chore: bump version from 0.9.11 to 0.9.12

Update the project version in pyproject.toml to prepare for the next release.

.github/workflows/backend-tests.yml

@@ -47,6 +47,30 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements-dev.txt
 
+      - name: Verify symlink support
+        run: |
+          python - <<'PY'
+          import os
+          import pathlib
+          import tempfile
+
+          root = pathlib.Path(tempfile.mkdtemp(prefix="lm-symlink-check-"))
+          target = root / "target"
+          target.mkdir()
+          link = root / "link"
+          try:
+              link.symlink_to(target, target_is_directory=True)
+          except OSError as exc:
+              raise SystemExit(f"Failed to create directory symlink in CI: {exc}")
+
+          is_link = os.path.islink(link)
+          is_dir = os.path.isdir(link)
+          realpath = os.path.realpath(link)
+          print(f"islink={is_link} isdir={is_dir} realpath={realpath}")
+          if not (is_link and is_dir and realpath == str(target)):
+              raise SystemExit("Directory symlink is not functioning correctly in CI; aborting.")
+          PY
+
       - name: Run pytest with coverage
         env:
           COVERAGE_FILE: coverage/backend/.coverage

.gitignore

@@ -1,4 +1,5 @@
 __pycache__/
+.pytest_cache/
 settings.json
 path_mappings.yaml
 output/*
@@ -10,3 +11,11 @@ node_modules/
 coverage/
 .coverage
 model_cache/
+
+# agent
+.opencode/
+
+# Vue widgets development cache (but keep build output)
+vue-widgets/node_modules/
+vue-widgets/.vite/
+vue-widgets/dist/

AGENTS.md

@@ -1,22 +1,192 @@
-# Repository Guidelines
+# AGENTS.md
 
-## Project Structure & Module Organization
-ComfyUI LoRA Manager pairs a Python backend with browser-side widgets. Backend modules live in <code>py/</code> with HTTP entry points in <code>py/routes/</code>, feature logic in <code>py/services/</code>, shared helpers in <code>py/utils/</code>, and custom nodes in <code>py/nodes/</code>. UI scripts extend ComfyUI from <code>web/comfyui/</code>, while deploy-ready assets remain in <code>static/</code> and <code>templates/</code>. Localization files live in <code>locales/</code>, example workflows in <code>example_workflows/</code>, and interim tests such as <code>test_i18n.py</code> sit beside their source until a dedicated <code>tests/</code> tree lands.
+This file provides guidance for agentic coding assistants working in this repository.
 
-## Build, Test, and Development Commands
-- <code>pip install -r requirements.txt</code> installs backend dependencies.
-- <code>python standalone.py --port 8188</code> launches the standalone server for iterative development.
-- <code>python -m pytest test_i18n.py</code> runs the current regression suite; target new files explicitly, e.g. <code>python -m pytest tests/test_recipes.py</code>.
-- <code>python scripts/sync_translation_keys.py</code> synchronizes locale keys after UI string updates.
+## Development Commands
 
-## Coding Style & Naming Conventions
-Follow PEP 8 with four-space indentation and descriptive snake_case file and function names such as <code>settings_manager.py</code>. Classes stay PascalCase, constants in UPPER_SNAKE_CASE, and loggers retrieved via <code>logging.getLogger(__name__)</code>. Prefer explicit type hints and docstrings on public APIs. JavaScript under <code>web/comfyui/</code> uses ES modules with camelCase helpers and the <code>_widget.js</code> suffix for UI components.
+### Backend Development
 
-## Testing Guidelines
-Pytest powers backend tests. Name modules <code>test_<feature>.py</code> and keep them near the code or in a future <code>tests/</code> package. Mock ComfyUI dependencies through helpers in <code>standalone.py</code>, keep filesystem fixtures deterministic, and ensure translations are covered. Run <code>python -m pytest</code> before submitting changes.
+```bash
+# Install dependencies
+pip install -r requirements.txt
+pip install -r requirements-dev.txt
 
-## Commit & Pull Request Guidelines
-Commits follow the conventional format, e.g. <code>feat(settings): add default model path</code>, and should stay focused on a single concern. Pull requests must outline the problem, summarize the solution, list manual verification steps (server run, targeted pytest), and link related issues. Include screenshots or GIFs for UI or locale updates and call out migration steps such as <code>settings.json</code> adjustments.
+# Run standalone server (port 8188 by default)
+python standalone.py --port 8188
+
+# Run all backend tests
+pytest
+
+# Run specific test file
+pytest tests/test_recipes.py
+
+# Run specific test function
+pytest tests/test_recipes.py::test_function_name
+
+# Run backend tests with coverage
+COVERAGE_FILE=coverage/backend/.coverage pytest \
+  --cov=py \
+  --cov=standalone \
+  --cov-report=term-missing \
+  --cov-report=html:coverage/backend/html \
+  --cov-report=xml:coverage/backend/coverage.xml \
+  --cov-report=json:coverage/backend/coverage.json
+```
+
+### Frontend Development
+
+```bash
+# Install frontend dependencies
+npm install
+
+# Run frontend tests
+npm test
+
+# Run frontend tests in watch mode
+npm run test:watch
+
+# Run frontend tests with coverage
+npm run test:coverage
+```
+
+## Python Code Style
+
+### Imports
+
+- Use `from __future__ import annotations` for forward references in type hints
+- Group imports: standard library, third-party, local (separated by blank lines)
+- Use absolute imports within `py/` package: `from ..services import X`
+- Mock ComfyUI dependencies in tests using `tests/conftest.py` patterns
+
+### Formatting & Types
+
+- PEP 8 with 4-space indentation
+- Type hints required for function signatures and class attributes
+- Use `TYPE_CHECKING` guard for type-checking-only imports
+- Prefer dataclasses for simple data containers
+- Use `Optional[T]` for nullable types, `Union[T, None]` only when necessary
+
+### Naming Conventions
+
+- Files: `snake_case.py` (e.g., `model_scanner.py`, `lora_service.py`)
+- Classes: `PascalCase` (e.g., `ModelScanner`, `LoraService`)
+- Functions/variables: `snake_case` (e.g., `get_instance`, `model_type`)
+- Constants: `UPPER_SNAKE_CASE` (e.g., `VALID_LORA_TYPES`)
+- Private members: `_single_underscore` (protected), `__double_underscore` (name-mangled)
+
+### Error Handling
+
+- Use `logging.getLogger(__name__)` for module-level loggers
+- Define custom exceptions in `py/services/errors.py`
+- Use `asyncio.Lock` for thread-safe singleton patterns
+- Raise specific exceptions with descriptive messages
+- Log errors at appropriate levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+
+### Async Patterns
+
+- Use `async def` for I/O-bound operations
+- Mark async tests with `@pytest.mark.asyncio`
+- Use `async with` for context managers
+- Singleton pattern with class-level locks: see `ModelScanner.get_instance()`
+- Use `aiohttp.web.Response` for HTTP responses
+
+### Testing Patterns
+
+- Use `pytest` with `--import-mode=importlib`
+- Fixtures in `tests/conftest.py` handle ComfyUI mocking
+- Use `@pytest.mark.no_settings_dir_isolation` for tests needing real paths
+- Test files: `tests/test_*.py`
+- Use `tmp_path_factory` for temporary directory isolation
+
+## JavaScript Code Style
+
+### Imports & Modules
+
+- ES modules with `import`/`export`
+- Use `import { app } from "../../scripts/app.js"` for ComfyUI integration
+- Export named functions/classes: `export function foo() {}`
+- Widget files use `*_widget.js` suffix
+
+### Naming & Formatting
+
+- camelCase for functions, variables, object properties
+- PascalCase for classes/constructors
+- Constants: `UPPER_SNAKE_CASE` (e.g., `CONVERTED_TYPE`)
+- Files: `snake_case.js` or `kebab-case.js`
+- 2-space indentation preferred (follow existing file conventions)
+
+### Widget Development
+
+- Use `app.registerExtension()` to register ComfyUI extensions
+- Use `node.addDOMWidget(name, type, element, options)` for custom widgets
+- Event handlers attached via `addEventListener` or widget callbacks
+- See `web/comfyui/utils.js` for shared utilities
+
+## Architecture Patterns
+
+### Service Layer
+
+- Use `ServiceRegistry` singleton for dependency injection
+- Services follow singleton pattern via `get_instance()` class method
+- Separate scanners (discovery) from services (business logic)
+- Handlers in `py/routes/handlers/` implement route logic
+
+### Model Types
+
+- BaseModelService is abstract base for LoRA, Checkpoint, Embedding services
+- ModelScanner provides file discovery and hash-based deduplication
+- Persistent cache in SQLite via `PersistentModelCache`
+- Metadata sync from CivitAI/CivArchive via `MetadataSyncService`
+
+### Routes & Handlers
+
+- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, etc.
+- Handlers are pure functions taking dependencies as parameters
+- Use `WebSocketManager` for real-time progress updates
+- Return `aiohttp.web.json_response` or `web.Response`
+
+### Recipe System
+
+- Base metadata in `py/recipes/base.py`
+- Enrichment adds model metadata: `RecipeEnrichmentService`
+- Parsers for different formats in `py/recipes/parsers/`
+
+## Important Notes
+
+- Always use English for comments (per copilot-instructions.md)
+- Dual mode: ComfyUI plugin (uses folder_paths) vs standalone (reads settings.json)
+- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+- Settings auto-saved in user directory or portable mode
+- WebSocket broadcasts for real-time updates (downloads, scans)
+- Symlink handling requires normalized paths
+- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*` patterns
+- Run `python scripts/sync_translation_keys.py` after UI string updates
+
+## Frontend UI Architecture
+
+This project has two distinct UI systems:
+
+### 1. Standalone Lora Manager Web UI
+- Location: `./static/` and `./templates/`
+- Purpose: Full-featured web application for managing LoRA models
+- Tech stack: Vanilla JS + CSS, served by the standalone server
+- Development: Uses npm for frontend testing (`npm test`, `npm run test:watch`, etc.)
+
+### 2. ComfyUI Custom Node Widgets
+- Location: `./web/comfyui/`
+- Purpose: Widgets and UI logic that ComfyUI loads as custom node extensions
+- Tech stack: Vanilla JS + Vue.js widgets (in `./vue-widgets/` and built to `./web/comfyui/vue-widgets/`)
+- Widget styling: Primary styles in `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
+- Development: No npm build step for these widgets (Vue widgets use build system)
+
+### Widget Development Guidelines
+- Use `app.registerExtension()` to register ComfyUI extensions (ComfyUI integration layer)
+- Use `node.addDOMWidget()` for custom DOM widgets
+- Widget styles should follow the patterns in `./web/comfyui/lm_styles.css`
+  - Selected state: `rgba(66, 153, 225, 0.3)` background, `rgba(66, 153, 225, 0.6)` border
+  - Hover state: `rgba(66, 153, 225, 0.2)` background
+  - Color palette matches the Lora Manager accent color (blue #4299e1)
+  - Use oklch() for color values when possible (defined in `./static/css/base.css`)
+- Vue widget components are in `./vue-widgets/src/components/` and built to `./web/comfyui/vue-widgets/`
+- When modifying widget styles, check `./web/comfyui/lm_styles.css` for consistency with other ComfyUI widgets
 
-## Configuration & Localization Tips
-Copy <code>settings.json.example</code> to <code>settings.json</code> and adapt model directories before running the standalone server. Store reference assets in <code>civitai/</code> or <code>docs/</code> to keep runtime directories deploy-ready. Whenever UI text changes, update every <code>locales/&lt;lang&gt;.json</code> file and rerun the translation sync script so ComfyUI surfaces localized strings.

CLAUDE.md

@@ -0,0 +1,211 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Overview
+
+ComfyUI LoRA Manager is a comprehensive LoRA management system for ComfyUI that combines a Python backend with browser-based widgets. It provides model organization, downloading from CivitAI/CivArchive, recipe management, and one-click workflow integration.
+
+## Development Commands
+
+### Backend Development
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Install development dependencies (for testing)
+pip install -r requirements-dev.txt
+
+# Run standalone server (port 8188 by default)
+python standalone.py --port 8188
+
+# Run backend tests with coverage
+COVERAGE_FILE=coverage/backend/.coverage pytest \
+  --cov=py \
+  --cov=standalone \
+  --cov-report=term-missing \
+  --cov-report=html:coverage/backend/html \
+  --cov-report=xml:coverage/backend/coverage.xml \
+  --cov-report=json:coverage/backend/coverage.json
+
+# Run specific test file
+pytest tests/test_recipes.py
+```
+
+### Frontend Development
+```bash
+# Install frontend dependencies
+npm install
+
+# Run frontend tests
+npm test
+
+# Run frontend tests in watch mode
+npm run test:watch
+
+# Run frontend tests with coverage
+npm run test:coverage
+```
+
+### Localization
+```bash
+# Sync translation keys after UI string updates
+python scripts/sync_translation_keys.py
+```
+
+## Architecture
+
+### Backend Structure (Python)
+
+**Core Entry Points:**
+- `__init__.py` - ComfyUI plugin entry point, registers nodes and routes
+- `standalone.py` - Standalone server that mocks ComfyUI dependencies
+- `py/lora_manager.py` - Main LoraManager class that registers HTTP routes
+
+**Service Layer** (`py/services/`):
+- `ServiceRegistry` - Singleton service registry for dependency management
+- `ModelServiceFactory` - Factory for creating model services (LoRA, Checkpoint, Embedding)
+- Scanner services (`lora_scanner.py`, `checkpoint_scanner.py`, `embedding_scanner.py`) - Model file discovery and indexing
+- `model_scanner.py` - Base scanner with hash-based deduplication and metadata extraction
+- `persistent_model_cache.py` - SQLite-based cache for model metadata
+- `metadata_sync_service.py` - Syncs metadata from CivitAI/CivArchive APIs
+- `civitai_client.py` / `civarchive_client.py` - API clients for external services
+- `downloader.py` / `download_manager.py` - Model download orchestration
+- `recipe_scanner.py` - Recipe file management and image association
+- `settings_manager.py` - Application settings with migration support
+- `websocket_manager.py` - WebSocket broadcasting for real-time updates
+- `use_cases/` - Business logic orchestration (auto-organize, bulk refresh, downloads)
+
+**Routes Layer** (`py/routes/`):
+- Route registrars organize endpoints by domain (models, recipes, previews, example images, updates)
+- `handlers/` - Request handlers implementing business logic
+- Routes use aiohttp and integrate with ComfyUI's PromptServer
+
+**Recipe System** (`py/recipes/`):
+- `base.py` - Base recipe metadata structure
+- `enrichment.py` - Enriches recipes with model metadata
+- `merger.py` - Merges recipe data from multiple sources
+- `parsers/` - Parsers for different recipe formats (PNG, JSON, workflow)
+
+**Custom Nodes** (`py/nodes/`):
+- `lora_loader.py` - LoRA loader nodes with preset support
+- `save_image.py` - Enhanced save image with pattern-based filenames
+- `trigger_word_toggle.py` - Toggle trigger words in prompts
+- `lora_stacker.py` - Stack multiple LoRAs
+- `prompt.py` - Prompt node with autocomplete
+- `wanvideo_lora_select.py` - WanVideo-specific LoRA selection
+
+**Configuration** (`py/config.py`):
+- Manages folder paths for models, checkpoints, embeddings
+- Handles symlink mappings for complex directory structures
+- Auto-saves paths to settings.json in ComfyUI mode
+
+### Frontend Structure (JavaScript)
+
+**ComfyUI Widgets** (`web/comfyui/`):
+- Vanilla JavaScript ES modules extending ComfyUI's LiteGraph-based UI
+- `loras_widget.js` - Main LoRA selection widget with preview
+- `loras_widget_events.js` - Event handling for widget interactions
+- `autocomplete.js` - Autocomplete for trigger words and embeddings
+- `preview_tooltip.js` - Preview tooltip for model cards
+- `top_menu_extension.js` - Adds "Launch LoRA Manager" menu item
+- `trigger_word_highlight.js` - Syntax highlighting for trigger words
+- `utils.js` - Shared utilities and API helpers
+
+**Widget Development:**
+- Widgets use `app.registerExtension` and `getCustomWidgets` hooks
+- `node.addDOMWidget(name, type, element, options)` embeds HTML in nodes
+- See `docs/dom_widget_dev_guide.md` for complete DOMWidget development guide
+
+**Web Source** (`web-src/`):
+- Modern frontend components (if migrating from static)
+- `components/` - Reusable UI components
+- `styles/` - CSS styling
+
+### Key Patterns
+
+**Dual Mode Operation:**
+- ComfyUI plugin mode: Integrates with ComfyUI's PromptServer, uses folder_paths
+- Standalone mode: Mocks ComfyUI dependencies via `standalone.py`, reads paths from settings.json
+- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+
+**Settings Management:**
+- Settings stored in user directory (via `platformdirs`) or portable mode (in repo)
+- Migration system tracks settings schema version
+- Template in `settings.json.example` defines defaults
+
+**Model Scanning Flow:**
+1. Scanner walks folder paths, computes file hashes
+2. Hash-based deduplication prevents duplicate processing
+3. Metadata extracted from safetensors headers
+4. Persistent cache stores results in SQLite
+5. Background sync fetches CivitAI/CivArchive metadata
+6. WebSocket broadcasts updates to connected clients
+
+**Recipe System:**
+- Recipes store LoRA combinations with parameters
+- Supports import from workflow JSON, PNG metadata
+- Images associated with recipes via sibling file detection
+- Enrichment adds model metadata for display
+
+**Frontend-Backend Communication:**
+- REST API for CRUD operations
+- WebSocket for real-time progress updates (downloads, scans)
+- API endpoints follow `/loras/*` pattern
+
+## Code Style
+
+**Python:**
+- PEP 8 with 4-space indentation
+- snake_case for files, functions, variables
+- PascalCase for classes
+- Type hints preferred
+- English comments only (per copilot-instructions.md)
+- Loggers via `logging.getLogger(__name__)`
+
+**JavaScript:**
+- ES modules with camelCase
+- Files use `*_widget.js` suffix for ComfyUI widgets
+- Prefer vanilla JS, avoid framework dependencies
+
+## Testing
+
+**Backend Tests:**
+- pytest with `--import-mode=importlib`
+- Test files: `tests/test_*.py`
+- Fixtures in `tests/conftest.py`
+- Mock ComfyUI dependencies using standalone.py patterns
+- Markers: `@pytest.mark.asyncio` for async tests, `@pytest.mark.no_settings_dir_isolation` for real paths
+
+**Frontend Tests:**
+- Vitest with jsdom environment
+- Test files: `tests/frontend/**/*.test.js`
+- Setup in `tests/frontend/setup.js`
+- Coverage via `npm run test:coverage`
+
+## Important Notes
+
+**Settings Location:**
+- ComfyUI mode: Auto-saves folder paths to user settings directory
+- Standalone mode: Use `settings.json` (copy from `settings.json.example`)
+- Portable mode: Set `"use_portable_settings": true` in settings.json
+
+**API Integration:**
+- CivitAI API key required for downloads (add to settings)
+- CivArchive API used as fallback for deleted models
+- Metadata archive database available for offline metadata
+
+**Symlink Handling:**
+- Config scans symlinks to map virtual paths to physical locations
+- Preview validation uses normalized preview root paths
+- Fingerprinting prevents redundant symlink rescans
+
+**ComfyUI Node Development:**
+- Nodes defined in `py/nodes/`, registered in `__init__.py`
+- Frontend widgets in `web/comfyui/`, matched by node type
+- Use `WEB_DIRECTORY = "./web/comfyui"` convention
+
+**Recipe Image Association:**
+- Recipes scan for sibling images in same directory
+- Supports repair/migration of recipe image paths
+- See `py/services/recipe_scanner.py` for implementation details

IFLOW.md

@@ -1,103 +0,0 @@
-# ComfyUI LoRA Manager - iFlow 上下文
-
-## 项目概述
-
-ComfyUI LoRA Manager 是一个全面的工具集，用于简化 ComfyUI 中 LoRA 模型的组织、下载和应用。它提供了强大的功能，如配方管理、检查点组织和一键工作流集成，使模型操作更快、更流畅、更简单。
-
-该项目是一个 Python 后端与 JavaScript 前端结合的 Web 应用程序，既可以作为 ComfyUI 的自定义节点运行，也可以作为独立应用程序运行。
-
-## 项目结构
-
-```
-D:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\
-├── py/                 # Python 后端代码
-│   ├── config.py       # 全局配置
-│   ├── lora_manager.py # 主入口点
-│   ├── controllers/    # 控制器
-│   ├── metadata_collector/ # 元数据收集器
-│   ├── middleware/     # 中间件
-│   ├── nodes/          # ComfyUI 节点
-│   ├── recipes/        # 配方相关
-│   ├── routes/         # API 路由
-│   ├── services/       # 业务逻辑服务
-│   ├── utils/          # 工具函数
-│   └── validators/     # 验证器
-├── static/             # 静态资源 (CSS, JS, 图片)
-├── templates/          # HTML 模板
-├── locales/            # 国际化文件
-├── tests/              # 测试代码
-├── standalone.py       # 独立模式入口
-├── requirements.txt    # Python 依赖
-├── package.json        # Node.js 依赖和脚本
-└── README.md           # 项目说明
-```
-
-## 核心组件
-
-### 后端 (Python)
-
-- **主入口**: `py/lora_manager.py` 和 `standalone.py`
-- **配置**: `py/config.py` 管理全局配置和路径
-- **路由**: `py/routes/` 目录下包含各种 API 路由
-- **服务**: `py/services/` 目录下包含业务逻辑，如模型扫描、下载管理等
-- **模型管理**: 使用 `ModelServiceFactory` 来管理不同类型的模型 (LoRA, Checkpoint, Embedding)
-
-### 前端 (JavaScript)
-
-- **构建工具**: 使用 Node.js 和 npm 进行依赖管理和测试
-- **测试**: 使用 Vitest 进行前端测试
-
-## 构建和运行
-
-### 安装依赖
-
-```bash
-# Python 依赖
-pip install -r requirements.txt
-
-# Node.js 依赖 (用于测试)
-npm install
-```
-
-### 运行 (ComfyUI 模式)
-
-作为 ComfyUI 的自定义节点安装后，在 ComfyUI 中启动即可。
-
-### 运行 (独立模式)
-
-```bash
-# 使用默认配置运行
-python standalone.py
-
-# 指定主机和端口
-python standalone.py --host 127.0.0.1 --port 9000
-```
-
-### 测试
-
-#### 后端测试
-
-```bash
-# 安装开发依赖
-pip install -r requirements-dev.txt
-
-# 运行测试
-pytest
-```
-
-#### 前端测试
-
-```bash
-# 运行测试
-npm run test
-
-# 运行测试并生成覆盖率报告
-npm run test:coverage
-```
-
-## 开发约定
-
-- **代码风格**: Python 代码应遵循 PEP 8 规范
-- **测试**: 新功能应包含相应的单元测试
-- **配置**: 使用 `settings.json` 文件进行用户配置
-- **日志**: 使用 Python 标准库 `logging` 模块进行日志记录

README.md

@@ -34,6 +34,26 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ## Release Notes
 
+### v0.9.12
+* **LoRA Randomizer System** - Introduced a comprehensive LoRA randomization system featuring LoRA Pool and LoRA Randomizer nodes for flexible and dynamic generation workflows.
+* **LoRA Randomizer Template** - Refer to the new "LoRA Randomizer" template workflow for detailed examples of flexible randomization modes, lock & reuse options, and other features.
+* **Recipe Folders** - Introduced a folder system for the Recipes page, allowing users to freely organize recipes just like they do with models.
+* **Recipe Bulk Operations** - Added bulk mode support for batch moving, deleting, and setting base models for selected recipes with intuitive controls like click-and-drag selection, drag-to-folder, and Ctrl+A (Select All).
+* **Prompt Search & Sorting** - Search recipes by prompt content and sort by Recipe Name, Imported Date, or LoRA Count for better browsing.
+* **Recipe Favorites** - Mark specific recipes as favorites for quick access.
+* **Video Recipe Support** - Enabled support for video recipes (import via LM extension or URL; video file import not supported).
+* **Performance Improvements** - Fixed performance issues for dramatically improved startup and loading speed. After first scan, subsequent loads are instant regardless of collection size.
+* **ComfyUI Nodes 2.0 Support** - Basic support for ComfyUI Nodes 2.0.
+
+### v0.9.10
+* **Smarter Update Matching** - Users can now choose to check and group updates by matching base model only or with no base-model constraint; version lists also support toggling between same-base versions or all versions.
+* **Flexible Tag Filtering** - The filter panel now supports tag exclusion: click a tag to include, click again to exclude, and click a third time to clear, enabling stronger and more flexible tag filters.
+* **License Visibility & Controls** - Model detail headers and ComfyUI preview popups now show Civitai license icons. The filter panel gains license include/exclude options, and a new global context menu action, "Refresh license metadata," fetches missing license data.
+* **Recipe Improvements** - Recipes now allow importing with zero LoRAs, and recipe detail pages show the related checkpoint for easier reference.
+* **Better ZIP Downloads** - When downloading models packaged in ZIPs, model files are extracted into the target model folder; ZIPs containing multiple model files (e.g., WanVideo high/low LoRA pairs) are added as separate models.
+* **Template Workflow Update** - Refreshed the "Illustrious Pony Example" template workflow with usage guidance for each LoRA Manager node.
+* **Bug Fixes & Stability** - General fixes and stability improvements.
+
 ### v0.9.9
 * **Check for Updates Feature** - Users can now check for updates for all models or selected models in bulk mode. Models with available updates will display an "update available" badge on their model card, and users can filter to show only models with updates.
 * **Model Versions Management** - Added a new Versions tab in the model modal that centralizes all versions of a model, providing download, delete, and ignore update functions.
@@ -71,34 +91,6 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 * **Automatic Filename Conflict Resolution** - Implemented automatic file renaming (`original name + short hash`) to prevent conflicts when downloading or moving models.
 * **Performance Optimizations & Bug Fixes** - Various performance improvements and bug fixes for a more stable and responsive experience.
 
-### v0.8.30
-* **Automatic Model Path Correction** - Added auto-correction for model paths in built-in nodes such as Load Checkpoint, Load Diffusion Model, Load LoRA, and other custom nodes with similar functionality. Workflows containing outdated or incorrect model paths will now be automatically updated to reflect the current location of your models.
-* **Node UI Enhancements** - Improved node interface for a smoother and more intuitive user experience.
-* **Bug Fixes** - Addressed various bugs to enhance stability and reliability.
-
-### v0.8.29
-* **Enhanced Recipe Imports** - Improved recipe importing with new target folder selection, featuring path input autocomplete and interactive folder tree navigation. Added a "Use Default Path" option when downloading missing LoRAs.
-* **WanVideo Lora Select Node Update** - Updated the WanVideo Lora Select node with a 'merge_loras' option to match the counterpart node in the WanVideoWrapper node package.
-* **Autocomplete Conflict Resolution** - Resolved an autocomplete feature conflict in LoRA nodes with pysssss autocomplete.
-* **Improved Download Functionality** - Enhanced download functionality with resumable downloads and improved error handling.
-* **Bug Fixes** - Addressed several bugs for improved stability and performance.
-
-### v0.8.28
-* **Autocomplete for Node Inputs** - Instantly find and add LoRAs by filename directly in Lora Loader, Lora Stacker, and WanVideo Lora Select nodes. Autocomplete suggestions include preview tooltips and preset weights, allowing you to quickly select LoRAs without opening the LoRA Manager UI.
-* **Duplicate Notification Control** - Added a switch to duplicates mode, enabling users to turn off duplicate model notifications for a more streamlined experience.
-* **Download Example Images from Context Menu** - Introduced a new context menu option to download example images for individual models.
-
-### v0.8.27
-* **User Experience Enhancements** - Improved the model download target folder selection with path input autocomplete and interactive folder tree navigation, making it easier and faster to choose where models are saved.
-* **Default Path Option for Downloads** - Added a "Use Default Path" option when downloading models. When enabled, models are automatically organized and stored according to your configured path template settings.
-* **Advanced Download Path Templates** - Expanded path template settings, allowing users to set individual templates for LoRA, checkpoint, and embedding models for greater flexibility. Introduced the `{author}` placeholder, enabling automatic organization of model files by creator name.
-* **Bug Fixes & Stability Improvements** - Addressed various bugs and improved overall stability for a smoother experience.
-
-### v0.8.26
-* **Creator Search Option** - Added ability to search models by creator name, making it easier to find models from specific authors.
-* **Enhanced Node Usability** - Improved user experience for Lora Loader, Lora Stacker, and WanVideo Lora Select nodes by fixing the maximum height of the text input area. Users can now freely and conveniently adjust the LoRA region within these nodes.
-* **Compatibility Fixes** - Resolved compatibility issues with ComfyUI and certain custom nodes, including ComfyUI-Custom-Scripts, ensuring smoother integration and operation.
-
 [View Update History](./update_logs.md)
 
 ---

__init__.py

@@ -4,12 +4,16 @@ try:  # pragma: no cover - import fallback for pytest collection
     from .py.nodes.trigger_word_toggle import TriggerWordToggle
     from .py.nodes.prompt import PromptLoraManager
     from .py.nodes.lora_stacker import LoraStacker
-    from .py.nodes.save_image import SaveImage
+    from .py.nodes.save_image import SaveImageLM
     from .py.nodes.debug_metadata import DebugMetadata
-    from .py.nodes.wanvideo_lora_select import WanVideoLoraSelect
+    from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
     from .py.nodes.wanvideo_lora_select_from_text import WanVideoLoraSelectFromText
+    from .py.nodes.lora_pool import LoraPoolNode
+    from .py.nodes.lora_randomizer import LoraRandomizerNode
     from .py.metadata_collector import init as init_metadata_collector
-except ImportError:  # pragma: no cover - allows running under pytest without package install
+except (
+    ImportError
+):  # pragma: no cover - allows running under pytest without package install
     import importlib
     import pathlib
     import sys
@@ -20,14 +24,28 @@ except ImportError:  # pragma: no cover - allows running under pytest without pa
 
     PromptLoraManager = importlib.import_module("py.nodes.prompt").PromptLoraManager
     LoraManager = importlib.import_module("py.lora_manager").LoraManager
-    LoraManagerLoader = importlib.import_module("py.nodes.lora_loader").LoraManagerLoader
-    LoraManagerTextLoader = importlib.import_module("py.nodes.lora_loader").LoraManagerTextLoader
-    TriggerWordToggle = importlib.import_module("py.nodes.trigger_word_toggle").TriggerWordToggle
+    LoraManagerLoader = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraManagerLoader
+    LoraManagerTextLoader = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraManagerTextLoader
+    TriggerWordToggle = importlib.import_module(
+        "py.nodes.trigger_word_toggle"
+    ).TriggerWordToggle
     LoraStacker = importlib.import_module("py.nodes.lora_stacker").LoraStacker
-    SaveImage = importlib.import_module("py.nodes.save_image").SaveImage
+    SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
     DebugMetadata = importlib.import_module("py.nodes.debug_metadata").DebugMetadata
-    WanVideoLoraSelect = importlib.import_module("py.nodes.wanvideo_lora_select").WanVideoLoraSelect
-    WanVideoLoraSelectFromText = importlib.import_module("py.nodes.wanvideo_lora_select_from_text").WanVideoLoraSelectFromText
+    WanVideoLoraSelectLM = importlib.import_module(
+        "py.nodes.wanvideo_lora_select"
+    ).WanVideoLoraSelectLM
+    WanVideoLoraSelectFromText = importlib.import_module(
+        "py.nodes.wanvideo_lora_select_from_text"
+    ).WanVideoLoraSelectFromText
+    LoraPoolNode = importlib.import_module("py.nodes.lora_pool").LoraPoolNode
+    LoraRandomizerNode = importlib.import_module(
+        "py.nodes.lora_randomizer"
+    ).LoraRandomizerNode
     init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
@@ -36,17 +54,38 @@ NODE_CLASS_MAPPINGS = {
     LoraManagerTextLoader.NAME: LoraManagerTextLoader,
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
-    SaveImage.NAME: SaveImage,
+    SaveImageLM.NAME: SaveImageLM,
     DebugMetadata.NAME: DebugMetadata,
-    WanVideoLoraSelect.NAME: WanVideoLoraSelect,
-    WanVideoLoraSelectFromText.NAME: WanVideoLoraSelectFromText
+    WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,
+    WanVideoLoraSelectFromText.NAME: WanVideoLoraSelectFromText,
+    LoraPoolNode.NAME: LoraPoolNode,
+    LoraRandomizerNode.NAME: LoraRandomizerNode,
 }
 
 WEB_DIRECTORY = "./web/comfyui"
 
+# Check and build Vue widgets if needed (development mode)
+try:
+    from .py.vue_widget_builder import check_and_build_vue_widgets
+
+    # Auto-build in development, warn only if fails
+    check_and_build_vue_widgets(auto_build=True, warn_only=True)
+except ImportError:
+    # Fallback for pytest
+    import importlib
+
+    check_and_build_vue_widgets = importlib.import_module(
+        "py.vue_widget_builder"
+    ).check_and_build_vue_widgets
+    check_and_build_vue_widgets(auto_build=True, warn_only=True)
+except Exception as e:
+    import logging
+
+    logging.warning(f"[LoRA Manager] Vue widget build check skipped: {e}")
+
 # Initialize metadata collector
 init_metadata_collector()
 
 # Register routes on import
 LoraManager.add_routes()
-__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']
+__all__ = ["NODE_CLASS_MAPPINGS", "WEB_DIRECTORY"]

docs/dom_widget_dev_guide.md

@@ -0,0 +1,544 @@
+# DOMWidget Development Guide
+
+This document provides a comprehensive guide for developing custom DOMWidgets in ComfyUI using Vanilla JavaScript. DOMWidgets allow you to embed standard HTML elements (div, video, canvas, input, etc.) into ComfyUI nodes while benefitting from the frontend's automatic layout and zoom management.
+
+## 1. Core Concepts
+
+In ComfyUI, a `DOMWidget` extends the default LiteGraph Canvas rendering logic. It maintains an HTML layer on top of the Canvas, making complex interactions and media displays significantly easier to implement than pure Canvas drawing.
+
+### Key APIs
+*   **`app.registerExtension`**: The entry point for registering extensions.
+*   **`getCustomWidgets`**: A hook for defining new widget types associated with specific input types.
+*   **`node.addDOMWidget`**: The core method to add HTML elements to a node.
+
+---
+
+## 2. Basic Structure
+
+A standard custom DOMWidget extension typically follows this structure:
+
+```javascript
+import { app } from "../../scripts/app.js";
+
+app.registerExtension({
+    name: "My.Custom.Extension",
+    async getCustomWidgets() {
+        return {
+            // Define a new widget type named "MY_WIDGET_TYPE"
+            MY_WIDGET_TYPE(node, inputName, inputData, app) {
+                // 1. Create the HTML element
+                const container = document.createElement("div");
+                container.innerHTML = "Hello <b>DOMWidget</b>!";
+                
+                // 2. Setup styles (Optional but recommended)
+                container.style.color = "white";
+                container.style.backgroundColor = "#222";
+                container.style.padding = "5px";
+
+                // 3. Add the DOMWidget and return the result
+                const widget = node.addDOMWidget(inputName, "MY_WIDGET_TYPE", container, {
+                    // Configuration options
+                    getValue() {
+                        return container.innerText;
+                    },
+                    setValue(v) {
+                        container.innerText = v;
+                    }
+                });
+
+                // 4. Return in the standard format
+                return { widget };
+            }
+        };
+    }
+});
+```
+
+---
+
+## ComfyUI Dual Rendering Modes
+
+ComfyUI frontend supports two rendering modes:
+
+| Mode | Description | DOM Structure |
+| :--- | :--- | :--- |
+| **Canvas Mode** | Traditional rendering where widgets are rendered on top of canvas using absolute positioning | Uses `.dom-widget` class on containers |
+| **Vue DOM Mode** | New rendering mode where nodes and widgets are rendered as Vue components | Uses `.lg-node-widget` class on containers with dynamic IDs (e.g., `v-1-0`) |
+
+### Mode Switching
+
+The frontend switches between modes via `LiteGraph.vueNodesMode` boolean:
+- `LiteGraph.vueNodesMode = true` → Vue DOM Mode
+- `LiteGraph.vueNodesMode = false` → Canvas Mode
+
+**Key Behavior**: Mode switching triggers DOM re-rendering WITHOUT page reload. Widget elements are destroyed and recreated, so any event listeners or references to old DOM elements become invalid.
+
+### Testing Mode Switches via Chrome DevTools MCP
+
+```javascript
+// Trigger render mode change
+LiteGraph.vueNodesMode = !LiteGraph.vueNodesMode;
+
+// Force canvas redraw (optional but helps trigger re-render)
+if (app.canvas) {
+    app.canvas.draw(true, true);
+}
+```
+
+### Development Notes
+
+When implementing widgets that attach event listeners or maintain external references:
+1. **Use `node.onRemoved`** to clean up when node is deleted
+2. **Detect DOM changes** by checking if widget input element is still in document: `document.body.contains(inputElement)`
+3. **Poll for mode changes** by watching `LiteGraph.vueNodesMode` and re-initializing when it changes
+4. **Use `loadedGraphNode` hook** for initial setup (guarantees DOM is fully rendered)
+
+
+---
+
+## 3. The `addDOMWidget` API
+
+```javascript
+node.addDOMWidget(name, type, element, options)
+```
+
+### Parameters
+1.  **`name`**: The internal name of the widget (usually matches the input name).
+2.  **`type`**: The type identifier for the widget.
+3.  **`element`**: The actual HTMLElement to embed.
+4.  **`options`**: (Object) Configuration for lifecycle, sizing, and persistence.
+
+### Common `options` Fields
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `getValue` | `Function` | Defines how to retrieve the widget's value for serialization. |
+| `setValue` | `Function` | Defines how to restore the widget's state from workflow data. |
+| `getMinHeight` | `Function` | Returns the minimum height in pixels. |
+| `getHeight` | `Function` | Returns the preferred height (supports numbers or percentage strings like `"50%"`). |
+| `onResize` | `Function` | Callback triggered when the widget is resized. |
+| `hideOnZoom`| `Boolean` | Whether to hide the DOM element when zoomed out to improve performance (default: `true`). |
+| `selectOn` | `string[]` | Events on the element that should trigger node selection (default: `['focus', 'click']`). |
+
+---
+
+## 4. Size Control
+
+Custom DOMWidgets must actively inform the parent Node of their size requirements to ensure the Node layout is calculated correctly and connection wires remain aligned.
+
+### 4.1 Core Mechanism
+
+Whether in Canvas Mode or Vue Mode, the underlying logic model (`LGraphNode`) calls the widget's `computeLayoutSize` method to determine dimensions. This logic is used to calculate the Node's total size and the position of input/output slots.
+
+### 4.2 Controlling Height
+
+It is recommended to use the `options` parameter to define height behavior.
+
+**Performance Note:** providing `getMinHeight` and `getHeight` via `options` allows the system to skip expensive DOM measurements (`getComputedStyle`) during rendering loop. This significantly improves performance and prevents FPS drops during node resizing.
+
+**Method 1: Using `options` (Recommended)**
+
+```javascript
+const widget = node.addDOMWidget("MyWidget", "custom", element, {
+    // Specify minimum height in pixels
+    getMinHeight: () => 150,
+    
+    // Or specify preferred height (pixels or percentage string)
+    // getHeight: () => "50%", 
+});
+```
+
+**Method 2: Using CSS Variables**
+
+You can also set specific CSS variables on the root element:
+
+```javascript
+element.style.setProperty("--comfy-widget-min-height", "150px");
+// or --comfy-widget-height
+```
+
+### 4.3 Controlling Width
+
+By default, a DOMWidget's width automatically stretches to fit the Node's width (which is determined by the Title or other Input Slots).
+
+If you must **force the Node to be wider** to accommodate your widget, you need to override the widget instance's `computeLayoutSize` method:
+
+```javascript
+const widget = node.addDOMWidget("WideWidget", "custom", element);
+
+// Override the default layout calculation
+widget.computeLayoutSize = (targetNode) => {
+    return {
+        minHeight: 150, // Must return height
+        minWidth: 300   // Force the Node to be at least 300px wide
+    };
+};
+```
+
+### 4.4 Dynamic Resizing
+
+If your widget's content changes dynamically (e.g., expanding sections, loading images, or CSS changes), the DOM element will resize, but the Canvas-rendered Node background and Slots will not automatically follow. You must manually trigger a synchronization.
+
+**The Update Sequence:**
+Whenever the **actual rendering height** of your DOM element changes, execute the following "three-step combo":
+
+```javascript
+// 1. Calculate the new optimal size for the node based on current widget requirements
+const newSize = node.computeSize();
+
+// 2. Apply the new size to the node model (updates bounding box and slot positions)
+node.setSize(newSize);
+
+// 3. Mark the canvas as dirty to trigger a redraw in the next animation frame
+node.setDirtyCanvas(true, true);
+```
+
+**Common Scenarios:**
+
+| Scenario | Actual Height Change? | Update Required? |
+| :--- | :--- | :--- |
+| **Expand/Collapse content** | **Yes** | ✅ **Yes**. Prevents widget from overflowing node boundaries. |
+| **Image/Video finished loading** | **Yes** | ✅ **Yes**. Initial height might be 0 until the media loads. |
+| **Changing `minHeight`** | **Maybe** | ❓ **Only if** the change causes the element's actual height to shift. |
+| **Changing font size/styles** | **Yes** | ✅ **Yes**. Text reflow often changes the total height. |
+| **User dragging node corner** | **Yes** | ❌ **No**. LiteGraph handles this internally. |
+
+---
+
+## 5. State Persistence (Serialization)
+
+### 5.1 Default Behavior
+
+DOMWidgets have **serialization enabled** by default (`serialize` property is `true`).
+*   **Saving**: ComfyUI attempts to read the widget's value to save into the Workflow file.
+*   **Loading**: ComfyUI reads the value from the Workflow file and assigns it to the widget.
+
+### 5.2 Custom Serialization
+
+To make persistence work effectively (saving internal DOM state and restoring it), you must implement `getValue` and `setValue` in the `options`:
+
+*   **`getValue`**: Returns the state to be saved (Number, String, or Object).
+*   **`setValue`**: Receives the restored value and updates the DOM element.
+
+**Example:**
+
+```javascript
+const inputEl = document.createElement("input");
+const widget = node.addDOMWidget("MyInput", "custom", inputEl, {
+    // 1. Called during Save
+    getValue: () => {
+        return inputEl.value;
+    },
+    // 2. Called during Load or Copy/Paste
+    setValue: (value) => {
+        inputEl.value = value || "";
+    }
+});
+
+// Optional: Listen for changes to update widget.value immediately
+inputEl.addEventListener("change", () => {
+    widget.value = inputEl.value; // Triggers callbacks
+});
+```
+
+### 5.3 The Restoration Mechanism (`configure`)
+
+*   **`configure(data)`**: When a Workflow is loaded, `LGraphNode` calls its `configure(data)` method.
+*   **`setValue` Chain**: During `configure`, the Node iterates over the saved `widgets_values` array and assigns each value (`widget.value = savedValue`). For DOMWidgets, this assignment triggers the `setValue` callback defined in your options.
+
+Therefore, `options.setValue` is the critical hook for restoring widget state.
+
+### 5.4 Disabling Serialization
+
+If your widget is purely for display (e.g., a real-time monitor or generated chart) and doesn't need to save state, disable serialization to reduce workflow file size.
+
+**Note**: You cannot set this via `options`. You must modify the widget instance directly.
+
+```javascript
+const widget = node.addDOMWidget("DisplayOnly", "custom", element);
+widget.serialize = false; // Explicitly disable
+```
+
+---
+
+## 6. Lifecycle & Events
+
+### 6.1 `onResize`
+
+When the Node size changes (e.g., user drags the corner), the widget can receive a notification via `options`:
+
+```javascript
+const widget = node.addDOMWidget("ResizingWidget", "custom", element, {
+    onResize: (w) => {
+        // 'w' is the widget instance
+        // Adjust internal DOM layout here if necessary
+        console.log("Widget resized");
+    }
+});
+```
+
+### 6.2 Construction & Mounting
+
+*   **Construction**: Occurs immediately when `addDOMWidget` is called.
+*   **Mounting**:
+    *   **Canvas Mode**: Appended to `.dom-widget-container` via `DomWidget.vue`.
+    *   **Vue Mode**: Appended inside the Node component via `WidgetDOM.vue`.
+    *   **Caution**: When `addDOMWidget` returns, the element may not be in the `document.body` yet. If you need to access layout properties like `getBoundingClientRect`, use `setTimeout` or wait for the first `onResize`.
+
+### 6.3 Cleanup
+
+If you create external references (like `setInterval` or global event listeners), ensure you clean them up using `node.onRemoved`:
+
+```javascript
+node.onRemoved = function() {
+    clearInterval(myInterval);
+    // Call original onRemoved if it existed
+};
+```
+
+---
+
+## 7. Styling & Best Practices
+
+### 7.1 Styling
+Since DOMWidgets are placed in absolute positioned containers or managed by Vue, ensure your container handles sizing gracefully:
+
+```javascript
+container.style.width = "100%";
+container.style.boxSizing = "border-box";
+```
+
+### 7.2 Path References
+When importing `app`, adjust the path based on your extension's folder depth. Typically:
+`import { app } from "../../scripts/app.js";`
+
+### 7.3 Security
+If setting `innerHTML` dynamically, ensure the content is sanitized or trusted to prevent XSS attacks.
+
+### 7.4 UI Constraints for ComfyUI Custom Node Widgets
+
+When developing DOMWidgets as internal UI widgets for ComfyUI custom nodes, keep the following constraints in mind:
+
+#### 7.4.1 Minimize Vertical Space
+
+ComfyUI nodes are often displayed in a compact graph view with many nodes visible simultaneously. Avoid excessive vertical spacing that could clutter the workspace.
+
+- Keep layouts compact and efficient
+- Use appropriate padding and margins (4-8px typically)
+- Stack related controls vertically but avoid unnecessary spacing
+
+#### 7.4.2 Avoid Dynamic Height Changes
+
+Dynamic height changes (expand/collapse sections, showing/hiding content) can cause node layout recalculations and affect connection wire positioning.
+
+- Prefer static layouts over expandable/collapsible sections
+- Use tooltips or overlays for additional information instead
+- If dynamic height is unavoidable, manually trigger layout updates (see Section 4.4)
+
+#### 7.4.3 Keep UI Simple and Intuitive
+
+As internal widgets for ComfyUI custom nodes, the UI should be accessible to users without technical implementation details.
+
+- Use clear, user-friendly terminology (avoid "frontend/backend roll" in favor of "fixed/always randomize")
+- Focus on user intent rather than implementation details
+- Avoid complex interactions that may confuse users
+
+#### 7.4.4 Forward Middle Mouse Events to Canvas
+
+By default, when a DOM widget receives pointer events (e.g., mouse clicks, drags), these events are captured by the widget and not forwarded to the ComfyUI canvas. This prevents users from panning the workflow using the middle mouse button when the cursor is over a DOM widget.
+
+To enable workflow panning over your widget, you should forward middle mouse events (button 1) to the canvas using the `forwardMiddleMouseToCanvas` utility function:
+
+```javascript
+import { forwardMiddleMouseToCanvas } from "./utils.js";
+
+// In your widget creation function
+const container = document.createElement("div");
+container.style.width = "100%";
+container.style.height = "100%";
+// ... other styles ...
+
+// Forward middle mouse events to canvas for panning
+forwardMiddleMouseToCanvas(container);
+
+const widget = node.addDOMWidget(name, type, container, { ... });
+```
+
+The `forwardMiddleMouseToCanvas` function:
+- Forwards `pointerdown` events with button 1 (middle mouse button) to `app.canvas.processMouseDown`
+- Forwards `pointermove` events while middle mouse button is pressed to `app.canvas.processMouseMove`
+- Forwards `pointerup` events with button 1 to `app.canvas.processMouseUp`
+
+This allows users to pan the workflow canvas even when their mouse cursor is hovering over your DOM widget.
+
+---
+
+## 8. Event Handling in Vue DOM Render Mode
+
+ComfyUI frontend supports two rendering modes for nodes:
+- **Legacy Canvas Mode**: Traditional rendering where widgets are rendered on top of the canvas using absolute positioning
+- **Vue DOM Render Mode**: New rendering mode where nodes and widgets are rendered as Vue components
+
+In Vue DOM render mode, event handling works differently. The frontend uses `useCanvasInteractions` composable to manage event forwarding to the canvas. This can cause custom event handlers in your widgets (e.g., mouse wheel for sliders, custom drag operations) to be intercepted by the canvas.
+
+### 8.1 Wheel Event Handling
+
+By default in Vue DOM render mode, wheel events on widgets may be forwarded to the canvas for workflow zoom, overriding your custom wheel handlers (e.g., adjusting slider values with mouse wheel).
+
+To fix this, use the `data-capture-wheel="true"` attribute on elements that should capture wheel events:
+
+```vue
+<!-- Vue component template -->
+<div class="my-slider" data-capture-wheel="true" @wheel="onWheel">
+  <!-- Slider content -->
+</div>
+
+<script setup lang="ts">
+const onWheel = (event: WheelEvent) => {
+  event.preventDefault()
+  // Custom wheel handling logic here
+}
+</script>
+```
+
+**How it works:**
+- ComfyUI's `useCanvasInteractions.ts` checks `target?.closest('[data-capture-wheel="true"]')` before forwarding wheel events
+- If an element (or its ancestor) has this attribute, wheel events are not forwarded to canvas
+- Your custom `@wheel` handler will work as expected
+
+**Granular control:**
+- Apply `data-capture-wheel="true"` to specific interactive elements (e.g., sliders, scrollable areas)
+- Widget container without this attribute will allow workflow zoom when wheel is used elsewhere
+- This allows users to both: adjust widget values with wheel, and zoom workflow with wheel in widget's non-interactive areas
+
+**Example from DualRangeSlider.vue:**
+```vue
+<template>
+  <div
+    class="dual-range-slider"
+    :class="{ disabled, 'is-dragging': dragging !== null }"
+    data-capture-wheel="true"
+    @wheel="onWheel"
+  >
+    <!-- Slider tracks and handles -->
+  </div>
+</template>
+```
+
+### 8.2 Pointer Event Handling
+
+In Vue DOM render mode, pointer events (click, drag, etc.) may also be captured by the canvas system. For custom drag operations:
+
+1. **Use event modifiers to stop propagation:**
+   ```vue
+   <div
+     @pointerdown.stop="startDrag"
+     @pointermove.stop="onDrag"
+     @pointerup.stop="stopDrag"
+   >
+   ```
+
+2. **Use pointer capture for reliable drag tracking:**
+   ```javascript
+   const startDrag = (event: PointerEvent) => {
+     const target = event.currentTarget as HTMLElement
+     target.setPointerCapture(event.pointerId)
+     // ... drag initialization
+   }
+
+   const stopDrag = (event: PointerEvent) => {
+     const target = event.currentTarget as HTMLElement
+     target.releasePointerCapture(event.pointerId)
+     // ... drag cleanup
+   }
+   ```
+
+3. **Use `touch-action: none` CSS for touch devices:**
+   ```css
+   .my-draggable {
+     touch-action: none;
+   }
+   ```
+
+### 8.3 Compatibility Checklist
+
+Ensure your widget works in both rendering modes:
+
+| Feature | Canvas Mode | Vue DOM Mode | Solution |
+|---------|-------------|--------------|----------|
+| Mouse wheel on sliders | Works by default | Needs `data-capture-wheel` | Add `data-capture-wheel="true"` to slider elements |
+| Custom drag operations | Works with `stopPropagation()` | Needs `stopPropagation()` | Use `.stop` modifier and pointer capture |
+| Middle mouse panning | Manual forwarding required | Manual forwarding required | Use `forwardMiddleMouseToCanvas()` |
+| Workflow zoom on widget edges | Works by default | Works by default | No action needed (works by default) |
+
+### 8.4 Testing Recommendations
+
+Test your widget in both rendering modes:
+1. Toggle between Canvas Mode and Vue DOM Mode in ComfyUI settings
+2. Verify custom interactions (wheel, drag, etc.) work in both modes
+3. Verify canvas interactions (zoom, pan) still work when cursor is over non-interactive widget areas
+4. Test with touch devices if applicable
+
+---
+
+## 9. Complete Example: Text Counter
+
+This example implements a simple widget that displays the character count of another text widget in the same node.
+
+```javascript
+import { app } from "../../scripts/app.js";
+
+app.registerExtension({
+    name: "Comfy.TextCounter",
+    getCustomWidgets() {
+        return {
+            TEXT_COUNTER(node, inputName) {
+                const el = document.createElement("div");
+                Object.assign(el.style, {
+                    background: "#222",
+                    border: "1px solid #444",
+                    padding: "8px",
+                    borderRadius: "4px",
+                    fontSize: "12px",
+                    color: "#eee"
+                });
+                
+                const label = document.createElement("span");
+                label.innerText = "Characters: 0";
+                el.appendChild(label);
+
+                const widget = node.addDOMWidget(inputName, "TEXT_COUNTER", el, {
+                    getValue() { return ""; }, // Nothing to save
+                    setValue(v) { },           // Nothing to restore
+                    getMinHeight() { return 40; }
+                });
+                
+                // Disable serialization for this display-only widget
+                widget.serialize = false;
+
+                // Custom method to update UI
+                widget.updateCount = (text) => {
+                    label.innerText = `Characters: ${text.length}`;
+                };
+
+                return { widget };
+            }
+        };
+    },
+    nodeCreated(node) {
+        // Logic to link widgets after the node is initialized
+        if (node.comfyClass === "MyTextNode") {
+            const counterWidget = node.widgets.find(w => w.type === "TEXT_COUNTER");
+            const textWidget = node.widgets.find(w => w.name === "text");
+            
+            if (counterWidget && textWidget) {
+                // Hook into the text widget's callback
+                const oldCallback = textWidget.callback;
+                textWidget.callback = function(v) {
+                    if (oldCallback) oldCallback.apply(this, arguments);
+                    counterWidget.updateCount(v);
+                };
+            }
+        }
+    }
+});
+```

docs/frontend-filtering-test-matrix.md

@@ -21,7 +21,7 @@ This matrix captures the scenarios that Phase 3 frontend tests should cover for
 | ID | Feature | Scenario | LoRAs Expectations | Checkpoints Expectations | Notes |
 | --- | --- | --- | --- | --- | --- |
 | F-01 | Search filter | Typing a query updates `pageState.filters.search`, persists to session, and triggers `resetAndReload` on submit | Validate `SearchManager` writes query and reloads via API stub; confirm LoRA cards pass query downstream | Same as LoRAs | Cover `enter` press and clicking search icon |
-| F-02 | Tag filter | Selecting a tag chip adds it to filters, applies active styling, and reloads results | Tag stored under `filters.tags`; `FilterManager.applyFilters` persists and triggers `resetAndReload(true)` | Same; ensure base model tag set is scoped to checkpoints dataset | Include removal path |
+| F-02 | Tag filter | Selecting a tag chip cycles include ➜ exclude ➜ clear, updates storage, and reloads results | Tag state stored under `filters.tags[tagName] = 'include'|'exclude'`; `FilterManager.applyFilters` persists and triggers `resetAndReload(true)` | Same; ensure base model tag set is scoped to checkpoints dataset | Include removal path |
 | F-03 | Base model filter | Toggling base model checkboxes updates `filters.baseModel`, persists, and reloads | Ensure only LoRA-supported models show; toggle multi-select | Ensure SDXL/Flux base models appear as expected | Capture UI state restored from storage on next init |
 | F-04 | Favorites-only | Clicking favorites toggle updates session flag and calls `resetAndReload(true)` | Button gains `.active` class and API called | Same | Verify duplicates badge refresh when active |
 | F-05 | Sort selection | Changing sort select saves preference (legacy + new format) and reloads | Confirm `PageControls.saveSortPreference` invoked with option and API called | Same with checkpoints-specific defaults | Cover `convertLegacySortFormat` branch |

locales/de.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "Update",
             "updateAvailable": "Update verfügbar"
+        },
+        "usage": {
+            "timesUsed": "Verwendungsanzahl"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "Keine Beispielbild-Ordner mussten bereinigt werden",
             "partial": "Bereinigung abgeschlossen, {failures} Ordner übersprungen",
             "error": "Fehler beim Bereinigen der Beispielbild-Ordner: {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}"
+        },
+        "repairRecipes": {
+            "label": "Recipe-Daten reparieren",
+            "loading": "Recipe-Daten werden repariert...",
+            "success": "{count} Rezepte erfolgreich repariert.",
+            "cancelled": "Reparatur abgebrochen. {count} Rezepte wurden repariert.",
+            "error": "Recipe-Reparatur fehlgeschlagen: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "Ersteller",
                 "title": "Rezept-Titel",
                 "loraName": "LoRA-Dateiname",
-                "loraModel": "LoRA-Modellname"
+                "loraModel": "LoRA-Modellname",
+                "prompt": "Prompt"
             }
         },
         "filter": {
             "title": "Modelle filtern",
             "baseModel": "Basis-Modell",
             "modelTags": "Tags (Top 20)",
+            "modelTypes": "Model Types",
+            "license": "Lizenz",
+            "noCreditRequired": "Kein Credit erforderlich",
+            "allowSellingGeneratedContent": "Verkauf erlaubt",
+            "noTags": "Keine Tags",
             "clearAll": "Alle Filter löschen"
         },
         "theme": {
@@ -210,7 +233,9 @@
             "label": "Einstellungsordner öffnen",
             "tooltip": "Den Ordner mit der settings.json öffnen",
             "success": "Einstellungsordner geöffnet",
-            "failed": "Einstellungsordner konnte nicht geöffnet werden"
+            "failed": "Einstellungsordner konnte nicht geöffnet werden",
+            "copied": "Einstellungspfad in die Zwischenablage kopiert: {{path}}",
+            "clipboardFallback": "Einstellungspfad: {{path}}"
         },
         "sections": {
             "contentFiltering": "Inhaltsfilterung",
@@ -220,10 +245,17 @@
             "priorityTags": "Prioritäts-Tags",
             "downloadPathTemplates": "Download-Pfad-Vorlagen",
             "exampleImages": "Beispielbilder",
+            "updateFlags": "Update-Markierungen",
+            "autoOrganize": "Auto-organize",
             "misc": "Verschiedenes",
             "metadataArchive": "Metadaten-Archiv-Datenbank",
+            "storageLocation": "Einstellungsort",
             "proxySettings": "Proxy-Einstellungen"
         },
+        "storage": {
+            "locationLabel": "Portabler Modus",
+            "locationHelp": "Aktiviere, um settings.json im Repository zu belassen; deaktiviere, um es im Benutzerkonfigurationsordner zu speichern."
+        },
         "contentFiltering": {
             "blurNsfwContent": "NSFW-Inhalte unscharf stellen",
             "blurNsfwContentHelp": "Nicht jugendfreie (NSFW) Vorschaubilder unscharf stellen",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "Videos bei Hover automatisch abspielen",
             "autoplayOnHoverHelp": "Video-Vorschauen nur beim Darüberfahren mit der Maus abspielen"
         },
+        "autoOrganizeExclusions": {
+            "label": "Auto-Organisierungs-Ausnahmen",
+            "placeholder": "Beispiel: curated/*, */backups/*; *_temp.safetensors",
+            "help": "Dateien überspringen, die mit diesen Wildcard-Mustern übereinstimmen. Mehrere Muster mit Kommas oder Semikolons trennen.",
+            "validation": {
+                "noPatterns": "Geben Sie mindestens ein Muster ein, getrennt durch Kommas oder Semikolons.",
+                "saveFailed": "Fehler beim Speichern der Ausschlüsse: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
@@ -256,7 +297,6 @@
                 "hover": "Bei Hover anzeigen"
             },
             "cardInfoDisplayHelp": "Wählen Sie, wann Modellinformationen und Aktionsschaltflächen angezeigt werden sollen",
-
             "modelCardFooterAction": "Aktion der Modellkarten-Schaltfläche",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Beispielbilder öffnen",
@@ -279,6 +319,8 @@
             "defaultLoraRootHelp": "Legen Sie den Standard-LoRA-Stammordner für Downloads, Importe und Verschiebungen fest",
             "defaultCheckpointRoot": "Standard-Checkpoint-Stammordner",
             "defaultCheckpointRootHelp": "Legen Sie den Standard-Checkpoint-Stammordner für Downloads, Importe und Verschiebungen fest",
+            "defaultUnetRoot": "Standard-Diffusion-Modell-Stammordner",
+            "defaultUnetRootHelp": "Legen Sie den Standard-Diffusion-Modell-(UNET)-Stammordner für Downloads, Importe und Verschiebungen fest",
             "defaultEmbeddingRoot": "Standard-Embedding-Stammordner",
             "defaultEmbeddingRootHelp": "Legen Sie den Standard-Embedding-Stammordner für Downloads, Importe und Verschiebungen fest",
             "noDefault": "Kein Standard"
@@ -350,6 +392,14 @@
             "download": "Herunterladen",
             "restartRequired": "Neustart erforderlich"
         },
+        "updateFlagStrategy": {
+            "label": "Strategie für Update-Markierungen",
+            "help": "Entscheide, ob Update-Badges nur dann erscheinen, wenn eine neue Version dasselbe Basismodell wie deine lokalen Dateien verwendet, oder sobald es irgendein neueres Release für dieses Modell gibt.",
+            "options": {
+                "sameBase": "Updates nach Basismodell abgleichen",
+                "any": "Jede verfügbare Aktualisierung markieren"
+            }
+        },
         "misc": {
             "includeTriggerWords": "Trigger Words in LoRA-Syntax einschließen",
             "includeTriggerWordsHelp": "Trainierte Trigger Words beim Kopieren der LoRA-Syntax in die Zwischenablage einschließen"
@@ -409,7 +459,10 @@
                 "dateAsc": "Älteste",
                 "size": "Dateigröße",
                 "sizeDesc": "Größte",
-                "sizeAsc": "Kleinste"
+                "sizeAsc": "Kleinste",
+                "usage": "Anzahl Nutzung",
+                "usageDesc": "Meiste",
+                "usageAsc": "Wenigste"
             },
             "refresh": {
                 "title": "Modelliste aktualisieren",
@@ -472,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Civitai-Daten aktualisieren",
+            "checkUpdates": "Updates prüfen",
             "relinkCivitai": "Mit Civitai neu verknüpfen",
             "copySyntax": "LoRA-Syntax kopieren",
             "copyFilename": "Modell-Dateiname kopieren",
@@ -483,6 +537,7 @@
             "replacePreview": "Vorschau ersetzen",
             "setContentRating": "Inhaltsbewertung festlegen",
             "moveToFolder": "In Ordner verschieben",
+            "repairMetadata": "Metadaten reparieren",
             "excludeModel": "Modell ausschließen",
             "deleteModel": "Modell löschen",
             "shareRecipe": "Rezept teilen",
@@ -493,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA-Rezepte",
+        "actions": {
+            "sendCheckpoint": "Send to ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "Importieren",
@@ -550,10 +608,26 @@
                     "selectLoraRoot": "Bitte wählen Sie ein LoRA-Stammverzeichnis aus"
                 }
             },
+            "sort": {
+                "title": "Rezepte sortieren nach...",
+                "name": "Name",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "Datum",
+                "dateDesc": "Neueste",
+                "dateAsc": "Älteste",
+                "lorasCount": "LoRA-Anzahl",
+                "lorasCountDesc": "Meiste",
+                "lorasCountAsc": "Wenigste"
+            },
             "refresh": {
                 "title": "Rezeptliste aktualisieren"
             },
-            "filteredByLora": "Gefiltert nach LoRA"
+            "filteredByLora": "Gefiltert nach LoRA",
+            "favorites": {
+                "title": "Nur Favoriten anzeigen",
+                "action": "Favoriten"
+            }
         },
         "duplicates": {
             "found": "{count} Duplikat-Gruppen gefunden",
@@ -579,11 +653,25 @@
                 "noMissingLoras": "Keine fehlenden LoRAs zum Herunterladen",
                 "getInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
                 "prepareError": "Fehler beim Vorbereiten der LoRAs für den Download: {message}"
+            },
+            "repair": {
+                "starting": "Rezept-Metadaten werden repariert...",
+                "success": "Rezept-Metadaten erfolgreich repariert",
+                "skipped": "Rezept bereits in der neuesten Version, keine Reparatur erforderlich",
+                "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
+                "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpoint-Modelle"
+        "title": "Checkpoint-Modelle",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben"
+        }
     },
     "embeddings": {
         "title": "Embedding-Modelle"
@@ -600,7 +688,8 @@
         "recursiveUnavailable": "Rekursive Suche ist nur in der Baumansicht verfügbar",
         "collapseAllDisabled": "Im Listenmodus nicht verfügbar",
         "dragDrop": {
-            "unableToResolveRoot": "Zielpfad für das Verschieben konnte nicht ermittelt werden."
+            "unableToResolveRoot": "Zielpfad für das Verschieben konnte nicht ermittelt werden.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -810,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "Dateispeicherort erfolgreich geöffnet",
-                "failed": "Fehler beim Öffnen des Dateispeicherorts"
+                "failed": "Fehler beim Öffnen des Dateispeicherorts",
+                "copied": "Pfad in die Zwischenablage kopiert: {{path}}",
+                "clipboardFallback": "Pfad: {{path}}"
             },
             "metadata": {
                 "version": "Version",
@@ -833,11 +924,13 @@
                 "addPresetParameter": "Voreingestellten Parameter hinzufügen...",
                 "strengthMin": "Stärke Min",
                 "strengthMax": "Stärke Max",
+                "strengthRange": "Stärkenbereich",
                 "strength": "Stärke",
                 "clipStrength": "Clip-Stärke",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Wert",
-                "add": "Hinzufügen"
+                "add": "Hinzufügen",
+                "invalidRange": "Ungültiges Bereichsformat. Verwenden Sie x.x-y.y"
             },
             "triggerWords": {
                 "label": "Trigger Words",
@@ -876,6 +969,23 @@
                 "recipes": "Rezepte",
                 "versions": "Versionen"
             },
+            "navigation": {
+                "label": "Modellnavigation",
+                "previousWithShortcut": "Vorheriges Modell (←)",
+                "nextWithShortcut": "Nächstes Modell (→)",
+                "noPrevious": "Kein vorheriges Modell verfügbar",
+                "noNext": "Kein weiteres Modell verfügbar"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "Ersteller-Angabe erforderlich",
+                "noDerivatives": "Keine gemeinsamen Zusammenführungen",
+                "noReLicense": "Gleiche Berechtigungen erforderlich",
+                "restrictionsLabel": "Lizenzbeschränkungen"
+            },
             "loading": {
                 "exampleImages": "Beispielbilder werden geladen...",
                 "description": "Modellbeschreibung wird geladen...",
@@ -909,6 +1019,18 @@
                     "viewLocalVersions": "Alle lokalen Versionen anzeigen",
                     "viewLocalTooltip": "Demnächst verfügbar"
                 },
+                "filters": {
+                    "label": "Basisfilter",
+                    "state": {
+                        "showAll": "Alle Versionen",
+                        "showSameBase": "Gleiches Basismodell"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "Wechseln, um alle Versionen anzuzeigen",
+                        "showSameBaseVersions": "Wechseln, um nur Versionen mit demselben Basismodell anzuzeigen"
+                    },
+                    "empty": "Keine Versionen entsprechen dem Filter für das aktuelle Basismodell."
+                },
                 "empty": "Noch keine Versionshistorie für dieses Modell vorhanden.",
                 "error": "Versionen konnten nicht geladen werden.",
                 "missingModelId": "Für dieses Modell ist keine Civitai-Model-ID vorhanden.",
@@ -1197,6 +1319,9 @@
             "cannotSend": "Kann Rezept nicht senden: Fehlende Rezept-ID",
             "sendFailed": "Fehler beim Senden des Rezepts an Workflow",
             "sendError": "Fehler beim Senden des Rezepts an Workflow",
+            "missingCheckpointPath": "Checkpoint-Pfad nicht verfügbar",
+            "missingCheckpointInfo": "Checkpoint-Informationen fehlen",
+            "downloadCheckpointFailed": "Checkpoint-Download fehlgeschlagen: {message}",
             "cannotDelete": "Kann Rezept nicht löschen: Fehlende Rezept-ID",
             "deleteConfirmationError": "Fehler beim Anzeigen der Löschbestätigung",
             "deletedSuccessfully": "Rezept erfolgreich gelöscht",
@@ -1254,6 +1379,7 @@
             "verificationCompleteSuccess": "Verifikation abgeschlossen. Alle Dateien sind bestätigte Duplikate.",
             "verificationFailed": "Fehler beim Verifizieren der Hashes: {message}",
             "noTagsToAdd": "Keine Tags zum Hinzufügen",
+            "bulkTagsUpdating": "Tags für {count} Modell(e) werden aktualisiert...",
             "tagsAddedSuccessfully": "Erfolgreich {tagCount} Tag(s) zu {count} {type}(s) hinzugefügt",
             "tagsReplacedSuccessfully": "Tags für {count} {type}(s) erfolgreich durch {tagCount} Tag(s) ersetzt",
             "tagsAddFailed": "Fehler beim Hinzufügen von Tags zu {count} Modell(en)",
@@ -1267,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "Fehler beim Laden der LoRA-Stammverzeichnisse: {message}",
             "checkpointRootsFailed": "Fehler beim Laden der Checkpoint-Stammverzeichnisse: {message}",
+            "unetRootsFailed": "Fehler beim Laden der Diffusion-Modell-Stammverzeichnisse: {message}",
             "embeddingRootsFailed": "Fehler beim Laden der Embedding-Stammverzeichnisse: {message}",
             "mappingsUpdated": "Basis-Modell-Pfad-Zuordnungen aktualisiert ({count} Zuordnung{plural})",
             "mappingsCleared": "Basis-Modell-Pfad-Zuordnungen gelöscht",
@@ -1303,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "Konnte trainierte Wörter nicht laden",
-            "tooLong": "Trigger Word sollte 30 Wörter nicht überschreiten",
+            "tooLong": "Trigger Word sollte 100 Wörter nicht überschreiten",
             "tooMany": "Maximal 30 Trigger Words erlaubt",
             "alreadyExists": "Dieses Trigger Word existiert bereits",
             "updateSuccess": "Trigger Words erfolgreich aktualisiert",
@@ -1374,6 +1501,8 @@
             "metadataRefreshed": "Metadaten erfolgreich aktualisiert",
             "metadataRefreshFailed": "Fehler beim Aktualisieren der Metadaten: {message}",
             "metadataUpdateComplete": "Metadaten-Update abgeschlossen",
+            "operationCancelled": "Vorgang vom Benutzer abgebrochen",
+            "operationCancelledPartial": "Vorgang abgebrochen. {success} Elemente verarbeitet.",
             "metadataFetchFailed": "Fehler beim Abrufen der Metadaten: {message}",
             "bulkMetadataCompleteAll": "Alle {count} {type}s erfolgreich aktualisiert",
             "bulkMetadataCompletePartial": "{success} von {total} {type}s aktualisiert",
@@ -1390,7 +1519,8 @@
             "bulkMoveFailures": "Fehlgeschlagene Verschiebungen:\n{failures}",
             "bulkMoveSuccess": "{successCount} {type}s erfolgreich verschoben",
             "exampleImagesDownloadSuccess": "Beispielbilder erfolgreich heruntergeladen!",
-            "exampleImagesDownloadFailed": "Fehler beim Herunterladen der Beispielbilder: {message}"
+            "exampleImagesDownloadFailed": "Fehler beim Herunterladen der Beispielbilder: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/en.json

@@ -32,7 +32,7 @@
             "korean": "한국어",
             "french": "Français",
             "spanish": "Español",
-			"Hebrew": "עברית"
+            "Hebrew": "עברית"
         },
         "fileSize": {
             "zero": "0 Bytes",
@@ -131,6 +131,9 @@
         "badges": {
             "update": "Update",
             "updateAvailable": "Update available"
+        },
+        "usage": {
+            "timesUsed": "Times used"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "No example image folders needed cleanup",
             "partial": "Cleanup completed with {failures} folder(s) skipped",
             "error": "Failed to clean example image folders: {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}"
+        },
+        "repairRecipes": {
+            "label": "Repair recipes data",
+            "loading": "Repairing recipe data...",
+            "success": "Successfully repaired {count} recipes.",
+            "cancelled": "Repair cancelled. {count} recipes were repaired.",
+            "error": "Recipe repair failed: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "Creator",
                 "title": "Recipe Title",
                 "loraName": "LoRA Filename",
-                "loraModel": "LoRA Model Name"
+                "loraModel": "LoRA Model Name",
+                "prompt": "Prompt"
             }
         },
         "filter": {
             "title": "Filter Models",
             "baseModel": "Base Model",
             "modelTags": "Tags (Top 20)",
+            "modelTypes": "Model Types",
+            "license": "License",
+            "noCreditRequired": "No Credit Required",
+            "allowSellingGeneratedContent": "Allow Selling",
+            "noTags": "No tags",
             "clearAll": "Clear All Filters"
         },
         "theme": {
@@ -208,9 +231,11 @@
         "civitaiApiKeyHelp": "Used for authentication when downloading models from Civitai",
         "openSettingsFileLocation": {
             "label": "Open settings folder",
-            "tooltip": "Open the folder containing settings.json",
+            "tooltip": "Open folder containing settings.json",
             "success": "Opened settings.json folder",
-            "failed": "Failed to open settings.json folder"
+            "failed": "Failed to open settings.json folder",
+            "copied": "Settings path copied to clipboard: {{path}}",
+            "clipboardFallback": "Settings path: {{path}}"
         },
         "sections": {
             "contentFiltering": "Content Filtering",
@@ -220,10 +245,17 @@
             "priorityTags": "Priority Tags",
             "downloadPathTemplates": "Download Path Templates",
             "exampleImages": "Example Images",
+            "updateFlags": "Update Flags",
+            "autoOrganize": "Auto-organize",
             "misc": "Misc.",
             "metadataArchive": "Metadata Archive Database",
+            "storageLocation": "Settings Location",
             "proxySettings": "Proxy Settings"
         },
+        "storage": {
+            "locationLabel": "Portable mode",
+            "locationHelp": "Enable to keep settings.json inside the repository; disable to store it in your user config directory."
+        },
         "contentFiltering": {
             "blurNsfwContent": "Blur NSFW Content",
             "blurNsfwContentHelp": "Blur mature (NSFW) content preview images",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "Autoplay Videos on Hover",
             "autoplayOnHoverHelp": "Only play video previews when hovering over them"
         },
+        "autoOrganizeExclusions": {
+            "label": "Auto-organize exclusions",
+            "placeholder": "Example: curated/*, */backups/*; *_temp.safetensors",
+            "help": "Skip moving files that match these wildcard patterns. Separate multiple patterns with commas or semicolons.",
+            "validation": {
+                "noPatterns": "Enter at least one pattern separated by commas or semicolons.",
+                "saveFailed": "Unable to save exclusions: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Display Density",
             "displayDensityOptions": {
@@ -275,11 +316,13 @@
             "loadingLibraries": "Loading libraries...",
             "noLibraries": "No libraries configured",
             "defaultLoraRoot": "Default LoRA Root",
-            "defaultLoraRootHelp": "Set the default LoRA root directory for downloads, imports and moves",
+            "defaultLoraRootHelp": "Set default LoRA root directory for downloads, imports and moves",
             "defaultCheckpointRoot": "Default Checkpoint Root",
-            "defaultCheckpointRootHelp": "Set the default checkpoint root directory for downloads, imports and moves",
+            "defaultCheckpointRootHelp": "Set default checkpoint root directory for downloads, imports and moves",
+            "defaultUnetRoot": "Default Diffusion Model Root",
+            "defaultUnetRootHelp": "Set default diffusion model (UNET) root directory for downloads, imports and moves",
             "defaultEmbeddingRoot": "Default Embedding Root",
-            "defaultEmbeddingRootHelp": "Set the default embedding root directory for downloads, imports and moves",
+            "defaultEmbeddingRootHelp": "Set default embedding root directory for downloads, imports and moves",
             "noDefault": "No Default"
         },
         "priorityTags": {
@@ -309,7 +352,7 @@
             "templateOptions": {
                 "flatStructure": "Flat Structure",
                 "byBaseModel": "By Base Model",
-                "byAuthor": "By Author", 
+                "byAuthor": "By Author",
                 "byFirstTag": "By First Tag",
                 "baseModelFirstTag": "Base Model + First Tag",
                 "baseModelAuthor": "Base Model + Author",
@@ -320,7 +363,7 @@
             "customTemplatePlaceholder": "Enter custom template (e.g., {base_model}/{author}/{first_tag})",
             "modelTypes": {
                 "lora": "LoRA",
-                "checkpoint": "Checkpoint", 
+                "checkpoint": "Checkpoint",
                 "embedding": "Embedding"
             },
             "baseModelPathMappings": "Base Model Path Mappings",
@@ -349,6 +392,14 @@
             "download": "Download",
             "restartRequired": "Requires restart"
         },
+        "updateFlagStrategy": {
+            "label": "Update Flag Strategy",
+            "help": "Decide whether update badges should only appear when a new release shares the same base model as your local files or whenever any newer version exists for that model.",
+            "options": {
+                "sameBase": "Match updates by base model",
+                "any": "Flag any available update"
+            }
+        },
         "misc": {
             "includeTriggerWords": "Include Trigger Words in LoRA Syntax",
             "includeTriggerWordsHelp": "Include trained trigger words when copying LoRA syntax to clipboard"
@@ -385,11 +436,11 @@
             "proxyHost": "Proxy Host",
             "proxyHostPlaceholder": "proxy.example.com",
             "proxyHostHelp": "The hostname or IP address of your proxy server",
-            "proxyPort": "Proxy Port", 
+            "proxyPort": "Proxy Port",
             "proxyPortPlaceholder": "8080",
             "proxyPortHelp": "The port number of your proxy server",
             "proxyUsername": "Username (Optional)",
-            "proxyUsernamePlaceholder": "username", 
+            "proxyUsernamePlaceholder": "username",
             "proxyUsernameHelp": "Username for proxy authentication (if required)",
             "proxyPassword": "Password (Optional)",
             "proxyPasswordPlaceholder": "password",
@@ -408,7 +459,10 @@
                 "dateAsc": "Oldest",
                 "size": "File Size",
                 "sizeDesc": "Largest",
-                "sizeAsc": "Smallest"
+                "sizeAsc": "Smallest",
+                "usage": "Use Count",
+                "usageDesc": "Most",
+                "usageAsc": "Least"
             },
             "refresh": {
                 "title": "Refresh model list",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Refresh Civitai Data",
+            "checkUpdates": "Check Updates",
             "relinkCivitai": "Re-link to Civitai",
             "copySyntax": "Copy LoRA Syntax",
             "copyFilename": "Copy Model Filename",
@@ -482,6 +537,7 @@
             "replacePreview": "Replace Preview",
             "setContentRating": "Set Content Rating",
             "moveToFolder": "Move to Folder",
+            "repairMetadata": "Repair metadata",
             "excludeModel": "Exclude Model",
             "deleteModel": "Delete Model",
             "shareRecipe": "Share Recipe",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA Recipes",
+        "actions": {
+            "sendCheckpoint": "Send to ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "Import",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "Please select a LoRA root directory"
                 }
             },
+            "sort": {
+                "title": "Sort recipes by...",
+                "name": "Name",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "Date",
+                "dateDesc": "Newest",
+                "dateAsc": "Oldest",
+                "lorasCount": "LoRA Count",
+                "lorasCountDesc": "Most",
+                "lorasCountAsc": "Least"
+            },
             "refresh": {
                 "title": "Refresh recipe list"
             },
-            "filteredByLora": "Filtered by LoRA"
+            "filteredByLora": "Filtered by LoRA",
+            "favorites": {
+                "title": "Show Favorites Only",
+                "action": "Favorites"
+            }
         },
         "duplicates": {
             "found": "Found {count} duplicate groups",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "No missing LoRAs to download",
                 "getInfoFailed": "Failed to get information for missing LoRAs",
                 "prepareError": "Error preparing LoRAs for download: {message}"
+            },
+            "repair": {
+                "starting": "Repairing recipe metadata...",
+                "success": "Recipe metadata repaired successfully",
+                "skipped": "Recipe already at latest version, no repair needed",
+                "failed": "Failed to repair recipe: {message}",
+                "missingId": "Cannot repair recipe: Missing recipe ID"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpoint Models"
+        "title": "Checkpoint Models",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "Move to {otherType} Folder"
+        }
     },
     "embeddings": {
         "title": "Embedding Models"
@@ -599,7 +688,8 @@
         "recursiveUnavailable": "Recursive search is available in tree view only",
         "collapseAllDisabled": "Not available in list view",
         "dragDrop": {
-            "unableToResolveRoot": "Unable to determine destination path for move."
+            "unableToResolveRoot": "Unable to determine destination path for move.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "File location opened successfully",
-                "failed": "Failed to open file location"
+                "failed": "Failed to open file location",
+                "copied": "Path copied to clipboard: {{path}}",
+                "clipboardFallback": "Path: {{path}}"
             },
             "metadata": {
                 "version": "Version",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "Add preset parameter...",
                 "strengthMin": "Strength Min",
                 "strengthMax": "Strength Max",
+                "strengthRange": "Strength Range",
                 "strength": "Strength",
                 "clipStrength": "Clip Strength",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Value",
-                "add": "Add"
+                "add": "Add",
+                "invalidRange": "Invalid range format. Use x.x-y.y"
             },
             "triggerWords": {
                 "label": "Trigger Words",
@@ -875,6 +969,23 @@
                 "recipes": "Recipes",
                 "versions": "Versions"
             },
+            "navigation": {
+                "label": "Model navigation",
+                "previousWithShortcut": "Previous model (←)",
+                "nextWithShortcut": "Next model (→)",
+                "noPrevious": "No previous model available",
+                "noNext": "No next model available"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "Creator credit required",
+                "noDerivatives": "No sharing merges",
+                "noReLicense": "Same permissions required",
+                "restrictionsLabel": "License restrictions"
+            },
             "loading": {
                 "exampleImages": "Loading example images...",
                 "description": "Loading model description...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "View all local versions",
                     "viewLocalTooltip": "Coming soon"
                 },
+                "filters": {
+                    "label": "Base filter",
+                    "state": {
+                        "showAll": "All versions",
+                        "showSameBase": "Same base"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "Switch to showing all versions",
+                        "showSameBaseVersions": "Switch to showing only versions that match the current base model"
+                    },
+                    "empty": "No versions match the current base model filter."
+                },
                 "empty": "No version history available for this model yet.",
                 "error": "Failed to load versions.",
                 "missingModelId": "This model is missing a Civitai model id.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "Cannot send recipe: Missing recipe ID",
             "sendFailed": "Failed to send recipe to workflow",
             "sendError": "Error sending recipe to workflow",
+            "missingCheckpointPath": "Checkpoint path not available",
+            "missingCheckpointInfo": "Missing checkpoint information",
+            "downloadCheckpointFailed": "Failed to download checkpoint: {message}",
             "cannotDelete": "Cannot delete recipe: Missing recipe ID",
             "deleteConfirmationError": "Error showing delete confirmation",
             "deletedSuccessfully": "Recipe deleted successfully",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "Verification complete. All files are confirmed duplicates.",
             "verificationFailed": "Failed to verify hashes: {message}",
             "noTagsToAdd": "No tags to add",
+            "bulkTagsUpdating": "Updating tags for {count} model(s)...",
             "tagsAddedSuccessfully": "Successfully added {tagCount} tag(s) to {count} {type}(s)",
             "tagsReplacedSuccessfully": "Successfully replaced tags for {count} {type}(s) with {tagCount} tag(s)",
             "tagsAddFailed": "Failed to add tags to {count} model(s)",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "Failed to load LoRA roots: {message}",
             "checkpointRootsFailed": "Failed to load checkpoint roots: {message}",
+            "unetRootsFailed": "Failed to load diffusion model roots: {message}",
             "embeddingRootsFailed": "Failed to load embedding roots: {message}",
             "mappingsUpdated": "Base model path mappings updated ({count} mapping{plural})",
             "mappingsCleared": "Base model path mappings cleared",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "Could not load trained words",
-            "tooLong": "Trigger word should not exceed 30 words",
+            "tooLong": "Trigger word should not exceed 100 words",
             "tooMany": "Maximum 30 trigger words allowed",
             "alreadyExists": "This trigger word already exists",
             "updateSuccess": "Trigger words updated successfully",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "Metadata refreshed successfully",
             "metadataRefreshFailed": "Failed to refresh metadata: {message}",
             "metadataUpdateComplete": "Metadata update complete",
+            "operationCancelled": "Operation cancelled by user",
+            "operationCancelledPartial": "Operation cancelled. {success} items processed.",
             "metadataFetchFailed": "Failed to fetch metadata: {message}",
             "bulkMetadataCompleteAll": "Successfully refreshed all {count} {type}s",
             "bulkMetadataCompletePartial": "Refreshed {success} of {total} {type}s",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "Failed moves:\n{failures}",
             "bulkMoveSuccess": "Successfully moved {successCount} {type}s",
             "exampleImagesDownloadSuccess": "Successfully downloaded example images!",
-            "exampleImagesDownloadFailed": "Failed to download example images: {message}"
+            "exampleImagesDownloadFailed": "Failed to download example images: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {
@@ -1407,4 +1538,4 @@
             "learnMore": "LM Civitai Extension Tutorial"
         }
     }
-}
+}

locales/es.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "Actualización",
             "updateAvailable": "Actualización disponible"
+        },
+        "usage": {
+            "timesUsed": "Veces usado"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "No hay carpetas de imágenes de ejemplo que necesiten limpieza",
             "partial": "Limpieza completada con {failures} carpeta(s) omitidas",
             "error": "No se pudieron limpiar las carpetas de imágenes de ejemplo: {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}"
+        },
+        "repairRecipes": {
+            "label": "Reparar datos de recetas",
+            "loading": "Reparando datos de recetas...",
+            "success": "Se repararon con éxito {count} recetas.",
+            "cancelled": "Reparación cancelada. {count} recetas fueron reparadas.",
+            "error": "Error al reparar recetas: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "Creador",
                 "title": "Título de la receta",
                 "loraName": "Nombre de archivo LoRA",
-                "loraModel": "Nombre del modelo LoRA"
+                "loraModel": "Nombre del modelo LoRA",
+                "prompt": "Prompt"
             }
         },
         "filter": {
             "title": "Filtrar modelos",
             "baseModel": "Modelo base",
             "modelTags": "Etiquetas (Top 20)",
+            "modelTypes": "Model Types",
+            "license": "Licencia",
+            "noCreditRequired": "Sin crédito requerido",
+            "allowSellingGeneratedContent": "Venta permitida",
+            "noTags": "Sin etiquetas",
             "clearAll": "Limpiar todos los filtros"
         },
         "theme": {
@@ -210,7 +233,9 @@
             "label": "Abrir carpeta de ajustes",
             "tooltip": "Abrir la carpeta que contiene settings.json",
             "success": "Carpeta de settings.json abierta",
-            "failed": "No se pudo abrir la carpeta de settings.json"
+            "failed": "No se pudo abrir la carpeta de settings.json",
+            "copied": "Ruta de configuración copiada al portapapeles: {{path}}",
+            "clipboardFallback": "Ruta de configuración: {{path}}"
         },
         "sections": {
             "contentFiltering": "Filtrado de contenido",
@@ -220,10 +245,17 @@
             "priorityTags": "Etiquetas prioritarias",
             "downloadPathTemplates": "Plantillas de rutas de descarga",
             "exampleImages": "Imágenes de ejemplo",
+            "updateFlags": "Indicadores de actualización",
+            "autoOrganize": "Auto-organize",
             "misc": "Varios",
             "metadataArchive": "Base de datos de archivo de metadatos",
+            "storageLocation": "Ubicación de ajustes",
             "proxySettings": "Configuración de proxy"
         },
+        "storage": {
+            "locationLabel": "Modo portátil",
+            "locationHelp": "Activa para mantener settings.json dentro del repositorio; desactívalo para guardarlo en tu directorio de configuración de usuario."
+        },
         "contentFiltering": {
             "blurNsfwContent": "Difuminar contenido NSFW",
             "blurNsfwContentHelp": "Difuminar imágenes de vista previa de contenido para adultos (NSFW)",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "Reproducir videos automáticamente al pasar el ratón",
             "autoplayOnHoverHelp": "Solo reproducir vistas previas de video al pasar el ratón sobre ellas"
         },
+        "autoOrganizeExclusions": {
+            "label": "Exclusiones de auto-organización",
+            "placeholder": "Ejemplo: curated/*, */backups/*; *_temp.safetensors",
+            "help": "Omitir archivos que coincidan con estos patrones comodín. Separe múltiples patrones con comas o puntos y comas.",
+            "validation": {
+                "noPatterns": "Ingrese al menos un patrón separado por comas o puntos y comas.",
+                "saveFailed": "No se pudieron guardar las exclusiones: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
@@ -278,6 +319,8 @@
             "defaultLoraRootHelp": "Establecer el directorio raíz predeterminado de LoRA para descargas, importaciones y movimientos",
             "defaultCheckpointRoot": "Raíz predeterminada de checkpoint",
             "defaultCheckpointRootHelp": "Establecer el directorio raíz predeterminado de checkpoint para descargas, importaciones y movimientos",
+            "defaultUnetRoot": "Raíz predeterminada de Diffusion Model",
+            "defaultUnetRootHelp": "Establecer el directorio raíz predeterminado de Diffusion Model (UNET) para descargas, importaciones y movimientos",
             "defaultEmbeddingRoot": "Raíz predeterminada de embedding",
             "defaultEmbeddingRootHelp": "Establecer el directorio raíz predeterminado de embedding para descargas, importaciones y movimientos",
             "noDefault": "Sin predeterminado"
@@ -349,6 +392,14 @@
             "download": "Descargar",
             "restartRequired": "Requiere reinicio"
         },
+        "updateFlagStrategy": {
+            "label": "Estrategia de indicadores de actualización",
+            "help": "Decide si las insignias de actualización deben mostrarse solo cuando una nueva versión comparte el mismo modelo base que tus archivos locales o siempre que exista cualquier versión más reciente de ese modelo.",
+            "options": {
+                "sameBase": "Coincidir actualizaciones por modelo base",
+                "any": "Marcar cualquier actualización disponible"
+            }
+        },
         "misc": {
             "includeTriggerWords": "Incluir palabras clave en la sintaxis de LoRA",
             "includeTriggerWordsHelp": "Incluir palabras clave entrenadas al copiar la sintaxis de LoRA al portapapeles"
@@ -408,7 +459,10 @@
                 "dateAsc": "Más antiguo",
                 "size": "Tamaño de archivo",
                 "sizeDesc": "Mayor",
-                "sizeAsc": "Menor"
+                "sizeAsc": "Menor",
+                "usage": "Número de usos",
+                "usageDesc": "Más",
+                "usageAsc": "Menos"
             },
             "refresh": {
                 "title": "Actualizar lista de modelos",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Actualizar datos de Civitai",
+            "checkUpdates": "Comprobar actualizaciones",
             "relinkCivitai": "Re-vincular a Civitai",
             "copySyntax": "Copiar sintaxis de LoRA",
             "copyFilename": "Copiar nombre de archivo del modelo",
@@ -482,6 +537,7 @@
             "replacePreview": "Reemplazar vista previa",
             "setContentRating": "Establecer clasificación de contenido",
             "moveToFolder": "Mover a carpeta",
+            "repairMetadata": "Reparar metadatos",
             "excludeModel": "Excluir modelo",
             "deleteModel": "Eliminar modelo",
             "shareRecipe": "Compartir receta",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "Recetas de LoRA",
+        "actions": {
+            "sendCheckpoint": "Enviar a ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "Importar",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "Por favor selecciona un directorio raíz de LoRA"
                 }
             },
+            "sort": {
+                "title": "Ordenar recetas por...",
+                "name": "Nombre",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "Fecha",
+                "dateDesc": "Más reciente",
+                "dateAsc": "Más antiguo",
+                "lorasCount": "Cant. de LoRAs",
+                "lorasCountDesc": "Más",
+                "lorasCountAsc": "Menos"
+            },
             "refresh": {
                 "title": "Actualizar lista de recetas"
             },
-            "filteredByLora": "Filtrado por LoRA"
+            "filteredByLora": "Filtrado por LoRA",
+            "favorites": {
+                "title": "Mostrar solo favoritos",
+                "action": "Favoritos"
+            }
         },
         "duplicates": {
             "found": "Se encontraron {count} grupos de duplicados",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "No hay LoRAs faltantes para descargar",
                 "getInfoFailed": "Error al obtener información de LoRAs faltantes",
                 "prepareError": "Error preparando LoRAs para descarga: {message}"
+            },
+            "repair": {
+                "starting": "Reparando metadatos de la receta...",
+                "success": "Metadatos de la receta reparados con éxito",
+                "skipped": "La receta ya está en la última versión, no se necesita reparación",
+                "failed": "Error al reparar la receta: {message}",
+                "missingId": "No se puede reparar la receta: falta el ID de la receta"
             }
         }
     },
     "checkpoints": {
-        "title": "Modelos checkpoint"
+        "title": "Modelos checkpoint",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}"
+        }
     },
     "embeddings": {
         "title": "Modelos embedding"
@@ -599,7 +688,8 @@
         "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."
+            "unableToResolveRoot": "No se puede determinar la ruta de destino para el movimiento.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "Ubicación del archivo abierta exitosamente",
-                "failed": "Error al abrir la ubicación del archivo"
+                "failed": "Error al abrir la ubicación del archivo",
+                "copied": "Ruta copiada al portapapeles: {{path}}",
+                "clipboardFallback": "Ruta: {{path}}"
             },
             "metadata": {
                 "version": "Versión",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "Añadir parámetro preestablecido...",
                 "strengthMin": "Fuerza mínima",
                 "strengthMax": "Fuerza máxima",
+                "strengthRange": "Rango de fuerza",
                 "strength": "Fuerza",
                 "clipStrength": "Fuerza de Clip",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Valor",
-                "add": "Añadir"
+                "add": "Añadir",
+                "invalidRange": "Formato de rango inválido. Use x.x-y.y"
             },
             "triggerWords": {
                 "label": "Palabras clave",
@@ -875,6 +969,23 @@
                 "recipes": "Recetas",
                 "versions": "Versiones"
             },
+            "navigation": {
+                "label": "Navegación de modelos",
+                "previousWithShortcut": "Modelo anterior (←)",
+                "nextWithShortcut": "Siguiente modelo (→)",
+                "noPrevious": "No hay modelo anterior disponible",
+                "noNext": "No hay siguiente modelo disponible"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "Crédito del creador requerido",
+                "noDerivatives": "No se permiten fusiones",
+                "noReLicense": "Se requieren mismos permisos",
+                "restrictionsLabel": "Restricciones de licencia"
+            },
             "loading": {
                 "exampleImages": "Cargando imágenes de ejemplo...",
                 "description": "Cargando descripción del modelo...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "Ver todas las versiones locales",
                     "viewLocalTooltip": "Disponible pronto"
                 },
+                "filters": {
+                    "label": "Filtro base",
+                    "state": {
+                        "showAll": "Todas las versiones",
+                        "showSameBase": "Mismo modelo base"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "Cambiar para mostrar todas las versiones",
+                        "showSameBaseVersions": "Cambiar para mostrar solo versiones del mismo modelo base"
+                    },
+                    "empty": "Ninguna versión coincide con el filtro del modelo base actual."
+                },
                 "empty": "Aún no hay historial de versiones para este modelo.",
                 "error": "No se pudieron cargar las versiones.",
                 "missingModelId": "Este modelo no tiene un ID de modelo de Civitai.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "No se puede enviar receta: Falta ID de receta",
             "sendFailed": "Error al enviar receta al flujo de trabajo",
             "sendError": "Error enviando receta al flujo de trabajo",
+            "missingCheckpointPath": "Ruta del checkpoint no disponible",
+            "missingCheckpointInfo": "Falta información del checkpoint",
+            "downloadCheckpointFailed": "Error al descargar el checkpoint: {message}",
             "cannotDelete": "No se puede eliminar receta: Falta ID de receta",
             "deleteConfirmationError": "Error mostrando confirmación de eliminación",
             "deletedSuccessfully": "Receta eliminada exitosamente",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "Verificación completa. Todos los archivos son confirmados duplicados.",
             "verificationFailed": "Error al verificar hashes: {message}",
             "noTagsToAdd": "No hay etiquetas para añadir",
+            "bulkTagsUpdating": "Actualizando etiquetas para {count} modelo(s)...",
             "tagsAddedSuccessfully": "Se añadieron exitosamente {tagCount} etiqueta(s) a {count} {type}(s)",
             "tagsReplacedSuccessfully": "Se reemplazaron exitosamente las etiquetas de {count} {type}(s) con {tagCount} etiqueta(s)",
             "tagsAddFailed": "Error al añadir etiquetas a {count} modelo(s)",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "Error al cargar raíces de LoRA: {message}",
             "checkpointRootsFailed": "Error al cargar raíces de checkpoint: {message}",
+            "unetRootsFailed": "Error al cargar raíces de Diffusion Model: {message}",
             "embeddingRootsFailed": "Error al cargar raíces de embedding: {message}",
             "mappingsUpdated": "Mapeos de rutas de modelo base actualizados ({count} mapeo{plural})",
             "mappingsCleared": "Mapeos de rutas de modelo base limpiados",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "No se pudieron cargar palabras entrenadas",
-            "tooLong": "La palabra clave no debe exceder 30 palabras",
+            "tooLong": "La palabra clave no debe exceder 100 palabras",
             "tooMany": "Máximo 30 palabras clave permitidas",
             "alreadyExists": "Esta palabra clave ya existe",
             "updateSuccess": "Palabras clave actualizadas exitosamente",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "Metadatos actualizados exitosamente",
             "metadataRefreshFailed": "Error al actualizar metadatos: {message}",
             "metadataUpdateComplete": "Actualización de metadatos completada",
+            "operationCancelled": "Operación cancelada por el usuario",
+            "operationCancelledPartial": "Operación cancelada. {success} elementos procesados.",
             "metadataFetchFailed": "Error al obtener metadatos: {message}",
             "bulkMetadataCompleteAll": "Actualizados exitosamente todos los {count} {type}s",
             "bulkMetadataCompletePartial": "Actualizados {success} de {total} {type}s",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "Movimientos fallidos:\n{failures}",
             "bulkMoveSuccess": "Movidos exitosamente {successCount} {type}s",
             "exampleImagesDownloadSuccess": "¡Imágenes de ejemplo descargadas exitosamente!",
-            "exampleImagesDownloadFailed": "Error al descargar imágenes de ejemplo: {message}"
+            "exampleImagesDownloadFailed": "Error al descargar imágenes de ejemplo: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/fr.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "Mise à jour",
             "updateAvailable": "Mise à jour disponible"
+        },
+        "usage": {
+            "timesUsed": "Nombre d'utilisations"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "Aucun dossier d'images d'exemple à nettoyer",
             "partial": "Nettoyage terminé avec {failures} dossier(s) ignoré(s)",
             "error": "Échec du nettoyage des dossiers d'images d'exemple : {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}"
+        },
+        "repairRecipes": {
+            "label": "Réparer les données de recettes",
+            "loading": "Réparation des données de recettes...",
+            "success": "{count} recettes réparées avec succès.",
+            "cancelled": "Réparation annulée. {count} recettes ont été réparées.",
+            "error": "Échec de la réparation des recettes : {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "Créateur",
                 "title": "Titre de la recipe",
                 "loraName": "Nom de fichier LoRA",
-                "loraModel": "Nom du modèle LoRA"
+                "loraModel": "Nom du modèle LoRA",
+                "prompt": "Prompt"
             }
         },
         "filter": {
             "title": "Filtrer les modèles",
             "baseModel": "Modèle de base",
             "modelTags": "Tags (Top 20)",
+            "modelTypes": "Model Types",
+            "license": "Licence",
+            "noCreditRequired": "Crédit non requis",
+            "allowSellingGeneratedContent": "Vente autorisée",
+            "noTags": "Aucun tag",
             "clearAll": "Effacer tous les filtres"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "Ouvrir le dossier des paramètres",
             "tooltip": "Ouvrir le dossier contenant settings.json",
             "success": "Dossier settings.json ouvert",
-            "failed": "Impossible d'ouvrir le dossier settings.json"
+            "failed": "Impossible d'ouvrir le dossier settings.json",
+            "copied": "Chemin des paramètres copié dans le presse-papiers: {{path}}",
+            "clipboardFallback": "Chemin des paramètres: {{path}}"
         },
         "sections": {
             "contentFiltering": "Filtrage du contenu",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
             "folderSettings": "Paramètres des dossiers",
+            "priorityTags": "Étiquettes prioritaires",
             "downloadPathTemplates": "Modèles de chemin de téléchargement",
             "exampleImages": "Images d'exemple",
+            "updateFlags": "Indicateurs de mise à jour",
+            "autoOrganize": "Auto-organize",
             "misc": "Divers",
             "metadataArchive": "Base de données d'archive des métadonnées",
-            "proxySettings": "Paramètres du proxy",
-            "priorityTags": "Étiquettes prioritaires"
+            "storageLocation": "Emplacement des paramètres",
+            "proxySettings": "Paramètres du proxy"
+        },
+        "storage": {
+            "locationLabel": "Mode portable",
+            "locationHelp": "Activez pour garder settings.json dans le dépôt ; désactivez pour le placer dans votre dossier de configuration utilisateur."
         },
         "contentFiltering": {
             "blurNsfwContent": "Flouter le contenu NSFW",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "Lecture automatique vidéo au survol",
             "autoplayOnHoverHelp": "Lire les aperçus vidéo uniquement lors du survol"
         },
+        "autoOrganizeExclusions": {
+            "label": "Exclusions de l'auto-organisation",
+            "placeholder": "Exemple : curated/*, */backups/*; *_temp.safetensors",
+            "help": "Ignorer les fichiers correspondant à ces motifs génériques. Séparez plusieurs motifs par des virgules ou des points-virgules.",
+            "validation": {
+                "noPatterns": "Entrez au moins un motif séparé par des virgules ou des points-virgules.",
+                "saveFailed": "Impossible d'enregistrer les exclusions : {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "Définir le répertoire racine LoRA par défaut pour les téléchargements, imports et déplacements",
             "defaultCheckpointRoot": "Racine Checkpoint par défaut",
             "defaultCheckpointRootHelp": "Définir le répertoire racine checkpoint par défaut pour les téléchargements, imports et déplacements",
+            "defaultUnetRoot": "Racine Diffusion Model par défaut",
+            "defaultUnetRootHelp": "Définir le répertoire racine Diffusion Model (UNET) par défaut pour les téléchargements, imports et déplacements",
             "defaultEmbeddingRoot": "Racine Embedding par défaut",
             "defaultEmbeddingRootHelp": "Définir le répertoire racine embedding par défaut pour les téléchargements, imports et déplacements",
             "noDefault": "Aucun par défaut"
         },
+        "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))",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "Ouvrir l'aide sur les étiquettes prioritaires",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "Checkpoint",
+                "embedding": "Embedding"
+            },
+            "saveSuccess": "Étiquettes prioritaires mises à jour.",
+            "saveError": "Échec de la mise à jour des étiquettes prioritaires.",
+            "loadingSuggestions": "Chargement des suggestions...",
+            "validation": {
+                "missingClosingParen": "L'entrée {index} n'a pas de parenthèse fermante.",
+                "missingCanonical": "L'entrée {index} doit inclure un nom d'étiquette canonique.",
+                "duplicateCanonical": "L'étiquette canonique \"{tag}\" apparaît plusieurs fois.",
+                "unknown": "Configuration d'étiquettes prioritaires invalide."
+            }
+        },
         "downloadPathTemplates": {
             "title": "Modèles de chemin de téléchargement",
             "help": "Configurer les structures de dossiers pour différents types de modèles lors du téléchargement depuis Civitai.",
@@ -329,6 +392,14 @@
             "download": "Télécharger",
             "restartRequired": "Redémarrage requis"
         },
+        "updateFlagStrategy": {
+            "label": "Stratégie des indicateurs de mise à jour",
+            "help": "Choisissez si les badges de mise à jour doivent apparaître uniquement lorsqu’une nouvelle version partage le même modèle de base que vos fichiers locaux, ou dès qu’il existe une version plus récente pour ce modèle.",
+            "options": {
+                "sameBase": "Faire correspondre les mises à jour par modèle de base",
+                "any": "Signaler n’importe quelle mise à jour disponible"
+            }
+        },
         "misc": {
             "includeTriggerWords": "Inclure les mots-clés dans la syntaxe LoRA",
             "includeTriggerWordsHelp": "Inclure les mots-clés d'entraînement lors de la copie de la syntaxe LoRA dans le presse-papiers"
@@ -374,26 +445,6 @@
             "proxyPassword": "Mot de passe (optionnel)",
             "proxyPasswordPlaceholder": "mot_de_passe",
             "proxyPasswordHelp": "Mot de passe pour l'authentification proxy (si nécessaire)"
-        },
-        "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))",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "Ouvrir l'aide sur les étiquettes prioritaires",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "Checkpoint",
-                "embedding": "Embedding"
-            },
-            "saveSuccess": "Étiquettes prioritaires mises à jour.",
-            "saveError": "Échec de la mise à jour des étiquettes prioritaires.",
-            "loadingSuggestions": "Chargement des suggestions...",
-            "validation": {
-                "missingClosingParen": "L'entrée {index} n'a pas de parenthèse fermante.",
-                "missingCanonical": "L'entrée {index} doit inclure un nom d'étiquette canonique.",
-                "duplicateCanonical": "L'étiquette canonique \"{tag}\" apparaît plusieurs fois.",
-                "unknown": "Configuration d'étiquettes prioritaires invalide."
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "Plus ancien",
                 "size": "Taille du fichier",
                 "sizeDesc": "Plus grand",
-                "sizeAsc": "Plus petit"
+                "sizeAsc": "Plus petit",
+                "usage": "Nombre d'utilisations",
+                "usageDesc": "Plus",
+                "usageAsc": "Moins"
             },
             "refresh": {
                 "title": "Actualiser la liste des modèles",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Actualiser les données Civitai",
+            "checkUpdates": "Vérifier les mises à jour",
             "relinkCivitai": "Relier à nouveau à Civitai",
             "copySyntax": "Copier la syntaxe LoRA",
             "copyFilename": "Copier le nom de fichier du modèle",
@@ -482,6 +537,7 @@
             "replacePreview": "Remplacer l'aperçu",
             "setContentRating": "Définir la classification du contenu",
             "moveToFolder": "Déplacer vers un dossier",
+            "repairMetadata": "Réparer les métadonnées",
             "excludeModel": "Exclure le modèle",
             "deleteModel": "Supprimer le modèle",
             "shareRecipe": "Partager la recipe",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA Recipes",
+        "actions": {
+            "sendCheckpoint": "Envoyer vers ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "Importer",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "Veuillez sélectionner un répertoire racine LoRA"
                 }
             },
+            "sort": {
+                "title": "Trier les recettes par...",
+                "name": "Nom",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "Date",
+                "dateDesc": "Plus récent",
+                "dateAsc": "Plus ancien",
+                "lorasCount": "Nombre de LoRAs",
+                "lorasCountDesc": "Plus",
+                "lorasCountAsc": "Moins"
+            },
             "refresh": {
                 "title": "Actualiser la liste des recipes"
             },
-            "filteredByLora": "Filtré par LoRA"
+            "filteredByLora": "Filtré par LoRA",
+            "favorites": {
+                "title": "Afficher uniquement les favoris",
+                "action": "Favoris"
+            }
         },
         "duplicates": {
             "found": "Trouvé {count} groupes de doublons",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "Aucun LoRA manquant à télécharger",
                 "getInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
                 "prepareError": "Erreur lors de la préparation des LoRAs pour le téléchargement : {message}"
+            },
+            "repair": {
+                "starting": "Réparation des métadonnées de la recette...",
+                "success": "Métadonnées de la recette réparées avec succès",
+                "skipped": "Recette déjà à la version la plus récente, aucune réparation nécessaire",
+                "failed": "Échec de la réparation de la recette : {message}",
+                "missingId": "Impossible de réparer la recette : ID de recette manquant"
             }
         }
     },
     "checkpoints": {
-        "title": "Modèles Checkpoint"
+        "title": "Modèles Checkpoint",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}"
+        }
     },
     "embeddings": {
         "title": "Modèles Embedding"
@@ -599,7 +688,8 @@
         "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."
+            "unableToResolveRoot": "Impossible de déterminer le chemin de destination pour le déplacement.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "Emplacement du fichier ouvert avec succès",
-                "failed": "Échec de l'ouverture de l'emplacement du fichier"
+                "failed": "Échec de l'ouverture de l'emplacement du fichier",
+                "copied": "Chemin copié dans le presse-papiers: {{path}}",
+                "clipboardFallback": "Chemin: {{path}}"
             },
             "metadata": {
                 "version": "Version",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "Ajouter un paramètre prédéfini...",
                 "strengthMin": "Force Min",
                 "strengthMax": "Force Max",
+                "strengthRange": "Gamme de force",
                 "strength": "Force",
                 "clipStrength": "Force Clip",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Valeur",
-                "add": "Ajouter"
+                "add": "Ajouter",
+                "invalidRange": "Format de plage invalide. Utilisez x.x-y.y"
             },
             "triggerWords": {
                 "label": "Mots-clés",
@@ -875,6 +969,23 @@
                 "recipes": "Recipes",
                 "versions": "Versions"
             },
+            "navigation": {
+                "label": "Navigation des modèles",
+                "previousWithShortcut": "Modèle précédent (←)",
+                "nextWithShortcut": "Modèle suivant (→)",
+                "noPrevious": "Aucun modèle précédent",
+                "noNext": "Aucun modèle suivant"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "Crédit du créateur requis",
+                "noDerivatives": "Pas de fusion de partage",
+                "noReLicense": "Mêmes autorisations requises",
+                "restrictionsLabel": "Restrictions de licence"
+            },
             "loading": {
                 "exampleImages": "Chargement des images d'exemple...",
                 "description": "Chargement de la description du modèle...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "Voir toutes les versions locales",
                     "viewLocalTooltip": "Bientôt disponible"
                 },
+                "filters": {
+                    "label": "Filtre de base",
+                    "state": {
+                        "showAll": "Toutes les versions",
+                        "showSameBase": "Même modèle de base"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "Passer à l'affichage de toutes les versions",
+                        "showSameBaseVersions": "Passer à l'affichage des versions du même modèle de base"
+                    },
+                    "empty": "Aucune version ne correspond au filtre du modèle de base actuel."
+                },
                 "empty": "Aucun historique de versions n'est disponible pour ce modèle pour le moment.",
                 "error": "Échec du chargement des versions.",
                 "missingModelId": "Ce modèle ne possède pas d'identifiant de modèle Civitai.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "Impossible d'envoyer la recipe : ID de recipe manquant",
             "sendFailed": "Échec de l'envoi de la recipe vers le workflow",
             "sendError": "Erreur lors de l'envoi de la recipe vers le workflow",
+            "missingCheckpointPath": "Chemin du checkpoint indisponible",
+            "missingCheckpointInfo": "Informations sur le checkpoint manquantes",
+            "downloadCheckpointFailed": "Échec du téléchargement du checkpoint : {message}",
             "cannotDelete": "Impossible de supprimer la recipe : ID de recipe manquant",
             "deleteConfirmationError": "Erreur lors de l'affichage de la confirmation de suppression",
             "deletedSuccessfully": "Recipe supprimée avec succès",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "Vérification terminée. Tous les fichiers sont confirmés comme doublons.",
             "verificationFailed": "Échec de la vérification des hash : {message}",
             "noTagsToAdd": "Aucun tag à ajouter",
+            "bulkTagsUpdating": "Mise à jour des tags pour {count} modèle(s)...",
             "tagsAddedSuccessfully": "{tagCount} tag(s) ajouté(s) avec succès à {count} {type}(s)",
             "tagsReplacedSuccessfully": "Tags remplacés avec succès pour {count} {type}(s) avec {tagCount} tag(s)",
             "tagsAddFailed": "Échec de l'ajout des tags à {count} modèle(s)",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "Échec du chargement des racines LoRA : {message}",
             "checkpointRootsFailed": "Échec du chargement des racines checkpoint : {message}",
+            "unetRootsFailed": "Échec du chargement des racines Diffusion Model : {message}",
             "embeddingRootsFailed": "Échec du chargement des racines embedding : {message}",
             "mappingsUpdated": "Mappages de chemin de modèle de base mis à jour ({count} mappage{plural})",
             "mappingsCleared": "Mappages de chemin de modèle de base effacés",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "Impossible de charger les mots entraînés",
-            "tooLong": "Le mot-clé ne doit pas dépasser 30 mots",
+            "tooLong": "Le mot-clé ne doit pas dépasser 100 mots",
             "tooMany": "Maximum 30 mots-clés autorisés",
             "alreadyExists": "Ce mot-clé existe déjà",
             "updateSuccess": "Mots-clés mis à jour avec succès",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "Métadonnées actualisées avec succès",
             "metadataRefreshFailed": "Échec de l'actualisation des métadonnées : {message}",
             "metadataUpdateComplete": "Mise à jour des métadonnées terminée",
+            "operationCancelled": "Opération annulée par l'utilisateur",
+            "operationCancelledPartial": "Opération annulée. {success} éléments traités.",
             "metadataFetchFailed": "Échec de la récupération des métadonnées : {message}",
             "bulkMetadataCompleteAll": "Actualisation réussie de tous les {count} {type}s",
             "bulkMetadataCompletePartial": "{success} sur {total} {type}s actualisés",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "Échecs de déplacement :\n{failures}",
             "bulkMoveSuccess": "{successCount} {type}s déplacés avec succès",
             "exampleImagesDownloadSuccess": "Images d'exemple téléchargées avec succès !",
-            "exampleImagesDownloadFailed": "Échec du téléchargement des images d'exemple : {message}"
+            "exampleImagesDownloadFailed": "Échec du téléchargement des images d'exemple : {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/he.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "עדכון",
             "updateAvailable": "עדכון זמין"
+        },
+        "usage": {
+            "timesUsed": "מספר שימושים"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "אין תיקיות תמונות דוגמה שזקוקות לניקוי",
             "partial": "הניקוי הושלם עם דילוג על {failures} תיקיות",
             "error": "ניקוי תיקיות תמונות הדוגמה נכשל: {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}"
+        },
+        "repairRecipes": {
+            "label": "תיקון נתוני מתכונים",
+            "loading": "מתקן נתוני מתכונים...",
+            "success": "תוקנו בהצלחה {count} מתכונים.",
+            "cancelled": "תיקון בוטל. {count} מתכונים תוקנו.",
+            "error": "תיקון המתכונים נכשל: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "יוצר",
                 "title": "כותרת מתכון",
                 "loraName": "שם קובץ LoRA",
-                "loraModel": "שם מודל LoRA"
+                "loraModel": "שם מודל LoRA",
+                "prompt": "הנחיה"
             }
         },
         "filter": {
             "title": "סנן מודלים",
             "baseModel": "מודל בסיס",
             "modelTags": "תגיות (20 המובילות)",
+            "modelTypes": "Model Types",
+            "license": "רישיון",
+            "noCreditRequired": "ללא קרדיט נדרש",
+            "allowSellingGeneratedContent": "אפשר מכירה",
+            "noTags": "ללא תגיות",
             "clearAll": "נקה את כל המסננים"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "פתח תיקיית הגדרות",
             "tooltip": "פתח את התיקייה שמכילה את ‎settings.json",
             "success": "תיקיית settings.json נפתחה",
-            "failed": "לא ניתן לפתוח את תיקיית settings.json"
+            "failed": "לא ניתן לפתוח את תיקיית settings.json",
+            "copied": "נתיב ההגדרות הועתק ללוח העריכה: {{path}}",
+            "clipboardFallback": "נתיב ההגדרות: {{path}}"
         },
         "sections": {
             "contentFiltering": "סינון תוכן",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
             "folderSettings": "הגדרות תיקייה",
+            "priorityTags": "תגיות עדיפות",
             "downloadPathTemplates": "תבניות נתיב הורדה",
             "exampleImages": "תמונות דוגמה",
+            "updateFlags": "תגי עדכון",
+            "autoOrganize": "Auto-organize",
             "misc": "שונות",
             "metadataArchive": "מסד נתונים של ארכיון מטא-דאטה",
-            "proxySettings": "הגדרות פרוקסי",
-            "priorityTags": "תגיות עדיפות"
+            "storageLocation": "מיקום ההגדרות",
+            "proxySettings": "הגדרות פרוקסי"
+        },
+        "storage": {
+            "locationLabel": "מצב נייד",
+            "locationHelp": "הפעל כדי לשמור את settings.json בתוך המאגר; בטל כדי לשמור אותו בתיקיית ההגדרות של המשתמש."
         },
         "contentFiltering": {
             "blurNsfwContent": "טשטש תוכן NSFW",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "נגן וידאו אוטומטית בריחוף",
             "autoplayOnHoverHelp": "נגן תצוגות מקדימות של וידאו רק בעת ריחוף מעליהן"
         },
+        "autoOrganizeExclusions": {
+            "label": "יוצא דופן של ארגון אוטומטי",
+            "placeholder": "דוגמה: curated/*, */backups/*; *_temp.safetensors",
+            "help": "דלג על העברת קבצים התואמים לתבניות אלו. הפרד תבניות מרובות בפסיקים או בנקודותיים.",
+            "validation": {
+                "noPatterns": "הזן לפחות תבנית אחת מופרדת בפסיקים או בנקודותיים.",
+                "saveFailed": "לא ניתן לשמור את ההוצאות: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
@@ -271,17 +312,39 @@
         },
         "folderSettings": {
             "activeLibrary": "ספרייה פעילה",
-            "activeLibraryHelp": "החלפה בין הספריות המוגדרות תעדכן את תיקיות ברירת המחדל. שינוי הבחירה ירענן את הדף.",
+            "activeLibraryHelp": "החלפה בין הספריות המוגדרות לעדכן את תיקיות ברירת המחדל. שינוי הבחירה ירענן את הדף.",
             "loadingLibraries": "טוען ספריות...",
             "noLibraries": "לא הוגדרו ספריות",
             "defaultLoraRoot": "תיקיית שורש ברירת מחדל של LoRA",
             "defaultLoraRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של LoRA להורדות, ייבוא והעברות",
             "defaultCheckpointRoot": "תיקיית שורש ברירת מחדל של Checkpoint",
             "defaultCheckpointRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של checkpoint להורדות, ייבוא והעברות",
+            "defaultUnetRoot": "תיקיית שורש ברירת מחדל של Diffusion Model",
+            "defaultUnetRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של Diffusion Model (UNET) להורדות, ייבוא והעברות",
             "defaultEmbeddingRoot": "תיקיית שורש ברירת מחדל של Embedding",
             "defaultEmbeddingRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של embedding להורדות, ייבוא והעברות",
             "noDefault": "אין ברירת מחדל"
         },
+        "priorityTags": {
+            "title": "תגיות עדיפות",
+            "description": "התאם את סדר העדיפות של התגיות עבור כל סוג מודל (לדוגמה: character, concept, style(toon|toon_style))",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "פתח עזרה בנושא תגיות עדיפות",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "Checkpoint",
+                "embedding": "Embedding"
+            },
+            "saveSuccess": "תגיות העדיפות עודכנו.",
+            "saveError": "עדכון תגיות העדיפות נכשל.",
+            "loadingSuggestions": "טוען הצעות...",
+            "validation": {
+                "missingClosingParen": "לרשומה {index} חסר סוגר סוגריים.",
+                "missingCanonical": "על הרשומה {index} לכלול שם תגית קנונית.",
+                "duplicateCanonical": "התגית הקנונית \"{tag}\" מופיעה יותר מפעם אחת.",
+                "unknown": "תצורת תגיות העדיפות שגויה."
+            }
+        },
         "downloadPathTemplates": {
             "title": "תבניות נתיב הורדה",
             "help": "הגדר מבני תיקיות לסוגי מודלים שונים בעת הורדה מ-Civitai.",
@@ -293,8 +356,8 @@
                 "byFirstTag": "לפי תגית ראשונה",
                 "baseModelFirstTag": "מודל בסיס + תגית ראשונה",
                 "baseModelAuthor": "מודל בסיס + יוצר",
-                "baseModelAuthorFirstTag": "מודל בסיס + יוצר + תגית ראשונה",
                 "authorFirstTag": "יוצר + תגית ראשונה",
+                "baseModelAuthorFirstTag": "מודל בסיס + יוצר + תגית ראשונה",
                 "customTemplate": "תבנית מותאמת אישית"
             },
             "customTemplatePlaceholder": "הזן תבנית מותאמת אישית (למשל, {base_model}/{author}/{first_tag})",
@@ -329,6 +392,14 @@
             "download": "הורד",
             "restartRequired": "דורש הפעלה מחדש"
         },
+        "updateFlagStrategy": {
+            "label": "אסטרטגיית תגי עדכון",
+            "help": "בחרו אם תוויות העדכון יוצגו רק כאשר גרסה חדשה חולקת את אותו דגם בסיס כמו הקבצים המקומיים שלכם או בכל מקרה שבו קיימת גרסה חדשה עבור אותו דגם.",
+            "options": {
+                "sameBase": "התאמת עדכונים לפי דגם בסיס",
+                "any": "תוויות לכל עדכון זמין"
+            }
+        },
         "misc": {
             "includeTriggerWords": "כלול מילות טריגר בתחביר LoRA",
             "includeTriggerWordsHelp": "כלול מילות טריגר מאומנות בעת העתקת תחביר LoRA ללוח"
@@ -374,26 +445,6 @@
             "proxyPassword": "סיסמה (אופציונלי)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "סיסמה לאימות מול הפרוקסי (אם נדרש)"
-        },
-        "priorityTags": {
-            "title": "תגיות עדיפות",
-            "description": "התאם את סדר העדיפות של התגיות עבור כל סוג מודל (לדוגמה: character, concept, style(toon|toon_style))",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "פתח עזרה בנושא תגיות עדיפות",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "Checkpoint",
-                "embedding": "Embedding"
-            },
-            "saveSuccess": "תגיות העדיפות עודכנו.",
-            "saveError": "עדכון תגיות העדיפות נכשל.",
-            "loadingSuggestions": "טוען הצעות...",
-            "validation": {
-                "missingClosingParen": "לרשומה {index} חסר סוגר סוגריים.",
-                "missingCanonical": "על הרשומה {index} לכלול שם תגית קנונית.",
-                "duplicateCanonical": "התגית הקנונית \"{tag}\" מופיעה יותר מפעם אחת.",
-                "unknown": "תצורת תגיות העדיפות שגויה."
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "הישן ביותר",
                 "size": "גודל קובץ",
                 "sizeDesc": "הגדול ביותר",
-                "sizeAsc": "הקטן ביותר"
+                "sizeAsc": "הקטן ביותר",
+                "usage": "מספר שימושים",
+                "usageDesc": "הכי הרבה",
+                "usageAsc": "הכי פחות"
             },
             "refresh": {
                 "title": "רענן רשימת מודלים",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "רענן נתוני Civitai",
+            "checkUpdates": "בדוק עדכונים",
             "relinkCivitai": "קשר מחדש ל-Civitai",
             "copySyntax": "העתק תחביר LoRA",
             "copyFilename": "העתק שם קובץ מודל",
@@ -482,6 +537,7 @@
             "replacePreview": "החלף תצוגה מקדימה",
             "setContentRating": "הגדר דירוג תוכן",
             "moveToFolder": "העבר לתיקייה",
+            "repairMetadata": "תיקון מטא-דאטה",
             "excludeModel": "החרג מודל",
             "deleteModel": "מחק מודל",
             "shareRecipe": "שתף מתכון",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "מתכוני LoRA",
+        "actions": {
+            "sendCheckpoint": "שלח ל-ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "ייבא",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "אנא בחר ספריית שורש של LoRA"
                 }
             },
+            "sort": {
+                "title": "מיון מתכונים לפי...",
+                "name": "שם",
+                "nameAsc": "א - ת",
+                "nameDesc": "ת - א",
+                "date": "תאריך",
+                "dateDesc": "הכי חדש",
+                "dateAsc": "הכי ישן",
+                "lorasCount": "מספר LoRAs",
+                "lorasCountDesc": "הכי הרבה",
+                "lorasCountAsc": "הכי פחות"
+            },
             "refresh": {
                 "title": "רענן רשימת מתכונים"
             },
-            "filteredByLora": "מסונן לפי LoRA"
+            "filteredByLora": "מסונן לפי LoRA",
+            "favorites": {
+                "title": "הצג מועדפים בלבד",
+                "action": "מועדפים"
+            }
         },
         "duplicates": {
             "found": "נמצאו {count} קבוצות כפולות",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "אין LoRAs חסרים להורדה",
                 "getInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
                 "prepareError": "שגיאה בהכנת LoRAs להורדה: {message}"
+            },
+            "repair": {
+                "starting": "מתקן מטא-דאטה של מתכון...",
+                "success": "מטא-דאטה של מתכון תוקן בהצלחה",
+                "skipped": "המתכון כבר בגרסה העדכנית ביותר, אין צורך בתיקון",
+                "failed": "תיקון המתכון נכשל: {message}",
+                "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
             }
         }
     },
     "checkpoints": {
-        "title": "מודלי Checkpoint"
+        "title": "מודלי Checkpoint",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}"
+        }
     },
     "embeddings": {
         "title": "מודלי Embedding"
@@ -599,7 +688,8 @@
         "recursiveUnavailable": "חיפוש רקורסיבי זמין רק בתצוגת עץ",
         "collapseAllDisabled": "לא זמין בתצוגת רשימה",
         "dragDrop": {
-            "unableToResolveRoot": "לא ניתן לקבוע את נתיב היעד להעברה."
+            "unableToResolveRoot": "לא ניתן לקבוע את נתיב היעד להעברה.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "מיקום הקובץ נפתח בהצלחה",
-                "failed": "פתיחת מיקום הקובץ נכשלה"
+                "failed": "פתיחת מיקום הקובץ נכשלה",
+                "copied": "הנתיב הועתק ללוח העריכה: {{path}}",
+                "clipboardFallback": "נתיב: {{path}}"
             },
             "metadata": {
                 "version": "גרסה",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "הוסף פרמטר קבוע מראש...",
                 "strengthMin": "חוזק מינימלי",
                 "strengthMax": "חוזק מקסימלי",
+                "strengthRange": "טווח עוצמה",
                 "strength": "חוזק",
                 "clipStrength": "עוצמת CLIP",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "ערך",
-                "add": "הוסף"
+                "add": "הוסף",
+                "invalidRange": "פורמט טווח לא תקין. השתמש ב-x.x-y.y"
             },
             "triggerWords": {
                 "label": "מילות טריגר",
@@ -875,6 +969,23 @@
                 "recipes": "מתכונים",
                 "versions": "גרסאות"
             },
+            "navigation": {
+                "label": "ניווט מודלים",
+                "previousWithShortcut": "המודל הקודם (←)",
+                "nextWithShortcut": "המודל הבא (→)",
+                "noPrevious": "אין מודל קודם זמין",
+                "noNext": "אין מודל נוסף זמין"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "נדרש ייחוס ליוצר",
+                "noDerivatives": "אין שיתוף מיזוגים",
+                "noReLicense": "נדרשות אותן הרשאות",
+                "restrictionsLabel": "הגבלות רישיון"
+            },
             "loading": {
                 "exampleImages": "טוען תמונות דוגמה...",
                 "description": "טוען תיאור מודל...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "הצג את כל הגרסאות המקומיות",
                     "viewLocalTooltip": "יגיע בקרוב"
                 },
+                "filters": {
+                    "label": "מסנן בסיס",
+                    "state": {
+                        "showAll": "כל הגרסאות",
+                        "showSameBase": "אותו מודל בסיס"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "החלף להצגת כל הגרסאות",
+                        "showSameBaseVersions": "החלף להצגת גרסאות עם אותו מודל בסיס"
+                    },
+                    "empty": "אין גרסאות התואמות את המסנן של מודל הבסיס הנוכחי."
+                },
                 "empty": "אין עדיין היסטוריית גרסאות למודל זה.",
                 "error": "טעינת הגרסאות נכשלה.",
                 "missingModelId": "למודל זה אין מזהה מודל של Civitai.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "לא ניתן לשלוח מתכון: חסר מזהה מתכון",
             "sendFailed": "שליחת המתכון ל-workflow נכשלה",
             "sendError": "שגיאה בשליחת המתכון ל-workflow",
+            "missingCheckpointPath": "נתיב ה-checkpoint אינו זמין",
+            "missingCheckpointInfo": "חסרים פרטי checkpoint",
+            "downloadCheckpointFailed": "הורדת checkpoint נכשלה: {message}",
             "cannotDelete": "לא ניתן למחוק מתכון: חסר מזהה מתכון",
             "deleteConfirmationError": "שגיאה בהצגת אישור המחיקה",
             "deletedSuccessfully": "המתכון נמחק בהצלחה",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "האימות הושלם. כל הקבצים אושרו ככפולים.",
             "verificationFailed": "אימות ה-hashes נכשל: {message}",
             "noTagsToAdd": "אין תגיות להוספה",
+            "bulkTagsUpdating": "מעדכן תגיות עבור {count} מודלים...",
             "tagsAddedSuccessfully": "נוספו בהצלחה {tagCount} תגית(ות) ל-{count} {type}(ים)",
             "tagsReplacedSuccessfully": "הוחלפו בהצלחה תגיות עבור {count} {type}(ים) ב-{tagCount} תגית(ות)",
             "tagsAddFailed": "הוספת תגיות ל-{count} מודל(ים) נכשלה",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "טעינת שורשי LoRA נכשלה: {message}",
             "checkpointRootsFailed": "טעינת שורשי checkpoint נכשלה: {message}",
+            "unetRootsFailed": "טעינת שורשי Diffusion Model נכשלה: {message}",
             "embeddingRootsFailed": "טעינת שורשי embedding נכשלה: {message}",
             "mappingsUpdated": "מיפויי נתיבי מודל בסיס עודכנו ({count} מיפוי{plural})",
             "mappingsCleared": "מיפויי נתיבי מודל בסיס נוקו",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "לא ניתן היה לטעון מילים מאומנות",
-            "tooLong": "מילת טריגר לא תעלה על 30 מילים",
+            "tooLong": "מילת טריגר לא תעלה על 100 מילים",
             "tooMany": "מותרות עד 30 מילות טריגר",
             "alreadyExists": "מילת טריגר זו כבר קיימת",
             "updateSuccess": "מילות הטריגר עודכנו בהצלחה",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "המטא-דאטה רועננה בהצלחה",
             "metadataRefreshFailed": "רענון המטא-דאטה נכשל: {message}",
             "metadataUpdateComplete": "עדכון המטא-דאטה הושלם",
+            "operationCancelled": "הפעולה בוטלה על ידי המשתמש",
+            "operationCancelledPartial": "הפעולה בוטלה. {success} פריטים עובדו.",
             "metadataFetchFailed": "אחזור המטא-דאטה נכשל: {message}",
             "bulkMetadataCompleteAll": "רועננו בהצלחה כל {count} ה-{type}s",
             "bulkMetadataCompletePartial": "רועננו {success} מתוך {total} {type}s",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "העברות שנכשלו:\n{failures}",
             "bulkMoveSuccess": "הועברו בהצלחה {successCount} {type}s",
             "exampleImagesDownloadSuccess": "תמונות הדוגמה הורדו בהצלחה!",
-            "exampleImagesDownloadFailed": "הורדת תמונות הדוגמה נכשלה: {message}"
+            "exampleImagesDownloadFailed": "הורדת תמונות הדוגמה נכשלה: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/ja.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "アップデート",
             "updateAvailable": "アップデートがあります"
+        },
+        "usage": {
+            "timesUsed": "使用回数"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "クリーンアップが必要な例画像フォルダはありません",
             "partial": "クリーンアップが完了しましたが、{failures} 個のフォルダはスキップされました",
             "error": "例画像フォルダのクリーンアップに失敗しました：{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}"
+        },
+        "repairRecipes": {
+            "label": "レシピデータの修復",
+            "loading": "レシピデータを修復中...",
+            "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}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "作成者",
                 "title": "レシピタイトル",
                 "loraName": "LoRAファイル名",
-                "loraModel": "LoRAモデル名"
+                "loraModel": "LoRAモデル名",
+                "prompt": "プロンプト"
             }
         },
         "filter": {
             "title": "モデルをフィルタ",
             "baseModel": "ベースモデル",
             "modelTags": "タグ（上位20）",
+            "modelTypes": "Model Types",
+            "license": "ライセンス",
+            "noCreditRequired": "クレジット不要",
+            "allowSellingGeneratedContent": "販売許可",
+            "noTags": "タグなし",
             "clearAll": "すべてのフィルタをクリア"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "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",
             "success": "settings.json フォルダーを開きました",
-            "failed": "settings.json フォルダーを開けませんでした"
+            "failed": "settings.json フォルダーを開けませんでした",
+            "copied": "設定パスをクリップボードにコピーしました: {{path}}",
+            "clipboardFallback": "設定パス: {{path}}"
         },
         "sections": {
             "contentFiltering": "コンテンツフィルタリング",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
             "folderSettings": "フォルダ設定",
+            "priorityTags": "優先タグ",
             "downloadPathTemplates": "ダウンロードパステンプレート",
             "exampleImages": "例画像",
+            "updateFlags": "アップデートフラグ",
+            "autoOrganize": "Auto-organize",
             "misc": "その他",
             "metadataArchive": "メタデータアーカイブデータベース",
-            "proxySettings": "プロキシ設定",
-            "priorityTags": "優先タグ"
+            "storageLocation": "設定の場所",
+            "proxySettings": "プロキシ設定"
+        },
+        "storage": {
+            "locationLabel": "ポータブルモード",
+            "locationHelp": "有効にすると settings.json をリポジトリ内に保持し、無効にするとユーザー設定ディレクトリに格納します。"
         },
         "contentFiltering": {
             "blurNsfwContent": "NSFWコンテンツをぼかす",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "ホバー時に動画を自動再生",
             "autoplayOnHoverHelp": "動画プレビューはホバー時にのみ再生されます"
         },
+        "autoOrganizeExclusions": {
+            "label": "自動整理除外設定",
+            "placeholder": "例: curated/*, */backups/*; *_temp.safetensors",
+            "help": "これらのワイルドカードパターンに一致するファイルの移動をスキップします。複数のパターンはカンマまたはセミコロンで区切ってください。",
+            "validation": {
+                "noPatterns": "カンマまたはセミコロンで区切られた少なくとも1つのパターンを入力してください。",
+                "saveFailed": "除外設定を保存できませんでした: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "表示密度",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "ダウンロード、インポート、移動用のデフォルトLoRAルートディレクトリを設定",
             "defaultCheckpointRoot": "デフォルトCheckpointルート",
             "defaultCheckpointRootHelp": "ダウンロード、インポート、移動用のデフォルトcheckpointルートディレクトリを設定",
+            "defaultUnetRoot": "デフォルトDiffusion Modelルート",
+            "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": "デフォルト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",
             "noDefault": "デフォルトなし"
         },
+        "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))",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "優先タグのヘルプを開く",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "チェックポイント",
+                "embedding": "埋め込み"
+            },
+            "saveSuccess": "優先タグを更新しました。",
+            "saveError": "優先タグの更新に失敗しました。",
+            "loadingSuggestions": "候補を読み込み中...",
+            "validation": {
+                "missingClosingParen": "エントリ {index} に閉じ括弧がありません。",
+                "missingCanonical": "エントリ {index} には正規タグ名を含める必要があります。",
+                "duplicateCanonical": "正規タグ \"{tag}\" が複数回登場しています。",
+                "unknown": "無効な優先タグ設定です。"
+            }
+        },
         "downloadPathTemplates": {
             "title": "ダウンロードパステンプレート",
             "help": "Civitaiからダウンロードする際の異なるモデルタイプのフォルダ構造を設定します。",
@@ -329,6 +392,14 @@
             "download": "ダウンロード",
             "restartRequired": "再起動が必要"
         },
+        "updateFlagStrategy": {
+            "label": "アップデートフラグの表示戦略",
+            "help": "新リリースがローカルファイルと同じベースモデルを共有する場合にのみ更新バッジを表示するか、そのモデルに新しいバージョンがあれば常に表示するかを決めます。",
+            "options": {
+                "sameBase": "ベースモデルで更新をマッチ",
+                "any": "利用可能な更新すべてを表示"
+            }
+        },
         "misc": {
             "includeTriggerWords": "LoRA構文にトリガーワードを含める",
             "includeTriggerWordsHelp": "LoRA構文をクリップボードにコピーする際、学習済みトリガーワードを含めます"
@@ -374,26 +445,6 @@
             "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）"
-        },
-        "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))",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "優先タグのヘルプを開く",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "チェックポイント",
-                "embedding": "埋め込み"
-            },
-            "saveSuccess": "優先タグを更新しました。",
-            "saveError": "優先タグの更新に失敗しました。",
-            "loadingSuggestions": "候補を読み込み中...",
-            "validation": {
-                "missingClosingParen": "エントリ {index} に閉じ括弧がありません。",
-                "missingCanonical": "エントリ {index} には正規タグ名を含める必要があります。",
-                "duplicateCanonical": "正規タグ \"{tag}\" が複数回登場しています。",
-                "unknown": "無効な優先タグ設定です。"
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "古い順",
                 "size": "ファイルサイズ",
                 "sizeDesc": "大きい順",
-                "sizeAsc": "小さい順"
+                "sizeAsc": "小さい順",
+                "usage": "使用回数",
+                "usageDesc": "多い",
+                "usageAsc": "少ない"
             },
             "refresh": {
                 "title": "モデルリストを更新",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Civitaiデータを更新",
+            "checkUpdates": "更新確認",
             "relinkCivitai": "Civitaiに再リンク",
             "copySyntax": "LoRA構文をコピー",
             "copyFilename": "モデルファイル名をコピー",
@@ -482,6 +537,7 @@
             "replacePreview": "プレビューを置換",
             "setContentRating": "コンテンツレーティングを設定",
             "moveToFolder": "フォルダに移動",
+            "repairMetadata": "メタデータを修復",
             "excludeModel": "モデルを除外",
             "deleteModel": "モデルを削除",
             "shareRecipe": "レシピを共有",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRAレシピ",
+        "actions": {
+            "sendCheckpoint": "ComfyUIへ送信"
+        },
         "controls": {
             "import": {
                 "action": "インポート",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "LoRAルートディレクトリを選択してください"
                 }
             },
+            "sort": {
+                "title": "レシピの並び替え...",
+                "name": "名前",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "日付",
+                "dateDesc": "新しい順",
+                "dateAsc": "古い順",
+                "lorasCount": "LoRA数",
+                "lorasCountDesc": "多い順",
+                "lorasCountAsc": "少ない順"
+            },
             "refresh": {
                 "title": "レシピリストを更新"
             },
-            "filteredByLora": "LoRAでフィルタ済み"
+            "filteredByLora": "LoRAでフィルタ済み",
+            "favorites": {
+                "title": "お気に入りのみ表示",
+                "action": "お気に入り"
+            }
         },
         "duplicates": {
             "found": "{count} 個の重複グループが見つかりました",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "ダウンロードする不足LoRAがありません",
                 "getInfoFailed": "不足LoRAの情報取得に失敗しました",
                 "prepareError": "ダウンロード用LoRAの準備中にエラー：{message}"
+            },
+            "repair": {
+                "starting": "レシピのメタデータを修復中...",
+                "success": "レシピのメタデータが正常に修復されました",
+                "skipped": "レシピはすでに最新バージョンです。修復は不要です",
+                "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"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpointモデル"
+        "title": "Checkpointモデル",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "{otherType} フォルダに移動"
+        }
     },
     "embeddings": {
         "title": "Embeddingモデル"
@@ -599,7 +688,8 @@
         "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。"
+            "unableToResolveRoot": "移動先のパスを特定できません。",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "ファイルの場所を正常に開きました",
-                "failed": "ファイルの場所を開くのに失敗しました"
+                "failed": "ファイルの場所を開くのに失敗しました",
+                "copied": "パスをクリップボードにコピーしました: {{path}}",
+                "clipboardFallback": "パス: {{path}}"
             },
             "metadata": {
                 "version": "バージョン",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "プリセットパラメータを追加...",
                 "strengthMin": "強度最小",
                 "strengthMax": "強度最大",
+                "strengthRange": "強度範囲",
                 "strength": "強度",
                 "clipStrength": "クリップ強度",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "値",
-                "add": "追加"
+                "add": "追加",
+                "invalidRange": "無効な範囲形式です。x.x-y.y を使用してください"
             },
             "triggerWords": {
                 "label": "トリガーワード",
@@ -875,6 +969,23 @@
                 "recipes": "レシピ",
                 "versions": "バージョン"
             },
+            "navigation": {
+                "label": "モデルナビゲーション",
+                "previousWithShortcut": "前のモデル（←）",
+                "nextWithShortcut": "次のモデル（→）",
+                "noPrevious": "前のモデルがありません",
+                "noNext": "次のモデルがありません"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "作成者のクレジットが必要",
+                "noDerivatives": "共有マージ不可",
+                "noReLicense": "同じ権限が必要",
+                "restrictionsLabel": "ライセンス制限"
+            },
             "loading": {
                 "exampleImages": "例画像を読み込み中...",
                 "description": "モデル説明を読み込み中...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "ローカルの全バージョンを表示",
                     "viewLocalTooltip": "近日対応予定"
                 },
+                "filters": {
+                    "label": "ベースフィルター",
+                    "state": {
+                        "showAll": "すべてのバージョン",
+                        "showSameBase": "同じベース"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "すべてのバージョンを表示する",
+                        "showSameBaseVersions": "同じベースモデルのバージョンのみ表示する"
+                    },
+                    "empty": "現在のベースモデルフィルターに一致するバージョンがありません。"
+                },
                 "empty": "このモデルにはまだバージョン履歴がありません。",
                 "error": "バージョンの読み込みに失敗しました。",
                 "missingModelId": "このモデルにはCivitaiのモデルIDがありません。",
@@ -1196,6 +1319,9 @@
             "cannotSend": "レシピを送信できません：レシピIDがありません",
             "sendFailed": "レシピのワークフローへの送信に失敗しました",
             "sendError": "レシピのワークフロー送信エラー",
+            "missingCheckpointPath": "チェックポイントのパスがありません",
+            "missingCheckpointInfo": "チェックポイント情報が不足しています",
+            "downloadCheckpointFailed": "チェックポイントのダウンロードに失敗しました: {message}",
             "cannotDelete": "レシピを削除できません：レシピIDがありません",
             "deleteConfirmationError": "削除確認の表示中にエラーが発生しました",
             "deletedSuccessfully": "レシピが正常に削除されました",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "検証完了。すべてのファイルが重複であることが確認されました。",
             "verificationFailed": "ハッシュの検証に失敗しました：{message}",
             "noTagsToAdd": "追加するタグがありません",
+            "bulkTagsUpdating": "{count} 個のモデルのタグを更新しています...",
             "tagsAddedSuccessfully": "{count} {type} に {tagCount} 個のタグを追加しました",
             "tagsReplacedSuccessfully": "{count} {type} のタグを {tagCount} 個に置換しました",
             "tagsAddFailed": "{count} モデルへのタグ追加に失敗しました",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "LoRAルートの読み込みに失敗しました：{message}",
             "checkpointRootsFailed": "checkpointルートの読み込みに失敗しました：{message}",
+            "unetRootsFailed": "Diffusion Modelルートの読み込みに失敗しました：{message}",
             "embeddingRootsFailed": "embeddingルートの読み込みに失敗しました：{message}",
             "mappingsUpdated": "ベースモデルパスマッピングが更新されました（{count} マッピング{plural}）",
             "mappingsCleared": "ベースモデルパスマッピングがクリアされました",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "学習済みワードを読み込めませんでした",
-            "tooLong": "トリガーワードは30ワードを超えてはいけません",
+            "tooLong": "トリガーワードは100ワードを超えてはいけません",
             "tooMany": "最大30トリガーワードまで許可されています",
             "alreadyExists": "このトリガーワードは既に存在します",
             "updateSuccess": "トリガーワードが正常に更新されました",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "メタデータが正常に更新されました",
             "metadataRefreshFailed": "メタデータの更新に失敗しました：{message}",
             "metadataUpdateComplete": "メタデータ更新完了",
+            "operationCancelled": "ユーザーによって操作がキャンセルされました",
+            "operationCancelledPartial": "操作がキャンセルされました。{success} 個の項目が処理されました。",
             "metadataFetchFailed": "メタデータの取得に失敗しました：{message}",
             "bulkMetadataCompleteAll": "{count} {type}すべてが正常に更新されました",
             "bulkMetadataCompletePartial": "{total} {type}のうち {success} が更新されました",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "失敗した移動：\n{failures}",
             "bulkMoveSuccess": "{successCount} {type}が正常に移動されました",
             "exampleImagesDownloadSuccess": "例画像が正常にダウンロードされました！",
-            "exampleImagesDownloadFailed": "例画像のダウンロードに失敗しました：{message}"
+            "exampleImagesDownloadFailed": "例画像のダウンロードに失敗しました：{message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/ko.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "업데이트",
             "updateAvailable": "업데이트 가능"
+        },
+        "usage": {
+            "timesUsed": "사용 횟수"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "정리가 필요한 예시 이미지 폴더가 없습니다",
             "partial": "정리가 완료되었으나 {failures}개의 폴더가 건너뛰어졌습니다",
             "error": "예시 이미지 폴더 정리에 실패했습니다: {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}"
+        },
+        "repairRecipes": {
+            "label": "레시피 데이터 복구",
+            "loading": "레시피 데이터 복구 중...",
+            "success": "{count}개의 레시피가 성공적으로 복구되었습니다.",
+            "cancelled": "수리가 취소되었습니다. {count}개의 레시피가 수리되었습니다.",
+            "error": "레시피 복구 실패: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "제작자",
                 "title": "레시피 제목",
                 "loraName": "LoRA 파일명",
-                "loraModel": "LoRA 모델명"
+                "loraModel": "LoRA 모델명",
+                "prompt": "프롬프트"
             }
         },
         "filter": {
             "title": "모델 필터",
             "baseModel": "베이스 모델",
             "modelTags": "태그 (상위 20개)",
+            "modelTypes": "Model Types",
+            "license": "라이선스",
+            "noCreditRequired": "크레딧 표기 없음",
+            "allowSellingGeneratedContent": "판매 허용",
+            "noTags": "태그 없음",
             "clearAll": "모든 필터 지우기"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "설정 폴더 열기",
             "tooltip": "settings.json이 있는 폴더를 엽니다",
             "success": "settings.json 폴더를 열었습니다",
-            "failed": "settings.json 폴더를 열지 못했습니다"
+            "failed": "settings.json 폴더를 열지 못했습니다",
+            "copied": "설정 경로가 클립보드에 복사되었습니다: {{path}}",
+            "clipboardFallback": "설정 경로: {{path}}"
         },
         "sections": {
             "contentFiltering": "콘텐츠 필터링",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
             "folderSettings": "폴더 설정",
+            "priorityTags": "우선순위 태그",
             "downloadPathTemplates": "다운로드 경로 템플릿",
             "exampleImages": "예시 이미지",
+            "updateFlags": "업데이트 표시",
+            "autoOrganize": "Auto-organize",
             "misc": "기타",
             "metadataArchive": "메타데이터 아카이브 데이터베이스",
-            "proxySettings": "프록시 설정",
-            "priorityTags": "우선순위 태그"
+            "storageLocation": "설정 위치",
+            "proxySettings": "프록시 설정"
+        },
+        "storage": {
+            "locationLabel": "휴대용 모드",
+            "locationHelp": "활성화하면 settings.json을 리포지토리에 유지하고, 비활성화하면 사용자 구성 디렉터리에 저장합니다."
         },
         "contentFiltering": {
             "blurNsfwContent": "NSFW 콘텐츠 블러 처리",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "호버 시 비디오 자동 재생",
             "autoplayOnHoverHelp": "마우스를 올렸을 때만 비디오 미리보기를 재생합니다"
         },
+        "autoOrganizeExclusions": {
+            "label": "자동 정리 제외 항목",
+            "placeholder": "예: curated/*, */backups/*; *_temp.safetensors",
+            "help": "이 와일드카드 패턴과 일치하는 파일 이동을 건너뜁니다. 여러 패턴은 쉼표 또는 세미콜론으로 구분하십시오.",
+            "validation": {
+                "noPatterns": "쉼표 또는 세미콜론으로 구분된 최소한 하나의 패턴을 입력하십시오.",
+                "saveFailed": "제외 항목을 저장할 수 없습니다: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 LoRA 루트 디렉토리를 설정합니다",
             "defaultCheckpointRoot": "기본 Checkpoint 루트",
             "defaultCheckpointRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Checkpoint 루트 디렉토리를 설정합니다",
+            "defaultUnetRoot": "기본 Diffusion Model 루트",
+            "defaultUnetRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Diffusion Model (UNET) 루트 디렉토리를 설정합니다",
             "defaultEmbeddingRoot": "기본 Embedding 루트",
             "defaultEmbeddingRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Embedding 루트 디렉토리를 설정합니다",
             "noDefault": "기본값 없음"
         },
+        "priorityTags": {
+            "title": "우선순위 태그",
+            "description": "모델 유형별 태그 우선순위를 사용자 지정합니다(예: character, concept, style(toon|toon_style)).",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "우선순위 태그 도움말 열기",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "체크포인트",
+                "embedding": "임베딩"
+            },
+            "saveSuccess": "우선순위 태그가 업데이트되었습니다.",
+            "saveError": "우선순위 태그를 업데이트하지 못했습니다.",
+            "loadingSuggestions": "추천을 불러오는 중...",
+            "validation": {
+                "missingClosingParen": "{index}번째 항목에 닫는 괄호가 없습니다.",
+                "missingCanonical": "{index}번째 항목에는 정식 태그 이름이 포함되어야 합니다.",
+                "duplicateCanonical": "정식 태그 \"{tag}\"가 여러 번 나타납니다.",
+                "unknown": "잘못된 우선순위 태그 구성입니다."
+            }
+        },
         "downloadPathTemplates": {
             "title": "다운로드 경로 템플릿",
             "help": "Civitai에서 다운로드할 때 다양한 모델 유형의 폴더 구조를 구성합니다.",
@@ -329,6 +392,14 @@
             "download": "다운로드",
             "restartRequired": "재시작 필요"
         },
+        "updateFlagStrategy": {
+            "label": "업데이트 표시 전략",
+            "help": "새 릴리스가 로컬 파일과 동일한 베이스 모델을 공유할 때만 업데이트 배지를 표시할지, 또는 해당 모델에 사용 가능한 새 버전이 있으면 항상 표시할지 결정합니다.",
+            "options": {
+                "sameBase": "베이스 모델로 업데이트 일치",
+                "any": "사용 가능한 모든 업데이트 표시"
+            }
+        },
         "misc": {
             "includeTriggerWords": "LoRA 문법에 트리거 단어 포함",
             "includeTriggerWordsHelp": "LoRA 문법을 클립보드에 복사할 때 학습된 트리거 단어를 포함합니다"
@@ -374,26 +445,6 @@
             "proxyPassword": "비밀번호 (선택사항)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "프록시 인증에 필요한 비밀번호 (필요한 경우)"
-        },
-        "priorityTags": {
-            "title": "우선순위 태그",
-            "description": "모델 유형별 태그 우선순위를 사용자 지정합니다(예: character, concept, style(toon|toon_style)).",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "우선순위 태그 도움말 열기",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "체크포인트",
-                "embedding": "임베딩"
-            },
-            "saveSuccess": "우선순위 태그가 업데이트되었습니다.",
-            "saveError": "우선순위 태그를 업데이트하지 못했습니다.",
-            "loadingSuggestions": "추천을 불러오는 중...",
-            "validation": {
-                "missingClosingParen": "{index}번째 항목에 닫는 괄호가 없습니다.",
-                "missingCanonical": "{index}번째 항목에는 정식 태그 이름이 포함되어야 합니다.",
-                "duplicateCanonical": "정식 태그 \"{tag}\"가 여러 번 나타납니다.",
-                "unknown": "잘못된 우선순위 태그 구성입니다."
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "오래된순",
                 "size": "파일 크기",
                 "sizeDesc": "큰 순서",
-                "sizeAsc": "작은 순서"
+                "sizeAsc": "작은 순서",
+                "usage": "사용 횟수",
+                "usageDesc": "많은 순",
+                "usageAsc": "적은 순"
             },
             "refresh": {
                 "title": "모델 목록 새로고침",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Civitai 데이터 새로고침",
+            "checkUpdates": "업데이트 확인",
             "relinkCivitai": "Civitai에 다시 연결",
             "copySyntax": "LoRA 문법 복사",
             "copyFilename": "모델 파일명 복사",
@@ -482,6 +537,7 @@
             "replacePreview": "미리보기 교체",
             "setContentRating": "콘텐츠 등급 설정",
             "moveToFolder": "폴더로 이동",
+            "repairMetadata": "메타데이터 복구",
             "excludeModel": "모델 제외",
             "deleteModel": "모델 삭제",
             "shareRecipe": "레시피 공유",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA 레시피",
+        "actions": {
+            "sendCheckpoint": "ComfyUI로 보내기"
+        },
         "controls": {
             "import": {
                 "action": "가져오기",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "LoRA 루트 디렉토리를 선택해주세요"
                 }
             },
+            "sort": {
+                "title": "레시피 정렬...",
+                "name": "이름",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "날짜",
+                "dateDesc": "최신순",
+                "dateAsc": "오래된순",
+                "lorasCount": "LoRA 수",
+                "lorasCountDesc": "많은순",
+                "lorasCountAsc": "적은순"
+            },
             "refresh": {
                 "title": "레시피 목록 새로고침"
             },
-            "filteredByLora": "LoRA로 필터링됨"
+            "filteredByLora": "LoRA로 필터링됨",
+            "favorites": {
+                "title": "즐겨찾기만 표시",
+                "action": "즐겨찾기"
+            }
         },
         "duplicates": {
             "found": "{count}개의 중복 그룹 발견",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "다운로드할 누락된 LoRA가 없습니다",
                 "getInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
                 "prepareError": "LoRA 다운로드 준비 중 오류: {message}"
+            },
+            "repair": {
+                "starting": "레시피 메타데이터 복구 중...",
+                "success": "레시피 메타데이터가 성공적으로 복구되었습니다",
+                "skipped": "레시피가 이미 최신 버전입니다. 복구가 필요하지 않습니다",
+                "failed": "레시피 복구 실패: {message}",
+                "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpoint 모델"
+        "title": "Checkpoint 모델",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "{otherType} 폴더로 이동"
+        }
     },
     "embeddings": {
         "title": "Embedding 모델"
@@ -599,7 +688,8 @@
         "recursiveUnavailable": "재귀 검색은 트리 보기에서만 사용할 수 있습니다",
         "collapseAllDisabled": "목록 보기에서는 사용할 수 없습니다",
         "dragDrop": {
-            "unableToResolveRoot": "이동할 대상 경로를 확인할 수 없습니다."
+            "unableToResolveRoot": "이동할 대상 경로를 확인할 수 없습니다.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "파일 위치가 성공적으로 열렸습니다",
-                "failed": "파일 위치 열기에 실패했습니다"
+                "failed": "파일 위치 열기에 실패했습니다",
+                "copied": "경로가 클립보드에 복사되었습니다: {{path}}",
+                "clipboardFallback": "경로: {{path}}"
             },
             "metadata": {
                 "version": "버전",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "프리셋 매개변수 추가...",
                 "strengthMin": "최소 강도",
                 "strengthMax": "최대 강도",
+                "strengthRange": "강도 범위",
                 "strength": "강도",
                 "clipStrength": "클립 강도",
                 "clipSkip": "클립 스킵",
                 "valuePlaceholder": "값",
-                "add": "추가"
+                "add": "추가",
+                "invalidRange": "잘못된 범위 형식입니다. x.x-y.y를 사용하세요"
             },
             "triggerWords": {
                 "label": "트리거 단어",
@@ -875,6 +969,23 @@
                 "recipes": "레시피",
                 "versions": "버전"
             },
+            "navigation": {
+                "label": "모델 탐색",
+                "previousWithShortcut": "이전 모델(←)",
+                "nextWithShortcut": "다음 모델(→)",
+                "noPrevious": "이전 모델이 없습니다",
+                "noNext": "다음 모델이 없습니다"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "제작자 크레딧 필요",
+                "noDerivatives": "공유 병합 불가",
+                "noReLicense": "동일한 권한 필요",
+                "restrictionsLabel": "라이선스 제한"
+            },
             "loading": {
                 "exampleImages": "예시 이미지 로딩 중...",
                 "description": "모델 설명 로딩 중...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "로컬 버전 모두 보기",
                     "viewLocalTooltip": "곧 제공 예정"
                 },
+                "filters": {
+                    "label": "기본 필터",
+                    "state": {
+                        "showAll": "모든 버전",
+                        "showSameBase": "같은 베이스"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "모든 버전을 표시하도록 전환",
+                        "showSameBaseVersions": "같은 베이스 모델 버전만 표시하도록 전환"
+                    },
+                    "empty": "현재 베이스 모델 필터와 일치하는 버전이 없습니다."
+                },
                 "empty": "이 모델에는 아직 버전 기록이 없습니다.",
                 "error": "버전을 불러오지 못했습니다.",
                 "missingModelId": "이 모델에는 Civitai 모델 ID가 없습니다.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "레시피를 전송할 수 없습니다: 레시피 ID 누락",
             "sendFailed": "레시피를 워크플로로 전송하는데 실패했습니다",
             "sendError": "레시피를 워크플로로 전송하는 중 오류",
+            "missingCheckpointPath": "체크포인트 경로를 사용할 수 없습니다",
+            "missingCheckpointInfo": "체크포인트 정보가 부족합니다",
+            "downloadCheckpointFailed": "체크포인트 다운로드 실패: {message}",
             "cannotDelete": "레시피를 삭제할 수 없습니다: 레시피 ID 누락",
             "deleteConfirmationError": "삭제 확인 표시 오류",
             "deletedSuccessfully": "레시피가 성공적으로 삭제되었습니다",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "검증 완료. 모든 파일이 중복임을 확인했습니다.",
             "verificationFailed": "해시 검증 실패: {message}",
             "noTagsToAdd": "추가할 태그가 없습니다",
+            "bulkTagsUpdating": "{count}개 모델의 태그를 업데이트 중입니다...",
             "tagsAddedSuccessfully": "{count}개의 {type}에 {tagCount}개의 태그가 성공적으로 추가되었습니다",
             "tagsReplacedSuccessfully": "{count}개의 {type}의 태그가 {tagCount}개의 태그로 성공적으로 교체되었습니다",
             "tagsAddFailed": "{count}개의 모델에 태그 추가에 실패했습니다",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "LoRA 루트 로딩 실패: {message}",
             "checkpointRootsFailed": "Checkpoint 루트 로딩 실패: {message}",
+            "unetRootsFailed": "Diffusion Model 루트 로딩 실패: {message}",
             "embeddingRootsFailed": "Embedding 루트 로딩 실패: {message}",
             "mappingsUpdated": "베이스 모델 경로 매핑이 업데이트되었습니다 ({count}개 매핑)",
             "mappingsCleared": "베이스 모델 경로 매핑이 지워졌습니다",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "학습된 단어를 로딩할 수 없습니다",
-            "tooLong": "트리거 단어는 30단어를 초과할 수 없습니다",
+            "tooLong": "트리거 단어는 100단어를 초과할 수 없습니다",
             "tooMany": "최대 30개의 트리거 단어만 허용됩니다",
             "alreadyExists": "이 트리거 단어는 이미 존재합니다",
             "updateSuccess": "트리거 단어가 성공적으로 업데이트되었습니다",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "메타데이터가 성공적으로 새로고침되었습니다",
             "metadataRefreshFailed": "메타데이터 새로고침 실패: {message}",
             "metadataUpdateComplete": "메타데이터 업데이트 완료",
+            "operationCancelled": "사용자에 의해 작업이 취소되었습니다",
+            "operationCancelledPartial": "작업이 취소되었습니다. {success}개 항목이 처리되었습니다.",
             "metadataFetchFailed": "메타데이터 가져오기 실패: {message}",
             "bulkMetadataCompleteAll": "모든 {count}개 {type}이(가) 성공적으로 새로고침되었습니다",
             "bulkMetadataCompletePartial": "{total}개 중 {success}개 {type}이(가) 새로고침되었습니다",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "실패한 이동:\n{failures}",
             "bulkMoveSuccess": "{successCount}개 {type}이(가) 성공적으로 이동되었습니다",
             "exampleImagesDownloadSuccess": "예시 이미지가 성공적으로 다운로드되었습니다!",
-            "exampleImagesDownloadFailed": "예시 이미지 다운로드 실패: {message}"
+            "exampleImagesDownloadFailed": "예시 이미지 다운로드 실패: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/ru.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "Обновление",
             "updateAvailable": "Доступно обновление"
+        },
+        "usage": {
+            "timesUsed": "Количество использований"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "Нет папок с примерами изображений, требующих очистки",
             "partial": "Очистка завершена, пропущено {failures} папок",
             "error": "Не удалось очистить папки с примерами изображений: {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}"
+        },
+        "repairRecipes": {
+            "label": "Восстановить данные рецептов",
+            "loading": "Восстановление данных рецептов...",
+            "success": "Успешно восстановлено {count} рецептов.",
+            "cancelled": "Восстановление отменено. {count} рецептов было восстановлено.",
+            "error": "Ошибка восстановления рецептов: {message}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "Автор",
                 "title": "Название рецепта",
                 "loraName": "Имя файла LoRA",
-                "loraModel": "Название модели LoRA"
+                "loraModel": "Название модели LoRA",
+                "prompt": "Запрос"
             }
         },
         "filter": {
             "title": "Фильтр моделей",
             "baseModel": "Базовая модель",
             "modelTags": "Теги (Топ 20)",
+            "modelTypes": "Model Types",
+            "license": "Лицензия",
+            "noCreditRequired": "Без указания авторства",
+            "allowSellingGeneratedContent": "Продажа разрешена",
+            "noTags": "Без тегов",
             "clearAll": "Очистить все фильтры"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "Открыть папку настроек",
             "tooltip": "Открыть папку, содержащую settings.json",
             "success": "Папка settings.json открыта",
-            "failed": "Не удалось открыть папку settings.json"
+            "failed": "Не удалось открыть папку settings.json",
+            "copied": "Путь настроек скопирован в буфер обмена: {{path}}",
+            "clipboardFallback": "Путь настроек: {{path}}"
         },
         "sections": {
             "contentFiltering": "Фильтрация контента",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
             "folderSettings": "Настройки папок",
+            "priorityTags": "Приоритетные теги",
             "downloadPathTemplates": "Шаблоны путей загрузки",
             "exampleImages": "Примеры изображений",
+            "updateFlags": "Метки обновлений",
+            "autoOrganize": "Auto-organize",
             "misc": "Разное",
             "metadataArchive": "Архив метаданных",
-            "proxySettings": "Настройки прокси",
-            "priorityTags": "Приоритетные теги"
+            "storageLocation": "Расположение настроек",
+            "proxySettings": "Настройки прокси"
+        },
+        "storage": {
+            "locationLabel": "Портативный режим",
+            "locationHelp": "Включите, чтобы хранить settings.json в репозитории; выключите, чтобы сохранить его в папке конфигурации пользователя."
         },
         "contentFiltering": {
             "blurNsfwContent": "Размывать NSFW контент",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "Автовоспроизведение видео при наведении",
             "autoplayOnHoverHelp": "Воспроизводить превью видео только при наведении курсора"
         },
+        "autoOrganizeExclusions": {
+            "label": "Исключения автосортировки",
+            "placeholder": "Пример: curated/*, */backups/*; *_temp.safetensors",
+            "help": "Пропускать перемещение файлов, соответствующих этим шаблонам. Разделяйте несколько шаблонов запятыми или точками с запятой.",
+            "validation": {
+                "noPatterns": "Введите хотя бы один шаблон, разделенный запятыми или точками с запятой.",
+                "saveFailed": "Не удалось сохранить исключения: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "Установить корневую папку LoRA по умолчанию для загрузок, импорта и перемещений",
             "defaultCheckpointRoot": "Корневая папка Checkpoint по умолчанию",
             "defaultCheckpointRootHelp": "Установить корневую папку checkpoint по умолчанию для загрузок, импорта и перемещений",
+            "defaultUnetRoot": "Корневая папка Diffusion Model по умолчанию",
+            "defaultUnetRootHelp": "Установить корневую папку Diffusion Model (UNET) по умолчанию для загрузок, импорта и перемещений",
             "defaultEmbeddingRoot": "Корневая папка Embedding по умолчанию",
             "defaultEmbeddingRootHelp": "Установить корневую папку embedding по умолчанию для загрузок, импорта и перемещений",
             "noDefault": "Не задано"
         },
+        "priorityTags": {
+            "title": "Приоритетные теги",
+            "description": "Настройте порядок приоритетов тегов для каждого типа моделей (например, character, concept, style(toon|toon_style)).",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "Открыть справку по приоритетным тегам",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "Чекпойнт",
+                "embedding": "Эмбеддинг"
+            },
+            "saveSuccess": "Приоритетные теги обновлены.",
+            "saveError": "Не удалось обновить приоритетные теги.",
+            "loadingSuggestions": "Загрузка подсказок...",
+            "validation": {
+                "missingClosingParen": "В записи {index} отсутствует закрывающая скобка.",
+                "missingCanonical": "Запись {index} должна содержать каноническое имя тега.",
+                "duplicateCanonical": "Канонический тег \"{tag}\" встречается более одного раза.",
+                "unknown": "Недопустимая конфигурация приоритетных тегов."
+            }
+        },
         "downloadPathTemplates": {
             "title": "Шаблоны путей загрузки",
             "help": "Настройте структуру папок для разных типов моделей при загрузке с Civitai.",
@@ -329,6 +392,14 @@
             "download": "Загрузить",
             "restartRequired": "Требует перезапуска"
         },
+        "updateFlagStrategy": {
+            "label": "Стратегия меток обновлений",
+            "help": "Выберите, отображать ли значки обновления только когда новая версия имеет тот же базовый модель, что и локальные файлы, или всегда при наличии любого нового релиза для этой модели.",
+            "options": {
+                "sameBase": "Совпадение обновлений по базовой модели",
+                "any": "Отмечать любые доступные обновления"
+            }
+        },
         "misc": {
             "includeTriggerWords": "Включать триггерные слова в синтаксис LoRA",
             "includeTriggerWordsHelp": "Включать обученные триггерные слова при копировании синтаксиса LoRA в буфер обмена"
@@ -374,26 +445,6 @@
             "proxyPassword": "Пароль (необязательно)",
             "proxyPasswordPlaceholder": "пароль",
             "proxyPasswordHelp": "Пароль для аутентификации на прокси (если требуется)"
-        },
-        "priorityTags": {
-            "title": "Приоритетные теги",
-            "description": "Настройте порядок приоритетов тегов для каждого типа моделей (например, character, concept, style(toon|toon_style)).",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "Открыть справку по приоритетным тегам",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "Чекпойнт",
-                "embedding": "Эмбеддинг"
-            },
-            "saveSuccess": "Приоритетные теги обновлены.",
-            "saveError": "Не удалось обновить приоритетные теги.",
-            "loadingSuggestions": "Загрузка подсказок...",
-            "validation": {
-                "missingClosingParen": "В записи {index} отсутствует закрывающая скобка.",
-                "missingCanonical": "Запись {index} должна содержать каноническое имя тега.",
-                "duplicateCanonical": "Канонический тег \"{tag}\" встречается более одного раза.",
-                "unknown": "Недопустимая конфигурация приоритетных тегов."
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "Старейшим",
                 "size": "Размеру файла",
                 "sizeDesc": "Наибольшим",
-                "sizeAsc": "Наименьшим"
+                "sizeAsc": "Наименьшим",
+                "usage": "Число использований",
+                "usageDesc": "Больше",
+                "usageAsc": "Меньше"
             },
             "refresh": {
                 "title": "Обновить список моделей",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "Обновить данные Civitai",
+            "checkUpdates": "Проверить обновления",
             "relinkCivitai": "Пересвязать с Civitai",
             "copySyntax": "Копировать синтаксис LoRA",
             "copyFilename": "Копировать имя файла модели",
@@ -482,6 +537,7 @@
             "replacePreview": "Заменить превью",
             "setContentRating": "Установить рейтинг контента",
             "moveToFolder": "Переместить в папку",
+            "repairMetadata": "Восстановить метаданные",
             "excludeModel": "Исключить модель",
             "deleteModel": "Удалить модель",
             "shareRecipe": "Поделиться рецептом",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "Рецепты LoRA",
+        "actions": {
+            "sendCheckpoint": "Отправить в ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "Импортировать",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "Пожалуйста, выберите корневую папку LoRA"
                 }
             },
+            "sort": {
+                "title": "Сортировка рецептов...",
+                "name": "Имя",
+                "nameAsc": "А - Я",
+                "nameDesc": "Я - А",
+                "date": "Дата",
+                "dateDesc": "Сначала новые",
+                "dateAsc": "Сначала старые",
+                "lorasCount": "Кол-во LoRA",
+                "lorasCountDesc": "Больше всего",
+                "lorasCountAsc": "Меньше всего"
+            },
             "refresh": {
                 "title": "Обновить список рецептов"
             },
-            "filteredByLora": "Фильтр по LoRA"
+            "filteredByLora": "Фильтр по LoRA",
+            "favorites": {
+                "title": "Только избранные",
+                "action": "Избранное"
+            }
         },
         "duplicates": {
             "found": "Найдено {count} групп дубликатов",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "Нет отсутствующих LoRAs для загрузки",
                 "getInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
                 "prepareError": "Ошибка подготовки LoRAs для загрузки: {message}"
+            },
+            "repair": {
+                "starting": "Восстановление метаданных рецепта...",
+                "success": "Метаданные рецепта успешно восстановлены",
+                "skipped": "Рецепт уже последней версии, восстановление не требуется",
+                "failed": "Не удалось восстановить рецепт: {message}",
+                "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
             }
         }
     },
     "checkpoints": {
-        "title": "Модели Checkpoint"
+        "title": "Модели Checkpoint",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "Переместить в папку {otherType}"
+        }
     },
     "embeddings": {
         "title": "Модели Embedding"
@@ -599,7 +688,8 @@
         "recursiveUnavailable": "Рекурсивный поиск доступен только в режиме дерева",
         "collapseAllDisabled": "Недоступно в виде списка",
         "dragDrop": {
-            "unableToResolveRoot": "Не удалось определить путь назначения для перемещения."
+            "unableToResolveRoot": "Не удалось определить путь назначения для перемещения.",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "Расположение файла успешно открыто",
-                "failed": "Не удалось открыть расположение файла"
+                "failed": "Не удалось открыть расположение файла",
+                "copied": "Путь скопирован в буфер обмена: {{path}}",
+                "clipboardFallback": "Путь: {{path}}"
             },
             "metadata": {
                 "version": "Версия",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "Добавить предустановленный параметр...",
                 "strengthMin": "Мин. сила",
                 "strengthMax": "Макс. сила",
+                "strengthRange": "Диапазон силы",
                 "strength": "Сила",
                 "clipStrength": "Сила клипа",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Значение",
-                "add": "Добавить"
+                "add": "Добавить",
+                "invalidRange": "Неверный формат диапазона. Используйте x.x-y.y"
             },
             "triggerWords": {
                 "label": "Триггерные слова",
@@ -875,6 +969,23 @@
                 "recipes": "Рецепты",
                 "versions": "Версии"
             },
+            "navigation": {
+                "label": "Навигация по моделям",
+                "previousWithShortcut": "Предыдущая модель (←)",
+                "nextWithShortcut": "Следующая модель (→)",
+                "noPrevious": "Предыдущая модель отсутствует",
+                "noNext": "Следующая модель отсутствует"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "Требуется указание авторства",
+                "noDerivatives": "Запрет на совместное использование производных работ",
+                "noReLicense": "Требуются те же права",
+                "restrictionsLabel": "Лицензионные ограничения"
+            },
             "loading": {
                 "exampleImages": "Загрузка примеров изображений...",
                 "description": "Загрузка описания модели...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "Показать все локальные версии",
                     "viewLocalTooltip": "Скоро появится"
                 },
+                "filters": {
+                    "label": "Фильтр по базе",
+                    "state": {
+                        "showAll": "Все версии",
+                        "showSameBase": "Тот же базовый"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "Переключиться на отображение всех версий",
+                        "showSameBaseVersions": "Переключиться на отображение только версий с тем же базовым"
+                    },
+                    "empty": "Нет версий, соответствующих текущему фильтру базовой модели."
+                },
                 "empty": "Для этой модели пока нет истории версий.",
                 "error": "Не удалось загрузить версии.",
                 "missingModelId": "У этой модели отсутствует идентификатор модели Civitai.",
@@ -1196,6 +1319,9 @@
             "cannotSend": "Невозможно отправить рецепт: отсутствует ID рецепта",
             "sendFailed": "Не удалось отправить рецепт в workflow",
             "sendError": "Ошибка отправки рецепта в workflow",
+            "missingCheckpointPath": "Путь к чекпойнту недоступен",
+            "missingCheckpointInfo": "Отсутствуют данные о чекпойнте",
+            "downloadCheckpointFailed": "Не удалось скачать чекпойнт: {message}",
             "cannotDelete": "Невозможно удалить рецепт: отсутствует ID рецепта",
             "deleteConfirmationError": "Ошибка отображения подтверждения удаления",
             "deletedSuccessfully": "Рецепт успешно удален",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "Проверка завершена. Все файлы подтверждены как дубликаты.",
             "verificationFailed": "Не удалось проверить хеши: {message}",
             "noTagsToAdd": "Нет тегов для добавления",
+            "bulkTagsUpdating": "Обновление тегов для {count} модел(ей)...",
             "tagsAddedSuccessfully": "Успешно добавлено {tagCount} тег(ов) к {count} {type}(ам)",
             "tagsReplacedSuccessfully": "Успешно заменены теги для {count} {type}(ов) на {tagCount} тег(ов)",
             "tagsAddFailed": "Не удалось добавить теги к {count} модель(ям)",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "Не удалось загрузить корни LoRA: {message}",
             "checkpointRootsFailed": "Не удалось загрузить корни checkpoint: {message}",
+            "unetRootsFailed": "Не удалось загрузить корни Diffusion Model: {message}",
             "embeddingRootsFailed": "Не удалось загрузить корни embedding: {message}",
             "mappingsUpdated": "Сопоставления путей базовых моделей обновлены ({count} сопоставлени{plural})",
             "mappingsCleared": "Сопоставления путей базовых моделей очищены",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "Не удалось загрузить обученные слова",
-            "tooLong": "Триггерное слово не должно превышать 30 слов",
+            "tooLong": "Триггерное слово не должно превышать 100 слов",
             "tooMany": "Максимум 30 триггерных слов разрешено",
             "alreadyExists": "Это триггерное слово уже существует",
             "updateSuccess": "Триггерные слова успешно обновлены",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "Метаданные успешно обновлены",
             "metadataRefreshFailed": "Не удалось обновить метаданные: {message}",
             "metadataUpdateComplete": "Обновление метаданных завершено",
+            "operationCancelled": "Операция отменена пользователем",
+            "operationCancelledPartial": "Операция отменена. Обработано {success} элементов.",
             "metadataFetchFailed": "Не удалось получить метаданные: {message}",
             "bulkMetadataCompleteAll": "Успешно обновлены все {count} {type}s",
             "bulkMetadataCompletePartial": "Обновлено {success} из {total} {type}s",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "Неудачные перемещения:\n{failures}",
             "bulkMoveSuccess": "Успешно перемещено {successCount} {type}s",
             "exampleImagesDownloadSuccess": "Примеры изображений успешно загружены!",
-            "exampleImagesDownloadFailed": "Не удалось загрузить примеры изображений: {message}"
+            "exampleImagesDownloadFailed": "Не удалось загрузить примеры изображений: {message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

locales/zh-CN.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "更新",
             "updateAvailable": "有可用更新"
+        },
+        "usage": {
+            "timesUsed": "使用次数"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "没有需要清理的示例图片文件夹",
             "partial": "清理完成，有 {failures} 个文件夹跳过",
             "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}"
+        },
+        "repairRecipes": {
+            "label": "修复配方数据",
+            "loading": "正在修复配方数据...",
+            "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}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "创作者",
                 "title": "配方标题",
                 "loraName": "LoRA 文件名",
-                "loraModel": "LoRA 模型名称"
+                "loraModel": "LoRA 模型名称",
+                "prompt": "提示词"
             }
         },
         "filter": {
             "title": "筛选模型",
             "baseModel": "基础模型",
             "modelTags": "标签（前20）",
+            "modelTypes": "Model Types",
+            "license": "许可证",
+            "noCreditRequired": "无需署名",
+            "allowSellingGeneratedContent": "允许销售",
+            "noTags": "无标签",
             "clearAll": "清除所有筛选"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "打开设置文件夹",
             "tooltip": "打开包含 settings.json 的文件夹",
             "success": "已打开 settings.json 文件夹",
-            "failed": "无法打开 settings.json 文件夹"
+            "failed": "无法打开 settings.json 文件夹",
+            "copied": "设置路径已复制到剪贴板：{{path}}",
+            "clipboardFallback": "设置路径：{{path}}"
         },
         "sections": {
             "contentFiltering": "内容过滤",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
             "folderSettings": "文件夹设置",
+            "priorityTags": "优先标签",
             "downloadPathTemplates": "下载路径模板",
             "exampleImages": "示例图片",
+            "updateFlags": "更新标记",
+            "autoOrganize": "Auto-organize",
             "misc": "其他",
             "metadataArchive": "元数据归档数据库",
-            "proxySettings": "代理设置",
-            "priorityTags": "优先标签"
+            "storageLocation": "设置位置",
+            "proxySettings": "代理设置"
+        },
+        "storage": {
+            "locationLabel": "便携模式",
+            "locationHelp": "开启可将 settings.json 保存在仓库中；关闭则保存在用户配置目录。"
         },
         "contentFiltering": {
             "blurNsfwContent": "模糊 NSFW 内容",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "悬停时自动播放视频",
             "autoplayOnHoverHelp": "仅在悬停时播放视频预览"
         },
+        "autoOrganizeExclusions": {
+            "label": "自动整理排除项",
+            "placeholder": "示例: curated/*, */backups/*; *_temp.safetensors",
+            "help": "跳过与这些通配符模式匹配的文件。多个模式用逗号或分号分隔。",
+            "validation": {
+                "noPatterns": "请输入至少一个用逗号或分号分隔的模式。",
+                "saveFailed": "无法保存排除项：{message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "显示密度",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "设置下载、导入和移动时的默认 LoRA 根目录",
             "defaultCheckpointRoot": "默认 Checkpoint 根目录",
             "defaultCheckpointRootHelp": "设置下载、导入和移动时的默认 Checkpoint 根目录",
+            "defaultUnetRoot": "默认 Diffusion Model 根目录",
+            "defaultUnetRootHelp": "设置下载、导入和移动时的默认 Diffusion Model (UNET) 根目录",
             "defaultEmbeddingRoot": "默认 Embedding 根目录",
             "defaultEmbeddingRootHelp": "设置下载、导入和移动时的默认 Embedding 根目录",
             "noDefault": "无默认"
         },
+        "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))",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "打开优先标签帮助",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "Checkpoint",
+                "embedding": "Embedding"
+            },
+            "saveSuccess": "优先标签已更新。",
+            "saveError": "优先标签更新失败。",
+            "loadingSuggestions": "正在加载建议...",
+            "validation": {
+                "missingClosingParen": "条目 {index} 缺少右括号。",
+                "missingCanonical": "条目 {index} 必须包含规范标签名称。",
+                "duplicateCanonical": "规范标签 \"{tag}\" 出现多次。",
+                "unknown": "优先标签配置无效。"
+            }
+        },
         "downloadPathTemplates": {
             "title": "下载路径模板",
             "help": "配置从 Civitai 下载不同模型类型的文件夹结构。",
@@ -329,6 +392,14 @@
             "download": "下载",
             "restartRequired": "需要重启"
         },
+        "updateFlagStrategy": {
+            "label": "更新标记策略",
+            "help": "决定更新徽章是否仅在新版本与本地文件共享相同基础模型时显示，或只要该模型有任何更新版本就显示。",
+            "options": {
+                "sameBase": "按基础模型匹配更新",
+                "any": "显示任何可用更新"
+            }
+        },
         "misc": {
             "includeTriggerWords": "复制 LoRA 语法时包含触发词",
             "includeTriggerWordsHelp": "复制 LoRA 语法到剪贴板时包含训练触发词"
@@ -374,26 +445,6 @@
             "proxyPassword": "密码 (可选)",
             "proxyPasswordPlaceholder": "密码",
             "proxyPasswordHelp": "代理认证的密码 (如果需要)"
-        },
-        "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))",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "打开优先标签帮助",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "Checkpoint",
-                "embedding": "Embedding"
-            },
-            "saveSuccess": "优先标签已更新。",
-            "saveError": "优先标签更新失败。",
-            "loadingSuggestions": "正在加载建议...",
-            "validation": {
-                "missingClosingParen": "条目 {index} 缺少右括号。",
-                "missingCanonical": "条目 {index} 必须包含规范标签名称。",
-                "duplicateCanonical": "规范标签 \"{tag}\" 出现多次。",
-                "unknown": "优先标签配置无效。"
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "最旧",
                 "size": "文件大小",
                 "sizeDesc": "最大",
-                "sizeAsc": "最小"
+                "sizeAsc": "最小",
+                "usage": "使用次数",
+                "usageDesc": "最多",
+                "usageAsc": "最少"
             },
             "refresh": {
                 "title": "刷新模型列表",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 数据",
+            "checkUpdates": "检查更新",
             "relinkCivitai": "重新关联到 Civitai",
             "copySyntax": "复制 LoRA 语法",
             "copyFilename": "复制模型文件名",
@@ -482,6 +537,7 @@
             "replacePreview": "替换预览",
             "setContentRating": "设置内容评级",
             "moveToFolder": "移动到文件夹",
+            "repairMetadata": "修复元数据",
             "excludeModel": "排除模型",
             "deleteModel": "删除模型",
             "shareRecipe": "分享配方",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA 配方",
+        "actions": {
+            "sendCheckpoint": "发送到 ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "导入",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "请选择 LoRA 根目录"
                 }
             },
+            "sort": {
+                "title": "配方排序...",
+                "name": "名称",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "时间",
+                "dateDesc": "最新",
+                "dateAsc": "最早",
+                "lorasCount": "LoRA 数量",
+                "lorasCountDesc": "最多",
+                "lorasCountAsc": "最少"
+            },
             "refresh": {
                 "title": "刷新配方列表"
             },
-            "filteredByLora": "按 LoRA 筛选"
+            "filteredByLora": "按 LoRA 筛选",
+            "favorites": {
+                "title": "仅显示收藏",
+                "action": "收藏"
+            }
         },
         "duplicates": {
             "found": "发现 {count} 个重复组",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "没有缺失的 LoRA 可下载",
                 "getInfoFailed": "获取缺失 LoRA 信息失败",
                 "prepareError": "准备下载 LoRA 时出错：{message}"
+            },
+            "repair": {
+                "starting": "正在修复配方元数据...",
+                "success": "配方元数据修复成功",
+                "skipped": "配方已是最新版本，无需修复",
+                "failed": "修复配方失败：{message}",
+                "missingId": "无法修复配方：缺少配方 ID"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpoint 模型"
+        "title": "Checkpoint 模型",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹"
+        }
     },
     "embeddings": {
         "title": "Embedding 模型"
@@ -599,7 +688,8 @@
         "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。"
+            "unableToResolveRoot": "无法确定移动的目标路径。",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "文件位置已成功打开",
-                "failed": "打开文件位置失败"
+                "failed": "打开文件位置失败",
+                "copied": "路径已复制到剪贴板：{{path}}",
+                "clipboardFallback": "路径：{{path}}"
             },
             "metadata": {
                 "version": "版本",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "添加预设参数...",
                 "strengthMin": "最小强度",
                 "strengthMax": "最大强度",
+                "strengthRange": "强度范围",
                 "strength": "强度",
                 "clipStrength": "Clip 强度",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "数值",
-                "add": "添加"
+                "add": "添加",
+                "invalidRange": "无效的范围格式。请使用 x.x-y.y"
             },
             "triggerWords": {
                 "label": "触发词",
@@ -875,6 +969,23 @@
                 "recipes": "配方",
                 "versions": "版本"
             },
+            "navigation": {
+                "label": "模型导航",
+                "previousWithShortcut": "上一个模型（←）",
+                "nextWithShortcut": "下一个模型（→）",
+                "noPrevious": "没有上一个模型",
+                "noNext": "没有下一个模型"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "需要创作者署名",
+                "noDerivatives": "禁止分享合并作品",
+                "noReLicense": "需要相同权限",
+                "restrictionsLabel": "许可证限制"
+            },
             "loading": {
                 "exampleImages": "正在加载示例图片...",
                 "description": "正在加载模型描述...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "查看所有本地版本",
                     "viewLocalTooltip": "敬请期待"
                 },
+                "filters": {
+                    "label": "基础筛选",
+                    "state": {
+                        "showAll": "全部版本",
+                        "showSameBase": "相同基模型"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "切换为显示所有版本",
+                        "showSameBaseVersions": "仅显示与当前基模型匹配的版本"
+                    },
+                    "empty": "没有与当前基模型筛选匹配的版本。"
+                },
                 "empty": "该模型还没有版本历史。",
                 "error": "加载版本失败。",
                 "missingModelId": "该模型缺少 Civitai 模型 ID。",
@@ -1196,6 +1319,9 @@
             "cannotSend": "无法发送配方：缺少配方 ID",
             "sendFailed": "发送配方到工作流失败",
             "sendError": "发送配方到工作流出错",
+            "missingCheckpointPath": "缺少检查点路径",
+            "missingCheckpointInfo": "缺少检查点信息",
+            "downloadCheckpointFailed": "下载检查点失败：{message}",
             "cannotDelete": "无法删除配方：缺少配方 ID",
             "deleteConfirmationError": "显示删除确认出错",
             "deletedSuccessfully": "配方删除成功",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "验证完成。所有文件均为重复项。",
             "verificationFailed": "验证哈希失败：{message}",
             "noTagsToAdd": "没有可添加的标签",
+            "bulkTagsUpdating": "正在更新 {count} 个模型的标签...",
             "tagsAddedSuccessfully": "已成功为 {count} 个 {type} 添加 {tagCount} 个标签",
             "tagsReplacedSuccessfully": "已成功为 {count} 个 {type} 替换为 {tagCount} 个标签",
             "tagsAddFailed": "为 {count} 个模型添加标签失败",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "加载 LoRA 根目录失败：{message}",
             "checkpointRootsFailed": "加载 Checkpoint 根目录失败：{message}",
+            "unetRootsFailed": "加载 Diffusion Model 根目录失败：{message}",
             "embeddingRootsFailed": "加载 Embedding 根目录失败：{message}",
             "mappingsUpdated": "基础模型路径映射已更新（{count} 条映射{plural}）",
             "mappingsCleared": "基础模型路径映射已清除",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "无法加载训练词",
-            "tooLong": "触发词不能超过30个词",
+            "tooLong": "触发词不能超过100个词",
             "tooMany": "最多允许30个触发词",
             "alreadyExists": "该触发词已存在",
             "updateSuccess": "触发词更新成功",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "元数据刷新成功",
             "metadataRefreshFailed": "刷新元数据失败：{message}",
             "metadataUpdateComplete": "元数据更新完成",
+            "operationCancelled": "操作已由用户取消",
+            "operationCancelledPartial": "操作已取消。已处理 {success} 个项目。",
             "metadataFetchFailed": "获取元数据失败：{message}",
             "bulkMetadataCompleteAll": "全部 {count} 个 {type} 元数据刷新成功",
             "bulkMetadataCompletePartial": "已刷新 {success}/{total} 个 {type} 元数据",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "移动失败：\n{failures}",
             "bulkMoveSuccess": "成功移动 {successCount} 个 {type}",
             "exampleImagesDownloadSuccess": "示例图片下载成功！",
-            "exampleImagesDownloadFailed": "示例图片下载失败：{message}"
+            "exampleImagesDownloadFailed": "示例图片下载失败：{message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {
@@ -1407,4 +1538,4 @@
             "learnMore": "浏览器插件教程"
         }
     }
-}
+}

locales/zh-TW.json

@@ -131,6 +131,9 @@
         "badges": {
             "update": "更新",
             "updateAvailable": "有可用更新"
+        },
+        "usage": {
+            "timesUsed": "使用次數"
         }
     },
     "globalContextMenu": {
@@ -152,6 +155,20 @@
             "none": "沒有需要清理的範例圖片資料夾",
             "partial": "清理完成，有 {failures} 個資料夾略過",
             "error": "清理範例圖片資料夾失敗：{message}"
+        },
+        "fetchMissingLicenses": {
+            "label": "重新整理授權中繼資料",
+            "loading": "正在重新整理 {typePlural} 的授權中繼資料...",
+            "success": "已更新 {count} 個 {typePlural} 的授權中繼資料",
+            "none": "所有 {typePlural} 已具備授權中繼資料",
+            "error": "重新整理 {typePlural} 授權中繼資料失敗：{message}"
+        },
+        "repairRecipes": {
+            "label": "修復配方資料",
+            "loading": "正在修復配方資料...",
+            "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}"
         }
     },
     "header": {
@@ -181,13 +198,19 @@
                 "creator": "創作者",
                 "title": "配方標題",
                 "loraName": "LoRA 檔案名稱",
-                "loraModel": "LoRA 模型名稱"
+                "loraModel": "LoRA 模型名稱",
+                "prompt": "提示詞"
             }
         },
         "filter": {
             "title": "篩選模型",
             "baseModel": "基礎模型",
             "modelTags": "標籤（前 20）",
+            "modelTypes": "Model Types",
+            "license": "授權",
+            "noCreditRequired": "無需署名",
+            "allowSellingGeneratedContent": "允許銷售",
+            "noTags": "無標籤",
             "clearAll": "清除所有篩選"
         },
         "theme": {
@@ -210,19 +233,28 @@
             "label": "開啟設定資料夾",
             "tooltip": "開啟包含 settings.json 的資料夾",
             "success": "已開啟 settings.json 資料夾",
-            "failed": "無法開啟 settings.json 資料夾"
+            "failed": "無法開啟 settings.json 資料夾",
+            "copied": "設定路徑已複製到剪貼簿：{{path}}",
+            "clipboardFallback": "設定路徑：{{path}}"
         },
         "sections": {
             "contentFiltering": "內容過濾",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
             "folderSettings": "資料夾設定",
+            "priorityTags": "優先標籤",
             "downloadPathTemplates": "下載路徑範本",
             "exampleImages": "範例圖片",
+            "updateFlags": "更新標記",
+            "autoOrganize": "Auto-organize",
             "misc": "其他",
             "metadataArchive": "中繼資料封存資料庫",
-            "proxySettings": "代理設定",
-            "priorityTags": "優先標籤"
+            "storageLocation": "設定位置",
+            "proxySettings": "代理設定"
+        },
+        "storage": {
+            "locationLabel": "可攜式模式",
+            "locationHelp": "啟用可將 settings.json 保存在儲存庫中；停用則保存在使用者設定目錄。"
         },
         "contentFiltering": {
             "blurNsfwContent": "模糊 NSFW 內容",
@@ -234,6 +266,15 @@
             "autoplayOnHover": "滑鼠懸停自動播放影片",
             "autoplayOnHoverHelp": "僅在滑鼠懸停時播放影片預覽"
         },
+        "autoOrganizeExclusions": {
+            "label": "自動整理排除項目",
+            "placeholder": "範例: curated/*, */backups/*; *_temp.safetensors",
+            "help": "跳過符合這些萬用字元模式的檔案。多個模式請用逗號或分號分隔。",
+            "validation": {
+                "noPatterns": "請輸入至少一個以逗號或分號分隔的模式。",
+                "saveFailed": "無法儲存排除項目：{message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
@@ -278,10 +319,32 @@
             "defaultLoraRootHelp": "設定下載、匯入和移動時的預設 LoRA 根目錄",
             "defaultCheckpointRoot": "預設 Checkpoint 根目錄",
             "defaultCheckpointRootHelp": "設定下載、匯入和移動時的預設 Checkpoint 根目錄",
+            "defaultUnetRoot": "預設 Diffusion Model 根目錄",
+            "defaultUnetRootHelp": "設定下載、匯入和移動時的預設 Diffusion Model (UNET) 根目錄",
             "defaultEmbeddingRoot": "預設 Embedding 根目錄",
             "defaultEmbeddingRootHelp": "設定下載、匯入和移動時的預設 Embedding 根目錄",
             "noDefault": "未設定預設"
         },
+        "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))",
+            "placeholder": "character, concept, style(toon|toon_style)",
+            "helpLinkLabel": "開啟優先標籤說明",
+            "modelTypes": {
+                "lora": "LoRA",
+                "checkpoint": "Checkpoint",
+                "embedding": "Embedding"
+            },
+            "saveSuccess": "優先標籤已更新。",
+            "saveError": "更新優先標籤失敗。",
+            "loadingSuggestions": "正在載入建議...",
+            "validation": {
+                "missingClosingParen": "項目 {index} 缺少右括號。",
+                "missingCanonical": "項目 {index} 必須包含正規標籤名稱。",
+                "duplicateCanonical": "正規標籤 \"{tag}\" 出現多於一次。",
+                "unknown": "優先標籤設定無效。"
+            }
+        },
         "downloadPathTemplates": {
             "title": "下載路徑範本",
             "help": "設定從 Civitai 下載時不同模型類型的資料夾結構。",
@@ -329,6 +392,14 @@
             "download": "下載",
             "restartRequired": "需要重新啟動"
         },
+        "updateFlagStrategy": {
+            "label": "更新標記策略",
+            "help": "決定更新徽章是否僅在新版本與本地檔案共享相同基礎模型時顯示，或只要該模型有任何更新版本就顯示。",
+            "options": {
+                "sameBase": "依基礎模型匹配更新",
+                "any": "顯示任何可用更新"
+            }
+        },
         "misc": {
             "includeTriggerWords": "在 LoRA 語法中包含觸發詞",
             "includeTriggerWordsHelp": "複製 LoRA 語法到剪貼簿時包含訓練觸發詞"
@@ -374,26 +445,6 @@
             "proxyPassword": "密碼（選填）",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "代理驗證所需的密碼（如有需要）"
-        },
-        "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))",
-            "placeholder": "character, concept, style(toon|toon_style)",
-            "helpLinkLabel": "開啟優先標籤說明",
-            "modelTypes": {
-                "lora": "LoRA",
-                "checkpoint": "Checkpoint",
-                "embedding": "Embedding"
-            },
-            "saveSuccess": "優先標籤已更新。",
-            "saveError": "更新優先標籤失敗。",
-            "loadingSuggestions": "正在載入建議...",
-            "validation": {
-                "missingClosingParen": "項目 {index} 缺少右括號。",
-                "missingCanonical": "項目 {index} 必須包含正規標籤名稱。",
-                "duplicateCanonical": "正規標籤 \"{tag}\" 出現多於一次。",
-                "unknown": "優先標籤設定無效。"
-            }
         }
     },
     "loras": {
@@ -408,7 +459,10 @@
                 "dateAsc": "最舊",
                 "size": "檔案大小",
                 "sizeDesc": "最大",
-                "sizeAsc": "最小"
+                "sizeAsc": "最小",
+                "usage": "使用次數",
+                "usageDesc": "最多",
+                "usageAsc": "最少"
             },
             "refresh": {
                 "title": "重新整理模型列表",
@@ -471,6 +525,7 @@
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 資料",
+            "checkUpdates": "檢查更新",
             "relinkCivitai": "重新連結 Civitai",
             "copySyntax": "複製 LoRA 語法",
             "copyFilename": "複製模型檔名",
@@ -482,6 +537,7 @@
             "replacePreview": "更換預覽圖",
             "setContentRating": "設定內容分級",
             "moveToFolder": "移動到資料夾",
+            "repairMetadata": "修復元數據",
             "excludeModel": "排除模型",
             "deleteModel": "刪除模型",
             "shareRecipe": "分享配方",
@@ -492,6 +548,9 @@
     },
     "recipes": {
         "title": "LoRA 配方",
+        "actions": {
+            "sendCheckpoint": "傳送到 ComfyUI"
+        },
         "controls": {
             "import": {
                 "action": "匯入",
@@ -549,10 +608,26 @@
                     "selectLoraRoot": "請選擇 LoRA 根目錄"
                 }
             },
+            "sort": {
+                "title": "配方排序...",
+                "name": "名稱",
+                "nameAsc": "A - Z",
+                "nameDesc": "Z - A",
+                "date": "時間",
+                "dateDesc": "最新",
+                "dateAsc": "最舊",
+                "lorasCount": "LoRA 數量",
+                "lorasCountDesc": "最多",
+                "lorasCountAsc": "最少"
+            },
             "refresh": {
                 "title": "重新整理配方列表"
             },
-            "filteredByLora": "已依 LoRA 篩選"
+            "filteredByLora": "已依 LoRA 篩選",
+            "favorites": {
+                "title": "僅顯示收藏",
+                "action": "收藏"
+            }
         },
         "duplicates": {
             "found": "發現 {count} 組重複項",
@@ -578,11 +653,25 @@
                 "noMissingLoras": "無缺少的 LoRA 可下載",
                 "getInfoFailed": "取得缺少 LoRA 資訊失敗",
                 "prepareError": "準備下載 LoRA 時發生錯誤：{message}"
+            },
+            "repair": {
+                "starting": "正在修復配方元數據...",
+                "success": "配方元數據修復成功",
+                "skipped": "配方已是最新版本，無需修復",
+                "failed": "修復配方失敗：{message}",
+                "missingId": "無法修復配方：缺少配方 ID"
             }
         }
     },
     "checkpoints": {
-        "title": "Checkpoint 模型"
+        "title": "Checkpoint 模型",
+        "modelTypes": {
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾"
+        }
     },
     "embeddings": {
         "title": "Embedding 模型"
@@ -599,7 +688,8 @@
         "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。"
+            "unableToResolveRoot": "無法確定移動的目標路徑。",
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -809,7 +899,9 @@
             },
             "openFileLocation": {
                 "success": "檔案位置已成功開啟",
-                "failed": "開啟檔案位置失敗"
+                "failed": "開啟檔案位置失敗",
+                "copied": "路徑已複製到剪貼簿：{{path}}",
+                "clipboardFallback": "路徑：{{path}}"
             },
             "metadata": {
                 "version": "版本",
@@ -832,11 +924,13 @@
                 "addPresetParameter": "新增預設參數...",
                 "strengthMin": "最小強度",
                 "strengthMax": "最大強度",
+                "strengthRange": "強度範圍",
                 "strength": "強度",
                 "clipStrength": "Clip 強度",
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "數值",
-                "add": "新增"
+                "add": "新增",
+                "invalidRange": "無效的範圍格式。請使用 x.x-y.y"
             },
             "triggerWords": {
                 "label": "觸發詞",
@@ -875,6 +969,23 @@
                 "recipes": "配方",
                 "versions": "版本"
             },
+            "navigation": {
+                "label": "模型導覽",
+                "previousWithShortcut": "上一個模型（←）",
+                "nextWithShortcut": "下一個模型（→）",
+                "noPrevious": "沒有上一個模型",
+                "noNext": "沒有下一個模型"
+            },
+            "license": {
+                "noImageSell": "No selling generated content",
+                "noRentCivit": "No Civitai generation",
+                "noRent": "No generation services",
+                "noSell": "No selling models",
+                "creditRequired": "需要創作者標示",
+                "noDerivatives": "禁止分享合併作品",
+                "noReLicense": "需要相同授權",
+                "restrictionsLabel": "授權限制"
+            },
             "loading": {
                 "exampleImages": "載入範例圖片中...",
                 "description": "載入模型描述中...",
@@ -908,6 +1019,18 @@
                     "viewLocalVersions": "檢視所有本地版本",
                     "viewLocalTooltip": "敬請期待"
                 },
+                "filters": {
+                    "label": "基礎篩選",
+                    "state": {
+                        "showAll": "所有版本",
+                        "showSameBase": "相同基礎模型"
+                    },
+                    "tooltip": {
+                        "showAllVersions": "切換為顯示所有版本",
+                        "showSameBaseVersions": "僅顯示與目前基礎模型相符的版本"
+                    },
+                    "empty": "沒有符合目前基礎模型篩選的版本。"
+                },
                 "empty": "此模型尚無版本歷史。",
                 "error": "載入版本失敗。",
                 "missingModelId": "此模型缺少 Civitai 模型 ID。",
@@ -1196,6 +1319,9 @@
             "cannotSend": "無法傳送配方：缺少配方 ID",
             "sendFailed": "傳送配方到工作流失敗",
             "sendError": "傳送配方到工作流錯誤",
+            "missingCheckpointPath": "缺少檢查點路徑",
+            "missingCheckpointInfo": "缺少檢查點資訊",
+            "downloadCheckpointFailed": "下載檢查點失敗：{message}",
             "cannotDelete": "無法刪除配方：缺少配方 ID",
             "deleteConfirmationError": "顯示刪除確認時發生錯誤",
             "deletedSuccessfully": "配方已成功刪除",
@@ -1253,6 +1379,7 @@
             "verificationCompleteSuccess": "驗證完成。所有檔案均確認為重複項。",
             "verificationFailed": "驗證雜湊失敗：{message}",
             "noTagsToAdd": "沒有可新增的標籤",
+            "bulkTagsUpdating": "正在更新 {count} 個模型的標籤...",
             "tagsAddedSuccessfully": "已成功將 {tagCount} 個標籤新增到 {count} 個 {type}",
             "tagsReplacedSuccessfully": "已成功以 {tagCount} 個標籤取代 {count} 個 {type} 的標籤",
             "tagsAddFailed": "新增標籤到 {count} 個模型失敗",
@@ -1266,6 +1393,7 @@
         "settings": {
             "loraRootsFailed": "載入 LoRA 根目錄失敗：{message}",
             "checkpointRootsFailed": "載入 checkpoint 根目錄失敗：{message}",
+            "unetRootsFailed": "載入 Diffusion Model 根目錄失敗：{message}",
             "embeddingRootsFailed": "載入 embedding 根目錄失敗：{message}",
             "mappingsUpdated": "基礎模型路徑對應已更新（{count} 個對應）",
             "mappingsCleared": "基礎模型路徑對應已清除",
@@ -1302,7 +1430,7 @@
         },
         "triggerWords": {
             "loadFailed": "無法載入訓練詞",
-            "tooLong": "觸發詞不可超過 30 個字",
+            "tooLong": "觸發詞不可超過 100 個字",
             "tooMany": "最多允許 30 個觸發詞",
             "alreadyExists": "此觸發詞已存在",
             "updateSuccess": "觸發詞已更新",
@@ -1373,6 +1501,8 @@
             "metadataRefreshed": "metadata 已成功刷新",
             "metadataRefreshFailed": "刷新 metadata 失敗：{message}",
             "metadataUpdateComplete": "metadata 更新完成",
+            "operationCancelled": "操作已由用戶取消",
+            "operationCancelledPartial": "操作已取消。已處理 {success} 個項目。",
             "metadataFetchFailed": "取得 metadata 失敗：{message}",
             "bulkMetadataCompleteAll": "已成功刷新全部 {count} 個 {type}",
             "bulkMetadataCompletePartial": "已刷新 {success} / {total} 個 {type}",
@@ -1389,7 +1519,8 @@
             "bulkMoveFailures": "移動失敗：\n{failures}",
             "bulkMoveSuccess": "已成功移動 {successCount} 個 {type}",
             "exampleImagesDownloadSuccess": "範例圖片下載成功！",
-            "exampleImagesDownloadFailed": "下載範例圖片失敗：{message}"
+            "exampleImagesDownloadFailed": "下載範例圖片失敗：{message}",
+            "moveFailed": "Failed to move item: {message}"
         }
     },
     "banners": {

package-lock.json

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

py/config.py

@@ -1,13 +1,15 @@
 import os
 import platform
+import threading
 from pathlib import Path
 import folder_paths # type: ignore
-from typing import Any, Dict, Iterable, List, Mapping, Optional, Set
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple
 import logging
 import json
 import urllib.parse
+import time
 
-from .utils.settings_paths import ensure_settings_file, 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"
@@ -80,6 +82,8 @@ class Config:
         self._path_mappings: Dict[str, str] = {}
         # Normalized preview root directories used to validate preview access
         self._preview_root_paths: Set[Path] = set()
+        # Fingerprint of the symlink layout from the last successful scan
+        self._cached_fingerprint: Optional[Dict[str, object]] = None
         self.loras_roots = self._init_lora_paths()
         self.checkpoints_roots = None
         self.unet_roots = None
@@ -87,8 +91,7 @@ class Config:
         self.base_models_roots = self._init_checkpoint_paths()
         self.embeddings_roots = self._init_embedding_paths()
         # Scan symbolic links during initialization
-        self._scan_symbolic_links()
-        self._rebuild_preview_roots()
+        self._initialize_symlink_mappings()
         
         if not standalone_mode:
             # Save the paths to settings.json when running in ComfyUI mode
@@ -220,45 +223,217 @@ class Config:
             logger.error(f"Error checking link status for {path}: {e}")
             return False
 
+    def _normalize_path(self, path: str) -> str:
+        return os.path.normpath(path).replace(os.sep, '/')
+
+    def _get_symlink_cache_path(self) -> Path:
+        cache_dir = Path(get_settings_dir(create=True)) / "cache"
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        return cache_dir / "symlink_map.json"
+
+    def _symlink_roots(self) -> List[str]:
+        roots: List[str] = []
+        roots.extend(self.loras_roots or [])
+        roots.extend(self.base_models_roots or [])
+        roots.extend(self.embeddings_roots or [])
+        return roots
+
+    def _build_symlink_fingerprint(self) -> Dict[str, object]:
+        roots = [self._normalize_path(path) for path in self._symlink_roots() if path]
+        unique_roots = sorted(set(roots))
+        # Fingerprint now only contains the root paths to avoid sensitivity to folder content changes.
+        return {"roots": unique_roots}
+
+    def _initialize_symlink_mappings(self) -> None:
+        start = time.perf_counter()
+        cache_loaded = self._load_persisted_cache_into_mappings()
+
+        if cache_loaded:
+            logger.info(
+                "Symlink mappings restored from cache in %.2f ms",
+                (time.perf_counter() - start) * 1000,
+            )
+            self._rebuild_preview_roots()
+
+            # Only rescan if target roots have changed. 
+            # This is stable across file additions/deletions.
+            current_fingerprint = self._build_symlink_fingerprint()
+            cached_fingerprint = self._cached_fingerprint
+            
+            if cached_fingerprint and current_fingerprint == cached_fingerprint:
+                return
+
+            logger.info("Symlink root paths changed; rescanning symbolic links")
+
+        self.rebuild_symlink_cache()
+        logger.info(
+            "Symlink mappings rebuilt and cached in %.2f ms",
+            (time.perf_counter() - start) * 1000,
+        )
+
+    def rebuild_symlink_cache(self) -> None:
+        """Force a fresh scan of all symbolic links and update the persistent cache."""
+        self._scan_symbolic_links()
+        self._save_symlink_cache()
+        self._rebuild_preview_roots()
+
+    def _load_persisted_cache_into_mappings(self) -> bool:
+        """Load the symlink cache and store its fingerprint for comparison."""
+        cache_path = self._get_symlink_cache_path()
+        if not cache_path.exists():
+            return False
+
+        try:
+            with cache_path.open("r", encoding="utf-8") as handle:
+                payload = json.load(handle)
+        except Exception as exc:
+            logger.info("Failed to load symlink cache %s: %s", cache_path, exc)
+            return False
+
+        if not isinstance(payload, dict):
+            return False
+
+        cached_mappings = payload.get("path_mappings")
+        if not isinstance(cached_mappings, Mapping):
+            return False
+
+        # Store the cached fingerprint for comparison during initialization
+        self._cached_fingerprint = payload.get("fingerprint")
+
+        normalized_mappings: Dict[str, str] = {}
+        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)
+
+        self._path_mappings = normalized_mappings
+        logger.info("Symlink cache loaded with %d mappings", len(self._path_mappings))
+        return True
+
+    def _save_symlink_cache(self) -> None:
+        cache_path = self._get_symlink_cache_path()
+        payload = {
+            "fingerprint": self._build_symlink_fingerprint(),
+            "path_mappings": self._path_mappings,
+        }
+
+        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))
+        except Exception as exc:
+            logger.info("Failed to write symlink cache %s: %s", cache_path, exc)
+
     def _scan_symbolic_links(self):
         """Scan all symbolic links in LoRA, Checkpoint, and Embedding root directories"""
-        for root in self.loras_roots:
-            self._scan_directory_links(root)
+        start = time.perf_counter()
         
-        for root in self.base_models_roots:
-            self._scan_directory_links(root)
-            
-        for root in self.embeddings_roots:
-            self._scan_directory_links(root)
+        # Reset mappings before rescanning to avoid stale entries
+        self._path_mappings.clear()
+        self._seed_root_symlink_mappings()
+        visited_dirs: Set[str] = set()
+        for root in self._symlink_roots():
+            self._scan_directory_links(root, visited_dirs)
+        logger.debug(
+            "Symlink scan finished in %.2f ms with %d mappings",
+            (time.perf_counter() - start) * 1000,
+            len(self._path_mappings),
+        )
 
-    def _scan_directory_links(self, root: str):
-        """Recursively scan symbolic links in a directory"""
+    def _scan_directory_links(self, root: str, visited_dirs: Set[str]):
+        """Iteratively scan directory symlinks to avoid deep recursion."""
         try:
-            with os.scandir(root) as it:
-                for entry in it:
-                    if self._is_link(entry.path):
-                        target_path = os.path.realpath(entry.path)
-                        if os.path.isdir(target_path):
-                            self.add_path_mapping(entry.path, target_path)
-                            self._scan_directory_links(target_path)
-                    elif entry.is_dir(follow_symlinks=False):
-                        self._scan_directory_links(entry.path)
-        except Exception as e:
-            logger.error(f"Error scanning links in {root}: {e}")
+            # Note: We only use realpath for the initial root if it's not already resolved
+            # to ensure we have a valid entry point.
+            root_real = self._normalize_path(os.path.realpath(root))
+        except OSError:
+            root_real = self._normalize_path(root)
+
+        if root_real in visited_dirs:
+            return
+
+        visited_dirs.add(root_real)
+        # Stack entries: (display_path, real_resolved_path)
+        stack: List[Tuple[str, str]] = [(root, root_real)]
+
+        while stack:
+            current_display, current_real = stack.pop()
+            try:
+                with os.scandir(current_display) as it:
+                    for entry in it:
+                        try:
+                            # 1. High speed detection using dirent data (is_symlink)
+                            is_link = entry.is_symlink()
+                            
+                            # On Windows, is_symlink handles reparse points
+                            if is_link:
+                                # Only resolve realpath when we actually find a link
+                                target_path = os.path.realpath(entry.path)
+                                if not os.path.isdir(target_path):
+                                    continue
+
+                                normalized_target = self._normalize_path(target_path)
+                                self.add_path_mapping(entry.path, target_path)
+                                
+                                if normalized_target in visited_dirs:
+                                    continue
+                                    
+                                visited_dirs.add(normalized_target)
+                                stack.append((target_path, normalized_target))
+                                continue
+
+                            # 2. Process normal directories
+                            if not entry.is_dir(follow_symlinks=False):
+                                continue
+
+                            # For normal directories, we avoid realpath() call by 
+                            # incrementally building the real path relative to current_real.
+                            # This is safe because 'entry' is NOT a symlink.
+                            entry_real = self._normalize_path(os.path.join(current_real, entry.name))
+                            
+                            if entry_real in visited_dirs:
+                                continue
+                            
+                            visited_dirs.add(entry_real)
+                            stack.append((entry.path, entry_real))
+                        except Exception as inner_exc:
+                            logger.debug(
+                                "Error processing directory entry %s: %s", entry.path, inner_exc
+                            )
+            except Exception as e:
+                logger.error(f"Error scanning links in {current_display}: {e}")
+
+
 
     def add_path_mapping(self, link_path: str, target_path: str):
         """Add a symbolic link path mapping
         target_path: actual target path
         link_path: symbolic link path
         """
-        normalized_link = os.path.normpath(link_path).replace(os.sep, '/')
-        normalized_target = os.path.normpath(target_path).replace(os.sep, '/')
+        normalized_link = self._normalize_path(link_path)
+        normalized_target = self._normalize_path(target_path)
         # Keep the original mapping: target path -> link path
         self._path_mappings[normalized_target] = normalized_link
         logger.info(f"Added path mapping: {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))
 
+    def _seed_root_symlink_mappings(self) -> None:
+        """Ensure symlinked root folders are recorded before deep scanning."""
+
+        for root in self._symlink_roots():
+            if not root:
+                continue
+            try:
+                if not self._is_link(root):
+                    continue
+                target_path = os.path.realpath(root)
+                if not os.path.isdir(target_path):
+                    continue
+                self.add_path_mapping(root, target_path)
+            except Exception as exc:
+                logger.debug("Skipping root symlink %s: %s", root, exc)
+
     def _expand_preview_root(self, path: str) -> Set[Path]:
         """Return normalized ``Path`` objects representing a preview root."""
 
@@ -321,7 +496,11 @@ class Config:
         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():
-            if normalized_path.startswith(target_path):
+            # 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 the path starts with the target path, replace with link path
                 mapped_path = normalized_path.replace(target_path, link_path, 1)
                 return mapped_path
@@ -331,10 +510,14 @@ class Config:
         """Map a symbolic link path back to the actual path"""
         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 in self._path_mappings.items():
-            if normalized_link.startswith(target_path):
-                # If the path starts with the target path, replace with actual path
-                mapped_path = normalized_link.replace(target_path, link_path, 1)
+        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 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
         return link_path
 
@@ -411,8 +594,7 @@ class Config:
         self.base_models_roots = self._prepare_checkpoint_paths(checkpoint_paths, unet_paths)
         self.embeddings_roots = self._prepare_embedding_paths(embedding_paths)
 
-        self._scan_symbolic_links()
-        self._rebuild_preview_roots()
+        self._initialize_symlink_mappings()
 
     def _init_lora_paths(self) -> List[str]:
         """Initialize and validate LoRA paths from ComfyUI settings"""

py/lora_manager.py

@@ -2,6 +2,15 @@ import asyncio
 import sys
 import os
 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"
+
+# Only setup logging prefix if not in standalone mode
+if not standalone_mode:
+    setup_logging()
+
 from server import PromptServer # type: ignore
 
 from .config import config
@@ -17,12 +26,10 @@ from .services.settings_manager import get_settings_manager
 from .utils.example_images_migration import ExampleImagesMigration
 from .services.websocket_manager import ws_manager
 from .services.example_images_cleanup_service import ExampleImagesCleanupService
+from .middleware.csp_middleware import relax_csp_for_remote_media
 
 logger = logging.getLogger(__name__)
 
-# Check if we're in standalone mode
-STANDALONE_MODE = 'nodes' not in sys.modules
-
 HEADER_SIZE_LIMIT = 16384
 
 
@@ -62,6 +69,23 @@ class LoraManager:
         """Initialize and register all routes using the new refactored architecture"""
         app = PromptServer.instance.app
 
+        if relax_csp_for_remote_media not in app.middlewares:
+            # Ensure CSP relaxer executes after ComfyUI's block_external_middleware so it can
+            # see and extend the restrictive header instead of being overwritten by it.
+            block_middleware_index = next(
+                (
+                    idx
+                    for idx, middleware in enumerate(app.middlewares)
+                    if getattr(middleware, "__name__", "") == "block_external_middleware"
+                ),
+                None,
+            )
+
+            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)
+
         # 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
         # limits. Cookies for unrelated apps are still sent to the plugin and
@@ -140,8 +164,6 @@ class LoraManager:
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
         
-        logger.info(f"LoRA Manager: Set up routes for {len(ModelServiceFactory.get_registered_types())} model types: {', '.join(ModelServiceFactory.get_registered_types())}")
-    
     @classmethod
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""

py/metadata_collector/metadata_processor.py

@@ -39,8 +39,39 @@ class MetadataProcessor:
                     if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
                         candidate_samplers[node_id] = metadata[SAMPLING][node_id]
                 
-                # If we found candidate samplers, apply primary sampler logic to these candidates only
-                if candidate_samplers:
+                    # If we found candidate samplers, apply primary sampler logic to these candidates only
+                    
+                    # PRE-PROCESS: Ensure all candidate samplers have their parameters populated
+                    # This is especially important for SamplerCustomAdvanced which needs tracing
+                    prompt = metadata.get("current_prompt")
+                    for node_id in candidate_samplers:
+                        # If a sampler is missing common parameters like steps or denoise, 
+                        # try to populate them using tracing before ranking
+                        sampler_info = candidate_samplers[node_id]
+                        params = sampler_info.get("parameters", {})
+                        
+                        if prompt and (params.get("steps") is None or params.get("denoise") is None):
+                            # Create a temporary params dict to use the handler
+                            temp_params = {
+                                "steps": params.get("steps"),
+                                "denoise": params.get("denoise"),
+                                "sampler": params.get("sampler_name"),
+                                "scheduler": params.get("scheduler")
+                            }
+                            
+                            # Check if it's SamplerCustomAdvanced
+                            if prompt.original_prompt and node_id in prompt.original_prompt:
+                                if prompt.original_prompt[node_id].get("class_type") == "SamplerCustomAdvanced":
+                                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, node_id, temp_params)
+                                    
+                                    # Update the actual parameters with found values
+                                    params["steps"] = temp_params.get("steps")
+                                    params["denoise"] = temp_params.get("denoise")
+                                    if temp_params.get("sampler"):
+                                        params["sampler_name"] = temp_params.get("sampler")
+                                    if temp_params.get("scheduler"):
+                                        params["scheduler"] = temp_params.get("scheduler")
+
                     # Collect potential primary samplers based on different criteria
                     custom_advanced_samplers = []
                     advanced_add_noise_samplers = []
@@ -49,7 +80,6 @@ class MetadataProcessor:
                     high_denoise_id = None
                     
                     # First, check for SamplerCustomAdvanced among candidates
-                    prompt = metadata.get("current_prompt")
                     if prompt and prompt.original_prompt:
                         for node_id in candidate_samplers:
                             node_info = prompt.original_prompt.get(node_id, {})
@@ -77,15 +107,16 @@ class MetadataProcessor:
                     # Combine all potential primary samplers
                     potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
                     
-                    # Find the most recent potential primary sampler (closest to downstream node)
-                    for i in range(downstream_index - 1, -1, -1):
+                    # Find the first potential primary sampler (prefer base sampler over refine)
+                    # Use forward search to prioritize the first one in execution order
+                    for i in range(downstream_index):
                         node_id = execution_order[i]
                         if node_id in potential_samplers:
                             return node_id, candidate_samplers[node_id]
                     
-                    # If no potential sampler found from our criteria, return the most recent sampler
+                    # If no potential sampler found from our criteria, return the first sampler
                     if candidate_samplers:
-                        for i in range(downstream_index - 1, -1, -1):
+                        for i in range(downstream_index):
                             node_id = execution_order[i]
                             if node_id in candidate_samplers:
                                 return node_id, candidate_samplers[node_id]
@@ -176,8 +207,11 @@ class MetadataProcessor:
                 found_node_id = input_value[0]  # Connected node_id
                 
                 # If we're looking for a specific node class
-                if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
-                    return found_node_id
+                if target_class:
+                    if found_node_id not in prompt.original_prompt:
+                        return None
+                    if prompt.original_prompt[found_node_id].get("class_type") == target_class:
+                        return found_node_id
                 
                 # If we're not looking for a specific class, update the last valid node
                 if not target_class:
@@ -185,11 +219,19 @@ class MetadataProcessor:
                 
                 # Continue tracing through intermediate nodes
                 current_node_id = found_node_id
-                # For most conditioning nodes, the input we want to follow is named "conditioning"
-                if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
+                
+                # Check if current source node exists
+                if current_node_id not in prompt.original_prompt:
+                    return found_node_id if not target_class else None
+                    
+                # Determine which input to follow next on the source node
+                source_node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
+                if input_name in source_node_inputs:
+                    current_input = input_name
+                elif "conditioning" in source_node_inputs:
                     current_input = "conditioning"
                 else:
-                    # If there's no "conditioning" input, return the current node
+                    # If there's no suitable input to follow, return the current node
                     # if we're not looking for a specific target_class
                     return found_node_id if not target_class else None
             else:
@@ -202,12 +244,89 @@ class MetadataProcessor:
         return last_valid_node if not target_class else None
     
     @staticmethod
-    def find_primary_checkpoint(metadata):
-        """Find the primary checkpoint model in the workflow"""
-        if not metadata.get(MODELS):
+    def trace_model_path(metadata, prompt, start_node_id):
+        """
+        Trace the model connection path upstream to find the checkpoint
+        """
+        if not prompt or not prompt.original_prompt:
             return None
             
-        # In most workflows, there's only one checkpoint, so we can just take the first one
+        current_node_id = start_node_id
+        depth = 0
+        max_depth = 50
+        
+        while depth < max_depth:
+            # Check if current node is a registered checkpoint in our metadata
+            # This handles cached nodes correctly because metadata contains info for all nodes in the graph
+            if current_node_id in metadata.get(MODELS, {}):
+                if metadata[MODELS][current_node_id].get("type") == "checkpoint":
+                    return current_node_id
+            
+            if current_node_id not in prompt.original_prompt:
+                return None
+                
+            node = prompt.original_prompt[current_node_id]
+            inputs = node.get("inputs", {})
+            class_type = node.get("class_type", "")
+            
+            # Determine which input to follow next
+            next_input_name = "model"
+            
+            # Special handling for initial node
+            if depth == 0:
+                if class_type == "SamplerCustomAdvanced":
+                    next_input_name = "guider"
+            
+            # If the specific input doesn't exist, try generic 'model' 
+            if next_input_name not in inputs:
+                if "model" in inputs:
+                    next_input_name = "model"
+                elif "basic_pipe" in inputs:
+                    # Handle pipe nodes like FromBasicPipe by following the pipeline
+                    next_input_name = "basic_pipe"
+                else:
+                    # Dead end - no model input to follow
+                    return None
+            
+            # Get connected node
+            input_val = inputs[next_input_name]
+            if isinstance(input_val, list) and len(input_val) > 0:
+                current_node_id = input_val[0]
+            else:
+                return None
+                
+            depth += 1
+            
+        return None
+
+    @staticmethod
+    def find_primary_checkpoint(metadata, downstream_id=None, primary_sampler_id=None):
+        """
+        Find the primary checkpoint model in the workflow
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - downstream_id: Optional ID of a downstream node to help identify the specific primary sampler
+        - primary_sampler_id: Optional ID of the primary sampler if already known
+        """
+        if not metadata.get(MODELS):
+            return None
+        
+        # Method 1: Topology-based tracing (More accurate for complex workflows)
+        # First, find the primary sampler if not provided
+        if not primary_sampler_id:
+            primary_sampler_id, _ = MetadataProcessor.find_primary_sampler(metadata, downstream_id)
+        
+        if primary_sampler_id:
+            prompt = metadata.get("current_prompt")
+            if prompt:
+                # Trace back from the sampler to find the checkpoint
+                checkpoint_id = MetadataProcessor.trace_model_path(metadata, prompt, primary_sampler_id)
+                if checkpoint_id and checkpoint_id in metadata.get(MODELS, {}):
+                    return metadata[MODELS][checkpoint_id].get("name")
+            
+        # Method 2: Fallback to the first available checkpoint (Original behavior)
+        # In most simple workflows, there's only one checkpoint, so we can just take the first one
         for node_id, model_info in metadata.get(MODELS, {}).items():
             if model_info.get("type") == "checkpoint":
                 return model_info.get("name")
@@ -311,7 +430,8 @@ class MetadataProcessor:
         primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
         
         # Directly get checkpoint from metadata instead of tracing
-        checkpoint = MetadataProcessor.find_primary_checkpoint(metadata)
+        # Pass primary_sampler_id to avoid redundant calculation
+        checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
         if checkpoint:
             params["checkpoint"] = checkpoint
         
@@ -445,6 +565,7 @@ class MetadataProcessor:
                 scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
                 params["steps"] = scheduler_params.get("steps")
                 params["scheduler"] = scheduler_params.get("scheduler")
+                params["denoise"] = scheduler_params.get("denoise")
         
         # 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
         if "sampler" in sampler_inputs:

py/metadata_collector/metadata_registry.py

@@ -196,9 +196,11 @@ class MetadataRegistry:
                     node_metadata[category] = {}
                 node_metadata[category][node_id] = current_metadata[category][node_id]
         
-        # Save to cache if we have any metadata for this node
+        # 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"""

py/metadata_collector/node_extractors.py

@@ -72,6 +72,18 @@ class GGUFLoaderExtractor(NodeMetadataExtractor):
         model_name = inputs.get("gguf_name")
         _store_checkpoint_metadata(metadata, node_id, model_name)
 
+
+class KJNodesModelLoaderExtractor(NodeMetadataExtractor):
+    """Extract metadata from KJNodes loaders that expose `model_name`."""
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "model_name" not in inputs:
+            return
+
+        model_name = inputs.get("model_name")
+        _store_checkpoint_metadata(metadata, node_id, model_name)
+
 class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -682,6 +694,7 @@ NODE_EXTRACTORS = {
     "KSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipeExtractor,    # comfyui-impact-pack
     "KSampler_inspire_pipe": KSamplerBasicPipeExtractor,    # comfyui-inspire-pack
     "KSamplerAdvanced_inspire_pipe": KSamplerAdvancedBasicPipeExtractor,  # comfyui-inspire-pack
+    "KSampler_inspire": SamplerExtractor,  # comfyui-inspire-pack
     # Sampling Selectors
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
@@ -695,6 +708,9 @@ NODE_EXTRACTORS = {
     "NunchakuQwenImageDiTLoader": NunchakuQwenImageDiTLoaderExtractor,  # ComfyUI-Nunchaku
     "LoaderGGUF": GGUFLoaderExtractor,  # calcuis gguf
     "LoaderGGUFAdvanced": GGUFLoaderExtractor,  # calcuis gguf
+    "GGUFLoaderKJ": KJNodesModelLoaderExtractor,  # KJNodes
+    "DiffusionModelLoaderKJ": KJNodesModelLoaderExtractor,  # KJNodes
+    "CheckpointLoaderKJ": CheckpointLoaderExtractor,  # KJNodes
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
     "LoraLoader": LoraLoaderExtractor,

py/middleware/csp_middleware.py

@@ -0,0 +1,65 @@
+"""Middleware helpers for adjusting Content Security Policy headers."""
+
+from typing import Awaitable, Callable, Dict, List
+
+from aiohttp import web
+
+REMOTE_MEDIA_SOURCES = (
+    "https://image.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]]
+) -> web.StreamResponse:
+    """Allow LoRA Manager media previews to load from trusted remote domains.
+
+    When ComfyUI is started with ``--disable-api-nodes`` it injects a restrictive
+    ``Content-Security-Policy`` header that blocks remote images and videos. The
+    LoRA Manager UI legitimately needs to fetch previews from Civitai and Genur,
+    so this middleware augments the existing CSP to whitelist those hosts while
+    preserving all other directives.
+    """
+
+    response: web.StreamResponse = await handler(request)
+    header_value = response.headers.get("Content-Security-Policy")
+
+    if not header_value:
+        return response
+
+    directive_order: List[str] = []
+    directives: Dict[str, List[str]] = {}
+
+    for raw_directive in header_value.split(";"):
+        directive = raw_directive.strip()
+        if not directive:
+            continue
+
+        parts = directive.split()
+        name, values = parts[0], parts[1:]
+        if name not in directive_order:
+            directive_order.append(name)
+        directives[name] = values
+
+    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:
+            if source not in existing:
+                existing.append(source)
+
+        directives[name] = existing
+        if name not in directive_order:
+            directive_order.append(name)
+
+    merge_sources("img-src", list(REMOTE_MEDIA_SOURCES))
+    merge_sources("media-src", ["'self'", *REMOTE_MEDIA_SOURCES], defaults=["'self'"])
+
+    updated_header = "; ".join(
+        f"{name} {' '.join(directives[name])}".rstrip() for name in directive_order
+    )
+
+    response.headers["Content-Security-Policy"] = f"{updated_header};"
+    return response

py/nodes/debug_metadata.py

@@ -1,15 +1,15 @@
 import logging
-from server import PromptServer   # type: ignore
 from ..metadata_collector.metadata_processor import MetadataProcessor
 
 logger = logging.getLogger(__name__)
 
+
 class DebugMetadata:
     NAME = "Debug Metadata (LoraManager)"
     CATEGORY = "Lora Manager/utils"
     DESCRIPTION = "Debug node to verify metadata_processor functionality"
     OUTPUT_NODE = True
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -25,21 +25,37 @@ class DebugMetadata:
     FUNCTION = "process_metadata"
 
     def process_metadata(self, images, id):
+        """
+        Process metadata from the execution context and return it for UI display.
+
+        The metadata is returned via the 'ui' key in the return dict, which triggers
+        node.onExecuted on the frontend to update the JsonDisplayWidget.
+
+        Args:
+            images: Input images (required for execution flow)
+            id: Node's unique ID (hidden)
+
+        Returns:
+            Dict with 'result' (empty tuple) and 'ui' (metadata dict for widget display)
+        """
         try:
             # Get the current execution context's metadata
             from ..metadata_collector import get_metadata
+
             metadata = get_metadata()
-            
-            # Use the MetadataProcessor to convert it to JSON string
-            metadata_json = MetadataProcessor.to_json(metadata, id)
-            
-            # Send metadata to frontend for display
-            PromptServer.instance.send_sync("metadata_update", {
-                "id": id,
-                "metadata": metadata_json
-            })
-            
+
+            # Use the MetadataProcessor to convert it to dict
+            metadata_dict = MetadataProcessor.to_dict(metadata, id)
+
+            return {
+                "result": (),
+                # ComfyUI expects ui values to be lists, wrap the dict in a list
+                "ui": {"metadata": [metadata_dict]},
+            }
+
         except Exception as e:
             logger.error(f"Error processing metadata: {e}")
-        
-        return ()
+            return {
+                "result": (),
+                "ui": {"metadata": [{"error": str(e)}]},
+            }

py/nodes/lora_pool.py

@@ -0,0 +1,87 @@
+"""
+LoRA Pool Node - Defines filter configuration for LoRA selection.
+
+This node provides a visual filter editor that generates a LORA_POOL_CONFIG
+object for use by downstream nodes (like LoRA Randomizer).
+"""
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LoraPoolNode:
+    """
+    A node that defines LoRA filter criteria through a Vue-based widget.
+
+    Outputs a LORA_POOL_CONFIG that can be consumed by:
+    - Frontend: LoRA Randomizer widget reads connected pool's widget value
+    - Backend: LoRA Randomizer receives config during workflow execution
+    """
+
+    NAME = "Lora Pool (LoraManager)"
+    CATEGORY = "Lora Manager/randomizer"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "pool_config": ("LORA_POOL_CONFIG", {}),
+            },
+            "hidden": {
+                # Hidden input to pass through unique node ID for frontend
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("POOL_CONFIG",)
+    RETURN_NAMES = ("POOL_CONFIG",)
+
+    FUNCTION = "process"
+    OUTPUT_NODE = False
+
+    def process(self, pool_config, unique_id=None):
+        """
+        Pass through the pool configuration filters.
+
+        The config is generated entirely by the frontend widget.
+        This function validates and returns only the filters field.
+
+        Args:
+            pool_config: Dict containing filter criteria from widget
+            unique_id: Node's unique ID (hidden)
+
+        Returns:
+            Tuple containing the filters dict from pool_config
+        """
+        # Validate required structure
+        if not isinstance(pool_config, dict):
+            logger.warning("Invalid pool_config type, using empty config")
+            pool_config = self._default_config()
+
+        # Ensure version field exists
+        if "version" not in pool_config:
+            pool_config["version"] = 1
+
+        # Extract filters field
+        filters = pool_config.get("filters", self._default_config()["filters"])
+
+        # Log for debugging
+        logger.debug(f"[LoraPoolNode] Processing filters: {filters}")
+
+        return (filters,)
+
+    @staticmethod
+    def _default_config():
+        """Return default empty configuration."""
+        return {
+            "version": 1,
+            "filters": {
+                "baseModels": [],
+                "tags": {"include": [], "exclude": []},
+                "folders": {"include": [], "exclude": []},
+                "favoritesOnly": False,
+                "license": {"noCreditRequired": False, "allowSelling": False},
+            },
+            "preview": {"matchCount": 0, "lastUpdated": 0},
+        }

py/nodes/lora_randomizer.py

@@ -0,0 +1,187 @@
+"""
+Lora Randomizer Node - Randomly selects LoRAs from a pool with configurable settings.
+
+This node accepts optional pool_config input to filter available LoRAs, and outputs
+a LORA_STACK with randomly selected LoRAs. Returns UI updates with new random LoRAs
+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__)
+
+
+class LoraRandomizerNode:
+    """Node that randomly selects LoRAs from a pool"""
+
+    NAME = "Lora Randomizer (LoraManager)"
+    CATEGORY = "Lora Manager/randomizer"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "randomizer_config": ("RANDOMIZER_CONFIG", {}),
+                "loras": ("LORAS", {}),
+            },
+            "optional": {
+                "pool_config": ("POOL_CONFIG", {}),
+            },
+        }
+
+    RETURN_TYPES = ("LORA_STACK",)
+    RETURN_NAMES = ("LORA_STACK",)
+
+    FUNCTION = "randomize"
+    OUTPUT_NODE = False
+
+    def _preprocess_loras_input(self, loras):
+        """
+        Preprocess loras input to handle different widget formats.
+
+        Args:
+            loras: Input from widget, either:
+                  - List of LoRA dicts (expected format)
+                  - Dict with '__value__' key containing the list
+
+        Returns:
+            List of LoRA dicts
+        """
+        if isinstance(loras, dict) and "__value__" in loras:
+            return loras["__value__"]
+        return loras
+
+    async def randomize(self, randomizer_config, loras, pool_config=None):
+        """
+        Randomize LoRAs based on configuration and pool filters.
+
+        Args:
+            randomizer_config: Dict with randomizer settings (count, strength ranges, roll_mode)
+            loras: List of LoRA dicts from LORAS widget (includes locked state)
+            pool_config: Optional config from LoRA Pool node for filtering
+
+        Returns:
+            Dictionary with 'result' (LORA_STACK tuple) and 'ui' (for widget display)
+        """
+        from ..services.service_registry import ServiceRegistry
+
+        loras = self._preprocess_loras_input(loras)
+
+        roll_mode = randomizer_config.get("roll_mode", "always")
+        logger.debug(f"[LoraRandomizerNode] roll_mode: {roll_mode}")
+
+        if roll_mode == "fixed":
+            ui_loras = loras
+        else:
+            scanner = await ServiceRegistry.get_lora_scanner()
+            ui_loras = await self._generate_random_loras_for_ui(
+                scanner, randomizer_config, loras, pool_config
+            )
+
+        print("pool config", pool_config)
+
+        execution_stack = self._build_execution_stack_from_input(loras)
+
+        return {
+            "result": (execution_stack,),
+            "ui": {"loras": ui_loras, "last_used": loras},
+        }
+
+    def _build_execution_stack_from_input(self, loras):
+        """
+        Build LORA_STACK tuple from input loras list for execution.
+
+        Args:
+            loras: List of LoRA dicts with name, strength, clipStrength, active
+
+        Returns:
+            List of tuples (lora_path, model_strength, clip_strength)
+        """
+        lora_stack = []
+        for lora in loras:
+            if not lora.get("active", False):
+                continue
+
+            # Get file path
+            lora_path, trigger_words = get_lora_info(lora["name"])
+            if not lora_path:
+                logger.warning(
+                    f"[LoraRandomizerNode] Could not find path for LoRA: {lora['name']}"
+                )
+                continue
+
+            # Normalize path separators
+            lora_path = lora_path.replace("/", os.sep)
+
+            # Extract strengths (convert to float to prevent string subtraction errors)
+            model_strength = float(lora.get("strength", 1.0))
+            clip_strength = float(lora.get("clipStrength", model_strength))
+
+            lora_stack.append((lora_path, model_strength, clip_strength))
+
+        return lora_stack
+
+    async def _generate_random_loras_for_ui(
+        self, scanner, randomizer_config, input_loras, pool_config=None
+    ):
+        """
+        Generate new random loras for UI display.
+
+        Args:
+            scanner: LoraScanner instance
+            randomizer_config: Dict with randomizer settings
+            input_loras: Current input loras (for extracting locked loras)
+            pool_config: Optional pool filters
+
+        Returns:
+            List of LoRA dicts for UI display
+        """
+        from ..services.lora_service import LoraService
+
+        # Parse randomizer settings (convert numeric values to float to prevent type errors)
+        count_mode = randomizer_config.get("count_mode", "range")
+        count_fixed = int(randomizer_config.get("count_fixed", 5))
+        count_min = int(randomizer_config.get("count_min", 3))
+        count_max = int(randomizer_config.get("count_max", 7))
+        model_strength_min = float(randomizer_config.get("model_strength_min", 0.0))
+        model_strength_max = float(randomizer_config.get("model_strength_max", 1.0))
+        use_same_clip_strength = randomizer_config.get("use_same_clip_strength", True)
+        clip_strength_min = float(randomizer_config.get("clip_strength_min", 0.0))
+        clip_strength_max = float(randomizer_config.get("clip_strength_max", 1.0))
+        use_recommended_strength = randomizer_config.get(
+            "use_recommended_strength", False
+        )
+        recommended_strength_scale_min = float(
+            randomizer_config.get("recommended_strength_scale_min", 0.5)
+        )
+        recommended_strength_scale_max = float(
+            randomizer_config.get("recommended_strength_scale_max", 1.0)
+        )
+
+        # Extract locked LoRAs from input
+        locked_loras = [lora for lora in input_loras if lora.get("locked", False)]
+
+        # Use LoraService to generate random LoRAs
+        lora_service = LoraService(scanner)
+        result_loras = await lora_service.get_random_loras(
+            count=count_fixed,
+            model_strength_min=model_strength_min,
+            model_strength_max=model_strength_max,
+            use_same_clip_strength=use_same_clip_strength,
+            clip_strength_min=clip_strength_min,
+            clip_strength_max=clip_strength_max,
+            locked_loras=locked_loras,
+            pool_config=pool_config,
+            count_mode=count_mode,
+            count_min=count_min,
+            count_max=count_max,
+            use_recommended_strength=use_recommended_strength,
+            recommended_strength_scale_min=recommended_strength_scale_min,
+            recommended_strength_scale_max=recommended_strength_scale_max,
+        )
+
+        return result_loras

py/nodes/save_image.py

@@ -9,7 +9,7 @@ from ..metadata_collector import get_metadata
 from PIL import Image, PngImagePlugin
 import piexif
 
-class SaveImage:
+class SaveImageLM:
     NAME = "Save Image (LoraManager)"
     CATEGORY = "Lora Manager/utils"
     DESCRIPTION = "Save images with embedded generation metadata in compatible format"
@@ -273,9 +273,15 @@ class SaveImage:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
-            elif key == "model" and 'checkpoint' in metadata_dict:
-                model = metadata_dict.get('checkpoint', '')
-                model = os.path.splitext(os.path.basename(model))[0]
+            elif key == "model":
+                model_value = metadata_dict.get('checkpoint')
+                if isinstance(model_value, (bytes, os.PathLike)):
+                    model_value = str(model_value)
+
+                if not isinstance(model_value, str) or not model_value:
+                    model = "model_unavailable"
+                else:
+                    model = os.path.splitext(os.path.basename(model_value))[0]
                 if len(parts) >= 2:
                     length = int(parts[1])
                     model = model[:length]
@@ -442,4 +448,4 @@ class SaveImage:
             add_counter_to_filename
         )
         
-        return (images,)
+        return (images,)

py/nodes/trigger_word_toggle.py

@@ -23,6 +23,10 @@ class TriggerWordToggle:
                     "default": True,
                     "tooltip": "Sets the default initial state (active or inactive) when trigger words are added."
                 }),
+                "allow_strength_adjustment": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "Enable mouse wheel adjustment of each trigger word's strength."
+                }),
             },
             "optional": FlexibleOptionalInputType(any_type),
             "hidden": {
@@ -47,7 +51,14 @@ class TriggerWordToggle:
         else:
             return data
 
-    def process_trigger_words(self, id, group_mode, default_active, **kwargs):
+    def process_trigger_words(
+        self,
+        id,
+        group_mode,
+        default_active,
+        allow_strength_adjustment=False,
+        **kwargs,
+    ):
         # Handle both old and new formats for trigger_words
         trigger_words_data = self._get_toggle_data(kwargs, 'orinalMessage')
         trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""
@@ -61,36 +72,81 @@ class TriggerWordToggle:
                 # Convert to list if it's a JSON string
                 if isinstance(trigger_data, str):
                     trigger_data = json.loads(trigger_data)
-                
-                # Create dictionaries to track active state of words or groups
-                active_state = {item['text']: item.get('active', False) for item in trigger_data}
-                
-                if group_mode:
-                    # Split by two or more consecutive commas to get groups
-                    groups = re.split(r',{2,}', trigger_words)
-                    # Remove leading/trailing whitespace from each group
-                    groups = [group.strip() for group in groups]
-                    
-                    # Filter groups: keep those not in toggle_trigger_words or those that are active
-                    filtered_groups = [group for group in groups if group not in active_state or active_state[group]]
-                    
-                    if filtered_groups:
-                        filtered_triggers = ', '.join(filtered_groups)
+
+                if isinstance(trigger_data, list):
+                    if group_mode:
+                        if allow_strength_adjustment:
+                            parsed_items = [
+                                self._parse_trigger_item(item, allow_strength_adjustment)
+                                for item in trigger_data
+                            ]
+                            filtered_groups = [
+                                self._format_word_output(
+                                    item["text"],
+                                    item["strength"],
+                                    allow_strength_adjustment,
+                                )
+                                for item in parsed_items
+                                if item["text"] and item["active"]
+                            ]
+                        else:
+                            filtered_groups = [
+                                (item.get('text') or "").strip()
+                                for item in trigger_data
+                                if (item.get('text') or "").strip() and item.get('active', False)
+                            ]
+                        filtered_triggers = ', '.join(filtered_groups) if filtered_groups else ""
                     else:
-                        filtered_triggers = ""
+                        parsed_items = [
+                            self._parse_trigger_item(item, allow_strength_adjustment)
+                            for item in trigger_data
+                        ]
+                        filtered_words = [
+                            self._format_word_output(
+                                item["text"],
+                                item["strength"],
+                                allow_strength_adjustment,
+                            )
+                            for item in parsed_items
+                            if item["text"] and item["active"]
+                        ]
+                        filtered_triggers = ', '.join(filtered_words) if filtered_words else ""
                 else:
-                    # Original behavior for individual words mode
-                    original_words = [word.strip() for word in trigger_words.split(',')]
-                    # Filter out empty strings
-                    original_words = [word for word in original_words if word]
-                    filtered_words = [word for word in original_words if word not in active_state or active_state[word]]
-                    
-                    if filtered_words:
-                        filtered_triggers = ', '.join(filtered_words)
+                    # Fallback to original message parsing if data is not in the expected list format
+                    if group_mode:
+                        groups = re.split(r',{2,}', trigger_words)
+                        groups = [group.strip() for group in groups if group.strip()]
+                        filtered_triggers = ', '.join(groups)
                     else:
-                        filtered_triggers = ""
-                    
+                        words = [word.strip() for word in trigger_words.split(',') if word.strip()]
+                        filtered_triggers = ', '.join(words)
+
             except Exception as e:
                 logger.error(f"Error processing trigger words: {e}")
             
-        return (filtered_triggers,)
+        return (filtered_triggers,)
+
+    def _parse_trigger_item(self, item, allow_strength_adjustment):
+        text = (item.get('text') or "").strip()
+        active = bool(item.get('active', False))
+        strength = item.get('strength')
+
+        strength_match = re.match(r'^\((.+):([\d.]+)\)$', text)
+        if strength_match:
+            text = strength_match.group(1).strip()
+            if strength is None:
+                try:
+                    strength = float(strength_match.group(2))
+                except ValueError:
+                    strength = None
+
+        return {
+            "text": text,
+            "active": active,
+            "strength": strength if allow_strength_adjustment else None,
+        }
+
+    def _format_word_output(self, base_word, strength, allow_strength_adjustment):
+        if allow_strength_adjustment and strength is not None:
+            return f"({base_word}:{strength:.2f})"
+        return base_word

py/nodes/utils.py

@@ -36,6 +36,7 @@ any_type = AnyType("*")
 import os
 import logging
 import copy
+import sys
 import folder_paths
 
 logger = logging.getLogger(__name__)
@@ -98,25 +99,37 @@ def to_diffusers(input_lora):
 
 def nunchaku_load_lora(model, lora_name, lora_strength):
     """Load a Flux LoRA for Nunchaku model"""   
-    model_wrapper = model.model.diffusion_model
-    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
-    
     # 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)
     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
 
-    ret_model_wrapper.loras.append((lora_path, lora_strength))
+    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)
+    copy_with_ctx = getattr(module, "copy_with_ctx", None)
+
+    if copy_with_ctx is not None:
+        # New logic using copy_with_ctx from ComfyUI-nunchaku 1.1.0+
+        ret_model_wrapper, ret_model = copy_with_ctx(model_wrapper)
+        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.")
+        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
+        ret_model_wrapper.loras.append((lora_path, lora_strength))
 
     # Convert the LoRA to diffusers format
     sd = to_diffusers(lora_path)

py/nodes/wanvideo_lora_select.py

@@ -5,7 +5,7 @@ import logging
 
 logger = logging.getLogger(__name__)
 
-class WanVideoLoraSelect:
+class WanVideoLoraSelectLM:
     NAME = "WanVideo Lora Select (LoraManager)"
     CATEGORY = "Lora Manager/stackers"
 

py/recipes/base.py

@@ -8,6 +8,7 @@ from typing import Dict, List, Any, Optional, Tuple
 from abc import ABC, abstractmethod
 from ..config import config
 from ..utils.constants import VALID_LORA_TYPES
+from ..utils.civitai_utils import rewrite_preview_url
 
 logger = logging.getLogger(__name__)
 
@@ -36,7 +37,8 @@ class RecipeMetadataParser(ABC):
         """
         pass
     
-    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+    @staticmethod
+    async def populate_lora_from_civitai(lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
         """
         Populate a lora entry with information from Civitai API response
@@ -78,7 +80,7 @@ class RecipeMetadataParser(ABC):
             # Update model name if available
             if 'model' in civitai_info and 'name' in civitai_info['model']:
                 lora_entry['name'] = civitai_info['model']['name']
-            
+
             lora_entry['id'] = civitai_info.get('id')
             lora_entry['modelId'] = civitai_info.get('modelId')
             
@@ -88,7 +90,10 @@ class RecipeMetadataParser(ABC):
             
             # Get thumbnail URL from first image
             if 'images' in civitai_info and civitai_info['images']:
-                lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                image_url = civitai_info['images'][0].get('url')
+                if image_url:
+                    rewritten_image_url, _ = rewrite_preview_url(image_url, media_type='image')
+                    lora_entry['thumbnailUrl'] = rewritten_image_url or image_url
             
             # Get base model
             current_base_model = civitai_info.get('baseModel', '')
@@ -144,40 +149,68 @@ class RecipeMetadataParser(ABC):
             logger.error(f"Error populating lora from Civitai info: {e}")
             
         return lora_entry
-        
-    async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+    
+    @staticmethod
+    async def populate_checkpoint_from_civitai(checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
         """
         Populate checkpoint information from Civitai API response
         
         Args:
             checkpoint: The checkpoint entry to populate
-            civitai_info: The response from Civitai API
+            civitai_info: The response from Civitai API or a (data, error_msg) tuple
             
         Returns:
             The populated checkpoint dict
         """
         try:
-            if civitai_info and civitai_info.get("error") != "Model not found":
-                # Update model name if available
-                if 'model' in civitai_info and 'name' in civitai_info['model']:
-                    checkpoint['name'] = civitai_info['model']['name']
-                
-                # Update version if available
-                if 'name' in civitai_info:
-                    checkpoint['version'] = civitai_info.get('name', '')
-                
-                # Get thumbnail URL from first image
-                if 'images' in civitai_info and civitai_info['images']:
-                    checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
-                
-                # Get base model
-                checkpoint['baseModel'] = civitai_info.get('baseModel', '')
-                
-                # Get download URL
-                checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
-            else:
-                # Model not found or deleted
+            civitai_data, error_msg = (
+                (civitai_info, None)
+                if not isinstance(civitai_info, tuple)
+                else civitai_info
+            )
+
+            if not civitai_data or error_msg == "Model not found":
                 checkpoint['isDeleted'] = True
+                return checkpoint
+
+            if 'model' in civitai_data and 'name' in civitai_data['model']:
+                checkpoint['name'] = civitai_data['model']['name']
+
+            if 'name' in civitai_data:
+                checkpoint['version'] = civitai_data.get('name', '')
+
+            if 'images' in civitai_data and civitai_data['images']:
+                image_url = civitai_data['images'][0].get('url')
+                if image_url:
+                    rewritten_image_url, _ = rewrite_preview_url(image_url, media_type='image')
+                    checkpoint['thumbnailUrl'] = rewritten_image_url or image_url
+
+            checkpoint['baseModel'] = civitai_data.get('baseModel', '')
+            checkpoint['downloadUrl'] = civitai_data.get('downloadUrl', '')
+
+            checkpoint['modelId'] = civitai_data.get('modelId', checkpoint.get('modelId', 0))
+            checkpoint['id'] = civitai_data.get('id', 0)
+
+            if 'files' in civitai_data:
+                model_file = next(
+                    (
+                        file
+                        for file in civitai_data.get('files', [])
+                        if file.get('type') == 'Model'
+                    ),
+                    None,
+                )
+
+                if model_file:
+                    checkpoint['size'] = model_file.get('sizeKB', 0) * 1024
+
+                    sha256 = model_file.get('hashes', {}).get('SHA256')
+                    if sha256:
+                        checkpoint['hash'] = sha256.lower()
+
+                    file_name = model_file.get('name', '')
+                    if file_name:
+                        checkpoint['file_name'] = os.path.splitext(file_name)[0]
         except Exception as e:
             logger.error(f"Error populating checkpoint from Civitai info: {e}")
             

py/recipes/enrichment.py

@@ -0,0 +1,216 @@
+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
+
+logger = logging.getLogger(__name__)
+
+class RecipeEnricher:
+    """Service to enrich recipe metadata from multiple sources (Civitai, Embedded, User)."""
+
+    @staticmethod
+    async def enrich_recipe(
+        recipe: Dict[str, Any],
+        civitai_client: Any,
+        request_params: Optional[Dict[str, Any]] = None
+    ) -> bool:
+        """
+        Enrich a recipe dictionary in-place with metadata from Civitai and embedded params.
+        
+        Args:
+            recipe: The recipe dictionary to enrich. Must have 'gen_params' initialized.
+            civitai_client: Authenticated Civitai client instance.
+            request_params: (Optional) Parameters from a user request (e.g. import).
+            
+        Returns:
+            bool: True if the recipe was modified, False otherwise.
+        """
+        updated = False
+        gen_params = recipe.get("gen_params", {})
+        
+        # 1. Fetch Civitai Info if available
+        civitai_meta = None
+        model_version_id = None
+        
+        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)
+            try:
+                image_info = await civitai_client.get_image_info(image_id)
+                if image_info:
+                    # Handle nested meta often found in Civitai API responses
+                    raw_meta = image_info.get("meta")
+                    if isinstance(raw_meta, dict):
+                        if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
+                            civitai_meta = raw_meta["meta"]
+                        else:
+                            civitai_meta = raw_meta
+                    
+                    model_version_id = image_info.get("modelVersionId")
+                    
+                    # If not at top level, check resources in meta
+                    if not model_version_id and civitai_meta:
+                        resources = civitai_meta.get("civitaiResources", [])
+                        for res in resources:
+                            if res.get("type") == "checkpoint":
+                                model_version_id = res.get("modelVersionId")
+                                break
+            except Exception as e:
+                logger.warning(f"Failed to fetch Civitai image info: {e}")
+
+        # 2. Merge Parameters
+        # Priority: request_params > civitai_meta > embedded (existing gen_params)
+        new_gen_params = GenParamsMerger.merge(
+            request_params=request_params,
+            civitai_meta=civitai_meta,
+            embedded_metadata=gen_params
+        )
+        
+        if new_gen_params != gen_params:
+            recipe["gen_params"] = new_gen_params
+            updated = True
+        
+        # 3. Checkpoint Enrichment
+        # If we have a checkpoint entry, or we can find one
+        # Use 'id' (from Civitai version) as a marker that it's been enriched
+        checkpoint_entry = recipe.get("checkpoint")
+        has_full_checkpoint = checkpoint_entry and checkpoint_entry.get("name") and checkpoint_entry.get("id")
+        
+        if not has_full_checkpoint:
+            # Helper to look up values in priority order
+            def start_lookup(keys):
+                for source in [request_params, civitai_meta, gen_params]:
+                    if source:
+                        if isinstance(keys, list):
+                            for k in keys:
+                                if k in source: return source[k]
+                        else:
+                            if keys in source: return source[keys]
+                return None
+
+            target_version_id = model_version_id or start_lookup("modelVersionId")
+            
+            # Also check existing checkpoint entry
+            if not target_version_id and checkpoint_entry:
+                target_version_id = checkpoint_entry.get("modelVersionId") or checkpoint_entry.get("id")
+            
+            # Check for version ID in resources (which might be a string in gen_params)
+            if not target_version_id:
+                # Look in all sources for "Civitai resources"
+                resources_val = start_lookup(["Civitai resources", "civitai_resources", "resources"])
+                if resources_val:
+                    target_version_id = RecipeEnricher._extract_version_id_from_resources({"Civitai resources": resources_val})
+            
+            target_hash = start_lookup(["Model hash", "checkpoint_hash", "hashes"])
+            if not target_hash and checkpoint_entry:
+                target_hash = checkpoint_entry.get("hash") or checkpoint_entry.get("model_hash")
+            
+            # Look for 'Model' which sometimes is the hash or name
+            model_val = start_lookup("Model")
+
+            # Look for Checkpoint name fallback
+            checkpoint_val = checkpoint_entry.get("name") if checkpoint_entry else None
+            if not checkpoint_val:
+                checkpoint_val = start_lookup(["Checkpoint", "checkpoint"])
+
+            checkpoint_updated = await RecipeEnricher._resolve_and_populate_checkpoint(
+                recipe, target_version_id, target_hash, model_val, checkpoint_val
+            )
+            if checkpoint_updated:
+                updated = True
+        else:
+            # Checkpoint exists, no need to sync to gen_params anymore.
+            pass
+        # base_model resolution moved to _resolve_and_populate_checkpoint to support strict formatting
+        return updated
+        
+    @staticmethod
+    def _extract_version_id_from_resources(gen_params: Dict[str, Any]) -> Optional[Any]:
+        """Try to find modelVersionId in Civitai resources parameter."""
+        civitai_resources_raw = gen_params.get("Civitai resources")
+        if not civitai_resources_raw:
+            return None
+            
+        resources_list = None
+        if isinstance(civitai_resources_raw, str):
+            try:
+                resources_list = json.loads(civitai_resources_raw)
+            except Exception:
+                pass
+        elif isinstance(civitai_resources_raw, list):
+            resources_list = civitai_resources_raw
+            
+        if isinstance(resources_list, list):
+            for res in resources_list:
+                if res.get("type") == "checkpoint":
+                    return res.get("modelVersionId")
+        return None
+
+    @staticmethod
+    async def _resolve_and_populate_checkpoint(
+        recipe: Dict[str, Any], 
+        target_version_id: Optional[Any], 
+        target_hash: Optional[str],
+        model_val: Optional[str],
+        checkpoint_val: Optional[str]
+    ) -> bool:
+        """Find checkpoint metadata and populate it in the recipe."""
+        metadata_provider = await get_default_metadata_provider()
+        civitai_info = None
+        
+        if target_version_id:
+            civitai_info = await metadata_provider.get_model_version_info(str(target_version_id))
+        elif target_hash:
+            civitai_info = await metadata_provider.get_model_by_hash(target_hash)
+        else:
+            # Look for 'Model' which sometimes is the hash or name
+            if model_val and len(model_val) == 10: # Likely a short hash
+                civitai_info = await metadata_provider.get_model_by_hash(model_val)
+                
+        if civitai_info and not (isinstance(civitai_info, tuple) and civitai_info[1] == "Model not found"):
+            # If we already have a partial checkpoint, use it as base
+            existing_cp = recipe.get("checkpoint")
+            if existing_cp is None:
+                existing_cp = {}
+            checkpoint_data = await RecipeMetadataParser.populate_checkpoint_from_civitai(existing_cp, civitai_info)
+            # 1. First, resolve base_model using full data before we format it away
+            current_base_model = recipe.get("base_model")
+            resolved_base_model = checkpoint_data.get("baseModel")
+            if resolved_base_model:
+                # Update if empty OR if it matches our generic prefix but is less specific
+                is_generic = not current_base_model or current_base_model.lower() in ["flux", "sdxl", "sd15"]
+                if is_generic and resolved_base_model != current_base_model:
+                    recipe["base_model"] = resolved_base_model
+            
+            # 2. Format according to requirements: type, modelId, modelVersionId, modelName, modelVersionName
+            formatted_checkpoint = {
+                "type": "checkpoint",
+                "modelId": checkpoint_data.get("modelId"),
+                "modelVersionId": checkpoint_data.get("id") or checkpoint_data.get("modelVersionId"),
+                "modelName": checkpoint_data.get("name"), # In base.py, 'name' is populated from civitai_data['model']['name']
+                "modelVersionName": checkpoint_data.get("version") # In base.py, 'version' is populated from civitai_data['name']
+            }
+            # Remove None values
+            recipe["checkpoint"] = {k: v for k, v in formatted_checkpoint.items() if v is not None}
+            
+            return True
+        else:
+            # Fallback to name extraction if we don't already have one
+            existing_cp = recipe.get("checkpoint")
+            if not existing_cp or not existing_cp.get("modelName"):
+                cp_name = checkpoint_val
+                if cp_name:
+                    recipe["checkpoint"] = {
+                        "type": "checkpoint",
+                        "modelName": cp_name
+                    }
+                    return True
+                
+        return False

py/recipes/merger.py

@@ -0,0 +1,98 @@
+from typing import Any, Dict, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+class GenParamsMerger:
+    """Utility to merge generation parameters from multiple sources with priority."""
+
+    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"
+    }
+    
+    NORMALIZATION_MAPPING = {
+        # Civitai specific
+        "cfgScale": "cfg_scale",
+        "clipSkip": "clip_skip",
+        "negativePrompt": "negative_prompt",
+        # Case variations
+        "Sampler": "sampler",
+        "Steps": "steps",
+        "Seed": "seed",
+        "Size": "size",
+        "Prompt": "prompt",
+        "Negative prompt": "negative_prompt",
+        "Cfg scale": "cfg_scale",
+        "Clip skip": "clip_skip",
+        "Denoising strength": "denoising_strength",
+    }
+
+    @staticmethod
+    def merge(
+        request_params: Optional[Dict[str, Any]] = None,
+        civitai_meta: 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)
+        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):
+                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
+
+    @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

py/recipes/parsers/automatic.py

@@ -1,6 +1,7 @@
 """Parser for Automatic1111 metadata format."""
 
 import re
+import os
 import json
 import logging
 from typing import Dict, Any
@@ -22,6 +23,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
     CIVITAI_METADATA_REGEX = r', Civitai metadata:\s*(\{.*?\})'
     EXTRANETS_REGEX = r'<(lora|hypernet):([^:]+):(-?[0-9.]+)>'
     MODEL_HASH_PATTERN = r'Model hash: ([a-zA-Z0-9]+)'
+    MODEL_NAME_PATTERN = r'Model: ([^,]+)'
     VAE_HASH_PATTERN = r'VAE hash: ([a-zA-Z0-9]+)'
     
     def is_metadata_matching(self, user_comment: str) -> bool:
@@ -115,6 +117,12 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         except json.JSONDecodeError:
                             logger.error("Error parsing hashes JSON")
                     
+                    # Pick up model hash from parsed hashes if available
+                    if "hashes" in metadata and not metadata.get("model_hash"):
+                        model_hash_from_hashes = metadata["hashes"].get("model")
+                        if model_hash_from_hashes:
+                            metadata["model_hash"] = model_hash_from_hashes
+                    
                     # Extract Lora hashes in alternative format
                     lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
                     if not hashes_match and lora_hashes_match:
@@ -137,6 +145,17 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                             params_section = params_section.replace(lora_hashes_match.group(0), '')
                         except Exception as e:
                             logger.error(f"Error parsing Lora hashes: {e}")
+
+                    # Extract checkpoint model hash/name when provided outside Civitai resources
+                    model_hash_match = re.search(self.MODEL_HASH_PATTERN, params_section)
+                    if model_hash_match:
+                        metadata["model_hash"] = model_hash_match.group(1).strip()
+                        params_section = params_section.replace(model_hash_match.group(0), '')
+
+                    model_name_match = re.search(self.MODEL_NAME_PATTERN, params_section)
+                    if model_name_match:
+                        metadata["model_name"] = model_name_match.group(1).strip()
+                        params_section = params_section.replace(model_name_match.group(0), '')
                     
                     # Extract basic parameters
                     param_pattern = r'([A-Za-z\s]+): ([^,]+)'
@@ -178,9 +197,10 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                     
                     metadata["gen_params"] = gen_params
             
-            # Extract LoRA information 
+            # Extract LoRA and checkpoint information 
             loras = []
             base_model_counts = {}
+            checkpoint = None
             
             # First use Civitai resources if available (more reliable source)
             if metadata.get("civitai_resources"):
@@ -202,6 +222,50 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                             resource["modelVersionId"] = air_modelVersionId
                     # --- End added ---
 
+                    if resource.get("type") == "checkpoint" and resource.get("modelVersionId"):
+                        version_id = resource.get("modelVersionId")
+                        version_id_str = str(version_id)
+                        checkpoint_entry = {
+                            'id': version_id,
+                            'modelId': resource.get("modelId", 0),
+                            'name': resource.get("modelName", "Unknown Checkpoint"),
+                            'version': resource.get("modelVersionName", resource.get("versionName", "")),
+                            '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 metadata_provider:
+                            try:
+                                civitai_info = await metadata_provider.get_model_version_info(version_id_str)
+                                checkpoint_entry = await self.populate_checkpoint_from_civitai(
+                                    checkpoint_entry,
+                                    civitai_info
+                                )
+                            except Exception as e:
+                                logger.error(
+                                    "Error fetching Civitai info for checkpoint version %s: %s",
+                                    version_id,
+                                    e,
+                                )
+
+                        # Prefer the first checkpoint found
+                        if checkpoint_entry.get("baseModel"):
+                            base_model_value = checkpoint_entry["baseModel"]
+                            base_model_counts[base_model_value] = base_model_counts.get(base_model_value, 0) + 1
+
+                        if checkpoint is None:
+                            checkpoint = checkpoint_entry
+
+                        continue
+
                     if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
                         # Initialize lora entry
                         lora_entry = {
@@ -237,6 +301,52 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         
                         loras.append(lora_entry)
             
+            # Fallback checkpoint parsing from generic "Model" and "Model hash" fields
+            if checkpoint is None:
+                model_hash = metadata.get("model_hash")
+                if not model_hash and metadata.get("hashes"):
+                    model_hash = metadata["hashes"].get("model")
+
+                model_name = metadata.get("model_name")
+                file_name = ""
+                if model_name:
+                    cleaned_name = re.split(r"[\\\\/]", model_name)[-1]
+                    file_name = os.path.splitext(cleaned_name)[0]
+
+                if model_hash or model_name:
+                    checkpoint_entry = {
+                        'id': 0,
+                        'modelId': 0,
+                        'name': model_name or "Unknown Checkpoint",
+                        'version': '',
+                        'type': 'checkpoint',
+                        'hash': model_hash or "",
+                        'existsLocally': False,
+                        'localPath': None,
+                        'file_name': file_name,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
+                    }
+
+                    if metadata_provider and model_hash:
+                        try:
+                            civitai_info = await metadata_provider.get_model_by_hash(model_hash)
+                            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 hash {model_hash}: {e}")
+
+                    if checkpoint_entry.get("baseModel"):
+                        base_model_value = checkpoint_entry["baseModel"]
+                        base_model_counts[base_model_value] = base_model_counts.get(base_model_value, 0) + 1
+
+                    checkpoint = checkpoint_entry
+
             # If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
             if not loras or len(loras) == 0:
                 # Extract lora weights from extranet tags in prompt (for later use)
@@ -300,7 +410,9 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                 
             # Try to get base model from resources or make educated guess
             base_model = None
-            if base_model_counts:
+            if checkpoint and checkpoint.get("baseModel"):
+                base_model = checkpoint.get("baseModel")
+            elif base_model_counts:
                 # Use the most common base model from the loras
                 base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
             
@@ -317,6 +429,10 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                 'gen_params': filtered_gen_params,
                 'from_automatic_metadata': True
             }
+
+            if checkpoint:
+                result['checkpoint'] = checkpoint
+                result['model'] = checkpoint
             
             return result
             

py/recipes/parsers/civitai_image.py

@@ -23,13 +23,48 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         """
         if not metadata or not isinstance(metadata, dict):
             return False
-            
-        # Check for key markers specific to Civitai image metadata
-        return any([
-            "resources" in metadata,
-            "civitaiResources" in metadata,
-            "additionalResources" in metadata
-        ])
+
+        def has_markers(payload: Dict[str, Any]) -> bool:
+            # Check for common CivitAI image metadata fields
+            civitai_image_fields = (
+                "resources",
+                "civitaiResources", 
+                "additionalResources",
+                "hashes",
+                "prompt",
+                "negativePrompt",
+                "steps",
+                "sampler",
+                "cfgScale",
+                "seed",
+                "width",
+                "height",
+                "Model",
+                "Model hash"
+            )
+            return any(key in payload for key in civitai_image_fields)
+
+        # Check the main metadata object
+        if has_markers(metadata):
+            return True
+
+        # Check for LoRA hash patterns
+        hashes = metadata.get("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)
+        nested_meta = metadata.get("meta")
+        if isinstance(nested_meta, dict):
+            if has_markers(nested_meta):
+                return True
+
+            # 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):
+                return True
+
+        return False
     
     async def parse_metadata(self, metadata, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
         """Parse metadata from Civitai image format
@@ -45,11 +80,32 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         try:
             # Get metadata provider instead of using civitai_client directly
             metadata_provider = await get_default_metadata_provider()
+
+            # Civitai image responses may wrap the actual metadata inside a "meta" key
+            if (
+                isinstance(metadata, dict)
+                and "meta" in metadata
+                and isinstance(metadata["meta"], dict)
+            ):
+                inner_meta = metadata["meta"]
+                if any(
+                    key in inner_meta
+                    for key in (
+                        "resources",
+                        "civitaiResources",
+                        "additionalResources",
+                        "hashes",
+                        "prompt",
+                        "negativePrompt",
+                    )
+                ):
+                    metadata = inner_meta
             
             # Initialize result structure
             result = {
                 'base_model': None,
                 'loras': [],
+                'model': None,
                 'gen_params': {},
                 'from_civitai_image': True
             }
@@ -61,8 +117,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             lora_hashes = {}
             if "hashes" in metadata and isinstance(metadata["hashes"], dict):
                 for key, hash_value in metadata["hashes"].items():
-                    if key.startswith("LORA:"):
-                        lora_name = key.replace("LORA:", "")
+                    key_str = str(key)
+                    if key_str.lower().startswith("lora:"):
+                        lora_name = key_str.split(":", 1)[1]
                         lora_hashes[lora_name] = hash_value
             
             # Extract prompt and negative prompt
@@ -174,13 +231,48 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Process civitaiResources array
             if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
                 for resource in metadata["civitaiResources"]:
-                    # Get unique identifier for deduplication
+                    # Get resource type and identifier
+                    resource_type = str(resource.get("type") or "").lower()
                     version_id = str(resource.get("modelVersionId", ""))
-                    
+
+                    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
+                        }
+
+                        if version_id and metadata_provider:
+                            try:
+                                civitai_info = await metadata_provider.get_model_version_info(version_id)
+
+                                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}")
+
+                        if result["model"] is None:
+                            result["model"] = checkpoint_entry
+
+                        continue
+
                     # Skip if we've already added this LoRA
                     if version_id and version_id in added_loras:
                         continue
-                    
+
                     # Initialize lora entry
                     lora_entry = {
                         'id': resource.get("modelVersionId", 0),
@@ -196,31 +288,31 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         '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)
-                            
+
                             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 in our deduplication dict
                     if version_id:
                         added_loras[version_id] = len(result["loras"])
-                        
+
                     result["loras"].append(lora_entry)
             
             # Process additionalResources array

py/recipes/parsers/comfy.py

@@ -36,9 +36,6 @@ class ComfyMetadataParser(RecipeMetadataParser):
             # Find all LoraLoader nodes
             lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
             
-            if not lora_nodes:
-                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
-            
             # Process each LoraLoader node
             for node_id, node in lora_nodes.items():
                 if 'inputs' not in node or 'lora_name' not in node['inputs']:

py/recipes/parsers/meta_format.py

@@ -1,5 +1,6 @@
 """Parser for meta format (Lora_N Model hash) metadata."""
 
+import os
 import re
 import logging
 from typing import Dict, Any
@@ -145,14 +146,53 @@ class MetaFormatParser(RecipeMetadataParser):
                 
                 loras.append(lora_entry)
             
-            # Extract model information
-            model = None
-            if 'model' in metadata:
-                model = metadata['model']
+            # Extract checkpoint information from generic Model/Model hash fields
+            checkpoint = None
+            model_hash = metadata.get("model_hash")
+            model_name = metadata.get("model")
+
+            if model_hash or model_name:
+                cleaned_name = None
+                if model_name:
+                    cleaned_name = re.split(r"[\\\\/]", model_name)[-1]
+                    cleaned_name = os.path.splitext(cleaned_name)[0]
+
+                checkpoint_entry = {
+                    'id': 0,
+                    'modelId': 0,
+                    'name': model_name or "Unknown Checkpoint",
+                    'version': '',
+                    'type': 'checkpoint',
+                    'hash': model_hash or "",
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': cleaned_name or (model_name or ""),
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+
+                if metadata_provider and model_hash:
+                    try:
+                        civitai_info = await metadata_provider.get_model_by_hash(model_hash)
+                        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 hash {model_hash}: {e}")
+
+                if checkpoint_entry.get("baseModel"):
+                    base_model_value = checkpoint_entry["baseModel"]
+                    base_model_counts[base_model_value] = base_model_counts.get(base_model_value, 0) + 1
+
+                checkpoint = checkpoint_entry
             
-            # Set base_model to the most common one from civitai_info
-            base_model = None
-            if base_model_counts:
+            # Set base_model to the most common one from civitai_info or checkpoint
+            base_model = checkpoint["baseModel"] if checkpoint and checkpoint.get("baseModel") else None
+            if not base_model and base_model_counts:
                 base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
             
             # Extract generation parameters for recipe metadata
@@ -170,7 +210,8 @@ class MetaFormatParser(RecipeMetadataParser):
                 'loras': loras,
                 'gen_params': gen_params,
                 'raw_metadata': metadata,
-                'from_meta_format': True
+                'from_meta_format': True,
+                **({'checkpoint': checkpoint, 'model': checkpoint} if checkpoint else {})
             }
             
         except Exception as e:

py/recipes/parsers/recipe_format.py

@@ -3,7 +3,7 @@
 import re
 import json
 import logging
-from typing import Dict, Any
+from typing import Dict, Any, Optional
 from ...config import config
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
@@ -16,6 +16,28 @@ class RecipeFormatParser(RecipeMetadataParser):
     
     # Regular expression pattern for extracting recipe metadata
     METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+
+    async def _get_lora_from_version_index(self, recipe_scanner, model_version_id: Any) -> Optional[Dict[str, Any]]:
+        """Return a cached LoRA entry by modelVersionId if available."""
+
+        if not recipe_scanner or not getattr(recipe_scanner, "_lora_scanner", None):
+            return None
+
+        try:
+            normalized_id = int(model_version_id)
+        except (TypeError, ValueError):
+            return None
+
+        try:
+            cache = await recipe_scanner._lora_scanner.get_cached_data()
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.debug("Unable to load lora cache for version lookup: %s", exc)
+            return None
+
+        if not cache or not getattr(cache, "version_index", None):
+            return None
+
+        return cache.version_index.get(normalized_id)
     
     def is_metadata_matching(self, user_comment: str) -> bool:
         """Check if the user comment matches the metadata format"""
@@ -53,49 +75,110 @@ class RecipeFormatParser(RecipeMetadataParser):
                     'type': 'lora',
                     'weight': lora.get('strength', 1.0),
                     'file_name': lora.get('file_name', ''),
-                    'hash': lora.get('hash', '')
+                    'hash': lora.get('hash', ''),
+                    'existsLocally': False,
+                    'inLibrary': False,
+                    'localPath': None,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'size': 0
                 }
                 
                 # Check if this LoRA exists locally by SHA256 hash
-                if lora.get('hash') and recipe_scanner:
+                if recipe_scanner:
                     lora_scanner = recipe_scanner._lora_scanner
-                    exists_locally = lora_scanner.has_hash(lora['hash'])
-                    if exists_locally:
-                        lora_cache = await lora_scanner.get_cached_data()
-                        lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
-                        if lora_item:
+
+                    if lora.get('hash'):
+                        exists_locally = lora_scanner.has_hash(lora['hash'])
+                        if exists_locally:
+                            lora_cache = await lora_scanner.get_cached_data()
+                            lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                            if lora_item:
+                                lora_entry['existsLocally'] = True
+                                lora_entry['inLibrary'] = True
+                                lora_entry['localPath'] = lora_item['file_path']
+                                lora_entry['file_name'] = lora_item['file_name']
+                                lora_entry['size'] = lora_item['size']
+                                lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                                
+                        else:
+                            lora_entry['existsLocally'] = False
+                            lora_entry['inLibrary'] = False
+                            lora_entry['localPath'] = None
+
+                    # If we still don't have a local match, try matching by modelVersionId
+                    if not lora_entry['existsLocally'] and lora.get('modelVersionId') is not None:
+                        cached_lora = await self._get_lora_from_version_index(recipe_scanner, lora.get('modelVersionId'))
+                        if cached_lora:
                             lora_entry['existsLocally'] = True
-                            lora_entry['localPath'] = lora_item['file_path']
-                            lora_entry['file_name'] = lora_item['file_name']
-                            lora_entry['size'] = lora_item['size']
-                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
-                            
-                    else:
-                        lora_entry['existsLocally'] = False
-                        lora_entry['localPath'] = None
-                        
-                        # Try to get additional info from Civitai if we have a model version ID
-                        if lora.get('modelVersionId') and metadata_provider:
-                            try:
-                                civitai_info_tuple = await metadata_provider.get_model_version_info(lora['modelVersionId'])
-                                # Populate lora entry with Civitai info
-                                populated_entry = await self.populate_lora_from_civitai(
-                                    lora_entry, 
-                                    civitai_info_tuple, 
-                                    recipe_scanner,
-                                    None,  # No need to track base model counts
-                                    lora['hash']
-                                )
-                                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 LoRA: {e}")
-                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                            lora_entry['inLibrary'] = True
+                            lora_entry['localPath'] = cached_lora.get('file_path')
+                            lora_entry['file_name'] = cached_lora.get('file_name') or lora_entry['file_name']
+                            lora_entry['size'] = cached_lora.get('size', lora_entry['size'])
+                            if cached_lora.get('sha256'):
+                                lora_entry['hash'] = cached_lora['sha256']
+                            preview_url = cached_lora.get('preview_url')
+                            if preview_url:
+                                lora_entry['thumbnailUrl'] = config.get_preview_static_url(preview_url)
+
+                    # Try to get additional info from Civitai if we have a model version ID and still missing locally
+                    if not lora_entry['existsLocally'] and lora.get('modelVersionId') and metadata_provider:
+                        try:
+                            civitai_info_tuple = await metadata_provider.get_model_version_info(lora['modelVersionId'])
+                            # Populate lora entry with Civitai info
+                            populated_entry = await self.populate_lora_from_civitai(
+                                lora_entry, 
+                                civitai_info_tuple, 
+                                recipe_scanner,
+                                None,  # No need to track base model counts
+                                lora_entry.get('hash', '')
+                            )
+                            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 LoRA: {e}")
+                            lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
                 
                 loras.append(lora_entry)
-
+            
             logger.info(f"Found {len(loras)} loras in recipe metadata")
+
+            # Process checkpoint information if present
+            checkpoint = None
+            checkpoint_data = recipe_metadata.get('checkpoint') or {}
+            if isinstance(checkpoint_data, dict) and checkpoint_data:
+                version_id = checkpoint_data.get('modelVersionId') or checkpoint_data.get('id')
+                checkpoint_entry = {
+                    'id': version_id or 0,
+                    'modelId': checkpoint_data.get('modelId', 0),
+                    'name': checkpoint_data.get('name', 'Unknown Checkpoint'),
+                    'version': checkpoint_data.get('version', ''),
+                    'type': checkpoint_data.get('type', 'checkpoint'),
+                    'hash': checkpoint_data.get('hash', ''),
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': checkpoint_data.get('file_name', ''),
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+
+                if metadata_provider:
+                    try:
+                        civitai_info = None
+                        if version_id:
+                            civitai_info = await metadata_provider.get_model_version_info(str(version_id))
+                        elif checkpoint_entry.get('hash'):
+                            civitai_info = await metadata_provider.get_model_by_hash(checkpoint_entry['hash'])
+
+                        if 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 in recipe metadata: {e}")
+
+                checkpoint = checkpoint_entry
             
             # Filter gen_params to only include recognized keys
             filtered_gen_params = {}
@@ -105,12 +188,13 @@ class RecipeFormatParser(RecipeMetadataParser):
                         filtered_gen_params[key] = value
             
             return {
-                'base_model': recipe_metadata.get('base_model', ''),
+                'base_model': checkpoint['baseModel'] if checkpoint and checkpoint.get('baseModel') else recipe_metadata.get('base_model', ''),
                 'loras': loras,
                 'gen_params': filtered_gen_params,
                 'tags': recipe_metadata.get('tags', []),
                 'title': recipe_metadata.get('title', ''),
-                'from_recipe_metadata': True
+                'from_recipe_metadata': True,
+                **({'checkpoint': checkpoint, 'model': checkpoint} if checkpoint else {})
             }
             
         except Exception as e:

py/routes/base_model_routes.py

@@ -120,12 +120,13 @@ class BaseModelRoutes(ABC):
         self.service = service
         self.model_type = service.model_type
         self.model_file_service = ModelFileService(service.scanner, service.model_type)
-        self.model_move_service = ModelMoveService(service.scanner)
+        self.model_move_service = ModelMoveService(service.scanner, service.model_type)
         self.model_lifecycle_service = ModelLifecycleService(
             scanner=service.scanner,
             metadata_manager=MetadataManager,
             metadata_loader=self._metadata_sync_service.load_local_metadata,
             recipe_scanner_factory=ServiceRegistry.get_recipe_scanner,
+            update_service=self._model_update_service,
         )
         self._handler_set = None
         self._handler_mapping = None
@@ -269,7 +270,7 @@ class BaseModelRoutes(ABC):
     def _ensure_move_service(self) -> ModelMoveService:
         if self.model_move_service is None:
             service = self._ensure_service()
-            self.model_move_service = ModelMoveService(service.scanner)
+            self.model_move_service = ModelMoveService(service.scanner, service.model_type)
         return self.model_move_service
 
     def _ensure_lifecycle_service(self) -> ModelLifecycleService:
@@ -297,4 +298,3 @@ class BaseModelRoutes(ABC):
         if self._model_update_service is None:
             raise RuntimeError("Model update service has not been attached")
         return self._model_update_service
-

py/routes/base_recipe_routes.py

@@ -79,26 +79,8 @@ class BaseRecipeRoutes:
             return
 
         app.on_startup.append(self.attach_dependencies)
-        app.on_startup.append(self.prewarm_cache)
         self._startup_hooks_registered = True
 
-    async def prewarm_cache(self, app: web.Application | None = None) -> None:
-        """Pre-load recipe and LoRA caches on startup."""
-
-        try:
-            await self.attach_dependencies(app)
-
-            if self.lora_scanner is not None:
-                await self.lora_scanner.get_cached_data()
-                hash_index = getattr(self.lora_scanner, "_hash_index", None)
-                if hash_index is not None and hasattr(hash_index, "_hash_to_path"):
-                    _ = len(hash_index._hash_to_path)
-
-            if self.recipe_scanner is not None:
-                await self.recipe_scanner.get_cached_data(force_refresh=True)
-        except Exception as exc:
-            logger.error("Error pre-warming recipe cache: %s", exc, exc_info=True)
-
     def to_route_mapping(self) -> Mapping[str, Callable]:
         """Return a mapping of handler name to coroutine for registrar binding."""
 
@@ -191,6 +173,8 @@ class BaseRecipeRoutes:
             logger=logger,
             persistence_service=persistence_service,
             analysis_service=analysis_service,
+            downloader_factory=get_downloader,
+            civitai_client_getter=civitai_client_getter,
         )
         analysis = RecipeAnalysisHandler(
             ensure_dependencies_ready=self.ensure_dependencies_ready,
@@ -214,4 +198,3 @@ class BaseRecipeRoutes:
             analysis=analysis,
             sharing=sharing,
         )
-

py/routes/checkpoint_routes.py

@@ -1,4 +1,5 @@
 import logging
+from typing import Dict
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
@@ -51,6 +52,19 @@ class CheckpointRoutes(BaseModelRoutes):
     def _get_expected_model_types(self) -> str:
         """Get expected model types string for error messages"""
         return "Checkpoint"
+
+    def _parse_specific_params(self, request: web.Request) -> Dict:
+        """Parse Checkpoint-specific parameters"""
+        params: Dict = {}
+
+        if 'checkpoint_hash' in request.query:
+            params['hash_filters'] = {'single_hash': request.query['checkpoint_hash'].lower()}
+        elif 'checkpoint_hashes' in request.query:
+            params['hash_filters'] = {
+                'multiple_hashes': [h.lower() for h in request.query['checkpoint_hashes'].split(',')]
+            }
+
+        return params
     
     async def get_checkpoint_info(self, request: web.Request) -> web.Response:
         """Get detailed information for a specific checkpoint by name"""

py/routes/example_images_route_registrar.py

@@ -29,6 +29,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/delete-example-image", "delete_example_image"),
     RouteDefinition("POST", "/api/lm/force-download-example-images", "force_download_example_images"),
     RouteDefinition("POST", "/api/lm/cleanup-example-image-folders", "cleanup_example_image_folders"),
+    RouteDefinition("POST", "/api/lm/example-images/set-nsfw-level", "set_example_image_nsfw_level"),
 )
 
 

py/routes/handlers/example_images_handlers.py

@@ -113,6 +113,9 @@ class ExampleImagesManagementHandler:
     async def delete_example_image(self, request: web.Request) -> web.StreamResponse:
         return await self._processor.delete_custom_image(request)
 
+    async def set_example_image_nsfw_level(self, request: web.Request) -> web.StreamResponse:
+        return await self._processor.set_example_image_nsfw_level(request)
+
     async def cleanup_example_image_folders(self, request: web.Request) -> web.StreamResponse:
         result = await self._cleanup_service.cleanup_example_image_folders()
 
@@ -160,6 +163,7 @@ class ExampleImagesHandlerSet:
             "force_download_example_images": self.download.force_download_example_images,
             "import_example_images": self.management.import_example_images,
             "delete_example_image": self.management.delete_example_image,
+            "set_example_image_nsfw_level": self.management.set_example_image_nsfw_level,
             "cleanup_example_image_folders": self.management.cleanup_example_image_folders,
             "open_example_images_folder": self.files.open_example_images_folder,
             "get_example_image_files": self.files.get_example_image_files,

py/routes/handlers/misc_handlers.py

@@ -43,12 +43,55 @@ from ...utils.usage_stats import UsageStats
 logger = logging.getLogger(__name__)
 
 
+def _is_wsl() -> bool:
+    """Check if running in WSL environment."""
+    try:
+        with open("/proc/version", "r") as f:
+            version_info = f.read().lower()
+            return "microsoft" in version_info or "wsl" in version_info
+    except (OSError, IOError):
+        return False
+
+
+def _is_docker() -> bool:
+    """Check if running in Docker container."""
+    dockerenv_exists = os.path.exists("/.dockerenv")
+    if dockerenv_exists:
+        return True
+
+    try:
+        with open("/proc/1/cgroup", "r") as f:
+            cgroup_content = f.read()
+            return (
+                "docker" in cgroup_content.lower()
+                or "kubepods" in cgroup_content.lower()
+            )
+    except (OSError, IOError):
+        return False
+
+
+def _wsl_to_windows_path(wsl_path: str) -> str | None:
+    """Convert WSL path to Windows path using wslpath."""
+    try:
+        result = subprocess.run(
+            ["wslpath", "-w", wsl_path],
+            capture_output=True,
+            text=True,
+            check=True,
+        )
+        return result.stdout.strip()
+    except (subprocess.CalledProcessError, FileNotFoundError, OSError):
+        return None
+
+
 class PromptServerProtocol(Protocol):
     """Subset of PromptServer used by the handlers."""
 
     instance: "PromptServerProtocol"
 
-    def send_sync(self, event: str, payload: dict) -> None:  # pragma: no cover - protocol
+    def send_sync(
+        self, event: str, payload: dict
+    ) -> None:  # pragma: no cover - protocol
         ...
 
 
@@ -63,7 +106,9 @@ class UsageStatsFactory(Protocol):
 
 
 class MetadataProviderProtocol(Protocol):
-    async def get_model_versions(self, model_id: int) -> dict | None:  # pragma: no cover - protocol
+    async def get_model_versions(
+        self, model_id: int
+    ) -> dict | None:  # pragma: no cover - protocol
         ...
 
 
@@ -109,7 +154,11 @@ class NodeRegistry:
                 raw_widget_names: list | None = node.get("widget_names")
                 if not isinstance(raw_widget_names, list):
                     capability_widget_names = capabilities.get("widget_names")
-                    raw_widget_names = capability_widget_names if isinstance(capability_widget_names, list) else None
+                    raw_widget_names = (
+                        capability_widget_names
+                        if isinstance(capability_widget_names, list)
+                        else None
+                    )
 
                 widget_names: list[str] = []
                 if isinstance(raw_widget_names, list):
@@ -175,11 +224,13 @@ class SettingsHandler:
         "civitai_api_key",
         "default_lora_root",
         "default_checkpoint_root",
+        "default_unet_root",
         "default_embedding_root",
         "base_model_path_mappings",
         "download_path_templates",
         "enable_metadata_archive_db",
         "language",
+        "use_portable_settings",
         "proxy_enabled",
         "proxy_type",
         "proxy_host",
@@ -200,16 +251,29 @@ class SettingsHandler:
         "priority_tags",
         "model_card_footer_action",
         "model_name_display",
+        "update_flag_strategy",
+        "auto_organize_exclusions",
     )
 
-    _PROXY_KEYS = {"proxy_enabled", "proxy_host", "proxy_port", "proxy_username", "proxy_password", "proxy_type"}
+    _PROXY_KEYS = {
+        "proxy_enabled",
+        "proxy_host",
+        "proxy_port",
+        "proxy_username",
+        "proxy_password",
+        "proxy_type",
+    }
 
     def __init__(
         self,
         *,
         settings_service=None,
-        metadata_provider_updater: Callable[[], Awaitable[None]] = update_metadata_providers,
-        downloader_factory: Callable[[], Awaitable[DownloaderProtocol]] = get_downloader,
+        metadata_provider_updater: Callable[
+            [], Awaitable[None]
+        ] = update_metadata_providers,
+        downloader_factory: Callable[
+            [], Awaitable[DownloaderProtocol]
+        ] = get_downloader,
     ) -> None:
         self._settings = settings_service or get_settings_manager()
         self._metadata_provider_updater = metadata_provider_updater
@@ -245,11 +309,13 @@ class SettingsHandler:
                 response_data["settings_file"] = settings_file
             messages_getter = getattr(self._settings, "get_startup_messages", None)
             messages = list(messages_getter()) if callable(messages_getter) else []
-            return web.json_response({
-                "success": True,
-                "settings": response_data,
-                "messages": messages,
-            })
+            return web.json_response(
+                {
+                    "success": True,
+                    "settings": response_data,
+                    "messages": messages,
+                }
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Error getting settings: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -268,8 +334,12 @@ class SettingsHandler:
         try:
             data = await request.json()
         except Exception as exc:  # pragma: no cover - defensive logging
-            logger.error("Error parsing activate library request: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": "Invalid JSON payload"}, status=400)
+            logger.error(
+                "Error parsing activate library request: %s", exc, exc_info=True
+            )
+            return web.json_response(
+                {"success": False, "error": "Invalid JSON payload"}, status=400
+            )
 
         library_name = data.get("library") or data.get("library_name")
         if not isinstance(library_name, str) or not library_name.strip():
@@ -294,7 +364,9 @@ class SettingsHandler:
             logger.debug("Attempted to activate unknown library '%s'", library_name)
             return web.json_response({"success": False, "error": str(exc)}, status=404)
         except Exception as exc:  # pragma: no cover - defensive logging
-            logger.error("Error activating library '%s': %s", library_name, exc, exc_info=True)
+            logger.error(
+                "Error activating library '%s': %s", library_name, exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def update_settings(self, request: web.Request) -> web.Response:
@@ -309,9 +381,14 @@ class SettingsHandler:
                 if key == "example_images_path" and value:
                     validation_error = self._validate_example_images_path(value)
                     if validation_error:
-                        return web.json_response({"success": False, "error": validation_error})
+                        return web.json_response(
+                            {"success": False, "error": validation_error}
+                        )
 
-                if value == "__DELETE__" and key in ("proxy_username", "proxy_password"):
+                if value == "__DELETE__" and key in (
+                    "proxy_username",
+                    "proxy_password",
+                ):
                     self._settings.delete(key)
                 else:
                     self._settings.set(key, value)
@@ -353,7 +430,9 @@ class UsageStatsHandler:
             data = await request.json()
             prompt_id = data.get("prompt_id")
             if not prompt_id:
-                return web.json_response({"success": False, "error": "Missing prompt_id"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing prompt_id"}, status=400
+                )
             usage_stats = self._usage_stats_factory()
             await usage_stats.process_execution(prompt_id)
             return web.json_response({"success": True})
@@ -384,18 +463,24 @@ class LoraCodeHandler:
             mode = data.get("mode", "append")
 
             if not lora_code:
-                return web.json_response({"success": False, "error": "Missing lora_code parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing lora_code parameter"},
+                    status=400,
+                )
 
             results = []
             if node_ids is None:
                 try:
                     self._prompt_server.instance.send_sync(
-                        "lora_code_update", {"id": -1, "lora_code": lora_code, "mode": mode}
+                        "lora_code_update",
+                        {"id": -1, "lora_code": lora_code, "mode": mode},
                     )
                     results.append({"node_id": "broadcast", "success": True})
                 except Exception as exc:  # pragma: no cover - defensive logging
                     logger.error("Error broadcasting lora code: %s", exc)
-                    results.append({"node_id": "broadcast", "success": False, "error": str(exc)})
+                    results.append(
+                        {"node_id": "broadcast", "success": False, "error": str(exc)}
+                    )
             else:
                 for entry in node_ids:
                     node_identifier = entry
@@ -468,11 +553,19 @@ class TrainedWordsHandler:
         try:
             file_path = request.query.get("file_path")
             if not file_path:
-                return web.json_response({"success": False, "error": "Missing file_path parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing file_path parameter"},
+                    status=400,
+                )
             if not os.path.exists(file_path):
-                return web.json_response({"success": False, "error": "File not found"}, status=404)
+                return web.json_response(
+                    {"success": False, "error": "File not found"}, status=404
+                )
             if not file_path.endswith(".safetensors"):
-                return web.json_response({"success": False, "error": "File must be a safetensors file"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "File must be a safetensors file"},
+                    status=400,
+                )
 
             trained_words, class_tokens = await extract_trained_words(file_path)
             return web.json_response(
@@ -492,10 +585,15 @@ class ModelExampleFilesHandler:
         try:
             model_path = request.query.get("model_path")
             if not model_path:
-                return web.json_response({"success": False, "error": "Missing model_path parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing model_path parameter"},
+                    status=400,
+                )
             model_dir = os.path.dirname(model_path)
             if not os.path.exists(model_dir):
-                return web.json_response({"success": False, "error": "Model directory not found"}, status=404)
+                return web.json_response(
+                    {"success": False, "error": "Model directory not found"}, status=404
+                )
 
             base_name = os.path.splitext(os.path.basename(model_path))[0]
             files = []
@@ -507,7 +605,10 @@ class ModelExampleFilesHandler:
                 if not os.path.isfile(file_full_path):
                     continue
                 file_ext = os.path.splitext(file)[1].lower()
-                if file_ext not in SUPPORTED_MEDIA_EXTENSIONS["images"] and file_ext not in SUPPORTED_MEDIA_EXTENSIONS["videos"]:
+                if (
+                    file_ext not in SUPPORTED_MEDIA_EXTENSIONS["images"]
+                    and file_ext not in SUPPORTED_MEDIA_EXTENSIONS["videos"]
+                ):
                     continue
                 try:
                     index = int(file[len(pattern) :].split(".")[0])
@@ -542,7 +643,13 @@ class ServiceRegistryAdapter:
 
 
 class ModelLibraryHandler:
-    def __init__(self, service_registry: ServiceRegistryAdapter, metadata_provider_factory: Callable[[], Awaitable[MetadataProviderProtocol | None]]) -> None:
+    def __init__(
+        self,
+        service_registry: ServiceRegistryAdapter,
+        metadata_provider_factory: Callable[
+            [], Awaitable[MetadataProviderProtocol | None]
+        ],
+    ) -> None:
         self._service_registry = service_registry
         self._metadata_provider_factory = metadata_provider_factory
 
@@ -551,11 +658,17 @@ class ModelLibraryHandler:
             model_id_str = request.query.get("modelId")
             model_version_id_str = request.query.get("modelVersionId")
             if not model_id_str:
-                return web.json_response({"success": False, "error": "Missing required parameter: modelId"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing required parameter: modelId"},
+                    status=400,
+                )
             try:
                 model_id = int(model_id_str)
             except ValueError:
-                return web.json_response({"success": False, "error": "Parameter modelId must be an integer"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Parameter modelId must be an integer"},
+                    status=400,
+                )
 
             lora_scanner = await self._service_registry.get_lora_scanner()
             checkpoint_scanner = await self._service_registry.get_checkpoint_scanner()
@@ -565,29 +678,55 @@ class ModelLibraryHandler:
                 try:
                     model_version_id = int(model_version_id_str)
                 except ValueError:
-                    return web.json_response({"success": False, "error": "Parameter modelVersionId must be an integer"}, status=400)
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": "Parameter modelVersionId must be an integer",
+                        },
+                        status=400,
+                    )
 
                 exists = False
                 model_type = None
                 if await lora_scanner.check_model_version_exists(model_version_id):
                     exists = True
                     model_type = "lora"
-                elif checkpoint_scanner and await checkpoint_scanner.check_model_version_exists(model_version_id):
+                elif (
+                    checkpoint_scanner
+                    and await checkpoint_scanner.check_model_version_exists(
+                        model_version_id
+                    )
+                ):
                     exists = True
                     model_type = "checkpoint"
-                elif embedding_scanner and await embedding_scanner.check_model_version_exists(model_version_id):
+                elif (
+                    embedding_scanner
+                    and await embedding_scanner.check_model_version_exists(
+                        model_version_id
+                    )
+                ):
                     exists = True
                     model_type = "embedding"
 
-                return web.json_response({"success": True, "exists": exists, "modelType": model_type if exists else None})
+                return web.json_response(
+                    {
+                        "success": True,
+                        "exists": exists,
+                        "modelType": model_type if exists else None,
+                    }
+                )
 
             lora_versions = await lora_scanner.get_model_versions_by_id(model_id)
             checkpoint_versions = []
             embedding_versions = []
             if not lora_versions and checkpoint_scanner:
-                checkpoint_versions = await checkpoint_scanner.get_model_versions_by_id(model_id)
+                checkpoint_versions = await checkpoint_scanner.get_model_versions_by_id(
+                    model_id
+                )
             if not lora_versions and not checkpoint_versions and embedding_scanner:
-                embedding_versions = await embedding_scanner.get_model_versions_by_id(model_id)
+                embedding_versions = await embedding_scanner.get_model_versions_by_id(
+                    model_id
+                )
 
             model_type = None
             versions = []
@@ -601,7 +740,9 @@ class ModelLibraryHandler:
                 model_type = "embedding"
                 versions = embedding_versions
 
-            return web.json_response({"success": True, "modelType": model_type, "versions": versions})
+            return web.json_response(
+                {"success": True, "modelType": model_type, "versions": versions}
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to check model existence: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -610,22 +751,35 @@ class ModelLibraryHandler:
         try:
             model_id_str = request.query.get("modelId")
             if not model_id_str:
-                return web.json_response({"success": False, "error": "Missing required parameter: modelId"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing required parameter: modelId"},
+                    status=400,
+                )
             try:
                 model_id = int(model_id_str)
             except ValueError:
-                return web.json_response({"success": False, "error": "Parameter modelId must be an integer"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Parameter modelId must be an integer"},
+                    status=400,
+                )
 
             metadata_provider = await self._metadata_provider_factory()
             if not metadata_provider:
-                return web.json_response({"success": False, "error": "Metadata provider not available"}, status=503)
+                return web.json_response(
+                    {"success": False, "error": "Metadata provider not available"},
+                    status=503,
+                )
 
             try:
                 response = await metadata_provider.get_model_versions(model_id)
             except ResourceNotFoundError:
-                return web.json_response({"success": False, "error": "Model not found"}, status=404)
+                return web.json_response(
+                    {"success": False, "error": "Model not found"}, status=404
+                )
             if not response or not response.get("modelVersions"):
-                return web.json_response({"success": False, "error": "Model not found"}, status=404)
+                return web.json_response(
+                    {"success": False, "error": "Model not found"}, status=404
+                )
 
             versions = response.get("modelVersions", [])
             model_name = response.get("name", "")
@@ -643,10 +797,22 @@ class ModelLibraryHandler:
                 scanner = await self._service_registry.get_embedding_scanner()
                 normalized_type = "embedding"
             else:
-                return web.json_response({"success": False, "error": f'Model type "{model_type}" is not supported'}, status=400)
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": f'Model type "{model_type}" is not supported',
+                    },
+                    status=400,
+                )
 
             if not scanner:
-                return web.json_response({"success": False, "error": f'Scanner for type "{normalized_type}" is not available'}, status=503)
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": f'Scanner for type "{normalized_type}" is not available',
+                    },
+                    status=503,
+                )
 
             local_versions = await scanner.get_model_versions_by_id(model_id)
             local_version_ids = {version["versionId"] for version in local_versions}
@@ -658,7 +824,9 @@ class ModelLibraryHandler:
                     {
                         "id": version_id,
                         "name": version.get("name", ""),
-                        "thumbnailUrl": version.get("images")[0]["url"] if version.get("images") else None,
+                        "thumbnailUrl": version.get("images")[0]["url"]
+                        if version.get("images")
+                        else None,
                         "inLibrary": version_id in local_version_ids,
                     }
                 )
@@ -680,19 +848,34 @@ class ModelLibraryHandler:
         try:
             username = request.query.get("username")
             if not username:
-                return web.json_response({"success": False, "error": "Missing required parameter: username"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing required parameter: username"},
+                    status=400,
+                )
 
             metadata_provider = await self._metadata_provider_factory()
             if not metadata_provider:
-                return web.json_response({"success": False, "error": "Metadata provider not available"}, status=503)
+                return web.json_response(
+                    {"success": False, "error": "Metadata provider not available"},
+                    status=503,
+                )
 
             try:
                 models = await metadata_provider.get_user_models(username)
             except NotImplementedError:
-                return web.json_response({"success": False, "error": "Metadata provider does not support user model queries"}, status=501)
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Metadata provider does not support user model queries",
+                    },
+                    status=501,
+                )
 
             if models is None:
-                return web.json_response({"success": False, "error": "Failed to fetch user models"}, status=502)
+                return web.json_response(
+                    {"success": False, "error": "Failed to fetch user models"},
+                    status=502,
+                )
 
             if not isinstance(models, list):
                 models = []
@@ -701,7 +884,9 @@ class ModelLibraryHandler:
             checkpoint_scanner = await self._service_registry.get_checkpoint_scanner()
             embedding_scanner = await self._service_registry.get_embedding_scanner()
 
-            normalized_allowed_types = {model_type.lower() for model_type in CIVITAI_USER_MODEL_TYPES}
+            normalized_allowed_types = {
+                model_type.lower() for model_type in CIVITAI_USER_MODEL_TYPES
+            }
             lora_type_aliases = {model_type.lower() for model_type in VALID_LORA_TYPES}
 
             type_scanner_map: Dict[str, object | None] = {
@@ -721,7 +906,13 @@ class ModelLibraryHandler:
 
                 scanner = type_scanner_map.get(model_type)
                 if scanner is None:
-                    return web.json_response({"success": False, "error": f'Scanner for type "{model_type}" is not available'}, status=503)
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": f'Scanner for type "{model_type}" is not available',
+                        },
+                        status=503,
+                    )
 
                 tags_value = model.get("tags")
                 tags = tags_value if isinstance(tags_value, list) else []
@@ -756,7 +947,9 @@ class ModelLibraryHandler:
                             rewritten_url, _ = rewrite_preview_url(raw_url, media_type)
                             thumbnail_url = rewritten_url
 
-                    in_library = await scanner.check_model_version_exists(version_id_int)
+                    in_library = await scanner.check_model_version_exists(
+                        version_id_int
+                    )
 
                     versions.append(
                         {
@@ -772,7 +965,9 @@ class ModelLibraryHandler:
                         }
                     )
 
-            return web.json_response({"success": True, "username": username, "versions": versions})
+            return web.json_response(
+                {"success": True, "username": username, "versions": versions}
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to get Civitai user models: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -782,9 +977,13 @@ class MetadataArchiveHandler:
     def __init__(
         self,
         *,
-        metadata_archive_manager_factory: Callable[[], Awaitable[MetadataArchiveManagerProtocol]] = get_metadata_archive_manager,
+        metadata_archive_manager_factory: Callable[
+            [], Awaitable[MetadataArchiveManagerProtocol]
+        ] = get_metadata_archive_manager,
         settings_service=None,
-        metadata_provider_updater: Callable[[], Awaitable[None]] = update_metadata_providers,
+        metadata_provider_updater: Callable[
+            [], Awaitable[None]
+        ] = update_metadata_providers,
     ) -> None:
         self._metadata_archive_manager_factory = metadata_archive_manager_factory
         self._settings = settings_service or get_settings_manager()
@@ -796,18 +995,37 @@ class MetadataArchiveHandler:
             download_id = request.query.get("download_id")
 
             def progress_callback(stage: str, message: str) -> None:
-                data = {"stage": stage, "message": message, "type": "metadata_archive_download"}
+                data = {
+                    "stage": stage,
+                    "message": message,
+                    "type": "metadata_archive_download",
+                }
                 if download_id:
-                    asyncio.create_task(ws_manager.broadcast_download_progress(download_id, data))
+                    asyncio.create_task(
+                        ws_manager.broadcast_download_progress(download_id, data)
+                    )
                 else:
                     asyncio.create_task(ws_manager.broadcast(data))
 
-            success = await archive_manager.download_and_extract_database(progress_callback)
+            success = await archive_manager.download_and_extract_database(
+                progress_callback
+            )
             if success:
                 self._settings.set("enable_metadata_archive_db", True)
                 await self._metadata_provider_updater()
-                return web.json_response({"success": True, "message": "Metadata archive database downloaded and extracted successfully"})
-            return web.json_response({"success": False, "error": "Failed to download and extract metadata archive database"}, status=500)
+                return web.json_response(
+                    {
+                        "success": True,
+                        "message": "Metadata archive database downloaded and extracted successfully",
+                    }
+                )
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "Failed to download and extract metadata archive database",
+                },
+                status=500,
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Error downloading metadata archive: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -819,8 +1037,19 @@ class MetadataArchiveHandler:
             if success:
                 self._settings.set("enable_metadata_archive_db", False)
                 await self._metadata_provider_updater()
-                return web.json_response({"success": True, "message": "Metadata archive database removed successfully"})
-            return web.json_response({"success": False, "error": "Failed to remove metadata archive database"}, status=500)
+                return web.json_response(
+                    {
+                        "success": True,
+                        "message": "Metadata archive database removed successfully",
+                    }
+                )
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "Failed to remove metadata archive database",
+                },
+                status=500,
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Error removing metadata archive: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -841,39 +1070,136 @@ class MetadataArchiveHandler:
                     "isAvailable": is_available,
                     "isEnabled": is_enabled,
                     "databaseSize": db_size,
-                    "databasePath": archive_manager.get_database_path() if is_available else None,
+                    "databasePath": archive_manager.get_database_path()
+                    if is_available
+                    else None,
                 }
             )
         except Exception as exc:  # pragma: no cover - defensive logging
-            logger.error("Error getting metadata archive status: %s", exc, exc_info=True)
+            logger.error(
+                "Error getting metadata archive status: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
 
 class FileSystemHandler:
+    def __init__(self, settings_service=None) -> None:
+        self._settings = settings_service or get_settings_manager()
+
     async def open_file_location(self, request: web.Request) -> web.Response:
         try:
             data = await request.json()
             file_path = data.get("file_path")
             if not file_path:
-                return web.json_response({"success": False, "error": "Missing file_path parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing file_path parameter"},
+                    status=400,
+                )
             file_path = os.path.abspath(file_path)
             if not os.path.isfile(file_path):
-                return web.json_response({"success": False, "error": "File does not exist"}, status=404)
+                return web.json_response(
+                    {"success": False, "error": "File does not exist"}, status=404
+                )
 
             if os.name == "nt":
                 subprocess.Popen(["explorer", "/select,", file_path])
             elif os.name == "posix":
-                if sys.platform == "darwin":
+                if _is_docker():
+                    return web.json_response(
+                        {
+                            "success": True,
+                            "message": "Running in Docker: Path available for copying",
+                            "path": file_path,
+                            "mode": "clipboard",
+                        }
+                    )
+                elif _is_wsl():
+                    windows_path = _wsl_to_windows_path(file_path)
+                    if windows_path:
+                        subprocess.Popen(["explorer.exe", "/select,", windows_path])
+                    else:
+                        logger.error(
+                            "Failed to convert WSL path to Windows path: %s", file_path
+                        )
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": "Failed to open file location: path conversion error",
+                            },
+                            status=500,
+                        )
+                elif sys.platform == "darwin":
                     subprocess.Popen(["open", "-R", file_path])
                 else:
                     folder = os.path.dirname(file_path)
                     subprocess.Popen(["xdg-open", folder])
 
-            return web.json_response({"success": True, "message": f"Opened folder and selected file: {file_path}"})
+            return web.json_response(
+                {
+                    "success": True,
+                    "message": f"Opened folder and selected file: {file_path}",
+                }
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to open file location: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def open_settings_location(self, request: web.Request) -> web.Response:
+        try:
+            settings_file = getattr(self._settings, "settings_file", None)
+            if not settings_file:
+                return web.json_response(
+                    {"success": False, "error": "Settings file not found"}, status=404
+                )
+
+            settings_file = os.path.abspath(settings_file)
+            if not os.path.isfile(settings_file):
+                return web.json_response(
+                    {"success": False, "error": "Settings file does not exist"},
+                    status=404,
+                )
+
+            if os.name == "nt":
+                subprocess.Popen(["explorer", "/select,", settings_file])
+            elif os.name == "posix":
+                if _is_docker():
+                    return web.json_response(
+                        {
+                            "success": True,
+                            "message": "Running in Docker: Path available for copying",
+                            "path": settings_file,
+                            "mode": "clipboard",
+                        }
+                    )
+                elif _is_wsl():
+                    windows_path = _wsl_to_windows_path(settings_file)
+                    if windows_path:
+                        subprocess.Popen(["explorer.exe", "/select,", windows_path])
+                    else:
+                        logger.error(
+                            "Failed to convert WSL path to Windows path: %s",
+                            settings_file,
+                        )
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": "Failed to open settings location: path conversion error",
+                            },
+                            status=500,
+                        )
+                elif sys.platform == "darwin":
+                    subprocess.Popen(["open", "-R", settings_file])
+                else:
+                    folder = os.path.dirname(settings_file)
+                    subprocess.Popen(["xdg-open", folder])
+
+            return web.json_response(
+                {"success": True, "message": f"Opened settings folder: {settings_file}"}
+            )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to open settings location: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class NodeRegistryHandler:
     def __init__(
@@ -892,21 +1218,44 @@ class NodeRegistryHandler:
             data = await request.json()
             nodes = data.get("nodes", [])
             if not isinstance(nodes, list):
-                return web.json_response({"success": False, "error": "nodes must be a list"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "nodes must be a list"}, status=400
+                )
             for index, node in enumerate(nodes):
                 if not isinstance(node, dict):
-                    return web.json_response({"success": False, "error": f"Node {index} must be an object"}, status=400)
+                    return web.json_response(
+                        {"success": False, "error": f"Node {index} must be an object"},
+                        status=400,
+                    )
                 node_id = node.get("node_id")
                 if node_id is None:
-                    return web.json_response({"success": False, "error": f"Node {index} missing node_id parameter"}, status=400)
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": f"Node {index} missing node_id parameter",
+                        },
+                        status=400,
+                    )
                 graph_id = node.get("graph_id")
                 if graph_id is None:
-                    return web.json_response({"success": False, "error": f"Node {index} missing graph_id parameter"}, status=400)
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": f"Node {index} missing graph_id parameter",
+                        },
+                        status=400,
+                    )
                 graph_name = node.get("graph_name")
                 try:
                     node["node_id"] = int(node_id)
                 except (TypeError, ValueError):
-                    return web.json_response({"success": False, "error": f"Node {index} node_id must be an integer"}, status=400)
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": f"Node {index} node_id must be an integer",
+                        },
+                        status=400,
+                    )
                 node["graph_id"] = str(graph_id)
                 if graph_name is None:
                     node["graph_name"] = None
@@ -916,7 +1265,12 @@ class NodeRegistryHandler:
                     node["graph_name"] = str(graph_name)
 
             await self._node_registry.register_nodes(nodes)
-            return web.json_response({"success": True, "message": f"{len(nodes)} nodes registered successfully"})
+            return web.json_response(
+                {
+                    "success": True,
+                    "message": f"{len(nodes)} nodes registered successfully",
+                }
+            )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to register nodes: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -964,7 +1318,10 @@ class NodeRegistryHandler:
             return web.json_response({"success": True, "data": registry_info})
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to get registry: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": "Internal Error", "message": str(exc)}, status=500)
+            return web.json_response(
+                {"success": False, "error": "Internal Error", "message": str(exc)},
+                status=500,
+            )
 
     async def update_node_widget(self, request: web.Request) -> web.Response:
         try:
@@ -974,10 +1331,15 @@ class NodeRegistryHandler:
             node_ids = data.get("node_ids")
 
             if not isinstance(widget_name, str) or not widget_name:
-                return web.json_response({"success": False, "error": "Missing widget_name parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing widget_name parameter"},
+                    status=400,
+                )
 
             if not isinstance(value, str) or not value:
-                return web.json_response({"success": False, "error": "Missing value parameter"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Missing value parameter"}, status=400
+                )
 
             if not isinstance(node_ids, list) or not node_ids:
                 return web.json_response(
@@ -1077,7 +1439,9 @@ class MiscHandlerSet:
         self.metadata_archive = metadata_archive
         self.filesystem = filesystem
 
-    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]]]:
         return {
             "health_check": self.health.health_check,
             "get_settings": self.settings.get_settings,
@@ -1100,6 +1464,7 @@ class MiscHandlerSet:
             "get_metadata_archive_status": self.metadata_archive.get_metadata_archive_status,
             "get_model_versions_status": self.model_library.get_model_versions_status,
             "open_file_location": self.filesystem.open_file_location,
+            "open_settings_location": self.filesystem.open_settings_location,
         }
 
 

py/routes/handlers/recipe_handlers.py

@@ -4,8 +4,11 @@ from __future__ import annotations
 import json
 import logging
 import os
+import re
+import asyncio
+import tempfile
 from dataclasses import dataclass
-from typing import Any, Awaitable, Callable, Dict, Mapping, Optional
+from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional
 
 from aiohttp import web
 
@@ -20,6 +23,12 @@ from ...services.recipes import (
     RecipeSharingService,
     RecipeValidationError,
 )
+from ...services.metadata_service import get_default_metadata_provider
+from ...utils.civitai_utils import 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
 
 Logger = logging.Logger
 EnsureDependenciesCallable = Callable[[], Awaitable[None]]
@@ -45,22 +54,33 @@ class RecipeHandlerSet:
             "render_page": self.page_view.render_page,
             "list_recipes": self.listing.list_recipes,
             "get_recipe": self.listing.get_recipe,
+            "import_remote_recipe": self.management.import_remote_recipe,
             "analyze_uploaded_image": self.analysis.analyze_uploaded_image,
             "analyze_local_image": self.analysis.analyze_local_image,
             "save_recipe": self.management.save_recipe,
             "delete_recipe": self.management.delete_recipe,
             "get_top_tags": self.query.get_top_tags,
             "get_base_models": self.query.get_base_models,
+            "get_roots": self.query.get_roots,
+            "get_folders": self.query.get_folders,
+            "get_folder_tree": self.query.get_folder_tree,
+            "get_unified_folder_tree": self.query.get_unified_folder_tree,
             "share_recipe": self.sharing.share_recipe,
             "download_shared_recipe": self.sharing.download_shared_recipe,
             "get_recipe_syntax": self.query.get_recipe_syntax,
             "update_recipe": self.management.update_recipe,
             "reconnect_lora": self.management.reconnect_lora,
             "find_duplicates": self.query.find_duplicates,
+            "move_recipes_bulk": self.management.move_recipes_bulk,
             "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,
             "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,
         }
 
 
@@ -144,22 +164,45 @@ class RecipeListingHandler:
             page_size = int(request.query.get("page_size", "20"))
             sort_by = request.query.get("sort_by", "date")
             search = request.query.get("search")
+            folder = request.query.get("folder")
+            recursive = request.query.get("recursive", "true").lower() == "true"
 
             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",
+                "prompt": request.query.get("search_prompt", "true").lower() == "true",
             }
 
-            filters: Dict[str, list[str]] = {}
+            filters: Dict[str, Any] = {}
             base_models = request.query.get("base_models")
             if base_models:
                 filters["base_model"] = base_models.split(",")
 
-            tags = request.query.get("tags")
-            if tags:
-                filters["tags"] = tags.split(",")
+            if request.query.get("favorite", "false").lower() == "true":
+                filters["favorite"] = True
+
+            tag_filters: Dict[str, str] = {}
+            legacy_tags = request.query.get("tags")
+            if legacy_tags:
+                for tag in legacy_tags.split(","):
+                    tag = tag.strip()
+                    if tag:
+                        tag_filters[tag] = "include"
+
+            include_tags = request.query.getall("tag_include", [])
+            for tag in include_tags:
+                if tag:
+                    tag_filters[tag] = "include"
+
+            exclude_tags = request.query.getall("tag_exclude", [])
+            for tag in exclude_tags:
+                if tag:
+                    tag_filters[tag] = "exclude"
+
+            if tag_filters:
+                filters["tags"] = tag_filters
 
             lora_hash = request.query.get("lora_hash")
 
@@ -171,6 +214,8 @@ class RecipeListingHandler:
                 filters=filters,
                 search_options=search_options,
                 lora_hash=lora_hash,
+                folder=folder,
+                recursive=recursive,
             )
 
             for item in result.get("items", []):
@@ -277,6 +322,58 @@ class RecipeQueryHandler:
             self._logger.error("Error retrieving base models: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_roots(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")
+
+            roots = [recipe_scanner.recipes_dir] if recipe_scanner.recipes_dir else []
+            return web.json_response({"success": True, "roots": roots})
+        except Exception as exc:
+            self._logger.error("Error retrieving recipe roots: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_folders(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")
+
+            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)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_folder_tree(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")
+
+            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)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_unified_folder_tree(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")
+
+            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)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def get_recipes_for_lora(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -387,12 +484,18 @@ class RecipeManagementHandler:
         logger: Logger,
         persistence_service: RecipePersistenceService,
         analysis_service: RecipeAnalysisService,
+        downloader_factory,
+        civitai_client_getter: CivitaiClientGetter,
+        ws_manager=default_ws_manager,
     ) -> None:
         self._ensure_dependencies_ready = ensure_dependencies_ready
         self._recipe_scanner_getter = recipe_scanner_getter
         self._logger = logger
         self._persistence_service = persistence_service
         self._analysis_service = analysis_service
+        self._downloader_factory = downloader_factory
+        self._civitai_client_getter = civitai_client_getter
+        self._ws_manager = ws_manager
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         try:
@@ -411,6 +514,7 @@ class RecipeManagementHandler:
                 name=payload["name"],
                 tags=payload["tags"],
                 metadata=payload["metadata"],
+                extension=payload.get("extension"),
             )
             return web.json_response(result.payload, status=result.status)
         except RecipeValidationError as exc:
@@ -419,6 +523,215 @@ class RecipeManagementHandler:
             self._logger.error("Error saving recipe: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def repair_recipes(self, request: web.Request) -> web.Response:
+        try:
+            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)
+
+            # 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)
+
+            recipe_scanner.reset_cancellation()
+
+            async def progress_callback(data):
+                await self._ws_manager.broadcast_recipe_repair_progress(data)
+
+            # Run in background to avoid timeout
+            async def run_repair():
+                try:
+                    await recipe_scanner.repair_all_recipes(
+                        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)
+                    })
+                finally:
+                    # Keep the final status for a while so the UI can see it
+                    await asyncio.sleep(5)
+                    # Don't cleanup if it was cancelled, let the UI see the cancelled state for a bit?
+                    # Actually cleanup_recipe_repair_progress is fine as long as we waited enough.
+                    self._ws_manager.cleanup_recipe_repair_progress()
+
+            asyncio.create_task(run_repair())
+
+            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)
+
+    async def cancel_repair(self, request: web.Request) -> web.Response:
+        try:
+            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)
+
+            recipe_scanner.cancel_task()
+            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)
+
+    async def repair_recipe(self, request: web.Request) -> web.Response:
+        try:
+            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)
+
+            recipe_id = request.match_info["recipe_id"]
+            result = await recipe_scanner.repair_recipe_by_id(recipe_id)
+            return web.json_response(result)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error repairing single recipe: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_repair_progress(self, request: web.Request) -> web.Response:
+        try:
+            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)
+        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:
+                raise RecipeValidationError("Missing required field: name")
+            if not resources_raw:
+                raise RecipeValidationError("Missing required field: resources")
+
+            checkpoint_entry, lora_entries = self._parse_resources_payload(resources_raw)
+            gen_params_request = self._parse_gen_params(params.get("gen_params"))
+            
+            # 2. Initial Metadata Construction
+            metadata: Dict[str, Any] = {
+                "base_model": params.get("base_model", "") or "",
+                "loras": lora_entries,
+                "gen_params": gen_params_request or {},
+                "source_url": image_url
+            }
+            
+            source_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)
+                    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)
+
+            # 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. 
+            # 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) 
+            # 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. 
+            # `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:
+                    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)
+                        if parser:
+                            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:
+                            embedded_gen_params = {"raw_metadata": raw_embedded}
+                finally:
+                    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)
+
+            # Pre-populate gen_params with embedded data so Enricher treats it as the "base" layer
+            if embedded_gen_params:
+                # Merge embedded into existing gen_params (which currently only has request params if any)
+                # 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, 
+                civitai_client=civitai_client,
+                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? 
+            # But usually `import_remote_recipe` is used with Civitai URLs.
+            # For now, relying on Enricher's internal fetch is consistent with repair.
+
+            result = await self._persistence_service.save_recipe(
+                recipe_scanner=recipe_scanner,
+                image_bytes=image_bytes,
+                image_base64=None,
+                name=name,
+                tags=tags,
+                metadata=metadata,
+                extension=extension,
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        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)
+            return web.json_response({"error": str(exc)}, status=500)
+
     async def delete_recipe(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -458,6 +771,64 @@ class RecipeManagementHandler:
             self._logger.error("Error updating recipe: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def move_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")
+
+            data = await request.json()
+            recipe_id = data.get("recipe_id")
+            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
+                )
+
+            result = await self._persistence_service.move_recipe(
+                recipe_scanner=recipe_scanner,
+                recipe_id=str(recipe_id),
+                target_path=str(target_path),
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error moving recipe: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def move_recipes_bulk(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")
+
+            data = await request.json()
+            recipe_ids = data.get("recipe_ids") or []
+            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
+                )
+
+            result = await self._persistence_service.move_recipes_bulk(
+                recipe_scanner=recipe_scanner,
+                recipe_ids=recipe_ids,
+                target_path=str(target_path),
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error moving recipes in bulk: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def reconnect_lora(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -539,6 +910,7 @@ class RecipeManagementHandler:
         name: Optional[str] = None
         tags: list[str] = []
         metadata: Optional[Dict[str, Any]] = None
+        extension: Optional[str] = None
 
         while True:
             field = await reader.next()
@@ -569,6 +941,8 @@ class RecipeManagementHandler:
                     metadata = json.loads(metadata_text)
                 except Exception:
                     metadata = {}
+            elif field.name == "extension":
+                extension = await field.text()
 
         return {
             "image_bytes": image_bytes,
@@ -576,8 +950,160 @@ class RecipeManagementHandler:
             "name": name,
             "tags": tags,
             "metadata": metadata,
+            "extension": extension,
         }
 
+    def _parse_tags(self, tag_text: Optional[str]) -> list[str]:
+        if not tag_text:
+            return []
+        return [tag.strip() for tag in tag_text.split(",") if tag.strip()]
+
+    def _parse_gen_params(self, payload: Optional[str]) -> Optional[Dict[str, Any]]:
+        if payload is None:
+            return None
+        if payload == "":
+            return {}
+        try:
+            parsed = json.loads(payload)
+        except json.JSONDecodeError as exc:
+            raise RecipeValidationError(f"Invalid gen_params payload: {exc}") from exc
+        if parsed is None:
+            return {}
+        if not isinstance(parsed, dict):
+            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]]]:
+        try:
+            payload = json.loads(payload_raw)
+        except json.JSONDecodeError as exc:
+            raise RecipeValidationError(f"Invalid resources payload: {exc}") from exc
+
+        if not isinstance(payload, list):
+            raise RecipeValidationError("Resources payload must be a list")
+
+        checkpoint_entry: Optional[Dict[str, Any]] = None
+        lora_entries: List[Dict[str, Any]] = []
+
+        for resource in payload:
+            if not isinstance(resource, dict):
+                continue
+            resource_type = str(resource.get("type") or "").lower()
+            if resource_type == "checkpoint":
+                checkpoint_entry = self._build_checkpoint_entry(resource)
+            elif resource_type in {"lora", "lycoris"}:
+                lora_entries.append(self._build_lora_entry(resource))
+
+        return checkpoint_entry, lora_entries
+
+    def _build_checkpoint_entry(self, resource: Dict[str, Any]) -> Dict[str, Any]:
+        return {
+            "type": resource.get("type", "checkpoint"),
+            "modelId": self._safe_int(resource.get("modelId")),
+            "modelVersionId": self._safe_int(resource.get("modelVersionId")),
+            "modelName": resource.get("modelName", ""),
+            "modelVersionName": resource.get("modelVersionName", ""),
+        }
+
+    def _build_lora_entry(self, resource: Dict[str, Any]) -> Dict[str, Any]:
+        weight_raw = resource.get("weight", 1.0)
+        try:
+            weight = float(weight_raw)
+        except (TypeError, ValueError):
+            weight = 1.0
+        return {
+            "file_name": resource.get("modelName", ""),
+            "weight": weight,
+            "id": self._safe_int(resource.get("modelVersionId")),
+            "name": resource.get("modelName", ""),
+            "version": resource.get("modelVersionName", ""),
+            "isDeleted": False,
+            "exclude": False,
+        }
+
+    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str]:
+        civitai_client = self._civitai_client_getter()
+        downloader = await self._downloader_factory()
+        temp_path = None
+        try:
+            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+                temp_path = temp_file.name
+            download_url = image_url
+            civitai_match = re.match(r"https://civitai\.com/images/(\d+)", image_url)
+            if civitai_match:
+                if civitai_client is None:
+                    raise RecipeDownloadError("Civitai client unavailable for image download")
+                image_info = await civitai_client.get_image_info(civitai_match.group(1))
+                if not image_info:
+                    raise RecipeDownloadError("Failed to fetch image information from Civitai")
+                
+                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)
+                if rewritten_url:
+                    download_url = rewritten_url
+                else:
+                    download_url = media_url
+
+            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]
+            extension = os.path.splitext(url_path)[1].lower()
+            if not extension:
+                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
+        except RecipeDownloadError:
+            raise
+        except RecipeValidationError:
+            raise
+        except Exception as exc:  # pragma: no cover - defensive guard
+            raise RecipeValidationError(f"Unable to download image: {exc}") from exc
+        finally:
+            if temp_path:
+                try:
+                    os.unlink(temp_path)
+                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:
+        version_id = self._safe_int(checkpoint_entry.get("modelVersionId"))
+
+        if not version_id:
+            return ""
+
+        try:
+            provider = await get_default_metadata_provider()
+            if not provider:
+                return ""
+
+            version_info = await provider.get_model_version_info(version_id)
+            if isinstance(version_info, tuple):
+                version_info = version_info[0]
+
+            if isinstance(version_info, dict):
+                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)
+
+        return ""
+
 
 class RecipeAnalysisHandler:
     """Analyze images to extract recipe metadata."""

py/routes/lora_routes.py

@@ -12,14 +12,15 @@ from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
 
+
 class LoraRoutes(BaseModelRoutes):
     """LoRA-specific route controller"""
-    
+
     def __init__(self):
         """Initialize LoRA routes with LoRA service"""
         super().__init__()
         self.template_name = "loras.html"
-    
+
     async def initialize_services(self):
         """Initialize services from ServiceRegistry"""
         lora_scanner = await ServiceRegistry.get_lora_scanner()
@@ -29,207 +30,276 @@ class LoraRoutes(BaseModelRoutes):
 
         # Attach service dependencies
         self.attach_service(self.service)
-    
+
     def setup_routes(self, app: web.Application):
         """Setup LoRA routes"""
         # Schedule service initialization on app startup
         app.on_startup.append(lambda _: self.initialize_services())
 
         # Setup common routes with 'loras' prefix (includes page route)
-        super().setup_routes(app, 'loras')
+        super().setup_routes(app, "loras")
 
     def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup LoRA-specific routes"""
         # LoRA-specific query routes
-        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/letter-counts', prefix, self.get_letter_counts)
-        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/get-trigger-words', prefix, self.get_lora_trigger_words)
-        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/usage-tips-by-path', prefix, self.get_lora_usage_tips_by_path)
+        registrar.add_prefixed_route(
+            "GET", "/api/lm/{prefix}/letter-counts", prefix, self.get_letter_counts
+        )
+        registrar.add_prefixed_route(
+            "GET",
+            "/api/lm/{prefix}/get-trigger-words",
+            prefix,
+            self.get_lora_trigger_words,
+        )
+        registrar.add_prefixed_route(
+            "GET",
+            "/api/lm/{prefix}/usage-tips-by-path",
+            prefix,
+            self.get_lora_usage_tips_by_path,
+        )
+
+        # Randomizer routes
+        registrar.add_prefixed_route(
+            "POST", "/api/lm/{prefix}/random-sample", prefix, self.get_random_loras
+        )
 
         # ComfyUI integration
-        registrar.add_prefixed_route('POST', '/api/lm/{prefix}/get_trigger_words', prefix, self.get_trigger_words)
-    
+        registrar.add_prefixed_route(
+            "POST", "/api/lm/{prefix}/get_trigger_words", prefix, self.get_trigger_words
+        )
+
     def _parse_specific_params(self, request: web.Request) -> Dict:
         """Parse LoRA-specific parameters"""
         params = {}
-        
+
         # LoRA-specific parameters
-        if 'first_letter' in request.query:
-            params['first_letter'] = request.query.get('first_letter')
-        
+        if "first_letter" in request.query:
+            params["first_letter"] = request.query.get("first_letter")
+
         # Handle fuzzy search parameter name variation
-        if request.query.get('fuzzy') == 'true':
-            params['fuzzy_search'] = True
-        
+        if request.query.get("fuzzy") == "true":
+            params["fuzzy_search"] = True
+
         # Handle additional filter parameters for LoRAs
-        if 'lora_hash' in request.query:
-            if not params.get('hash_filters'):
-                params['hash_filters'] = {}
-            params['hash_filters']['single_hash'] = request.query['lora_hash'].lower()
-        elif 'lora_hashes' in request.query:
-            if not params.get('hash_filters'):
-                params['hash_filters'] = {}
-            params['hash_filters']['multiple_hashes'] = [h.lower() for h in request.query['lora_hashes'].split(',')]
-        
+        if "lora_hash" in request.query:
+            if not params.get("hash_filters"):
+                params["hash_filters"] = {}
+            params["hash_filters"]["single_hash"] = request.query["lora_hash"].lower()
+        elif "lora_hashes" in request.query:
+            if not params.get("hash_filters"):
+                params["hash_filters"] = {}
+            params["hash_filters"]["multiple_hashes"] = [
+                h.lower() for h in request.query["lora_hashes"].split(",")
+            ]
+
         return params
-    
+
     def _validate_civitai_model_type(self, model_type: str) -> bool:
         """Validate CivitAI model type for LoRA"""
         from ..utils.constants import VALID_LORA_TYPES
+
         return model_type.lower() in VALID_LORA_TYPES
-    
+
     def _get_expected_model_types(self) -> str:
         """Get expected model types string for error messages"""
         return "LORA, LoCon, or DORA"
-    
+
     # LoRA-specific route handlers
     async def get_letter_counts(self, request: web.Request) -> web.Response:
         """Get count of LoRAs for each letter of the alphabet"""
         try:
             letter_counts = await self.service.get_letter_counts()
-            return web.json_response({
-                'success': True,
-                'letter_counts': letter_counts
-            })
+            return web.json_response({"success": True, "letter_counts": letter_counts})
         except Exception as e:
             logger.error(f"Error getting letter counts: {e}")
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_lora_notes(self, request: web.Request) -> web.Response:
         """Get notes for a specific LoRA file"""
         try:
-            lora_name = request.query.get('name')
+            lora_name = request.query.get("name")
             if not lora_name:
-                return web.Response(text='Lora file name is required', status=400)
-            
+                return web.Response(text="Lora file name is required", status=400)
+
             notes = await self.service.get_lora_notes(lora_name)
             if notes is not None:
-                return web.json_response({
-                    'success': True,
-                    'notes': notes
-                })
+                return web.json_response({"success": True, "notes": notes})
             else:
-                return web.json_response({
-                    'success': False,
-                    'error': 'LoRA not found in cache'
-                }, status=404)
-                
+                return web.json_response(
+                    {"success": False, "error": "LoRA not found in cache"}, status=404
+                )
+
         except Exception as e:
             logger.error(f"Error getting lora notes: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_lora_trigger_words(self, request: web.Request) -> web.Response:
         """Get trigger words for a specific LoRA file"""
         try:
-            lora_name = request.query.get('name')
+            lora_name = request.query.get("name")
             if not lora_name:
-                return web.Response(text='Lora file name is required', status=400)
-            
+                return web.Response(text="Lora file name is required", status=400)
+
             trigger_words = await self.service.get_lora_trigger_words(lora_name)
-            return web.json_response({
-                'success': True,
-                'trigger_words': trigger_words
-            })
-            
+            return web.json_response({"success": True, "trigger_words": trigger_words})
+
         except Exception as e:
             logger.error(f"Error getting lora trigger words: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_lora_usage_tips_by_path(self, request: web.Request) -> web.Response:
         """Get usage tips for a LoRA by its relative path"""
         try:
-            relative_path = request.query.get('relative_path')
+            relative_path = request.query.get("relative_path")
             if not relative_path:
-                return web.Response(text='Relative path is required', status=400)
-            
-            usage_tips = await self.service.get_lora_usage_tips_by_relative_path(relative_path)
-            return web.json_response({
-                'success': True,
-                'usage_tips': usage_tips or ''
-            })
-            
+                return web.Response(text="Relative path is required", status=400)
+
+            usage_tips = await self.service.get_lora_usage_tips_by_relative_path(
+                relative_path
+            )
+            return web.json_response({"success": True, "usage_tips": usage_tips or ""})
+
         except Exception as e:
             logger.error(f"Error getting lora usage tips by path: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_lora_preview_url(self, request: web.Request) -> web.Response:
         """Get the static preview URL for a LoRA file"""
         try:
-            lora_name = request.query.get('name')
+            lora_name = request.query.get("name")
             if not lora_name:
-                return web.Response(text='Lora file name is required', status=400)
-            
+                return web.Response(text="Lora file name is required", status=400)
+
             preview_url = await self.service.get_lora_preview_url(lora_name)
             if preview_url:
-                return web.json_response({
-                    'success': True,
-                    'preview_url': preview_url
-                })
+                return web.json_response({"success": True, "preview_url": preview_url})
             else:
-                return web.json_response({
-                    'success': False,
-                    'error': 'No preview URL found for the specified lora'
-                }, status=404)
-                
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "No preview URL found for the specified lora",
+                    },
+                    status=404,
+                )
+
         except Exception as e:
             logger.error(f"Error getting lora preview URL: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_lora_civitai_url(self, request: web.Request) -> web.Response:
         """Get the Civitai URL for a LoRA file"""
         try:
-            lora_name = request.query.get('name')
+            lora_name = request.query.get("name")
             if not lora_name:
-                return web.Response(text='Lora file name is required', status=400)
-            
+                return web.Response(text="Lora file name is required", status=400)
+
             result = await self.service.get_lora_civitai_url(lora_name)
-            if result['civitai_url']:
-                return web.json_response({
-                    'success': True,
-                    **result
-                })
+            if result["civitai_url"]:
+                return web.json_response({"success": True, **result})
             else:
-                return web.json_response({
-                    'success': False,
-                    'error': 'No Civitai data found for the specified lora'
-                }, status=404)
-                
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "No Civitai data found for the specified lora",
+                    },
+                    status=404,
+                )
+
         except Exception as e:
             logger.error(f"Error getting lora Civitai URL: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
+    async def get_random_loras(self, request: web.Request) -> web.Response:
+        """Get random LoRAs based on filters and strength ranges"""
+        try:
+            json_data = await request.json()
+
+            # Parse parameters
+            count = json_data.get("count", 5)
+            count_min = json_data.get("count_min")
+            count_max = json_data.get("count_max")
+            model_strength_min = float(json_data.get("model_strength_min", 0.0))
+            model_strength_max = float(json_data.get("model_strength_max", 1.0))
+            use_same_clip_strength = json_data.get("use_same_clip_strength", True)
+            clip_strength_min = float(json_data.get("clip_strength_min", 0.0))
+            clip_strength_max = float(json_data.get("clip_strength_max", 1.0))
+            locked_loras = json_data.get("locked_loras", [])
+            pool_config = json_data.get("pool_config")
+            use_recommended_strength = json_data.get("use_recommended_strength", False)
+            recommended_strength_scale_min = float(
+                json_data.get("recommended_strength_scale_min", 0.5)
+            )
+            recommended_strength_scale_max = float(
+                json_data.get("recommended_strength_scale_max", 1.0)
+            )
+
+            # Determine target count
+            if count_min is not None and count_max is not None:
+                import random
+
+                target_count = random.randint(count_min, count_max)
+            else:
+                target_count = count
+
+            # Validate parameters
+            if target_count < 1 or target_count > 100:
+                return web.json_response(
+                    {"success": False, "error": "Count must be between 1 and 100"},
+                    status=400,
+                )
+
+            if model_strength_min < -10 or model_strength_max > 10:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Model strength must be between -10 and 10",
+                    },
+                    status=400,
+                )
+
+            # Get random LoRAs from service
+            result_loras = await self.service.get_random_loras(
+                count=target_count,
+                model_strength_min=model_strength_min,
+                model_strength_max=model_strength_max,
+                use_same_clip_strength=use_same_clip_strength,
+                clip_strength_min=clip_strength_min,
+                clip_strength_max=clip_strength_max,
+                locked_loras=locked_loras,
+                pool_config=pool_config,
+                use_recommended_strength=use_recommended_strength,
+                recommended_strength_scale_min=recommended_strength_scale_min,
+                recommended_strength_scale_max=recommended_strength_scale_max,
+            )
+
+            return web.json_response(
+                {"success": True, "loras": result_loras, "count": len(result_loras)}
+            )
+
+        except ValueError as e:
+            logger.error(f"Invalid parameter for random LoRAs: {e}")
+            return web.json_response({"success": False, "error": str(e)}, status=400)
+        except Exception as e:
+            logger.error(f"Error getting random LoRAs: {e}", exc_info=True)
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
     async def get_trigger_words(self, request: web.Request) -> web.Response:
         """Get trigger words for specified LoRA models"""
         try:
             json_data = await request.json()
             lora_names = json_data.get("lora_names", [])
             node_ids = json_data.get("node_ids", [])
-            
+
             all_trigger_words = []
             for lora_name in lora_names:
                 _, trigger_words = get_lora_info(lora_name)
                 all_trigger_words.extend(trigger_words)
-            
+
             # Format the trigger words
-            trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-            
+            trigger_words_text = (
+                ",, ".join(all_trigger_words) if all_trigger_words else ""
+            )
+
             # Send update to all connected trigger word toggle nodes
             for entry in node_ids:
                 node_identifier = entry
@@ -243,21 +313,15 @@ class LoraRoutes(BaseModelRoutes):
                 except (TypeError, ValueError):
                     parsed_node_id = node_identifier
 
-                payload = {
-                    "id": parsed_node_id,
-                    "message": trigger_words_text
-                }
+                payload = {"id": parsed_node_id, "message": trigger_words_text}
 
                 if graph_identifier is not None:
                     payload["graph_id"] = str(graph_identifier)
 
                 PromptServer.instance.send_sync("trigger_word_update", payload)
-            
+
             return web.json_response({"success": True})
 
         except Exception as e:
             logger.error(f"Error getting trigger words: {e}")
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
+            return web.json_response({"success": False, "error": str(e)}, status=500)

py/routes/misc_route_registrar.py

@@ -41,6 +41,7 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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/settings/open-location", "open_settings_location"),
 )
 
 

py/routes/misc_routes.py

@@ -107,7 +107,7 @@ class MiscRoutes:
             settings_service=self._settings,
             metadata_provider_updater=self._metadata_provider_updater,
         )
-        filesystem = FileSystemHandler()
+        filesystem = FileSystemHandler(settings_service=self._settings)
         node_registry_handler = NodeRegistryHandler(
             node_registry=self._node_registry,
             prompt_server=self._prompt_server,

py/routes/model_route_registrar.py

@@ -39,6 +39,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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"),
     RouteDefinition("GET", "/api/lm/{prefix}/scan", "scan_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/roots", "get_model_roots"),
     RouteDefinition("GET", "/api/lm/{prefix}/folders", "get_folders"),
@@ -56,6 +57,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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"),
@@ -66,6 +68,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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("POST", "/api/lm/{prefix}/cancel-task", "cancel_task"),
     RouteDefinition("GET", "/{prefix}", "handle_models_page"),
 )
 
@@ -103,4 +106,3 @@ class ModelRouteRegistrar:
         add_method_name = self._METHOD_MAP[method.upper()]
         add_method = getattr(self._app.router, add_method_name)
         add_method(path, handler)
-

py/routes/recipe_route_registrar.py

@@ -20,22 +20,33 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/loras/recipes", "render_page"),
     RouteDefinition("GET", "/api/lm/recipes", "list_recipes"),
     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/save", "save_recipe"),
     RouteDefinition("DELETE", "/api/lm/recipe/{recipe_id}", "delete_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/top-tags", "get_top_tags"),
     RouteDefinition("GET", "/api/lm/recipes/base-models", "get_base_models"),
+    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/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}/syntax", "get_recipe_syntax"),
     RouteDefinition("PUT", "/api/lm/recipe/{recipe_id}/update", "update_recipe"),
+    RouteDefinition("POST", "/api/lm/recipe/move", "move_recipe"),
+    RouteDefinition("POST", "/api/lm/recipes/move-bulk", "move_recipes_bulk"),
     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("GET", "/api/lm/recipes/for-lora", "get_recipes_for_lora"),
     RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
+    RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
+    RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
+    RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
+    RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
 )
 
 
@@ -61,4 +72,3 @@ class RecipeRouteRegistrar:
         add_method_name = self._METHOD_MAP[method.upper()]
         add_method = getattr(self._app.router, add_method_name)
         add_method(path, handler)
-

py/routes/update_routes.py

@@ -344,6 +344,11 @@ class UpdateRoutes:
             origin.fetch()
             
             if nightly:
+                # Reset to discard any local changes
+                repo.git.reset('--hard')
+                # Clean untracked files
+                repo.git.clean('-fd')
+                
                 # Switch to main branch and pull latest
                 main_branch = 'main'
                 if main_branch not in [branch.name for branch in repo.branches]:
@@ -357,6 +362,11 @@ class UpdateRoutes:
                 new_version = f"main-{repo.head.commit.hexsha[:7]}"
                 
             else:
+                # Reset to discard any local changes
+                repo.git.reset('--hard')
+                # Clean untracked files
+                repo.git.clean('-fd')
+                
                 # Get latest release tag
                 tags = sorted(repo.tags, key=lambda t: t.commit.committed_datetime, reverse=True)
                 if not tags:

py/services/base_model_service.py

@@ -1,12 +1,23 @@
 from abc import ABC, abstractmethod
 import asyncio
-from typing import Dict, List, Optional, Type, TYPE_CHECKING
+from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
 import logging
 import os
+import time
 
+from ..utils.constants import VALID_LORA_TYPES
 from ..utils.models import BaseModelMetadata
 from ..utils.metadata_manager import MetadataManager
-from .model_query import FilterCriteria, ModelCacheRepository, ModelFilterSet, SearchStrategy, SettingsProvider
+from ..utils.usage_stats import UsageStats
+from .model_query import (
+    FilterCriteria,
+    ModelCacheRepository,
+    ModelFilterSet,
+    SearchStrategy,
+    SettingsProvider,
+    normalize_civitai_model_type,
+    resolve_civitai_model_type,
+)
 from .settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
@@ -14,9 +25,10 @@ logger = logging.getLogger(__name__)
 if TYPE_CHECKING:
     from .model_update_service import ModelUpdateService
 
+
 class BaseModelService(ABC):
     """Base service class for all model types"""
-    
+
     def __init__(
         self,
         model_type: str,
@@ -49,35 +61,51 @@ class BaseModelService(ABC):
         self.filter_set = filter_set or ModelFilterSet(self.settings)
         self.search_strategy = search_strategy or SearchStrategy()
         self.update_service = update_service
-    
+
     async def get_paginated_data(
         self,
         page: int,
         page_size: int,
-        sort_by: str = 'name',
+        sort_by: str = "name",
         folder: str = None,
+        folder_include: list = None,
+        folder_exclude: list = None,
         search: str = None,
         fuzzy_search: bool = False,
         base_models: list = None,
-        tags: list = None,
+        model_types: list = None,
+        tags: Optional[Dict[str, str]] = None,
         search_options: dict = None,
         hash_filters: dict = None,
         favorites_only: bool = False,
         update_available_only: bool = False,
+        credit_required: Optional[bool] = None,
+        allow_selling_generated_content: Optional[bool] = None,
         **kwargs,
     ) -> Dict:
         """Get paginated and filtered model data"""
+        overall_start = time.perf_counter()
 
         sort_params = self.cache_repository.parse_sort(sort_by)
-        sorted_data = await self.cache_repository.fetch_sorted(sort_params)
+        t0 = time.perf_counter()
+        if sort_params.key == "usage":
+            sorted_data = await self._fetch_with_usage_sort(sort_params)
+        else:
+            sorted_data = await self.cache_repository.fetch_sorted(sort_params)
+        fetch_duration = time.perf_counter() - t0
+        initial_count = len(sorted_data)
 
+        t1 = time.perf_counter()
         if hash_filters:
             filtered_data = await self._apply_hash_filters(sorted_data, hash_filters)
         else:
             filtered_data = await self._apply_common_filters(
                 sorted_data,
                 folder=folder,
+                folder_include=folder_include,
+                folder_exclude=folder_exclude,
                 base_models=base_models,
+                model_types=model_types,
                 tags=tags,
                 favorites_only=favorites_only,
                 search_options=search_options,
@@ -93,54 +121,124 @@ class BaseModelService(ABC):
 
             filtered_data = await self._apply_specific_filters(filtered_data, **kwargs)
 
+            # Apply license-based filters
+            if credit_required is not None:
+                filtered_data = await self._apply_credit_required_filter(
+                    filtered_data, credit_required
+                )
+
+            if allow_selling_generated_content is not None:
+                filtered_data = await self._apply_allow_selling_filter(
+                    filtered_data, allow_selling_generated_content
+                )
+        filter_duration = time.perf_counter() - t1
+        post_filter_count = len(filtered_data)
+
         annotated_for_filter: Optional[List[Dict]] = None
+        t2 = time.perf_counter()
         if update_available_only:
             annotated_for_filter = await self._annotate_update_flags(filtered_data)
             filtered_data = [
-                item for item in annotated_for_filter
-                if item.get('update_available')
+                item for item in annotated_for_filter if item.get("update_available")
             ]
+        update_filter_duration = time.perf_counter() - t2
+        final_count = len(filtered_data)
 
+        t3 = time.perf_counter()
         paginated = self._paginate(filtered_data, page, page_size)
+        pagination_duration = time.perf_counter() - t3
 
+        t4 = time.perf_counter()
         if update_available_only:
             # Items already include update flags thanks to the pre-filter annotation.
-            paginated['items'] = list(paginated['items'])
+            paginated["items"] = list(paginated["items"])
         else:
-            paginated['items'] = await self._annotate_update_flags(
-                paginated['items'],
+            paginated["items"] = await self._annotate_update_flags(
+                paginated["items"],
             )
+        annotate_duration = time.perf_counter() - t4
+
+        overall_duration = time.perf_counter() - overall_start
+        logger.debug(
+            "%s.get_paginated_data took %.3fs (fetch: %.3fs, filter: %.3fs, update_filter: %.3fs, pagination: %.3fs, annotate: %.3fs). "
+            "Counts: initial=%d, post_filter=%d, final=%d",
+            self.__class__.__name__,
+            overall_duration,
+            fetch_duration,
+            filter_duration,
+            update_filter_duration,
+            pagination_duration,
+            annotate_duration,
+            initial_count,
+            post_filter_count,
+            final_count,
+        )
         return paginated
 
-    
-    async def _apply_hash_filters(self, data: List[Dict], hash_filters: Dict) -> List[Dict]:
+    async def _fetch_with_usage_sort(self, sort_params):
+        """Fetch data sorted by usage count (desc/asc)."""
+        cache = await self.cache_repository.get_cache()
+        raw_items = cache.raw_data or []
+
+        # Map model type to usage stats bucket
+        bucket_map = {
+            "lora": "loras",
+            "checkpoint": "checkpoints",
+            # 'embedding': 'embeddings',  # TODO: Enable when embedding usage tracking is implemented
+        }
+        bucket_key = bucket_map.get(self.model_type, "")
+
+        usage_stats = UsageStats()
+        stats = await usage_stats.get_stats()
+        usage_bucket = stats.get(bucket_key, {}) if bucket_key else {}
+
+        annotated = []
+        for item in raw_items:
+            sha = (item.get("sha256") or "").lower()
+            usage_info = (
+                usage_bucket.get(sha, {}) if isinstance(usage_bucket, dict) else {}
+            )
+            usage_count = (
+                usage_info.get("total", 0) if isinstance(usage_info, dict) else 0
+            )
+            annotated.append({**item, "usage_count": usage_count})
+
+        reverse = sort_params.order == "desc"
+        annotated.sort(
+            key=lambda x: (x.get("usage_count", 0), x.get("model_name", "").lower()),
+            reverse=reverse,
+        )
+        return annotated
+
+    async def _apply_hash_filters(
+        self, data: List[Dict], hash_filters: Dict
+    ) -> List[Dict]:
         """Apply hash-based filtering"""
-        single_hash = hash_filters.get('single_hash')
-        multiple_hashes = hash_filters.get('multiple_hashes')
-        
+        single_hash = hash_filters.get("single_hash")
+        multiple_hashes = hash_filters.get("multiple_hashes")
+
         if single_hash:
             # Filter by single hash
             single_hash = single_hash.lower()
             return [
-                item for item in data
-                if item.get('sha256', '').lower() == single_hash
+                item for item in data if item.get("sha256", "").lower() == single_hash
             ]
         elif multiple_hashes:
             # Filter by multiple hashes
             hash_set = set(hash.lower() for hash in multiple_hashes)
-            return [
-                item for item in data
-                if item.get('sha256', '').lower() in hash_set
-            ]
-        
+            return [item for item in data if item.get("sha256", "").lower() in hash_set]
+
         return data
-    
+
     async def _apply_common_filters(
         self,
         data: List[Dict],
         folder: str = None,
+        folder_include: list = None,
+        folder_exclude: list = None,
         base_models: list = None,
-        tags: list = None,
+        model_types: list = None,
+        tags: Optional[Dict[str, str]] = None,
         favorites_only: bool = False,
         search_options: dict = None,
     ) -> List[Dict]:
@@ -148,13 +246,16 @@ class BaseModelService(ABC):
         normalized_options = self.search_strategy.normalize_options(search_options)
         criteria = FilterCriteria(
             folder=folder,
+            folder_include=folder_include,
+            folder_exclude=folder_exclude,
             base_models=base_models,
+            model_types=model_types,
             tags=tags,
             favorites_only=favorites_only,
             search_options=normalized_options,
         )
         return self.filter_set.apply(data, criteria)
-    
+
     async def _apply_search_filters(
         self,
         data: List[Dict],
@@ -164,12 +265,77 @@ class BaseModelService(ABC):
     ) -> List[Dict]:
         """Apply search filtering"""
         normalized_options = self.search_strategy.normalize_options(search_options)
-        return self.search_strategy.apply(data, search, normalized_options, fuzzy_search)
-    
+        return self.search_strategy.apply(
+            data, search, normalized_options, fuzzy_search
+        )
+
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
         """Apply model-specific filters - to be overridden by subclasses if needed"""
         return data
 
+    async def _apply_credit_required_filter(
+        self, data: List[Dict], credit_required: bool
+    ) -> List[Dict]:
+        """Apply credit required filtering based on license_flags.
+
+        Args:
+            data: List of model data items
+            credit_required:
+                - True: Return items where credit is required (allowNoCredit=False)
+                - False: Return items where credit is not required (allowNoCredit=True)
+        """
+        filtered_data = []
+        for item in data:
+            license_flags = item.get(
+                "license_flags", 127
+            )  # Default to all permissions enabled
+
+            # Bit 0 represents allowNoCredit (1 = no credit required, 0 = credit required)
+            allow_no_credit = bool(license_flags & (1 << 0))
+
+            # If credit_required is True, we want items where allowNoCredit is False (credit required)
+            # If credit_required is False, we want items where allowNoCredit is True (no credit required)
+            if credit_required:
+                if not allow_no_credit:  # Credit is required
+                    filtered_data.append(item)
+            else:
+                if allow_no_credit:  # Credit is not required
+                    filtered_data.append(item)
+
+        return filtered_data
+
+    async def _apply_allow_selling_filter(
+        self, data: List[Dict], allow_selling: bool
+    ) -> List[Dict]:
+        """Apply allow selling generated content filtering based on license_flags.
+
+        Args:
+            data: List of model data items
+            allow_selling:
+                - True: Return items where selling generated content is allowed (allowCommercialUse contains Image)
+                - False: Return items where selling generated content is not allowed (allowCommercialUse does not contain Image)
+        """
+        filtered_data = []
+        for item in data:
+            license_flags = item.get(
+                "license_flags", 127
+            )  # Default to all permissions enabled
+
+            # Bits 1-4 represent commercial use permissions
+            # Bit 1 specifically represents Image permission (allowCommercialUse contains Image)
+            has_image_permission = bool(license_flags & (1 << 1))
+
+            # If allow_selling is True, we want items where Image permission is granted
+            # If allow_selling is False, we want items where Image permission is not granted
+            if allow_selling:
+                if has_image_permission:  # Selling generated content is allowed
+                    filtered_data.append(item)
+            else:
+                if not has_image_permission:  # Selling generated content is not allowed
+                    filtered_data.append(item)
+
+        return filtered_data
+
     async def _annotate_update_flags(
         self,
         items: List[Dict],
@@ -185,7 +351,7 @@ class BaseModelService(ABC):
 
         if self.update_service is None:
             for item in annotated:
-                item['update_available'] = False
+                item["update_available"] = False
             return annotated
 
         id_to_items: Dict[int, List[Dict]] = {}
@@ -193,7 +359,7 @@ class BaseModelService(ABC):
         for item in annotated:
             model_id = self._extract_model_id(item)
             if model_id is None:
-                item['update_available'] = False
+                item["update_available"] = False
                 continue
             if model_id not in id_to_items:
                 id_to_items[model_id] = []
@@ -203,20 +369,49 @@ class BaseModelService(ABC):
         if not ordered_ids:
             return annotated
 
+        strategy_value = self.settings.get("update_flag_strategy")
+        if isinstance(strategy_value, str) and strategy_value.strip():
+            strategy = strategy_value.strip().lower()
+        else:
+            strategy = "same_base"
+        same_base_mode = strategy == "same_base"
+
+        records = None
         resolved: Optional[Dict[int, bool]] = None
-        bulk_method = getattr(self.update_service, "has_updates_bulk", None)
-        if callable(bulk_method):
-            try:
-                resolved = await bulk_method(self.model_type, ordered_ids)
-            except Exception as exc:
-                logger.error(
-                    "Failed to resolve update status in bulk for %s models (%s): %s",
-                    self.model_type,
-                    ordered_ids,
-                    exc,
-                    exc_info=True,
-                )
-                resolved = None
+        if same_base_mode:
+            record_method = getattr(self.update_service, "get_records_bulk", None)
+            if callable(record_method):
+                try:
+                    records = await record_method(self.model_type, ordered_ids)
+                    resolved = {
+                        model_id: record.has_update()
+                        for model_id, record in records.items()
+                    }
+                except Exception as exc:
+                    logger.error(
+                        "Failed to resolve update records in bulk for %s models (%s): %s",
+                        self.model_type,
+                        ordered_ids,
+                        exc,
+                        exc_info=True,
+                    )
+                    records = None
+                    resolved = None
+
+        if resolved is None:
+            bulk_method = getattr(self.update_service, "has_updates_bulk", None)
+            if callable(bulk_method):
+                try:
+                    resolved = await bulk_method(self.model_type, ordered_ids)
+                except Exception as exc:
+                    logger.error(
+                        "Failed to resolve update status in bulk for %s models (%s): %s",
+                        self.model_type,
+                        ordered_ids,
+                        exc,
+                        exc_info=True,
+                    )
+                    resolved = None
 
         if resolved is None:
             tasks = [
@@ -237,96 +432,226 @@ class BaseModelService(ABC):
                 resolved[model_id] = bool(result)
 
         for model_id, items_for_id in id_to_items.items():
-            flag = bool(resolved.get(model_id, False))
+            default_flag = bool(resolved.get(model_id, False)) if resolved else False
+            record = records.get(model_id) if records else None
+            base_highest_versions = (
+                self._build_highest_local_versions_by_base(record)
+                if same_base_mode and record
+                else {}
+            )
             for item in items_for_id:
-                item['update_available'] = flag
+                if same_base_mode and record is not None:
+                    base_model = self._extract_base_model(item)
+                    normalized_base = self._normalize_base_model_name(base_model)
+                    threshold_version = (
+                        base_highest_versions.get(normalized_base)
+                        if normalized_base
+                        else None
+                    )
+                    if threshold_version is None:
+                        threshold_version = self._extract_version_id(item)
+                    flag = record.has_update_for_base(
+                        threshold_version,
+                        base_model,
+                    )
+                else:
+                    flag = default_flag
+                item["update_available"] = flag
 
         return annotated
 
     @staticmethod
     def _extract_model_id(item: Dict) -> Optional[int]:
-        civitai = item.get('civitai') if isinstance(item, dict) else None
+        civitai = item.get("civitai") if isinstance(item, dict) else None
         if not isinstance(civitai, dict):
             return None
         try:
-            value = civitai.get('modelId')
+            value = civitai.get("modelId")
             if value is None:
                 return None
             return int(value)
         except (TypeError, ValueError):
             return None
-    
+
+    @staticmethod
+    def _extract_version_id(item: Dict) -> Optional[int]:
+        civitai = item.get("civitai") if isinstance(item, dict) else None
+        if not isinstance(civitai, dict):
+            return None
+        value = civitai.get("id")
+        if value is None:
+            return None
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return None
+
+    @staticmethod
+    def _extract_base_model(item: Dict) -> Optional[str]:
+        value = item.get("base_model")
+        if value is None:
+            return None
+        if isinstance(value, str):
+            candidate = value.strip()
+        else:
+            try:
+                candidate = str(value).strip()
+            except Exception:
+                return None
+        return candidate if candidate else None
+
+    @staticmethod
+    def _normalize_base_model_name(value: Optional[str]) -> Optional[str]:
+        """Return a lowercased, trimmed base model name for comparison."""
+
+        if value is None:
+            return None
+        if isinstance(value, str):
+            candidate = value.strip()
+        else:
+            try:
+                candidate = str(value).strip()
+            except Exception:
+                return None
+        return candidate.lower() if candidate else None
+
+    def _build_highest_local_versions_by_base(self, record) -> Dict[str, int]:
+        """Return the highest local version id known for each normalized base model."""
+
+        if record is None:
+            return {}
+
+        highest_by_base: Dict[str, int] = {}
+        for version in getattr(record, "versions", []):
+            if not getattr(version, "is_in_library", False):
+                continue
+            normalized_base = self._normalize_base_model_name(
+                getattr(version, "base_model", None)
+            )
+            if normalized_base is None:
+                continue
+            version_id = getattr(version, "version_id", None)
+            if version_id is None:
+                continue
+            current_max = highest_by_base.get(normalized_base)
+            if current_max is None or version_id > current_max:
+                highest_by_base[normalized_base] = version_id
+
+        return highest_by_base
+
     def _paginate(self, data: List[Dict], page: int, page_size: int) -> Dict:
         """Apply pagination to filtered data"""
         total_items = len(data)
         start_idx = (page - 1) * page_size
         end_idx = min(start_idx + page_size, total_items)
-        
+
         return {
-            'items': data[start_idx:end_idx],
-            'total': total_items,
-            'page': page,
-            'page_size': page_size,
-            'total_pages': (total_items + page_size - 1) // page_size
+            "items": data[start_idx:end_idx],
+            "total": total_items,
+            "page": page,
+            "page_size": page_size,
+            "total_pages": (total_items + page_size - 1) // page_size,
         }
-    
+
     @abstractmethod
     async def format_response(self, model_data: Dict) -> Dict:
         """Format model data for API response - must be implemented by subclasses"""
         pass
-    
+
     # Common service methods that delegate to scanner
     async def get_top_tags(self, limit: int = 20) -> List[Dict]:
         """Get top tags sorted by frequency"""
         return await self.scanner.get_top_tags(limit)
-    
+
     async def get_base_models(self, limit: int = 20) -> List[Dict]:
         """Get base models sorted by frequency"""
         return await self.scanner.get_base_models(limit)
-    
+
+    async def get_model_types(self, limit: int = 20) -> List[Dict[str, Any]]:
+        """Get counts of normalized CivitAI model types present in the cache."""
+        cache = await self.scanner.get_cached_data()
+
+        type_counts: Dict[str, int] = {}
+        for entry in cache.raw_data:
+            normalized_type = normalize_civitai_model_type(
+                resolve_civitai_model_type(entry)
+            )
+            if not normalized_type or normalized_type not in VALID_LORA_TYPES:
+                continue
+            type_counts[normalized_type] = type_counts.get(normalized_type, 0) + 1
+
+        sorted_types = sorted(
+            [
+                {"type": model_type, "count": count}
+                for model_type, count in type_counts.items()
+            ],
+            key=lambda value: value["count"],
+            reverse=True,
+        )
+
+        return sorted_types[:limit]
+
     def has_hash(self, sha256: str) -> bool:
         """Check if a model with given hash exists"""
         return self.scanner.has_hash(sha256)
-    
+
     def get_path_by_hash(self, sha256: str) -> Optional[str]:
         """Get file path for a model by its hash"""
         return self.scanner.get_path_by_hash(sha256)
-    
+
     def get_hash_by_path(self, file_path: str) -> Optional[str]:
         """Get hash for a model by its file path"""
         return self.scanner.get_hash_by_path(file_path)
-    
-    async def scan_models(self, force_refresh: bool = False, rebuild_cache: bool = False):
+
+    async def scan_models(
+        self, force_refresh: bool = False, rebuild_cache: bool = False
+    ):
         """Trigger model scanning"""
-        return await self.scanner.get_cached_data(force_refresh=force_refresh, rebuild_cache=rebuild_cache)
-    
+        return await self.scanner.get_cached_data(
+            force_refresh=force_refresh, rebuild_cache=rebuild_cache
+        )
+
     async def get_model_info_by_name(self, name: str):
         """Get model information by name"""
         return await self.scanner.get_model_info_by_name(name)
-    
+
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
         return self.scanner.get_model_roots()
-    
+
     def filter_civitai_data(self, data: Dict, minimal: bool = False) -> Dict:
         """Filter relevant fields from CivitAI data"""
         if not data:
             return {}
 
-        fields = ["id", "modelId", "name", "trainedWords"] if minimal else [
-            "id", "modelId", "name", "createdAt", "updatedAt",
-            "publishedAt", "trainedWords", "baseModel", "description",
-            "model", "images", "customImages", "creator"
-        ]
+        fields = (
+            ["id", "modelId", "name", "trainedWords"]
+            if minimal
+            else [
+                "id",
+                "modelId",
+                "name",
+                "createdAt",
+                "updatedAt",
+                "publishedAt",
+                "trainedWords",
+                "baseModel",
+                "description",
+                "model",
+                "images",
+                "customImages",
+                "creator",
+            ]
+        )
         return {k: data[k] for k in fields if k in data}
-    
+
     async def get_folder_tree(self, model_root: str) -> Dict:
         """Get hierarchical folder tree for a specific model root"""
         cache = await self.scanner.get_cached_data()
-        
+
         # Build tree structure from folders
         tree = {}
-        
+
         for folder in cache.folders:
             # Check if this folder belongs to the specified model root
             folder_belongs_to_root = False
@@ -334,95 +659,96 @@ class BaseModelService(ABC):
                 if root == model_root:
                     folder_belongs_to_root = True
                     break
-            
+
             if not folder_belongs_to_root:
                 continue
-            
+
             # Split folder path into components
-            parts = folder.split('/') if folder else []
+            parts = folder.split("/") if folder else []
             current_level = tree
-            
+
             for part in parts:
                 if part not in current_level:
                     current_level[part] = {}
                 current_level = current_level[part]
-        
+
         return tree
-    
+
     async def get_unified_folder_tree(self) -> Dict:
         """Get unified folder tree across all model roots"""
         cache = await self.scanner.get_cached_data()
-        
+
         # Build unified tree structure by analyzing all relative paths
         unified_tree = {}
-        
+
         # Get all model roots for path normalization
         model_roots = self.scanner.get_model_roots()
-        
+
         for folder in cache.folders:
             if not folder:  # Skip empty folders
                 continue
-            
+
             # Find which root this folder belongs to by checking the actual file paths
             # This is a simplified approach - we'll use the folder as-is since it should already be relative
             relative_path = folder
-            
+
             # Split folder path into components
-            parts = relative_path.split('/')
+            parts = relative_path.split("/")
             current_level = unified_tree
-            
+
             for part in parts:
                 if part not in current_level:
                     current_level[part] = {}
                 current_level = current_level[part]
-        
+
         return unified_tree
 
     async def get_model_notes(self, model_name: str) -> Optional[str]:
         """Get notes for a specific model file"""
         cache = await self.scanner.get_cached_data()
-        
+
         for model in cache.raw_data:
-            if model['file_name'] == model_name:
-                return model.get('notes', '')
-        
+            if model["file_name"] == model_name:
+                return model.get("notes", "")
+
         return None
-    
+
     async def get_model_preview_url(self, model_name: str) -> Optional[str]:
         """Get the static preview URL for a model file"""
         cache = await self.scanner.get_cached_data()
-        
+
         for model in cache.raw_data:
-            if model['file_name'] == model_name:
-                preview_url = model.get('preview_url')
+            if model["file_name"] == model_name:
+                preview_url = model.get("preview_url")
                 if preview_url:
                     from ..config import config
+
                     return config.get_preview_static_url(preview_url)
-        
-        return '/loras_static/images/no-preview.png'
-    
+
+        return "/loras_static/images/no-preview.png"
+
     async def get_model_civitai_url(self, model_name: str) -> Dict[str, Optional[str]]:
         """Get the Civitai URL for a model file"""
         cache = await self.scanner.get_cached_data()
-        
+
         for model in cache.raw_data:
-            if model['file_name'] == model_name:
-                civitai_data = model.get('civitai', {})
-                model_id = civitai_data.get('modelId')
-                version_id = civitai_data.get('id')
-                
+            if model["file_name"] == model_name:
+                civitai_data = model.get("civitai", {})
+                model_id = civitai_data.get("modelId")
+                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}"
-                    
+
                     return {
-                        'civitai_url': civitai_url,
-                        'model_id': str(model_id),
-                        'version_id': str(version_id) if version_id else None
+                        "civitai_url": civitai_url,
+                        "model_id": str(model_id),
+                        "version_id": str(version_id) if version_id else None,
                     }
-        
-        return {'civitai_url': None, 'model_id': None, 'version_id': None}
+
+        return {"civitai_url": None, "model_id": None, "version_id": None}
 
     async def get_model_metadata(self, file_path: str) -> Optional[Dict]:
         """Load full metadata for a single model.
@@ -430,58 +756,116 @@ class BaseModelService(ABC):
         Listing/search endpoints return lightweight cache entries; this method performs
         a lazy read of the on-disk metadata snapshot when callers need full detail.
         """
-        metadata, should_skip = await MetadataManager.load_metadata(file_path, self.metadata_class)
+        metadata, should_skip = await MetadataManager.load_metadata(
+            file_path, self.metadata_class
+        )
         if should_skip or metadata is None:
             return None
         return self.filter_civitai_data(metadata.to_dict().get("civitai", {}))
 
-
     async def get_model_description(self, file_path: str) -> Optional[str]:
         """Return the stored modelDescription field for a model."""
-        metadata, should_skip = await MetadataManager.load_metadata(file_path, self.metadata_class)
+        metadata, should_skip = await MetadataManager.load_metadata(
+            file_path, self.metadata_class
+        )
         if should_skip or metadata is None:
             return None
-        return metadata.modelDescription or ''
+        return metadata.modelDescription or ""
 
+    @staticmethod
+    def _parse_search_tokens(search_term: str) -> tuple[List[str], List[str]]:
+        """Split a search string into include and exclude tokens."""
+        include_terms: List[str] = []
+        exclude_terms: List[str] = []
 
-    async def search_relative_paths(self, search_term: str, limit: int = 15) -> List[str]:
+        for raw_term in search_term.split():
+            term = raw_term.strip()
+            if not term:
+                continue
+
+            if term.startswith("-") and len(term) > 1:
+                exclude_terms.append(term[1:].lower())
+            else:
+                include_terms.append(term.lower())
+
+        return include_terms, exclude_terms
+
+    @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):
+            return False
+
+        for term in include_terms:
+            if term and term not in path_lower:
+                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()
+        prefix_hits = sum(
+            1 for term in include_terms if term and path_lower.startswith(term)
+        )
+        match_positions = [
+            path_lower.find(term)
+            for term in include_terms
+            if term and term in path_lower
+        ]
+        first_match_index = min(match_positions) if match_positions else 0
+
+        return (-prefix_hits, first_match_index, len(relative_path), path_lower)
+
+    async def search_relative_paths(
+        self, search_term: str, limit: int = 15
+    ) -> List[str]:
         """Search model relative file paths for autocomplete functionality"""
         cache = await self.scanner.get_cached_data()
-        
+        include_terms, exclude_terms = self._parse_search_tokens(search_term)
+
         matching_paths = []
-        search_lower = search_term.lower()
-        
+
         # Get model roots for path calculation
         model_roots = self.scanner.get_model_roots()
-        
+
         for model in cache.raw_data:
-            file_path = model.get('file_path', '')
+            file_path = model.get("file_path", "")
             if not file_path:
                 continue
-                
+
             # Calculate relative path from model root
             relative_path = None
             for root in model_roots:
                 # Normalize paths for comparison
                 normalized_root = os.path.normpath(root)
                 normalized_file = os.path.normpath(file_path)
-                
+
                 if normalized_file.startswith(normalized_root):
                     # Remove root and leading separator to get relative path
-                    relative_path = normalized_file[len(normalized_root):].lstrip(os.sep)
+                    relative_path = normalized_file[len(normalized_root) :].lstrip(
+                        os.sep
+                    )
                     break
-            
-            if relative_path and search_lower in relative_path.lower():
+
+            if not relative_path:
+                continue
+
+            relative_lower = relative_path.lower()
+            if self._relative_path_matches_tokens(
+                relative_lower, include_terms, exclude_terms
+            ):
                 matching_paths.append(relative_path)
-                
+
                 if len(matching_paths) >= limit * 2:  # Get more for better sorting
                     break
-        
-        # Sort by relevance (exact matches first, then by length)
-        matching_paths.sort(key=lambda x: (
-            not x.lower().startswith(search_lower),  # Exact prefix matches first
-            len(x),  # Then by length (shorter first)
-            x.lower()  # Then alphabetically
-        ))
-        
+
+        # 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]

py/services/checkpoint_service.py

@@ -35,6 +35,7 @@ class CheckpointService(BaseModelService):
             "modified": checkpoint_data.get("modified", ""),
             "tags": checkpoint_data.get("tags", []),
             "from_civitai": checkpoint_data.get("from_civitai", True),
+            "usage_count": checkpoint_data.get("usage_count", 0),
             "notes": checkpoint_data.get("notes", ""),
             "model_type": checkpoint_data.get("model_type", "checkpoint"),
             "favorite": checkpoint_data.get("favorite", False),

py/services/civarchive_client.py

@@ -1,4 +1,3 @@
-import os
 import json
 import logging
 import asyncio
@@ -8,22 +7,6 @@ from .model_metadata_provider import CivArchiveModelMetadataProvider, ModelMetad
 from .downloader import get_downloader
 from .errors import RateLimitError
 
-try:
-    from bs4 import BeautifulSoup
-except ImportError as exc:
-    BeautifulSoup = None  # type: ignore[assignment]
-    _BS4_IMPORT_ERROR = exc
-else:
-    _BS4_IMPORT_ERROR = None
-
-def _require_beautifulsoup():
-    if BeautifulSoup is None:
-        raise RuntimeError(
-            "BeautifulSoup (bs4) is required for CivArchive client. "
-            "Install it with 'pip install beautifulsoup4'."
-        ) from _BS4_IMPORT_ERROR
-    return BeautifulSoup
-
 logger = logging.getLogger(__name__)
 
 class CivArchiveClient:
@@ -446,109 +429,3 @@ class CivArchiveClient:
         if version is None:
             return None, "Model not found"
         return version, None
-
-    async def get_model_by_url(self, url) -> Optional[Dict]:
-        """Get specific model version by parsing CivArchive HTML page (legacy method)
-        
-        This is the original HTML scraping implementation, kept for reference and new sites added not in api.
-        The primary get_model_version() now uses the API instead.
-        """
-
-        try:
-            # Construct CivArchive URL
-            url = f"https://civarchive.com/{url}"
-            downloader = await get_downloader()
-            session = await downloader.session
-            async with session.get(url) as response:
-                if response.status != 200:
-                    return None
-                
-                html_content = await response.text()
-                
-            # Parse HTML to extract JSON data
-            soup_parser = _require_beautifulsoup()
-            soup = soup_parser(html_content, 'html.parser')
-            script_tag = soup.find('script', {'id': '__NEXT_DATA__', 'type': 'application/json'})
-            
-            if not script_tag:
-                return None
-                
-            # Parse JSON content
-            json_data = json.loads(script_tag.string)
-            model_data = json_data.get('props', {}).get('pageProps', {}).get('model')
-            
-            if not model_data or 'version' not in model_data:
-                return None
-            
-            # Extract version data as base
-            version = model_data['version'].copy()
-            
-            # Restructure stats
-            if 'downloadCount' in version and 'ratingCount' in version and 'rating' in version:
-                version['stats'] = {
-                    'downloadCount': version.pop('downloadCount'),
-                    'ratingCount': version.pop('ratingCount'),
-                    'rating': version.pop('rating')
-                }
-            
-            # Rename trigger to trainedWords
-            if 'trigger' in version:
-                version['trainedWords'] = version.pop('trigger')
-            
-            # Transform files data to expected format
-            if 'files' in version:
-                transformed_files = []
-                for file_data in version['files']:
-                    # Find first available mirror (deletedAt is null)
-                    available_mirror = None
-                    for mirror in file_data.get('mirrors', []):
-                        if mirror.get('deletedAt') is None:
-                            available_mirror = mirror
-                            break
-                    
-                    # Create transformed file entry
-                    transformed_file = {
-                        'id': file_data.get('id'),
-                        'sizeKB': file_data.get('sizeKB'),
-                        'name': available_mirror.get('filename', file_data.get('name')) if available_mirror else file_data.get('name'),
-                        'type': file_data.get('type'),
-                        'downloadUrl': available_mirror.get('url') if available_mirror else None,
-                        'primary': file_data.get('is_primary', False),
-                        'mirrors': file_data.get('mirrors', [])
-                    }
-                    
-                    # Transform hash format
-                    if 'sha256' in file_data:
-                        transformed_file['hashes'] = {
-                            'SHA256': file_data['sha256'].upper()
-                        }
-                    
-                    transformed_files.append(transformed_file)
-                
-                version['files'] = transformed_files
-            
-            # Add model information
-            version['model'] = {
-                'name': model_data.get('name'),
-                'type': model_data.get('type'),
-                'nsfw': model_data.get('is_nsfw', False),
-                'description': model_data.get('description'),
-                'tags': model_data.get('tags', [])
-            }
-
-            version['creator'] = {
-                'username': model_data.get('username'),
-                'image': ''
-            }
-            
-            # Add source identifier
-            version['source'] = 'civarchive'
-            version['is_deleted'] = json_data.get('query', {}).get('is_deleted', False)
-            
-            return version
-            
-        except RateLimitError:
-            raise
-        except Exception as e:
-            logger.error(f"Error fetching CivArchive model version (scraping) {url}: {e}")
-            return None

py/services/civitai_client.py

@@ -6,6 +6,7 @@ from typing import Any, Optional, Dict, Tuple, List, Sequence
 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__)
 
@@ -103,44 +104,32 @@ class CivitaiClient:
 
     async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
         try:
-            success, result = await self._make_request(
+            success, version = await self._make_request(
                 'GET',
                 f"{self.base_url}/model-versions/by-hash/{model_hash}",
                 use_auth=True
             )
-            if success:
-                # Get model ID from version data
-                model_id = result.get('modelId')
-                if model_id:
-                    # Fetch additional model metadata
-                    success_model, data = await self._make_request(
-                        'GET',
-                        f"{self.base_url}/models/{model_id}",
-                        use_auth=True
-                    )
-                    if success_model:
-                        # Enrich version_info with model data
-                        result['model']['description'] = data.get("description")
-                        result['model']['tags'] = data.get("tags", [])
+            if not success:
+                message = str(version)
+                if "not found" in message.lower():
+                    return None, "Model not found"
 
-                        # Add creator from model data
-                        result['creator'] = data.get("creator")
+                logger.error("Failed to fetch model info for %s: %s", model_hash[:10], message)
+                return None, message
 
-                self._remove_comfy_metadata(result)
-                return result, None
-            
-            # Handle specific error cases
-            if "not found" in str(result):
-                return None, "Model not found"
-            
-            # Other error cases
-            logger.error(f"Failed to fetch model info for {model_hash[:10]}: {result}")
-            return None, str(result)
+            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
         except RateLimitError:
             raise
-        except Exception as e:
-            logger.error(f"API Error: {str(e)}")
-            return None, str(e)
+        except Exception as exc:
+            logger.error("API Error: %s", exc)
+            return None, str(exc)
 
     async def download_preview_image(self, image_url: str, save_path: str):
         try:
@@ -257,6 +246,10 @@ class CivitaiClient:
                     '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:
@@ -420,6 +413,10 @@ class CivitaiClient:
         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]]:
         """Fetch model version metadata from Civitai
         

py/services/downloader.py

@@ -128,6 +128,7 @@ class Downloader:
         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
@@ -148,7 +149,10 @@ class Downloader:
     async def session(self) -> aiohttp.ClientSession:
         """Get or create the global aiohttp session with optimized settings"""
         if self._session is None or self._should_refresh_session():
-            await self._create_session()
+            async with self._session_lock:
+                # Double check after acquiring lock
+                if self._session is None or self._should_refresh_session():
+                    await self._create_session()
         return self._session
 
     @property
@@ -197,10 +201,18 @@ class Downloader:
         return False
     
     async def _create_session(self):
-        """Create a new aiohttp session with optimized settings"""
+        """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:
-            await self._session.close()
+            try:
+                await self._session.close()
+            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
@@ -808,7 +820,8 @@ class Downloader:
     
     async def refresh_session(self):
         """Force refresh the HTTP session (useful when proxy settings change)"""
-        await self._create_session()
+        async with self._session_lock:
+            await self._create_session()
         logger.info("HTTP session refreshed due to settings change")
 
     @staticmethod

py/services/embedding_service.py

@@ -35,6 +35,7 @@ class EmbeddingService(BaseModelService):
             "modified": embedding_data.get("modified", ""),
             "tags": embedding_data.get("tags", []),
             "from_civitai": embedding_data.get("from_civitai", True),
+            # "usage_count": embedding_data.get("usage_count", 0), # TODO: Enable when embedding usage tracking is implemented
             "notes": embedding_data.get("notes", ""),
             "model_type": embedding_data.get("model_type", "embedding"),
             "favorite": embedding_data.get("favorite", False),

py/services/lora_service.py

@@ -8,24 +8,27 @@ from ..config import config
 
 logger = logging.getLogger(__name__)
 
+
 class LoraService(BaseModelService):
     """LoRA-specific service implementation"""
-    
+
     def __init__(self, scanner, update_service=None):
         """Initialize LoRA service
-        
+
         Args:
             scanner: LoRA scanner instance
             update_service: Optional service for remote update tracking.
         """
         super().__init__("lora", scanner, LoraMetadata, update_service=update_service)
-    
+
     async def format_response(self, lora_data: Dict) -> Dict:
         """Format LoRA data for API response"""
         return {
             "model_name": lora_data["model_name"],
             "file_name": lora_data["file_name"],
-            "preview_url": config.get_preview_static_url(lora_data.get("preview_url", "")),
+            "preview_url": config.get_preview_static_url(
+                lora_data.get("preview_url", "")
+            ),
             "preview_nsfw_level": lora_data.get("preview_nsfw_level", 0),
             "base_model": lora_data.get("base_model", ""),
             "folder": lora_data["folder"],
@@ -35,149 +38,438 @@ class LoraService(BaseModelService):
             "modified": lora_data.get("modified", ""),
             "tags": lora_data.get("tags", []),
             "from_civitai": lora_data.get("from_civitai", True),
+            "usage_count": lora_data.get("usage_count", 0),
             "usage_tips": lora_data.get("usage_tips", ""),
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
             "update_available": bool(lora_data.get("update_available", False)),
-            "civitai": self.filter_civitai_data(lora_data.get("civitai", {}), minimal=True)
+            "civitai": self.filter_civitai_data(
+                lora_data.get("civitai", {}), minimal=True
+            ),
         }
-    
+
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
         """Apply LoRA-specific filters"""
         # Handle first_letter filter for LoRAs
-        first_letter = kwargs.get('first_letter')
+        first_letter = kwargs.get("first_letter")
         if first_letter:
             data = self._filter_by_first_letter(data, first_letter)
-        
+
         return data
-    
+
     def _filter_by_first_letter(self, data: List[Dict], letter: str) -> List[Dict]:
         """Filter data by first letter of model name
-        
+
         Special handling:
         - '#': Numbers (0-9)
         - '@': Special characters (not alphanumeric)
         - '漢': CJK characters
         """
         filtered_data = []
-        
+
         for lora in data:
-            model_name = lora.get('model_name', '')
+            model_name = lora.get("model_name", "")
             if not model_name:
                 continue
-                
+
             first_char = model_name[0].upper()
-            
-            if letter == '#' and first_char.isdigit():
+
+            if letter == "#" and first_char.isdigit():
                 filtered_data.append(lora)
-            elif letter == '@' and not first_char.isalnum():
+            elif letter == "@" and not first_char.isalnum():
                 # Special characters (not alphanumeric)
                 filtered_data.append(lora)
-            elif letter == '漢' and self._is_cjk_character(first_char):
+            elif letter == "漢" and self._is_cjk_character(first_char):
                 # CJK characters
                 filtered_data.append(lora)
             elif letter.upper() == first_char:
                 # Regular alphabet matching
                 filtered_data.append(lora)
-                
+
         return filtered_data
-    
+
     def _is_cjk_character(self, char: str) -> bool:
         """Check if character is a CJK character"""
         # Define Unicode ranges for CJK characters
         cjk_ranges = [
-            (0x4E00, 0x9FFF),   # CJK Unified Ideographs
-            (0x3400, 0x4DBF),   # CJK Unified Ideographs Extension A
-            (0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
-            (0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
-            (0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
-            (0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
-            (0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
-            (0x30000, 0x3134F), # CJK Unified Ideographs Extension G
-            (0xF900, 0xFAFF),   # CJK Compatibility Ideographs
-            (0x3300, 0x33FF),   # CJK Compatibility
-            (0x3200, 0x32FF),   # Enclosed CJK Letters and Months
-            (0x3100, 0x312F),   # Bopomofo
-            (0x31A0, 0x31BF),   # Bopomofo Extended
-            (0x3040, 0x309F),   # Hiragana
-            (0x30A0, 0x30FF),   # Katakana
-            (0x31F0, 0x31FF),   # Katakana Phonetic Extensions
-            (0xAC00, 0xD7AF),   # Hangul Syllables
-            (0x1100, 0x11FF),   # Hangul Jamo
-            (0xA960, 0xA97F),   # Hangul Jamo Extended-A
-            (0xD7B0, 0xD7FF),   # Hangul Jamo Extended-B
+            (0x4E00, 0x9FFF),  # CJK Unified Ideographs
+            (0x3400, 0x4DBF),  # CJK Unified Ideographs Extension A
+            (0x20000, 0x2A6DF),  # CJK Unified Ideographs Extension B
+            (0x2A700, 0x2B73F),  # CJK Unified Ideographs Extension C
+            (0x2B740, 0x2B81F),  # CJK Unified Ideographs Extension D
+            (0x2B820, 0x2CEAF),  # CJK Unified Ideographs Extension E
+            (0x2CEB0, 0x2EBEF),  # CJK Unified Ideographs Extension F
+            (0x30000, 0x3134F),  # CJK Unified Ideographs Extension G
+            (0xF900, 0xFAFF),  # CJK Compatibility Ideographs
+            (0x3300, 0x33FF),  # CJK Compatibility
+            (0x3200, 0x32FF),  # Enclosed CJK Letters and Months
+            (0x3100, 0x312F),  # Bopomofo
+            (0x31A0, 0x31BF),  # Bopomofo Extended
+            (0x3040, 0x309F),  # Hiragana
+            (0x30A0, 0x30FF),  # Katakana
+            (0x31F0, 0x31FF),  # Katakana Phonetic Extensions
+            (0xAC00, 0xD7AF),  # Hangul Syllables
+            (0x1100, 0x11FF),  # Hangul Jamo
+            (0xA960, 0xA97F),  # Hangul Jamo Extended-A
+            (0xD7B0, 0xD7FF),  # Hangul Jamo Extended-B
         ]
-        
+
         code_point = ord(char)
         return any(start <= code_point <= end for start, end in cjk_ranges)
-    
+
     # LoRA-specific methods
     async def get_letter_counts(self) -> Dict[str, int]:
         """Get count of LoRAs for each letter of the alphabet"""
         cache = await self.scanner.get_cached_data()
         data = cache.raw_data
-        
+
         # Define letter categories
         letters = {
-            '#': 0,  # Numbers
-            'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
-            'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
-            'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
-            'Y': 0, 'Z': 0,
-            '@': 0,  # Special characters
-            '漢': 0   # CJK characters
+            "#": 0,  # Numbers
+            "A": 0,
+            "B": 0,
+            "C": 0,
+            "D": 0,
+            "E": 0,
+            "F": 0,
+            "G": 0,
+            "H": 0,
+            "I": 0,
+            "J": 0,
+            "K": 0,
+            "L": 0,
+            "M": 0,
+            "N": 0,
+            "O": 0,
+            "P": 0,
+            "Q": 0,
+            "R": 0,
+            "S": 0,
+            "T": 0,
+            "U": 0,
+            "V": 0,
+            "W": 0,
+            "X": 0,
+            "Y": 0,
+            "Z": 0,
+            "@": 0,  # Special characters
+            "漢": 0,  # CJK characters
         }
-        
+
         # Count models for each letter
         for lora in data:
-            model_name = lora.get('model_name', '')
+            model_name = lora.get("model_name", "")
             if not model_name:
                 continue
-                
+
             first_char = model_name[0].upper()
-            
+
             if first_char.isdigit():
-                letters['#'] += 1
+                letters["#"] += 1
             elif first_char in letters:
                 letters[first_char] += 1
             elif self._is_cjk_character(first_char):
-                letters['漢'] += 1
+                letters["漢"] += 1
             elif not first_char.isalnum():
-                letters['@'] += 1
-                
+                letters["@"] += 1
+
         return letters
-    
+
     async def get_lora_trigger_words(self, lora_name: str) -> List[str]:
         """Get trigger words for a specific LoRA file"""
         cache = await self.scanner.get_cached_data()
-        
+
         for lora in cache.raw_data:
-            if lora['file_name'] == lora_name:
-                civitai_data = lora.get('civitai', {})
-                return civitai_data.get('trainedWords', [])
-        
+            if lora["file_name"] == lora_name:
+                civitai_data = lora.get("civitai", {})
+                return civitai_data.get("trainedWords", [])
+
         return []
-    
-    async def get_lora_usage_tips_by_relative_path(self, relative_path: str) -> Optional[str]:
+
+    async def get_lora_usage_tips_by_relative_path(
+        self, relative_path: str
+    ) -> Optional[str]:
         """Get usage tips for a LoRA by its relative path"""
         cache = await self.scanner.get_cached_data()
-        
+
         for lora in cache.raw_data:
-            file_path = lora.get('file_path', '')
+            file_path = lora.get("file_path", "")
             if file_path:
                 # Convert to forward slashes and extract relative path
-                file_path_normalized = file_path.replace('\\', '/')
-                relative_path = relative_path.replace('\\', '/')
+                file_path_normalized = file_path.replace("\\", "/")
+                relative_path = relative_path.replace("\\", "/")
                 # Find the relative path part by looking for the relative_path in the full path
-                if file_path_normalized.endswith(relative_path) or relative_path in file_path_normalized:
-                    return lora.get('usage_tips', '')
-        
+                if (
+                    file_path_normalized.endswith(relative_path)
+                    or relative_path in file_path_normalized
+                ):
+                    return lora.get("usage_tips", "")
+
         return None
-    
+
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
-    
+
     def find_duplicate_filenames(self) -> Dict:
         """Find LoRAs with conflicting filenames"""
         return self.scanner._hash_index.get_duplicate_filenames()
+
+    async def get_random_loras(
+        self,
+        count: int,
+        model_strength_min: float = 0.0,
+        model_strength_max: float = 1.0,
+        use_same_clip_strength: bool = True,
+        clip_strength_min: float = 0.0,
+        clip_strength_max: float = 1.0,
+        locked_loras: Optional[List[Dict]] = None,
+        pool_config: Optional[Dict] = None,
+        count_mode: str = "fixed",
+        count_min: int = 3,
+        count_max: int = 7,
+        use_recommended_strength: bool = False,
+        recommended_strength_scale_min: float = 0.5,
+        recommended_strength_scale_max: float = 1.0,
+    ) -> List[Dict]:
+        """
+        Get random LoRAs with specified strength ranges.
+
+        Args:
+            count: Number of LoRAs to select (if count_mode='fixed')
+            model_strength_min: Minimum model strength
+            model_strength_max: Maximum model strength
+            use_same_clip_strength: Whether to use same strength for clip
+            clip_strength_min: Minimum clip strength
+            clip_strength_max: Maximum clip strength
+            locked_loras: List of locked LoRA dicts to preserve
+            pool_config: Optional pool config for filtering
+            count_mode: How to determine count ('fixed' or 'range')
+            count_min: Minimum count for range mode
+            count_max: Maximum count for range mode
+            use_recommended_strength: Whether to use recommended strength from usage_tips
+            recommended_strength_scale_min: Minimum scale factor for recommended strength
+            recommended_strength_scale_max: Maximum scale factor for recommended strength
+
+        Returns:
+            List of LoRA dicts with randomized strengths
+        """
+        import random
+        import json
+
+        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 = []
+
+        # Determine target count based on count_mode
+        if count_mode == "fixed":
+            target_count = count
+        else:
+            target_count = random.randint(count_min, count_max)
+
+        # Get available loras from cache
+        cache = await self.scanner.get_cached_data(force_refresh=False)
+        available_loras = cache.raw_data if cache else []
+
+        # Apply pool filters if provided
+        if pool_config:
+            available_loras = await self._apply_pool_filters(
+                available_loras, pool_config
+            )
+
+        # Calculate slots needed (total - locked)
+        locked_count = len(locked_loras)
+        slots_needed = target_count - locked_count
+
+        if slots_needed < 0:
+            slots_needed = 0
+            # Too many locked, trim to target
+            locked_loras = locked_loras[:target_count]
+
+        # Filter out locked LoRAs from available pool
+        locked_names = {lora["name"] for lora in locked_loras}
+        available_pool = [
+            l for l in available_loras if l["file_name"] not in locked_names
+        ]
+
+        # Ensure we don't try to select more than available
+        if slots_needed > len(available_pool):
+            slots_needed = len(available_pool)
+
+        # Random sample
+        selected = []
+        if slots_needed > 0:
+            selected = random.sample(available_pool, slots_needed)
+
+        # Generate random strengths for selected LoRAs
+        result_loras = []
+        for lora in selected:
+            if use_recommended_strength:
+                recommended_strength = get_recommended_strength(lora)
+                if recommended_strength is not None:
+                    scale = random.uniform(
+                        recommended_strength_scale_min, recommended_strength_scale_max
+                    )
+                    model_str = round(recommended_strength * scale, 2)
+                else:
+                    model_str = round(
+                        random.uniform(model_strength_min, model_strength_max), 2
+                    )
+            else:
+                model_str = round(
+                    random.uniform(model_strength_min, model_strength_max), 2
+                )
+
+            if use_same_clip_strength:
+                clip_str = model_str
+            elif use_recommended_strength:
+                recommended_clip_strength = get_recommended_clip_strength(lora)
+                if recommended_clip_strength is not None:
+                    scale = random.uniform(
+                        recommended_strength_scale_min, recommended_strength_scale_max
+                    )
+                    clip_str = round(recommended_clip_strength * scale, 2)
+                else:
+                    clip_str = round(
+                        random.uniform(clip_strength_min, clip_strength_max), 2
+                    )
+            else:
+                clip_str = round(
+                    random.uniform(clip_strength_min, clip_strength_max), 2
+                )
+
+            result_loras.append(
+                {
+                    "name": lora["file_name"],
+                    "strength": model_str,
+                    "clipStrength": clip_str,
+                    "active": True,
+                    "expanded": abs(model_str - clip_str) > 0.001,
+                    "locked": False,
+                }
+            )
+
+        # Merge with locked LoRAs
+        result_loras.extend(locked_loras)
+
+        return result_loras
+
+    async def _apply_pool_filters(
+        self, available_loras: List[Dict], pool_config: Dict
+    ) -> List[Dict]:
+        """
+        Apply pool_config filters to available LoRAs.
+
+        Args:
+            available_loras: List of all LoRA dicts
+            pool_config: Dict with filter settings from LoRA Pool node
+
+        Returns:
+            Filtered list of LoRA dicts
+        """
+        from .model_query import FilterCriteria
+
+        filter_section = pool_config
+
+        # Extract filter parameters
+        selected_base_models = filter_section.get("baseModels", [])
+        tags_dict = filter_section.get("tags", {})
+        include_tags = tags_dict.get("include", [])
+        exclude_tags = tags_dict.get("exclude", [])
+        folders_dict = filter_section.get("folders", {})
+        include_folders = folders_dict.get("include", [])
+        exclude_folders = folders_dict.get("exclude", [])
+        license_dict = filter_section.get("license", {})
+        no_credit_required = license_dict.get("noCreditRequired", False)
+        allow_selling = license_dict.get("allowSelling", False)
+
+        # Build tag filters dict
+        tag_filters = {}
+        for tag in include_tags:
+            tag_filters[tag] = "include"
+        for tag in exclude_tags:
+            tag_filters[tag] = "exclude"
+
+        # Build folder filter
+        if include_folders or exclude_folders:
+            filtered = []
+            for lora in available_loras:
+                folder = lora.get("folder", "")
+
+                # Check exclude folders first
+                excluded = False
+                for exclude_folder in exclude_folders:
+                    if folder.startswith(exclude_folder):
+                        excluded = True
+                        break
+
+                if excluded:
+                    continue
+
+                # Check include folders
+                if include_folders:
+                    included = False
+                    for include_folder in include_folders:
+                        if folder.startswith(include_folder):
+                            included = True
+                            break
+                    if not included:
+                        continue
+
+                filtered.append(lora)
+
+            available_loras = filtered
+
+        # Apply base model filter
+        if selected_base_models:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if lora.get("base_model") in selected_base_models
+            ]
+
+        # Apply tag filters
+        if tag_filters:
+            criteria = FilterCriteria(tags=tag_filters)
+            available_loras = self.filter_set.apply(available_loras, criteria)
+
+        # Apply license filters
+        # no_credit_required=True means keep only models where credit is NOT required
+        # (i.e., allowNoCredit=True, which is bit 0 = 1 in license_flags)
+        if no_credit_required:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if bool(lora.get("license_flags", 127) & (1 << 0))
+            ]
+
+        # allow_selling=True means keep only models where selling generated content is allowed
+        if allow_selling:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if bool(lora.get("license_flags", 127) & (1 << 1))
+            ]
+
+        return available_loras

py/services/metadata_service.py

@@ -2,11 +2,12 @@ import os
 import logging
 from .model_metadata_provider import (
     ModelMetadataProvider,
-    ModelMetadataProviderManager, 
+    ModelMetadataProviderManager,
     SQLiteModelMetadataProvider,
     CivitaiModelMetadataProvider,
     CivArchiveModelMetadataProvider,
-    FallbackMetadataProvider
+    FallbackMetadataProvider,
+    RateLimitRetryingProvider,
 )
 from .settings_manager import get_settings_manager
 from .metadata_archive_manager import MetadataArchiveManager
@@ -108,14 +109,24 @@ async def get_metadata_archive_manager():
     base_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
     return MetadataArchiveManager(base_path)
 
+def _wrap_provider_with_rate_limit(provider_name: str | None, provider: ModelMetadataProvider) -> ModelMetadataProvider:
+    if isinstance(provider, (FallbackMetadataProvider, RateLimitRetryingProvider)):
+        return provider
+    return RateLimitRetryingProvider(provider, label=provider_name)
+
+
 async def get_metadata_provider(provider_name: str = None):
-    """Get a specific metadata provider or default provider"""
+    """Get a specific metadata provider or default provider with rate-limit handling."""
+
     provider_manager = await ModelMetadataProviderManager.get_instance()
-    
-    if provider_name:
-        return provider_manager._get_provider(provider_name)
-    
-    return provider_manager._get_provider()
+
+    provider = (
+        provider_manager._get_provider(provider_name)
+        if provider_name
+        else provider_manager._get_provider()
+    )
+
+    return _wrap_provider_with_rate_limit(provider_name, provider)
 
 async def get_default_metadata_provider():
     """Get the default metadata provider (fallback or single provider)"""

py/services/metadata_sync_service.py

@@ -9,6 +9,7 @@ from datetime import datetime
 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 .errors import RateLimitError
 
@@ -75,7 +76,7 @@ class MetadataSyncService:
         files = meta.get("files")
         images = meta.get("images")
         source = meta.get("source")
-        return bool(files) and bool(images) and source != "archive_db"
+        return bool(files) and bool(images) and source not in ("archive_db", "civarchive")
 
     async def update_model_metadata(
         self,
@@ -89,11 +90,11 @@ class MetadataSyncService:
         existing_civitai = local_metadata.get("civitai") or {}
 
         if (
-            civitai_metadata.get("source") == "archive_db"
+            not self.is_civitai_api_metadata(civitai_metadata)
             and self.is_civitai_api_metadata(existing_civitai)
         ):
             logger.info(
-                "Skip civitai update for %s (%s)",
+                "Skip civitai update for %s (%s) - existing metadata is higher quality",
                 local_metadata.get("model_name", ""),
                 existing_civitai.get("name", ""),
             )
@@ -135,6 +136,17 @@ class MetadataSyncService:
             ):
                 local_metadata.setdefault("civitai", {})["creator"] = model_data["creator"]
 
+            merged_civitai = local_metadata.get("civitai") or {}
+            civitai_model = merged_civitai.get("model")
+            if not isinstance(civitai_model, dict):
+                civitai_model = {}
+
+            license_payload = resolve_license_payload(model_data)
+            civitai_model.update(license_payload)
+
+            merged_civitai["model"] = civitai_model
+            local_metadata["civitai"] = merged_civitai
+
         local_metadata["base_model"] = determine_base_model(
             civitai_metadata.get("baseModel")
         )
@@ -202,6 +214,7 @@ class MetadataSyncService:
             metadata_provider: Optional[MetadataProviderProtocol] = None
             provider_used: Optional[str] = None
             last_error: Optional[str] = None
+            civitai_api_not_found = False
 
             for provider_name, provider in provider_attempts:
                 try:
@@ -216,19 +229,24 @@ class MetadataSyncService:
                 if provider_name == "sqlite":
                     sqlite_attempted = True
 
+                is_default_provider = provider_name is None
+
                 if civitai_metadata_candidate:
                     civitai_metadata = civitai_metadata_candidate
                     metadata_provider = provider
                     provider_used = provider_name
                     break
 
+                if is_default_provider and error == "Model not found":
+                    civitai_api_not_found = True
+
                 last_error = error or last_error
 
             if civitai_metadata is None or metadata_provider is None:
                 if sqlite_attempted:
                     model_data["db_checked"] = True
 
-                if last_error == "Model not found":
+                if civitai_api_not_found:
                     model_data["from_civitai"] = False
                     model_data["civitai_deleted"] = True
                     model_data["db_checked"] = sqlite_attempted or (enable_archive and model_data.get("db_checked", False))
@@ -254,7 +272,10 @@ class MetadataSyncService:
                 return False, error_msg
 
             model_data["from_civitai"] = True
-            model_data["civitai_deleted"] = civitai_metadata.get("source") == "archive_db" or civitai_metadata.get("source") == "civarchive"
+            if provider_used is None:
+                model_data["civitai_deleted"] = False
+            elif civitai_api_not_found:
+                model_data["civitai_deleted"] = True
             model_data["db_checked"] = enable_archive and (
                 civitai_metadata.get("source") == "archive_db" or sqlite_attempted
             )
@@ -295,6 +316,7 @@ class MetadataSyncService:
                 "preview_url": local_metadata.get("preview_url"),
                 "civitai": local_metadata.get("civitai"),
             }
+
             model_data.update(update_payload)
 
             await update_cache_func(file_path, file_path, local_metadata)
@@ -436,4 +458,3 @@ class MetadataSyncService:
                 results["verified_as_duplicates"] = False
 
         return results
-

py/services/model_cache.py

@@ -1,4 +1,8 @@
 import asyncio
+import time
+import logging
+
+logger = logging.getLogger(__name__)
 from typing import Any, Dict, List, Optional, Tuple
 from dataclasses import dataclass, field
 from operator import itemgetter
@@ -13,7 +17,10 @@ SUPPORTED_SORT_MODES = [
     ('date', 'desc'),
     ('size', 'asc'),
     ('size', 'desc'),
+    ('usage', 'asc'),
+    ('usage', 'desc'),
 ]
+# Is this in use?
 
 DISPLAY_NAME_MODES = {"model_name", "file_name"}
 
@@ -212,40 +219,63 @@ class ModelCache:
 
     def _sort_data(self, data: List[Dict], sort_key: str, order: str) -> List[Dict]:
         """Sort data by sort_key and order"""
+        start_time = time.perf_counter()
         reverse = (order == 'desc')
         if sort_key == 'name':
             # Natural sort by configured display name, case-insensitive
-            return natsorted(
+            result = natsorted(
                 data,
                 key=lambda x: self._get_display_name(x).lower(),
                 reverse=reverse
             )
         elif sort_key == 'date':
             # Sort by modified timestamp
-            return sorted(
+            result = sorted(
                 data,
                 key=itemgetter('modified'),
                 reverse=reverse
             )
         elif sort_key == 'size':
             # Sort by file size
-            return sorted(
+            result = sorted(
                 data,
                 key=itemgetter('size'),
                 reverse=reverse
             )
+        elif sort_key == 'usage':
+            # Sort by usage count, fallback to 0, then name for stability
+            return sorted(
+                data,
+                key=lambda x: (
+                    x.get('usage_count', 0),
+                    self._get_display_name(x).lower()
+                ),
+                reverse=reverse
+            )
         else:
             # Fallback: no sort
-            return list(data)
+            result = list(data)
+        
+        duration = time.perf_counter() - start_time
+        if duration > 0.05:
+            logger.debug("ModelCache._sort_data(%s, %s) for %d items took %.3fs", sort_key, order, len(data), duration)
+        return result
 
     async def get_sorted_data(self, sort_key: str = 'name', order: str = 'asc') -> List[Dict]:
         """Get sorted data by sort_key and order, using cache if possible"""
         async with self._lock:
             if (sort_key, order) == self._last_sort:
                 return self._last_sorted_data
+            
+            start_time = time.perf_counter()
             sorted_data = self._sort_data(self.raw_data, sort_key, order)
             self._last_sort = (sort_key, order)
             self._last_sorted_data = sorted_data
+            
+            duration = time.perf_counter() - start_time
+            if duration > 0.1:
+                logger.debug("ModelCache.get_sorted_data(%s, %s) took %.3fs", sort_key, order, duration)
+            
             return sorted_data
 
     async def update_name_display_mode(self, display_mode: str) -> None:

py/services/model_file_service.py

@@ -1,7 +1,8 @@
 import asyncio
+import fnmatch
 import os
 import logging
-from typing import List, Dict, Optional, Any, Set
+from typing import Any, Dict, List, Optional, Sequence, Set
 from abc import ABC, abstractmethod
 
 from ..utils.utils import calculate_relative_path_for_model, remove_empty_dirs
@@ -35,11 +36,13 @@ class AutoOrganizeResult:
         self.results_truncated: bool = False
         self.sample_results: List[Dict[str, Any]] = []
         self.is_flat_structure: bool = False
+        self.status: str = 'success'
     
     def to_dict(self) -> Dict[str, Any]:
         """Convert result to dictionary"""
         result = {
-            'success': True,
+            'success': self.status != 'error',
+            'status': self.status,
             'message': f'Auto-organize {self.operation_type} completed: {self.success_count} moved, {self.skipped_count} skipped, {self.failure_count} failed out of {self.total} total',
             'summary': {
                 'total': self.total,
@@ -79,9 +82,10 @@ class ModelFileService:
         return self.scanner.get_model_roots()
     
     async def auto_organize_models(
-        self, 
+        self,
         file_paths: Optional[List[str]] = None,
-        progress_callback: Optional[ProgressCallback] = None
+        progress_callback: Optional[ProgressCallback] = None,
+        exclusion_patterns: Optional[Sequence[str]] = None,
     ) -> AutoOrganizeResult:
         """Auto-organize models based on current settings
         
@@ -96,10 +100,19 @@ class ModelFileService:
         result = AutoOrganizeResult()
         source_directories: Set[str] = set()
         
+        self.scanner.reset_cancellation()
+        
         try:
             # Get all models from cache
             cache = await self.scanner.get_cached_data()
             all_models = cache.raw_data
+
+            settings_manager = get_settings_manager()
+            normalized_exclusions = settings_manager.normalize_auto_organize_exclusions(
+                exclusion_patterns
+                if exclusion_patterns is not None
+                else settings_manager.get_auto_organize_exclusions()
+            )
             
             # Filter models if specific file paths are provided
             if file_paths:
@@ -107,11 +120,19 @@ class ModelFileService:
                 result.operation_type = 'bulk'
             else:
                 result.operation_type = 'all'
-            
-            # Get model roots for this scanner
+
             model_roots = self.get_model_roots()
             if not model_roots:
                 raise ValueError('No model roots configured')
+
+            if normalized_exclusions:
+                all_models = [
+                    model
+                    for model in all_models
+                    if not self._should_exclude_model(
+                        model.get('file_path'), normalized_exclusions, model_roots
+                    )
+                ]
             
             # Check if flat structure is configured for this model type
             settings_manager = get_settings_manager()
@@ -133,7 +154,34 @@ class ModelFileService:
                     'skipped': 0,
                     'operation_type': result.operation_type
                 })
-            
+
+            if result.total == 0:
+                if progress_callback:
+                    await asyncio.sleep(0.1)
+                    payload = {
+                        'type': 'auto_organize_progress',
+                        'total': 0,
+                        'processed': 0,
+                        'success': 0,
+                        'failures': 0,
+                        'skipped': 0,
+                        'operation_type': result.operation_type
+                    }
+                    await progress_callback.on_progress({**payload, 'status': 'processing'})
+                    await progress_callback.on_progress({
+                        **payload,
+                        'status': 'cleaning',
+                        'message': 'Cleaning up empty directories...'
+                    })
+                    result.cleanup_counts = {}
+                    await progress_callback.on_progress({
+                        **payload,
+                        'status': 'completed',
+                        'cleanup': result.cleanup_counts
+                    })
+
+                return result
+
             # Process models in batches
             await self._process_models_in_batches(
                 all_models, 
@@ -142,6 +190,21 @@ class ModelFileService:
                 progress_callback,
                 source_directories  # Pass the set to track source directories
             )
+
+            if self.scanner.is_cancelled():
+                result.status = 'cancelled'
+                if progress_callback:
+                    await progress_callback.on_progress({
+                        'type': 'auto_organize_progress',
+                        'status': 'cancelled',
+                        'total': result.total,
+                        'processed': result.processed,
+                        'success': result.success_count,
+                        'failures': result.failure_count,
+                        'skipped': result.skipped_count,
+                        'operation_type': result.operation_type
+                    })
+                return result
             
             # Send cleanup progress
             if progress_callback:
@@ -202,9 +265,15 @@ class ModelFileService:
         """Process models in batches to avoid overwhelming the system"""
         
         for i in range(0, result.total, AUTO_ORGANIZE_BATCH_SIZE):
+            if self.scanner.is_cancelled():
+                logger.info(f"{self.model_type.capitalize()} File Service: Auto-organize cancelled by user")
+                break
+
             batch = all_models[i:i + AUTO_ORGANIZE_BATCH_SIZE]
             
             for model in batch:
+                if self.scanner.is_cancelled():
+                    break
                 await self._process_single_model(model, model_roots, result, source_directories)
                 result.processed += 1
             
@@ -301,10 +370,43 @@ class ModelFileService:
             # Normalize paths for comparison
             normalized_root = os.path.normpath(root).replace(os.sep, '/')
             normalized_file = os.path.normpath(file_path).replace(os.sep, '/')
-            
+
             if normalized_file.startswith(normalized_root):
                 return root
         return None
+
+    def _should_exclude_model(
+        self,
+        file_path: Optional[str],
+        patterns: Sequence[str],
+        model_roots: Sequence[str],
+    ) -> bool:
+        if not file_path or not patterns:
+            return False
+
+        normalized_path = os.path.normpath(file_path).replace(os.sep, '/')
+        filename = os.path.basename(normalized_path)
+        relative_path = None
+
+        if model_roots:
+            root = self._find_model_root(file_path, list(model_roots))
+            if root:
+                normalized_root = os.path.normpath(root)
+                try:
+                    relative = os.path.relpath(file_path, normalized_root)
+                except ValueError:
+                    relative = None
+                if relative is not None:
+                    relative_path = relative.replace(os.sep, '/')
+
+        for pattern in patterns:
+            if fnmatch.fnmatch(filename, pattern):
+                return True
+            if relative_path and fnmatch.fnmatch(relative_path, pattern):
+                return True
+            if fnmatch.fnmatch(normalized_path, pattern):
+                return True
+        return False
     
     async def _calculate_target_directory(
         self, 
@@ -369,25 +471,46 @@ class ModelFileService:
 class ModelMoveService:
     """Service for handling individual model moves"""
     
-    def __init__(self, scanner):
+    def __init__(self, scanner, model_type: str):
         """Initialize the service
         
         Args:
             scanner: Model scanner instance
+            model_type: Type of model (e.g., 'lora', 'checkpoint')
         """
         self.scanner = scanner
+        self.model_type = model_type
     
-    async def move_model(self, file_path: str, target_path: str) -> Dict[str, Any]:
+    async def move_model(self, file_path: str, target_path: str, use_default_paths: bool = False) -> Dict[str, Any]:
         """Move a single model file
         
         Args:
             file_path: Source file path
-            target_path: Target directory path
+            target_path: Target directory path (used as root if use_default_paths is True)
+            use_default_paths: Whether to use default path template for organization
             
         Returns:
             Dictionary with move result
         """
         try:
+            if use_default_paths:
+                # Find the model in cache to get metadata
+                cache = await self.scanner.get_cached_data()
+                model_data = next((m for m in cache.raw_data if m.get('file_path') == file_path), None)
+                
+                if model_data:
+                    from ..utils.utils import calculate_relative_path_for_model
+                    relative_path = calculate_relative_path_for_model(model_data, self.model_type)
+                    if relative_path:
+                        target_path = os.path.join(target_path, relative_path).replace(os.sep, '/')
+                    elif not get_settings_manager().get_download_path_template(self.model_type):
+                        # Flat structure, target_path remains the root
+                        pass
+                    else:
+                        # Could not calculate relative path (e.g. missing metadata)
+                        # Fallback to manual target_path or skip?
+                        pass
+
             source_dir = os.path.dirname(file_path)
             if os.path.normpath(source_dir) == os.path.normpath(target_path):
                 logger.info(f"Source and target directories are the same: {source_dir}")
@@ -398,12 +521,15 @@ class ModelMoveService:
                     'new_file_path': file_path
                 }
 
-            new_file_path = await self.scanner.move_model(file_path, target_path)
-            if new_file_path:
+            move_result = await self.scanner.move_model(file_path, target_path)
+            if move_result:
+                new_file_path = move_result.get("new_path")
+                cache_entry = move_result.get("cache_entry")
                 return {
                     'success': True, 
                     'original_file_path': file_path,
-                    'new_file_path': new_file_path
+                    'new_file_path': new_file_path,
+                    'cache_entry': cache_entry
                 }
             else:
                 return {
@@ -421,26 +547,32 @@ class ModelMoveService:
                 'new_file_path': None
             }
     
-    async def move_models_bulk(self, file_paths: List[str], target_path: str) -> Dict[str, Any]:
+    async def move_models_bulk(self, file_paths: List[str], target_path: str, use_default_paths: bool = False) -> Dict[str, Any]:
         """Move multiple model files
         
         Args:
             file_paths: List of source file paths
-            target_path: Target directory path
+            target_path: Target directory path (used as root if use_default_paths is True)
+            use_default_paths: Whether to use default path template for organization
             
         Returns:
             Dictionary with bulk move results
         """
         try:
             results = []
+            self.scanner.reset_cancellation()
             
             for file_path in file_paths:
-                result = await self.move_model(file_path, target_path)
+                if self.scanner.is_cancelled():
+                    logger.info(f"{self.model_type.capitalize()} Move Service: Bulk move cancelled by user")
+                    break
+                result = await self.move_model(file_path, target_path, use_default_paths=use_default_paths)
                 results.append({
                     "original_file_path": file_path,
                     "new_file_path": result.get('new_file_path'),
                     "success": result['success'],
-                    "message": result.get('message', result.get('error', 'Unknown'))
+                    "message": result.get('message', result.get('error', 'Unknown')),
+                    "cache_entry": result.get('cache_entry')
                 })
             
             success_count = sum(1 for r in results if r["success"])
@@ -461,4 +593,4 @@ class ModelMoveService:
                 'results': [],
                 'success_count': 0,
                 'failure_count': len(file_paths)
-            }
+            }

py/services/model_lifecycle_service.py

@@ -4,26 +4,29 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Awaitable, Callable, Dict, Iterable, List, Optional
+from typing import Any, Awaitable, Callable, Dict, Iterable, List, Mapping, Optional, TYPE_CHECKING
 
 from ..services.service_registry import ServiceRegistry
 from ..utils.constants import PREVIEW_EXTENSIONS
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from ..services.model_update_service import ModelUpdateService
 
-async def delete_model_artifacts(target_dir: str, file_name: str) -> List[str]:
+
+async def delete_model_artifacts(
+    target_dir: str, file_name: str, main_extension: str | None = None
+) -> List[str]:
     """Delete the primary model artefacts within ``target_dir``."""
 
-    patterns = [
-        f"{file_name}.safetensors",
-        f"{file_name}.metadata.json",
-    ]
+    main_extension = ".safetensors" if main_extension is None else main_extension
+    main_file = f"{file_name}{main_extension}" if main_extension else file_name
+    patterns = [main_file, f"{file_name}.metadata.json"]
     for ext in PREVIEW_EXTENSIONS:
         patterns.append(f"{file_name}{ext}")
 
     deleted: List[str] = []
-    main_file = patterns[0]
     main_path = os.path.join(target_dir, main_file).replace(os.sep, "/")
 
     if os.path.exists(main_path):
@@ -54,6 +57,7 @@ class ModelLifecycleService:
         metadata_manager,
         metadata_loader: Callable[[str], Awaitable[Dict[str, object]]],
         recipe_scanner_factory: Callable[[], Awaitable] | None = None,
+        update_service: "ModelUpdateService" | None = None,
     ) -> None:
         self._scanner = scanner
         self._metadata_manager = metadata_manager
@@ -61,6 +65,7 @@ class ModelLifecycleService:
         self._recipe_scanner_factory = (
             recipe_scanner_factory or ServiceRegistry.get_recipe_scanner
         )
+        self._update_service = update_service
 
     async def delete_model(self, file_path: str) -> Dict[str, object]:
         """Delete a model file and associated artefacts."""
@@ -68,20 +73,103 @@ class ModelLifecycleService:
         if not file_path:
             raise ValueError("Model path is required")
 
-        target_dir = os.path.dirname(file_path)
-        file_name = os.path.splitext(os.path.basename(file_path))[0]
-
-        deleted_files = await delete_model_artifacts(target_dir, file_name)
-
         cache = await self._scanner.get_cached_data()
-        cache.raw_data = [item for item in cache.raw_data if item["file_path"] != file_path]
-        await cache.resort()
+
+        cached_entry = None
+        if cache and hasattr(cache, "raw_data"):
+            cached_entry = next(
+                (item for item in cache.raw_data if item.get("file_path") == file_path),
+                None,
+            )
+
+        metadata_payload = {}
+        try:
+            metadata_payload = await self._metadata_manager.load_metadata_payload(file_path)
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.debug("Failed to load metadata payload for %s: %s", file_path, exc)
+
+        model_id = (
+            self._extract_model_id_from_payload(metadata_payload)
+            or self._extract_model_id_from_payload(cached_entry)
+        )
+
+        target_dir = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        file_name, main_extension = os.path.splitext(base_name)
+        deleted_files = await delete_model_artifacts(
+            target_dir, file_name, main_extension=main_extension
+        )
+
+        if cache:
+            cache.raw_data = [
+                item for item in cache.raw_data if item.get("file_path") != file_path
+            ]
+            await cache.resort()
 
         if hasattr(self._scanner, "_hash_index") and self._scanner._hash_index:
             self._scanner._hash_index.remove_by_path(file_path)
 
+        await self._sync_update_for_model(model_id)
         return {"success": True, "deleted_files": deleted_files}
 
+    @staticmethod
+    def _extract_model_id_from_payload(payload: Any) -> Optional[int]:
+        if not isinstance(payload, Mapping):
+            return None
+        civitai = payload.get("civitai")
+        if isinstance(civitai, Mapping):
+            candidate = civitai.get("modelId") or civitai.get("model_id")
+            if candidate is None:
+                model_section = civitai.get("model")
+                if isinstance(model_section, Mapping):
+                    candidate = model_section.get("id")
+            normalized = ModelLifecycleService._coerce_int(candidate)
+            if normalized is not None:
+                return normalized
+        fallback = payload.get("model_id") or payload.get("civitai_model_id")
+        return ModelLifecycleService._coerce_int(fallback)
+
+    @staticmethod
+    def _coerce_int(value: Any) -> Optional[int]:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return None
+
+    async def _sync_update_for_model(self, model_id: Optional[int]) -> None:
+        if self._update_service is None or model_id is None:
+            return
+
+        try:
+            versions = await self._scanner.get_model_versions_by_id(model_id)
+        except Exception as exc:  # pragma: no cover - defensive log
+            logger.debug(
+                "Failed to collect local versions for model %s: %s", model_id, exc
+            )
+            versions = []
+
+        version_ids = set()
+        for version in versions or []:
+            candidate = (
+                version.get("versionId")
+                or version.get("id")
+                or version.get("version_id")
+            )
+            normalized = ModelLifecycleService._coerce_int(candidate)
+            if normalized is not None:
+                version_ids.add(normalized)
+
+        try:
+            await self._update_service.update_in_library_versions(
+                self._scanner.model_type,
+                model_id,
+                sorted(version_ids),
+            )
+        except Exception as exc:  # pragma: no cover - defensive log
+            logger.debug(
+                "Failed to sync update record for model %s: %s", model_id, exc
+            )
+
     async def exclude_model(self, file_path: str) -> Dict[str, object]:
         """Mark a model as excluded and prune cache references."""
 
@@ -146,16 +234,19 @@ class ModelLifecycleService:
             raise ValueError("Invalid characters in file name")
 
         target_dir = os.path.dirname(file_path)
-        old_file_name = os.path.splitext(os.path.basename(file_path))[0]
-        new_file_path = os.path.join(target_dir, f"{new_file_name}.safetensors").replace(
-            os.sep, "/"
-        )
+        base_name = os.path.basename(file_path)
+        old_file_name, old_extension = os.path.splitext(base_name)
+        if not old_extension:
+            old_extension = ".safetensors"
+        new_file_path = os.path.join(
+            target_dir, f"{new_file_name}{old_extension}"
+        ).replace(os.sep, "/")
 
         if os.path.exists(new_file_path):
             raise ValueError("A file with this name already exists")
 
         patterns = [
-            f"{old_file_name}.safetensors",
+            f"{old_file_name}{old_extension}",
             f"{old_file_name}.metadata.json",
             f"{old_file_name}.metadata.json.bak",
         ]
@@ -248,7 +339,7 @@ class ModelLifecycleService:
                 return suffix
 
         basename = os.path.basename(filename)
-        dot_index = basename.find(".")
+        dot_index = basename.rfind(".")
         if dot_index != -1:
             return basename[dot_index:]
 

py/services/model_metadata_provider.py

@@ -41,6 +41,55 @@ def _require_aiosqlite() -> Any:
 
 logger = logging.getLogger(__name__)
 
+
+class _RateLimitRetryHelper:
+    """Coordinate exponential backoff retries after rate limiting."""
+
+    def __init__(
+        self,
+        *,
+        retry_limit: int = 3,
+        base_delay: float = 1.5,
+        max_delay: float = 30.0,
+        jitter_ratio: float = 0.2,
+    ) -> None:
+        self._retry_limit = max(1, retry_limit)
+        self._base_delay = base_delay
+        self._max_delay = max_delay
+        self._jitter_ratio = max(0.0, jitter_ratio)
+
+    async def run(self, label: str, func, *args, **kwargs):
+        attempt = 0
+        while True:
+            try:
+                return await func(*args, **kwargs)
+            except RateLimitError as exc:
+                attempt += 1
+                if attempt >= self._retry_limit:
+                    exc.provider = exc.provider or label
+                    raise
+
+                delay = self._calculate_delay(exc.retry_after, attempt)
+                logger.warning(
+                    "Provider %s rate limited request; retrying in %.2fs (attempt %s/%s)",
+                    label,
+                    delay,
+                    attempt,
+                    self._retry_limit,
+                )
+                await asyncio.sleep(delay)
+
+    def _calculate_delay(self, retry_after: Optional[float], attempt: int) -> float:
+        if retry_after is not None:
+            return min(self._max_delay, max(0.0, retry_after))
+
+        base_delay = self._base_delay * (2 ** max(0, attempt - 1))
+        jitter_span = base_delay * self._jitter_ratio
+        if jitter_span > 0:
+            base_delay += random.uniform(-jitter_span, jitter_span)
+
+        return min(self._max_delay, max(0.0, base_delay))
+
 class ModelMetadataProvider(ABC):
     """Base abstract class for all model metadata providers"""
     
@@ -390,6 +439,12 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         self._rate_limit_base_delay = rate_limit_base_delay
         self._rate_limit_max_delay = rate_limit_max_delay
         self._rate_limit_jitter_ratio = max(0.0, rate_limit_jitter_ratio)
+        self._rate_limit_helper = _RateLimitRetryHelper(
+            retry_limit=self._rate_limit_retry_limit,
+            base_delay=self._rate_limit_base_delay,
+            max_delay=self._rate_limit_max_delay,
+            jitter_ratio=self._rate_limit_jitter_ratio,
+        )
 
     async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
         for provider, label in self._iter_providers():
@@ -485,44 +540,80 @@ class FallbackMetadataProvider(ModelMetadataProvider):
     def _iter_providers(self):
         return zip(self.providers, self._provider_labels)
 
-    async def _call_with_rate_limit(
+    async def _call_with_rate_limit(self, label: str, func, *args, **kwargs):
+        return await self._rate_limit_helper.run(label, func, *args, **kwargs)
+
+
+class RateLimitRetryingProvider(ModelMetadataProvider):
+    """Adapter that retries individual provider calls after rate limiting."""
+
+    def __init__(
         self,
-        label: str,
-        func,
-        *args,
-        **kwargs,
-    ):
-        attempt = 0
-        while True:
-            try:
-                return await func(*args, **kwargs)
-            except RateLimitError as exc:
-                attempt += 1
-                if attempt >= self._rate_limit_retry_limit:
-                    exc.provider = exc.provider or label
-                    raise exc
-                delay = self._calculate_rate_limit_delay(exc.retry_after, attempt)
-                logger.warning(
-                    "Provider %s rate limited request; retrying in %.2fs (attempt %s/%s)",
-                    label,
-                    delay,
-                    attempt,
-                    self._rate_limit_retry_limit,
-                )
-                await asyncio.sleep(delay)
-            except Exception:
-                raise
+        provider: ModelMetadataProvider,
+        label: Optional[str] = None,
+        *,
+        rate_limit_retry_limit: int = 3,
+        rate_limit_base_delay: float = 1.5,
+        rate_limit_max_delay: float = 30.0,
+        rate_limit_jitter_ratio: float = 0.2,
+    ) -> None:
+        self._provider = provider
+        self._label = label or provider.__class__.__name__
+        self._rate_limit_helper = _RateLimitRetryHelper(
+            retry_limit=rate_limit_retry_limit,
+            base_delay=rate_limit_base_delay,
+            max_delay=rate_limit_max_delay,
+            jitter_ratio=rate_limit_jitter_ratio,
+        )
 
-    def _calculate_rate_limit_delay(self, retry_after: Optional[float], attempt: int) -> float:
-        if retry_after is not None:
-            return min(self._rate_limit_max_delay, max(0.0, retry_after))
+    def __getattr__(self, item):
+        return getattr(self._provider, item)
 
-        base_delay = self._rate_limit_base_delay * (2 ** max(0, attempt - 1))
-        jitter_span = base_delay * self._rate_limit_jitter_ratio
-        if jitter_span > 0:
-            base_delay += random.uniform(-jitter_span, jitter_span)
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_by_hash,
+            model_hash,
+        )
 
-        return min(self._rate_limit_max_delay, max(0.0, base_delay))
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_versions,
+            model_id,
+        )
+
+    async def get_model_versions_bulk(
+        self,
+        model_ids: Sequence[int],
+    ) -> Optional[Dict[int, Dict]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_versions_bulk,
+            model_ids,
+        )
+
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_version,
+            model_id,
+            version_id,
+        )
+
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_version_info,
+            version_id,
+        )
+
+    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_user_models,
+            username,
+        )
 
 class ModelMetadataProviderManager:
     """Manager for selecting and using model metadata providers"""

py/services/model_query.py

@@ -1,17 +1,68 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Protocol, Callable
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Protocol,
+    Callable,
+)
 
 from ..utils.constants import NSFW_LEVELS
 from ..utils.utils import fuzzy_match as default_fuzzy_match
+import time
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+DEFAULT_CIVITAI_MODEL_TYPE = "LORA"
+
+
+def _coerce_to_str(value: Any) -> Optional[str]:
+    if value is None:
+        return None
+
+    candidate = str(value).strip()
+    return candidate if candidate else None
+
+
+def normalize_civitai_model_type(value: Any) -> Optional[str]:
+    """Return a lowercase string suitable for comparisons."""
+    candidate = _coerce_to_str(value)
+    return candidate.lower() if candidate else None
+
+
+def resolve_civitai_model_type(entry: Mapping[str, Any]) -> str:
+    """Extract the model type from CivitAI metadata, defaulting to LORA."""
+    if not isinstance(entry, Mapping):
+        return DEFAULT_CIVITAI_MODEL_TYPE
+
+    civitai = entry.get("civitai")
+    if isinstance(civitai, Mapping):
+        civitai_model = civitai.get("model")
+        if isinstance(civitai_model, Mapping):
+            model_type = _coerce_to_str(civitai_model.get("type"))
+            if model_type:
+                return model_type
+
+    model_type = _coerce_to_str(entry.get("model_type"))
+    if model_type:
+        return model_type
+
+    return DEFAULT_CIVITAI_MODEL_TYPE
 
 
 class SettingsProvider(Protocol):
     """Protocol describing the SettingsManager contract used by query helpers."""
 
-    def get(self, key: str, default: Any = None) -> Any:
-        ...
+    def get(self, key: str, default: Any = None) -> Any: ...
 
 
 @dataclass(frozen=True)
@@ -27,10 +78,13 @@ class FilterCriteria:
     """Container for model list filtering options."""
 
     folder: Optional[str] = None
+    folder_include: Optional[Sequence[str]] = None
+    folder_exclude: Optional[Sequence[str]] = None
     base_models: Optional[Sequence[str]] = None
-    tags: Optional[Sequence[str]] = None
+    tags: Optional[Dict[str, str]] = None
     favorites_only: bool = False
     search_options: Optional[Dict[str, Any]] = None
+    model_types: Optional[Sequence[str]] = None
 
 
 class ModelCacheRepository:
@@ -71,51 +125,222 @@ class ModelCacheRepository:
 class ModelFilterSet:
     """Applies common filtering rules to the model collection."""
 
-    def __init__(self, settings: SettingsProvider, nsfw_levels: Optional[Dict[str, int]] = None) -> None:
+    def __init__(
+        self, settings: SettingsProvider, nsfw_levels: Optional[Dict[str, int]] = None
+    ) -> None:
         self._settings = settings
         self._nsfw_levels = nsfw_levels or NSFW_LEVELS
 
-    def apply(self, data: Iterable[Dict[str, Any]], criteria: FilterCriteria) -> List[Dict[str, Any]]:
+    def apply(
+        self, data: Iterable[Dict[str, Any]], criteria: FilterCriteria
+    ) -> List[Dict[str, Any]]:
         """Return items that satisfy the provided criteria."""
+        overall_start = time.perf_counter()
         items = list(data)
+        initial_count = len(items)
 
         if self._settings.get("show_only_sfw", False):
+            t0 = time.perf_counter()
             threshold = self._nsfw_levels.get("R", 0)
             items = [
-                item for item in items
-                if not item.get("preview_nsfw_level") or item.get("preview_nsfw_level") < threshold
+                item
+                for item in items
+                if not item.get("preview_nsfw_level")
+                or item.get("preview_nsfw_level") < threshold
             ]
+            sfw_duration = time.perf_counter() - t0
+        else:
+            sfw_duration = 0
 
+        favorites_duration = 0
         if criteria.favorites_only:
+            t0 = time.perf_counter()
             items = [item for item in items if item.get("favorite", False)]
+            favorites_duration = time.perf_counter() - t0
 
+        folder_duration = 0
         folder = criteria.folder
+        folder_include = criteria.folder_include or []
+        folder_exclude = criteria.folder_exclude or []
         options = criteria.search_options or {}
         recursive = bool(options.get("recursive", True))
+
+        # Apply folder exclude filters first
+        if folder_exclude:
+            t0 = time.perf_counter()
+            for exclude_folder in folder_exclude:
+                if exclude_folder:
+                    # Check exact match OR prefix match (for subfolders)
+                    # Normalize exclude_folder for prefix matching
+                    if not exclude_folder.endswith("/"):
+                        exclude_prefix = f"{exclude_folder}/"
+                    else:
+                        exclude_prefix = exclude_folder
+                    items = [
+                        item
+                        for item in items
+                        if item.get("folder") != exclude_folder
+                        and not item.get("folder", "").startswith(exclude_prefix)
+                    ]
+            folder_duration = time.perf_counter() - t0
+
+        # Apply folder include filters
         if folder is not None:
+            t0 = time.perf_counter()
             if recursive:
                 if folder:
                     folder_with_sep = f"{folder}/"
                     items = [
-                        item for item in items
-                        if item.get("folder") == folder or item.get("folder", "").startswith(folder_with_sep)
+                        item
+                        for item in items
+                        if item.get("folder") == folder
+                        or item.get("folder", "").startswith(folder_with_sep)
                     ]
             else:
                 items = [item for item in items if item.get("folder") == folder]
+            folder_duration = time.perf_counter() - t0 + folder_duration
 
+        # Apply folder include filters
+        if folder_include:
+            t0 = time.perf_counter()
+            matched_items = []
+            for include_folder in folder_include:
+                if include_folder:
+                    if recursive:
+                        # Normalize folder for prefix matching (similar to exclude logic)
+                        if not include_folder.endswith("/"):
+                            folder_prefix = f"{include_folder}/"
+                        else:
+                            folder_prefix = include_folder
+                        folder_items = [
+                            item
+                            for item in items
+                            if item.get("folder") == include_folder
+                            or item.get("folder", "").startswith(folder_prefix)
+                        ]
+                    else:
+                        folder_items = [
+                            item
+                            for item in items
+                            if item.get("folder") == include_folder
+                        ]
+                    matched_items.extend(folder_items)
+            # Remove duplicates while preserving order
+            seen = set()
+            items = []
+            for item in matched_items:
+                # Use sha256 or id as unique identifier if available, otherwise use tuple representation
+                item_id = item.get("sha256") or item.get("id")
+                if item_id is not None:
+                    identifier = item_id
+                else:
+                    # For items without explicit id, use a tuple of key values
+                    identifier = tuple(sorted((k, str(v)) for k, v in item.items()))
+                if identifier not in seen:
+                    seen.add(identifier)
+                    items.append(item)
+            folder_duration = time.perf_counter() - t0 + folder_duration
+        # Apply folder include filters (legacy single folder)
+        elif folder is not None:
+            t0 = time.perf_counter()
+            if recursive:
+                if folder:
+                    # Normalize folder for prefix matching
+                    if not folder.endswith("/"):
+                        folder_prefix = f"{folder}/"
+                    else:
+                        folder_prefix = folder
+                    items = [
+                        item
+                        for item in items
+                        if item.get("folder") == folder
+                        or item.get("folder", "").startswith(folder_prefix)
+                    ]
+            else:
+                items = [item for item in items if item.get("folder") == folder]
+            folder_duration = time.perf_counter() - t0 + folder_duration
+
+        base_models_duration = 0
         base_models = criteria.base_models or []
         if base_models:
+            t0 = time.perf_counter()
             base_model_set = set(base_models)
             items = [item for item in items if item.get("base_model") in base_model_set]
+            base_models_duration = time.perf_counter() - t0
 
-        tags = criteria.tags or []
-        if tags:
-            tag_set = set(tags)
-            items = [
-                item for item in items
-                if any(tag in tag_set for tag in item.get("tags", []))
-            ]
+        tags_duration = 0
+        tag_filters = criteria.tags or {}
+        if tag_filters:
+            t0 = time.perf_counter()
+            include_tags = set()
+            exclude_tags = set()
+            if isinstance(tag_filters, dict):
+                for tag, state in tag_filters.items():
+                    if not tag:
+                        continue
+                    if state == "exclude":
+                        exclude_tags.add(tag)
+                    else:
+                        include_tags.add(tag)
+            else:
+                include_tags = {tag for tag in tag_filters if tag}
 
+            if include_tags:
+
+                def matches_include(item_tags):
+                    if not item_tags and "__no_tags__" in include_tags:
+                        return True
+                    return any(tag in include_tags for tag in (item_tags or []))
+
+                items = [item for item in items if matches_include(item.get("tags"))]
+
+            if exclude_tags:
+
+                def matches_exclude(item_tags):
+                    if not item_tags and "__no_tags__" in exclude_tags:
+                        return True
+                    return any(tag in exclude_tags for tag in (item_tags or []))
+
+                items = [
+                    item for item in items if not matches_exclude(item.get("tags"))
+                ]
+            tags_duration = time.perf_counter() - t0
+
+        model_types_duration = 0
+        model_types = criteria.model_types or []
+        if model_types:
+            t0 = time.perf_counter()
+            normalized_model_types = {
+                model_type
+                for model_type in (
+                    normalize_civitai_model_type(value) for value in model_types
+                )
+                if model_type
+            }
+            if normalized_model_types:
+                items = [
+                    item
+                    for item in items
+                    if normalize_civitai_model_type(resolve_civitai_model_type(item))
+                    in normalized_model_types
+                ]
+            model_types_duration = time.perf_counter() - t0
+
+        duration = time.perf_counter() - overall_start
+        if duration > 0.1:  # Only log if it's potentially slow
+            logger.debug(
+                "ModelFilterSet.apply took %.3fs (sfw: %.3fs, fav: %.3fs, folder: %.3fs, base: %.3fs, tags: %.3fs, types: %.3fs). "
+                "Count: %d -> %d",
+                duration,
+                sfw_duration,
+                favorites_duration,
+                folder_duration,
+                base_models_duration,
+                tags_duration,
+                model_types_duration,
+                initial_count,
+                len(items),
+            )
         return items
 
 
@@ -130,7 +355,9 @@ class SearchStrategy:
         "creator": False,
     }
 
-    def __init__(self, fuzzy_matcher: Optional[Callable[[str, str], bool]] = None) -> None:
+    def __init__(
+        self, fuzzy_matcher: Optional[Callable[[str, str], bool]] = None
+    ) -> None:
         self._fuzzy_match = fuzzy_matcher or default_fuzzy_match
 
     def normalize_options(self, options: Optional[Dict[str, Any]]) -> Dict[str, Any]:
@@ -169,7 +396,9 @@ class SearchStrategy:
 
             if options.get("tags", False):
                 tags = item.get("tags", []) or []
-                if any(self._matches(tag, search_term, search_lower, fuzzy) for tag in tags):
+                if any(
+                    self._matches(tag, search_term, search_lower, fuzzy) for tag in tags
+                ):
                     results.append(item)
                     continue
 
@@ -180,13 +409,17 @@ class SearchStrategy:
                     creator = civitai.get("creator")
                     if isinstance(creator, dict):
                         creator_username = creator.get("username", "")
-                if creator_username and self._matches(creator_username, search_term, search_lower, fuzzy):
+                if creator_username and self._matches(
+                    creator_username, search_term, search_lower, fuzzy
+                ):
                     results.append(item)
                     continue
 
         return results
 
-    def _matches(self, candidate: str, search_term: str, search_lower: str, fuzzy: bool) -> bool:
+    def _matches(
+        self, candidate: str, search_term: str, search_lower: str, fuzzy: bool
+    ) -> bool:
         if not isinstance(candidate, str):
             candidate = "" if candidate is None else str(candidate)
 

py/services/model_scanner.py

@@ -11,6 +11,7 @@ from ..utils.models import BaseModelMetadata
 from ..config import config
 from ..utils.file_utils import find_preview_file, get_preview_extension
 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
@@ -83,6 +84,7 @@ class ModelScanner:
         self._excluded_models = []  # List to track excluded models
         self._persistent_cache = get_persistent_cache()
         self._name_display_mode = self._resolve_name_display_mode()
+        self._cancel_requested = False  # Flag for cancellation
         try:
             loop = asyncio.get_running_loop()
         except RuntimeError:
@@ -160,6 +162,12 @@ class ModelScanner:
         if trained_words:
             slim['trainedWords'] = list(trained_words) if isinstance(trained_words, list) else trained_words
 
+        civitai_model = civitai.get('model')
+        if isinstance(civitai_model, Mapping):
+            model_type_value = civitai_model.get('type')
+            if model_type_value not in (None, '', []):
+                slim['model'] = {'type': model_type_value}
+
         return slim or None
 
     def _build_cache_entry(
@@ -175,7 +183,17 @@ class ModelScanner:
         def get_value(key: str, default: Any = None) -> Any:
             if is_mapping:
                 return source.get(key, default)
-            return getattr(source, key, default)
+
+            sentinel = object()
+            value = getattr(source, key, sentinel)
+            if value is not sentinel:
+                return value
+
+            unknown = getattr(source, "_unknown_fields", None)
+            if isinstance(unknown, dict) and key in unknown:
+                return unknown[key]
+
+            return default
 
         file_path = file_path_override or get_value('file_path', '') or ''
         normalized_path = file_path.replace('\\', '/')
@@ -197,7 +215,8 @@ class ModelScanner:
         else:
             preview_url = ''
 
-        civitai_slim = self._slim_civitai_payload(get_value('civitai'))
+        civitai_full = get_value('civitai')
+        civitai_slim = self._slim_civitai_payload(civitai_full)
         usage_tips = get_value('usage_tips', '') or ''
         if not isinstance(usage_tips, str):
             usage_tips = str(usage_tips)
@@ -229,12 +248,76 @@ class ModelScanner:
             'civitai_deleted': bool(get_value('civitai_deleted', False)),
         }
 
+        license_source: Dict[str, Any] = {}
+        if isinstance(civitai_full, Mapping):
+            civitai_model = civitai_full.get('model')
+            if isinstance(civitai_model, Mapping):
+                for key in (
+                    'allowNoCredit',
+                    'allowCommercialUse',
+                    'allowDerivatives',
+                    'allowDifferentLicense',
+                ):
+                    if key in civitai_model:
+                        license_source[key] = civitai_model.get(key)
+
+        for key in (
+            'allowNoCredit',
+            'allowCommercialUse',
+            'allowDerivatives',
+            'allowDifferentLicense',
+        ):
+            if key not in license_source:
+                value = get_value(key)
+                if value is not None:
+                    license_source[key] = value
+
+        _, license_flags = resolve_license_info(license_source or {})
+        entry['license_flags'] = license_flags
+
         model_type = get_value('model_type', None)
         if model_type:
             entry['model_type'] = model_type
 
         return entry
 
+    def _ensure_license_flags(self, entry: Dict[str, Any]) -> None:
+        """Ensure cached entries include an integer license flag bitset."""
+
+        if not isinstance(entry, dict):
+            return
+
+        license_value = entry.get('license_flags')
+        if license_value is not None:
+            try:
+                entry['license_flags'] = int(license_value)
+            except (TypeError, ValueError):
+                _, fallback_flags = resolve_license_info({})
+                entry['license_flags'] = fallback_flags
+            return
+
+        license_source = {
+            'allowNoCredit': entry.get('allowNoCredit'),
+            'allowCommercialUse': entry.get('allowCommercialUse'),
+            'allowDerivatives': entry.get('allowDerivatives'),
+            'allowDifferentLicense': entry.get('allowDifferentLicense'),
+        }
+        civitai_full = entry.get('civitai')
+        if isinstance(civitai_full, Mapping):
+            civitai_model = civitai_full.get('model')
+            if isinstance(civitai_model, Mapping):
+                for key in (
+                    'allowNoCredit',
+                    'allowCommercialUse',
+                    'allowDerivatives',
+                    'allowDifferentLicense',
+                ):
+                    if key in civitai_model:
+                        license_source[key] = civitai_model.get(key)
+
+        _, license_flags = resolve_license_info(license_source)
+        entry['license_flags'] = license_flags
+
     async def initialize_in_background(self) -> None:
         """Initialize cache in background using thread pool"""
         try:
@@ -567,14 +650,21 @@ class ModelScanner:
 
     async def _initialize_cache(self) -> None:
         """Initialize or refresh the cache"""
+        print("init start", flush=True)
         self._is_initializing = True  # Set flag
         try:
             start_time = time.time()
+            
+            # Manually trigger a symlink rescan during a full rebuild.
+            # This ensures that any new symlink mappings are correctly picked up.
+            config.rebuild_symlink_cache()
+
             # Determine the page type based on model type
             # Scan for new data
             scan_result = await self._gather_model_data()
             await self._apply_scan_result(scan_result)
             await self._save_persistent_cache(scan_result)
+            print("init end", flush=True)
 
             logger.info(
                 f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
@@ -594,6 +684,7 @@ class ModelScanner:
 
     async def _reconcile_cache(self) -> None:
         """Fast cache reconciliation - only process differences between cache and filesystem"""
+        self.reset_cancellation()
         self._is_initializing = True # Set flag for reconciliation duration
         try:
             start_time = time.time()
@@ -653,6 +744,9 @@ class ModelScanner:
                     
                     # Yield control periodically
                     await asyncio.sleep(0)
+                    if self.is_cancelled():
+                        logger.info(f"{self.model_type.capitalize()} Scanner: Reconcile scan cancelled")
+                        return
             
             # Process new files in batches
             total_added = 0
@@ -681,6 +775,7 @@ class ModelScanner:
                                     model_data = self.adjust_cached_entry(dict(model_data))
                                     if not model_data:
                                         continue
+                                    self._ensure_license_flags(model_data)
                                     # Add to cache
                                     self._cache.raw_data.append(model_data)
                                     self._cache.add_to_version_index(model_data)
@@ -699,6 +794,10 @@ class ModelScanner:
                                 logger.error(f"Could not determine root path for {path}")
                         except Exception as e:
                             logger.error(f"Error adding {path} to cache: {e}")
+                        
+                        if self.is_cancelled():
+                            logger.info(f"{self.model_type.capitalize()} Scanner: Reconcile processing cancelled")
+                            return
             
             # Find missing files (in cache but not in filesystem)
             missing_files = cached_paths - found_paths
@@ -753,6 +852,19 @@ class ModelScanner:
         """Check if the scanner is currently initializing"""
         return self._is_initializing
     
+    def cancel_task(self) -> None:
+        """Request cancellation of the current long-running task."""
+        self._cancel_requested = True
+        logger.info(f"{self.model_type.capitalize()} Scanner: Cancellation requested")
+
+    def reset_cancellation(self) -> None:
+        """Reset the cancellation flag."""
+        self._cancel_requested = False
+
+    def is_cancelled(self) -> bool:
+        """Check if cancellation has been requested."""
+        return self._cancel_requested
+    
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
         raise NotImplementedError("Subclasses must implement get_model_roots")
@@ -842,7 +954,7 @@ class ModelScanner:
                         metadata = self.model_class.from_civitai_info(version_info, file_info, file_path)
                         metadata.preview_url = find_preview_file(file_name, os.path.dirname(file_path))
                         await MetadataManager.save_metadata(file_path, metadata)
-                        logger.debug(f"Created metadata from .civitai.info for {file_path}")
+                        logger.info(f"Created metadata from .civitai.info for {file_path} (Reason: .civitai.info was found but .metadata.json was missing)")
                 except Exception as e:
                     logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
         else:
@@ -945,6 +1057,8 @@ class ModelScanner:
             except Exception as exc:  # pragma: no cover - defensive logging
                 logger.error(f"Error reporting progress for {self.model_type}: {exc}")
 
+        self.reset_cancellation()
+
         async def scan_recursive(current_path: str, root_path: str, visited_paths: Set[str]) -> None:
             nonlocal processed_files
 
@@ -975,6 +1089,7 @@ class ModelScanner:
                             processed_files += 1
 
                             if result:
+                                self._ensure_license_flags(result)
                                 raw_data.append(result)
 
                                 sha_value = result.get('sha256')
@@ -987,6 +1102,8 @@ class ModelScanner:
 
                             await handle_progress()
                             await asyncio.sleep(0)
+                            if self.is_cancelled():
+                                return
                         elif entry.is_dir(follow_symlinks=True):
                             await scan_recursive(entry.path, root_path, visited_paths)
                     except Exception as entry_error:
@@ -994,6 +1111,9 @@ class ModelScanner:
             except Exception as scan_error:
                 logger.error(f"Error scanning {current_path}: {scan_error}")
 
+            if self.is_cancelled():
+                return
+
         for model_root in self.get_model_roots():
             if not os.path.exists(model_root):
                 continue
@@ -1130,9 +1250,12 @@ class ModelScanner:
                 except Exception as e:
                     logger.error(f"Error moving metadata file: {e}")
             
-            await self.update_single_model_cache(source_path, target_file, metadata)
+            update_result = await self.update_single_model_cache(source_path, target_file, metadata, recalculate_type=True)
             
-            return target_file
+            return {
+                "new_path": target_file,
+                "cache_entry": update_result if isinstance(update_result, dict) else None
+            }
             
         except Exception as e:
             logger.error(f"Error moving model: {e}", exc_info=True)
@@ -1164,7 +1287,7 @@ class ModelScanner:
             logger.error(f"Error updating metadata paths: {e}", exc_info=True)
             return None
 
-    async def update_single_model_cache(self, original_path: str, new_path: str, metadata: Dict) -> bool:
+    async def update_single_model_cache(self, original_path: str, new_path: str, metadata: Dict, recalculate_type: bool = False) -> Union[bool, Dict]:
         """Update cache after a model has been moved or modified"""
         cache = await self.get_cached_data()
 
@@ -1201,6 +1324,9 @@ class ModelScanner:
                 file_path_override=normalized_new_path,
             )
 
+            if recalculate_type:
+                cache_entry = self.adjust_cached_entry(cache_entry)
+
             cache.raw_data.append(cache_entry)
             cache.add_to_version_index(cache_entry)
 
@@ -1221,7 +1347,7 @@ class ModelScanner:
         if cache_modified:
             await self._persist_current_cache()
 
-        return True
+        return cache_entry if metadata else True
         
     def has_hash(self, sha256: str) -> bool:
         """Check if a model with given hash exists"""
@@ -1356,13 +1482,19 @@ class ModelScanner:
             deleted_models = []
             
             for file_path in file_paths:
+                if self.is_cancelled():
+                    logger.info(f"{self.model_type.capitalize()} Scanner: Bulk delete cancelled by user")
+                    break
+
                 try:
                     target_dir = os.path.dirname(file_path)
-                    file_name = os.path.splitext(os.path.basename(file_path))[0]
-                    
+                    base_name = os.path.basename(file_path)
+                    file_name, main_extension = os.path.splitext(base_name)
+
                     deleted_files = await delete_model_artifacts(
                         target_dir,
-                        file_name
+                        file_name,
+                        main_extension=main_extension,
                     )
                     
                     if deleted_files:
@@ -1394,6 +1526,7 @@ class ModelScanner:
                 
             return {
                 'success': True,
+                'status': 'cancelled' if self.is_cancelled() else 'success',
                 'total_deleted': total_deleted,
                 'total_attempted': len(file_paths),
                 'cache_updated': cache_updated,

py/services/model_service_factory.py

@@ -22,7 +22,6 @@ class ModelServiceFactory:
         """
         cls._services[model_type] = service_class
         cls._routes[model_type] = route_class
-        logger.info(f"Registered model type '{model_type}' with service {service_class.__name__} and routes {route_class.__name__}")
     
     @classmethod
     def get_service_class(cls, model_type: str) -> Type:
@@ -80,13 +79,10 @@ class ModelServiceFactory:
         Args:
             app: The aiohttp application instance
         """
-        logger.info(f"Setting up routes for {len(cls._services)} registered model types")
-        
         for model_type in cls._services.keys():
             try:
                 routes_instance = cls.get_route_instance(model_type)
                 routes_instance.setup_routes(app)
-                logger.info(f"Successfully set up routes for {model_type}")
             except Exception as e:
                 logger.error(f"Failed to setup routes for {model_type}: {e}", exc_info=True)
     
@@ -137,6 +133,4 @@ def register_default_model_types():
     ModelServiceFactory.register_model_type('checkpoint', CheckpointService, CheckpointRoutes)
     
     # Register Embedding model type
-    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
-    
-    logger.info("Registered default model types: lora, checkpoint, embedding")
+    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)

py/services/model_update_service.py

@@ -17,6 +17,41 @@ from ..utils.preview_selection import select_preview_media
 logger = logging.getLogger(__name__)
 
 
+def _normalize_int(value) -> Optional[int]:
+    """Safely convert a value to an integer."""
+
+    try:
+        if value is None:
+            return None
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _normalize_string(value) -> Optional[str]:
+    """Return a stripped string or None if the value is empty."""
+
+    if value is None:
+        return None
+    if isinstance(value, str):
+        stripped = value.strip()
+        return stripped or None
+    try:
+        normalized = str(value).strip()
+        return normalized or None
+    except Exception:
+        return None
+
+
+def _normalize_base_model(value) -> Optional[str]:
+    """Normalize base-model names for case-insensitive comparison."""
+
+    normalized = _normalize_string(value)
+    if normalized is None:
+        return None
+    return normalized.lower()
+
+
 @dataclass
 class ModelVersionRecord:
     """Persisted metadata for a single model version."""
@@ -85,6 +120,47 @@ class ModelUpdateRecord:
                 return True
         return False
 
+    def has_update_for_base(
+        self,
+        local_version_id: Optional[int],
+        local_base_model: Optional[str],
+    ) -> bool:
+        """Return True when a newer remote version with the same base model exists."""
+
+        if self.should_ignore_model:
+            return False
+
+        normalized_base = _normalize_base_model(local_base_model)
+        if normalized_base is None:
+            return False
+
+        threshold = _normalize_int(local_version_id)
+        if threshold is None:
+            highest_local = None
+            for version in self.versions:
+                if not version.is_in_library:
+                    continue
+                version_base = _normalize_base_model(version.base_model)
+                if version_base != normalized_base:
+                    continue
+                if highest_local is None or version.version_id > highest_local:
+                    highest_local = version.version_id
+            threshold = highest_local
+
+        if threshold is None:
+            return False
+
+        for version in self.versions:
+            if version.is_in_library or version.should_ignore:
+                continue
+            version_base = _normalize_base_model(version.base_model)
+            if version_base != normalized_base:
+                continue
+            if version.version_id > threshold:
+                return True
+
+        return False
+
 
 class ModelUpdateService:
     """Persist and query remote model version metadata."""
@@ -390,6 +466,7 @@ class ModelUpdateService:
         target_model_ids: Optional[Sequence[int]] = None,
     ) -> Dict[int, ModelUpdateRecord]:
         """Refresh update information for every model present in the cache."""
+        scanner.reset_cancellation()
 
         normalized_targets = (
             self._normalize_sequence(target_model_ids)
@@ -466,6 +543,9 @@ class ModelUpdateService:
                 force_refresh=force_refresh,
                 prefetched_response=prefetched.get(model_id),
             )
+            if scanner.is_cancelled():
+                logger.info(f"{model_type.capitalize()} Update Service: Refresh cancelled by user")
+                return results
             if record:
                 results[model_id] = record
             if index % progress_interval == 0 or index == total_models:
@@ -509,6 +589,8 @@ class ModelUpdateService:
         model_type: str,
         model_id: int,
         version_ids: Sequence[int],
+        *,
+        version_info: Optional[Mapping] = None,
     ) -> ModelUpdateRecord:
         """Persist a new set of in-library version identifiers."""
 
@@ -520,6 +602,7 @@ class ModelUpdateService:
                 normalized_versions,
                 model_type=model_type,
                 model_id=model_id,
+                version_info=version_info,
             )
             self._upsert_record(record)
             return record
@@ -628,6 +711,20 @@ class ModelUpdateService:
             for model_id in normalized_ids
         }
 
+    async def get_records_bulk(
+        self,
+        model_type: str,
+        model_ids: Sequence[int],
+    ) -> Dict[int, ModelUpdateRecord]:
+        """Return cached update records for the requested models."""
+
+        normalized_ids = self._normalize_sequence(model_ids)
+        if not normalized_ids:
+            return {}
+
+        async with self._lock:
+            return self._get_records_bulk(model_type, normalized_ids)
+
     async def _refresh_single_model(
         self,
         model_type: str,
@@ -799,7 +896,7 @@ class ModelUpdateService:
                 )
                 continue
             for key, value in response.items():
-                normalized_key = self._normalize_int(key)
+                normalized_key = _normalize_int(key)
                 if normalized_key is None:
                     continue
                 if isinstance(value, Mapping):
@@ -832,8 +929,8 @@ class ModelUpdateService:
             civitai = item.get("civitai") if isinstance(item, dict) else None
             if not isinstance(civitai, dict):
                 continue
-            model_id = self._normalize_int(civitai.get("modelId"))
-            version_id = self._normalize_int(civitai.get("id"))
+            model_id = _normalize_int(civitai.get("modelId"))
+            version_id = _normalize_int(civitai.get("id"))
             if model_id is None or version_id is None:
                 continue
             if target_set is not None and model_id not in target_set:
@@ -850,6 +947,7 @@ class ModelUpdateService:
         model_type: Optional[str] = None,
         model_id: Optional[int] = None,
         last_checked_at: Optional[float] = None,
+        version_info: Optional[Mapping] = None,
     ) -> ModelUpdateRecord:
         local_set = set(normalized_local)
         versions: List[ModelVersionRecord] = []
@@ -871,19 +969,26 @@ class ModelUpdateService:
 
         seen_ids = {version.version_id for version in versions}
         for missing_id in sorted(local_set - seen_ids):
-            versions.append(
-                ModelVersionRecord(
-                    version_id=missing_id,
-                    name=None,
-                    base_model=None,
-                    released_at=None,
-                    size_bytes=None,
-                    preview_url=None,
-                    is_in_library=True,
-                    should_ignore=ignore_map.get(missing_id, False),
-                    sort_index=len(versions),
+            new_version: Optional[ModelVersionRecord] = None
+            if version_info and _normalize_int(version_info.get("id")) == missing_id:
+                new_version = self._extract_single_version(version_info, index=len(versions))
+
+            if new_version:
+                versions.append(replace(new_version, is_in_library=True))
+            else:
+                versions.append(
+                    ModelVersionRecord(
+                        version_id=missing_id,
+                        name=None,
+                        base_model=None,
+                        released_at=None,
+                        size_bytes=None,
+                        preview_url=None,
+                        is_in_library=True,
+                        should_ignore=ignore_map.get(missing_id, False),
+                        sort_index=len(versions),
+                    )
                 )
-            )
 
         return ModelUpdateRecord(
             model_type=model_type,
@@ -973,35 +1078,14 @@ class ModelUpdateService:
             return True
         return (now - record.last_checked_at) >= self._ttl_seconds
 
-    @staticmethod
-    def _normalize_int(value) -> Optional[int]:
-        try:
-            if value is None:
-                return None
-            return int(value)
-        except (TypeError, ValueError):
-            return None
-
     def _normalize_sequence(self, values: Sequence[int]) -> List[int]:
         normalized = [
             item
-            for item in (self._normalize_int(value) for value in values)
+            for item in (_normalize_int(value) for value in values)
             if item is not None
         ]
         return sorted(dict.fromkeys(normalized))
 
-    @staticmethod
-    def _normalize_string(value) -> Optional[str]:
-        if value is None:
-            return None
-        if isinstance(value, str):
-            stripped = value.strip()
-            return stripped or None
-        try:
-            return str(value)
-        except Exception:  # pragma: no cover - defensive conversion
-            return None
-
     def _extract_versions(self, response) -> Optional[List[ModelVersionRecord]]:
         if not isinstance(response, Mapping):
             return None
@@ -1010,33 +1094,45 @@ class ModelUpdateService:
             return []
         if not isinstance(versions, Iterable):
             return None
+
         extracted: List[ModelVersionRecord] = []
         for index, entry in enumerate(versions):
-            if not isinstance(entry, Mapping):
-                continue
-            version_id = self._normalize_int(entry.get("id"))
-            if version_id is None:
-                continue
-            name = self._normalize_string(entry.get("name"))
-            base_model = self._normalize_string(entry.get("baseModel"))
-            released_at = self._normalize_string(entry.get("publishedAt") or entry.get("createdAt"))
-            size_bytes = self._extract_size_bytes(entry.get("files"))
-            preview_url = self._extract_preview_url(entry.get("images"))
-            extracted.append(
-                ModelVersionRecord(
-                    version_id=version_id,
-                    name=name,
-                    base_model=base_model,
-                    released_at=released_at,
-                    size_bytes=size_bytes,
-                    preview_url=preview_url,
-                    is_in_library=False,
-                    should_ignore=False,
-                    sort_index=index,
-                )
-            )
+            version_record = self._extract_single_version(entry, index)
+            if version_record:
+                extracted.append(version_record)
+
         return extracted
 
+    def _extract_single_version(
+        self, entry: Any, index: int = 0
+    ) -> Optional[ModelVersionRecord]:
+        """Convert a raw metadata entry into a structured record."""
+
+        if not isinstance(entry, Mapping):
+            return None
+
+        version_id = _normalize_int(entry.get("id"))
+        if version_id is None:
+            return None
+
+        name = _normalize_string(entry.get("name"))
+        base_model = _normalize_string(entry.get("baseModel"))
+        released_at = _normalize_string(entry.get("publishedAt") or entry.get("createdAt"))
+        size_bytes = self._extract_size_bytes(entry.get("files"))
+        preview_url = self._extract_preview_url(entry.get("images"))
+
+        return ModelVersionRecord(
+            version_id=version_id,
+            name=name,
+            base_model=base_model,
+            released_at=released_at,
+            size_bytes=size_bytes,
+            preview_url=preview_url,
+            is_in_library=False,
+            should_ignore=False,
+            sort_index=index,
+        )
+
     def _extract_size_bytes(self, files) -> Optional[int]:
         if not isinstance(files, Iterable):
             return None
@@ -1152,11 +1248,11 @@ class ModelUpdateService:
                     name=row["name"],
                     base_model=row["base_model"],
                     released_at=row["released_at"],
-                    size_bytes=self._normalize_int(row["size_bytes"]),
+                    size_bytes=_normalize_int(row["size_bytes"]),
                     preview_url=row["preview_url"],
                     is_in_library=bool(row["is_in_library"]),
                     should_ignore=bool(row["should_ignore"]),
-                    sort_index=self._normalize_int(row["sort_index"]) or 0,
+                    sort_index=_normalize_int(row["sort_index"]) or 0,
                 )
             )
 

py/services/persistent_model_cache.py

@@ -5,7 +5,7 @@ import re
 import sqlite3
 import threading
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Sequence, Tuple
+from typing import Dict, List, Mapping, Optional, Sequence, Tuple
 
 from ..utils.settings_paths import get_project_root, get_settings_dir
 
@@ -21,6 +21,9 @@ class PersistedCacheData:
     excluded_models: List[str]
 
 
+DEFAULT_LICENSE_FLAGS = 127  # 127 (0b1111111) encodes default CivitAI permissions with all commercial modes enabled.
+
+
 class PersistentModelCache:
     """Persist core model metadata and hash index data in SQLite."""
 
@@ -44,9 +47,11 @@ class PersistentModelCache:
         "metadata_source",
         "civitai_id",
         "civitai_model_id",
+        "civitai_model_type",
         "civitai_name",
         "civitai_creator_username",
         "trained_words",
+        "license_flags",
         "civitai_deleted",
         "exclude",
         "db_checked",
@@ -134,7 +139,8 @@ class PersistentModelCache:
             creator_username = row["civitai_creator_username"]
             civitai: Optional[Dict] = None
             civitai_has_data = any(
-                row[col] is not None for col in ("civitai_id", "civitai_model_id", "civitai_name")
+                row[col] is not None
+                for col in ("civitai_id", "civitai_model_id", "civitai_model_type", "civitai_name")
             ) or trained_words or creator_username
             if civitai_has_data:
                 civitai = {}
@@ -148,6 +154,13 @@ class PersistentModelCache:
                     civitai["trainedWords"] = trained_words
                 if creator_username:
                     civitai.setdefault("creator", {})["username"] = creator_username
+                model_type_value = row["civitai_model_type"]
+                if model_type_value:
+                    civitai.setdefault("model", {})["type"] = model_type_value
+
+            license_value = row["license_flags"]
+            if license_value is None:
+                license_value = DEFAULT_LICENSE_FLAGS
 
             item = {
                 "file_path": file_path,
@@ -171,6 +184,7 @@ class PersistentModelCache:
                 "tags": tags.get(file_path, []),
                 "civitai": civitai,
                 "civitai_deleted": bool(row["civitai_deleted"]),
+                "license_flags": int(license_value),
             }
             raw_data.append(item)
 
@@ -434,6 +448,7 @@ class PersistentModelCache:
                             metadata_source TEXT,
                             civitai_id INTEGER,
                             civitai_model_id INTEGER,
+                            civitai_model_type TEXT,
                             civitai_name TEXT,
                             civitai_creator_username TEXT,
                             trained_words TEXT,
@@ -483,7 +498,10 @@ class PersistentModelCache:
         required_columns = {
             "metadata_source": "TEXT",
             "civitai_creator_username": "TEXT",
+            "civitai_model_type": "TEXT",
             "civitai_deleted": "INTEGER DEFAULT 0",
+            # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
+            "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
         }
 
         for column, definition in required_columns.items():
@@ -517,6 +535,17 @@ class PersistentModelCache:
         creator_data = civitai.get("creator") if isinstance(civitai, dict) else None
         if isinstance(creator_data, dict):
             creator_username = creator_data.get("username") or None
+        model_type_value = None
+        if isinstance(civitai, Mapping):
+            civitai_model_info = civitai.get("model")
+            if isinstance(civitai_model_info, Mapping):
+                candidate_type = civitai_model_info.get("type")
+                if candidate_type not in (None, "", []):
+                    model_type_value = candidate_type
+
+        license_flags = item.get("license_flags")
+        if license_flags is None:
+            license_flags = DEFAULT_LICENSE_FLAGS
 
         return (
             model_type,
@@ -537,9 +566,11 @@ class PersistentModelCache:
             metadata_source,
             civitai.get("id"),
             civitai.get("modelId"),
+            model_type_value,
             civitai.get("name"),
             creator_username,
             trained_words_json,
+            int(license_flags),
             1 if item.get("civitai_deleted") else 0,
             1 if item.get("exclude") else 0,
             1 if item.get("db_checked") else 0,

py/services/recipe_cache.py

@@ -7,12 +7,18 @@ from natsort import natsorted
 @dataclass
 class RecipeCache:
     """Cache structure for Recipe data"""
+
     raw_data: List[Dict]
     sorted_by_name: List[Dict]
     sorted_by_date: List[Dict]
+    folders: List[str] | None = None
+    folder_tree: Dict | None = None
 
     def __post_init__(self):
         self._lock = asyncio.Lock()
+        # Normalize optional metadata containers
+        self.folders = self.folders or []
+        self.folder_tree = self.folder_tree or {}
 
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""

py/services/recipe_fts_index.py

@@ -0,0 +1,547 @@
+"""SQLite FTS5-based full-text search index for recipes.
+
+This module provides fast recipe search using SQLite's FTS5 extension,
+enabling sub-100ms search times even with 20k+ recipes.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import re
+import sqlite3
+import threading
+import time
+from typing import Any, Dict, List, Optional, Set
+
+from ..utils.settings_paths import get_settings_dir
+
+logger = logging.getLogger(__name__)
+
+
+class RecipeFTSIndex:
+    """SQLite FTS5-based full-text search index for recipes.
+
+    Provides fast prefix-based search across multiple recipe fields:
+    - title
+    - tags
+    - lora_names (file names)
+    - lora_models (model names)
+    - prompt
+    - negative_prompt
+    """
+
+    _DEFAULT_FILENAME = "recipe_fts.sqlite"
+
+    # Map of search option keys to FTS column names
+    FIELD_MAP = {
+        'title': ['title'],
+        'tags': ['tags'],
+        'lora_name': ['lora_names'],
+        'lora_model': ['lora_models'],
+        'prompt': ['prompt', 'negative_prompt'],
+    }
+
+    def __init__(self, db_path: Optional[str] = None) -> None:
+        """Initialize the FTS index.
+
+        Args:
+            db_path: Optional path to the SQLite database file.
+                     If not provided, uses the default location in settings directory.
+        """
+        self._db_path = db_path or self._resolve_default_path()
+        self._lock = threading.Lock()
+        self._ready = threading.Event()
+        self._indexing_in_progress = False
+        self._schema_initialized = False
+        self._warned_not_ready = False
+
+        # Ensure directory exists
+        try:
+            directory = os.path.dirname(self._db_path)
+            if directory:
+                os.makedirs(directory, exist_ok=True)
+        except Exception as exc:
+            logger.warning("Could not create FTS index directory %s: %s", directory, exc)
+
+    def _resolve_default_path(self) -> str:
+        """Resolve the default database path."""
+        override = os.environ.get("LORA_MANAGER_RECIPE_FTS_DB")
+        if override:
+            return override
+
+        try:
+            settings_dir = get_settings_dir(create=True)
+        except Exception as exc:
+            logger.warning("Falling back to current directory for FTS index: %s", exc)
+            settings_dir = "."
+
+        return os.path.join(settings_dir, self._DEFAULT_FILENAME)
+
+    def get_database_path(self) -> str:
+        """Return the resolved database path."""
+        return self._db_path
+
+    def is_ready(self) -> bool:
+        """Check if the FTS index is ready for queries."""
+        return self._ready.is_set()
+
+    def is_indexing(self) -> bool:
+        """Check if indexing is currently in progress."""
+        return self._indexing_in_progress
+
+    def initialize(self) -> None:
+        """Initialize the database schema."""
+        if self._schema_initialized:
+            return
+
+        with self._lock:
+            if self._schema_initialized:
+                return
+
+            try:
+                conn = self._connect()
+                try:
+                    conn.execute("PRAGMA journal_mode=WAL")
+                    conn.executescript("""
+                        -- FTS5 virtual table for full-text search
+                        -- Note: We use a regular FTS5 table (not contentless) so we can retrieve recipe_id
+                        CREATE VIRTUAL TABLE IF NOT EXISTS recipe_fts USING fts5(
+                            recipe_id,
+                            title,
+                            tags,
+                            lora_names,
+                            lora_models,
+                            prompt,
+                            negative_prompt,
+                            tokenize='unicode61 remove_diacritics 2'
+                        );
+
+                        -- Recipe ID to rowid mapping for fast lookups and deletions
+                        CREATE TABLE IF NOT EXISTS recipe_rowid (
+                            recipe_id TEXT PRIMARY KEY,
+                            fts_rowid INTEGER UNIQUE
+                        );
+
+                        -- Index version tracking
+                        CREATE TABLE IF NOT EXISTS fts_metadata (
+                            key TEXT PRIMARY KEY,
+                            value TEXT
+                        );
+                    """)
+                    conn.commit()
+                    self._schema_initialized = True
+                    logger.debug("FTS index schema initialized at %s", self._db_path)
+                finally:
+                    conn.close()
+            except Exception as exc:
+                logger.error("Failed to initialize FTS schema: %s", exc)
+
+    def build_index(self, recipes: List[Dict[str, Any]]) -> None:
+        """Build or rebuild the entire FTS index from recipe data.
+
+        Args:
+            recipes: List of recipe dictionaries to index.
+        """
+        if self._indexing_in_progress:
+            logger.warning("FTS indexing already in progress, skipping")
+            return
+
+        self._indexing_in_progress = True
+        self._ready.clear()
+        start_time = time.time()
+
+        try:
+            self.initialize()
+            if not self._schema_initialized:
+                logger.error("Cannot build FTS index: schema not initialized")
+                return
+
+            with self._lock:
+                conn = self._connect()
+                try:
+                    conn.execute("BEGIN")
+
+                    # Clear existing data
+                    conn.execute("DELETE FROM recipe_fts")
+                    conn.execute("DELETE FROM recipe_rowid")
+
+                    # Batch insert for performance
+                    batch_size = 500
+                    total = len(recipes)
+                    inserted = 0
+
+                    for i in range(0, total, batch_size):
+                        batch = recipes[i:i + batch_size]
+                        rows = []
+                        rowid_mappings = []
+
+                        for recipe in batch:
+                            recipe_id = str(recipe.get('id', ''))
+                            if not recipe_id:
+                                continue
+
+                            row = self._prepare_fts_row(recipe)
+                            rows.append(row)
+                            inserted += 1
+
+                        if rows:
+                            # Insert into FTS table
+                            conn.executemany(
+                                """INSERT INTO recipe_fts (recipe_id, title, tags, lora_names,
+                                   lora_models, prompt, negative_prompt)
+                                   VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                                rows
+                            )
+
+                            # Build rowid mappings
+                            for row in rows:
+                                recipe_id = row[0]
+                                cursor = conn.execute(
+                                    "SELECT rowid FROM recipe_fts WHERE recipe_id = ?",
+                                    (recipe_id,)
+                                )
+                                result = cursor.fetchone()
+                                if result:
+                                    rowid_mappings.append((recipe_id, result[0]))
+
+                            if rowid_mappings:
+                                conn.executemany(
+                                    "INSERT OR REPLACE INTO recipe_rowid (recipe_id, fts_rowid) VALUES (?, ?)",
+                                    rowid_mappings
+                                )
+
+                    # Update metadata
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        ('last_build_time', str(time.time()))
+                    )
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        ('recipe_count', str(inserted))
+                    )
+
+                    conn.commit()
+                    elapsed = time.time() - start_time
+                    logger.info("FTS index built: %d recipes indexed in %.2fs", inserted, elapsed)
+                finally:
+                    conn.close()
+
+            self._ready.set()
+
+        except Exception as exc:
+            logger.error("Failed to build FTS index: %s", exc, exc_info=True)
+        finally:
+            self._indexing_in_progress = False
+
+    def search(self, query: str, fields: Optional[Set[str]] = None) -> Set[str]:
+        """Search recipes using FTS5 with prefix matching.
+
+        Args:
+            query: The search query string.
+            fields: Optional set of field names to search. If None, searches all fields.
+                    Valid fields: 'title', 'tags', 'lora_name', 'lora_model', 'prompt'
+
+        Returns:
+            Set of matching recipe IDs.
+        """
+        if not self.is_ready():
+            if not self._warned_not_ready:
+                logger.debug("FTS index not ready, returning empty results")
+                self._warned_not_ready = True
+            return set()
+
+        if not query or not query.strip():
+            return set()
+
+        fts_query = self._build_fts_query(query, fields)
+        if not fts_query:
+            return set()
+
+        try:
+            with self._lock:
+                conn = self._connect(readonly=True)
+                try:
+                    cursor = conn.execute(
+                        "SELECT recipe_id FROM recipe_fts WHERE recipe_fts MATCH ?",
+                        (fts_query,)
+                    )
+                    return {row[0] for row in cursor.fetchall()}
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("FTS search error for query '%s': %s", query, exc)
+            return set()
+
+    def add_recipe(self, recipe: Dict[str, Any]) -> bool:
+        """Add a single recipe to the FTS index.
+
+        Args:
+            recipe: The recipe dictionary to add.
+
+        Returns:
+            True if successful, False otherwise.
+        """
+        if not self.is_ready():
+            return False
+
+        recipe_id = str(recipe.get('id', ''))
+        if not recipe_id:
+            return False
+
+        try:
+            with self._lock:
+                conn = self._connect()
+                try:
+                    # Remove existing entry if present
+                    self._remove_recipe_locked(conn, recipe_id)
+
+                    # Insert new entry
+                    row = self._prepare_fts_row(recipe)
+                    conn.execute(
+                        """INSERT INTO recipe_fts (recipe_id, title, tags, lora_names,
+                           lora_models, prompt, negative_prompt)
+                           VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                        row
+                    )
+
+                    # Update rowid mapping
+                    cursor = conn.execute(
+                        "SELECT rowid FROM recipe_fts WHERE recipe_id = ?",
+                        (recipe_id,)
+                    )
+                    result = cursor.fetchone()
+                    if result:
+                        conn.execute(
+                            "INSERT OR REPLACE INTO recipe_rowid (recipe_id, fts_rowid) VALUES (?, ?)",
+                            (recipe_id, result[0])
+                        )
+
+                    conn.commit()
+                    return True
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("Failed to add recipe %s to FTS index: %s", recipe_id, exc)
+            return False
+
+    def remove_recipe(self, recipe_id: str) -> bool:
+        """Remove a recipe from the FTS index.
+
+        Args:
+            recipe_id: The ID of the recipe to remove.
+
+        Returns:
+            True if successful, False otherwise.
+        """
+        if not self.is_ready():
+            return False
+
+        if not recipe_id:
+            return False
+
+        try:
+            with self._lock:
+                conn = self._connect()
+                try:
+                    self._remove_recipe_locked(conn, recipe_id)
+                    conn.commit()
+                    return True
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("Failed to remove recipe %s from FTS index: %s", recipe_id, exc)
+            return False
+
+    def update_recipe(self, recipe: Dict[str, Any]) -> bool:
+        """Update a recipe in the FTS index.
+
+        Args:
+            recipe: The updated recipe dictionary.
+
+        Returns:
+            True if successful, False otherwise.
+        """
+        return self.add_recipe(recipe)  # add_recipe handles removal and re-insertion
+
+    def clear(self) -> bool:
+        """Clear all data from the FTS index.
+
+        Returns:
+            True if successful, False otherwise.
+        """
+        try:
+            with self._lock:
+                conn = self._connect()
+                try:
+                    conn.execute("DELETE FROM recipe_fts")
+                    conn.execute("DELETE FROM recipe_rowid")
+                    conn.commit()
+                    self._ready.clear()
+                    return True
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.error("Failed to clear FTS index: %s", exc)
+            return False
+
+    def get_indexed_count(self) -> int:
+        """Return the number of recipes currently indexed."""
+        if not self._schema_initialized:
+            return 0
+
+        try:
+            with self._lock:
+                conn = self._connect(readonly=True)
+                try:
+                    cursor = conn.execute("SELECT COUNT(*) FROM recipe_fts")
+                    result = cursor.fetchone()
+                    return result[0] if result else 0
+                finally:
+                    conn.close()
+        except Exception:
+            return 0
+
+    # Internal helpers
+
+    def _connect(self, readonly: bool = False) -> sqlite3.Connection:
+        """Create a database connection."""
+        uri = False
+        path = self._db_path
+        if readonly:
+            if not os.path.exists(path):
+                raise FileNotFoundError(path)
+            path = f"file:{path}?mode=ro"
+            uri = True
+        conn = sqlite3.connect(path, check_same_thread=False, uri=uri)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _remove_recipe_locked(self, conn: sqlite3.Connection, recipe_id: str) -> None:
+        """Remove a recipe entry. Caller must hold the lock."""
+        # Get the rowid for deletion
+        cursor = conn.execute(
+            "SELECT fts_rowid FROM recipe_rowid WHERE recipe_id = ?",
+            (recipe_id,)
+        )
+        result = cursor.fetchone()
+        if result:
+            fts_rowid = result[0]
+            # Delete from FTS using rowid
+            conn.execute(
+                "DELETE FROM recipe_fts WHERE rowid = ?",
+                (fts_rowid,)
+            )
+        # Also try direct delete by recipe_id (handles edge cases)
+        conn.execute(
+            "DELETE FROM recipe_fts WHERE recipe_id = ?",
+            (recipe_id,)
+        )
+        conn.execute(
+            "DELETE FROM recipe_rowid WHERE recipe_id = ?",
+            (recipe_id,)
+        )
+
+    def _prepare_fts_row(self, recipe: Dict[str, Any]) -> tuple:
+        """Prepare a row tuple for FTS insertion."""
+        recipe_id = str(recipe.get('id', ''))
+        title = str(recipe.get('title', ''))
+
+        # Extract tags as space-separated string
+        tags_list = recipe.get('tags', [])
+        tags = ' '.join(str(t) for t in tags_list if t) if tags_list else ''
+
+        # Extract LoRA file names and model names
+        loras = recipe.get('loras', [])
+        lora_names = []
+        lora_models = []
+        for lora in loras:
+            if isinstance(lora, dict):
+                file_name = lora.get('file_name', '')
+                if file_name:
+                    lora_names.append(str(file_name))
+                model_name = lora.get('modelName', '')
+                if model_name:
+                    lora_models.append(str(model_name))
+
+        lora_names_str = ' '.join(lora_names)
+        lora_models_str = ' '.join(lora_models)
+
+        # Extract prompts from gen_params
+        gen_params = recipe.get('gen_params', {})
+        prompt = str(gen_params.get('prompt', '')) if gen_params else ''
+        negative_prompt = str(gen_params.get('negative_prompt', '')) if gen_params else ''
+
+        return (recipe_id, title, tags, lora_names_str, lora_models_str, prompt, negative_prompt)
+
+    def _build_fts_query(self, query: str, fields: Optional[Set[str]] = None) -> str:
+        """Build an FTS5 query string with prefix matching and field restrictions.
+
+        Args:
+            query: The user's search query.
+            fields: Optional set of field names to restrict search to.
+
+        Returns:
+            FTS5 query string.
+        """
+        # Split query into words and clean them
+        words = query.lower().split()
+        if not words:
+            return ''
+
+        # Escape and add prefix wildcard to each word
+        prefix_terms = []
+        for word in words:
+            escaped = self._escape_fts_query(word)
+            if escaped:
+                # Add prefix wildcard for substring-like matching
+                # FTS5 prefix queries: word* matches words starting with "word"
+                prefix_terms.append(f'{escaped}*')
+
+        if not prefix_terms:
+            return ''
+
+        # Combine terms with implicit AND (all words must match)
+        term_expr = ' '.join(prefix_terms)
+
+        # If no field restriction, search all indexed fields (not recipe_id)
+        if not fields:
+            return term_expr
+
+        # Build field-restricted query with OR between fields
+        field_clauses = []
+        for field in fields:
+            if field in self.FIELD_MAP:
+                cols = self.FIELD_MAP[field]
+                for col in cols:
+                    # FTS5 column filter syntax: column:term
+                    # Need to handle multiple terms properly
+                    for term in prefix_terms:
+                        field_clauses.append(f'{col}:{term}')
+
+        if not field_clauses:
+            return term_expr
+
+        # Combine field clauses with OR
+        return ' OR '.join(field_clauses)
+
+    def _escape_fts_query(self, text: str) -> str:
+        """Escape special FTS5 characters.
+
+        FTS5 special characters: " ( ) * : ^ -
+        We keep * for prefix matching but escape others.
+        """
+        if not text:
+            return ''
+
+        # Replace FTS5 special characters with space
+        # Keep alphanumeric, CJK characters, and common punctuation
+        special = ['"', '(', ')', '*', ':', '^', '-', '{', '}', '[', ']']
+        result = text
+        for char in special:
+            result = result.replace(char, ' ')
+
+        # Collapse multiple spaces and strip
+        result = re.sub(r'\s+', ' ', result).strip()
+        return result

py/services/recipes/analysis_service.py

@@ -13,6 +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 .errors import (
     RecipeDownloadError,
     RecipeNotFoundError,
@@ -94,36 +95,72 @@ class RecipeAnalysisService:
         if civitai_client is None:
             raise RecipeServiceError("Civitai client unavailable")
 
-        temp_path = self._create_temp_path()
+        temp_path = None
         metadata: Optional[dict[str, Any]] = None
+        is_video = False
+        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))
                 if not image_info:
                     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"))
+                if rewritten_url:
+                    image_url = rewritten_url
+
+                if is_video:
+                    # Extract extension from URL
+                    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)
+                    and "meta" in metadata
+                    and isinstance(metadata["meta"], dict)
+                ):
+                    metadata = metadata["meta"]
             else:
+                # Basic extension detection for non-Civitai URLs
+                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)
 
-            if metadata is None:
+            if metadata is None and not is_video:
                 metadata = self._exif_utils.extract_image_metadata(temp_path)
 
-            if not metadata:
-                return self._metadata_not_found_response(temp_path)
-
             return await self._parse_metadata(
-                metadata,
+                metadata or {},
                 recipe_scanner=recipe_scanner,
                 image_path=temp_path,
                 include_image_base64=True,
+                is_video=is_video,
+                extension=extension,
             )
         finally:
-            self._safe_cleanup(temp_path)
+            if temp_path:
+                self._safe_cleanup(temp_path)
 
     async def analyze_local_image(
         self,
@@ -192,12 +229,16 @@ class RecipeAnalysisService:
         recipe_scanner,
         image_path: Optional[str],
         include_image_base64: bool,
+        is_video: bool = False,
+        extension: str = ".jpg",
     ) -> AnalysisResult:
         parser = self._recipe_parser_factory.create_parser(metadata)
         if parser is None:
             payload = {"error": "No parser found for this image", "loras": []}
             if include_image_base64 and image_path:
                 payload["image_base64"] = self._encode_file(image_path)
+            payload["is_video"] = is_video
+            payload["extension"] = extension
             return AnalysisResult(payload)
 
         result = await parser.parse_metadata(metadata, recipe_scanner=recipe_scanner)
@@ -205,6 +246,9 @@ class RecipeAnalysisService:
         if include_image_base64 and image_path:
             result["image_base64"] = self._encode_file(image_path)
 
+        result["is_video"] = is_video
+        result["extension"] = extension
+
         if "error" in result and not result.get("loras"):
             return AnalysisResult(result)
 
@@ -235,8 +279,8 @@ class RecipeAnalysisService:
             temp_file.write(data)
             return temp_file.name
 
-    def _create_temp_path(self) -> str:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+    def _create_temp_path(self, suffix: str = ".jpg") -> str:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
             return temp_file.name
 
     def _safe_cleanup(self, path: Optional[str]) -> None:

py/services/recipes/persistence_service.py

@@ -5,6 +5,7 @@ import base64
 import json
 import os
 import re
+import shutil
 import time
 import uuid
 from dataclasses import dataclass
@@ -46,6 +47,7 @@ class RecipePersistenceService:
         name: str | None,
         tags: Iterable[str],
         metadata: Optional[dict[str, Any]],
+        extension: str | None = None,
     ) -> PersistenceResult:
         """Persist a user uploaded recipe."""
 
@@ -64,13 +66,21 @@ class RecipePersistenceService:
         os.makedirs(recipes_dir, exist_ok=True)
 
         recipe_id = str(uuid.uuid4())
-        optimized_image, extension = self._exif_utils.optimize_image(
-            image_data=resolved_image_bytes,
-            target_width=self._card_preview_width,
-            format="webp",
-            quality=85,
-            preserve_metadata=True,
-        )
+        
+        # Handle video formats by bypassing optimization and metadata embedding
+        is_video = extension in [".mp4", ".webm"]
+        if is_video:
+            optimized_image = resolved_image_bytes
+            # extension is already set
+        else:
+            optimized_image, extension = self._exif_utils.optimize_image(
+                image_data=resolved_image_bytes,
+                target_width=self._card_preview_width,
+                format="webp",
+                quality=85,
+                preserve_metadata=True,
+            )
+            
         image_filename = f"{recipe_id}{extension}"
         image_path = os.path.join(recipes_dir, image_filename)
         normalized_image_path = os.path.normpath(image_path)
@@ -78,15 +88,15 @@ class RecipePersistenceService:
             file_obj.write(optimized_image)
 
         current_time = time.time()
-        loras_data = [self._normalise_lora_entry(lora) for lora in metadata.get("loras", [])]
+        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", {})
+        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", ""),
-                "checkpoint": raw_metadata.get("checkpoint", {}),
                 "steps": raw_metadata.get("steps", ""),
                 "sampler": raw_metadata.get("sampler", ""),
                 "cfg_scale": raw_metadata.get("cfg_scale", ""),
@@ -95,6 +105,9 @@ class RecipePersistenceService:
                 "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)
+
         fingerprint = calculate_recipe_fingerprint(loras_data)
         recipe_data: Dict[str, Any] = {
             "id": recipe_id,
@@ -107,6 +120,8 @@ class RecipePersistenceService:
             "gen_params": gen_params,
             "fingerprint": fingerprint,
         }
+        if checkpoint_entry:
+            recipe_data["checkpoint"] = checkpoint_entry
 
         tags_list = list(tags)
         if tags_list:
@@ -121,7 +136,8 @@ class RecipePersistenceService:
         with open(json_path, "w", encoding="utf-8") as file_obj:
             json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
 
-        self._exif_utils.append_recipe_metadata(normalized_image_path, recipe_data)
+        if not is_video:
+            self._exif_utils.append_recipe_metadata(normalized_image_path, recipe_data)
 
         matching_recipes = await self._find_matching_recipes(recipe_scanner, fingerprint, exclude_id=recipe_id)
         await recipe_scanner.add_recipe(recipe_data)
@@ -139,12 +155,8 @@ class RecipePersistenceService:
     async def delete_recipe(self, *, recipe_scanner, recipe_id: str) -> PersistenceResult:
         """Delete an existing recipe."""
 
-        recipes_dir = recipe_scanner.recipes_dir
-        if not recipes_dir or not os.path.exists(recipes_dir):
-            raise RecipeNotFoundError("Recipes directory not found")
-
-        recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
-        if not os.path.exists(recipe_json_path):
+        recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
             raise RecipeNotFoundError("Recipe not found")
 
         with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
@@ -161,9 +173,9 @@ 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")):
+        if not any(key in updates for key in ("title", "tags", "source_path", "preview_nsfw_level", "favorite")):
             raise RecipeValidationError(
-                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level)"
+                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite)"
             )
 
         success = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
@@ -172,6 +184,163 @@ class RecipePersistenceService:
 
         return PersistenceResult({"success": True, "recipe_id": recipe_id, "updates": updates})
 
+    def _normalize_target_path(self, recipe_scanner, target_path: str) -> tuple[str, str]:
+        """Normalize and validate the target path for recipe moves."""
+
+        if not target_path:
+            raise RecipeValidationError("Target path is required")
+
+        recipes_root = recipe_scanner.recipes_dir
+        if not recipes_root:
+            raise RecipeNotFoundError("Recipes directory not found")
+
+        normalized_target = os.path.normpath(target_path)
+        recipes_root = os.path.normpath(recipes_root)
+        if not os.path.isabs(normalized_target):
+            normalized_target = os.path.normpath(os.path.join(recipes_root, normalized_target))
+
+        try:
+            common_root = os.path.commonpath([normalized_target, recipes_root])
+        except ValueError as exc:
+            raise RecipeValidationError("Invalid target path") from exc
+
+        if common_root != recipes_root:
+            raise RecipeValidationError("Target path must be inside the recipes directory")
+
+        return normalized_target, recipes_root
+
+    async def _move_recipe_files(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        normalized_target: str,
+        recipes_root: str,
+    ) -> dict[str, Any]:
+        """Move the recipe's JSON and preview image into the normalized target."""
+
+        recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        recipe_data = await recipe_scanner.get_recipe_by_id(recipe_id)
+        if not recipe_data:
+            raise RecipeNotFoundError("Recipe not found")
+
+        current_json_dir = os.path.dirname(recipe_json_path)
+        normalized_image_path = os.path.normpath(recipe_data.get("file_path") or "") if recipe_data.get("file_path") else None
+
+        os.makedirs(normalized_target, exist_ok=True)
+
+        if os.path.normpath(current_json_dir) == normalized_target:
+            return {
+                "success": True,
+                "message": "Recipe is already in the target folder",
+                "recipe_id": recipe_id,
+                "original_file_path": recipe_data.get("file_path"),
+                "new_file_path": recipe_data.get("file_path"),
+            }
+
+        new_json_path = os.path.normpath(os.path.join(normalized_target, os.path.basename(recipe_json_path)))
+        shutil.move(recipe_json_path, new_json_path)
+
+        new_image_path = normalized_image_path
+        if normalized_image_path:
+            target_image_path = os.path.normpath(os.path.join(normalized_target, os.path.basename(normalized_image_path)))
+            if os.path.exists(normalized_image_path) and normalized_image_path != target_image_path:
+                shutil.move(normalized_image_path, target_image_path)
+            new_image_path = target_image_path
+
+        relative_folder = os.path.relpath(normalized_target, recipes_root)
+        if relative_folder in (".", ""):
+            relative_folder = ""
+        updates = {"file_path": new_image_path or recipe_data.get("file_path"), "folder": relative_folder.replace(os.path.sep, "/")}
+
+        updated = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
+        if not updated:
+            raise RecipeNotFoundError("Recipe not found after move")
+
+        return {
+            "success": True,
+            "recipe_id": recipe_id,
+            "original_file_path": recipe_data.get("file_path"),
+            "new_file_path": updates["file_path"],
+            "json_path": new_json_path,
+            "folder": updates["folder"],
+        }
+
+    async def move_recipe(self, *, recipe_scanner, recipe_id: str, target_path: str) -> PersistenceResult:
+        """Move a recipe's assets into a new folder under the recipes root."""
+
+        normalized_target, recipes_root = self._normalize_target_path(recipe_scanner, target_path)
+        result = await self._move_recipe_files(
+            recipe_scanner=recipe_scanner,
+            recipe_id=recipe_id,
+            normalized_target=normalized_target,
+            recipes_root=recipes_root,
+        )
+        return PersistenceResult(result)
+
+    async def move_recipes_bulk(
+        self,
+        *,
+        recipe_scanner,
+        recipe_ids: Iterable[str],
+        target_path: str,
+    ) -> PersistenceResult:
+        """Move multiple recipes to a new folder."""
+
+        recipe_ids = list(recipe_ids)
+        if not recipe_ids:
+            raise RecipeValidationError("No recipe IDs provided")
+
+        normalized_target, recipes_root = self._normalize_target_path(recipe_scanner, target_path)
+
+        results: list[dict[str, Any]] = []
+        success_count = 0
+        failure_count = 0
+
+        for recipe_id in recipe_ids:
+            try:
+                move_result = await self._move_recipe_files(
+                    recipe_scanner=recipe_scanner,
+                    recipe_id=str(recipe_id),
+                    normalized_target=normalized_target,
+                    recipes_root=recipes_root,
+                )
+                results.append(
+                    {
+                        "recipe_id": recipe_id,
+                        "original_file_path": move_result.get("original_file_path"),
+                        "new_file_path": move_result.get("new_file_path"),
+                        "success": True,
+                        "message": move_result.get("message", ""),
+                        "folder": move_result.get("folder", ""),
+                    }
+                )
+                success_count += 1
+            except Exception as exc:  # pragma: no cover - per-item error handling
+                results.append(
+                    {
+                        "recipe_id": recipe_id,
+                        "original_file_path": None,
+                        "new_file_path": None,
+                        "success": False,
+                        "message": str(exc),
+                    }
+                )
+                failure_count += 1
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "message": f"Moved {success_count} of {len(recipe_ids)} recipes",
+                "results": results,
+                "success_count": success_count,
+                "failure_count": failure_count,
+            }
+        )
+
     async def reconnect_lora(
         self,
         *,
@@ -182,8 +351,8 @@ class RecipePersistenceService:
     ) -> PersistenceResult:
         """Reconnect a LoRA entry within an existing recipe."""
 
-        recipe_path = os.path.join(recipe_scanner.recipes_dir, f"{recipe_id}.recipe.json")
-        if not os.path.exists(recipe_path):
+        recipe_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_path or not os.path.exists(recipe_path):
             raise RecipeNotFoundError("Recipe not found")
 
         target_lora = await recipe_scanner.get_local_lora(target_name)
@@ -228,16 +397,12 @@ class RecipePersistenceService:
         if not recipe_ids:
             raise RecipeValidationError("No recipe IDs provided")
 
-        recipes_dir = recipe_scanner.recipes_dir
-        if not recipes_dir or not os.path.exists(recipes_dir):
-            raise RecipeNotFoundError("Recipes directory not found")
-
         deleted_recipes: list[str] = []
         failed_recipes: list[dict[str, Any]] = []
 
         for recipe_id in recipe_ids:
-            recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
-            if not os.path.exists(recipe_json_path):
+            recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+            if not recipe_json_path or not os.path.exists(recipe_json_path):
                 failed_recipes.append({"id": recipe_id, "reason": "Recipe not found"})
                 continue
 
@@ -295,8 +460,6 @@ class RecipePersistenceService:
 
         lora_stack = metadata.get("loras", "")
         lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", lora_stack)
-        if not lora_matches:
-            raise RecipeValidationError("No LoRAs found in the generation metadata")
 
         loras_data = []
         base_model_counts: Dict[str, int] = {}
@@ -332,7 +495,7 @@ class RecipePersistenceService:
             "created_date": time.time(),
             "base_model": most_common_base_model,
             "loras": loras_data,
-            "checkpoint": metadata.get("checkpoint", ""),
+            "checkpoint": self._sanitize_checkpoint_entry(metadata.get("checkpoint", "")),
             "gen_params": {
                 key: value
                 for key, value in metadata.items()
@@ -361,6 +524,30 @@ class RecipePersistenceService:
 
     # Helper methods ---------------------------------------------------
 
+    def _extract_checkpoint_entry(self, metadata: dict[str, Any]) -> Optional[dict[str, Any]]:
+        """Pull a checkpoint entry from various metadata locations."""
+
+        checkpoint_entry = metadata.get("checkpoint") or metadata.get("model")
+        if not checkpoint_entry:
+            gen_params = metadata.get("gen_params") or {}
+            checkpoint_entry = gen_params.get("checkpoint")
+
+        return checkpoint_entry if isinstance(checkpoint_entry, dict) else None
+
+    def _sanitize_checkpoint_entry(self, checkpoint_entry: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]:
+        """Remove transient/local-only fields from checkpoint metadata."""
+
+        if not checkpoint_entry:
+            return None
+
+        if not isinstance(checkpoint_entry, dict):
+            return checkpoint_entry
+
+        pruned = dict(checkpoint_entry)
+        for key in ("existsLocally", "localPath", "thumbnailUrl", "size", "downloadUrl"):
+            pruned.pop(key, None)
+        return pruned
+
     def _resolve_image_bytes(self, image_bytes: bytes | None, image_base64: str | None) -> bytes:
         if image_bytes is not None:
             return image_bytes

py/services/settings_manager.py

@@ -2,14 +2,17 @@ import asyncio
 import copy
 import json
 import os
+import shutil
 import logging
 from pathlib import Path
 from datetime import datetime, timezone
 from threading import Lock
 from typing import Any, Awaitable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple
 
-from ..utils.constants import DEFAULT_PRIORITY_TAG_CONFIG
-from ..utils.settings_paths import ensure_settings_file
+from platformdirs import user_config_dir
+
+from ..utils.constants import DEFAULT_HASH_CHUNK_SIZE_MB, DEFAULT_PRIORITY_TAG_CONFIG
+from ..utils.settings_paths import APP_NAME, ensure_settings_file, get_legacy_settings_path
 from ..utils.tag_priorities import (
     PriorityTagEntry,
     collect_canonical_tags,
@@ -29,6 +32,7 @@ CORE_USER_SETTING_KEYS: Tuple[str, ...] = (
 DEFAULT_SETTINGS: Dict[str, Any] = {
     "civitai_api_key": "",
     "use_portable_settings": False,
+    "hash_chunk_size_mb": DEFAULT_HASH_CHUNK_SIZE_MB,
     "language": "en",
     "show_only_sfw": False,
     "enable_metadata_archive_db": False,
@@ -40,6 +44,7 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "proxy_type": "http",
     "default_lora_root": "",
     "default_checkpoint_root": "",
+    "default_unet_root": "",
     "default_embedding_root": "",
     "base_model_path_mappings": {},
     "download_path_templates": {},
@@ -57,12 +62,15 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "priority_tags": DEFAULT_PRIORITY_TAG_CONFIG.copy(),
     "model_name_display": "model_name",
     "model_card_footer_action": "example_images",
+    "update_flag_strategy": "same_base",
+    "auto_organize_exclusions": [],
 }
 
 
 class SettingsManager:
     def __init__(self):
         self.settings_file = ensure_settings_file(logger)
+        self._pending_portable_switch: Optional[Dict[str, str]] = None
         self._standalone_mode = self._detect_standalone_mode()
         self._startup_messages: List[Dict[str, Any]] = []
         self._needs_initial_save = False
@@ -208,6 +216,7 @@ class SettingsManager:
                 folder_paths=merged.get("folder_paths", {}),
                 default_lora_root=merged.get("default_lora_root"),
                 default_checkpoint_root=merged.get("default_checkpoint_root"),
+                default_unet_root=merged.get("default_unet_root"),
                 default_embedding_root=merged.get("default_embedding_root"),
             )
         }
@@ -233,6 +242,17 @@ class SettingsManager:
             )
             inserted_defaults = True
 
+        if "auto_organize_exclusions" in self.settings:
+            normalized_exclusions = self.normalize_auto_organize_exclusions(
+                self.settings.get("auto_organize_exclusions")
+            )
+            if normalized_exclusions != self.settings.get("auto_organize_exclusions"):
+                self.settings["auto_organize_exclusions"] = normalized_exclusions
+                updated_existing = True
+        else:
+            self.settings["auto_organize_exclusions"] = []
+            inserted_defaults = True
+
         for key, value in defaults.items():
             if key == "priority_tags":
                 continue
@@ -282,6 +302,7 @@ class SettingsManager:
                 folder_paths=normalized_top_level_paths,
                 default_lora_root=self.settings.get("default_lora_root", ""),
                 default_checkpoint_root=self.settings.get("default_checkpoint_root", ""),
+                default_unet_root=self.settings.get("default_unet_root", ""),
                 default_embedding_root=self.settings.get("default_embedding_root", ""),
             )
             libraries = {library_name: library_payload}
@@ -324,6 +345,7 @@ class SettingsManager:
                 folder_paths=candidate_folder_paths,
                 default_lora_root=data.get("default_lora_root"),
                 default_checkpoint_root=data.get("default_checkpoint_root"),
+                default_unet_root=data.get("default_unet_root"),
                 default_embedding_root=data.get("default_embedding_root"),
                 metadata=data.get("metadata"),
                 base=data,
@@ -362,6 +384,7 @@ class SettingsManager:
         self.settings["folder_paths"] = folder_paths
         self.settings["default_lora_root"] = active_library.get("default_lora_root", "")
         self.settings["default_checkpoint_root"] = active_library.get("default_checkpoint_root", "")
+        self.settings["default_unet_root"] = active_library.get("default_unet_root", "")
         self.settings["default_embedding_root"] = active_library.get("default_embedding_root", "")
 
         if save:
@@ -376,6 +399,7 @@ class SettingsManager:
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
+        default_unet_root: Optional[str] = None,
         default_embedding_root: Optional[str] = None,
         metadata: Optional[Mapping[str, Any]] = None,
         base: Optional[Mapping[str, Any]] = None,
@@ -398,6 +422,11 @@ class SettingsManager:
         else:
             payload.setdefault("default_checkpoint_root", "")
 
+        if default_unet_root is not None:
+            payload["default_unet_root"] = default_unet_root
+        else:
+            payload.setdefault("default_unet_root", "")
+
         if default_embedding_root is not None:
             payload["default_embedding_root"] = default_embedding_root
         else:
@@ -499,6 +528,7 @@ class SettingsManager:
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
+        default_unet_root: Optional[str] = None,
         default_embedding_root: Optional[str] = None,
     ) -> bool:
         libraries = self.settings.get("libraries", {})
@@ -523,6 +553,10 @@ class SettingsManager:
             library["default_checkpoint_root"] = default_checkpoint_root
             changed = True
 
+        if default_unet_root is not None and library.get("default_unet_root") != default_unet_root:
+            library["default_unet_root"] = default_unet_root
+            changed = True
+
         if default_embedding_root is not None and library.get("default_embedding_root") != default_embedding_root:
             library["default_embedding_root"] = default_embedding_root
             changed = True
@@ -578,7 +612,11 @@ class SettingsManager:
             logger.info("Migration completed")
 
     def _auto_set_default_roots(self):
-        """Auto set default root paths when only one folder is present and the current default is unset or not among the options."""
+        """Auto set default root paths when the current default is unset or not among the options.
+
+        For single-path cases, always use that path.
+        For multi-path cases, only set if current default is empty or invalid.
+        """
         folder_paths = self.settings.get('folder_paths', {})
         updated = False
         # loras
@@ -595,6 +633,14 @@ class SettingsManager:
             if current_checkpoint_root not in checkpoints:
                 self.settings['default_checkpoint_root'] = checkpoints[0]
                 updated = True
+        # unet (diffusion models) - auto-set if empty or invalid
+        unet_paths = folder_paths.get('unet', [])
+        if isinstance(unet_paths, list) and len(unet_paths) >= 1:
+            current_unet_root = self.settings.get('default_unet_root')
+            # Set to first path if current is empty or not in the valid paths
+            if not current_unet_root or current_unet_root not in unet_paths:
+                self.settings['default_unet_root'] = unet_paths[0]
+                updated = True
         # embeddings
         embeddings = folder_paths.get('embeddings', [])
         if isinstance(embeddings, list) and len(embeddings) == 1:
@@ -606,6 +652,7 @@ class SettingsManager:
             self._update_active_library_entry(
                 default_lora_root=self.settings.get('default_lora_root'),
                 default_checkpoint_root=self.settings.get('default_checkpoint_root'),
+                default_unet_root=self.settings.get('default_unet_root'),
                 default_embedding_root=self.settings.get('default_embedding_root'),
             )
             if self._bootstrap_reason == "missing":
@@ -713,6 +760,7 @@ class SettingsManager:
         defaults['download_path_templates'] = {}
         defaults['priority_tags'] = DEFAULT_PRIORITY_TAG_CONFIG.copy()
         defaults.setdefault('folder_paths', {})
+        defaults['auto_organize_exclusions'] = []
 
         library_name = defaults.get("active_library") or "default"
         default_library = self._build_library_payload(
@@ -738,6 +786,35 @@ class SettingsManager:
 
         return normalized
 
+    def normalize_auto_organize_exclusions(self, value: Any) -> List[str]:
+        if value is None:
+            return []
+
+        if isinstance(value, str):
+            candidates: Iterable[str] = (
+                value.replace("\n", ",").replace(";", ",").split(",")
+            )
+        elif isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray, str)):
+            candidates = value
+        else:
+            return []
+
+        patterns: List[str] = []
+        for raw in candidates:
+            if isinstance(raw, str):
+                token = raw.strip()
+                if token:
+                    patterns.append(token)
+
+        unique_patterns: List[str] = []
+        seen = set()
+        for pattern in patterns:
+            if pattern not in seen:
+                seen.add(pattern)
+                unique_patterns.append(pattern)
+
+        return unique_patterns
+
     def get_priority_tag_config(self) -> Dict[str, str]:
         stored_value = self.settings.get("priority_tags")
         normalized = self._normalize_priority_tag_config(stored_value)
@@ -746,6 +823,15 @@ class SettingsManager:
             self._save_settings()
         return normalized.copy()
 
+    def get_auto_organize_exclusions(self) -> List[str]:
+        exclusions = self.normalize_auto_organize_exclusions(
+            self.settings.get("auto_organize_exclusions")
+        )
+        if exclusions != self.settings.get("auto_organize_exclusions"):
+            self.settings["auto_organize_exclusions"] = exclusions
+            self._save_settings()
+        return exclusions
+
     def get_startup_messages(self) -> List[Dict[str, Any]]:
         return [message.copy() for message in self._startup_messages]
 
@@ -781,18 +867,28 @@ class SettingsManager:
 
     def set(self, key: str, value: Any) -> None:
         """Set setting value and save"""
+        if key == "auto_organize_exclusions":
+            value = self.normalize_auto_organize_exclusions(value)
         self.settings[key] = value
+        portable_switch_pending = False
+        if key == "use_portable_settings" and isinstance(value, bool):
+            portable_switch_pending = True
+            self._prepare_portable_switch(value)
         if key == 'folder_paths' and isinstance(value, Mapping):
             self._update_active_library_entry(folder_paths=value)  # type: ignore[arg-type]
         elif key == 'default_lora_root':
             self._update_active_library_entry(default_lora_root=str(value))
         elif key == 'default_checkpoint_root':
             self._update_active_library_entry(default_checkpoint_root=str(value))
+        elif key == 'default_unet_root':
+            self._update_active_library_entry(default_unet_root=str(value))
         elif key == 'default_embedding_root':
             self._update_active_library_entry(default_embedding_root=str(value))
         elif key == 'model_name_display':
             self._notify_model_name_display_change(value)
         self._save_settings()
+        if portable_switch_pending:
+            self._finalize_portable_switch()
 
     def delete(self, key: str) -> None:
         """Delete setting key and save"""
@@ -801,6 +897,119 @@ class SettingsManager:
             self._save_settings()
             logger.info(f"Deleted setting: {key}")
 
+    def _prepare_portable_switch(self, use_portable: bool) -> None:
+        """Prepare switching the settings storage location."""
+
+        legacy_path = get_legacy_settings_path()
+        user_dir = self._get_user_config_directory()
+        user_settings_path = os.path.join(user_dir, "settings.json")
+
+        target_path = legacy_path if use_portable else user_settings_path
+        other_path = user_settings_path if use_portable else legacy_path
+        target_dir = os.path.dirname(target_path)
+        os.makedirs(target_dir, exist_ok=True)
+
+        previous_path = self.settings_file or target_path
+        previous_dir = os.path.dirname(previous_path) or target_dir
+
+        if os.path.abspath(previous_path) != os.path.abspath(target_path):
+            self._copy_model_cache_directory(previous_dir, target_dir)
+            logger.info("Switching settings file to: %s", target_path)
+
+        self._pending_portable_switch = {"other_path": other_path}
+        self.settings_file = target_path
+
+    def _finalize_portable_switch(self) -> None:
+        """Mirror the latest settings file to the secondary location."""
+
+        info = self._pending_portable_switch
+        if not info:
+            return
+
+        other_path = info.get("other_path")
+        current_path = self.settings_file
+
+        if not other_path or not current_path:
+            self._pending_portable_switch = None
+            return
+
+        if os.path.abspath(other_path) == os.path.abspath(current_path):
+            self._pending_portable_switch = None
+            return
+
+        other_dir = os.path.dirname(other_path) or os.path.dirname(current_path)
+        if other_dir:
+            os.makedirs(other_dir, exist_ok=True)
+
+        try:
+            shutil.copy2(current_path, other_path)
+        except Exception as exc:
+            logger.warning("Failed to mirror settings.json to %s: %s", other_path, exc)
+        finally:
+            self._pending_portable_switch = None
+
+    def _copy_model_cache_directory(self, source_dir: str, target_dir: str) -> None:
+        """Copy model_cache artifacts when switching storage locations."""
+
+        if not source_dir or not target_dir:
+            return
+
+        source_cache_dir = os.path.join(source_dir, "model_cache")
+        target_cache_dir = os.path.join(target_dir, "model_cache")
+        if (
+            os.path.isdir(source_cache_dir)
+            and os.path.abspath(source_cache_dir) != os.path.abspath(target_cache_dir)
+        ):
+            try:
+                shutil.copytree(
+                    source_cache_dir,
+                    target_cache_dir,
+                    dirs_exist_ok=True,
+                    ignore=shutil.ignore_patterns("*.sqlite-shm", "*.sqlite-wal"),
+                )
+            except Exception as exc:
+                logger.warning(
+                    "Failed to copy model_cache directory from %s to %s: %s",
+                    source_cache_dir,
+                    target_cache_dir,
+                    exc,
+                )
+
+        source_cache_file = os.path.join(source_dir, "model_cache.sqlite")
+        target_cache_file = os.path.join(target_dir, "model_cache.sqlite")
+        if (
+            os.path.isfile(source_cache_file)
+            and os.path.abspath(source_cache_file) != os.path.abspath(target_cache_file)
+        ):
+            try:
+                shutil.copy2(source_cache_file, target_cache_file)
+            except Exception as exc:
+                logger.warning(
+                    "Failed to copy model_cache.sqlite from %s to %s: %s",
+                    source_cache_file,
+                    target_cache_file,
+                    exc,
+                )
+
+    def _get_user_config_directory(self) -> str:
+        """Return the user configuration directory, falling back to ~/.config."""
+
+        try:
+            config_dir = user_config_dir(APP_NAME, appauthor=False) or ""
+        except Exception as exc:  # pragma: no cover - defensive fallback
+            logger.warning("Failed to determine user config directory: %s", exc)
+            config_dir = ""
+
+        if not config_dir:
+            config_dir = os.path.join(os.path.expanduser("~"), f".config/{APP_NAME}")
+
+        try:
+            os.makedirs(config_dir, exist_ok=True)
+        except Exception as exc:
+            logger.warning("Failed to create user config directory %s: %s", config_dir, exc)
+
+        return config_dir
+
     def _notify_model_name_display_change(self, value: Any) -> None:
         """Trigger cache resorting when the model name display preference updates."""
 
@@ -953,6 +1162,7 @@ class SettingsManager:
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
+        default_unet_root: Optional[str] = None,
         default_embedding_root: Optional[str] = None,
         metadata: Optional[Mapping[str, Any]] = None,
         activate: bool = False,
@@ -977,6 +1187,11 @@ class SettingsManager:
                 if default_checkpoint_root is not None
                 else existing.get("default_checkpoint_root")
             ),
+            default_unet_root=(
+                default_unet_root
+                if default_unet_root is not None
+                else existing.get("default_unet_root")
+            ),
             default_embedding_root=(
                 default_embedding_root
                 if default_embedding_root is not None
@@ -1006,6 +1221,7 @@ class SettingsManager:
         folder_paths: Mapping[str, Iterable[str]],
         default_lora_root: str = "",
         default_checkpoint_root: str = "",
+        default_unet_root: str = "",
         default_embedding_root: str = "",
         metadata: Optional[Mapping[str, Any]] = None,
         activate: bool = False,
@@ -1021,6 +1237,7 @@ class SettingsManager:
             folder_paths=folder_paths,
             default_lora_root=default_lora_root,
             default_checkpoint_root=default_checkpoint_root,
+            default_unet_root=default_unet_root,
             default_embedding_root=default_embedding_root,
             metadata=metadata,
             activate=activate,
@@ -1078,6 +1295,7 @@ class SettingsManager:
         *,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
+        default_unet_root: Optional[str] = None,
         default_embedding_root: Optional[str] = None,
     ) -> None:
         """Update folder paths for the active library."""
@@ -1088,6 +1306,7 @@ class SettingsManager:
             folder_paths=folder_paths,
             default_lora_root=default_lora_root,
             default_checkpoint_root=default_checkpoint_root,
+            default_unet_root=default_unet_root,
             default_embedding_root=default_embedding_root,
             activate=True,
         )

py/services/tag_update_service.py

@@ -33,7 +33,8 @@ class TagUpdateService:
         tags_added: List[str] = []
         for tag in new_tags:
             if isinstance(tag, str) and tag.strip():
-                normalized = tag.strip()
+                # Convert all tags to lowercase to avoid case sensitivity issues on Windows
+                normalized = tag.strip().lower()
                 if normalized.lower() not in existing_lower:
                     existing_tags.append(normalized)
                     existing_lower.append(normalized.lower())

py/services/use_cases/auto_organize_use_case.py

@@ -39,6 +39,7 @@ class AutoOrganizeUseCase:
         *,
         file_paths: Optional[Sequence[str]] = None,
         progress_callback: Optional[ProgressCallback] = None,
+        exclusion_patterns: Optional[Sequence[str]] = None,
     ) -> AutoOrganizeResult:
         """Run the auto-organize routine guarded by a shared lock."""
 
@@ -53,4 +54,5 @@ class AutoOrganizeUseCase:
             return await self._file_service.auto_organize_models(
                 file_paths=list(file_paths) if file_paths is not None else None,
                 progress_callback=progress_callback,
+                exclusion_patterns=exclusion_patterns,
             )

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -59,6 +59,8 @@ class BulkMetadataRefreshUseCase:
         success = 0
         needs_resort = False
 
+        self._service.scanner.reset_cancellation()
+
         async def emit(status: str, **extra: Any) -> None:
             if progress_callback is None:
                 return
@@ -69,6 +71,10 @@ class BulkMetadataRefreshUseCase:
         await emit("started")
 
         for model in to_process:
+            if self._service.scanner.is_cancelled():
+                self._logger.info("Bulk metadata refresh cancelled by user")
+                await emit("cancelled", processed=processed, success=success)
+                return {"success": False, "message": "Operation cancelled", "processed": processed, "updated": success, "total": total_models}
             try:
                 original_name = model.get("model_name")
                 await MetadataManager.hydrate_model_data(model)

py/services/websocket_manager.py

@@ -20,6 +20,8 @@ class WebSocketManager:
         self._last_init_progress: Dict[str, Dict] = {}
         # Add auto-organize progress tracking
         self._auto_organize_progress: Optional[Dict] = None
+        # Add recipe repair progress tracking
+        self._recipe_repair_progress: Optional[Dict] = None
         self._auto_organize_lock = asyncio.Lock()
         
     async def handle_connection(self, request: web.Request) -> web.WebSocketResponse:
@@ -189,6 +191,14 @@ class WebSocketManager:
         # Broadcast via WebSocket
         await self.broadcast(data)
     
+    async def broadcast_recipe_repair_progress(self, data: Dict):
+        """Broadcast recipe repair progress to connected clients"""
+        # Store progress data in memory
+        self._recipe_repair_progress = data
+        
+        # Broadcast via WebSocket
+        await self.broadcast(data)
+    
     def get_auto_organize_progress(self) -> Optional[Dict]:
         """Get current auto-organize progress"""
         return self._auto_organize_progress
@@ -197,6 +207,22 @@ class WebSocketManager:
         """Clear auto-organize progress data"""
         self._auto_organize_progress = None
     
+    def get_recipe_repair_progress(self) -> Optional[Dict]:
+        """Get current recipe repair progress"""
+        return self._recipe_repair_progress
+    
+    def cleanup_recipe_repair_progress(self):
+        """Clear recipe repair progress data if it is in a finished state"""
+        if self._recipe_repair_progress and self._recipe_repair_progress.get('status') in ['completed', 'cancelled', 'error']:
+            self._recipe_repair_progress = None
+    
+    def is_recipe_repair_running(self) -> bool:
+        """Check if recipe repair is currently running"""
+        if not self._recipe_repair_progress:
+            return False
+        status = self._recipe_repair_progress.get('status')
+        return status in ['started', 'processing']
+    
     def is_auto_organize_running(self) -> bool:
         """Check if auto-organize is currently running"""
         if not self._auto_organize_progress:

py/utils/civitai_utils.py

@@ -2,9 +2,152 @@
 
 from __future__ import annotations
 
+from typing import Any, Dict, Iterable, Mapping, Sequence
 from urllib.parse import urlparse, urlunparse
 
 
+_DEFAULT_ALLOW_COMMERCIAL_USE: Sequence[str] = ("Sell",)
+_LICENSE_DEFAULTS: Dict[str, Any] = {
+    "allowNoCredit": True,
+    "allowCommercialUse": _DEFAULT_ALLOW_COMMERCIAL_USE,
+    "allowDerivatives": True,
+    "allowDifferentLicense": True,
+}
+_COMMERCIAL_ALLOWED_VALUES = {"sell", "rent", "rentcivit", "image"}
+_COMMERCIAL_SHIFT = 1
+
+
+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("}"))
+        if not looks_aggregate:
+            return [value_str]
+
+        trimmed = stripped
+        if trimmed.startswith("{") and trimmed.endswith("}"):
+            trimmed = trimmed[1:-1]
+
+        parts = [part.strip() for part in trimmed.split(",")]
+        result = [part for part in parts if part]
+        return result or [value_str]
+
+    if value is None:
+        return list(_DEFAULT_ALLOW_COMMERCIAL_USE)
+
+    if isinstance(value, str):
+        return _split_aggregate(value)
+
+    if isinstance(value, Iterable):
+        result = []
+        for item in value:
+            if item is None:
+                continue
+            if isinstance(item, str):
+                result.extend(_split_aggregate(item))
+                continue
+            result.append(str(item))
+        if result:
+            return result
+        try:
+            if len(value) == 0:  # type: ignore[arg-type]
+                return []
+        except TypeError:
+            pass
+
+    return list(_DEFAULT_ALLOW_COMMERCIAL_USE)
+
+
+def _to_bool(value: Any, fallback: bool) -> bool:
+    if value is None:
+        return fallback
+    return bool(value)
+
+
+def resolve_license_payload(model_data: Mapping[str, Any] | None) -> Dict[str, Any]:
+    """Extract license fields from model metadata applying documented defaults."""
+
+    payload: Dict[str, Any] = {}
+
+    allow_no_credit = payload["allowNoCredit"] = _to_bool(
+        (model_data or {}).get("allowNoCredit"),
+        _LICENSE_DEFAULTS["allowNoCredit"],
+    )
+
+    commercial = _normalize_commercial_values(
+        (model_data or {}).get("allowCommercialUse"),
+    )
+    payload["allowCommercialUse"] = list(commercial)
+
+    allow_derivatives = payload["allowDerivatives"] = _to_bool(
+        (model_data or {}).get("allowDerivatives"),
+        _LICENSE_DEFAULTS["allowDerivatives"],
+    )
+
+    allow_different_license = payload["allowDifferentLicense"] = _to_bool(
+        (model_data or {}).get("allowDifferentLicense"),
+        _LICENSE_DEFAULTS["allowDifferentLicense"],
+    )
+
+    # Ensure booleans are plain bool instances
+    payload["allowNoCredit"] = bool(allow_no_credit)
+    payload["allowDerivatives"] = bool(allow_derivatives)
+    payload["allowDifferentLicense"] = bool(allow_different_license)
+
+    return payload
+
+
+def _resolve_commercial_bits(values: Sequence[str]) -> int:
+    normalized_values = set()
+    for value in values:
+        normalized = str(value).strip().lower().replace("_", "").replace("-", "")
+        if normalized in _COMMERCIAL_ALLOWED_VALUES:
+            normalized_values.add(normalized)
+
+    has_sell = "sell" in normalized_values
+    has_rent = has_sell or "rent" in normalized_values
+    has_rentcivit = has_rent or "rentcivit" in normalized_values
+    has_image = has_sell or "image" in normalized_values
+
+    commercial_bits = (
+        (1 if has_sell else 0) << 3
+        | (1 if has_rent else 0) << 2
+        | (1 if has_rentcivit else 0) << 1
+        | (1 if has_image else 0)
+    )
+    return commercial_bits << _COMMERCIAL_SHIFT
+
+
+def build_license_flags(payload: Mapping[str, Any] | None) -> int:
+    """Encode license payload into a compact bitset for cache storage."""
+
+    resolved = resolve_license_payload(payload or {})
+
+    flags = 0
+    if resolved.get("allowNoCredit", True):
+        flags |= 1 << 0
+
+    commercial_bits = _resolve_commercial_bits(resolved.get("allowCommercialUse", ()))
+    flags |= commercial_bits
+
+    if resolved.get("allowDerivatives", True):
+        flags |= 1 << 5
+
+    if resolved.get("allowDifferentLicense", True):
+        flags |= 1 << 6
+
+    return flags
+
+
+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]:
     """Rewrite Civitai preview URLs to use optimized renditions.
 
@@ -43,5 +186,9 @@ def rewrite_preview_url(source_url: str | None, media_type: str | None = None) -
     return rewritten, True
 
 
-__all__ = ["rewrite_preview_url"]
-
+__all__ = [
+    "build_license_flags",
+    "resolve_license_payload",
+    "resolve_license_info",
+    "rewrite_preview_url",
+]

py/utils/constants.py

@@ -4,14 +4,14 @@ NSFW_LEVELS = {
     "R": 4,
     "X": 8,
     "XXX": 16,
-    "Blocked": 32, # Probably not actually visible through the API without being logged in on model owner account?
+    "Blocked": 32,  # Probably not actually visible through the API without being logged in on model owner account?
 }
 
 # Node type constants
 NODE_TYPES = {
     "Lora Loader (LoraManager)": 1,
     "Lora Stacker (LoraManager)": 2,
-    "WanVideo Lora Select (LoraManager)": 3
+    "WanVideo Lora Select (LoraManager)": 3,
 }
 
 # Default ComfyUI node color when bgcolor is null
@@ -19,18 +19,18 @@ DEFAULT_NODE_COLOR = "#353535"
 
 # preview extensions
 PREVIEW_EXTENSIONS = [
-    '.webp',
-    '.preview.webp',
-    '.preview.png',
-    '.preview.jpeg',
-    '.preview.jpg',
-    '.preview.mp4',
-    '.png',
-    '.jpeg',
-    '.jpg',
-    '.mp4',
-    '.gif',
-    '.webm'
+    ".webp",
+    ".preview.webp",
+    ".preview.png",
+    ".preview.jpeg",
+    ".preview.jpg",
+    ".preview.mp4",
+    ".png",
+    ".jpeg",
+    ".jpg",
+    ".mp4",
+    ".gif",
+    ".webm",
 ]
 
 # Card preview image width
@@ -41,34 +41,70 @@ EXAMPLE_IMAGE_WIDTH = 832
 
 # Supported media extensions for example downloads
 SUPPORTED_MEDIA_EXTENSIONS = {
-    'images': ['.jpg', '.jpeg', '.png', '.webp', '.gif'],
-    'videos': ['.mp4', '.webm']
+    "images": [".jpg", ".jpeg", ".png", ".webp", ".gif"],
+    "videos": [".mp4", ".webm"],
 }
 
 # Valid Lora types
-VALID_LORA_TYPES = ['lora', 'locon', 'dora']
+VALID_LORA_TYPES = ["lora", "locon", "dora"]
 
 # Supported Civitai model types for user model queries (case-insensitive)
 CIVITAI_USER_MODEL_TYPES = [
     *VALID_LORA_TYPES,
-    'textualinversion',
-    'checkpoint',
+    "textualinversion",
+    "checkpoint",
 ]
 
+# Default chunk size in megabytes used for hashing large files.
+DEFAULT_HASH_CHUNK_SIZE_MB = 4
+
 # Auto-organize settings
-AUTO_ORGANIZE_BATCH_SIZE = 50  # Process models in batches to avoid overwhelming the system
+AUTO_ORGANIZE_BATCH_SIZE = (
+    50  # Process models in batches to avoid overwhelming the system
+)
 
 # Civitai model tags in priority order for subfolder organization
 CIVITAI_MODEL_TAGS = [
-    'character', 'concept', 'clothing',
-    'realistic', 'anime', 'toon', 'furry', 'style',
-    'poses', 'background', 'tool', 'vehicle', 'buildings',
-    'objects', 'assets', 'animal', 'action'
+    "character",
+    "concept",
+    "clothing",
+    "realistic",
+    "anime",
+    "toon",
+    "furry",
+    "style",
+    "poses",
+    "background",
+    "tool",
+    "vehicle",
+    "buildings",
+    "objects",
+    "assets",
+    "animal",
+    "action",
 ]
 
 # Default priority tag configuration strings for each model type
 DEFAULT_PRIORITY_TAG_CONFIG = {
-    'lora': ', '.join(CIVITAI_MODEL_TAGS),
-    'checkpoint': ', '.join(CIVITAI_MODEL_TAGS),
-    'embedding': ', '.join(CIVITAI_MODEL_TAGS),
+    "lora": ", ".join(CIVITAI_MODEL_TAGS),
+    "checkpoint": ", ".join(CIVITAI_MODEL_TAGS),
+    "embedding": ", ".join(CIVITAI_MODEL_TAGS),
 }
+
+# baseModel values from CivitAI that should be treated as diffusion models (unet)
+# These model types are incorrectly labeled as "checkpoint" by CivitAI but are actually diffusion models
+DIFFUSION_MODEL_BASE_MODELS = frozenset(
+    [
+        "ZImageTurbo",
+        "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 I2V-A14B",
+        "Wan Video 2.2 T2V-A14B",
+        "Wan Video 2.5 T2V",
+        "Wan Video 2.5 I2V",
+        "Qwen",
+    ]
+)

py/utils/example_images_processor.py

@@ -593,5 +593,114 @@ class ExampleImagesProcessor:
                 'error': str(e)
             }, status=500)
 
+    @staticmethod
+    async def set_example_image_nsfw_level(request: web.Request) -> web.StreamResponse:
+        """
+        Update the NSFW level for a single example image (regular or custom).
+        """
+        try:
+            data = await request.json()
+        except Exception:
+            return web.json_response({'success': False, 'error': 'Invalid JSON body'}, status=400)
+
+        model_hash = data.get('model_hash')
+        raw_level = data.get('nsfw_level')
+        source = (data.get('source') or 'civitai').lower()
+        index = data.get('index')
+        image_id = data.get('id')
+
+        if model_hash is None or raw_level is None:
+            return web.json_response(
+                {'success': False, 'error': 'Missing required parameters: model_hash and nsfw_level'},
+                status=400,
+            )
+
+        try:
+            nsfw_level = int(raw_level)
+        except (TypeError, ValueError):
+            return web.json_response(
+                {'success': False, 'error': 'nsfw_level must be an integer'}, status=400
+            )
+
+        if source == 'custom':
+            if not image_id:
+                return web.json_response(
+                    {'success': False, 'error': 'Custom images require an id field'}, status=400
+                )
+        else:
+            try:
+                index = int(index)
+            except (TypeError, ValueError):
+                return web.json_response(
+                    {'success': False, 'error': 'Regular images require a numeric index'}, status=400
+                )
+
+        try:
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+
+            model_data = None
+            scanner = None
+
+            for scan_obj in [lora_scanner, checkpoint_scanner, embedding_scanner]:
+                if scan_obj.has_hash(model_hash):
+                    cache = await scan_obj.get_cached_data()
+                    for item in cache.raw_data:
+                        if item.get('sha256') == model_hash:
+                            model_data = item
+                            scanner = scan_obj
+                            break
+                if model_data:
+                    break
+
+            if not model_data:
+                return web.json_response(
+                    {'success': False, 'error': f"Model with hash {model_hash} not found in cache"},
+                    status=404,
+                )
+
+            await MetadataManager.hydrate_model_data(model_data)
+            civitai_data = model_data.setdefault('civitai', {})
+            regular_images = civitai_data.get('images') or []
+            custom_images = civitai_data.get('customImages') or []
+
+            target_image = None
+            if source == 'custom':
+                for image in custom_images:
+                    if image.get('id') == image_id:
+                        target_image = image
+                        break
+            else:
+                if 0 <= index < len(regular_images):
+                    target_image = regular_images[index]
+
+            if target_image is None:
+                return web.json_response(
+                    {'success': False, 'error': 'Target image not found'}, status=404
+                )
+
+            target_image['nsfwLevel'] = nsfw_level
+            civitai_data['images'] = regular_images
+            civitai_data['customImages'] = custom_images
+
+            file_path = model_data.get('file_path')
+            if file_path:
+                model_copy = model_data.copy()
+                model_copy.pop('folder', None)
+                await MetadataManager.save_metadata(file_path, model_copy)
+                await scanner.update_single_model_cache(file_path, file_path, model_data)
+
+            return web.json_response({
+                'success': True,
+                'regular_images': regular_images,
+                'custom_images': custom_images,
+                'model_file_path': model_data.get('file_path', ''),
+                'nsfw_level': nsfw_level
+            })
+        except Exception as exc:
+            logger.error("Failed to update example image NSFW level: %s", exc, exc_info=True)
+            return web.json_response({'success': False, 'error': str(exc)}, status=500)
+
 
     

py/utils/exif_utils.py

@@ -22,6 +22,12 @@ class ExifUtils:
             Optional[str]: Extracted metadata or None if not found
         """
         try:
+            # Skip for video files
+            if image_path:
+                ext = os.path.splitext(image_path)[1].lower()
+                if ext in ['.mp4', '.webm']:
+                    return None
+
             # First try to open the image
             with Image.open(image_path) as img:
                 # Method 1: Check for parameters in image info
@@ -80,6 +86,12 @@ class ExifUtils:
             str: Path to the updated image
         """
         try:
+            # Skip for video files
+            if image_path:
+                ext = os.path.splitext(image_path)[1].lower()
+                if ext in ['.mp4', '.webm']:
+                    return image_path
+
             # Load the image and check its format
             with Image.open(image_path) as img:
                 img_format = img.format
@@ -133,6 +145,12 @@ class ExifUtils:
     def append_recipe_metadata(image_path, recipe_data) -> str:
         """Append recipe metadata to an image's EXIF data"""
         try:
+            # Skip for video files
+            if image_path:
+                ext = os.path.splitext(image_path)[1].lower()
+                if ext in ['.mp4', '.webm']:
+                    return image_path
+
             # First, extract existing metadata
             metadata = ExifUtils.extract_image_metadata(image_path)
             
@@ -140,6 +158,28 @@ class ExifUtils:
             if metadata:
                 # Remove any existing recipe metadata
                 metadata = ExifUtils.remove_recipe_metadata(metadata)
+
+            # Prepare checkpoint data
+            checkpoint_data = recipe_data.get("checkpoint") or {}
+            simplified_checkpoint = None
+            if isinstance(checkpoint_data, dict) and checkpoint_data:
+                simplified_checkpoint = {
+                    "type": checkpoint_data.get("type", "checkpoint"),
+                    "modelId": checkpoint_data.get("modelId", 0),
+                    "modelVersionId": checkpoint_data.get("modelVersionId")
+                    or checkpoint_data.get("id", 0),
+                    "modelName": checkpoint_data.get(
+                        "modelName", checkpoint_data.get("name", "")
+                    ),
+                    "modelVersionName": checkpoint_data.get(
+                        "modelVersionName", checkpoint_data.get("version", "")
+                    ),
+                    "hash": checkpoint_data.get("hash", "").lower()
+                    if checkpoint_data.get("hash")
+                    else "",
+                    "file_name": checkpoint_data.get("file_name", ""),
+                    "baseModel": checkpoint_data.get("baseModel", ""),
+                }
             
             # Prepare simplified loras data
             simplified_loras = []
@@ -160,7 +200,8 @@ class ExifUtils:
                 'base_model': recipe_data.get('base_model', ''),
                 'loras': simplified_loras,
                 'gen_params': recipe_data.get('gen_params', {}),
-                'tags': recipe_data.get('tags', [])
+                'tags': recipe_data.get('tags', []),
+                **({'checkpoint': simplified_checkpoint} if simplified_checkpoint else {})
             }
             
             # Convert to JSON string
@@ -219,6 +260,16 @@ class ExifUtils:
             Tuple of (optimized_image_data, extension)
         """
         try:
+            # Skip for video files early if it's a file path
+            if isinstance(image_data, str) and os.path.exists(image_data):
+                ext = os.path.splitext(image_data)[1].lower()
+                if ext in ['.mp4', '.webm']:
+                    try:
+                        with open(image_data, 'rb') as f:
+                            return f.read(), ext
+                    except Exception:
+                        return image_data, ext
+
             # First validate the image data is usable
             img = None
             if isinstance(image_data, str) and os.path.exists(image_data):
@@ -359,4 +410,4 @@ class ExifUtils:
                         return f.read(), os.path.splitext(image_data)[1]
                 except Exception:
                     return image_data, '.jpg'  # Last resort fallback
-            return image_data, '.jpg'
+            return image_data, '.jpg'

py/utils/file_utils.py

@@ -1,17 +1,41 @@
+
+import hashlib
 import logging
 import os
-import hashlib
 
-from .constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH
+from .constants import (
+    CARD_PREVIEW_WIDTH,
+    DEFAULT_HASH_CHUNK_SIZE_MB,
+    PREVIEW_EXTENSIONS,
+)
 from .exif_utils import ExifUtils
+from ..services.settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
 
+
+def _get_hash_chunk_size_bytes() -> int:
+    """Return the chunk size used for hashing, in bytes."""
+
+    settings_manager = get_settings_manager()
+    chunk_size_mb = settings_manager.get("hash_chunk_size_mb", DEFAULT_HASH_CHUNK_SIZE_MB)
+    try:
+        chunk_size_value = float(chunk_size_mb)
+    except (TypeError, ValueError):
+        chunk_size_value = float(DEFAULT_HASH_CHUNK_SIZE_MB)
+
+    if chunk_size_value <= 0:
+        chunk_size_value = float(DEFAULT_HASH_CHUNK_SIZE_MB)
+
+    return max(1, int(chunk_size_value * 1024 * 1024))
+
+
 async def calculate_sha256(file_path: str) -> str:
     """Calculate SHA256 hash of a file"""
     sha256_hash = hashlib.sha256()
+    chunk_size = _get_hash_chunk_size_bytes()
     with open(file_path, "rb") as f:
-        for byte_block in iter(lambda: f.read(128 * 1024), b""):
+        for byte_block in iter(lambda: f.read(chunk_size), b""):
             sha256_hash.update(byte_block)
     return sha256_hash.hexdigest()
 
@@ -81,4 +105,4 @@ def get_preview_extension(preview_path: str) -> str:
 
 def normalize_path(path: str) -> str:
     """Normalize file path to use forward slashes"""
-    return path.replace(os.sep, "/") if path else path
+    return path.replace(os.sep, "/") if path else path

py/utils/logging_config.py

@@ -0,0 +1,26 @@
+import logging
+import os
+
+def setup_logging():
+    """
+    Sets up a global log record factory that prepends '[LoRA-Manager]' to all logs
+    generated by this extension.
+    """
+    # project_root should be the parent of the directory containing this file (py/utils/logging_config.py)
+    # So project_root is ComfyUI-Lora-Manager/
+    project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    prefix = "[LoRA-Manager] "
+
+    old_factory = logging.getLogRecordFactory()
+    def factory(*args, **kwargs):
+        record = old_factory(*args, **kwargs)
+        
+        # Check if the log is coming from our extension
+        # We use pathname to verify if it's within our project directory
+        if record.pathname and os.path.abspath(record.pathname).startswith(project_root):
+            if isinstance(record.msg, str) and not record.msg.startswith(prefix):
+                record.msg = f"{prefix}{record.msg}"
+        
+        return record
+
+    logging.setLogRecordFactory(factory)

py/utils/metadata_manager.py

@@ -2,6 +2,7 @@ from datetime import datetime
 import os
 import json
 import logging
+import time
 from typing import Any, Dict, Optional, Type, Union
 
 from .models import BaseModelMetadata, LoraMetadata
@@ -22,7 +23,7 @@ class MetadataManager:
     """
     
     @staticmethod
-    async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
+    async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> tuple[Optional[BaseModelMetadata], bool]:
         """
         Load metadata safely.
         
@@ -203,7 +204,11 @@ class MetadataManager:
             preview_url = find_preview_file(base_name, dir_path)
             
             # Calculate file hash
+            start_hash_time = time.perf_counter()
+            logger.debug(f"Calculating SHA256 hash for {real_path}...")
             sha256 = await calculate_sha256(real_path)
+            hash_duration = time.perf_counter() - start_hash_time
+            logger.info(f"SHA256 hash calculated for {real_path} in {hash_duration:.3f}s")
             
             # Create instance based on model type
             if model_class.__name__ == "CheckpointMetadata":
@@ -255,6 +260,7 @@ class MetadataManager:
             # await MetadataManager._enrich_metadata(metadata, real_path)
             
             # Save the created metadata
+            logger.info(f"Creating new .metadata.json for {file_path} (Reason: No existing metadata found)")
             await MetadataManager.save_metadata(file_path, metadata)
             
             return metadata