feat(release): update version to 0.9.6 and add release notes for critical performance optimizations and new features

fix: throttle model card video lazy loading

.github/copilot-instructions.md

@@ -0,0 +1 @@
+Always use English for comments.

.github/workflows/backend-tests.yml

@@ -0,0 +1,69 @@
+name: Backend Tests
+
+on:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - 'py/**'
+      - 'standalone.py'
+      - 'tests/**'
+      - 'requirements.txt'
+      - 'requirements-dev.txt'
+      - 'pyproject.toml'
+      - 'pytest.ini'
+      - '.github/workflows/backend-tests.yml'
+  pull_request:
+    paths:
+      - 'py/**'
+      - 'standalone.py'
+      - 'tests/**'
+      - 'requirements.txt'
+      - 'requirements-dev.txt'
+      - 'pyproject.toml'
+      - 'pytest.ini'
+      - '.github/workflows/backend-tests.yml'
+
+jobs:
+  pytest:
+    name: Run pytest with coverage
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+          cache: 'pip'
+          cache-dependency-path: |
+            requirements.txt
+            requirements-dev.txt
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-dev.txt
+
+      - name: Run pytest with coverage
+        env:
+          COVERAGE_FILE: coverage/backend/.coverage
+        run: |
+          mkdir -p coverage/backend
+          python -m pytest \
+            --cov=py \
+            --cov=standalone \
+            --cov-report=term-missing \
+            --cov-report=xml:coverage/backend/coverage.xml \
+            --cov-report=html:coverage/backend/html \
+            --cov-report=json:coverage/backend/coverage.json
+
+      - name: Upload coverage artifact
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: backend-coverage
+          path: coverage/backend
+          if-no-files-found: warn

.github/workflows/frontend-tests.yml

@@ -0,0 +1,52 @@
+name: Frontend Tests
+
+on:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - 'package.json'
+      - 'package-lock.json'
+      - 'vitest.config.js'
+      - 'tests/frontend/**'
+      - 'static/js/**'
+      - 'scripts/run_frontend_coverage.js'
+      - '.github/workflows/frontend-tests.yml'
+  pull_request:
+    paths:
+      - 'package.json'
+      - 'package-lock.json'
+      - 'vitest.config.js'
+      - 'tests/frontend/**'
+      - 'static/js/**'
+      - 'scripts/run_frontend_coverage.js'
+      - '.github/workflows/frontend-tests.yml'
+
+jobs:
+  vitest:
+    name: Run Vitest with coverage
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Use Node.js 20
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: 'npm'
+
+      - name: Install dependencies
+        run: npm ci
+
+      - name: Run frontend tests with coverage
+        run: npm run test:coverage
+
+      - name: Upload coverage artifact
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: frontend-coverage
+          path: coverage/frontend
+          if-no-files-found: warn

.gitignore

@@ -5,3 +5,7 @@ output/*
 py/run_test.py
 .vscode/
 cache/
+civitai/
+node_modules/
+coverage/
+.coverage

AGENTS.md

@@ -0,0 +1,22 @@
+# Repository Guidelines
+
+## 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.
+
+## 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.
+
+## 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.
+
+## 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.
+
+## 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.
+
+## 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.

README.md

@@ -34,68 +34,61 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ## Release Notes
 
+### v0.9.6
+* **Critical Performance Optimization** - Introduced persistent model cache that dramatically accelerates initialization after startup and significantly reduces Python backend memory footprint for improved application performance.
+* **Cross-Browser Settings Synchronization** - Migrated nearly all settings to the backend, ensuring your preferences sync automatically across all browsers for a seamless multi-browser experience.
+* **Protected User Settings Location** - Relocated user settings (settings.json) to the user config directory (accessible via the link icon in Settings), preventing accidental deletion during reinstalls or updates.
+* **Global Context Menu** - Added a new global context menu accessible by right-clicking on empty page areas, providing quick access to global operations with more features coming in future updates.
+* **Multi-Library Support** - Introduced support for managing multiple libraries, allowing you to easily switch between different model collections (advanced usage, documentation in progress).
+* **Bug Fixes & Stability Improvements** - Various bug fixes and enhancements for improved stability and reliability.
+
+### v0.9.3
+* **Metadata Archive Database Support** - Added the ability to download and utilize a metadata archive database, enabling access to metadata for models that have been deleted from CivitAI.
+* **App-Level Proxy Settings** - Introduced support for configuring a global proxy within the application, making it easier to use the manager behind network restrictions.
+* **Bug Fixes** - Various bug fixes for improved stability and reliability.
+
+### v0.9.2
+* **Bulk Auto-Organization Action** - Added a new bulk auto-organization feature. You can now select multiple models and automatically organize them according to your current path template settings for streamlined management.
+* **Bug Fixes** - Addressed several bugs to improve stability and reliability.
+
+### v0.9.1
+* **Enhanced Bulk Operations** - Improved bulk operations with Marquee Selection and a bulk operation context menu, providing a more intuitive, desktop-application-like user experience.
+* **New Bulk Actions** - Added bulk operations for adding tags and setting base models to multiple models simultaneously.
+
+### v0.9.0
+* **UI Overhaul for Enhanced Navigation** - Replaced the top flat folder tags with a new folder sidebar and breadcrumb navigation system for a more intuitive folder browsing and selection experience.
+* **Dual-Mode Folder Sidebar** - The new folder sidebar offers two display modes: 'List Mode,' which mirrors the classic folder view, and 'Tree Mode,' which presents a hierarchical folder structure for effortless navigation through nested directories.
+* **Internationalization Support** - Introduced multi-language support, now available in English, Simplified Chinese, Traditional Chinese, Spanish, Japanese, Korean, French, Russian, and German. Feedback from native speakers is welcome to improve the translations.
+* **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.
-
-### v0.8.25
-* **LoRA List Reordering**  
-  - Drag & Drop: Easily rearrange LoRA entries using the drag handle.
-  - Keyboard Shortcuts:  
-    - Arrow keys: Navigate between LoRAs  
-    - Ctrl/Cmd + Arrow: Move selected LoRA up/down  
-    - Ctrl/Cmd + Home/End: Move selected LoRA to top/bottom  
-    - Delete/Backspace: Remove selected LoRA  
-  - Context Menu: Right-click for quick actions like Move Up, Move Down, Move to Top, Move to Bottom.
-* **Bulk Operations for Checkpoints & Embeddings**  
-  - Bulk Mode: Select multiple checkpoints or embeddings for batch actions.
-  - Bulk Refresh: Update Civitai metadata for selected models.
-  - Bulk Delete: Remove multiple models at once.
-  - Bulk Move (Embeddings): Move selected embeddings to a different folder.
-* **New Setting: Auto Download Example Images**  
-  - Automatically fetch example images for models missing previews (requires download location to be set). Enabled by default.
-* **General Improvements**  
-  - Various user experience enhancements and stability fixes.
-
-### v0.8.22
-* **Embeddings Management** - Added Embeddings page for comprehensive embedding model management.
-* **Advanced Sorting Options** - Introduced flexible sorting controls, allowing sorting by name, added date, or file size in both ascending and descending order.
-* **Custom Download Path Templates & Base Model Mapping** - Implemented UI settings for configuring download path templates and base model path mappings, allowing customized model organization and storage location when downloading models via LM Civitai Extension.
-* **LM Civitai Extension Enhancements** - Improved concurrent download performance and stability, with new support for canceling active downloads directly from the extension interface.
-* **Update Feature** - Added update functionality, allowing users to update LoRA Manager to the latest release version directly from the LoRA Manager UI.
-* **Bulk Operations: Refresh All** - Added bulk refresh functionality, allowing users to update Civitai metadata across multiple LoRAs.
-
-### v0.8.20
-* **LM Civitai Extension** - Released [browser extension through Chrome Web Store](https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) that works seamlessly with LoRA Manager to enhance Civitai browsing experience, showing which models are already in your local library, enabling one-click downloads, and providing queue and parallel download support
-* **Enhanced Lora Loader** - Added support for nunchaku, improving convenience when working with ComfyUI-nunchaku workflows, plus new template workflows for quick onboarding
-* **WanVideo Integration** - Introduced WanVideo Lora Select (LoraManager) node compatible with ComfyUI-WanVideoWrapper for streamlined lora usage in video workflows, including a template workflow to help you get started quickly
-
-### v0.8.19
-* **Analytics Dashboard** - Added new Statistics page providing comprehensive visual analysis of model collection and usage patterns for better library insights
-* **Target Node Selection** - Enhanced workflow integration with intelligent target choosing when sending LoRAs/recipes to workflows with multiple loader/stacker nodes; a visual selector now appears showing node color, type, ID, and title for precise targeting
-* **Enhanced NSFW Controls** - Added support for setting NSFW levels on recipes with automatic content blurring based on user preferences
-* **Customizable Card Display** - New display settings allowing users to choose whether card information and action buttons are always visible or only revealed on hover
-* **Expanded Compatibility** - Added support for efficiency-nodes-comfyui in Save Recipe and Save Image nodes, plus fixed compatibility with ComfyUI_Custom_Nodes_AlekPet
-
-### v0.8.18
-* **Custom Example Images** - Added ability to import your own example images for LoRAs and checkpoints with automatic metadata extraction from embedded information
-* **Enhanced Example Management** - New action buttons to set specific examples as previews or delete custom examples
-* **Improved Duplicate Detection** - Enhanced "Find Duplicates" with hash verification feature to eliminate false positives when identifying duplicate models
-* **Tag Management** - Added tag editing functionality allowing users to customize and manage model tags
-* **Advanced Selection Controls** - Implemented Ctrl+A shortcut for quickly selecting all filtered LoRAs, automatically entering bulk mode when needed
-* **Note**: Cache file functionality temporarily disabled pending rework
-
-### v0.8.17
-* **Duplicate Model Detection** - Added "Find Duplicates" functionality for LoRAs and checkpoints using model file hash detection, enabling convenient viewing and batch deletion of duplicate models
-* **Enhanced URL Recipe Imports** - Optimized import recipe via URL functionality using CivitAI API calls instead of web scraping, now supporting all rated images (including NSFW) for recipe imports
-* **Improved TriggerWord Control** - Enhanced TriggerWord Toggle node with new default_active switch to set the initial state (active/inactive) when trigger words are added
-* **Centralized Example Management** - Added "Migrate Existing Example Images" feature to consolidate downloaded example images from model folders into central storage with customizable naming patterns
-* **Intelligent Word Suggestions** - Implemented smart trigger word suggestions by reading class tokens and tag frequency from safetensors files, displaying recommendations when editing trigger words
-* **Model Version Management** - Added "Re-link to CivitAI" context menu option for connecting models to different CivitAI versions when needed
+* **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)
 
@@ -154,10 +147,11 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ### Option 2: **Portable Standalone Edition** (No ComfyUI required)
 
-1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.15/lora_manager_portable.7z)
+1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.9.2/lora_manager_portable.7z)
 2. Copy the provided `settings.json.example` file to create a new file named `settings.json` in `comfyui-lora-manager` folder
-3. Edit `settings.json` to include your correct model folder paths and CivitAI API key
+3. Edit the new `settings.json` to include your correct model folder paths and CivitAI API key
 4. Run run.bat
+    - To change the startup port, edit `run.bat` and modify the parameter (e.g. `--port 9001`)
 
 ### Option 3: **Manual Installation**
 
@@ -223,7 +217,7 @@ You can combine multiple patterns to create detailed, organized filenames for yo
 You can now run LoRA Manager independently from ComfyUI:
 
 1. **For ComfyUI users**:
-   - Launch ComfyUI with LoRA Manager at least once to initialize the necessary path information in the `settings.json` file.
+   - Launch ComfyUI with LoRA Manager at least once to initialize the necessary path information in the `settings.json` file located in your user settings folder (see paths above).
    - Make sure dependencies are installed: `pip install -r requirements.txt`
    - From your ComfyUI root directory, run:
      ```bash
@@ -245,8 +239,37 @@ You can now run LoRA Manager independently from ComfyUI:
      ```
    - Access the interface through your browser at: `http://localhost:8188/loras`
 
+   > **Note:** Existing installations automatically migrate the legacy `settings.json` from the plugin folder to the user settings directory the first time you launch this version.
+
 This standalone mode provides a lightweight option for managing your model and recipe collection without needing to run the full ComfyUI environment, making it useful even for users who primarily use other stable diffusion interfaces.
 
+## Testing & Coverage
+
+### Backend
+
+Install the development dependencies and run pytest with coverage reports:
+
+```bash
+pip install -r requirements-dev.txt
+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
+```
+
+HTML, XML, and JSON artifacts are stored under `coverage/backend/` so you can inspect hot spots locally or from CI artifacts.
+
+### Frontend
+
+Run the Vitest coverage suite to analyze widget hot spots:
+
+```bash
+npm run test:coverage
+```
+
 ---
 
 ## Contributing
@@ -287,3 +310,6 @@ Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
 
 ---
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=willmiao/ComfyUI-Lora-Manager&type=Date)](https://star-history.com/#willmiao/ComfyUI-Lora-Manager&Date)

__init__.py

@@ -1,20 +1,42 @@
-from .py.lora_manager import LoraManager
-from .py.nodes.lora_loader import LoraManagerLoader
-from .py.nodes.trigger_word_toggle import TriggerWordToggle
-from .py.nodes.lora_stacker import LoraStacker
-from .py.nodes.save_image import SaveImage
-from .py.nodes.debug_metadata import DebugMetadata
-from .py.nodes.wanvideo_lora_select import WanVideoLoraSelect
-# Import metadata collector to install hooks on startup
-from .py.metadata_collector import init as init_metadata_collector
+try:  # pragma: no cover - import fallback for pytest collection
+    from .py.lora_manager import LoraManager
+    from .py.nodes.lora_loader import LoraManagerLoader, LoraManagerTextLoader
+    from .py.nodes.trigger_word_toggle import TriggerWordToggle
+    from .py.nodes.lora_stacker import LoraStacker
+    from .py.nodes.save_image import SaveImage
+    from .py.nodes.debug_metadata import DebugMetadata
+    from .py.nodes.wanvideo_lora_select import WanVideoLoraSelect
+    from .py.nodes.wanvideo_lora_select_from_text import WanVideoLoraSelectFromText
+    from .py.metadata_collector import init as init_metadata_collector
+except ImportError:  # pragma: no cover - allows running under pytest without package install
+    import importlib
+    import pathlib
+    import sys
+
+    package_root = pathlib.Path(__file__).resolve().parent
+    if str(package_root) not in sys.path:
+        sys.path.append(str(package_root))
+
+    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
+    LoraStacker = importlib.import_module("py.nodes.lora_stacker").LoraStacker
+    SaveImage = importlib.import_module("py.nodes.save_image").SaveImage
+    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
+    init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
     LoraManagerLoader.NAME: LoraManagerLoader,
+    LoraManagerTextLoader.NAME: LoraManagerTextLoader,
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
     SaveImage.NAME: SaveImage,
     DebugMetadata.NAME: DebugMetadata,
-    WanVideoLoraSelect.NAME: WanVideoLoraSelect
+    WanVideoLoraSelect.NAME: WanVideoLoraSelect,
+    WanVideoLoraSelectFromText.NAME: WanVideoLoraSelectFromText
 }
 
 WEB_DIRECTORY = "./web/comfyui"

docs/LM-Extension-Wiki.md

@@ -0,0 +1,180 @@
+## Overview
+
+The **LoRA Manager Civitai Extension** is a Browser extension designed to work seamlessly with [LoRA Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) to significantly enhance your browsing experience on [Civitai](https://civitai.com).
+It also supports browsing on [CivArchive](https://civarchive.com/) (formerly CivitaiArchive).
+
+With this extension, you can:
+
+✅ Instantly see which models are already present in your local library  
+✅ Download new models with a single click  
+✅ Manage downloads efficiently with queue and parallel download support  
+✅ Keep your downloaded models automatically organized according to your custom settings    
+
+![Civitai Models page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-models-page.png)  
+![CivArchive Models page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civarchive-models-page.png)  
+
+---
+
+## Why Are All Features for Supporters Only?
+
+I love building tools for the Stable Diffusion and ComfyUI communities, and LoRA Manager is a passion project that I've poured countless hours into. When I created this companion extension, my hope was to offer its core features for free, as a thank-you to all of you.
+
+Unfortunately, I've reached a point where I need to be realistic. The level of support from the free model has been far lower than what's needed to justify the continuous development and maintenance for both projects. It was a difficult decision, but I've chosen to make the extension's features exclusive to supporters.
+
+This change is crucial for me to be able to continue dedicating my time to improving the free and open-source LoRA Manager, which I'm committed to keeping available for everyone.
+
+Your support does more than just unlock a few features—it allows me to keep innovating and ensures the core LoRA Manager project thrives. I'm incredibly grateful for your understanding and any support you can offer. ❤️
+
+(_For those who previously supported me on Ko-fi with a one-time donation, I'll be sending out license keys individually as a thank-you._)
+
+
+---
+
+## Installation
+
+### Supported Browsers & Installation Methods
+
+| Browser            | Installation Method                                                                 |
+|--------------------|-------------------------------------------------------------------------------------|
+| **Google Chrome**   | [Chrome Web Store link](https://chromewebstore.google.com/detail/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) |
+| **Microsoft Edge**  | Install via Chrome Web Store (compatible)                                          |
+| **Brave Browser**   | Install via Chrome Web Store (compatible)                                          |
+| **Opera**           | Install via Chrome Web Store (compatible)                                          |
+| **Firefox**         | <div id="firefox-install" class="install-ok"><a href="https://github.com/willmiao/lm-civitai-extension-firefox/releases/latest/download/extension.xpi">📦 Install Firefox Extension (reviewed and verified by Mozilla)</a></div>             |
+
+For non-Chrome browsers (e.g., Microsoft Edge), you can typically install extensions from the Chrome Web Store by following these steps: open the extension’s Chrome Web Store page, click 'Get extension', then click 'Allow' when prompted to enable installations from other stores, and finally click 'Add extension' to complete the installation.
+
+---
+
+## Privacy & Security
+
+I understand concerns around browser extensions and privacy, and I want to be fully transparent about how the **LM Civitai Extension** works:
+
+- **Reviewed and Verified**  
+  This extension has been **manually reviewed and approved by the Chrome Web Store**. The Firefox version uses the **exact same code** (only the packaging format differs) and has passed **Mozilla’s Add-on review**.
+
+- **Minimal Network Access**  
+  The only external server this extension connects to is:  
+  **`https://willmiao.shop`** — used solely for **license validation**.
+
+  It does **not collect, transmit, or store any personal or usage data**.  
+  No browsing history, no user IDs, no analytics, no hidden trackers.
+
+- **Local-Only Model Detection**  
+  Model detection and LoRA Manager communication all happen **locally** within your browser, directly interacting with your local LoRA Manager backend.
+
+I value your trust and are committed to keeping your local setup private and secure. If you have any questions, feel free to reach out!
+
+---
+
+## How to Use
+
+After installing the extension, you'll automatically receive a **7-day trial** to explore all features.
+
+When the extension is correctly installed and your license is valid:
+
+- Open **Civitai**, and you'll see visual indicators added by the extension on model cards, showing:
+  - ✅ Models already present in your local library  
+  - ⬇️ A download button for models not in your library  
+
+Clicking the download button adds the corresponding model version to the download queue, waiting to be downloaded. You can set up to **5 models to download simultaneously**.
+
+### Visual Indicators Appear On:
+
+- **Home Page** — Featured models  
+- **Models Page**  
+- **Creator Profiles** — If the creator has set their models to be visible  
+- **Recommended Resources** — On individual model pages  
+
+### Version Buttons on Model Pages
+
+On a specific model page, visual indicators also appear on version buttons, showing which versions are already in your local library.
+
+When switching to a specific version by clicking a version button:
+
+- Clicking the download button will open a dropdown:
+  - Download via **LoRA Manager**  
+  - Download via **Original Download** (browser download)  
+
+You can check **Remember my choice** to set your preferred default. You can change this setting anytime in the extension's settings.
+
+![Civitai Model Page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-model-page.png)
+
+### Resources on Image Pages (2025-08-05) — now shows in-library indicators for image resources. ‘Import image as recipe’ coming soon!
+
+![Civitai Image Page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-image-page.jpg)
+
+---
+
+## Model Download Location & LoRA Manager Settings
+
+To use the **one-click download function**, you must first set:
+
+- Your **Default LoRAs Root**  
+- Your **Default Checkpoints Root**  
+
+These are set within LoRA Manager's settings.
+
+When everything is configured, downloaded model files will be placed in:  
+
+`<Default_Models_Root>/<Base_Model_of_the_Model>/<First_Tag_of_the_Model>`
+
+
+### Update: Default Path Customization (2025-07-21)  
+
+A new setting to customize the default download path has been added in the nightly version. You can now personalize where models are saved when downloading via the LM Civitai Extension.
+
+![Default Path Customization](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/default-path-customization.png)
+
+The previous YAML path mapping file will be deprecated—settings will now be unified in settings.json to simplify configuration.
+
+---
+
+## Backend Port Configuration
+
+If your **ComfyUI** or **LoRA Manager** backend is running on a port **other than the default 8188**, you must configure the backend port in the extension's settings.
+
+After correctly setting and saving the port, you'll see in the extension's header area:  
+- A **Healthy** status with the tooltip:  `Connected to LoRA Manager on port xxxx`
+
+
+---
+
+## Advanced Usage
+
+### Connecting to a Remote LoRA Manager
+
+If your LoRA Manager is running on another computer, you can still connect from your browser using port forwarding.
+
+> **Why can't you set a remote IP directly?**
+>
+> For privacy and security, the extension only requests access to `http://127.0.0.1/*`. Supporting remote IPs would require much broader permissions, which may be rejected by browser stores and could raise user concerns.
+
+**Solution: Port Forwarding with `socat`**
+
+On your browser computer, run:
+
+`socat TCP-LISTEN:8188,bind=127.0.0.1,fork TCP:REMOTE.IP.ADDRESS.HERE:8188`
+
+- Replace `REMOTE.IP.ADDRESS.HERE` with the IP of the machine running LoRA Manager.
+- Adjust the port if needed.
+
+This lets the extension connect to `127.0.0.1:8188` as usual, with traffic forwarded to your remote server.
+
+_Thanks to user **Temikus** for sharing this solution!_
+
+---
+
+## Roadmap
+
+The extension will evolve alongside **LoRA Manager** improvements. Planned features include:
+
+- [x] Support for **additional model types** (e.g., embeddings)
+- [ ] One-click **Recipe Import**  
+- [x] Display of in-library status for all resources in the **Resources Used** section of the image page  
+- [x] One-click **Auto-organize Models**
+
+**Stay tuned — and thank you for your support!**
+
+---
+

docs/architecture/example_images_routes.md

@@ -0,0 +1,93 @@
+# Example image route architecture
+
+The example image routing stack mirrors the layered model route stack described in
+[`docs/architecture/model_routes.md`](model_routes.md). HTTP wiring, controller setup,
+handler orchestration, and long-running workflows now live in clearly separated modules so
+we can extend download/import behaviour without touching the entire feature surface.
+
+```mermaid
+graph TD
+    subgraph HTTP
+        A[ExampleImagesRouteRegistrar] -->|binds| B[ExampleImagesRoutes controller]
+    end
+    subgraph Application
+        B --> C[ExampleImagesHandlerSet]
+        C --> D1[Handlers]
+        D1 --> E1[Use cases]
+        E1 --> F1[Download manager / processor / file manager]
+    end
+    subgraph Side Effects
+        F1 --> G1[Filesystem]
+        F1 --> G2[Model metadata]
+        F1 --> G3[WebSocket progress]
+    end
+```
+
+## Layer responsibilities
+
+| Layer | Module(s) | Responsibility |
+| --- | --- | --- |
+| Registrar | `py/routes/example_images_route_registrar.py` | Declarative catalogue of every example image endpoint plus helpers that bind them to an `aiohttp` router. Keeps HTTP concerns symmetrical with the model registrar. |
+| Controller | `py/routes/example_images_routes.py` | Lazily constructs `ExampleImagesHandlerSet`, injects defaults for the download manager, processor, and file manager, and exposes the registrar-ready mapping just like `BaseModelRoutes`. |
+| Handler set | `py/routes/handlers/example_images_handlers.py` | Groups HTTP adapters by concern (downloads, imports/deletes, filesystem access). Each handler translates domain errors into HTTP responses and defers to a use case or utility service. |
+| Use cases | `py/services/use_cases/example_images/*.py` | Encapsulate orchestration for downloads and imports. They validate input, translate concurrency/configuration errors, and keep handler logic declarative. |
+| Supporting services | `py/utils/example_images_download_manager.py`, `py/utils/example_images_processor.py`, `py/utils/example_images_file_manager.py` | Execute long-running work: pull assets from Civitai, persist uploads, clean metadata, expose filesystem actions with guardrails, and broadcast progress snapshots. |
+
+## Handler responsibilities & invariants
+
+`ExampleImagesHandlerSet` flattens the handler objects into the `{"handler_name": coroutine}`
+mapping consumed by the registrar. The table below outlines how each handler collaborates
+with the use cases and utilities.
+
+| Handler | Key endpoints | Collaborators | Contracts |
+| --- | --- | --- | --- |
+| `ExampleImagesDownloadHandler` | `/api/lm/download-example-images`, `/api/lm/example-images-status`, `/api/lm/pause-example-images`, `/api/lm/resume-example-images`, `/api/lm/force-download-example-images` | `DownloadExampleImagesUseCase`, `DownloadManager` | Delegates payload validation and concurrency checks to the use case; progress/status endpoints expose the same snapshot used for WebSocket broadcasts; pause/resume surface `DownloadNotRunningError` as HTTP 400 instead of 500. |
+| `ExampleImagesManagementHandler` | `/api/lm/import-example-images`, `/api/lm/delete-example-image` | `ImportExampleImagesUseCase`, `ExampleImagesProcessor` | Multipart uploads are streamed to disk via the use case; validation failures return HTTP 400 with no filesystem side effects; deletion funnels through the processor to prune metadata and cached images consistently. |
+| `ExampleImagesFileHandler` | `/api/lm/open-example-images-folder`, `/api/lm/example-image-files`, `/api/lm/has-example-images` | `ExampleImagesFileManager` | Centralises filesystem access, enforcing settings-based root paths and returning HTTP 400/404 for missing configuration or folders; responses always include `success`/`has_images` booleans for UI consumption. |
+
+## Use case boundaries
+
+| Use case | Entry point | Dependencies | Guarantees |
+| --- | --- | --- | --- |
+| `DownloadExampleImagesUseCase` | `execute(payload)` | `DownloadManager.start_download`, download configuration errors | Raises `DownloadExampleImagesInProgressError` when the manager reports an active job, rewraps configuration errors into `DownloadExampleImagesConfigurationError`, and lets `ExampleImagesDownloadError` bubble as 500s so handlers do not duplicate logging. |
+| `ImportExampleImagesUseCase` | `execute(request)` | `ExampleImagesProcessor.import_images`, temporary file helpers | Supports multipart or JSON payloads, normalises file paths into a single list, cleans up temp files even on failure, and maps validation issues to `ImportExampleImagesValidationError` for HTTP 400 responses. |
+
+## Maintaining critical invariants
+
+* **Shared progress snapshots** - The download handler returns the same snapshot built by
+  `DownloadManager`, guaranteeing parity between HTTP polling endpoints and WebSocket
+  progress events.
+* **Safe filesystem access** - All folder/file actions flow through
+  `ExampleImagesFileManager`, which validates the configured example image root and ensures
+  responses never leak absolute paths outside the allowed directory.
+* **Metadata hygiene** - Import/delete operations run through `ExampleImagesProcessor`,
+  which updates model metadata via `MetadataManager` and notifies the relevant scanners so
+  cache state stays in sync.
+
+## Migration notes
+
+The refactor brings the example image stack in line with the model/recipe stacks:
+
+1. `ExampleImagesRouteRegistrar` now owns the declarative route list. Downstream projects
+   should rely on `ExampleImagesRoutes.to_route_mapping()` instead of manually wiring
+   handler callables.
+2. `ExampleImagesRoutes` caches its `ExampleImagesHandlerSet` just like
+   `BaseModelRoutes`. If you previously instantiated handlers directly, inject custom
+   collaborators via the controller constructor (`download_manager`, `processor`,
+   `file_manager`) to keep test seams predictable.
+3. Tests that mocked `ExampleImagesRoutes.setup_routes` should switch to patching
+   `DownloadExampleImagesUseCase`/`ImportExampleImagesUseCase` at import time. The handlers
+   expect those abstractions to surface validation/concurrency errors, and bypassing them
+   will skip the HTTP-friendly error mapping.
+
+## Extending the stack
+
+1. Add the endpoint to `ROUTE_DEFINITIONS` with a unique `handler_name`.
+2. Expose the coroutine on an existing handler class (or create a new handler and extend
+   `ExampleImagesHandlerSet`).
+3. Wire additional services or factories inside `_build_handler_set` on
+   `ExampleImagesRoutes`, mirroring how the model stack introduces new use cases.
+
+`tests/routes/test_example_images_routes.py` exercises registrar binding, download pause
+flows, and import validations. Use it as a template when introducing new handler
+collaborators or error mappings.

docs/architecture/model_routes.md

@@ -0,0 +1,100 @@
+# Base model route architecture
+
+The model routing stack now splits HTTP wiring, orchestration logic, and
+business rules into discrete layers.  The goal is to make it obvious where a
+new collaborator should live and which contract it must honour.  The diagram
+below captures the end-to-end flow for a typical request:
+
+```mermaid
+graph TD
+    subgraph HTTP
+        A[ModelRouteRegistrar] -->|binds| B[BaseModelRoutes handler proxy]
+    end
+    subgraph Application
+        B --> C[ModelHandlerSet]
+        C --> D1[Handlers]
+        D1 --> E1[Use cases]
+        E1 --> F1[Services / scanners]
+    end
+    subgraph Side Effects
+        F1 --> G1[Cache & metadata]
+        F1 --> G2[Filesystem]
+        F1 --> G3[WebSocket state]
+    end
+```
+
+Every box maps to a concrete module:
+
+| Layer | Module(s) | Responsibility |
+| --- | --- | --- |
+| Registrar | `py/routes/model_route_registrar.py` | Declarative list of routes shared by every model type and helper methods for binding them to an `aiohttp` application. |
+| Route controller | `py/routes/base_model_routes.py` | Constructs the handler graph, injects shared services, exposes proxies that surface `503 Service not ready` when the model service has not been attached. |
+| Handler set | `py/routes/handlers/model_handlers.py` | Thin HTTP adapters grouped by concern (page rendering, listings, mutations, queries, downloads, CivitAI integration, move operations, auto-organize). |
+| Use cases | `py/services/use_cases/*.py` | Encapsulate long-running flows (`DownloadModelUseCase`, `BulkMetadataRefreshUseCase`, `AutoOrganizeUseCase`).  They normalise validation errors and concurrency constraints before returning control to the handlers. |
+| Services | `py/services/*.py` | Existing services and scanners that mutate caches, write metadata, move files, and broadcast WebSocket updates. |
+
+## Handler responsibilities & contracts
+
+`ModelHandlerSet` flattens the handler objects into the exact callables used by
+the registrar.  The table below highlights the separation of concerns within
+the set and the invariants that must hold after each handler returns.
+
+| Handler | Key endpoints | Collaborators | Contracts |
+| --- | --- | --- | --- |
+| `ModelPageView` | `/{prefix}` | `SettingsManager`, `server_i18n`, Jinja environment, `service.scanner` | Template is rendered with `is_initializing` flag when caches are cold; i18n filter is registered exactly once per environment instance. |
+| `ModelListingHandler` | `/api/lm/{prefix}/list` | `service.get_paginated_data`, `service.format_response` | Listings respect pagination query parameters and cap `page_size` at 100; every item is formatted before response. |
+| `ModelManagementHandler` | Mutations (delete, exclude, metadata, preview, tags, rename, bulk delete, duplicate verification) | `ModelLifecycleService`, `MetadataSyncService`, `PreviewAssetService`, `TagUpdateService`, scanner cache/index | Cache state mirrors filesystem changes: deletes prune cache & hash index, preview replacements synchronise metadata and cache NSFW levels, metadata saves trigger cache resort when names change. |
+| `ModelQueryHandler` | Read-only queries (top tags, folders, duplicates, metadata, URLs) | Service query helpers & scanner cache | Outputs always wrapped in `{"success": True}` when no error; duplicate/filename grouping omits empty entries; invalid parameters (e.g. missing `model_root`) return HTTP 400. |
+| `ModelDownloadHandler` | `/api/lm/download-model`, `/download-model-get`, `/download-progress/{id}`, `/cancel-download-get` | `DownloadModelUseCase`, `DownloadCoordinator`, `WebSocketManager` | Payload validation errors become HTTP 400 without mutating download progress cache; early-access failures surface as HTTP 401; successful downloads cache progress snapshots that back both WebSocket broadcasts and polling endpoints. |
+| `ModelCivitaiHandler` | CivitAI metadata routes | `MetadataSyncService`, metadata provider factory, `BulkMetadataRefreshUseCase` | `fetch_all_civitai` streams progress via `WebSocketBroadcastCallback`; version lookups validate model type before returning; local availability fields derive from hash lookups without mutating cache state. |
+| `ModelMoveHandler` | `move_model`, `move_models_bulk` | `ModelMoveService` | Moves execute atomically per request; bulk operations aggregate success/failure per file set. |
+| `ModelAutoOrganizeHandler` | `/api/lm/{prefix}/auto-organize` (GET/POST), `/auto-organize-progress` | `AutoOrganizeUseCase`, `WebSocketProgressCallback`, `WebSocketManager` | Enforces single-flight execution using the shared lock; progress broadcasts remain available to polling clients until explicitly cleared; conflicts return HTTP 409 with a descriptive error. |
+
+## Use case boundaries
+
+Each use case exposes a narrow asynchronous API that hides the underlying
+services.  Their error mapping is essential for predictable HTTP responses.
+
+| Use case | Entry point | Dependencies | Guarantees |
+| --- | --- | --- | --- |
+| `DownloadModelUseCase` | `execute(payload)` | `DownloadCoordinator.schedule_download` | Translates `ValueError` into `DownloadModelValidationError` for HTTP 400, recognises early-access errors (`"401"` in message) and surfaces them as `DownloadModelEarlyAccessError`, forwards success dictionaries untouched. |
+| `AutoOrganizeUseCase` | `execute(file_paths, progress_callback)` | `ModelFileService.auto_organize_models`, `WebSocketManager` lock | Guarded by `ws_manager` lock + status checks; raises `AutoOrganizeInProgressError` before invoking the file service when another run is already active. |
+| `BulkMetadataRefreshUseCase` | `execute_with_error_handling(progress_callback)` | `MetadataSyncService`, `SettingsManager`, `WebSocketBroadcastCallback` | Iterates through cached models, applies metadata sync, emits progress snapshots that handlers broadcast unchanged. |
+
+## Maintaining legacy contracts
+
+The refactor preserves the invariants called out in the previous architecture
+notes.  The most critical ones are reiterated here to emphasise the
+collaboration points:
+
+1. **Cache mutations** – Delete, exclude, rename, and bulk delete operations are
+   channelled through `ModelManagementHandler`.  The handler delegates to
+   `ModelLifecycleService` or `MetadataSyncService`, and the scanner cache is
+   mutated in-place before the handler returns.  The accompanying tests assert
+   that `scanner._cache.raw_data` and `scanner._hash_index` stay in sync after
+   each mutation.
+2. **Preview updates** – `PreviewAssetService.replace_preview` writes the new
+   asset, `MetadataSyncService` persists the JSON metadata, and
+   `scanner.update_preview_in_cache` mirrors the change.  The handler returns
+   the static URL produced by `config.get_preview_static_url`, keeping browser
+   clients in lockstep with disk state.
+3. **Download progress** – `DownloadCoordinator.schedule_download` generates the
+   download identifier, registers a WebSocket progress callback, and caches the
+   latest numeric progress via `WebSocketManager`.  Both `download_model`
+   responses and `/download-progress/{id}` polling read from the same cache to
+   guarantee consistent progress reporting across transports.
+
+## Extending the stack
+
+To add a new shared route:
+
+1. Declare it in `COMMON_ROUTE_DEFINITIONS` using a unique handler name.
+2. Implement the corresponding coroutine on one of the handlers inside
+   `ModelHandlerSet` (or introduce a new handler class when the concern does not
+   fit existing ones).
+3. Inject additional dependencies in `BaseModelRoutes._create_handler_set` by
+   wiring services or use cases through the constructor parameters.
+
+Model-specific routes should continue to be registered inside the subclass
+implementation of `setup_specific_routes`, reusing the shared registrar where
+possible.

docs/architecture/multi_library_design.md

@@ -0,0 +1,34 @@
+# Multi-Library Management for Standalone Mode
+
+## Requirements Summary
+- **Independent libraries**: In standalone mode, users can maintain multiple libraries, where each library represents a distinct set of model folders (LoRAs, checkpoints, embeddings, etc.). Only one library is active at any given time, but users need a fast way to switch between them.
+- **Library-specific settings**: The fields that vary per library are `folder_paths`, `default_lora_root`, `default_checkpoint_root`, and `default_embedding_root` inside `settings.json`.
+- **Persistent caches**: Every library must have its own SQLite persistent model cache so that metadata generated for one library does not leak into another.
+- **Backward compatibility**: Existing single-library setups should continue to work. When no multi-library configuration is provided, the application should behave exactly as before.
+
+## Proposed Design
+1. **Library registry**
+   - Extend the standalone configuration to hold a list of libraries, each identified by a unique name.
+   - Each entry stores the folder path configuration plus any library-scoped metadata (e.g. creation time, display name).
+   - The active library key is stored separately to allow quick switching without rewriting the full config.
+2. **Settings management**
+   - Update `settings_manager` to load and persist the library registry. When a library is activated, hydrate the in-memory settings object with that library's folder configuration.
+   - Provide helper methods for creating, renaming, and deleting libraries, ensuring validation for duplicate names and path collisions.
+   - Continue writing the active library settings to `settings.json` for compatibility, while storing the registry in a new section such as `libraries`.
+3. **Persistent model cache**
+   - Derive the SQLite file path from the active library, e.g. `model_cache_<library>.sqlite` or a nested directory structure like `model_cache/<library>/models.sqlite`.
+   - Update `PersistentModelCache` so it resolves the database path dynamically whenever the active library changes. Ensure connections are closed before switching to avoid locking issues.
+   - Migrate existing single cache files by treating them as the default library's cache.
+4. **Model scanning workflow**
+   - Modify `ModelScanner` and related services to react to library switches by clearing in-memory caches, re-reading folder paths, and rehydrating metadata from the library-specific SQLite cache.
+   - Provide API endpoints in standalone mode to list libraries, activate one, and trigger a rescan.
+5. **UI/UX considerations**
+   - In the standalone UI, introduce a library selector component that surfaces available libraries and offers quick switching.
+   - Offer feedback when switching libraries (e.g. spinner while rescanning) and guard destructive actions with confirmation prompts.
+
+## Implementation Notes
+- **Data migration**: On startup, detect if the old `settings.json` structure is present. If so, create a default library entry using the current folder paths and point the active library to it.
+- **Thread safety**: Ensure that any long-running scans are cancelled or awaited before switching libraries to prevent race conditions in cache writes.
+- **Testing**: Add unit tests for the settings manager to cover library CRUD operations and cache path resolution. Include integration tests that simulate switching libraries and verifying that the correct models are loaded.
+- **Documentation**: Update user guides to explain how to define libraries, switch between them, and where the new cache files are stored.
+- **Extensibility**: Keep the design open to future per-library settings (e.g. auto-refresh intervals, metadata overrides) by storing library data as objects instead of flat maps.

docs/architecture/recipe_routes.md

@@ -0,0 +1,89 @@
+# Recipe route architecture
+
+The recipe routing stack now mirrors the modular model route design. HTTP
+bindings, controller wiring, handler orchestration, and business rules live in
+separate layers so new behaviours can be added without re-threading the entire
+feature. The diagram below outlines the flow for a typical request:
+
+```mermaid
+graph TD
+    subgraph HTTP
+        A[RecipeRouteRegistrar] -->|binds| B[RecipeRoutes controller]
+    end
+    subgraph Application
+        B --> C[RecipeHandlerSet]
+        C --> D1[Handlers]
+        D1 --> E1[Use cases]
+        E1 --> F1[Services / scanners]
+    end
+    subgraph Side Effects
+        F1 --> G1[Cache & fingerprint index]
+        F1 --> G2[Metadata files]
+        F1 --> G3[Temporary shares]
+    end
+```
+
+## Layer responsibilities
+
+| Layer | Module(s) | Responsibility |
+| --- | --- | --- |
+| Registrar | `py/routes/recipe_route_registrar.py` | Declarative list of every recipe endpoint and helper methods that bind them to an `aiohttp` application. |
+| Controller | `py/routes/base_recipe_routes.py`, `py/routes/recipe_routes.py` | Lazily resolves scanners/clients from the service registry, wires shared templates/i18n, instantiates `RecipeHandlerSet`, and exposes a `{handler_name: coroutine}` mapping for the registrar. |
+| Handler set | `py/routes/handlers/recipe_handlers.py` | Thin HTTP adapters grouped by concern (page view, listings, queries, mutations, sharing). They normalise responses and translate service exceptions into HTTP status codes. |
+| Services & scanners | `py/services/recipes/*.py`, `py/services/recipe_scanner.py`, `py/services/service_registry.py` | Concrete business logic: metadata parsing, persistence, sharing, fingerprint/index maintenance, and cache refresh. |
+
+## Handler responsibilities & invariants
+
+`RecipeHandlerSet` flattens purpose-built handler objects into the callables the
+registrar binds. Each handler is responsible for a narrow concern and enforces a
+set of invariants before returning:
+
+| Handler | Key endpoints | Collaborators | Contracts |
+| --- | --- | --- | --- |
+| `RecipePageView` | `/loras/recipes` | `SettingsManager`, `server_i18n`, Jinja environment, recipe scanner getter | Template rendered with `is_initializing` flag when caches are still warming; i18n filter registered exactly once per environment instance. |
+| `RecipeListingHandler` | `/api/lm/recipes`, `/api/lm/recipe/{id}` | `recipe_scanner.get_paginated_data`, `recipe_scanner.get_recipe_by_id` | Listings respect pagination and search filters; every item receives a `file_url` fallback even when metadata is incomplete; missing recipes become HTTP 404. |
+| `RecipeQueryHandler` | Tag/base-model stats, syntax, LoRA lookups | Recipe scanner cache, `format_recipe_file_url` helper | Cache snapshots are reused without forcing refresh; duplicate lookups collapse groups by fingerprint; syntax lookups return helpful errors when LoRAs are absent. |
+| `RecipeManagementHandler` | Save, update, reconnect, bulk delete, widget ingest | `RecipePersistenceService`, `RecipeAnalysisService`, recipe scanner | Persistence results propagate HTTP status codes; fingerprint/index updates flow through the scanner before returning; validation errors surface as HTTP 400 without touching disk. |
+| `RecipeAnalysisHandler` | Uploaded/local/remote analysis | `RecipeAnalysisService`, `civitai_client`, recipe scanner | Unsupported content types map to HTTP 400; download errors (`RecipeDownloadError`) are not retried; every response includes a `loras` array for client compatibility. |
+| `RecipeSharingHandler` | Share + download | `RecipeSharingService`, recipe scanner | Share responses provide a stable download URL and filename; expired shares surface as HTTP 404; downloads stream via `web.FileResponse` with attachment headers. |
+
+## Use case boundaries
+
+The dedicated services encapsulate long-running work so handlers stay thin.
+
+| Use case | Entry point | Dependencies | Guarantees |
+| --- | --- | --- | --- |
+| `RecipeAnalysisService` | `analyze_uploaded_image`, `analyze_remote_image`, `analyze_local_image`, `analyze_widget_metadata` | `ExifUtils`, `RecipeParserFactory`, downloader factory, optional metadata collector/processor | Normalises missing/invalid payloads into `RecipeValidationError`; generates consistent fingerprint data to keep duplicate detection stable; temporary files are cleaned up after every analysis path. |
+| `RecipePersistenceService` | `save_recipe`, `delete_recipe`, `update_recipe`, `reconnect_lora`, `bulk_delete`, `save_recipe_from_widget` | `ExifUtils`, recipe scanner, card preview sizing constants | Writes images/JSON metadata atomically; updates scanner caches and hash indices before returning; recalculates fingerprints whenever LoRA assignments change. |
+| `RecipeSharingService` | `share_recipe`, `prepare_download` | `tempfile`, recipe scanner | Copies originals to TTL-managed temp files; metadata lookups re-use the scanner; expired shares trigger cleanup and `RecipeNotFoundError`. |
+
+## Maintaining critical invariants
+
+* **Cache updates** – Mutations (`save`, `delete`, `bulk_delete`, `update`) call
+  back into the recipe scanner to mutate the in-memory cache and fingerprint
+  index before returning a response. Tests assert that these methods are invoked
+  even when stubbing persistence.
+* **Fingerprint management** – `RecipePersistenceService` recomputes
+  fingerprints whenever LoRA metadata changes and duplicate lookups use those
+  fingerprints to group recipes. Handlers bubble the resulting IDs so clients
+  can merge duplicates without an extra fetch.
+* **Metadata synchronisation** – Saving or reconnecting a recipe updates the
+  JSON sidecar, refreshes embedded metadata via `ExifUtils`, and instructs the
+  scanner to resort its cache. Sharing relies on this metadata to generate
+  filenames and ensure downloads stay in sync with on-disk state.
+
+## Extending the stack
+
+1. Declare the new endpoint in `ROUTE_DEFINITIONS` with a unique handler name.
+2. Implement the coroutine on an existing handler or introduce a new handler
+   class inside `py/routes/handlers/recipe_handlers.py` when the concern does
+   not fit existing ones.
+3. Wire additional collaborators inside
+   `BaseRecipeRoutes._create_handler_set` (inject new services or factories) and
+   expose helper getters on the handler owner if the handler needs to share
+   utilities.
+
+Integration tests in `tests/routes/test_recipe_routes.py` exercise the listing,
+mutation, analysis-error, and sharing paths end-to-end, ensuring the controller
+and handler wiring remains valid as new capabilities are added.
+

docs/frontend-dom-fixtures.md

@@ -0,0 +1,51 @@
+# Frontend DOM Fixture Strategy
+
+This guide outlines how to reproduce the markup emitted by the Django templates while running Vitest in jsdom.  The aim is to make it straightforward to write integration-style unit tests for managers and UI helpers without having to duplicate template fragments inline.
+
+## Loading Template Markup
+
+Vitest executes inside Node, so we can read the same HTML templates that ship with the extension:
+
+1. Use the helper utilities from `tests/frontend/utils/domFixtures.js` to read files under the `templates/` directory.
+2. Mount the returned markup into `document.body` (or any custom container) before importing the module under test so its query selectors resolve correctly.
+
+```js
+import { renderTemplate } from '../utils/domFixtures.js'; // adjust the relative path to your spec
+
+beforeEach(() => {
+  renderTemplate('loras.html', {
+    dataset: { page: 'loras' }
+  });
+});
+```
+
+The helper ensures the dataset is applied to the container, which mirrors how Django sets `data-page` in production.
+
+## Working with Partial Components
+
+Many features are implemented as template partials located under `templates/components/`.  When a test only needs a fragment (for example, the progress panel or context menu markup), load the component file directly:
+
+```js
+const container = renderTemplate('components/progress_panel.html');
+
+const progressPanel = container.querySelector('#progress-panel');
+```
+
+This pattern avoids hand-written fixture strings and keeps the tests aligned with the actual markup.
+
+## Resetting Between Tests
+
+The shared Vitest setup clears `document.body` and storage APIs before each test.  If a suite adds additional DOM nodes outside of the body or needs to reset custom attributes mid-test, use `resetDom()` exported from `domFixtures.js`.
+
+```js
+import { resetDom } from '../utils/domFixtures.js';
+
+afterEach(() => {
+  resetDom();
+});
+```
+
+## Future Enhancements
+
+- Provide typed helpers for injecting mock script tags (e.g., replicating ComfyUI globals).
+- Compose higher-level fixtures that mimic specific pages (loras, checkpoints, recipes) once those managers receive dedicated suites.

docs/frontend-filtering-test-matrix.md

@@ -0,0 +1,44 @@
+# LoRA & Checkpoints Filtering/Sorting Test Matrix
+
+This matrix captures the scenarios that Phase 3 frontend tests should cover for the LoRA and Checkpoint managers. It focuses on how search, filter, sort, and duplicate badge toggles interact so future specs can share fixtures and expectations.
+
+## Scope
+
+- **Components**: `PageControls`, `FilterManager`, `SearchManager`, and `ModelDuplicatesManager` wiring invoked through `CheckpointsPageManager` and `LorasPageManager`.
+- **Templates**: `templates/loras.html` and `templates/checkpoints.html` along with shared filter panel and toolbar partials.
+- **APIs**: Requests issued through `baseModelApi.fetchModels` (via `resetAndReload`/`refreshModels`) and duplicates badge updates.
+
+## Shared Setup Considerations
+
+1. Render full page templates using `renderLorasPage` / `renderCheckpointsPage` helpers before importing modules so DOM queries resolve.
+2. Stub storage helpers (`getStorageItem`, `setStorageItem`, `getSessionItem`, `setSessionItem`) to observe persistence behavior without mutating real storage.
+3. Mock `sidebarManager` to capture refresh calls triggered after sort/filter actions.
+4. Provide fake API implementations exposing `resetAndReload`, `refreshModels`, `fetchFromCivitai`, `toggleBulkMode`, and `clearCustomFilter` so control events remain asynchronous but deterministic.
+5. Supply a minimal `ModelDuplicatesManager` mock exposing `toggleDuplicateMode`, `checkDuplicatesCount`, and `updateDuplicatesBadgeAfterRefresh` to validate duplicate badge wiring.
+
+## Scenario Matrix
+
+| 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-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 |
+| F-06 | Filter persistence | Re-initializing manager loads stored filters/sort and updates DOM | Filters pre-populate chips/checkboxes; favorites state restored | Same | Requires simulating repeated construction |
+| F-07 | Combined filters | Applying search + tag + base model yields aggregated query params for fetch | Assert API receives merged filter payload | Same | Validate toast messaging for active filters |
+| F-08 | Clearing filters | Using "Clear filters" resets state, storage, and reloads list | `FilterManager.clearFilters` empties `filters`, removes active class, shows toast | Same | Ensure favorites-only toggle unaffected |
+| F-09 | Duplicate badge toggle | Pressing "Find duplicates" toggles duplicate mode and updates badge counts post-refresh | `ModelDuplicatesManager.toggleDuplicateMode` invoked and badge refresh called after API rebuild | Same plus checkpoint-specific duplicate badge dataset | Connects to future duplicate-specific specs |
+| F-10 | Bulk actions menu | Opening bulk dropdown keeps filters intact and closes on outside click | Validate dropdown class toggling and no unintended reload | Same | Guard against regression when dropdown interacts with filters |
+
+## Automation Coverage Status
+
+- ✅ F-01 Search filter, F-02 Tag filter, F-03 Base model filter, F-04 Favorites-only toggle, F-05 Sort selection, and F-09 Duplicate badge toggle are covered by `tests/frontend/components/pageControls.filtering.test.js` for both LoRA and checkpoint pages.
+- ⏳ F-06 Filter persistence, F-07 Combined filters, F-08 Clearing filters, and F-10 Bulk actions remain to be automated alongside upcoming bulk mode refinements.
+
+## Coverage Gaps & Follow-Ups
+
+- Write Vitest suites that exercise the matrix for both managers, sharing fixtures through page helpers to avoid duplication.
+- Capture API parameter assertions by inspecting `baseModelApi.fetchModels` mocks rather than relying solely on state mutations.
+- Add regression cases for legacy storage migrations (old filter keys) once fixtures exist for older payloads.
+- Extend duplicate badge coverage with scenarios where `checkDuplicatesCount` signals zero duplicates versus pending calculations.

docs/frontend-testing-roadmap.md

@@ -0,0 +1,33 @@
+# Frontend Automation Testing Roadmap
+
+This roadmap tracks the planned rollout of automated testing for the ComfyUI LoRA Manager frontend. Each phase builds on the infrastructure introduced in this change set and records progress so future contributors can quickly identify the next tasks.
+
+## Phase Overview
+
+| Phase | Goal | Primary Focus | Status | Notes |
+| --- | --- | --- | --- | --- |
+| Phase 0 | Establish baseline tooling | Add Node test runner, jsdom environment, and seed smoke tests | ✅ Complete | Vitest + jsdom configured, example state tests committed |
+| Phase 1 | Cover state management logic | Unit test selectors, derived data helpers, and storage utilities under `static/js/state` and `static/js/utils` | ✅ Complete | Storage helpers and state selectors now exercised via deterministic suites |
+| Phase 2 | Test AppCore orchestration | Simulate page bootstrapping, infinite scroll hooks, and manager registration using JSDOM DOM fixtures | ✅ Complete | AppCore initialization + page feature suites now validate manager wiring, infinite scroll hooks, and onboarding gating |
+| Phase 3 | Validate page-specific managers | Add focused suites for `loras`, `checkpoints`, `embeddings`, and `recipes` managers covering filtering, sorting, and bulk actions | ✅ Complete | LoRA/checkpoint suites expanded; embeddings + recipes managers now covered with initialization, filtering, and duplicate workflows |
+| Phase 4 | Interaction-level regression tests | Exercise template fragments, modals, and menus to ensure UI wiring remains intact | ✅ Complete | Vitest DOM suites cover NSFW selector, recipe modal editing, and global context menus |
+| Phase 5 | Continuous integration & coverage | Integrate frontend tests into CI workflow and track coverage metrics | ✅ Complete | CI workflow runs Vitest and aggregates V8 coverage into `coverage/frontend` via a dedicated script |
+
+## Next Steps Checklist
+
+- [x] Expand unit tests for `storageHelpers` covering migrations and namespace behavior.
+- [x] Document DOM fixture strategy for reproducing template structures in tests.
+- [x] Prototype AppCore initialization test that verifies manager bootstrapping with stubbed dependencies.
+- [x] Add AppCore page feature suite exercising context menu creation and infinite scroll registration via DOM fixtures.
+- [x] Extend AppCore orchestration tests to cover manager wiring, bulk menu setup, and onboarding gating scenarios.
+- [x] Add interaction regression suites for context menus and recipe modals to complete Phase 4.
+- [x] Evaluate integrating coverage reporting once test surface grows (> 20 specs).
+- [x] Create shared fixtures for the loras and checkpoints pages once dedicated manager suites are added.
+- [x] Draft focused test matrix for loras/checkpoints manager filtering and sorting paths ahead of Phase 3.
+- [x] Implement LoRAs manager filtering/sorting specs for scenarios F-01–F-05 & F-09; queue remaining edge cases after duplicate/bulk flows stabilize.
+- [x] Implement checkpoints manager filtering/sorting specs for scenarios F-01–F-05 & F-09; cover remaining paths alongside bulk action work.
+- [x] Implement checkpoints page manager smoke tests covering initialization and duplicate badge wiring.
+- [x] Outline focused checkpoints scenarios (filtering, sorting, duplicate badge toggles) to feed into the shared test matrix.
+- [ ] Add duplicate badge regression coverage for zero/pending states after API refreshes.
+
+Maintaining this roadmap alongside code changes will make it easier to append new automated test tasks and update their progress.

docs/library-switching.md

@@ -0,0 +1,28 @@
+# Library Switching and Preview Routes
+
+Library switching no longer requires restarting the backend. The preview
+thumbnails shown in the UI are now served through a dynamic endpoint that
+resolves files against the folders registered for the active library at request
+time. This allows the multi-library flow to update model roots without touching
+the aiohttp router, so previews remain available immediately after a switch.
+
+## How the dynamic preview endpoint works
+
+* `config.get_preview_static_url()` now returns `/api/lm/previews?path=<encoded>`
+  for any preview path. The raw filesystem location is URL encoded so that it
+  can be passed through the query string without leaking directory structure in
+  the route itself.【F:py/config.py†L398-L404】
+* `PreviewRoutes` exposes the `/api/lm/previews` handler which validates the
+  decoded path against the directories registered for the current library. The
+  request is rejected if it falls outside those roots or if the file does not
+  exist.【F:py/routes/preview_routes.py†L5-L21】【F:py/routes/handlers/preview_handlers.py†L9-L48】
+* `Config` keeps an up-to-date cache of allowed preview roots. Every time a
+  library is applied the cache is rebuilt using the declared LoRA, checkpoint
+  and embedding directories (including symlink targets). The validation logic
+  checks preview requests against this cache.【F:py/config.py†L51-L68】【F:py/config.py†L180-L248】【F:py/config.py†L332-L346】
+
+Both the ComfyUI runtime (`LoraManager.add_routes`) and the standalone launcher
+(`StandaloneLoraManager.add_routes`) register the new preview routes instead of
+mounting a static directory per root. Switching libraries therefore works
+without restarting the application, and preview URLs generated before or after a
+switch continue to resolve correctly.【F:py/lora_manager.py†L21-L82】【F:standalone.py†L302-L315】

package.json

@@ -0,0 +1,15 @@
+{
+  "name": "comfyui-lora-manager-frontend",
+  "version": "0.1.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "test": "vitest run",
+    "test:watch": "vitest",
+    "test:coverage": "node scripts/run_frontend_coverage.js"
+  },
+  "devDependencies": {
+    "jsdom": "^24.0.0",
+    "vitest": "^1.6.0"
+  }
+}

py/__init__.py

@@ -0,0 +1,12 @@
+"""Project namespace package."""
+
+# pytest's internal compatibility layer still imports ``py.path.local`` from the
+# historical ``py`` dependency.  Because this project reuses the ``py`` package
+# name, we expose a minimal shim so ``py.path.local`` resolves to ``pathlib.Path``
+# during test runs without pulling in the external dependency.
+from pathlib import Path
+from types import SimpleNamespace
+
+path = SimpleNamespace(local=Path)
+
+__all__ = ["path"]

py/config.py

@@ -1,26 +1,61 @@
 import os
 import platform
+from pathlib import Path
 import folder_paths # type: ignore
-from typing import List
+from typing import Dict, Iterable, List, Mapping, Set
 import logging
-import sys
 import json
+import urllib.parse
 
-# Check if running in standalone mode
-standalone_mode = 'nodes' not in sys.modules
+from .utils.settings_paths import ensure_settings_file
+
+# 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"
 
 logger = logging.getLogger(__name__)
 
+
+def _normalize_folder_paths_for_comparison(
+    folder_paths: Mapping[str, Iterable[str]]
+) -> Dict[str, Set[str]]:
+    """Normalize folder paths for comparison across libraries."""
+
+    normalized: Dict[str, Set[str]] = {}
+    for key, values in folder_paths.items():
+        if isinstance(values, str):
+            candidate_values: Iterable[str] = [values]
+        else:
+            try:
+                candidate_values = iter(values)
+            except TypeError:
+                continue
+
+        normalized_values: Set[str] = set()
+        for value in candidate_values:
+            if not isinstance(value, str):
+                continue
+            stripped = value.strip()
+            if not stripped:
+                continue
+            normalized_values.add(os.path.normcase(os.path.normpath(stripped)))
+
+        if normalized_values:
+            normalized[key] = normalized_values
+
+    return normalized
+
+
 class Config:
     """Global configuration for LoRA Manager"""
     
     def __init__(self):
         self.templates_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'templates')
         self.static_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'static')
+        self.i18n_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'locales')
         # Path mapping dictionary, target to link mapping
-        self._path_mappings = {}
-        # Static route mapping dictionary, target to route mapping
-        self._route_mappings = {}
+        self._path_mappings: Dict[str, str] = {}
+        # Normalized preview root directories used to validate preview access
+        self._preview_root_paths: Set[Path] = set()
         self.loras_roots = self._init_lora_paths()
         self.checkpoints_roots = None
         self.unet_roots = None
@@ -29,45 +64,73 @@ class Config:
         self.embeddings_roots = self._init_embedding_paths()
         # Scan symbolic links during initialization
         self._scan_symbolic_links()
+        self._rebuild_preview_roots()
         
         if not standalone_mode:
             # Save the paths to settings.json when running in ComfyUI mode
             self.save_folder_paths_to_settings()
 
     def save_folder_paths_to_settings(self):
-        """Save folder paths to settings.json for standalone mode to use later"""
+        """Persist ComfyUI-derived folder paths to the multi-library settings."""
         try:
-            # Check if we're running in ComfyUI mode (not standalone)           
-            # Load existing settings
-            settings_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings.json')
-            settings = {}
-            if os.path.exists(settings_path):
-                with open(settings_path, 'r', encoding='utf-8') as f:
-                    settings = json.load(f)
-            
-            # Update settings with paths
-            settings['folder_paths'] = {
-                'loras': self.loras_roots,
-                'checkpoints': self.checkpoints_roots,
-                'unet': self.unet_roots,
-                'embeddings': self.embeddings_roots,
-            }
-            
-            # Add default roots if there's only one item and key doesn't exist
-            if len(self.loras_roots) == 1 and "default_lora_root" not in settings:
-                settings["default_lora_root"] = self.loras_roots[0]
-            
-            if self.checkpoints_roots and len(self.checkpoints_roots) == 1 and "default_checkpoint_root" not in settings:
-                settings["default_checkpoint_root"] = self.checkpoints_roots[0]
+            ensure_settings_file(logger)
+            from .services.settings_manager import settings as settings_service
 
-            if self.embeddings_roots and len(self.embeddings_roots) == 1 and "default_embedding_root" not in settings:
-                settings["default_embedding_root"] = self.embeddings_roots[0]
-            
-            # Save settings
-            with open(settings_path, 'w', encoding='utf-8') as f:
-                json.dump(settings, f, indent=2)
-            
-            logger.info("Saved folder paths to settings.json")
+            libraries = settings_service.get_libraries()
+            comfy_library = libraries.get("comfyui", {})
+            default_library = libraries.get("default", {})
+
+            target_folder_paths = {
+                'loras': list(self.loras_roots),
+                'checkpoints': list(self.checkpoints_roots or []),
+                'unet': list(self.unet_roots or []),
+                'embeddings': list(self.embeddings_roots or []),
+            }
+
+            normalized_target_paths = _normalize_folder_paths_for_comparison(target_folder_paths)
+
+            if (not comfy_library and default_library and normalized_target_paths and
+                    _normalize_folder_paths_for_comparison(default_library.get("folder_paths", {})) ==
+                    normalized_target_paths):
+                try:
+                    settings_service.rename_library("default", "comfyui")
+                    logger.info("Renamed legacy 'default' library to 'comfyui'")
+                    libraries = settings_service.get_libraries()
+                    comfy_library = libraries.get("comfyui", {})
+                except Exception as rename_error:
+                    logger.debug(
+                        "Failed to rename legacy 'default' library: %s", rename_error
+                    )
+
+            default_lora_root = comfy_library.get("default_lora_root", "")
+            if not default_lora_root and len(self.loras_roots) == 1:
+                default_lora_root = self.loras_roots[0]
+
+            default_checkpoint_root = comfy_library.get("default_checkpoint_root", "")
+            if (not default_checkpoint_root and self.checkpoints_roots and
+                    len(self.checkpoints_roots) == 1):
+                default_checkpoint_root = self.checkpoints_roots[0]
+
+            default_embedding_root = comfy_library.get("default_embedding_root", "")
+            if (not default_embedding_root and self.embeddings_roots and
+                    len(self.embeddings_roots) == 1):
+                default_embedding_root = self.embeddings_roots[0]
+
+            metadata = dict(comfy_library.get("metadata", {}))
+            metadata.setdefault("display_name", "ComfyUI")
+            metadata["source"] = "comfyui"
+
+            settings_service.upsert_library(
+                "comfyui",
+                folder_paths=target_folder_paths,
+                default_lora_root=default_lora_root,
+                default_checkpoint_root=default_checkpoint_root,
+                default_embedding_root=default_embedding_root,
+                metadata=metadata,
+                activate=True,
+            )
+
+            logger.info("Updated 'comfyui' library with current folder paths")
         except Exception as e:
             logger.warning(f"Failed to save folder paths: {e}")
 
@@ -124,12 +187,65 @@ class Config:
         # 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 add_route_mapping(self, path: str, route: str):
-        """Add a static route mapping"""
-        normalized_path = os.path.normpath(path).replace(os.sep, '/')
-        self._route_mappings[normalized_path] = route
-        # logger.info(f"Added route mapping: {normalized_path} -> {route}")
+    def _expand_preview_root(self, path: str) -> Set[Path]:
+        """Return normalized ``Path`` objects representing a preview root."""
+
+        roots: Set[Path] = set()
+        if not path:
+            return roots
+
+        try:
+            raw_path = Path(path).expanduser()
+        except Exception:
+            return roots
+
+        if raw_path.is_absolute():
+            roots.add(raw_path)
+
+        try:
+            resolved = raw_path.resolve(strict=False)
+        except RuntimeError:
+            resolved = raw_path.absolute()
+        roots.add(resolved)
+
+        try:
+            real_path = raw_path.resolve()
+        except (FileNotFoundError, RuntimeError):
+            real_path = resolved
+        roots.add(real_path)
+
+        normalized: Set[Path] = set()
+        for candidate in roots:
+            if candidate.is_absolute():
+                normalized.add(candidate)
+            else:
+                try:
+                    normalized.add(candidate.resolve(strict=False))
+                except RuntimeError:
+                    normalized.add(candidate.absolute())
+
+        return normalized
+
+    def _rebuild_preview_roots(self) -> None:
+        """Recompute the cache of directories permitted for previews."""
+
+        preview_roots: Set[Path] = set()
+
+        for root in self.loras_roots or []:
+            preview_roots.update(self._expand_preview_root(root))
+        for root in self.base_models_roots or []:
+            preview_roots.update(self._expand_preview_root(root))
+        for root in self.embeddings_roots or []:
+            preview_roots.update(self._expand_preview_root(root))
+
+        for target, link in self._path_mappings.items():
+            preview_roots.update(self._expand_preview_root(target))
+            preview_roots.update(self._expand_preview_root(link))
+
+        self._preview_root_paths = {path for path in preview_roots if path.is_absolute()}
 
     def map_path_to_link(self, path: str) -> str:
         """Map a target path back to its symbolic link path"""
@@ -153,31 +269,93 @@ class Config:
                 return mapped_path
         return link_path
 
+    def _dedupe_existing_paths(self, raw_paths: Iterable[str]) -> Dict[str, str]:
+        dedup: Dict[str, str] = {}
+        for path in raw_paths:
+            if not isinstance(path, str):
+                continue
+            if not os.path.exists(path):
+                continue
+            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+            normalized = os.path.normpath(path).replace(os.sep, '/')
+            if real_path not in dedup:
+                dedup[real_path] = normalized
+        return dedup
+
+    def _prepare_lora_paths(self, raw_paths: Iterable[str]) -> List[str]:
+        path_map = self._dedupe_existing_paths(raw_paths)
+        unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+
+        for original_path in unique_paths:
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            if real_path != original_path:
+                self.add_path_mapping(original_path, real_path)
+
+        return unique_paths
+
+    def _prepare_checkpoint_paths(
+        self, checkpoint_paths: Iterable[str], unet_paths: Iterable[str]
+    ) -> List[str]:
+        checkpoint_map = self._dedupe_existing_paths(checkpoint_paths)
+        unet_map = self._dedupe_existing_paths(unet_paths)
+
+        merged_map: Dict[str, str] = {}
+        for real_path, original in {**checkpoint_map, **unet_map}.items():
+            if real_path not in merged_map:
+                merged_map[real_path] = original
+
+        unique_paths = sorted(merged_map.values(), key=lambda p: p.lower())
+
+        checkpoint_values = set(checkpoint_map.values())
+        unet_values = set(unet_map.values())
+        self.checkpoints_roots = [p for p in unique_paths if p in checkpoint_values]
+        self.unet_roots = [p for p in unique_paths if p in unet_values]
+
+        for original_path in unique_paths:
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            if real_path != original_path:
+                self.add_path_mapping(original_path, real_path)
+
+        return unique_paths
+
+    def _prepare_embedding_paths(self, raw_paths: Iterable[str]) -> List[str]:
+        path_map = self._dedupe_existing_paths(raw_paths)
+        unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+
+        for original_path in unique_paths:
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            if real_path != original_path:
+                self.add_path_mapping(original_path, real_path)
+
+        return unique_paths
+
+    def _apply_library_paths(self, folder_paths: Mapping[str, Iterable[str]]) -> None:
+        self._path_mappings.clear()
+        self._preview_root_paths = set()
+
+        lora_paths = folder_paths.get('loras', []) or []
+        checkpoint_paths = folder_paths.get('checkpoints', []) or []
+        unet_paths = folder_paths.get('unet', []) or []
+        embedding_paths = folder_paths.get('embeddings', []) or []
+
+        self.loras_roots = self._prepare_lora_paths(lora_paths)
+        self.base_models_roots = self._prepare_checkpoint_paths(checkpoint_paths, unet_paths)
+        self.embeddings_roots = self._prepare_embedding_paths(embedding_paths)
+
+        self._scan_symbolic_links()
+        self._rebuild_preview_roots()
+
     def _init_lora_paths(self) -> List[str]:
         """Initialize and validate LoRA paths from ComfyUI settings"""
         try:
             raw_paths = folder_paths.get_folder_paths("loras")
-            
-            # Normalize and resolve symlinks, store mapping from resolved -> original
-            path_map = {}
-            for path in raw_paths:
-                if os.path.exists(path):
-                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-                    path_map[real_path] = path_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
-            
-            # Now sort and use only the deduplicated real paths
-            unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+            unique_paths = self._prepare_lora_paths(raw_paths)
             logger.info("Found LoRA roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
-            
+
             if not unique_paths:
                 logger.warning("No valid loras folders found in ComfyUI configuration")
                 return []
-            
-            for original_path in unique_paths:
-                real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
-                if real_path != original_path:
-                    self.add_path_mapping(original_path, real_path)
-            
+
             return unique_paths
         except Exception as e:
             logger.warning(f"Error initializing LoRA paths: {e}")
@@ -186,52 +364,17 @@ class Config:
     def _init_checkpoint_paths(self) -> List[str]:
         """Initialize and validate checkpoint paths from ComfyUI settings"""
         try:
-            # Get checkpoint paths from folder_paths
             raw_checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
             raw_unet_paths = folder_paths.get_folder_paths("unet")
-            
-            # Normalize and resolve symlinks for checkpoints, store mapping from resolved -> original
-            checkpoint_map = {}
-            for path in raw_checkpoint_paths:
-                if os.path.exists(path):
-                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-                    checkpoint_map[real_path] = checkpoint_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
-            
-            # Normalize and resolve symlinks for unet, store mapping from resolved -> original
-            unet_map = {}
-            for path in raw_unet_paths:
-                if os.path.exists(path):
-                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-                    unet_map[real_path] = unet_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
-            
-            # Merge both maps and deduplicate by real path
-            merged_map = {}
-            for real_path, orig_path in {**checkpoint_map, **unet_map}.items():
-                if real_path not in merged_map:
-                    merged_map[real_path] = orig_path
+            unique_paths = self._prepare_checkpoint_paths(raw_checkpoint_paths, raw_unet_paths)
 
-            # Now sort and use only the deduplicated real paths
-            unique_paths = sorted(merged_map.values(), key=lambda p: p.lower())
-            
-            # Split back into checkpoints and unet roots for class properties
-            self.checkpoints_roots = [p for p in unique_paths if p in checkpoint_map.values()]
-            self.unet_roots = [p for p in unique_paths if p in unet_map.values()]
-            
-            all_paths = unique_paths
-            
-            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(all_paths) if all_paths else "[]"))
-            
-            if not all_paths:
+            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+
+            if not unique_paths:
                 logger.warning("No valid checkpoint folders found in ComfyUI configuration")
                 return []
-            
-            # Initialize path mappings
-            for original_path in all_paths:
-                real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
-                if real_path != original_path:
-                    self.add_path_mapping(original_path, real_path)
-            
-            return all_paths
+
+            return unique_paths
         except Exception as e:
             logger.warning(f"Error initializing checkpoint paths: {e}")
             return []
@@ -240,45 +383,76 @@ class Config:
         """Initialize and validate embedding paths from ComfyUI settings"""
         try:
             raw_paths = folder_paths.get_folder_paths("embeddings")
-            
-            # Normalize and resolve symlinks, store mapping from resolved -> original
-            path_map = {}
-            for path in raw_paths:
-                if os.path.exists(path):
-                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-                    path_map[real_path] = path_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
-            
-            # Now sort and use only the deduplicated real paths
-            unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+            unique_paths = self._prepare_embedding_paths(raw_paths)
             logger.info("Found embedding roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
-            
+
             if not unique_paths:
                 logger.warning("No valid embeddings folders found in ComfyUI configuration")
                 return []
-            
-            for original_path in unique_paths:
-                real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
-                if real_path != original_path:
-                    self.add_path_mapping(original_path, real_path)
-            
+
             return unique_paths
         except Exception as e:
             logger.warning(f"Error initializing embedding paths: {e}")
             return []
 
     def get_preview_static_url(self, preview_path: str) -> str:
-        """Convert local preview path to static URL"""
         if not preview_path:
             return ""
-        
-        real_path = os.path.realpath(preview_path).replace(os.sep, '/')
 
-        for path, route in self._route_mappings.items():
-            if real_path.startswith(path):
-                relative_path = os.path.relpath(real_path, path)
-                return f'{route}/{relative_path.replace(os.sep, "/")}'
+        normalized = os.path.normpath(preview_path).replace(os.sep, '/')
+        encoded_path = urllib.parse.quote(normalized, safe='')
+        return f'/api/lm/previews?path={encoded_path}'
 
-        return ""
+    def is_preview_path_allowed(self, preview_path: str) -> bool:
+        """Return ``True`` if ``preview_path`` is within an allowed directory."""
+
+        if not preview_path:
+            return False
+
+        try:
+            candidate = Path(preview_path).expanduser().resolve(strict=False)
+        except Exception:
+            return False
+
+        for root in self._preview_root_paths:
+            try:
+                candidate.relative_to(root)
+                return True
+            except ValueError:
+                continue
+
+        return False
+
+    def apply_library_settings(self, library_config: Mapping[str, object]) -> None:
+        """Update runtime paths to match the provided library configuration."""
+        folder_paths = library_config.get('folder_paths') if isinstance(library_config, Mapping) else {}
+        if not isinstance(folder_paths, Mapping):
+            folder_paths = {}
+
+        self._apply_library_paths(folder_paths)
+
+        logger.info(
+            "Applied library settings with %d lora roots, %d checkpoint roots, and %d embedding roots",
+            len(self.loras_roots or []),
+            len(self.base_models_roots or []),
+            len(self.embeddings_roots or []),
+        )
+
+    def get_library_registry_snapshot(self) -> Dict[str, object]:
+        """Return the current library registry and active library name."""
+
+        try:
+            from .services.settings_manager import settings as settings_service
+
+            libraries = settings_service.get_libraries()
+            active_library = settings_service.get_active_library_name()
+            return {
+                "active_library": active_library,
+                "libraries": libraries,
+            }
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.debug("Failed to collect library registry snapshot: %s", exc)
+            return {"active_library": "", "libraries": {}}
 
 # Global config instance
 config = Config()

py/lora_manager.py

@@ -2,7 +2,6 @@ import asyncio
 import sys
 import os
 import logging
-from pathlib import Path
 from server import PromptServer # type: ignore
 
 from .config import config
@@ -11,11 +10,13 @@ from .routes.recipe_routes import RecipeRoutes
 from .routes.stats_routes import StatsRoutes
 from .routes.update_routes import UpdateRoutes
 from .routes.misc_routes import MiscRoutes
+from .routes.preview_routes import PreviewRoutes
 from .routes.example_images_routes import ExampleImagesRoutes
 from .services.service_registry import ServiceRegistry
 from .services.settings_manager import settings
 from .utils.example_images_migration import ExampleImagesMigration
 from .services.websocket_manager import ws_manager
+from .services.example_images_cleanup_service import ExampleImagesCleanupService
 
 logger = logging.getLogger(__name__)
 
@@ -49,103 +50,18 @@ class LoraManager:
         asyncio_logger = logging.getLogger("asyncio")
         asyncio_logger.addFilter(ConnectionResetFilter())
 
-        added_targets = set()  # Track already added target paths
-        
         # Add static route for example images if the path exists in settings
         example_images_path = settings.get('example_images_path')
         logger.info(f"Example images path: {example_images_path}")
         if example_images_path and os.path.exists(example_images_path):
             app.router.add_static('/example_images_static', example_images_path)
             logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
-        
-        # Add static routes for each lora root
-        for idx, root in enumerate(config.loras_roots, start=1):
-            preview_path = f'/loras_static/root{idx}/preview'
-            
-            real_root = root
-            if root in config._path_mappings.values():
-                for target, link in config._path_mappings.items():
-                    if link == root:
-                        real_root = target
-                        break
-            # Add static route for original path
-            app.router.add_static(preview_path, real_root)
-            logger.info(f"Added static route {preview_path} -> {real_root}")
-            
-            # Record route mapping
-            config.add_route_mapping(real_root, preview_path)
-            added_targets.add(real_root)
-        
-        # Add static routes for each checkpoint root
-        for idx, root in enumerate(config.base_models_roots, start=1):
-            preview_path = f'/checkpoints_static/root{idx}/preview'
-            
-            real_root = root
-            if root in config._path_mappings.values():
-                for target, link in config._path_mappings.items():
-                    if link == root:
-                        real_root = target
-                        break
-            # Add static route for original path
-            app.router.add_static(preview_path, real_root)
-            logger.info(f"Added static route {preview_path} -> {real_root}")
-            
-            # Record route mapping
-            config.add_route_mapping(real_root, preview_path)
-            added_targets.add(real_root)
-        
-        # Add static routes for each embedding root
-        for idx, root in enumerate(config.embeddings_roots, start=1):
-            preview_path = f'/embeddings_static/root{idx}/preview'
-            
-            real_root = root
-            if root in config._path_mappings.values():
-                for target, link in config._path_mappings.items():
-                    if link == root:
-                        real_root = target
-                        break
-            # Add static route for original path
-            app.router.add_static(preview_path, real_root)
-            logger.info(f"Added static route {preview_path} -> {real_root}")
-            
-            # Record route mapping
-            config.add_route_mapping(real_root, preview_path)
-            added_targets.add(real_root)
-        
-        # Add static routes for symlink target paths
-        link_idx = {
-            'lora': 1,
-            'checkpoint': 1,
-            'embedding': 1
-        }
-        
-        for target_path, link_path in config._path_mappings.items():
-            if target_path not in added_targets:
-                # Determine if this is a checkpoint, lora, or embedding link based on path
-                is_checkpoint = any(cp_root in link_path for cp_root in config.base_models_roots)
-                is_checkpoint = is_checkpoint or any(cp_root in target_path for cp_root in config.base_models_roots)
-                is_embedding = any(emb_root in link_path for emb_root in config.embeddings_roots)
-                is_embedding = is_embedding or any(emb_root in target_path for emb_root in config.embeddings_roots)
-                
-                if is_checkpoint:
-                    route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
-                    link_idx["checkpoint"] += 1
-                elif is_embedding:
-                    route_path = f'/embeddings_static/link_{link_idx["embedding"]}/preview'
-                    link_idx["embedding"] += 1
-                else:
-                    route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
-                    link_idx["lora"] += 1
-                
-                try:
-                    app.router.add_static(route_path, Path(target_path).resolve(strict=False))
-                    logger.info(f"Added static route for link target {route_path} -> {target_path}")
-                    config.add_route_mapping(target_path, route_path)
-                    added_targets.add(target_path)
-                except Exception as e:
-                    logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
-                    continue
-        
+
+        # Add static route for locales JSON files
+        if os.path.exists(config.i18n_path):
+            app.router.add_static('/locales', config.i18n_path)
+            logger.info(f"Added static route for locales: /locales -> {config.i18n_path}")
+
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
         
@@ -161,7 +77,8 @@ class LoraManager:
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)  
         MiscRoutes.setup_routes(app)
-        ExampleImagesRoutes.setup_routes(app)
+        ExampleImagesRoutes.setup_routes(app, ws_manager=ws_manager)
+        PreviewRoutes.setup_routes(app)
         
         # Setup WebSocket routes that are shared across all model types
         app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
@@ -185,6 +102,9 @@ class LoraManager:
 
             # Register DownloadManager with ServiceRegistry
             await ServiceRegistry.get_download_manager()
+
+            from .services.metadata_service import initialize_metadata_providers
+            await initialize_metadata_providers()
             
             # Initialize WebSocket manager
             await ServiceRegistry.get_websocket_manager()
@@ -198,29 +118,188 @@ class LoraManager:
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
             
             # Create low-priority initialization tasks
-            asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init')
-            asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init')
-            asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init')
-            asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
+            init_tasks = [
+                asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init'),
+                asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init'),
+                asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init'),
+                asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
+            ]
 
             await ExampleImagesMigration.check_and_run_migrations()
             
-            logger.info("LoRA Manager: All services initialized and background tasks scheduled")
+            # Schedule post-initialization tasks to run after scanners complete
+            asyncio.create_task(
+                cls._run_post_initialization_tasks(init_tasks), 
+                name='post_init_tasks'
+            )
+            
+            logger.debug("LoRA Manager: All services initialized and background tasks scheduled")
                 
         except Exception as e:
             logger.error(f"LoRA Manager: Error initializing services: {e}", exc_info=True)
     
+    @classmethod
+    async def _run_post_initialization_tasks(cls, init_tasks):
+        """Run post-initialization tasks after all scanners complete"""
+        try:
+            logger.debug("LoRA Manager: Waiting for scanner initialization to complete...")
+            
+            # Wait for all scanner initialization tasks to complete
+            await asyncio.gather(*init_tasks, return_exceptions=True)
+
+            logger.debug("LoRA Manager: Scanner initialization completed, starting post-initialization tasks...")
+            
+            # Run post-initialization tasks
+            post_tasks = [
+                asyncio.create_task(cls._cleanup_backup_files(), name='cleanup_bak_files'),
+                # Add more post-initialization tasks here as needed
+                # asyncio.create_task(cls._another_post_task(), name='another_task'),
+            ]
+            
+            # Run all post-initialization tasks
+            results = await asyncio.gather(*post_tasks, return_exceptions=True)
+            
+            # Log results
+            for i, result in enumerate(results):
+                task_name = post_tasks[i].get_name()
+                if isinstance(result, Exception):
+                    logger.error(f"Post-initialization task '{task_name}' failed: {result}")
+                else:
+                    logger.debug(f"Post-initialization task '{task_name}' completed successfully")
+                    
+            logger.debug("LoRA Manager: All post-initialization tasks completed")
+            
+        except Exception as e:
+            logger.error(f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True)
+    
+    @classmethod
+    async def _cleanup_backup_files(cls):
+        """Clean up .bak files in all model roots"""
+        try:
+            logger.debug("Starting cleanup of .bak files in model directories...")
+            
+            # Collect all model roots
+            all_roots = set()
+            all_roots.update(config.loras_roots)
+            all_roots.update(config.base_models_roots) 
+            all_roots.update(config.embeddings_roots)
+            
+            total_deleted = 0
+            total_size_freed = 0
+            
+            for root_path in all_roots:
+                if not os.path.exists(root_path):
+                    continue
+                    
+                try:
+                    deleted_count, size_freed = await cls._cleanup_backup_files_in_directory(root_path)
+                    total_deleted += deleted_count
+                    total_size_freed += size_freed
+                    
+                    if deleted_count > 0:
+                        logger.debug(f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024*1024):.2f} MB)")
+                        
+                except Exception as e:
+                    logger.error(f"Error cleaning up .bak files in {root_path}: {e}")
+                    
+                # Yield control periodically
+                await asyncio.sleep(0.01)
+            
+            if total_deleted > 0:
+                logger.debug(f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024*1024):.2f} MB total")
+            else:
+                logger.debug("Backup cleanup completed: no .bak files found")
+
+        except Exception as e:
+            logger.error(f"Error during backup file cleanup: {e}", exc_info=True)
+    
+    @classmethod
+    async def _cleanup_backup_files_in_directory(cls, directory_path: str):
+        """Clean up .bak files in a specific directory recursively
+        
+        Args:
+            directory_path: Path to the directory to clean
+            
+        Returns:
+            Tuple[int, int]: (number of files deleted, total size freed in bytes)
+        """
+        deleted_count = 0
+        size_freed = 0
+        visited_paths = set()
+        
+        def cleanup_recursive(path):
+            nonlocal deleted_count, size_freed
+            
+            try:
+                real_path = os.path.realpath(path)
+                if real_path in visited_paths:
+                    return
+                visited_paths.add(real_path)
+                
+                with os.scandir(path) as it:
+                    for entry in it:
+                        try:
+                            if entry.is_file(follow_symlinks=True) and entry.name.endswith('.bak'):
+                                file_size = entry.stat().st_size
+                                os.remove(entry.path)
+                                deleted_count += 1
+                                size_freed += file_size
+                                logger.debug(f"Deleted .bak file: {entry.path}")
+                                
+                            elif entry.is_dir(follow_symlinks=True):
+                                cleanup_recursive(entry.path)
+                                
+                        except Exception as e:
+                            logger.warning(f"Could not delete .bak file {entry.path}: {e}")
+                            
+            except Exception as e:
+                logger.error(f"Error scanning directory {path} for .bak files: {e}")
+        
+        # Run the recursive cleanup in a thread pool to avoid blocking
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, cleanup_recursive, directory_path)
+        
+        return deleted_count, size_freed
+    
+    @classmethod
+    async def _cleanup_example_images_folders(cls):
+        """Invoke the example images cleanup service for manual execution."""
+        try:
+            service = ExampleImagesCleanupService()
+            result = await service.cleanup_example_image_folders()
+
+            if result.get('success'):
+                logger.debug(
+                    "Manual example images cleanup completed: moved=%s",
+                    result.get('moved_total'),
+                )
+            elif result.get('partial_success'):
+                logger.warning(
+                    "Manual example images cleanup partially succeeded: moved=%s failures=%s",
+                    result.get('moved_total'),
+                    result.get('move_failures'),
+                )
+            else:
+                logger.debug(
+                    "Manual example images cleanup skipped or failed: %s",
+                    result.get('error', 'no changes'),
+                )
+
+            return result
+
+        except Exception as e:  # pragma: no cover - defensive guard
+            logger.error(f"Error during example images cleanup: {e}", exc_info=True)
+            return {
+                'success': False,
+                'error': str(e),
+                'error_code': 'unexpected_error',
+            }
+
     @classmethod
     async def _cleanup(cls, app):
         """Cleanup resources using ServiceRegistry"""
         try:
             logger.info("LoRA Manager: Cleaning up services")
                 
-            # Close CivitaiClient gracefully
-            civitai_client = await ServiceRegistry.get_service("civitai_client")
-            if civitai_client:
-                await civitai_client.close()
-                logger.info("Closed CivitaiClient connection")
-                
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/__init__.py

@@ -1,9 +1,7 @@
 import os
-import importlib
-import sys
 
 # Check if running in standalone mode
-standalone_mode = 'nodes' not in sys.modules
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
 if not standalone_mode:
     from .metadata_hook import MetadataHook

py/metadata_collector/metadata_processor.py

@@ -1,9 +1,9 @@
 import json
-import sys
+import os
 from .constants import IMAGES
 
 # Check if running in standalone mode
-standalone_mode = 'nodes' not in sys.modules
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
 from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
 
@@ -295,7 +295,7 @@ class MetadataProcessor:
             "seed": None,
             "steps": None,
             "cfg_scale": None,
-            "guidance": None,  # Add guidance parameter
+            # "guidance": None,  # Add guidance parameter
             "sampler": None,
             "scheduler": None,
             "checkpoint": None,
@@ -339,44 +339,8 @@ class MetadataProcessor:
                     is_custom_advanced = prompt.original_prompt[primary_sampler_id].get("class_type") == "SamplerCustomAdvanced"
                 
                 if is_custom_advanced:
-                    # For SamplerCustomAdvanced, trace specific inputs
-                    
-                    # 1. Trace sigmas input to find BasicScheduler
-                    scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", "BasicScheduler", max_depth=5)
-                    if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
-                        scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
-                        params["steps"] = scheduler_params.get("steps")
-                        params["scheduler"] = scheduler_params.get("scheduler")
-                    
-                    # 2. Trace sampler input to find KSamplerSelect
-                    sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
-                    if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
-                        sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
-                        params["sampler"] = sampler_params.get("sampler_name")
-                    
-                    # 3. Trace guider input for CFGGuider and CLIPTextEncode
-                    guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
-                    if guider_node_id and guider_node_id in prompt.original_prompt:
-                        # Check if the guider node is a CFGGuider
-                        if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
-                            # Extract cfg value from the CFGGuider
-                            if guider_node_id in metadata.get(SAMPLING, {}):
-                                cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
-                                params["cfg_scale"] = cfg_params.get("cfg")
-                            
-                            # Find CLIPTextEncode for positive prompt
-                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
-                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
-                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
-                            
-                            # Find CLIPTextEncode for negative prompt
-                            negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
-                            if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
-                                params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
-                        else:
-                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
-                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
-                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    # For SamplerCustomAdvanced, use the new handler method
+                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
                 
                 else:
                     # For standard samplers, match conditioning objects to prompts
@@ -401,6 +365,9 @@ class MetadataProcessor:
                         negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
                         if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                             params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                    
+                    # For SamplerCustom, handle any additional parameters
+                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
                 
             # Size extraction is same for all sampler types
             # Check if the sampler itself has size information (from latent_image)
@@ -454,3 +421,59 @@ class MetadataProcessor:
         """Convert metadata to JSON string"""
         params = MetadataProcessor.to_dict(metadata, id)
         return json.dumps(params, indent=4)
+    
+    @staticmethod
+    def handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params):
+        """
+        Handle parameter extraction for SamplerCustomAdvanced nodes
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - prompt: The prompt object containing node connections
+        - primary_sampler_id: ID of the SamplerCustomAdvanced node
+        - params: Parameters dictionary to update
+        """
+        if not prompt.original_prompt or primary_sampler_id not in prompt.original_prompt:
+            return
+            
+        sampler_inputs = prompt.original_prompt[primary_sampler_id].get("inputs", {})
+        
+        # 1. Trace sigmas input to find BasicScheduler (only if sigmas input exists)
+        if "sigmas" in sampler_inputs:
+            scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", None, max_depth=5)
+            if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
+                scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
+                params["steps"] = scheduler_params.get("steps")
+                params["scheduler"] = scheduler_params.get("scheduler")
+        
+        # 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
+        if "sampler" in sampler_inputs:
+            sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
+            if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
+                sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
+                params["sampler"] = sampler_params.get("sampler_name")
+        
+        # 3. Trace guider input for CFGGuider and CLIPTextEncode
+        if "guider" in sampler_inputs:
+            guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
+            if guider_node_id and guider_node_id in prompt.original_prompt:
+                # Check if the guider node is a CFGGuider
+                if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
+                    # Extract cfg value from the CFGGuider
+                    if guider_node_id in metadata.get(SAMPLING, {}):
+                        cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
+                        params["cfg_scale"] = cfg_params.get("cfg")
+                    
+                    # Find CLIPTextEncode for positive prompt
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
+                    if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                        params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    
+                    # Find CLIPTextEncode for negative prompt
+                    negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
+                    if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
+                        params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                else:
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                    if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                        params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")

py/metadata_collector/node_extractors.py

@@ -642,7 +642,9 @@ NODE_EXTRACTORS = {
     # Sampling
     "KSampler": SamplerExtractor,
     "KSamplerAdvanced": KSamplerAdvancedExtractor,
+    "SamplerCustom": KSamplerAdvancedExtractor,
     "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,
+    "ClownsharKSampler_Beta": SamplerExtractor,
     "TSC_KSampler": TSCKSamplerExtractor,   # Efficient Nodes
     "TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor,  # Efficient Nodes
     "KSamplerBasicPipe": KSamplerBasicPipeExtractor,    # comfyui-impact-pack
@@ -652,9 +654,11 @@ NODE_EXTRACTORS = {
     # Sampling Selectors
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
+    "AlignYourStepsScheduler": BasicSchedulerExtractor,  # Add AlignYourStepsScheduler
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
     "comfyLoader": CheckpointLoaderExtractor,  # easy comfyLoader
+    "CheckpointLoaderSimpleWithImages": CheckpointLoaderExtractor,  # CheckpointLoader|pysssss
     "TSC_EfficientLoader": TSCCheckpointLoaderExtractor,  # Efficient Nodes
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
@@ -667,6 +671,7 @@ NODE_EXTRACTORS = {
     "AdvancedCLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
     "smZ_CLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/shiimizu/ComfyUI_smZNodes
     "CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor,  # Add CR_ApplyControlNetStack
+    "PCTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/asagi4/comfyui-prompt-control
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux

py/middleware/__init__.py

@@ -0,0 +1 @@
+"""Server middleware modules"""

py/middleware/cache_middleware.py

@@ -0,0 +1,53 @@
+"""Cache control middleware for ComfyUI server"""
+
+from aiohttp import web
+from typing import Callable, Awaitable
+
+# Time in seconds
+ONE_HOUR: int = 3600
+ONE_DAY: int = 86400
+IMG_EXTENSIONS = (
+    ".jpg",
+    ".jpeg",
+    ".png",
+    ".ppm",
+    ".bmp",
+    ".pgm",
+    ".tif",
+    ".tiff",
+    ".webp",
+    ".mp4"
+)
+
+
+@web.middleware
+async def cache_control(
+    request: web.Request, handler: Callable[[web.Request], Awaitable[web.Response]]
+) -> web.Response:
+    """Cache control middleware that sets appropriate cache headers based on file type and response status"""
+    response: web.Response = await handler(request)
+
+    if (
+        request.path.endswith(".js")
+        or request.path.endswith(".css")
+        or request.path.endswith("index.json")
+    ):
+        response.headers.setdefault("Cache-Control", "no-cache")
+        return response
+
+    # Early return for non-image files - no cache headers needed
+    if not request.path.lower().endswith(IMG_EXTENSIONS):
+        return response
+
+    # Handle image files
+    if response.status == 404:
+        response.headers.setdefault("Cache-Control", f"public, max-age={ONE_HOUR}")
+    elif response.status in (200, 201, 202, 203, 204, 205, 206, 301, 308):
+        # Success responses and permanent redirects - cache for 1 day
+        response.headers.setdefault("Cache-Control", f"public, max-age={ONE_DAY}")
+    elif response.status in (302, 303, 307):
+        # Temporary redirects - no cache
+        response.headers.setdefault("Cache-Control", "no-cache")
+    # Note: 304 Not Modified falls through - no cache headers set
+
+    return response

py/nodes/lora_loader.py

@@ -1,4 +1,5 @@
 import logging
+import re
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
 from ..utils.utils import get_lora_info
@@ -17,7 +18,8 @@ class LoraManagerLoader:
                 "model": ("MODEL",),
                 # "clip": ("CLIP",),
                 "text": (IO.STRING, {
-                    "multiline": True, 
+                    "multiline": True,
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"
@@ -109,6 +111,144 @@ class LoraManagerLoader:
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
         
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0]
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
+
+        return (model, clip, trigger_words_text, formatted_loras_text)
+
+class LoraManagerTextLoader:
+    NAME = "LoRA Text Loader (LoraManager)"
+    CATEGORY = "Lora Manager/loaders"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "lora_syntax": (IO.STRING, {
+                    "defaultInput": True,
+                    "forceInput": True,
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
+                }),
+            },
+            "optional": {
+                "clip": ("CLIP",),
+                "lora_stack": ("LORA_STACK",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
+    FUNCTION = "load_loras_from_text"
+    
+    def parse_lora_syntax(self, text):
+        """Parse LoRA syntax from text input."""
+        # Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
+        pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
+        matches = re.findall(pattern, text, re.IGNORECASE)
+        
+        loras = []
+        for match in matches:
+            lora_name = match[0]
+            model_strength = float(match[1])
+            clip_strength = float(match[2]) if match[2] else model_strength
+            
+            loras.append({
+                'name': lora_name,
+                'model_strength': model_strength,
+                'clip_strength': clip_strength
+            })
+        
+        return loras
+    
+    def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
+        """Load LoRAs based on text syntax input."""
+        loaded_loras = []
+        all_trigger_words = []
+        
+        # Check if model is a Nunchaku Flux model - simplified approach
+        is_nunchaku_model = False
+        
+        try:
+            model_wrapper = model.model.diffusion_model
+            # Check if model is a Nunchaku Flux model using only class name
+            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
+                is_nunchaku_model = True
+                logger.info("Detected Nunchaku Flux model")
+        except (AttributeError, TypeError):
+            # Not a model with the expected structure
+            pass
+        
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the appropriate loader
+                if is_nunchaku_model:
+                    # Use our custom function for Flux models
+                    model = nunchaku_load_lora(model, lora_path, model_strength)
+                    # clip remains unchanged for Nunchaku models
+                else:
+                    # Use default loader for standard models
+                    model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                
+                # Extract lora name for trigger words lookup
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = get_lora_info(lora_name)
+                
+                all_trigger_words.extend(trigger_words)
+                # Add clip strength to output if different from model strength (except for Nunchaku models)
+                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Parse and process LoRAs from text syntax
+        parsed_loras = self.parse_lora_syntax(lora_syntax)
+        for lora in parsed_loras:
+            lora_name = lora['name']
+            model_strength = lora['model_strength']
+            clip_strength = lora['clip_strength']
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = get_lora_info(lora_name)
+            
+            # Apply the LoRA using the appropriate loader
+            if is_nunchaku_model:
+                # For Nunchaku models, use our custom function
+                model = nunchaku_load_lora(model, lora_path, model_strength)
+                # clip remains unchanged
+            else:
+                # Use default loader for standard models
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength and not a Nunchaku model
+            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
         # Format loaded_loras with support for both formats
         formatted_loras = []
         for item in loaded_loras:

py/nodes/lora_stacker.py

@@ -17,6 +17,7 @@ class LoraStacker:
             "required": {
                 "text": (IO.STRING, {
                     "multiline": True, 
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"

py/nodes/save_image.py

@@ -418,11 +418,15 @@ class SaveImage:
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
         
-        # Ensure images is always a list of images
-        if len(images.shape) == 3:  # Single image (height, width, channels)
-            images = [images]
-        else:  # Multiple images (batch, height, width, channels)
-            images = [img for img in images]
+        # If images is already a list or array of images, do nothing; otherwise, convert to list
+        if isinstance(images, (list, np.ndarray)):
+            pass
+        else:
+            # Ensure images is always a list of images
+            if len(images.shape) == 3:  # Single image (height, width, channels)
+                images = [images]
+            else:  # Multiple images (batch, height, width, channels)
+                images = [img for img in images]
         
         # Save all images
         results = self.save_images(

py/nodes/wanvideo_lora_select.py

@@ -14,9 +14,11 @@ class WanVideoLoraSelect:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}),
+                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load LORA models with less VRAM usage, slower loading. This affects ALL LoRAs, not just the current ones. No effect if merge_loras is False"}),
+                "merge_loras": ("BOOLEAN", {"default": True, "tooltip": "Merge LoRAs into the model, otherwise they are loaded on the fly. Always disabled for GGUF and scaled fp8 models. This affects ALL LoRAs, not just the current one"}),
                 "text": (IO.STRING, {
                     "multiline": True, 
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"
@@ -29,7 +31,7 @@ class WanVideoLoraSelect:
     RETURN_NAMES = ("lora", "trigger_words", "active_loras")
     FUNCTION = "process_loras"
     
-    def process_loras(self, text, low_mem_load=False, **kwargs):
+    def process_loras(self, text, low_mem_load=False, merge_loras=True, **kwargs):
         loras_list = []
         all_trigger_words = []
         active_loras = []
@@ -38,6 +40,9 @@ class WanVideoLoraSelect:
         prev_lora = kwargs.get('prev_lora', None)
         if prev_lora is not None:
             loras_list.extend(prev_lora)
+
+        if not merge_loras:
+            low_mem_load = False  # Unmerged LoRAs don't need low_mem_load
         
         # Get blocks if available
         blocks = kwargs.get('blocks', {})
@@ -65,6 +70,7 @@ class WanVideoLoraSelect:
                 "blocks": selected_blocks,
                 "layer_filter": layer_filter,
                 "low_mem_load": low_mem_load,
+                "merge_loras": merge_loras,
             }
             
             # Add to list and collect active loras

py/nodes/wanvideo_lora_select_from_text.py

@@ -0,0 +1,127 @@
+from comfy.comfy_types import IO
+import folder_paths
+from ..utils.utils import get_lora_info
+from .utils import any_type 
+import logging
+
+# 初始化日志记录器
+logger = logging.getLogger(__name__)
+
+# 定义新节点的类
+class WanVideoLoraSelectFromText:
+    # 节点在UI中显示的名称
+    NAME = "WanVideo Lora Select From Text (LoraManager)"
+    # 节点所属的分类
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load LORA models with less VRAM usage, slower loading. This affects ALL LoRAs, not just the current ones. No effect if merge_loras is False"}),
+                "merge_lora": ("BOOLEAN", {"default": True, "tooltip": "Merge LoRAs into the model, otherwise they are loaded on the fly. Always disabled for GGUF and scaled fp8 models. This affects ALL LoRAs, not just the current one"}),
+                "lora_syntax": (IO.STRING, {
+                    "multiline": True,
+                    "defaultInput": True,
+                    "forceInput": True,
+                    "tooltip": "Connect a TEXT output for LoRA syntax: <lora:name:strength>"
+                }),
+            },
+
+            "optional": {
+                "prev_lora": ("WANVIDLORA",), 
+                "blocks": ("BLOCKS",)        
+            }
+        }
+
+    RETURN_TYPES = ("WANVIDLORA", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("lora", "trigger_words", "active_loras")
+
+    FUNCTION = "process_loras_from_syntax"
+    
+    def process_loras_from_syntax(self, lora_syntax, low_mem_load=False, merge_lora=True, **kwargs):
+        text_to_process = lora_syntax
+
+        blocks = kwargs.get('blocks', {})
+        selected_blocks = blocks.get("selected_blocks", {})
+        layer_filter = blocks.get("layer_filter", "")
+
+        loras_list = []
+        all_trigger_words = []
+        active_loras = []
+        
+        prev_lora = kwargs.get('prev_lora', None)
+        if prev_lora is not None:
+            loras_list.extend(prev_lora)
+
+        if not merge_lora:
+            low_mem_load = False
+
+        parts = text_to_process.split('<lora:')
+        for part in parts[1:]:
+            end_index = part.find('>')
+            if end_index == -1:
+                continue
+
+            content = part[:end_index]
+            lora_parts = content.split(':')
+            
+            lora_name_raw = ""
+            model_strength = 1.0
+            clip_strength = 1.0
+
+            if len(lora_parts) == 2:
+                lora_name_raw = lora_parts[0].strip()
+                try:
+                    model_strength = float(lora_parts[1])
+                    clip_strength = model_strength
+                except (ValueError, IndexError):
+                    logger.warning(f"Invalid strength for LoRA '{lora_name_raw}'. Skipping.")
+                    continue
+            elif len(lora_parts) >= 3:
+                lora_name_raw = lora_parts[0].strip()
+                try:
+                    model_strength = float(lora_parts[1])
+                    clip_strength = float(lora_parts[2])
+                except (ValueError, IndexError):
+                    logger.warning(f"Invalid strengths for LoRA '{lora_name_raw}'. Skipping.")
+                    continue
+            else:
+                continue
+
+            lora_path, trigger_words = get_lora_info(lora_name_raw)
+
+            lora_item = {
+                "path": folder_paths.get_full_path("loras", lora_path),
+                "strength": model_strength,
+                "name": lora_path.split(".")[0],
+                "blocks": selected_blocks,
+                "layer_filter": layer_filter,
+                "low_mem_load": low_mem_load,
+                "merge_loras": merge_lora,
+            }
+
+            loras_list.append(lora_item)
+            active_loras.append((lora_name_raw, model_strength, clip_strength))
+            all_trigger_words.extend(trigger_words)
+
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+                
+        active_loras_text = " ".join(formatted_loras)
+
+        return (loras_list, trigger_words_text, active_loras_text)
+
+NODE_CLASS_MAPPINGS = {
+    "WanVideoLoraSelectFromText": WanVideoLoraSelectFromText
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "WanVideoLoraSelectFromText": "WanVideo Lora Select From Text (LoraManager)"
+}

py/recipes/base.py

@@ -55,7 +55,7 @@ class RecipeMetadataParser(ABC):
             # Unpack the tuple to get the actual data
             civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
             
-            if not civitai_info or civitai_info.get("error") == "Model not found":
+            if not civitai_info or error_msg == "Model not found":
                 # Model not found or deleted
                 lora_entry['isDeleted'] = True
                 lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'

py/recipes/parsers/automatic.py

@@ -6,6 +6,7 @@ import logging
 from typing import Dict, Any
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
+from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
@@ -30,6 +31,9 @@ class AutomaticMetadataParser(RecipeMetadataParser):
     async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
         """Parse metadata from Automatic1111 format"""
         try:
+            # Get metadata provider instead of using civitai_client directly
+            metadata_provider = await get_default_metadata_provider()
+            
             # Split on Negative prompt if it exists
             if "Negative prompt:" in user_comment:
                 parts = user_comment.split('Negative prompt:', 1)
@@ -216,9 +220,9 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         }
                         
                         # Get additional info from Civitai
-                        if civitai_client:
+                        if metadata_provider:
                             try:
-                                civitai_info = await civitai_client.get_model_version_info(resource.get("modelVersionId"))
+                                civitai_info = await metadata_provider.get_model_version_info(resource.get("modelVersionId"))
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry,
                                     civitai_info,
@@ -271,11 +275,11 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         }
                         
                         # Try to get info from Civitai
-                        if civitai_client:
+                        if metadata_provider:
                             try:
                                 if lora_hash:
                                     # If we have hash, use it for lookup
-                                    civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                    civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                                 else:
                                     civitai_info = None
                                 

py/recipes/parsers/civitai_image.py

@@ -5,6 +5,7 @@ import logging
 from typing import Dict, Any, Union
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
+from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
@@ -36,12 +37,15 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         Args:
             metadata: The metadata from the image (dict)
             recipe_scanner: Optional recipe scanner service
-            civitai_client: Optional Civitai API client
+            civitai_client: Optional Civitai API client (deprecated, use metadata_provider instead)
             
         Returns:
             Dict containing parsed recipe data
         """
         try:
+            # Get metadata provider instead of using civitai_client directly
+            metadata_provider = await get_default_metadata_provider()
+            
             # Initialize result structure
             result = {
                 'base_model': None,
@@ -53,6 +57,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Track already added LoRAs to prevent duplicates
             added_loras = {}  # key: model_version_id or hash, value: index in result["loras"]
             
+            # Extract hash information from hashes field for LoRA matching
+            lora_hashes = {}
+            if "hashes" in metadata and isinstance(metadata["hashes"], dict):
+                for key, hash_value in metadata["hashes"].items():
+                    if key.startswith("LORA:"):
+                        lora_name = key.replace("LORA:", "")
+                        lora_hashes[lora_name] = hash_value
+            
             # Extract prompt and negative prompt
             if "prompt" in metadata:
                 result["gen_params"]["prompt"] = metadata["prompt"]
@@ -77,9 +89,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Extract base model information - directly if available
             if "baseModel" in metadata:
                 result["base_model"] = metadata["baseModel"]
-            elif "Model hash" in metadata and civitai_client:
+            elif "Model hash" in metadata and metadata_provider:
                 model_hash = metadata["Model hash"]
-                model_info = await civitai_client.get_model_by_hash(model_hash)
+                model_info, error = await metadata_provider.get_model_by_hash(model_hash)
                 if model_info:
                     result["base_model"] = model_info.get("baseModel", "")
             elif "Model" in metadata and isinstance(metadata.get("resources"), list):
@@ -87,8 +99,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 for resource in metadata.get("resources", []):
                     if resource.get("type") == "model" and resource.get("name") == metadata.get("Model"):
                         # This is likely the checkpoint model
-                        if civitai_client and resource.get("hash"):
-                            model_info = await civitai_client.get_model_by_hash(resource.get("hash"))
+                        if metadata_provider and resource.get("hash"):
+                            model_info, error = await metadata_provider.get_model_by_hash(resource.get("hash"))
                             if model_info:
                                 result["base_model"] = model_info.get("baseModel", "")
             
@@ -101,6 +113,15 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if resource.get("type", "lora") == "lora":
                         lora_hash = resource.get("hash", "")
                         
+                        # Try to get hash from the hashes field if not present in resource
+                        if not lora_hash and resource.get("name"):
+                            lora_hash = lora_hashes.get(resource["name"], "")
+                        
+                        # Skip LoRAs without proper identification (hash or modelVersionId)
+                        if not lora_hash and not resource.get("modelVersionId"):
+                            logger.debug(f"Skipping LoRA resource '{resource.get('name', 'Unknown')}' - no hash or modelVersionId")
+                            continue
+                        
                         # Skip if we've already added this LoRA by hash
                         if lora_hash and lora_hash in added_loras:
                             continue
@@ -121,9 +142,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         }
                         
                         # Try to get info from Civitai if hash is available
-                        if lora_entry['hash'] and civitai_client:
+                        if lora_entry['hash'] and metadata_provider:
                             try:
-                                civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                                 
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry,
@@ -177,14 +198,10 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     }
                     
                     # Try to get info from Civitai if modelVersionId is available
-                    if version_id and civitai_client:
+                    if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info instead of get_model_version
-                            civitai_info, error = await civitai_client.get_model_version_info(version_id)
-                            
-                            if error:
-                                logger.warning(f"Error getting model version info: {error}")
-                                continue
+                            civitai_info = await metadata_provider.get_model_version_info(version_id)
                             
                             populated_entry = await self.populate_lora_from_civitai(
                                 lora_entry,
@@ -242,35 +259,84 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         'isDeleted': False
                     }
                     
-                    # If we have a version ID and civitai client, try to get more info
-                    if version_id and civitai_client:
+                    # If we have a version ID and metadata provider, try to get more info
+                    if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info with the version ID
-                            civitai_info, error = await civitai_client.get_model_version_info(version_id)
+                            civitai_info = await metadata_provider.get_model_version_info(version_id)
                             
-                            if error:
-                                logger.warning(f"Error getting model version info: {error}")
-                            else:
-                                populated_entry = await self.populate_lora_from_civitai(
-                                    lora_entry,
-                                    civitai_info,
-                                    recipe_scanner,
-                                    base_model_counts
-                                )
+                            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
                                 
-                                if populated_entry is None:
-                                    continue  # Skip invalid LoRA types
-                                    
-                                lora_entry = populated_entry
-                                
-                                # Track this LoRA for deduplication
-                                if version_id:
-                                    added_loras[version_id] = len(result["loras"])
+                            lora_entry = populated_entry
+                            
+                            # Track this LoRA for deduplication
+                            if version_id:
+                                added_loras[version_id] = len(result["loras"])
                         except Exception as e:
                             logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
                     
                         result["loras"].append(lora_entry)
-            
+
+            # If we found LoRA hashes in the metadata but haven't already
+            # populated entries for them, fall back to creating LoRAs from
+            # the hashes section. Some Civitai image responses only include
+            # LoRA information here without explicit resources entries.
+            for lora_name, lora_hash in lora_hashes.items():
+                if not lora_hash:
+                    continue
+
+                # Skip LoRAs we've already added via resources or other fields
+                if lora_hash in added_loras:
+                    continue
+
+                lora_entry = {
+                    'name': lora_name,
+                    'type': "lora",
+                    'weight': 1.0,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+
+                if metadata_provider:
+                    try:
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry,
+                            civitai_info,
+                            recipe_scanner,
+                            base_model_counts,
+                            lora_hash
+                        )
+
+                        if populated_entry is None:
+                            continue
+
+                        lora_entry = populated_entry
+
+                        if 'id' in lora_entry and lora_entry['id']:
+                            added_loras[str(lora_entry['id'])] = len(result["loras"])
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}")
+
+                added_loras[lora_hash] = len(result["loras"])
+                result["loras"].append(lora_entry)
+
             # Check for LoRA info in the format "Lora_0 Model hash", "Lora_0 Model name", etc.
             lora_index = 0
             while f"Lora_{lora_index} Model hash" in metadata and f"Lora_{lora_index} Model name" in metadata:
@@ -299,9 +365,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 }
                 
                 # Try to get info from Civitai if hash is available
-                if lora_entry['hash'] and civitai_client:
+                if lora_entry['hash'] and metadata_provider:
                     try:
-                        civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                         
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry,

py/recipes/parsers/comfy.py

@@ -6,6 +6,7 @@ import logging
 from typing import Dict, Any
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
+from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
@@ -26,6 +27,9 @@ class ComfyMetadataParser(RecipeMetadataParser):
     async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
         """Parse metadata from Civitai ComfyUI metadata format"""
         try:
+            # Get metadata provider instead of using civitai_client directly
+            metadata_provider = await get_default_metadata_provider()
+            
             data = json.loads(user_comment)
             loras = []
             
@@ -73,10 +77,10 @@ class ComfyMetadataParser(RecipeMetadataParser):
                     'isDeleted': False
                 }
                 
-                # Get additional info from Civitai if client is available
-                if civitai_client:
+                # Get additional info from Civitai if metadata provider is available
+                if metadata_provider:
                     try:
-                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
+                        civitai_info_tuple = await metadata_provider.get_model_version_info(model_version_id)
                         # Populate lora entry with Civitai info
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry, 
@@ -116,9 +120,9 @@ class ComfyMetadataParser(RecipeMetadataParser):
                         }
                         
                         # Get additional checkpoint info from Civitai
-                        if civitai_client:
+                        if metadata_provider:
                             try:
-                                civitai_info_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info_tuple = await metadata_provider.get_model_version_info(checkpoint_version_id)
                                 civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
                                 # Populate checkpoint with Civitai info
                                 checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)

py/recipes/parsers/meta_format.py

@@ -5,6 +5,7 @@ import logging
 from typing import Dict, Any
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
+from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
@@ -18,8 +19,11 @@ class MetaFormatParser(RecipeMetadataParser):
         return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
     
     async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
-        """Parse metadata from images with meta format metadata"""
+        """Parse metadata from images with meta format metadata (Lora_N Model hash format)"""
         try:
+            # Get metadata provider instead of using civitai_client directly
+            metadata_provider = await get_default_metadata_provider()
+            
             # Extract prompt and negative prompt
             parts = user_comment.split('Negative prompt:', 1)
             prompt = parts[0].strip()
@@ -122,9 +126,9 @@ class MetaFormatParser(RecipeMetadataParser):
                 }
                 
                 # Get info from Civitai by hash if available
-                if civitai_client and hash_value:
+                if metadata_provider and hash_value:
                     try:
-                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        civitai_info = await metadata_provider.get_model_by_hash(hash_value)
                         # Populate lora entry with Civitai info
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry, 

py/recipes/parsers/recipe_format.py

@@ -7,6 +7,7 @@ from typing import Dict, Any
 from ...config import config
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
+from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
@@ -23,6 +24,9 @@ class RecipeFormatParser(RecipeMetadataParser):
     async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
         """Parse metadata from images with dedicated recipe metadata format"""
         try:
+            # Get metadata provider instead of using civitai_client directly
+            metadata_provider = await get_default_metadata_provider()
+            
             # Extract recipe metadata from user comment
             try:
                 # Look for recipe metadata section
@@ -71,9 +75,9 @@ class RecipeFormatParser(RecipeMetadataParser):
                         lora_entry['localPath'] = None
                         
                         # Try to get additional info from Civitai if we have a model version ID
-                        if lora.get('modelVersionId') and civitai_client:
+                        if lora.get('modelVersionId') and metadata_provider:
                             try:
-                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                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, 

py/routes/base_model_routes.py

@@ -1,699 +1,275 @@
-from abc import ABC, abstractmethod
-import asyncio
-import json
+from __future__ import annotations
+
 import logging
-from aiohttp import web
-from typing import Dict
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Mapping
 
 import jinja2
+from aiohttp import web
 
-from ..utils.routes_common import ModelRouteUtils
-from ..services.websocket_manager import ws_manager
-from ..services.settings_manager import settings
 from ..config import config
+from ..services.download_coordinator import DownloadCoordinator
+from ..services.downloader import get_downloader
+from ..services.metadata_service import get_default_metadata_provider, get_metadata_provider
+from ..services.metadata_sync_service import MetadataSyncService
+from ..services.model_file_service import ModelFileService, ModelMoveService
+from ..services.model_lifecycle_service import ModelLifecycleService
+from ..services.preview_asset_service import PreviewAssetService
+from ..services.server_i18n import server_i18n as default_server_i18n
+from ..services.service_registry import ServiceRegistry
+from ..services.settings_manager import settings as default_settings
+from ..services.tag_update_service import TagUpdateService
+from ..services.websocket_manager import ws_manager as default_ws_manager
+from ..services.use_cases import (
+    AutoOrganizeUseCase,
+    BulkMetadataRefreshUseCase,
+    DownloadModelUseCase,
+)
+from ..services.websocket_progress_callback import (
+    WebSocketBroadcastCallback,
+    WebSocketProgressCallback,
+)
+from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
+from .model_route_registrar import COMMON_ROUTE_DEFINITIONS, ModelRouteRegistrar
+from .handlers.model_handlers import (
+    ModelAutoOrganizeHandler,
+    ModelCivitaiHandler,
+    ModelDownloadHandler,
+    ModelHandlerSet,
+    ModelListingHandler,
+    ModelManagementHandler,
+    ModelMoveHandler,
+    ModelPageView,
+    ModelQueryHandler,
+)
 
 logger = logging.getLogger(__name__)
 
+
 class BaseModelRoutes(ABC):
-    """Base route controller for all model types"""
-    
-    def __init__(self, service):
-        """Initialize the route controller
-        
-        Args:
-            service: Model service instance (LoraService, CheckpointService, etc.)
-        """
-        self.service = service
-        self.model_type = service.model_type
+    """Base route controller for all model types."""
+
+    template_name: str | None = None
+
+    def __init__(
+        self,
+        service=None,
+        *,
+        settings_service=default_settings,
+        ws_manager=default_ws_manager,
+        server_i18n=default_server_i18n,
+        metadata_provider_factory=get_default_metadata_provider,
+    ) -> None:
+        self.service = None
+        self.model_type = ""
+        self._settings = settings_service
+        self._ws_manager = ws_manager
+        self._server_i18n = server_i18n
+        self._metadata_provider_factory = metadata_provider_factory
+
         self.template_env = jinja2.Environment(
             loader=jinja2.FileSystemLoader(config.templates_path),
-            autoescape=True
+            autoescape=True,
         )
-    
-    def setup_routes(self, app: web.Application, prefix: str):
-        """Setup common routes for the model type
-        
-        Args:
-            app: aiohttp application
-            prefix: URL prefix (e.g., 'loras', 'checkpoints')
-        """
-        # Common model management routes
-        app.router.add_get(f'/api/{prefix}/list', self.get_models)
-        app.router.add_post(f'/api/{prefix}/delete', self.delete_model)
-        app.router.add_post(f'/api/{prefix}/exclude', self.exclude_model)
-        app.router.add_post(f'/api/{prefix}/fetch-civitai', self.fetch_civitai)
-        app.router.add_post(f'/api/{prefix}/relink-civitai', self.relink_civitai)
-        app.router.add_post(f'/api/{prefix}/replace-preview', self.replace_preview)
-        app.router.add_post(f'/api/{prefix}/save-metadata', self.save_metadata)
-        app.router.add_post(f'/api/{prefix}/rename', self.rename_model)
-        app.router.add_post(f'/api/{prefix}/bulk-delete', self.bulk_delete_models)
-        app.router.add_post(f'/api/{prefix}/verify-duplicates', self.verify_duplicates)
-        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
-        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
-        
-        # Common query routes
-        app.router.add_get(f'/api/{prefix}/top-tags', self.get_top_tags)
-        app.router.add_get(f'/api/{prefix}/base-models', self.get_base_models)
-        app.router.add_get(f'/api/{prefix}/scan', self.scan_models)
-        app.router.add_get(f'/api/{prefix}/roots', self.get_model_roots)
-        app.router.add_get(f'/api/{prefix}/folders', self.get_folders)
-        app.router.add_get(f'/api/{prefix}/find-duplicates', self.find_duplicate_models)
-        app.router.add_get(f'/api/{prefix}/find-filename-conflicts', self.find_filename_conflicts)
 
-        # Common Download management
-        app.router.add_post(f'/api/download-model', self.download_model)
-        app.router.add_get(f'/api/download-model-get', self.download_model_get)
-        app.router.add_get(f'/api/cancel-download-get', self.cancel_download_get)
-        app.router.add_get(f'/api/download-progress/{{download_id}}', self.get_download_progress)
-        
-        # CivitAI integration routes
-        app.router.add_post(f'/api/{prefix}/fetch-all-civitai', self.fetch_all_civitai)
-        # app.router.add_get(f'/api/civitai/versions/{{model_id}}', self.get_civitai_versions)
-        
-        # Add generic page route
-        app.router.add_get(f'/{prefix}', self.handle_models_page)
-        
-        # Setup model-specific routes
-        self.setup_specific_routes(app, prefix)
-    
+        self.model_file_service: ModelFileService | None = None
+        self.model_move_service: ModelMoveService | None = None
+        self.model_lifecycle_service: ModelLifecycleService | None = None
+        self.websocket_progress_callback = WebSocketProgressCallback()
+        self.metadata_progress_callback = WebSocketBroadcastCallback()
+
+        self._handler_set: ModelHandlerSet | None = None
+        self._handler_mapping: Dict[str, Callable[[web.Request], web.StreamResponse]] | None = None
+
+        self._preview_service = PreviewAssetService(
+            metadata_manager=MetadataManager,
+            downloader_factory=get_downloader,
+            exif_utils=ExifUtils,
+        )
+        self._metadata_sync_service = MetadataSyncService(
+            metadata_manager=MetadataManager,
+            preview_service=self._preview_service,
+            settings=settings_service,
+            default_metadata_provider_factory=metadata_provider_factory,
+            metadata_provider_selector=get_metadata_provider,
+        )
+        self._tag_update_service = TagUpdateService(metadata_manager=MetadataManager)
+        self._download_coordinator = DownloadCoordinator(
+            ws_manager=self._ws_manager,
+            download_manager_factory=ServiceRegistry.get_download_manager,
+        )
+
+        if service is not None:
+            self.attach_service(service)
+
+    def attach_service(self, service) -> None:
+        """Attach a model service and rebuild handler dependencies."""
+        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_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,
+        )
+        self._handler_set = None
+        self._handler_mapping = None
+
+    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+        if self._handler_mapping is None:
+            handler_set = self._create_handler_set()
+            self._handler_set = handler_set
+            self._handler_mapping = handler_set.to_route_mapping()
+        return self._handler_mapping
+
+    def _create_handler_set(self) -> ModelHandlerSet:
+        service = self._ensure_service()
+        page_view = ModelPageView(
+            template_env=self.template_env,
+            template_name=self.template_name or "",
+            service=service,
+            settings_service=self._settings,
+            server_i18n=self._server_i18n,
+            logger=logger,
+        )
+        listing = ModelListingHandler(
+            service=service,
+            parse_specific_params=self._parse_specific_params,
+            logger=logger,
+        )
+        management = ModelManagementHandler(
+            service=service,
+            logger=logger,
+            metadata_sync=self._metadata_sync_service,
+            preview_service=self._preview_service,
+            tag_update_service=self._tag_update_service,
+            lifecycle_service=self._ensure_lifecycle_service(),
+        )
+        query = ModelQueryHandler(service=service, logger=logger)
+        download_use_case = DownloadModelUseCase(download_coordinator=self._download_coordinator)
+        download = ModelDownloadHandler(
+            ws_manager=self._ws_manager,
+            logger=logger,
+            download_use_case=download_use_case,
+            download_coordinator=self._download_coordinator,
+        )
+        metadata_refresh_use_case = BulkMetadataRefreshUseCase(
+            service=service,
+            metadata_sync=self._metadata_sync_service,
+            settings_service=self._settings,
+            logger=logger,
+        )
+        civitai = ModelCivitaiHandler(
+            service=service,
+            settings_service=self._settings,
+            ws_manager=self._ws_manager,
+            logger=logger,
+            metadata_provider_factory=self._metadata_provider_factory,
+            validate_model_type=self._validate_civitai_model_type,
+            expected_model_types=self._get_expected_model_types,
+            find_model_file=self._find_model_file,
+            metadata_sync=self._metadata_sync_service,
+            metadata_refresh_use_case=metadata_refresh_use_case,
+            metadata_progress_callback=self.metadata_progress_callback,
+        )
+        move = ModelMoveHandler(move_service=self._ensure_move_service(), logger=logger)
+        auto_organize_use_case = AutoOrganizeUseCase(
+            file_service=self._ensure_file_service(),
+            lock_provider=self._ws_manager,
+        )
+        auto_organize = ModelAutoOrganizeHandler(
+            use_case=auto_organize_use_case,
+            progress_callback=self.websocket_progress_callback,
+            ws_manager=self._ws_manager,
+            logger=logger,
+        )
+        return ModelHandlerSet(
+            page_view=page_view,
+            listing=listing,
+            management=management,
+            query=query,
+            download=download,
+            civitai=civitai,
+            move=move,
+            auto_organize=auto_organize,
+        )
+
+    @property
+    def route_handlers(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+        return self._ensure_handler_mapping()
+
+    def setup_routes(self, app: web.Application, prefix: str) -> None:
+        registrar = ModelRouteRegistrar(app)
+        handler_lookup = {
+            definition.handler_name: self._make_handler_proxy(definition.handler_name)
+            for definition in COMMON_ROUTE_DEFINITIONS
+        }
+        registrar.register_common_routes(prefix, handler_lookup)
+        self.setup_specific_routes(registrar, prefix)
+
     @abstractmethod
-    def setup_specific_routes(self, app: web.Application, prefix: str):
-        """Setup model-specific routes - to be implemented by subclasses"""
-        pass
-    
-    async def handle_models_page(self, request: web.Request) -> web.Response:
-        """
-        Generic handler for model pages (e.g., /loras, /checkpoints).
-        Subclasses should set self.template_env and template_name.
-        """
-        try:
-            # Check if the scanner is initializing
-            is_initializing = (
-                self.service.scanner._cache is None or
-                (hasattr(self.service.scanner, 'is_initializing') and callable(self.service.scanner.is_initializing) and self.service.scanner.is_initializing()) or
-                (hasattr(self.service.scanner, '_is_initializing') and self.service.scanner._is_initializing)
-            )
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str) -> None:
+        """Setup model-specific routes."""
+        raise NotImplementedError
 
-            template_name = getattr(self, "template_name", None)
-            if not self.template_env or not template_name:
-                return web.Response(text="Template environment or template name not set", status=500)
-
-            if is_initializing:
-                rendered = self.template_env.get_template(template_name).render(
-                    folders=[],
-                    is_initializing=True,
-                    settings=settings,
-                    request=request
-                )
-            else:
-                try:
-                    cache = await self.service.scanner.get_cached_data(force_refresh=False)
-                    rendered = self.template_env.get_template(template_name).render(
-                        folders=getattr(cache, "folders", []),
-                        is_initializing=False,
-                        settings=settings,
-                        request=request
-                    )
-                except Exception as cache_error:
-                    logger.error(f"Error loading cache data: {cache_error}")
-                    rendered = self.template_env.get_template(template_name).render(
-                        folders=[],
-                        is_initializing=True,
-                        settings=settings,
-                        request=request
-                    )
-            return web.Response(
-                text=rendered,
-                content_type='text/html'
-            )
-        except Exception as e:
-            logger.error(f"Error handling models page: {e}", exc_info=True)
-            return web.Response(
-                text="Error loading models page",
-                status=500
-            )
-    
-    async def get_models(self, request: web.Request) -> web.Response:
-        """Get paginated model data"""
-        try:
-            # Parse common query parameters
-            params = self._parse_common_params(request)
-            
-            # Get data from service
-            result = await self.service.get_paginated_data(**params)
-            
-            # Format response items
-            formatted_result = {
-                'items': [await self.service.format_response(item) for item in result['items']],
-                'total': result['total'],
-                'page': result['page'],
-                'page_size': result['page_size'],
-                'total_pages': result['total_pages']
-            }
-            
-            return web.json_response(formatted_result)
-            
-        except Exception as e:
-            logger.error(f"Error in get_{self.model_type}s: {e}", exc_info=True)
-            return web.json_response({"error": str(e)}, status=500)
-    
-    def _parse_common_params(self, request: web.Request) -> Dict:
-        """Parse common query parameters"""
-        # Parse basic pagination and sorting
-        page = int(request.query.get('page', '1'))
-        page_size = min(int(request.query.get('page_size', '20')), 100)
-        sort_by = request.query.get('sort_by', 'name')
-        folder = request.query.get('folder', None)
-        search = request.query.get('search', None)
-        fuzzy_search = request.query.get('fuzzy_search', 'false').lower() == 'true'
-        
-        # Parse filter arrays
-        base_models = request.query.getall('base_model', [])
-        tags = request.query.getall('tag', [])
-        favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'
-        
-        # Parse search options
-        search_options = {
-            'filename': request.query.get('search_filename', 'true').lower() == 'true',
-            'modelname': request.query.get('search_modelname', 'true').lower() == 'true',
-            'tags': request.query.get('search_tags', 'false').lower() == 'true',
-            'creator': request.query.get('search_creator', 'false').lower() == 'true',
-            'recursive': request.query.get('recursive', 'false').lower() == 'true',
-        }
-        
-        # Parse hash filters if provided
-        hash_filters = {}
-        if 'hash' in request.query:
-            hash_filters['single_hash'] = request.query['hash']
-        elif 'hashes' in request.query:
-            try:
-                hash_list = json.loads(request.query['hashes'])
-                if isinstance(hash_list, list):
-                    hash_filters['multiple_hashes'] = hash_list
-            except (json.JSONDecodeError, TypeError):
-                pass
-        
-        return {
-            'page': page,
-            'page_size': page_size,
-            'sort_by': sort_by,
-            'folder': folder,
-            'search': search,
-            'fuzzy_search': fuzzy_search,
-            'base_models': base_models,
-            'tags': tags,
-            'search_options': search_options,
-            'hash_filters': hash_filters,
-            'favorites_only': favorites_only,
-            # Add model-specific parameters
-            **self._parse_specific_params(request)
-        }
-    
     def _parse_specific_params(self, request: web.Request) -> Dict:
-        """Parse model-specific parameters - to be overridden by subclasses"""
+        """Parse model-specific parameters - to be overridden by subclasses."""
         return {}
-    
-    # Common route handlers
-    async def delete_model(self, request: web.Request) -> web.Response:
-        """Handle model deletion request"""
-        return await ModelRouteUtils.handle_delete_model(request, self.service.scanner)
-    
-    async def exclude_model(self, request: web.Request) -> web.Response:
-        """Handle model exclusion request"""
-        return await ModelRouteUtils.handle_exclude_model(request, self.service.scanner)
-    
-    async def fetch_civitai(self, request: web.Request) -> web.Response:
-        """Handle CivitAI metadata fetch request"""
-        response = await ModelRouteUtils.handle_fetch_civitai(request, self.service.scanner)
-        
-        # If successful, format the metadata before returning
-        if response.status == 200:
-            data = json.loads(response.body.decode('utf-8'))
-            if data.get("success") and data.get("metadata"):
-                formatted_metadata = await self.service.format_response(data["metadata"])
-                return web.json_response({
-                    "success": True,
-                    "metadata": formatted_metadata
-                })
-        
-        return response
-    
-    async def relink_civitai(self, request: web.Request) -> web.Response:
-        """Handle CivitAI metadata re-linking request"""
-        return await ModelRouteUtils.handle_relink_civitai(request, self.service.scanner)
-    
-    async def replace_preview(self, request: web.Request) -> web.Response:
-        """Handle preview image replacement"""
-        return await ModelRouteUtils.handle_replace_preview(request, self.service.scanner)
-    
-    async def save_metadata(self, request: web.Request) -> web.Response:
-        """Handle saving metadata updates"""
-        return await ModelRouteUtils.handle_save_metadata(request, self.service.scanner)
-    
-    async def rename_model(self, request: web.Request) -> web.Response:
-        """Handle renaming a model file and its associated files"""
-        return await ModelRouteUtils.handle_rename_model(request, self.service.scanner)
-    
-    async def bulk_delete_models(self, request: web.Request) -> web.Response:
-        """Handle bulk deletion of models"""
-        return await ModelRouteUtils.handle_bulk_delete_models(request, self.service.scanner)
-    
-    async def verify_duplicates(self, request: web.Request) -> web.Response:
-        """Handle verification of duplicate model hashes"""
-        return await ModelRouteUtils.handle_verify_duplicates(request, self.service.scanner)
-    
-    async def get_top_tags(self, request: web.Request) -> web.Response:
-        """Handle request for top tags sorted by frequency"""
-        try:
-            limit = int(request.query.get('limit', '20'))
-            if limit < 1 or limit > 100:
-                limit = 20
-                
-            top_tags = await self.service.get_top_tags(limit)
-            
-            return web.json_response({
-                'success': True,
-                'tags': top_tags
-            })
-            
-        except Exception as e:
-            logger.error(f"Error getting top tags: {str(e)}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': 'Internal server error'
-            }, status=500)
-    
-    async def get_base_models(self, request: web.Request) -> web.Response:
-        """Get base models used in models"""
-        try:
-            limit = int(request.query.get('limit', '20'))
-            if limit < 1 or limit > 100:
-                limit = 20
-                
-            base_models = await self.service.get_base_models(limit)
-            
-            return web.json_response({
-                'success': True,
-                'base_models': base_models
-            })
-        except Exception as e:
-            logger.error(f"Error retrieving base models: {e}")
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    async def scan_models(self, request: web.Request) -> web.Response:
-        """Force a rescan of model files"""
-        try:
-            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
-            
-            await self.service.scan_models(force_refresh=True, rebuild_cache=full_rebuild)
-            return web.json_response({
-                "status": "success", 
-                "message": f"{self.model_type.capitalize()} scan completed"
-            })
-        except Exception as e:
-            logger.error(f"Error in scan_{self.model_type}s: {e}", exc_info=True)
-            return web.json_response({"error": str(e)}, status=500)
-    
-    async def get_model_roots(self, request: web.Request) -> web.Response:
-        """Return the model root directories"""
-        try:
-            roots = self.service.get_model_roots()
-            return web.json_response({
-                "success": True,
-                "roots": roots
-            })
-        except Exception as e:
-            logger.error(f"Error getting {self.model_type} roots: {e}", exc_info=True)
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-        
-    async def get_folders(self, request: web.Request) -> web.Response:
-        """Get all folders in the cache"""
-        try:
-            cache = await self.service.scanner.get_cached_data()
-            return web.json_response({
-                'folders': cache.folders
-            })
-        except Exception as e:
-            logger.error(f"Error getting folders: {e}")
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    async def find_duplicate_models(self, request: web.Request) -> web.Response:
-        """Find models with duplicate SHA256 hashes"""
-        try:
-            # Get duplicate hashes from service
-            duplicates = self.service.find_duplicate_hashes()
-            
-            # Format the response
-            result = []
-            cache = await self.service.scanner.get_cached_data()
-            
-            for sha256, paths in duplicates.items():
-                group = {
-                    "hash": sha256,
-                    "models": []
-                }
-                # Find matching models for each path
-                for path in paths:
-                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
-                    if model:
-                        group["models"].append(await self.service.format_response(model))
-                
-                # Add the primary model too
-                primary_path = self.service.get_path_by_hash(sha256)
-                if primary_path and primary_path not in paths:
-                    primary_model = next((m for m in cache.raw_data if m['file_path'] == primary_path), None)
-                    if primary_model:
-                        group["models"].insert(0, await self.service.format_response(primary_model))
-                
-                if len(group["models"]) > 1:  # Only include if we found multiple models
-                    result.append(group)
-                
-            return web.json_response({
-                "success": True,
-                "duplicates": result,
-                "count": len(result)
-            })
-        except Exception as e:
-            logger.error(f"Error finding duplicate {self.model_type}s: {e}", exc_info=True)
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-    
-    async def find_filename_conflicts(self, request: web.Request) -> web.Response:
-        """Find models with conflicting filenames"""
-        try:
-            # Get duplicate filenames from service
-            duplicates = self.service.find_duplicate_filenames()
-            
-            # Format the response
-            result = []
-            cache = await self.service.scanner.get_cached_data()
-            
-            for filename, paths in duplicates.items():
-                group = {
-                    "filename": filename,
-                    "models": []
-                }
-                # Find matching models for each path
-                for path in paths:
-                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
-                    if model:
-                        group["models"].append(await self.service.format_response(model))
-                
-                # Find the model from the main index too
-                hash_val = self.service.scanner.get_hash_by_filename(filename)
-                if hash_val:
-                    main_path = self.service.get_path_by_hash(hash_val)
-                    if main_path and main_path not in paths:
-                        main_model = next((m for m in cache.raw_data if m['file_path'] == main_path), None)
-                        if main_model:
-                            group["models"].insert(0, await self.service.format_response(main_model))
-                
-                if group["models"]:
-                    result.append(group)
-                
-            return web.json_response({
-                "success": True,
-                "conflicts": result,
-                "count": len(result)
-            })
-        except Exception as e:
-            logger.error(f"Error finding filename conflicts for {self.model_type}s: {e}", exc_info=True)
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-        
-    # Download management methods
-    async def download_model(self, request: web.Request) -> web.Response:
-        """Handle model download request"""
-        return await ModelRouteUtils.handle_download_model(request)
-    
-    async def download_model_get(self, request: web.Request) -> web.Response:
-        """Handle model download request via GET method"""
-        try:
-            # Extract query parameters
-            model_id = request.query.get('model_id')
-            if not model_id:
-                return web.Response(
-                    status=400, 
-                    text="Missing required parameter: Please provide 'model_id'"
-                )
-            
-            # Get optional parameters
-            model_version_id = request.query.get('model_version_id')
-            download_id = request.query.get('download_id')
-            use_default_paths = request.query.get('use_default_paths', 'false').lower() == 'true'
-            
-            # Create a data dictionary that mimics what would be received from a POST request
-            data = {
-                'model_id': model_id
-            }
-            
-            # Add optional parameters only if they are provided
-            if model_version_id:
-                data['model_version_id'] = model_version_id
-                
-            if download_id:
-                data['download_id'] = download_id
-                
-            data['use_default_paths'] = use_default_paths
-            
-            # Create a mock request object with the data
-            future = asyncio.get_event_loop().create_future()
-            future.set_result(data)
-            
-            mock_request = type('MockRequest', (), {
-                'json': lambda self=None: future
-            })()
-            
-            # Call the existing download handler
-            return await ModelRouteUtils.handle_download_model(mock_request)
-            
-        except Exception as e:
-            error_message = str(e)
-            logger.error(f"Error downloading model via GET: {error_message}", exc_info=True)
-            return web.Response(status=500, text=error_message)
-    
-    async def cancel_download_get(self, request: web.Request) -> web.Response:
-        """Handle GET request for cancelling a download by download_id"""
-        try:
-            download_id = request.query.get('download_id')
-            if not download_id:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Download ID is required'
-                }, status=400)
-            
-            # Create a mock request with match_info for compatibility
-            mock_request = type('MockRequest', (), {
-                'match_info': {'download_id': download_id}
-            })()
-            return await ModelRouteUtils.handle_cancel_download(mock_request)
-        except Exception as e:
-            logger.error(f"Error cancelling download via GET: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    async def get_download_progress(self, request: web.Request) -> web.Response:
-        """Handle request for download progress by download_id"""
-        try:
-            # Get download_id from URL path
-            download_id = request.match_info.get('download_id')
-            if not download_id:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Download ID is required'
-                }, status=400)
-            
-            progress_data = ws_manager.get_download_progress(download_id)
-            
-            if progress_data is None:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Download ID not found'
-                }, status=404)
-            
-            return web.json_response({
-                'success': True,
-                'progress': progress_data.get('progress', 0)
-            })
-        except Exception as e:
-            logger.error(f"Error getting download progress: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    async def fetch_all_civitai(self, request: web.Request) -> web.Response:
-        """Fetch CivitAI metadata for all models in the background"""
-        try:
-            cache = await self.service.scanner.get_cached_data()
-            total = len(cache.raw_data)
-            processed = 0
-            success = 0
-            needs_resort = False
-            
-            # Prepare models to process
-            to_process = [
-                model for model in cache.raw_data 
-                if model.get('sha256') and (not model.get('civitai') or 'id' not in model.get('civitai')) and model.get('from_civitai', True)
-            ]
-            total_to_process = len(to_process)
-            
-            # Send initial progress
-            await ws_manager.broadcast({
-                'status': 'started',
-                'total': total_to_process,
-                'processed': 0,
-                'success': 0
-            })
-            
-            # Process each model
-            for model in to_process:
-                try:
-                    original_name = model.get('model_name')
-                    if await ModelRouteUtils.fetch_and_update_model(
-                        sha256=model['sha256'],
-                        file_path=model['file_path'],
-                        model_data=model,
-                        update_cache_func=self.service.scanner.update_single_model_cache
-                    ):
-                        success += 1
-                        if original_name != model.get('model_name'):
-                            needs_resort = True
-                    
-                    processed += 1
-                    
-                    # Send progress update
-                    await ws_manager.broadcast({
-                        'status': 'processing',
-                        'total': total_to_process,
-                        'processed': processed,
-                        'success': success,
-                        'current_name': model.get('model_name', 'Unknown')
-                    })
-                    
-                except Exception as e:
-                    logger.error(f"Error fetching CivitAI data for {model['file_path']}: {e}")
-            
-            if needs_resort:
-                await cache.resort()
-            
-            # Send completion message
-            await ws_manager.broadcast({
-                'status': 'completed',
-                'total': total_to_process,
-                'processed': processed,
-                'success': success
-            })
-                    
-            return web.json_response({
-                "success": True,
-                "message": f"Successfully updated {success} of {processed} processed {self.model_type}s (total: {total})"
-            })
-            
-        except Exception as e:
-            # Send error message
-            await ws_manager.broadcast({
-                'status': 'error',
-                'error': str(e)
-            })
-            logger.error(f"Error in fetch_all_civitai for {self.model_type}s: {e}")
-            return web.Response(text=str(e), status=500)
-    
-    async def get_civitai_versions(self, request: web.Request) -> web.Response:
-        """Get available versions for a Civitai model with local availability info"""
-        # This will be implemented by subclasses as they need CivitAI client access
-        return web.json_response({
-            "error": "Not implemented in base class"
-        }, status=501)
-    
-    # Common model move handlers
-    async def move_model(self, request: web.Request) -> web.Response:
-        """Handle model move request"""
-        try:
-            data = await request.json()
-            file_path = data.get('file_path')
-            target_path = data.get('target_path')
-            if not file_path or not target_path:
-                return web.Response(text='File path and target path are required', status=400)
-            import os
-            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}")
-                return web.json_response({'success': True, 'message': 'Source and target directories are the same'})
-            file_name = os.path.basename(file_path)
-            target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-            if os.path.exists(target_file_path):
-                return web.json_response({
-                    'success': False, 
-                    'error': f"Target file already exists: {target_file_path}"
-                }, status=409)
-            success = await self.service.scanner.move_model(file_path, target_path)
-            if success:
-                return web.json_response({'success': True, 'new_file_path': target_file_path})
-            else:
-                return web.Response(text='Failed to move model', status=500)
-        except Exception as e:
-            logger.error(f"Error moving model: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
 
-    async def move_models_bulk(self, request: web.Request) -> web.Response:
-        """Handle bulk model move request"""
-        try:
-            data = await request.json()
-            file_paths = data.get('file_paths', [])
-            target_path = data.get('target_path')
-            if not file_paths or not target_path:
-                return web.Response(text='File paths and target path are required', status=400)
-            results = []
-            import os
-            for file_path in file_paths:
-                source_dir = os.path.dirname(file_path)
-                if os.path.normpath(source_dir) == os.path.normpath(target_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": True, 
-                        "message": "Source and target directories are the same"
-                    })
-                    continue
-                file_name = os.path.basename(file_path)
-                target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-                if os.path.exists(target_file_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": False, 
-                        "message": f"Target file already exists: {target_file_path}"
-                    })
-                    continue
-                success = await self.service.scanner.move_model(file_path, target_path)
-                results.append({
-                    "path": file_path, 
-                    "success": success,
-                    "message": "Success" if success else "Failed to move model"
-                })
-            success_count = sum(1 for r in results if r["success"])
-            failure_count = len(results) - success_count
-            return web.json_response({
-                'success': True,
-                'message': f'Moved {success_count} of {len(file_paths)} models',
-                'results': results,
-                'success_count': success_count,
-                'failure_count': failure_count
-            })
-        except Exception as e:
-            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
+    def _validate_civitai_model_type(self, model_type: str) -> bool:
+        """Validate CivitAI model type - to be overridden by subclasses."""
+        return True
+
+    def _get_expected_model_types(self) -> str:
+        """Get expected model types string for error messages - to be overridden by subclasses."""
+        return "any model type"
+
+    def _find_model_file(self, files):
+        """Find the appropriate model file from the files list - can be overridden by subclasses."""
+        return next((file for file in files if file.get("type") == "Model" and file.get("primary") is True), None)
+
+    def get_handler(self, name: str) -> Callable[[web.Request], web.StreamResponse]:
+        """Expose handlers for subclasses or tests."""
+        return self._ensure_handler_mapping()[name]
+
+    def _ensure_service(self):
+        if self.service is None:
+            raise RuntimeError("Model service has not been attached")
+        return self.service
+
+    def _ensure_file_service(self) -> ModelFileService:
+        if self.model_file_service is None:
+            service = self._ensure_service()
+            self.model_file_service = ModelFileService(service.scanner, service.model_type)
+        return self.model_file_service
+
+    def _ensure_move_service(self) -> ModelMoveService:
+        if self.model_move_service is None:
+            service = self._ensure_service()
+            self.model_move_service = ModelMoveService(service.scanner)
+        return self.model_move_service
+
+    def _ensure_lifecycle_service(self) -> ModelLifecycleService:
+        if self.model_lifecycle_service is None:
+            service = self._ensure_service()
+            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,
+            )
+        return self.model_lifecycle_service
+
+    def _make_handler_proxy(self, name: str) -> Callable[[web.Request], web.StreamResponse]:
+        async def proxy(request: web.Request) -> web.StreamResponse:
+            try:
+                handler = self.get_handler(name)
+            except RuntimeError:
+                return web.json_response({"success": False, "error": "Service not ready"}, status=503)
+            return await handler(request)
+
+        return proxy
+

py/routes/base_recipe_routes.py

@@ -0,0 +1,217 @@
+"""Base infrastructure shared across recipe routes."""
+from __future__ import annotations
+
+import logging
+import os
+from typing import Callable, Mapping
+
+import jinja2
+from aiohttp import web
+
+from ..config import config
+from ..recipes import RecipeParserFactory
+from ..services.downloader import get_downloader
+from ..services.recipes import (
+    RecipeAnalysisService,
+    RecipePersistenceService,
+    RecipeSharingService,
+)
+from ..services.server_i18n import server_i18n
+from ..services.service_registry import ServiceRegistry
+from ..services.settings_manager import settings
+from ..utils.constants import CARD_PREVIEW_WIDTH
+from ..utils.exif_utils import ExifUtils
+from .handlers.recipe_handlers import (
+    RecipeAnalysisHandler,
+    RecipeHandlerSet,
+    RecipeListingHandler,
+    RecipeManagementHandler,
+    RecipePageView,
+    RecipeQueryHandler,
+    RecipeSharingHandler,
+)
+from .recipe_route_registrar import ROUTE_DEFINITIONS
+
+logger = logging.getLogger(__name__)
+
+
+class BaseRecipeRoutes:
+    """Common dependency and startup wiring for recipe routes."""
+
+    _HANDLER_NAMES: tuple[str, ...] = tuple(
+        definition.handler_name for definition in ROUTE_DEFINITIONS
+    )
+
+    template_name: str = "recipes.html"
+
+    def __init__(self) -> None:
+        self.recipe_scanner = None
+        self.lora_scanner = None
+        self.civitai_client = None
+        self.settings = settings
+        self.server_i18n = server_i18n
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader(config.templates_path),
+            autoescape=True,
+        )
+
+        self._i18n_registered = False
+        self._startup_hooks_registered = False
+        self._handler_set: RecipeHandlerSet | None = None
+        self._handler_mapping: dict[str, Callable] | None = None
+
+    async def attach_dependencies(self, app: web.Application | None = None) -> None:
+        """Resolve shared services from the registry."""
+
+        await self._ensure_services()
+        self._ensure_i18n_filter()
+
+    async def ensure_dependencies_ready(self) -> None:
+        """Ensure dependencies are available for request handlers."""
+
+        if self.recipe_scanner is None or self.civitai_client is None:
+            await self.attach_dependencies()
+
+    def register_startup_hooks(self, app: web.Application) -> None:
+        """Register startup hooks once for dependency wiring."""
+
+        if self._startup_hooks_registered:
+            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."""
+
+        if self._handler_mapping is None:
+            handler_set = self._create_handler_set()
+            self._handler_set = handler_set
+            self._handler_mapping = handler_set.to_route_mapping()
+        return self._handler_mapping
+
+    # Internal helpers -------------------------------------------------
+
+    async def _ensure_services(self) -> None:
+        if self.recipe_scanner is None:
+            self.recipe_scanner = await ServiceRegistry.get_recipe_scanner()
+            self.lora_scanner = getattr(self.recipe_scanner, "_lora_scanner", None)
+
+        if self.civitai_client is None:
+            self.civitai_client = await ServiceRegistry.get_civitai_client()
+
+    def _ensure_i18n_filter(self) -> None:
+        if not self._i18n_registered:
+            self.template_env.filters["t"] = self.server_i18n.create_template_filter()
+            self._i18n_registered = True
+
+    def get_handler_owner(self):
+        """Return the object supplying bound handler coroutines."""
+
+        if self._handler_set is None:
+            self._handler_set = self._create_handler_set()
+        return self._handler_set
+
+    def _create_handler_set(self) -> RecipeHandlerSet:
+        recipe_scanner_getter = lambda: self.recipe_scanner
+        civitai_client_getter = lambda: self.civitai_client
+
+        standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
+        if not standalone_mode:
+            from ..metadata_collector import get_metadata  # type: ignore[import-not-found]
+            from ..metadata_collector.metadata_processor import (  # type: ignore[import-not-found]
+                MetadataProcessor,
+            )
+            from ..metadata_collector.metadata_registry import (  # type: ignore[import-not-found]
+                MetadataRegistry,
+            )
+        else:  # pragma: no cover - optional dependency path
+            get_metadata = None  # type: ignore[assignment]
+            MetadataProcessor = None  # type: ignore[assignment]
+            MetadataRegistry = None  # type: ignore[assignment]
+
+        analysis_service = RecipeAnalysisService(
+            exif_utils=ExifUtils,
+            recipe_parser_factory=RecipeParserFactory,
+            downloader_factory=get_downloader,
+            metadata_collector=get_metadata,
+            metadata_processor_cls=MetadataProcessor,
+            metadata_registry_cls=MetadataRegistry,
+            standalone_mode=standalone_mode,
+            logger=logger,
+        )
+        persistence_service = RecipePersistenceService(
+            exif_utils=ExifUtils,
+            card_preview_width=CARD_PREVIEW_WIDTH,
+            logger=logger,
+        )
+        sharing_service = RecipeSharingService(logger=logger)
+
+        page_view = RecipePageView(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            settings_service=self.settings,
+            server_i18n=self.server_i18n,
+            template_env=self.template_env,
+            template_name=self.template_name,
+            recipe_scanner_getter=recipe_scanner_getter,
+            logger=logger,
+        )
+        listing = RecipeListingHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            logger=logger,
+        )
+        query = RecipeQueryHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            format_recipe_file_url=listing.format_recipe_file_url,
+            logger=logger,
+        )
+        management = RecipeManagementHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            logger=logger,
+            persistence_service=persistence_service,
+            analysis_service=analysis_service,
+        )
+        analysis = RecipeAnalysisHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            logger=logger,
+            analysis_service=analysis_service,
+        )
+        sharing = RecipeSharingHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            logger=logger,
+            sharing_service=sharing_service,
+        )
+
+        return RecipeHandlerSet(
+            page_view=page_view,
+            listing=listing,
+            query=query,
+            management=management,
+            analysis=analysis,
+            sharing=sharing,
+        )
+

py/routes/checkpoint_routes.py

@@ -2,6 +2,7 @@ import logging
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
+from .model_route_registrar import ModelRouteRegistrar
 from ..services.checkpoint_service import CheckpointService
 from ..services.service_registry import ServiceRegistry
 from ..config import config
@@ -13,19 +14,16 @@ class CheckpointRoutes(BaseModelRoutes):
     
     def __init__(self):
         """Initialize Checkpoint routes with Checkpoint service"""
-        # Service will be initialized later via setup_routes
-        self.service = None
-        self.civitai_client = None
+        super().__init__()
         self.template_name = "checkpoints.html"
     
     async def initialize_services(self):
         """Initialize services from ServiceRegistry"""
         checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
         self.service = CheckpointService(checkpoint_scanner)
-        self.civitai_client = await ServiceRegistry.get_civitai_client()
         
-        # Initialize parent with the service
-        super().__init__(self.service)
+        # Attach service dependencies
+        self.attach_service(self.service)
     
     def setup_routes(self, app: web.Application):
         """Setup Checkpoint routes"""
@@ -35,17 +33,22 @@ class CheckpointRoutes(BaseModelRoutes):
         # Setup common routes with 'checkpoints' prefix (includes page route)
         super().setup_routes(app, 'checkpoints')
     
-    def setup_specific_routes(self, app: web.Application, prefix: str):
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup Checkpoint-specific routes"""
-        # Checkpoint-specific CivitAI integration
-        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_checkpoint)
-        
         # Checkpoint info by name
-        app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_checkpoint_info)
-        
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/info/{name}', prefix, self.get_checkpoint_info)
+
         # Checkpoint roots and Unet roots
-        app.router.add_get(f'/api/{prefix}/checkpoints_roots', self.get_checkpoints_roots)
-        app.router.add_get(f'/api/{prefix}/unet_roots', self.get_unet_roots)
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/checkpoints_roots', prefix, self.get_checkpoints_roots)
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/unet_roots', prefix, self.get_unet_roots)
+    
+    def _validate_civitai_model_type(self, model_type: str) -> bool:
+        """Validate CivitAI model type for Checkpoint"""
+        return model_type.lower() == 'checkpoint'
+    
+    def _get_expected_model_types(self) -> str:
+        """Get expected model types string for error messages"""
+        return "Checkpoint"
     
     async def get_checkpoint_info(self, request: web.Request) -> web.Response:
         """Get detailed information for a specific checkpoint by name"""
@@ -62,53 +65,6 @@ class CheckpointRoutes(BaseModelRoutes):
             logger.error(f"Error in get_checkpoint_info: {e}", exc_info=True)
             return web.json_response({"error": str(e)}, status=500)
     
-    async def get_civitai_versions_checkpoint(self, request: web.Request) -> web.Response:
-        """Get available versions for a Civitai checkpoint model with local availability info"""
-        try:
-            model_id = request.match_info['model_id']
-            response = await self.civitai_client.get_model_versions(model_id)
-            if not response or not response.get('modelVersions'):
-                return web.Response(status=404, text="Model not found")
-            
-            versions = response.get('modelVersions', [])
-            model_type = response.get('type', '')
-            
-            # Check model type - should be Checkpoint
-            if model_type.lower() != 'checkpoint':
-                return web.json_response({
-                    'error': f"Model type mismatch. Expected Checkpoint, got {model_type}"
-                }, status=400)
-            
-            # Check local availability for each version
-            for version in versions:
-                # Find the primary model file (type="Model" and primary=true) in the files list
-                model_file = next((file for file in version.get('files', []) 
-                                  if file.get('type') == 'Model' and file.get('primary') == True), None)
-                
-                # If no primary file found, try to find any model file
-                if not model_file:
-                    model_file = next((file for file in version.get('files', []) 
-                                      if file.get('type') == 'Model'), None)
-                
-                if model_file:
-                    sha256 = model_file.get('hashes', {}).get('SHA256')
-                    if sha256:
-                        # Set existsLocally and localPath at the version level
-                        version['existsLocally'] = self.service.has_hash(sha256)
-                        if version['existsLocally']:
-                            version['localPath'] = self.service.get_path_by_hash(sha256)
-                        
-                        # Also set the model file size at the version level for easier access
-                        version['modelSizeKB'] = model_file.get('sizeKB')
-                else:
-                    # No model file found in this version
-                    version['existsLocally'] = False
-                    
-            return web.json_response(versions)
-        except Exception as e:
-            logger.error(f"Error fetching checkpoint model versions: {e}")
-            return web.Response(status=500, text=str(e))
-    
     async def get_checkpoints_roots(self, request: web.Request) -> web.Response:
         """Return the list of checkpoint roots from config"""
         try:

py/routes/embedding_routes.py

@@ -2,6 +2,7 @@ import logging
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
+from .model_route_registrar import ModelRouteRegistrar
 from ..services.embedding_service import EmbeddingService
 from ..services.service_registry import ServiceRegistry
 
@@ -12,19 +13,16 @@ class EmbeddingRoutes(BaseModelRoutes):
     
     def __init__(self):
         """Initialize Embedding routes with Embedding service"""
-        # Service will be initialized later via setup_routes
-        self.service = None
-        self.civitai_client = None
+        super().__init__()
         self.template_name = "embeddings.html"
     
     async def initialize_services(self):
         """Initialize services from ServiceRegistry"""
         embedding_scanner = await ServiceRegistry.get_embedding_scanner()
         self.service = EmbeddingService(embedding_scanner)
-        self.civitai_client = await ServiceRegistry.get_civitai_client()
         
-        # Initialize parent with the service
-        super().__init__(self.service)
+        # Attach service dependencies
+        self.attach_service(self.service)
     
     def setup_routes(self, app: web.Application):
         """Setup Embedding routes"""
@@ -34,13 +32,18 @@ class EmbeddingRoutes(BaseModelRoutes):
         # Setup common routes with 'embeddings' prefix (includes page route)
         super().setup_routes(app, 'embeddings')
     
-    def setup_specific_routes(self, app: web.Application, prefix: str):
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup Embedding-specific routes"""
-        # Embedding-specific CivitAI integration
-        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_embedding)
-        
         # Embedding info by name
-        app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_embedding_info)
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/info/{name}', prefix, self.get_embedding_info)
+    
+    def _validate_civitai_model_type(self, model_type: str) -> bool:
+        """Validate CivitAI model type for Embedding"""
+        return model_type.lower() == 'textualinversion'
+    
+    def _get_expected_model_types(self) -> str:
+        """Get expected model types string for error messages"""
+        return "TextualInversion"
     
     async def get_embedding_info(self, request: web.Request) -> web.Response:
         """Get detailed information for a specific embedding by name"""
@@ -56,50 +59,3 @@ class EmbeddingRoutes(BaseModelRoutes):
         except Exception as e:
             logger.error(f"Error in get_embedding_info: {e}", exc_info=True)
             return web.json_response({"error": str(e)}, status=500)
-    
-    async def get_civitai_versions_embedding(self, request: web.Request) -> web.Response:
-        """Get available versions for a Civitai embedding model with local availability info"""
-        try:
-            model_id = request.match_info['model_id']
-            response = await self.civitai_client.get_model_versions(model_id)
-            if not response or not response.get('modelVersions'):
-                return web.Response(status=404, text="Model not found")
-            
-            versions = response.get('modelVersions', [])
-            model_type = response.get('type', '')
-            
-            # Check model type - should be TextualInversion (Embedding)
-            if model_type.lower() not in ['textualinversion', 'embedding']:
-                return web.json_response({
-                    'error': f"Model type mismatch. Expected TextualInversion/Embedding, got {model_type}"
-                }, status=400)
-            
-            # Check local availability for each version
-            for version in versions:
-                # Find the primary model file (type="Model" and primary=true) in the files list
-                model_file = next((file for file in version.get('files', []) 
-                                  if file.get('type') == 'Model' and file.get('primary') == True), None)
-                
-                # If no primary file found, try to find any model file
-                if not model_file:
-                    model_file = next((file for file in version.get('files', []) 
-                                      if file.get('type') == 'Model'), None)
-                
-                if model_file:
-                    sha256 = model_file.get('hashes', {}).get('SHA256')
-                    if sha256:
-                        # Set existsLocally and localPath at the version level
-                        version['existsLocally'] = self.service.has_hash(sha256)
-                        if version['existsLocally']:
-                            version['localPath'] = self.service.get_path_by_hash(sha256)
-                        
-                        # Also set the model file size at the version level for easier access
-                        version['modelSizeKB'] = model_file.get('sizeKB')
-                else:
-                    # No model file found in this version
-                    version['existsLocally'] = False
-                    
-            return web.json_response(versions)
-        except Exception as e:
-            logger.error(f"Error fetching embedding model versions: {e}")
-            return web.Response(status=500, text=str(e))

py/routes/example_images_route_registrar.py

@@ -0,0 +1,62 @@
+"""Route registrar for example image endpoints."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Iterable, Mapping
+
+from aiohttp import web
+
+
+@dataclass(frozen=True)
+class RouteDefinition:
+    """Declarative configuration for a HTTP route."""
+
+    method: str
+    path: str
+    handler_name: str
+
+
+ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
+    RouteDefinition("POST", "/api/lm/download-example-images", "download_example_images"),
+    RouteDefinition("POST", "/api/lm/import-example-images", "import_example_images"),
+    RouteDefinition("GET", "/api/lm/example-images-status", "get_example_images_status"),
+    RouteDefinition("POST", "/api/lm/pause-example-images", "pause_example_images"),
+    RouteDefinition("POST", "/api/lm/resume-example-images", "resume_example_images"),
+    RouteDefinition("POST", "/api/lm/open-example-images-folder", "open_example_images_folder"),
+    RouteDefinition("GET", "/api/lm/example-image-files", "get_example_image_files"),
+    RouteDefinition("GET", "/api/lm/has-example-images", "has_example_images"),
+    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"),
+)
+
+
+class ExampleImagesRouteRegistrar:
+    """Bind declarative example image routes to an aiohttp router."""
+
+    _METHOD_MAP = {
+        "GET": "add_get",
+        "POST": "add_post",
+        "PUT": "add_put",
+        "DELETE": "add_delete",
+    }
+
+    def __init__(self, app: web.Application) -> None:
+        self._app = app
+
+    def register_routes(
+        self,
+        handler_lookup: Mapping[str, Callable[[web.Request], object]],
+        *,
+        definitions: Iterable[RouteDefinition] = ROUTE_DEFINITIONS,
+    ) -> None:
+        """Register each route definition using the supplied handlers."""
+
+        for definition in definitions:
+            handler = handler_lookup[definition.handler_name]
+            self._bind_route(definition.method, definition.path, handler)
+
+    def _bind_route(self, method: str, path: str, handler: Callable[[web.Request], object]) -> None:
+        add_method_name = self._METHOD_MAP[method.upper()]
+        add_method = getattr(self._app.router, add_method_name)
+        add_method(path, handler)

py/routes/example_images_routes.py

@@ -1,67 +1,88 @@
+from __future__ import annotations
+
 import logging
-from ..utils.example_images_download_manager import DownloadManager
-from ..utils.example_images_processor import ExampleImagesProcessor
+from typing import Callable, Mapping
+
+from aiohttp import web
+
+from .example_images_route_registrar import ExampleImagesRouteRegistrar
+from .handlers.example_images_handlers import (
+    ExampleImagesDownloadHandler,
+    ExampleImagesFileHandler,
+    ExampleImagesHandlerSet,
+    ExampleImagesManagementHandler,
+)
+from ..services.use_cases.example_images import (
+    DownloadExampleImagesUseCase,
+    ImportExampleImagesUseCase,
+)
+from ..utils.example_images_download_manager import (
+    DownloadManager,
+    get_default_download_manager,
+)
 from ..utils.example_images_file_manager import ExampleImagesFileManager
+from ..utils.example_images_processor import ExampleImagesProcessor
+from ..services.example_images_cleanup_service import ExampleImagesCleanupService
 
 logger = logging.getLogger(__name__)
 
+
 class ExampleImagesRoutes:
-    """Routes for example images related functionality"""
-    
-    @staticmethod
-    def setup_routes(app):
-        """Register example images routes"""
-        app.router.add_post('/api/download-example-images', ExampleImagesRoutes.download_example_images)
-        app.router.add_post('/api/import-example-images', ExampleImagesRoutes.import_example_images)
-        app.router.add_get('/api/example-images-status', ExampleImagesRoutes.get_example_images_status)
-        app.router.add_post('/api/pause-example-images', ExampleImagesRoutes.pause_example_images)
-        app.router.add_post('/api/resume-example-images', ExampleImagesRoutes.resume_example_images)
-        app.router.add_post('/api/open-example-images-folder', ExampleImagesRoutes.open_example_images_folder)
-        app.router.add_get('/api/example-image-files', ExampleImagesRoutes.get_example_image_files)
-        app.router.add_get('/api/has-example-images', ExampleImagesRoutes.has_example_images)
-        app.router.add_post('/api/delete-example-image', ExampleImagesRoutes.delete_example_image)
+    """Route controller for example image endpoints."""
 
-    @staticmethod
-    async def download_example_images(request):
-        """Download example images for models from Civitai"""
-        return await DownloadManager.start_download(request)
+    def __init__(
+        self,
+        *,
+        ws_manager,
+        download_manager: DownloadManager | None = None,
+        processor=ExampleImagesProcessor,
+        file_manager=ExampleImagesFileManager,
+        cleanup_service: ExampleImagesCleanupService | None = None,
+    ) -> None:
+        if ws_manager is None:
+            raise ValueError("ws_manager is required")
+        self._download_manager = download_manager or get_default_download_manager(ws_manager)
+        self._processor = processor
+        self._file_manager = file_manager
+        self._cleanup_service = cleanup_service or ExampleImagesCleanupService()
+        self._handler_set: ExampleImagesHandlerSet | None = None
+        self._handler_mapping: Mapping[str, Callable[[web.Request], web.StreamResponse]] | None = None
 
-    @staticmethod
-    async def get_example_images_status(request):
-        """Get the current status of example images download"""
-        return await DownloadManager.get_status(request)
+    @classmethod
+    def setup_routes(cls, app: web.Application, *, ws_manager) -> None:
+        """Register routes on the given aiohttp application using default wiring."""
 
-    @staticmethod
-    async def pause_example_images(request):
-        """Pause the example images download"""
-        return await DownloadManager.pause_download(request)
+        controller = cls(ws_manager=ws_manager)
+        controller.register(app)
 
-    @staticmethod
-    async def resume_example_images(request):
-        """Resume the example images download"""
-        return await DownloadManager.resume_download(request)
-        
-    @staticmethod
-    async def open_example_images_folder(request):
-        """Open the example images folder for a specific model"""
-        return await ExampleImagesFileManager.open_folder(request)
+    def register(self, app: web.Application) -> None:
+        """Bind the controller's handlers to the aiohttp router."""
 
-    @staticmethod
-    async def get_example_image_files(request):
-        """Get list of example image files for a specific model"""
-        return await ExampleImagesFileManager.get_files(request)
+        registrar = ExampleImagesRouteRegistrar(app)
+        registrar.register_routes(self.to_route_mapping())
 
-    @staticmethod
-    async def import_example_images(request):
-        """Import local example images for a model"""
-        return await ExampleImagesProcessor.import_images(request)
-        
-    @staticmethod
-    async def has_example_images(request):
-        """Check if example images folder exists and is not empty for a model"""
-        return await ExampleImagesFileManager.has_images(request)
+    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+        """Return the registrar-compatible mapping of handler names to callables."""
 
-    @staticmethod
-    async def delete_example_image(request):
-        """Delete a custom example image for a model"""
-        return await ExampleImagesProcessor.delete_custom_image(request)
+        if self._handler_mapping is None:
+            handler_set = self._build_handler_set()
+            self._handler_set = handler_set
+            self._handler_mapping = handler_set.to_route_mapping()
+        return self._handler_mapping
+
+    def _build_handler_set(self) -> ExampleImagesHandlerSet:
+        logger.debug("Building ExampleImagesHandlerSet with %s, %s, %s", self._download_manager, self._processor, self._file_manager)
+        download_use_case = DownloadExampleImagesUseCase(download_manager=self._download_manager)
+        download_handler = ExampleImagesDownloadHandler(download_use_case, self._download_manager)
+        import_use_case = ImportExampleImagesUseCase(processor=self._processor)
+        management_handler = ExampleImagesManagementHandler(
+            import_use_case,
+            self._processor,
+            self._cleanup_service,
+        )
+        file_handler = ExampleImagesFileHandler(self._file_manager)
+        return ExampleImagesHandlerSet(
+            download=download_handler,
+            management=management_handler,
+            files=file_handler,
+        )

py/routes/handlers/example_images_handlers.py

@@ -0,0 +1,159 @@
+"""Handler set for example image routes."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Mapping
+
+from aiohttp import web
+
+from ...services.use_cases.example_images import (
+    DownloadExampleImagesConfigurationError,
+    DownloadExampleImagesInProgressError,
+    DownloadExampleImagesUseCase,
+    ImportExampleImagesUseCase,
+    ImportExampleImagesValidationError,
+)
+from ...utils.example_images_download_manager import (
+    DownloadConfigurationError,
+    DownloadInProgressError,
+    DownloadNotRunningError,
+    ExampleImagesDownloadError,
+)
+from ...utils.example_images_processor import ExampleImagesImportError
+
+
+class ExampleImagesDownloadHandler:
+    """HTTP adapters for download-related example image endpoints."""
+
+    def __init__(
+        self,
+        download_use_case: DownloadExampleImagesUseCase,
+        download_manager,
+    ) -> None:
+        self._download_use_case = download_use_case
+        self._download_manager = download_manager
+
+    async def download_example_images(self, request: web.Request) -> web.StreamResponse:
+        try:
+            payload = await request.json()
+            result = await self._download_use_case.execute(payload)
+            return web.json_response(result)
+        except DownloadExampleImagesInProgressError as exc:
+            response = {
+                'success': False,
+                'error': str(exc),
+                'status': exc.progress,
+            }
+            return web.json_response(response, status=400)
+        except DownloadExampleImagesConfigurationError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=400)
+        except ExampleImagesDownloadError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=500)
+
+    async def get_example_images_status(self, request: web.Request) -> web.StreamResponse:
+        result = await self._download_manager.get_status(request)
+        return web.json_response(result)
+
+    async def pause_example_images(self, request: web.Request) -> web.StreamResponse:
+        try:
+            result = await self._download_manager.pause_download(request)
+            return web.json_response(result)
+        except DownloadNotRunningError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=400)
+
+    async def resume_example_images(self, request: web.Request) -> web.StreamResponse:
+        try:
+            result = await self._download_manager.resume_download(request)
+            return web.json_response(result)
+        except DownloadNotRunningError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=400)
+
+    async def force_download_example_images(self, request: web.Request) -> web.StreamResponse:
+        try:
+            payload = await request.json()
+            result = await self._download_manager.start_force_download(payload)
+            return web.json_response(result)
+        except DownloadInProgressError as exc:
+            response = {
+                'success': False,
+                'error': str(exc),
+                'status': exc.progress_snapshot,
+            }
+            return web.json_response(response, status=400)
+        except DownloadConfigurationError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=400)
+        except ExampleImagesDownloadError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=500)
+
+
+class ExampleImagesManagementHandler:
+    """HTTP adapters for import/delete endpoints."""
+
+    def __init__(self, import_use_case: ImportExampleImagesUseCase, processor, cleanup_service) -> None:
+        self._import_use_case = import_use_case
+        self._processor = processor
+        self._cleanup_service = cleanup_service
+
+    async def import_example_images(self, request: web.Request) -> web.StreamResponse:
+        try:
+            result = await self._import_use_case.execute(request)
+            return web.json_response(result)
+        except ImportExampleImagesValidationError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=400)
+        except ExampleImagesImportError as exc:
+            return web.json_response({'success': False, 'error': str(exc)}, status=500)
+
+    async def delete_example_image(self, request: web.Request) -> web.StreamResponse:
+        return await self._processor.delete_custom_image(request)
+
+    async def cleanup_example_image_folders(self, request: web.Request) -> web.StreamResponse:
+        result = await self._cleanup_service.cleanup_example_image_folders()
+
+        if result.get('success') or result.get('partial_success'):
+            return web.json_response(result, status=200)
+
+        error_code = result.get('error_code')
+        status = 400 if error_code in {'path_not_configured', 'path_not_found'} else 500
+        return web.json_response(result, status=status)
+
+
+class ExampleImagesFileHandler:
+    """HTTP adapters for filesystem-centric endpoints."""
+
+    def __init__(self, file_manager) -> None:
+        self._file_manager = file_manager
+
+    async def open_example_images_folder(self, request: web.Request) -> web.StreamResponse:
+        return await self._file_manager.open_folder(request)
+
+    async def get_example_image_files(self, request: web.Request) -> web.StreamResponse:
+        return await self._file_manager.get_files(request)
+
+    async def has_example_images(self, request: web.Request) -> web.StreamResponse:
+        return await self._file_manager.has_images(request)
+
+
+@dataclass(frozen=True)
+class ExampleImagesHandlerSet:
+    """Aggregate of handlers exposed to the registrar."""
+
+    download: ExampleImagesDownloadHandler
+    management: ExampleImagesManagementHandler
+    files: ExampleImagesFileHandler
+
+    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+        """Flatten handler methods into the registrar mapping."""
+
+        return {
+            "download_example_images": self.download.download_example_images,
+            "get_example_images_status": self.download.get_example_images_status,
+            "pause_example_images": self.download.pause_example_images,
+            "resume_example_images": self.download.resume_example_images,
+            "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,
+            "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,
+            "has_example_images": self.files.has_example_images,
+        }

py/routes/handlers/misc_handlers.py

@@ -0,0 +1,795 @@
+"""Handlers for miscellaneous routes.
+
+The legacy :mod:`py.routes.misc_routes` module bundled HTTP wiring and
+business logic in a single class.  This module mirrors the model route
+architecture by splitting the responsibilities into dedicated handler
+objects that can be composed by the route controller.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import subprocess
+import sys
+from dataclasses import dataclass
+from typing import Awaitable, Callable, Dict, Mapping, Protocol
+
+from aiohttp import web
+
+from ...config import config
+from ...services.metadata_service import (
+    get_metadata_archive_manager,
+    update_metadata_providers,
+)
+from ...services.service_registry import ServiceRegistry
+from ...services.settings_manager import settings as default_settings
+from ...services.websocket_manager import ws_manager
+from ...services.downloader import get_downloader
+from ...utils.constants import DEFAULT_NODE_COLOR, NODE_TYPES, SUPPORTED_MEDIA_EXTENSIONS
+from ...utils.example_images_paths import is_valid_example_images_root
+from ...utils.lora_metadata import extract_trained_words
+from ...utils.usage_stats import UsageStats
+
+logger = logging.getLogger(__name__)
+
+
+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
+        ...
+
+
+class DownloaderProtocol(Protocol):
+    async def refresh_session(self) -> None:  # pragma: no cover - protocol
+        ...
+
+
+class UsageStatsFactory(Protocol):
+    def __call__(self) -> UsageStats:  # pragma: no cover - protocol
+        ...
+
+
+class MetadataProviderProtocol(Protocol):
+    async def get_model_versions(self, model_id: int) -> dict | None:  # pragma: no cover - protocol
+        ...
+
+
+class MetadataArchiveManagerProtocol(Protocol):
+    async def download_and_extract_database(
+        self, progress_callback: Callable[[str, str], None]
+    ) -> bool:  # pragma: no cover - protocol
+        ...
+
+    async def remove_database(self) -> bool:  # pragma: no cover - protocol
+        ...
+
+    def is_database_available(self) -> bool:  # pragma: no cover - protocol
+        ...
+
+    def get_database_path(self) -> str | None:  # pragma: no cover - protocol
+        ...
+
+
+class NodeRegistry:
+    """Thread-safe registry for tracking LoRA nodes in active workflows."""
+
+    def __init__(self) -> None:
+        self._lock = asyncio.Lock()
+        self._nodes: Dict[int, dict] = {}
+        self._registry_updated = asyncio.Event()
+
+    async def register_nodes(self, nodes: list[dict]) -> None:
+        async with self._lock:
+            self._nodes.clear()
+            for node in nodes:
+                node_id = node["node_id"]
+                node_type = node.get("type", "")
+                type_id = NODE_TYPES.get(node_type, 0)
+                bgcolor = node.get("bgcolor") or DEFAULT_NODE_COLOR
+                self._nodes[node_id] = {
+                    "id": node_id,
+                    "bgcolor": bgcolor,
+                    "title": node.get("title"),
+                    "type": type_id,
+                    "type_name": node_type,
+                }
+            logger.debug("Registered %s nodes in registry", len(nodes))
+            self._registry_updated.set()
+
+    async def get_registry(self) -> dict:
+        async with self._lock:
+            return {
+                "nodes": dict(self._nodes),
+                "node_count": len(self._nodes),
+            }
+
+    async def wait_for_update(self, timeout: float = 1.0) -> bool:
+        self._registry_updated.clear()
+        try:
+            await asyncio.wait_for(self._registry_updated.wait(), timeout=timeout)
+            return True
+        except asyncio.TimeoutError:
+            return False
+
+
+class HealthCheckHandler:
+    async def health_check(self, request: web.Request) -> web.Response:
+        return web.json_response({"status": "ok"})
+
+
+class SettingsHandler:
+    """Sync settings between backend and frontend."""
+
+    _SYNC_KEYS = (
+        "civitai_api_key",
+        "default_lora_root",
+        "default_checkpoint_root",
+        "default_embedding_root",
+        "base_model_path_mappings",
+        "download_path_templates",
+        "enable_metadata_archive_db",
+        "language",
+        "proxy_enabled",
+        "proxy_type",
+        "proxy_host",
+        "proxy_port",
+        "proxy_username",
+        "proxy_password",
+        "example_images_path",
+        "optimize_example_images",
+        "auto_download_example_images",
+        "blur_mature_content",
+        "autoplay_on_hover",
+        "display_density",
+        "card_info_display",
+        "include_trigger_words",
+        "show_only_sfw",
+        "compact_mode",
+    )
+
+    _PROXY_KEYS = {"proxy_enabled", "proxy_host", "proxy_port", "proxy_username", "proxy_password", "proxy_type"}
+
+    def __init__(
+        self,
+        *,
+        settings_service=default_settings,
+        metadata_provider_updater: Callable[[], Awaitable[None]] = update_metadata_providers,
+        downloader_factory: Callable[[], Awaitable[DownloaderProtocol]] = get_downloader,
+    ) -> None:
+        self._settings = settings_service
+        self._metadata_provider_updater = metadata_provider_updater
+        self._downloader_factory = downloader_factory
+
+    async def get_libraries(self, request: web.Request) -> web.Response:
+        """Return the registered libraries and the active selection."""
+
+        try:
+            snapshot = config.get_library_registry_snapshot()
+            libraries = snapshot.get("libraries", {})
+            active_library = snapshot.get("active_library", "")
+            return web.json_response(
+                {
+                    "success": True,
+                    "libraries": libraries,
+                    "active_library": active_library,
+                }
+            )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Error getting library registry: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_settings(self, request: web.Request) -> web.Response:
+        try:
+            response_data = {}
+            for key in self._SYNC_KEYS:
+                value = self._settings.get(key)
+                if value is not None:
+                    response_data[key] = value
+            return web.json_response({"success": True, "settings": response_data})
+        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)
+
+    async def activate_library(self, request: web.Request) -> web.Response:
+        """Activate the selected library."""
+
+        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)
+
+        library_name = data.get("library") or data.get("library_name")
+        if not isinstance(library_name, str) or not library_name.strip():
+            return web.json_response(
+                {"success": False, "error": "Library name is required"}, status=400
+            )
+
+        try:
+            normalized_name = library_name.strip()
+            self._settings.activate_library(normalized_name)
+            snapshot = config.get_library_registry_snapshot()
+            libraries = snapshot.get("libraries", {})
+            active_library = snapshot.get("active_library", "")
+            return web.json_response(
+                {
+                    "success": True,
+                    "active_library": active_library,
+                    "libraries": libraries,
+                }
+            )
+        except KeyError as exc:
+            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)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def update_settings(self, request: web.Request) -> web.Response:
+        try:
+            data = await request.json()
+            proxy_changed = False
+
+            for key, value in data.items():
+                if value == self._settings.get(key):
+                    continue
+
+                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})
+
+                if value == "__DELETE__" and key in ("proxy_username", "proxy_password"):
+                    self._settings.delete(key)
+                else:
+                    self._settings.set(key, value)
+
+                if key == "enable_metadata_archive_db":
+                    await self._metadata_provider_updater()
+
+                if key in self._PROXY_KEYS:
+                    proxy_changed = True
+
+            if proxy_changed:
+                downloader = await self._downloader_factory()
+                await downloader.refresh_session()
+
+            return web.json_response({"success": True})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Error updating settings: %s", exc, exc_info=True)
+            return web.Response(status=500, text=str(exc))
+
+    def _validate_example_images_path(self, folder_path: str) -> str | None:
+        if not os.path.exists(folder_path):
+            return f"Path does not exist: {folder_path}"
+        if not os.path.isdir(folder_path):
+            return "Please set a dedicated folder for example images."
+        if not self._is_dedicated_example_images_folder(folder_path):
+            return "Please set a dedicated folder for example images."
+        return None
+
+    def _is_dedicated_example_images_folder(self, folder_path: str) -> bool:
+        return is_valid_example_images_root(folder_path)
+
+
+class UsageStatsHandler:
+    def __init__(self, usage_stats_factory: UsageStatsFactory = UsageStats) -> None:
+        self._usage_stats_factory = usage_stats_factory
+
+    async def update_usage_stats(self, request: web.Request) -> web.Response:
+        try:
+            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)
+            usage_stats = self._usage_stats_factory()
+            await usage_stats.process_execution(prompt_id)
+            return web.json_response({"success": True})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to update usage stats: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_usage_stats(self, request: web.Request) -> web.Response:
+        try:
+            usage_stats = self._usage_stats_factory()
+            stats = await usage_stats.get_stats()
+            stats_response = {"success": True, "data": stats, "format_version": 2}
+            return web.json_response(stats_response)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to get usage stats: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+class LoraCodeHandler:
+    def __init__(self, prompt_server: type[PromptServerProtocol]) -> None:
+        self._prompt_server = prompt_server
+
+    async def update_lora_code(self, request: web.Request) -> web.Response:
+        try:
+            data = await request.json()
+            node_ids = data.get("node_ids")
+            lora_code = data.get("lora_code", "")
+            mode = data.get("mode", "append")
+
+            if not lora_code:
+                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}
+                    )
+                    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)})
+            else:
+                for node_id in node_ids:
+                    try:
+                        self._prompt_server.instance.send_sync(
+                            "lora_code_update",
+                            {"id": node_id, "lora_code": lora_code, "mode": mode},
+                        )
+                        results.append({"node_id": node_id, "success": True})
+                    except Exception as exc:  # pragma: no cover - defensive logging
+                        logger.error("Error sending lora code to node %s: %s", node_id, exc)
+                        results.append({"node_id": node_id, "success": False, "error": str(exc)})
+
+            return web.json_response({"success": True, "results": results})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to update lora code: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+class TrainedWordsHandler:
+    async def get_trained_words(self, request: web.Request) -> web.Response:
+        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)
+            if not os.path.exists(file_path):
+                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)
+
+            trained_words, class_tokens = await extract_trained_words(file_path)
+            return web.json_response(
+                {
+                    "success": True,
+                    "trained_words": trained_words,
+                    "class_tokens": class_tokens,
+                }
+            )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to get trained words: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+class ModelExampleFilesHandler:
+    async def get_model_example_files(self, request: web.Request) -> web.Response:
+        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)
+            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)
+
+            base_name = os.path.splitext(os.path.basename(model_path))[0]
+            files = []
+            pattern = f"{base_name}.example."
+            for file in os.listdir(model_dir):
+                if not file.startswith(pattern):
+                    continue
+                file_full_path = os.path.join(model_dir, file)
+                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"]:
+                    continue
+                try:
+                    index = int(file[len(pattern) :].split(".")[0])
+                except (ValueError, IndexError):
+                    index = float("inf")
+                static_url = config.get_preview_static_url(file_full_path)
+                files.append(
+                    {
+                        "name": file,
+                        "path": static_url,
+                        "extension": file_ext,
+                        "is_video": file_ext in SUPPORTED_MEDIA_EXTENSIONS["videos"],
+                        "index": index,
+                    }
+                )
+
+            files.sort(key=lambda item: item["index"])
+            for file in files:
+                file.pop("index", None)
+
+            return web.json_response({"success": True, "files": files})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to get model example files: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+@dataclass
+class ServiceRegistryAdapter:
+    get_lora_scanner: Callable[[], Awaitable]
+    get_checkpoint_scanner: Callable[[], Awaitable]
+    get_embedding_scanner: Callable[[], Awaitable]
+
+
+class ModelLibraryHandler:
+    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
+
+    async def check_model_exists(self, request: web.Request) -> web.Response:
+        try:
+            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)
+            try:
+                model_id = int(model_id_str)
+            except ValueError:
+                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()
+            embedding_scanner = await self._service_registry.get_embedding_scanner()
+
+            if model_version_id_str:
+                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)
+
+                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):
+                    exists = True
+                    model_type = "checkpoint"
+                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})
+
+            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)
+            if not lora_versions and not checkpoint_versions and embedding_scanner:
+                embedding_versions = await embedding_scanner.get_model_versions_by_id(model_id)
+
+            model_type = None
+            versions = []
+            if lora_versions:
+                model_type = "lora"
+                versions = lora_versions
+            elif checkpoint_versions:
+                model_type = "checkpoint"
+                versions = checkpoint_versions
+            elif embedding_versions:
+                model_type = "embedding"
+                versions = embedding_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)
+
+    async def get_model_versions_status(self, request: web.Request) -> web.Response:
+        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)
+            try:
+                model_id = int(model_id_str)
+            except ValueError:
+                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)
+
+            response = await metadata_provider.get_model_versions(model_id)
+            if not response or not response.get("modelVersions"):
+                return web.json_response({"success": False, "error": "Model not found"}, status=404)
+
+            versions = response.get("modelVersions", [])
+            model_name = response.get("name", "")
+            model_type = response.get("type", "").lower()
+
+            scanner = None
+            normalized_type = None
+            if model_type in {"lora", "locon", "dora"}:
+                scanner = await self._service_registry.get_lora_scanner()
+                normalized_type = "lora"
+            elif model_type == "checkpoint":
+                scanner = await self._service_registry.get_checkpoint_scanner()
+                normalized_type = "checkpoint"
+            elif model_type == "textualinversion":
+                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)
+
+            if not scanner:
+                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}
+
+            enriched_versions = []
+            for version in versions:
+                version_id = version.get("id")
+                enriched_versions.append(
+                    {
+                        "id": version_id,
+                        "name": version.get("name", ""),
+                        "thumbnailUrl": version.get("images")[0]["url"] if version.get("images") else None,
+                        "inLibrary": version_id in local_version_ids,
+                    }
+                )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "modelId": model_id,
+                    "modelName": model_name,
+                    "modelType": model_type,
+                    "versions": enriched_versions,
+                }
+            )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to get model versions status: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+class MetadataArchiveHandler:
+    def __init__(
+        self,
+        *,
+        metadata_archive_manager_factory: Callable[[], Awaitable[MetadataArchiveManagerProtocol]] = get_metadata_archive_manager,
+        settings_service=default_settings,
+        metadata_provider_updater: Callable[[], Awaitable[None]] = update_metadata_providers,
+    ) -> None:
+        self._metadata_archive_manager_factory = metadata_archive_manager_factory
+        self._settings = settings_service
+        self._metadata_provider_updater = metadata_provider_updater
+
+    async def download_metadata_archive(self, request: web.Request) -> web.Response:
+        try:
+            archive_manager = await self._metadata_archive_manager_factory()
+            download_id = request.query.get("download_id")
+
+            def progress_callback(stage: str, message: str) -> None:
+                data = {"stage": stage, "message": message, "type": "metadata_archive_download"}
+                if download_id:
+                    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)
+            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)
+        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)
+
+    async def remove_metadata_archive(self, request: web.Request) -> web.Response:
+        try:
+            archive_manager = await self._metadata_archive_manager_factory()
+            success = await archive_manager.remove_database()
+            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)
+        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)
+
+    async def get_metadata_archive_status(self, request: web.Request) -> web.Response:
+        try:
+            archive_manager = await self._metadata_archive_manager_factory()
+            is_available = archive_manager.is_database_available()
+            is_enabled = self._settings.get("enable_metadata_archive_db", False)
+            db_size = 0
+            if is_available:
+                db_path = archive_manager.get_database_path()
+                if db_path and os.path.exists(db_path):
+                    db_size = os.path.getsize(db_path)
+            return web.json_response(
+                {
+                    "success": True,
+                    "isAvailable": is_available,
+                    "isEnabled": is_enabled,
+                    "databaseSize": db_size,
+                    "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)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+
+class FileSystemHandler:
+    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)
+            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)
+
+            if os.name == "nt":
+                subprocess.Popen(["explorer", "/select,", file_path])
+            elif os.name == "posix":
+                if 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}"})
+        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)
+
+
+class NodeRegistryHandler:
+    def __init__(
+        self,
+        node_registry: NodeRegistry,
+        prompt_server: type[PromptServerProtocol],
+        *,
+        standalone_mode: bool,
+    ) -> None:
+        self._node_registry = node_registry
+        self._prompt_server = prompt_server
+        self._standalone_mode = standalone_mode
+
+    async def register_nodes(self, request: web.Request) -> web.Response:
+        try:
+            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)
+            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)
+                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)
+                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)
+
+            await self._node_registry.register_nodes(nodes)
+            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)
+
+    async def get_registry(self, request: web.Request) -> web.Response:
+        try:
+            if self._standalone_mode:
+                logger.warning("Registry refresh not available in standalone mode")
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Standalone Mode Active",
+                        "message": "Cannot interact with ComfyUI in standalone mode.",
+                    },
+                    status=503,
+                )
+
+            try:
+                self._prompt_server.instance.send_sync("lora_registry_refresh", {})
+                logger.debug("Sent registry refresh request to frontend")
+            except Exception as exc:
+                logger.error("Failed to send registry refresh message: %s", exc)
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Communication Error",
+                        "message": f"Failed to communicate with ComfyUI frontend: {exc}",
+                    },
+                    status=500,
+                )
+
+            registry_updated = await self._node_registry.wait_for_update(timeout=1.0)
+            if not registry_updated:
+                logger.warning("Registry refresh timeout after 1 second")
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Timeout Error",
+                        "message": "Registry refresh timeout - ComfyUI frontend may not be responsive",
+                    },
+                    status=408,
+                )
+
+            registry_info = await self._node_registry.get_registry()
+            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)
+
+
+class MiscHandlerSet:
+    """Aggregate handlers into a lookup compatible with the registrar."""
+
+    def __init__(
+        self,
+        *,
+        health: HealthCheckHandler,
+        settings: SettingsHandler,
+        usage_stats: UsageStatsHandler,
+        lora_code: LoraCodeHandler,
+        trained_words: TrainedWordsHandler,
+        model_examples: ModelExampleFilesHandler,
+        node_registry: NodeRegistryHandler,
+        model_library: ModelLibraryHandler,
+        metadata_archive: MetadataArchiveHandler,
+        filesystem: FileSystemHandler,
+    ) -> None:
+        self.health = health
+        self.settings = settings
+        self.usage_stats = usage_stats
+        self.lora_code = lora_code
+        self.trained_words = trained_words
+        self.model_examples = model_examples
+        self.node_registry = node_registry
+        self.model_library = model_library
+        self.metadata_archive = metadata_archive
+        self.filesystem = filesystem
+
+    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,
+            "update_settings": self.settings.update_settings,
+            "get_settings_libraries": self.settings.get_libraries,
+            "activate_library": self.settings.activate_library,
+            "update_usage_stats": self.usage_stats.update_usage_stats,
+            "get_usage_stats": self.usage_stats.get_usage_stats,
+            "update_lora_code": self.lora_code.update_lora_code,
+            "get_trained_words": self.trained_words.get_trained_words,
+            "get_model_example_files": self.model_examples.get_model_example_files,
+            "register_nodes": self.node_registry.register_nodes,
+            "get_registry": self.node_registry.get_registry,
+            "check_model_exists": self.model_library.check_model_exists,
+            "download_metadata_archive": self.metadata_archive.download_metadata_archive,
+            "remove_metadata_archive": self.metadata_archive.remove_metadata_archive,
+            "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,
+        }
+
+
+def build_service_registry_adapter() -> ServiceRegistryAdapter:
+    return ServiceRegistryAdapter(
+        get_lora_scanner=ServiceRegistry.get_lora_scanner,
+        get_checkpoint_scanner=ServiceRegistry.get_checkpoint_scanner,
+        get_embedding_scanner=ServiceRegistry.get_embedding_scanner,
+    )

py/routes/handlers/preview_handlers.py

@@ -0,0 +1,56 @@
+"""Handlers responsible for serving preview assets dynamically."""
+
+from __future__ import annotations
+
+import logging
+import urllib.parse
+from pathlib import Path
+
+from aiohttp import web
+
+from ...config import config as global_config
+
+logger = logging.getLogger(__name__)
+
+
+class PreviewHandler:
+    """Serve preview assets for the active library at request time."""
+
+    def __init__(self, *, config=global_config) -> None:
+        self._config = config
+
+    async def serve_preview(self, request: web.Request) -> web.StreamResponse:
+        """Return the preview file referenced by the encoded ``path`` query."""
+
+        raw_path = request.query.get("path", "")
+        if not raw_path:
+            raise web.HTTPBadRequest(text="Missing 'path' query parameter")
+
+        try:
+            decoded_path = urllib.parse.unquote(raw_path)
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.debug("Failed to decode preview path %s: %s", raw_path, exc)
+            raise web.HTTPBadRequest(text="Invalid preview path encoding") from exc
+
+        normalized = decoded_path.replace("\\", "/")
+        candidate = Path(normalized)
+        try:
+            resolved = candidate.expanduser().resolve(strict=False)
+        except Exception as exc:
+            logger.debug("Failed to resolve preview path %s: %s", normalized, exc)
+            raise web.HTTPBadRequest(text="Unable to resolve preview path") from exc
+
+        resolved_str = str(resolved)
+        if not self._config.is_preview_path_allowed(resolved_str):
+            logger.debug("Rejected preview outside allowed roots: %s", resolved_str)
+            raise web.HTTPForbidden(text="Preview path is not within an allowed directory")
+
+        if not resolved.is_file():
+            logger.debug("Preview file not found at %s", resolved_str)
+            raise web.HTTPNotFound(text="Preview file not found")
+
+        # aiohttp's FileResponse handles range requests and content headers for us.
+        return web.FileResponse(path=resolved, chunk_size=256 * 1024)
+
+
+__all__ = ["PreviewHandler"]

py/routes/handlers/recipe_handlers.py

@@ -0,0 +1,723 @@
+"""Dedicated handler objects for recipe-related routes."""
+from __future__ import annotations
+
+import json
+import logging
+import os
+from dataclasses import dataclass
+from typing import Any, Awaitable, Callable, Dict, Mapping, Optional
+
+from aiohttp import web
+
+from ...config import config
+from ...services.server_i18n import server_i18n as default_server_i18n
+from ...services.settings_manager import SettingsManager
+from ...services.recipes import (
+    RecipeAnalysisService,
+    RecipeDownloadError,
+    RecipeNotFoundError,
+    RecipePersistenceService,
+    RecipeSharingService,
+    RecipeValidationError,
+)
+
+Logger = logging.Logger
+EnsureDependenciesCallable = Callable[[], Awaitable[None]]
+RecipeScannerGetter = Callable[[], Any]
+CivitaiClientGetter = Callable[[], Any]
+
+
+@dataclass(frozen=True)
+class RecipeHandlerSet:
+    """Group of handlers providing recipe route implementations."""
+
+    page_view: "RecipePageView"
+    listing: "RecipeListingHandler"
+    query: "RecipeQueryHandler"
+    management: "RecipeManagementHandler"
+    analysis: "RecipeAnalysisHandler"
+    sharing: "RecipeSharingHandler"
+
+    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+        """Expose handler coroutines keyed by registrar handler names."""
+
+        return {
+            "render_page": self.page_view.render_page,
+            "list_recipes": self.listing.list_recipes,
+            "get_recipe": self.listing.get_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,
+            "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,
+            "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,
+        }
+
+
+class RecipePageView:
+    """Render the recipe shell page."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        settings_service: SettingsManager,
+        server_i18n=default_server_i18n,
+        template_env,
+        template_name: str,
+        recipe_scanner_getter: RecipeScannerGetter,
+        logger: Logger,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._settings = settings_service
+        self._server_i18n = server_i18n
+        self._template_env = template_env
+        self._template_name = template_name
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._logger = logger
+
+    async def render_page(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:  # pragma: no cover - defensive guard
+                raise RuntimeError("Recipe scanner not available")
+
+            user_language = self._settings.get("language", "en")
+            self._server_i18n.set_locale(user_language)
+
+            try:
+                await recipe_scanner.get_cached_data(force_refresh=False)
+                rendered = self._template_env.get_template(self._template_name).render(
+                    recipes=[],
+                    is_initializing=False,
+                    settings=self._settings,
+                    request=request,
+                    t=self._server_i18n.get_translation,
+                )
+            except Exception as cache_error:  # pragma: no cover - logging path
+                self._logger.error("Error loading recipe cache data: %s", cache_error)
+                rendered = self._template_env.get_template(self._template_name).render(
+                    is_initializing=True,
+                    settings=self._settings,
+                    request=request,
+                    t=self._server_i18n.get_translation,
+                )
+            return web.Response(text=rendered, content_type="text/html")
+        except Exception as exc:  # pragma: no cover - logging path
+            self._logger.error("Error handling recipes request: %s", exc, exc_info=True)
+            return web.Response(text="Error loading recipes page", status=500)
+
+
+class RecipeListingHandler:
+    """Provide listing and detail APIs for recipes."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        logger: Logger,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._logger = logger
+
+    async def list_recipes(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")
+
+            page = int(request.query.get("page", "1"))
+            page_size = int(request.query.get("page_size", "20"))
+            sort_by = request.query.get("sort_by", "date")
+            search = request.query.get("search")
+
+            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",
+            }
+
+            filters: Dict[str, list[str]] = {}
+            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(",")
+
+            lora_hash = request.query.get("lora_hash")
+
+            result = await recipe_scanner.get_paginated_data(
+                page=page,
+                page_size=page_size,
+                sort_by=sort_by,
+                search=search,
+                filters=filters,
+                search_options=search_options,
+                lora_hash=lora_hash,
+            )
+
+            for item in result.get("items", []):
+                file_path = item.get("file_path")
+                if file_path:
+                    item["file_url"] = self.format_recipe_file_url(file_path)
+                else:
+                    item.setdefault("file_url", "/loras_static/images/no-preview.png")
+                item.setdefault("loras", [])
+                item.setdefault("base_model", "")
+
+            return web.json_response(result)
+        except Exception as exc:
+            self._logger.error("Error retrieving recipes: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def get_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")
+
+            recipe_id = request.match_info["recipe_id"]
+            recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+
+            if not recipe:
+                return web.json_response({"error": "Recipe not found"}, status=404)
+            return web.json_response(recipe)
+        except Exception as exc:
+            self._logger.error("Error retrieving recipe details: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    def format_recipe_file_url(self, file_path: str) -> str:
+        try:
+            normalized_path = os.path.normpath(file_path)
+            static_url = config.get_preview_static_url(normalized_path)
+            if static_url:
+                return static_url
+        except Exception as exc:  # pragma: no cover - logging path
+            self._logger.error("Error formatting recipe file URL: %s", exc, exc_info=True)
+            return "/loras_static/images/no-preview.png"
+
+        return "/loras_static/images/no-preview.png"
+
+
+class RecipeQueryHandler:
+    """Provide read-only insights on recipe data."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        format_recipe_file_url: Callable[[str], str],
+        logger: Logger,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._format_recipe_file_url = format_recipe_file_url
+        self._logger = logger
+
+    async def get_top_tags(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")
+
+            limit = int(request.query.get("limit", "20"))
+            cache = await recipe_scanner.get_cached_data()
+
+            tag_counts: Dict[str, int] = {}
+            for recipe in getattr(cache, "raw_data", []):
+                for tag in recipe.get("tags", []) or []:
+                    tag_counts[tag] = tag_counts.get(tag, 0) + 1
+
+            sorted_tags = [{"tag": tag, "count": count} for tag, count in tag_counts.items()]
+            sorted_tags.sort(key=lambda entry: entry["count"], reverse=True)
+            return web.json_response({"success": True, "tags": sorted_tags[:limit]})
+        except Exception as exc:
+            self._logger.error("Error retrieving top tags: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_base_models(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")
+
+            cache = await recipe_scanner.get_cached_data()
+
+            base_model_counts: Dict[str, int] = {}
+            for recipe in getattr(cache, "raw_data", []):
+                base_model = recipe.get("base_model")
+                if base_model:
+                    base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+
+            sorted_models = [{"name": model, "count": count} for model, count in base_model_counts.items()]
+            sorted_models.sort(key=lambda entry: entry["count"], reverse=True)
+            return web.json_response({"success": True, "base_models": sorted_models})
+        except Exception as exc:
+            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_recipes_for_lora(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")
+
+            lora_hash = request.query.get("hash")
+            if not lora_hash:
+                return web.json_response({"success": False, "error": "Lora hash is required"}, status=400)
+
+            matching_recipes = await recipe_scanner.get_recipes_for_lora(lora_hash)
+            return web.json_response({"success": True, "recipes": matching_recipes})
+        except Exception as exc:
+            self._logger.error("Error getting recipes for Lora: %s", exc)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def scan_recipes(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            self._logger.info("Manually triggering recipe cache rebuild")
+            await recipe_scanner.get_cached_data(force_refresh=True)
+            return web.json_response({"success": True, "message": "Recipe cache refreshed successfully"})
+        except Exception as exc:
+            self._logger.error("Error refreshing recipe cache: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def find_duplicates(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")
+
+            duplicate_groups = await recipe_scanner.find_all_duplicate_recipes()
+            response_data = []
+
+            for fingerprint, recipe_ids in duplicate_groups.items():
+                if len(recipe_ids) <= 1:
+                    continue
+
+                recipes = []
+                for recipe_id in recipe_ids:
+                    recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+                    if recipe:
+                        recipes.append(
+                            {
+                                "id": recipe.get("id"),
+                                "title": recipe.get("title"),
+                                "file_url": recipe.get("file_url")
+                                or self._format_recipe_file_url(recipe.get("file_path", "")),
+                                "modified": recipe.get("modified"),
+                                "created_date": recipe.get("created_date"),
+                                "lora_count": len(recipe.get("loras", [])),
+                            }
+                        )
+
+                if len(recipes) >= 2:
+                    recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
+                    response_data.append(
+                        {
+                            "fingerprint": fingerprint,
+                            "count": len(recipes),
+                            "recipes": recipes,
+                        }
+                    )
+
+            response_data.sort(key=lambda entry: entry["count"], reverse=True)
+            return web.json_response({"success": True, "duplicate_groups": response_data})
+        except Exception as exc:
+            self._logger.error("Error finding duplicate recipes: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_recipe_syntax(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")
+
+            recipe_id = request.match_info["recipe_id"]
+            try:
+                syntax_parts = await recipe_scanner.get_recipe_syntax_tokens(recipe_id)
+            except RecipeNotFoundError:
+                return web.json_response({"error": "Recipe not found"}, status=404)
+
+            if not syntax_parts:
+                return web.json_response({"error": "No LoRAs found in this recipe"}, status=400)
+
+            return web.json_response({"success": True, "syntax": " ".join(syntax_parts)})
+        except Exception as exc:
+            self._logger.error("Error generating recipe syntax: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+
+class RecipeManagementHandler:
+    """Handle create/update/delete style recipe operations."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        logger: Logger,
+        persistence_service: RecipePersistenceService,
+        analysis_service: RecipeAnalysisService,
+    ) -> 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
+
+    async def save_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")
+
+            reader = await request.multipart()
+            payload = await self._parse_save_payload(reader)
+
+            result = await self._persistence_service.save_recipe(
+                recipe_scanner=recipe_scanner,
+                image_bytes=payload["image_bytes"],
+                image_base64=payload["image_base64"],
+                name=payload["name"],
+                tags=payload["tags"],
+                metadata=payload["metadata"],
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error("Error saving recipe: %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()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            recipe_id = request.match_info["recipe_id"]
+            result = await self._persistence_service.delete_recipe(
+                recipe_scanner=recipe_scanner, recipe_id=recipe_id
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error deleting recipe: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def update_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")
+
+            recipe_id = request.match_info["recipe_id"]
+            data = await request.json()
+            result = await self._persistence_service.update_recipe(
+                recipe_scanner=recipe_scanner, recipe_id=recipe_id, updates=data
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error updating recipe: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def reconnect_lora(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()
+            for field in ("recipe_id", "lora_index", "target_name"):
+                if field not in data:
+                    raise RecipeValidationError(f"Missing required field: {field}")
+
+            result = await self._persistence_service.reconnect_lora(
+                recipe_scanner=recipe_scanner,
+                recipe_id=data["recipe_id"],
+                lora_index=int(data["lora_index"]),
+                target_name=data["target_name"],
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error reconnecting LoRA: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def bulk_delete(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", [])
+            result = await self._persistence_service.bulk_delete(
+                recipe_scanner=recipe_scanner, recipe_ids=recipe_ids
+            )
+            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 performing bulk delete: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def save_recipe_from_widget(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")
+
+            analysis = await self._analysis_service.analyze_widget_metadata(
+                recipe_scanner=recipe_scanner
+            )
+            metadata = analysis.payload.get("metadata")
+            image_bytes = analysis.payload.get("image_bytes")
+            if not metadata or image_bytes is None:
+                raise RecipeValidationError("Unable to extract metadata from widget")
+
+            result = await self._persistence_service.save_recipe_from_widget(
+                recipe_scanner=recipe_scanner,
+                metadata=metadata,
+                image_bytes=image_bytes,
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error("Error saving recipe from widget: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def _parse_save_payload(self, reader) -> dict[str, Any]:
+        image_bytes: Optional[bytes] = None
+        image_base64: Optional[str] = None
+        name: Optional[str] = None
+        tags: list[str] = []
+        metadata: Optional[Dict[str, Any]] = None
+
+        while True:
+            field = await reader.next()
+            if field is None:
+                break
+            if field.name == "image":
+                image_chunks = bytearray()
+                while True:
+                    chunk = await field.read_chunk()
+                    if not chunk:
+                        break
+                    image_chunks.extend(chunk)
+                image_bytes = bytes(image_chunks)
+            elif field.name == "image_base64":
+                image_base64 = await field.text()
+            elif field.name == "name":
+                name = await field.text()
+            elif field.name == "tags":
+                tags_text = await field.text()
+                try:
+                    parsed_tags = json.loads(tags_text)
+                    tags = parsed_tags if isinstance(parsed_tags, list) else []
+                except Exception:
+                    tags = []
+            elif field.name == "metadata":
+                metadata_text = await field.text()
+                try:
+                    metadata = json.loads(metadata_text)
+                except Exception:
+                    metadata = {}
+
+        return {
+            "image_bytes": image_bytes,
+            "image_base64": image_base64,
+            "name": name,
+            "tags": tags,
+            "metadata": metadata,
+        }
+
+
+class RecipeAnalysisHandler:
+    """Analyze images to extract recipe metadata."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        civitai_client_getter: CivitaiClientGetter,
+        logger: Logger,
+        analysis_service: RecipeAnalysisService,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._civitai_client_getter = civitai_client_getter
+        self._logger = logger
+        self._analysis_service = analysis_service
+
+    async def analyze_uploaded_image(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            civitai_client = self._civitai_client_getter()
+            if recipe_scanner is None or civitai_client is None:
+                raise RuntimeError("Required services unavailable")
+
+            content_type = request.headers.get("Content-Type", "")
+            if "multipart/form-data" in content_type:
+                reader = await request.multipart()
+                field = await reader.next()
+                if field is None or field.name != "image":
+                    raise RecipeValidationError("No image field found")
+                image_chunks = bytearray()
+                while True:
+                    chunk = await field.read_chunk()
+                    if not chunk:
+                        break
+                    image_chunks.extend(chunk)
+                result = await self._analysis_service.analyze_uploaded_image(
+                    image_bytes=bytes(image_chunks),
+                    recipe_scanner=recipe_scanner,
+                )
+                return web.json_response(result.payload, status=result.status)
+
+            if "application/json" in content_type:
+                data = await request.json()
+                result = await self._analysis_service.analyze_remote_image(
+                    url=data.get("url"),
+                    recipe_scanner=recipe_scanner,
+                    civitai_client=civitai_client,
+                )
+                return web.json_response(result.payload, status=result.status)
+
+            raise RecipeValidationError("Unsupported content type")
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc), "loras": []}, status=400)
+        except RecipeDownloadError as exc:
+            return web.json_response({"error": str(exc), "loras": []}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc), "loras": []}, status=404)
+        except Exception as exc:
+            self._logger.error("Error analyzing recipe image: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc), "loras": []}, status=500)
+
+    async def analyze_local_image(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()
+            result = await self._analysis_service.analyze_local_image(
+                file_path=data.get("path"),
+                recipe_scanner=recipe_scanner,
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc), "loras": []}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc), "loras": []}, status=404)
+        except Exception as exc:
+            self._logger.error("Error analyzing local image: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc), "loras": []}, status=500)
+
+
+class RecipeSharingHandler:
+    """Serve endpoints related to recipe sharing."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        logger: Logger,
+        sharing_service: RecipeSharingService,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._logger = logger
+        self._sharing_service = sharing_service
+
+    async def share_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")
+
+            recipe_id = request.match_info["recipe_id"]
+            result = await self._sharing_service.share_recipe(
+                recipe_scanner=recipe_scanner, recipe_id=recipe_id
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error sharing recipe: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def download_shared_recipe(self, request: web.Request) -> web.StreamResponse:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            recipe_id = request.match_info["recipe_id"]
+            download_info = await self._sharing_service.prepare_download(
+                recipe_scanner=recipe_scanner, recipe_id=recipe_id
+            )
+            return web.FileResponse(
+                download_info.file_path,
+                headers={
+                    "Content-Disposition": f'attachment; filename="{download_info.download_filename}"'
+                },
+            )
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error downloading shared recipe: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)

py/routes/lora_routes.py

@@ -5,9 +5,9 @@ from typing import Dict
 from server import PromptServer  # type: ignore
 
 from .base_model_routes import BaseModelRoutes
+from .model_route_registrar import ModelRouteRegistrar
 from ..services.lora_service import LoraService
 from ..services.service_registry import ServiceRegistry
-from ..utils.routes_common import ModelRouteUtils
 from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
@@ -17,45 +17,34 @@ class LoraRoutes(BaseModelRoutes):
     
     def __init__(self):
         """Initialize LoRA routes with LoRA service"""
-        # Service will be initialized later via setup_routes
-        self.service = None
-        self.civitai_client = None
+        super().__init__()
         self.template_name = "loras.html"
     
     async def initialize_services(self):
         """Initialize services from ServiceRegistry"""
         lora_scanner = await ServiceRegistry.get_lora_scanner()
         self.service = LoraService(lora_scanner)
-        self.civitai_client = await ServiceRegistry.get_civitai_client()
         
-        # Initialize parent with the service
-        super().__init__(self.service)
+        # 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')
-    
-    def setup_specific_routes(self, app: web.Application, prefix: str):
+
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup LoRA-specific routes"""
         # LoRA-specific query routes
-        app.router.add_get(f'/api/{prefix}/letter-counts', self.get_letter_counts)
-        app.router.add_get(f'/api/{prefix}/get-notes', self.get_lora_notes)
-        app.router.add_get(f'/api/{prefix}/get-trigger-words', self.get_lora_trigger_words)
-        app.router.add_get(f'/api/{prefix}/preview-url', self.get_lora_preview_url)
-        app.router.add_get(f'/api/{prefix}/civitai-url', self.get_lora_civitai_url)
-        app.router.add_get(f'/api/{prefix}/model-description', self.get_lora_model_description)
-        
-        # CivitAI integration with LoRA-specific validation
-        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_lora)
-        app.router.add_get(f'/api/{prefix}/civitai/model/version/{{modelVersionId}}', self.get_civitai_model_by_version)
-        app.router.add_get(f'/api/{prefix}/civitai/model/hash/{{hash}}', self.get_civitai_model_by_hash)
-        
+        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)
+
         # ComfyUI integration
-        app.router.add_post(f'/api/{prefix}/get_trigger_words', 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"""
@@ -81,6 +70,15 @@ class LoraRoutes(BaseModelRoutes):
         
         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"""
@@ -143,6 +141,26 @@ class LoraRoutes(BaseModelRoutes):
                 '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')
+            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 ''
+            })
+            
+        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)
+    
     async def get_lora_preview_url(self, request: web.Request) -> web.Response:
         """Get the static preview URL for a LoRA file"""
         try:
@@ -195,159 +213,6 @@ class LoraRoutes(BaseModelRoutes):
                 'error': str(e)
             }, status=500)
     
-    # CivitAI integration methods
-    async def get_civitai_versions_lora(self, request: web.Request) -> web.Response:
-        """Get available versions for a Civitai LoRA model with local availability info"""
-        try:
-            model_id = request.match_info['model_id']
-            response = await self.civitai_client.get_model_versions(model_id)
-            if not response or not response.get('modelVersions'):
-                return web.Response(status=404, text="Model not found")
-            
-            versions = response.get('modelVersions', [])
-            model_type = response.get('type', '')
-            
-            # Check model type - should be LORA, LoCon, or DORA
-            from ..utils.constants import VALID_LORA_TYPES
-            if model_type.lower() not in VALID_LORA_TYPES:
-                return web.json_response({
-                    'error': f"Model type mismatch. Expected LORA or LoCon, got {model_type}"
-                }, status=400)
-            
-            # Check local availability for each version
-            for version in versions:
-                # Find the model file (type="Model") in the files list
-                model_file = next((file for file in version.get('files', []) 
-                                  if file.get('type') == 'Model'), None)
-                
-                if model_file:
-                    sha256 = model_file.get('hashes', {}).get('SHA256')
-                    if sha256:
-                        # Set existsLocally and localPath at the version level
-                        version['existsLocally'] = self.service.has_hash(sha256)
-                        if version['existsLocally']:
-                            version['localPath'] = self.service.get_path_by_hash(sha256)
-                        
-                        # Also set the model file size at the version level for easier access
-                        version['modelSizeKB'] = model_file.get('sizeKB')
-                else:
-                    # No model file found in this version
-                    version['existsLocally'] = False
-                    
-            return web.json_response(versions)
-        except Exception as e:
-            logger.error(f"Error fetching LoRA model versions: {e}")
-            return web.Response(status=500, text=str(e))
-    
-    async def get_civitai_model_by_version(self, request: web.Request) -> web.Response:
-        """Get CivitAI model details by model version ID"""
-        try:
-            model_version_id = request.match_info.get('modelVersionId')
-            
-            # Get model details from Civitai API    
-            model, error_msg = await self.civitai_client.get_model_version_info(model_version_id)
-            
-            if not model:
-                # Log warning for failed model retrieval
-                logger.warning(f"Failed to fetch model version {model_version_id}: {error_msg}")
-                
-                # Determine status code based on error message
-                status_code = 404 if error_msg and "not found" in error_msg.lower() else 500
-                
-                return web.json_response({
-                    "success": False,
-                    "error": error_msg or "Failed to fetch model information"
-                }, status=status_code)
-                
-            return web.json_response(model)
-        except Exception as e:
-            logger.error(f"Error fetching model details: {e}")
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-    
-    async def get_civitai_model_by_hash(self, request: web.Request) -> web.Response:
-        """Get CivitAI model details by hash"""
-        try:
-            hash = request.match_info.get('hash')
-            model = await self.civitai_client.get_model_by_hash(hash)
-            return web.json_response(model)
-        except Exception as e:
-            logger.error(f"Error fetching model details by hash: {e}")
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-    
-    async def get_lora_model_description(self, request: web.Request) -> web.Response:
-        """Get model description for a Lora model"""
-        try:
-            # Get parameters
-            model_id = request.query.get('model_id')
-            file_path = request.query.get('file_path')
-            
-            if not model_id:
-                return web.json_response({
-                    'success': False, 
-                    'error': 'Model ID is required'
-                }, status=400)
-            
-            # Check if we already have the description stored in metadata
-            description = None
-            tags = []
-            creator = {}
-            if file_path:
-                import os
-                from ..utils.metadata_manager import MetadataManager
-                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-                description = metadata.get('modelDescription')
-                tags = metadata.get('tags', [])
-                creator = metadata.get('creator', {})
-            
-            # If description is not in metadata, fetch from CivitAI
-            if not description:
-                logger.info(f"Fetching model metadata for model ID: {model_id}")
-                model_metadata, _ = await self.civitai_client.get_model_metadata(model_id)
-                
-                if model_metadata:
-                    description = model_metadata.get('description')
-                    tags = model_metadata.get('tags', [])
-                    creator = model_metadata.get('creator', {})
-                
-                    # Save the metadata to file if we have a file path and got metadata
-                    if file_path:
-                        try:
-                            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-                            
-                            metadata['modelDescription'] = description
-                            metadata['tags'] = tags
-                            # Ensure the civitai dict exists
-                            if 'civitai' not in metadata:
-                                metadata['civitai'] = {}
-                            # Store creator in the civitai nested structure
-                            metadata['civitai']['creator'] = creator
-                            
-                            await MetadataManager.save_metadata(file_path, metadata, True)
-                        except Exception as e:
-                            logger.error(f"Error saving model metadata: {e}")
-            
-            return web.json_response({
-                'success': True,
-                'description': description or "<p>No model description available.</p>",
-                'tags': tags,
-                'creator': creator
-            })
-            
-        except Exception as e:
-            logger.error(f"Error getting model metadata: {e}")
-            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:

py/routes/misc_route_registrar.py

@@ -0,0 +1,69 @@
+"""Route registrar for miscellaneous endpoints.
+
+This module mirrors the model route registrar architecture so that
+miscellaneous endpoints share a consistent registration flow.
+"""
+
+from dataclasses import dataclass
+from typing import Callable, Iterable, Mapping
+
+from aiohttp import web
+
+
+@dataclass(frozen=True)
+class RouteDefinition:
+    """Declarative definition for a HTTP route."""
+
+    method: str
+    path: str
+    handler_name: str
+
+
+MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
+    RouteDefinition("GET", "/api/lm/settings", "get_settings"),
+    RouteDefinition("POST", "/api/lm/settings", "update_settings"),
+    RouteDefinition("GET", "/api/lm/settings/libraries", "get_settings_libraries"),
+    RouteDefinition("POST", "/api/lm/settings/libraries/activate", "activate_library"),
+    RouteDefinition("GET", "/api/lm/health-check", "health_check"),
+    RouteDefinition("POST", "/api/lm/open-file-location", "open_file_location"),
+    RouteDefinition("POST", "/api/lm/update-usage-stats", "update_usage_stats"),
+    RouteDefinition("GET", "/api/lm/get-usage-stats", "get_usage_stats"),
+    RouteDefinition("POST", "/api/lm/update-lora-code", "update_lora_code"),
+    RouteDefinition("GET", "/api/lm/trained-words", "get_trained_words"),
+    RouteDefinition("GET", "/api/lm/model-example-files", "get_model_example_files"),
+    RouteDefinition("POST", "/api/lm/register-nodes", "register_nodes"),
+    RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
+    RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
+    RouteDefinition("POST", "/api/lm/download-metadata-archive", "download_metadata_archive"),
+    RouteDefinition("POST", "/api/lm/remove-metadata-archive", "remove_metadata_archive"),
+    RouteDefinition("GET", "/api/lm/metadata-archive-status", "get_metadata_archive_status"),
+    RouteDefinition("GET", "/api/lm/model-versions-status", "get_model_versions_status"),
+)
+
+
+class MiscRouteRegistrar:
+    """Bind miscellaneous route definitions to an aiohttp router."""
+
+    _METHOD_MAP = {
+        "GET": "add_get",
+        "POST": "add_post",
+        "PUT": "add_put",
+        "DELETE": "add_delete",
+    }
+
+    def __init__(self, app: web.Application) -> None:
+        self._app = app
+
+    def register_routes(
+        self,
+        handler_lookup: Mapping[str, Callable[[web.Request], object]],
+        *,
+        definitions: Iterable[RouteDefinition] = MISC_ROUTE_DEFINITIONS,
+    ) -> None:
+        for definition in definitions:
+            self._bind(definition.method, definition.path, handler_lookup[definition.handler_name])
+
+    def _bind(self, method: str, path: str, handler: Callable) -> None:
+        add_method_name = self._METHOD_MAP[method.upper()]
+        add_method = getattr(self._app.router, add_method_name)
+        add_method(path, handler)

py/routes/misc_routes.py

@@ -1,710 +1,135 @@
-import json
+"""Route controller for miscellaneous endpoints."""
+
+from __future__ import annotations
+
 import logging
 import os
-import sys
-import threading
-import asyncio
-from server import PromptServer # type: ignore
+from typing import Awaitable, Callable, Mapping
+
 from aiohttp import web
+from server import PromptServer  # type: ignore
+
+from ..services.metadata_service import (
+    get_metadata_archive_manager,
+    get_metadata_provider,
+    update_metadata_providers,
+)
 from ..services.settings_manager import settings
+from ..services.downloader import get_downloader
 from ..utils.usage_stats import UsageStats
-from ..utils.lora_metadata import extract_trained_words
-from ..config import config
-from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS, NODE_TYPES, DEFAULT_NODE_COLOR
-from ..services.service_registry import ServiceRegistry
-import re
+from .handlers.misc_handlers import (
+    FileSystemHandler,
+    HealthCheckHandler,
+    LoraCodeHandler,
+    MetadataArchiveHandler,
+    MiscHandlerSet,
+    ModelExampleFilesHandler,
+    ModelLibraryHandler,
+    NodeRegistry,
+    NodeRegistryHandler,
+    SettingsHandler,
+    TrainedWordsHandler,
+    UsageStatsHandler,
+    build_service_registry_adapter,
+)
+from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
 
-standalone_mode = 'nodes' not in sys.modules
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get(
+    "HF_HUB_DISABLE_TELEMETRY", "0"
+) == "0"
 
-# Node registry for tracking active workflow nodes
-class NodeRegistry:
-    """Thread-safe registry for tracking Lora nodes in active workflows"""
-    
-    def __init__(self):
-        self._lock = threading.RLock()
-        self._nodes = {}  # node_id -> node_info
-        self._registry_updated = threading.Event()
-    
-    def register_nodes(self, nodes):
-        """Register multiple nodes at once, replacing existing registry"""
-        with self._lock:
-            # Clear existing registry
-            self._nodes.clear()
-            
-            # Register all new nodes
-            for node in nodes:
-                node_id = node['node_id']
-                node_type = node.get('type', '')
-                
-                # Convert node type name to integer
-                type_id = NODE_TYPES.get(node_type, 0)  # 0 for unknown types
-                
-                # Handle null bgcolor with default color
-                bgcolor = node.get('bgcolor')
-                if bgcolor is None:
-                    bgcolor = DEFAULT_NODE_COLOR
-                
-                self._nodes[node_id] = {
-                    'id': node_id,
-                    'bgcolor': bgcolor,
-                    'title': node.get('title'),
-                    'type': type_id,
-                    'type_name': node_type
-                }
-            
-            logger.debug(f"Registered {len(nodes)} nodes in registry")
-            
-            # Signal that registry has been updated
-            self._registry_updated.set()
-    
-    def get_registry(self):
-        """Get current registry information"""
-        with self._lock:
-            return {
-                'nodes': dict(self._nodes),  # Return a copy
-                'node_count': len(self._nodes)
-            }
-    
-    def clear_registry(self):
-        """Clear the entire registry"""
-        with self._lock:
-            self._nodes.clear()
-            logger.info("Node registry cleared")
-    
-    def wait_for_update(self, timeout=1.0):
-        """Wait for registry update with timeout"""
-        self._registry_updated.clear()
-        return self._registry_updated.wait(timeout)
-
-# Global registry instance
-node_registry = NodeRegistry()
 
 class MiscRoutes:
-    """Miscellaneous routes for various utility functions"""
-    
-    @staticmethod
-    def setup_routes(app):
-        """Register miscellaneous routes"""
-        app.router.add_post('/api/settings', MiscRoutes.update_settings)
-        
-        # Add new route for clearing cache
-        app.router.add_post('/api/clear-cache', MiscRoutes.clear_cache)
+    """Route controller that mirrors the model route architecture."""
 
-        app.router.add_get('/api/health-check', lambda request: web.json_response({'status': 'ok'}))
+    def __init__(
+        self,
+        *,
+        settings_service=settings,
+        usage_stats_factory: Callable[[], UsageStats] = UsageStats,
+        prompt_server: type[PromptServer] = PromptServer,
+        service_registry_adapter=build_service_registry_adapter(),
+        metadata_provider_factory=get_metadata_provider,
+        metadata_archive_manager_factory=get_metadata_archive_manager,
+        metadata_provider_updater=update_metadata_providers,
+        downloader_factory=get_downloader,
+        registrar_factory=MiscRouteRegistrar,
+        handler_set_factory=MiscHandlerSet,
+        node_registry: NodeRegistry | None = None,
+        standalone_mode_flag: bool = standalone_mode,
+    ) -> None:
+        self._settings = settings_service
+        self._usage_stats_factory = usage_stats_factory
+        self._prompt_server = prompt_server
+        self._service_registry_adapter = service_registry_adapter
+        self._metadata_provider_factory = metadata_provider_factory
+        self._metadata_archive_manager_factory = metadata_archive_manager_factory
+        self._metadata_provider_updater = metadata_provider_updater
+        self._downloader_factory = downloader_factory
+        self._registrar_factory = registrar_factory
+        self._handler_set_factory = handler_set_factory
+        self._node_registry = node_registry or NodeRegistry()
+        self._standalone_mode = standalone_mode_flag
 
-        # Usage stats routes
-        app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
-        app.router.add_get('/api/get-usage-stats', MiscRoutes.get_usage_stats)
-        
-        # Lora code update endpoint
-        app.router.add_post('/api/update-lora-code', MiscRoutes.update_lora_code)
-
-        # Add new route for getting trained words
-        app.router.add_get('/api/trained-words', MiscRoutes.get_trained_words)
-        
-        # Add new route for getting model example files
-        app.router.add_get('/api/model-example-files', MiscRoutes.get_model_example_files)
-        
-        # Node registry endpoints
-        app.router.add_post('/api/register-nodes', MiscRoutes.register_nodes)
-        app.router.add_get('/api/get-registry', MiscRoutes.get_registry)
-        
-        # Add new route for checking if a model exists in the library
-        app.router.add_get('/api/check-model-exists', MiscRoutes.check_model_exists)
+        self._handler_mapping: Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]] | None = None
 
     @staticmethod
-    async def clear_cache(request):
-        """Clear all cache files from the cache folder"""
-        try:
-            # Get the cache folder path (relative to project directory)
-            project_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-            cache_folder = os.path.join(project_dir, 'cache')
-            
-            # Check if cache folder exists
-            if not os.path.exists(cache_folder):
-                logger.info("Cache folder does not exist, nothing to clear")
-                return web.json_response({'success': True, 'message': 'No cache folder found'})
-            
-            # Get list of cache files before deleting for reporting
-            cache_files = [f for f in os.listdir(cache_folder) if os.path.isfile(os.path.join(cache_folder, f))]
-            deleted_files = []
-            
-            # Delete each .msgpack file in the cache folder
-            for filename in cache_files:
-                if filename.endswith('.msgpack'):
-                    file_path = os.path.join(cache_folder, filename)
-                    try:
-                        os.remove(file_path)
-                        deleted_files.append(filename)
-                        logger.info(f"Deleted cache file: {filename}")
-                    except Exception as e:
-                        logger.error(f"Failed to delete {filename}: {e}")
-                        return web.json_response({
-                            'success': False,
-                            'error': f"Failed to delete {filename}: {str(e)}"
-                        }, status=500)
-            
-            return web.json_response({
-                'success': True,
-                'message': f"Successfully cleared {len(deleted_files)} cache files",
-                'deleted_files': deleted_files
-            })
-            
-        except Exception as e:
-            logger.error(f"Error clearing cache files: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+    def setup_routes(app: web.Application) -> None:
+        """Entry point used by the application bootstrap."""
+        controller = MiscRoutes()
+        controller.bind(app)
 
-    @staticmethod
-    async def update_settings(request):
-        """Update application settings"""
-        try:
-            data = await request.json()
-            
-            # Validate and update settings
-            for key, value in data.items():
-                if value == settings.get(key):
-                    # No change, skip
-                    continue
-                # Special handling for example_images_path - verify path exists
-                if key == 'example_images_path' and value:
-                    if not os.path.exists(value):
-                        return web.json_response({
-                            'success': False,
-                            'error': f"Path does not exist: {value}"
-                        })
-                    
-                    # Path changed - server restart required for new path to take effect
-                    old_path = settings.get('example_images_path')
-                    if old_path != value:
-                        logger.info(f"Example images path changed to {value} - server restart required")
-                
-                # Special handling for base_model_path_mappings - parse JSON string
-                if key == 'base_model_path_mappings' and value:
-                    try:
-                        value = json.loads(value)
-                    except json.JSONDecodeError:
-                        return web.json_response({
-                            'success': False,
-                            'error': f"Invalid JSON format for base_model_path_mappings: {value}"
-                        })
-                
-                # Save to settings
-                settings.set(key, value)
-            
-            return web.json_response({'success': True})
-        except Exception as e:
-            logger.error(f"Error updating settings: {e}", exc_info=True)
-            return web.Response(status=500, text=str(e))
-    
-    @staticmethod
-    async def update_usage_stats(request):
-        """
-        Update usage statistics based on a prompt_id
-        
-        Expects a JSON body with:
-        {
-            "prompt_id": "string"
-        }
-        """
-        try:
-            # Parse the request body
-            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)
-            
-            # Call the UsageStats to process this prompt_id synchronously
-            usage_stats = UsageStats()
-            await usage_stats.process_execution(prompt_id)
-            
-            return web.json_response({
-                'success': True
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to update usage stats: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    @staticmethod
-    async def get_usage_stats(request):
-        """Get current usage statistics"""
-        try:
-            usage_stats = UsageStats()
-            stats = await usage_stats.get_stats()
-            
-            # Add version information to help clients handle format changes
-            stats_response = {
-                'success': True,
-                'data': stats,
-                'format_version': 2  # Indicate this is the new format with history
-            }
-            
-            return web.json_response(stats_response)
-            
-        except Exception as e:
-            logger.error(f"Failed to get usage stats: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    @staticmethod
-    async def update_lora_code(request):
-        """
-        Update Lora code in ComfyUI nodes
-        
-        Expects a JSON body with:
-        {
-            "node_ids": [123, 456], # Optional - List of node IDs to update (for browser mode)
-            "lora_code": "<lora:modelname:1.0>", # The Lora code to send
-            "mode": "append" # or "replace" - whether to append or replace existing code
-        }
-        """
-        try:
-            # Parse the request body
-            data = await request.json()
-            node_ids = data.get('node_ids')
-            lora_code = data.get('lora_code', '')
-            mode = data.get('mode', 'append')
-            
-            if not lora_code:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Missing lora_code parameter'
-                }, status=400)
-            
-            results = []
-            
-            # Desktop mode: no specific node_ids provided
-            if node_ids is None:
-                try:
-                    # Send broadcast message with id=-1 to all Lora Loader nodes
-                    PromptServer.instance.send_sync("lora_code_update", {
-                        "id": -1,
-                        "lora_code": lora_code,
-                        "mode": mode
-                    })
-                    results.append({
-                        'node_id': 'broadcast',
-                        'success': True
-                    })
-                except Exception as e:
-                    logger.error(f"Error broadcasting lora code: {e}")
-                    results.append({
-                        'node_id': 'broadcast',
-                        'success': False,
-                        'error': str(e)
-                    })
-            else:
-                # Browser mode: send to specific nodes
-                for node_id in node_ids:
-                    try:
-                        # Send the message to the frontend
-                        PromptServer.instance.send_sync("lora_code_update", {
-                            "id": node_id,
-                            "lora_code": lora_code,
-                            "mode": mode
-                        })
-                        results.append({
-                            'node_id': node_id,
-                            'success': True
-                        })
-                    except Exception as e:
-                        logger.error(f"Error sending lora code to node {node_id}: {e}")
-                        results.append({
-                            'node_id': node_id,
-                            'success': False,
-                            'error': str(e)
-                        })
-            
-            return web.json_response({
-                'success': True,
-                'results': results
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to update lora code: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+    def bind(self, app: web.Application) -> None:
+        registrar = self._registrar_factory(app)
+        registrar.register_routes(self._ensure_handler_mapping())
 
-    @staticmethod
-    async def get_trained_words(request):
-        """
-        Get trained words from a safetensors file, sorted by frequency
-        
-        Expects a query parameter:
-        file_path: Path to the safetensors file
-        """
-        try:
-            # Get file path from query parameters
-            file_path = request.query.get('file_path')
-            
-            if not file_path:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Missing file_path parameter'
-                }, status=400)
-            
-            # Check if file exists and is a safetensors file
-            if not os.path.exists(file_path):
-                return web.json_response({
-                    'success': False,
-                    'error': f"File not found: {file_path}"
-                }, status=404)
-                
-            if not file_path.lower().endswith('.safetensors'):
-                return web.json_response({
-                    'success': False,
-                    'error': 'File is not a safetensors file'
-                }, status=400)
-            
-            # Extract trained words and class_tokens
-            trained_words, class_tokens = await extract_trained_words(file_path)
-            
-            # Return result with both trained words and class tokens
-            return web.json_response({
-                'success': True,
-                'trained_words': trained_words,
-                'class_tokens': class_tokens
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to get trained words: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+        if self._handler_mapping is None:
+            handler_set = self._create_handler_set()
+            self._handler_mapping = handler_set.to_route_mapping()
+        return self._handler_mapping
 
-    @staticmethod
-    async def get_model_example_files(request):
-        """
-        Get list of example image files for a specific model based on file path
-        
-        Expects:
-        - file_path in query parameters
-        
-        Returns:
-        - List of image files with their paths as static URLs
-        """
-        try:
-            # Get the model file path from query parameters
-            file_path = request.query.get('file_path')
-            
-            if not file_path:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Missing file_path parameter'
-                }, status=400)
-            
-            # Extract directory and base filename
-            model_dir = os.path.dirname(file_path)
-            model_filename = os.path.basename(file_path)
-            model_name = os.path.splitext(model_filename)[0]
-            
-            # Check if the directory exists
-            if not os.path.exists(model_dir):
-                return web.json_response({
-                    'success': False, 
-                    'error': 'Model directory not found',
-                    'files': []
-                }, status=404)
-            
-            # Look for files matching the pattern modelname.example.<index>.<ext>
-            files = []
-            pattern = f"{model_name}.example."
-            
-            for file in os.listdir(model_dir):
-                file_lower = file.lower()
-                if file_lower.startswith(pattern.lower()):
-                    file_full_path = os.path.join(model_dir, file)
-                    if os.path.isfile(file_full_path):
-                        # Check if the file is a supported media file
-                        file_ext = os.path.splitext(file)[1].lower()
-                        if (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
-                            file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
-                            
-                            # Extract the index from the filename
-                            try:
-                                # Extract the part after '.example.' and before file extension
-                                index_part = file[len(pattern):].split('.')[0]
-                                # Try to parse it as an integer
-                                index = int(index_part)
-                            except (ValueError, IndexError):
-                                # If we can't parse the index, use infinity to sort at the end
-                                index = float('inf')
-                            
-                            # Convert file path to static URL
-                            static_url = config.get_preview_static_url(file_full_path)
-                            
-                            files.append({
-                                'name': file,
-                                'path': static_url,
-                                'extension': file_ext,
-                                'is_video': file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'],
-                                'index': index
-                            })
-            
-            # Sort files by their index for consistent ordering
-            files.sort(key=lambda x: x['index'])
-            # Remove the index field as it's only used for sorting
-            for file in files:
-                file.pop('index', None)
-            
-            return web.json_response({
-                'success': True,
-                'files': files
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to get model example files: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+    def _create_handler_set(self) -> MiscHandlerSet:
+        health = HealthCheckHandler()
+        settings_handler = SettingsHandler(
+            settings_service=self._settings,
+            metadata_provider_updater=self._metadata_provider_updater,
+            downloader_factory=self._downloader_factory,
+        )
+        usage_stats = UsageStatsHandler(usage_stats_factory=self._usage_stats_factory)
+        lora_code = LoraCodeHandler(prompt_server=self._prompt_server)
+        trained_words = TrainedWordsHandler()
+        model_examples = ModelExampleFilesHandler()
+        metadata_archive = MetadataArchiveHandler(
+            metadata_archive_manager_factory=self._metadata_archive_manager_factory,
+            settings_service=self._settings,
+            metadata_provider_updater=self._metadata_provider_updater,
+        )
+        filesystem = FileSystemHandler()
+        node_registry_handler = NodeRegistryHandler(
+            node_registry=self._node_registry,
+            prompt_server=self._prompt_server,
+            standalone_mode=self._standalone_mode,
+        )
+        model_library = ModelLibraryHandler(
+            service_registry=self._service_registry_adapter,
+            metadata_provider_factory=self._metadata_provider_factory,
+        )
 
-    @staticmethod
-    async def register_nodes(request):
-        """
-        Register multiple Lora nodes at once
-        
-        Expects a JSON body with:
-        {
-            "nodes": [
-                {
-                    "node_id": 123,
-                    "bgcolor": "#535",
-                    "title": "Lora Loader (LoraManager)"
-                },
-                ...
-            ]
-        }
-        """
-        try:
-            data = await request.json()
-            
-            # Validate required fields
-            nodes = data.get('nodes', [])
-            
-            if not isinstance(nodes, list):
-                return web.json_response({
-                    'success': False,
-                    'error': 'nodes must be a list'
-                }, status=400)
-            
-            # Validate each node
-            for i, node in enumerate(nodes):
-                if not isinstance(node, dict):
-                    return web.json_response({
-                        'success': False,
-                        'error': f'Node {i} 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 {i} missing node_id parameter'
-                    }, status=400)
-                
-                # Validate node_id is an integer
-                try:
-                    node['node_id'] = int(node_id)
-                except (ValueError, TypeError):
-                    return web.json_response({
-                        'success': False,
-                        'error': f'Node {i} node_id must be an integer'
-                    }, status=400)
-            
-            # Register all nodes
-            node_registry.register_nodes(nodes)
-            
-            return web.json_response({
-                'success': True,
-                'message': f'{len(nodes)} nodes registered successfully'
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to register nodes: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    @staticmethod
-    async def get_registry(request):
-        """Get current node registry information by refreshing from frontend"""
-        try:
-            # Check if running in standalone mode
-            if standalone_mode:
-                logger.warning("Registry refresh not available in standalone mode")
-                return web.json_response({
-                    'success': False,
-                    'error': 'Standalone Mode Active',
-                    'message': 'Cannot interact with ComfyUI in standalone mode.'
-                }, status=503)
-            
-            # Send message to frontend to refresh registry
-            try:
-                PromptServer.instance.send_sync("lora_registry_refresh", {})
-                logger.debug("Sent registry refresh request to frontend")
-            except Exception as e:
-                logger.error(f"Failed to send registry refresh message: {e}")
-                return web.json_response({
-                    'success': False,
-                    'error': 'Communication Error',
-                    'message': f'Failed to communicate with ComfyUI frontend: {str(e)}'
-                }, status=500)
-            
-            # Wait for registry update with timeout
-            def wait_for_registry():
-                return node_registry.wait_for_update(timeout=1.0)
-            
-            # Run the wait in a thread to avoid blocking the event loop
-            loop = asyncio.get_event_loop()
-            registry_updated = await loop.run_in_executor(None, wait_for_registry)
-            
-            if not registry_updated:
-                logger.warning("Registry refresh timeout after 1 second")
-                return web.json_response({
-                    'success': False,
-                    'error': 'Timeout Error',
-                    'message': 'Registry refresh timeout - ComfyUI frontend may not be responsive'
-                }, status=408)
-            
-            # Get updated registry
-            registry_info = node_registry.get_registry()
-            
-            return web.json_response({
-                'success': True,
-                'data': registry_info
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to get registry: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': 'Internal Error',
-                'message': str(e)
-            }, status=500)
+        return self._handler_set_factory(
+            health=health,
+            settings=settings_handler,
+            usage_stats=usage_stats,
+            lora_code=lora_code,
+            trained_words=trained_words,
+            model_examples=model_examples,
+            node_registry=node_registry_handler,
+            model_library=model_library,
+            metadata_archive=metadata_archive,
+            filesystem=filesystem,
+        )
 
-    @staticmethod
-    async def check_model_exists(request):
-        """
-        Check if a model with specified modelId and optionally modelVersionId exists in the library
-        
-        Expects query parameters:
-        - modelId: int - Civitai model ID (required)
-        - modelVersionId: int - Civitai model version ID (optional)
-        
-        Returns:
-        - If modelVersionId is provided: JSON with a boolean 'exists' field
-        - If modelVersionId is not provided: JSON with a list of modelVersionIds that exist in the library
-        """
-        try:
-            # Get the modelId and modelVersionId from query parameters
-            model_id_str = request.query.get('modelId')
-            model_version_id_str = request.query.get('modelVersionId')
-            
-            # Validate modelId parameter (required)
-            if not model_id_str:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Missing required parameter: modelId'
-                }, status=400)
-                
-            try:
-                # Convert modelId to integer
-                model_id = int(model_id_str)
-            except ValueError:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Parameter modelId must be an integer'
-                }, status=400)
-            
-            # Get all scanners
-            lora_scanner = await ServiceRegistry.get_lora_scanner()
-            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
-            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-            
-            # If modelVersionId is provided, check for specific version
-            if model_version_id_str:
-                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)
-                
-                # Check lora scanner first
-                exists = False
-                model_type = None
 
-                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
-                    exists = True
-                    model_type = 'lora'
-                elif checkpoint_scanner and await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
-                    exists = True
-                    model_type = 'checkpoint'
-                elif embedding_scanner and await embedding_scanner.check_model_version_exists(model_id, model_version_id):
-                    exists = True
-                    model_type = 'embedding'
-                
-                return web.json_response({
-                    'success': True,
-                    'exists': exists,
-                    'modelType': model_type if exists else None
-                })
-            
-            # If modelVersionId is not provided, return all version IDs for the model
-            else:
-                lora_versions = await lora_scanner.get_model_versions_by_id(model_id)
-                checkpoint_versions = []
-                embedding_versions = []
-
-                # 优先lora，其次checkpoint，最后embedding
-                if not lora_versions:
-                    checkpoint_versions = await checkpoint_scanner.get_model_versions_by_id(model_id)
-                if not lora_versions and not checkpoint_versions:
-                    embedding_versions = await embedding_scanner.get_model_versions_by_id(model_id)
-
-                model_type = None
-                versions = []
-
-                if lora_versions:
-                    model_type = 'lora'
-                    versions = lora_versions
-                elif checkpoint_versions:
-                    model_type = 'checkpoint'
-                    versions = checkpoint_versions
-                elif embedding_versions:
-                    model_type = 'embedding'
-                    versions = embedding_versions
-
-                return web.json_response({
-                    'success': True,
-                    'modelId': model_id,
-                    'modelType': model_type,
-                    'versions': versions
-                })
-            
-        except Exception as e:
-            logger.error(f"Failed to check model existence: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+__all__ = ["MiscRoutes"]

py/routes/model_route_registrar.py

@@ -0,0 +1,99 @@
+"""Route registrar for model endpoints."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Iterable, Mapping
+
+from aiohttp import web
+
+
+@dataclass(frozen=True)
+class RouteDefinition:
+    """Declarative definition for a HTTP route."""
+
+    method: str
+    path_template: str
+    handler_name: str
+
+    def build_path(self, prefix: str) -> str:
+        return self.path_template.replace("{prefix}", prefix)
+
+
+COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
+    RouteDefinition("GET", "/api/lm/{prefix}/list", "get_models"),
+    RouteDefinition("POST", "/api/lm/{prefix}/delete", "delete_model"),
+    RouteDefinition("POST", "/api/lm/{prefix}/exclude", "exclude_model"),
+    RouteDefinition("POST", "/api/lm/{prefix}/fetch-civitai", "fetch_civitai"),
+    RouteDefinition("POST", "/api/lm/{prefix}/fetch-all-civitai", "fetch_all_civitai"),
+    RouteDefinition("POST", "/api/lm/{prefix}/relink-civitai", "relink_civitai"),
+    RouteDefinition("POST", "/api/lm/{prefix}/replace-preview", "replace_preview"),
+    RouteDefinition("POST", "/api/lm/{prefix}/save-metadata", "save_metadata"),
+    RouteDefinition("POST", "/api/lm/{prefix}/add-tags", "add_tags"),
+    RouteDefinition("POST", "/api/lm/{prefix}/rename", "rename_model"),
+    RouteDefinition("POST", "/api/lm/{prefix}/bulk-delete", "bulk_delete_models"),
+    RouteDefinition("POST", "/api/lm/{prefix}/verify-duplicates", "verify_duplicates"),
+    RouteDefinition("POST", "/api/lm/{prefix}/move_model", "move_model"),
+    RouteDefinition("POST", "/api/lm/{prefix}/move_models_bulk", "move_models_bulk"),
+    RouteDefinition("GET", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
+    RouteDefinition("POST", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
+    RouteDefinition("GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"),
+    RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
+    RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
+    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"),
+    RouteDefinition("GET", "/api/lm/{prefix}/folder-tree", "get_folder_tree"),
+    RouteDefinition("GET", "/api/lm/{prefix}/unified-folder-tree", "get_unified_folder_tree"),
+    RouteDefinition("GET", "/api/lm/{prefix}/find-duplicates", "find_duplicate_models"),
+    RouteDefinition("GET", "/api/lm/{prefix}/find-filename-conflicts", "find_filename_conflicts"),
+    RouteDefinition("GET", "/api/lm/{prefix}/get-notes", "get_model_notes"),
+    RouteDefinition("GET", "/api/lm/{prefix}/preview-url", "get_model_preview_url"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai-url", "get_model_civitai_url"),
+    RouteDefinition("GET", "/api/lm/{prefix}/metadata", "get_model_metadata"),
+    RouteDefinition("GET", "/api/lm/{prefix}/model-description", "get_model_description"),
+    RouteDefinition("GET", "/api/lm/{prefix}/relative-paths", "get_relative_paths"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/version/{modelVersionId}", "get_civitai_model_by_version"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/hash/{hash}", "get_civitai_model_by_hash"),
+    RouteDefinition("POST", "/api/lm/download-model", "download_model"),
+    RouteDefinition("GET", "/api/lm/download-model-get", "download_model_get"),
+    RouteDefinition("GET", "/api/lm/cancel-download-get", "cancel_download_get"),
+    RouteDefinition("GET", "/api/lm/download-progress/{download_id}", "get_download_progress"),
+    RouteDefinition("GET", "/{prefix}", "handle_models_page"),
+)
+
+
+class ModelRouteRegistrar:
+    """Bind declarative definitions to an aiohttp router."""
+
+    _METHOD_MAP = {
+        "GET": "add_get",
+        "POST": "add_post",
+        "PUT": "add_put",
+        "DELETE": "add_delete",
+    }
+
+    def __init__(self, app: web.Application) -> None:
+        self._app = app
+
+    def register_common_routes(
+        self,
+        prefix: str,
+        handler_lookup: Mapping[str, Callable[[web.Request], object]],
+        *,
+        definitions: Iterable[RouteDefinition] = COMMON_ROUTE_DEFINITIONS,
+    ) -> None:
+        for definition in definitions:
+            self._bind_route(definition.method, definition.build_path(prefix), handler_lookup[definition.handler_name])
+
+    def add_route(self, method: str, path: str, handler: Callable) -> None:
+        self._bind_route(method, path, handler)
+
+    def add_prefixed_route(self, method: str, path_template: str, prefix: str, handler: Callable) -> None:
+        self._bind_route(method, path_template.replace("{prefix}", prefix), handler)
+
+    def _bind_route(self, method: str, path: str, handler: Callable) -> None:
+        add_method_name = self._METHOD_MAP[method.upper()]
+        add_method = getattr(self._app.router, add_method_name)
+        add_method(path, handler)
+

py/routes/preview_routes.py

@@ -0,0 +1,25 @@
+"""Route controller for preview asset delivery."""
+
+from __future__ import annotations
+
+from aiohttp import web
+
+from .handlers.preview_handlers import PreviewHandler
+
+
+class PreviewRoutes:
+    """Register routes that expose preview assets."""
+
+    def __init__(self, *, handler: PreviewHandler | None = None) -> None:
+        self._handler = handler or PreviewHandler()
+
+    @classmethod
+    def setup_routes(cls, app: web.Application) -> None:
+        controller = cls()
+        controller.register(app)
+
+    def register(self, app: web.Application) -> None:
+        app.router.add_get('/api/lm/previews', self._handler.serve_preview)
+
+
+__all__ = ["PreviewRoutes"]

py/routes/recipe_route_registrar.py

@@ -0,0 +1,64 @@
+"""Route registrar for recipe endpoints."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Mapping
+
+from aiohttp import web
+
+
+@dataclass(frozen=True)
+class RouteDefinition:
+    """Declarative definition for a recipe HTTP route."""
+
+    method: str
+    path: str
+    handler_name: str
+
+
+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("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/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/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"),
+)
+
+
+class RecipeRouteRegistrar:
+    """Bind declarative recipe definitions to an aiohttp router."""
+
+    _METHOD_MAP = {
+        "GET": "add_get",
+        "POST": "add_post",
+        "PUT": "add_put",
+        "DELETE": "add_delete",
+    }
+
+    def __init__(self, app: web.Application) -> None:
+        self._app = app
+
+    def register_routes(self, handler_lookup: Mapping[str, Callable[[web.Request], object]]) -> None:
+        for definition in ROUTE_DEFINITIONS:
+            handler = handler_lookup[definition.handler_name]
+            self._bind_route(definition.method, definition.path, handler)
+
+    def _bind_route(self, method: str, path: str, handler: Callable) -> None:
+        add_method_name = self._METHOD_MAP[method.upper()]
+        add_method = getattr(self._app.router, add_method_name)
+        add_method(path, handler)
+

py/routes/stats_routes.py

@@ -9,6 +9,7 @@ from typing import Dict, List, Any
 
 from ..config import config
 from ..services.settings_manager import settings
+from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
 from ..utils.usage_stats import UsageStats
 
@@ -32,7 +33,13 @@ class StatsRoutes:
         self.lora_scanner = await ServiceRegistry.get_lora_scanner()
         self.checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
         self.embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-        self.usage_stats = UsageStats()
+        
+        # Only initialize usage stats if we have valid paths configured
+        try:
+            self.usage_stats = UsageStats()
+        except RuntimeError as e:
+            logger.warning(f"Could not initialize usage statistics: {e}")
+            self.usage_stats = None
 
     async def handle_stats_page(self, request: web.Request) -> web.Response:
         """Handle GET /statistics request"""
@@ -58,11 +65,23 @@ class StatsRoutes:
             
             is_initializing = lora_initializing or checkpoint_initializing or embedding_initializing
 
+            # 获取用户语言设置
+            user_language = settings.get('language', 'en')
+            
+            # 设置服务端i18n语言
+            server_i18n.set_locale(user_language)
+            
+            # 为模板环境添加i18n过滤器
+            if not hasattr(self.template_env, '_i18n_filter_added'):
+                self.template_env.filters['t'] = server_i18n.create_template_filter()
+                self.template_env._i18n_filter_added = True
+
             template = self.template_env.get_template('statistics.html')
             rendered = template.render(
                 is_initializing=is_initializing,
                 settings=settings,
-                request=request
+                request=request,
+                t=server_i18n.get_translation,
             )
             
             return web.Response(
@@ -488,12 +507,12 @@ class StatsRoutes:
         app.router.add_get('/statistics', self.handle_stats_page)
         
         # Register API routes
-        app.router.add_get('/api/stats/collection-overview', self.get_collection_overview)
-        app.router.add_get('/api/stats/usage-analytics', self.get_usage_analytics)
-        app.router.add_get('/api/stats/base-model-distribution', self.get_base_model_distribution)
-        app.router.add_get('/api/stats/tag-analytics', self.get_tag_analytics)
-        app.router.add_get('/api/stats/storage-analytics', self.get_storage_analytics)
-        app.router.add_get('/api/stats/insights', self.get_insights)
+        app.router.add_get('/api/lm/stats/collection-overview', self.get_collection_overview)
+        app.router.add_get('/api/lm/stats/usage-analytics', self.get_usage_analytics)
+        app.router.add_get('/api/lm/stats/base-model-distribution', self.get_base_model_distribution)
+        app.router.add_get('/api/lm/stats/tag-analytics', self.get_tag_analytics)
+        app.router.add_get('/api/lm/stats/storage-analytics', self.get_storage_analytics)
+        app.router.add_get('/api/lm/stats/insights', self.get_insights)
         
     async def _on_startup(self, app):
         """Initialize services when the app starts"""

py/routes/update_routes.py

@@ -1,26 +1,31 @@
 import os
-import aiohttp
 import logging
 import toml
 import git
 import zipfile
 import shutil
 import tempfile
-from aiohttp import web
+import asyncio
+from aiohttp import web, ClientError
 from typing import Dict, List
 
+from ..utils.settings_paths import ensure_settings_file
+from ..services.downloader import get_downloader
 
 logger = logging.getLogger(__name__)
 
+NETWORK_EXCEPTIONS = (ClientError, OSError, asyncio.TimeoutError)
+
+
 class UpdateRoutes:
     """Routes for handling plugin update checks"""
     
     @staticmethod
     def setup_routes(app):
         """Register update check routes"""
-        app.router.add_get('/api/check-updates', UpdateRoutes.check_updates)
-        app.router.add_get('/api/version-info', UpdateRoutes.get_version_info)
-        app.router.add_post('/api/perform-update', UpdateRoutes.perform_update)
+        app.router.add_get('/api/lm/check-updates', UpdateRoutes.check_updates)
+        app.router.add_get('/api/lm/version-info', UpdateRoutes.get_version_info)
+        app.router.add_post('/api/lm/perform-update', UpdateRoutes.perform_update)
     
     @staticmethod
     async def check_updates(request):
@@ -64,6 +69,12 @@ class UpdateRoutes:
                 'nightly': nightly
             })
             
+        except NETWORK_EXCEPTIONS as e:
+            logger.warning("Network unavailable during update check: %s", e)
+            return web.json_response({
+                'success': False,
+                'error': 'Network unavailable for update check'
+            })
         except Exception as e:
             logger.error(f"Failed to check for updates: {e}", exc_info=True)
             return web.json_response({
@@ -112,7 +123,7 @@ class UpdateRoutes:
             current_dir = os.path.dirname(os.path.abspath(__file__))
             plugin_root = os.path.dirname(os.path.dirname(current_dir))
 
-            settings_path = os.path.join(plugin_root, 'settings.json')
+            settings_path = ensure_settings_file(logger)
             settings_backup = None
             if os.path.exists(settings_path):
                 with open(settings_path, 'r', encoding='utf-8') as f:
@@ -155,51 +166,66 @@ class UpdateRoutes:
     async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
         """
         Download latest release ZIP from GitHub and replace plugin files.
-        Skips settings.json. Writes extracted file list to .tracking.
+        Skips settings.json and civitai folder. Writes extracted file list to .tracking.
         """
         repo_owner = "willmiao"
         repo_name = "ComfyUI-Lora-Manager"
         github_api = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
 
         try:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(github_api) as resp:
-                    if resp.status != 200:
-                        logger.error(f"Failed to fetch release info: {resp.status}")
-                        return False, ""
-                    data = await resp.json()
-                    zip_url = data.get("zipball_url")
-                    version = data.get("tag_name", "unknown")
+            downloader = await get_downloader()
+            
+            # Get release info
+            success, data = await downloader.make_request(
+                'GET',
+                github_api,
+                use_auth=False
+            )
+            if not success:
+                logger.error(f"Failed to fetch release info: {data}")
+                return False, ""
+            
+            zip_url = data.get("zipball_url")
+            version = data.get("tag_name", "unknown")
 
-                # Download ZIP
-                async with session.get(zip_url) as zip_resp:
-                    if zip_resp.status != 200:
-                        logger.error(f"Failed to download ZIP: {zip_resp.status}")
-                        return False, ""
-                    with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
-                        tmp_zip.write(await zip_resp.read())
-                        zip_path = tmp_zip.name
+            # Download ZIP to temporary file
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
+                tmp_zip_path = tmp_zip.name
+            
+            success, result = await downloader.download_file(
+                url=zip_url,
+                save_path=tmp_zip_path,
+                use_auth=False,
+                allow_resume=False
+            )
+            
+            if not success:
+                logger.error(f"Failed to download ZIP: {result}")
+                return False, ""
 
-                UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json'])
+            zip_path = tmp_zip_path
 
-                # Extract ZIP to temp dir
-                with tempfile.TemporaryDirectory() as tmp_dir:
+            # Skip both settings.json and civitai folder
+            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json', 'civitai'])
+
+            # Extract ZIP to temp dir
+            with tempfile.TemporaryDirectory() as tmp_dir:
                     with zipfile.ZipFile(zip_path, 'r') as zip_ref:
                         zip_ref.extractall(tmp_dir)
                     # Find extracted folder (GitHub ZIP contains a root folder)
                     extracted_root = next(os.scandir(tmp_dir)).path
 
-                    # Copy files, skipping settings.json
+                    # Copy files, skipping settings.json and civitai folder
                     for item in os.listdir(extracted_root):
+                        if item == 'settings.json' or item == 'civitai':
+                            continue
                         src = os.path.join(extracted_root, item)
                         dst = os.path.join(plugin_root, item)
                         if os.path.isdir(src):
                             if os.path.exists(dst):
                                 shutil.rmtree(dst)
-                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json'))
+                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json', 'civitai'))
                         else:
-                            if item == 'settings.json':
-                                continue
                             shutil.copy2(src, dst)
 
                     # Write .tracking file: list all files under extracted_root, relative to extracted_root
@@ -207,15 +233,22 @@ class UpdateRoutes:
                     tracking_info_file = os.path.join(plugin_root, '.tracking')
                     tracking_files = []
                     for root, dirs, files in os.walk(extracted_root):
+                        # Skip civitai folder and its contents
+                        rel_root = os.path.relpath(root, extracted_root)
+                        if rel_root == 'civitai' or rel_root.startswith('civitai' + os.sep):
+                            continue
                         for file in files:
                             rel_path = os.path.relpath(os.path.join(root, file), extracted_root)
+                            # Skip settings.json and any file under civitai
+                            if rel_path == 'settings.json' or rel_path.startswith('civitai' + os.sep):
+                                continue
                             tracking_files.append(rel_path.replace("\\", "/"))
                     with open(tracking_info_file, "w", encoding='utf-8') as file:
                         file.write('\n'.join(tracking_files))
 
-                os.remove(zip_path)
-                logger.info(f"Updated plugin via ZIP to {version}")
-                return True, version
+            os.remove(zip_path)
+            logger.info(f"Updated plugin via ZIP to {version}")
+            return True, version
 
         except Exception as e:
             logger.error(f"ZIP update failed: {e}", exc_info=True)
@@ -244,24 +277,27 @@ class UpdateRoutes:
         github_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/commits/main"
         
         try:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(github_url, headers={'Accept': 'application/vnd.github+json'}) as response:
-                    if response.status != 200:
-                        logger.warning(f"Failed to fetch GitHub commit: {response.status}")
-                        return "main", []
-                    
-                    data = await response.json()
-                    commit_sha = data.get('sha', '')[:7]  # Short hash
-                    commit_message = data.get('commit', {}).get('message', '')
-                    
-                    # Format as "main-{short_hash}"
-                    version = f"main-{commit_sha}"
-                    
-                    # Use commit message as changelog
-                    changelog = [commit_message] if commit_message else []
-                    
-                    return version, changelog
+            downloader = await get_downloader()
+            success, data = await downloader.make_request('GET', github_url, custom_headers={'Accept': 'application/vnd.github+json'})
+            
+            if not success:
+                logger.warning(f"Failed to fetch GitHub commit: {data}")
+                return "main", []
+            
+            commit_sha = data.get('sha', '')[:7]  # Short hash
+            commit_message = data.get('commit', {}).get('message', '')
+            
+            # Format as "main-{short_hash}"
+            version = f"main-{commit_sha}"
+            
+            # Use commit message as changelog
+            changelog = [commit_message] if commit_message else []
+            
+            return version, changelog
         
+        except NETWORK_EXCEPTIONS as e:
+            logger.warning("Unable to reach GitHub for nightly version: %s", e)
+            return "main", []
         except Exception as e:
             logger.error(f"Error fetching nightly version: {e}", exc_info=True)
             return "main", []
@@ -410,23 +446,26 @@ class UpdateRoutes:
         github_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
         
         try:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(github_url, headers={'Accept': 'application/vnd.github+json'}) as response:
-                    if response.status != 200:
-                        logger.warning(f"Failed to fetch GitHub release: {response.status}")
-                        return "v0.0.0", []
-                    
-                    data = await response.json()
-                    version = data.get('tag_name', '')
-                    if not version.startswith('v'):
-                        version = f"v{version}"
-                    
-                    # Extract changelog from release notes
-                    body = data.get('body', '')
-                    changelog = UpdateRoutes._parse_changelog(body)
-                    
-                    return version, changelog
+            downloader = await get_downloader()
+            success, data = await downloader.make_request('GET', github_url, custom_headers={'Accept': 'application/vnd.github+json'})
+            
+            if not success:
+                logger.warning(f"Failed to fetch GitHub release: {data}")
+                return "v0.0.0", []
+            
+            version = data.get('tag_name', '')
+            if not version.startswith('v'):
+                version = f"v{version}"
+            
+            # Extract changelog from release notes
+            body = data.get('body', '')
+            changelog = UpdateRoutes._parse_changelog(body)
+            
+            return version, changelog
         
+        except NETWORK_EXCEPTIONS as e:
+            logger.warning("Unable to reach GitHub for release info: %s", e)
+            return "v0.0.0", []
         except Exception as e:
             logger.error(f"Error fetching remote version: {e}", exc_info=True)
             return "v0.0.0", []

py/services/base_model_service.py

@@ -1,100 +1,92 @@
 from abc import ABC, abstractmethod
 from typing import Dict, List, Optional, Type
 import logging
+import os
 
 from ..utils.models import BaseModelMetadata
-from ..utils.constants import NSFW_LEVELS
-from .settings_manager import settings
-from ..utils.utils import fuzzy_match
+from ..utils.metadata_manager import MetadataManager
+from .model_query import FilterCriteria, ModelCacheRepository, ModelFilterSet, SearchStrategy, SettingsProvider
+from .settings_manager import settings as default_settings
 
 logger = logging.getLogger(__name__)
 
 class BaseModelService(ABC):
     """Base service class for all model types"""
     
-    def __init__(self, model_type: str, scanner, metadata_class: Type[BaseModelMetadata]):
-        """Initialize the service
-        
+    def __init__(
+        self,
+        model_type: str,
+        scanner,
+        metadata_class: Type[BaseModelMetadata],
+        *,
+        cache_repository: Optional[ModelCacheRepository] = None,
+        filter_set: Optional[ModelFilterSet] = None,
+        search_strategy: Optional[SearchStrategy] = None,
+        settings_provider: Optional[SettingsProvider] = None,
+    ):
+        """Initialize the service.
+
         Args:
-            model_type: Type of model (lora, checkpoint, etc.)
-            scanner: Model scanner instance
-            metadata_class: Metadata class for this model type
+            model_type: Type of model (lora, checkpoint, etc.).
+            scanner: Model scanner instance.
+            metadata_class: Metadata class for this model type.
+            cache_repository: Custom repository for cache access (primarily for tests).
+            filter_set: Filter component controlling folder/tag/favorites logic.
+            search_strategy: Search component for fuzzy/text matching.
+            settings_provider: Settings object; defaults to the global settings manager.
         """
         self.model_type = model_type
         self.scanner = scanner
         self.metadata_class = metadata_class
+        self.settings = settings_provider or default_settings
+        self.cache_repository = cache_repository or ModelCacheRepository(scanner)
+        self.filter_set = filter_set or ModelFilterSet(self.settings)
+        self.search_strategy = search_strategy or SearchStrategy()
     
-    async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name', 
-                               folder: str = None, search: str = None, fuzzy_search: bool = False,
-                               base_models: list = None, tags: list = None,
-                               search_options: dict = None, hash_filters: dict = None,
-                               favorites_only: bool = False, **kwargs) -> Dict:
-        """Get paginated and filtered model data
-        
-        Args:
-            page: Page number (1-based)
-            page_size: Number of items per page
-            sort_by: Sort criteria, e.g. 'name', 'name:asc', 'name:desc', 'date', 'date:asc', 'date:desc'
-            folder: Folder filter
-            search: Search term
-            fuzzy_search: Whether to use fuzzy search
-            base_models: List of base models to filter by
-            tags: List of tags to filter by
-            search_options: Search options dict
-            hash_filters: Hash filtering options
-            favorites_only: Filter for favorites only
-            **kwargs: Additional model-specific filters
-            
-        Returns:
-            Dict containing paginated results
-        """
-        cache = await self.scanner.get_cached_data()
+    async def get_paginated_data(
+        self,
+        page: int,
+        page_size: int,
+        sort_by: str = 'name',
+        folder: str = None,
+        search: str = None,
+        fuzzy_search: bool = False,
+        base_models: list = None,
+        tags: list = None,
+        search_options: dict = None,
+        hash_filters: dict = None,
+        favorites_only: bool = False,
+        **kwargs,
+    ) -> Dict:
+        """Get paginated and filtered model data"""
+        sort_params = self.cache_repository.parse_sort(sort_by)
+        sorted_data = await self.cache_repository.fetch_sorted(sort_params)
 
-        # Parse sort_by into sort_key and order
-        if ':' in sort_by:
-            sort_key, order = sort_by.split(':', 1)
-            sort_key = sort_key.strip()
-            order = order.strip().lower()
-            if order not in ('asc', 'desc'):
-                order = 'asc'
-        else:
-            sort_key = sort_by.strip()
-            order = 'asc'
-
-        # Get default search options if not provided
-        if search_options is None:
-            search_options = {
-                'filename': True,
-                'modelname': True,
-                'tags': False,
-                'recursive': False,
-            }
-
-        # Get the base data set using new sort logic
-        filtered_data = await cache.get_sorted_data(sort_key, order)
-        
-        # Apply hash filtering if provided (highest priority)
         if hash_filters:
-            filtered_data = await self._apply_hash_filters(filtered_data, hash_filters)
-            
-            # Jump to pagination for hash filters
+            filtered_data = await self._apply_hash_filters(sorted_data, hash_filters)
             return self._paginate(filtered_data, page, page_size)
-        
-        # Apply common filters
+
         filtered_data = await self._apply_common_filters(
-            filtered_data, folder, base_models, tags, favorites_only, search_options
+            sorted_data,
+            folder=folder,
+            base_models=base_models,
+            tags=tags,
+            favorites_only=favorites_only,
+            search_options=search_options,
         )
-        
-        # Apply search filtering
+
         if search:
             filtered_data = await self._apply_search_filters(
-                filtered_data, search, fuzzy_search, search_options
+                filtered_data,
+                search,
+                fuzzy_search,
+                search_options,
             )
-        
-        # Apply model-specific filters
+
         filtered_data = await self._apply_specific_filters(filtered_data, **kwargs)
-        
+
         return self._paginate(filtered_data, page, page_size)
+
     
     async def _apply_hash_filters(self, data: List[Dict], hash_filters: Dict) -> List[Dict]:
         """Apply hash-based filtering"""
@@ -118,105 +110,36 @@ class BaseModelService(ABC):
         
         return data
     
-    async def _apply_common_filters(self, data: List[Dict], folder: str = None, 
-                                  base_models: list = None, tags: list = None,
-                                  favorites_only: bool = False, search_options: dict = None) -> List[Dict]:
+    async def _apply_common_filters(
+        self,
+        data: List[Dict],
+        folder: str = None,
+        base_models: list = None,
+        tags: list = None,
+        favorites_only: bool = False,
+        search_options: dict = None,
+    ) -> List[Dict]:
         """Apply common filters that work across all model types"""
-        # Apply SFW filtering if enabled in settings
-        if settings.get('show_only_sfw', False):
-            data = [
-                item for item in data
-                if not item.get('preview_nsfw_level') or item.get('preview_nsfw_level') < NSFW_LEVELS['R']
-            ]
-        
-        # Apply favorites filtering if enabled
-        if favorites_only:
-            data = [
-                item for item in data
-                if item.get('favorite', False) is True
-            ]
-        
-        # Apply folder filtering
-        if folder is not None:
-            if search_options and search_options.get('recursive', False):
-                # Recursive folder filtering - include all subfolders
-                data = [
-                    item for item in data
-                    if item['folder'].startswith(folder)
-                ]
-            else:
-                # Exact folder filtering
-                data = [
-                    item for item in data
-                    if item['folder'] == folder
-                ]
-        
-        # Apply base model filtering
-        if base_models and len(base_models) > 0:
-            data = [
-                item for item in data
-                if item.get('base_model') in base_models
-            ]
-        
-        # Apply tag filtering
-        if tags and len(tags) > 0:
-            data = [
-                item for item in data
-                if any(tag in item.get('tags', []) for tag in tags)
-            ]
-        
-        return data
+        normalized_options = self.search_strategy.normalize_options(search_options)
+        criteria = FilterCriteria(
+            folder=folder,
+            base_models=base_models,
+            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], search: str, 
-                                  fuzzy_search: bool, search_options: dict) -> List[Dict]:
+    async def _apply_search_filters(
+        self,
+        data: List[Dict],
+        search: str,
+        fuzzy_search: bool,
+        search_options: dict,
+    ) -> List[Dict]:
         """Apply search filtering"""
-        search_results = []
-        
-        for item in data:
-            # Search by file name
-            if search_options.get('filename', True):
-                if fuzzy_search:
-                    if fuzzy_match(item.get('file_name', ''), search):
-                        search_results.append(item)
-                        continue
-                elif search.lower() in item.get('file_name', '').lower():
-                    search_results.append(item)
-                    continue
-            
-            # Search by model name
-            if search_options.get('modelname', True):
-                if fuzzy_search:
-                    if fuzzy_match(item.get('model_name', ''), search):
-                        search_results.append(item)
-                        continue
-                elif search.lower() in item.get('model_name', '').lower():
-                    search_results.append(item)
-                    continue
-            
-            # Search by tags
-            if search_options.get('tags', False) and 'tags' in item:
-                if any((fuzzy_match(tag, search) if fuzzy_search else search.lower() in tag.lower()) 
-                       for tag in item['tags']):
-                    search_results.append(item)
-                    continue
-            
-            # Search by creator
-            civitai = item.get('civitai')
-            creator_username = ''
-            if civitai and isinstance(civitai, dict):
-                creator = civitai.get('creator')
-                if creator and isinstance(creator, dict):
-                    creator_username = creator.get('username', '')
-            if search_options.get('creator', False) and creator_username:
-                if fuzzy_search:
-                    if fuzzy_match(creator_username, search):
-                        search_results.append(item)
-                        continue
-                elif search.lower() in creator_username.lower():
-                    search_results.append(item)
-                    continue
-        
-        return search_results
+        normalized_options = self.search_strategy.normalize_options(search_options)
+        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"""
@@ -272,4 +195,182 @@ class BaseModelService(ABC):
     
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
-        return self.scanner.get_model_roots()
+        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"
+        ]
+        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
+            for root in self.scanner.get_model_roots():
+                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 []
+            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('/')
+            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', '')
+        
+        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 preview_url:
+                    from ..config import config
+                    return config.get_preview_static_url(preview_url)
+        
+        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_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
+                    }
+        
+        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.
+
+        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)
+        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)
+        if should_skip or metadata is None:
+            return None
+        return metadata.modelDescription or ''
+
+
+    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()
+        
+        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', '')
+            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)
+                    break
+            
+            if relative_path and search_lower in relative_path.lower():
+                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
+        ))
+        
+        return matching_paths[:limit]

py/services/checkpoint_scanner.py

@@ -13,7 +13,7 @@ class CheckpointScanner(ModelScanner):
     
     def __init__(self):
         # Define supported file extensions
-        file_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.sft', '.gguf'}
+        file_extensions = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft', '.gguf'}
         super().__init__(
             model_type="checkpoint",
             model_class=CheckpointMetadata,

py/services/checkpoint_service.py

@@ -1,11 +1,10 @@
 import os
 import logging
-from typing import Dict, List, Optional
+from typing import Dict
 
 from .base_model_service import BaseModelService
 from ..utils.models import CheckpointMetadata
 from ..config import config
-from ..utils.routes_common import ModelRouteUtils
 
 logger = logging.getLogger(__name__)
 
@@ -34,12 +33,11 @@ class CheckpointService(BaseModelService):
             "file_size": checkpoint_data.get("size", 0),
             "modified": checkpoint_data.get("modified", ""),
             "tags": checkpoint_data.get("tags", []),
-            "modelDescription": checkpoint_data.get("modelDescription", ""),
             "from_civitai": checkpoint_data.get("from_civitai", True),
             "notes": checkpoint_data.get("notes", ""),
             "model_type": checkpoint_data.get("model_type", "checkpoint"),
             "favorite": checkpoint_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(checkpoint_data.get("civitai", {}))
+            "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True)
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/civitai_client.py

@@ -1,11 +1,10 @@
-from datetime import datetime
-import aiohttp
 import os
+import copy
 import logging
 import asyncio
-from email.parser import Parser
 from typing import Optional, Dict, Tuple, List
-from urllib.parse import unquote
+from .model_metadata_provider import CivitaiModelMetadataProvider, ModelMetadataProviderManager
+from .downloader import get_downloader
 
 logger = logging.getLogger(__name__)
 
@@ -19,6 +18,11 @@ class CivitaiClient:
         async with cls._lock:
             if cls._instance is None:
                 cls._instance = cls()
+                
+                # Register this client as a metadata provider
+                provider_manager = await ModelMetadataProviderManager.get_instance()
+                provider_manager.register_provider('civitai', CivitaiModelMetadataProvider(cls._instance), True)
+                
             return cls._instance
 
     def __init__(self):
@@ -28,81 +32,27 @@ class CivitaiClient:
         self._initialized = True
         
         self.base_url = "https://civitai.com/api/v1"
-        self.headers = {
-            'User-Agent': 'ComfyUI-LoRA-Manager/1.0'
-        }
-        self._session = None
-        self._session_created_at = None
-        # Set default buffer size to 1MB for higher throughput
-        self.chunk_size = 1024 * 1024
+
+    @staticmethod
+    def _remove_comfy_metadata(model_version: Optional[Dict]) -> None:
+        """Remove Comfy-specific metadata from model version images."""
+        if not isinstance(model_version, dict):
+            return
+
+        images = model_version.get("images")
+        if not isinstance(images, list):
+            return
+
+        for image in images:
+            if not isinstance(image, dict):
+                continue
+
+            meta = image.get("meta")
+            if isinstance(meta, dict) and "comfy" in meta:
+                meta.pop("comfy", None)
     
-    @property
-    async def session(self) -> aiohttp.ClientSession:
-        """Lazy initialize the session"""
-        if self._session is None:
-            # Optimize TCP connection parameters
-            connector = aiohttp.TCPConnector(
-                ssl=True,
-                limit=8,  # Increase from 3 to 8 for better parallelism
-                ttl_dns_cache=300,  # Enable DNS caching with reasonable timeout
-                force_close=False,  # Keep connections for reuse
-                enable_cleanup_closed=True
-            )
-            trust_env = True  # Allow using system environment proxy settings
-            # Configure timeout parameters - increase read timeout for large files
-            timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=120)
-            self._session = aiohttp.ClientSession(
-                connector=connector, 
-                trust_env=trust_env,
-                timeout=timeout
-            )
-            self._session_created_at = datetime.now()
-        return self._session
-    
-    async def _ensure_fresh_session(self):
-        """Refresh session if it's been open too long"""
-        if self._session is not None:
-            if not hasattr(self, '_session_created_at') or \
-               (datetime.now() - self._session_created_at).total_seconds() > 300:  # 5 minutes
-                await self.close()
-                self._session = None
-        
-        return await self.session
-
-    def _parse_content_disposition(self, header: str) -> str:
-        """Parse filename from content-disposition header"""
-        if not header:
-            return None
-        
-        # Handle quoted filenames
-        if 'filename="' in header:
-            start = header.index('filename="') + 10
-            end = header.index('"', start)
-            return unquote(header[start:end])
-        
-        # Fallback to original parsing
-        disposition = Parser().parsestr(f'Content-Disposition: {header}')
-        filename = disposition.get_param('filename')
-        if filename:
-            return unquote(filename)
-        return None
-
-    def _get_request_headers(self) -> dict:
-        """Get request headers with optional API key"""
-        headers = {
-            'User-Agent': 'ComfyUI-LoRA-Manager/1.0',
-            'Content-Type': 'application/json'
-        }
-        
-        from .settings_manager import settings
-        api_key = settings.get('civitai_api_key')
-        if (api_key):
-            headers['Authorization'] = f'Bearer {api_key}'
-            
-        return headers
-
-    async def _download_file(self, url: str, save_dir: str, default_filename: str, progress_callback=None) -> Tuple[bool, str]:
-        """Download file with content-disposition support and progress tracking
+    async def download_file(self, url: str, save_dir: str, default_filename: str, progress_callback=None) -> Tuple[bool, str]:
+        """Download file with resumable downloads and retry mechanism
 
         Args:
             url: Download URL
@@ -113,159 +63,230 @@ class CivitaiClient:
         Returns:
             Tuple[bool, str]: (success, save_path or error message)
         """
-        logger.debug(f"Resolving DNS for: {url}")
-        session = await self._ensure_fresh_session()
+        downloader = await get_downloader()
+        save_path = os.path.join(save_dir, default_filename)
+        
+        # Use unified downloader with CivitAI authentication
+        success, result = await downloader.download_file(
+            url=url,
+            save_path=save_path,
+            progress_callback=progress_callback,
+            use_auth=True,  # Enable CivitAI authentication
+            allow_resume=True
+        )
+        
+        return success, result
+
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
         try:
-            headers = self._get_request_headers()
+            downloader = await get_downloader()
+            success, result = await downloader.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 downloader.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", [])
+
+                        # Add creator from model data
+                        result['creator'] = data.get("creator")
+
+                self._remove_comfy_metadata(result)
+                return result, None
             
-            # Add Range header to allow resumable downloads
-            headers['Accept-Encoding'] = 'identity'  # Disable compression for better chunked downloads
+            # Handle specific error cases
+            if "not found" in str(result):
+                return None, "Model not found"
             
-            logger.debug(f"Starting download from: {url}")
-            async with session.get(url, headers=headers, allow_redirects=True) as response:
-                if response.status != 200:
-                    # Handle 401 unauthorized responses
-                    if response.status == 401:
-                        logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
-                        
-                        return False, "Invalid or missing CivitAI API key, or early access restriction."
-                    
-                    # Handle other client errors that might be permission-related
-                    if response.status == 403:
-                        logger.warning(f"Forbidden access to resource: {url} (Status 403)")
-                        return False, "Access forbidden: You don't have permission to download this file."
-                    
-                    # Generic error response for other status codes
-                    logger.error(f"Download failed for {url} with status {response.status}")
-                    return False, f"Download failed with status {response.status}"
-
-                # Get filename from content-disposition header
-                content_disposition = response.headers.get('Content-Disposition')
-                filename = self._parse_content_disposition(content_disposition)
-                if not filename:
-                    filename = default_filename
-                
-                save_path = os.path.join(save_dir, filename)
-                
-                # Get total file size for progress calculation
-                total_size = int(response.headers.get('content-length', 0))
-                current_size = 0
-                last_progress_report_time = datetime.now()
-
-                # Stream download to file with progress updates using larger buffer
-                with open(save_path, 'wb') as f:
-                    async for chunk in response.content.iter_chunked(self.chunk_size):
-                        if chunk:
-                            f.write(chunk)
-                            current_size += len(chunk)
-                            
-                            # Limit progress update frequency to reduce overhead
-                            now = datetime.now()
-                            time_diff = (now - last_progress_report_time).total_seconds()
-                            
-                            if progress_callback and total_size and time_diff >= 1.0:
-                                progress = (current_size / total_size) * 100
-                                await progress_callback(progress)
-                                last_progress_report_time = now
-                
-                # Ensure 100% progress is reported
-                if progress_callback:
-                    await progress_callback(100)
-                        
-                return True, save_path
-                
-        except aiohttp.ClientError as e:
-            logger.error(f"Network error during download: {e}")
-            return False, f"Network error: {str(e)}"
-        except Exception as e:
-            logger.error(f"Download error: {e}")
-            return False, str(e)
-
-    async def get_model_by_hash(self, model_hash: str) -> Optional[Dict]:
-        try:
-            session = await self._ensure_fresh_session()
-            async with session.get(f"{self.base_url}/model-versions/by-hash/{model_hash}") as response:
-                if response.status == 200:
-                    return await response.json()
-                return None
+            # Other error cases
+            logger.error(f"Failed to fetch model info for {model_hash[:10]}: {result}")
+            return None, str(result)
         except Exception as e:
             logger.error(f"API Error: {str(e)}")
-            return None
+            return None, str(e)
 
     async def download_preview_image(self, image_url: str, save_path: str):
         try:
-            session = await self._ensure_fresh_session()
-            async with session.get(image_url) as response:
-                if response.status == 200:
-                    content = await response.read()
-                    with open(save_path, 'wb') as f:
-                        f.write(content)
-                    return True
-                return False
+            downloader = await get_downloader()
+            success, content, headers = await downloader.download_to_memory(
+                image_url,
+                use_auth=False  # Preview images don't need auth
+            )
+            if success:
+                # Ensure directory exists
+                os.makedirs(os.path.dirname(save_path), exist_ok=True)
+                with open(save_path, 'wb') as f:
+                    f.write(content)
+                return True
+            return False
         except Exception as e:
-            print(f"Download Error: {str(e)}")
+            logger.error(f"Download Error: {str(e)}")
             return False
             
     async def get_model_versions(self, model_id: str) -> List[Dict]:
         """Get all versions of a model with local availability info"""
         try:
-            session = await self._ensure_fresh_session()  # Use fresh session
-            async with session.get(f"{self.base_url}/models/{model_id}") as response:
-                if response.status != 200:
-                    return None
-                data = await response.json()
+            downloader = await get_downloader()
+            success, result = await downloader.make_request(
+                'GET',
+                f"{self.base_url}/models/{model_id}",
+                use_auth=True
+            )
+            if success:
                 # Also return model type along with versions
                 return {
-                    'modelVersions': data.get('modelVersions', []),
-                    'type': data.get('type', '')
+                    'modelVersions': result.get('modelVersions', []),
+                    'type': result.get('type', ''),
+                    'name': result.get('name', '')
                 }
+            return None
         except Exception as e:
             logger.error(f"Error fetching model versions: {e}")
             return None
             
-    async def get_model_version(self, model_id: int, version_id: int = None) -> Optional[Dict]:
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
         """Get specific model version with additional metadata
         
         Args:
-            model_id: The Civitai model ID
+            model_id: The Civitai model ID (optional if version_id is provided)
             version_id: Optional specific version ID to retrieve
             
         Returns:
             Optional[Dict]: The model version data with additional fields or None if not found
         """
         try:
-            session = await self._ensure_fresh_session()
+            downloader = await get_downloader()
             
-            # Step 1: Get model data to find version_id if not provided and get additional metadata
-            async with session.get(f"{self.base_url}/models/{model_id}") as response:
-                if response.status != 200:
+            # Case 1: Only version_id is provided
+            if model_id is None and version_id is not None:
+                # First get the version info to extract model_id
+                success, version = await downloader.make_request(
+                    'GET',
+                    f"{self.base_url}/model-versions/{version_id}",
+                    use_auth=True
+                )
+                if not success:
                     return None
-                    
-                data = await response.json()
+                
+                model_id = version.get('modelId')
+                if not model_id:
+                    logger.error(f"No modelId found in version {version_id}")
+                    return None
+            
+                # Now get the model data for additional metadata
+                success, model_data = await downloader.make_request(
+                    'GET',
+                    f"{self.base_url}/models/{model_id}",
+                    use_auth=True
+                )
+                if success:
+                    # Enrich version with model data
+                    version['model']['description'] = model_data.get("description")
+                    version['model']['tags'] = model_data.get("tags", [])
+                    version['creator'] = model_data.get("creator")
+
+                self._remove_comfy_metadata(version)
+                return version
+            
+            # Case 2: model_id is provided (with or without version_id)
+            elif model_id is not None:
+                # Step 1: Get model data to find version_id if not provided and get additional metadata
+                success, data = await downloader.make_request(
+                    'GET',
+                    f"{self.base_url}/models/{model_id}",
+                    use_auth=True
+                )
+                if not success:
+                    return None
+
                 model_versions = data.get('modelVersions', [])
-                
-                # Step 2: Determine the version_id to use
-                target_version_id = version_id
-                if target_version_id is None:
-                    target_version_id = model_versions[0].get('id')
-            
-            # Step 3: Get detailed version info using the version_id
-            headers = self._get_request_headers()
-            async with session.get(f"{self.base_url}/model-versions/{target_version_id}", headers=headers) as response:
-                if response.status != 200:
+                if not model_versions:
+                    logger.warning(f"No model versions found for model {model_id}")
                     return None
-                
-                version = await response.json()
-                
+
+                # Step 2: Determine the target version entry to use
+                target_version = None
+                if version_id is not None:
+                    target_version = next(
+                        (item for item in model_versions if item.get('id') == version_id),
+                        None
+                    )
+                    if target_version is None:
+                        logger.warning(
+                            f"Version {version_id} not found for model {model_id}, defaulting to first version"
+                        )
+                if target_version is None:
+                    target_version = model_versions[0]
+
+                target_version_id = target_version.get('id')
+
+                # Step 3: Get detailed version info using the SHA256 hash
+                model_hash = None
+                for file_info in target_version.get('files', []):
+                    if file_info.get('type') == 'Model' and file_info.get('primary'):
+                        model_hash = file_info.get('hashes', {}).get('SHA256')
+                        if model_hash:
+                            break
+
+                version = None
+                if model_hash:
+                    success, version = await downloader.make_request(
+                        'GET',
+                        f"{self.base_url}/model-versions/by-hash/{model_hash}",
+                        use_auth=True
+                    )
+                    if not success:
+                        logger.warning(
+                            f"Failed to fetch version by hash for model {model_id} version {target_version_id}: {version}"
+                        )
+                        version = None
+                else:
+                    logger.warning(
+                        f"No primary model hash found for model {model_id} version {target_version_id}"
+                    )
+
+                if version is None:
+                    version = copy.deepcopy(target_version)
+                    version.pop('index', None)
+                    version['modelId'] = model_id
+                    version['model'] = {
+                        'name': data.get('name'),
+                        'type': data.get('type'),
+                        'nsfw': data.get('nsfw'),
+                        'poi': data.get('poi')
+                    }
+
                 # Step 4: Enrich version_info with model data
                 # Add description and tags from model data
-                version['model']['description'] = data.get("description")
-                version['model']['tags'] = data.get("tags", [])
-                
+                model_info = version.get('model')
+                if not isinstance(model_info, dict):
+                    model_info = {}
+                    version['model'] = model_info
+                model_info['description'] = data.get("description")
+                model_info['tags'] = data.get("tags", [])
+
                 # Add creator from model data
                 version['creator'] = data.get("creator")
-                
+
+                self._remove_comfy_metadata(version)
                 return version
+            
+            # Case 3: Neither model_id nor version_id provided
+            else:
+                logger.error("Either model_id or version_id must be provided")
+                return None
                 
         except Exception as e:
             logger.error(f"Error fetching model version: {e}")
@@ -283,116 +304,35 @@ class CivitaiClient:
                 - An error message if there was an error, or None on success
         """
         try:
-            session = await self._ensure_fresh_session()
+            downloader = await get_downloader()
             url = f"{self.base_url}/model-versions/{version_id}"
-            headers = self._get_request_headers()
             
             logger.debug(f"Resolving DNS for model version info: {url}")
-            async with session.get(url, headers=headers) as response:
-                if response.status == 200:
-                    logger.debug(f"Successfully fetched model version info for: {version_id}")
-                    return await response.json(), None
-                
-                # Handle specific error cases
-                if response.status == 404:
-                    # Try to parse the error message
-                    try:
-                        error_data = await response.json()
-                        error_msg = error_data.get('error', f"Model not found (status 404)")
-                        logger.warning(f"Model version not found: {version_id} - {error_msg}")
-                        return None, error_msg
-                    except:
-                        return None, "Model not found (status 404)"
-                
-                # Other error cases
-                logger.error(f"Failed to fetch model info for {version_id} (status {response.status})")
-                return None, f"Failed to fetch model info (status {response.status})"
+            success, result = await downloader.make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
+            
+            if success:
+                logger.debug(f"Successfully fetched model version info for: {version_id}")
+                self._remove_comfy_metadata(result)
+                return result, None
+            
+            # Handle specific error cases
+            if "not found" in str(result):
+                error_msg = f"Model not found"
+                logger.warning(f"Model version not found: {version_id} - {error_msg}")
+                return None, error_msg
+            
+            # Other error cases
+            logger.error(f"Failed to fetch model info for {version_id}: {result}")
+            return None, str(result)
         except Exception as e:
             error_msg = f"Error fetching model version info: {e}"
             logger.error(error_msg)
             return None, error_msg
 
-    async def get_model_metadata(self, model_id: str) -> Tuple[Optional[Dict], int]:
-        """Fetch model metadata (description, tags, and creator info) from Civitai API
-        
-        Args:
-            model_id: The Civitai model ID
-            
-        Returns:
-            Tuple[Optional[Dict], int]: A tuple containing:
-                - A dictionary with model metadata or None if not found
-                - The HTTP status code from the request
-        """
-        try:
-            session = await self._ensure_fresh_session()
-            headers = self._get_request_headers()
-            url = f"{self.base_url}/models/{model_id}"
-            
-            async with session.get(url, headers=headers) as response:
-                status_code = response.status
-                
-                if status_code != 200:
-                    logger.warning(f"Failed to fetch model metadata: Status {status_code}")
-                    return None, status_code
-                
-                data = await response.json()
-                
-                # Extract relevant metadata
-                metadata = {
-                    "description": data.get("description") or "No model description available",
-                    "tags": data.get("tags", []),
-                    "creator": {
-                        "username": data.get("creator", {}).get("username"),
-                        "image": data.get("creator", {}).get("image")
-                    }
-                }
-                
-                if metadata["description"] or metadata["tags"] or metadata["creator"]["username"]:
-                    return metadata, status_code
-                else:
-                    logger.warning(f"No metadata found for model {model_id}")
-                    return None, status_code
-                
-        except Exception as e:
-            logger.error(f"Error fetching model metadata: {e}", exc_info=True)
-            return None, 0
-
-    # Keep old method for backward compatibility, delegating to the new one
-    async def get_model_description(self, model_id: str) -> Optional[str]:
-        """Fetch the model description from Civitai API (Legacy method)"""
-        metadata, _ = await self.get_model_metadata(model_id)
-        return metadata.get("description") if metadata else None
-
-    async def close(self):
-        """Close the session if it exists"""
-        if self._session is not None:
-            await self._session.close()
-            self._session = None
-
-    async def _get_hash_from_civitai(self, model_version_id: str) -> Optional[str]:
-        """Get hash from Civitai API"""
-        try:
-            session = await self._ensure_fresh_session()
-            if not session:
-                return None
-            
-            version_info = await session.get(f"{self.base_url}/model-versions/{model_version_id}")
-            
-            if not version_info or not version_info.json().get('files'):
-                return None
-            
-            # Get hash from the first file
-            for file_info in version_info.json().get('files', []):
-                if file_info.get('hashes', {}).get('SHA256'):
-                    # Convert hash to lowercase to standardize
-                    hash_value = file_info['hashes']['SHA256'].lower()
-                    return hash_value
-                
-            return None
-        except Exception as e:
-            logger.error(f"Error getting hash from Civitai: {e}")
-            return None
-
     async def get_image_info(self, image_id: str) -> Optional[Dict]:
         """Fetch image information from Civitai API
         
@@ -403,22 +343,25 @@ class CivitaiClient:
             Optional[Dict]: The image data or None if not found
         """
         try:
-            session = await self._ensure_fresh_session()
-            headers = self._get_request_headers()
+            downloader = await get_downloader()
             url = f"{self.base_url}/images?imageId={image_id}&nsfw=X"
             
             logger.debug(f"Fetching image info for ID: {image_id}")
-            async with session.get(url, headers=headers) as response:
-                if response.status == 200:
-                    data = await response.json()
-                    if data and "items" in data and len(data["items"]) > 0:
-                        logger.debug(f"Successfully fetched image info for ID: {image_id}")
-                        return data["items"][0]
-                    logger.warning(f"No image found with ID: {image_id}")
-                    return None
-                
-                logger.error(f"Failed to fetch image info for ID: {image_id} (status {response.status})")
+            success, result = await downloader.make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
+            
+            if success:
+                if result and "items" in result and len(result["items"]) > 0:
+                    logger.debug(f"Successfully fetched image info for ID: {image_id}")
+                    return result["items"][0]
+                logger.warning(f"No image found with ID: {image_id}")
                 return None
+            
+            logger.error(f"Failed to fetch image info for ID: {image_id}: {result}")
+            return None
         except Exception as e:
             error_msg = f"Error fetching image info: {e}"
             logger.error(error_msg)

py/services/download_coordinator.py

@@ -0,0 +1,100 @@
+"""Service wrapper for coordinating download lifecycle events."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Awaitable, Callable, Dict, Optional
+
+
+logger = logging.getLogger(__name__)
+
+
+class DownloadCoordinator:
+    """Manage download scheduling, cancellation and introspection."""
+
+    def __init__(
+        self,
+        *,
+        ws_manager,
+        download_manager_factory: Callable[[], Awaitable],
+    ) -> None:
+        self._ws_manager = ws_manager
+        self._download_manager_factory = download_manager_factory
+
+    async def schedule_download(self, payload: Dict[str, Any]) -> Dict[str, Any]:
+        """Schedule a download using the provided payload."""
+
+        download_manager = await self._download_manager_factory()
+
+        download_id = payload.get("download_id") or self._ws_manager.generate_download_id()
+        payload.setdefault("download_id", download_id)
+
+        async def progress_callback(progress: Any) -> None:
+            await self._ws_manager.broadcast_download_progress(
+                download_id,
+                {
+                    "status": "progress",
+                    "progress": progress,
+                    "download_id": download_id,
+                },
+            )
+
+        model_id = self._parse_optional_int(payload.get("model_id"), "model_id")
+        model_version_id = self._parse_optional_int(
+            payload.get("model_version_id"), "model_version_id"
+        )
+
+        if model_id is None and model_version_id is None:
+            raise ValueError(
+                "Missing required parameter: Please provide either 'model_id' or 'model_version_id'"
+            )
+
+        result = await download_manager.download_from_civitai(
+            model_id=model_id,
+            model_version_id=model_version_id,
+            save_dir=payload.get("model_root"),
+            relative_path=payload.get("relative_path", ""),
+            use_default_paths=payload.get("use_default_paths", False),
+            progress_callback=progress_callback,
+            download_id=download_id,
+            source=payload.get("source"),
+        )
+
+        result["download_id"] = download_id
+        return result
+
+    async def cancel_download(self, download_id: str) -> Dict[str, Any]:
+        """Cancel an active download and emit a broadcast event."""
+
+        download_manager = await self._download_manager_factory()
+        result = await download_manager.cancel_download(download_id)
+
+        await self._ws_manager.broadcast_download_progress(
+            download_id,
+            {
+                "status": "cancelled",
+                "progress": 0,
+                "download_id": download_id,
+                "message": "Download cancelled by user",
+            },
+        )
+
+        return result
+
+    async def list_active_downloads(self) -> Dict[str, Any]:
+        """Return the active download map from the underlying manager."""
+
+        download_manager = await self._download_manager_factory()
+        return await download_manager.get_active_downloads()
+
+    def _parse_optional_int(self, value: Any, field: str) -> Optional[int]:
+        """Parse an optional integer from user input."""
+
+        if value is None or value == "":
+            return None
+
+        try:
+            return int(value)
+        except (TypeError, ValueError) as exc:
+            raise ValueError(f"Invalid {field}: Must be an integer") from exc
+

py/services/download_manager.py

@@ -3,13 +3,15 @@ import os
 import asyncio
 from collections import OrderedDict
 import uuid
-from typing import Dict
+from typing import Dict, List
 from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 from ..utils.constants import CARD_PREVIEW_WIDTH, VALID_LORA_TYPES, CIVITAI_MODEL_TAGS
 from ..utils.exif_utils import ExifUtils
 from ..utils.metadata_manager import MetadataManager
 from .service_registry import ServiceRegistry
 from .settings_manager import settings
+from .metadata_service import get_default_metadata_provider
+from .downloader import get_downloader
 
 # Download to temporary file first
 import tempfile
@@ -34,17 +36,10 @@ class DownloadManager:
             return
         self._initialized = True
         
-        self._civitai_client = None  # Will be lazily initialized
         # Add download management
         self._active_downloads = OrderedDict()  # download_id -> download_info
         self._download_semaphore = asyncio.Semaphore(5)  # Limit concurrent downloads
         self._download_tasks = {}  # download_id -> asyncio.Task
-
-    async def _get_civitai_client(self):
-        """Lazily initialize CivitaiClient from registry"""
-        if self._civitai_client is None:
-            self._civitai_client = await ServiceRegistry.get_civitai_client()
-        return self._civitai_client
     
     async def _get_lora_scanner(self):
         """Get the lora scanner from registry"""
@@ -54,24 +49,29 @@ class DownloadManager:
         """Get the checkpoint scanner from registry"""
         return await ServiceRegistry.get_checkpoint_scanner()
     
-    async def download_from_civitai(self, model_id: int, model_version_id: int, 
+    async def download_from_civitai(self, model_id: int = None, model_version_id: int = None, 
                                   save_dir: str = None, relative_path: str = '', 
                                   progress_callback=None, use_default_paths: bool = False,
-                                  download_id: str = None) -> Dict:
+                                  download_id: str = None, source: str = None) -> Dict:
         """Download model from Civitai with task tracking and concurrency control
         
         Args:
-            model_id: Civitai model ID
-            model_version_id: Civitai model version ID
+            model_id: Civitai model ID (optional if model_version_id is provided)
+            model_version_id: Civitai model version ID (optional if model_id is provided)
             save_dir: Directory to save the model
             relative_path: Relative path within save_dir
             progress_callback: Callback function for progress updates
             use_default_paths: Flag to use default paths
             download_id: Unique identifier for this download task
+            source: Optional source parameter to specify metadata provider
             
         Returns:
             Dict with download result
         """
+        # Validate that at least one identifier is provided
+        if not model_id and not model_version_id:
+            return {'success': False, 'error': 'Either model_id or model_version_id must be provided'}
+        
         # Use provided download_id or generate new one
         task_id = download_id or str(uuid.uuid4())
         
@@ -87,7 +87,7 @@ class DownloadManager:
         download_task = asyncio.create_task(
             self._download_with_semaphore(
                 task_id, model_id, model_version_id, save_dir, 
-                relative_path, progress_callback, use_default_paths
+                relative_path, progress_callback, use_default_paths, source
             )
         )
         
@@ -108,7 +108,8 @@ class DownloadManager:
 
     async def _download_with_semaphore(self, task_id: str, model_id: int, model_version_id: int,
                                      save_dir: str, relative_path: str, 
-                                     progress_callback=None, use_default_paths: bool = False):
+                                     progress_callback=None, use_default_paths: bool = False,
+                                     source: str = None):
         """Execute download with semaphore to limit concurrency"""
         # Update status to waiting
         if task_id in self._active_downloads:
@@ -138,7 +139,7 @@ class DownloadManager:
                     result = await self._execute_original_download(
                         model_id, model_version_id, save_dir,
                         relative_path, tracking_callback, use_default_paths,
-                        task_id
+                        task_id, source
                     )
                     
                     # Update status based on result
@@ -173,7 +174,7 @@ class DownloadManager:
 
     async def _execute_original_download(self, model_id, model_version_id, save_dir, 
                                      relative_path, progress_callback, use_default_paths,
-                                     download_id=None):
+                                     download_id=None, source=None):
         """Wrapper for original download_from_civitai implementation"""
         try:
             # Check if model version already exists in library
@@ -181,20 +182,29 @@ class DownloadManager:
                 # Check both scanners
                 lora_scanner = await self._get_lora_scanner()
                 checkpoint_scanner = await self._get_checkpoint_scanner()
+                embedding_scanner = await ServiceRegistry.get_embedding_scanner()
                 
                 # Check lora scanner first
-                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
+                if await lora_scanner.check_model_version_exists(model_version_id):
                     return {'success': False, 'error': 'Model version already exists in lora library'}
                 
                 # Check checkpoint scanner
-                if await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
+                if await checkpoint_scanner.check_model_version_exists(model_version_id):
                     return {'success': False, 'error': 'Model version already exists in checkpoint library'}
+                
+                # Check embedding scanner
+                if await embedding_scanner.check_model_version_exists(model_version_id):
+                    return {'success': False, 'error': 'Model version already exists in embedding library'}
 
-            # Get civitai client
-            civitai_client = await self._get_civitai_client()
+            # Get metadata provider based on source parameter
+            if source == 'civarchive':
+                from .metadata_service import get_metadata_provider
+                metadata_provider = await get_metadata_provider('civarchive')
+            else:
+                metadata_provider = await get_default_metadata_provider()
 
             # Get version info based on the provided identifier
-            version_info = await civitai_client.get_model_version(model_id, model_version_id)
+            version_info = await metadata_provider.get_model_version(model_id, model_version_id)
             
             if not version_info:
                 return {'success': False, 'error': 'Failed to fetch model metadata'}
@@ -211,23 +221,22 @@ class DownloadManager:
             
             # Case 2: model_version_id was None, check after getting version_info
             if model_version_id is None:
-                version_model_id = version_info.get('modelId')
                 version_id = version_info.get('id')
                 
                 if model_type == 'lora':
                     # Check lora scanner
                     lora_scanner = await self._get_lora_scanner()
-                    if await lora_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await lora_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in lora library'}
                 elif model_type == 'checkpoint':
                     # Check checkpoint scanner
                     checkpoint_scanner = await self._get_checkpoint_scanner()
-                    if await checkpoint_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await checkpoint_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in checkpoint library'}
                 elif model_type == 'embedding':
                     # Embeddings are not checked in scanners, but we can still check if it exists
                     embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-                    if await embedding_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await embedding_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in embedding library'}
             
             # Handle use_default_paths
@@ -250,7 +259,7 @@ class DownloadManager:
                     save_dir = default_path
                     
                 # Calculate relative path using template
-                relative_path = self._calculate_relative_path(version_info)
+                relative_path = self._calculate_relative_path(version_info, model_type)
 
             # Update save directory with relative path if provided
             if relative_path:
@@ -266,9 +275,9 @@ class DownloadManager:
                     from datetime import datetime
                     date_obj = datetime.fromisoformat(early_access_date.replace('Z', '+00:00'))
                     formatted_date = date_obj.strftime('%Y-%m-%d')
-                    early_access_msg = f"This model requires early access payment (until {formatted_date}). "
+                    early_access_msg = f"This model requires payment (until {formatted_date}). "
                 except:
-                    early_access_msg = "This model requires early access payment. "
+                    early_access_msg = "This model requires payment. "
                 
                 early_access_msg += "Please ensure you have purchased early access and are logged in to Civitai."
                 logger.warning(f"Early access model detected: {version_info.get('name', 'Unknown')}")
@@ -285,6 +294,19 @@ class DownloadManager:
             file_info = next((f for f in version_info.get('files', []) if f.get('primary')), None)
             if not file_info:
                 return {'success': False, 'error': 'No primary file found in metadata'}
+            mirrors = file_info.get('mirrors') or []
+            download_urls = []
+            if mirrors:
+                for mirror in mirrors:
+                    if mirror.get('deletedAt') is None and mirror.get('url'):
+                        download_urls.append(mirror['url'])
+            else:
+                download_url = file_info.get('downloadUrl')
+                if download_url:
+                    download_urls.append(download_url)
+
+            if not download_urls:
+                return {'success': False, 'error': 'No download URL found for primary file'}
 
             # 3. Prepare download
             file_name = file_info['name']
@@ -303,7 +325,7 @@ class DownloadManager:
             
             # 6. Start download process
             result = await self._execute_download(
-                download_url=file_info.get('downloadUrl', ''),
+                download_urls=download_urls,
                 save_dir=save_dir,
                 metadata=metadata,
                 version_info=version_info,
@@ -313,6 +335,10 @@ class DownloadManager:
                 download_id=download_id
             )
 
+            # If early_access_msg exists and download failed, replace error message
+            if 'early_access_msg' in locals() and not result.get('success', False):
+                result['error'] = early_access_msg
+
             return result
 
         except Exception as e:
@@ -323,17 +349,18 @@ class DownloadManager:
                 return {'success': False, 'error': f"Early access restriction: {str(e)}. Please ensure you have purchased early access and are logged in to Civitai."}
             return {'success': False, 'error': str(e)}
 
-    def _calculate_relative_path(self, version_info: Dict) -> str:
+    def _calculate_relative_path(self, version_info: Dict, model_type: str = 'lora') -> str:
         """Calculate relative path using template from settings
         
         Args:
             version_info: Version info from Civitai API
+            model_type: Type of model ('lora', 'checkpoint', 'embedding')
             
         Returns:
             Relative path string
         """
-        # Get path template from settings, default to '{base_model}/{first_tag}'
-        path_template = settings.get('download_path_template', '{base_model}/{first_tag}')
+        # Get path template from settings for specific model type
+        path_template = settings.get_download_path_template(model_type)
         
         # If template is empty, return empty path (flat structure)
         if not path_template:
@@ -342,6 +369,13 @@ class DownloadManager:
         # Get base model name
         base_model = version_info.get('baseModel', '')
         
+        # Get author from creator data
+        creator_info = version_info.get('creator')
+        if creator_info and isinstance(creator_info, dict):
+            author = creator_info.get('username') or 'Anonymous'
+        else:
+            author = 'Anonymous'
+        
         # Apply mapping if available
         base_model_mappings = settings.get('base_model_path_mappings', {})
         mapped_base_model = base_model_mappings.get(base_model, base_model)
@@ -364,22 +398,52 @@ class DownloadManager:
         formatted_path = path_template
         formatted_path = formatted_path.replace('{base_model}', mapped_base_model)
         formatted_path = formatted_path.replace('{first_tag}', first_tag)
-        
+        formatted_path = formatted_path.replace('{author}', author)
+
+        if model_type == 'embedding':
+            formatted_path = formatted_path.replace(' ', '_')
+
         return formatted_path
 
-    async def _execute_download(self, download_url: str, save_dir: str, 
-                              metadata, version_info: Dict, 
-                              relative_path: str, progress_callback=None,
-                              model_type: str = "lora", download_id: str = None) -> Dict:
+    async def _execute_download(self, download_urls: List[str], save_dir: str,
+                          metadata, version_info: Dict,
+                          relative_path: str, progress_callback=None,
+                          model_type: str = "lora", download_id: str = None) -> Dict:
         """Execute the actual download process including preview images and model files"""
         try:
-            civitai_client = await self._get_civitai_client()
-            save_path = metadata.file_path
+            # Extract original filename details
+            original_filename = os.path.basename(metadata.file_path)
+            base_name, extension = os.path.splitext(original_filename)
+            
+            # Check for filename conflicts and generate unique filename if needed
+            # Use the hash from metadata for conflict resolution
+            def hash_provider():
+                return metadata.sha256
+            
+            unique_filename = metadata.generate_unique_filename(
+                save_dir, 
+                base_name, 
+                extension, 
+                hash_provider=hash_provider
+            )
+            
+            # Update paths if filename changed
+            if unique_filename != original_filename:
+                logger.info(f"Filename conflict detected. Changing '{original_filename}' to '{unique_filename}'")
+                save_path = os.path.join(save_dir, unique_filename)
+                # Update metadata with new file path and name
+                metadata.file_path = save_path.replace(os.sep, '/')
+                metadata.file_name = os.path.splitext(unique_filename)[0]
+            else:
+                save_path = metadata.file_path
+                
+            part_path = save_path + '.part'
             metadata_path = os.path.splitext(save_path)[0] + '.metadata.json'
             
-            # Store file path in active_downloads for potential cleanup
+            # Store file paths in active_downloads for potential cleanup
             if download_id and download_id in self._active_downloads:
                 self._active_downloads[download_id]['file_path'] = save_path
+                self._active_downloads[download_id]['part_path'] = part_path
 
             # Download preview image if available
             images = version_info.get('images', [])
@@ -396,8 +460,14 @@ class DownloadManager:
                     preview_ext = '.mp4'
                     preview_path = os.path.splitext(save_path)[0] + preview_ext
                     
-                    # Download video directly
-                    if await civitai_client.download_preview_image(images[0]['url'], preview_path):
+                    # Download video directly using downloader
+                    downloader = await get_downloader()
+                    success, result = await downloader.download_file(
+                        images[0]['url'], 
+                        preview_path, 
+                        use_auth=False  # Preview images typically don't need auth
+                    )
+                    if success:
                         metadata.preview_url = preview_path.replace(os.sep, '/')
                         metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
                 else:
@@ -405,8 +475,16 @@ class DownloadManager:
                     with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
                         temp_path = temp_file.name
                     
-                    # Download the original image to temp path
-                    if await civitai_client.download_preview_image(images[0]['url'], temp_path):
+                    # Download the original image to temp path using downloader
+                    downloader = await get_downloader()
+                    success, content, headers = await downloader.download_to_memory(
+                        images[0]['url'], 
+                        use_auth=False
+                    )
+                    if success:
+                        # Save to temp file
+                        with open(temp_path, 'wb') as f:
+                            f.write(content)
                         # Optimize and convert to WebP
                         preview_path = os.path.splitext(save_path)[0] + '.webp'
                         
@@ -437,26 +515,52 @@ class DownloadManager:
                 if progress_callback:
                     await progress_callback(3)  # 3% progress after preview download
 
-            # Download model file with progress tracking
-            success, result = await civitai_client._download_file(
-                download_url, 
-                save_dir,
-                os.path.basename(save_path),
-                progress_callback=lambda p: self._handle_download_progress(p, progress_callback)
-            )
+            # Download model file with progress tracking using downloader
+            downloader = await get_downloader()
+            last_error = None
+            for download_url in download_urls:
+                use_auth = download_url.startswith("https://civitai.com/api/download/")
+                success, result = await downloader.download_file(
+                    download_url,
+                    save_path,  # Use full path instead of separate dir and filename
+                    progress_callback=lambda p: self._handle_download_progress(p, progress_callback),
+                    use_auth=use_auth  # Only use authentication for Civitai downloads
+                )
 
-            if not success:
-                # Clean up files on failure
-                for path in [save_path, metadata_path, metadata.preview_url]:
+                if success:
+                    break
+
+                last_error = result
+                if os.path.exists(save_path):
+                    try:
+                        os.remove(save_path)
+                    except Exception as e:
+                        logger.warning(f"Failed to remove incomplete file {save_path}: {e}")
+            else:
+                # Clean up files on failure, but preserve .part file for resume
+                cleanup_files = [metadata_path]
+                preview_path_value = getattr(metadata, 'preview_url', None)
+                if preview_path_value and os.path.exists(preview_path_value):
+                    cleanup_files.append(preview_path_value)
+
+                for path in cleanup_files:
                     if path and os.path.exists(path):
-                        os.remove(path)
-                return {'success': False, 'error': result}
+                        try:
+                            os.remove(path)
+                        except Exception as e:
+                            logger.warning(f"Failed to cleanup file {path}: {e}")
+
+                # Log but don't remove .part file to allow resume
+                if os.path.exists(part_path):
+                    logger.info(f"Preserving partial download for resume: {part_path}")
+
+                return {'success': False, 'error': last_error or 'Failed to download file'}
 
             # 4. Update file information (size and modified time)
             metadata.update_file_info(save_path)
 
             # 5. Final metadata update
-            await MetadataManager.save_metadata(save_path, metadata, True)
+            await MetadataManager.save_metadata(save_path, metadata)
 
             # 6. Update cache based on model type
             if model_type == "checkpoint":
@@ -485,10 +589,18 @@ class DownloadManager:
 
         except Exception as e:
             logger.error(f"Error in _execute_download: {e}", exc_info=True)
-            # Clean up partial downloads
-            for path in [save_path, metadata_path]:
+            # Clean up partial downloads except .part file
+            cleanup_files = [metadata_path]
+            if hasattr(metadata, 'preview_url') and metadata.preview_url and os.path.exists(metadata.preview_url):
+                cleanup_files.append(metadata.preview_url)
+            
+            for path in cleanup_files:
                 if path and os.path.exists(path):
-                    os.remove(path)
+                    try:
+                        os.remove(path)
+                    except Exception as e:
+                        logger.warning(f"Failed to cleanup file {path}: {e}")
+            
             return {'success': False, 'error': str(e)}
 
     async def _handle_download_progress(self, file_progress: float, progress_callback):
@@ -530,35 +642,48 @@ class DownloadManager:
             except (asyncio.CancelledError, asyncio.TimeoutError):
                 pass
             
-            # Clean up partial downloads
+            # Clean up ALL files including .part when user cancels
             download_info = self._active_downloads.get(download_id)
-            if download_info and 'file_path' in download_info:
-                # Delete the partial file
-                file_path = download_info['file_path']
-                if os.path.exists(file_path):
-                    try:
-                        os.unlink(file_path)
-                        logger.debug(f"Deleted partial download: {file_path}")
-                    except Exception as e:
-                        logger.error(f"Error deleting partial file: {e}")
+            if download_info:
+                # Delete the main file
+                if 'file_path' in download_info:
+                    file_path = download_info['file_path']
+                    if os.path.exists(file_path):
+                        try:
+                            os.unlink(file_path)
+                            logger.debug(f"Deleted cancelled download: {file_path}")
+                        except Exception as e:
+                            logger.error(f"Error deleting file: {e}")
+                
+                # Delete the .part file (only on user cancellation)
+                if 'part_path' in download_info:
+                    part_path = download_info['part_path']
+                    if os.path.exists(part_path):
+                        try:
+                            os.unlink(part_path)
+                            logger.debug(f"Deleted partial download: {part_path}")
+                        except Exception as e:
+                            logger.error(f"Error deleting part file: {e}")
                 
                 # Delete metadata file if exists
-                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                if os.path.exists(metadata_path):
-                    try:
-                        os.unlink(metadata_path)
-                    except Exception as e:
-                        logger.error(f"Error deleting metadata file: {e}")
-
-                # Delete preview file if exists (.webp or .mp4)
-                for preview_ext in ['.webp', '.mp4']:
-                    preview_path = os.path.splitext(file_path)[0] + preview_ext
-                    if os.path.exists(preview_path):
+                if 'file_path' in download_info:
+                    file_path = download_info['file_path']
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                    if os.path.exists(metadata_path):
                         try:
-                            os.unlink(preview_path)
-                            logger.debug(f"Deleted preview file: {preview_path}")
+                            os.unlink(metadata_path)
                         except Exception as e:
-                            logger.error(f"Error deleting preview file: {e}")
+                            logger.error(f"Error deleting metadata file: {e}")
+
+                    # Delete preview file if exists (.webp or .mp4)
+                    for preview_ext in ['.webp', '.mp4']:
+                        preview_path = os.path.splitext(file_path)[0] + preview_ext
+                        if os.path.exists(preview_path):
+                            try:
+                                os.unlink(preview_path)
+                                logger.debug(f"Deleted preview file: {preview_path}")
+                            except Exception as e:
+                                logger.error(f"Error deleting preview file: {e}")
             
             return {'success': True, 'message': 'Download cancelled successfully'}
         except Exception as e:

py/services/downloader.py

@@ -0,0 +1,539 @@
+"""
+Unified download manager for all HTTP/HTTPS downloads in the application.
+
+This module provides a centralized download service with:
+- Singleton pattern for global session management
+- Support for authenticated downloads (e.g., CivitAI API key)
+- Resumable downloads with automatic retry
+- Progress tracking and callbacks
+- Optimized connection pooling and timeouts
+- Unified error handling and logging
+"""
+
+import os
+import logging
+import asyncio
+import aiohttp
+from datetime import datetime
+from typing import Optional, Dict, Tuple, Callable, Union
+from ..services.settings_manager import settings
+
+logger = logging.getLogger(__name__)
+
+
+class Downloader:
+    """Unified downloader for all HTTP/HTTPS downloads in the application."""
+    
+    _instance = None
+    _lock = asyncio.Lock()
+    
+    @classmethod
+    async def get_instance(cls):
+        """Get singleton instance of Downloader"""
+        async with cls._lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+    
+    def __init__(self):
+        """Initialize the downloader with optimal settings"""
+        # Check if already initialized for singleton pattern
+        if hasattr(self, '_initialized'):
+            return
+        self._initialized = True
+        
+        # Session management
+        self._session = None
+        self._session_created_at = None
+        self._proxy_url = None  # Store proxy URL for current session
+        
+        # Configuration
+        self.chunk_size = 4 * 1024 * 1024  # 4MB chunks for better throughput
+        self.max_retries = 5
+        self.base_delay = 2.0  # Base delay for exponential backoff
+        self.session_timeout = 300  # 5 minutes
+        
+        # Default headers
+        self.default_headers = {
+            'User-Agent': 'ComfyUI-LoRA-Manager/1.0'
+        }
+    
+    @property
+    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()
+        return self._session
+    
+    @property
+    def proxy_url(self) -> Optional[str]:
+        """Get the current proxy URL (initialize if needed)"""
+        if not hasattr(self, '_proxy_url'):
+            self._proxy_url = None
+        return self._proxy_url
+    
+    def _should_refresh_session(self) -> bool:
+        """Check if session should be refreshed"""
+        if self._session is None:
+            return True
+        
+        if not hasattr(self, '_session_created_at') or self._session_created_at is None:
+            return True
+        
+        # Refresh if session is older than timeout
+        if (datetime.now() - self._session_created_at).total_seconds() > self.session_timeout:
+            return True
+        
+        return False
+    
+    async def _create_session(self):
+        """Create a new aiohttp session with optimized settings"""
+        # Close existing session if any
+        if self._session is not None:
+            await self._session.close()
+        
+        # Check for app-level proxy settings
+        proxy_url = None
+        if settings.get('proxy_enabled', False):
+            proxy_host = settings.get('proxy_host', '').strip()
+            proxy_port = settings.get('proxy_port', '').strip()
+            proxy_type = settings.get('proxy_type', 'http').lower()
+            proxy_username = settings.get('proxy_username', '').strip()
+            proxy_password = settings.get('proxy_password', '').strip()
+            
+            if proxy_host and proxy_port:
+                # Build proxy URL
+                if proxy_username and proxy_password:
+                    proxy_url = f"{proxy_type}://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}"
+                else:
+                    proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
+                
+                logger.debug(f"Using app-level proxy: {proxy_type}://{proxy_host}:{proxy_port}")
+                logger.debug("Proxy mode: app-level proxy is active.")
+        else:
+            logger.debug("Proxy mode: system-level proxy (trust_env) will be used if configured in environment.")
+        # Optimize TCP connection parameters
+        connector = aiohttp.TCPConnector(
+            ssl=True,
+            limit=8,  # Concurrent connections
+            ttl_dns_cache=300,  # DNS cache timeout
+            force_close=False,  # Keep connections for reuse
+            enable_cleanup_closed=True
+        )
+        
+        # Configure timeout parameters
+        timeout = aiohttp.ClientTimeout(
+            total=None,  # No total timeout for large downloads
+            connect=60,  # Connection timeout
+            sock_read=300  # 5 minute socket read timeout
+        )
+        
+        self._session = aiohttp.ClientSession(
+            connector=connector,
+            trust_env=proxy_url is None,  # Only use system proxy if no app-level proxy is set
+            timeout=timeout
+        )
+        
+        # Store proxy URL for use in requests
+        self._proxy_url = proxy_url
+        self._session_created_at = datetime.now()
+        
+        logger.debug("Created new HTTP session with proxy settings. App-level proxy: %s, System-level proxy (trust_env): %s", bool(proxy_url), proxy_url is None)
+    
+    def _get_auth_headers(self, use_auth: bool = False) -> Dict[str, str]:
+        """Get headers with optional authentication"""
+        headers = self.default_headers.copy()
+        
+        if use_auth:
+            # Add CivitAI API key if available
+            api_key = settings.get('civitai_api_key')
+            if api_key:
+                headers['Authorization'] = f'Bearer {api_key}'
+                headers['Content-Type'] = 'application/json'
+        
+        return headers
+    
+    async def download_file(
+        self,
+        url: str,
+        save_path: str,
+        progress_callback: Optional[Callable[[float], None]] = None,
+        use_auth: bool = False,
+        custom_headers: Optional[Dict[str, str]] = None,
+        allow_resume: bool = True
+    ) -> Tuple[bool, str]:
+        """
+        Download a file with resumable downloads and retry mechanism
+        
+        Args:
+            url: Download URL
+            save_path: Full path where the file should be saved
+            progress_callback: Optional callback for progress updates (0-100)
+            use_auth: Whether to include authentication headers (e.g., CivitAI API key)
+            custom_headers: Additional headers to include in request
+            allow_resume: Whether to support resumable downloads
+            
+        Returns:
+            Tuple[bool, str]: (success, save_path or error message)
+        """
+        retry_count = 0
+        part_path = save_path + '.part' if allow_resume else save_path
+        
+        # Prepare headers
+        headers = self._get_auth_headers(use_auth)
+        if custom_headers:
+            headers.update(custom_headers)
+        
+        # Get existing file size for resume
+        resume_offset = 0
+        if allow_resume and os.path.exists(part_path):
+            resume_offset = os.path.getsize(part_path)
+            logger.info(f"Resuming download from offset {resume_offset} bytes")
+        
+        total_size = 0
+        
+        while retry_count <= self.max_retries:
+            try:
+                session = await self.session
+                # Debug log for proxy mode at request time
+                if self.proxy_url:
+                    logger.debug(f"[download_file] Using app-level proxy: {self.proxy_url}")
+                else:
+                    logger.debug("[download_file] Using system-level proxy (trust_env) if configured.")
+                
+                # Add Range header for resume if we have partial data
+                request_headers = headers.copy()
+                if allow_resume and resume_offset > 0:
+                    request_headers['Range'] = f'bytes={resume_offset}-'
+                
+                # Disable compression for better chunked downloads
+                request_headers['Accept-Encoding'] = 'identity'
+                
+                logger.debug(f"Download attempt {retry_count + 1}/{self.max_retries + 1} from: {url}")
+                if resume_offset > 0:
+                    logger.debug(f"Requesting range from byte {resume_offset}")
+                
+                async with session.get(url, headers=request_headers, allow_redirects=True, proxy=self.proxy_url) as response:
+                    # Handle different response codes
+                    if response.status == 200:
+                        # Full content response
+                        if resume_offset > 0:
+                            # Server doesn't support ranges, restart from beginning
+                            logger.warning("Server doesn't support range requests, restarting download")
+                            resume_offset = 0
+                            if os.path.exists(part_path):
+                                os.remove(part_path)
+                    elif response.status == 206:
+                        # Partial content response (resume successful)
+                        content_range = response.headers.get('Content-Range')
+                        if content_range:
+                            # Parse total size from Content-Range header (e.g., "bytes 1024-2047/2048")
+                            range_parts = content_range.split('/')
+                            if len(range_parts) == 2:
+                                total_size = int(range_parts[1])
+                        logger.info(f"Successfully resumed download from byte {resume_offset}")
+                    elif response.status == 416:
+                        # Range not satisfiable - file might be complete or corrupted
+                        if allow_resume and os.path.exists(part_path):
+                            part_size = os.path.getsize(part_path)
+                            logger.warning(f"Range not satisfiable. Part file size: {part_size}")
+                            # Try to get actual file size
+                            head_response = await session.head(url, headers=headers, proxy=self.proxy_url)
+                            if head_response.status == 200:
+                                actual_size = int(head_response.headers.get('content-length', 0))
+                                if part_size == actual_size:
+                                    # File is complete, just rename it
+                                    if allow_resume:
+                                        os.rename(part_path, save_path)
+                                    if progress_callback:
+                                        await progress_callback(100)
+                                    return True, save_path
+                            # Remove corrupted part file and restart
+                            os.remove(part_path)
+                            resume_offset = 0
+                            continue
+                    elif response.status == 401:
+                        logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
+                        return False, "Invalid or missing API key, or early access restriction."
+                    elif response.status == 403:
+                        logger.warning(f"Forbidden access to resource: {url} (Status 403)")
+                        return False, "Access forbidden: You don't have permission to download this file."
+                    elif response.status == 404:
+                        logger.warning(f"Resource not found: {url} (Status 404)")
+                        return False, "File not found - the download link may be invalid or expired."
+                    else:
+                        logger.error(f"Download failed for {url} with status {response.status}")
+                        return False, f"Download failed with status {response.status}"
+                    
+                    # Get total file size for progress calculation (if not set from Content-Range)
+                    if total_size == 0:
+                        total_size = int(response.headers.get('content-length', 0))
+                        if response.status == 206:
+                            # For partial content, add the offset to get total file size
+                            total_size += resume_offset
+                    
+                    current_size = resume_offset
+                    last_progress_report_time = datetime.now()
+                    
+                    # Ensure directory exists
+                    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+                    
+                    # Stream download to file with progress updates
+                    loop = asyncio.get_running_loop()
+                    mode = 'ab' if (allow_resume and resume_offset > 0) else 'wb'
+                    with open(part_path, mode) as f:
+                        async for chunk in response.content.iter_chunked(self.chunk_size):
+                            if chunk:
+                                # Run blocking file write in executor
+                                await loop.run_in_executor(None, f.write, chunk)
+                                current_size += len(chunk)
+                                
+                                # Limit progress update frequency to reduce overhead
+                                now = datetime.now()
+                                time_diff = (now - last_progress_report_time).total_seconds()
+                                
+                                if progress_callback and total_size and time_diff >= 1.0:
+                                    progress = (current_size / total_size) * 100
+                                    await progress_callback(progress)
+                                    last_progress_report_time = now
+                    
+                    # Download completed successfully
+                    # Verify file size if total_size was provided
+                    final_size = os.path.getsize(part_path)
+                    if total_size > 0 and final_size != total_size:
+                        logger.warning(f"File size mismatch. Expected: {total_size}, Got: {final_size}")
+                        # Don't treat this as fatal error, continue anyway
+                    
+                    # Atomically rename .part to final file (only if using resume)
+                    if allow_resume and part_path != save_path:
+                        max_rename_attempts = 5
+                        rename_attempt = 0
+                        rename_success = False
+                        
+                        while rename_attempt < max_rename_attempts and not rename_success:
+                            try:
+                                # If the destination file exists, remove it first (Windows safe)
+                                if os.path.exists(save_path):
+                                    os.remove(save_path)
+
+                                os.rename(part_path, save_path)
+                                rename_success = True
+                            except PermissionError as e:
+                                rename_attempt += 1
+                                if rename_attempt < max_rename_attempts:
+                                    logger.info(f"File still in use, retrying rename in 2 seconds (attempt {rename_attempt}/{max_rename_attempts})")
+                                    await asyncio.sleep(2)
+                                else:
+                                    logger.error(f"Failed to rename file after {max_rename_attempts} attempts: {e}")
+                                    return False, f"Failed to finalize download: {str(e)}"
+                    
+                    # Ensure 100% progress is reported
+                    if progress_callback:
+                        await progress_callback(100)
+                    
+                    return True, save_path
+                    
+            except (aiohttp.ClientError, aiohttp.ClientPayloadError,
+                    aiohttp.ServerDisconnectedError, asyncio.TimeoutError) as e:
+                retry_count += 1
+                logger.warning(f"Network error during download (attempt {retry_count}/{self.max_retries + 1}): {e}")
+                
+                if retry_count <= self.max_retries:
+                    # Calculate delay with exponential backoff
+                    delay = self.base_delay * (2 ** (retry_count - 1))
+                    logger.info(f"Retrying in {delay} seconds...")
+                    await asyncio.sleep(delay)
+                    
+                    # Update resume offset for next attempt
+                    if allow_resume and os.path.exists(part_path):
+                        resume_offset = os.path.getsize(part_path)
+                        logger.info(f"Will resume from byte {resume_offset}")
+                    
+                    # Refresh session to get new connection
+                    await self._create_session()
+                    continue
+                else:
+                    logger.error(f"Max retries exceeded for download: {e}")
+                    return False, f"Network error after {self.max_retries + 1} attempts: {str(e)}"
+                    
+            except Exception as e:
+                logger.error(f"Unexpected download error: {e}")
+                return False, str(e)
+        
+        return False, f"Download failed after {self.max_retries + 1} attempts"
+    
+    async def download_to_memory(
+        self,
+        url: str,
+        use_auth: bool = False,
+        custom_headers: Optional[Dict[str, str]] = None,
+        return_headers: bool = False
+    ) -> Tuple[bool, Union[bytes, str], Optional[Dict]]:
+        """
+        Download a file to memory (for small files like preview images)
+        
+        Args:
+            url: Download URL
+            use_auth: Whether to include authentication headers
+            custom_headers: Additional headers to include in request
+            return_headers: Whether to return response headers along with content
+            
+        Returns:
+            Tuple[bool, Union[bytes, str], Optional[Dict]]: (success, content or error message, response headers if requested)
+        """
+        try:
+            session = await self.session
+            # Debug log for proxy mode at request time
+            if self.proxy_url:
+                logger.debug(f"[download_to_memory] Using app-level proxy: {self.proxy_url}")
+            else:
+                logger.debug("[download_to_memory] Using system-level proxy (trust_env) if configured.")
+            
+            # Prepare headers
+            headers = self._get_auth_headers(use_auth)
+            if custom_headers:
+                headers.update(custom_headers)
+            
+            async with session.get(url, headers=headers, proxy=self.proxy_url) as response:
+                if response.status == 200:
+                    content = await response.read()
+                    if return_headers:
+                        return True, content, dict(response.headers)
+                    else:
+                        return True, content, None
+                elif response.status == 401:
+                    error_msg = "Unauthorized access - invalid or missing API key"
+                    return False, error_msg, None
+                elif response.status == 403:
+                    error_msg = "Access forbidden"
+                    return False, error_msg, None
+                elif response.status == 404:
+                    error_msg = "File not found"
+                    return False, error_msg, None
+                else:
+                    error_msg = f"Download failed with status {response.status}"
+                    return False, error_msg, None
+                    
+        except Exception as e:
+            logger.error(f"Error downloading to memory from {url}: {e}")
+            return False, str(e), None
+    
+    async def get_response_headers(
+        self,
+        url: str,
+        use_auth: bool = False,
+        custom_headers: Optional[Dict[str, str]] = None
+    ) -> Tuple[bool, Union[Dict, str]]:
+        """
+        Get response headers without downloading the full content
+        
+        Args:
+            url: URL to check
+            use_auth: Whether to include authentication headers
+            custom_headers: Additional headers to include in request
+            
+        Returns:
+            Tuple[bool, Union[Dict, str]]: (success, headers dict or error message)
+        """
+        try:
+            session = await self.session
+            # Debug log for proxy mode at request time
+            if self.proxy_url:
+                logger.debug(f"[get_response_headers] Using app-level proxy: {self.proxy_url}")
+            else:
+                logger.debug("[get_response_headers] Using system-level proxy (trust_env) if configured.")
+            
+            # Prepare headers
+            headers = self._get_auth_headers(use_auth)
+            if custom_headers:
+                headers.update(custom_headers)
+            
+            async with session.head(url, headers=headers, proxy=self.proxy_url) as response:
+                if response.status == 200:
+                    return True, dict(response.headers)
+                else:
+                    return False, f"Head request failed with status {response.status}"
+                    
+        except Exception as e:
+            logger.error(f"Error getting headers from {url}: {e}")
+            return False, str(e)
+    
+    async def make_request(
+        self,
+        method: str,
+        url: str,
+        use_auth: bool = False,
+        custom_headers: Optional[Dict[str, str]] = None,
+        **kwargs
+    ) -> Tuple[bool, Union[Dict, str]]:
+        """
+        Make a generic HTTP request and return JSON response
+        
+        Args:
+            method: HTTP method (GET, POST, etc.)
+            url: Request URL
+            use_auth: Whether to include authentication headers
+            custom_headers: Additional headers to include in request
+            **kwargs: Additional arguments for aiohttp request
+            
+        Returns:
+            Tuple[bool, Union[Dict, str]]: (success, response data or error message)
+        """
+        try:
+            session = await self.session
+            # Debug log for proxy mode at request time
+            if self.proxy_url:
+                logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
+            else:
+                logger.debug("[make_request] Using system-level proxy (trust_env) if configured.")
+            
+            # Prepare headers
+            headers = self._get_auth_headers(use_auth)
+            if custom_headers:
+                headers.update(custom_headers)
+            
+            # Add proxy to kwargs if not already present
+            if 'proxy' not in kwargs:
+                kwargs['proxy'] = self.proxy_url
+            
+            async with session.request(method, url, headers=headers, **kwargs) as response:
+                if response.status == 200:
+                    # Try to parse as JSON, fall back to text
+                    try:
+                        data = await response.json()
+                        return True, data
+                    except:
+                        text = await response.text()
+                        return True, text
+                elif response.status == 401:
+                    return False, "Unauthorized access - invalid or missing API key"
+                elif response.status == 403:
+                    return False, "Access forbidden"
+                elif response.status == 404:
+                    return False, "Resource not found"
+                else:
+                    return False, f"Request failed with status {response.status}"
+                    
+        except Exception as e:
+            logger.error(f"Error making {method} request to {url}: {e}")
+            return False, str(e)
+    
+    async def close(self):
+        """Close the HTTP session"""
+        if self._session is not None:
+            await self._session.close()
+            self._session = None
+            self._session_created_at = None
+            self._proxy_url = None
+            logger.debug("Closed HTTP session")
+    
+    async def refresh_session(self):
+        """Force refresh the HTTP session (useful when proxy settings change)"""
+        await self._create_session()
+        logger.info("HTTP session refreshed due to settings change")
+
+
+# Global instance accessor
+async def get_downloader() -> Downloader:
+    """Get the global downloader instance"""
+    return await Downloader.get_instance()

py/services/embedding_service.py

@@ -1,11 +1,10 @@
 import os
 import logging
-from typing import Dict, List, Optional
+from typing import Dict
 
 from .base_model_service import BaseModelService
 from ..utils.models import EmbeddingMetadata
 from ..config import config
-from ..utils.routes_common import ModelRouteUtils
 
 logger = logging.getLogger(__name__)
 
@@ -34,12 +33,11 @@ class EmbeddingService(BaseModelService):
             "file_size": embedding_data.get("size", 0),
             "modified": embedding_data.get("modified", ""),
             "tags": embedding_data.get("tags", []),
-            "modelDescription": embedding_data.get("modelDescription", ""),
             "from_civitai": embedding_data.get("from_civitai", True),
             "notes": embedding_data.get("notes", ""),
             "model_type": embedding_data.get("model_type", "embedding"),
             "favorite": embedding_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(embedding_data.get("civitai", {}))
+            "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True)
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/example_images_cleanup_service.py

@@ -0,0 +1,296 @@
+"""Service for cleaning up example image folders."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import shutil
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+from .service_registry import ServiceRegistry
+from .settings_manager import settings
+from ..utils.example_images_paths import iter_library_roots
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(slots=True)
+class CleanupResult:
+    """Structured result returned from cleanup operations."""
+
+    success: bool
+    checked_folders: int
+    moved_empty_folders: int
+    moved_orphaned_folders: int
+    skipped_non_hash: int
+    move_failures: int
+    errors: List[str]
+    deleted_root: str | None
+    partial_success: bool
+
+    def to_dict(self) -> Dict[str, object]:
+        """Convert the dataclass to a serialisable dictionary."""
+
+        data = {
+            "success": self.success,
+            "checked_folders": self.checked_folders,
+            "moved_empty_folders": self.moved_empty_folders,
+            "moved_orphaned_folders": self.moved_orphaned_folders,
+            "moved_total": self.moved_empty_folders + self.moved_orphaned_folders,
+            "skipped_non_hash": self.skipped_non_hash,
+            "move_failures": self.move_failures,
+            "errors": self.errors,
+            "deleted_root": self.deleted_root,
+            "partial_success": self.partial_success,
+        }
+
+        return data
+
+
+class ExampleImagesCleanupService:
+    """Encapsulates logic for cleaning example image folders."""
+
+    DELETED_FOLDER_NAME = "_deleted"
+
+    def __init__(self, deleted_folder_name: str | None = None) -> None:
+        self._deleted_folder_name = deleted_folder_name or self.DELETED_FOLDER_NAME
+
+    async def cleanup_example_image_folders(self) -> Dict[str, object]:
+        """Clean empty or orphaned example image folders by moving them under a deleted bucket."""
+
+        example_images_path = settings.get("example_images_path")
+        if not example_images_path:
+            logger.debug("Cleanup skipped: example images path not configured")
+            return {
+                "success": False,
+                "error": "Example images path is not configured.",
+                "error_code": "path_not_configured",
+            }
+
+        base_root = Path(example_images_path)
+        if not base_root.exists():
+            logger.debug("Cleanup skipped: example images path missing -> %s", base_root)
+            return {
+                "success": False,
+                "error": "Example images path does not exist.",
+                "error_code": "path_not_found",
+            }
+
+        try:
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.error("Failed to acquire scanners for cleanup: %s", exc, exc_info=True)
+            return {
+                "success": False,
+                "error": f"Failed to load model scanners: {exc}",
+                "error_code": "scanner_initialization_failed",
+            }
+
+        checked_folders = 0
+        moved_empty = 0
+        moved_orphaned = 0
+        skipped_non_hash = 0
+        move_failures = 0
+        errors: List[str] = []
+
+        resolved_base = base_root.resolve()
+        library_paths: List[Tuple[str, Path]] = []
+        processed_paths = {resolved_base}
+
+        for library_name, library_path in iter_library_roots():
+            if not library_path:
+                continue
+            library_root = Path(library_path)
+            try:
+                resolved = library_root.resolve()
+            except FileNotFoundError:
+                continue
+            if resolved in processed_paths:
+                continue
+            if not library_root.exists():
+                logger.debug(
+                    "Skipping cleanup for library '%s': folder missing (%s)",
+                    library_name,
+                    library_root,
+                )
+                continue
+            processed_paths.add(resolved)
+            library_paths.append((library_name, library_root))
+
+        deleted_roots: List[Path] = []
+
+        # Build list of (label, root) pairs including the base root for legacy layouts
+        cleanup_targets: List[Tuple[str, Path]] = [("__base__", base_root)] + library_paths
+
+        library_root_set = {root.resolve() for _, root in library_paths}
+
+        for label, root_path in cleanup_targets:
+            deleted_bucket = root_path / self._deleted_folder_name
+            deleted_bucket.mkdir(exist_ok=True)
+            deleted_roots.append(deleted_bucket)
+
+            for entry in os.scandir(root_path):
+                if not entry.is_dir(follow_symlinks=False):
+                    continue
+
+                if entry.name == self._deleted_folder_name:
+                    continue
+
+                entry_path = Path(entry.path)
+
+                if label == "__base__":
+                    try:
+                        resolved_entry = entry_path.resolve()
+                    except FileNotFoundError:
+                        continue
+                    if resolved_entry in library_root_set:
+                        # Skip library-specific folders tracked separately
+                        continue
+
+                checked_folders += 1
+
+                try:
+                    if self._is_folder_empty(entry_path):
+                        if await self._remove_empty_folder(entry_path):
+                            moved_empty += 1
+                        else:
+                            move_failures += 1
+                        continue
+
+                    if not self._is_hash_folder(entry.name):
+                        skipped_non_hash += 1
+                        continue
+
+                    hash_exists = (
+                        lora_scanner.has_hash(entry.name)
+                        or checkpoint_scanner.has_hash(entry.name)
+                        or embedding_scanner.has_hash(entry.name)
+                    )
+
+                    if not hash_exists:
+                        if await self._move_folder(entry_path, deleted_bucket):
+                            moved_orphaned += 1
+                        else:
+                            move_failures += 1
+
+                except Exception as exc:  # pragma: no cover - filesystem guard
+                    move_failures += 1
+                    error_message = f"{entry.name}: {exc}"
+                    errors.append(error_message)
+                    logger.error(
+                        "Error processing example images folder %s: %s",
+                        entry_path,
+                        exc,
+                        exc_info=True,
+                    )
+
+        partial_success = move_failures > 0 and (moved_empty > 0 or moved_orphaned > 0)
+        success = move_failures == 0 and not errors
+
+        result = CleanupResult(
+            success=success,
+            checked_folders=checked_folders,
+            moved_empty_folders=moved_empty,
+            moved_orphaned_folders=moved_orphaned,
+            skipped_non_hash=skipped_non_hash,
+            move_failures=move_failures,
+            errors=errors,
+            deleted_root=str(deleted_roots[0]) if deleted_roots else None,
+            partial_success=partial_success,
+        )
+
+        summary = result.to_dict()
+        summary["deleted_roots"] = [str(path) for path in deleted_roots]
+        if success:
+            logger.info(
+                "Example images cleanup complete: checked=%s, moved_empty=%s, moved_orphaned=%s",
+                checked_folders,
+                moved_empty,
+                moved_orphaned,
+            )
+        elif partial_success:
+            logger.warning(
+                "Example images cleanup partially complete: moved=%s, failures=%s",
+                summary["moved_total"],
+                move_failures,
+            )
+        else:
+            logger.error(
+                "Example images cleanup failed: move_failures=%s, errors=%s",
+                move_failures,
+                errors,
+            )
+
+        return summary
+
+    @staticmethod
+    def _is_folder_empty(folder_path: Path) -> bool:
+        try:
+            with os.scandir(folder_path) as iterator:
+                return not any(iterator)
+        except FileNotFoundError:
+            return True
+        except OSError as exc:  # pragma: no cover - defensive guard
+            logger.debug("Failed to inspect folder %s: %s", folder_path, exc)
+            return False
+
+    @staticmethod
+    def _is_hash_folder(name: str) -> bool:
+        if len(name) != 64:
+            return False
+        hex_chars = set("0123456789abcdefABCDEF")
+        return all(char in hex_chars for char in name)
+
+    async def _remove_empty_folder(self, folder_path: Path) -> bool:
+        loop = asyncio.get_running_loop()
+
+        try:
+            await loop.run_in_executor(
+                None,
+                shutil.rmtree,
+                str(folder_path),
+            )
+            logger.debug("Removed empty example images folder %s", folder_path)
+            return True
+        except Exception as exc:  # pragma: no cover - filesystem guard
+            logger.error("Failed to remove empty example images folder %s: %s", folder_path, exc, exc_info=True)
+            return False
+
+    async def _move_folder(self, folder_path: Path, deleted_bucket: Path) -> bool:
+        destination = self._build_destination(folder_path.name, deleted_bucket)
+        loop = asyncio.get_running_loop()
+
+        try:
+            await loop.run_in_executor(
+                None,
+                shutil.move,
+                str(folder_path),
+                str(destination),
+            )
+            logger.debug("Moved example images folder %s -> %s", folder_path, destination)
+            return True
+        except Exception as exc:  # pragma: no cover - filesystem guard
+            logger.error(
+                "Failed to move example images folder %s to %s: %s",
+                folder_path,
+                destination,
+                exc,
+                exc_info=True,
+            )
+            return False
+
+    def _build_destination(self, folder_name: str, deleted_bucket: Path) -> Path:
+        destination = deleted_bucket / folder_name
+        suffix = 1
+
+        while destination.exists():
+            destination = deleted_bucket / f"{folder_name}_{suffix}"
+            suffix += 1
+
+        return destination

py/services/lora_service.py

@@ -5,7 +5,6 @@ from typing import Dict, List, Optional
 from .base_model_service import BaseModelService
 from ..utils.models import LoraMetadata
 from ..config import config
-from ..utils.routes_common import ModelRouteUtils
 
 logger = logging.getLogger(__name__)
 
@@ -34,12 +33,11 @@ class LoraService(BaseModelService):
             "file_size": lora_data.get("size", 0),
             "modified": lora_data.get("modified", ""),
             "tags": lora_data.get("tags", []),
-            "modelDescription": lora_data.get("modelDescription", ""),
             "from_civitai": lora_data.get("from_civitai", True),
             "usage_tips": lora_data.get("usage_tips", ""),
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(lora_data.get("civitai", {}))
+            "civitai": self.filter_civitai_data(lora_data.get("civitai", {}), minimal=True)
         }
     
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
@@ -147,16 +145,6 @@ class LoraService(BaseModelService):
                 
         return letters
     
-    async def get_lora_notes(self, lora_name: str) -> Optional[str]:
-        """Get notes for a specific LoRA file"""
-        cache = await self.scanner.get_cached_data()
-        
-        for lora in cache.raw_data:
-            if lora['file_name'] == lora_name:
-                return lora.get('notes', '')
-        
-        return None
-    
     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()
@@ -168,41 +156,22 @@ class LoraService(BaseModelService):
         
         return []
     
-    async def get_lora_preview_url(self, lora_name: str) -> Optional[str]:
-        """Get the static preview URL for a LoRA file"""
+    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:
-            if lora['file_name'] == lora_name:
-                preview_url = lora.get('preview_url')
-                if preview_url:
-                    return config.get_preview_static_url(preview_url)
+            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('\\', '/')
+                # 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', '')
         
         return None
     
-    async def get_lora_civitai_url(self, lora_name: str) -> Dict[str, Optional[str]]:
-        """Get the Civitai URL for a 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', {})
-                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
-                    }
-        
-        return {'civitai_url': None, 'model_id': None, 'version_id': None}
-    
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()

py/services/metadata_archive_manager.py

@@ -0,0 +1,151 @@
+import zipfile
+import logging
+import asyncio
+from pathlib import Path
+from typing import Optional
+from .downloader import get_downloader
+
+logger = logging.getLogger(__name__)
+
+class MetadataArchiveManager:
+    """Manages downloading and extracting Civitai metadata archive database"""
+    
+    DOWNLOAD_URLS = [
+        "https://github.com/willmiao/civitai-metadata-archive-db/releases/download/db-2025-08-08/civitai.zip",
+        "https://huggingface.co/datasets/willmiao/civitai-metadata-archive-db/blob/main/civitai.zip"
+    ]
+    
+    def __init__(self, base_path: str):
+        """Initialize with base path where files will be stored"""
+        self.base_path = Path(base_path)
+        self.civitai_folder = self.base_path / "civitai"
+        self.archive_path = self.base_path / "civitai.zip"
+        self.db_path = self.civitai_folder / "civitai.sqlite"
+        
+    def is_database_available(self) -> bool:
+        """Check if the SQLite database is available and valid"""
+        return self.db_path.exists() and self.db_path.stat().st_size > 0
+        
+    def get_database_path(self) -> Optional[str]:
+        """Get the path to the SQLite database if available"""
+        if self.is_database_available():
+            return str(self.db_path)
+        return None
+        
+    async def download_and_extract_database(self, progress_callback=None) -> bool:
+        """Download and extract the metadata archive database
+        
+        Args:
+            progress_callback: Optional callback function to report progress
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create directories if they don't exist
+            self.base_path.mkdir(parents=True, exist_ok=True)
+            self.civitai_folder.mkdir(parents=True, exist_ok=True)
+            
+            # Download the archive
+            if not await self._download_archive(progress_callback):
+                return False
+                
+            # Extract the archive
+            if not await self._extract_archive(progress_callback):
+                return False
+                
+            # Clean up the archive file
+            if self.archive_path.exists():
+                self.archive_path.unlink()
+                
+            logger.info(f"Successfully downloaded and extracted metadata database to {self.db_path}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error downloading and extracting metadata database: {e}", exc_info=True)
+            return False
+            
+    async def _download_archive(self, progress_callback=None) -> bool:
+        """Download the zip archive from one of the available URLs"""
+        downloader = await get_downloader()
+        
+        for url in self.DOWNLOAD_URLS:
+            try:
+                logger.info(f"Attempting to download from {url}")
+                
+                if progress_callback:
+                    progress_callback("download", f"Downloading from {url}")
+                
+                # Custom progress callback to report download progress
+                async def download_progress(progress):
+                    if progress_callback:
+                        progress_callback("download", f"Downloading archive... {progress:.1f}%")
+                
+                success, result = await downloader.download_file(
+                    url=url,
+                    save_path=str(self.archive_path),
+                    progress_callback=download_progress,
+                    use_auth=False,  # Public download, no auth needed
+                    allow_resume=True
+                )
+                
+                if success:
+                    logger.info(f"Successfully downloaded archive from {url}")
+                    return True
+                else:
+                    logger.warning(f"Failed to download from {url}: {result}")
+                    continue
+                            
+            except Exception as e:
+                logger.warning(f"Error downloading from {url}: {e}")
+                continue
+                
+        logger.error("Failed to download archive from any URL")
+        return False
+        
+    async def _extract_archive(self, progress_callback=None) -> bool:
+        """Extract the zip archive to the civitai folder"""
+        try:
+            if progress_callback:
+                progress_callback("extract", "Extracting archive...")
+                
+            # Run extraction in thread pool to avoid blocking
+            loop = asyncio.get_event_loop()
+            await loop.run_in_executor(None, self._extract_zip_sync)
+            
+            if progress_callback:
+                progress_callback("extract", "Extraction completed")
+                
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error extracting archive: {e}", exc_info=True)
+            return False
+            
+    def _extract_zip_sync(self):
+        """Synchronous zip extraction (runs in thread pool)"""
+        with zipfile.ZipFile(self.archive_path, 'r') as archive:
+            archive.extractall(path=self.base_path)
+            
+    async def remove_database(self) -> bool:
+        """Remove the metadata database and folder"""
+        try:
+            if self.civitai_folder.exists():
+                # Remove all files in the civitai folder
+                for file_path in self.civitai_folder.iterdir():
+                    if file_path.is_file():
+                        file_path.unlink()
+                        
+                # Remove the folder itself
+                self.civitai_folder.rmdir()
+                
+            # Also remove the archive file if it exists
+            if self.archive_path.exists():
+                self.archive_path.unlink()
+                
+            logger.info("Successfully removed metadata database")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error removing metadata database: {e}", exc_info=True)
+            return False

py/services/metadata_service.py

@@ -0,0 +1,117 @@
+import os
+import logging
+from .model_metadata_provider import (
+    ModelMetadataProviderManager, 
+    SQLiteModelMetadataProvider,
+    CivitaiModelMetadataProvider,
+    FallbackMetadataProvider
+)
+from .settings_manager import settings
+from .metadata_archive_manager import MetadataArchiveManager
+from .service_registry import ServiceRegistry
+
+logger = logging.getLogger(__name__)
+
+async def initialize_metadata_providers():
+    """Initialize and configure all metadata providers based on settings"""
+    provider_manager = await ModelMetadataProviderManager.get_instance()
+    
+    # Clear existing providers to allow reinitialization
+    provider_manager.providers.clear()
+    provider_manager.default_provider = None
+    
+    # Get settings
+    enable_archive_db = settings.get('enable_metadata_archive_db', False)
+    
+    providers = []
+    
+    # Initialize archive database provider if enabled
+    if enable_archive_db:
+        try:
+            # Initialize archive manager
+            base_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+            archive_manager = MetadataArchiveManager(base_path)
+            
+            db_path = archive_manager.get_database_path()
+            if db_path and os.path.exists(db_path):
+                sqlite_provider = SQLiteModelMetadataProvider(db_path)
+                provider_manager.register_provider('sqlite', sqlite_provider)
+                providers.append(('sqlite', sqlite_provider))
+                logger.debug(f"SQLite metadata provider registered with database: {db_path}")
+            else:
+                logger.warning("Metadata archive database is enabled but database file not found")
+        except Exception as e:
+            logger.error(f"Failed to initialize SQLite metadata provider: {e}")
+    
+    # Initialize Civitai API provider (always available as fallback)
+    try:
+        civitai_client = await ServiceRegistry.get_civitai_client()
+        civitai_provider = CivitaiModelMetadataProvider(civitai_client)
+        provider_manager.register_provider('civitai_api', civitai_provider)
+        providers.append(('civitai_api', civitai_provider))
+        logger.debug("Civitai API metadata provider registered")
+    except Exception as e:
+        logger.error(f"Failed to initialize Civitai API metadata provider: {e}")
+
+    # Register CivArchive provider, but do NOT add to fallback providers
+    try:
+        from .model_metadata_provider import CivArchiveModelMetadataProvider
+        civarchive_provider = CivArchiveModelMetadataProvider()
+        provider_manager.register_provider('civarchive', civarchive_provider)
+        logger.debug("CivArchive metadata provider registered (not included in fallback)")
+    except Exception as e:
+        logger.error(f"Failed to initialize CivArchive metadata provider: {e}")
+
+    # Set up fallback provider based on available providers
+    if len(providers) > 1:
+        # Always use Civitai API first, then Archive DB
+        ordered_providers = []
+        ordered_providers.extend([p[1] for p in providers if p[0] == 'civitai_api'])
+        ordered_providers.extend([p[1] for p in providers if p[0] == 'sqlite'])
+        
+        if ordered_providers:
+            fallback_provider = FallbackMetadataProvider(ordered_providers)
+            provider_manager.register_provider('fallback', fallback_provider, is_default=True)
+            logger.debug(f"Fallback metadata provider registered with {len(ordered_providers)} providers, Civitai API first")
+    elif len(providers) == 1:
+        # Only one provider available, set it as default
+        provider_name, provider = providers[0]
+        provider_manager.register_provider(provider_name, provider, is_default=True)
+        logger.debug(f"Single metadata provider registered as default: {provider_name}")
+    else:
+        logger.warning("No metadata providers available - this may cause metadata lookup failures")
+    
+    return provider_manager
+
+async def update_metadata_providers():
+    """Update metadata providers based on current settings"""
+    try:
+        # Get current settings
+        enable_archive_db = settings.get('enable_metadata_archive_db', False)
+        
+        # Reinitialize all providers with new settings
+        provider_manager = await initialize_metadata_providers()
+        
+        logger.info(f"Updated metadata providers, archive_db enabled: {enable_archive_db}")
+        return provider_manager
+    except Exception as e:
+        logger.error(f"Failed to update metadata providers: {e}")
+        return await ModelMetadataProviderManager.get_instance()
+
+async def get_metadata_archive_manager():
+    """Get metadata archive manager instance"""
+    base_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+    return MetadataArchiveManager(base_path)
+
+async def get_metadata_provider(provider_name: str = None):
+    """Get a specific metadata provider or default provider"""
+    provider_manager = await ModelMetadataProviderManager.get_instance()
+    
+    if provider_name:
+        return provider_manager._get_provider(provider_name)
+    
+    return provider_manager._get_provider()
+
+async def get_default_metadata_provider():
+    """Get the default metadata provider (fallback or single provider)"""
+    return await get_metadata_provider()

py/services/metadata_sync_service.py

@@ -0,0 +1,356 @@
+"""Services for synchronising metadata with remote providers."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from datetime import datetime
+from typing import Any, Awaitable, Callable, Dict, Iterable, Optional
+
+from ..services.settings_manager import SettingsManager
+from ..utils.model_utils import determine_base_model
+
+logger = logging.getLogger(__name__)
+
+
+class MetadataProviderProtocol:
+    """Subset of metadata provider interface consumed by the sync service."""
+
+    async def get_model_by_hash(self, sha256: str) -> tuple[Optional[Dict[str, Any]], Optional[str]]:
+        ...
+
+    async def get_model_version(
+        self, model_id: int, model_version_id: Optional[int]
+    ) -> Optional[Dict[str, Any]]:
+        ...
+
+
+class MetadataSyncService:
+    """High level orchestration for metadata synchronisation flows."""
+
+    def __init__(
+        self,
+        *,
+        metadata_manager,
+        preview_service,
+        settings: SettingsManager,
+        default_metadata_provider_factory: Callable[[], Awaitable[MetadataProviderProtocol]],
+        metadata_provider_selector: Callable[[str], Awaitable[MetadataProviderProtocol]],
+    ) -> None:
+        self._metadata_manager = metadata_manager
+        self._preview_service = preview_service
+        self._settings = settings
+        self._get_default_provider = default_metadata_provider_factory
+        self._get_provider = metadata_provider_selector
+
+    async def load_local_metadata(self, metadata_path: str) -> Dict[str, Any]:
+        """Load metadata JSON from disk, returning an empty structure when missing."""
+
+        if not os.path.exists(metadata_path):
+            return {}
+
+        try:
+            with open(metadata_path, "r", encoding="utf-8") as handle:
+                return json.load(handle)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Error loading metadata from %s: %s", metadata_path, exc)
+            return {}
+
+    async def mark_not_found_on_civitai(
+        self, metadata_path: str, local_metadata: Dict[str, Any]
+    ) -> None:
+        """Persist the not-found flag for a metadata payload."""
+
+        local_metadata["from_civitai"] = False
+        await self._metadata_manager.save_metadata(metadata_path, local_metadata)
+
+    @staticmethod
+    def is_civitai_api_metadata(meta: Dict[str, Any]) -> bool:
+        """Determine if the metadata originated from the CivitAI public API."""
+
+        if not isinstance(meta, dict):
+            return False
+        files = meta.get("files")
+        images = meta.get("images")
+        source = meta.get("source")
+        return bool(files) and bool(images) and source != "archive_db"
+
+    async def update_model_metadata(
+        self,
+        metadata_path: str,
+        local_metadata: Dict[str, Any],
+        civitai_metadata: Dict[str, Any],
+        metadata_provider: Optional[MetadataProviderProtocol] = None,
+    ) -> Dict[str, Any]:
+        """Merge remote metadata into the local record and persist the result."""
+
+        existing_civitai = local_metadata.get("civitai") or {}
+
+        if (
+            civitai_metadata.get("source") == "archive_db"
+            and self.is_civitai_api_metadata(existing_civitai)
+        ):
+            logger.info(
+                "Skip civitai update for %s (%s)",
+                local_metadata.get("model_name", ""),
+                existing_civitai.get("name", ""),
+            )
+        else:
+            merged_civitai = existing_civitai.copy()
+            merged_civitai.update(civitai_metadata)
+
+            if civitai_metadata.get("source") == "archive_db":
+                model_name = civitai_metadata.get("model", {}).get("name", "")
+                version_name = civitai_metadata.get("name", "")
+                logger.info(
+                    "Recovered metadata from archive_db for deleted model: %s (%s)",
+                    model_name,
+                    version_name,
+                )
+
+            if "trainedWords" in existing_civitai:
+                existing_trained = existing_civitai.get("trainedWords", [])
+                new_trained = civitai_metadata.get("trainedWords", [])
+                merged_trained = list(set(existing_trained + new_trained))
+                merged_civitai["trainedWords"] = merged_trained
+
+            local_metadata["civitai"] = merged_civitai
+
+        if "model" in civitai_metadata and civitai_metadata["model"]:
+            model_data = civitai_metadata["model"]
+
+            if model_data.get("name"):
+                local_metadata["model_name"] = model_data["name"]
+
+            if not local_metadata.get("modelDescription") and model_data.get("description"):
+                local_metadata["modelDescription"] = model_data["description"]
+
+            if not local_metadata.get("tags") and model_data.get("tags"):
+                local_metadata["tags"] = model_data["tags"]
+
+            if model_data.get("creator") and not local_metadata.get("civitai", {}).get(
+                "creator"
+            ):
+                local_metadata.setdefault("civitai", {})["creator"] = model_data["creator"]
+
+        local_metadata["base_model"] = determine_base_model(
+            civitai_metadata.get("baseModel")
+        )
+
+        await self._preview_service.ensure_preview_for_metadata(
+            metadata_path, local_metadata, civitai_metadata.get("images", [])
+        )
+
+        await self._metadata_manager.save_metadata(metadata_path, local_metadata)
+        return local_metadata
+
+    async def fetch_and_update_model(
+        self,
+        *,
+        sha256: str,
+        file_path: str,
+        model_data: Dict[str, Any],
+        update_cache_func: Callable[[str, str, Dict[str, Any]], Awaitable[bool]],
+    ) -> tuple[bool, Optional[str]]:
+        """Fetch metadata for a model and update both disk and cache state."""
+
+        if not isinstance(model_data, dict):
+            error = f"Invalid model_data type: {type(model_data)}"
+            logger.error(error)
+            return False, error
+
+        metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
+        enable_archive = self._settings.get("enable_metadata_archive_db", False)
+
+        try:
+            if model_data.get("civitai_deleted") is True:
+                if not enable_archive or model_data.get("db_checked") is True:
+                    if not enable_archive:
+                        error_msg = "CivitAI model is deleted and metadata archive DB is not enabled"
+                    else:
+                        error_msg = "CivitAI model is deleted and not found in metadata archive DB"
+                    return (False, error_msg)
+                metadata_provider = await self._get_provider("sqlite")
+            else:
+                metadata_provider = await self._get_default_provider()
+
+            civitai_metadata, error = await metadata_provider.get_model_by_hash(sha256)
+            if not civitai_metadata:
+                if error == "Model not found":
+                    model_data["from_civitai"] = False
+                    model_data["civitai_deleted"] = True
+                    model_data["db_checked"] = enable_archive
+                    model_data["last_checked_at"] = datetime.now().timestamp()
+
+                    data_to_save = model_data.copy()
+                    data_to_save.pop("folder", None)
+                    await self._metadata_manager.save_metadata(file_path, data_to_save)
+
+                error_msg = (
+                    f"Error fetching metadata: {error} (model_name={model_data.get('model_name', '')})"
+                )
+                logger.error(error_msg)
+                return False, error_msg
+
+            model_data["from_civitai"] = True
+            model_data["civitai_deleted"] = civitai_metadata.get("source") == "archive_db"
+            model_data["db_checked"] = enable_archive
+            model_data["last_checked_at"] = datetime.now().timestamp()
+
+            local_metadata = model_data.copy()
+            local_metadata.pop("folder", None)
+
+            await self.update_model_metadata(
+                metadata_path,
+                local_metadata,
+                civitai_metadata,
+                metadata_provider,
+            )
+
+            update_payload = {
+                "model_name": local_metadata.get("model_name"),
+                "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)
+            return True, None
+        except KeyError as exc:
+            error_msg = f"Error fetching metadata - Missing key: {exc} in model_data={model_data}"
+            logger.error(error_msg)
+            return False, error_msg
+        except Exception as exc:  # pragma: no cover - error path
+            error_msg = f"Error fetching metadata: {exc}"
+            logger.error(error_msg, exc_info=True)
+            return False, error_msg
+
+    async def fetch_metadata_by_sha(
+        self, sha256: str, metadata_provider: Optional[MetadataProviderProtocol] = None
+    ) -> tuple[Optional[Dict[str, Any]], Optional[str]]:
+        """Fetch metadata for a SHA256 hash from the configured provider."""
+
+        provider = metadata_provider or await self._get_default_provider()
+        return await provider.get_model_by_hash(sha256)
+
+    async def relink_metadata(
+        self,
+        *,
+        file_path: str,
+        metadata: Dict[str, Any],
+        model_id: int,
+        model_version_id: Optional[int],
+    ) -> Dict[str, Any]:
+        """Relink a local metadata record to a specific CivitAI model version."""
+
+        provider = await self._get_default_provider()
+        civitai_metadata = await provider.get_model_version(model_id, model_version_id)
+        if not civitai_metadata:
+            raise ValueError(
+                f"Model version not found on CivitAI for ID: {model_id}"
+                + (f" with version: {model_version_id}" if model_version_id else "")
+            )
+
+        primary_model_file: Optional[Dict[str, Any]] = None
+        for file_info in civitai_metadata.get("files", []):
+            if file_info.get("primary", False) and file_info.get("type") == "Model":
+                primary_model_file = file_info
+                break
+
+        if primary_model_file and primary_model_file.get("hashes", {}).get("SHA256"):
+            metadata["sha256"] = primary_model_file["hashes"]["SHA256"].lower()
+
+        metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
+        await self.update_model_metadata(
+            metadata_path,
+            metadata,
+            civitai_metadata,
+            provider,
+        )
+
+        return metadata
+
+    async def save_metadata_updates(
+        self,
+        *,
+        file_path: str,
+        updates: Dict[str, Any],
+        metadata_loader: Callable[[str], Awaitable[Dict[str, Any]]],
+        update_cache: Callable[[str, str, Dict[str, Any]], Awaitable[bool]],
+    ) -> Dict[str, Any]:
+        """Apply metadata updates and persist to disk and cache."""
+
+        metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
+        metadata = await metadata_loader(metadata_path)
+
+        for key, value in updates.items():
+            if isinstance(value, dict) and isinstance(metadata.get(key), dict):
+                metadata[key].update(value)
+            else:
+                metadata[key] = value
+
+        await self._metadata_manager.save_metadata(file_path, metadata)
+        await update_cache(file_path, file_path, metadata)
+
+        if "model_name" in updates:
+            logger.debug("Metadata update touched model_name; cache resort required")
+
+        return metadata
+
+    async def verify_duplicate_hashes(
+        self,
+        *,
+        file_paths: Iterable[str],
+        metadata_loader: Callable[[str], Awaitable[Dict[str, Any]]],
+        hash_calculator: Callable[[str], Awaitable[str]],
+        update_cache: Callable[[str, str, Dict[str, Any]], Awaitable[bool]],
+    ) -> Dict[str, Any]:
+        """Verify a collection of files share the same SHA256 hash."""
+
+        file_paths = list(file_paths)
+        if not file_paths:
+            raise ValueError("No file paths provided for verification")
+
+        results = {
+            "verified_as_duplicates": True,
+            "mismatched_files": [],
+            "new_hash_map": {},
+        }
+
+        expected_hash: Optional[str] = None
+        first_metadata_path = os.path.splitext(file_paths[0])[0] + ".metadata.json"
+        first_metadata = await metadata_loader(first_metadata_path)
+        if first_metadata and "sha256" in first_metadata:
+            expected_hash = first_metadata["sha256"].lower()
+
+        for path in file_paths:
+            if not os.path.exists(path):
+                continue
+
+            try:
+                actual_hash = await hash_calculator(path)
+                metadata_path = os.path.splitext(path)[0] + ".metadata.json"
+                metadata = await metadata_loader(metadata_path)
+                stored_hash = metadata.get("sha256", "").lower()
+
+                if not expected_hash:
+                    expected_hash = stored_hash
+
+                if actual_hash != expected_hash:
+                    results["verified_as_duplicates"] = False
+                    results["mismatched_files"].append(path)
+                    results["new_hash_map"][path] = actual_hash
+
+                if actual_hash != stored_hash:
+                    metadata["sha256"] = actual_hash
+                    await self._metadata_manager.save_metadata(path, metadata)
+                    await update_cache(path, path, metadata)
+            except Exception as exc:  # pragma: no cover - defensive path
+                logger.error("Error verifying hash for %s: %s", path, exc)
+                results["mismatched_files"].append(path)
+                results["new_hash_map"][path] = "error_calculating_hash"
+                results["verified_as_duplicates"] = False
+
+        return results
+

py/services/model_file_service.py

@@ -0,0 +1,463 @@
+import asyncio
+import os
+import logging
+from typing import List, Dict, Optional, Any, Set
+from abc import ABC, abstractmethod
+
+from ..utils.utils import calculate_relative_path_for_model, remove_empty_dirs
+from ..utils.constants import AUTO_ORGANIZE_BATCH_SIZE
+from ..services.settings_manager import settings
+
+logger = logging.getLogger(__name__)
+
+
+class ProgressCallback(ABC):
+    """Abstract callback interface for progress reporting"""
+    
+    @abstractmethod
+    async def on_progress(self, progress_data: Dict[str, Any]) -> None:
+        """Called when progress is updated"""
+        pass
+
+
+class AutoOrganizeResult:
+    """Result object for auto-organize operations"""
+    
+    def __init__(self):
+        self.total: int = 0
+        self.processed: int = 0
+        self.success_count: int = 0
+        self.failure_count: int = 0
+        self.skipped_count: int = 0
+        self.operation_type: str = 'unknown'
+        self.cleanup_counts: Dict[str, int] = {}
+        self.results: List[Dict[str, Any]] = []
+        self.results_truncated: bool = False
+        self.sample_results: List[Dict[str, Any]] = []
+        self.is_flat_structure: bool = False
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary"""
+        result = {
+            'success': True,
+            '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,
+                'success': self.success_count,
+                'skipped': self.skipped_count,
+                'failures': self.failure_count,
+                'organization_type': 'flat' if self.is_flat_structure else 'structured',
+                'cleaned_dirs': self.cleanup_counts,
+                'operation_type': self.operation_type
+            }
+        }
+        
+        if self.results_truncated:
+            result['results_truncated'] = True
+            result['sample_results'] = self.sample_results
+        else:
+            result['results'] = self.results
+        
+        return result
+
+
+class ModelFileService:
+    """Service for handling model file operations and organization"""
+    
+    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
+    
+    def get_model_roots(self) -> List[str]:
+        """Get model root directories"""
+        return self.scanner.get_model_roots()
+    
+    async def auto_organize_models(
+        self, 
+        file_paths: Optional[List[str]] = None,
+        progress_callback: Optional[ProgressCallback] = None
+    ) -> AutoOrganizeResult:
+        """Auto-organize models based on current settings
+        
+        Args:
+            file_paths: Optional list of specific file paths to organize. 
+                       If None, organizes all models.
+            progress_callback: Optional callback for progress updates
+            
+        Returns:
+            AutoOrganizeResult object with operation results
+        """
+        result = AutoOrganizeResult()
+        source_directories: Set[str] = set()
+        
+        try:
+            # Get all models from cache
+            cache = await self.scanner.get_cached_data()
+            all_models = cache.raw_data
+            
+            # Filter models if specific file paths are provided
+            if file_paths:
+                all_models = [model for model in all_models if model.get('file_path') in file_paths]
+                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')
+            
+            # Check if flat structure is configured for this model type
+            path_template = settings.get_download_path_template(self.model_type)
+            result.is_flat_structure = not path_template
+            
+            # Initialize tracking
+            result.total = len(all_models)
+            
+            # Send initial progress
+            if progress_callback:
+                await progress_callback.on_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'started',
+                    'total': result.total,
+                    'processed': 0,
+                    'success': 0,
+                    'failures': 0,
+                    'skipped': 0,
+                    'operation_type': result.operation_type
+                })
+            
+            # Process models in batches
+            await self._process_models_in_batches(
+                all_models, 
+                model_roots, 
+                result, 
+                progress_callback,
+                source_directories  # Pass the set to track source directories
+            )
+            
+            # Send cleanup progress
+            if progress_callback:
+                await progress_callback.on_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'cleaning',
+                    'total': result.total,
+                    'processed': result.processed,
+                    'success': result.success_count,
+                    'failures': result.failure_count,
+                    'skipped': result.skipped_count,
+                    'message': 'Cleaning up empty directories...',
+                    'operation_type': result.operation_type
+                })
+            
+            # Clean up empty directories - only in affected directories for bulk operations
+            cleanup_paths = list(source_directories) if result.operation_type == 'bulk' else model_roots
+            result.cleanup_counts = await self._cleanup_empty_directories(cleanup_paths)
+            
+            # Send completion message
+            if progress_callback:
+                await progress_callback.on_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'completed',
+                    'total': result.total,
+                    'processed': result.processed,
+                    'success': result.success_count,
+                    'failures': result.failure_count,
+                    'skipped': result.skipped_count,
+                    'cleanup': result.cleanup_counts,
+                    'operation_type': result.operation_type
+                })
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error in auto_organize_models: {e}", exc_info=True)
+            
+            # Send error message
+            if progress_callback:
+                await progress_callback.on_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'error',
+                    'error': str(e),
+                    'operation_type': result.operation_type
+                })
+            
+            raise e
+    
+    async def _process_models_in_batches(
+        self, 
+        all_models: List[Dict[str, Any]], 
+        model_roots: List[str], 
+        result: AutoOrganizeResult,
+        progress_callback: Optional[ProgressCallback],
+        source_directories: Optional[Set[str]] = None
+    ) -> None:
+        """Process models in batches to avoid overwhelming the system"""
+        
+        for i in range(0, result.total, AUTO_ORGANIZE_BATCH_SIZE):
+            batch = all_models[i:i + AUTO_ORGANIZE_BATCH_SIZE]
+            
+            for model in batch:
+                await self._process_single_model(model, model_roots, result, source_directories)
+                result.processed += 1
+            
+            # Send progress update after each batch
+            if progress_callback:
+                await progress_callback.on_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'processing',
+                    'total': result.total,
+                    'processed': result.processed,
+                    'success': result.success_count,
+                    'failures': result.failure_count,
+                    'skipped': result.skipped_count,
+                    'operation_type': result.operation_type
+                })
+            
+            # Small delay between batches
+            await asyncio.sleep(0.1)
+    
+    async def _process_single_model(
+        self, 
+        model: Dict[str, Any], 
+        model_roots: List[str], 
+        result: AutoOrganizeResult,
+        source_directories: Optional[Set[str]] = None
+    ) -> None:
+        """Process a single model for organization"""
+        try:
+            file_path = model.get('file_path')
+            model_name = model.get('model_name', 'Unknown')
+            
+            if not file_path:
+                self._add_result(result, model_name, False, "No file path found")
+                result.failure_count += 1
+                return
+            
+            # Find which model root this file belongs to
+            current_root = self._find_model_root(file_path, model_roots)
+            if not current_root:
+                self._add_result(result, model_name, False, 
+                               "Model file not found in any configured root directory")
+                result.failure_count += 1
+                return
+            
+            # Determine target directory
+            target_dir = await self._calculate_target_directory(
+                model, current_root, result.is_flat_structure
+            )
+            
+            if target_dir is None:
+                self._add_result(result, model_name, False, 
+                               "Skipped - insufficient metadata for organization")
+                result.skipped_count += 1
+                return
+            
+            current_dir = os.path.dirname(file_path)
+            
+            # Skip if already in correct location
+            if current_dir.replace(os.sep, '/') == target_dir.replace(os.sep, '/'):
+                result.skipped_count += 1
+                return
+            
+            # Check for conflicts
+            file_name = os.path.basename(file_path)
+            target_file_path = os.path.join(target_dir, file_name)
+            
+            if os.path.exists(target_file_path):
+                self._add_result(result, model_name, False, 
+                               f"Target file already exists: {target_file_path}")
+                result.failure_count += 1
+                return
+            
+            # Store the source directory for potential cleanup
+            if source_directories is not None:
+                source_directories.add(current_dir)
+            
+            # Perform the move
+            success = await self.scanner.move_model(file_path, target_dir)
+            
+            if success:
+                result.success_count += 1
+            else:
+                self._add_result(result, model_name, False, "Failed to move model")
+                result.failure_count += 1
+            
+        except Exception as e:
+            logger.error(f"Error processing model {model.get('model_name', 'Unknown')}: {e}", exc_info=True)
+            self._add_result(result, model.get('model_name', 'Unknown'), False, f"Error: {str(e)}")
+            result.failure_count += 1
+    
+    def _find_model_root(self, file_path: str, model_roots: List[str]) -> Optional[str]:
+        """Find which model root the file belongs to"""
+        for root in model_roots:
+            # 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
+    
+    async def _calculate_target_directory(
+        self, 
+        model: Dict[str, Any], 
+        current_root: str, 
+        is_flat_structure: bool
+    ) -> Optional[str]:
+        """Calculate the target directory for a model"""
+        if is_flat_structure:
+            file_path = model.get('file_path')
+            current_dir = os.path.dirname(file_path)
+            
+            # Check if already in root directory
+            if os.path.normpath(current_dir) == os.path.normpath(current_root):
+                return None  # Signal to skip
+            
+            return current_root
+        else:
+            # Calculate new relative path based on settings
+            new_relative_path = calculate_relative_path_for_model(model, self.model_type)
+            
+            if not new_relative_path:
+                return None  # Signal to skip
+            
+            return os.path.join(current_root, new_relative_path).replace(os.sep, '/')
+    
+    def _add_result(
+        self, 
+        result: AutoOrganizeResult, 
+        model_name: str, 
+        success: bool, 
+        message: str
+    ) -> None:
+        """Add a result entry if under the limit"""
+        if len(result.results) < 100:  # Limit detailed results
+            result.results.append({
+                "model": model_name,
+                "success": success,
+                "message": message
+            })
+        elif len(result.results) == 100:
+            # Mark as truncated and save sample
+            result.results_truncated = True
+            result.sample_results = result.results[:50]
+    
+    async def _cleanup_empty_directories(self, paths: List[str]) -> Dict[str, int]:
+        """Clean up empty directories after organizing
+        
+        Args:
+            paths: List of paths to check for empty directories
+            
+        Returns:
+            Dictionary with counts of removed directories by root path
+        """
+        cleanup_counts = {}
+        for path in paths:
+            removed = remove_empty_dirs(path)
+            cleanup_counts[path] = removed
+        return cleanup_counts
+
+
+class ModelMoveService:
+    """Service for handling individual model moves"""
+    
+    def __init__(self, scanner):
+        """Initialize the service
+        
+        Args:
+            scanner: Model scanner instance
+        """
+        self.scanner = scanner
+    
+    async def move_model(self, file_path: str, target_path: str) -> Dict[str, Any]:
+        """Move a single model file
+        
+        Args:
+            file_path: Source file path
+            target_path: Target directory path
+            
+        Returns:
+            Dictionary with move result
+        """
+        try:
+            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}")
+                return {
+                    'success': True, 
+                    'message': 'Source and target directories are the same',
+                    'original_file_path': file_path,
+                    'new_file_path': file_path
+                }
+
+            new_file_path = await self.scanner.move_model(file_path, target_path)
+            if new_file_path:
+                return {
+                    'success': True, 
+                    'original_file_path': file_path,
+                    'new_file_path': new_file_path
+                }
+            else:
+                return {
+                    'success': False,
+                    'error': 'Failed to move model',
+                    'original_file_path': file_path,
+                    'new_file_path': None
+                }
+        except Exception as e:
+            logger.error(f"Error moving model: {e}", exc_info=True)
+            return {
+                'success': False,
+                'error': str(e),
+                'original_file_path': file_path,
+                'new_file_path': None
+            }
+    
+    async def move_models_bulk(self, file_paths: List[str], target_path: str) -> Dict[str, Any]:
+        """Move multiple model files
+        
+        Args:
+            file_paths: List of source file paths
+            target_path: Target directory path
+            
+        Returns:
+            Dictionary with bulk move results
+        """
+        try:
+            results = []
+            
+            for file_path in file_paths:
+                result = await self.move_model(file_path, target_path)
+                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'))
+                })
+            
+            success_count = sum(1 for r in results if r["success"])
+            failure_count = len(results) - success_count
+            
+            return {
+                'success': True,
+                'message': f'Moved {success_count} of {len(file_paths)} models',
+                'results': results,
+                'success_count': success_count,
+                'failure_count': failure_count
+            }
+        except Exception as e:
+            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
+            return {
+                'success': False,
+                'error': str(e),
+                'results': [],
+                'success_count': 0,
+                'failure_count': len(file_paths)
+            }

py/services/model_hash_index.py

@@ -31,29 +31,34 @@ class ModelHashIndex:
                 if file_path not in self._duplicate_hashes.get(sha256, []):
                     self._duplicate_hashes.setdefault(sha256, []).append(file_path)
         
-        # Track duplicates by filename
+        # Track duplicates by filename - FIXED LOGIC
         if filename in self._filename_to_hash:
-            old_hash = self._filename_to_hash[filename]
-            if old_hash != sha256:  # Different models with the same name
-                old_path = self._hash_to_path.get(old_hash)
-                if old_path:
-                    if filename not in self._duplicate_filenames:
-                        self._duplicate_filenames[filename] = [old_path]
-                    if file_path not in self._duplicate_filenames.get(filename, []):
-                        self._duplicate_filenames.setdefault(filename, []).append(file_path)
+            existing_hash = self._filename_to_hash[filename]
+            existing_path = self._hash_to_path.get(existing_hash)
+            
+            # If this is a different file with the same filename
+            if existing_path and existing_path != file_path:
+                # Initialize duplicates tracking if needed
+                if filename not in self._duplicate_filenames:
+                    self._duplicate_filenames[filename] = [existing_path]
+                
+                # Add current file to duplicates if not already present
+                if file_path not in self._duplicate_filenames[filename]:
+                    self._duplicate_filenames[filename].append(file_path)
         
         # Remove old path mapping if hash exists
         if sha256 in self._hash_to_path:
             old_path = self._hash_to_path[sha256]
             old_filename = self._get_filename_from_path(old_path)
-            if old_filename in self._filename_to_hash:
+            if old_filename in self._filename_to_hash and self._filename_to_hash[old_filename] == sha256:
                 del self._filename_to_hash[old_filename]
         
-        # Remove old hash mapping if filename exists
+        # Remove old hash mapping if filename exists and points to different hash
         if filename in self._filename_to_hash:
             old_hash = self._filename_to_hash[filename]
-            if old_hash in self._hash_to_path:
-                del self._hash_to_path[old_hash]
+            if old_hash != sha256 and old_hash in self._hash_to_path:
+                # Don't delete the old hash mapping, just update filename mapping
+                pass
         
         # Add new mappings
         self._hash_to_path[sha256] = file_path

py/services/model_lifecycle_service.py

@@ -0,0 +1,245 @@
+"""Service routines for model lifecycle mutations."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Awaitable, Callable, Dict, Iterable, List, Optional
+
+from ..services.service_registry import ServiceRegistry
+from ..utils.constants import PREVIEW_EXTENSIONS
+
+logger = logging.getLogger(__name__)
+
+
+async def delete_model_artifacts(target_dir: str, file_name: str) -> List[str]:
+    """Delete the primary model artefacts within ``target_dir``."""
+
+    patterns = [
+        f"{file_name}.safetensors",
+        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):
+        os.remove(main_path)
+        deleted.append(main_path)
+    else:
+        logger.warning("Model file not found: %s", main_file)
+
+    for pattern in patterns[1:]:
+        path = os.path.join(target_dir, pattern)
+        if os.path.exists(path):
+            try:
+                os.remove(path)
+                deleted.append(pattern)
+            except Exception as exc:  # pragma: no cover - defensive path
+                logger.warning("Failed to delete %s: %s", pattern, exc)
+
+    return deleted
+
+
+class ModelLifecycleService:
+    """Co-ordinate destructive and mutating model operations."""
+
+    def __init__(
+        self,
+        *,
+        scanner,
+        metadata_manager,
+        metadata_loader: Callable[[str], Awaitable[Dict[str, object]]],
+        recipe_scanner_factory: Callable[[], Awaitable] | None = None,
+    ) -> None:
+        self._scanner = scanner
+        self._metadata_manager = metadata_manager
+        self._metadata_loader = metadata_loader
+        self._recipe_scanner_factory = (
+            recipe_scanner_factory or ServiceRegistry.get_recipe_scanner
+        )
+
+    async def delete_model(self, file_path: str) -> Dict[str, object]:
+        """Delete a model file and associated artefacts."""
+
+        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()
+
+        if hasattr(self._scanner, "_hash_index") and self._scanner._hash_index:
+            self._scanner._hash_index.remove_by_path(file_path)
+
+        return {"success": True, "deleted_files": deleted_files}
+
+    async def exclude_model(self, file_path: str) -> Dict[str, object]:
+        """Mark a model as excluded and prune cache references."""
+
+        if not file_path:
+            raise ValueError("Model path is required")
+
+        metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
+        metadata = await self._metadata_loader(metadata_path)
+        metadata["exclude"] = True
+
+        await self._metadata_manager.save_metadata(file_path, metadata)
+
+        cache = await self._scanner.get_cached_data()
+        model_to_remove = next(
+            (item for item in cache.raw_data if item["file_path"] == file_path),
+            None,
+        )
+
+        if model_to_remove:
+            for tag in model_to_remove.get("tags", []):
+                if tag in getattr(self._scanner, "_tags_count", {}):
+                    self._scanner._tags_count[tag] = max(
+                        0, self._scanner._tags_count[tag] - 1
+                    )
+                    if self._scanner._tags_count[tag] == 0:
+                        del self._scanner._tags_count[tag]
+
+            if hasattr(self._scanner, "_hash_index") and self._scanner._hash_index:
+                self._scanner._hash_index.remove_by_path(file_path)
+
+            cache.raw_data = [
+                item for item in cache.raw_data if item["file_path"] != file_path
+            ]
+            await cache.resort()
+
+        excluded = getattr(self._scanner, "_excluded_models", None)
+        if isinstance(excluded, list):
+            excluded.append(file_path)
+
+        message = f"Model {os.path.basename(file_path)} excluded"
+        return {"success": True, "message": message}
+
+    async def bulk_delete_models(self, file_paths: Iterable[str]) -> Dict[str, object]:
+        """Delete a collection of models via the scanner bulk operation."""
+
+        file_paths = list(file_paths)
+        if not file_paths:
+            raise ValueError("No file paths provided for deletion")
+
+        return await self._scanner.bulk_delete_models(file_paths)
+
+    async def rename_model(
+        self, *, file_path: str, new_file_name: str
+    ) -> Dict[str, object]:
+        """Rename a model and its companion artefacts."""
+
+        if not file_path or not new_file_name:
+            raise ValueError("File path and new file name are required")
+
+        invalid_chars = {"/", "\\", ":", "*", "?", '"', "<", ">", "|"}
+        if any(char in new_file_name for char in invalid_chars):
+            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, "/"
+        )
+
+        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}.metadata.json",
+            f"{old_file_name}.metadata.json.bak",
+        ]
+        for ext in PREVIEW_EXTENSIONS:
+            patterns.append(f"{old_file_name}{ext}")
+
+        existing_files: List[tuple[str, str]] = []
+        for pattern in patterns:
+            path = os.path.join(target_dir, pattern)
+            if os.path.exists(path):
+                existing_files.append((path, pattern))
+
+        metadata_path = os.path.join(target_dir, f"{old_file_name}.metadata.json")
+        metadata: Optional[Dict[str, object]] = None
+        hash_value: Optional[str] = None
+
+        if os.path.exists(metadata_path):
+            metadata = await self._metadata_loader(metadata_path)
+            hash_value = metadata.get("sha256") if isinstance(metadata, dict) else None
+
+        renamed_files: List[str] = []
+        new_metadata_path: Optional[str] = None
+        new_preview: Optional[str] = None
+
+        for old_path, pattern in existing_files:
+            ext = self._get_multipart_ext(pattern)
+            new_path = os.path.join(target_dir, f"{new_file_name}{ext}").replace(
+                os.sep, "/"
+            )
+            os.rename(old_path, new_path)
+            renamed_files.append(new_path)
+
+            if ext == ".metadata.json":
+                new_metadata_path = new_path
+
+        if metadata and new_metadata_path:
+            metadata["file_name"] = new_file_name
+            metadata["file_path"] = new_file_path
+
+            if metadata.get("preview_url"):
+                old_preview = str(metadata["preview_url"])
+                ext = self._get_multipart_ext(old_preview)
+                new_preview = os.path.join(target_dir, f"{new_file_name}{ext}").replace(
+                    os.sep, "/"
+                )
+                metadata["preview_url"] = new_preview
+
+            await self._metadata_manager.save_metadata(new_file_path, metadata)
+
+        if metadata:
+            await self._scanner.update_single_model_cache(
+                file_path, new_file_path, metadata
+            )
+
+            if hash_value and getattr(self._scanner, "model_type", "") == "lora":
+                recipe_scanner = await self._recipe_scanner_factory()
+                if recipe_scanner:
+                    try:
+                        await recipe_scanner.update_lora_filename_by_hash(
+                            hash_value, new_file_name
+                        )
+                    except Exception as exc:  # pragma: no cover - defensive logging
+                        logger.error(
+                            "Error updating recipe references for %s: %s",
+                            file_path,
+                            exc,
+                        )
+
+        return {
+            "success": True,
+            "new_file_path": new_file_path,
+            "new_preview_path": new_preview,
+            "renamed_files": renamed_files,
+            "reload_required": False,
+        }
+
+    @staticmethod
+    def _get_multipart_ext(filename: str) -> str:
+        """Return the extension for files with compound suffixes."""
+
+        parts = filename.split(".")
+        if len(parts) == 3:
+            return "." + ".".join(parts[-2:])
+        if len(parts) >= 4:
+            return "." + ".".join(parts[-3:])
+        return os.path.splitext(filename)[1]
+

py/services/model_metadata_provider.py

@@ -0,0 +1,534 @@
+from abc import ABC, abstractmethod
+import json
+import logging
+from typing import Optional, Dict, Tuple, Any
+from .downloader import get_downloader
+
+try:
+    from bs4 import BeautifulSoup
+except ImportError as exc:
+    BeautifulSoup = None  # type: ignore[assignment]
+    _BS4_IMPORT_ERROR = exc
+else:
+    _BS4_IMPORT_ERROR = None
+
+try:
+    import aiosqlite
+except ImportError as exc:
+    aiosqlite = None  # type: ignore[assignment]
+    _AIOSQLITE_IMPORT_ERROR = exc
+else:
+    _AIOSQLITE_IMPORT_ERROR = None
+
+def _require_beautifulsoup() -> Any:
+    if BeautifulSoup is None:
+        raise RuntimeError(
+            "BeautifulSoup (bs4) is required for CivArchiveModelMetadataProvider. "
+            "Install it with 'pip install beautifulsoup4'."
+        ) from _BS4_IMPORT_ERROR
+    return BeautifulSoup
+
+def _require_aiosqlite() -> Any:
+    if aiosqlite is None:
+        raise RuntimeError(
+            "aiosqlite is required for SQLiteModelMetadataProvider. "
+            "Install it with 'pip install aiosqlite'."
+        ) from _AIOSQLITE_IMPORT_ERROR
+    return aiosqlite
+
+logger = logging.getLogger(__name__)
+
+class ModelMetadataProvider(ABC):
+    """Base abstract class for all model metadata providers"""
+    
+    @abstractmethod
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Find model by hash value"""
+        pass
+        
+    @abstractmethod
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        """Get all versions of a model with their details"""
+        pass
+        
+    @abstractmethod
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        """Get specific model version with additional metadata"""
+        pass
+        
+    @abstractmethod
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Fetch model version metadata"""
+        pass
+
+class CivitaiModelMetadataProvider(ModelMetadataProvider):
+    """Provider that uses Civitai API for metadata"""
+    
+    def __init__(self, civitai_client):
+        self.client = civitai_client
+        
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        return await self.client.get_model_by_hash(model_hash)
+        
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        return await self.client.get_model_versions(model_id)
+        
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        return await self.client.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.client.get_model_version_info(version_id)
+
+class CivArchiveModelMetadataProvider(ModelMetadataProvider):
+    """Provider that uses CivArchive HTML page parsing for metadata"""
+    
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Not supported by CivArchive provider"""
+        return None, "CivArchive provider does not support hash lookup"
+        
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        """Not supported by CivArchive provider"""
+        return None
+        
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        """Get specific model version by parsing CivArchive HTML page"""
+        if model_id is None or version_id is None:
+            return None
+        
+        try:
+            # Construct CivArchive URL
+            url = f"https://civarchive.com/models/{model_id}?modelVersionId={version_id}"
+
+            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': True,
+                        '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 Exception as e:
+            logger.error(f"Error fetching CivArchive model version {model_id}/{version_id}: {e}")
+            return None
+        
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Not supported by CivArchive provider - requires both model_id and version_id"""
+        return None, "CivArchive provider requires both model_id and version_id"
+
+class SQLiteModelMetadataProvider(ModelMetadataProvider):
+    """Provider that uses SQLite database for metadata"""
+    
+    def __init__(self, db_path: str):
+        self.db_path = db_path
+        self._aiosqlite = _require_aiosqlite()
+        
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Find model by hash value from SQLite database"""
+        async with self._aiosqlite.connect(self.db_path) as db:
+            # Look up in model_files table to get model_id and version_id
+            query = """
+                SELECT model_id, version_id 
+                FROM model_files 
+                WHERE sha256 = ? 
+                LIMIT 1
+            """
+            db.row_factory = self._aiosqlite.Row
+            cursor = await db.execute(query, (model_hash.upper(),))
+            file_row = await cursor.fetchone()
+            
+            if not file_row:
+                return None, "Model not found"
+                
+            # Get version details
+            model_id = file_row['model_id']
+            version_id = file_row['version_id']
+            
+            # Build response in the same format as Civitai API
+            result = await self._get_version_with_model_data(db, model_id, version_id)
+            return result, None if result else "Error retrieving model data"
+            
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        """Get all versions of a model from SQLite database"""
+        async with self._aiosqlite.connect(self.db_path) as db:
+            db.row_factory = self._aiosqlite.Row
+            
+            # First check if model exists
+            model_query = "SELECT * FROM models WHERE id = ?"
+            cursor = await db.execute(model_query, (model_id,))
+            model_row = await cursor.fetchone()
+            
+            if not model_row:
+                return None
+                
+            model_data = json.loads(model_row['data'])
+            model_type = model_row['type']
+            model_name = model_row['name']
+            
+            # Get all versions for this model
+            versions_query = """
+                SELECT id, name, base_model, data, position, published_at
+                FROM model_versions
+                WHERE model_id = ?
+                ORDER BY position ASC
+            """
+            cursor = await db.execute(versions_query, (model_id,))
+            version_rows = await cursor.fetchall()
+            
+            if not version_rows:
+                return {'modelVersions': [], 'type': model_type}
+                
+            # Format versions similar to Civitai API
+            model_versions = []
+            for row in version_rows:
+                version_data = json.loads(row['data'])
+                # Add fields from the row to ensure we have the basic fields
+                version_entry = {
+                    'id': row['id'],
+                    'modelId': int(model_id),
+                    'name': row['name'],
+                    'baseModel': row['base_model'],
+                    'model': {
+                        'name': model_row['name'],
+                        'type': model_type,
+                    },
+                    'source': 'archive_db'
+                }
+                # Update with any additional data
+                version_entry.update(version_data)
+                model_versions.append(version_entry)
+                
+            return {
+                'modelVersions': model_versions,
+                'type': model_type,
+                'name': model_name
+            }
+    
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        """Get specific model version with additional metadata from SQLite database"""
+        if not model_id and not version_id:
+            return None
+            
+        async with self._aiosqlite.connect(self.db_path) as db:
+            db.row_factory = self._aiosqlite.Row
+            
+            # Case 1: Only version_id is provided
+            if model_id is None and version_id is not None:
+                # First get the version info to extract model_id
+                version_query = "SELECT model_id FROM model_versions WHERE id = ?"
+                cursor = await db.execute(version_query, (version_id,))
+                version_row = await cursor.fetchone()
+                
+                if not version_row:
+                    return None
+                    
+                model_id = version_row['model_id']
+            
+            # Case 2: model_id is provided but version_id is not
+            elif model_id is not None and version_id is None:
+                # Find the latest version
+                version_query = """
+                    SELECT id FROM model_versions 
+                    WHERE model_id = ? 
+                    ORDER BY position ASC
+                    LIMIT 1
+                """
+                cursor = await db.execute(version_query, (model_id,))
+                version_row = await cursor.fetchone()
+                
+                if not version_row:
+                    return None
+                    
+                version_id = version_row['id']
+            
+            # Now we have both model_id and version_id, get the full data
+            return await self._get_version_with_model_data(db, model_id, version_id)
+    
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Fetch model version metadata from SQLite database"""
+        async with self._aiosqlite.connect(self.db_path) as db:
+            db.row_factory = self._aiosqlite.Row
+            
+            # Get version details
+            version_query = "SELECT model_id FROM model_versions WHERE id = ?"
+            cursor = await db.execute(version_query, (version_id,))
+            version_row = await cursor.fetchone()
+            
+            if not version_row:
+                return None, "Model version not found"
+                
+            model_id = version_row['model_id']
+            
+            # Build complete version data with model info
+            version_data = await self._get_version_with_model_data(db, model_id, version_id)
+            return version_data, None
+    
+    async def _get_version_with_model_data(self, db, model_id, version_id) -> Optional[Dict]:
+        """Helper to build version data with model information"""
+        # Get version details
+        version_query = "SELECT name, base_model, data FROM model_versions WHERE id = ? AND model_id = ?"
+        cursor = await db.execute(version_query, (version_id, model_id))
+        version_row = await cursor.fetchone()
+        
+        if not version_row:
+            return None
+            
+        # Get model details
+        model_query = "SELECT name, type, data, username FROM models WHERE id = ?"
+        cursor = await db.execute(model_query, (model_id,))
+        model_row = await cursor.fetchone()
+        
+        if not model_row:
+            return None
+            
+        # Parse JSON data
+        try:
+            version_data = json.loads(version_row['data'])
+            model_data = json.loads(model_row['data'])
+            
+            # Build response
+            result = {
+                "id": int(version_id),
+                "modelId": int(model_id),
+                "name": version_row['name'],
+                "baseModel": version_row['base_model'],
+                "model": {
+                    "name": model_row['name'],
+                    "description": model_data.get("description"),
+                    "type": model_row['type'],
+                    "tags": model_data.get("tags", [])
+                },
+                "creator": {
+                    "username": model_row['username'] or model_data.get("creator", {}).get("username"),
+                    "image": model_data.get("creator", {}).get("image")
+                },
+                "source": "archive_db"
+            }
+            
+            # Add any additional fields from version data
+            result.update(version_data)
+            
+            # Attach files associated with this version from model_files table
+            files_query = """
+                SELECT data
+                FROM model_files
+                WHERE version_id = ? AND type = 'Model'
+                ORDER BY id ASC
+            """
+            cursor = await db.execute(files_query, (version_id,))
+            file_rows = await cursor.fetchall()
+            
+            files = []
+            for file_row in file_rows:
+                try:
+                    file_data = json.loads(file_row['data'])
+                except json.JSONDecodeError:
+                    logger.warning(
+                        "Skipping model_files entry with invalid JSON for version_id %s", version_id
+                    )
+                    continue
+                # Remove 'modelId' and 'modelVersionId' fields if present
+                file_data.pop('modelId', None)
+                file_data.pop('modelVersionId', None)
+                files.append(file_data)
+            
+            if 'files' in result:
+                existing_files = result['files']
+                if isinstance(existing_files, list):
+                    existing_files.extend(files)
+                    result['files'] = existing_files
+                else:
+                    merged_files = files.copy()
+                    if existing_files:
+                        merged_files.insert(0, existing_files)
+                    result['files'] = merged_files
+            elif files:
+                result['files'] = files
+            else:
+                result['files'] = []
+            
+            return result
+        except json.JSONDecodeError:
+            return None
+        
+class FallbackMetadataProvider(ModelMetadataProvider):
+    """Try providers in order, return first successful result."""
+    def __init__(self, providers: list):
+        self.providers = providers
+
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+        for provider in self.providers:
+            try:
+                result, error = await provider.get_model_by_hash(model_hash)
+                if result:
+                    return result, error
+            except Exception as e:
+                logger.debug(f"Provider failed for get_model_by_hash: {e}")
+                continue
+        return None, "Model not found"
+
+    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        for provider in self.providers:
+            try:
+                result = await provider.get_model_versions(model_id)
+                if result:
+                    return result
+            except Exception as e:
+                logger.debug(f"Provider failed for get_model_versions: {e}")
+                continue
+        return None
+
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+        for provider in self.providers:
+            try:
+                result = await provider.get_model_version(model_id, version_id)
+                if result:
+                    return result
+            except Exception as e:
+                logger.debug(f"Provider failed for get_model_version: {e}")
+                continue
+        return None
+
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        for provider in self.providers:
+            try:
+                result, error = await provider.get_model_version_info(version_id)
+                if result:
+                    return result, error
+            except Exception as e:
+                logger.debug(f"Provider failed for get_model_version_info: {e}")
+                continue
+        return None, "No provider could retrieve the data"
+
+class ModelMetadataProviderManager:
+    """Manager for selecting and using model metadata providers"""
+    
+    _instance = None
+    
+    @classmethod
+    async def get_instance(cls):
+        """Get singleton instance of ModelMetadataProviderManager"""
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance
+    
+    def __init__(self):
+        self.providers = {}
+        self.default_provider = None
+        
+    def register_provider(self, name: str, provider: ModelMetadataProvider, is_default: bool = False):
+        """Register a metadata provider"""
+        self.providers[name] = provider
+        if is_default or self.default_provider is None:
+            self.default_provider = name
+            
+    async def get_model_by_hash(self, model_hash: str, provider_name: str = None) -> Tuple[Optional[Dict], Optional[str]]:
+        """Find model by hash using specified or default provider"""
+        provider = self._get_provider(provider_name)
+        return await provider.get_model_by_hash(model_hash)
+        
+    async def get_model_versions(self, model_id: str, provider_name: str = None) -> Optional[Dict]:
+        """Get model versions using specified or default provider"""
+        provider = self._get_provider(provider_name)
+        return await provider.get_model_versions(model_id)
+        
+    async def get_model_version(self, model_id: int = None, version_id: int = None, provider_name: str = None) -> Optional[Dict]:
+        """Get specific model version using specified or default provider"""
+        provider = self._get_provider(provider_name)
+        return await provider.get_model_version(model_id, version_id)
+        
+    async def get_model_version_info(self, version_id: str, provider_name: str = None) -> Tuple[Optional[Dict], Optional[str]]:
+        """Fetch model version info using specified or default provider"""
+        provider = self._get_provider(provider_name)
+        return await provider.get_model_version_info(version_id)
+        
+    def _get_provider(self, provider_name: str = None) -> ModelMetadataProvider:
+        """Get provider by name or default provider"""
+        if provider_name and provider_name in self.providers:
+            return self.providers[provider_name]
+        
+        if self.default_provider is None:
+            raise ValueError("No default provider set and no valid provider specified")
+            
+        return self.providers[self.default_provider]

py/services/model_query.py

@@ -0,0 +1,196 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Protocol, Callable
+
+from ..utils.constants import NSFW_LEVELS
+from ..utils.utils import fuzzy_match as default_fuzzy_match
+
+
+class SettingsProvider(Protocol):
+    """Protocol describing the SettingsManager contract used by query helpers."""
+
+    def get(self, key: str, default: Any = None) -> Any:
+        ...
+
+
+@dataclass(frozen=True)
+class SortParams:
+    """Normalized representation of sorting instructions."""
+
+    key: str
+    order: str
+
+
+@dataclass(frozen=True)
+class FilterCriteria:
+    """Container for model list filtering options."""
+
+    folder: Optional[str] = None
+    base_models: Optional[Sequence[str]] = None
+    tags: Optional[Sequence[str]] = None
+    favorites_only: bool = False
+    search_options: Optional[Dict[str, Any]] = None
+
+
+class ModelCacheRepository:
+    """Adapter around scanner cache access and sort normalisation."""
+
+    def __init__(self, scanner) -> None:
+        self._scanner = scanner
+
+    async def get_cache(self):
+        """Return the underlying cache instance from the scanner."""
+        return await self._scanner.get_cached_data()
+
+    async def fetch_sorted(self, params: SortParams) -> List[Dict[str, Any]]:
+        """Fetch cached data pre-sorted according to ``params``."""
+        cache = await self.get_cache()
+        return await cache.get_sorted_data(params.key, params.order)
+
+    @staticmethod
+    def parse_sort(sort_by: str) -> SortParams:
+        """Parse an incoming sort string into key/order primitives."""
+        if not sort_by:
+            return SortParams(key="name", order="asc")
+
+        if ":" in sort_by:
+            raw_key, raw_order = sort_by.split(":", 1)
+            sort_key = raw_key.strip().lower() or "name"
+            order = raw_order.strip().lower()
+        else:
+            sort_key = sort_by.strip().lower() or "name"
+            order = "asc"
+
+        if order not in ("asc", "desc"):
+            order = "asc"
+
+        return SortParams(key=sort_key, order=order)
+
+
+class ModelFilterSet:
+    """Applies common filtering rules to the model collection."""
+
+    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]]:
+        """Return items that satisfy the provided criteria."""
+        items = list(data)
+
+        if self._settings.get("show_only_sfw", False):
+            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
+            ]
+
+        if criteria.favorites_only:
+            items = [item for item in items if item.get("favorite", False)]
+
+        folder = criteria.folder
+        options = criteria.search_options or {}
+        recursive = bool(options.get("recursive", True))
+        if folder is not None:
+            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)
+                    ]
+            else:
+                items = [item for item in items if item.get("folder") == folder]
+
+        base_models = criteria.base_models or []
+        if base_models:
+            base_model_set = set(base_models)
+            items = [item for item in items if item.get("base_model") in base_model_set]
+
+        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", []))
+            ]
+
+        return items
+
+
+class SearchStrategy:
+    """Encapsulates text and fuzzy matching behaviour for model queries."""
+
+    DEFAULT_OPTIONS: Dict[str, Any] = {
+        "filename": True,
+        "modelname": True,
+        "tags": False,
+        "recursive": True,
+        "creator": False,
+    }
+
+    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]:
+        """Merge provided options with defaults without mutating input."""
+        normalized = dict(self.DEFAULT_OPTIONS)
+        if options:
+            normalized.update(options)
+        return normalized
+
+    def apply(
+        self,
+        data: Iterable[Dict[str, Any]],
+        search_term: str,
+        options: Dict[str, Any],
+        fuzzy: bool = False,
+    ) -> List[Dict[str, Any]]:
+        """Return items matching the search term using the configured strategy."""
+        if not search_term:
+            return list(data)
+
+        search_lower = search_term.lower()
+        results: List[Dict[str, Any]] = []
+
+        for item in data:
+            if options.get("filename", True):
+                candidate = item.get("file_name", "")
+                if self._matches(candidate, search_term, search_lower, fuzzy):
+                    results.append(item)
+                    continue
+
+            if options.get("modelname", True):
+                candidate = item.get("model_name", "")
+                if self._matches(candidate, search_term, search_lower, fuzzy):
+                    results.append(item)
+                    continue
+
+            if options.get("tags", False):
+                tags = item.get("tags", []) or []
+                if any(self._matches(tag, search_term, search_lower, fuzzy) for tag in tags):
+                    results.append(item)
+                    continue
+
+            if options.get("creator", False):
+                creator_username = ""
+                civitai = item.get("civitai")
+                if isinstance(civitai, dict):
+                    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):
+                    results.append(item)
+                    continue
+
+        return results
+
+    def _matches(self, candidate: str, search_term: str, search_lower: str, fuzzy: bool) -> bool:
+        if not candidate:
+            return False
+
+        candidate_lower = candidate.lower()
+        if fuzzy:
+            return self._fuzzy_match(candidate, search_term)
+        return search_lower in candidate_lower

py/services/persistent_model_cache.py

@@ -0,0 +1,357 @@
+import json
+import logging
+import os
+import re
+import sqlite3
+import threading
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Sequence, Tuple
+
+from ..utils.settings_paths import get_settings_dir
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class PersistedCacheData:
+    """Lightweight structure returned by the persistent cache."""
+
+    raw_data: List[Dict]
+    hash_rows: List[Tuple[str, str]]
+    excluded_models: List[str]
+
+
+class PersistentModelCache:
+    """Persist core model metadata and hash index data in SQLite."""
+
+    _DEFAULT_FILENAME = "model_cache.sqlite"
+    _instances: Dict[str, "PersistentModelCache"] = {}
+    _instance_lock = threading.Lock()
+
+    def __init__(self, library_name: str = "default", db_path: Optional[str] = None) -> None:
+        self._library_name = library_name or "default"
+        self._db_path = db_path or self._resolve_default_path(self._library_name)
+        self._db_lock = threading.Lock()
+        self._schema_initialized = False
+        try:
+            directory = os.path.dirname(self._db_path)
+            if directory:
+                os.makedirs(directory, exist_ok=True)
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.warning("Could not create cache directory %s: %s", directory, exc)
+        if self.is_enabled():
+            self._initialize_schema()
+
+    @classmethod
+    def get_default(cls, library_name: Optional[str] = None) -> "PersistentModelCache":
+        name = (library_name or "default")
+        with cls._instance_lock:
+            if name not in cls._instances:
+                cls._instances[name] = cls(name)
+            return cls._instances[name]
+
+    def is_enabled(self) -> bool:
+        return os.environ.get("LORA_MANAGER_DISABLE_PERSISTENT_CACHE", "0") != "1"
+
+    def load_cache(self, model_type: str) -> Optional[PersistedCacheData]:
+        if not self.is_enabled():
+            return None
+        if not self._schema_initialized:
+            self._initialize_schema()
+        if not self._schema_initialized:
+            return None
+        try:
+            with self._db_lock:
+                conn = self._connect(readonly=True)
+                try:
+                    rows = conn.execute(
+                        "SELECT file_path, file_name, model_name, folder, size, modified, sha256, base_model,"
+                        " preview_url, preview_nsfw_level, from_civitai, favorite, notes, usage_tips,"
+                        " civitai_id, civitai_model_id, civitai_name, trained_words, exclude, db_checked,"
+                        " last_checked_at"
+                        " FROM models WHERE model_type = ?",
+                        (model_type,),
+                    ).fetchall()
+
+                    if not rows:
+                        return None
+
+                    tags = self._load_tags(conn, model_type)
+                    hash_rows = conn.execute(
+                        "SELECT sha256, file_path FROM hash_index WHERE model_type = ?",
+                        (model_type,),
+                    ).fetchall()
+                    excluded = conn.execute(
+                        "SELECT file_path FROM excluded_models WHERE model_type = ?",
+                        (model_type,),
+                    ).fetchall()
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.warning("Failed to load persisted cache for %s: %s", model_type, exc)
+            return None
+
+        raw_data: List[Dict] = []
+        for row in rows:
+            file_path: str = row["file_path"]
+            trained_words = []
+            if row["trained_words"]:
+                try:
+                    trained_words = json.loads(row["trained_words"])
+                except json.JSONDecodeError:
+                    trained_words = []
+
+            civitai: Optional[Dict] = None
+            if any(row[col] is not None for col in ("civitai_id", "civitai_model_id", "civitai_name")):
+                civitai = {}
+                if row["civitai_id"] is not None:
+                    civitai["id"] = row["civitai_id"]
+                if row["civitai_model_id"] is not None:
+                    civitai["modelId"] = row["civitai_model_id"]
+                if row["civitai_name"]:
+                    civitai["name"] = row["civitai_name"]
+                if trained_words:
+                    civitai["trainedWords"] = trained_words
+
+            item = {
+                "file_path": file_path,
+                "file_name": row["file_name"],
+                "model_name": row["model_name"],
+                "folder": row["folder"] or "",
+                "size": row["size"] or 0,
+                "modified": row["modified"] or 0.0,
+                "sha256": row["sha256"] or "",
+                "base_model": row["base_model"] or "",
+                "preview_url": row["preview_url"] or "",
+                "preview_nsfw_level": row["preview_nsfw_level"] or 0,
+                "from_civitai": bool(row["from_civitai"]),
+                "favorite": bool(row["favorite"]),
+                "notes": row["notes"] or "",
+                "usage_tips": row["usage_tips"] or "",
+                "exclude": bool(row["exclude"]),
+                "db_checked": bool(row["db_checked"]),
+                "last_checked_at": row["last_checked_at"] or 0.0,
+                "tags": tags.get(file_path, []),
+                "civitai": civitai,
+            }
+            raw_data.append(item)
+
+        hash_pairs = [(entry["sha256"].lower(), entry["file_path"]) for entry in hash_rows if entry["sha256"]]
+        if not hash_pairs:
+            # Fall back to hashes stored on the model rows
+            for item in raw_data:
+                sha_value = item.get("sha256")
+                if sha_value:
+                    hash_pairs.append((sha_value.lower(), item["file_path"]))
+
+        excluded_paths = [row["file_path"] for row in excluded]
+        return PersistedCacheData(raw_data=raw_data, hash_rows=hash_pairs, excluded_models=excluded_paths)
+
+    def save_cache(self, model_type: str, raw_data: Sequence[Dict], hash_index: Dict[str, List[str]], excluded_models: Sequence[str]) -> None:
+        if not self.is_enabled():
+            return
+        if not self._schema_initialized:
+            self._initialize_schema()
+        if not self._schema_initialized:
+            return
+        try:
+            with self._db_lock:
+                conn = self._connect()
+                try:
+                    conn.execute("PRAGMA foreign_keys = ON")
+                    conn.execute("DELETE FROM models WHERE model_type = ?", (model_type,))
+                    conn.execute("DELETE FROM model_tags WHERE model_type = ?", (model_type,))
+                    conn.execute("DELETE FROM hash_index WHERE model_type = ?", (model_type,))
+                    conn.execute("DELETE FROM excluded_models WHERE model_type = ?", (model_type,))
+
+                    model_rows = [self._prepare_model_row(model_type, item) for item in raw_data]
+                    conn.executemany(self._insert_model_sql(), model_rows)
+
+                    tag_rows = []
+                    for item in raw_data:
+                        file_path = item.get("file_path")
+                        if not file_path:
+                            continue
+                        for tag in item.get("tags") or []:
+                            tag_rows.append((model_type, file_path, tag))
+                    if tag_rows:
+                        conn.executemany(
+                            "INSERT INTO model_tags (model_type, file_path, tag) VALUES (?, ?, ?)",
+                            tag_rows,
+                        )
+
+                    hash_rows: List[Tuple[str, str, str]] = []
+                    for sha_value, paths in hash_index.items():
+                        for path in paths:
+                            if not sha_value or not path:
+                                continue
+                            hash_rows.append((model_type, sha_value.lower(), path))
+                    if hash_rows:
+                        conn.executemany(
+                            "INSERT OR IGNORE INTO hash_index (model_type, sha256, file_path) VALUES (?, ?, ?)",
+                            hash_rows,
+                        )
+
+                    excluded_rows = [(model_type, path) for path in excluded_models]
+                    if excluded_rows:
+                        conn.executemany(
+                            "INSERT OR IGNORE INTO excluded_models (model_type, file_path) VALUES (?, ?)",
+                            excluded_rows,
+                        )
+                    conn.commit()
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.warning("Failed to persist cache for %s: %s", model_type, exc)
+
+    # Internal helpers -------------------------------------------------
+
+    def _resolve_default_path(self, library_name: str) -> str:
+        override = os.environ.get("LORA_MANAGER_CACHE_DB")
+        if override:
+            return override
+        try:
+            settings_dir = get_settings_dir(create=True)
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.warning("Falling back to project directory for cache: %s", exc)
+            settings_dir = os.path.dirname(os.path.dirname(self._db_path)) if hasattr(self, "_db_path") else os.getcwd()
+        safe_name = re.sub(r"[^A-Za-z0-9_.-]", "_", library_name or "default")
+        if safe_name.lower() in ("default", ""):
+            legacy_path = os.path.join(settings_dir, self._DEFAULT_FILENAME)
+            if os.path.exists(legacy_path):
+                return legacy_path
+        return os.path.join(settings_dir, "model_cache", f"{safe_name}.sqlite")
+
+    def _initialize_schema(self) -> None:
+        with self._db_lock:
+            if self._schema_initialized:
+                return
+            try:
+                with self._connect() as conn:
+                    conn.execute("PRAGMA journal_mode=WAL")
+                    conn.execute("PRAGMA foreign_keys = ON")
+                    conn.executescript(
+                        """
+                        CREATE TABLE IF NOT EXISTS models (
+                            model_type TEXT NOT NULL,
+                            file_path TEXT NOT NULL,
+                            file_name TEXT,
+                            model_name TEXT,
+                            folder TEXT,
+                            size INTEGER,
+                            modified REAL,
+                            sha256 TEXT,
+                            base_model TEXT,
+                            preview_url TEXT,
+                            preview_nsfw_level INTEGER,
+                            from_civitai INTEGER,
+                            favorite INTEGER,
+                            notes TEXT,
+                            usage_tips TEXT,
+                            civitai_id INTEGER,
+                            civitai_model_id INTEGER,
+                            civitai_name TEXT,
+                            trained_words TEXT,
+                            exclude INTEGER,
+                            db_checked INTEGER,
+                            last_checked_at REAL,
+                            PRIMARY KEY (model_type, file_path)
+                        );
+
+                        CREATE TABLE IF NOT EXISTS model_tags (
+                            model_type TEXT NOT NULL,
+                            file_path TEXT NOT NULL,
+                            tag TEXT NOT NULL,
+                            PRIMARY KEY (model_type, file_path, tag)
+                        );
+
+                        CREATE TABLE IF NOT EXISTS hash_index (
+                            model_type TEXT NOT NULL,
+                            sha256 TEXT NOT NULL,
+                            file_path TEXT NOT NULL,
+                            PRIMARY KEY (model_type, sha256, file_path)
+                        );
+
+                        CREATE TABLE IF NOT EXISTS excluded_models (
+                            model_type TEXT NOT NULL,
+                            file_path TEXT NOT NULL,
+                            PRIMARY KEY (model_type, file_path)
+                        );
+                        """
+                    )
+                    conn.commit()
+                self._schema_initialized = True
+            except Exception as exc:  # pragma: no cover - defensive guard
+                logger.warning("Failed to initialize persistent cache schema: %s", exc)
+
+    def _connect(self, readonly: bool = False) -> sqlite3.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, detect_types=sqlite3.PARSE_DECLTYPES)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _prepare_model_row(self, model_type: str, item: Dict) -> Tuple:
+        civitai = item.get("civitai") or {}
+        trained_words = civitai.get("trainedWords")
+        if isinstance(trained_words, str):
+            trained_words_json = trained_words
+        elif trained_words is None:
+            trained_words_json = None
+        else:
+            trained_words_json = json.dumps(trained_words)
+
+        return (
+            model_type,
+            item.get("file_path"),
+            item.get("file_name"),
+            item.get("model_name"),
+            item.get("folder"),
+            int(item.get("size") or 0),
+            float(item.get("modified") or 0.0),
+            (item.get("sha256") or "").lower() or None,
+            item.get("base_model"),
+            item.get("preview_url"),
+            int(item.get("preview_nsfw_level") or 0),
+            1 if item.get("from_civitai", True) else 0,
+            1 if item.get("favorite") else 0,
+            item.get("notes"),
+            item.get("usage_tips"),
+            civitai.get("id"),
+            civitai.get("modelId"),
+            civitai.get("name"),
+            trained_words_json,
+            1 if item.get("exclude") else 0,
+            1 if item.get("db_checked") else 0,
+            float(item.get("last_checked_at") or 0.0),
+        )
+
+    def _insert_model_sql(self) -> str:
+        return (
+            "INSERT INTO models (model_type, file_path, file_name, model_name, folder, size, modified, sha256,"
+            " base_model, preview_url, preview_nsfw_level, from_civitai, favorite, notes, usage_tips,"
+            " civitai_id, civitai_model_id, civitai_name, trained_words, exclude, db_checked, last_checked_at)"
+            " VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)"
+        )
+
+    def _load_tags(self, conn: sqlite3.Connection, model_type: str) -> Dict[str, List[str]]:
+        tag_rows = conn.execute(
+            "SELECT file_path, tag FROM model_tags WHERE model_type = ?",
+            (model_type,),
+        ).fetchall()
+        result: Dict[str, List[str]] = {}
+        for row in tag_rows:
+            result.setdefault(row["file_path"], []).append(row["tag"])
+        return result
+
+
+def get_persistent_cache() -> PersistentModelCache:
+    from .settings_manager import settings as settings_service  # Local import to avoid cycles
+
+    library_name = settings_service.get_active_library_name()
+    return PersistentModelCache.get_default(library_name)

py/services/preview_asset_service.py

@@ -0,0 +1,168 @@
+"""Service for processing preview assets for models."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Awaitable, Callable, Dict, Optional, Sequence
+
+from ..utils.constants import CARD_PREVIEW_WIDTH, PREVIEW_EXTENSIONS
+
+logger = logging.getLogger(__name__)
+
+
+class PreviewAssetService:
+    """Manage fetching and persisting preview assets."""
+
+    def __init__(
+        self,
+        *,
+        metadata_manager,
+        downloader_factory: Callable[[], Awaitable],
+        exif_utils,
+    ) -> None:
+        self._metadata_manager = metadata_manager
+        self._downloader_factory = downloader_factory
+        self._exif_utils = exif_utils
+
+    async def ensure_preview_for_metadata(
+        self,
+        metadata_path: str,
+        local_metadata: Dict[str, object],
+        images: Sequence[Dict[str, object]] | None,
+    ) -> None:
+        """Ensure preview assets exist for the supplied metadata entry."""
+
+        if local_metadata.get("preview_url") and os.path.exists(
+            str(local_metadata["preview_url"])
+        ):
+            return
+
+        if not images:
+            return
+
+        first_preview = images[0]
+        base_name = os.path.splitext(os.path.splitext(os.path.basename(metadata_path))[0])[0]
+        preview_dir = os.path.dirname(metadata_path)
+        is_video = first_preview.get("type") == "video"
+
+        if is_video:
+            extension = ".mp4"
+            preview_path = os.path.join(preview_dir, base_name + extension)
+            downloader = await self._downloader_factory()
+            success, result = await downloader.download_file(
+                first_preview["url"], preview_path, use_auth=False
+            )
+            if success:
+                local_metadata["preview_url"] = preview_path.replace(os.sep, "/")
+                local_metadata["preview_nsfw_level"] = first_preview.get("nsfwLevel", 0)
+        else:
+            extension = ".webp"
+            preview_path = os.path.join(preview_dir, base_name + extension)
+            downloader = await self._downloader_factory()
+            success, content, _headers = await downloader.download_to_memory(
+                first_preview["url"], use_auth=False
+            )
+            if not success:
+                return
+
+            try:
+                optimized_data, _ = self._exif_utils.optimize_image(
+                    image_data=content,
+                    target_width=CARD_PREVIEW_WIDTH,
+                    format="webp",
+                    quality=85,
+                    preserve_metadata=False,
+                )
+                with open(preview_path, "wb") as handle:
+                    handle.write(optimized_data)
+            except Exception as exc:  # pragma: no cover - defensive path
+                logger.error("Error optimizing preview image: %s", exc)
+                try:
+                    with open(preview_path, "wb") as handle:
+                        handle.write(content)
+                except Exception as save_exc:
+                    logger.error("Error saving preview image: %s", save_exc)
+                    return
+
+            local_metadata["preview_url"] = preview_path.replace(os.sep, "/")
+            local_metadata["preview_nsfw_level"] = first_preview.get("nsfwLevel", 0)
+
+    async def replace_preview(
+        self,
+        *,
+        model_path: str,
+        preview_data: bytes,
+        content_type: str,
+        original_filename: Optional[str],
+        nsfw_level: int,
+        update_preview_in_cache: Callable[[str, str, int], Awaitable[bool]],
+        metadata_loader: Callable[[str], Awaitable[Dict[str, object]]],
+    ) -> Dict[str, object]:
+        """Replace an existing preview asset for a model."""
+
+        base_name = os.path.splitext(os.path.basename(model_path))[0]
+        folder = os.path.dirname(model_path)
+
+        extension, optimized_data = await self._convert_preview(
+            preview_data, content_type, original_filename
+        )
+
+        for ext in PREVIEW_EXTENSIONS:
+            existing_preview = os.path.join(folder, base_name + ext)
+            if os.path.exists(existing_preview):
+                try:
+                    os.remove(existing_preview)
+                except Exception as exc:  # pragma: no cover - defensive path
+                    logger.warning(
+                        "Failed to delete existing preview %s: %s", existing_preview, exc
+                    )
+
+        preview_path = os.path.join(folder, base_name + extension).replace(os.sep, "/")
+        with open(preview_path, "wb") as handle:
+            handle.write(optimized_data)
+
+        metadata_path = os.path.splitext(model_path)[0] + ".metadata.json"
+        metadata = await metadata_loader(metadata_path)
+        metadata["preview_url"] = preview_path
+        metadata["preview_nsfw_level"] = nsfw_level
+        await self._metadata_manager.save_metadata(model_path, metadata)
+
+        await update_preview_in_cache(model_path, preview_path, nsfw_level)
+
+        return {"preview_path": preview_path, "preview_nsfw_level": nsfw_level}
+
+    async def _convert_preview(
+        self, data: bytes, content_type: str, original_filename: Optional[str]
+    ) -> tuple[str, bytes]:
+        """Convert preview bytes to the persisted representation."""
+
+        if content_type.startswith("video/"):
+            extension = self._resolve_video_extension(content_type, original_filename)
+            return extension, data
+
+        original_ext = (original_filename or "").lower()
+        if original_ext.endswith(".gif") or content_type.lower() == "image/gif":
+            return ".gif", data
+
+        optimized_data, _ = self._exif_utils.optimize_image(
+            image_data=data,
+            target_width=CARD_PREVIEW_WIDTH,
+            format="webp",
+            quality=85,
+            preserve_metadata=False,
+        )
+        return ".webp", optimized_data
+
+    def _resolve_video_extension(self, content_type: str, original_filename: Optional[str]) -> str:
+        """Infer the best extension for a video preview."""
+
+        if original_filename:
+            extension = os.path.splitext(original_filename)[1].lower()
+            if extension in {".mp4", ".webm", ".mov", ".avi"}:
+                return extension
+
+        if "webm" in content_type:
+            return ".webm"
+        return ".mp4"
+

py/services/recipe_cache.py

@@ -1,5 +1,5 @@
 import asyncio
-from typing import List, Dict
+from typing import Iterable, List, Dict, Optional
 from dataclasses import dataclass
 from operator import itemgetter
 from natsort import natsorted
@@ -10,77 +10,115 @@ class RecipeCache:
     raw_data: List[Dict]
     sorted_by_name: List[Dict]
     sorted_by_date: List[Dict]
-    
+
     def __post_init__(self):
         self._lock = asyncio.Lock()
 
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = natsorted(
-                self.raw_data, 
-                key=lambda x: x.get('title', '').lower()  # Case-insensitive sort
-            )
-            if not name_only:
-                self.sorted_by_date = sorted(
-                    self.raw_data, 
-                    key=itemgetter('created_date', 'file_path'), 
-                    reverse=True
-                )
-    
-    async def update_recipe_metadata(self, recipe_id: str, metadata: Dict) -> bool:
+            self._resort_locked(name_only=name_only)
+
+    async def update_recipe_metadata(self, recipe_id: str, metadata: Dict, *, resort: bool = True) -> bool:
         """Update metadata for a specific recipe in all cached data
-        
+
         Args:
             recipe_id: The ID of the recipe to update
             metadata: The new metadata
-            
+
         Returns:
             bool: True if the update was successful, False if the recipe wasn't found
         """
+        async with self._lock:
+            for item in self.raw_data:
+                if str(item.get('id')) == str(recipe_id):
+                    item.update(metadata)
+                    if resort:
+                        self._resort_locked()
+                    return True
+        return False  # Recipe not found
+
+    async def add_recipe(self, recipe_data: Dict, *, resort: bool = False) -> None:
+        """Add a new recipe to the cache."""
 
-        # Update in raw_data
-        for item in self.raw_data:
-            if item.get('id') == recipe_id:
-                item.update(metadata)
-                break
-        else:
-            return False  # Recipe not found
-            
-        # Resort to reflect changes
-        await self.resort()
-        return True
-            
-    async def add_recipe(self, recipe_data: Dict) -> None:
-        """Add a new recipe to the cache
-        
-        Args:
-            recipe_data: The recipe data to add
-        """
         async with self._lock:
             self.raw_data.append(recipe_data)
-            await self.resort() 
+            if resort:
+                self._resort_locked()
+
+    async def remove_recipe(self, recipe_id: str, *, resort: bool = False) -> Optional[Dict]:
+        """Remove a recipe from the cache by ID.
 
-    async def remove_recipe(self, recipe_id: str) -> bool:
-        """Remove a recipe from the cache by ID
-        
         Args:
             recipe_id: The ID of the recipe to remove
-            
+
         Returns:
-            bool: True if the recipe was found and removed, False otherwise
+            The removed recipe data if found, otherwise ``None``.
         """
-        # Find the recipe in raw_data
-        recipe_index = next((i for i, recipe in enumerate(self.raw_data) 
-                             if recipe.get('id') == recipe_id), None)
-        
-        if recipe_index is None:
-            return False
-        
-        # Remove from raw_data
-        self.raw_data.pop(recipe_index)
-        
-        # Resort to update sorted lists
-        await self.resort()
-        
-        return True 
+
+        async with self._lock:
+            for index, recipe in enumerate(self.raw_data):
+                if str(recipe.get('id')) == str(recipe_id):
+                    removed = self.raw_data.pop(index)
+                    if resort:
+                        self._resort_locked()
+                    return removed
+        return None
+
+    async def bulk_remove(self, recipe_ids: Iterable[str], *, resort: bool = False) -> List[Dict]:
+        """Remove multiple recipes from the cache."""
+
+        id_set = {str(recipe_id) for recipe_id in recipe_ids}
+        if not id_set:
+            return []
+
+        async with self._lock:
+            removed = [item for item in self.raw_data if str(item.get('id')) in id_set]
+            if not removed:
+                return []
+
+            self.raw_data = [item for item in self.raw_data if str(item.get('id')) not in id_set]
+            if resort:
+                self._resort_locked()
+            return removed
+
+    async def replace_recipe(self, recipe_id: str, new_data: Dict, *, resort: bool = False) -> bool:
+        """Replace cached data for a recipe."""
+
+        async with self._lock:
+            for index, recipe in enumerate(self.raw_data):
+                if str(recipe.get('id')) == str(recipe_id):
+                    self.raw_data[index] = new_data
+                    if resort:
+                        self._resort_locked()
+                    return True
+        return False
+
+    async def get_recipe(self, recipe_id: str) -> Optional[Dict]:
+        """Return a shallow copy of a cached recipe."""
+
+        async with self._lock:
+            for recipe in self.raw_data:
+                if str(recipe.get('id')) == str(recipe_id):
+                    return dict(recipe)
+        return None
+
+    async def snapshot(self) -> List[Dict]:
+        """Return a copy of all cached recipes."""
+
+        async with self._lock:
+            return [dict(item) for item in self.raw_data]
+
+    def _resort_locked(self, *, name_only: bool = False) -> None:
+        """Sort cached views. Caller must hold ``_lock``."""
+
+        self.sorted_by_name = natsorted(
+            self.raw_data,
+            key=lambda x: x.get('title', '').lower()
+        )
+        if not name_only:
+            self.sorted_by_date = sorted(
+                self.raw_data,
+                key=itemgetter('created_date', 'file_path'),
+                reverse=True
+            )

py/services/recipe_scanner.py

@@ -3,12 +3,14 @@ import logging
 import asyncio
 import json
 import time
-from typing import List, Dict, Optional, Any, Tuple
+from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
 from ..config import config
 from .recipe_cache import RecipeCache
 from .service_registry import ServiceRegistry
 from .lora_scanner import LoraScanner
-from ..utils.utils import fuzzy_match
+from .metadata_service import get_default_metadata_provider
+from .recipes.errors import RecipeNotFoundError
+from ..utils.utils import calculate_recipe_fingerprint, fuzzy_match
 from natsort import natsorted
 import sys
 
@@ -45,9 +47,36 @@ class RecipeScanner:
             self._initialization_lock = asyncio.Lock()
             self._initialization_task: Optional[asyncio.Task] = None
             self._is_initializing = False
+            self._mutation_lock = asyncio.Lock()
+            self._resort_tasks: Set[asyncio.Task] = set()
             if lora_scanner:
                 self._lora_scanner = lora_scanner
             self._initialized = True
+
+    def on_library_changed(self) -> None:
+        """Reset cached state when the active library changes."""
+
+        # Cancel any in-flight initialization or resorting work so the next
+        # access rebuilds the cache for the new library.
+        if self._initialization_task and not self._initialization_task.done():
+            self._initialization_task.cancel()
+
+        for task in list(self._resort_tasks):
+            if not task.done():
+                task.cancel()
+        self._resort_tasks.clear()
+
+        self._cache = None
+        self._initialization_task = None
+        self._is_initializing = False
+
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = None
+
+        if loop and not loop.is_closed():
+            loop.create_task(self.initialize_in_background())
     
     async def _get_civitai_client(self):
         """Lazily initialize CivitaiClient from registry"""
@@ -190,6 +219,22 @@ class RecipeScanner:
             # Clean up the event loop
             loop.close()
 
+    def _schedule_resort(self, *, name_only: bool = False) -> None:
+        """Schedule a background resort of the recipe cache."""
+
+        if not self._cache:
+            return
+
+        async def _resort_wrapper() -> None:
+            try:
+                await self._cache.resort(name_only=name_only)
+            except Exception as exc:  # pragma: no cover - defensive logging
+                logger.error("Recipe Scanner: error resorting cache: %s", exc, exc_info=True)
+
+        task = asyncio.create_task(_resort_wrapper())
+        self._resort_tasks.add(task)
+        task.add_done_callback(lambda finished: self._resort_tasks.discard(finished))
+
     @property
     def recipes_dir(self) -> str:
         """Get path to recipes directory"""
@@ -254,7 +299,45 @@ class RecipeScanner:
         
         # Return the cache (may be empty or partially initialized)
         return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
-    
+
+    async def refresh_cache(self, force: bool = False) -> RecipeCache:
+        """Public helper to refresh or return the recipe cache."""
+
+        return await self.get_cached_data(force_refresh=force)
+
+    async def add_recipe(self, recipe_data: Dict[str, Any]) -> None:
+        """Add a recipe to the in-memory cache."""
+
+        if not recipe_data:
+            return
+
+        cache = await self.get_cached_data()
+        await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+    async def remove_recipe(self, recipe_id: str) -> bool:
+        """Remove a recipe from the cache by ID."""
+
+        if not recipe_id:
+            return False
+
+        cache = await self.get_cached_data()
+        removed = await cache.remove_recipe(recipe_id, resort=False)
+        if removed is None:
+            return False
+
+        self._schedule_resort()
+        return True
+
+    async def bulk_remove(self, recipe_ids: Iterable[str]) -> int:
+        """Remove multiple recipes from the cache."""
+
+        cache = await self.get_cached_data()
+        removed = await cache.bulk_remove(recipe_ids, resort=False)
+        if removed:
+            self._schedule_resort()
+        return len(removed)
+
     async def scan_all_recipes(self) -> List[Dict]:
         """Scan all recipe JSON files and return metadata"""
         recipes = []
@@ -325,7 +408,6 @@ class RecipeScanner:
 
             # Calculate and update fingerprint if missing
             if 'loras' in recipe_data and 'fingerprint' not in recipe_data:
-                from ..utils.utils import calculate_recipe_fingerprint
                 fingerprint = calculate_recipe_fingerprint(recipe_data['loras'])
                 recipe_data['fingerprint'] = fingerprint
                 
@@ -367,27 +449,29 @@ class RecipeScanner:
             # If has modelVersionId but no hash, look in lora cache first, then fetch from Civitai
             if 'modelVersionId' in lora and not lora.get('hash'):
                 model_version_id = lora['modelVersionId']
+                # Check if model_version_id is an integer and > 0
+                if isinstance(model_version_id, int) and model_version_id > 0:
 
-                # Try to find in lora cache first
-                hash_from_cache = await self._find_hash_in_lora_cache(model_version_id)
-                if hash_from_cache:
-                    lora['hash'] = hash_from_cache
-                    metadata_updated = True
-                else:
-                    # If not in cache, fetch from Civitai
-                    result = await self._get_hash_from_civitai(model_version_id)
-                    if isinstance(result, tuple):
-                        hash_from_civitai, is_deleted = result
-                        if hash_from_civitai:
-                            lora['hash'] = hash_from_civitai
-                            metadata_updated = True
-                        elif is_deleted:
-                            # Mark the lora as deleted if it was not found on Civitai
-                            lora['isDeleted'] = True
-                            logger.warning(f"Marked lora with modelVersionId {model_version_id} as deleted")
-                            metadata_updated = True
+                    # Try to find in lora cache first
+                    hash_from_cache = await self._find_hash_in_lora_cache(model_version_id)
+                    if hash_from_cache:
+                        lora['hash'] = hash_from_cache
+                        metadata_updated = True
                     else:
-                        logger.debug(f"Could not get hash for modelVersionId {model_version_id}")
+                        # If not in cache, fetch from Civitai
+                        result = await self._get_hash_from_civitai(model_version_id)
+                        if isinstance(result, tuple):
+                            hash_from_civitai, is_deleted = result
+                            if hash_from_civitai:
+                                lora['hash'] = hash_from_civitai
+                                metadata_updated = True
+                            elif is_deleted:
+                                # Mark the lora as deleted if it was not found on Civitai
+                                lora['isDeleted'] = True
+                                logger.warning(f"Marked lora with modelVersionId {model_version_id} as deleted")
+                                metadata_updated = True
+                        else:
+                            logger.debug(f"Could not get hash for modelVersionId {model_version_id}")
             
             # If has hash but no file_name, look up in lora library
             if 'hash' in lora and (not lora.get('file_name') or not lora['file_name']):
@@ -431,13 +515,13 @@ class RecipeScanner:
     async def _get_hash_from_civitai(self, model_version_id: str) -> Optional[str]:
         """Get hash from Civitai API"""
         try:
-            # Get CivitaiClient from ServiceRegistry
-            civitai_client = await self._get_civitai_client()
-            if not civitai_client:
-                logger.error("Failed to get CivitaiClient from ServiceRegistry")
+            # Get metadata provider instead of civitai client directly
+            metadata_provider = await get_default_metadata_provider()
+            if not metadata_provider:
+                logger.error("Failed to get metadata provider")
                 return None
                 
-            version_info, error_msg = await civitai_client.get_model_version_info(model_version_id)
+            version_info, error_msg = await metadata_provider.get_model_version_info(model_version_id)
             
             if not version_info:
                 if error_msg and "model not found" in error_msg.lower():
@@ -496,9 +580,36 @@ class RecipeScanner:
             logger.error(f"Error getting base model for lora: {e}")
             return None
 
+    def _enrich_lora_entry(self, lora: Dict[str, Any]) -> Dict[str, Any]:
+        """Populate convenience fields for a LoRA entry."""
+
+        if not lora or not self._lora_scanner:
+            return lora
+
+        hash_value = (lora.get('hash') or '').lower()
+        if not hash_value:
+            return lora
+
+        try:
+            lora['inLibrary'] = self._lora_scanner.has_hash(hash_value)
+            lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(hash_value)
+            lora['localPath'] = self._lora_scanner.get_path_by_hash(hash_value)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.debug("Error enriching lora entry %s: %s", hash_value, exc)
+
+        return lora
+
+    async def get_local_lora(self, name: str) -> Optional[Dict[str, Any]]:
+        """Lookup a local LoRA model by name."""
+
+        if not self._lora_scanner or not name:
+            return None
+
+        return await self._lora_scanner.get_model_info_by_name(name)
+
     async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'date', search: str = None, filters: dict = None, search_options: dict = None, lora_hash: str = None, bypass_filters: bool = True):
         """Get paginated and filtered recipe data
-        
+
         Args:
             page: Current page number (1-based)
             page_size: Number of items per page
@@ -597,16 +708,12 @@ class RecipeScanner:
         
         # Get paginated items
         paginated_items = filtered_data[start_idx:end_idx]
-        
+
         # Add inLibrary information for each lora
         for item in paginated_items:
             if 'loras' in item:
-                for lora in item['loras']:
-                    if 'hash' in lora and lora['hash']:
-                        lora['inLibrary'] = self._lora_scanner.has_hash(lora['hash'].lower())
-                        lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(lora['hash'].lower())
-                        lora['localPath'] = self._lora_scanner.get_path_by_hash(lora['hash'].lower())
-        
+                item['loras'] = [self._enrich_lora_entry(dict(lora)) for lora in item['loras']]
+
         result = {
             'items': paginated_items,
             'total': total_items,
@@ -652,33 +759,25 @@ class RecipeScanner:
                 
         # Add lora metadata
         if 'loras' in formatted_recipe:
-            for lora in formatted_recipe['loras']:
-                if 'hash' in lora and lora['hash']:
-                    lora_hash = lora['hash'].lower()
-                    lora['inLibrary'] = self._lora_scanner.has_hash(lora_hash)
-                    lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(lora_hash)
-                    lora['localPath'] = self._lora_scanner.get_path_by_hash(lora_hash)
-                    
+            formatted_recipe['loras'] = [self._enrich_lora_entry(dict(lora)) for lora in formatted_recipe['loras']]
+
         return formatted_recipe
         
     def _format_file_url(self, file_path: str) -> str:
         """Format file path as URL for serving in web UI"""
         if not file_path:
             return '/loras_static/images/no-preview.png'
-            
+
         try:
-            # Format file path as a URL that will work with static file serving
-            recipes_dir = os.path.join(config.loras_roots[0], "recipes").replace(os.sep, '/')
-            if file_path.replace(os.sep, '/').startswith(recipes_dir):
-                relative_path = os.path.relpath(file_path, config.loras_roots[0]).replace(os.sep, '/')
-                return f"/loras_static/root1/preview/{relative_path}"
-                
-            # If not in recipes dir, try to create a valid URL from the file name
-            file_name = os.path.basename(file_path)
-            return f"/loras_static/root1/preview/recipes/{file_name}"
+            normalized_path = os.path.normpath(file_path)
+            static_url = config.get_preview_static_url(normalized_path)
+            if static_url:
+                return static_url
         except Exception as e:
             logger.error(f"Error formatting file URL: {e}")
             return '/loras_static/images/no-preview.png'
+
+        return '/loras_static/images/no-preview.png'
     
     def _format_timestamp(self, timestamp: float) -> str:
         """Format timestamp for display"""
@@ -716,26 +815,159 @@ class RecipeScanner:
             # Save updated recipe
             with open(recipe_json_path, 'w', encoding='utf-8') as f:
                 json.dump(recipe_data, f, indent=4, ensure_ascii=False)
-                
+
             # Update the cache if it exists
             if self._cache is not None:
-                await self._cache.update_recipe_metadata(recipe_id, metadata)
-                
+                await self._cache.update_recipe_metadata(recipe_id, metadata, resort=False)
+                self._schedule_resort()
+
             # If the recipe has an image, update its EXIF metadata
             from ..utils.exif_utils import ExifUtils
             image_path = recipe_data.get('file_path')
             if image_path and os.path.exists(image_path):
                 ExifUtils.append_recipe_metadata(image_path, recipe_data)
-                
+
             return True
         except Exception as e:
             import logging
             logging.getLogger(__name__).error(f"Error updating recipe metadata: {e}", exc_info=True)
             return False
 
+    async def update_lora_entry(
+        self,
+        recipe_id: str,
+        lora_index: int,
+        *,
+        target_name: str,
+        target_lora: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Update a specific LoRA entry within a recipe.
+
+        Returns the updated recipe data and the refreshed LoRA metadata.
+        """
+
+        if target_name is None:
+            raise ValueError("target_name must be provided")
+
+        recipe_json_path = os.path.join(self.recipes_dir, f"{recipe_id}.recipe.json")
+        if not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, 'r', encoding='utf-8') as file_obj:
+                recipe_data = json.load(file_obj)
+
+            loras = recipe_data.get('loras', [])
+            if lora_index >= len(loras):
+                raise RecipeNotFoundError("LoRA index out of range in recipe")
+
+            lora_entry = loras[lora_index]
+            lora_entry['isDeleted'] = False
+            lora_entry['exclude'] = False
+            lora_entry['file_name'] = target_name
+
+            if target_lora is not None:
+                sha_value = target_lora.get('sha256') or target_lora.get('sha')
+                if sha_value:
+                    lora_entry['hash'] = sha_value.lower()
+
+                civitai_info = target_lora.get('civitai') or {}
+                if civitai_info:
+                    lora_entry['modelName'] = civitai_info.get('model', {}).get('name', '')
+                    lora_entry['modelVersionName'] = civitai_info.get('name', '')
+                    lora_entry['modelVersionId'] = civitai_info.get('id')
+
+            recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
+            recipe_data['modified'] = time.time()
+
+            with open(recipe_json_path, 'w', encoding='utf-8') as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        updated_lora = dict(lora_entry)
+        if target_lora is not None:
+            preview_url = target_lora.get('preview_url')
+            if preview_url:
+                updated_lora['preview_url'] = config.get_preview_static_url(preview_url)
+            if target_lora.get('file_path'):
+                updated_lora['localPath'] = target_lora['file_path']
+
+        updated_lora = self._enrich_lora_entry(updated_lora)
+        return recipe_data, updated_lora
+
+    async def get_recipes_for_lora(self, lora_hash: str) -> List[Dict[str, Any]]:
+        """Return recipes that reference a given LoRA hash."""
+
+        if not lora_hash:
+            return []
+
+        normalized_hash = lora_hash.lower()
+        cache = await self.get_cached_data()
+        matching_recipes: List[Dict[str, Any]] = []
+
+        for recipe in cache.raw_data:
+            loras = recipe.get('loras', [])
+            if any((entry.get('hash') or '').lower() == normalized_hash for entry in loras):
+                recipe_copy = {**recipe}
+                recipe_copy['loras'] = [self._enrich_lora_entry(dict(entry)) for entry in loras]
+                recipe_copy['file_url'] = self._format_file_url(recipe.get('file_path'))
+                matching_recipes.append(recipe_copy)
+
+        return matching_recipes
+
+    async def get_recipe_syntax_tokens(self, recipe_id: str) -> List[str]:
+        """Build LoRA syntax tokens for a recipe."""
+
+        cache = await self.get_cached_data()
+        recipe = await cache.get_recipe(recipe_id)
+        if recipe is None:
+            raise RecipeNotFoundError("Recipe not found")
+
+        loras = recipe.get('loras', [])
+        if not loras:
+            return []
+
+        lora_cache = None
+        if self._lora_scanner is not None:
+            lora_cache = await self._lora_scanner.get_cached_data()
+
+        syntax_parts: List[str] = []
+        for lora in loras:
+            if lora.get('isDeleted', False):
+                continue
+
+            file_name = None
+            hash_value = (lora.get('hash') or '').lower()
+            if hash_value and self._lora_scanner is not None and hasattr(self._lora_scanner, '_hash_index'):
+                file_path = self._lora_scanner._hash_index.get_path(hash_value)
+                if file_path:
+                    file_name = os.path.splitext(os.path.basename(file_path))[0]
+
+            if not file_name and lora.get('modelVersionId') and lora_cache is not None:
+                for cached_lora in getattr(lora_cache, 'raw_data', []):
+                    civitai_info = cached_lora.get('civitai')
+                    if civitai_info and civitai_info.get('id') == lora.get('modelVersionId'):
+                        cached_path = cached_lora.get('path') or cached_lora.get('file_path')
+                        if cached_path:
+                            file_name = os.path.splitext(os.path.basename(cached_path))[0]
+                        break
+
+            if not file_name:
+                file_name = lora.get('file_name', 'unknown-lora')
+
+            strength = lora.get('strength', 1.0)
+            syntax_parts.append(f"<lora:{file_name}:{strength}>")
+
+        return syntax_parts
+
     async def update_lora_filename_by_hash(self, hash_value: str, new_file_name: str) -> Tuple[int, int]:
         """Update file_name in all recipes that contain a LoRA with the specified hash.
-        
+
         Args:
             hash_value: The SHA256 hash value of the LoRA
             new_file_name: The new file_name to set

py/services/recipes/__init__.py

@@ -0,0 +1,23 @@
+"""Recipe service layer implementations."""
+
+from .analysis_service import RecipeAnalysisService
+from .persistence_service import RecipePersistenceService
+from .sharing_service import RecipeSharingService
+from .errors import (
+    RecipeServiceError,
+    RecipeValidationError,
+    RecipeNotFoundError,
+    RecipeDownloadError,
+    RecipeConflictError,
+)
+
+__all__ = [
+    "RecipeAnalysisService",
+    "RecipePersistenceService",
+    "RecipeSharingService",
+    "RecipeServiceError",
+    "RecipeValidationError",
+    "RecipeNotFoundError",
+    "RecipeDownloadError",
+    "RecipeConflictError",
+]

py/services/recipes/analysis_service.py

@@ -0,0 +1,289 @@
+"""Services responsible for recipe metadata analysis."""
+from __future__ import annotations
+
+import base64
+import io
+import os
+import re
+import tempfile
+from dataclasses import dataclass
+from typing import Any, Callable, Optional
+
+import numpy as np
+from PIL import Image
+
+from ...utils.utils import calculate_recipe_fingerprint
+from .errors import (
+    RecipeDownloadError,
+    RecipeNotFoundError,
+    RecipeServiceError,
+    RecipeValidationError,
+)
+
+
+@dataclass(frozen=True)
+class AnalysisResult:
+    """Return payload from analysis operations."""
+
+    payload: dict[str, Any]
+    status: int = 200
+
+
+class RecipeAnalysisService:
+    """Extract recipe metadata from various image sources."""
+
+    def __init__(
+        self,
+        *,
+        exif_utils,
+        recipe_parser_factory,
+        downloader_factory: Callable[[], Any],
+        metadata_collector: Optional[Callable[[], Any]] = None,
+        metadata_processor_cls: Optional[type] = None,
+        metadata_registry_cls: Optional[type] = None,
+        standalone_mode: bool = False,
+        logger,
+    ) -> None:
+        self._exif_utils = exif_utils
+        self._recipe_parser_factory = recipe_parser_factory
+        self._downloader_factory = downloader_factory
+        self._metadata_collector = metadata_collector
+        self._metadata_processor_cls = metadata_processor_cls
+        self._metadata_registry_cls = metadata_registry_cls
+        self._standalone_mode = standalone_mode
+        self._logger = logger
+
+    async def analyze_uploaded_image(
+        self,
+        *,
+        image_bytes: bytes | None,
+        recipe_scanner,
+    ) -> AnalysisResult:
+        """Analyze an uploaded image payload."""
+
+        if not image_bytes:
+            raise RecipeValidationError("No image data provided")
+
+        temp_path = self._write_temp_file(image_bytes)
+        try:
+            metadata = self._exif_utils.extract_image_metadata(temp_path)
+            if not metadata:
+                return AnalysisResult({"error": "No metadata found in this image", "loras": []})
+
+            return await self._parse_metadata(
+                metadata,
+                recipe_scanner=recipe_scanner,
+                image_path=None,
+                include_image_base64=False,
+            )
+        finally:
+            self._safe_cleanup(temp_path)
+
+    async def analyze_remote_image(
+        self,
+        *,
+        url: str | None,
+        recipe_scanner,
+        civitai_client,
+    ) -> AnalysisResult:
+        """Analyze an image accessible via URL, including Civitai integration."""
+
+        if not url:
+            raise RecipeValidationError("No URL provided")
+
+        if civitai_client is None:
+            raise RecipeServiceError("Civitai client unavailable")
+
+        temp_path = self._create_temp_path()
+        metadata: Optional[dict[str, Any]] = None
+        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")
+                await self._download_image(image_url, temp_path)
+                metadata = image_info.get("meta") if "meta" in image_info else None
+            else:
+                await self._download_image(url, temp_path)
+
+            if metadata is None:
+                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,
+                recipe_scanner=recipe_scanner,
+                image_path=temp_path,
+                include_image_base64=True,
+            )
+        finally:
+            self._safe_cleanup(temp_path)
+
+    async def analyze_local_image(
+        self,
+        *,
+        file_path: str | None,
+        recipe_scanner,
+    ) -> AnalysisResult:
+        """Analyze a file already present on disk."""
+
+        if not file_path:
+            raise RecipeValidationError("No file path provided")
+
+        normalized_path = os.path.normpath(file_path.strip('"').strip("'"))
+        if not os.path.isfile(normalized_path):
+            raise RecipeNotFoundError("File not found")
+
+        metadata = self._exif_utils.extract_image_metadata(normalized_path)
+        if not metadata:
+            return self._metadata_not_found_response(normalized_path)
+
+        return await self._parse_metadata(
+            metadata,
+            recipe_scanner=recipe_scanner,
+            image_path=normalized_path,
+            include_image_base64=True,
+        )
+
+    async def analyze_widget_metadata(self, *, recipe_scanner) -> AnalysisResult:
+        """Analyse the most recent generation metadata for widget saves."""
+
+        if self._metadata_collector is None or self._metadata_processor_cls is None:
+            raise RecipeValidationError("Metadata collection not available")
+
+        raw_metadata = self._metadata_collector()
+        metadata_dict = self._metadata_processor_cls.to_dict(raw_metadata)
+        if not metadata_dict:
+            raise RecipeValidationError("No generation metadata found")
+
+        latest_image = None
+        if not self._standalone_mode and self._metadata_registry_cls is not None:
+            metadata_registry = self._metadata_registry_cls()
+            latest_image = metadata_registry.get_first_decoded_image()
+
+        if latest_image is None:
+            raise RecipeValidationError(
+                "No recent images found to use for recipe. Try generating an image first."
+            )
+
+        image_bytes = self._convert_tensor_to_png_bytes(latest_image)
+        if image_bytes is None:
+            raise RecipeValidationError("Cannot handle this data shape from metadata registry")
+
+        return AnalysisResult(
+            {
+                "metadata": metadata_dict,
+                "image_bytes": image_bytes,
+            }
+        )
+
+    # Internal helpers -------------------------------------------------
+
+    async def _parse_metadata(
+        self,
+        metadata: dict[str, Any],
+        *,
+        recipe_scanner,
+        image_path: Optional[str],
+        include_image_base64: bool,
+    ) -> 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)
+            return AnalysisResult(payload)
+
+        result = await parser.parse_metadata(metadata, recipe_scanner=recipe_scanner)
+
+        if include_image_base64 and image_path:
+            result["image_base64"] = self._encode_file(image_path)
+
+        if "error" in result and not result.get("loras"):
+            return AnalysisResult(result)
+
+        fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+        result["fingerprint"] = fingerprint
+
+        matching_recipes: list[str] = []
+        if fingerprint:
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+        result["matching_recipes"] = matching_recipes
+
+        return AnalysisResult(result)
+
+    async def _download_image(self, url: str, temp_path: str) -> None:
+        downloader = await self._downloader_factory()
+        success, result = await downloader.download_file(url, temp_path, use_auth=False)
+        if not success:
+            raise RecipeDownloadError(f"Failed to download image from URL: {result}")
+
+    def _metadata_not_found_response(self, path: str) -> AnalysisResult:
+        payload: dict[str, Any] = {"error": "No metadata found in this image", "loras": []}
+        if os.path.exists(path):
+            payload["image_base64"] = self._encode_file(path)
+        return AnalysisResult(payload)
+
+    def _write_temp_file(self, data: bytes) -> str:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+            temp_file.write(data)
+            return temp_file.name
+
+    def _create_temp_path(self) -> str:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+            return temp_file.name
+
+    def _safe_cleanup(self, path: Optional[str]) -> None:
+        if path and os.path.exists(path):
+            try:
+                os.unlink(path)
+            except Exception as exc:  # pragma: no cover - defensive logging
+                self._logger.error("Error deleting temporary file: %s", exc)
+
+    def _encode_file(self, path: str) -> str:
+        with open(path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode("utf-8")
+
+    def _convert_tensor_to_png_bytes(self, latest_image: Any) -> Optional[bytes]:
+        try:
+            if isinstance(latest_image, tuple):
+                tensor_image = latest_image[0] if latest_image else None
+                if tensor_image is None:
+                    return None
+            else:
+                tensor_image = latest_image
+
+            if hasattr(tensor_image, "shape"):
+                self._logger.debug(
+                    "Tensor shape: %s, dtype: %s", tensor_image.shape, getattr(tensor_image, "dtype", None)
+                )
+
+            import torch  # type: ignore[import-not-found]
+
+            if isinstance(tensor_image, torch.Tensor):
+                image_np = tensor_image.cpu().numpy()
+            else:
+                image_np = np.array(tensor_image)
+
+            while len(image_np.shape) > 3:
+                image_np = image_np[0]
+
+            if image_np.dtype in (np.float32, np.float64) and image_np.max() <= 1.0:
+                image_np = (image_np * 255).astype(np.uint8)
+
+            if len(image_np.shape) == 3 and image_np.shape[2] == 3:
+                pil_image = Image.fromarray(image_np)
+                img_byte_arr = io.BytesIO()
+                pil_image.save(img_byte_arr, format="PNG")
+                return img_byte_arr.getvalue()
+        except Exception as exc:  # pragma: no cover - defensive logging path
+            self._logger.error("Error processing image data: %s", exc, exc_info=True)
+            return None
+
+        return None

py/services/recipes/errors.py

@@ -0,0 +1,22 @@
+"""Shared exceptions for recipe services."""
+from __future__ import annotations
+
+
+class RecipeServiceError(Exception):
+    """Base exception for recipe service failures."""
+
+
+class RecipeValidationError(RecipeServiceError):
+    """Raised when a request payload fails validation."""
+
+
+class RecipeNotFoundError(RecipeServiceError):
+    """Raised when a recipe resource cannot be located."""
+
+
+class RecipeDownloadError(RecipeServiceError):
+    """Raised when remote recipe assets cannot be downloaded."""
+
+
+class RecipeConflictError(RecipeServiceError):
+    """Raised when a conflicting recipe state is detected."""

py/services/recipes/persistence_service.py

@@ -0,0 +1,407 @@
+"""Services encapsulating recipe persistence workflows."""
+from __future__ import annotations
+
+import base64
+import json
+import os
+import re
+import time
+import uuid
+from dataclasses import dataclass
+from typing import Any, Dict, Iterable, Optional
+
+from ...config import config
+from ...utils.utils import calculate_recipe_fingerprint
+from .errors import RecipeNotFoundError, RecipeValidationError
+
+
+@dataclass(frozen=True)
+class PersistenceResult:
+    """Return payload from persistence operations."""
+
+    payload: dict[str, Any]
+    status: int = 200
+
+
+class RecipePersistenceService:
+    """Coordinate recipe persistence tasks across storage and caches."""
+
+    def __init__(
+        self,
+        *,
+        exif_utils,
+        card_preview_width: int,
+        logger,
+    ) -> None:
+        self._exif_utils = exif_utils
+        self._card_preview_width = card_preview_width
+        self._logger = logger
+
+    async def save_recipe(
+        self,
+        *,
+        recipe_scanner,
+        image_bytes: bytes | None,
+        image_base64: str | None,
+        name: str | None,
+        tags: Iterable[str],
+        metadata: Optional[dict[str, Any]],
+    ) -> PersistenceResult:
+        """Persist a user uploaded recipe."""
+
+        missing_fields = []
+        if not name:
+            missing_fields.append("name")
+        if metadata is None:
+            missing_fields.append("metadata")
+        if missing_fields:
+            raise RecipeValidationError(
+                f"Missing required fields: {', '.join(missing_fields)}"
+            )
+
+        resolved_image_bytes = self._resolve_image_bytes(image_bytes, image_base64)
+        recipes_dir = recipe_scanner.recipes_dir
+        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,
+        )
+        image_filename = f"{recipe_id}{extension}"
+        image_path = os.path.join(recipes_dir, image_filename)
+        with open(image_path, "wb") as file_obj:
+            file_obj.write(optimized_image)
+
+        current_time = time.time()
+        loras_data = [self._normalise_lora_entry(lora) for lora in metadata.get("loras", [])]
+
+        gen_params = metadata.get("gen_params", {})
+        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", ""),
+                "seed": raw_metadata.get("seed", ""),
+                "size": raw_metadata.get("size", ""),
+                "clip_skip": raw_metadata.get("clip_skip", ""),
+            }
+
+        fingerprint = calculate_recipe_fingerprint(loras_data)
+        recipe_data: Dict[str, Any] = {
+            "id": recipe_id,
+            "file_path": image_path,
+            "title": name,
+            "modified": current_time,
+            "created_date": current_time,
+            "base_model": metadata.get("base_model", ""),
+            "loras": loras_data,
+            "gen_params": gen_params,
+            "fingerprint": fingerprint,
+        }
+
+        tags_list = list(tags)
+        if tags_list:
+            recipe_data["tags"] = tags_list
+
+        if metadata.get("source_path"):
+            recipe_data["source_path"] = metadata.get("source_path")
+
+        json_filename = f"{recipe_id}.recipe.json"
+        json_path = os.path.join(recipes_dir, json_filename)
+        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(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)
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "image_path": image_path,
+                "json_path": json_path,
+                "matching_recipes": matching_recipes,
+            }
+        )
+
+    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):
+            raise RecipeNotFoundError("Recipe not found")
+
+        with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+            recipe_data = json.load(file_obj)
+
+        image_path = recipe_data.get("file_path")
+        os.remove(recipe_json_path)
+        if image_path and os.path.exists(image_path):
+            os.remove(image_path)
+
+        await recipe_scanner.remove_recipe(recipe_id)
+        return PersistenceResult({"success": True, "message": "Recipe deleted successfully"})
+
+    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")):
+            raise RecipeValidationError(
+                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level)"
+            )
+
+        success = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
+        if not success:
+            raise RecipeNotFoundError("Recipe not found or update failed")
+
+        return PersistenceResult({"success": True, "recipe_id": recipe_id, "updates": updates})
+
+    async def reconnect_lora(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        lora_index: int,
+        target_name: str,
+    ) -> 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):
+            raise RecipeNotFoundError("Recipe not found")
+
+        target_lora = await recipe_scanner.get_local_lora(target_name)
+        if not target_lora:
+            raise RecipeNotFoundError(f"Local LoRA not found with name: {target_name}")
+
+        recipe_data, updated_lora = await recipe_scanner.update_lora_entry(
+            recipe_id,
+            lora_index,
+            target_name=target_name,
+            target_lora=target_lora,
+        )
+
+        image_path = recipe_data.get("file_path")
+        if image_path and os.path.exists(image_path):
+            self._exif_utils.append_recipe_metadata(image_path, recipe_data)
+
+        matching_recipes = []
+        if "fingerprint" in recipe_data:
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(recipe_data["fingerprint"])
+            if recipe_id in matching_recipes:
+                matching_recipes.remove(recipe_id)
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "updated_lora": updated_lora,
+                "matching_recipes": matching_recipes,
+            }
+        )
+
+    async def bulk_delete(
+        self,
+        *,
+        recipe_scanner,
+        recipe_ids: Iterable[str],
+    ) -> PersistenceResult:
+        """Delete multiple recipes in a single request."""
+
+        recipe_ids = list(recipe_ids)
+        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):
+                failed_recipes.append({"id": recipe_id, "reason": "Recipe not found"})
+                continue
+
+            try:
+                with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                    recipe_data = json.load(file_obj)
+                image_path = recipe_data.get("file_path")
+                os.remove(recipe_json_path)
+                if image_path and os.path.exists(image_path):
+                    os.remove(image_path)
+                deleted_recipes.append(recipe_id)
+            except Exception as exc:
+                failed_recipes.append({"id": recipe_id, "reason": str(exc)})
+
+        if deleted_recipes:
+            await recipe_scanner.bulk_remove(deleted_recipes)
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "deleted": deleted_recipes,
+                "failed": failed_recipes,
+                "total_deleted": len(deleted_recipes),
+                "total_failed": len(failed_recipes),
+            }
+        )
+
+    async def save_recipe_from_widget(
+        self,
+        *,
+        recipe_scanner,
+        metadata: dict[str, Any],
+        image_bytes: bytes,
+    ) -> PersistenceResult:
+        """Save a recipe constructed from widget metadata."""
+
+        if not metadata:
+            raise RecipeValidationError("No generation metadata found")
+
+        recipes_dir = recipe_scanner.recipes_dir
+        os.makedirs(recipes_dir, exist_ok=True)
+
+        recipe_id = str(uuid.uuid4())
+        optimized_image, extension = self._exif_utils.optimize_image(
+            image_data=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)
+        with open(image_path, "wb") as file_obj:
+            file_obj.write(optimized_image)
+
+        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] = {}
+
+        for name, strength in lora_matches:
+            lora_info = await recipe_scanner.get_local_lora(name)
+            lora_data = {
+                "file_name": name,
+                "strength": float(strength),
+                "hash": (lora_info.get("sha256") or "").lower() if lora_info else "",
+                "modelVersionId": (lora_info.get("civitai") or {}).get("id", 0) if lora_info else 0,
+                "modelName": ((lora_info.get("civitai") or {}).get("model") or {}).get("name", name) if lora_info else "",
+                "modelVersionName": (lora_info.get("civitai") or {}).get("name", "") if lora_info else "",
+                "isDeleted": False,
+                "exclude": False,
+            }
+            loras_data.append(lora_data)
+
+            if lora_info and "base_model" in lora_info:
+                base_model = lora_info["base_model"]
+                base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+
+        recipe_name = self._derive_recipe_name(lora_matches)
+        most_common_base_model = (
+            max(base_model_counts.items(), key=lambda item: item[1])[0] if base_model_counts else ""
+        )
+
+        recipe_data = {
+            "id": recipe_id,
+            "file_path": image_path,
+            "title": recipe_name,
+            "modified": time.time(),
+            "created_date": time.time(),
+            "base_model": most_common_base_model,
+            "loras": loras_data,
+            "checkpoint": metadata.get("checkpoint", ""),
+            "gen_params": {
+                key: value
+                for key, value in metadata.items()
+                if key not in ["checkpoint", "loras"]
+            },
+            "loras_stack": lora_stack,
+        }
+
+        json_filename = f"{recipe_id}.recipe.json"
+        json_path = os.path.join(recipes_dir, json_filename)
+        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(image_path, recipe_data)
+        await recipe_scanner.add_recipe(recipe_data)
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "image_path": image_path,
+                "json_path": json_path,
+                "recipe_name": recipe_name,
+            }
+        )
+
+    # Helper methods ---------------------------------------------------
+
+    def _resolve_image_bytes(self, image_bytes: bytes | None, image_base64: str | None) -> bytes:
+        if image_bytes is not None:
+            return image_bytes
+        if image_base64:
+            try:
+                payload = image_base64.split(",", 1)[1] if "," in image_base64 else image_base64
+                return base64.b64decode(payload)
+            except Exception as exc:  # pragma: no cover - validation guard
+                raise RecipeValidationError(f"Invalid base64 image data: {exc}") from exc
+        raise RecipeValidationError("No image data provided")
+
+    def _normalise_lora_entry(self, lora: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "file_name": lora.get("file_name", "")
+            or (
+                os.path.splitext(os.path.basename(lora.get("localPath", "")))[0]
+                if lora.get("localPath")
+                else ""
+            ),
+            "hash": (lora.get("hash") or "").lower(),
+            "strength": float(lora.get("weight", 1.0)),
+            "modelVersionId": lora.get("id", 0),
+            "modelName": lora.get("name", ""),
+            "modelVersionName": lora.get("version", ""),
+            "isDeleted": lora.get("isDeleted", False),
+            "exclude": lora.get("exclude", False),
+        }
+
+    async def _find_matching_recipes(
+        self,
+        recipe_scanner,
+        fingerprint: str | None,
+        *,
+        exclude_id: Optional[str] = None,
+    ) -> list[str]:
+        if not fingerprint:
+            return []
+        matches = await recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+        if exclude_id and exclude_id in matches:
+            matches.remove(exclude_id)
+        return matches
+
+    def _derive_recipe_name(self, lora_matches: list[tuple[str, str]]) -> str:
+        recipe_name_parts = [f"{name.strip()}-{float(strength):.2f}" for name, strength in lora_matches[:3]]
+        recipe_name = "_".join(recipe_name_parts)
+        return recipe_name or "recipe"

py/services/recipes/sharing_service.py

@@ -0,0 +1,105 @@
+"""Services handling recipe sharing and downloads."""
+from __future__ import annotations
+
+import os
+import shutil
+import tempfile
+import time
+from dataclasses import dataclass
+from typing import Any, Dict
+
+from .errors import RecipeNotFoundError
+
+
+@dataclass(frozen=True)
+class SharingResult:
+    """Return payload for share operations."""
+
+    payload: dict[str, Any]
+    status: int = 200
+
+
+@dataclass(frozen=True)
+class DownloadInfo:
+    """Information required to stream a shared recipe file."""
+
+    file_path: str
+    download_filename: str
+
+
+class RecipeSharingService:
+    """Prepare temporary recipe downloads with TTL cleanup."""
+
+    def __init__(self, *, ttl_seconds: int = 300, logger) -> None:
+        self._ttl_seconds = ttl_seconds
+        self._logger = logger
+        self._shared_recipes: Dict[str, Dict[str, Any]] = {}
+
+    async def share_recipe(self, *, recipe_scanner, recipe_id: str) -> SharingResult:
+        """Prepare a temporary downloadable copy of a recipe image."""
+
+        recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+        if not recipe:
+            raise RecipeNotFoundError("Recipe not found")
+
+        image_path = recipe.get("file_path")
+        if not image_path or not os.path.exists(image_path):
+            raise RecipeNotFoundError("Recipe image not found")
+
+        ext = os.path.splitext(image_path)[1]
+        with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as temp_file:
+            temp_path = temp_file.name
+
+        shutil.copy2(image_path, temp_path)
+        timestamp = int(time.time())
+        self._shared_recipes[recipe_id] = {
+            "path": temp_path,
+            "timestamp": timestamp,
+            "expires": time.time() + self._ttl_seconds,
+        }
+        self._cleanup_shared_recipes()
+
+        safe_title = recipe.get("title", "").replace(" ", "_").lower()
+        filename = f"recipe_{safe_title}{ext}" if safe_title else f"recipe_{recipe_id}{ext}"
+        url_path = f"/api/recipe/{recipe_id}/share/download?t={timestamp}"
+        return SharingResult({"success": True, "download_url": url_path, "filename": filename})
+
+    async def prepare_download(self, *, recipe_scanner, recipe_id: str) -> DownloadInfo:
+        """Return file path and filename for a prepared shared recipe."""
+
+        shared_info = self._shared_recipes.get(recipe_id)
+        if not shared_info or time.time() > shared_info.get("expires", 0):
+            self._cleanup_entry(recipe_id)
+            raise RecipeNotFoundError("Shared recipe not found or expired")
+
+        file_path = shared_info["path"]
+        if not os.path.exists(file_path):
+            self._cleanup_entry(recipe_id)
+            raise RecipeNotFoundError("Shared recipe file not found")
+
+        recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+        filename_base = (
+            f"recipe_{recipe.get('title', '').replace(' ', '_').lower()}" if recipe else recipe_id
+        )
+        ext = os.path.splitext(file_path)[1]
+        download_filename = f"{filename_base}{ext}"
+        return DownloadInfo(file_path=file_path, download_filename=download_filename)
+
+    def _cleanup_shared_recipes(self) -> None:
+        for recipe_id in list(self._shared_recipes.keys()):
+            shared = self._shared_recipes.get(recipe_id)
+            if not shared:
+                continue
+            if time.time() > shared.get("expires", 0):
+                self._cleanup_entry(recipe_id)
+
+    def _cleanup_entry(self, recipe_id: str) -> None:
+        shared_info = self._shared_recipes.pop(recipe_id, None)
+        if not shared_info:
+            return
+        file_path = shared_info.get("path")
+        if file_path and os.path.exists(file_path):
+            try:
+                os.unlink(file_path)
+            except Exception as exc:  # pragma: no cover - defensive logging
+                self._logger.error("Error cleaning up shared recipe %s: %s", recipe_id, exc)

py/services/server_i18n.py

@@ -0,0 +1,114 @@
+import os
+import json
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+class ServerI18nManager:
+    """Server-side internationalization manager for template rendering"""
+    
+    def __init__(self):
+        self.translations = {}
+        self.current_locale = 'en'
+        self._load_translations()
+    
+    def _load_translations(self):
+        """Load all translation files from the locales directory"""
+        i18n_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 
+            'locales'
+        )
+        
+        if not os.path.exists(i18n_path):
+            logger.warning(f"I18n directory not found: {i18n_path}")
+            return
+        
+        # Load all available locale files
+        for filename in os.listdir(i18n_path):
+            if filename.endswith('.json'):
+                locale_code = filename[:-5]  # Remove .json extension
+                try:
+                    self._load_locale_file(i18n_path, filename, locale_code)
+                except Exception as e:
+                    logger.error(f"Error loading locale file {filename}: {e}")
+    
+    def _load_locale_file(self, path: str, filename: str, locale_code: str):
+        """Load a single locale JSON file"""
+        file_path = os.path.join(path, filename)
+        
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                translations = json.load(f)
+                
+            self.translations[locale_code] = translations
+            logger.debug(f"Loaded translations for {locale_code} from {filename}")
+            
+        except Exception as e:
+            logger.error(f"Error parsing locale file {filename}: {e}")
+    
+    def set_locale(self, locale: str):
+        """Set the current locale"""
+        if locale in self.translations:
+            self.current_locale = locale
+        else:
+            logger.warning(f"Locale {locale} not found, using 'en'")
+            self.current_locale = 'en'
+    
+    def get_translation(self, key: str, params: Dict[str, Any] = None, **kwargs) -> str:
+        """Get translation for a key with optional parameters (supports both dict and keyword args)"""
+        # Merge kwargs into params for convenience
+        if params is None:
+            params = {}
+        if kwargs:
+            params = {**params, **kwargs}
+        
+        if self.current_locale not in self.translations:
+            return key
+        
+        # Navigate through nested object using dot notation
+        keys = key.split('.')
+        value = self.translations[self.current_locale]
+        
+        for k in keys:
+            if isinstance(value, dict) and k in value:
+                value = value[k]
+            else:
+                # Fallback to English if current locale doesn't have the key
+                if self.current_locale != 'en' and 'en' in self.translations:
+                    en_value = self.translations['en']
+                    for k in keys:
+                        if isinstance(en_value, dict) and k in en_value:
+                            en_value = en_value[k]
+                        else:
+                            return key
+                    value = en_value
+                else:
+                    return key
+                break
+        
+        if not isinstance(value, str):
+            return key
+        
+        # Replace parameters if provided
+        if params:
+            for param_key, param_value in params.items():
+                placeholder = f"{{{param_key}}}"
+                double_placeholder = f"{{{{{param_key}}}}}"
+                value = value.replace(placeholder, str(param_value))
+                value = value.replace(double_placeholder, str(param_value))
+        
+        return value
+    
+    def get_available_locales(self) -> list:
+        """Get list of available locales"""
+        return list(self.translations.keys())
+    
+    def create_template_filter(self):
+        """Create a Jinja2 filter function for templates"""
+        def t_filter(key: str, **params) -> str:
+            return self.get_translation(key, params)
+        return t_filter
+
+# Create global instance
+server_i18n = ServerI18nManager()

py/services/settings_manager.py

@@ -1,14 +1,51 @@
-import os
+import copy
 import json
+import os
 import logging
-from typing import Any, Dict
+from datetime import datetime, timezone
+from typing import Any, Dict, Iterable, List, Mapping, Optional
+
+from ..utils.settings_paths import ensure_settings_file
 
 logger = logging.getLogger(__name__)
 
+
+DEFAULT_SETTINGS: Dict[str, Any] = {
+    "civitai_api_key": "",
+    "language": "en",
+    "show_only_sfw": False,
+    "enable_metadata_archive_db": False,
+    "proxy_enabled": False,
+    "proxy_host": "",
+    "proxy_port": "",
+    "proxy_username": "",
+    "proxy_password": "",
+    "proxy_type": "http",
+    "default_lora_root": "",
+    "default_checkpoint_root": "",
+    "default_embedding_root": "",
+    "base_model_path_mappings": {},
+    "download_path_templates": {},
+    "example_images_path": "",
+    "optimize_example_images": True,
+    "auto_download_example_images": False,
+    "blur_mature_content": True,
+    "autoplay_on_hover": False,
+    "display_density": "default",
+    "card_info_display": "always",
+    "include_trigger_words": False,
+    "compact_mode": False,
+}
+
+
 class SettingsManager:
     def __init__(self):
-        self.settings_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'settings.json')
+        self.settings_file = ensure_settings_file(logger)
         self.settings = self._load_settings()
+        self._migrate_setting_keys()
+        self._ensure_default_settings()
+        self._migrate_to_library_registry()
+        self._migrate_download_path_template()
         self._auto_set_default_roots()
         self._check_environment_variables()
 
@@ -22,26 +59,310 @@ class SettingsManager:
                 logger.error(f"Error loading settings: {e}")
         return self._get_default_settings()
 
+    def _ensure_default_settings(self) -> None:
+        """Ensure all default settings keys exist"""
+        updated = False
+        for key, value in self._get_default_settings().items():
+            if key not in self.settings:
+                if isinstance(value, dict):
+                    self.settings[key] = value.copy()
+                else:
+                    self.settings[key] = value
+                updated = True
+        if updated:
+            self._save_settings()
+
+    def _migrate_to_library_registry(self) -> None:
+        """Ensure settings include the multi-library registry structure."""
+        libraries = self.settings.get("libraries")
+        active_name = self.settings.get("active_library")
+
+        if not isinstance(libraries, dict) or not libraries:
+            library_name = active_name or "default"
+            library_payload = self._build_library_payload(
+                folder_paths=self.settings.get("folder_paths", {}),
+                default_lora_root=self.settings.get("default_lora_root", ""),
+                default_checkpoint_root=self.settings.get("default_checkpoint_root", ""),
+                default_embedding_root=self.settings.get("default_embedding_root", ""),
+            )
+            libraries = {library_name: library_payload}
+            self.settings["libraries"] = libraries
+            self.settings["active_library"] = library_name
+            self._sync_active_library_to_root(save=False)
+            self._save_settings()
+            return
+
+        sanitized_libraries: Dict[str, Dict[str, Any]] = {}
+        changed = False
+        for name, data in libraries.items():
+            if not isinstance(data, dict):
+                data = {}
+                changed = True
+            payload = self._build_library_payload(
+                folder_paths=data.get("folder_paths"),
+                default_lora_root=data.get("default_lora_root"),
+                default_checkpoint_root=data.get("default_checkpoint_root"),
+                default_embedding_root=data.get("default_embedding_root"),
+                metadata=data.get("metadata"),
+                base=data,
+            )
+            sanitized_libraries[name] = payload
+            if payload is not data:
+                changed = True
+
+        if changed:
+            self.settings["libraries"] = sanitized_libraries
+
+        if not active_name or active_name not in sanitized_libraries:
+            if sanitized_libraries:
+                self.settings["active_library"] = next(iter(sanitized_libraries.keys()))
+            else:
+                self.settings["active_library"] = "default"
+
+        self._sync_active_library_to_root(save=changed)
+
+    def _sync_active_library_to_root(self, *, save: bool = False) -> None:
+        """Update top-level folder path settings to mirror the active library."""
+        libraries = self.settings.get("libraries", {})
+        active_name = self.settings.get("active_library")
+        if not libraries:
+            return
+
+        if active_name not in libraries:
+            active_name = next(iter(libraries.keys()))
+            self.settings["active_library"] = active_name
+
+        active_library = libraries.get(active_name, {})
+        folder_paths = copy.deepcopy(active_library.get("folder_paths", {}))
+        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_embedding_root"] = active_library.get("default_embedding_root", "")
+
+        if save:
+            self._save_settings()
+
+    def _current_timestamp(self) -> str:
+        return datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+
+    def _build_library_payload(
+        self,
+        *,
+        folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
+        default_lora_root: Optional[str] = None,
+        default_checkpoint_root: Optional[str] = None,
+        default_embedding_root: Optional[str] = None,
+        metadata: Optional[Mapping[str, Any]] = None,
+        base: Optional[Mapping[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        payload: Dict[str, Any] = dict(base or {})
+        timestamp = self._current_timestamp()
+
+        if folder_paths is not None:
+            payload["folder_paths"] = self._normalize_folder_paths(folder_paths)
+        else:
+            payload.setdefault("folder_paths", {})
+
+        if default_lora_root is not None:
+            payload["default_lora_root"] = default_lora_root
+        else:
+            payload.setdefault("default_lora_root", "")
+
+        if default_checkpoint_root is not None:
+            payload["default_checkpoint_root"] = default_checkpoint_root
+        else:
+            payload.setdefault("default_checkpoint_root", "")
+
+        if default_embedding_root is not None:
+            payload["default_embedding_root"] = default_embedding_root
+        else:
+            payload.setdefault("default_embedding_root", "")
+
+        if metadata:
+            merged_meta = dict(payload.get("metadata", {}))
+            merged_meta.update(metadata)
+            payload["metadata"] = merged_meta
+
+        payload.setdefault("created_at", timestamp)
+        payload["updated_at"] = timestamp
+        return payload
+
+    def _normalize_folder_paths(
+        self, folder_paths: Mapping[str, Iterable[str]]
+    ) -> Dict[str, List[str]]:
+        normalized: Dict[str, List[str]] = {}
+        for key, values in folder_paths.items():
+            if not isinstance(values, Iterable):
+                continue
+            cleaned: List[str] = []
+            seen = set()
+            for value in values:
+                if not isinstance(value, str):
+                    continue
+                stripped = value.strip()
+                if not stripped:
+                    continue
+                if stripped not in seen:
+                    cleaned.append(stripped)
+                    seen.add(stripped)
+            normalized[key] = cleaned
+        return normalized
+
+    def _validate_folder_paths(
+        self,
+        library_name: str,
+        folder_paths: Mapping[str, Iterable[str]],
+    ) -> None:
+        """Ensure folder paths do not overlap with other libraries."""
+        libraries = self.settings.get("libraries", {})
+        normalized_new: Dict[str, Dict[str, str]] = {}
+        for key, values in folder_paths.items():
+            path_map: Dict[str, str] = {}
+            for value in values:
+                if not isinstance(value, str):
+                    continue
+                stripped = value.strip()
+                if not stripped:
+                    continue
+                normalized_value = os.path.normcase(os.path.normpath(stripped))
+                path_map[normalized_value] = stripped
+            if path_map:
+                normalized_new[key] = path_map
+
+        if not normalized_new:
+            return
+
+        for other_name, other in libraries.items():
+            if other_name == library_name:
+                continue
+            other_paths = other.get("folder_paths", {})
+            for key, new_paths in normalized_new.items():
+                existing = {
+                    os.path.normcase(os.path.normpath(path))
+                    for path in other_paths.get(key, [])
+                    if isinstance(path, str) and path
+                }
+                overlap = existing.intersection(new_paths.keys())
+                if overlap:
+                    collisions = ", ".join(sorted(new_paths[value] for value in overlap))
+                    raise ValueError(
+                        f"Folder path(s) {collisions} already assigned to library '{other_name}'"
+                    )
+
+    def _update_active_library_entry(
+        self,
+        *,
+        folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
+        default_lora_root: Optional[str] = None,
+        default_checkpoint_root: Optional[str] = None,
+        default_embedding_root: Optional[str] = None,
+    ) -> bool:
+        libraries = self.settings.get("libraries", {})
+        active_name = self.settings.get("active_library")
+        if not active_name or active_name not in libraries:
+            return False
+
+        library = libraries[active_name]
+        changed = False
+
+        if folder_paths is not None:
+            normalized_paths = self._normalize_folder_paths(folder_paths)
+            if library.get("folder_paths") != normalized_paths:
+                library["folder_paths"] = normalized_paths
+                changed = True
+
+        if default_lora_root is not None and library.get("default_lora_root") != default_lora_root:
+            library["default_lora_root"] = default_lora_root
+            changed = True
+
+        if default_checkpoint_root is not None and library.get("default_checkpoint_root") != default_checkpoint_root:
+            library["default_checkpoint_root"] = default_checkpoint_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
+
+        if changed:
+            library.setdefault("created_at", self._current_timestamp())
+            library["updated_at"] = self._current_timestamp()
+
+        return changed
+
+    def _migrate_setting_keys(self) -> None:
+        """Migrate legacy camelCase setting keys to snake_case"""
+        key_migrations = {
+            'optimizeExampleImages': 'optimize_example_images',
+            'autoDownloadExampleImages': 'auto_download_example_images',
+            'blurMatureContent': 'blur_mature_content',
+            'autoplayOnHover': 'autoplay_on_hover',
+            'displayDensity': 'display_density',
+            'cardInfoDisplay': 'card_info_display',
+            'includeTriggerWords': 'include_trigger_words',
+            'compactMode': 'compact_mode',
+        }
+
+        updated = False
+        for old_key, new_key in key_migrations.items():
+            if old_key in self.settings:
+                if new_key not in self.settings:
+                    self.settings[new_key] = self.settings[old_key]
+                del self.settings[old_key]
+                updated = True
+
+        if updated:
+            logger.info("Migrated legacy setting keys to snake_case")
+            self._save_settings()
+
+    def _migrate_download_path_template(self):
+        """Migrate old download_path_template to new download_path_templates"""
+        old_template = self.settings.get('download_path_template')
+        templates = self.settings.get('download_path_templates')
+
+        # If old template exists and new templates don't exist, migrate
+        if old_template is not None and not templates:
+            logger.info("Migrating download_path_template to download_path_templates")
+            self.settings['download_path_templates'] = {
+                'lora': old_template,
+                'checkpoint': old_template,
+                'embedding': old_template
+            }
+            # Remove old setting
+            del self.settings['download_path_template']
+            self._save_settings()
+            logger.info("Migration completed")
+
     def _auto_set_default_roots(self):
-        """Auto set default root paths if only one folder is present and default is empty."""
+        """Auto set default root paths when only one folder is present and the current default is unset or not among the options."""
         folder_paths = self.settings.get('folder_paths', {})
         updated = False
         # loras
         loras = folder_paths.get('loras', [])
-        if isinstance(loras, list) and len(loras) == 1 and not self.settings.get('default_lora_root'):
-            self.settings['default_lora_root'] = loras[0]
-            updated = True
+        if isinstance(loras, list) and len(loras) == 1:
+            current_lora_root = self.settings.get('default_lora_root')
+            if current_lora_root not in loras:
+                self.settings['default_lora_root'] = loras[0]
+                updated = True
         # checkpoints
         checkpoints = folder_paths.get('checkpoints', [])
-        if isinstance(checkpoints, list) and len(checkpoints) == 1 and not self.settings.get('default_checkpoint_root'):
-            self.settings['default_checkpoint_root'] = checkpoints[0]
-            updated = True
+        if isinstance(checkpoints, list) and len(checkpoints) == 1:
+            current_checkpoint_root = self.settings.get('default_checkpoint_root')
+            if current_checkpoint_root not in checkpoints:
+                self.settings['default_checkpoint_root'] = checkpoints[0]
+                updated = True
         # embeddings
         embeddings = folder_paths.get('embeddings', [])
-        if isinstance(embeddings, list) and len(embeddings) == 1 and not self.settings.get('default_embedding_root'):
-            self.settings['default_embedding_root'] = embeddings[0]
-            updated = True
+        if isinstance(embeddings, list) and len(embeddings) == 1:
+            current_embedding_root = self.settings.get('default_embedding_root')
+            if current_embedding_root not in embeddings:
+                self.settings['default_embedding_root'] = embeddings[0]
+                updated = True
         if updated:
+            self._update_active_library_entry(
+                default_lora_root=self.settings.get('default_lora_root'),
+                default_checkpoint_root=self.settings.get('default_checkpoint_root'),
+                default_embedding_root=self.settings.get('default_embedding_root'),
+            )
             self._save_settings()
 
     def _check_environment_variables(self) -> None:
@@ -59,10 +380,11 @@ class SettingsManager:
 
     def _get_default_settings(self) -> Dict[str, Any]:
         """Return default settings"""
-        return {
-            "civitai_api_key": "",
-            "show_only_sfw": False
-        }
+        defaults = DEFAULT_SETTINGS.copy()
+        # Ensure nested dicts are independent copies
+        defaults['base_model_path_mappings'] = {}
+        defaults['download_path_templates'] = {}
+        return defaults
 
     def get(self, key: str, default: Any = None) -> Any:
         """Get setting value"""
@@ -71,8 +393,23 @@ class SettingsManager:
     def set(self, key: str, value: Any) -> None:
         """Set setting value and save"""
         self.settings[key] = 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_embedding_root':
+            self._update_active_library_entry(default_embedding_root=str(value))
         self._save_settings()
 
+    def delete(self, key: str) -> None:
+        """Delete setting key and save"""
+        if key in self.settings:
+            del self.settings[key]
+            self._save_settings()
+            logger.info(f"Deleted setting: {key}")
+
     def _save_settings(self) -> None:
         """Save settings to file"""
         try:
@@ -81,4 +418,274 @@ class SettingsManager:
         except Exception as e:
             logger.error(f"Error saving settings: {e}")
 
+    def get_libraries(self) -> Dict[str, Dict[str, Any]]:
+        """Return a copy of the registered libraries."""
+        libraries = self.settings.get("libraries", {})
+        return copy.deepcopy(libraries)
+
+    def get_active_library_name(self) -> str:
+        """Return the currently active library name."""
+        libraries = self.settings.get("libraries", {})
+        active_name = self.settings.get("active_library")
+        if active_name and active_name in libraries:
+            return active_name
+        if libraries:
+            return next(iter(libraries.keys()))
+        return "default"
+
+    def get_active_library(self) -> Dict[str, Any]:
+        """Return a copy of the active library configuration."""
+        libraries = self.settings.get("libraries", {})
+        active_name = self.get_active_library_name()
+        return copy.deepcopy(libraries.get(active_name, {}))
+
+    def activate_library(self, library_name: str) -> None:
+        """Activate a library by name and refresh dependent services."""
+        libraries = self.settings.get("libraries", {})
+        if library_name not in libraries:
+            raise KeyError(f"Library '{library_name}' does not exist")
+
+        current_active = self.get_active_library_name()
+        if current_active == library_name:
+            # Ensure root settings stay in sync even if already active
+            self._sync_active_library_to_root(save=False)
+            self._save_settings()
+            self._notify_library_change(library_name)
+            return
+
+        self.settings["active_library"] = library_name
+        self._sync_active_library_to_root(save=False)
+        self._save_settings()
+        self._notify_library_change(library_name)
+
+    def upsert_library(
+        self,
+        library_name: str,
+        *,
+        folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
+        default_lora_root: Optional[str] = None,
+        default_checkpoint_root: Optional[str] = None,
+        default_embedding_root: Optional[str] = None,
+        metadata: Optional[Mapping[str, Any]] = None,
+        activate: bool = False,
+    ) -> Dict[str, Any]:
+        """Create or update a library definition."""
+
+        name = library_name.strip()
+        if not name:
+            raise ValueError("Library name cannot be empty")
+
+        if folder_paths is not None:
+            self._validate_folder_paths(name, folder_paths)
+
+        libraries = self.settings.setdefault("libraries", {})
+        existing = libraries.get(name, {})
+
+        payload = self._build_library_payload(
+            folder_paths=folder_paths if folder_paths is not None else existing.get("folder_paths"),
+            default_lora_root=default_lora_root if default_lora_root is not None else existing.get("default_lora_root"),
+            default_checkpoint_root=(
+                default_checkpoint_root
+                if default_checkpoint_root is not None
+                else existing.get("default_checkpoint_root")
+            ),
+            default_embedding_root=(
+                default_embedding_root
+                if default_embedding_root is not None
+                else existing.get("default_embedding_root")
+            ),
+            metadata=metadata if metadata is not None else existing.get("metadata"),
+            base=existing,
+        )
+
+        libraries[name] = payload
+
+        if activate or not self.settings.get("active_library"):
+            self.settings["active_library"] = name
+
+        self._sync_active_library_to_root(save=False)
+        self._save_settings()
+
+        if self.settings.get("active_library") == name:
+            self._notify_library_change(name)
+
+        return payload
+
+    def create_library(
+        self,
+        library_name: str,
+        *,
+        folder_paths: Mapping[str, Iterable[str]],
+        default_lora_root: str = "",
+        default_checkpoint_root: str = "",
+        default_embedding_root: str = "",
+        metadata: Optional[Mapping[str, Any]] = None,
+        activate: bool = False,
+    ) -> Dict[str, Any]:
+        """Create a new library entry."""
+
+        libraries = self.settings.get("libraries", {})
+        if library_name in libraries:
+            raise ValueError(f"Library '{library_name}' already exists")
+
+        return self.upsert_library(
+            library_name,
+            folder_paths=folder_paths,
+            default_lora_root=default_lora_root,
+            default_checkpoint_root=default_checkpoint_root,
+            default_embedding_root=default_embedding_root,
+            metadata=metadata,
+            activate=activate,
+        )
+
+    def rename_library(self, old_name: str, new_name: str) -> None:
+        """Rename an existing library."""
+
+        libraries = self.settings.get("libraries", {})
+        if old_name not in libraries:
+            raise KeyError(f"Library '{old_name}' does not exist")
+        new_name_stripped = new_name.strip()
+        if not new_name_stripped:
+            raise ValueError("New library name cannot be empty")
+        if new_name_stripped in libraries:
+            raise ValueError(f"Library '{new_name_stripped}' already exists")
+
+        libraries[new_name_stripped] = libraries.pop(old_name)
+        if self.settings.get("active_library") == old_name:
+            self.settings["active_library"] = new_name_stripped
+            active_name = new_name_stripped
+        else:
+            active_name = self.settings.get("active_library")
+
+        self._sync_active_library_to_root(save=False)
+        self._save_settings()
+
+        if active_name == new_name_stripped:
+            self._notify_library_change(new_name_stripped)
+
+    def delete_library(self, library_name: str) -> None:
+        """Remove a library definition."""
+
+        libraries = self.settings.get("libraries", {})
+        if library_name not in libraries:
+            raise KeyError(f"Library '{library_name}' does not exist")
+        if len(libraries) == 1:
+            raise ValueError("At least one library must remain")
+
+        was_active = self.settings.get("active_library") == library_name
+        libraries.pop(library_name)
+
+        if was_active:
+            new_active = next(iter(libraries.keys()))
+            self.settings["active_library"] = new_active
+        self._sync_active_library_to_root(save=False)
+        self._save_settings()
+
+        if was_active:
+            self._notify_library_change(self.settings["active_library"])
+
+    def update_active_library_paths(
+        self,
+        folder_paths: Mapping[str, Iterable[str]],
+        *,
+        default_lora_root: Optional[str] = None,
+        default_checkpoint_root: Optional[str] = None,
+        default_embedding_root: Optional[str] = None,
+    ) -> None:
+        """Update folder paths for the active library."""
+
+        active_name = self.get_active_library_name()
+        self.upsert_library(
+            active_name,
+            folder_paths=folder_paths,
+            default_lora_root=default_lora_root,
+            default_checkpoint_root=default_checkpoint_root,
+            default_embedding_root=default_embedding_root,
+            activate=True,
+        )
+
+    def _notify_library_change(self, library_name: str) -> None:
+        """Notify dependent services that the active library changed."""
+        libraries = self.settings.get("libraries", {})
+        library_config = libraries.get(library_name, {})
+        library_snapshot = copy.deepcopy(library_config)
+
+        try:
+            from ..config import config  # Local import to avoid circular dependency
+
+            config.apply_library_settings(library_snapshot)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.debug("Failed to apply library settings to config: %s", exc)
+
+        try:
+            from .service_registry import ServiceRegistry  # type: ignore
+
+            for service_name in (
+                "lora_scanner",
+                "checkpoint_scanner",
+                "embedding_scanner",
+                "recipe_scanner",
+            ):
+                service = ServiceRegistry.get_service_sync(service_name)
+                if service and hasattr(service, "on_library_changed"):
+                    try:
+                        service.on_library_changed()
+                    except Exception as service_exc:  # pragma: no cover - defensive logging
+                        logger.debug(
+                            "Service %s failed to handle library change: %s",
+                            service_name,
+                            service_exc,
+                        )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.debug("Failed to notify services about library change: %s", exc)
+
+    def get_download_path_template(self, model_type: str) -> str:
+        """Get download path template for specific model type
+        
+        Args:
+            model_type: The type of model ('lora', 'checkpoint', 'embedding')
+            
+        Returns:
+            Template string for the model type, defaults to '{base_model}/{first_tag}'
+        """
+        templates = self.settings.get('download_path_templates', {})
+        
+        # Handle edge case where templates might be stored as JSON string
+        if isinstance(templates, str):
+            try:
+                # Try to parse JSON string
+                parsed_templates = json.loads(templates)
+                if isinstance(parsed_templates, dict):
+                    # Update settings with parsed dictionary
+                    self.settings['download_path_templates'] = parsed_templates
+                    self._save_settings()
+                    templates = parsed_templates
+                    logger.info("Successfully parsed download_path_templates from JSON string")
+                else:
+                    raise ValueError("Parsed JSON is not a dictionary")
+            except (json.JSONDecodeError, ValueError) as e:
+                # If parsing fails, set default values
+                logger.warning(f"Failed to parse download_path_templates JSON string: {e}. Setting default values.")
+                default_template = '{base_model}/{first_tag}'
+                templates = {
+                    'lora': default_template,
+                    'checkpoint': default_template,
+                    'embedding': default_template
+                }
+                self.settings['download_path_templates'] = templates
+                self._save_settings()
+        
+        # Ensure templates is a dictionary
+        if not isinstance(templates, dict):
+            default_template = '{base_model}/{first_tag}'
+            templates = {
+                'lora': default_template,
+                'checkpoint': default_template,
+                'embedding': default_template
+            }
+            self.settings['download_path_templates'] = templates
+            self._save_settings()
+        
+        return templates.get(model_type, '{base_model}/{first_tag}')
+
 settings = SettingsManager()

py/services/tag_update_service.py

@@ -0,0 +1,47 @@
+"""Service for updating tag collections on metadata records."""
+
+from __future__ import annotations
+
+import os
+
+from typing import Awaitable, Callable, Dict, List, Sequence
+
+
+class TagUpdateService:
+    """Encapsulate tag manipulation for models."""
+
+    def __init__(self, *, metadata_manager) -> None:
+        self._metadata_manager = metadata_manager
+
+    async def add_tags(
+        self,
+        *,
+        file_path: str,
+        new_tags: Sequence[str],
+        metadata_loader: Callable[[str], Awaitable[Dict[str, object]]],
+        update_cache: Callable[[str, str, Dict[str, object]], Awaitable[bool]],
+    ) -> List[str]:
+        """Add tags to a metadata entry while keeping case-insensitive uniqueness."""
+
+        base, _ = os.path.splitext(file_path)
+        metadata_path = f"{base}.metadata.json"
+        metadata = await metadata_loader(metadata_path)
+
+        existing_tags = list(metadata.get("tags", []))
+        existing_lower = [tag.lower() for tag in existing_tags]
+
+        tags_added: List[str] = []
+        for tag in new_tags:
+            if isinstance(tag, str) and tag.strip():
+                normalized = tag.strip()
+                if normalized.lower() not in existing_lower:
+                    existing_tags.append(normalized)
+                    existing_lower.append(normalized.lower())
+                    tags_added.append(normalized)
+
+        metadata["tags"] = existing_tags
+        await self._metadata_manager.save_metadata(file_path, metadata)
+        await update_cache(file_path, file_path, metadata)
+
+        return existing_tags
+

py/services/use_cases/__init__.py

@@ -0,0 +1,37 @@
+"""Application-level orchestration services for model routes."""
+
+from .auto_organize_use_case import (
+    AutoOrganizeInProgressError,
+    AutoOrganizeUseCase,
+)
+from .bulk_metadata_refresh_use_case import (
+    BulkMetadataRefreshUseCase,
+    MetadataRefreshProgressReporter,
+)
+from .download_model_use_case import (
+    DownloadModelEarlyAccessError,
+    DownloadModelUseCase,
+    DownloadModelValidationError,
+)
+from .example_images import (
+    DownloadExampleImagesConfigurationError,
+    DownloadExampleImagesInProgressError,
+    DownloadExampleImagesUseCase,
+    ImportExampleImagesUseCase,
+    ImportExampleImagesValidationError,
+)
+
+__all__ = [
+    "AutoOrganizeInProgressError",
+    "AutoOrganizeUseCase",
+    "BulkMetadataRefreshUseCase",
+    "MetadataRefreshProgressReporter",
+    "DownloadModelEarlyAccessError",
+    "DownloadModelUseCase",
+    "DownloadModelValidationError",
+    "DownloadExampleImagesConfigurationError",
+    "DownloadExampleImagesInProgressError",
+    "DownloadExampleImagesUseCase",
+    "ImportExampleImagesUseCase",
+    "ImportExampleImagesValidationError",
+]

py/services/use_cases/auto_organize_use_case.py

@@ -0,0 +1,56 @@
+"""Auto-organize use case orchestrating concurrency and progress handling."""
+
+from __future__ import annotations
+
+import asyncio
+from typing import Optional, Protocol, Sequence
+
+from ..model_file_service import AutoOrganizeResult, ModelFileService, ProgressCallback
+
+
+class AutoOrganizeLockProvider(Protocol):
+    """Minimal protocol for objects exposing auto-organize locking primitives."""
+
+    def is_auto_organize_running(self) -> bool:
+        """Return ``True`` when an auto-organize operation is in-flight."""
+
+    async def get_auto_organize_lock(self) -> asyncio.Lock:
+        """Return the asyncio lock guarding auto-organize operations."""
+
+
+class AutoOrganizeInProgressError(RuntimeError):
+    """Raised when an auto-organize run is already active."""
+
+
+class AutoOrganizeUseCase:
+    """Coordinate auto-organize execution behind a shared lock."""
+
+    def __init__(
+        self,
+        *,
+        file_service: ModelFileService,
+        lock_provider: AutoOrganizeLockProvider,
+    ) -> None:
+        self._file_service = file_service
+        self._lock_provider = lock_provider
+
+    async def execute(
+        self,
+        *,
+        file_paths: Optional[Sequence[str]] = None,
+        progress_callback: Optional[ProgressCallback] = None,
+    ) -> AutoOrganizeResult:
+        """Run the auto-organize routine guarded by a shared lock."""
+
+        if self._lock_provider.is_auto_organize_running():
+            raise AutoOrganizeInProgressError("Auto-organize is already running")
+
+        lock = await self._lock_provider.get_auto_organize_lock()
+        if lock.locked():
+            raise AutoOrganizeInProgressError("Auto-organize is already running")
+
+        async with lock:
+            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,
+            )