refactor(recipes): reuse shared local hash cache in create-from-example

Fix ~790 basedpyright errors across the test suite:
- Type stub subclasses of real production classes with super().__init__()
- Add missing generic type arguments and Dict[str, Any] annotations
- Add None guards before subscript/member access
- Adapt tests to production API changes (removed dead handlers,
  PersistentModelCache.get_default, _i18n_filter_added location)

.docs/batch-import-design.md

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

.gitignore

@@ -7,17 +7,25 @@ py/run_test.py
 .vscode/
 cache/
 civitai/
+stats/
+wildcards/
+backups/
+logs/
 node_modules/
 coverage/
 .coverage
 model_cache/
 
-# agent
+# agent / dev tooling
 .opencode/
 .claude/
 .sisyphus/
 .codex
 .omo
+reasonix.toml
+.reasonix/
+.codegraph/
+.playwright-mcp/
 
 # Vue widgets development cache (but keep build output)
 vue-widgets/node_modules/
@@ -26,3 +34,10 @@ vue-widgets/dist/
 
 # Hypothesis test cache
 .hypothesis/
+
+# Working/research notes (not committed)
+.docs/
+
+# HF enrichment validation baseline snapshots (contain potentially
+# NSFW README content fetched from community model repos)
+tests/enrich_hf_validation/baselines/

.omo/plans/embeddings-hybrid-approach.md

@@ -0,0 +1,181 @@
+# Embeddings Usage Tracking — Hybrid Approach (Plan C)
+
+> **Status**: Reference document for future implementation
+> **Current implementation**: Plan A (prompt text parsing only, see `usage_stats.py:_process_embeddings`)
+> **Next step**: Add Plan B as a supplement when edge-case coverage is needed
+
+## Problem
+
+Embeddings in ComfyUI are not loaded through dedicated ComfyUI nodes like LoRAs or
+Checkpoints. They are resolved during CLIP tokenization when the prompt text contains
+`embedding:<name>` syntax (see `comfy/sd1_clip.py:SDTokenizer.tokenize_with_weights`).
+
+This means the existing metadata_collector hook (which intercepts node execution via
+`_map_node_over_list`) cannot capture embeddings the same way it captures LoRAs and
+checkpoints — there is no "EmbeddingLoader" node to intercept.
+
+## Solution Architecture
+
+The hybrid approach combines **two complementary mechanisms** to capture embedding
+usage from all possible paths.
+
+```
+┌─────────────────────────────────────────────────────────┐
+│                 Plan A (已实现)                          │
+│                                                         │
+│  MetadataRegistry.prompt_metadata["prompts"]            │
+│       │                                                  │
+│       ▼                                                  │
+│  _process_embeddings()                                   │
+│       │                                                  │
+│       ├─ Iterate all prompt node texts                   │
+│       ├─ regex extract "embedding:<name>"                │
+│       ├─ resolve name → sha256 via EmbeddingScanner      │
+│       └─ UsageStats.stats["embeddings"][sha256]++        │
+│                                                         │
+│  Coverage: ~95% — all CLIPTextEncode/Flux/etc nodes     │
+│                                                         │
+│  Gap: Custom nodes that load embeddings programmatically │
+│       without putting embedding:name in prompt text      │
+└─────────────────────────────────────────────────────────┘
+
+                         +
+                         ↓  (future: enable Plan B when needed)
+
+┌─────────────────────────────────────────────────────────┐
+│                 Plan B (未来 — monkey-patch)              │
+│                                                         │
+│  comfy/sd1_clip.py:load_embed()                         │
+│       │                                                  │
+│       ▼                                                  │
+│  Monkey-patch intercepts EVERY embedding file load       │
+│       │                                                  │
+│       ├─ Records embedding_name + success/failure        │
+│       ├─ Associates with current prompt_id (via registry)│
+│       └─ Feeds into UsageStats same as Plan A            │
+│                                                         │
+│  Coverage: 100% — catches ALL embedding loads            │
+│                                                         │
+│  Cost: Requires patching into ComfyUI internals          │
+│        (sd1_clip.py, sdxl_clip.py, some text_encoders)   │
+└─────────────────────────────────────────────────────────┘
+```
+
+## Plan B Detail — Monkey-patch `load_embed`
+
+### Target Function
+
+**`comfy.sd1_clip.load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None)`**
+at line 415 of `sd1_clip.py`.
+
+This is the **single choke point** for all embedding file loads in ComfyUI. Every
+CLIP variant (SD1, SDXL, SD3, Flux) calls this same function.
+
+### Implementation Sketch
+
+```python
+# In metadata_collector/metadata_hook.py (or a new module)
+import comfy.sd1_clip as sd1_clip
+
+_original_load_embed = sd1_clip.load_embed
+
+def _patched_load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
+    result = _original_load_embed(
+        embedding_name, embedding_directory, embedding_size, embed_key
+    )
+    if result is not None:
+        _record_embedding_usage(embedding_name)
+    return result
+
+sd1_clip.load_embed = _patched_load_embed
+```
+
+### Prompt ID Association
+
+The challenge is associating the `load_embed` call with the current `prompt_id`.
+Options:
+
+1. **Thread-local / contextvar**: Store current `prompt_id` in a `contextvars.ContextVar`
+   that the metadata_collector sets at the start of each prompt execution.
+
+2. **MetadataRegistry singleton**: The MetadataRegistry already has `current_prompt_id`.
+   The patch can read it directly since both run in the same thread.
+
+3. **Lazy aggregation**: Instead of associating with prompt_id at load time, collect
+   all loaded embedding names in a global set during execution, then flush to
+   UsageStats after the prompt completes.
+
+### Files to Patch
+
+| File | Function | Coverage |
+|------|----------|----------|
+| `comfy/sd1_clip.py:415` | `load_embed()` | Primary — SD1.x, SDXL, SD3, Flux |
+| `comfy/sdxl_clip.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
+| `comfy/text_encoders/sd3_clip.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
+| `comfy/text_encoders/flux.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
+
+The SD1 tokenizer is the base class for all CLIP variants' tokenizers, so patching
+`load_embed` covers them all.
+
+### Edge Cases
+
+| Edge Case | Plan A | Plan B |
+|-----------|--------|--------|
+| `embedding:name` in CLIPTextEncode | ✅ | ✅ |
+| `embedding:name` in CLIPTextEncodeFlux | ✅ | ✅ |
+| `embedding:name` in PromptLM (LoRA Manager) | ✅ | ✅ |
+| `embedding:name` in WAS_Text_to_Conditioning | ✅ | ✅ |
+| Custom node that loads embedding programmatically | ❌ | ✅ |
+| Embedding loaded multiple times in same prompt | ✅ (dedup via set) | ✅ (dedup via set) |
+| Embedding file not found | N/A | ✅ (can log) |
+| Embedding dimension mismatch | N/A | ✅ (can log) |
+| Text encoder with non-standard tokenizer (LLaMA, T5...) | Partial | ✅ (if it calls load_embed) |
+
+## Migration Path: Standalone → Hybrid
+
+### Phase 1 — Plan A (当前状态)
+- Prompt text parsing only
+- No monkey-patching required
+- Covers all standard workflows
+
+### Phase 2 — Enable Plan B (未来工作)
+1. Add monkey-patch of `load_embed` in `metadata_collector/metadata_hook.py` (alongside
+   the existing `_map_node_over_list` hook)
+2. Collect loaded embedding names in a `set()` on the registry
+3. In `UsageStats._process_embeddings()`, merge the Plan A results (from prompt text)
+   with the Plan B results (from the patch)
+4. Add `prompt_data` field on MetadataRegistry to store loaded embeddings per prompt
+
+### Deduplication
+
+```python
+# Merge Plan A + Plan B results in _process_embeddings
+plan_a_names = extract_from_prompt_texts(prompts_data)
+plan_b_names = registry.get_loaded_embeddings(prompt_id)
+
+all_names = plan_a_names | plan_b_names
+```
+
+## Testing the Hybrid
+
+| Scenario | What to verify |
+|----------|---------------|
+| Standard `embedding:name` in prompt | Plan A captures it |
+| Embedding loaded by custom node script | Plan B captures it |
+| Both paths fire for same embedding | No double-counting (dedup) |
+| Embedding name resolves to hash | EmbeddingScanner.get_hash_by_filename works |
+| No embedding scanner available | Graceful skip, no crash |
+| Missing embedding file | Plan B logs warning, Plan A skips gracefully |
+| Empty prompt | No crash, no entries |
+| Standalone mode | Both plans disabled gracefully |
+
+## Key Files Reference
+
+| File | Role |
+|------|------|
+| `py/utils/usage_stats.py` | Core — `_process_embeddings()` for Plan A |
+| `py/metadata_collector/constants.py` | `EMBEDDINGS` category constant |
+| `py/metadata_collector/metadata_hook.py` | Future — monkey-patch for Plan B |
+| `py/services/embedding_scanner.py` | Hash resolution service |
+| `py/routes/stats_routes.py` | Already handles `usage_data.get('embeddings', {})` |
+| `comfy/sd1_clip.py` (ComfyUI) | `load_embed()` — Plan B target |

AGENTS.md

@@ -31,7 +31,7 @@ COVERAGE_FILE=coverage/backend/.coverage pytest \
   --cov-report=xml:coverage/backend/coverage.xml
 ```
 
-### Frontend Development (Standalone Web UI)
+### Frontend Development (LoRA Manager Web UI)
 
 ```bash
 npm install
@@ -102,6 +102,7 @@ npm run test:coverage       # Generate coverage report
 - ComfyUI: `app.registerExtension()`, `node.addDOMWidget(name, type, element, options)`
 - Event handlers via `addEventListener` or widget callbacks
 - Shared utilities: `web/comfyui/utils.js`
+- Dual-mode rendering patterns (canvas vs Vue): see `docs/comfyui-dual-mode-widgets.md`
 
 ### Vue Composables Pattern
 
@@ -136,7 +137,13 @@ npm run test:coverage       # Generate coverage report
 - Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
 - Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
 - Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
-- Symlinks require normalized paths
+- Symlinks require normalized paths.
+  **Business paths vs real paths**: All stored paths and operation routing use the
+  original paths as they appear under configured model roots — symlinks are NOT
+  resolved. `os.path.realpath` is only for scanner dedup and the symlink cache.
+  Any path passed to `os.remove`/`os.rename`/`shutil.move` or validated by a
+  containment check MUST use the business path (i.e. `os.path.abspath`, not
+  `realpath`).
 
 ## Git / Commit Messages
 
@@ -147,9 +154,9 @@ npm run test:coverage       # Generate coverage report
 
 ## Frontend UI Architecture
 
-### 1. Standalone Web UI
+### 1. LoRA Manager Web UI
 - Location: `./static/` and `./templates/`
-- Tech: Vanilla JS + CSS, served by standalone server
+- Tech: Vanilla JS + CSS, served by the hosting server (ComfyUI app in plugin mode, `standalone.py` in standalone mode)
 - Tests via npm in root directory
 
 ### 2. ComfyUI Custom Node Widgets

__init__.py

@@ -15,6 +15,10 @@ try:  # pragma: no cover - import fallback for pytest collection
     from .py.nodes.lora_pool import LoraPoolLM
     from .py.nodes.lora_randomizer import LoraRandomizerLM
     from .py.nodes.lora_cycler import LoraCyclerLM
+    from .py.nodes.lora_info import LoraInfoLM
+    from .py.nodes.lora_syntax_to_path import LoraSyntaxToPath
+    from .py.nodes.create_hook_lora import CreateHookLoraLM
+    from .py.nodes.metadata_overwrite import MetadataOverwriteLM
     from .py.metadata_collector import init as init_metadata_collector
 except (
     ImportError
@@ -56,6 +60,16 @@ except (
         "py.nodes.lora_randomizer"
     ).LoraRandomizerLM
     LoraCyclerLM = importlib.import_module("py.nodes.lora_cycler").LoraCyclerLM
+    LoraInfoLM = importlib.import_module("py.nodes.lora_info").LoraInfoLM
+    LoraSyntaxToPath = importlib.import_module(
+        "py.nodes.lora_syntax_to_path"
+    ).LoraSyntaxToPath
+    CreateHookLoraLM = importlib.import_module(
+        "py.nodes.create_hook_lora"
+    ).CreateHookLoraLM
+    MetadataOverwriteLM = importlib.import_module(
+        "py.nodes.metadata_overwrite"
+    ).MetadataOverwriteLM
     init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
@@ -75,6 +89,10 @@ NODE_CLASS_MAPPINGS = {
     LoraPoolLM.NAME: LoraPoolLM,
     LoraRandomizerLM.NAME: LoraRandomizerLM,
     LoraCyclerLM.NAME: LoraCyclerLM,
+    LoraInfoLM.NAME: LoraInfoLM,
+    LoraSyntaxToPath.NAME: LoraSyntaxToPath,
+    CreateHookLoraLM.NAME: CreateHookLoraLM,
+    MetadataOverwriteLM.NAME: MetadataOverwriteLM,
 }
 
 WEB_DIRECTORY = "./web/comfyui"

docs/agent_skills.md

@@ -0,0 +1,208 @@
+# Agent Skills System
+
+The LoRA Manager agent skills system enables LLM-powered metadata enrichment and other AI-driven tasks. Users configure their own LLM provider (BYOK), and skills are executed through right-click context menu actions.
+
+## Architecture
+
+```
+┌──────────────────────────────────────────────┐
+│              LoRA Manager Backend             │
+│                                               │
+│  ┌──────────────┐    ┌────────────────┐       │
+│  │ LLMService    │───▶│ LLM Provider   │       │
+│  │ (BYOK config, │◀───│ (OpenAI/Ollama │       │
+│  │  API calls)   │    │ /custom)       │       │
+│  └───────┬───────┘    └────────────────┘       │
+│          │                                     │
+│  ┌───────▼───────────────────────┐             │
+│  │     AgentService              │             │
+│  │  (orchestration: validate     │             │
+│  │   → LLM call → post-process   │             │
+│  │   → WebSocket broadcast)      │             │
+│  └───────┬───────────────────────┘             │
+│          │                                     │
+│  ┌───────▼───────────────────────┐             │
+│  │     SkillRegistry             │             │
+│  │  ┌─────────────────────────┐  │             │
+│  │  │ enrich_hf_metadata:     │  │             │
+│  │  │  - skill.yaml           │  │             │
+│  │  │  - prompt.md            │  │             │
+│  │  │  - handler.py           │  │             │
+│  │  └─────────────────────────┘  │             │
+│  └───────────────────────────────┘             │
+└──────────────────────────────────────────────┘
+```
+
+### Key Design Principle
+
+**Skills define *what* to do (prompt + post-processing). The AgentService handles *how* (LLM calls, validation, progress).**
+
+Skills never call the LLM directly. This keeps BYOK configuration centralized and provider-agnostic.
+
+## BYOK Configuration
+
+Users configure their LLM provider in **Settings → AI Provider**:
+
+| Setting | Description | Example |
+|---|---|---|
+| `llm_provider` | Provider type | `openai`, `ollama`, or `custom` |
+| `llm_api_key` | API key (not needed for local Ollama) | `sk-...` |
+| `llm_api_base` | Custom API base URL (empty = provider default) | `https://api.openai.com/v1` |
+| `llm_model` | Model name | `gpt-4o-mini` |
+
+Environment variable overrides: `LLM_API_KEY`, `LLM_MODEL`, `LLM_API_BASE`, `LLM_PROVIDER`.
+
+### Supported Providers
+
+- **OpenAI**: Uses `https://api.openai.com/v1` by default
+- **Ollama** (local): Uses `http://localhost:11434/v1`, no API key required
+- **Custom**: Any OpenAI-compatible endpoint (vLLM, LM Studio, etc.) — set `llm_api_base` explicitly
+
+## Available Skills
+
+### enrich_hf_metadata
+
+Enriches HuggingFace-downloaded models with metadata extracted by an LLM from the HF model card.
+
+**Entry point**: Right-click context menu → "Enrich Metadata (Agent)"
+
+**What it does**:
+1. Reads the model's `.metadata.json` to get the `hf_url`
+2. Fetches the README.md from the HuggingFace repository
+3. Sends the README + local metadata to the LLM for structured extraction
+4. Writes extracted fields to `.metadata.json`:
+   - `base_model` — only if current value is empty
+   - `trainedWords` — trigger words (LoRA only, if none exist)
+   - `modelDescription` — concise summary (if none exists)
+   - `tags` — merged with existing tags, deduplicated
+   - `metadata_source` — audit trail: `agent:enrich_hf_metadata`
+   - `llm_enriched_at` — ISO timestamp
+5. Downloads and optimizes preview image (if LLM found one in the README)
+6. Updates the scanner cache
+7. Broadcasts WebSocket progress events
+
+**Model types**: LoRA, Checkpoint, Embedding
+
+## Adding a New Skill
+
+### 1. Create the skill directory
+
+```
+py/services/agent/skills/<skill_name>/
+├── skill.yaml      # Skill metadata and schemas
+├── prompt.md       # LLM prompt template
+└── handler.py      # Pre-processing and post-processing
+```
+
+### 2. Write skill.yaml
+
+```yaml
+name: my_skill
+title: "My Skill"
+description: "What this skill does"
+llm_required: true
+model_type_filter: ["lora"]  # or null for all types
+input_schema:
+  type: object
+  properties:
+    model_paths:
+      type: array
+      items:
+        type: string
+  required:
+    - model_paths
+output_schema:
+  type: object
+  properties:
+    # ... JSON schema for LLM output
+permissions:
+  write_metadata: true
+  write_previews: false
+  network_domains:
+    - "example.com"
+```
+
+### 3. Write prompt.md
+
+Use `{{variable}}` placeholders that will be replaced with data from the `prepare` function:
+
+```markdown
+You are an expert assistant...
+
+Model URL: {{hf_url}}
+README content:
+{{readme_content}}
+
+Current metadata:
+{{current_metadata}}
+```
+
+### 4. Write handler.py
+
+```python
+async def prepare(model_path: str, input_data: dict) -> dict:
+    """Gather context for the LLM prompt. Returns variables for template rendering."""
+    return {
+        "model_path": model_path,
+        # ... other variables used in prompt.md
+    }
+
+async def post_process(context) -> dict:
+    """Apply the LLM-extracted data to the model."""
+    llm_response = context.llm_response
+    # ... write metadata, download previews, update cache
+    return {
+        "success": True,
+        "updated_fields": ["base_model", "tags"],
+        "errors": [],
+    }
+```
+
+**Important**: Use absolute imports (`from py.utils.metadata_manager import MetadataManager`) because skills are loaded via `importlib.util.spec_from_file_location`, which doesn't support relative imports.
+
+### 5. Test
+
+The skill is automatically discovered by `SkillRegistry` on startup. Test with:
+
+```python
+pytest tests/services/test_agent_service.py
+```
+
+## API Endpoints
+
+| Method | Path | Description |
+|---|---|---|
+| GET | `/api/lm/agent/skills` | List available skills |
+| POST | `/api/lm/agent/execute/{skill_name}` | Execute a skill (body: `{"model_paths": [...]}`) |
+| POST | `/api/lm/agent/cancel` | Cancel running skill (stub) |
+
+## WebSocket Events
+
+| Type | When | Key fields |
+|---|---|---|
+| `agent_progress` | Skill started/processing | `skill`, `status`, `total`, `processed`, `success`, `current_path` |
+| `agent_progress` | Skill completed | `skill`, `status`, `updated_models`, `errors`, `summary` |
+| `agent_progress` | Skill error | `skill`, `status`, `error` |
+
+## Security Model
+
+Skills declare permissions in `skill.yaml`:
+- `write_metadata` — can write `.metadata.json` files
+- `write_previews` — can download/replace preview images
+- `network_domains` — allowed domains for HTTP requests
+
+These are declarative constraints checked by `AgentService`. They are defense-in-depth, not a sandbox — the Python process can technically do anything, but the contract is clear and auditable.
+
+## File Locations
+
+| Component | Path |
+|---|---|
+| LLMService | `py/services/llm_service.py` |
+| AgentService | `py/services/agent/agent_service.py` |
+| SkillRegistry | `py/services/agent/skill_registry.py` |
+| SkillDefinition | `py/services/agent/skill_definition.py` |
+| Skills directory | `py/services/agent/skills/` |
+| Route handlers | `py/routes/handlers/agent_handlers.py` |
+| Frontend manager | `static/js/managers/AgentManager.js` |
+| Settings UI | `templates/components/modals/settings_modal.html` |
+| Context menu | `templates/components/context_menu.html` |

docs/comfyui-dual-mode-widgets.md

@@ -0,0 +1,65 @@
+# ComfyUI Dual-Mode Widget Rendering
+
+ComfyUI custom node widgets render in one of two modes. Patterns that work in one often fail silently in the other. Test both.
+
+## Mode Detection
+
+```js
+typeof LiteGraph !== 'undefined' && LiteGraph.vueNodesMode
+```
+
+In Vue SFCs, `window.LiteGraph` is unavailable — pass as a prop from `main.ts`.
+
+## Canvas Mode Layout
+
+Uses `computeLayoutSize()` + `distributeSpace()` to allocate widget height within the node. Widgets with `computeLayoutSize` participate in space distribution; those with `computeSize` have fixed height.
+
+- `getMinHeight()` in `addDOMWidget` options → minimum widget height
+- `widget.computeLayoutSize()` → `{ minHeight, minWidth, maxHeight? }`
+- Avoid `getMaxHeight()` unless the widget genuinely needs a fixed cap (prevents user resize)
+
+## Vue Mode Layout
+
+Uses CSS Grid (`grid-template-rows`) + `ResizeObserver`. The ResizeObserver watches the widget's DOM and feeds back into grid row sizing. This creates a feedback loop: content grows → row resizes → more space for content → content reflows/grows → row resizes again.
+
+### Height Containment
+
+The fix: `contain: layout size` on the widget root. This tells the browser the element's intrinsic size is CSS-determined, not driven by descendant content. The ResizeObserver sees a stable size and the loop is broken.
+
+```css
+.widget-root.lm-vue-node {
+  height: 100%;
+  min-height: var(--comfy-widget-min-height, 200px);
+  contain: layout size;
+}
+```
+
+Existing examples: `.lm-loras-container.lm-vue-node` and `.comfy-tags-container.lm-vue-node` in `web/comfyui/lm_styles.css`.
+
+**Do NOT** fix height issues with `maxHeight`, `getMaxHeight()`, or inline `max-height` — these prevent the user from resizing the node.
+
+## Scroll Wheel Isolation
+
+Both modes need to distinguish "user wants to scroll widget content" from "user wants to zoom canvas".
+
+**Canvas mode:** Add `@wheel` on widget root. Check `event.target.closest(selector)` for scrollable sub-areas. If scrollable → `event.stopPropagation()`. Otherwise → `app.canvas.processMouseWheel(event)`.
+
+**Vue mode:** Add CSS class `lm-wheel-scrollable` to scrollable elements. The global capture-phase hook in `web/comfyui/utils.js` (`enableListWheelScroll`) detects wheel events on marked elements and manually scrolls them via `element.scrollTop`, consuming the event before canvas zoom sees it.
+
+## DOM Structure
+
+`main.ts` creates an outer `<div>` container, then `vueApp.mount(container)`. The Vue app renders its own root element inside.
+
+- `container.id` / `container.style.*` → outer element
+- Vue scoped `<style>` → `[data-v-hash]` applies only to Vue root
+
+Classes needed by scoped Vue CSS must go on the Vue root element. Pass data as props and bind with `:class` rather than manipulating the DOM from `main.ts`.
+
+## Serialization
+
+For stateful widgets that need workflow persistence:
+
+- `serialize: true` in `addDOMWidget` options
+- `serializeValue()` → state snapshot (called on workflow save)
+- `onSetValue(v)` → restore state (called on workflow load)
+- Always handle missing keys in restored value for backward compatibility with old workflows

docs/metadata-json-schema.md

@@ -39,6 +39,7 @@ These fields are present in all model metadata files.
 | `metadata_source` | string\|null | ❌ No | ✅ Yes | Last provider that supplied metadata (see below) |
 | `last_checked_at` | float | ❌ No (default: `0`) | ✅ Yes | Unix timestamp of last metadata check |
 | `hash_status` | string | ❌ No (default: `"completed"`) | ✅ Yes | Hash calculation status: `"pending"`, `"calculating"`, `"completed"`, `"failed"` |
+| `autov3` | string\|null | ❌ No | ✅ Yes | CivitAI AutoV3 hash (first 12 chars, lowercase hex) sourced from the safetensors embedded metadata (`sshs_model_hash` / `modelspec.hash_sha256`). **Absent** = not yet checked (may be backfilled later); **`null`** = checked but unavailable (header has no recognized hash); **12-char hex string** = value |
 
 ---
 
@@ -287,6 +288,7 @@ These fields are automatically synchronized with the filesystem:
 - `preview_url` — Updated if preview file is moved/removed
 - `sha256` — Updated during hash calculation (when `hash_status="pending"`)
 - `hash_status` — Updated during hash calculation
+- `autov3` — Set when metadata is first created (from safetensors header); may be backfilled later for entries where it is absent
 - `last_checked_at` — Timestamp of scan
 - `metadata_source` — Set based on metadata provider
 
@@ -345,6 +347,7 @@ These fields can be edited by users at any time through the Lora Manager UI or b
 | `metadata_source` | `null` |
 | `last_checked_at` | `0` |
 | `hash_status` | `"completed"` |
+| `autov3` | absent (not checked) or `null` (checked, no value) |
 | `usage_tips` | `"{}"` (LoRA only) |
 | `model_type` | `"checkpoint"` or `"embedding"` (not present in LoRA models) |
 
@@ -354,6 +357,7 @@ These fields can be edited by users at any time through the Lora Manager UI or b
 
 | Version | Date | Changes |
 |---------|------|---------|
+| 1.1 | 2026-08 | Added `autov3` field (CivitAI AutoV3 hash with three-state semantics) |
 | 1.0 | 2026-03 | Initial schema documentation |
 
 ---

locales/de.json

@@ -16,10 +16,13 @@
             "help": "Hilfe",
             "add": "Hinzufügen",
             "close": "Schließen",
-            "menu": "Menü"
+            "menu": "Menü",
+            "remove": "Entfernen",
+            "change": "Ändern"
         },
         "status": {
             "loading": "Wird geladen...",
+            "cancelling": "Abbrechen...",
             "unknown": "Unbekannt",
             "date": "Datum",
             "version": "Version",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "Aus Favoriten entfernen",
             "viewOnCivitai": "Auf Civitai anzeigen",
             "notAvailableFromCivitai": "Nicht auf Civitai verfügbar",
+            "viewOnHuggingFace": "Auf Hugging Face ansehen",
             "sendToWorkflow": "An ComfyUI senden (Klick: Anhängen, Shift+Klick: Ersetzen)",
             "copyLoRASyntax": "LoRA-Syntax kopieren",
             "checkpointNameCopied": "Checkpoint-Name kopiert",
@@ -111,6 +115,7 @@
             "replacePreview": "Vorschau ersetzen",
             "copyCheckpointName": "Checkpoint-Name kopieren",
             "copyEmbeddingName": "Embedding-Name kopieren",
+            "embeddingNameCopied": "Embedding-Syntax kopiert",
             "sendCheckpointToWorkflow": "An ComfyUI senden",
             "sendEmbeddingToWorkflow": "An ComfyUI senden"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Verwendungsanzahl"
+        },
+        "footer": {
+            "versionCount": "{count} Versionen",
+            "viewAllVersions": "Alle lokalen Versionen anzeigen"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Ausgeschlossene Modelle verwalten"
+        },
+        "groupByModel": {
+            "label": "Nach Modell gruppieren"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "Statistiken"
         },
         "search": {
-            "placeholder": "Suchen...",
-            "placeholders": {
-                "loras": "LoRAs suchen...",
-                "recipes": "Rezepte suchen...",
-                "checkpoints": "Checkpoints suchen...",
-                "embeddings": "Embeddings suchen..."
-            },
+            "placeholder": "Suchen",
             "options": "Suchoptionen",
             "searchIn": "Suchen in:",
             "notAvailable": "Suche auf Statistikseite nicht verfügbar",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "Voreinstellungsname...",
             "baseModel": "Basis-Modell",
             "baseModelSearchPlaceholder": "Basismodelle durchsuchen...",
-            "modelTags": "Tags (Top 20)",
+            "modelTags": "Tags",
             "modelTypes": "Modelltypen",
             "license": "Lizenz",
             "noCreditRequired": "Kein Credit erforderlich",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Verkauf generierter Bilder erlauben",
             "noCreditRequiredTooltip": "Modell ohne Nennung des Erstellers verwenden",
             "noTags": "Keine Tags",
+            "tagSearchPlaceholder": "Tags durchsuchen...",
+            "noTagMatches": "Keine Tags entsprechen der aktuellen Suche.",
             "autoTags": "Auto-Tags",
             "noBaseModelMatches": "Keine Basismodelle entsprechen der aktuellen Suche.",
             "clearAll": "Alle Filter löschen",
@@ -247,7 +255,18 @@
             "toggle": "Theme wechseln",
             "switchToLight": "Zu hellem Theme wechseln",
             "switchToDark": "Zu dunklem Theme wechseln",
-            "switchToAuto": "Zu automatischem Theme wechseln"
+            "switchToAuto": "Zu automatischem Theme wechseln",
+            "presets": "Theme-Voreinstellungen",
+            "default": "Standard",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Modus",
+            "light": "Hell",
+            "dark": "Dunkel",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Updates prüfen",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Geben Sie Ihren Civitai API Key ein",
         "civitaiApiKeyHelp": "Wird für die Authentifizierung beim Herunterladen von Modellen von Civitai verwendet",
+        "civitaiApiKeyConfigured": "Konfiguriert",
+        "civitaiApiKeyNotConfigured": "Nicht konfiguriert",
+        "civitaiApiKeySet": "Einrichten",
         "civitaiHost": {
             "label": "Civitai-Host",
             "help": "Wählen Sie aus, welche Civitai-Seite geöffnet wird, wenn Sie „View on Civitai“-Links verwenden.",
@@ -299,6 +321,7 @@
             "downloads": "Downloads",
             "videoSettings": "Video-Einstellungen",
             "layoutSettings": "Layout-Einstellungen",
+            "licenseIcons": "Lizenzsymbole",
             "misc": "Verschiedenes",
             "backup": "Backups",
             "folderSettings": "Standard-Roots",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "Zusätzliche Ordnerpfade",
             "downloadPathTemplates": "Download-Pfad-Vorlagen",
             "priorityTags": "Prioritäts-Tags",
-            "updateFlags": "Update-Markierungen",
+            "versionScope": "Update-Markierungen",
             "exampleImages": "Beispielbilder",
             "autoOrganize": "Auto-Organisierung",
             "metadata": "Metadaten",
@@ -411,6 +434,8 @@
             "help": "Wenn aktiviert, überspringt LoRA Manager den Download einer Modellversion, wenn der Download-Verlaufsdienst diese spezifische Version als bereits heruntergeladen erfasst hat. Gilt für alle Download-Abläufe."
         },
         "layoutSettings": {
+            "groupByModel": "Nach Modell gruppieren",
+            "groupByModelHelp": "Wenn aktiviert, wird nur die neueste Version jedes Civitai-Modells als einzelne Karte angezeigt. Ältere Versionen werden ausgeblendet.",
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
                 "default": "Standard",
@@ -424,6 +449,12 @@
                 "compact": "7 (1080p), 8 (2K), 10 (4K)"
             },
             "displayDensityWarning": "Warnung: Höhere Dichten können bei Systemen mit begrenzten Ressourcen zu Performance-Problemen führen.",
+            "recipesLayout": "Rezepte-Layout",
+            "recipesLayoutHelp": "Wählen Sie, wie Rezeptkarten angeordnet werden: ein einheitliches Raster oder ein Masonry-Layout (Pinterest-Stil), das das Seitenverhältnis jedes Bildes beibehält.",
+            "recipesLayoutOptions": {
+                "grid": "Raster",
+                "masonry": "Masonry"
+            },
             "showFolderSidebar": "Ordner-Seitenleiste anzeigen",
             "showFolderSidebarHelp": "Blenden Sie die Ordner-Navigationsleiste auf den Modellseiten ein oder aus. Wenn deaktiviert, bleiben Seitenleiste und Hoverbereich verborgen.",
             "cardInfoDisplay": "Karten-Info-Anzeige",
@@ -445,7 +476,9 @@
                 "modelName": "Modellname",
                 "fileName": "Dateiname"
             },
-            "modelNameDisplayHelp": "Wählen Sie aus, was in der Fußzeile der Modellkarte angezeigt werden soll"
+            "modelNameDisplayHelp": "Wählen Sie aus, was in der Fußzeile der Modellkarte angezeigt werden soll",
+            "cardBlurAmount": "Karten-Overlay-Unschärfe",
+            "cardBlurAmountHelp": "Passen Sie die Unschärfeintensität der Kopf- und Fußzeilen-Overlays auf Modell- und Rezeptkarten an (0 = keine Unschärfe, 20 = maximale Unschärfe)."
         },
         "folderSettings": {
             "activeLibrary": "Aktive Bibliothek",
@@ -480,7 +513,9 @@
             "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert. Neustart erforderlich, um Änderungen anzuwenden.",
             "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
             "validation": {
-                "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
+                "duplicatePath": "Dieser Pfad ist bereits konfiguriert",
+                "checkpointUnetOverlap": "Derselbe Pfad kann nicht für Checkpoints und Diffusionsmodelle verwendet werden: {paths}",
+                "checkpointUnetOverlapInline": "Dieser Pfad wird bereits für einen anderen Modelltyp verwendet. Bitte verwenden Sie separate Ordner für Checkpoints und Diffusionsmodelle."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "Herunterladen",
             "restartRequired": "Neustart erforderlich"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Strategie für Update-Markierungen",
             "help": "Entscheide, ob Update-Badges nur dann erscheinen, wenn eine neue Version dasselbe Basismodell wie deine lokalen Dateien verwendet, oder sobald es irgendein neueres Release für dieses Modell gibt.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "Früher Zugriff Updates ausblenden",
             "help": "Nur Early-Access-Updates"
         },
+        "licenseIcons": {
+            "useNewStyle": "Aktualisierte Lizenzsymbole verwenden",
+            "useNewStyleHelp": "Lizenzberechtigungen mit farbigen Indikatoren (neuer Stil) oder nur Einschränkungssymbolen (klassischer Stil) anzeigen. Orientiert sich am aktuellen CivitAI-Design."
+        },
         "misc": {
             "includeTriggerWords": "Trigger Words in LoRA-Syntax einschließen",
             "includeTriggerWordsHelp": "Trainierte Trigger Words beim Kopieren der LoRA-Syntax in die Zwischenablage einschließen",
@@ -609,7 +648,13 @@
             "preparing": "Download wird vorbereitet...",
             "connecting": "Verbindung zum Download-Server wird hergestellt...",
             "completed": "Abgeschlossen",
-            "downloadComplete": "Download erfolgreich abgeschlossen"
+            "downloadComplete": "Download erfolgreich abgeschlossen",
+            "enableCivarchiveApi": "CivArchive API als Metadaten-Anbieter aktivieren",
+            "enableCivarchiveApiHelp": "Wenn aktiviert, wird die CivArchive API als alternative Quelle für Modell-Metadaten verwendet (z. B. für von CivitAI gelöschte Modelle). Deaktivieren, um die Ratenbegrenzungen von CivArchive vollständig zu vermeiden.",
+            "providerOrder": "Reihenfolge der Metadaten-Anbieter",
+            "providerOrderHelp": "Die CivitAI API wird immer zuerst versucht. Wählen Sie die Reihenfolge der übrigen Anbieter bei der Metadatensuche.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "App-Proxy aktivieren",
@@ -628,6 +673,33 @@
             "proxyPassword": "Passwort (optional)",
             "proxyPasswordPlaceholder": "passwort",
             "proxyPasswordHelp": "Passwort für die Proxy-Authentifizierung (falls erforderlich)"
+        },
+        "aiProvider": {
+            "title": "KI-Anbieter",
+            "provider": "Anbieter",
+            "providerHelp": "Wählen Sie Ihren LLM-Anbieter. OpenAI und Ollama verwenden voreingestellte API-Endpunkte. Mit \"Benutzerdefiniert\" können Sie jeden OpenAI-kompatiblen Endpunkt angeben.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (lokal)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "Benutzerdefiniert (OpenAI-kompatibel)"
+            },
+            "apiBase": "API-Basis-URL",
+            "apiBaseHelp": "Die Basis-URL für die LLM-API (z.B. https://api.openai.com/v1). Leer lassen, um die Anbietervoreinstellung zu verwenden.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API-Schlüssel",
+            "apiKeyHelp": "Ihr LLM-API-Schlüssel. Wird lokal gespeichert und niemals an einen anderen Server außer Ihrem gewählten LLM-Anbieter gesendet.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Nicht festgelegt",
+            "apiKeyConfigured": "Konfiguriert",
+            "apiKeySet": "Einrichten",
+            "model": "Modell",
+            "modelHelp": "Der zu verwendende Modellname (z.B. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Prüfen Sie Ihren Anbieter auf verfügbare Modelle.",
+            "modelPlaceholder": "Modell auswählen..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "Kleinste",
                 "usage": "Anzahl Nutzung",
                 "usageDesc": "Meiste",
-                "usageAsc": "Wenigste"
+                "usageAsc": "Wenigste",
+                "versionsCount": "Lokale Versionen",
+                "versionsCountDesc": "Meiste Versionen zuerst",
+                "versionsCountAsc": "Wenigste Versionen zuerst",
+                "versionIdDesc": "Neueste Version zuerst",
+                "random": "Zufällig",
+                "randomAction": "Zufällig mischen"
             },
             "refresh": {
                 "title": "Modelliste aktualisieren",
@@ -690,6 +768,7 @@
             "copyAll": "Alle Syntax kopieren",
             "refreshAll": "Alle Metadaten aktualisieren",
             "repairMetadata": "Metadaten der Auswahl reparieren",
+            "reimportMetadata": "Aus Quelle neu importieren",
             "checkUpdates": "Auswahl auf Updates prüfen",
             "moveAll": "Alle in Ordner verschieben",
             "autoOrganize": "Automatisch organisieren",
@@ -701,6 +780,8 @@
             "deleteAll": "Ausgewählte löschen",
             "downloadMissingLoras": "Fehlende LoRAs herunterladen",
             "downloadExamples": "Beispielbilder herunterladen",
+            "downloadMissingExamples": "Fehlende herunterladen",
+            "reprocessExamples": "Alle erneut verarbeiten",
             "clear": "Auswahl löschen",
             "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
             "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
@@ -720,12 +801,15 @@
                 "completed": "Abgeschlossen: {success} verschoben, {skipped} übersprungen, {failures} fehlgeschlagen",
                 "complete": "Automatische Organisation abgeschlossen",
                 "error": "Fehler: {error}"
-            }
+            },
+            "enrichHfAgent": "HF-Metadaten mit KI anreichern"
         },
         "contextMenu": {
             "refreshMetadata": "Civitai-Daten aktualisieren",
             "checkUpdates": "Updates prüfen",
-            "relinkCivitai": "Mit Civitai neu verknüpfen",
+            "linkModel": "Modell verknüpfen",
+            "linkCivitai": "Mit Civitai neu verknüpfen",
+            "linkHuggingFace": "Mit HuggingFace verknüpfen",
             "copySyntax": "LoRA-Syntax kopieren",
             "copyFilename": "Modell-Dateiname kopieren",
             "copyRecipeSyntax": "Rezept-Syntax kopieren",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "An Workflow senden (Ersetzen)",
             "openExamples": "Beispiele-Ordner öffnen",
             "downloadExamples": "Beispielbilder herunterladen",
+            "downloadMissingExamples": "Fehlende herunterladen",
+            "reprocessExamples": "Alle erneut verarbeiten",
             "replacePreview": "Vorschau ersetzen",
             "setContentRating": "Inhaltsbewertung festlegen",
             "moveToFolder": "In Ordner verschieben",
             "repairMetadata": "Metadaten reparieren",
+            "reimportMetadata": "Aus Quelle neu importieren",
             "excludeModel": "Modell ausschließen",
             "restoreModel": "Modell wiederherstellen",
             "deleteModel": "Modell löschen",
             "shareRecipe": "Rezept teilen",
             "viewAllLoras": "Alle LoRAs anzeigen",
             "downloadMissingLoras": "Fehlende LoRAs herunterladen",
-            "deleteRecipe": "Rezept löschen"
+            "deleteRecipe": "Rezept löschen",
+            "enrichHfAgent": "HF-Metadaten mit KI anreichern"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "Rezept bereits in der neuesten Version, keine Reparatur erforderlich",
                 "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
                 "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
+            },
+            "reimport": {
+                "starting": "Rezept wird aus Quelle neu importiert...",
+                "success": "Rezept erfolgreich neu importiert",
+                "noSourceUrl": "Rezept hat keine Quell-URL, Neuimport nicht möglich",
+                "failed": "Neuimport des Rezepts fehlgeschlagen: {message}",
+                "missingId": "Neuimport nicht möglich: Rezept-ID fehlt"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "Stammverzeichnis",
         "collapseAll": "Alle Ordner einklappen",
-        "pinSidebar": "Sidebar anheften",
-        "unpinSidebar": "Sidebar lösen",
+        "hideOnThisPage": "Seitenleiste auf dieser Seite ausblenden",
+        "showSidebar": "Seitenleiste anzeigen",
+        "sidebarHiddenNotification": "Seitenleiste auf der Seite {page} ausgeblendet",
         "switchToListView": "Zur Listenansicht wechseln",
         "switchToTreeView": "Zur Baumansicht wechseln",
         "recursiveOn": "Unterordner einbeziehen",
@@ -981,6 +1077,18 @@
             "storage": "Speicher",
             "insights": "Erkenntnisse"
         },
+        "metrics": {
+            "totalModels": "Modelle gesamt",
+            "totalStorage": "Speicher gesamt",
+            "totalGenerations": "Generationen gesamt",
+            "usageRate": "Nutzungsrate",
+            "loras": "LoRAs",
+            "checkpoints": "Checkpoints",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Einzigartige Tags",
+            "unusedModels": "Ungenutzte Modelle",
+            "avgUsesPerModel": "Ø Nutzungen/Modell"
+        },
         "usage": {
             "mostUsedLoras": "Meistgenutzte LoRAs",
             "mostUsedCheckpoints": "Meistgenutzte Checkpoints",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "Intelligente Erkenntnisse",
-            "recommendations": "Empfehlungen"
+            "recommendations": "Empfehlungen",
+            "noInsights": "Keine Erkenntnisse verfügbar",
+            "unusedLoras": {
+                "high": {
+                    "title": "Hohe Anzahl ungenutzter LoRAs",
+                    "description": "{percent}% Ihrer LoRAs ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Erwägen Sie, ungenutzte Modelle zu organisieren oder zu archivieren, um Speicherplatz freizugeben."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Ungenutzte Checkpoints erkannt",
+                    "description": "{percent}% Ihrer Checkpoints ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Überprüfen Sie nicht mehr benötigte Checkpoints und erwägen Sie deren Entfernung."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Hohe Anzahl ungenutzter Embeddings",
+                    "description": "{percent}% Ihrer Embeddings ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Organisieren oder archivieren Sie ungenutzte Embeddings, um Ihre Sammlung zu optimieren."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Große Sammlung erkannt",
+                    "description": "Ihre Modellsammlung verwendet {size} Speicher.",
+                    "suggestion": "Erwägen Sie externe Speicher- oder Cloud-Lösungen für eine bessere Organisation."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Aktiver Benutzer",
+                    "description": "Sie haben {count} Generationen abgeschlossen!",
+                    "suggestion": "Entdecken und erstellen Sie weiterhin großartige Inhalte mit Ihren Modellen."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Sammlungsübersicht",
             "baseModelDistribution": "Basis-Modell-Verteilung",
             "usageTrends": "Nutzungstrends (Letzte 30 Tage)",
-            "usageDistribution": "Nutzungsverteilung"
+            "usageDistribution": "Nutzungsverteilung",
+            "date": "Datum",
+            "usageCount": "Nutzungsanzahl",
+            "fileSizeBytes": "Dateigröße (Bytes)",
+            "models": "Modelle",
+            "loraUsage": "LoRA-Nutzung",
+            "checkpointUsage": "Checkpoint-Nutzung",
+            "embeddingUsage": "Embedding-Nutzung"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusionsmodell",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Lädt...",
+            "noModels": "Keine Modelle gefunden",
+            "errorLoading": "Fehler beim Laden der Daten",
+            "noStorageData": "Keine Speicherdaten verfügbar",
+            "rootFolder": "Root",
+            "chartLibraryMissing": "Diagramm benötigt Chart.js-Bibliothek"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} Modelle",
+            "chartUsage": "{name}: {size}, {count} Nutzungen",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "Modell von URL herunterladen",
             "titleWithType": "{type} von URL herunterladen",
-            "url": "Civitai URL",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "Geben Sie eine CivitAI-, CivArchive- oder Hugging Face-URL pro Zeile ein. Unterstützt mehrere URLs für den Batch-Download.",
+            "selectHfFiles": "Datei(en) zum Herunterladen aus diesem Repository auswählen:",
+            "selectAll": "Alle auswählen",
+            "fetchingRepoFiles": "Repository-Dateien werden abgerufen...",
             "locationPreview": "Download-Speicherort Vorschau",
             "useDefaultPath": "Standardpfad verwenden",
             "useDefaultPathTooltip": "Wenn aktiviert, werden Dateien automatisch mit konfigurierten Pfadvorlagen organisiert",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "Ungültiges Civitai URL-Format",
-                "noVersions": "Keine Versionen für dieses Modell verfügbar"
+                "noVersions": "Keine Versionen für dieses Modell verfügbar",
+                "mixedSources": "CivitAI- und Hugging Face-URLs können nicht in derselben Charge gemischt werden.",
+                "noModelFiles": "In diesem Repository wurden keine Modelldateien gefunden."
             },
             "status": {
                 "preparing": "Download wird vorbereitet...",
                 "downloadedPreview": "Vorschaubild heruntergeladen",
                 "downloadingFile": "{type}-Datei wird heruntergeladen",
-                "finalizing": "Download wird abgeschlossen..."
+                "finalizing": "Download wird abgeschlossen...",
+                "cancelling": "Download wird abgebrochen...",
+                "cancelled": "Download abgebrochen"
             },
             "progress": {
                 "currentFile": "Aktuelle Datei:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "Ordnerpfad eingeben oder aus Baum unten auswählen...",
             "root": "Stammverzeichnis"
         },
+        "linkHuggingFace": {
+            "title": "Mit HuggingFace verknüpfen",
+            "infoText": "Fügen Sie die HuggingFace-Repository-URL ein, um dieses Modell zuzuordnen. Dies ermöglicht die KI-gestützte Metadatenanreicherung.",
+            "urlLabel": "HuggingFace-Repository-URL:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Geben Sie die vollständige URL des HuggingFace-Repositorys ein.",
+            "confirmAction": "Speichern & Verknüpfen"
+        },
         "relinkCivitai": {
             "title": "Mit Civitai neu verknüpfen",
             "warning": "Warnung:",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "Versionsname bearbeiten",
                 "viewOnCivitai": "Auf Civitai anzeigen",
                 "viewOnCivitaiText": "Auf Civitai anzeigen",
+                "viewOnHuggingFace": "Auf Hugging Face ansehen",
+                "viewOnHuggingFaceText": "Auf Hugging Face ansehen",
                 "viewCreatorProfile": "Ersteller-Profil anzeigen",
                 "openFileLocation": "Dateispeicherort öffnen",
                 "sendToWorkflow": "An ComfyUI senden",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "Zusätzliche Notizen",
                 "notesHint": "Enter zum Speichern, Shift+Enter für neue Zeile",
                 "addNotesPlaceholder": "Fügen Sie hier Ihre Notizen hinzu...",
-                "aboutThisVersion": "Über diese Version"
+                "aboutThisVersion": "Über diese Version",
+                "baseModelSearchPlaceholder": "Basismodell suchen…",
+                "baseModelSuggested": "Vorschlag",
+                "baseModelNoMatch": "Keine passenden Basismodelle"
             },
             "notes": {
                 "saved": "Notizen erfolgreich gespeichert",
-                "saveFailed": "Fehler beim Speichern der Notizen"
+                "saveFailed": "Fehler beim Speichern der Notizen",
+                "showMore": "Mehr anzeigen",
+                "showLess": "Weniger anzeigen"
             },
             "usageTips": {
                 "addPresetParameter": "Voreingestellten Parameter hinzufügen...",
@@ -1367,6 +1561,7 @@
                 "empty": "Noch keine Versionshistorie für dieses Modell vorhanden.",
                 "error": "Versionen konnten nicht geladen werden.",
                 "missingModelId": "Für dieses Modell ist keine Civitai-Model-ID vorhanden.",
+                "hfGroupInfo": "Dies ist eine HuggingFace-Modellgruppe. Öffnen Sie die Bibliothek, um alle Versionen im Raster zu sehen.",
                 "confirm": {
                     "delete": "Diese Version aus Ihrer Bibliothek löschen?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "Version gelöscht"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "Metadaten abrufen — Zusammenfassung",
+            "statSuccess": "Erfolgreich",
+            "statFailed": "Fehlgeschlagen",
+            "statSkipped": "Übersprungen",
+            "statTotal": "Gesamt geprüft",
+            "statDuration": "Dauer",
+            "successMessage": "Alle {count} {type}s erfolgreich aktualisiert!",
+            "failedItems": "Fehlgeschlagene Elemente ({count})",
+            "close": "Schließen",
+            "copyReport": "Bericht kopieren",
+            "downloadCsv": "CSV herunterladen",
+            "columnModelName": "Modellname",
+            "columnError": "Fehler"
+        },
+        "downloadBatchSummary": {
+            "title": "Zusammenfassung des Batch-Downloads",
+            "statSuccess": "Erfolgreich",
+            "statFailed": "Fehlgeschlagen",
+            "statTotal": "Gesamt",
+            "successMessage": "Alle {count} Modelle erfolgreich heruntergeladen",
+            "completedWithErrors": "Abgeschlossen, aber mit Fehlern",
+            "failed": "Download fehlgeschlagen",
+            "failedItems": "Fehlgeschlagene Elemente ({count})",
+            "columnName": "Modellname",
+            "columnError": "Fehler",
+            "close": "Schließen",
+            "copyReport": "Bericht kopieren",
+            "retryFailed": "Fehlgeschlagene erneut versuchen ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "Dieser Tag existiert bereits"
         }
     },
-    "keyboard": {
-        "navigation": "Tastatur-Navigation:",
-        "shortcuts": {
-            "pageUp": "Eine Seite nach oben scrollen",
-            "pageDown": "Eine Seite nach unten scrollen",
-            "home": "Zum Anfang springen",
-            "end": "Zum Ende springen"
-        }
-    },
     "initialization": {
         "title": "Initialisierung",
         "message": "Ihr Arbeitsbereich wird vorbereitet...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "Keine kompatiblen Knoten im aktuellen Workflow verfügbar",
             "noTargetNodeSelected": "Kein Zielknoten ausgewählt",
             "modelUpdated": "Modell im Workflow aktualisiert",
-            "modelFailed": "Fehler beim Aktualisieren des Modellknotens"
+            "modelFailed": "Fehler beim Aktualisieren des Modellknotens",
+            "embeddingAdded": "Embedding zum Workflow hinzugefügt",
+            "embeddingFailed": "Fehler beim Hinzufügen des Embeddings",
+            "promptSent": "Prompt an Workflow gesendet",
+            "promptFailed": "Fehler beim Senden des Prompts"
         },
         "nodeSelector": {
             "recipe": "Rezept",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Ersetzen",
             "append": "Anhängen",
             "selectTargetNode": "Zielknoten auswählen",
@@ -1558,6 +1780,12 @@
         "checkingMessage": "Bitte warten Sie, während wir nach der neuesten Version suchen.",
         "showNotifications": "Update-Benachrichtigungen anzeigen",
         "latestBadge": "Neueste",
+        "latestMain": "Main-Branch",
+        "channel": "Update-Kanal",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Update wird vorbereitet...",
             "installing": "Update wird installiert...",
@@ -1578,6 +1806,15 @@
             "warning": "Warnung: Nightly Builds können experimentelle Funktionen enthalten und könnten instabil sein.",
             "enable": "Nightly Updates aktivieren"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Zu Nightly-Kanal wechseln",
+            "nightlyMessage": "Der Wechsel zu Nightly initialisiert ein Git-Repository und verfolgt die neuesten Commits des main-Branches. Updates sind häufiger, können aber instabil sein. Sie können jederzeit zu Release zurückwechseln.",
+            "releaseTitle": "Zu Release-Kanal wechseln",
+            "releaseMessage": "Der Wechsel zu Release checkt den neuesten stabilen Versions-Tag aus. Sie können jederzeit zu Nightly zurückwechseln.",
+            "switching": "Wechsle zu {channel}-Kanal...",
+            "completed": "Erfolgreich zu {channel}-Kanal gewechselt",
+            "failed": "Kanalwechsel fehlgeschlagen"
+        },
         "banners": {
             "recent": "Neueste Mitteilungen",
             "empty": "Keine aktuellen Banner verfügbar.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "Bitte geben Sie einen LoRA-Namen oder Syntax ein",
             "reconnectedSuccessfully": "LoRA erfolgreich neu verbunden",
             "reconnectFailed": "Fehler beim Neuverbinden des LoRA: {message}",
+            "noPromptToSend": "Kein zu sendender Prompt",
             "cannotSend": "Kann Rezept nicht senden: Fehlende Rezept-ID",
             "sendFailed": "Fehler beim Senden des Rezepts an Workflow",
             "sendError": "Fehler beim Senden des Rezepts an Workflow",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "Reparatur abgeschlossen: {repaired} repariert, {skipped} übersprungen (von {total})",
             "repairBulkSkipped": "Keine Reparatur für die {total} ausgewählten Rezepte erforderlich",
             "repairBulkFailed": "Reparatur der ausgewählten Rezepte fehlgeschlagen: {message}",
+            "reimporting": "Rezept wird aus Quelle neu importiert...",
+            "reimportSuccess": "Rezept erfolgreich neu importiert",
+            "reimportBulkComplete": "Neuimport abgeschlossen: {completed} importiert, {failed} fehlgeschlagen (von {total})",
+            "reimportBulkFailed": "Neuimport einiger Rezepte fehlgeschlagen",
             "noMissingLorasInSelection": "Keine fehlenden LoRAs in ausgewählten Rezepten gefunden",
             "noLoraRootConfigured": "Kein LoRA-Stammverzeichnis konfiguriert. Bitte legen Sie ein Standard-LoRA-Stammverzeichnis in den Einstellungen fest."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "Voreinstellungsname darf maximal {max} Zeichen haben",
             "presetNameInvalidChars": "Voreinstellungsname enthält ungültige Zeichen",
             "presetNameExists": "Eine Voreinstellung mit diesem Namen existiert bereits",
-            "maxPresetsReached": "Maximal {max} Voreinstellungen erlaubt. Löschen Sie eine, um weitere hinzuzufügen.",
             "presetNotFound": "Voreinstellung nicht gefunden",
             "invalidPreset": "Ungültige Voreinstellungsdaten",
             "deletePresetFailed": "Fehler beim Löschen der Voreinstellung",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "Beispielbilder {action} abgeschlossen",
             "imagesFailed": "Beispielbilder {action} fehlgeschlagen",
             "loadError": "Fehler beim Laden der Downloads: {message}",
-            "downloadError": "Download-Fehler: {message}"
+            "downloadError": "Download-Fehler: {message}",
+            "downloadStopped": "Download abgebrochen"
         },
         "import": {
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "Fehler beim Setzen der Inhaltsbewertung: {message}",
             "relinkSuccess": "Modell erfolgreich mit Civitai neu verknüpft",
             "relinkFailed": "Fehler: {message}",
+            "linkHfSuccess": "Modell erfolgreich mit HuggingFace verknüpft",
+            "linkHfFailed": "Fehler: {message}",
             "fetchMetadataFirst": "Bitte rufen Sie zuerst Metadaten von CivitAI ab",
             "noCivitaiInfo": "Keine CivitAI-Informationen verfügbar",
             "missingHash": "Modell-Hash nicht verfügbar"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "{successCount} {type}s erfolgreich verschoben",
             "exampleImagesDownloadSuccess": "Beispielbilder erfolgreich heruntergeladen!",
             "exampleImagesDownloadFailed": "Fehler beim Herunterladen der Beispielbilder: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "In die Zwischenablage kopiert",
+            "downloadStarted": "Download gestartet"
+        },
+        "agent": {
+            "llmNotConfigured": "KI-Anbieter nicht konfiguriert. Aktivieren Sie ihn unter Einstellungen → KI-Anbieter.",
+            "enrichStarted": "Metadaten werden mit KI angereichert...",
+            "enrichComplete": "Metadatenanreicherung abgeschlossen: {{summary}}",
+            "enrichFailed": "Metadatenanreicherung fehlgeschlagen: {{error}}"
         }
     },
     "doctor": {

locales/es.json

@@ -16,10 +16,13 @@
             "help": "Ayuda",
             "add": "Añadir",
             "close": "Cerrar",
-            "menu": "Menú"
+            "menu": "Menú",
+            "remove": "Eliminar",
+            "change": "Cambiar"
         },
         "status": {
             "loading": "Cargando...",
+            "cancelling": "Cancelando...",
             "unknown": "Desconocido",
             "date": "Fecha",
             "version": "Versión",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "Eliminar de favoritos",
             "viewOnCivitai": "Ver en Civitai",
             "notAvailableFromCivitai": "No disponible en Civitai",
+            "viewOnHuggingFace": "Ver en Hugging Face",
             "sendToWorkflow": "Enviar a ComfyUI (Clic: Añadir, Shift+Clic: Reemplazar)",
             "copyLoRASyntax": "Copiar sintaxis de LoRA",
             "checkpointNameCopied": "Nombre del checkpoint copiado",
@@ -111,6 +115,7 @@
             "replacePreview": "Reemplazar vista previa",
             "copyCheckpointName": "Copiar nombre del checkpoint",
             "copyEmbeddingName": "Copiar nombre del embedding",
+            "embeddingNameCopied": "Sintaxis de embedding copiada",
             "sendCheckpointToWorkflow": "Enviar a ComfyUI",
             "sendEmbeddingToWorkflow": "Enviar a ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Veces usado"
+        },
+        "footer": {
+            "versionCount": "{count} versiones",
+            "viewAllVersions": "Ver todas las versiones locales"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Gestionar modelos excluidos"
+        },
+        "groupByModel": {
+            "label": "Agrupar por modelo"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "Estadísticas"
         },
         "search": {
-            "placeholder": "Buscar...",
-            "placeholders": {
-                "loras": "Buscar LoRAs...",
-                "recipes": "Buscar recetas...",
-                "checkpoints": "Buscar checkpoints...",
-                "embeddings": "Buscar embeddings..."
-            },
+            "placeholder": "Buscar",
             "options": "Opciones de búsqueda",
             "searchIn": "Buscar en:",
             "notAvailable": "Búsqueda no disponible en la página de estadísticas",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "Nombre del preajuste...",
             "baseModel": "Modelo base",
             "baseModelSearchPlaceholder": "Buscar modelos base...",
-            "modelTags": "Etiquetas (Top 20)",
+            "modelTags": "Etiquetas",
             "modelTypes": "Tipos de modelos",
             "license": "Licencia",
             "noCreditRequired": "Sin crédito requerido",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Permitir la venta de imágenes generadas",
             "noCreditRequiredTooltip": "Usar el modelo sin atribuir al creador",
             "noTags": "Sin etiquetas",
+            "tagSearchPlaceholder": "Buscar etiquetas...",
+            "noTagMatches": "Ninguna etiqueta coincide con la búsqueda actual.",
             "autoTags": "Etiquetas automáticas",
             "noBaseModelMatches": "Ningún modelo base coincide con la búsqueda actual.",
             "clearAll": "Limpiar todos los filtros",
@@ -247,7 +255,18 @@
             "toggle": "Cambiar tema",
             "switchToLight": "Cambiar a tema claro",
             "switchToDark": "Cambiar a tema oscuro",
-            "switchToAuto": "Cambiar a tema automático"
+            "switchToAuto": "Cambiar a tema automático",
+            "presets": "Preajustes de tema",
+            "default": "Predeterminado",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Modo",
+            "light": "Claro",
+            "dark": "Oscuro",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Comprobar actualizaciones",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Clave API de Civitai",
         "civitaiApiKeyPlaceholder": "Introduce tu clave API de Civitai",
         "civitaiApiKeyHelp": "Utilizada para autenticación al descargar modelos de Civitai",
+        "civitaiApiKeyConfigured": "Configurado",
+        "civitaiApiKeyNotConfigured": "No configurado",
+        "civitaiApiKeySet": "Configurar",
         "civitaiHost": {
             "label": "Host de Civitai",
             "help": "Elige qué sitio de Civitai se abre al usar los enlaces de \"View on Civitai\".",
@@ -299,6 +321,7 @@
             "downloads": "Descargas",
             "videoSettings": "Configuración de video",
             "layoutSettings": "Configuración de diseño",
+            "licenseIcons": "Iconos de licencia",
             "misc": "Varios",
             "backup": "Copias de seguridad",
             "folderSettings": "Raíces predeterminadas",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "Rutas de carpetas adicionales",
             "downloadPathTemplates": "Plantillas de rutas de descarga",
             "priorityTags": "Etiquetas prioritarias",
-            "updateFlags": "Indicadores de actualización",
+            "versionScope": "Indicadores de actualización",
             "exampleImages": "Imágenes de ejemplo",
             "autoOrganize": "Organización automática",
             "metadata": "Metadatos",
@@ -411,6 +434,8 @@
             "help": "Cuando está habilitado, LoRA Manager omitirá la descarga de una versión de modelo si el servicio de historial de descargas registra esa versión exacta como ya descargada. Aplica a todos los flujos de descarga."
         },
         "layoutSettings": {
+            "groupByModel": "Agrupar por modelo",
+            "groupByModelHelp": "Cuando está activado, solo se muestra la versión más reciente de cada modelo de Civitai como una tarjeta única. Las versiones anteriores están ocultas.",
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
                 "default": "Predeterminado",
@@ -424,6 +449,12 @@
                 "compact": "7 (1080p), 8 (2K), 10 (4K)"
             },
             "displayDensityWarning": "Advertencia: Densidades más altas pueden causar problemas de rendimiento en sistemas con recursos limitados.",
+            "recipesLayout": "Diseño de recetas",
+            "recipesLayoutHelp": "Elige cómo se organizan las tarjetas de recetas: una cuadrícula uniforme o un diseño masonry (estilo Pinterest) que conserva la proporción de aspecto de cada imagen.",
+            "recipesLayoutOptions": {
+                "grid": "Cuadrícula",
+                "masonry": "Masonry"
+            },
             "showFolderSidebar": "Mostrar barra lateral de carpetas",
             "showFolderSidebarHelp": "Activa o desactiva la barra lateral de navegación de carpetas en las páginas de modelos. Cuando está desactivada, la barra lateral y el área de desplazamiento permanecen ocultas.",
             "cardInfoDisplay": "Visualización de información de tarjeta",
@@ -445,7 +476,9 @@
                 "modelName": "Nombre del modelo",
                 "fileName": "Nombre del archivo"
             },
-            "modelNameDisplayHelp": "Elige qué mostrar en el pie de la tarjeta del modelo"
+            "modelNameDisplayHelp": "Elige qué mostrar en el pie de la tarjeta del modelo",
+            "cardBlurAmount": "Desenfoque de superposición de tarjetas",
+            "cardBlurAmountHelp": "Ajuste la intensidad de desenfoque de las superposiciones del encabezado y pie de página en las tarjetas de modelos y recetas (0 = sin desenfoque, 20 = desenfoque máximo)."
         },
         "folderSettings": {
             "activeLibrary": "Biblioteca activa",
@@ -480,7 +513,9 @@
             "saveSuccess": "Rutas de carpetas adicionales actualizadas. Se requiere reinicio para aplicar los cambios.",
             "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
             "validation": {
-                "duplicatePath": "Esta ruta ya está configurada"
+                "duplicatePath": "Esta ruta ya está configurada",
+                "checkpointUnetOverlap": "No se puede usar la misma ruta para checkpoints y modelos de difusión: {paths}",
+                "checkpointUnetOverlapInline": "Esta ruta ya se usa para otro tipo de modelo. Use carpetas separadas para checkpoints y modelos de difusión."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "Descargar",
             "restartRequired": "Requiere reinicio"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Estrategia de indicadores de actualización",
             "help": "Decide si las insignias de actualización deben mostrarse solo cuando una nueva versión comparte el mismo modelo base que tus archivos locales o siempre que exista cualquier versión más reciente de ese modelo.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "Ocultar actualizaciones de acceso temprano",
             "help": "Solo actualizaciones de acceso temprano"
         },
+        "licenseIcons": {
+            "useNewStyle": "Usar iconos de licencia actualizados",
+            "useNewStyleHelp": "Mostrar permisos de licencia con indicadores de color (nuevo estilo) o solo iconos de restricción (estilo clásico). Refleja el diseño actual de CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Incluir palabras clave en la sintaxis de LoRA",
             "includeTriggerWordsHelp": "Incluir palabras clave entrenadas al copiar la sintaxis de LoRA al portapapeles",
@@ -609,7 +648,13 @@
             "preparing": "Preparando descarga...",
             "connecting": "Conectando al servidor de descarga...",
             "completed": "Completado",
-            "downloadComplete": "Descarga completada exitosamente"
+            "downloadComplete": "Descarga completada exitosamente",
+            "enableCivarchiveApi": "Habilitar CivArchive API como proveedor de metadatos",
+            "enableCivarchiveApiHelp": "Al activarlo, la API de CivArchive se usa como fuente alternativa de metadatos de modelos (p. ej. para modelos eliminados de CivitAI). Desactívelo para evitar por completo los límites de velocidad de CivArchive.",
+            "providerOrder": "Orden de proveedores de metadatos de respaldo",
+            "providerOrderHelp": "La API de CivitAI siempre se intenta primero. Elija el orden de los demás proveedores al buscar metadatos.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Habilitar proxy a nivel de aplicación",
@@ -628,6 +673,33 @@
             "proxyPassword": "Contraseña (opcional)",
             "proxyPasswordPlaceholder": "contraseña",
             "proxyPasswordHelp": "Contraseña para autenticación de proxy (si es necesario)"
+        },
+        "aiProvider": {
+            "title": "Proveedor de IA",
+            "provider": "Proveedor",
+            "providerHelp": "Elija su proveedor de LLM. OpenAI y Ollama usan endpoints predefinidos. Personalizado le permite especificar cualquier endpoint compatible con OpenAI.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (local)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "Personalizado (compatible con OpenAI)"
+            },
+            "apiBase": "URL base de la API",
+            "apiBaseHelp": "La URL base para la API LLM (p.ej. https://api.openai.com/v1). Déjelo vacío para usar el valor predeterminado del proveedor.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "Clave de API",
+            "apiKeyHelp": "Su clave de API del proveedor LLM. Se almacena localmente y nunca se envía a ningún servidor excepto a su proveedor LLM elegido.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "No configurada",
+            "apiKeyConfigured": "Configurada",
+            "apiKeySet": "Configurar",
+            "model": "Modelo",
+            "modelHelp": "El nombre del modelo a usar (p.ej. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Consulte a su proveedor para ver los modelos disponibles.",
+            "modelPlaceholder": "Seleccionar un modelo..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "Menor",
                 "usage": "Número de usos",
                 "usageDesc": "Más",
-                "usageAsc": "Menos"
+                "usageAsc": "Menos",
+                "versionsCount": "Versiones locales",
+                "versionsCountDesc": "Más versiones primero",
+                "versionsCountAsc": "Menos versiones primero",
+                "versionIdDesc": "Versión más nueva primero",
+                "random": "Aleatorio",
+                "randomAction": "Aleatorizar (barajar)"
             },
             "refresh": {
                 "title": "Actualizar lista de modelos",
@@ -690,6 +768,7 @@
             "copyAll": "Copiar toda la sintaxis",
             "refreshAll": "Actualizar todos los metadatos",
             "repairMetadata": "Reparar metadatos de la selección",
+            "reimportMetadata": "Reimportar desde origen",
             "checkUpdates": "Comprobar actualizaciones para la selección",
             "moveAll": "Mover todos a carpeta",
             "autoOrganize": "Auto-organizar seleccionados",
@@ -701,6 +780,8 @@
             "deleteAll": "Eliminar seleccionados",
             "downloadMissingLoras": "Descargar LoRAs faltantes",
             "downloadExamples": "Descargar imágenes de ejemplo",
+            "downloadMissingExamples": "Descargar faltantes",
+            "reprocessExamples": "Reprocesar todo",
             "clear": "Limpiar selección",
             "skipMetadataRefreshCount": "Omitir（{count} modelos）",
             "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
@@ -720,12 +801,15 @@
                 "completed": "Completado: {success} movidos, {skipped} omitidos, {failures} fallidos",
                 "complete": "Auto-organización completada",
                 "error": "Error: {error}"
-            }
+            },
+            "enrichHfAgent": "Enriquecer metadatos HF (IA)"
         },
         "contextMenu": {
             "refreshMetadata": "Actualizar datos de Civitai",
             "checkUpdates": "Comprobar actualizaciones",
-            "relinkCivitai": "Re-vincular a Civitai",
+            "linkModel": "Vincular modelo",
+            "linkCivitai": "Re-vincular a Civitai",
+            "linkHuggingFace": "Vincular a HuggingFace",
             "copySyntax": "Copiar sintaxis de LoRA",
             "copyFilename": "Copiar nombre de archivo del modelo",
             "copyRecipeSyntax": "Copiar sintaxis de receta",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "Enviar al flujo de trabajo (Reemplazar)",
             "openExamples": "Abrir carpeta de ejemplos",
             "downloadExamples": "Descargar imágenes de ejemplo",
+            "downloadMissingExamples": "Descargar faltantes",
+            "reprocessExamples": "Reprocesar todo",
             "replacePreview": "Reemplazar vista previa",
             "setContentRating": "Establecer clasificación de contenido",
             "moveToFolder": "Mover a carpeta",
             "repairMetadata": "Reparar metadatos",
+            "reimportMetadata": "Reimportar desde origen",
             "excludeModel": "Excluir modelo",
             "restoreModel": "Restaurar modelo",
             "deleteModel": "Eliminar modelo",
             "shareRecipe": "Compartir receta",
             "viewAllLoras": "Ver todos los LoRAs",
             "downloadMissingLoras": "Descargar LoRAs faltantes",
-            "deleteRecipe": "Eliminar receta"
+            "deleteRecipe": "Eliminar receta",
+            "enrichHfAgent": "Enriquecer metadatos HF (IA)"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "La receta ya está en la última versión, no se necesita reparación",
                 "failed": "Error al reparar la receta: {message}",
                 "missingId": "No se puede reparar la receta: falta el ID de la receta"
+            },
+            "reimport": {
+                "starting": "Reimportando receta desde origen...",
+                "success": "Receta reimportada exitosamente",
+                "noSourceUrl": "La receta no tiene URL de origen, no se puede reimportar",
+                "failed": "Error al reimportar la receta: {message}",
+                "missingId": "No se puede reimportar la receta: falta el ID"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "Raíz",
         "collapseAll": "Colapsar todas las carpetas",
-        "pinSidebar": "Fijar barra lateral",
-        "unpinSidebar": "Desfijar barra lateral",
+        "hideOnThisPage": "Ocultar barra lateral en esta página",
+        "showSidebar": "Mostrar barra lateral",
+        "sidebarHiddenNotification": "Barra lateral oculta en la página {page}",
         "switchToListView": "Cambiar a vista de lista",
         "switchToTreeView": "Cambiar a vista de árbol",
         "recursiveOn": "Incluir subcarpetas",
@@ -981,6 +1077,18 @@
             "storage": "Almacenamiento",
             "insights": "Perspectivas"
         },
+        "metrics": {
+            "totalModels": "Total de modelos",
+            "totalStorage": "Almacenamiento total",
+            "totalGenerations": "Generaciones totales",
+            "usageRate": "Tasa de uso",
+            "loras": "LoRAs",
+            "checkpoints": "Puntos de control",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Etiquetas únicas",
+            "unusedModels": "Modelos no usados",
+            "avgUsesPerModel": "Prom. usos/modelo"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs más utilizados",
             "mostUsedCheckpoints": "Checkpoints más utilizados",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "Perspectivas inteligentes",
-            "recommendations": "Recomendaciones"
+            "recommendations": "Recomendaciones",
+            "noInsights": "No hay información disponible",
+            "unusedLoras": {
+                "high": {
+                    "title": "Alta cantidad de LoRAs no utilizadas",
+                    "description": "El {percent}% de tus LoRAs ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Considera organizar o archivar modelos no utilizados para liberar espacio."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Puntos de control no utilizados detectados",
+                    "description": "El {percent}% de tus puntos de control ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Revisa y considera eliminar los puntos de control que ya no necesites."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Alta cantidad de Embeddings no utilizados",
+                    "description": "El {percent}% de tus embeddings ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Considera organizar o archivar embeddings no utilizados para optimizar tu colección."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Colección grande detectada",
+                    "description": "Tu colección de modelos está usando {size} de almacenamiento.",
+                    "suggestion": "Considera usar almacenamiento externo o soluciones en la nube para una mejor organización."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Usuario activo",
+                    "description": "¡Has completado {count} generaciones hasta ahora!",
+                    "suggestion": "Sigue explorando y creando contenido increíble con tus modelos."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Resumen de colección",
             "baseModelDistribution": "Distribución de modelo base",
             "usageTrends": "Tendencias de uso (Últimos 30 días)",
-            "usageDistribution": "Distribución de uso"
+            "usageDistribution": "Distribución de uso",
+            "date": "Fecha",
+            "usageCount": "Conteo de uso",
+            "fileSizeBytes": "Tamaño del archivo (bytes)",
+            "models": "Modelos",
+            "loraUsage": "Uso de LoRA",
+            "checkpointUsage": "Uso de Checkpoint",
+            "embeddingUsage": "Uso de Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Punto de control",
+            "diffusion_model": "Modelo de difusión",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Cargando...",
+            "noModels": "No se encontraron modelos",
+            "errorLoading": "Error al cargar datos",
+            "noStorageData": "No hay datos de almacenamiento disponibles",
+            "rootFolder": "Raíz",
+            "chartLibraryMissing": "El gráfico requiere la librería Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} modelos",
+            "chartUsage": "{name}: {size}, {count} usos",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "Descargar modelo desde URL",
             "titleWithType": "Descargar {type} desde URL",
-            "url": "URL de Civitai",
             "civitaiUrl": "URL de Civitai:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "Ingrese una URL de CivitAI, CivArchive o Hugging Face por línea. Admite múltiples URLs para descarga por lotes.",
+            "selectHfFiles": "Seleccione el/los archivo(s) para descargar de este repositorio:",
+            "selectAll": "Seleccionar todo",
+            "fetchingRepoFiles": "Obteniendo archivos del repositorio...",
             "locationPreview": "Vista previa de ubicación de descarga",
             "useDefaultPath": "Usar ruta predeterminada",
             "useDefaultPathTooltip": "Cuando está habilitado, los archivos se organizan automáticamente usando plantillas de rutas configuradas",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "Formato de URL de Civitai inválido",
-                "noVersions": "No hay versiones disponibles para este modelo"
+                "noVersions": "No hay versiones disponibles para este modelo",
+                "mixedSources": "No se pueden mezclar URL de CivitAI y Hugging Face en el mismo lote.",
+                "noModelFiles": "No se encontraron archivos de modelo en este repositorio."
             },
             "status": {
                 "preparing": "Preparando descarga...",
                 "downloadedPreview": "Imagen de vista previa descargada",
                 "downloadingFile": "Descargando archivo de {type}",
-                "finalizing": "Finalizando descarga..."
+                "finalizing": "Finalizando descarga...",
+                "cancelling": "Cancelando descarga...",
+                "cancelled": "Descarga cancelada"
             },
             "progress": {
                 "currentFile": "Archivo actual:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "Escribe la ruta de la carpeta o selecciona del árbol de abajo...",
             "root": "Raíz"
         },
+        "linkHuggingFace": {
+            "title": "Vincular a HuggingFace",
+            "infoText": "Pegue la URL del repositorio de HuggingFace para asociar este modelo. Esto permite el enriquecimiento de metadatos con IA.",
+            "urlLabel": "URL del repositorio de HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Ingrese la URL completa del repositorio de HuggingFace.",
+            "confirmAction": "Guardar y vincular"
+        },
         "relinkCivitai": {
             "title": "Re-vincular a Civitai",
             "warning": "Advertencia:",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "Editar nombre de versión",
                 "viewOnCivitai": "Ver en Civitai",
                 "viewOnCivitaiText": "Ver en Civitai",
+                "viewOnHuggingFace": "Ver en Hugging Face",
+                "viewOnHuggingFaceText": "Ver en Hugging Face",
                 "viewCreatorProfile": "Ver perfil del creador",
                 "openFileLocation": "Abrir ubicación del archivo",
                 "sendToWorkflow": "Enviar a ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "Notas adicionales",
                 "notesHint": "Presiona Enter para guardar, Shift+Enter para nueva línea",
                 "addNotesPlaceholder": "Añade tus notas aquí...",
-                "aboutThisVersion": "Acerca de esta versión"
+                "aboutThisVersion": "Acerca de esta versión",
+                "baseModelSearchPlaceholder": "Buscar modelo base…",
+                "baseModelSuggested": "Sugerido",
+                "baseModelNoMatch": "No hay modelos base que coincidan"
             },
             "notes": {
                 "saved": "Notas guardadas exitosamente",
-                "saveFailed": "Error al guardar notas"
+                "saveFailed": "Error al guardar notas",
+                "showMore": "Mostrar más",
+                "showLess": "Mostrar menos"
             },
             "usageTips": {
                 "addPresetParameter": "Añadir parámetro preestablecido...",
@@ -1367,6 +1561,7 @@
                 "empty": "Aún no hay historial de versiones para este modelo.",
                 "error": "No se pudieron cargar las versiones.",
                 "missingModelId": "Este modelo no tiene un ID de modelo de Civitai.",
+                "hfGroupInfo": "Este es un grupo de modelos de HuggingFace. Abra la biblioteca para ver todas las versiones en la cuadrícula.",
                 "confirm": {
                     "delete": "¿Eliminar esta versión de tu biblioteca?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "Versión eliminada"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "Resumen de obtención de metadatos",
+            "statSuccess": "Éxito",
+            "statFailed": "Fallido",
+            "statSkipped": "Omitido",
+            "statTotal": "Total escaneado",
+            "statDuration": "Duración",
+            "successMessage": "¡Todos los {count} {type}s actualizados correctamente!",
+            "failedItems": "Elementos fallidos ({count})",
+            "close": "Cerrar",
+            "copyReport": "Copiar informe",
+            "downloadCsv": "Descargar CSV",
+            "columnModelName": "Nombre del modelo",
+            "columnError": "Error"
+        },
+        "downloadBatchSummary": {
+            "title": "Resumen de descarga por lotes",
+            "statSuccess": "Correctos",
+            "statFailed": "Fallidos",
+            "statTotal": "Total",
+            "successMessage": "Todos los {count} modelos se descargaron correctamente",
+            "completedWithErrors": "Completado con errores",
+            "failed": "Descarga fallida",
+            "failedItems": "Elementos fallidos ({count})",
+            "columnName": "Nombre del modelo",
+            "columnError": "Error",
+            "close": "Cerrar",
+            "copyReport": "Copiar informe",
+            "retryFailed": "Reintentar fallidos ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "Esta etiqueta ya existe"
         }
     },
-    "keyboard": {
-        "navigation": "Navegación por teclado:",
-        "shortcuts": {
-            "pageUp": "Desplazar hacia arriba una página",
-            "pageDown": "Desplazar hacia abajo una página",
-            "home": "Saltar al inicio",
-            "end": "Saltar al final"
-        }
-    },
     "initialization": {
         "title": "Inicializando",
         "message": "Preparando tu espacio de trabajo...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "No hay nodos compatibles disponibles en el flujo de trabajo actual",
             "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino",
             "modelUpdated": "Modelo actualizado en el flujo de trabajo",
-            "modelFailed": "Error al actualizar nodo de modelo"
+            "modelFailed": "Error al actualizar nodo de modelo",
+            "embeddingAdded": "Embedding añadido al flujo de trabajo",
+            "embeddingFailed": "Error al añadir el embedding",
+            "promptSent": "Prompt enviado al flujo de trabajo",
+            "promptFailed": "Error al enviar el prompt"
         },
         "nodeSelector": {
             "recipe": "Receta",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Reemplazar",
             "append": "Añadir",
             "selectTargetNode": "Seleccionar nodo de destino",
@@ -1557,7 +1779,13 @@
         "checkingUpdates": "Comprobando actualizaciones...",
         "checkingMessage": "Por favor espera mientras comprobamos la última versión.",
         "showNotifications": "Mostrar notificaciones de actualización",
-        "latestBadge": "Último",
+        "latestBadge": "Última",
+        "latestMain": "Rama main",
+        "channel": "Canal de actualizacion",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Preparando actualización...",
             "installing": "Instalando actualización...",
@@ -1578,6 +1806,15 @@
             "warning": "Advertencia: Las compilaciones nocturnas pueden contener características experimentales y podrían ser inestables.",
             "enable": "Habilitar actualizaciones nocturnas"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Cambiar a canal Nightly",
+            "nightlyMessage": "Cambiar a Nightly inicializara un repositorio Git y seguira los ultimos commits de la rama main. Las actualizaciones son mas frecuentes pero pueden ser inestables. Puede volver a Release en cualquier momento.",
+            "releaseTitle": "Cambiar a canal Release",
+            "releaseMessage": "Cambiar a Release hara checkout de la ultima etiqueta de version estable. Puede volver a Nightly en cualquier momento.",
+            "switching": "Cambiando a canal {channel}...",
+            "completed": "Cambio a canal {channel} exitoso",
+            "failed": "Error al cambiar de canal"
+        },
         "banners": {
             "recent": "Notificaciones recientes",
             "empty": "No hay banners recientes.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "Por favor introduce un nombre de LoRA o sintaxis",
             "reconnectedSuccessfully": "LoRA reconectado exitosamente",
             "reconnectFailed": "Error reconectando LoRA: {message}",
+            "noPromptToSend": "No hay prompt para enviar",
             "cannotSend": "No se puede enviar receta: Falta ID de receta",
             "sendFailed": "Error al enviar receta al flujo de trabajo",
             "sendError": "Error enviando receta al flujo de trabajo",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "Reparación completa: {repaired} reparadas, {skipped} omitidas (de {total})",
             "repairBulkSkipped": "No se necesita reparación para ninguna de las {total} recetas seleccionadas",
             "repairBulkFailed": "Error al reparar las recetas seleccionadas: {message}",
+            "reimporting": "Reimportando receta desde origen...",
+            "reimportSuccess": "Receta reimportada exitosamente",
+            "reimportBulkComplete": "Reimportación completa: {completed} reimportadas, {failed} fallidas (de {total})",
+            "reimportBulkFailed": "Error al reimportar algunas recetas",
             "noMissingLorasInSelection": "No se encontraron LoRAs faltantes en las recetas seleccionadas",
             "noLoraRootConfigured": "No se ha configurado el directorio raíz de LoRA. Por favor, establezca un directorio raíz de LoRA predeterminado en la configuración."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "El nombre del preajuste debe tener {max} caracteres o menos",
             "presetNameInvalidChars": "El nombre del preajuste contiene caracteres inválidos",
             "presetNameExists": "Ya existe un preajuste con este nombre",
-            "maxPresetsReached": "Máximo {max} preajustes permitidos. Elimine uno para agregar más.",
             "presetNotFound": "Preajuste no encontrado",
             "invalidPreset": "Datos de preajuste inválidos",
             "deletePresetFailed": "Error al eliminar el preajuste",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "Imágenes de ejemplo {action} completadas",
             "imagesFailed": "Imágenes de ejemplo {action} fallidas",
             "loadError": "Error al cargar descargas: {message}",
-            "downloadError": "Error de descarga: {message}"
+            "downloadError": "Error de descarga: {message}",
+            "downloadStopped": "Descarga cancelada"
         },
         "import": {
             "folderTreeFailed": "Error al cargar árbol de carpetas",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "Error al establecer clasificación de contenido: {message}",
             "relinkSuccess": "Modelo re-vinculado exitosamente a Civitai",
             "relinkFailed": "Error: {message}",
+            "linkHfSuccess": "Modelo vinculado a HuggingFace exitosamente",
+            "linkHfFailed": "Error: {message}",
             "fetchMetadataFirst": "Por favor obtén metadatos de CivitAI primero",
             "noCivitaiInfo": "No hay información de CivitAI disponible",
             "missingHash": "Hash del modelo no disponible"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "Movidos exitosamente {successCount} {type}s",
             "exampleImagesDownloadSuccess": "¡Imágenes de ejemplo descargadas exitosamente!",
             "exampleImagesDownloadFailed": "Error al descargar imágenes de ejemplo: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "Copiado al portapapeles",
+            "downloadStarted": "Descarga iniciada"
+        },
+        "agent": {
+            "llmNotConfigured": "Proveedor de IA no configurado. Actívelo en Configuración → Proveedor de IA.",
+            "enrichStarted": "Enriqueciendo metadatos con IA...",
+            "enrichComplete": "Enriquecimiento de metadatos completado: {{summary}}",
+            "enrichFailed": "Enriquecimiento de metadatos fallido: {{error}}"
         }
     },
     "doctor": {

locales/fr.json

@@ -16,10 +16,13 @@
             "help": "Aide",
             "add": "Ajouter",
             "close": "Fermer",
-            "menu": "Menu"
+            "menu": "Menu",
+            "remove": "Supprimer",
+            "change": "Modifier"
         },
         "status": {
             "loading": "Chargement...",
+            "cancelling": "Annulation...",
             "unknown": "Inconnu",
             "date": "Date",
             "version": "Version",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "Retirer des favoris",
             "viewOnCivitai": "Voir sur Civitai",
             "notAvailableFromCivitai": "Non disponible sur Civitai",
+            "viewOnHuggingFace": "Voir sur Hugging Face",
             "sendToWorkflow": "Envoyer vers ComfyUI (Clic: Ajouter, Maj+Clic: Remplacer)",
             "copyLoRASyntax": "Copier la syntaxe LoRA",
             "checkpointNameCopied": "Nom du checkpoint copié",
@@ -111,6 +115,7 @@
             "replacePreview": "Remplacer l'aperçu",
             "copyCheckpointName": "Copier le nom du checkpoint",
             "copyEmbeddingName": "Copier le nom de l'embedding",
+            "embeddingNameCopied": "Syntaxe dembedding copiée",
             "sendCheckpointToWorkflow": "Envoyer vers ComfyUI",
             "sendEmbeddingToWorkflow": "Envoyer vers ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Nombre d'utilisations"
+        },
+        "footer": {
+            "versionCount": "{count} versions",
+            "viewAllVersions": "Voir toutes les versions locales"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Gérer les modèles exclus"
+        },
+        "groupByModel": {
+            "label": "Grouper par modèle"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "Statistiques"
         },
         "search": {
-            "placeholder": "Rechercher...",
-            "placeholders": {
-                "loras": "Rechercher des LoRAs...",
-                "recipes": "Rechercher des recipes...",
-                "checkpoints": "Rechercher des checkpoints...",
-                "embeddings": "Rechercher des embeddings..."
-            },
+            "placeholder": "Rechercher",
             "options": "Options de recherche",
             "searchIn": "Rechercher dans :",
             "notAvailable": "Recherche non disponible sur la page de statistiques",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "Nom du préréglage...",
             "baseModel": "Modèle de base",
             "baseModelSearchPlaceholder": "Rechercher des modèles de base...",
-            "modelTags": "Tags (Top 20)",
+            "modelTags": "Tags",
             "modelTypes": "Types de modèles",
             "license": "Licence",
             "noCreditRequired": "Crédit non requis",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Autoriser la vente d\"images générées",
             "noCreditRequiredTooltip": "Utiliser le modèle sans créditer le créateur",
             "noTags": "Aucun tag",
+            "tagSearchPlaceholder": "Rechercher des tags...",
+            "noTagMatches": "Aucun tag ne correspond à la recherche actuelle.",
             "autoTags": "Auto-Tags",
             "noBaseModelMatches": "Aucun modèle de base ne correspond à la recherche actuelle.",
             "clearAll": "Effacer tous les filtres",
@@ -247,7 +255,18 @@
             "toggle": "Basculer le thème",
             "switchToLight": "Passer au thème clair",
             "switchToDark": "Passer au thème sombre",
-            "switchToAuto": "Passer au thème automatique"
+            "switchToAuto": "Passer au thème automatique",
+            "presets": "Préréglages de thème",
+            "default": "Par défaut",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Mode",
+            "light": "Clair",
+            "dark": "Sombre",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Vérifier les mises à jour",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Clé API Civitai",
         "civitaiApiKeyPlaceholder": "Entrez votre clé API Civitai",
         "civitaiApiKeyHelp": "Utilisée pour l'authentification lors du téléchargement de modèles depuis Civitai",
+        "civitaiApiKeyConfigured": "Configuré",
+        "civitaiApiKeyNotConfigured": "Non configuré",
+        "civitaiApiKeySet": "Configurer",
         "civitaiHost": {
             "label": "Hôte Civitai",
             "help": "Choisissez quel site Civitai s'ouvre lorsque vous utilisez les liens « View on Civitai ».",
@@ -299,6 +321,7 @@
             "downloads": "Téléchargements",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
+            "licenseIcons": "Icônes de licence",
             "misc": "Divers",
             "backup": "Sauvegardes",
             "folderSettings": "Racines par défaut",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "Chemins de dossiers supplémentaires",
             "downloadPathTemplates": "Modèles de chemin de téléchargement",
             "priorityTags": "Étiquettes prioritaires",
-            "updateFlags": "Indicateurs de mise à jour",
+            "versionScope": "Indicateurs de mise à jour",
             "exampleImages": "Images d'exemple",
             "autoOrganize": "Organisation automatique",
             "metadata": "Métadonnées",
@@ -411,6 +434,8 @@
             "help": "Lorsque activé, LoRA Manager ignorera le téléchargement d'une version de modèle si le service d'historique des téléchargements enregistre cette version exacte comme déjà téléchargée. S'applique à tous les flux de téléchargement."
         },
         "layoutSettings": {
+            "groupByModel": "Grouper par modèle",
+            "groupByModelHelp": "Lorsque activé, seule la version la plus récente de chaque modèle Civitai s'affiche sous forme de carte unique. Les versions plus anciennes sont masquées.",
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
                 "default": "Par défaut",
@@ -424,6 +449,12 @@
                 "compact": "7 (1080p), 8 (2K), 10 (4K)"
             },
             "displayDensityWarning": "Attention : Des densités plus élevées peuvent causer des problèmes de performance sur les systèmes avec des ressources limitées.",
+            "recipesLayout": "Disposition des recettes",
+            "recipesLayoutHelp": "Choisissez comment les cartes de recettes sont organisées : une grille uniforme ou une disposition masonry (style Pinterest) qui préserve le rapport d'aspect de chaque image.",
+            "recipesLayoutOptions": {
+                "grid": "Grille",
+                "masonry": "Masonry"
+            },
             "showFolderSidebar": "Afficher la barre latérale des dossiers",
             "showFolderSidebarHelp": "Activez ou désactivez la barre latérale de navigation des dossiers sur les pages de modèles. Lorsqu'elle est désactivée, la barre latérale et la zone de survol restent masquées.",
             "cardInfoDisplay": "Affichage des informations de carte",
@@ -445,7 +476,9 @@
                 "modelName": "Nom du modèle",
                 "fileName": "Nom du fichier"
             },
-            "modelNameDisplayHelp": "Choisissez ce qui doit être affiché dans le pied de page de la carte du modèle"
+            "modelNameDisplayHelp": "Choisissez ce qui doit être affiché dans le pied de page de la carte du modèle",
+            "cardBlurAmount": "Flou de superposition des cartes",
+            "cardBlurAmountHelp": "Ajustez l'intensité du flou des superpositions d'en-tête et de pied de page sur les cartes de modèles et de recettes (0 = aucun flou, 20 = flou maximal)."
         },
         "folderSettings": {
             "activeLibrary": "Bibliothèque active",
@@ -480,7 +513,9 @@
             "saveSuccess": "Chemins de dossiers supplémentaires mis à jour. Redémarrage requis pour appliquer les changements.",
             "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
             "validation": {
-                "duplicatePath": "Ce chemin est déjà configuré"
+                "duplicatePath": "Ce chemin est déjà configuré",
+                "checkpointUnetOverlap": "Impossible d'utiliser le même chemin pour les checkpoints et les modèles de diffusion : {paths}",
+                "checkpointUnetOverlapInline": "Ce chemin est déjà utilisé pour un autre type de modèle. Utilisez des dossiers séparés pour les checkpoints et les modèles de diffusion."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "Télécharger",
             "restartRequired": "Redémarrage requis"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Stratégie des indicateurs de mise à jour",
             "help": "Choisissez si les badges de mise à jour doivent apparaître uniquement lorsqu’une nouvelle version partage le même modèle de base que vos fichiers locaux, ou dès qu’il existe une version plus récente pour ce modèle.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "Masquer les mises à jour en accès anticipé",
             "help": "Seulement les mises à jour en accès anticipé"
         },
+        "licenseIcons": {
+            "useNewStyle": "Utiliser les icônes de licence mises à jour",
+            "useNewStyleHelp": "Afficher les permissions de licence avec des indicateurs colorés (nouveau style) ou des icônes de restriction uniquement (style classique). Reprend le design actuel de CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Inclure les mots-clés dans la syntaxe LoRA",
             "includeTriggerWordsHelp": "Inclure les mots-clés d'entraînement lors de la copie de la syntaxe LoRA dans le presse-papiers",
@@ -609,7 +648,13 @@
             "preparing": "Préparation du téléchargement...",
             "connecting": "Connexion au serveur de téléchargement...",
             "completed": "Terminé",
-            "downloadComplete": "Téléchargement terminé avec succès"
+            "downloadComplete": "Téléchargement terminé avec succès",
+            "enableCivarchiveApi": "Activer l'API CivArchive comme fournisseur de métadonnées",
+            "enableCivarchiveApiHelp": "Lorsqu'elle est activée, l'API CivArchive est utilisée comme source de secours pour les métadonnées des modèles (par ex. pour les modèles supprimés de CivitAI). Désactivez pour éviter entièrement les limites de débit de CivArchive.",
+            "providerOrder": "Ordre de secours des fournisseurs de métadonnées",
+            "providerOrderHelp": "L'API CivitAI est toujours essayée en premier. Choisissez l'ordre des autres fournisseurs lors de la recherche de métadonnées.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Activer le proxy au niveau de l'application",
@@ -628,6 +673,33 @@
             "proxyPassword": "Mot de passe (optionnel)",
             "proxyPasswordPlaceholder": "mot_de_passe",
             "proxyPasswordHelp": "Mot de passe pour l'authentification proxy (si nécessaire)"
+        },
+        "aiProvider": {
+            "title": "Fournisseur d'IA",
+            "provider": "Fournisseur",
+            "providerHelp": "Choisissez votre fournisseur LLM. OpenAI et Ollama utilisent des endpoints prédéfinis. Personnalisé vous permet de spécifier n'importe quel endpoint compatible OpenAI.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (local)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "Personnalisé (compatible OpenAI)"
+            },
+            "apiBase": "URL de base de l'API",
+            "apiBaseHelp": "L'URL de base pour l'API LLM (ex. https://api.openai.com/v1). Laissez vide pour utiliser le fournisseur par défaut.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "Clé API",
+            "apiKeyHelp": "Votre clé API du fournisseur LLM. Stockée localement, jamais envoyée à un serveur autre que votre fournisseur LLM choisi.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Non définie",
+            "apiKeyConfigured": "Configurée",
+            "apiKeySet": "Configurer",
+            "model": "Modèle",
+            "modelHelp": "Le nom du modèle à utiliser (ex. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Consultez votre fournisseur pour les modèles disponibles.",
+            "modelPlaceholder": "Sélectionner un modèle..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "Plus petit",
                 "usage": "Nombre d'utilisations",
                 "usageDesc": "Plus",
-                "usageAsc": "Moins"
+                "usageAsc": "Moins",
+                "versionsCount": "Versions locales",
+                "versionsCountDesc": "Plus de versions d'abord",
+                "versionsCountAsc": "Moins de versions d'abord",
+                "versionIdDesc": "Version la plus récente d'abord",
+                "random": "Aléatoire",
+                "randomAction": "Aléatoire (mélanger)"
             },
             "refresh": {
                 "title": "Actualiser la liste des modèles",
@@ -690,6 +768,7 @@
             "copyAll": "Copier toute la syntaxe",
             "refreshAll": "Actualiser toutes les métadonnées",
             "repairMetadata": "Réparer les métadonnées de la sélection",
+            "reimportMetadata": "Ré-importer depuis la source",
             "checkUpdates": "Vérifier les mises à jour pour la sélection",
             "moveAll": "Déplacer tout vers un dossier",
             "autoOrganize": "Auto-organiser la sélection",
@@ -701,6 +780,8 @@
             "deleteAll": "Supprimer la sélection",
             "downloadMissingLoras": "Télécharger les LoRAs manquants",
             "downloadExamples": "Télécharger les images d'exemple",
+            "downloadMissingExamples": "Télécharger les manquantes",
+            "reprocessExamples": "Tout retraiter",
             "clear": "Effacer la sélection",
             "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
             "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
@@ -720,12 +801,15 @@
                 "completed": "Terminé : {success} déplacés, {skipped} ignorés, {failures} échecs",
                 "complete": "Auto-organisation terminée",
                 "error": "Erreur : {error}"
-            }
+            },
+            "enrichHfAgent": "Enrichir les métadonnées HF (IA)"
         },
         "contextMenu": {
             "refreshMetadata": "Actualiser les données Civitai",
             "checkUpdates": "Vérifier les mises à jour",
-            "relinkCivitai": "Relier à nouveau à Civitai",
+            "linkModel": "Lier le modèle",
+            "linkCivitai": "Relier à nouveau à Civitai",
+            "linkHuggingFace": "Lier à HuggingFace",
             "copySyntax": "Copier la syntaxe LoRA",
             "copyFilename": "Copier le nom de fichier du modèle",
             "copyRecipeSyntax": "Copier la syntaxe de la recipe",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "Envoyer vers le workflow (Remplacer)",
             "openExamples": "Ouvrir le dossier d'exemples",
             "downloadExamples": "Télécharger les images d'exemple",
+            "downloadMissingExamples": "Télécharger les manquantes",
+            "reprocessExamples": "Tout retraiter",
             "replacePreview": "Remplacer l'aperçu",
             "setContentRating": "Définir la classification du contenu",
             "moveToFolder": "Déplacer vers un dossier",
             "repairMetadata": "Réparer les métadonnées",
+            "reimportMetadata": "Ré-importer depuis la source",
             "excludeModel": "Exclure le modèle",
             "restoreModel": "Restaurer le modèle",
             "deleteModel": "Supprimer le modèle",
             "shareRecipe": "Partager la recipe",
             "viewAllLoras": "Voir tous les LoRAs",
             "downloadMissingLoras": "Télécharger les LoRAs manquants",
-            "deleteRecipe": "Supprimer la recipe"
+            "deleteRecipe": "Supprimer la recipe",
+            "enrichHfAgent": "Enrichir les métadonnées HF (IA)"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "Recette déjà à la version la plus récente, aucune réparation nécessaire",
                 "failed": "Échec de la réparation de la recette : {message}",
                 "missingId": "Impossible de réparer la recette : ID de recette manquant"
+            },
+            "reimport": {
+                "starting": "Ré-import de la recette depuis la source...",
+                "success": "Recette ré-importée avec succès",
+                "noSourceUrl": "La recette n'a pas d'URL source, ré-import impossible",
+                "failed": "Échec du ré-import de la recette : {message}",
+                "missingId": "Impossible de ré-importer la recette : ID de recette manquant"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "Racine",
         "collapseAll": "Réduire tous les dossiers",
-        "pinSidebar": "Épingler la barre latérale",
-        "unpinSidebar": "Désépingler la barre latérale",
+        "hideOnThisPage": "Masquer la barre latérale sur cette page",
+        "showSidebar": "Afficher la barre latérale",
+        "sidebarHiddenNotification": "Barre latérale masquée sur la page {page}",
         "switchToListView": "Passer en vue liste",
         "switchToTreeView": "Passer en vue arborescence",
         "recursiveOn": "Inclure les sous-dossiers",
@@ -981,6 +1077,18 @@
             "storage": "Stockage",
             "insights": "Aperçus"
         },
+        "metrics": {
+            "totalModels": "Total des modèles",
+            "totalStorage": "Stockage total",
+            "totalGenerations": "Générations totales",
+            "usageRate": "Taux d'utilisation",
+            "loras": "LoRAs",
+            "checkpoints": "Points de contrôle",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Tags uniques",
+            "unusedModels": "Modèles inutilisés",
+            "avgUsesPerModel": "Moy. utilisations/modèle"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs les plus utilisés",
             "mostUsedCheckpoints": "Checkpoints les plus utilisés",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "Aperçus intelligents",
-            "recommendations": "Recommandations"
+            "recommendations": "Recommandations",
+            "noInsights": "Aucun aperçu disponible",
+            "unusedLoras": {
+                "high": {
+                    "title": "Nombre élevé de LoRAs inutilisées",
+                    "description": "{percent}% de vos LoRAs ({count}/{total}) n'ont jamais été utilisées.",
+                    "suggestion": "Envisagez d'organiser ou d'archiver les modèles inutilisés pour libérer de l'espace."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Points de contrôle inutilisés détectés",
+                    "description": "{percent}% de vos points de contrôle ({count}/{total}) n'ont jamais été utilisés.",
+                    "suggestion": "Examinez et envisagez de supprimer les points de contrôle dont vous n'avez plus besoin."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Nombre élevé d'Embeddings inutilisées",
+                    "description": "{percent}% de vos embeddings ({count}/{total}) n'ont jamais été utilisées.",
+                    "suggestion": "Envisagez d'organiser ou d'archiver les embeddings inutilisées pour optimiser votre collection."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Grande collection détectée",
+                    "description": "Votre collection de modèles utilise {size} de stockage.",
+                    "suggestion": "Envisagez d'utiliser un stockage externe ou des solutions cloud pour une meilleure organisation."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Utilisateur actif",
+                    "description": "Vous avez effectué {count} générations jusqu'à présent !",
+                    "suggestion": "Continuez à explorer et à créer du contenu formidable avec vos modèles."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Aperçu de la collection",
             "baseModelDistribution": "Distribution des modèles de base",
             "usageTrends": "Tendances d'utilisation (30 derniers jours)",
-            "usageDistribution": "Distribution de l'utilisation"
+            "usageDistribution": "Distribution de l'utilisation",
+            "date": "Date",
+            "usageCount": "Nombre d'utilisations",
+            "fileSizeBytes": "Taille du fichier (octets)",
+            "models": "Modèles",
+            "loraUsage": "Utilisation LoRA",
+            "checkpointUsage": "Utilisation Checkpoint",
+            "embeddingUsage": "Utilisation Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Point de contrôle",
+            "diffusion_model": "Modèle de diffusion",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Chargement...",
+            "noModels": "Aucun modèle trouvé",
+            "errorLoading": "Erreur de chargement des données",
+            "noStorageData": "Aucune donnée de stockage disponible",
+            "rootFolder": "Racine",
+            "chartLibraryMissing": "Le graphique nécessite la bibliothèque Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} modèles",
+            "chartUsage": "{name}: {size}, {count} utilisations",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "Télécharger un modèle depuis une URL",
             "titleWithType": "Télécharger {type} depuis une URL",
-            "url": "URL Civitai",
             "civitaiUrl": "URL Civitai :",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "Entrez une URL CivitAI, CivArchive ou Hugging Face par ligne. Prend en charge plusieurs URL pour le téléchargement par lot.",
+            "selectHfFiles": "Sélectionnez le(s) fichier(s) à télécharger depuis ce dépôt :",
+            "selectAll": "Tout sélectionner",
+            "fetchingRepoFiles": "Récupération des fichiers du dépôt...",
             "locationPreview": "Aperçu de l'emplacement de téléchargement",
             "useDefaultPath": "Utiliser le chemin par défaut",
             "useDefaultPathTooltip": "Lorsque activé, les fichiers sont automatiquement organisés selon les modèles de chemin configurés",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "Format d'URL Civitai invalide",
-                "noVersions": "Aucune version disponible pour ce modèle"
+                "noVersions": "Aucune version disponible pour ce modèle",
+                "mixedSources": "Impossible de mélanger les URL CivitAI et Hugging Face dans le même lot.",
+                "noModelFiles": "Aucun fichier de modèle trouvé dans ce dépôt."
             },
             "status": {
                 "preparing": "Préparation du téléchargement...",
                 "downloadedPreview": "Image d'aperçu téléchargée",
                 "downloadingFile": "Téléchargement du fichier {type}",
-                "finalizing": "Finalisation du téléchargement..."
+                "finalizing": "Finalisation du téléchargement...",
+                "cancelling": "Annulation du téléchargement...",
+                "cancelled": "Téléchargement annulé"
             },
             "progress": {
                 "currentFile": "Fichier actuel :",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "Tapez le chemin du dossier ou sélectionnez dans l'arbre ci-dessous...",
             "root": "Racine"
         },
+        "linkHuggingFace": {
+            "title": "Lier à HuggingFace",
+            "infoText": "Collez l'URL du dépôt HuggingFace pour associer ce modèle à sa source. Cela permet l'enrichissement des métadonnées par IA.",
+            "urlLabel": "URL du dépôt HuggingFace :",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Entrez l'URL complète du dépôt HuggingFace.",
+            "confirmAction": "Enregistrer & lier"
+        },
         "relinkCivitai": {
             "title": "Relier à nouveau à Civitai",
             "warning": "Attention :",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "Modifier le nom de la version",
                 "viewOnCivitai": "Voir sur Civitai",
                 "viewOnCivitaiText": "Voir sur Civitai",
+                "viewOnHuggingFace": "Voir sur Hugging Face",
+                "viewOnHuggingFaceText": "Voir sur Hugging Face",
                 "viewCreatorProfile": "Voir le profil du créateur",
                 "openFileLocation": "Ouvrir l'emplacement du fichier",
                 "sendToWorkflow": "Envoyer vers ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "Notes supplémentaires",
                 "notesHint": "Appuyez sur Entrée pour sauvegarder, Maj+Entrée pour nouvelle ligne",
                 "addNotesPlaceholder": "Ajoutez vos notes ici...",
-                "aboutThisVersion": "À propos de cette version"
+                "aboutThisVersion": "À propos de cette version",
+                "baseModelSearchPlaceholder": "Rechercher un modèle de base…",
+                "baseModelSuggested": "Suggéré",
+                "baseModelNoMatch": "Aucun modèle de base correspondant"
             },
             "notes": {
                 "saved": "Notes sauvegardées avec succès",
-                "saveFailed": "Échec de la sauvegarde des notes"
+                "saveFailed": "Échec de la sauvegarde des notes",
+                "showMore": "Afficher plus",
+                "showLess": "Afficher moins"
             },
             "usageTips": {
                 "addPresetParameter": "Ajouter un paramètre prédéfini...",
@@ -1367,6 +1561,7 @@
                 "empty": "Aucun historique de versions n'est disponible pour ce modèle pour le moment.",
                 "error": "Échec du chargement des versions.",
                 "missingModelId": "Ce modèle ne possède pas d'identifiant de modèle Civitai.",
+                "hfGroupInfo": "Ceci est un groupe de modèles HuggingFace. Ouvrez la bibliothèque pour voir toutes les versions dans la grille.",
                 "confirm": {
                     "delete": "Supprimer cette version de votre bibliothèque ?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "Version supprimée"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "Récapitulatif de la récupération des métadonnées",
+            "statSuccess": "Réussi",
+            "statFailed": "Échoué",
+            "statSkipped": "Ignoré",
+            "statTotal": "Total scanné",
+            "statDuration": "Durée",
+            "successMessage": "Tous les {count} {type}s mis à jour avec succès !",
+            "failedItems": "Éléments échoués ({count})",
+            "close": "Fermer",
+            "copyReport": "Copier le rapport",
+            "downloadCsv": "Télécharger CSV",
+            "columnModelName": "Nom du modèle",
+            "columnError": "Erreur"
+        },
+        "downloadBatchSummary": {
+            "title": "Résumé du téléchargement groupé",
+            "statSuccess": "Réussis",
+            "statFailed": "Échoués",
+            "statTotal": "Total",
+            "successMessage": "Les {count} modèles ont été téléchargés avec succès",
+            "completedWithErrors": "Terminé avec des erreurs",
+            "failed": "Échec du téléchargement",
+            "failedItems": "Éléments échoués ({count})",
+            "columnName": "Nom du modèle",
+            "columnError": "Erreur",
+            "close": "Fermer",
+            "copyReport": "Copier le rapport",
+            "retryFailed": "Réessayer les échecs ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "Ce tag existe déjà"
         }
     },
-    "keyboard": {
-        "navigation": "Navigation au clavier :",
-        "shortcuts": {
-            "pageUp": "Défiler d'une page vers le haut",
-            "pageDown": "Défiler d'une page vers le bas",
-            "home": "Aller en haut",
-            "end": "Aller en bas"
-        }
-    },
     "initialization": {
         "title": "Initialisation",
         "message": "Préparation de votre espace de travail...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "Aucun nœud compatible disponible dans le workflow actuel",
             "noTargetNodeSelected": "Aucun nœud cible sélectionné",
             "modelUpdated": "Modèle mis à jour dans le workflow",
-            "modelFailed": "Échec de la mise à jour du nœud modèle"
+            "modelFailed": "Échec de la mise à jour du nœud modèle",
+            "embeddingAdded": "Embedding ajouté au workflow",
+            "embeddingFailed": "Échec de l'ajout de l'embedding",
+            "promptSent": "Prompt envoyé au workflow",
+            "promptFailed": "Échec de l'envoi du prompt"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Remplacer",
             "append": "Ajouter",
             "selectTargetNode": "Sélectionner le nœud cible",
@@ -1557,7 +1779,13 @@
         "checkingUpdates": "Vérification des mises à jour...",
         "checkingMessage": "Veuillez patienter pendant la vérification de la dernière version.",
         "showNotifications": "Afficher les notifications de mise à jour",
-        "latestBadge": "Dernier",
+        "latestBadge": "Dernière",
+        "latestMain": "Branche main",
+        "channel": "Canal de mise a jour",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Préparation de la mise à jour...",
             "installing": "Installation de la mise à jour...",
@@ -1578,6 +1806,15 @@
             "warning": "Attention : Les versions nightly peuvent contenir des fonctionnalités expérimentales et être instables.",
             "enable": "Activer les mises à jour nightly"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Passer au canal Nightly",
+            "nightlyMessage": "Passer a Nightly initialisera un depot Git et suivra les derniers commits de la branche main. Les mises a jour sont plus frequentes mais peuvent etre instables. Vous pouvez revenir a Release a tout moment.",
+            "releaseTitle": "Passer au canal Release",
+            "releaseMessage": "Passer a Release passera au dernier tag de version stable. Vous pouvez revenir a Nightly a tout moment.",
+            "switching": "Passage au canal {channel}...",
+            "completed": "Basculement vers le canal {channel} reussi",
+            "failed": "Echec du changement de canal"
+        },
         "banners": {
             "recent": "Messages récents",
             "empty": "Aucune bannière récente.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "Veuillez entrer un nom ou une syntaxe LoRA",
             "reconnectedSuccessfully": "LoRA reconnecté avec succès",
             "reconnectFailed": "Erreur lors de la reconnexion du LoRA : {message}",
+            "noPromptToSend": "Aucun prompt à envoyer",
             "cannotSend": "Impossible d'envoyer la recipe : ID de recipe manquant",
             "sendFailed": "Échec de l'envoi de la recipe vers le workflow",
             "sendError": "Erreur lors de l'envoi de la recipe vers le workflow",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "Réparation terminée : {repaired} réparée(s), {skipped} ignorée(s) (sur {total})",
             "repairBulkSkipped": "Aucune réparation nécessaire parmi les {total} recettes sélectionnées",
             "repairBulkFailed": "Échec de la réparation des recettes sélectionnées : {message}",
+            "reimporting": "Ré-import de la recette depuis la source...",
+            "reimportSuccess": "Recette ré-importée avec succès",
+            "reimportBulkComplete": "Ré-import terminé : {completed} ré-importé(s), {failed} échec(s) (sur {total})",
+            "reimportBulkFailed": "Échec du ré-import de certaines recettes",
             "noMissingLorasInSelection": "Aucun LoRA manquant trouvé dans les recettes sélectionnées",
             "noLoraRootConfigured": "Aucun répertoire racine LoRA configuré. Veuillez définir un répertoire racine LoRA par défaut dans les paramètres."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "Le nom du préréglage doit contenir au maximum {max} caractères",
             "presetNameInvalidChars": "Le nom du préréglage contient des caractères invalides",
             "presetNameExists": "Un préréglage avec ce nom existe déjà",
-            "maxPresetsReached": "Maximum {max} préréglages autorisés. Supprimez-en un pour en ajouter plus.",
             "presetNotFound": "Préréglage non trouvé",
             "invalidPreset": "Données de préréglage invalides",
             "deletePresetFailed": "Échec de la suppression du préréglage",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "Images d'exemple {action} terminées",
             "imagesFailed": "Images d'exemple {action} échouées",
             "loadError": "Erreur lors du chargement des téléchargements : {message}",
-            "downloadError": "Erreur de téléchargement : {message}"
+            "downloadError": "Erreur de téléchargement : {message}",
+            "downloadStopped": "Téléchargement annulé"
         },
         "import": {
             "folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "Échec de la définition de la classification du contenu : {message}",
             "relinkSuccess": "Modèle relié à Civitai avec succès",
             "relinkFailed": "Erreur : {message}",
+            "linkHfSuccess": "Modèle lié à HuggingFace avec succès",
+            "linkHfFailed": "Erreur : {message}",
             "fetchMetadataFirst": "Veuillez d'abord récupérer les métadonnées depuis CivitAI",
             "noCivitaiInfo": "Aucune information CivitAI disponible",
             "missingHash": "Hash du modèle non disponible"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "{successCount} {type}s déplacés avec succès",
             "exampleImagesDownloadSuccess": "Images d'exemple téléchargées avec succès !",
             "exampleImagesDownloadFailed": "Échec du téléchargement des images d'exemple : {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "Copié dans le presse-papiers",
+            "downloadStarted": "Téléchargement démarré"
+        },
+        "agent": {
+            "llmNotConfigured": "Fournisseur d'IA non configuré. Activez-le dans Paramètres → Fournisseur d'IA.",
+            "enrichStarted": "Enrichissement des métadonnées par IA...",
+            "enrichComplete": "Enrichissement des métadonnées terminé : {{summary}}",
+            "enrichFailed": "Échec de l'enrichissement des métadonnées : {{error}}"
         }
     },
     "doctor": {

locales/he.json

@@ -16,10 +16,13 @@
             "help": "עזרה",
             "add": "הוספה",
             "close": "סגור",
-            "menu": "תפריט"
+            "menu": "תפריט",
+            "remove": "הסר",
+            "change": "שנה"
         },
         "status": {
             "loading": "טוען...",
+            "cancelling": "מבטל...",
             "unknown": "לא ידוע",
             "date": "תאריך",
             "version": "גרסה",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "הסר מהמועדפים",
             "viewOnCivitai": "הצג ב-Civitai",
             "notAvailableFromCivitai": "לא זמין מ-Civitai",
+            "viewOnHuggingFace": "צפייה ב-Hugging Face",
             "sendToWorkflow": "שלח ל-ComfyUI (לחיצה: הוסף, Shift+לחיצה: החלף)",
             "copyLoRASyntax": "העתק תחביר LoRA",
             "checkpointNameCopied": "שם Checkpoint הועתק",
@@ -111,6 +115,7 @@
             "replacePreview": "החלף תצוגה מקדימה",
             "copyCheckpointName": "העתק שם Checkpoint",
             "copyEmbeddingName": "העתק שם Embedding",
+            "embeddingNameCopied": "תחביר Embedding הועתק",
             "sendCheckpointToWorkflow": "שלח ל-ComfyUI",
             "sendEmbeddingToWorkflow": "שלח ל-ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "מספר שימושים"
+        },
+        "footer": {
+            "versionCount": "{count} גרסאות",
+            "viewAllVersions": "הצג את כל הגרסאות המקומיות"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "ניהול מודלים מוחרגים"
+        },
+        "groupByModel": {
+            "label": "קיבוץ לפי דגם"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "סטטיסטיקה"
         },
         "search": {
-            "placeholder": "חפש...",
-            "placeholders": {
-                "loras": "חפש LoRAs...",
-                "recipes": "חפש מתכונים...",
-                "checkpoints": "חפש checkpoints...",
-                "embeddings": "חפש embeddings..."
-            },
+            "placeholder": "חיפוש",
             "options": "אפשרויות חיפוש",
             "searchIn": "חפש ב:",
             "notAvailable": "חיפוש לא זמין בדף הסטטיסטיקה",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "שם קביעה מראש...",
             "baseModel": "מודל בסיס",
             "baseModelSearchPlaceholder": "חפש מודלי בסיס...",
-            "modelTags": "תגיות (20 המובילות)",
+            "modelTags": "תגיות",
             "modelTypes": "סוגי מודלים",
             "license": "רישיון",
             "noCreditRequired": "ללא קרדיט נדרש",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "אפשר מכירת תמונות שנוצרו",
             "noCreditRequiredTooltip": "שימוש במודל ללא מתן קרדיט ליוצר",
             "noTags": "ללא תגיות",
+            "tagSearchPlaceholder": "חיפוש תגיות...",
+            "noTagMatches": "אין תגיות שתואמות את החיפוש הנוכחי.",
             "autoTags": "תגיות אוטומטיות",
             "noBaseModelMatches": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
             "clearAll": "נקה את כל המסננים",
@@ -247,7 +255,18 @@
             "toggle": "החלף ערכת נושא",
             "switchToLight": "עבור לערכת נושא בהירה",
             "switchToDark": "עבור לערכת נושא כהה",
-            "switchToAuto": "עבור לערכת נושא אוטומטית"
+            "switchToAuto": "עבור לערכת נושא אוטומטית",
+            "presets": "ערכות נושא מוגדרות",
+            "default": "ברירת מחדל",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "מצב",
+            "light": "בהיר",
+            "dark": "כהה",
+            "auto": "אוטומטי"
         },
         "actions": {
             "checkUpdates": "בדוק עדכונים",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "מפתח API של Civitai",
         "civitaiApiKeyPlaceholder": "הזן את מפתח ה-API שלך מ-Civitai",
         "civitaiApiKeyHelp": "משמש לאימות בעת הורדת מודלים מ-Civitai",
+        "civitaiApiKeyConfigured": "מוגדר",
+        "civitaiApiKeyNotConfigured": "לא מוגדר",
+        "civitaiApiKeySet": "הגדר",
         "civitaiHost": {
             "label": "מארח Civitai",
             "help": "בחר איזה אתר של Civitai ייפתח בעת שימוש בקישורי \"View on Civitai\".",
@@ -299,6 +321,7 @@
             "downloads": "הורדות",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
+            "licenseIcons": "סמלי רישיון",
             "misc": "שונות",
             "backup": "גיבויים",
             "folderSettings": "תיקיות ברירת מחדל",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "נתיבי תיקיות נוספים",
             "downloadPathTemplates": "תבניות נתיב הורדה",
             "priorityTags": "תגיות עדיפות",
-            "updateFlags": "תגי עדכון",
+            "versionScope": "תגי עדכון",
             "exampleImages": "תמונות דוגמה",
             "autoOrganize": "ארגון אוטומטי",
             "metadata": "מטא-נתונים",
@@ -411,6 +434,8 @@
             "help": "כאשר מופעל, LoRA Manager ידלג על הורדת גרסת מודל אם שירות היסטוריית ההורדות רושם את הגרסה המדויקת הזו ככבר שהורדה. חל על כל תהליכי ההורדה."
         },
         "layoutSettings": {
+            "groupByModel": "קיבוץ לפי דגם",
+            "groupByModelHelp": "כאשר מופעל, רק הגרסה העדכנית ביותר של כל דגם Civitai מוצגת ככרטיס בודד. גרסאות ישנות יותר מוסתרות.",
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
                 "default": "ברירת מחדל",
@@ -424,6 +449,12 @@
                 "compact": "7 (1080p), 8 (2K), 10 (4K)"
             },
             "displayDensityWarning": "אזהרה: צפיפויות גבוהות יותר עלולות לגרום לבעיות ביצועים במערכות עם משאבים מוגבלים.",
+            "recipesLayout": "פריסת מתכונים",
+            "recipesLayoutHelp": "בחר כיצד יסודרו כרטיסי המתכונים: רשת אחידה או פריסת Masonry (בסגנון Pinterest) השומרת על יחס הגובה-רוחב של כל תמונה.",
+            "recipesLayoutOptions": {
+                "grid": "רשת",
+                "masonry": "Masonry"
+            },
             "showFolderSidebar": "הצג סרגל צד תיקיות",
             "showFolderSidebarHelp": "הפעל או כבה את סרגל הצד לניווט תיקיות בדפי המודל. כאשר הוא כבוי, סרגל הצד ואזור הריחוף נשארים מוסתרים.",
             "cardInfoDisplay": "תצוגת מידע בכרטיס",
@@ -445,7 +476,9 @@
                 "modelName": "שם מודל",
                 "fileName": "שם קובץ"
             },
-            "modelNameDisplayHelp": "בחר מה להציג בכותרת התחתונה של כרטיס המודל"
+            "modelNameDisplayHelp": "בחר מה להציג בכותרת התחתונה של כרטיס המודל",
+            "cardBlurAmount": "עוצמת טשטוש שכבת-על בכרטיס",
+            "cardBlurAmountHelp": "כוונן את עוצמת הטשטוש של שכבת-העל בכותרת ובכותרות תחתונה בכרטיסי מודל ומתכונים (0 = ללא טשטוש, 20 = טשטוש מקסימלי)."
         },
         "folderSettings": {
             "activeLibrary": "ספרייה פעילה",
@@ -480,7 +513,9 @@
             "saveSuccess": "נתיבי תיקיות נוספים עודכנו. נדרשת הפעלה מחדש כדי להחיל את השינויים.",
             "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
             "validation": {
-                "duplicatePath": "נתיב זה כבר מוגדר"
+                "duplicatePath": "נתיב זה כבר מוגדר",
+                "checkpointUnetOverlap": "לא ניתן להשתמש באותו נתיב עבור checkpoints ומודלי דיפוזיה: {paths}",
+                "checkpointUnetOverlapInline": "הנתיב הזה כבר נמצא בשימוש עבור סוג מודל אחר. יש להשתמש בתיקיות נפרדות עבור checkpoints ומודלי דיפוזיה."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "הורד",
             "restartRequired": "דורש הפעלה מחדש"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "אסטרטגיית תגי עדכון",
             "help": "בחרו אם תוויות העדכון יוצגו רק כאשר גרסה חדשה חולקת את אותו דגם בסיס כמו הקבצים המקומיים שלכם או בכל מקרה שבו קיימת גרסה חדשה עבור אותו דגם.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "הסתר עדכוני גישה מוקדמת",
             "help": "רק עדכוני גישה מוקדמת"
         },
+        "licenseIcons": {
+            "useNewStyle": "השתמש בסמלי רישיון מעודכנים",
+            "useNewStyleHelp": "הצג הרשאות רישיון עם מחוונים צבעוניים (סגנון חדש) או סמלי הגבלה בלבד (סגנון קלאסי). משקף את העיצוב העדכני של CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "כלול מילות טריגר בתחביר LoRA",
             "includeTriggerWordsHelp": "כלול מילות טריגר מאומנות בעת העתקת תחביר LoRA ללוח",
@@ -609,7 +648,13 @@
             "preparing": "מכין הורדה...",
             "connecting": "מתחבר לשרת ההורדות...",
             "completed": "הושלם",
-            "downloadComplete": "ההורדה הושלמה בהצלחה"
+            "downloadComplete": "ההורדה הושלמה בהצלחה",
+            "enableCivarchiveApi": "הפעל את CivArchive API כספק מטא-נתונים",
+            "enableCivarchiveApiHelp": "כאשר מופעל, CivArchive API משמש כמקור גיבוי למטא-נתונים של מודלים (למשל עבור מודלים שנמחקו מ-CivitAI). כבה כדי להימנע לחלוטין ממגבלות הקצב של CivArchive.",
+            "providerOrder": "סדר ספקי מטא-נתונים לגיבוי",
+            "providerOrderHelp": "CivitAI API תמיד מנוסה ראשון. בחר את סדר הספקים הנותרים בעת חיפוש מטא-נתונים.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "הפעל פרוקסי ברמת האפליקציה",
@@ -628,6 +673,33 @@
             "proxyPassword": "סיסמה (אופציונלי)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "סיסמה לאימות מול הפרוקסי (אם נדרש)"
+        },
+        "aiProvider": {
+            "title": "ספק AI",
+            "provider": "ספק",
+            "providerHelp": "בחר את ספק ה-LLM שלך. OpenAI ו-Ollama משתמשים בנקודות קצה מוגדרות מראש. מותאם אישית מאפשר לך לציין כל נקודת קצה תואמת OpenAI.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (מקומי)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "מותאם אישית (תואם OpenAI)"
+            },
+            "apiBase": "כתובת בסיס API",
+            "apiBaseHelp": "כתובת ה-URL הבסיסית ל-API של LLM (לדוגמה https://api.openai.com/v1). השאר ריק לשימוש בברירת המחדל של הספק.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "מפתח API",
+            "apiKeyHelp": "מפתח ה-API של ספק ה-LLM שלך. נשמר מקומית, לעולם לא נשלח לשרת כלשהו מלבד ספק ה-LLM שבחרת.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "לא הוגדר",
+            "apiKeyConfigured": "הוגדר",
+            "apiKeySet": "הגדר",
+            "model": "מודל",
+            "modelHelp": "שם המודל לשימוש (לדוגמה deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). בדוק אצל הספק שלך אילו מודלים זמינים.",
+            "modelPlaceholder": "בחר מודל..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "הקטן ביותר",
                 "usage": "מספר שימושים",
                 "usageDesc": "הכי הרבה",
-                "usageAsc": "הכי פחות"
+                "usageAsc": "הכי פחות",
+                "versionsCount": "גרסאות מקומיות",
+                "versionsCountDesc": "הכי הרבה גרסאות ראשונות",
+                "versionsCountAsc": "הכי מעט גרסאות ראשונות",
+                "versionIdDesc": "גרסה חדשה ביותר ראשונה",
+                "random": "אקראי",
+                "randomAction": "ערבוב אקראי"
             },
             "refresh": {
                 "title": "רענן רשימת מודלים",
@@ -690,6 +768,7 @@
             "copyAll": "העתק את כל התחבירים",
             "refreshAll": "רענן את כל המטא-דאטה",
             "repairMetadata": "תקן מטא-דאטה עבור הנבחרים",
+            "reimportMetadata": "ייבא מחדש ממקור",
             "checkUpdates": "בדוק עדכונים לבחירה",
             "moveAll": "העבר הכל לתיקייה",
             "autoOrganize": "ארגן אוטומטית נבחרים",
@@ -701,6 +780,8 @@
             "deleteAll": "מחק נבחרים",
             "downloadMissingLoras": "הורדת LoRAs חסרים",
             "downloadExamples": "הורד תמונות דוגמה",
+            "downloadMissingExamples": "הורדת חסרים",
+            "reprocessExamples": "עיבוד מחדש של הכול",
             "clear": "נקה בחירה",
             "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
             "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
@@ -720,12 +801,15 @@
                 "completed": "הושלם: {success} הועברו, {skipped} דולגו, {failures} נכשלו",
                 "complete": "ארגון אוטומטי הושלם",
                 "error": "שגיאה: {error}"
-            }
+            },
+            "enrichHfAgent": "העשרת HF מטא-דאטה (AI)"
         },
         "contextMenu": {
             "refreshMetadata": "רענן נתוני Civitai",
             "checkUpdates": "בדוק עדכונים",
-            "relinkCivitai": "קשר מחדש ל-Civitai",
+            "linkModel": "קישור מודל",
+            "linkCivitai": "קשר מחדש ל-Civitai",
+            "linkHuggingFace": "קישור ל-HuggingFace",
             "copySyntax": "העתק תחביר LoRA",
             "copyFilename": "העתק שם קובץ מודל",
             "copyRecipeSyntax": "העתק תחביר מתכון",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "שלח ל-Workflow (החלף)",
             "openExamples": "פתח תיקיית דוגמאות",
             "downloadExamples": "הורד תמונות דוגמה",
+            "downloadMissingExamples": "הורדת חסרים",
+            "reprocessExamples": "עיבוד מחדש של הכול",
             "replacePreview": "החלף תצוגה מקדימה",
             "setContentRating": "הגדר דירוג תוכן",
             "moveToFolder": "העבר לתיקייה",
             "repairMetadata": "תיקון מטא-דאטה",
+            "reimportMetadata": "ייבא מחדש ממקור",
             "excludeModel": "החרג מודל",
             "restoreModel": "שחזור מודל",
             "deleteModel": "מחק מודל",
             "shareRecipe": "שתף מתכון",
             "viewAllLoras": "הצג את כל ה-LoRAs",
             "downloadMissingLoras": "הורד LoRAs חסרים",
-            "deleteRecipe": "מחק מתכון"
+            "deleteRecipe": "מחק מתכון",
+            "enrichHfAgent": "העשרת HF מטא-דאטה (AI)"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "המתכון כבר בגרסה העדכנית ביותר, אין צורך בתיקון",
                 "failed": "תיקון המתכון נכשל: {message}",
                 "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
+            },
+            "reimport": {
+                "starting": "מייבא מתכון מחדש מהמקור...",
+                "success": "המתכון יובא מחדש בהצלחה",
+                "noSourceUrl": "למתכון אין כתובת מקור, לא ניתן לייבא מחדש",
+                "failed": "ייבוא המתכון מחדש נכשל: {message}",
+                "missingId": "לא ניתן לייבא מחדש: חסר מזהה מתכון"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "שורש",
         "collapseAll": "כווץ את כל התיקיות",
-        "pinSidebar": "נעל סרגל צד",
-        "unpinSidebar": "שחרר סרגל צד",
+        "hideOnThisPage": "הסתר סרגל צד בדף זה",
+        "showSidebar": "הצג סרגל צד",
+        "sidebarHiddenNotification": "סרגל הצד מוסתר בדף {page}",
         "switchToListView": "עבור לתצוגת רשימה",
         "switchToTreeView": "תצוגת עץ",
         "recursiveOn": "כלול תיקיות משנה",
@@ -981,6 +1077,18 @@
             "storage": "אחסון",
             "insights": "תובנות"
         },
+        "metrics": {
+            "totalModels": "סה\"כ דגמים",
+            "totalStorage": "סה\"כ אחסון",
+            "totalGenerations": "סה\"כ יצירות",
+            "usageRate": "שיעור שימוש",
+            "loras": "LoRA",
+            "checkpoints": "נקודות ביקורת",
+            "embeddings": "הטמעות",
+            "uniqueTags": "תגיות ייחודיות",
+            "unusedModels": "דגמים שאינם בשימוש",
+            "avgUsesPerModel": "ממוצע שימושים/דגם"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs הנפוצים ביותר",
             "mostUsedCheckpoints": "Checkpoints הנפוצים ביותר",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "תובנות חכמות",
-            "recommendations": "המלצות"
+            "recommendations": "המלצות",
+            "noInsights": "אין תובנות זמינות",
+            "unusedLoras": {
+                "high": {
+                    "title": "כמות גבוהה של LoRAs שאינן בשימוש",
+                    "description": "{percent}% מה-LoRAs שלך ({count}/{total}) מעולם לא נעשה בהם שימוש.",
+                    "suggestion": "שקול לארגן או לאחסן בארכיון מודלים שאינם בשימוש כדי לפנות שטח אחסון."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "התגלו נקודות ביקורת שאינן בשימוש",
+                    "description": "{percent}% מנקודות הביקורת שלך ({count}/{total}) מעולם לא נעשה בהן שימוש.",
+                    "suggestion": "בדוק ושקול להסיר נקודות ביקורת שאינך צריך עוד."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "כמות גבוהה של Embeddings שאינם בשימוש",
+                    "description": "{percent}% מה-Embeddings שלך ({count}/{total}) מעולם לא נעשה בהם שימוש.",
+                    "suggestion": "שקול לארגן או לאחסן בארכיון Embeddings שאינם בשימוש כדי לייעל את האוסף."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "התגלה אוסף גדול",
+                    "description": "אוסף המודלים שלך משתמש ב-{size} של אחסון.",
+                    "suggestion": "שקול להשתמש באחסון חיצוני או בפתרונות ענן לארגון טוב יותר."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "משתמש פעיל",
+                    "description": "השלמת {count} יצירות עד כה!",
+                    "suggestion": "המשך לחקור וליצור תוכן מדהים עם המודלים שלך."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "סקירת אוסף",
             "baseModelDistribution": "התפלגות מודלי בסיס",
             "usageTrends": "מגמות שימוש (30 יום אחרונים)",
-            "usageDistribution": "התפלגות שימוש"
+            "usageDistribution": "התפלגות שימוש",
+            "date": "תאריך",
+            "usageCount": "מספר שימושים",
+            "fileSizeBytes": "גודל קובץ (בתים)",
+            "models": "דגמים",
+            "loraUsage": "שימוש ב-LoRA",
+            "checkpointUsage": "שימוש ב-Checkpoint",
+            "embeddingUsage": "שימוש ב-Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "נקודת ביקורת",
+            "diffusion_model": "מודל דיפוזיה",
+            "embedding": "הטמעות"
+        },
+        "placeholders": {
+            "loading": "טוען...",
+            "noModels": "לא נמצאו דגמים",
+            "errorLoading": "שגיאה בטעינת נתונים",
+            "noStorageData": "אין נתוני אחסון זמינים",
+            "rootFolder": "שורש",
+            "chartLibraryMissing": "הגרף דורש את ספריית Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} דגמים",
+            "chartUsage": "{name}: {size}, {count} שימושים",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "הורד מודל מכתובת URL",
             "titleWithType": "הורד {type} מכתובת URL",
-            "url": "כתובת URL של Civitai",
             "civitaiUrl": "כתובת URL של Civitai:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "יש להזין כתובת URL אחת של CivitAI, CivArchive או Hugging Face בכל שורה. תומך במספר כתובות URL להורדה בקבוצה.",
+            "selectHfFiles": "בחר קבצים להורדה ממאגר זה:",
+            "selectAll": "בחר הכל",
+            "fetchingRepoFiles": "מביא קבצים מהמאגר...",
             "locationPreview": "תצוגה מקדימה של מיקום ההורדה",
             "useDefaultPath": "השתמש בנתיב ברירת מחדל",
             "useDefaultPathTooltip": "כאשר מופעל, קבצים מאורגנים אוטומטית באמצעות תבניות נתיב מוגדרות",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "פורמט URL של Civitai לא חוקי",
-                "noVersions": "אין גרסאות זמינות למודל זה"
+                "noVersions": "אין גרסאות זמינות למודל זה",
+                "mixedSources": "לא ניתן לערבב כתובות URL של CivitAI ו-Hugging Face באותה קבוצה.",
+                "noModelFiles": "לא נמצאו קבצי מודל במאגר זה."
             },
             "status": {
                 "preparing": "מכין הורדה...",
                 "downloadedPreview": "תמונת תצוגה מקדימה הורדה",
                 "downloadingFile": "מוריד קובץ {type}",
-                "finalizing": "מסיים הורדה..."
+                "finalizing": "מסיים הורדה...",
+                "cancelling": "מבטל הורדה...",
+                "cancelled": "ההורדה בוטלה"
             },
             "progress": {
                 "currentFile": "הקובץ הנוכחי:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "הקלד נתיב תיקייה או בחר מהעץ למטה...",
             "root": "שורש"
         },
+        "linkHuggingFace": {
+            "title": "קישור ל-HuggingFace",
+            "infoText": "הדבק את כתובת ה-URL של מאגר HuggingFace כדי לשייך מודל זה למקורו. פעולה זו מאפשרת העשרת מטא-דאטה באמצעות AI.",
+            "urlLabel": "כתובת URL של מאגר HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "הזן את כתובת ה-URL המלאה של מאגר HuggingFace.",
+            "confirmAction": "שמור וקשר"
+        },
         "relinkCivitai": {
             "title": "קשר מחדש ל-Civitai",
             "warning": "אזהרה:",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "ערוך שם גרסה",
                 "viewOnCivitai": "הצג ב-Civitai",
                 "viewOnCivitaiText": "הצג ב-Civitai",
+                "viewOnHuggingFace": "צפייה ב-Hugging Face",
+                "viewOnHuggingFaceText": "צפייה ב-Hugging Face",
                 "viewCreatorProfile": "הצג פרופיל יוצר",
                 "openFileLocation": "פתח מיקום קובץ",
                 "sendToWorkflow": "שלח ל-ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "הערות נוספות",
                 "notesHint": "לחץ Enter לשמירה, Shift+Enter לשורה חדשה",
                 "addNotesPlaceholder": "הוסף את ההערות שלך כאן...",
-                "aboutThisVersion": "אודות גרסה זו"
+                "aboutThisVersion": "אודות גרסה זו",
+                "baseModelSearchPlaceholder": "חפש מודל בסיס…",
+                "baseModelSuggested": "מוצע",
+                "baseModelNoMatch": "אין מודלי בסיס תואמים"
             },
             "notes": {
                 "saved": "הערות נשמרו בהצלחה",
-                "saveFailed": "שמירת ההערות נכשלה"
+                "saveFailed": "שמירת ההערות נכשלה",
+                "showMore": "הצג עוד",
+                "showLess": "הצג פחות"
             },
             "usageTips": {
                 "addPresetParameter": "הוסף פרמטר קבוע מראש...",
@@ -1367,6 +1561,7 @@
                 "empty": "אין עדיין היסטוריית גרסאות למודל זה.",
                 "error": "טעינת הגרסאות נכשלה.",
                 "missingModelId": "למודל זה אין מזהה מודל של Civitai.",
+                "hfGroupInfo": "זוהי קבוצת דגמים של HuggingFace. פתח את הספרייה כדי לראות את כל הגרסאות ברשת.",
                 "confirm": {
                     "delete": "למחוק גרסה זו מהספרייה שלך?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "הגרסה נמחקה"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "סיכום שליפת מטא-דאטה",
+            "statSuccess": "הצלחה",
+            "statFailed": "נכשל",
+            "statSkipped": "דולג",
+            "statTotal": "סה\"כ נסרק",
+            "statDuration": "משך",
+            "successMessage": "כל {count} {type}s עודכנו בהצלחה!",
+            "failedItems": "פריטים נכשלים ({count})",
+            "close": "סגור",
+            "copyReport": "העתק דוח",
+            "downloadCsv": "הורד CSV",
+            "columnModelName": "שם המודל",
+            "columnError": "שגיאה"
+        },
+        "downloadBatchSummary": {
+            "title": "סיכום הורדה בכמות",
+            "statSuccess": "הצליחו",
+            "statFailed": "נכשלו",
+            "statTotal": "סה\"כ",
+            "successMessage": "כל {count} הדגמים הורדו בהצלחה",
+            "completedWithErrors": "הושלם עם שגיאות",
+            "failed": "ההורדה נכשלה",
+            "failedItems": "פריטים שנכשלו ({count})",
+            "columnName": "שם הדגם",
+            "columnError": "שגיאה",
+            "close": "סגור",
+            "copyReport": "העתק דוח",
+            "retryFailed": "נסה שוב ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "תגית זו כבר קיימת"
         }
     },
-    "keyboard": {
-        "navigation": "ניווט במקלדת:",
-        "shortcuts": {
-            "pageUp": "גלול עמוד אחד למעלה",
-            "pageDown": "גלול עמוד אחד למטה",
-            "home": "קפוץ להתחלה",
-            "end": "קפוץ לסוף"
-        }
-    },
     "initialization": {
         "title": "מאתחל",
         "message": "מכין את סביבת העבודה שלך...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "אין צמתים תואמים זמינים ב-workflow הנוכחי",
             "noTargetNodeSelected": "לא נבחר צומת יעד",
             "modelUpdated": "מודל עודכן ב-workflow",
-            "modelFailed": "עדכון צומת המודל נכשל"
+            "modelFailed": "עדכון צומת המודל נכשל",
+            "embeddingAdded": "Embedding נוסף ל-workflow",
+            "embeddingFailed": "הוספת Embedding נכשלה",
+            "promptSent": "הנחיה נשלחה ל-workflow",
+            "promptFailed": "שליחת ההנחיה נכשלה"
         },
         "nodeSelector": {
             "recipe": "מתכון",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "הנחיה",
             "replace": "החלף",
             "append": "הוסף",
             "selectTargetNode": "בחר צומת יעד",
@@ -1557,7 +1779,13 @@
         "checkingUpdates": "בודק עדכונים...",
         "checkingMessage": "אנא המתן בזמן שאנו בודקים את הגרסה האחרונה.",
         "showNotifications": "הצג התראות עדכון",
-        "latestBadge": "עדכן",
+        "latestBadge": "אחרון",
+        "latestMain": "ענף main",
+        "channel": "ערוץ עדכון",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "מכין עדכון...",
             "installing": "מתקין עדכון...",
@@ -1578,6 +1806,15 @@
             "warning": "אזהרה: גרסאות ליליות עשויות להכיל תכונות ניסיוניות ועלולות להיות לא יציבות.",
             "enable": "הפעל עדכונים ליליים"
         },
+        "channelSwitch": {
+            "nightlyTitle": "מעבר לערוץ Nightly",
+            "nightlyMessage": "מעבר ל-Nightly יאתחל מאגר Git ויעקוב אחר הקומיטים האחרונים בענף main. העדכונים תכופים יותר אך עשויים להיות לא יציבים. ניתן לחזור ל-Release בכל עת.",
+            "releaseTitle": "מעבר לערוץ Release",
+            "releaseMessage": "מעבר ל-Release יעבור לתגית הגרסה היציבה האחרונה. ניתן לחזור ל-Nightly בכל עת.",
+            "switching": "מעבר לערוץ {channel}...",
+            "completed": "המעבר לערוץ {channel} הושלם",
+            "failed": "החלפת ערוץ נכשלה"
+        },
         "banners": {
             "recent": "הודעות אחרונות",
             "empty": "אין כרגע באנרים אחרונים.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "אנא הזן שם LoRA או תחביר",
             "reconnectedSuccessfully": "LoRA קושר מחדש בהצלחה",
             "reconnectFailed": "שגיאה בקישור מחדש של LoRA: {message}",
+            "noPromptToSend": "אין הנחיה לשליחה",
             "cannotSend": "לא ניתן לשלוח מתכון: חסר מזהה מתכון",
             "sendFailed": "שליחת המתכון ל-workflow נכשלה",
             "sendError": "שגיאה בשליחת המתכון ל-workflow",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "התיקון הושלם: {repaired} תוקנו, {skipped} דולגו (מתוך {total})",
             "repairBulkSkipped": "אין צורך בתיקון עבור {total} המתכונים הנבחרים",
             "repairBulkFailed": "תיקון המתכונים הנבחרים נכשל: {message}",
+            "reimporting": "מייבא מתכון מחדש מהמקור...",
+            "reimportSuccess": "המתכון יובא מחדש בהצלחה",
+            "reimportBulkComplete": "ייבוא מחדש הושלם: {completed} יובאו, {failed} נכשלו (מתוך {total})",
+            "reimportBulkFailed": "ייבוא מחדש של חלק מהמתכונים נכשל",
             "noMissingLorasInSelection": "לא נמצאו LoRAs חסרים במתכונים שנבחרו",
             "noLoraRootConfigured": "תיקיית השורש של LoRA לא מוגדרת. אנא הגדר תיקיית שורש LoRA ברירת מחדל בהגדרות."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "שם קביעה מראש חייב להיות {max} תווים או פחות",
             "presetNameInvalidChars": "שם קביעה מראש מכיל תווים לא חוקיים",
             "presetNameExists": "קביעה מראש עם שם זה כבר קיימת",
-            "maxPresetsReached": "מותר מקסימום {max} קביעות מראש. מחק אחת כדי להוסיף עוד.",
             "presetNotFound": "קביעה מראש לא נמצאה",
             "invalidPreset": "נתוני קביעה מראש לא חוקיים",
             "deletePresetFailed": "מחיקת קביעה מראש נכשלה",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "{action} תמונות הדוגמה הושלם",
             "imagesFailed": "{action} תמונות הדוגמה נכשל",
             "loadError": "שגיאה בטעינת הורדות: {message}",
-            "downloadError": "שגיאת הורדה: {message}"
+            "downloadError": "שגיאת הורדה: {message}",
+            "downloadStopped": "ההורדה בוטלה"
         },
         "import": {
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "הגדרת דירוג התוכן נכשלה: {message}",
             "relinkSuccess": "המודל קושר מחדש ל-Civitai בהצלחה",
             "relinkFailed": "שגיאה: {message}",
+            "linkHfSuccess": "המודל נקשר בהצלחה ל-HuggingFace",
+            "linkHfFailed": "שגיאה: {message}",
             "fetchMetadataFirst": "אנא אחזר מטא-דאטה מ-CivitAI תחילה",
             "noCivitaiInfo": "אין מידע מ-CivitAI זמין",
             "missingHash": "ה-hash של המודל אינו זמין"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "הועברו בהצלחה {successCount} {type}s",
             "exampleImagesDownloadSuccess": "תמונות הדוגמה הורדו בהצלחה!",
             "exampleImagesDownloadFailed": "הורדת תמונות הדוגמה נכשלה: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "הועתק ללוח",
+            "downloadStarted": "ההורדה החלה"
+        },
+        "agent": {
+            "llmNotConfigured": "ספק AI לא הוגדר. הפעל אותו בהגדרות → ספק AI.",
+            "enrichStarted": "מעשיר מטא-דאטה באמצעות AI...",
+            "enrichComplete": "העשרת מטא-דאטה הושלמה: {{summary}}",
+            "enrichFailed": "העשרת מטא-דאטה נכשלה: {{error}}"
         }
     },
     "doctor": {

locales/ja.json

@@ -16,10 +16,13 @@
             "help": "ヘルプ",
             "add": "追加",
             "close": "閉じる",
-            "menu": "メニュー"
+            "menu": "メニュー",
+            "remove": "削除",
+            "change": "変更"
         },
         "status": {
             "loading": "読み込み中...",
+            "cancelling": "キャンセル中...",
             "unknown": "不明",
             "date": "日付",
             "version": "バージョン",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "お気に入りから削除",
             "viewOnCivitai": "Civitaiで表示",
             "notAvailableFromCivitai": "Civitaiでは利用できません",
+            "viewOnHuggingFace": "Hugging Face で見る",
             "sendToWorkflow": "ComfyUIに送信（クリック：追加、Shift+クリック：置換）",
             "copyLoRASyntax": "LoRA構文をコピー",
             "checkpointNameCopied": "checkpointの名前をコピーしました",
@@ -111,6 +115,7 @@
             "replacePreview": "プレビューを置換",
             "copyCheckpointName": "checkpoint名をコピー",
             "copyEmbeddingName": "embedding名をコピー",
+            "embeddingNameCopied": "Embedding構文をコピーしました",
             "sendCheckpointToWorkflow": "ComfyUIに送信",
             "sendEmbeddingToWorkflow": "ComfyUIに送信"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用回数"
+        },
+        "footer": {
+            "versionCount": "{count} バージョン",
+            "viewAllVersions": "ローカルの全バージョンを表示"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "除外モデルを管理"
+        },
+        "groupByModel": {
+            "label": "モデルでグループ化"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "統計"
         },
         "search": {
-            "placeholder": "検索...",
-            "placeholders": {
-                "loras": "LoRAを検索...",
-                "recipes": "レシピを検索...",
-                "checkpoints": "checkpointを検索...",
-                "embeddings": "embeddingを検索..."
-            },
+            "placeholder": "検索",
             "options": "検索オプション",
             "searchIn": "検索対象：",
             "notAvailable": "統計ページでは検索は利用できません",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "プリセット名...",
             "baseModel": "ベースモデル",
             "baseModelSearchPlaceholder": "ベースモデルを検索...",
-            "modelTags": "タグ（上位20）",
+            "modelTags": "タグ",
             "modelTypes": "モデルタイプ",
             "license": "ライセンス",
             "noCreditRequired": "クレジット不要",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "生成した画像の販売を許可",
             "noCreditRequiredTooltip": "クレジット表記なしでモデルを使用可能",
             "noTags": "タグなし",
+            "tagSearchPlaceholder": "タグを検索...",
+            "noTagMatches": "現在の検索に一致するタグはありません。",
             "autoTags": "自動タグ",
             "noBaseModelMatches": "現在の検索に一致するベースモデルはありません。",
             "clearAll": "すべてのフィルタをクリア",
@@ -247,7 +255,18 @@
             "toggle": "テーマの切り替え",
             "switchToLight": "ライトテーマに切り替え",
             "switchToDark": "ダークテーマに切り替え",
-            "switchToAuto": "自動テーマに切り替え"
+            "switchToAuto": "自動テーマに切り替え",
+            "presets": "テーマプリセット",
+            "default": "デフォルト",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "モード",
+            "light": "ライト",
+            "dark": "ダーク",
+            "auto": "自動"
         },
         "actions": {
             "checkUpdates": "更新確認",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Civitai APIキー",
         "civitaiApiKeyPlaceholder": "Civitai APIキーを入力してください",
         "civitaiApiKeyHelp": "Civitaiからモデルをダウンロードするときの認証に使用されます",
+        "civitaiApiKeyConfigured": "設定済み",
+        "civitaiApiKeyNotConfigured": "未設定",
+        "civitaiApiKeySet": "設定",
         "civitaiHost": {
             "label": "Civitai ホスト",
             "help": "「View on Civitai」リンクを使うときに開く Civitai サイトを選択します。",
@@ -299,6 +321,7 @@
             "downloads": "ダウンロード",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
+            "licenseIcons": "ライセンスアイコン",
             "misc": "その他",
             "backup": "バックアップ",
             "folderSettings": "デフォルトルート",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "追加フォルダーパス",
             "downloadPathTemplates": "ダウンロードパステンプレート",
             "priorityTags": "優先タグ",
-            "updateFlags": "アップデートフラグ",
+            "versionScope": "アップデートフラグ",
             "exampleImages": "例画像",
             "autoOrganize": "自動整理",
             "metadata": "メタデータ",
@@ -411,6 +434,8 @@
             "help": "有効にすると、ダウンロード履歴サービスがそのバージョンが既にダウンロード済みと記録している場合、LoRA Managerはそのモデルバージョンのダウンロードをスキップします。すべてのダウンロードフローに適用されます。"
         },
         "layoutSettings": {
+            "groupByModel": "モデルでグループ化",
+            "groupByModelHelp": "有効にすると、各Civitaiモデルの最新バージョンのみが1枚のカードとして表示され、古いバージョンは非表示になります。",
             "displayDensity": "表示密度",
             "displayDensityOptions": {
                 "default": "デフォルト",
@@ -424,6 +449,12 @@
                 "compact": "7（1080p）、8（2K）、10（4K）"
             },
             "displayDensityWarning": "警告：高密度設定は、リソースが限られたシステムでパフォーマンスの問題を引き起こす可能性があります。",
+            "recipesLayout": "レシピのレイアウト",
+            "recipesLayoutHelp": "レシピカードの配置方法を選択：均一なグリッド、または各画像のアスペクト比を保持するメイソンリー（Pinterest スタイル）レイアウト。",
+            "recipesLayoutOptions": {
+                "grid": "グリッド",
+                "masonry": "メイソンリー"
+            },
             "showFolderSidebar": "フォルダサイドバーを表示",
             "showFolderSidebarHelp": "モデルページのフォルダナビゲーションサイドバーを表示/非表示にします。無効にするとサイドバーとホバーエリアは表示されません。",
             "cardInfoDisplay": "カード情報表示",
@@ -445,7 +476,9 @@
                 "modelName": "モデル名",
                 "fileName": "ファイル名"
             },
-            "modelNameDisplayHelp": "モデルカードのフッターに表示する内容を選択"
+            "modelNameDisplayHelp": "モデルカードのフッターに表示する内容を選択",
+            "cardBlurAmount": "カードオーバーレイのぼかし",
+            "cardBlurAmountHelp": "モデルカードとレシピカードのヘッダー・フッターオーバーレイのぼかし強度を調整します（0 = ぼかしなし、20 = 最大ぼかし）。"
         },
         "folderSettings": {
             "activeLibrary": "アクティブライブラリ",
@@ -480,7 +513,9 @@
             "saveSuccess": "追加フォルダーパスを更新しました。変更を適用するには再起動が必要です。",
             "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
             "validation": {
-                "duplicatePath": "このパスはすでに設定されています"
+                "duplicatePath": "このパスはすでに設定されています",
+                "checkpointUnetOverlap": "checkpoints と diffusion models に同じパスは使用できません：{paths}",
+                "checkpointUnetOverlapInline": "このパスは別のモデルタイプですでに使用されています。checkpoints と diffusion models には別々のフォルダを使用してください。"
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "ダウンロード",
             "restartRequired": "再起動が必要"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "アップデートフラグの表示戦略",
             "help": "新リリースがローカルファイルと同じベースモデルを共有する場合にのみ更新バッジを表示するか、そのモデルに新しいバージョンがあれば常に表示するかを決めます。",
             "options": {
@@ -577,6 +612,10 @@
             "label": "早期アクセス更新を非表示",
             "help": "早期アクセスのみの更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "更新されたライセンスアイコンを使用",
+            "useNewStyleHelp": "カラーインジケーター付きでライセンス許可を表示（新スタイル）するか、制限のみのアイコンを表示（クラシックスタイル）します。現在のCivitAIデザインを反映しています。"
+        },
         "misc": {
             "includeTriggerWords": "LoRA構文にトリガーワードを含める",
             "includeTriggerWordsHelp": "LoRA構文をクリップボードにコピーする際、学習済みトリガーワードを含めます",
@@ -609,7 +648,13 @@
             "preparing": "ダウンロードを準備中...",
             "connecting": "ダウンロードサーバーに接続中...",
             "completed": "完了",
-            "downloadComplete": "ダウンロードが正常に完了しました"
+            "downloadComplete": "ダウンロードが正常に完了しました",
+            "enableCivarchiveApi": "CivArchive API をメタデータプロバイダーとして有効化",
+            "enableCivarchiveApiHelp": "有効にすると、CivArchive API がモデルメタデータの代替ソースとして使用されます（例：CivitAI から削除されたモデルの場合）。オフにすると、CivArchive のレート制限を完全に回避できます。",
+            "providerOrder": "メタデータプロバイダーのフォールバック順序",
+            "providerOrderHelp": "CivitAI API が常に最初に試行されます。メタデータ検索時の残りのプロバイダーの順序を選択してください。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "アプリレベルのプロキシを有効化",
@@ -628,6 +673,33 @@
             "proxyPassword": "パスワード（任意）",
             "proxyPasswordPlaceholder": "パスワード",
             "proxyPasswordHelp": "プロキシ認証用のパスワード（必要な場合）"
+        },
+        "aiProvider": {
+            "title": "AIプロバイダー",
+            "provider": "プロバイダー",
+            "providerHelp": "LLMプロバイダーを選択してください。OpenAIとOllamaはプリセットのAPIエンドポイントを使用します。カスタムでは任意のOpenAI互換エンドポイントを指定できます。",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama（ローカル）",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "カスタム（OpenAI 互換）"
+            },
+            "apiBase": "APIベースURL",
+            "apiBaseHelp": "LLM APIのベースURL（例：https://api.openai.com/v1）。空の場合はプロバイダーのデフォルトが使用されます。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "APIキー",
+            "apiKeyHelp": "LLMプロバイダーのAPIキー。ローカルに保存され、選択したLLMプロバイダー以外のサーバーに送信されることはありません。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未設定",
+            "apiKeyConfigured": "設定済み",
+            "apiKeySet": "設定",
+            "model": "モデル",
+            "modelHelp": "使用するモデル名（例：deepseek-v4-flash, gemini-2.5-flash, gemma4:12b）。プロバイダーで利用可能なモデルをご確認ください。",
+            "modelPlaceholder": "モデルを選択..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "小さい順",
                 "usage": "使用回数",
                 "usageDesc": "多い",
-                "usageAsc": "少ない"
+                "usageAsc": "少ない",
+                "versionsCount": "ローカルバージョン数",
+                "versionsCountDesc": "バージョン数の多い順",
+                "versionsCountAsc": "バージョン数の少ない順",
+                "versionIdDesc": "最新バージョン順",
+                "random": "ランダム",
+                "randomAction": "シャッフル（ランダム）"
             },
             "refresh": {
                 "title": "モデルリストを更新",
@@ -690,6 +768,7 @@
             "copyAll": "すべての構文をコピー",
             "refreshAll": "すべてのメタデータを更新",
             "repairMetadata": "選択したレシピのメタデータを修復",
+            "reimportMetadata": "ソースから再インポート",
             "checkUpdates": "選択項目の更新を確認",
             "moveAll": "すべてをフォルダに移動",
             "autoOrganize": "自動整理を実行",
@@ -701,6 +780,8 @@
             "deleteAll": "選択したものを削除",
             "downloadMissingLoras": "不足している LoRA をダウンロード",
             "downloadExamples": "例画像をダウンロード",
+            "downloadMissingExamples": "不足分をダウンロード",
+            "reprocessExamples": "すべて再処理",
             "clear": "選択をクリア",
             "skipMetadataRefreshCount": "スキップ（{count}モデル）",
             "resumeMetadataRefreshCount": "再開（{count}モデル）",
@@ -720,12 +801,15 @@
                 "completed": "完了：{success} 移動、{skipped} スキップ、{failures} 失敗",
                 "complete": "自動整理が完了しました",
                 "error": "エラー：{error}"
-            }
+            },
+            "enrichHfAgent": "HF メタデータをAIで補完"
         },
         "contextMenu": {
             "refreshMetadata": "Civitaiデータを更新",
             "checkUpdates": "更新確認",
-            "relinkCivitai": "Civitaiに再リンク",
+            "linkModel": "モデルをリンク",
+            "linkCivitai": "Civitai にリンク",
+            "linkHuggingFace": "HuggingFace にリンク",
             "copySyntax": "LoRA構文をコピー",
             "copyFilename": "モデルファイル名をコピー",
             "copyRecipeSyntax": "レシピ構文をコピー",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "ワークフローに送信（置換）",
             "openExamples": "例画像フォルダを開く",
             "downloadExamples": "例画像をダウンロード",
+            "downloadMissingExamples": "不足分をダウンロード",
+            "reprocessExamples": "すべて再処理",
             "replacePreview": "プレビューを置換",
             "setContentRating": "コンテンツレーティングを設定",
             "moveToFolder": "フォルダに移動",
             "repairMetadata": "メタデータを修復",
+            "reimportMetadata": "ソースから再インポート",
             "excludeModel": "モデルを除外",
             "restoreModel": "モデルを復元",
             "deleteModel": "モデルを削除",
             "shareRecipe": "レシピを共有",
             "viewAllLoras": "すべてのLoRAを表示",
             "downloadMissingLoras": "不足しているLoRAをダウンロード",
-            "deleteRecipe": "レシピを削除"
+            "deleteRecipe": "レシピを削除",
+            "enrichHfAgent": "HF メタデータをAIで補完"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "レシピはすでに最新バージョンです。修復は不要です",
                 "failed": "レシピの修復に失敗しました: {message}",
                 "missingId": "レシピを修復できません: レシピIDがありません"
+            },
+            "reimport": {
+                "starting": "ソースからレシピを再インポート中...",
+                "success": "レシピの再インポートが完了しました",
+                "noSourceUrl": "レシピにソースURLがありません。再インポートできません",
+                "failed": "レシピの再インポートに失敗しました: {message}",
+                "missingId": "レシピを再インポートできません: レシピIDがありません"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "ルート",
         "collapseAll": "すべてのフォルダを折りたたむ",
-        "pinSidebar": "サイドバーを固定",
-        "unpinSidebar": "サイドバーの固定を解除",
+        "hideOnThisPage": "このページでサイドバーを非表示",
+        "showSidebar": "サイドバーを表示",
+        "sidebarHiddenNotification": "{page}ページでサイドバーが非表示になっています",
         "switchToListView": "リストビューに切り替え",
         "switchToTreeView": "ツリー表示に切り替え",
         "recursiveOn": "サブフォルダーを含める",
@@ -981,6 +1077,18 @@
             "storage": "ストレージ",
             "insights": "インサイト"
         },
+        "metrics": {
+            "totalModels": "モデル総数",
+            "totalStorage": "ストレージ合計",
+            "totalGenerations": "生成回数合計",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "ユニークタグ",
+            "unusedModels": "未使用モデル",
+            "avgUsesPerModel": "平均使用回数/モデル"
+        },
         "usage": {
             "mostUsedLoras": "最も使用されているLoRA",
             "mostUsedCheckpoints": "最も使用されているCheckpoint",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "スマートインサイト",
-            "recommendations": "推奨事項"
+            "recommendations": "推奨事項",
+            "noInsights": "インサイトはありません",
+            "unusedLoras": {
+                "high": {
+                    "title": "未使用のLoRAが多数あります",
+                    "description": "LoRAの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "未使用のモデルを整理またはアーカイブしてストレージを解放してください。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "未使用のCheckpointを検出",
+                    "description": "Checkpointの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "不要なCheckpointを確認して削除を検討してください。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "未使用のEmbeddingが多数あります",
+                    "description": "Embeddingの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "未使用のEmbeddingを整理またはアーカイブしてコレクションを最適化してください。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "大規模コレクションを検出",
+                    "description": "モデルコレクションが{size}のストレージを使用しています。",
+                    "suggestion": "外部ストレージやクラウドソリューションの使用を検討してください。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "アクティブユーザー",
+                    "description": "これまでに{count}回の生成を完了しました！",
+                    "suggestion": "モデルを使って素晴らしいコンテンツを作り続けてください。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "コレクション概要",
             "baseModelDistribution": "ベースモデル分布",
             "usageTrends": "使用傾向（過去30日）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日付",
+            "usageCount": "使用回数",
+            "fileSizeBytes": "ファイルサイズ（バイト）",
+            "models": "モデル",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "拡散モデル",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "読み込み中...",
+            "noModels": "モデルが見つかりません",
+            "errorLoading": "データ読み込みエラー",
+            "noStorageData": "ストレージデータがありません",
+            "rootFolder": "ルート",
+            "chartLibraryMissing": "Chart.js ライブラリが必要です"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} モデル",
+            "chartUsage": "{name}: {size}, {count} 回使用",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "URLからモデルをダウンロード",
             "titleWithType": "URLから{type}をダウンロード",
-            "url": "Civitai URL",
             "civitaiUrl": "Civitai URL：",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "1行に1つのCivitAI、CivArchive、またはHugging Face URLを入力してください。複数のURLを一括ダウンロードできます。",
+            "selectHfFiles": "このリポジトリからダウンロードするファイルを選択してください：",
+            "selectAll": "すべて選択",
+            "fetchingRepoFiles": "リポジトリのファイルを取得中...",
             "locationPreview": "ダウンロード場所プレビュー",
             "useDefaultPath": "デフォルトパスを使用",
             "useDefaultPathTooltip": "有効にすると、設定されたパステンプレートを使用してファイルが自動的に整理されます",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "無効なCivitai URL形式",
-                "noVersions": "このモデルの利用可能なバージョンがありません"
+                "noVersions": "このモデルの利用可能なバージョンがありません",
+                "mixedSources": "同じバッチ内でCivitAIとHugging FaceのURLを混在させることはできません。",
+                "noModelFiles": "このリポジトリにモデルファイルが見つかりませんでした。"
             },
             "status": {
                 "preparing": "ダウンロードを準備中...",
                 "downloadedPreview": "プレビュー画像をダウンロードしました",
                 "downloadingFile": "{type}ファイルをダウンロード中",
-                "finalizing": "ダウンロードを完了中..."
+                "finalizing": "ダウンロードを完了中...",
+                "cancelling": "ダウンロードをキャンセル中...",
+                "cancelled": "ダウンロードをキャンセルしました"
             },
             "progress": {
                 "currentFile": "現在のファイル:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "フォルダパスを入力するか、下のツリーから選択...",
             "root": "ルート"
         },
+        "linkHuggingFace": {
+            "title": "HuggingFace にリンク",
+            "infoText": "HuggingFace リポジトリの URL を貼り付けてモデルを関連付けます。AI によるメタデータ補完が有効になります。",
+            "urlLabel": "HuggingFace リポジトリ URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "完全な HuggingFace リポジトリ URL を入力してください。",
+            "confirmAction": "保存＆リンク"
+        },
         "relinkCivitai": {
             "title": "Civitaiに再リンク",
             "warning": "警告：",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "バージョン名を編集",
                 "viewOnCivitai": "Civitaiで表示",
                 "viewOnCivitaiText": "Civitaiで表示",
+                "viewOnHuggingFace": "Hugging Face で見る",
+                "viewOnHuggingFaceText": "Hugging Face で見る",
                 "viewCreatorProfile": "作成者プロフィールを表示",
                 "openFileLocation": "ファイルの場所を開く",
                 "sendToWorkflow": "ComfyUI に送信",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "追加メモ",
                 "notesHint": "Enterで保存、Shift+Enterで改行",
                 "addNotesPlaceholder": "メモをここに追加...",
-                "aboutThisVersion": "このバージョンについて"
+                "aboutThisVersion": "このバージョンについて",
+                "baseModelSearchPlaceholder": "ベースモデルを検索…",
+                "baseModelSuggested": "おすすめ",
+                "baseModelNoMatch": "該当するベースモデルがありません"
             },
             "notes": {
                 "saved": "メモが正常に保存されました",
-                "saveFailed": "メモの保存に失敗しました"
+                "saveFailed": "メモの保存に失敗しました",
+                "showMore": "もっと見る",
+                "showLess": "折りたたむ"
             },
             "usageTips": {
                 "addPresetParameter": "プリセットパラメータを追加...",
@@ -1367,6 +1561,7 @@
                 "empty": "このモデルにはまだバージョン履歴がありません。",
                 "error": "バージョンの読み込みに失敗しました。",
                 "missingModelId": "このモデルにはCivitaiのモデルIDがありません。",
+                "hfGroupInfo": "これは HuggingFace モデルグループです。ライブラリを開いてグリッドですべてのバージョンを表示してください。",
                 "confirm": {
                     "delete": "このバージョンをライブラリから削除しますか？"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "バージョンを削除しました"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "メタデータ取得サマリー",
+            "statSuccess": "成功",
+            "statFailed": "失敗",
+            "statSkipped": "スキップ",
+            "statTotal": "スキャン合計",
+            "statDuration": "所要時間",
+            "successMessage": "すべての{count}件の{type}を正常に更新しました",
+            "failedItems": "失敗したアイテム ({count})",
+            "close": "閉じる",
+            "copyReport": "レポートをコピー",
+            "downloadCsv": "CSVをダウンロード",
+            "columnModelName": "モデル名",
+            "columnError": "エラー"
+        },
+        "downloadBatchSummary": {
+            "title": "バッチダウンロードの概要",
+            "statSuccess": "成功",
+            "statFailed": "失敗",
+            "statTotal": "合計",
+            "successMessage": "{count} 個のモデルがすべて正常にダウンロードされました",
+            "completedWithErrors": "エラーありで完了",
+            "failed": "ダウンロードに失敗しました",
+            "failedItems": "失敗した項目（{count}）",
+            "columnName": "モデル名",
+            "columnError": "エラー",
+            "close": "閉じる",
+            "copyReport": "レポートをコピー",
+            "retryFailed": "失敗した項目を再試行（{count}）"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "このタグは既に存在します"
         }
     },
-    "keyboard": {
-        "navigation": "キーボードナビゲーション：",
-        "shortcuts": {
-            "pageUp": "1ページ上にスクロール",
-            "pageDown": "1ページ下にスクロール",
-            "home": "トップにジャンプ",
-            "end": "ボトムにジャンプ"
-        }
-    },
     "initialization": {
         "title": "初期化中",
         "message": "ワークスペースを準備中...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "現在のワークフローには互換性のあるノードがありません",
             "noTargetNodeSelected": "ターゲットノードが選択されていません",
             "modelUpdated": "モデルがワークフローで更新されました",
-            "modelFailed": "モデルノードの更新に失敗しました"
+            "modelFailed": "モデルノードの更新に失敗しました",
+            "embeddingAdded": "Embeddingをワークフローに追加しました",
+            "embeddingFailed": "Embeddingの追加に失敗しました",
+            "promptSent": "プロンプトをワークフローに送信しました",
+            "promptFailed": "プロンプトの送信に失敗しました"
         },
         "nodeSelector": {
             "recipe": "レシピ",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "プロンプト",
             "replace": "置換",
             "append": "追加",
             "selectTargetNode": "ターゲットノードを選択",
@@ -1558,6 +1780,12 @@
         "checkingMessage": "最新バージョンを確認しています。お待ちください。",
         "showNotifications": "更新通知を表示",
         "latestBadge": "最新",
+        "latestMain": "Main ブランチ",
+        "channel": "更新チャンネル",
+        "channels": {
+            "release": "リリース",
+            "nightly": "ナイトリー"
+        },
         "updateProgress": {
             "preparing": "更新を準備中...",
             "installing": "更新をインストール中...",
@@ -1578,6 +1806,15 @@
             "warning": "警告：ナイトリービルドには実験的機能が含まれており、不安定な場合があります。",
             "enable": "ナイトリー更新を有効にする"
         },
+        "channelSwitch": {
+            "nightlyTitle": "ナイトリーチャンネルに切り替え",
+            "nightlyMessage": "ナイトリーに切り替えると、Gitリポジトリが初期化され、mainブランチの最新コミットを追跡します。更新頻度は高くなりますが、不安定な場合があります。いつでもリリース版に戻せます。",
+            "releaseTitle": "リリースチャンネルに切り替え",
+            "releaseMessage": "リリースに切り替えると、最新の安定版タグにチェックアウトされます。いつでもNightlyに戻せます。",
+            "switching": "{channel} チャンネルに切り替え中...",
+            "completed": "{channel} チャンネルに切り替えました",
+            "failed": "チャンネルの切り替えに失敗しました"
+        },
         "banners": {
             "recent": "最近の通知",
             "empty": "最近のバナーはありません。",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "LoRA名または構文を入力してください",
             "reconnectedSuccessfully": "LoRAが正常に再接続されました",
             "reconnectFailed": "LoRA再接続エラー：{message}",
+            "noPromptToSend": "送信するプロンプトがありません",
             "cannotSend": "レシピを送信できません：レシピIDがありません",
             "sendFailed": "レシピのワークフローへの送信に失敗しました",
             "sendError": "レシピのワークフロー送信エラー",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "修復完了：{repaired} 件修復、{skipped} 件スキップ（合計 {total} 件）",
             "repairBulkSkipped": "選択した {total} 件のレシピは修復不要です",
             "repairBulkFailed": "選択したレシピの修復に失敗しました：{message}",
+            "reimporting": "ソースからレシピを再インポート中...",
+            "reimportSuccess": "レシピの再インポートが完了しました",
+            "reimportBulkComplete": "再インポート完了：{completed} 件成功、{failed} 件失敗（合計 {total} 件）",
+            "reimportBulkFailed": "一部のレシピの再インポートに失敗しました",
             "noMissingLorasInSelection": "選択したレシピに不足している LoRA が見つかりませんでした",
             "noLoraRootConfigured": "LoRA ルートディレクトリが設定されていません。設定でデフォルトの LoRA ルートを設定してください。"
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "プリセット名は{max}文字以内にしてください",
             "presetNameInvalidChars": "プリセット名に使用できない文字が含まれています",
             "presetNameExists": "同じ名前のプリセットが既に存在します",
-            "maxPresetsReached": "プリセットは最大{max}個までです。追加するには既存のものを削除してください。",
             "presetNotFound": "プリセットが見つかりません",
             "invalidPreset": "無効なプリセットデータです",
             "deletePresetFailed": "プリセットの削除に失敗しました",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "例画像 {action} が完了しました",
             "imagesFailed": "例画像 {action} が失敗しました",
             "loadError": "ダウンロード読み込みエラー：{message}",
-            "downloadError": "ダウンロードエラー：{message}"
+            "downloadError": "ダウンロードエラー：{message}",
+            "downloadStopped": "ダウンロードをキャンセルしました"
         },
         "import": {
             "folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "コンテンツレーティングの設定に失敗しました：{message}",
             "relinkSuccess": "モデルがCivitaiに正常に再リンクされました",
             "relinkFailed": "エラー：{message}",
+            "linkHfSuccess": "モデルを HuggingFace にリンクしました",
+            "linkHfFailed": "エラー：{message}",
             "fetchMetadataFirst": "最初にCivitAIからメタデータを取得してください",
             "noCivitaiInfo": "CivitAI情報が利用できません",
             "missingHash": "モデルハッシュが利用できません"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "{successCount} {type}が正常に移動されました",
             "exampleImagesDownloadSuccess": "例画像が正常にダウンロードされました！",
             "exampleImagesDownloadFailed": "例画像のダウンロードに失敗しました：{message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "クリップボードにコピーしました",
+            "downloadStarted": "ダウンロードを開始しました"
+        },
+        "agent": {
+            "llmNotConfigured": "AIプロバイダーが設定されていません。設定 → AIプロバイダーで有効にしてください。",
+            "enrichStarted": "AIでメタデータを補完中...",
+            "enrichComplete": "メタデータの補完が完了しました：{{summary}}",
+            "enrichFailed": "メタデータの補完に失敗しました：{{error}}"
         }
     },
     "doctor": {

locales/ko.json

@@ -16,10 +16,13 @@
             "help": "도움말",
             "add": "추가",
             "close": "닫기",
-            "menu": "메뉴"
+            "menu": "메뉴",
+            "remove": "제거",
+            "change": "변경"
         },
         "status": {
             "loading": "로딩 중...",
+            "cancelling": "취소 중...",
             "unknown": "알 수 없음",
             "date": "날짜",
             "version": "버전",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "즐겨찾기에서 제거",
             "viewOnCivitai": "Civitai에서 보기",
             "notAvailableFromCivitai": "Civitai에서 사용할 수 없음",
+            "viewOnHuggingFace": "Hugging Face에서 보기",
             "sendToWorkflow": "ComfyUI로 전송 (클릭: 추가, Shift+클릭: 교체)",
             "copyLoRASyntax": "LoRA 문법 복사",
             "checkpointNameCopied": "Checkpoint 이름 복사됨",
@@ -111,6 +115,7 @@
             "replacePreview": "미리보기 교체",
             "copyCheckpointName": "Checkpoint 이름 복사",
             "copyEmbeddingName": "Embedding 이름 복사",
+            "embeddingNameCopied": "Embedding 구문 복사됨",
             "sendCheckpointToWorkflow": "ComfyUI로 전송",
             "sendEmbeddingToWorkflow": "ComfyUI로 전송"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "사용 횟수"
+        },
+        "footer": {
+            "versionCount": "{count}개 버전",
+            "viewAllVersions": "모든 로컬 버전 보기"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "제외된 모델 관리"
+        },
+        "groupByModel": {
+            "label": "모델별 그룹화"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "통계"
         },
         "search": {
-            "placeholder": "검색...",
-            "placeholders": {
-                "loras": "LoRA 검색...",
-                "recipes": "레시피 검색...",
-                "checkpoints": "Checkpoint 검색...",
-                "embeddings": "Embedding 검색..."
-            },
+            "placeholder": "검색",
             "options": "검색 옵션",
             "searchIn": "검색 범위:",
             "notAvailable": "통계 페이지에서는 검색을 사용할 수 없습니다",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "프리셋 이름...",
             "baseModel": "베이스 모델",
             "baseModelSearchPlaceholder": "베이스 모델 검색...",
-            "modelTags": "태그 (상위 20개)",
+            "modelTags": "태그",
             "modelTypes": "모델 유형",
             "license": "라이선스",
             "noCreditRequired": "크레딧 표기 없음",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "생성된 이미지 판매 허용",
             "noCreditRequiredTooltip": "크리에이터 저작자 표시 없이 모델 사용 가능",
             "noTags": "태그 없음",
+            "tagSearchPlaceholder": "태그 검색...",
+            "noTagMatches": "현재 검색과 일치하는 태그가 없습니다.",
             "autoTags": "자동 태그",
             "noBaseModelMatches": "현재 검색과 일치하는 베이스 모델이 없습니다.",
             "clearAll": "모든 필터 지우기",
@@ -247,7 +255,18 @@
             "toggle": "테마 토글",
             "switchToLight": "라이트 테마로 전환",
             "switchToDark": "다크 테마로 전환",
-            "switchToAuto": "자동 테마로 전환"
+            "switchToAuto": "자동 테마로 전환",
+            "presets": "테마 프리셋",
+            "default": "기본",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "모드",
+            "light": "라이트",
+            "dark": "다크",
+            "auto": "자동"
         },
         "actions": {
             "checkUpdates": "업데이트 확인",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Civitai API 키",
         "civitaiApiKeyPlaceholder": "Civitai API 키를 입력하세요",
         "civitaiApiKeyHelp": "Civitai에서 모델을 다운로드할 때 인증에 사용됩니다",
+        "civitaiApiKeyConfigured": "설정됨",
+        "civitaiApiKeyNotConfigured": "설정되지 않음",
+        "civitaiApiKeySet": "설정",
         "civitaiHost": {
             "label": "Civitai 호스트",
             "help": "\"View on Civitai\" 링크를 사용할 때 어떤 Civitai 사이트를 열지 선택합니다.",
@@ -299,6 +321,7 @@
             "downloads": "다운로드",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
+            "licenseIcons": "라이선스 아이콘",
             "misc": "기타",
             "backup": "백업",
             "folderSettings": "기본 루트",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "추가 폴다 경로",
             "downloadPathTemplates": "다운로드 경로 템플릿",
             "priorityTags": "우선순위 태그",
-            "updateFlags": "업데이트 표시",
+            "versionScope": "업데이트 표시",
             "exampleImages": "예시 이미지",
             "autoOrganize": "자동 정리",
             "metadata": "메타데이터",
@@ -411,6 +434,8 @@
             "help": "활성화하면 다운로드 기록 서비스가 해당 버전이 이미 다운로드되었음을 기록한 경우 LoRA Manager는 해당 모델 버전 다운로드를 건너뜁니다. 모든 다운로드 플로우에 적용됩니다."
         },
         "layoutSettings": {
+            "groupByModel": "모델별 그룹화",
+            "groupByModelHelp": "활성화하면 각 Civitai 모델의 최신 버전만 단일 카드로 표시되며, 이전 버전은 숨겨집니다.",
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
                 "default": "기본",
@@ -424,6 +449,12 @@
                 "compact": "7개 (1080p), 8개 (2K), 10개 (4K)"
             },
             "displayDensityWarning": "경고: 높은 밀도는 리소스가 제한된 시스템에서 성능 문제를 일으킬 수 있습니다.",
+            "recipesLayout": "레시피 레이아웃",
+            "recipesLayoutHelp": "레시피 카드의 배열 방식을 선택하세요: 균일한 그리드 또는 각 이미지의 종횡비를 유지하는 메이슨리(Pinterest 스타일) 레이아웃.",
+            "recipesLayoutOptions": {
+                "grid": "그리드",
+                "masonry": "메이슨리"
+            },
             "showFolderSidebar": "폴더 사이드바 표시",
             "showFolderSidebarHelp": "모델 페이지에서 폴더 탐색 사이드바를 켜거나 끕니다. 비활성화하면 사이드바와 호버 영역이 표시되지 않습니다.",
             "cardInfoDisplay": "카드 정보 표시",
@@ -445,7 +476,9 @@
                 "modelName": "모델명",
                 "fileName": "파일명"
             },
-            "modelNameDisplayHelp": "모델 카드 하단에 표시할 내용을 선택하세요"
+            "modelNameDisplayHelp": "모델 카드 하단에 표시할 내용을 선택하세요",
+            "cardBlurAmount": "카드 오버레이 흐림 강도",
+            "cardBlurAmountHelp": "모델 및 레시피 카드의 헤더와 푸터 오버레이 흐림 강도를 조정합니다 (0 = 흐림 없음, 20 = 최대 흐림)."
         },
         "folderSettings": {
             "activeLibrary": "활성 라이브러리",
@@ -480,7 +513,9 @@
             "saveSuccess": "추가 폴다 경로가 업데이트되었습니다. 변경 사항을 적용하려면 재시작이 필요합니다.",
             "saveError": "추가 폴다 경로 업데이트 실패: {message}",
             "validation": {
-                "duplicatePath": "이 경로는 이미 구성되어 있습니다"
+                "duplicatePath": "이 경로는 이미 구성되어 있습니다",
+                "checkpointUnetOverlap": "checkpoints와 diffusion models에 동일한 경로를 사용할 수 없습니다: {paths}",
+                "checkpointUnetOverlapInline": "이 경로는 다른 모델 유형에 이미 사용 중입니다. checkpoints와 diffusion models에 별도의 폴더를 사용하세요."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "다운로드",
             "restartRequired": "재시작 필요"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "업데이트 표시 전략",
             "help": "새 릴리스가 로컬 파일과 동일한 베이스 모델을 공유할 때만 업데이트 배지를 표시할지, 또는 해당 모델에 사용 가능한 새 버전이 있으면 항상 표시할지 결정합니다.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "얼리 액세스 업데이트 숨기기",
             "help": "얼리 액세스 업데이트만"
         },
+        "licenseIcons": {
+            "useNewStyle": "업데이트된 라이선스 아이콘 사용",
+            "useNewStyleHelp": "색상 표시기가 있는 라이선스 권한(새 스타일) 또는 제한 전용 아이콘(클래식 스타일)을 표시합니다. 현재 CivitAI 디자인을 반영합니다."
+        },
         "misc": {
             "includeTriggerWords": "LoRA 문법에 트리거 단어 포함",
             "includeTriggerWordsHelp": "LoRA 문법을 클립보드에 복사할 때 학습된 트리거 단어를 포함합니다",
@@ -609,7 +648,13 @@
             "preparing": "다운로드 준비 중...",
             "connecting": "다운로드 서버에 연결 중...",
             "completed": "완료됨",
-            "downloadComplete": "다운로드가 성공적으로 완료되었습니다"
+            "downloadComplete": "다운로드가 성공적으로 완료되었습니다",
+            "enableCivarchiveApi": "CivArchive API를 메타데이터 제공자로 활성화",
+            "enableCivarchiveApiHelp": "활성화하면 CivArchive API가 모델 메타데이터의 대체 소스로 사용됩니다 (예: CivitAI에서 삭제된 모델의 경우). 비활성화하면 CivArchive의 속도 제한을 완전히 피할 수 있습니다.",
+            "providerOrder": "메타데이터 제공자 폴백 순서",
+            "providerOrderHelp": "CivitAI API가 항상 먼저 시도됩니다. 메타데이터 조회 시 나머지 제공자의 순서를 선택하세요.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "앱 수준 프록시 활성화",
@@ -628,6 +673,33 @@
             "proxyPassword": "비밀번호 (선택사항)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "프록시 인증에 필요한 비밀번호 (필요한 경우)"
+        },
+        "aiProvider": {
+            "title": "AI 제공자",
+            "provider": "제공자",
+            "providerHelp": "LLM 제공자를 선택하세요. OpenAI와 Ollama는 사전 설정된 API 엔드포인트를 사용합니다. 사용자 정의를 선택하면 모든 OpenAI 호환 엔드포인트를 지정할 수 있습니다.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (로컬)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "사용자 정의 (OpenAI 호환)"
+            },
+            "apiBase": "API 기본 URL",
+            "apiBaseHelp": "LLM API의 기본 URL입니다 (예: https://api.openai.com/v1). 비워두면 제공자 기본값이 사용됩니다.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 키",
+            "apiKeyHelp": "LLM 제공자의 API 키입니다. 로컬에 저장되며 선택한 LLM 제공자 외의 서버로 전송되지 않습니다.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "설정되지 않음",
+            "apiKeyConfigured": "설정됨",
+            "apiKeySet": "설정",
+            "model": "모델",
+            "modelHelp": "사용할 모델 이름 (예: deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). 제공자에서 사용 가능한 모델을 확인하세요.",
+            "modelPlaceholder": "모델 선택..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "작은 순서",
                 "usage": "사용 횟수",
                 "usageDesc": "많은 순",
-                "usageAsc": "적은 순"
+                "usageAsc": "적은 순",
+                "versionsCount": "로컬 버전 수",
+                "versionsCountDesc": "버전 수 많은 순",
+                "versionsCountAsc": "버전 수 적은 순",
+                "versionIdDesc": "최신 버전순",
+                "random": "랜덤",
+                "randomAction": "셔플 (무작위)"
             },
             "refresh": {
                 "title": "모델 목록 새로고침",
@@ -690,6 +768,7 @@
             "copyAll": "모든 문법 복사",
             "refreshAll": "모든 메타데이터 새로고침",
             "repairMetadata": "선택한 레시피 메타데이터 복구",
+            "reimportMetadata": "소스에서 다시 가져오기",
             "checkUpdates": "선택 항목 업데이트 확인",
             "moveAll": "모두 폴더로 이동",
             "autoOrganize": "자동 정리 선택",
@@ -701,6 +780,8 @@
             "deleteAll": "선택된 항목 삭제",
             "downloadMissingLoras": "누락된 LoRA 다운로드",
             "downloadExamples": "예시 이미지 다운로드",
+            "downloadMissingExamples": "누락된 것만 다운로드",
+            "reprocessExamples": "모두 다시 처리",
             "clear": "선택 지우기",
             "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
             "resumeMetadataRefreshCount": "재개（{count}개 모델）",
@@ -720,12 +801,15 @@
                 "completed": "완료: {success}개 이동, {skipped}개 건너뜀, {failures}개 실패",
                 "complete": "자동 정리 완료",
                 "error": "오류: {error}"
-            }
+            },
+            "enrichHfAgent": "HF AI로 메타데이터 보강"
         },
         "contextMenu": {
             "refreshMetadata": "Civitai 데이터 새로고침",
             "checkUpdates": "업데이트 확인",
-            "relinkCivitai": "Civitai에 다시 연결",
+            "linkModel": "모델 연결",
+            "linkCivitai": "Civitai에 연결",
+            "linkHuggingFace": "HuggingFace에 연결",
             "copySyntax": "LoRA 문법 복사",
             "copyFilename": "모델 파일명 복사",
             "copyRecipeSyntax": "레시피 문법 복사",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "워크플로로 전송 (교체)",
             "openExamples": "예시 폴더 열기",
             "downloadExamples": "예시 이미지 다운로드",
+            "downloadMissingExamples": "누락된 것만 다운로드",
+            "reprocessExamples": "모두 다시 처리",
             "replacePreview": "미리보기 교체",
             "setContentRating": "콘텐츠 등급 설정",
             "moveToFolder": "폴더로 이동",
             "repairMetadata": "메타데이터 복구",
+            "reimportMetadata": "소스에서 다시 가져오기",
             "excludeModel": "모델 제외",
             "restoreModel": "모델 복원",
             "deleteModel": "모델 삭제",
             "shareRecipe": "레시피 공유",
             "viewAllLoras": "모든 LoRA 보기",
             "downloadMissingLoras": "누락된 LoRA 다운로드",
-            "deleteRecipe": "레시피 삭제"
+            "deleteRecipe": "레시피 삭제",
+            "enrichHfAgent": "HF AI로 메타데이터 보강"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "레시피가 이미 최신 버전입니다. 복구가 필요하지 않습니다",
                 "failed": "레시피 복구 실패: {message}",
                 "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
+            },
+            "reimport": {
+                "starting": "소스에서 레시피를 다시 가져오는 중...",
+                "success": "레시피를 다시 가져왔습니다",
+                "noSourceUrl": "레시피에 소스 URL이 없어 다시 가져올 수 없습니다",
+                "failed": "레시피 다시 가져오기 실패: {message}",
+                "missingId": "레시피를 다시 가져올 수 없음: 레시피 ID 누락"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "루트",
         "collapseAll": "모든 폴더 접기",
-        "pinSidebar": "사이드바 고정",
-        "unpinSidebar": "사이드바 고정 해제",
+        "hideOnThisPage": "이 페이지에서 사이드바 숨기기",
+        "showSidebar": "사이드바 표시",
+        "sidebarHiddenNotification": "{page} 페이지에서 사이드바가 숨겨져 있습니다",
         "switchToListView": "목록 보기로 전환",
         "switchToTreeView": "트리 보기로 전환",
         "recursiveOn": "하위 폴더 포함",
@@ -981,6 +1077,18 @@
             "storage": "저장소",
             "insights": "인사이트"
         },
+        "metrics": {
+            "totalModels": "모델 총계",
+            "totalStorage": "총 저장 공간",
+            "totalGenerations": "총 생성 횟수",
+            "usageRate": "사용률",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "고유 태그",
+            "unusedModels": "미사용 모델",
+            "avgUsesPerModel": "모델당 평균 사용"
+        },
         "usage": {
             "mostUsedLoras": "가장 많이 사용된 LoRA",
             "mostUsedCheckpoints": "가장 많이 사용된 Checkpoint",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "스마트 인사이트",
-            "recommendations": "추천"
+            "recommendations": "추천",
+            "noInsights": "인사이트 없음",
+            "unusedLoras": {
+                "high": {
+                    "title": "사용하지 않은 LoRA가 많음",
+                    "description": "LoRA의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "사용하지 않는 모델을 정리하거나 보관하여 저장 공간을 확보하세요."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "사용하지 않은 Checkpoint 감지",
+                    "description": "Checkpoint의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "더 이상 필요하지 않은 Checkpoint를 검토하고 제거하세요."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "사용하지 않은 Embedding이 많음",
+                    "description": "Embedding의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "사용하지 않는 Embedding을 정리하여 컬렉션을 최적화하세요."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "대규모 컬렉션 감지",
+                    "description": "모델 컬렉션이 {size}의 저장 공간을 사용 중입니다.",
+                    "suggestion": "더 나은 관리를 위해 외부 저장소나 클라우드 솔루션을 고려하세요."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "활성 사용자",
+                    "description": "지금까지 {count}번의 생성을 완료했습니다!",
+                    "suggestion": "모델로 계속해서 멋진 콘텐츠를 탐색하고 만들어보세요."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "컬렉션 개요",
             "baseModelDistribution": "베이스 모델 분포",
             "usageTrends": "사용량 트렌드 (최근 30일)",
-            "usageDistribution": "사용량 분포"
+            "usageDistribution": "사용량 분포",
+            "date": "날짜",
+            "usageCount": "사용 횟수",
+            "fileSizeBytes": "파일 크기(바이트)",
+            "models": "모델",
+            "loraUsage": "LoRA 사용량",
+            "checkpointUsage": "Checkpoint 사용량",
+            "embeddingUsage": "Embedding 사용량"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "확산 모델",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "로딩 중...",
+            "noModels": "모델을 찾을 수 없음",
+            "errorLoading": "데이터 로딩 오류",
+            "noStorageData": "저장 데이터 없음",
+            "rootFolder": "루트",
+            "chartLibraryMissing": "Chart.js 라이브러리가 필요합니다"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count}개 모델",
+            "chartUsage": "{name}: {size}, {count}회 사용",
+            "chartPercentage": "{label}: {value}({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "URL에서 모델 다운로드",
             "titleWithType": "URL에서 {type} 다운로드",
-            "url": "Civitai URL",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "한 줄에 하나의 CivitAI, CivArchive 또는 Hugging Face URL을 입력하세요. 여러 URL을 일괄 다운로드할 수 있습니다.",
+            "selectHfFiles": "이 저장소에서 다운로드할 파일을 선택하세요：",
+            "selectAll": "모두 선택",
+            "fetchingRepoFiles": "저장소 파일을 가져오는 중...",
             "locationPreview": "다운로드 위치 미리보기",
             "useDefaultPath": "기본 경로 사용",
             "useDefaultPathTooltip": "활성화하면 구성된 경로 템플릿을 사용하여 파일이 자동으로 정리됩니다",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "잘못된 Civitai URL 형식",
-                "noVersions": "이 모델에 사용 가능한 버전이 없습니다"
+                "noVersions": "이 모델에 사용 가능한 버전이 없습니다",
+                "mixedSources": "동일한 배치에서 CivitAI와 Hugging Face URL을 혼합할 수 없습니다.",
+                "noModelFiles": "이 저장소에서 모델 파일을 찾을 수 없습니다."
             },
             "status": {
                 "preparing": "다운로드 준비 중...",
                 "downloadedPreview": "미리보기 이미지 다운로드됨",
                 "downloadingFile": "{type} 파일 다운로드 중",
-                "finalizing": "다운로드 완료 중..."
+                "finalizing": "다운로드 완료 중...",
+                "cancelling": "다운로드 취소 중...",
+                "cancelled": "다운로드가 취소되었습니다"
             },
             "progress": {
                 "currentFile": "현재 파일:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "폴더 경로를 입력하거나 아래 트리에서 선택하세요...",
             "root": "루트"
         },
+        "linkHuggingFace": {
+            "title": "HuggingFace에 연결",
+            "infoText": "HuggingFace 저장소 URL을 붙여넣어 모델을 연결합니다. AI 메타데이터 보강 기능을 사용할 수 있습니다.",
+            "urlLabel": "HuggingFace 저장소 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "전체 HuggingFace 저장소 URL을 입력하세요.",
+            "confirmAction": "저장 및 연결"
+        },
         "relinkCivitai": {
             "title": "Civitai에 다시 연결",
             "warning": "경고:",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "버전명 편집",
                 "viewOnCivitai": "Civitai에서 보기",
                 "viewOnCivitaiText": "Civitai에서 보기",
+                "viewOnHuggingFace": "Hugging Face에서 보기",
+                "viewOnHuggingFaceText": "Hugging Face에서 보기",
                 "viewCreatorProfile": "제작자 프로필 보기",
                 "openFileLocation": "파일 위치 열기",
                 "sendToWorkflow": "ComfyUI로 보내기",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "추가 메모",
                 "notesHint": "Enter로 저장, Shift+Enter로 줄바꿈",
                 "addNotesPlaceholder": "메모를 여기에 추가하세요...",
-                "aboutThisVersion": "이 버전에 대해"
+                "aboutThisVersion": "이 버전에 대해",
+                "baseModelSearchPlaceholder": "베이스 모델 검색…",
+                "baseModelSuggested": "추천",
+                "baseModelNoMatch": "일치하는 베이스 모델 없음"
             },
             "notes": {
                 "saved": "메모가 성공적으로 저장됨",
-                "saveFailed": "메모 저장 실패"
+                "saveFailed": "메모 저장 실패",
+                "showMore": "더 보기",
+                "showLess": "접기"
             },
             "usageTips": {
                 "addPresetParameter": "프리셋 매개변수 추가...",
@@ -1367,6 +1561,7 @@
                 "empty": "이 모델에는 아직 버전 기록이 없습니다.",
                 "error": "버전을 불러오지 못했습니다.",
                 "missingModelId": "이 모델에는 Civitai 모델 ID가 없습니다.",
+                "hfGroupInfo": "HuggingFace 모델 그룹입니다. 라이브러리를 열어 그리드에서 모든 버전을 확인하세요.",
                 "confirm": {
                     "delete": "이 버전을 라이브러리에서 삭제하시겠습니까?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "버전이 삭제되었습니다"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "메타데이터 가져오기 요약",
+            "statSuccess": "성공",
+            "statFailed": "실패",
+            "statSkipped": "건너뜀",
+            "statTotal": "총 스캔",
+            "statDuration": "소요 시간",
+            "successMessage": "모든 {count}개 {type}이(가) 성공적으로 업데이트되었습니다",
+            "failedItems": "실패한 항목 ({count})",
+            "close": "닫기",
+            "copyReport": "보고서 복사",
+            "downloadCsv": "CSV 다운로드",
+            "columnModelName": "모델 이름",
+            "columnError": "오류"
+        },
+        "downloadBatchSummary": {
+            "title": "일괄 다운로드 요약",
+            "statSuccess": "성공",
+            "statFailed": "실패",
+            "statTotal": "전체",
+            "successMessage": "{count}개 모델이 모두 성공적으로 다운로드되었습니다",
+            "completedWithErrors": "오류와 함께 완료됨",
+            "failed": "다운로드 실패",
+            "failedItems": "실패한 항목 ({count})",
+            "columnName": "모델 이름",
+            "columnError": "오류",
+            "close": "닫기",
+            "copyReport": "보고서 복사",
+            "retryFailed": "실패 항목 재시도 ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "이 태그는 이미 존재합니다"
         }
     },
-    "keyboard": {
-        "navigation": "키보드 내비게이션:",
-        "shortcuts": {
-            "pageUp": "한 페이지 위로 스크롤",
-            "pageDown": "한 페이지 아래로 스크롤",
-            "home": "맨 위로 이동",
-            "end": "맨 아래로 이동"
-        }
-    },
     "initialization": {
         "title": "초기화 중",
         "message": "작업공간을 준비하고 있습니다...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "현재 워크플로에서 호환되는 노드가 없습니다",
             "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다",
             "modelUpdated": "모델이 워크플로에서 업데이트되었습니다",
-            "modelFailed": "모델 노드 업데이트 실패"
+            "modelFailed": "모델 노드 업데이트 실패",
+            "embeddingAdded": "Embedding을 워크플로에 추가했습니다",
+            "embeddingFailed": "Embedding 추가 실패",
+            "promptSent": "프롬프트를 워크플로에 보냈습니다",
+            "promptFailed": "프롬프트 보내기 실패"
         },
         "nodeSelector": {
             "recipe": "레시피",
             "lora": "LoRA",
+            "embedding": "임베딩",
+            "prompt": "프롬프트",
             "replace": "교체",
             "append": "추가",
             "selectTargetNode": "대상 노드 선택",
@@ -1558,6 +1780,12 @@
         "checkingMessage": "최신 버전을 확인하는 동안 잠시 기다려주세요.",
         "showNotifications": "업데이트 알림 표시",
         "latestBadge": "최신",
+        "latestMain": "Main 브랜치",
+        "channel": "업데이트 채널",
+        "channels": {
+            "release": "릴리스",
+            "nightly": "나이틀리"
+        },
         "updateProgress": {
             "preparing": "업데이트 준비 중...",
             "installing": "업데이트 설치 중...",
@@ -1578,6 +1806,15 @@
             "warning": "경고: 나이틀리 빌드는 실험적 기능을 포함할 수 있으며 불안정할 수 있습니다.",
             "enable": "나이틀리 업데이트 활성화"
         },
+        "channelSwitch": {
+            "nightlyTitle": "나이틀리 채널로 전환",
+            "nightlyMessage": "나이틀리로 전환하면 Git 저장소가 초기화되고 main 브랜치의 최신 커밋을 추적합니다. 업데이트 빈도는 높지만 불안정할 수 있습니다. 언제든지 릴리스로 돌아갈 수 있습니다.",
+            "releaseTitle": "릴리스 채널로 전환",
+            "releaseMessage": "릴리스로 전환하면 최신 안정 버전 태그로 체크아웃됩니다. 언제든지 나이틀리로 돌아갈 수 있습니다.",
+            "switching": "{channel} 채널로 전환 중...",
+            "completed": "{channel} 채널로 전환 완료",
+            "failed": "채널 전환 실패"
+        },
         "banners": {
             "recent": "최근 알림",
             "empty": "최근 배너가 없습니다.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "LoRA 이름 또는 문법을 입력해주세요",
             "reconnectedSuccessfully": "LoRA가 성공적으로 다시 연결되었습니다",
             "reconnectFailed": "LoRA 다시 연결 오류: {message}",
+            "noPromptToSend": "보낼 프롬프트가 없습니다",
             "cannotSend": "레시피를 전송할 수 없습니다: 레시피 ID 누락",
             "sendFailed": "레시피를 워크플로로 전송하는데 실패했습니다",
             "sendError": "레시피를 워크플로로 전송하는 중 오류",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "복구 완료: {repaired}개 복구, {skipped}개 건너뜀 (총 {total}개)",
             "repairBulkSkipped": "선택한 {total}개 레시피는 복구가 필요하지 않습니다",
             "repairBulkFailed": "선택한 레시피 복구 실패: {message}",
+            "reimporting": "소스에서 레시피를 다시 가져오는 중...",
+            "reimportSuccess": "레시피를 다시 가져왔습니다",
+            "reimportBulkComplete": "다시 가져오기 완료: {completed}개 성공, {failed}개 실패 (총 {total}개)",
+            "reimportBulkFailed": "일부 레시피를 다시 가져오지 못했습니다",
             "noMissingLorasInSelection": "선택한 레시피에서 누락된 LoRA를 찾을 수 없습니다",
             "noLoraRootConfigured": "LoRA 루트 디렉토리가 구성되지 않았습니다. 설정에서 기본 LoRA 루트를 설정하세요."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "프리셋 이름은 {max}자 이하여야 합니다",
             "presetNameInvalidChars": "프리셋 이름에 유효하지 않은 문자가 포함되어 있습니다",
             "presetNameExists": "동일한 이름의 프리셋이 이미 존재합니다",
-            "maxPresetsReached": "최대 {max}개의 프리셋만 허용됩니다. 더 추가하려면 기존 것을 삭제하세요.",
             "presetNotFound": "프리셋을 찾을 수 없습니다",
             "invalidPreset": "잘못된 프리셋 데이터입니다",
             "deletePresetFailed": "프리셋 삭제에 실패했습니다",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "예시 이미지 {action}이(가) 완료되었습니다",
             "imagesFailed": "예시 이미지 {action}이(가) 실패했습니다",
             "loadError": "다운로드 로딩 오류: {message}",
-            "downloadError": "다운로드 오류: {message}"
+            "downloadError": "다운로드 오류: {message}",
+            "downloadStopped": "다운로드가 취소되었습니다"
         },
         "import": {
             "folderTreeFailed": "폴더 트리 로딩 실패",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "콘텐츠 등급 설정 실패: {message}",
             "relinkSuccess": "모델이 Civitai에 성공적으로 다시 연결되었습니다",
             "relinkFailed": "오류: {message}",
+            "linkHfSuccess": "모델이 HuggingFace에 연결되었습니다",
+            "linkHfFailed": "오류: {message}",
             "fetchMetadataFirst": "먼저 CivitAI에서 메타데이터를 가져와주세요",
             "noCivitaiInfo": "사용 가능한 CivitAI 정보가 없습니다",
             "missingHash": "모델 해시를 사용할 수 없습니다"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "{successCount}개 {type}이(가) 성공적으로 이동되었습니다",
             "exampleImagesDownloadSuccess": "예시 이미지가 성공적으로 다운로드되었습니다!",
             "exampleImagesDownloadFailed": "예시 이미지 다운로드 실패: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "클립보드에 복사됨",
+            "downloadStarted": "다운로드 시작됨"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 제공자가 설정되지 않았습니다. 설정 → AI 제공자에서 활성화하세요.",
+            "enrichStarted": "AI로 메타데이터 보강 중...",
+            "enrichComplete": "메타데이터 보강 완료: {{summary}}",
+            "enrichFailed": "메타데이터 보강 실패: {{error}}"
         }
     },
     "doctor": {

locales/ru.json

@@ -16,10 +16,13 @@
             "help": "Справка",
             "add": "Добавить",
             "close": "Закрыть",
-            "menu": "Меню"
+            "menu": "Меню",
+            "remove": "Удалить",
+            "change": "Изменить"
         },
         "status": {
             "loading": "Загрузка...",
+            "cancelling": "Отмена...",
             "unknown": "Неизвестно",
             "date": "Дата",
             "version": "Версия",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "Удалить из избранного",
             "viewOnCivitai": "Посмотреть на Civitai",
             "notAvailableFromCivitai": "Недоступно на Civitai",
+            "viewOnHuggingFace": "Открыть Hugging Face",
             "sendToWorkflow": "Отправить в ComfyUI (Клик: Добавить, Shift+Клик: Заменить)",
             "copyLoRASyntax": "Копировать синтаксис LoRA",
             "checkpointNameCopied": "Имя checkpoint скопировано",
@@ -111,6 +115,7 @@
             "replacePreview": "Заменить превью",
             "copyCheckpointName": "Копировать имя checkpoint",
             "copyEmbeddingName": "Копировать имя embedding",
+            "embeddingNameCopied": "Синтаксис embedding скопирован",
             "sendCheckpointToWorkflow": "Отправить в ComfyUI",
             "sendEmbeddingToWorkflow": "Отправить в ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Количество использований"
+        },
+        "footer": {
+            "versionCount": "{count} версий",
+            "viewAllVersions": "Показать все локальные версии"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Управление исключёнными моделями"
+        },
+        "groupByModel": {
+            "label": "Группировать по модели"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "Статистика"
         },
         "search": {
-            "placeholder": "Поиск...",
-            "placeholders": {
-                "loras": "Поиск LoRAs...",
-                "recipes": "Поиск рецептов...",
-                "checkpoints": "Поиск checkpoints...",
-                "embeddings": "Поиск embeddings..."
-            },
+            "placeholder": "Поиск",
             "options": "Опции поиска",
             "searchIn": "Искать в:",
             "notAvailable": "Поиск недоступен на странице статистики",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "Имя пресета...",
             "baseModel": "Базовая модель",
             "baseModelSearchPlaceholder": "Поиск базовых моделей...",
-            "modelTags": "Теги (Топ 20)",
+            "modelTags": "Теги",
             "modelTypes": "Типы моделей",
             "license": "Лицензия",
             "noCreditRequired": "Без указания авторства",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Разрешить продажу сгенерированных изображений",
             "noCreditRequiredTooltip": "Использование модели без указания автора",
             "noTags": "Без тегов",
+            "tagSearchPlaceholder": "Поиск тегов...",
+            "noTagMatches": "Нет тегов, соответствующих текущему поиску.",
             "autoTags": "Авто-теги",
             "noBaseModelMatches": "Нет базовых моделей, соответствующих текущему поиску.",
             "clearAll": "Очистить все фильтры",
@@ -247,7 +255,18 @@
             "toggle": "Переключить тему",
             "switchToLight": "Переключить на светлую тему",
             "switchToDark": "Переключить на тёмную тему",
-            "switchToAuto": "Переключить на автоматическую тему"
+            "switchToAuto": "Переключить на автоматическую тему",
+            "presets": "Предустановки тем",
+            "default": "По умолчанию",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Режим",
+            "light": "Светлый",
+            "dark": "Тёмный",
+            "auto": "Авто"
         },
         "actions": {
             "checkUpdates": "Проверить обновления",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Ключ API Civitai",
         "civitaiApiKeyPlaceholder": "Введите ваш ключ API Civitai",
         "civitaiApiKeyHelp": "Используется для аутентификации при загрузке моделей с Civitai",
+        "civitaiApiKeyConfigured": "Настроен",
+        "civitaiApiKeyNotConfigured": "Не настроен",
+        "civitaiApiKeySet": "Настроить",
         "civitaiHost": {
             "label": "Хост Civitai",
             "help": "Выберите, какой сайт Civitai будет открываться при использовании ссылок «View on Civitai».",
@@ -299,6 +321,7 @@
             "downloads": "Загрузки",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
+            "licenseIcons": "Значки лицензии",
             "misc": "Разное",
             "backup": "Резервные копии",
             "folderSettings": "Корневые папки",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "Дополнительные пути к папкам",
             "downloadPathTemplates": "Шаблоны путей загрузки",
             "priorityTags": "Приоритетные теги",
-            "updateFlags": "Метки обновлений",
+            "versionScope": "Метки обновлений",
             "exampleImages": "Примеры изображений",
             "autoOrganize": "Автоорганизация",
             "metadata": "Метаданные",
@@ -411,6 +434,8 @@
             "help": "Если включено, LoRA Manager будет пропускать загрузку версии модели, если сервис истории загрузок записал, что эта конкретная версия уже загружена. Применяется ко всем потокам загрузки."
         },
         "layoutSettings": {
+            "groupByModel": "Группировать по модели",
+            "groupByModelHelp": "При включении отображается только последняя версия каждой модели Civitai в виде одной карточки. Старые версии скрыты.",
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
                 "default": "По умолчанию",
@@ -424,6 +449,12 @@
                 "compact": "7 (1080p), 8 (2K), 10 (4K)"
             },
             "displayDensityWarning": "Предупреждение: Высокая плотность может вызвать проблемы с производительностью на системах с ограниченными ресурсами.",
+            "recipesLayout": "Макет рецептов",
+            "recipesLayoutHelp": "Выберите, как располагаются карточки рецептов: единая сетка или masonry-макет (в стиле Pinterest), сохраняющий пропорции каждого изображения.",
+            "recipesLayoutOptions": {
+                "grid": "Сетка",
+                "masonry": "Masonry"
+            },
             "showFolderSidebar": "Показывать боковую панель папок",
             "showFolderSidebarHelp": "Включает или выключает боковую панель навигации по папкам на страницах моделей. При отключении панель и область наведения скрыты.",
             "cardInfoDisplay": "Отображение информации карточки",
@@ -445,7 +476,9 @@
                 "modelName": "Название модели",
                 "fileName": "Имя файла"
             },
-            "modelNameDisplayHelp": "Выберите, что отображать в нижней части карточки модели"
+            "modelNameDisplayHelp": "Выберите, что отображать в нижней части карточки модели",
+            "cardBlurAmount": "Размытие наложения карточек",
+            "cardBlurAmountHelp": "Настройте интенсивность размытия наложений верхнего и нижнего колонтитулов на карточках моделей и рецептов (0 = без размытия, 20 = максимальное размытие)."
         },
         "folderSettings": {
             "activeLibrary": "Активная библиотека",
@@ -480,7 +513,9 @@
             "saveSuccess": "Дополнительные пути к папкам обновлены. Требуется перезапуск для применения изменений.",
             "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
             "validation": {
-                "duplicatePath": "Этот путь уже настроен"
+                "duplicatePath": "Этот путь уже настроен",
+                "checkpointUnetOverlap": "Нельзя использовать один и тот же путь для checkpoints и diffusion models: {paths}",
+                "checkpointUnetOverlapInline": "Этот путь уже используется для другого типа модели. Используйте отдельные папки для checkpoints и diffusion models."
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "Загрузить",
             "restartRequired": "Требует перезапуска"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Стратегия меток обновлений",
             "help": "Выберите, отображать ли значки обновления только когда новая версия имеет тот же базовый модель, что и локальные файлы, или всегда при наличии любого нового релиза для этой модели.",
             "options": {
@@ -577,6 +612,10 @@
             "label": "Скрыть обновления раннего доступа",
             "help": "Только обновления раннего доступа"
         },
+        "licenseIcons": {
+            "useNewStyle": "Использовать обновлённые значки лицензии",
+            "useNewStyleHelp": "Отображать разрешения лицензии с цветными индикаторами (новый стиль) или только значки ограничений (классический стиль). Соответствует текущему дизайну CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Включать триггерные слова в синтаксис LoRA",
             "includeTriggerWordsHelp": "Включать обученные триггерные слова при копировании синтаксиса LoRA в буфер обмена",
@@ -609,7 +648,13 @@
             "preparing": "Подготовка к загрузке...",
             "connecting": "Подключение к серверу загрузки...",
             "completed": "Завершено",
-            "downloadComplete": "Загрузка успешно завершена"
+            "downloadComplete": "Загрузка успешно завершена",
+            "enableCivarchiveApi": "Включить CivArchive API как источник метаданных",
+            "enableCivarchiveApiHelp": "При включении CivArchive API используется как резервный источник метаданных моделей (например, для моделей, удалённых с CivitAI). Отключите, чтобы полностью избежать ограничений скорости CivArchive.",
+            "providerOrder": "Порядок резервных источников метаданных",
+            "providerOrderHelp": "CivitAI API всегда проверяется первым. Выберите порядок остальных источников при поиске метаданных.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Включить прокси на уровне приложения",
@@ -628,6 +673,33 @@
             "proxyPassword": "Пароль (необязательно)",
             "proxyPasswordPlaceholder": "пароль",
             "proxyPasswordHelp": "Пароль для аутентификации на прокси (если требуется)"
+        },
+        "aiProvider": {
+            "title": "Поставщик ИИ",
+            "provider": "Поставщик",
+            "providerHelp": "Выберите поставщика LLM. OpenAI и Ollama используют предустановленные API-эндпоинты. Пользовательский позволяет указать любой совместимый с OpenAI эндпоинт.",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama (локальный)",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "Пользовательский (совместимый с OpenAI)"
+            },
+            "apiBase": "Базовый URL API",
+            "apiBaseHelp": "Базовый URL для LLM API (например, https://api.openai.com/v1). Оставьте пустым, чтобы использовать значение по умолчанию.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API-ключ",
+            "apiKeyHelp": "Ваш API-ключ поставщика LLM. Хранится локально и никогда не отправляется на другие серверы, кроме выбранного поставщика LLM.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Не задан",
+            "apiKeyConfigured": "Настроен",
+            "apiKeySet": "Настроить",
+            "model": "Модель",
+            "modelHelp": "Имя модели для использования (например, deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Проверьте доступные модели у вашего поставщика.",
+            "modelPlaceholder": "Выберите модель..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "Наименьшим",
                 "usage": "Число использований",
                 "usageDesc": "Больше",
-                "usageAsc": "Меньше"
+                "usageAsc": "Меньше",
+                "versionsCount": "Локальные версии",
+                "versionsCountDesc": "Сначала больше версий",
+                "versionsCountAsc": "Сначала меньше версий",
+                "versionIdDesc": "Сначала новые версии",
+                "random": "Случайно",
+                "randomAction": "Перемешать"
             },
             "refresh": {
                 "title": "Обновить список моделей",
@@ -690,6 +768,7 @@
             "copyAll": "Копировать весь синтаксис",
             "refreshAll": "Обновить все метаданные",
             "repairMetadata": "Восстановить метаданные для выбранных",
+            "reimportMetadata": "Переимпортировать из источника",
             "checkUpdates": "Проверить обновления для выбранных",
             "moveAll": "Переместить все в папку",
             "autoOrganize": "Автоматически организовать выбранные",
@@ -701,6 +780,8 @@
             "deleteAll": "Удалить выбранные",
             "downloadMissingLoras": "Скачать отсутствующие LoRAs",
             "downloadExamples": "Загрузить примеры изображений",
+            "downloadMissingExamples": "Скачать недостающие",
+            "reprocessExamples": "Обработать всё заново",
             "clear": "Очистить выбор",
             "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
             "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
@@ -720,12 +801,15 @@
                 "completed": "Завершено: {success} перемещено, {skipped} пропущено, {failures} не удалось",
                 "complete": "Автоматическая организация завершена",
                 "error": "Ошибка: {error}"
-            }
+            },
+            "enrichHfAgent": "Обогатить HF метаданные (ИИ)"
         },
         "contextMenu": {
             "refreshMetadata": "Обновить данные Civitai",
             "checkUpdates": "Проверить обновления",
-            "relinkCivitai": "Пересвязать с Civitai",
+            "linkModel": "Связать модель",
+            "linkCivitai": "Пересвязать с Civitai",
+            "linkHuggingFace": "Связать с HuggingFace",
             "copySyntax": "Копировать синтаксис LoRA",
             "copyFilename": "Копировать имя файла модели",
             "copyRecipeSyntax": "Копировать синтаксис рецепта",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "Отправить в Workflow (Заменить)",
             "openExamples": "Открыть папку примеров",
             "downloadExamples": "Загрузить примеры изображений",
+            "downloadMissingExamples": "Скачать недостающие",
+            "reprocessExamples": "Обработать всё заново",
             "replacePreview": "Заменить превью",
             "setContentRating": "Установить рейтинг контента",
             "moveToFolder": "Переместить в папку",
             "repairMetadata": "Восстановить метаданные",
+            "reimportMetadata": "Переимпортировать из источника",
             "excludeModel": "Исключить модель",
             "restoreModel": "Восстановить модель",
             "deleteModel": "Удалить модель",
             "shareRecipe": "Поделиться рецептом",
             "viewAllLoras": "Посмотреть все LoRAs",
             "downloadMissingLoras": "Загрузить отсутствующие LoRAs",
-            "deleteRecipe": "Удалить рецепт"
+            "deleteRecipe": "Удалить рецепт",
+            "enrichHfAgent": "Обогатить HF метаданные (ИИ)"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "Рецепт уже последней версии, восстановление не требуется",
                 "failed": "Не удалось восстановить рецепт: {message}",
                 "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
+            },
+            "reimport": {
+                "starting": "Переимпорт рецепта из источника...",
+                "success": "Рецепт успешно переимпортирован",
+                "noSourceUrl": "У рецепта нет URL источника, переимпорт невозможен",
+                "failed": "Не удалось переимпортировать рецепт: {message}",
+                "missingId": "Невозможно переимпортировать рецепт: отсутствует ID"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "Корень",
         "collapseAll": "Свернуть все папки",
-        "pinSidebar": "Закрепить боковую панель",
-        "unpinSidebar": "Открепить боковую панель",
+        "hideOnThisPage": "Скрыть боковую панель на этой странице",
+        "showSidebar": "Показать боковую панель",
+        "sidebarHiddenNotification": "Боковая панель скрыта на странице {page}",
         "switchToListView": "Переключить на вид списка",
         "switchToTreeView": "Переключить на древовидный вид",
         "recursiveOn": "Включать вложенные папки",
@@ -981,6 +1077,18 @@
             "storage": "Хранение",
             "insights": "Аналитика"
         },
+        "metrics": {
+            "totalModels": "Всего моделей",
+            "totalStorage": "Всего хранилища",
+            "totalGenerations": "Всего генераций",
+            "usageRate": "Коэффициент использования",
+            "loras": "LoRA",
+            "checkpoints": "Контрольные точки",
+            "embeddings": "Эмбеддинги",
+            "uniqueTags": "Уникальные теги",
+            "unusedModels": "Неиспользуемые модели",
+            "avgUsesPerModel": "Сред. использований/модель"
+        },
         "usage": {
             "mostUsedLoras": "Наиболее используемые LoRAs",
             "mostUsedCheckpoints": "Наиболее используемые Checkpoints",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "Умная аналитика",
-            "recommendations": "Рекомендации"
+            "recommendations": "Рекомендации",
+            "noInsights": "Нет доступных данных",
+            "unusedLoras": {
+                "high": {
+                    "title": "Большое количество неиспользуемых LoRA",
+                    "description": "{percent}% ваших LoRA ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Рассмотрите возможность организации или архивирования неиспользуемых моделей для освобождения места."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Обнаружены неиспользуемые контрольные точки",
+                    "description": "{percent}% ваших контрольных точек ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Проверьте и удалите ненужные контрольные точки."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Большое количество неиспользуемых эмбеддингов",
+                    "description": "{percent}% ваших эмбеддингов ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Организуйте или архивируйте неиспользуемые эмбеддинги для оптимизации коллекции."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Обнаружена большая коллекция",
+                    "description": "Ваша коллекция моделей использует {size} хранилища.",
+                    "suggestion": "Рассмотрите внешнее хранилище или облачные решения для лучшей организации."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Активный пользователь",
+                    "description": "Вы завершили {count} генераций!",
+                    "suggestion": "Продолжайте исследовать и создавать удивительный контент с вашими моделями."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Обзор коллекции",
             "baseModelDistribution": "Распределение базовых моделей",
             "usageTrends": "Тенденции использования (за последние 30 дней)",
-            "usageDistribution": "Распределение использования"
+            "usageDistribution": "Распределение использования",
+            "date": "Дата",
+            "usageCount": "Количество использований",
+            "fileSizeBytes": "Размер файла (байты)",
+            "models": "Модели",
+            "loraUsage": "Использование LoRA",
+            "checkpointUsage": "Использование Checkpoint",
+            "embeddingUsage": "Использование Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Контрольная точка",
+            "diffusion_model": "Диффузионная модель",
+            "embedding": "Эмбеддинги"
+        },
+        "placeholders": {
+            "loading": "Загрузка...",
+            "noModels": "Модели не найдены",
+            "errorLoading": "Ошибка загрузки данных",
+            "noStorageData": "Нет данных о хранилище",
+            "rootFolder": "Корень",
+            "chartLibraryMissing": "Для графика требуется библиотека Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} моделей",
+            "chartUsage": "{name}: {size}, {count} использований",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "Скачать модель по URL",
             "titleWithType": "Скачать {type} по URL",
-            "url": "Civitai URL",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "Введите один URL CivitAI, CivArchive или Hugging Face в каждой строке. Поддерживает несколько URL для пакетной загрузки.",
+            "selectHfFiles": "Выберите файл(ы) для загрузки из этого репозитория：",
+            "selectAll": "Выбрать все",
+            "fetchingRepoFiles": "Получение файлов репозитория...",
             "locationPreview": "Предпросмотр места загрузки",
             "useDefaultPath": "Использовать путь по умолчанию",
             "useDefaultPathTooltip": "При включении файлы автоматически организуются с использованием настроенных шаблонов путей",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "Неверный формат URL Civitai",
-                "noVersions": "Нет доступных версий для этой модели"
+                "noVersions": "Нет доступных версий для этой модели",
+                "mixedSources": "Нельзя смешивать URL-адреса CivitAI и Hugging Face в одном пакете.",
+                "noModelFiles": "В этом репозитории не найдено файлов моделей."
             },
             "status": {
                 "preparing": "Подготовка загрузки...",
                 "downloadedPreview": "Превью изображение загружено",
                 "downloadingFile": "Загрузка файла {type}",
-                "finalizing": "Завершение загрузки..."
+                "finalizing": "Завершение загрузки...",
+                "cancelling": "Отмена загрузки...",
+                "cancelled": "Загрузка отменена"
             },
             "progress": {
                 "currentFile": "Текущий файл:",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "Введите путь к папке или выберите из дерева ниже...",
             "root": "Корень"
         },
+        "linkHuggingFace": {
+            "title": "Связать с HuggingFace",
+            "infoText": "Вставьте URL репозитория HuggingFace, чтобы связать эту модель с её источником. Это позволит обогащать метаданные с помощью ИИ.",
+            "urlLabel": "URL репозитория HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Введите полный URL репозитория HuggingFace.",
+            "confirmAction": "Сохранить и связать"
+        },
         "relinkCivitai": {
             "title": "Пересвязать с Civitai",
             "warning": "Предупреждение:",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "Редактировать название версии",
                 "viewOnCivitai": "Посмотреть на Civitai",
                 "viewOnCivitaiText": "Посмотреть на Civitai",
+                "viewOnHuggingFace": "Открыть Hugging Face",
+                "viewOnHuggingFaceText": "Открыть Hugging Face",
                 "viewCreatorProfile": "Посмотреть профиль создателя",
                 "openFileLocation": "Открыть расположение файла",
                 "sendToWorkflow": "Отправить в ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "Дополнительные заметки",
                 "notesHint": "Нажмите Enter для сохранения, Shift+Enter для новой строки",
                 "addNotesPlaceholder": "Добавьте ваши заметки здесь...",
-                "aboutThisVersion": "Об этой версии"
+                "aboutThisVersion": "Об этой версии",
+                "baseModelSearchPlaceholder": "Поиск базовой модели…",
+                "baseModelSuggested": "Предполагаемые",
+                "baseModelNoMatch": "Нет подходящих базовых моделей"
             },
             "notes": {
                 "saved": "Заметки успешно сохранены",
-                "saveFailed": "Не удалось сохранить заметки"
+                "saveFailed": "Не удалось сохранить заметки",
+                "showMore": "Показать больше",
+                "showLess": "Свернуть"
             },
             "usageTips": {
                 "addPresetParameter": "Добавить предустановленный параметр...",
@@ -1367,6 +1561,7 @@
                 "empty": "Для этой модели пока нет истории версий.",
                 "error": "Не удалось загрузить версии.",
                 "missingModelId": "У этой модели отсутствует идентификатор модели Civitai.",
+                "hfGroupInfo": "Это группа моделей HuggingFace. Откройте библиотеку, чтобы увидеть все версии в сетке.",
                 "confirm": {
                     "delete": "Удалить эту версию из библиотеки?"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "Версия удалена"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "Сводка получения метаданных",
+            "statSuccess": "Успешно",
+            "statFailed": "Ошибка",
+            "statSkipped": "Пропущено",
+            "statTotal": "Всего проверено",
+            "statDuration": "Длительность",
+            "successMessage": "Все {count} {type}s успешно обновлены",
+            "failedItems": "Ошибочные элементы ({count})",
+            "close": "Закрыть",
+            "copyReport": "Копировать отчет",
+            "downloadCsv": "Скачать CSV",
+            "columnModelName": "Имя модели",
+            "columnError": "Ошибка"
+        },
+        "downloadBatchSummary": {
+            "title": "Сводка пакетной загрузки",
+            "statSuccess": "Успешно",
+            "statFailed": "Ошибки",
+            "statTotal": "Всего",
+            "successMessage": "Все {count} моделей успешно загружены",
+            "completedWithErrors": "Завершено с ошибками",
+            "failed": "Не удалось загрузить",
+            "failedItems": "Неудачные элементы ({count})",
+            "columnName": "Имя модели",
+            "columnError": "Ошибка",
+            "close": "Закрыть",
+            "copyReport": "Скопировать отчёт",
+            "retryFailed": "Повторить неудачные ({count})"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "Этот тег уже существует"
         }
     },
-    "keyboard": {
-        "navigation": "Навигация с клавиатуры:",
-        "shortcuts": {
-            "pageUp": "Прокрутить на страницу вверх",
-            "pageDown": "Прокрутить на страницу вниз",
-            "home": "Перейти к началу",
-            "end": "Перейти к концу"
-        }
-    },
     "initialization": {
         "title": "Инициализация",
         "message": "Подготовка вашего рабочего пространства...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "В текущем workflow нет совместимых узлов",
             "noTargetNodeSelected": "Целевой узел не выбран",
             "modelUpdated": "Модель обновлена в workflow",
-            "modelFailed": "Не удалось обновить узел модели"
+            "modelFailed": "Не удалось обновить узел модели",
+            "embeddingAdded": "Embedding добавлен в workflow",
+            "embeddingFailed": "Не удалось добавить embedding",
+            "promptSent": "Запрос отправлен в workflow",
+            "promptFailed": "Не удалось отправить запрос"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
             "lora": "LoRA",
+            "embedding": "Эмбеддинг",
+            "prompt": "Запрос",
             "replace": "Заменить",
             "append": "Добавить",
             "selectTargetNode": "Выберите целевой узел",
@@ -1557,7 +1779,13 @@
         "checkingUpdates": "Проверка обновлений...",
         "checkingMessage": "Пожалуйста, подождите, пока мы проверяем последнюю версию.",
         "showNotifications": "Показывать уведомления об обновлениях",
-        "latestBadge": "Последний",
+        "latestBadge": "Последняя",
+        "latestMain": "Ветка main",
+        "channel": "Канал обновлений",
+        "channels": {
+            "release": "Релиз",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Подготовка обновления...",
             "installing": "Установка обновления...",
@@ -1578,6 +1806,15 @@
             "warning": "Предупреждение: Ночные сборки могут содержать экспериментальные функции и могут быть нестабильными.",
             "enable": "Включить ночные обновления"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Переключиться на Nightly",
+            "nightlyMessage": "Переключение на Nightly инициализирует Git-репозиторий и отслеживает последние коммиты ветки main. Обновления чаще, но могут быть нестабильными. Вы можете вернуться к Release в любое время.",
+            "releaseTitle": "Переключиться на Release",
+            "releaseMessage": "Переключение на Release выполнит checkout последнего стабильного тега. Вы можете вернуться к Nightly в любое время.",
+            "switching": "Переключение на канал {channel}...",
+            "completed": "Успешно переключено на канал {channel}",
+            "failed": "Не удалось переключить канал"
+        },
         "banners": {
             "recent": "Недавние уведомления",
             "empty": "Недавних баннеров нет.",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "Пожалуйста, введите название LoRA или синтаксис",
             "reconnectedSuccessfully": "LoRA успешно переподключена",
             "reconnectFailed": "Ошибка переподключения LoRA: {message}",
+            "noPromptToSend": "Нет запроса для отправки",
             "cannotSend": "Невозможно отправить рецепт: отсутствует ID рецепта",
             "sendFailed": "Не удалось отправить рецепт в workflow",
             "sendError": "Ошибка отправки рецепта в workflow",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "Восстановление завершено: {repaired} восстановлено, {skipped} пропущено (из {total})",
             "repairBulkSkipped": "Ни один из {total} выбранных рецептов не требует восстановления",
             "repairBulkFailed": "Не удалось восстановить выбранные рецепты: {message}",
+            "reimporting": "Переимпорт рецепта из источника...",
+            "reimportSuccess": "Рецепт успешно переимпортирован",
+            "reimportBulkComplete": "Переимпорт завершён: {completed} переимпортировано, {failed} ошибок (из {total})",
+            "reimportBulkFailed": "Не удалось переимпортировать некоторые рецепты",
             "noMissingLorasInSelection": "В выбранных рецептах не найдены отсутствующие LoRAs",
             "noLoraRootConfigured": "Корневой каталог LoRA не настроен. Пожалуйста, установите корневой каталог LoRA по умолчанию в настройках."
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "Имя пресета должно содержать не более {max} символов",
             "presetNameInvalidChars": "Имя пресета содержит недопустимые символы",
             "presetNameExists": "Пресет с таким именем уже существует",
-            "maxPresetsReached": "Допустимо максимум {max} пресетов. Удалите один, чтобы добавить больше.",
             "presetNotFound": "Пресет не найден",
             "invalidPreset": "Недопустимые данные пресета",
             "deletePresetFailed": "Не удалось удалить пресет",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "Примеры изображений {action} завершены",
             "imagesFailed": "Примеры изображений {action} не удались",
             "loadError": "Ошибка загрузки downloads: {message}",
-            "downloadError": "Ошибка загрузки: {message}"
+            "downloadError": "Ошибка загрузки: {message}",
+            "downloadStopped": "Загрузка отменена"
         },
         "import": {
             "folderTreeFailed": "Не удалось загрузить дерево папок",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "Не удалось установить рейтинг контента: {message}",
             "relinkSuccess": "Модель успешно пересвязана с Civitai",
             "relinkFailed": "Ошибка: {message}",
+            "linkHfSuccess": "Модель успешно связана с HuggingFace",
+            "linkHfFailed": "Ошибка: {message}",
             "fetchMetadataFirst": "Пожалуйста, сначала получите метаданные с CivitAI",
             "noCivitaiInfo": "Информация CivitAI недоступна",
             "missingHash": "Хеш модели недоступен"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "Успешно перемещено {successCount} {type}s",
             "exampleImagesDownloadSuccess": "Примеры изображений успешно загружены!",
             "exampleImagesDownloadFailed": "Не удалось загрузить примеры изображений: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "Скопировано в буфер обмена",
+            "downloadStarted": "Загрузка начата"
+        },
+        "agent": {
+            "llmNotConfigured": "Поставщик ИИ не настроен. Включите его в Настройки → Поставщик ИИ.",
+            "enrichStarted": "Обогащение метаданных с помощью ИИ...",
+            "enrichComplete": "Обогащение метаданных завершено: {{summary}}",
+            "enrichFailed": "Ошибка обогащения метаданных: {{error}}"
         }
     },
     "doctor": {

locales/zh-CN.json

@@ -16,10 +16,13 @@
             "help": "帮助",
             "add": "添加",
             "close": "关闭",
-            "menu": "菜单"
+            "menu": "菜单",
+            "remove": "移除",
+            "change": "更换"
         },
         "status": {
             "loading": "加载中...",
+            "cancelling": "取消中...",
             "unknown": "未知",
             "date": "日期",
             "version": "版本",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "从收藏移除",
             "viewOnCivitai": "在 Civitai 查看",
             "notAvailableFromCivitai": "Civitai 上不可用",
+            "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "发送到 ComfyUI（点击：追加，Shift+点击：替换）",
             "copyLoRASyntax": "复制 LoRA 语法",
             "checkpointNameCopied": "检查点名称已复制",
@@ -111,6 +115,7 @@
             "replacePreview": "替换预览",
             "copyCheckpointName": "复制 Checkpoint 名称",
             "copyEmbeddingName": "复制 Embedding 名称",
+            "embeddingNameCopied": "已复制 Embedding 语法",
             "sendCheckpointToWorkflow": "发送到 ComfyUI",
             "sendEmbeddingToWorkflow": "发送到 ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用次数"
+        },
+        "footer": {
+            "versionCount": "{count} 个版本",
+            "viewAllVersions": "查看所有本地版本"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
+        },
+        "groupByModel": {
+            "label": "按模型分组"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "统计"
         },
         "search": {
-            "placeholder": "搜索...",
-            "placeholders": {
-                "loras": "搜索 LoRA...",
-                "recipes": "搜索配方...",
-                "checkpoints": "搜索 Checkpoint...",
-                "embeddings": "搜索 Embedding..."
-            },
+            "placeholder": "搜索",
             "options": "搜索选项",
             "searchIn": "搜索范围：",
             "notAvailable": "统计页面不可用搜索",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "预设名称...",
             "baseModel": "基础模型",
             "baseModelSearchPlaceholder": "搜索基础模型...",
-            "modelTags": "标签（前20）",
+            "modelTags": "标签",
             "modelTypes": "模型类型",
             "license": "许可证",
             "noCreditRequired": "无需署名",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "允许出售生成的图片",
             "noCreditRequiredTooltip": "使用模型时无需注明原作者",
             "noTags": "无标签",
+            "tagSearchPlaceholder": "搜索标签...",
+            "noTagMatches": "没有匹配当前搜索的标签。",
             "autoTags": "自动标签",
             "noBaseModelMatches": "没有基础模型符合当前搜索。",
             "clearAll": "清除所有筛选",
@@ -247,7 +255,18 @@
             "toggle": "切换主题",
             "switchToLight": "切换到浅色主题",
             "switchToDark": "切换到深色主题",
-            "switchToAuto": "切换到自动主题"
+            "switchToAuto": "切换到自动主题",
+            "presets": "主题预设",
+            "default": "默认",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "模式",
+            "light": "浅色",
+            "dark": "深色",
+            "auto": "自动"
         },
         "actions": {
             "checkUpdates": "检查更新",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Civitai API 密钥",
         "civitaiApiKeyPlaceholder": "请输入你的 Civitai API 密钥",
         "civitaiApiKeyHelp": "用于从 Civitai 下载模型时的身份验证",
+        "civitaiApiKeyConfigured": "已配置",
+        "civitaiApiKeyNotConfigured": "未配置",
+        "civitaiApiKeySet": "设置",
         "civitaiHost": {
             "label": "Civitai 站点",
             "help": "选择使用“在 Civitai 中查看”时默认打开的 Civitai 站点。",
@@ -299,6 +321,7 @@
             "downloads": "下载",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
+            "licenseIcons": "许可协议图标",
             "misc": "其他",
             "backup": "备份",
             "folderSettings": "默认根目录",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "额外文件夹路径",
             "downloadPathTemplates": "下载路径模板",
             "priorityTags": "优先标签",
-            "updateFlags": "更新标记",
+            "versionScope": "版本范围",
             "exampleImages": "示例图片",
             "autoOrganize": "自动整理",
             "metadata": "元数据",
@@ -411,6 +434,8 @@
             "help": "启用后，如果下载历史服务记录显示该版本已下载，LoRA Manager 将跳过下载该模型版本。适用于所有下载流程。"
         },
         "layoutSettings": {
+            "groupByModel": "按模型分组",
+            "groupByModelHelp": "开启后，每个 Civitai 模型仅显示最新版本的单张卡片，旧版本将被隐藏。",
             "displayDensity": "显示密度",
             "displayDensityOptions": {
                 "default": "默认",
@@ -424,6 +449,12 @@
                 "compact": "7（1080p），8（2K），10（4K）"
             },
             "displayDensityWarning": "警告：高密度可能导致资源有限的系统性能下降。",
+            "recipesLayout": "配方布局",
+            "recipesLayoutHelp": "选择配方卡片的排列方式：统一网格，或保留每张图片原始宽高比的瀑布流（Pinterest 风格）布局。",
+            "recipesLayoutOptions": {
+                "grid": "网格",
+                "masonry": "瀑布流"
+            },
             "showFolderSidebar": "显示文件夹侧边栏",
             "showFolderSidebarHelp": "在模型页面启用或禁用文件夹导航侧边栏。关闭后，侧边栏和悬停区域将保持隐藏。",
             "cardInfoDisplay": "卡片信息显示",
@@ -445,7 +476,9 @@
                 "modelName": "模型名称",
                 "fileName": "文件名"
             },
-            "modelNameDisplayHelp": "选择在模型卡片底部显示的内容"
+            "modelNameDisplayHelp": "选择在模型卡片底部显示的内容",
+            "cardBlurAmount": "卡片叠加模糊强度",
+            "cardBlurAmountHelp": "调整模型和配方卡片上页眉和页脚叠加层的模糊强度（0 = 无模糊，20 = 最大模糊）。"
         },
         "folderSettings": {
             "activeLibrary": "活动库",
@@ -480,7 +513,9 @@
             "saveSuccess": "额外文件夹路径已更新，需要重启才能生效。",
             "saveError": "更新额外文件夹路径失败：{message}",
             "validation": {
-                "duplicatePath": "此路径已配置"
+                "duplicatePath": "此路径已配置",
+                "checkpointUnetOverlap": "checkpoints 和 diffusion models 不能使用相同的路径：{paths}",
+                "checkpointUnetOverlapInline": "此路径已被用于另一种模型类型。请为 checkpoints 和 diffusion models 使用不同的文件夹。"
             }
         },
         "priorityTags": {
@@ -565,18 +600,22 @@
             "download": "下载",
             "restartRequired": "需要重启"
         },
-        "updateFlagStrategy": {
-            "label": "更新标记策略",
-            "help": "决定更新徽章是否仅在新版本与本地文件共享相同基础模型时显示，或只要该模型有任何更新版本就显示。",
+        "versionGrouping": {
+            "label": "版本分组",
+            "help": "控制版本在 UI 中的分组方式：按基础模型分组或合并显示。同时影响更新徽章逻辑和版本列表的筛选行为。",
             "options": {
-                "sameBase": "按基础模型匹配更新",
-                "any": "显示任何可用更新"
+                "sameBase": "按基础模型分组",
+                "any": "显示所有版本"
             }
         },
         "hideEarlyAccessUpdates": {
             "label": "隐藏抢先体验更新",
             "help": "抢先体验更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "使用新版许可协议图标",
+            "useNewStyleHelp": "以彩色指示器显示许可权限（新样式），或仅显示限制图标（经典样式）。与当前 CivitAI 设计保持一致。"
+        },
         "misc": {
             "includeTriggerWords": "复制 LoRA 语法时包含触发词",
             "includeTriggerWordsHelp": "复制 LoRA 语法到剪贴板时包含训练触发词",
@@ -609,7 +648,13 @@
             "preparing": "正在准备下载...",
             "connecting": "正在连接下载服务器...",
             "completed": "已完成",
-            "downloadComplete": "下载成功完成"
+            "downloadComplete": "下载成功完成",
+            "enableCivarchiveApi": "启用 CivArchive API 作为元数据提供者",
+            "enableCivarchiveApiHelp": "开启后，CivArchive API 将作为模型元数据的备用来源（例如用于已从 CivitAI 删除的模型）。关闭可完全避免 CivArchive 的速率限制。",
+            "providerOrder": "元数据提供者回退顺序",
+            "providerOrderHelp": "CivitAI API 始终优先尝试。选择查找元数据时其余提供者的顺序。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "启用应用级代理",
@@ -628,6 +673,33 @@
             "proxyPassword": "密码 (可选)",
             "proxyPasswordPlaceholder": "密码",
             "proxyPasswordHelp": "代理认证的密码 (如果需要)"
+        },
+        "aiProvider": {
+            "title": "AI 提供商",
+            "provider": "提供商",
+            "providerHelp": "选择您的 LLM 提供商。OpenAI 和 Ollama 使用预设的 API 端点。自定义允许您指定任何兼容 OpenAI 的端点。",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama（本地）",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "自定义（OpenAI 兼容）"
+            },
+            "apiBase": "API 基础地址",
+            "apiBaseHelp": "LLM API 的基础地址。选择预设或输入自定义地址，下拉框显示所有支持的提供商预设。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 密钥",
+            "apiKeyHelp": "LLM 提供商的 API 密钥。本地存储，除您选择的 LLM 提供商外不会发送到任何服务器。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未设置",
+            "apiKeyConfigured": "已配置",
+            "apiKeySet": "设置",
+            "model": "模型",
+            "modelHelp": "要使用的模型。从下拉框选择（从提供商获取）或输入自定义模型名称。",
+            "modelPlaceholder": "选择一个模型..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "最小",
                 "usage": "使用次数",
                 "usageDesc": "最多",
-                "usageAsc": "最少"
+                "usageAsc": "最少",
+                "versionsCount": "本地版本数",
+                "versionsCountDesc": "版本数从多到少",
+                "versionsCountAsc": "版本数从少到多",
+                "versionIdDesc": "最新版本优先",
+                "random": "随机",
+                "randomAction": "随机排序（洗牌）"
             },
             "refresh": {
                 "title": "刷新模型列表",
@@ -690,6 +768,7 @@
             "copyAll": "复制所选中语法",
             "refreshAll": "刷新所选中元数据",
             "repairMetadata": "修复所选中元数据",
+            "reimportMetadata": "从源重新导入",
             "checkUpdates": "检查所选更新",
             "moveAll": "移动所选中到文件夹",
             "autoOrganize": "自动整理所选模型",
@@ -701,6 +780,8 @@
             "deleteAll": "删除已选",
             "downloadMissingLoras": "下载缺失的 LoRAs",
             "downloadExamples": "下载示例图片",
+            "downloadMissingExamples": "下载缺失的",
+            "reprocessExamples": "重新处理全部",
             "clear": "清除选择",
             "skipMetadataRefreshCount": "跳过（{count} 个模型）",
             "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
@@ -720,12 +801,15 @@
                 "completed": "完成：已移动 {success} 个，跳过 {skipped} 个，失败 {failures} 个",
                 "complete": "自动整理已完成",
                 "error": "错误：{error}"
-            }
+            },
+            "enrichHfAgent": "AI HF 元数据增强"
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 数据",
             "checkUpdates": "检查更新",
-            "relinkCivitai": "重新关联到 Civitai",
+            "linkModel": "链接模型",
+            "linkCivitai": "链接到 Civitai",
+            "linkHuggingFace": "链接到 HuggingFace",
             "copySyntax": "复制 LoRA 语法",
             "copyFilename": "复制模型文件名",
             "copyRecipeSyntax": "复制配方语法",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "发送到工作流（替换）",
             "openExamples": "打开示例文件夹",
             "downloadExamples": "下载示例图片",
+            "downloadMissingExamples": "下载缺失的",
+            "reprocessExamples": "重新处理全部",
             "replacePreview": "替换预览",
             "setContentRating": "设置内容评级",
             "moveToFolder": "移动到文件夹",
             "repairMetadata": "修复元数据",
+            "reimportMetadata": "从源重新导入",
             "excludeModel": "排除模型",
             "restoreModel": "恢复模型",
             "deleteModel": "删除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "查看所有 LoRA",
             "downloadMissingLoras": "下载缺失的 LoRA",
-            "deleteRecipe": "删除配方"
+            "deleteRecipe": "删除配方",
+            "enrichHfAgent": "AI HF 元数据增强"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "配方已是最新版本，无需修复",
                 "failed": "修复配方失败：{message}",
                 "missingId": "无法修复配方：缺少配方 ID"
+            },
+            "reimport": {
+                "starting": "正在从源重新导入配方...",
+                "success": "配方已从源重新导入成功",
+                "noSourceUrl": "配方没有源URL，无法重新导入",
+                "failed": "重新导入配方失败：{message}",
+                "missingId": "无法重新导入配方：缺少配方ID"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "根目录",
         "collapseAll": "折叠所有文件夹",
-        "pinSidebar": "固定侧边栏",
-        "unpinSidebar": "取消固定侧边栏",
+        "hideOnThisPage": "隐藏此页面侧边栏",
+        "showSidebar": "显示侧边栏",
+        "sidebarHiddenNotification": "{page}页面的文件夹侧边栏已隐藏",
         "switchToListView": "切换到列表视图",
         "switchToTreeView": "切换到树状视图",
         "recursiveOn": "包含子文件夹",
@@ -981,6 +1077,18 @@
             "storage": "存储",
             "insights": "洞察"
         },
+        "metrics": {
+            "totalModels": "模型总数",
+            "totalStorage": "总存储空间",
+            "totalGenerations": "总生成次数",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "唯一标签",
+            "unusedModels": "未使用模型",
+            "avgUsesPerModel": "平均使用次数/模型"
+        },
         "usage": {
             "mostUsedLoras": "最常用 LoRA",
             "mostUsedCheckpoints": "最常用 Checkpoint",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "智能洞察",
-            "recommendations": "推荐"
+            "recommendations": "推荐",
+            "noInsights": "暂无可用洞察",
+            "unusedLoras": {
+                "high": {
+                    "title": "大量未使用的 LoRA",
+                    "description": "你的 LoRA 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "考虑整理或归档未使用的模型以释放存储空间。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "检测到未使用的 Checkpoint",
+                    "description": "你的 Checkpoint 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "审查并考虑删除不再需要的 Checkpoint。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "大量未使用的 Embedding",
+                    "description": "你的 Embedding 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "考虑整理或归档未使用的 Embedding 以优化你的收藏。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "检测到大型收藏",
+                    "description": "你的模型收藏正在使用 {size} 的存储空间。",
+                    "suggestion": "考虑使用外部存储或云解决方案以获得更好的组织。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "活跃用户",
+                    "description": "你已经完成了 {count} 次生成！",
+                    "suggestion": "继续探索并用你的模型创作精彩内容。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "收藏概览",
             "baseModelDistribution": "基础模型分布",
             "usageTrends": "使用趋势（最近30天）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日期",
+            "usageCount": "使用次数",
+            "fileSizeBytes": "文件大小（字节）",
+            "models": "模型",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "扩散模型",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "加载中...",
+            "noModels": "未找到模型",
+            "errorLoading": "数据加载失败",
+            "noStorageData": "暂无存储数据",
+            "rootFolder": "根目录",
+            "chartLibraryMissing": "需要 Chart.js 库来显示图表"
+        },
+        "tooltips": {
+            "tagCount": "{tag}：{count} 个模型",
+            "chartUsage": "{name}：{size}，{count} 次使用",
+            "chartPercentage": "{label}：{value}（{pct}%）"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "从 URL 下载模型",
             "titleWithType": "从 URL 下载 {type}",
-            "url": "Civitai URL",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "每行输入一个 CivitAI、CivArchive 或 Hugging Face URL。支持批量下载多个 URL。",
+            "selectHfFiles": "选择从此仓库下载的文件：",
+            "selectAll": "全选",
+            "fetchingRepoFiles": "正在获取仓库文件...",
             "locationPreview": "下载位置预览",
             "useDefaultPath": "使用默认路径",
             "useDefaultPathTooltip": "启用后，文件将自动按配置的路径模板进行整理",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "无效的 Civitai URL 格式",
-                "noVersions": "此模型没有可用版本"
+                "noVersions": "此模型没有可用版本",
+                "mixedSources": "无法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
+                "noModelFiles": "在此仓库中未找到模型文件。"
             },
             "status": {
                 "preparing": "正在准备下载...",
                 "downloadedPreview": "预览图片已下载",
                 "downloadingFile": "正在下载 {type} 文件",
-                "finalizing": "正在完成下载..."
+                "finalizing": "正在完成下载...",
+                "cancelling": "取消下载中...",
+                "cancelled": "下载已取消"
             },
             "progress": {
                 "currentFile": "当前文件：",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "输入文件夹路径或从下方树中选择...",
             "root": "根目录"
         },
+        "linkHuggingFace": {
+            "title": "链接到 HuggingFace",
+            "infoText": "粘贴 HuggingFace 仓库 URL 以关联此模型。关联后可启用 AI 元数据增强功能。",
+            "urlLabel": "HuggingFace 仓库 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "请输入完整的 HuggingFace 仓库 URL。",
+            "confirmAction": "保存并链接"
+        },
         "relinkCivitai": {
             "title": "重新关联到 Civitai",
             "warning": "警告：",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "编辑版本名称",
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnHuggingFace": "在 Hugging Face 查看",
+                "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看创作者主页",
                 "openFileLocation": "打开文件位置",
                 "sendToWorkflow": "发送到 ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "附加备注",
                 "notesHint": "回车保存，Shift+回车换行",
                 "addNotesPlaceholder": "在此添加你的备注...",
-                "aboutThisVersion": "关于此版本"
+                "aboutThisVersion": "关于此版本",
+                "baseModelSearchPlaceholder": "搜索基础模型…",
+                "baseModelSuggested": "推荐",
+                "baseModelNoMatch": "没有匹配的基础模型"
             },
             "notes": {
                 "saved": "备注保存成功",
-                "saveFailed": "备注保存失败"
+                "saveFailed": "备注保存失败",
+                "showMore": "展开",
+                "showLess": "收起"
             },
             "usageTips": {
                 "addPresetParameter": "添加预设参数...",
@@ -1367,6 +1561,7 @@
                 "empty": "该模型还没有版本历史。",
                 "error": "加载版本失败。",
                 "missingModelId": "该模型缺少 Civitai 模型 ID。",
+                "hfGroupInfo": "这是一个 HuggingFace 模型组。打开库页面即可在网格中查看所有版本。",
                 "confirm": {
                     "delete": "从库中删除此版本？"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "版本已删除"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "元数据获取摘要",
+            "statSuccess": "成功",
+            "statFailed": "失败",
+            "statSkipped": "已跳过",
+            "statTotal": "总计扫描",
+            "statDuration": "耗时",
+            "successMessage": "全部 {count} 个 {type} 更新成功！",
+            "failedItems": "失败项目 ({count})",
+            "close": "关闭",
+            "copyReport": "复制报告",
+            "downloadCsv": "下载 CSV",
+            "columnModelName": "模型名称",
+            "columnError": "错误"
+        },
+        "downloadBatchSummary": {
+            "title": "批量下载摘要",
+            "statSuccess": "成功",
+            "statFailed": "失败",
+            "statTotal": "总数",
+            "successMessage": "全部 {count} 个模型下载成功",
+            "completedWithErrors": "已完成，但有错误",
+            "failed": "下载失败",
+            "failedItems": "失败项（{count}）",
+            "columnName": "模型名称",
+            "columnError": "错误",
+            "close": "关闭",
+            "copyReport": "复制报告",
+            "retryFailed": "重试失败项（{count}）"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "该标签已存在"
         }
     },
-    "keyboard": {
-        "navigation": "键盘导航：",
-        "shortcuts": {
-            "pageUp": "向上一页滚动",
-            "pageDown": "向下一页滚动",
-            "home": "跳到顶部",
-            "end": "跳到底部"
-        }
-    },
     "initialization": {
         "title": "初始化",
         "message": "正在准备你的工作空间...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "当前工作流中没有兼容的节点",
             "noTargetNodeSelected": "未选择目标节点",
             "modelUpdated": "模型已更新到工作流",
-            "modelFailed": "更新模型节点失败"
+            "modelFailed": "更新模型节点失败",
+            "embeddingAdded": "Embedding 已追加到工作流",
+            "embeddingFailed": "添加 Embedding 失败",
+            "promptSent": "提示词已发送到工作流",
+            "promptFailed": "提示词发送失败"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "提示词",
             "replace": "替换",
             "append": "追加",
             "selectTargetNode": "选择目标节点",
@@ -1558,6 +1780,12 @@
         "checkingMessage": "请稍候，正在检查最新版本。",
         "showNotifications": "显示更新通知",
         "latestBadge": "最新",
+        "latestMain": "Main 分支",
+        "channel": "更新频道",
+        "channels": {
+            "release": "稳定版",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "正在准备更新...",
             "installing": "正在安装更新...",
@@ -1578,6 +1806,15 @@
             "warning": "警告：Nightly 版本可能包含实验性功能，可能不稳定。",
             "enable": "启用 Nightly 更新"
         },
+        "channelSwitch": {
+            "nightlyTitle": "切换到 Nightly",
+            "nightlyMessage": "切换到 Nightly 将初始化 Git 仓库并跟踪 main 分支的最新提交。更新更频繁但可能不稳定，可随时切回稳定版。",
+            "releaseTitle": "切换到稳定版",
+            "releaseMessage": "切换到稳定版将检出最新的发布标签。可随时切换回每日构建版。",
+            "switching": "正在切换到 {channel} 频道...",
+            "completed": "已切换到 {channel} 频道",
+            "failed": "切换频道失败"
+        },
         "banners": {
             "recent": "最近的通知",
             "empty": "暂无最近的横幅通知。",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "请输入 LoRA 名称或语法",
             "reconnectedSuccessfully": "LoRA 重新连接成功",
             "reconnectFailed": "LoRA 重新连接出错：{message}",
+            "noPromptToSend": "没有可发送的提示词",
             "cannotSend": "无法发送配方：缺少配方 ID",
             "sendFailed": "发送配方到工作流失败",
             "sendError": "发送配方到工作流出错",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "修复完成：{repaired} 个已修复，{skipped} 个已跳过（共 {total} 个）",
             "repairBulkSkipped": "所选 {total} 个配方无需修复",
             "repairBulkFailed": "修复所选配方失败：{message}",
+            "reimporting": "正在从源重新导入配方...",
+            "reimportSuccess": "配方已从源重新导入成功",
+            "reimportBulkComplete": "重新导入完成：{completed} 个已导入，{failed} 个失败（共 {total} 个）",
+            "reimportBulkFailed": "重新导入某些配方失败",
             "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
             "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "预设名称不能超过 {max} 个字符",
             "presetNameInvalidChars": "预设名称包含无效字符",
             "presetNameExists": "已存在同名预设",
-            "maxPresetsReached": "最多允许 {max} 个预设。删除一个以添加更多。",
             "presetNotFound": "预设未找到",
             "invalidPreset": "无效的预设数据",
             "deletePresetFailed": "删除预设失败",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "示例图片{action}完成",
             "imagesFailed": "示例图片{action}失败",
             "loadError": "加载下载项出错：{message}",
-            "downloadError": "下载错误：{message}"
+            "downloadError": "下载错误：{message}",
+            "downloadStopped": "下载已取消"
         },
         "import": {
             "folderTreeFailed": "加载文件夹树失败",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "设置内容评级失败：{message}",
             "relinkSuccess": "模型已成功重新关联到 Civitai",
             "relinkFailed": "错误：{message}",
+            "linkHfSuccess": "模型已成功链接到 HuggingFace",
+            "linkHfFailed": "错误：{message}",
             "fetchMetadataFirst": "请先从 CivitAI 获取元数据",
             "noCivitaiInfo": "无 CivitAI 信息",
             "missingHash": "模型哈希不可用"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "成功移动 {successCount} 个 {type}",
             "exampleImagesDownloadSuccess": "示例图片下载成功！",
             "exampleImagesDownloadFailed": "示例图片下载失败：{message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "已复制到剪贴板",
+            "downloadStarted": "下载已开始"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 提供商未配置。请在 设置 → AI 提供商 中进行配置。",
+            "enrichStarted": "正在使用 AI 增强元数据...",
+            "enrichComplete": "元数据增强完成：{{summary}}",
+            "enrichFailed": "元数据增强失败：{{error}}"
         }
     },
     "doctor": {

locales/zh-TW.json

@@ -16,10 +16,13 @@
             "help": "說明",
             "add": "新增",
             "close": "關閉",
-            "menu": "選單"
+            "menu": "選單",
+            "remove": "移除",
+            "change": "更換"
         },
         "status": {
             "loading": "載入中...",
+            "cancelling": "取消中...",
             "unknown": "未知",
             "date": "日期",
             "version": "版本",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "移除收藏",
             "viewOnCivitai": "在 Civitai 查看",
             "notAvailableFromCivitai": "Civitai 不提供",
+            "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "傳送到 ComfyUI（點擊：附加，Shift+點擊：取代）",
             "copyLoRASyntax": "複製 LoRA 語法",
             "checkpointNameCopied": "Checkpoint 名稱已複製",
@@ -111,6 +115,7 @@
             "replacePreview": "更換預覽圖",
             "copyCheckpointName": "複製檢查點名稱",
             "copyEmbeddingName": "複製嵌入名稱",
+            "embeddingNameCopied": "已複製 Embedding 語法",
             "sendCheckpointToWorkflow": "傳送到 ComfyUI",
             "sendEmbeddingToWorkflow": "傳送到 ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用次數"
+        },
+        "footer": {
+            "versionCount": "{count} 個版本",
+            "viewAllVersions": "檢視所有本地版本"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
+        },
+        "groupByModel": {
+            "label": "按模型分組"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "統計"
         },
         "search": {
-            "placeholder": "搜尋...",
-            "placeholders": {
-                "loras": "搜尋 LoRA...",
-                "recipes": "搜尋配方...",
-                "checkpoints": "搜尋 checkpoint...",
-                "embeddings": "搜尋 embedding..."
-            },
+            "placeholder": "搜尋",
             "options": "搜尋選項",
             "searchIn": "搜尋範圍：",
             "notAvailable": "統計頁面無法搜尋",
@@ -227,7 +233,7 @@
             "presetNamePlaceholder": "預設名稱...",
             "baseModel": "基礎模型",
             "baseModelSearchPlaceholder": "搜尋基礎模型...",
-            "modelTags": "標籤（前 20）",
+            "modelTags": "標籤",
             "modelTypes": "模型類型",
             "license": "授權",
             "noCreditRequired": "無需署名",
@@ -235,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "允許出售生成的圖片",
             "noCreditRequiredTooltip": "使用模型時無需註明原作者",
             "noTags": "無標籤",
+            "tagSearchPlaceholder": "搜尋標籤...",
+            "noTagMatches": "沒有符合目前搜尋的標籤。",
             "autoTags": "自動標籤",
             "noBaseModelMatches": "沒有基礎模型符合目前的搜尋。",
             "clearAll": "清除所有篩選",
@@ -247,7 +255,18 @@
             "toggle": "切換主題",
             "switchToLight": "切換至淺色主題",
             "switchToDark": "切換至深色主題",
-            "switchToAuto": "自動主題"
+            "switchToAuto": "自動主題",
+            "presets": "主題預設",
+            "default": "預設",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "模式",
+            "light": "淺色",
+            "dark": "深色",
+            "auto": "自動"
         },
         "actions": {
             "checkUpdates": "檢查更新",
@@ -259,6 +278,9 @@
         "civitaiApiKey": "Civitai API 金鑰",
         "civitaiApiKeyPlaceholder": "請輸入您的 Civitai API 金鑰",
         "civitaiApiKeyHelp": "用於從 Civitai 下載模型時的身份驗證",
+        "civitaiApiKeyConfigured": "已設定",
+        "civitaiApiKeyNotConfigured": "未設定",
+        "civitaiApiKeySet": "設定",
         "civitaiHost": {
             "label": "Civitai 站點",
             "help": "選擇使用「在 Civitai 中查看」時預設開啟的 Civitai 站點。",
@@ -299,6 +321,7 @@
             "downloads": "下載",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
+            "licenseIcons": "許可協議圖標",
             "misc": "其他",
             "backup": "備份",
             "folderSettings": "預設根目錄",
@@ -306,7 +329,7 @@
             "extraFolderPaths": "額外資料夾路徑",
             "downloadPathTemplates": "下載路徑範本",
             "priorityTags": "優先標籤",
-            "updateFlags": "更新標記",
+            "versionScope": "版本範圍",
             "exampleImages": "範例圖片",
             "autoOrganize": "自動整理",
             "metadata": "中繼資料",
@@ -411,6 +434,8 @@
             "help": "啟用後，如果下載歷史服務記錄顯示該版本已下載，LoRA Manager 將跳過下載該模型版本。適用於所有下載流程。"
         },
         "layoutSettings": {
+            "groupByModel": "按模型分組",
+            "groupByModelHelp": "啟用後，每個 Civitai 模型僅顯示最新版本的單張卡片，舊版本將被隱藏。",
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
                 "default": "預設",
@@ -424,6 +449,12 @@
                 "compact": "7（1080p）、8（2K）、10（4K）"
             },
             "displayDensityWarning": "警告：較高密度可能導致資源有限的系統效能下降。",
+            "recipesLayout": "配方版面",
+            "recipesLayoutHelp": "選擇配方卡片的排列方式：統一網格，或保留每張圖片原始寬高比的瀑布流（Pinterest 風格）版面。",
+            "recipesLayoutOptions": {
+                "grid": "網格",
+                "masonry": "瀑布流"
+            },
             "showFolderSidebar": "顯示資料夾側邊欄",
             "showFolderSidebarHelp": "在模型頁面啟用或停用資料夾導覽側邊欄。停用後，側邊欄與滑鼠懸停區域將保持隱藏。",
             "cardInfoDisplay": "卡片資訊顯示",
@@ -445,7 +476,9 @@
                 "modelName": "模型名稱",
                 "fileName": "檔案名稱"
             },
-            "modelNameDisplayHelp": "選擇在模型卡片底部顯示的內容"
+            "modelNameDisplayHelp": "選擇在模型卡片底部顯示的內容",
+            "cardBlurAmount": "卡片疊加模糊強度",
+            "cardBlurAmountHelp": "調整模型和配方卡片上頁首和頁尾疊加層的模糊強度（0 = 無模糊，20 = 最大模糊）。"
         },
         "folderSettings": {
             "activeLibrary": "使用中的資料庫",
@@ -480,7 +513,9 @@
             "saveSuccess": "額外資料夾路徑已更新，需要重啟才能生效。",
             "saveError": "更新額外資料夾路徑失敗：{message}",
             "validation": {
-                "duplicatePath": "此路徑已設定"
+                "duplicatePath": "此路徑已設定",
+                "checkpointUnetOverlap": "checkpoints 和 diffusion models 不能使用相同的路徑：{paths}",
+                "checkpointUnetOverlapInline": "此路徑已被用於另一種模型類型。請為 checkpoints 和 diffusion models 使用不同的資料夾。"
             }
         },
         "priorityTags": {
@@ -565,7 +600,7 @@
             "download": "下載",
             "restartRequired": "需要重新啟動"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "更新標記策略",
             "help": "決定更新徽章是否僅在新版本與本地檔案共享相同基礎模型時顯示，或只要該模型有任何更新版本就顯示。",
             "options": {
@@ -577,6 +612,10 @@
             "label": "隱藏搶先體驗更新",
             "help": "搶先體驗更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "使用新版許可協議圖標",
+            "useNewStyleHelp": "以彩色指示器顯示許可權限（新樣式），或僅顯示限制圖標（經典樣式）。與當前 CivitAI 設計保持一致。"
+        },
         "misc": {
             "includeTriggerWords": "在 LoRA 語法中包含觸發詞",
             "includeTriggerWordsHelp": "複製 LoRA 語法到剪貼簿時包含訓練觸發詞",
@@ -609,7 +648,13 @@
             "preparing": "準備下載中...",
             "connecting": "正在連接下載伺服器...",
             "completed": "已完成",
-            "downloadComplete": "下載成功完成"
+            "downloadComplete": "下載成功完成",
+            "enableCivarchiveApi": "啟用 CivArchive API 作為中繼資料提供者",
+            "enableCivarchiveApiHelp": "開啟後，CivArchive API 將作為模型中繼資料的備用來源（例如用於已從 CivitAI 刪除的模型）。關閉可完全避免 CivArchive 的速率限制。",
+            "providerOrder": "中繼資料提供者回退順序",
+            "providerOrderHelp": "CivitAI API 始終優先嘗試。選擇查詢中繼資料時其餘提供者的順序。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "啟用應用程式代理",
@@ -628,6 +673,33 @@
             "proxyPassword": "密碼（選填）",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "代理驗證所需的密碼（如有需要）"
+        },
+        "aiProvider": {
+            "title": "AI 提供者",
+            "provider": "提供者",
+            "providerHelp": "選擇您的 LLM 提供者。OpenAI 和 Ollama 使用預設 API 端點。自訂允許您指定任何相容 OpenAI 的端點。",
+            "providerOptions": {
+                "openai": "OpenAI",
+                "ollama": "Ollama（本地）",
+                "deepseek": "DeepSeek",
+                "groq": "Groq",
+                "openrouter": "OpenRouter",
+                "google": "Gemini",
+                "opencode-go": "OpenCode Go",
+                "custom": "自訂（OpenAI 相容）"
+            },
+            "apiBase": "API 基礎網址",
+            "apiBaseHelp": "LLM API 的基礎網址。選擇預設或輸入自訂網址，下拉選單顯示所有支援的提供者預設。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 金鑰",
+            "apiKeyHelp": "LLM 提供者的 API 金鑰。儲存在本地，除您選擇的 LLM 提供者外不會傳送到任何伺服器。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未設定",
+            "apiKeyConfigured": "已設定",
+            "apiKeySet": "設定",
+            "model": "模型",
+            "modelHelp": "要使用的模型。從下拉選單選擇（從提供者取得）或輸入自訂模型名稱。",
+            "modelPlaceholder": "選擇一個模型..."
         }
     },
     "loras": {
@@ -645,7 +717,13 @@
                 "sizeAsc": "最小",
                 "usage": "使用次數",
                 "usageDesc": "最多",
-                "usageAsc": "最少"
+                "usageAsc": "最少",
+                "versionsCount": "本地版本數",
+                "versionsCountDesc": "版本數從多到少",
+                "versionsCountAsc": "版本數從少到多",
+                "versionIdDesc": "最新版本優先",
+                "random": "隨機",
+                "randomAction": "隨機排序（洗牌）"
             },
             "refresh": {
                 "title": "重新整理模型列表",
@@ -690,6 +768,7 @@
             "copyAll": "複製全部語法",
             "refreshAll": "刷新全部 metadata",
             "repairMetadata": "修復所選中元數據",
+            "reimportMetadata": "從來源重新匯入",
             "checkUpdates": "檢查所選更新",
             "moveAll": "全部移動到資料夾",
             "autoOrganize": "自動整理所選模型",
@@ -701,6 +780,8 @@
             "deleteAll": "刪除所選",
             "downloadMissingLoras": "下載缺失的 LoRAs",
             "downloadExamples": "下載範例圖片",
+            "downloadMissingExamples": "下載缺少的",
+            "reprocessExamples": "重新處理全部",
             "clear": "清除選取",
             "skipMetadataRefreshCount": "跳過（{count} 個模型）",
             "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
@@ -720,12 +801,15 @@
                 "completed": "完成：已移動 {success}，已略過 {skipped}，失敗 {failures}",
                 "complete": "自動整理完成",
                 "error": "錯誤：{error}"
-            }
+            },
+            "enrichHfAgent": "AI HF 中繼資料增強"
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 資料",
             "checkUpdates": "檢查更新",
-            "relinkCivitai": "重新連結 Civitai",
+            "linkModel": "連結模型",
+            "linkCivitai": "連結到 Civitai",
+            "linkHuggingFace": "連結到 HuggingFace",
             "copySyntax": "複製 LoRA 語法",
             "copyFilename": "複製模型檔名",
             "copyRecipeSyntax": "複製配方語法",
@@ -733,17 +817,21 @@
             "sendToWorkflowReplace": "傳送到工作流（取代）",
             "openExamples": "開啟範例資料夾",
             "downloadExamples": "下載範例圖片",
+            "downloadMissingExamples": "下載缺少的",
+            "reprocessExamples": "重新處理全部",
             "replacePreview": "更換預覽圖",
             "setContentRating": "設定內容分級",
             "moveToFolder": "移動到資料夾",
             "repairMetadata": "修復元數據",
+            "reimportMetadata": "從來源重新匯入",
             "excludeModel": "排除模型",
             "restoreModel": "還原模型",
             "deleteModel": "刪除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "檢視全部 LoRA",
             "downloadMissingLoras": "下載缺少的 LoRA",
-            "deleteRecipe": "刪除配方"
+            "deleteRecipe": "刪除配方",
+            "enrichHfAgent": "AI HF 中繼資料增強"
         }
     },
     "recipes": {
@@ -864,6 +952,13 @@
                 "skipped": "配方已是最新版本，無需修復",
                 "failed": "修復配方失敗：{message}",
                 "missingId": "無法修復配方：缺少配方 ID"
+            },
+            "reimport": {
+                "starting": "正在從來源重新匯入配方...",
+                "success": "配方已從來源重新匯入成功",
+                "noSourceUrl": "配方沒有來源URL，無法重新匯入",
+                "failed": "重新匯入配方失敗：{message}",
+                "missingId": "無法重新匯入配方：缺少配方ID"
             }
         },
         "batchImport": {
@@ -942,8 +1037,9 @@
     "sidebar": {
         "modelRoot": "根目錄",
         "collapseAll": "全部摺疊資料夾",
-        "pinSidebar": "固定側邊欄",
-        "unpinSidebar": "取消固定側邊欄",
+        "hideOnThisPage": "隱藏此頁面側邊欄",
+        "showSidebar": "顯示側邊欄",
+        "sidebarHiddenNotification": "{page}頁面的資料夾側邊欄已隱藏",
         "switchToListView": "切換至列表檢視",
         "switchToTreeView": "切換到樹狀檢視",
         "recursiveOn": "包含子資料夾",
@@ -981,6 +1077,18 @@
             "storage": "儲存空間",
             "insights": "洞察"
         },
+        "metrics": {
+            "totalModels": "模型總數",
+            "totalStorage": "總儲存空間",
+            "totalGenerations": "總生成次數",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "唯一標籤",
+            "unusedModels": "未使用模型",
+            "avgUsesPerModel": "平均使用次數/模型"
+        },
         "usage": {
             "mostUsedLoras": "最常用的 LoRA",
             "mostUsedCheckpoints": "最常用的 Checkpoint",
@@ -998,13 +1106,77 @@
         },
         "insights": {
             "smartInsights": "智慧洞察",
-            "recommendations": "推薦"
+            "recommendations": "推薦",
+            "noInsights": "暫無可用洞察",
+            "unusedLoras": {
+                "high": {
+                    "title": "大量未使用的 LoRA",
+                    "description": "你的 LoRA 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "考慮整理或封存未使用的模型以釋放儲存空間。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "檢測到未使用的 Checkpoint",
+                    "description": "你的 Checkpoint 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "審查並考慮刪除不再需要的 Checkpoint。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "大量未使用的 Embedding",
+                    "description": "你的 Embedding 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "考慮整理或封存未使用的 Embedding 以優化你的收藏。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "檢測到大型收藏",
+                    "description": "你的模型收藏正在使用 {size} 的儲存空間。",
+                    "suggestion": "考慮使用外部儲存或雲端解決方案以獲得更好的組織。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "活躍用戶",
+                    "description": "你已經完成了 {count} 次生成！",
+                    "suggestion": "繼續探索並用你的模型創作精彩內容。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "收藏總覽",
             "baseModelDistribution": "基礎模型分布",
             "usageTrends": "使用趨勢（最近 30 天）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日期",
+            "usageCount": "使用次數",
+            "fileSizeBytes": "檔案大小（位元組）",
+            "models": "模型",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "擴散模型",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "載入中...",
+            "noModels": "找不到模型",
+            "errorLoading": "資料載入失敗",
+            "noStorageData": "暫無儲存資料",
+            "rootFolder": "根目錄",
+            "chartLibraryMissing": "需要 Chart.js 函式庫來顯示圖表"
+        },
+        "tooltips": {
+            "tagCount": "{tag}：{count} 個模型",
+            "chartUsage": "{name}：{size}，{count} 次使用",
+            "chartPercentage": "{label}：{value}（{pct}%）"
         }
     },
     "modals": {
@@ -1014,9 +1186,12 @@
         "download": {
             "title": "從網址下載模型",
             "titleWithType": "從網址下載 {type}",
-            "url": "Civitai 網址",
             "civitaiUrl": "Civitai 網址：",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "每行輸入一個 CivitAI、CivArchive 或 Hugging Face URL。支援批量下載多個 URL。",
+            "selectHfFiles": "選擇從此倉庫下載的檔案：",
+            "selectAll": "全選",
+            "fetchingRepoFiles": "正在獲取倉庫檔案...",
             "locationPreview": "下載位置預覽",
             "useDefaultPath": "使用預設路徑",
             "useDefaultPathTooltip": "啟用後，檔案將依照設定的路徑範本自動整理",
@@ -1045,13 +1220,17 @@
             },
             "errors": {
                 "invalidUrl": "Civitai 網址格式無效",
-                "noVersions": "此模型無可用版本"
+                "noVersions": "此模型無可用版本",
+                "mixedSources": "無法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
+                "noModelFiles": "在此倉庫中未找到模型檔案。"
             },
             "status": {
                 "preparing": "準備下載中...",
                 "downloadedPreview": "已下載預覽圖片",
                 "downloadingFile": "正在下載 {type} 檔案",
-                "finalizing": "完成下載中..."
+                "finalizing": "完成下載中...",
+                "cancelling": "取消下載中...",
+                "cancelled": "下載已取消"
             },
             "progress": {
                 "currentFile": "目前檔案：",
@@ -1167,6 +1346,14 @@
             "pathPlaceholder": "輸入資料夾路徑或從下方樹狀結構選擇...",
             "root": "根目錄"
         },
+        "linkHuggingFace": {
+            "title": "連結到 HuggingFace",
+            "infoText": "貼上 HuggingFace 倉庫 URL 以關聯此模型。關聯後可啟用 AI 中繼資料增強功能。",
+            "urlLabel": "HuggingFace 倉庫 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "請輸入完整的 HuggingFace 倉庫 URL。",
+            "confirmAction": "儲存並連結"
+        },
         "relinkCivitai": {
             "title": "重新連結至 Civitai",
             "warning": "警告：",
@@ -1196,6 +1383,8 @@
                 "editVersionName": "編輯版本名稱",
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnHuggingFace": "在 Hugging Face 查看",
+                "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
                 "openFileLocation": "開啟檔案位置",
                 "sendToWorkflow": "傳送到 ComfyUI",
@@ -1221,11 +1410,16 @@
                 "additionalNotes": "附加備註",
                 "notesHint": "按 Enter 儲存，Shift+Enter 換行",
                 "addNotesPlaceholder": "在此新增備註...",
-                "aboutThisVersion": "關於此版本"
+                "aboutThisVersion": "關於此版本",
+                "baseModelSearchPlaceholder": "搜尋基礎模型…",
+                "baseModelSuggested": "推薦",
+                "baseModelNoMatch": "沒有符合的基礎模型"
             },
             "notes": {
                 "saved": "備註已儲存",
-                "saveFailed": "儲存備註失敗"
+                "saveFailed": "儲存備註失敗",
+                "showMore": "展開",
+                "showLess": "收起"
             },
             "usageTips": {
                 "addPresetParameter": "新增預設參數...",
@@ -1367,6 +1561,7 @@
                 "empty": "此模型尚無版本歷史。",
                 "error": "載入版本失敗。",
                 "missingModelId": "此模型缺少 Civitai 模型 ID。",
+                "hfGroupInfo": "這是一個 HuggingFace 模型組。打開庫頁面即可在網格中查看所有版本。",
                 "confirm": {
                     "delete": "要從庫中刪除此版本嗎？"
                 },
@@ -1378,6 +1573,36 @@
                     "versionDeleted": "已刪除此版本"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "元資料獲取摘要",
+            "statSuccess": "成功",
+            "statFailed": "失敗",
+            "statSkipped": "已跳過",
+            "statTotal": "總計掃描",
+            "statDuration": "耗時",
+            "successMessage": "全部 {count} 個 {type} 更新成功！",
+            "failedItems": "失敗項目 ({count})",
+            "close": "關閉",
+            "copyReport": "複製報告",
+            "downloadCsv": "下載 CSV",
+            "columnModelName": "模型名稱",
+            "columnError": "錯誤"
+        },
+        "downloadBatchSummary": {
+            "title": "批次下載摘要",
+            "statSuccess": "成功",
+            "statFailed": "失敗",
+            "statTotal": "總數",
+            "successMessage": "全部 {count} 個模型下載成功",
+            "completedWithErrors": "已完成，但有錯誤",
+            "failed": "下載失敗",
+            "failedItems": "失敗項目（{count}）",
+            "columnName": "模型名稱",
+            "columnError": "錯誤",
+            "close": "關閉",
+            "copyReport": "複製報告",
+            "retryFailed": "重試失敗項目（{count}）"
         }
     },
     "modelTags": {
@@ -1391,15 +1616,6 @@
             "duplicate": "此標籤已存在"
         }
     },
-    "keyboard": {
-        "navigation": "鍵盤導覽：",
-        "shortcuts": {
-            "pageUp": "向上捲動一頁",
-            "pageDown": "向下捲動一頁",
-            "home": "跳至頂部",
-            "end": "跳至底部"
-        }
-    },
     "initialization": {
         "title": "初始化",
         "message": "正在準備您的工作區...",
@@ -1487,11 +1703,17 @@
             "noMatchingNodes": "目前工作流程中沒有相容的節點",
             "noTargetNodeSelected": "未選擇目標節點",
             "modelUpdated": "模型已更新到工作流",
-            "modelFailed": "更新模型節點失敗"
+            "modelFailed": "更新模型節點失敗",
+            "embeddingAdded": "Embedding 已附加到工作流",
+            "embeddingFailed": "傳送 Embedding 到工作流失敗",
+            "promptSent": "提示詞已發送到工作流",
+            "promptFailed": "提示詞發送失敗"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "提示詞",
             "replace": "取代",
             "append": "附加",
             "selectTargetNode": "選擇目標節點",
@@ -1558,6 +1780,12 @@
         "checkingMessage": "請稍候，正在檢查最新版本。",
         "showNotifications": "顯示更新通知",
         "latestBadge": "最新",
+        "latestMain": "Main 分支",
+        "channel": "更新頻道",
+        "channels": {
+            "release": "稳定版",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "正在準備更新...",
             "installing": "正在安裝更新...",
@@ -1578,6 +1806,15 @@
             "warning": "警告：Nightly 版本可能包含實驗性功能且可能不穩定。",
             "enable": "啟用 Nightly 更新"
         },
+        "channelSwitch": {
+            "nightlyTitle": "切换到 Nightly",
+            "nightlyMessage": "切换到 Nightly 将初始化 Git 仓库并跟踪 main 分支的最新提交。更新更频繁但可能不稳定，可随时切回稳定版。",
+            "releaseTitle": "切换到稳定版",
+            "releaseMessage": "切換到穩定版將檢出最新的發布標籤。可隨時切換回每日構建版。",
+            "switching": "正在切換到 {channel} 頻道...",
+            "completed": "已切換到 {channel} 頻道",
+            "failed": "切換頻道失敗"
+        },
         "banners": {
             "recent": "最新通知",
             "empty": "目前沒有最近的橫幅通知。",
@@ -1678,6 +1915,7 @@
             "enterLoraName": "請輸入 LoRA 名稱或語法",
             "reconnectedSuccessfully": "LoRA 重新連結成功",
             "reconnectFailed": "LoRA 重新連結錯誤：{message}",
+            "noPromptToSend": "沒有可發送的提示詞",
             "cannotSend": "無法傳送配方：缺少配方 ID",
             "sendFailed": "傳送配方到工作流失敗",
             "sendError": "傳送配方到工作流錯誤",
@@ -1713,6 +1951,10 @@
             "repairBulkComplete": "修復完成：{repaired} 個已修復，{skipped} 個已跳過（共 {total} 個）",
             "repairBulkSkipped": "所選 {total} 個配方無需修復",
             "repairBulkFailed": "修復所選配方失敗：{message}",
+            "reimporting": "正在從來源重新匯入配方...",
+            "reimportSuccess": "配方已從來源重新匯入成功",
+            "reimportBulkComplete": "重新匯入完成：{completed} 個已匯入，{failed} 個失敗（共 {total} 個）",
+            "reimportBulkFailed": "重新匯入某些配方失敗",
             "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
             "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
         },
@@ -1817,7 +2059,6 @@
             "presetNameTooLong": "預設名稱不能超過 {max} 個字元",
             "presetNameInvalidChars": "預設名稱包含無效字元",
             "presetNameExists": "已存在同名預設",
-            "maxPresetsReached": "最多允許 {max} 個預設。刪除一個以新增更多。",
             "presetNotFound": "預設未找到",
             "invalidPreset": "無效的預設資料",
             "deletePresetFailed": "刪除預設失敗",
@@ -1827,7 +2068,8 @@
             "imagesCompleted": "範例圖片{action}完成",
             "imagesFailed": "範例圖片{action}失敗",
             "loadError": "載入下載時發生錯誤：{message}",
-            "downloadError": "下載錯誤：{message}"
+            "downloadError": "下載錯誤：{message}",
+            "downloadStopped": "下載已取消"
         },
         "import": {
             "folderTreeFailed": "載入資料夾樹狀結構失敗",
@@ -1872,6 +2114,8 @@
             "contentRatingFailed": "設定內容分級失敗：{message}",
             "relinkSuccess": "模型已成功重新連結至 Civitai",
             "relinkFailed": "錯誤：{message}",
+            "linkHfSuccess": "模型已成功連結到 HuggingFace",
+            "linkHfFailed": "錯誤：{message}",
             "fetchMetadataFirst": "請先從 CivitAI 取得 metadata",
             "noCivitaiInfo": "無 CivitAI 資訊",
             "missingHash": "模型雜湊不可用"
@@ -1930,7 +2174,15 @@
             "bulkMoveSuccess": "已成功移動 {successCount} 個 {type}",
             "exampleImagesDownloadSuccess": "範例圖片下載成功！",
             "exampleImagesDownloadFailed": "下載範例圖片失敗：{message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "已複製到剪貼簿",
+            "downloadStarted": "下載已開始"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 提供者尚未設定。請在 設定 → AI 提供者 中進行設定。",
+            "enrichStarted": "正在使用 AI 增強中繼資料...",
+            "enrichComplete": "中繼資料增強完成：{{summary}}",
+            "enrichFailed": "中繼資料增強失敗：{{error}}"
         }
     },
     "doctor": {

py/config.py

@@ -1,13 +1,19 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import os
 import platform
 import posixpath
 import threading
 from pathlib import Path
-import folder_paths  # type: ignore
+import folder_paths  # pyright: ignore[reportMissingImports]
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple
 import logging
 import json
 import urllib.parse
+import sys as _sys
+import types as _types
 import time
 
 from .utils.cache_paths import CacheType, get_cache_file_path, get_legacy_cache_paths
@@ -88,7 +94,7 @@ def _resolve_valid_default_root(
 
 
 def _normalize_folder_paths_for_comparison(
-    folder_paths: Mapping[str, Iterable[str]],
+    folder_paths: Mapping[str, Any],
 ) -> Dict[str, Set[str]]:
     """Normalize folder paths for comparison across libraries."""
 
@@ -175,8 +181,7 @@ class Config:
 
         # Load extra folder paths from active library settings before symlink scan
         # so both primary and extra paths are discovered in a single pass.
-        if not standalone_mode:
-            self._load_extra_paths_from_settings()
+        self._load_extra_paths_from_settings()
 
         # Scan symbolic links during initialization
         self._initialize_symlink_mappings()
@@ -191,7 +196,7 @@ class Config:
         Called during ``Config.__init__`` before the symlink scan so both primary and
         extra paths are discovered in a single pass.  Mirrors the extra-path
         portion of ``_apply_library_paths`` without replacing the primary roots
-        that were already resolved from ComfyUI's ``folder_paths``.
+        that were already resolved via ``folder_paths.get_folder_paths``.
         """
         try:
             from .services.settings_manager import get_settings_manager
@@ -207,6 +212,12 @@ class Config:
             if not isinstance(library_config, dict):
                 return
 
+            # Always read recipes_path — it is independent of extra folder paths
+            # and must be set before any early returns below.
+            recipes_path = library_config.get("recipes_path", "")
+            if isinstance(recipes_path, str) and recipes_path:
+                self.recipes_path = recipes_path
+
             extra_folder_paths = library_config.get("extra_folder_paths")
             if not isinstance(extra_folder_paths, dict):
                 return
@@ -232,10 +243,6 @@ class Config:
                 extra_embedding
             )
 
-            recipes_path = library_config.get("recipes_path", "")
-            if isinstance(recipes_path, str) and recipes_path:
-                self.recipes_path = recipes_path
-
             if self.extra_loras_roots:
                 logger.info(
                     "Found extra LoRA roots:"
@@ -356,6 +363,47 @@ class Config:
                         "Failed to rename legacy 'default' library: %s", rename_error
                     )
 
+            # Clean up a stale "default" library entry that has no meaningful
+            # paths configured (e.g. leftover bootstrap artifact).  This only
+            # fires when "comfyui" already exists so we never delete the last
+            # remaining library.
+            if (
+                "default" in libraries
+                and "comfyui" in libraries
+                and isinstance(default_library, Mapping)
+            ):
+                default_folder_paths = _normalize_library_folder_paths(
+                    default_library
+                )
+                default_extra_paths = default_library.get("extra_folder_paths", {})
+                has_meaningful_paths = bool(default_folder_paths) or bool(
+                    default_extra_paths
+                ) or any(
+                    default_library.get(key)
+                    for key in (
+                        "default_lora_root",
+                        "default_checkpoint_root",
+                        "default_unet_root",
+                        "default_embedding_root",
+                        "recipes_path",
+                    )
+                )
+                if not has_meaningful_paths:
+                    try:
+                        settings_service.delete_library("default")
+                        libraries_changed = True
+                        logger.info(
+                            "Removed stale 'default' library entry "
+                            "with no meaningful paths configured"
+                        )
+                        libraries = settings_service.get_libraries()
+                        comfy_library = libraries.get("comfyui", {})
+                    except Exception as delete_error:
+                        logger.debug(
+                            "Failed to remove stale 'default' library: %s",
+                            delete_error,
+                        )
+
             default_lora_root = _resolve_valid_default_root(
                 comfy_library.get("default_lora_root", ""),
                 list(self.loras_roots or []),
@@ -438,7 +486,7 @@ class Config:
                     import ctypes
 
                     FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))  # type: ignore[attr-defined]
+                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))  # pyright: ignore[reportAttributeAccessIssue]
                     return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
                 except Exception as e:
                     logger.error(f"Error checking Windows reparse point: {e}")
@@ -447,7 +495,7 @@ class Config:
             logger.error(f"Error checking link status for {path}: {e}")
             return False
 
-    def _entry_is_symlink(self, entry: os.DirEntry) -> bool:
+    def _entry_is_symlink(self, entry: os.DirEntry[str]) -> bool:
         """Check if a directory entry is a symlink, including Windows junctions."""
         if entry.is_symlink():
             return True
@@ -456,7 +504,7 @@ class Config:
                 import ctypes
 
                 FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)  # type: ignore[attr-defined]
+                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)  # pyright: ignore[reportAttributeAccessIssue]
                 return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
             except Exception:
                 pass
@@ -1082,8 +1130,8 @@ class Config:
 
     def _apply_library_paths(
         self,
-        folder_paths: Mapping[str, Iterable[str]],
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
+        folder_paths: Mapping[str, Any],
+        extra_folder_paths: Optional[Mapping[str, Any]] = None,
         recipes_path: str = "",
     ) -> None:
         self._path_mappings.clear()
@@ -1380,4 +1428,21 @@ class Config:
 
 
 # Global config instance
-config = Config()
+# NOTE: Guard against re-import.  When ServiceRegistry.get_lora_scanner() triggers
+# a fresh import of lora_scanner → config, we must NOT re-execute Config.__init__()
+# (which re-scans all roots, re-registers libraries, etc.).
+#
+# Strategy: store the config instance in a dedicated sentinel module
+# ('_lm_config_cache') that is NEVER removed from sys.modules (its key does
+# NOT start with 'py.'), so it survives re-imports of py.* modules.
+_CONFIG_SENTINEL = "_lm_config_cache"
+config: Config
+if _CONFIG_SENTINEL in _sys.modules:
+    # Re-import: reuse the existing singleton from the sentinel.
+    config = _sys.modules[_CONFIG_SENTINEL].config
+else:
+    config = Config()
+    # Register the sentinel so re-imports of py.config find us.
+    _sentinel_mod = _types.ModuleType(_CONFIG_SENTINEL)
+    setattr(_sentinel_mod, "config", config)
+    _sys.modules[_CONFIG_SENTINEL] = _sentinel_mod

py/lora_manager.py

@@ -14,7 +14,7 @@ standalone_mode = (
 if not standalone_mode:
     setup_logging()
 
-from server import PromptServer  # type: ignore
+from server import PromptServer  # pyright: ignore[reportMissingImports]
 
 from .config import config
 from .services.model_service_factory import (
@@ -33,6 +33,7 @@ from .utils.example_images_migration import ExampleImagesMigration
 from .services.websocket_manager import ws_manager
 from .services.example_images_cleanup_service import ExampleImagesCleanupService
 from .middleware.csp_middleware import relax_csp_for_remote_media
+from .middleware.error_middleware import api_json_error
 
 logger = logging.getLogger(__name__)
 
@@ -76,6 +77,11 @@ class LoraManager:
         """Initialize and register all routes using the new refactored architecture"""
         app = PromptServer.instance.app
 
+        # Register JSON error middleware for /api/* routes as the outermost
+        # middleware so it catches errors from all other middlewares.
+        if api_json_error not in app.middlewares:
+            app.middlewares.insert(0, api_json_error)
+
         if relax_csp_for_remote_media not in app.middlewares:
             # Ensure CSP relaxer executes after ComfyUI's block_external_middleware so it can
             # see and extend the restrictive header instead of being overwritten by it.
@@ -189,6 +195,10 @@ class LoraManager:
 
             # Register DownloadManager with ServiceRegistry
             await ServiceRegistry.get_download_manager()
+
+            # Initialize DownloadQueueService for persistent queue/history
+            await ServiceRegistry.get_download_queue_service()
+
             await ServiceRegistry.get_backup_service()
 
             from .services.metadata_service import initialize_metadata_providers
@@ -198,6 +208,10 @@ class LoraManager:
             # Initialize WebSocket manager
             await ServiceRegistry.get_websocket_manager()
 
+            # Preload LLM model catalog (background task, non-blocking)
+            from .services.llm_service import LLMService
+            await LLMService.get_instance()
+
             # Initialize scanners in background
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
@@ -426,5 +440,21 @@ class LoraManager:
         try:
             logger.info("LoRA Manager: Cleaning up services")
 
+            # Cancel any in-flight scanner initialization tasks so thread-pool
+            # workers (e.g. _initialize_cache_sync) can break out of their loops
+            # when the server shuts down (e.g. Ctrl+C on WSL).
+            for name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner"):
+                scanner = ServiceRegistry.get_service_sync(name)
+                if scanner is not None and hasattr(scanner, "cancel_task"):
+                    scanner.cancel_task()
+                    logger.debug("LoRA Manager: Cancelled %s", name)
+
+            # Close shared aiohttp sessions to avoid "Unclosed client session" warnings
+            try:
+                from py.routes.handlers.hf_handlers import close_hf_api_session
+                await close_hf_api_session()
+            except Exception as exc:
+                logger.debug("Error closing HF API session: %s", exc)
+
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/__init__.py

@@ -22,7 +22,7 @@ if not standalone_mode:
 
         logger.info("ComfyUI Metadata Collector initialized")
 
-    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
+    def get_metadata(prompt_id=None):  # pyright: ignore[reportRedeclaration]
         """Helper function to get metadata from the registry"""
         registry = MetadataRegistry()
         return registry.get_metadata(prompt_id)
@@ -31,6 +31,6 @@ else:
     def init():
         logger.info("ComfyUI Metadata Collector disabled in standalone mode")
 
-    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
+    def get_metadata(prompt_id=None):  # pyright: ignore[reportRedeclaration]
         """Dummy implementation for standalone mode"""
         return {}

py/metadata_collector/constants.py

@@ -1,13 +1,28 @@
 """Constants used by the metadata collector"""
 
+# Sentinel value for clip_skip to distinguish "unconnected / widget default"
+# from "user wired value 0".  Both ComfyUI CLIPSetLastLayer (-24..-1) and
+# A1111 conventions treat 0 as meaningless for clip skipping, but users may
+# explicitly wire 0 to the overwrite node to express "no clip skip / default".
+CLIP_SKIP_SENTINEL = -25
+
 # Metadata categories
 MODELS = "models"
 PROMPTS = "prompts"
 SAMPLING = "sampling"
 LORAS = "loras"
+EMBEDDINGS = "embeddings"
 SIZE = "size"
 IMAGES = "images"
 IS_SAMPLER = "is_sampler"  # New constant to mark sampler nodes
+OVERWRITE = "overwrite"  # Manual metadata overwrite from MetadataOverwriteLM node
+
+# Field names that the MetadataOverwriteLM node and its extractor share
+METADATA_OVERWRITE_FIELDS = (
+    "prompt", "negative_prompt", "seed", "steps", "cfg_scale",
+    "sampler", "scheduler", "model", "loras", "size",
+    "clip_skip", "additional_data",
+)
 
 # Complete list of categories to track
-METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES, OVERWRITE]

py/metadata_collector/metadata_hook.py

@@ -16,7 +16,7 @@ class MetadataHook:
             execution = None
             try:
                 # Try direct import first
-                import execution # type: ignore
+                import execution # pyright: ignore[reportMissingImports]
             except ImportError:
                 # Try to locate from system modules
                 for module_name in sys.modules:
@@ -83,7 +83,8 @@ class MetadataHook:
                         
                         # Record inputs before execution
                         if node_id is not None:
-                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
             
@@ -114,7 +115,8 @@ class MetadataHook:
                         
                         # Record outputs after execution
                         if node_id is not None:
-                            registry.update_node_execution(node_id, class_type, results)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.update_node_execution(node_id, class_type, results, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (post-execution): {str(e)}")
             
@@ -135,10 +137,13 @@ class MetadataHook:
                 # Store the dynprompt reference for node lookups
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
-            
+
+                # Store extra_data for accessing full workflow node properties
+                registry.set_extra_data(extra_data)
+
             # Execute the original function
             return original_execute(*args, **kwargs)
-            
+
         # Replace the functions
         execution._map_node_over_list = map_node_over_list_with_metadata
         execution.execute = execute_with_prompt_tracking
@@ -163,7 +168,8 @@ class MetadataHook:
                         class_type = obj.__class__.__name__
                         node_id = unique_id
                         if node_id is not None:
-                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
 
@@ -180,7 +186,8 @@ class MetadataHook:
                         class_type = obj.__class__.__name__
                         node_id = unique_id
                         if node_id is not None:
-                            registry.update_node_execution(node_id, class_type, results)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.update_node_execution(node_id, class_type, results, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (post-execution): {str(e)}")
 
@@ -202,6 +209,9 @@ class MetadataHook:
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
 
+                # Store extra_data for accessing full workflow node properties
+                registry.set_extra_data(extra_data)
+
             # Execute the original function
             return await original_execute(*args, **kwargs)
 

py/metadata_collector/metadata_processor.py

@@ -1,15 +1,68 @@
 import json
+import logging
 import os
 from .constants import IMAGES
 
 # Check if running in standalone mode
 standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER, OVERWRITE
+from .node_extractors import NODE_EXTRACTORS
+
+logger = logging.getLogger(__name__)
+
+# Keys that identify metadata hint marks stored in node.properties.lm_marker_role
+_META_MARK_PREFIX = "meta_"
+_MARK_PRIMARY_MODEL = "primary_model"
+_MARK_PRIMARY_SAMPLER = "primary_sampler"
+_MARK_POSITIVE_PROMPT = "positive_prompt"
+_MARK_NEGATIVE_PROMPT = "negative_prompt"
 
 class MetadataProcessor:
     """Process and format collected metadata"""
-    
+
+    @staticmethod
+    def _get_user_marks(metadata):
+        """Scan workflow nodes (from extra_data.extra_pnginfo.workflow) for user-assigned
+        metadata hint marks stored in node.properties.lm_marker_role.
+
+        Returns a dict mapping mark type keys to node IDs.
+        Example: {'primary_model': '42', 'primary_sampler': '17'}
+        """
+        marks: dict[str, str] = {}
+
+        # Primary source: extra_data.extra_pnginfo.workflow.nodes (has full properties)
+        extra_data = metadata.get("extra_data")
+        if extra_data and isinstance(extra_data, dict):
+            extra_pnginfo = extra_data.get("extra_pnginfo", {})
+            if isinstance(extra_pnginfo, dict):
+                workflow = extra_pnginfo.get("workflow", {})
+                nodes = workflow.get("nodes", [])
+                for node in nodes:
+                    node_id = str(node.get("id", ""))
+                    role = node.get("properties", {}).get("lm_marker_role", "")
+                    if role.startswith(_META_MARK_PREFIX):
+                        mark_type = role[len(_META_MARK_PREFIX):]
+                        if mark_type in marks:
+                            logger.warning(
+                                "Duplicate meta hint '%s': node %s (previous: %s), "
+                                "last match wins",
+                                mark_type, node_id, marks[mark_type],
+                            )
+                        marks[mark_type] = node_id
+
+        # Fallback: try prompt.original_prompt (API-only submissions may not have workflow)
+        if not marks:
+            prompt = metadata.get("current_prompt")
+            if prompt and getattr(prompt, "original_prompt", None):
+                for node_id, node_data in prompt.original_prompt.items():
+                    role = node_data.get("properties", {}).get("lm_marker_role", "")
+                    if role.startswith(_META_MARK_PREFIX):
+                        mark_type = role[len(_META_MARK_PREFIX):]
+                        marks[mark_type] = node_id
+
+        return marks
+
     @staticmethod
     def find_primary_sampler(metadata, downstream_id=None):
         """
@@ -471,20 +524,57 @@ class MetadataProcessor:
             "checkpoint": None,
             "loras": "",
             "size": None,
-            "clip_skip": None
+            "clip_skip": None,
+            "additional_data": "",
         }
         
         # Get the prompt object for node relationship tracing
         prompt = metadata.get("current_prompt")
-        
-        # Find the primary KSampler node
-        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
-        
-        # Directly get checkpoint from metadata instead of tracing
-        # Pass primary_sampler_id to avoid redundant calculation
-        checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
-        if checkpoint:
-            params["checkpoint"] = checkpoint
+
+        # ---- User marks: override heuristic inference with user-assigned hints ----
+        user_marks = MetadataProcessor._get_user_marks(metadata)
+
+        # Find the primary KSampler node (user mark takes priority)
+        primary_sampler_id = None
+        primary_sampler = None
+        if _MARK_PRIMARY_SAMPLER in user_marks:
+            marked_id = user_marks[_MARK_PRIMARY_SAMPLER]
+            sampler_data = metadata.get(SAMPLING, {}).get(marked_id)
+            if sampler_data and sampler_data.get(IS_SAMPLER):
+                primary_sampler_id = marked_id
+                primary_sampler = sampler_data
+            else:
+                logger.warning(
+                    "User-marked primary sampler %s has no runtime metadata, "
+                    "falling back to heuristic",
+                    marked_id,
+                )
+        if primary_sampler is None:
+            primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
+
+        # Resolve checkpoint / model (user mark takes priority)
+        if _MARK_PRIMARY_MODEL in user_marks:
+            marked_id = user_marks[_MARK_PRIMARY_MODEL]
+            if marked_id in metadata.get(MODELS, {}):
+                params["checkpoint"] = metadata[MODELS][marked_id].get("name")
+            else:
+                extra_data = metadata.get("extra_data")
+                extra_pnginfo = extra_data.get("extra_pnginfo", {}) if extra_data and isinstance(extra_data, dict) else {}
+                workflow = extra_pnginfo.get("workflow", {}) if isinstance(extra_pnginfo, dict) else {}
+                node_type = "unknown"
+                for n in workflow.get("nodes", []):
+                    if str(n.get("id", "")) == marked_id:
+                        node_type = n.get("type", "unknown")
+                        break
+                logger.warning(
+                    "User-marked primary model %s (type=%s, registered=%s) has no runtime metadata, "
+                    "falling back to heuristic",
+                    marked_id, node_type, node_type in NODE_EXTRACTORS,
+                )
+        if params["checkpoint"] is None:
+            checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
+            if checkpoint:
+                params["checkpoint"] = checkpoint
         
         # Check if guidance parameter exists in any sampling node
         for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
@@ -539,7 +629,22 @@ class MetadataProcessor:
                     
                     # For SamplerCustom, handle any additional parameters
                     MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
-                
+
+            # ---- User marks: override prompts with explicitly tagged nodes ----
+            prompts_data = metadata.get(PROMPTS, {})
+            if _MARK_POSITIVE_PROMPT in user_marks:
+                pos_id = user_marks[_MARK_POSITIVE_PROMPT]
+                if pos_id in prompts_data:
+                    prompt_text = prompts_data[pos_id].get("text") or prompts_data[pos_id].get("positive_text")
+                    if prompt_text:
+                        params["prompt"] = prompt_text
+            if _MARK_NEGATIVE_PROMPT in user_marks:
+                neg_id = user_marks[_MARK_NEGATIVE_PROMPT]
+                if neg_id in prompts_data:
+                    prompt_text = prompts_data[neg_id].get("text") or prompts_data[neg_id].get("negative_text")
+                    if prompt_text:
+                        params["negative_prompt"] = prompt_text
+
             # Size extraction is same for all sampler types
             # Check if the sampler itself has size information (from latent_image)
             if primary_sampler_id in metadata.get(SIZE, {}):
@@ -568,7 +673,26 @@ class MetadataProcessor:
                 break
         if params["clip_skip"] is None:
             params["clip_skip"] = "1"
-        
+
+        # ---- Apply manual metadata overwrites ----
+        for overwrite_info in metadata.get(OVERWRITE, {}).values():
+            overwrite_params = overwrite_info.get("parameters", {})
+            for key, value in overwrite_params.items():
+                if key == "clip_skip":
+                    # Accept any value from overwrite node (sentinel -25 already
+                    # filtered upstream).  Needed because falsy check treats 0
+                    # as "not set" even though 0 is a valid wired input here.
+                    params[key] = value
+                elif value:  # truthy check — only overwrite when user provided a real value
+                    params[key] = value
+
+        # Bridge: the overwrite node exposes the field as "model" (more accurate),
+        # but the internal pipeline key remains "checkpoint" for backward compatibility
+        # with A1111 metadata format and downstream consumers.
+        if params.get("model"):
+            params["checkpoint"] = params["model"]
+            del params["model"]
+
         return params
     
     @staticmethod

py/metadata_collector/metadata_registry.py

@@ -1,7 +1,8 @@
 import time
-from nodes import NODE_CLASS_MAPPINGS  # type: ignore
+from typing import Any
+from nodes import NODE_CLASS_MAPPINGS  # pyright: ignore[reportMissingImports, reportAttributeAccessIssue]
 from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
-from .constants import METADATA_CATEGORIES, IMAGES
+from .constants import METADATA_CATEGORIES, IMAGES, OVERWRITE
 
 
 class MetadataRegistry:
@@ -9,6 +10,15 @@ class MetadataRegistry:
 
     _instance = None
 
+    current_prompt_id: Any = None
+    current_prompt: Any = None
+    metadata: dict[str, Any] = {}
+    prompt_metadata: dict[str, Any] = {}
+    executed_nodes: set[str] = set()
+    node_cache: dict[str, Any] = {}
+    max_prompt_history: int = 3
+    metadata_categories: list[str] = METADATA_CATEGORIES
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
@@ -61,6 +71,7 @@ class MetadataRegistry:
             {
                 "execution_order": [],
                 "current_prompt": None,  # Will store the prompt object
+                "extra_data": None,  # Will store the API extra_data for workflow metadata
                 "timestamp": time.time(),
             }
         )
@@ -75,6 +86,11 @@ class MetadataRegistry:
             # Store the prompt in the metadata for later relationship tracing
             self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
 
+    def set_extra_data(self, extra_data):
+        """Store the API extra_data (contains extra_pnginfo.workflow with node properties)"""
+        if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
+            self.prompt_metadata[self.current_prompt_id]["extra_data"] = extra_data
+
     def get_metadata(self, prompt_id=None):
         """Get collected metadata for a prompt"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
@@ -122,20 +138,28 @@ class MetadataRegistry:
             cache_key = f"{node_id}:{class_type}"
 
             # Check if this node type is relevant for metadata collection
-            if class_type in NODE_EXTRACTORS:
+            if class_type in NODE_EXTRACTORS or cache_key in self.node_cache:
                 # Check if we have cached metadata for this node
                 if cache_key in self.node_cache:
                     cached_data = self.node_cache[cache_key]
 
+                    # Detect bypass (mode=4) / mute (mode=2) — these nodes
+                    # were intentionally disabled and should not contribute
+                    # overwrite values from a previous execution's cache.
+                    node_mode = node_data.get("mode", 0)
+                    node_is_disabled = node_mode in (2, 4)
+
                     # Apply cached metadata to the current metadata
                     for category in self.metadata_categories:
+                        if category == OVERWRITE and node_is_disabled:
+                            continue
                         if category in cached_data and node_id in cached_data[category]:
                             if node_id not in metadata[category]:
                                 metadata[category][node_id] = cached_data[category][
                                     node_id
                                 ]
 
-    def record_node_execution(self, node_id, class_type, inputs, outputs):
+    def record_node_execution(self, node_id, class_type, inputs, outputs, return_types=None):
         """Record information about a node's execution"""
         if not self.current_prompt_id:
             return
@@ -158,17 +182,18 @@ class MetadataRegistry:
 
         # Extract node-specific metadata
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
-        extractor.extract(
-            node_id,
-            processed_inputs,
-            outputs,
-            self.prompt_metadata[self.current_prompt_id],
-        )
+        if extractor is GenericNodeExtractor:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id],
+                              return_types=return_types)
+        else:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id])
 
         # Cache this node's metadata
         self._cache_node_metadata(node_id, class_type)
 
-    def update_node_execution(self, node_id, class_type, outputs):
+    def update_node_execution(self, node_id, class_type, outputs, return_types=None):
         """Update node metadata with output information"""
         if not self.current_prompt_id:
             return
@@ -179,9 +204,17 @@ class MetadataRegistry:
         # Use the same extractor to update with outputs
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
         if hasattr(extractor, "update"):
-            extractor.update(
-                node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
-            )
+            if extractor is GenericNodeExtractor:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                    return_types=return_types,
+                )
+            else:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                )
 
         # Update the cached metadata for this node
         self._cache_node_metadata(node_id, class_type)

py/metadata_collector/node_extractors.py

@@ -2,7 +2,8 @@ import json
 import os
 import re
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE
+from .overwrite_utils import collect_overwrite_params
 
 
 def _store_checkpoint_metadata(metadata, node_id, model_name):
@@ -31,11 +32,78 @@ class NodeMetadataExtractor:
         pass
         
 class GenericNodeExtractor(NodeMetadataExtractor):
-    """Default extractor for nodes without specific handling"""
+    """Fallback extractor with type-signature-based detection.
+
+    When a node is not in the NODE_EXTRACTORS registry, the hook layer
+    passes ``return_types`` from ``obj.RETURN_TYPES``:
+
+    * ``MODEL`` output: common input fields (ckpt_name, unet_name, etc.)
+      are checked for a model file name and stored as checkpoint metadata.
+    * ``CONDITIONING`` output: common text input fields are checked for
+      prompt text and stored as prompt metadata.
+    """
+
+    # Input field names that carry a model path in loader-style nodes.
+    _MODEL_NAME_FIELDS = (
+        "ckpt_name", "unet_name", "model_path", "model_name", "gguf_name",
+    )
+
+    # Extensions used by checkpoint_scanner.py — only record values that look
+    # like real model filenames to avoid capturing unrelated string fields.
+    _MODEL_EXTENSIONS = {
+        ".ckpt", ".pt", ".pt2", ".bin", ".pth", ".safetensors", ".pkl", ".sft", ".gguf",
+    }
+
+    # Input field names that may carry prompt text in encoder-style nodes.
+    _TEXT_FIELDS = ("text", "clip_l", "t5xxl", "prompt", "positive", "negative")
+
     @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        pass
-        
+    def extract(node_id, inputs, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+
+        # — MODEL loader detection (checkpoint / UNET / GGUF) —
+        if "MODEL" in return_types or any("MODEL" in str(t) for t in return_types):
+            for field in GenericNodeExtractor._MODEL_NAME_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    name = val.strip()
+                    if not any(name.lower().endswith(ext) for ext in GenericNodeExtractor._MODEL_EXTENSIONS):
+                        continue
+                    _store_checkpoint_metadata(metadata, node_id, name)
+                    return
+
+        # — CONDITIONING encoder detection (CLIPTextEncode, Flux, custom) —
+        if "CONDITIONING" in return_types or any("CONDITIONING" in str(t) for t in return_types):
+            text = None
+            for field in GenericNodeExtractor._TEXT_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    text = val.strip()
+                    break
+            if text:
+                prompt_data = metadata.setdefault(PROMPTS, {})
+                prompt_data[node_id] = {
+                    "text": text,
+                    "node_id": node_id,
+                }
+
+    @staticmethod
+    def update(node_id, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+        if "CONDITIONING" not in return_types and not any(
+            "CONDITIONING" in str(t) for t in return_types
+        ):
+            return
+        if node_id not in metadata.get(PROMPTS, {}):
+            return
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
+                cond = outputs[0][0]
+                if cond is not None:
+                    metadata[PROMPTS][node_id]["conditioning"] = cond
+
 class CheckpointLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -901,6 +969,55 @@ class LoraLoaderManagerExtractor(NodeMetadataExtractor):
                 "node_id": node_id
             }
 
+class LoraTextLoaderManagerExtractor(NodeMetadataExtractor):
+    """Extract LoRA metadata from LoraTextLoaderLM (LoRA Text Loader).
+
+    The node accepts a `lora_syntax` STRING containing <lora:name:strength> tags
+    (same format as the ComfyUI prompt), plus an optional `lora_stack`.
+    This extractor parses the syntax string using the same regex as the node.
+    """
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        active_loras = []
+
+        # Process lora_stack if available (optional input)
+        if "lora_stack" in inputs:
+            lora_stack = inputs.get("lora_stack", [])
+            for item in lora_stack:
+                # lora_stack entries are (path, model_strength, clip_strength) tuples
+                if isinstance(item, (list, tuple)) and len(item) >= 2:
+                    lora_path = item[0]
+                    model_strength = item[1]
+                    lora_name = os.path.splitext(os.path.basename(lora_path))[0]
+                    active_loras.append({
+                        "name": lora_name,
+                        "strength": round(float(model_strength), 2)
+                    })
+
+        # Process lora_syntax string input
+        if "lora_syntax" in inputs:
+            lora_syntax = inputs.get("lora_syntax", "")
+            if lora_syntax and isinstance(lora_syntax, str):
+                pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
+                matches = re.findall(pattern, lora_syntax, re.IGNORECASE)
+                for match in matches:
+                    lora_name = match[0]
+                    model_strength = float(match[1])
+                    active_loras.append({
+                        "name": lora_name,
+                        "strength": round(model_strength, 2)
+                    })
+
+        if active_loras:
+            metadata[LORAS][node_id] = {
+                "lora_list": active_loras,
+                "node_id": node_id
+            }
+
+
 class FluxGuidanceExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1105,6 +1222,28 @@ class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
                 metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
                 metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
 
+class MetadataOverwriteExtractor(NodeMetadataExtractor):
+    """Extract manually specified metadata from MetadataOverwriteLM node.
+
+    Stores truthy input values under the OVERWRITE category so that
+    extract_generation_params can merge them over the inferred params.
+    """
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        overwrite_params = collect_overwrite_params(inputs)
+
+        if overwrite_params:
+            metadata.setdefault(OVERWRITE, {})
+            metadata[OVERWRITE][node_id] = {
+                "parameters": overwrite_params,
+                "node_id": node_id,
+            }
+
+
 # Registry of node-specific extractors
 # Keys are node class names
 NODE_EXTRACTORS = {
@@ -1146,6 +1285,7 @@ NODE_EXTRACTORS = {
     "UNETLoaderLM": UNETLoaderExtractor,  # LoRA Manager
     "LoraLoader": LoraLoaderExtractor,
     "LoraLoaderLM": LoraLoaderManagerExtractor,
+    "LoraTextLoaderLM": LoraTextLoaderManagerExtractor,
     "RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
     "TensorRTLoader": TensorRTLoaderExtractor,
     # Conditioning
@@ -1171,5 +1311,7 @@ NODE_EXTRACTORS = {
     "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider
     # Image
     "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
+    # Metadata overwrite
+    "MetadataOverwriteLM": MetadataOverwriteExtractor,
     # Add other nodes as needed
 }

py/metadata_collector/overwrite_utils.py

@@ -0,0 +1,42 @@
+"""Shared helpers for Metadata Overwrite node metadata collection.
+
+Used by both the MetadataOverwriteLM node (execution time) and the
+MetadataOverwriteExtractor (hook time) so the conversion/filtering logic
+cannot drift between the two paths.
+"""
+
+import logging
+from typing import Any, Dict
+
+from ..utils.utils import model_patcher_to_name
+from .constants import CLIP_SKIP_SENTINEL, METADATA_OVERWRITE_FIELDS
+
+logger = logging.getLogger(__name__)
+
+
+def collect_overwrite_params(values: Dict[str, Any]) -> Dict[str, Any]:
+    """Convert node input values into non-default overwrite parameters.
+
+    For most fields, a falsy value (empty string, 0) means "not set" and is
+    skipped.  clip_skip uses a dedicated sentinel (-25) so that a wired value
+    of 0 is preserved.  The ``model`` field accepts either a manual string or
+    a wired MODEL (ModelPatcher) connection; in the latter case the source
+    model name is extracted from the patcher's ``cached_patcher_init`` and
+    stored as a ComfyUI-style relative path.
+    """
+    result: Dict[str, Any] = {}
+    for key in METADATA_OVERWRITE_FIELDS:
+        value = values.get(key)
+        if key == "model" and not isinstance(value, str):
+            value = model_patcher_to_name(value)
+            if value is None:
+                logger.warning(
+                    "Could not extract model name from wired MODEL input "
+                    "(no cached_patcher_init); model metadata overwrite skipped"
+                )
+        if key == "clip_skip":
+            if value != CLIP_SKIP_SENTINEL:
+                result[key] = value
+        elif value:
+            result[key] = value
+    return result

py/metadata_ops/__init__.py

@@ -0,0 +1,233 @@
+"""Metadata operations — thin in-process wrappers around LoRA Manager internal services.
+
+All functions are simple Python async functions that delegate to the
+appropriate internal service.  They use **relative imports** within the
+``py`` package, so ``sys.modules`` caching works normally and there is no
+risk of double import or circular dependencies.
+
+Usage (in-process, primary)::
+
+    from py.metadata_ops import list_base_models, read_metadata
+
+    models = await list_base_models()
+    meta   = await read_metadata("/path/to/model.safetensors")
+
+Usage (subprocess, debugging / external)::
+
+    python -m py.metadata_ops base-models list
+    python -m py.metadata_ops metadata read /path/to/model.safetensors
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+SCANNER_TYPE_MAP: dict[str, str] = {
+    "get_lora_scanner": "lora",
+    "get_checkpoint_scanner": "checkpoint",
+    "get_embedding_scanner": "embedding",
+}
+
+SCANNER_GETTER_NAMES = tuple(SCANNER_TYPE_MAP.keys())
+
+
+async def _find_model_entry(
+    model_path: str,
+) -> tuple[Any, object, str | None] | tuple[None, None, None]:
+    """Iterate all scanners and return the first (scanner, entry, getter_name)
+    that owns *model_path*.  Returns ``(None, None, None)`` when no scanner
+    claims it.
+    """
+    from ..services.service_registry import ServiceRegistry
+
+    normalized = os.path.normpath(model_path)
+    for getter_name in SCANNER_GETTER_NAMES:
+        getter = getattr(ServiceRegistry, getter_name, None)
+        if getter is None:
+            continue
+        try:
+            scanner = await getter()
+            if scanner is None:
+                continue
+            cache = await scanner.get_cached_data()
+            for entry in cache.raw_data:
+                if os.path.normpath(entry.get("file_path", "")) == normalized:
+                    return scanner, entry, getter_name
+        except Exception as exc:
+            logger.debug(
+                "Scanner %s check failed for %s: %s",
+                getter_name, model_path, exc,
+            )
+    return None, None, None
+
+
+async def _find_scanner_for_model(
+    model_path: str,
+) -> tuple[Any, object] | tuple[None, None]:
+    """Find the (scanner, cache_entry) responsible for *model_path*."""
+    scanner, entry, _ = await _find_model_entry(model_path)
+    return scanner, entry
+
+
+async def identify_model_type(model_path: str) -> str:
+    """Determine the model type (``\"lora\"``, ``\"checkpoint\"``, or
+    ``\"embedding\"``) for *model_path*.
+
+    Falls back to ``\"lora\"`` when unknown.
+    """
+    _, _, getter_name = await _find_model_entry(model_path)
+    return SCANNER_TYPE_MAP[getter_name] if getter_name else "lora"
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+async def list_base_models(limit: int = 0) -> List[str]:
+    """Return all valid CivitAI base model names.
+
+    Uses ``CivitaiBaseModelService.get_base_models()`` which merges a
+    hardcoded list (``SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS``) with remote
+    models fetched from the CivitAI API.  Never empty — the hardcoded
+    fallback always provides a complete set.
+
+    The result is sorted alphabetically.  Pass *limit* = 0 for all models.
+    """
+    from ..services.civitai_base_model_service import (
+        CivitaiBaseModelService,
+    )
+
+    try:
+        service = await CivitaiBaseModelService.get_instance()
+        response = await service.get_base_models()
+        names: List[str] = response.get("models", [])
+    except Exception as exc:
+        logger.warning("list_base_models failed: %s", exc)
+        names = []
+    if limit > 0:
+        return names[:limit]
+    return names
+
+
+async def read_metadata(model_path: str) -> Dict[str, Any]:
+    """Load the full metadata payload for *model_path* from disk.
+
+    Returns an empty dict when the metadata file does not exist or cannot
+    be parsed — never raises.
+    """
+    from ..utils.metadata_manager import MetadataManager
+
+    try:
+        return await MetadataManager.load_metadata_payload(model_path) or {}
+    except Exception as exc:
+        logger.warning("read_metadata failed for %s: %s", model_path, exc)
+        return {}
+
+
+async def apply_metadata_updates(
+    model_path: str,
+    updates: Dict[str, Any],
+) -> List[str]:
+    """Merge *updates* into the model's on-disk metadata and persist.
+
+    Returns the list of field names that actually changed.
+    """
+    from ..utils.metadata_manager import MetadataManager
+
+    metadata = await read_metadata(model_path)
+    updated_fields: List[str] = []
+    for key, value in updates.items():
+        old = metadata.get(key)
+        if old != value:
+            metadata[key] = value
+            updated_fields.append(key)
+    if updated_fields:
+        await MetadataManager.save_metadata(model_path, metadata)
+    return updated_fields
+
+
+async def download_preview(
+    model_path: str,
+    url: str,
+    *,
+    target_width: int = 480,
+    quality: int = 85,
+) -> str | None:
+    """Download a preview image from *url*, optimise to .webp, and save it.
+
+    The output file is placed alongside the model file with a ``.webp``
+    extension.  Returns the local file path on success, ``None`` on failure.
+    """
+    from ..services.downloader import get_downloader
+    from ..utils.exif_utils import ExifUtils
+
+    if not url or not url.strip():
+        return None
+
+    base_name = os.path.splitext(os.path.basename(model_path))[0]
+    preview_dir = os.path.dirname(model_path)
+    output_path = os.path.join(preview_dir, base_name + ".webp")
+
+    downloader = await get_downloader()
+
+    # Try in-memory download + optimise first
+    success, content, _headers = await downloader.download_to_memory(
+        url, use_auth=False,
+    )
+    if success and content:
+        try:
+            optimized_data, _ = ExifUtils.optimize_image(
+                image_data=content,
+                target_width=target_width,
+                format="webp",
+                quality=quality,
+                preserve_metadata=False,
+            )
+            with open(output_path, "wb") as f:
+                f.write(optimized_data)
+            return output_path
+        except Exception as exc:
+            logger.warning("Preview optimisation failed, saving raw: %s", exc)
+            # Fall through to raw save
+
+    # Fallback: download directly to file
+    try:
+        ok, _ = await downloader.download_file(url, output_path, use_auth=False)
+        if ok:
+            return output_path
+    except Exception as exc:
+        logger.warning("Preview fallback download failed for %s: %s", model_path, exc)
+
+    return None
+
+
+async def refresh_cache(model_path: str) -> bool:
+    """Invalidate and reload the scanner cache entry for *model_path*.
+
+    Returns ``True`` when the model was found and the cache was refreshed.
+    """
+    scanner, entry = await _find_scanner_for_model(model_path)
+    if scanner is None:
+        logger.warning("refresh_cache: no scanner found for %s", model_path)
+        return False
+    try:
+        metadata = await read_metadata(model_path)
+        if not metadata:
+            logger.warning("refresh_cache: no metadata for %s", model_path)
+            return False
+        await scanner.update_single_model_cache(model_path, model_path, metadata)
+        return True
+    except Exception as exc:
+        logger.warning("refresh_cache failed for %s: %s", model_path, exc)
+        return False

py/metadata_ops/__main__.py

@@ -0,0 +1,113 @@
+"""Subprocess entry point for ``metadata_ops`` (debugging / external use).
+
+Usage::
+
+    python -m py.metadata_ops base-models list [--limit N]
+    python -m py.metadata_ops metadata read <path>
+    python -m py.metadata_ops metadata update <path> --json '{...}'
+    python -m py.metadata_ops preview download <path> --url <url>
+    python -m py.metadata_ops cache refresh <path>
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import sys
+from typing import Any, Dict, List
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="lmcli", description="LoRA Manager Agent CLI")
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # base-models list
+    base_models = sub.add_parser("base-models", aliases=["bm"])
+    base_models_cmds = base_models.add_subparsers(dest="subcommand", required=True)
+    base_models_list = base_models_cmds.add_parser("list")
+    base_models_list.add_argument(
+        "--limit", type=int, default=0, help="Max number of models (0 = all)"
+    )
+
+    # metadata read
+    meta = sub.add_parser("metadata", aliases=["md"])
+    meta_cmds = meta.add_subparsers(dest="subcommand", required=True)
+    meta_read = meta_cmds.add_parser("read")
+    meta_read.add_argument("path", type=str, help="Model file path")
+
+    # metadata update
+    meta_update = meta_cmds.add_parser("update")
+    meta_update.add_argument("path", type=str, help="Model file path")
+    meta_update.add_argument(
+        "--json",
+        type=str,
+        required=True,
+        help='JSON object of fields to update, e.g. \'{"base_model": "SDXL 1.0"}\'',
+    )
+
+    # preview download
+    prev = sub.add_parser("preview", aliases=["pv"])
+    prev_cmds = prev.add_subparsers(dest="subcommand", required=True)
+    prev_dl = prev_cmds.add_parser("download")
+    prev_dl.add_argument("path", type=str, help="Model file path")
+    prev_dl.add_argument("--url", type=str, required=True, help="Preview image URL")
+
+    # cache refresh
+    cache = sub.add_parser("cache")
+    cache_cmds = cache.add_subparsers(dest="subcommand", required=True)
+    cache_refresh = cache_cmds.add_parser("refresh")
+    cache_refresh.add_argument("path", type=str, help="Model file path")
+
+    return parser
+
+
+async def _run(args: argparse.Namespace) -> Any:
+    from . import (  # lazy import so startup is fast
+        list_base_models,
+        read_metadata,
+        apply_metadata_updates,
+        download_preview,
+        refresh_cache,
+    )
+
+    cmd = args.command
+    sub = args.subcommand
+
+    if cmd in ("base-models", "bm") and sub == "list":
+        return await list_base_models(limit=args.limit)
+
+    if cmd in ("metadata", "md") and sub == "read":
+        return await read_metadata(args.path)
+
+    if cmd in ("metadata", "md") and sub == "update":
+        updates: Dict[str, Any] = json.loads(args.json)
+        return await apply_metadata_updates(args.path, updates)
+
+    if cmd in ("preview", "pv") and sub == "download":
+        return await download_preview(args.path, args.url)
+
+    if cmd == "cache" and sub == "refresh":
+        return await refresh_cache(args.path)
+
+    raise ValueError(f"Unknown command: {cmd} {sub}")
+
+
+def main() -> None:
+    parser = _build_parser()
+    args = parser.parse_args()
+
+    result = asyncio.run(_run(args))
+    # Always print as JSON so callers can parse reliably
+    if isinstance(result, list):
+        for item in result:
+            print(item)
+    elif isinstance(result, dict):
+        json.dump(result, sys.stdout, ensure_ascii=False, indent=2)
+        print()
+    else:
+        print(json.dumps(result))
+
+
+if __name__ == "__main__":
+    main()

py/middleware/cache_middleware.py

@@ -16,6 +16,8 @@ IMG_EXTENSIONS = (
     ".tif",
     ".tiff",
     ".webp",
+    ".avif",
+    ".jxl",
     ".mp4"
 )
 

py/middleware/error_middleware.py

@@ -0,0 +1,76 @@
+"""JSON error middleware for API routes.
+
+Ensures all responses to /api/* requests return valid JSON that the
+browser-extension frontend can JSON.parse() without crashing, even when
+the route does not exist (404) or the handler raises an exception (500).
+
+Extension consumers call response.json() unconditionally — an HTML error
+page causes ``SyntaxError: unexpected end of data`` that leaks into the
+popup UI as a toast notification.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Awaitable, Callable
+
+from aiohttp import web
+
+logger = logging.getLogger(__name__)
+
+
+@web.middleware
+async def api_json_error(
+    request: web.Request,
+    handler: Callable[[web.Request], Awaitable[web.Response]],
+) -> web.Response:
+    """Return JSON ``{"success": false, "error": "..."}`` for API errors.
+
+    Only intercepts paths starting with ``/api/`` — all other routes
+    (frontend pages, static files, WebSocket upgrades) pass through
+    unchanged.
+    """
+    if not request.path.startswith("/api/"):
+        return await handler(request)
+
+    try:
+        response = await handler(request)
+        return response
+    except web.HTTPException as exc:
+        # Let redirects (301, 302, 307, 308) propagate — they are not errors.
+        if exc.status < 400:
+            raise
+
+        # Preview 404 is routine (file deleted from disk) — not worth a warning.
+        logger_method = logger.warning
+        if request.path.startswith("/api/lm/previews") and exc.status == 404:
+            logger_method = logger.debug
+
+        logger_method(
+            "API %s %s returned HTTP %d: %s",
+            request.method,
+            request.path,
+            exc.status,
+            exc.reason,
+        )
+
+        return web.json_response(
+            {"success": False, "error": f"{exc.status}: {exc.reason}"},
+            status=exc.status,
+        )
+    except Exception as exc:
+        logger.error(
+            "API %s %s raised unhandled exception: %s",
+            request.method,
+            request.path,
+            exc,
+            exc_info=True,
+        )
+
+        return web.json_response(
+            {
+                "success": False,
+                "error": f"500: Internal Server Error ({type(exc).__name__})",
+            },
+            status=500,
+        )

py/nodes/checkpoint_loader.py

@@ -1,7 +1,7 @@
 import logging
-from typing import List, Tuple
-import comfy.sd # type: ignore
-import folder_paths # type: ignore
+from typing import Any, List, Tuple
+import comfy.sd  # pyright: ignore[reportMissingImports]
+import folder_paths  # pyright: ignore[reportMissingImports]
 from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
 
 logger = logging.getLogger(__name__)
@@ -18,9 +18,9 @@ class CheckpointLoaderLM:
     CATEGORY = "Lora Manager/loaders"
 
     @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
         # Get list of checkpoint names from scanner (includes extra folder paths)
-        checkpoint_names = s._get_checkpoint_names()
+        checkpoint_names = cls._get_checkpoint_names()
         return {
             "required": {
                 "ckpt_name": (
@@ -89,7 +89,7 @@ class CheckpointLoaderLM:
             logger.error(f"Error getting checkpoint names: {e}")
             return []
 
-    def load_checkpoint(self, ckpt_name: str) -> Tuple:
+    def load_checkpoint(self, ckpt_name: str) -> Tuple[Any, Any, Any]:
         """Load a checkpoint by name, supporting extra folder paths
 
         Args:

py/nodes/create_hook_lora.py

@@ -0,0 +1,117 @@
+"""Create Hook LoRA (LoraManager) — multi-LoRA hook node compatible with ComfyUI's built-in hook pipeline.
+
+Produces ``("HOOKS",)`` output that chains seamlessly with downstream hook consumers
+(ConditioningSetProperties, SetHookKeyframes, CombineHooks, SetClipHooks, etc.).
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+from ..utils.utils import get_lora_info_absolute
+from .utils import (
+    FlexibleOptionalInputType,
+    any_type,
+    apply_lora_syntax_format,
+    get_loras_list,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class CreateHookLoraLM:
+    NAME = "Create Hook LoRA (LoraManager)"
+    CATEGORY = "Lora Manager/hooks"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": (
+                    "AUTOCOMPLETE_TEXT_LORAS",
+                    {
+                        "placeholder": "Search LoRAs to add...",
+                        "tooltip": (
+                            "Search and select LoRAs. Each LoRA gets its own "
+                            "model/clip strength. Hooks chain with prev_hooks."
+                        ),
+                    },
+                ),
+            },
+            "optional": FlexibleOptionalInputType(any_type),
+        }
+
+    RETURN_TYPES = ("HOOKS", "STRING", "STRING")
+    RETURN_NAMES = ("HOOKS", "trigger_words", "active_loras")
+    FUNCTION = "create_hook"
+
+    def create_hook(self, text: str, **kwargs):
+        """Create a HookGroup from the selected LoRAs, chained with prev_hooks.
+
+        Each active LoRA from the widget is loaded and wrapped in a WeightHook
+        via :func:`comfy.hooks.create_hook_lora`.  All hooks are combined into a
+        single group and returned alongside trigger words and a human-readable
+        summary of the active LoRAs.
+        """
+        del text  # used by the frontend widget only
+
+        # Lazy imports: comfy is not available in CI/test environment at module level
+        import comfy.hooks  # pyright: ignore[reportMissingImports]  # noqa: C0415
+        import comfy.utils  # pyright: ignore[reportMissingImports]  # noqa: C0415
+
+        prev_hooks: comfy.hooks.HookGroup | None = kwargs.get("prev_hooks")
+
+        hook_group = prev_hooks.clone() if prev_hooks is not None else comfy.hooks.HookGroup()
+
+        all_trigger_words: list[str] = []
+        active_loras: list[tuple[str, float, float]] = []
+
+        for lora in get_loras_list(kwargs):
+            if not lora.get("active", False):
+                continue
+
+            lora_name = apply_lora_syntax_format(lora["name"])
+            model_strength = float(lora["strength"])
+            clip_strength = float(lora.get("clipStrength", model_strength))
+
+            # Skip useless no-op entries (both strengths are zero)
+            if model_strength == 0.0 and clip_strength == 0.0:
+                continue
+
+            lora_path, trigger_words = get_lora_info_absolute(lora_name)
+            if not lora_path or not os.path.isfile(lora_path):
+                logger.warning("LoRA '%s' not found — skipping", lora_name)
+                continue
+
+            try:
+                lora_weights = comfy.utils.load_torch_file(lora_path, safe_load=True)
+
+                lora_hooks = comfy.hooks.create_hook_lora(
+                    lora=lora_weights,
+                    strength_model=model_strength,
+                    strength_clip=clip_strength,
+                )
+            except Exception:
+                logger.exception("Failed to load LoRA '%s' — skipping", lora_name)
+                continue
+            hook_group = hook_group.clone_and_combine(lora_hooks)
+
+            active_loras.append((lora_name, model_strength, clip_strength))
+            all_trigger_words.extend(trigger_words)
+
+        # Format trigger words (group mode separator)
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
+        # Format active LoRAs summary
+        formatted_loras = []
+        for name, model_s, clip_s in active_loras:
+            if abs(model_s - clip_s) > 0.001:
+                formatted_loras.append(
+                    f"<lora:{name}:{model_s}:{clip_s}>"
+                )
+            else:
+                formatted_loras.append(f"<lora:{name}:{model_s}>")
+        active_loras_text = " ".join(formatted_loras)
+
+        return (hook_group, trigger_words_text, active_loras_text)

py/nodes/lora_info.py

@@ -0,0 +1,45 @@
+"""Lora Info display node — pure frontend node for showing selected LoRA info.
+
+This node does NOT participate in workflow execution. Its single optional
+"lora_source" input exists solely as a wire-connection anchor so that the
+frontend can traverse the graph and push selection data to connected info nodes.
+"""
+
+from __future__ import annotations
+
+
+class LoraInfoLM:
+    """Display node that shows filename and notes for the selected LoRA."""
+
+    NAME = "Lora Info (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+    DESCRIPTION = (
+        "Displays information (filename, notes) about the currently selected "
+        "LoRA. Connect any output from a LoRA Loader or Stacker to the "
+        "lora_source input, then select a LoRA in the source widget — the "
+        "info updates automatically. Does not affect workflow execution."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ()
+    RETURN_NAMES = ()
+    OUTPUT_NODE = False
+    FUNCTION = "noop"
+
+    def noop(self, **kwargs):
+        # This node is display-only — no workflow execution needed.
+        return ()
+
+
+NODE_CLASS_MAPPINGS = {
+    LoraInfoLM.NAME: LoraInfoLM,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    LoraInfoLM.NAME: "Lora Info (LoraManager)",
+}

py/nodes/lora_loader.py

@@ -1,9 +1,8 @@
 import importlib
 import logging
-import re
 
-import comfy.sd  # type: ignore
-import comfy.utils  # type: ignore
+import comfy.sd  # pyright: ignore[reportMissingImports]
+import comfy.utils  # pyright: ignore[reportMissingImports]
 
 from ..utils.utils import get_lora_info_absolute
 from .utils import (
@@ -14,6 +13,7 @@ from .utils import (
     extract_lora_name,
     get_loras_list,
     nunchaku_load_lora,
+    parse_lora_syntax,
 )
 
 logger = logging.getLogger(__name__)
@@ -189,25 +189,10 @@ class LoraTextLoaderLM:
     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 = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
-        matches = re.findall(pattern, text, re.IGNORECASE)
-
-        loras = []
-        for match in matches:
-            model_strength = float(match[1])
-            loras.append({
-                "name": match[0],
-                "model_strength": model_strength,
-                "clip_strength": float(match[2]) if match[2] else model_strength,
-            })
-        return loras
-
     def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
         """Load LoRAs based on text syntax input."""
         lora_entries = _collect_stack_entries(lora_stack)
-        for lora in self.parse_lora_syntax(lora_syntax):
+        for lora in parse_lora_syntax(lora_syntax):
             lora_path, trigger_words = get_lora_info_absolute(lora["name"])
             lora_entries.append({
                 "name": lora["name"],

py/nodes/lora_stack_combiner.py

@@ -1,26 +1,102 @@
+from __future__ import annotations
+
+import inspect
+import re
+from typing import Any
+
+_STACK_INPUT_PATTERN = re.compile(r"^lora_stack(?:_([ab])|(\d+))$")
+
+
+def _is_stack_input(name: str) -> bool:
+    return bool(_STACK_INPUT_PATTERN.match(name))
+
+
+def _stack_slot_number(name: str) -> int:
+    """Numeric slot used to order stack inputs; legacy a/b map to 1/2."""
+    match = _STACK_INPUT_PATTERN.match(name)
+    if not match:
+        return -1
+    letter, digits = match.group(1), match.group(2)
+    if digits is not None:
+        return int(digits)
+    return 1 if letter == "a" else 2
+
+
+class _LoraStackOptionalInputs:
+    """Lookup that preserves explicit optional inputs and dynamic lora_stack slots."""
+
+    def __init__(self, explicit_inputs: dict[str, tuple[str, dict[str, Any]]]) -> None:
+        self._explicit_inputs = explicit_inputs
+
+    def __contains__(self, item: object) -> bool:
+        if not isinstance(item, str):
+            return False
+        return item in self._explicit_inputs or _is_stack_input(item)
+
+    def __getitem__(self, key: str) -> tuple[str, dict[str, Any]]:
+        if key in self._explicit_inputs:
+            return self._explicit_inputs[key]
+        if _is_stack_input(key):
+            return (
+                "LORA_STACK",
+                {
+                    "tooltip": "A LoRA stack to combine. Connect to add more inputs.",
+                },
+            )
+        raise KeyError(key)
+
+
 class LoraStackCombinerLM:
     NAME = "Lora Stack Combiner (LoraManager)"
     CATEGORY = "Lora Manager/stackers"
+    DESCRIPTION = (
+        "Combines multiple LoRA stacks into a single stack. "
+        "Supports dynamic inputs: connect a stack to add more inputs."
+    )
 
     @classmethod
     def INPUT_TYPES(cls):
+        optional_inputs: dict[str, tuple[str, dict[str, Any]]] = {
+            "lora_stack1": (
+                "LORA_STACK",
+                {
+                    "tooltip": "A LoRA stack to combine. Connect to add more inputs.",
+                },
+            ),
+            "lora_stack2": (
+                "LORA_STACK",
+                {
+                    "tooltip": "A LoRA stack to combine. Connect to add more inputs.",
+                },
+            ),
+        }
+
+        stack = inspect.stack()
+        if len(stack) > 2 and stack[2].function == "get_input_info":
+            optional_inputs = _LoraStackOptionalInputs(optional_inputs)  # pyright: ignore[reportAssignmentType]
+
         return {
-            "required": {
-                "lora_stack_a": ("LORA_STACK",),
-                "lora_stack_b": ("LORA_STACK",),
-            },
+            "required": {},
+            "optional": optional_inputs,
         }
 
     RETURN_TYPES = ("LORA_STACK",)
     RETURN_NAMES = ("LORA_STACK",)
     FUNCTION = "combine_stacks"
 
-    def combine_stacks(self, lora_stack_a, lora_stack_b):
-        combined_stack = []
+    def combine_stacks(self, lora_stack1=None, lora_stack2=None, **kwargs):
+        stacks = {
+            "lora_stack1": lora_stack1,
+            "lora_stack2": lora_stack2,
+        }
+        for key, value in kwargs.items():
+            if _is_stack_input(key) and value is not None:
+                stacks[key] = value
 
-        if lora_stack_a:
-            combined_stack.extend(lora_stack_a)
-        if lora_stack_b:
-            combined_stack.extend(lora_stack_b)
+        combined_stack = []
+        for key in sorted(stacks, key=_stack_slot_number):
+            stack = stacks[key]
+            if stack:
+                combined_stack.extend(stack)
 
         return (combined_stack,)

py/nodes/lora_syntax_to_path.py

@@ -0,0 +1,62 @@
+"""Node to resolve `<lora:name:strength>` syntax to absolute file system paths.
+
+Takes the loaded_loras / active_loras STRING output from LoraLoaderLM or
+LoraStackerLM and resolves each lora name to its absolute path on disk via
+the scanner cache. Unknown names are returned as-is.
+"""
+
+import logging
+
+from ..utils.utils import get_lora_info_absolute
+from .utils import parse_lora_syntax
+
+logger = logging.getLogger(__name__)
+
+
+class LoraSyntaxToPath:
+    NAME = "LoRA Syntax → Path (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_syntax": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "multiline": True,
+                        "tooltip": (
+                            "<lora:name:strength> formatted text from "
+                            "loaded_loras / active_loras output"
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("paths",)
+    FUNCTION = "resolve"
+
+    def resolve(self, lora_syntax: str) -> tuple[str]:
+        """Parse <lora:...> syntax and resolve each name to its absolute path."""
+        if not lora_syntax or not lora_syntax.strip():
+            logger.info("Received empty lora_syntax input")
+            return ("",)
+
+        parsed = parse_lora_syntax(lora_syntax)
+        if not parsed:
+            logger.info("No valid <lora:...> entries found in input")
+            return ("",)
+
+        paths: list[str] = []
+        for entry in parsed:
+            try:
+                absolute_path, _ = get_lora_info_absolute(entry["name"])
+                paths.append(absolute_path)
+            except Exception:
+                logger.warning("Failed to resolve lora '%s', skipping", entry["name"])
+                continue
+
+        return ("\n".join(paths),)

py/nodes/metadata_overwrite.py

@@ -0,0 +1,169 @@
+"""Metadata Overwrite node — allows users to manually specify generation parameters
+that override the automatically collected/inferred metadata.
+
+Most inputs have falsy defaults (empty string / 0) which are skipped.
+clip_skip uses a sentinel default (-25) so that a wired value of 0 is
+preserved — both ComfyUI and A1111 conventions have no meaningful 0 value,
+but users may wire 0 to express "no clip skip / default".
+"""
+
+from typing import Any
+
+from ..metadata_collector.constants import CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL
+from ..metadata_collector.overwrite_utils import collect_overwrite_params
+
+
+class MetadataOverwriteLM:
+    NAME = "Metadata Overwrite (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+    DESCRIPTION = (
+        "Manually specify generation parameters to override automatically collected "
+        "metadata. Only filled/connected inputs will take effect — empty defaults "
+        "are ignored."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, Any]:
+        return {
+            "optional": {
+                "prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Positive prompt. Only overwrites when non-empty.",
+                    },
+                ),
+                "negative_prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Negative prompt. Only overwrites when non-empty.",
+                    },
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": False,
+                        "tooltip": "Seed value. Only overwrites when > 0.",
+                    },
+                ),
+                "steps": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 10000,
+                        "tooltip": "Number of steps. Only overwrites when > 0.",
+                    },
+                ),
+                "cfg_scale": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "tooltip": "CFG scale. Only overwrites when > 0.",
+                    },
+                ),
+                "sampler": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Sampler name. Only overwrites when non-empty.",
+                    },
+                ),
+                "scheduler": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Scheduler name. Only overwrites when non-empty.",
+                    },
+                ),
+                "model": (
+                    "STRING,MODEL",
+                    {
+                        "default": "",
+                        "widgetType": "STRING",
+                        "tooltip": (
+                            "The checkpoint or diffusion model (UNet) used "
+                            "for generation. Fill in the name manually or "
+                            "connect a MODEL output — the model name is then "
+                            "extracted automatically. Only overwrites when "
+                            "non-empty."
+                        ),
+                    },
+                ),
+                "loras": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": (
+                            "LoRA syntax, e.g. <lora:name:strength> "
+                            "or <lora:name:model_strength:clip_strength>, "
+                            "separated by spaces. Only overwrites when non-empty."
+                        ),
+                    },
+                ),
+                "size": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": (
+                            "Image size in WIDTHxHEIGHT format (e.g. 512x768). "
+                            "Only overwrites when non-empty."
+                        ),
+                    },
+                ),
+                "clip_skip": (
+                    "INT",
+                    {
+                        "default": _CLIP_SKIP_SENTINEL,
+                        "min": -25,
+                        "max": 24,
+                        "tooltip": (
+                            "Clip skip (ComfyUI: -24..-1, A1111: 1+). "
+                            "Default -25 means not set — any other value "
+                            "overwrites."
+                        ),
+                    },
+                ),
+                "additional_data": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": (
+                            "Additional data to embed in the image metadata. "
+                            "Inserted between Clip skip and Model hash in the "
+                            "A1111-compatible parameters string. "
+                            'Example: "Copyright": "Some license info"'
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("METADATA",)
+    RETURN_NAMES = ("metadata",)
+    FUNCTION = "collect_metadata"
+    OUTPUT_NODE = True
+
+    def collect_metadata(self, **kwargs: Any) -> tuple[dict[str, Any]]:
+        """Collect non-default input values into a metadata dict.
+
+        For most fields, a falsy value (empty string, 0) means "not set"
+        and is skipped.  clip_skip uses a dedicated sentinel (-25) so that
+        a wired value of 0 is preserved and reaches the metadata pipeline.
+
+        The ``model`` field accepts either a manual string or a wired MODEL
+        (ModelPatcher) connection; in the latter case the underlying model
+        name is extracted from the patcher's ``cached_patcher_init`` and
+        stored as a ComfyUI-style relative path.
+        """
+        return (collect_overwrite_params(kwargs),)

py/nodes/nunchaku_qwen.py

@@ -15,15 +15,15 @@ import os
 import re
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union, cast
 
-import comfy.utils  # type: ignore
-import folder_paths  # type: ignore
+import comfy.utils  # pyright: ignore[reportMissingImports]
+import folder_paths  # pyright: ignore[reportMissingImports]
 import torch
 import torch.nn as nn
 from safetensors import safe_open
 
-from nunchaku.lora.flux.nunchaku_converter import (
+from nunchaku.lora.flux.nunchaku_converter import (  # pyright: ignore[reportMissingTypeStubs]
     pack_lowrank_weight,
     unpack_lowrank_weight,
 )
@@ -87,10 +87,6 @@ def _rename_layer_underscore_layer_name(old_name: str) -> str:
     return new_name
 
 
-def _is_indexable_module(module):
-    return isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple))
-
-
 def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
     if not name:
         return model
@@ -100,7 +96,7 @@ def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
             continue
         if hasattr(module, part):
             module = getattr(module, part)
-        elif part.isdigit() and _is_indexable_module(module):
+        elif part.isdigit() and isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple)):
             try:
                 module = module[int(part)]
             except (IndexError, TypeError):
@@ -267,7 +263,9 @@ def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_k
     return result, consumed
 
 
-def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: nn.Module) -> None:
+def _apply_lora_to_module(module: Any, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: Any) -> None:
+    # These modules are dynamic torch containers; monkey-patched attributes
+    # below are set at runtime, so the module/model types are deliberately Any.
     if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
         raise ValueError(f"{module_name}: unsupported module without in/out features")
     if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
@@ -336,7 +334,7 @@ def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: t
     raise ValueError(f"{module_name}: unsupported module type {type(module)}")
 
 
-def reset_lora_v2(model: nn.Module) -> None:
+def reset_lora_v2(model: Any) -> None:
     slots = getattr(model, "_lora_slots", None)
     if not slots:
         return
@@ -344,6 +342,7 @@ def reset_lora_v2(model: nn.Module) -> None:
         module = _get_module_by_name(model, name)
         if module is None:
             continue
+        module = cast(Any, module)
         module_type = info.get("type", "nunchaku")
         if module_type == "nunchaku":
             base_rank = info["base_rank"]
@@ -371,7 +370,7 @@ def reset_lora_v2(model: nn.Module) -> None:
 def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
     del apply_awq_mod  # retained for interface compatibility
     reset_lora_v2(model)
-    aggregated_weights: Dict[str, List[Dict[str, object]]] = defaultdict(list)
+    aggregated_weights: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
     saw_supported_format = False
     unresolved_targets = 0
 
@@ -471,7 +470,7 @@ def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path,
 class ComfyQwenImageWrapperLM(nn.Module):
     def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
         super().__init__()
-        self.model = model
+        self.model: Any = model
         self.config = {} if config is None else config
         self.dtype = next(model.parameters()).dtype
         self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []

py/nodes/prompt.py

@@ -67,7 +67,7 @@ class PromptLM:
 
         stack = inspect.stack()
         if len(stack) > 2 and stack[2].function == "get_input_info":
-            optional_inputs = _PromptOptionalInputs(optional_inputs)  # type: ignore[assignment]
+            optional_inputs = _PromptOptionalInputs(optional_inputs)  # pyright: ignore[reportAssignmentType]
 
         return {
             "required": {
@@ -126,7 +126,7 @@ class PromptLM:
         else:
             prompt = expanded_text
 
-        from nodes import CLIPTextEncode  # type: ignore
+        from nodes import CLIPTextEncode  # pyright: ignore[reportMissingImports, reportAttributeAccessIssue]
 
         conditioning = CLIPTextEncode().encode(clip, prompt)[0]
         return (conditioning, prompt)

py/nodes/save_image.py

@@ -5,17 +5,167 @@ import time
 import uuid
 from typing import Any, Dict, Optional
 import numpy as np
-import folder_paths  # type: ignore
+import folder_paths  # pyright: ignore[reportMissingImports]
 from ..services.service_registry import ServiceRegistry
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
 from ..utils.constants import CARD_PREVIEW_WIDTH
 from ..utils.exif_utils import ExifUtils
-from ..utils.utils import calculate_recipe_fingerprint
+from ..utils.utils import calculate_recipe_fingerprint, sanitize_folder_name
 from PIL import Image, PngImagePlugin
-import piexif
+import piexif  # pyright: ignore[reportMissingTypeStubs]
 import logging
 
+# Civitai-compatible sampler name mapping: ComfyUI internal → A1111 display name
+CIVITAI_SAMPLER_MAP = {
+    "euler": "Euler",
+    "euler_ancestral": "Euler a",
+    "lms": "LMS",
+    "heun": "Heun",
+    "dpm_2": "DPM2",
+    "dpm_2_ancestral": "DPM2 a",
+    "dpmpp_2s_ancestral": "DPM++ 2S a",
+    "dpmpp_2m": "DPM++ 2M",
+    "dpmpp_sde": "DPM++ SDE",
+    "dpmpp_sde_gpu": "DPM++ SDE",
+    "dpmpp_2m_sde": "DPM++ 2M SDE",
+    "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
+    "dpmpp_3m_sde": "DPM++ 3M SDE",
+    "dpm_fast": "DPM fast",
+    "dpm_adaptive": "DPM adaptive",
+    "ddim": "DDIM",
+    "plms": "PLMS",
+    "uni_pc_bh2": "UniPC",
+    "uni_pc": "UniPC",
+    "lcm": "LCM",
+}
+
+# Base model display name → AIR URN slug
+# Sourced from civitai source: src/shared/constants/basemodel.constants.ts
+BASE_MODEL_AIR_SLUG = {
+    # Stable Diffusion family
+    "SD 1.4": "sd1",
+    "SD 1.5": "sd1",
+    "SD 1.5 LCM": "sd1",
+    "SD 1.5 Hyper": "sd1",
+    "SD 2.0": "sd2",
+    "SD 2.0 768": "sd2",
+    "SD 2.1": "sd2",
+    "SD 2.1 768": "sd2",
+    "SD 2.1 Unclip": "sd2",
+    "SD 3.0": "sd3",
+    "SD 3.5": "sd35",
+    "SD 3.5 Large": "sd35",
+    "SD 3.5 Large Turbo": "sd35",
+    "SD 3.5 Medium": "sd35",
+    "SDXL 0.9": "sdxl",
+    "SDXL 1.0": "sdxl",
+    "SDXL 1.0 LCM": "sdxl",
+    "SDXL Lightning": "sdxl",
+    "SDXL Hyper": "sdxl",
+    "SDXL Turbo": "sdxl",
+    "SDXL Distilled": "sdxldistilled",
+    "Stable Cascade": "scascade",
+    "Stable Video Diffusion": "svd",
+    "SVD": "svd",
+    "SVD XT": "svdxt",
+
+    # SDXL community fine-tunes
+    "Pony": "pony",
+    "Pony Diffusion": "pony",
+    "Illustrious": "illustrious",
+    "NoobAI": "noobai",
+    "Animagine": "illustrious",
+
+    # Flux family
+    "Flux.1": "flux1",
+    "Flux.1 D": "flux1",
+    "Flux.1 S": "flux1",
+    "Flux.1 Krea": "fluxkrea",
+    "Flux.1 Kontext": "flux1kontext",
+    "Flux.2": "flux2",
+    "Flux.2 D": "flux2",
+    "Flux.2 Klein 9B": "flux2klein_9b",
+    "Flux.2 Klein 9B Base": "flux2klein_9b_base",
+    "Flux.2 Klein 4B": "flux2klein_4b",
+    "Flux.2 Klein 4B Base": "flux2klein_4b_base",
+
+    # Other image models (sorted alphabetically)
+    "AuraFlow": "auraflow",
+    "Chroma": "chroma",
+    "HiDream": "hidream",
+    "HiDream-O1": "hidream-o1",
+    "Hunyuan DiT": "hydit1",
+    "Hunyuan Video": "hyv1",
+    "Kolors": "kolors",
+    "Lumina": "lumina",
+    "Mochi": "mochi",
+    "ODOR": "odor",
+    "PixArt Alpha": "pixarta",
+    "PixArt Sigma": "pixarte",
+    "Playground v2": "playgroundv2",
+    "Playground v2.5": "playgroundv2",
+    "Pony Diffusion V7": "ponyv7",
+
+    # Video models
+    "CogVideoX": "cogvideox",
+    "LTX Video": "ltxv",
+    "LTX Video 2": "ltxv2",
+    "LTX Video 2.3": "ltxv23",
+    "Wan Video": "wanvideo",
+    "Wan Video 1.3B T2V": "wanvideo_13b_t2v",
+    "Wan Video 14B T2V": "wanvideo_14b_t2v",
+    "Wan Video 14B I2V 480p": "wanvideo_14b_i2v_480p",
+    "Wan Video 14B I2V 720p": "wanvideo_14b_i2v_720p",
+
+    # Third-party / proprietary image models
+    "Boogu": "boogu",
+    "Ernie": "ernie",
+    "Grok": "grok",
+    "HappyHorse": "happyhorse",
+    "Ideogram": "ideogram",
+    "Ideogram 4.0": "ideogram",
+    "Imagen": "imagen4",
+    "Imagen 4": "imagen4",
+    "Krea": "krea2",
+    "Krea 2": "krea2",
+    "Lens": "lens",
+    "MAI": "mai",
+    "Nano Banana": "nanobanana",
+    "OpenAI": "openai",
+    "Reve": "reve",
+    "Reve 2": "reve",
+    "Reve 2.1": "reve",
+    "Seedream": "seedream",
+    "Sora": "sora2",
+    "Sora 2": "sora2",
+    "Veo": "veo3",
+    "Veo 2": "veo3",
+    "Veo 3": "veo3",
+    "ZImageTurbo": "zimageturbo",
+    "ZImageBase": "zimagebase",
+    "ZImage": "zimagebase",
+
+    # Third-party video models
+    "Hailuo by MiniMax": "minimax",
+    "Haiper": "haiper",
+    "Kling": "kling",
+    "Lightricks": "lightricks",
+    "Seedance": "seedance",
+    "Vidu": "vidu",
+
+    # Qwen family
+    "Qwen": "qwen",
+    "Qwen 2": "qwen2",
+
+    # Anima
+    "Anima": "anima",
+
+    # Special
+    "Upscaler": "upscaler",
+    "Other": "other",
+}
+
 logger = logging.getLogger(__name__)
 
 
@@ -70,11 +220,29 @@ class SaveImageLM:
                         "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files.",
                     },
                 ),
+                "webp_method": (
+                    "INT",
+                    {
+                        "default": 6,
+                        "min": 0,
+                        "max": 6,
+                        "tooltip": "WebP compression method (0-6). 0=fastest/largest, 6=slowest/smallest. Only applies when file_format is 'webp'.",
+                    },
+                ),
+                "jpeg_subsampling": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2,
+                        "tooltip": "JPEG chroma subsampling level. 0=4:4:4 (best quality), 1=4:2:2, 2=4:2:0 (smallest files). Only applies when file_format is 'jpeg'.",
+                    },
+                ),
                 "embed_workflow": (
                     "BOOLEAN",
                     {
                         "default": False,
-                        "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
+                        "tooltip": "When enabled, saved images store the complete workflow. Drag the image back into ComfyUI to restore the original node graph. PNG and WebP only.",
                     },
                 ),
                 "save_with_metadata": (
@@ -84,6 +252,13 @@ class SaveImageLM:
                         "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
                     },
                 ),
+                "add_loras_to_prompt": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "When enabled, appends the LoRA syntax line (e.g. <lora:name:strength>) after the positive prompt in the saved metadata.",
+                    },
+                ),
                 "add_counter_to_filename": (
                     "BOOLEAN",
                     {
@@ -142,148 +317,197 @@ class SaveImageLM:
 
         return None
 
-    def format_metadata(self, metadata_dict):
-        """Format metadata in the requested format similar to userComment example"""
-        if not metadata_dict:
-            return ""
+    def _resolve_model_cache_entry(self, scanner_type: str, name: str):
+        """Resolve model hash, civitai metadata, and base_model from scanner cache.
+        Returns (hash_str, civitai_dict, base_model_str). All values are empty defaults when not found."""
+        scanner = ServiceRegistry.get_service_sync(scanner_type)
+        if scanner is None or not name:
+            return "", {}, ""
 
-        # Helper function to only add parameter if value is not None
-        def add_param_if_not_none(param_list, label, value):
-            if value is not None:
-                param_list.append(f"{label}: {value}")
+        entry = self._get_cached_model_by_name(scanner, name)
+        if entry is None:
+            basename = os.path.splitext(os.path.basename(name))[0]
+            hash_val = scanner.get_hash_by_filename(basename)
+            return (hash_val or "").lower(), {}, ""
+
+        hash_val = (entry.get("sha256") or "").lower()
+        civitai = entry.get("civitai") or {}
+        base_model = entry.get("base_model") or ""
+        return hash_val, civitai, base_model
+
+    @staticmethod
+    def _get_civitai_sampler_name(sampler_name: str, scheduler: str) -> str:
+        if sampler_name in CIVITAI_SAMPLER_MAP:
+            civitai_name = CIVITAI_SAMPLER_MAP[sampler_name]
+            if scheduler == "karras":
+                civitai_name += " Karras"
+            elif scheduler == "exponential":
+                civitai_name += " Exponential"
+            return civitai_name
+        else:
+            if scheduler and scheduler != "normal":
+                return f"{sampler_name}_{scheduler}"
+            return sampler_name
+
+    @staticmethod
+    def _build_air_string(base_model: str, model_type: str, model_id: int, version_id: int) -> str:
+        slug = BASE_MODEL_AIR_SLUG.get(base_model, "other")
+        type_lower = model_type.lower() if model_type else "other"
+        return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
+
+    def format_metadata(self, metadata_dict: dict[str, Any], add_loras_to_prompt: bool = False) -> str:
+        """Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
+        if not metadata_dict: return ""
 
-        # Extract the prompt and negative prompt
         prompt = metadata_dict.get("prompt", "")
         negative_prompt = metadata_dict.get("negative_prompt", "")
-
-        # Extract loras from the prompt if present
+        steps = metadata_dict.get("steps")
+        cfg = metadata_dict.get("guidance")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg_scale")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg")
+        seed = metadata_dict.get("seed")
+        size = metadata_dict.get("size")
+        sampler = metadata_dict.get("sampler") or ""
+        scheduler = metadata_dict.get("scheduler") or "normal"
+        checkpoint = metadata_dict.get("checkpoint") or ""
         loras_text = metadata_dict.get("loras", "")
-        lora_hashes = {}
+        clip_skip = metadata_dict.get("clip_skip")
 
-        # If loras are found, add them on a new line after the prompt
+        # Parse LoRA entries from <lora:name:strength> format
+        lora_entries: list[tuple[str, float]] = []
         if loras_text:
-            prompt_with_loras = f"{prompt}\n{loras_text}"
+            for match in re.findall(r"<lora:([^:]+):([^>]+)>", loras_text):
+                lora_name, strength_str = match
+                try:
+                    strength = float(strength_str)
+                except (ValueError, TypeError):
+                    strength = 1.0
+                lora_entries.append((lora_name, strength))
 
-            # Extract lora names from the format <lora:name:strength>
-            lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
+        # Resolve checkpoint hash and Civitai data from local cache
+        ckpt_hash, ckpt_civitai, ckpt_base_model = "", {}, ""
+        ckpt_display_name = ""
+        if checkpoint:
+            ckpt_hash, ckpt_civitai, ckpt_base_model = self._resolve_model_cache_entry(
+                "checkpoint_scanner", checkpoint
+            )
+            ckpt_display_name = os.path.splitext(os.path.basename(checkpoint))[0]
 
-            # Get hash for each lora
-            for lora_name, strength in lora_matches:
-                hash_value = self.get_lora_hash(lora_name)
-                if hash_value:
-                    lora_hashes[lora_name] = hash_value
-        else:
-            prompt_with_loras = prompt
+        # Resolve LoRA hash and Civitai data from local cache
+        loras_data: list[dict[str, Any]] = []
+        for lora_name, strength in lora_entries:
+            lora_hash, lora_civitai, lora_base_model = self._resolve_model_cache_entry(
+                "lora_scanner", lora_name
+            )
+            loras_data.append({
+                "name": lora_name,
+                "strength": strength,
+                "hash": lora_hash,
+                "civitai": lora_civitai,
+                "base_model": lora_base_model,
+            })
 
-        # Format the first part (prompt and loras)
-        metadata_parts = [prompt_with_loras]
+        # Build Hashes JSON (A1111 / Civitai standard format)
+        hashes: dict[str, str] = {}
+        if ckpt_hash:
+            hashes["model"] = ckpt_hash[:10].upper()
+        for lora in loras_data:
+            if lora["hash"]:
+                hashes[f"LORA:{lora['name']}"] = lora["hash"][:10].upper()
 
-        # Add negative prompt
+        # Build Civitai resources JSON array
+        civitai_resources: list[dict[str, Any]] = []
+        if ckpt_civitai.get("id", 0) > 0:
+            ckpt_resource: dict[str, Any] = {}
+            ckpt_type = (ckpt_civitai.get("model") or {}).get("type", "Checkpoint")
+            model_id = ckpt_civitai.get("modelId", 0)
+            version_id = ckpt_civitai.get("id", 0)
+            if model_id and version_id:
+                ckpt_resource["air"] = self._build_air_string(
+                    ckpt_base_model, ckpt_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                ckpt_resource["modelVersionId"] = int(version_id)
+            if ckpt_civitai.get("name"):
+                ckpt_resource["versionName"] = ckpt_civitai["name"]
+            if ckpt_resource:
+                civitai_resources.append(ckpt_resource)
+
+        for lora in loras_data:
+            lora_civitai = lora["civitai"]
+            if not lora_civitai or lora_civitai.get("id", 0) <= 0:
+                continue
+            lora_resource: dict[str, Any] = {"weight": lora["strength"]}
+            lora_type = (lora_civitai.get("model") or {}).get("type", "LORA")
+            model_id = lora_civitai.get("modelId", 0)
+            version_id = lora_civitai.get("id", 0)
+            if model_id and version_id:
+                lora_resource["air"] = self._build_air_string(
+                    lora["base_model"], lora_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                lora_resource["modelVersionId"] = int(version_id)
+            if lora_civitai.get("name"):
+                lora_resource["versionName"] = lora_civitai["name"]
+            civitai_resources.append(lora_resource)
+
+        sampler_name = CIVITAI_SAMPLER_MAP.get(sampler, sampler) if sampler else None
+
+        scheduler_mapping = {
+            "normal": "Normal",
+            "karras": "Karras",
+            "exponential": "Exponential",
+            "sgm_uniform": "SGM Uniform",
+            "sgm_quadratic": "SGM Quadratic",
+        }
+        scheduler_name = scheduler_mapping.get(scheduler, scheduler) if scheduler else None
+
+        # Build output lines
+        prompt_line = prompt if prompt else ""
+        if add_loras_to_prompt and loras_text:
+            prompt_line = f"{prompt_line}\n{loras_text}" if prompt_line else loras_text
+        lines = [prompt_line] if prompt_line else [""]
         if negative_prompt:
-            metadata_parts.append(f"Negative prompt: {negative_prompt}")
+            lines.append(f"Negative prompt: {negative_prompt}")
 
-        # Format the second part (generation parameters)
-        params = []
-
-        # Add standard parameters in the correct order
-        if "steps" in metadata_dict:
-            add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
-
-        # Combine sampler and scheduler information
-        sampler_name = None
-        scheduler_name = None
-
-        if "sampler" in metadata_dict:
-            sampler = metadata_dict.get("sampler")
-            # Convert ComfyUI sampler names to user-friendly names
-            sampler_mapping = {
-                "euler": "Euler",
-                "euler_ancestral": "Euler a",
-                "dpm_2": "DPM2",
-                "dpm_2_ancestral": "DPM2 a",
-                "heun": "Heun",
-                "dpm_fast": "DPM fast",
-                "dpm_adaptive": "DPM adaptive",
-                "lms": "LMS",
-                "dpmpp_2s_ancestral": "DPM++ 2S a",
-                "dpmpp_sde": "DPM++ SDE",
-                "dpmpp_sde_gpu": "DPM++ SDE",
-                "dpmpp_2m": "DPM++ 2M",
-                "dpmpp_2m_sde": "DPM++ 2M SDE",
-                "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
-                "ddim": "DDIM",
-            }
-            sampler_name = sampler_mapping.get(sampler, sampler)
-
-        if "scheduler" in metadata_dict:
-            scheduler = metadata_dict.get("scheduler")
-            scheduler_mapping = {
-                "normal": "Simple",
-                "karras": "Karras",
-                "exponential": "Exponential",
-                "sgm_uniform": "SGM Uniform",
-                "sgm_quadratic": "SGM Quadratic",
-            }
-            scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-
-        # Add combined sampler and scheduler information
+        params: list[str] = []
+        if steps is not None:
+            params.append(f"Steps: {steps}")
         if sampler_name:
             if scheduler_name:
                 params.append(f"Sampler: {sampler_name} {scheduler_name}")
             else:
                 params.append(f"Sampler: {sampler_name}")
+        if cfg is not None:
+            params.append(f"CFG scale: {cfg}")
+        if seed is not None:
+            params.append(f"Seed: {seed}")
+        if size:
+            params.append(f"Size: {size}")
+        if clip_skip is not None:
+            try:
+                params.append(f"Clip skip: {abs(int(clip_skip))}")
+            except (ValueError, TypeError):
+                pass
+        additional_data = metadata_dict.get("additional_data", "")
+        if additional_data:
+            params.append(additional_data)
+        if ckpt_hash:
+            params.append(f"Model hash: {ckpt_hash[:10].upper()}")
+        if ckpt_display_name:
+            params.append(f"Model: {ckpt_display_name}")
+        if hashes:
+            params.append(f"Hashes: {json.dumps(hashes, separators=(',', ':'))}")
+        params.append("Version: ComfyUI")
+        if civitai_resources:
+            params.append(
+                f"Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}"
+            )
 
-        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
-        if "guidance" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
-        elif "cfg_scale" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
-        elif "cfg" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
-
-        # Seed
-        if "seed" in metadata_dict:
-            add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
-
-        # Size
-        if "size" in metadata_dict:
-            add_param_if_not_none(params, "Size", metadata_dict.get("size"))
-
-        # Model info
-        if "checkpoint" in metadata_dict:
-            # Ensure checkpoint is a string before processing
-            checkpoint = metadata_dict.get("checkpoint")
-            if checkpoint is not None:
-                # Get model hash
-                model_hash = self.get_checkpoint_hash(checkpoint)
-
-                # Extract basename without path
-                checkpoint_name = os.path.basename(checkpoint)
-                # Remove extension if present
-                checkpoint_name = os.path.splitext(checkpoint_name)[0]
-
-                # Add model hash if available
-                if model_hash:
-                    params.append(
-                        f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
-                    )
-                else:
-                    params.append(f"Model: {checkpoint_name}")
-
-        # Add LoRA hashes if available
-        if lora_hashes:
-            lora_hash_parts = []
-            for lora_name, hash_value in lora_hashes.items():
-                lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
-
-            if lora_hash_parts:
-                params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
-
-        # Combine all parameters with commas
-        metadata_parts.append(", ".join(params))
-
-        # Join all parts with a new line
-        return "\n".join(metadata_parts)
+        lines.append(", ".join(params))
+        return "\n".join(lines)
 
     # credit to nkchocoai
     # Add format_filename method to handle pattern substitution
@@ -298,7 +522,12 @@ class SaveImageLM:
             key = parts[0]
 
             if key == "seed" and "seed" in metadata_dict:
-                filename = filename.replace(segment, str(metadata_dict.get("seed", "")))
+                seed_value = metadata_dict.get("seed")
+                if seed_value is not None:
+                    filename = filename.replace(segment, str(seed_value))
+                else:
+                    # Fallback if seed was not captured by metadata collector
+                    filename = filename.replace(segment, "0")
             elif key == "width" and "size" in metadata_dict:
                 size = metadata_dict.get("size", "x")
                 w = size.split("x")[0] if isinstance(size, str) else size[0]
@@ -309,12 +538,14 @@ class SaveImageLM:
                 filename = filename.replace(segment, str(h))
             elif key == "pprompt" and "prompt" in metadata_dict:
                 prompt = metadata_dict.get("prompt", "").replace("\n", " ")
+                prompt = sanitize_folder_name(prompt)
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
             elif key == "nprompt" and "negative_prompt" in metadata_dict:
                 prompt = metadata_dict.get("negative_prompt", "").replace("\n", " ")
+                prompt = sanitize_folder_name(prompt)
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
@@ -328,6 +559,7 @@ class SaveImageLM:
                     model = "model_unavailable"
                 else:
                     model = os.path.splitext(os.path.basename(model_value))[0]
+                    model = sanitize_folder_name(model)
                 if len(parts) >= 2:
                     length = int(parts[1])
                     model = model[:length]
@@ -565,10 +797,13 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Save images with metadata"""
         results = []
@@ -577,7 +812,7 @@ class SaveImageLM:
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
 
-        metadata = self.format_metadata(metadata_dict)
+        metadata = self.format_metadata(metadata_dict, add_loras_to_prompt)
 
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)
@@ -600,7 +835,7 @@ class SaveImageLM:
             img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
 
             # Generate filename with counter if needed
-            base_filename = filename
+            base_filename = filename.replace("%batch_num%", str(i))
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i
@@ -619,15 +854,14 @@ class SaveImageLM:
             elif file_format == "jpeg":
                 file = base_filename + ".jpg"
                 file_extension = ".jpg"
-                save_kwargs = {"quality": quality, "optimize": True}
+                save_kwargs = {"quality": quality, "optimize": True, "subsampling": jpeg_subsampling}
             elif file_format == "webp":
                 file = base_filename + ".webp"
                 file_extension = ".webp"
-                # Add optimization param to control performance
                 save_kwargs = {
                     "quality": quality,
                     "lossless": lossless_webp,
-                    "method": 0,
+                    "method": webp_method,
                 }
             else:
                 raise ValueError(f"Unsupported file format: {file_format}")
@@ -714,10 +948,13 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -743,10 +980,13 @@ class SaveImageLM:
             extra_pnginfo,
             lossless_webp,
             quality,
+            webp_method,
+            jpeg_subsampling,
             embed_workflow,
             save_with_metadata,
             add_counter_to_filename,
             save_as_recipe,
+            add_loras_to_prompt,
         )
 
         return {

py/nodes/unet_loader.py

@@ -1,12 +1,27 @@
 import logging
 import os
-from typing import List, Tuple
-import comfy.sd # type: ignore
+from typing import Any, List, Tuple
+import comfy.sd  # pyright: ignore[reportMissingImports]
 from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
 
 logger = logging.getLogger(__name__)
 
 
+def _reload_gguf_unet(
+    unet_path: str, weight_dtype: str, disable_dynamic: bool = False
+) -> object:
+    """Reload a GGUF diffusion model from disk (cached_patcher_init factory).
+
+    Mirrors the GGUF branch of UNETLoaderLM.load_unet so ModelPatcher
+    deepclone/dynamic machinery can rebuild GGUF models with the correct
+    GGMLOps. ``disable_dynamic`` is accepted for signature compatibility
+    with core ComfyUI loaders.
+    """
+    loader = UNETLoaderLM()
+    model, = loader._load_gguf_unet(unet_path, unet_path, weight_dtype)
+    return model
+
+
 class UNETLoaderLM:
     """UNET Loader with support for extra folder paths
 
@@ -19,9 +34,9 @@ class UNETLoaderLM:
     CATEGORY = "Lora Manager/loaders"
 
     @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
         # Get list of unet names from scanner (includes extra folder paths)
-        unet_names = s._get_unet_names()
+        unet_names = cls._get_unet_names()
         return {
             "required": {
                 "unet_name": (
@@ -90,7 +105,7 @@ class UNETLoaderLM:
             logger.error(f"Error getting unet names: {e}")
             return []
 
-    def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple:
+    def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple[Any, ...]:
         """Load a diffusion model by name, supporting extra folder paths
 
         Args:
@@ -133,7 +148,7 @@ class UNETLoaderLM:
 
     def _load_gguf_unet(
         self, unet_path: str, unet_name: str, weight_dtype: str
-    ) -> Tuple:
+    ) -> Tuple[Any, ...]:
         """Load a GGUF format diffusion model
 
         Args:
@@ -196,6 +211,12 @@ class UNETLoaderLM:
             # Wrap with GGUFModelPatcher
             model = GGUFModelPatcher.clone(model)
 
+            # Register a reload factory so the MODEL carries its source path
+            # (cached_patcher_init) like core ComfyUI loaders do — required
+            # for model-name extraction downstream and for ModelPatcher
+            # deepclone/dynamic machinery.
+            model.cached_patcher_init = (_reload_gguf_unet, (unet_path, weight_dtype))
+
             return (model,)
 
         except Exception as e:

py/nodes/utils.py

@@ -1,3 +1,6 @@
+from typing import Any
+
+
 class AnyType(str):
     """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
 
@@ -6,7 +9,7 @@ class AnyType(str):
 
 
 # Credit to Regis Gaughan, III (rgthree)
-class FlexibleOptionalInputType(dict):
+class FlexibleOptionalInputType(dict[str, Any]):
     """A special class to make flexible nodes that pass data to our python handlers.
 
     Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
@@ -23,6 +26,7 @@ class FlexibleOptionalInputType(dict):
     """
 
     def __init__(self, type):
+        super().__init__()
         self.type = type
 
     def __getitem__(self, key):
@@ -36,10 +40,11 @@ any_type = AnyType("*")
 
 # Common methods extracted from lora_loader.py and lora_stacker.py
 import os
+import re
 import logging
 import copy
 import sys
-import folder_paths  # type: ignore
+import folder_paths  # pyright: ignore[reportMissingImports]
 
 logger = logging.getLogger(__name__)
 
@@ -69,6 +74,25 @@ def extract_lora_name(lora_path):
     return apply_lora_syntax_format(name_no_ext)
 
 
+def parse_lora_syntax(text: str) -> list[dict[str, Any]]:
+    """Parse <lora:name:strength> syntax from text input into a list of dicts.
+
+    Each entry contains: name, model_strength, clip_strength.
+    Supports both ``<lora:name:strength>`` and ``<lora:name:model_strength:clip_strength>``.
+    """
+    pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
+    matches = re.findall(pattern, text, re.IGNORECASE)
+    loras = []
+    for match in matches:
+        model_strength = float(match[1])
+        loras.append({
+            "name": match[0],
+            "model_strength": model_strength,
+            "clip_strength": float(match[2]) if match[2] else model_strength,
+        })
+    return loras
+
+
 def get_loras_list(kwargs):
     """Helper to extract loras list from either old or new kwargs format"""
     if "loras" not in kwargs:

py/recipes/base.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 """Base classes for recipe parsers."""
 
 import json
@@ -38,7 +42,7 @@ class RecipeMetadataParser(ABC):
         pass
     
     @staticmethod
-    async def populate_lora_from_civitai(lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+    async def populate_lora_from_civitai(lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any] | None, str | None] | Dict[str, Any],
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
         """
         Populate a lora entry with information from Civitai API response
@@ -175,10 +179,18 @@ class RecipeMetadataParser(ABC):
                                 lora_entry['localPath'] = local_path
                                 lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
                                 
-                                # Get thumbnail from local preview if available
+                                # Get thumbnail from local preview if available.
+                                # Match the cache item by local path first (get_path_by_hash
+                                # cascade: 10-char autov2 / 12-char autov3), then by hash.
                                 lora_cache = await lora_scanner.get_cached_data()
-                                lora_item = next((item for item in lora_cache.raw_data 
-                                                    if item['sha256'].lower() == lora_entry['hash'].lower()), None)
+                                h = (lora_entry.get("hash") or "").lower()
+                                lora_item = next((item for item in lora_cache.raw_data
+                                                  if (item.get("file_path") or "") == local_path), None)
+                                if lora_item is None:
+                                    lora_item = next((item for item in lora_cache.raw_data
+                                                      if (item.get("sha256") or "").lower() == h
+                                                      or (item.get("autov3") or "").lower() == h
+                                                      or (item.get("sha256") or "")[:10].lower() == h), None)
                                 if lora_item and 'preview_url' in lora_item:
                                     lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
                             except Exception as e:
@@ -194,7 +206,7 @@ class RecipeMetadataParser(ABC):
         return lora_entry
     
     @staticmethod
-    async def populate_checkpoint_from_civitai(checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+    async def populate_checkpoint_from_civitai(checkpoint: Dict[str, Any], civitai_info: Dict[str, Any] | Tuple[Dict[str, Any] | None, str | None] | None) -> Dict[str, Any]:
         """
         Populate checkpoint information from Civitai API response
         

py/recipes/enrichment.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import logging
 import json
 import os

py/recipes/factory.py

@@ -1,6 +1,7 @@
 """Factory for creating recipe metadata parsers."""
 
 import logging
+from typing import Any
 from .parsers import (
     RecipeFormatParser,
     ComfyMetadataParser,
@@ -31,7 +32,8 @@ class RecipeParserFactory:
         # First, try CivitaiApiMetadataParser for dict input
         if isinstance(metadata, dict):
             try:
-                if CivitaiApiMetadataParser().is_metadata_matching(metadata):
+                user_comment: Any = metadata
+                if CivitaiApiMetadataParser().is_metadata_matching(user_comment):
                     return CivitaiApiMetadataParser()
             except Exception as e:
                 logger.debug(f"CivitaiApiMetadataParser check failed: {e}")

py/recipes/parsers/automatic.py

@@ -52,7 +52,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                     negative_and_params = ""
             
             # Initialize metadata
-            metadata = {
+            metadata: Dict[str, Any] = {
                 "prompt": prompt,
                 "loras": []
             }
@@ -123,24 +123,39 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         if model_hash_from_hashes:
                             metadata["model_hash"] = model_hash_from_hashes
                     
-                    # Extract Lora hashes in alternative format
+                    # Extract Lora hashes in alternative format.
+                    # Run unconditionally (not just as fallback) so that
+                    # non-empty hashes from Lora hashes fill in the gaps left
+                    # by empty values in the Hashes JSON dict.  Some WebUI
+                    # builds write real hash values only to Lora hashes and
+                    # leave the Hashes JSON values empty.
                     lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
-                    if not hashes_match and lora_hashes_match:
+                    if lora_hashes_match:
                         try:
                             lora_hashes_str = lora_hashes_match.group(1)
                             lora_hash_entries = lora_hashes_str.split(', ')
-                            
-                            # Initialize hashes dict if it doesn't exist
-                            if "hashes" not in metadata:
-                                metadata["hashes"] = {}
-                                
+
                             # Parse each lora hash entry (format: "name: hash")
                             for entry in lora_hash_entries:
                                 if ': ' in entry:
                                     lora_name, lora_hash = entry.split(': ', 1)
-                                    # Add as lora type in the same format as regular hashes
-                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
-                            
+                                    lora_hash = lora_hash.strip()
+                                    if not lora_hash:
+                                        # Skip entries without a hash value
+                                        continue
+                                    # Initialize hashes dict if it doesn't exist
+                                    if "hashes" not in metadata:
+                                        metadata["hashes"] = {}
+                                    # Add as lora type in the same format as
+                                    # regular hashes.  Only override an
+                                    # existing entry if its value is empty
+                                    # (Lora hashes is the more reliable
+                                    # source when Hashes JSON has blanks).
+                                    key = f"lora:{lora_name}"
+                                    existing = metadata["hashes"].get(key, "")
+                                    if not existing:
+                                        metadata["hashes"][key] = lora_hash
+
                             # Remove lora hashes from params section
                             params_section = params_section.replace(lora_hashes_match.group(0), '')
                         except Exception as e:
@@ -362,6 +377,12 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Only process lora or hypernet types
                         if not hash_key.startswith(("lora:", "hypernet:")):
                             continue
+                        
+                        # Skip entries without a hash value — they can't be
+                        # resolved via CivitAI and would only produce a
+                        # useless "Deleted" entry in the recipe.
+                        if not lora_hash:
+                            continue
                             
                         lora_type, lora_name = hash_key.split(':', 1)
                         
@@ -387,11 +408,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Try to get info from Civitai
                         if metadata_provider:
                             try:
-                                if lora_hash:
-                                    # If we have hash, use it for lookup
-                                    civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                                else:
-                                    civitai_info = None
+                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                                 
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry, 

py/recipes/parsers/civitai_image.py

@@ -4,7 +4,7 @@ import json
 import logging
 from typing import Dict, Any, Union
 from ..base import RecipeMetadataParser
-from ..constants import GEN_PARAM_KEYS
+from ..constants import GEN_PARAM_KEYS, VALID_LORA_TYPES
 from ...services.metadata_service import get_default_metadata_provider
 from ...config import config
 
@@ -14,15 +14,16 @@ logger = logging.getLogger(__name__)
 class CivitaiApiMetadataParser(RecipeMetadataParser):
     """Parser for Civitai image metadata format"""
 
-    def is_metadata_matching(self, metadata) -> bool:
+    def is_metadata_matching(self, user_comment) -> bool:
         """Check if the metadata matches the Civitai image metadata format
 
         Args:
-            metadata: The metadata from the image (dict)
+            user_comment: The metadata from the image (dict)
 
         Returns:
             bool: True if this parser can handle the metadata
         """
+        metadata = user_comment
         if not metadata or not isinstance(metadata, dict):
             return False
 
@@ -73,7 +74,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
         return False
 
-    async def parse_metadata(  # type: ignore[override]
+    async def parse_metadata(  # pyright: ignore[reportIncompatibleMethodOverride]
         self, user_comment, recipe_scanner=None, civitai_client=None,
         local_cache: dict[str, Any] | None = None,
     ) -> Dict[str, Any]:
@@ -89,8 +90,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         Returns:
             Dict containing parsed recipe data
         """
-        metadata: Dict[str, Any] = user_comment  # type: ignore[assignment]
-        metadata = user_comment
+        metadata: Dict[str, Any] = user_comment
         try:
             # Get metadata provider instead of using civitai_client directly
             metadata_provider = await get_default_metadata_provider()
@@ -116,7 +116,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     metadata = inner_meta
 
             # Initialize result structure
-            result = {
+            result: Dict[str, Any] = {
                 "base_model": None,
                 "loras": [],
                 "model": None,
@@ -125,10 +125,10 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             }
 
             # Track already added LoRAs to prevent duplicates
-            added_loras = {}  # key: model_version_id or hash, value: index in result["loras"]
+            added_loras: Dict[str, Any] = {}  # key: model_version_id or hash, value: index in result["loras"]
 
             # Extract hash information from hashes field for LoRA matching
-            lora_hashes = {}
+            lora_hashes: Dict[str, Any] = {}
             if "hashes" in metadata and isinstance(metadata["hashes"], dict):
                 for key, hash_value in metadata["hashes"].items():
                     key_str = str(key)
@@ -184,7 +184,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                             if model_info:
                                 result["base_model"] = model_info.get("baseModel", "")
 
-            base_model_counts = {}
+            base_model_counts: Dict[str, int] = {}
 
             # Process standard resources array
             if "resources" in metadata and isinstance(metadata["resources"], list):
@@ -196,7 +196,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     # identification because it has an explicit type field and hash,
                     # unlike modelVersionIds which is a flat list with no type info.
                     if resource_type == "model":
-                        checkpoint_entry = {
+                        checkpoint_entry: Dict[str, Any] = {
                             "id": 0,
                             "modelId": 0,
                             "name": resource.get("name", "Unknown Model"),
@@ -216,7 +216,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         # Try to look up base model from the checkpoint hash
                         cp_hash = checkpoint_entry.get("hash")
                         if cp_hash and metadata_provider:
-                            local_cached = local_cache.get(cp_hash) if local_cache else None
+                            # local_cache keys are stored lowercase
+                            local_cached = local_cache.get(cp_hash.lower()) if local_cache else None
                             if local_cached:
                                 self._populate_entry_from_cache(
                                     checkpoint_entry, local_cached
@@ -294,8 +295,15 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                         # Try to get info from Civitai if hash is available
                         if lora_hash and metadata_provider:
-                            local_cached = local_cache.get(lora_hash) if local_cache else None
+                            # local_cache keys are stored lowercase
+                            local_cached = local_cache.get(lora_hash.lower()) if local_cache else None
                             if local_cached:
+                                cached_type = self._cache_item_model_type(local_cached)
+                                if cached_type and cached_type not in VALID_LORA_TYPES:
+                                    logger.debug(
+                                        f"Skipping non-LoRA cache item for hash {lora_hash}"
+                                    )
+                                    continue
                                 self._populate_entry_from_cache(
                                     lora_entry, local_cached
                                 )
@@ -304,6 +312,12 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                                     added_loras[str(lora_entry["id"])] = len(
                                         result["loras"]
                                     )
+                                # Mirror base.py:150-151 counts for API-path loras
+                                bm = local_cached.get("base_model") or ""
+                                if bm:
+                                    base_model_counts[bm] = base_model_counts.get(
+                                        bm, 0
+                                    ) + 1
                             else:
                                 try:
                                     civitai_info = (
@@ -514,11 +528,21 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                         result["loras"].append(lora_entry)
 
-            # Process modelVersionIds from Civitai image API
-            # These are model version IDs returned at root level when meta doesn't contain resources
-            if "modelVersionIds" in metadata and isinstance(
-                metadata["modelVersionIds"], list
+            # Process modelVersionIds from Civitai image API.
+            # These are version IDs returned at root level of the API response.
+            # When resources or civitaiResources are already present in metadata
+            # (which they are when ?withMeta=true is passed), those sections have
+            # complete hash/type information — modelVersionIds is a fallback for
+            # when meta is null and only the flat ID list is available. Skipping
+            # it here avoids duplicates: the same file hash often resolves to
+            # different version IDs via hash lookup (resources) vs the original
+            # version ID in modelVersionIds, and both paths would create entries.
+            if (
+                "modelVersionIds" in metadata
+                and isinstance(metadata["modelVersionIds"], list)
+                and not result.get("loras")
             ):
+
                 for version_id in metadata["modelVersionIds"]:
                     version_id_str = str(version_id)
 
@@ -526,6 +550,13 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if version_id_str in added_loras:
                         continue
 
+                    # Skip if this version ID is already the recipe's checkpoint
+                    # (resolved earlier from embedded resources/Model hash,
+                    # avoiding a duplicate CivitAI API call).
+                    existing_model = result.get("model")
+                    if existing_model and str(existing_model.get("id")) == version_id_str:
+                        continue
+
                     # Initialize lora entry with version ID
                     lora_entry = {
                         "id": version_id,
@@ -559,9 +590,40 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                             )
 
                             if populated_entry is None:
-                                continue  # Skip invalid LoRA types
+                                # Not a LoRA — try as checkpoint (only if we
+                                # don't already have one).  Reuses the same
+                                # civitai_info from the API call above so no
+                                # extra query is made.
+                                if result["model"] is None:
+                                    checkpoint_entry = {
+                                        "id": version_id,
+                                        "modelId": 0,
+                                        "name": "Unknown Model",
+                                        "version": "",
+                                        "type": "checkpoint",
+                                        "existsLocally": False,
+                                        "localPath": None,
+                                        "file_name": "",
+                                        "hash": "",
+                                        "thumbnailUrl": (
+                                            "/loras_static/images/no-preview.png"
+                                        ),
+                                        "baseModel": "",
+                                        "size": 0,
+                                        "downloadUrl": "",
+                                        "isDeleted": False,
+                                    }
+                                    cp_populated = await (
+                                        self.populate_checkpoint_from_civitai(
+                                            checkpoint_entry, civitai_info
+                                        )
+                                    )
+                                    if cp_populated.get("modelId"):
+                                        result["model"] = cp_populated
+                                continue  # Not a LoRA, don't add to loras
 
                             lora_entry = populated_entry
+
                         except Exception as e:
                             logger.error(
                                 f"Error fetching Civitai info for model version {version_id}: {e}"
@@ -601,30 +663,47 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 }
 
                 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:
+                    # local_cache keys are stored lowercase
+                    local_cached = local_cache.get(lora_hash.lower()) if local_cache else None
+                    if local_cached:
+                        cached_type = self._cache_item_model_type(local_cached)
+                        if cached_type and cached_type not in VALID_LORA_TYPES:
+                            logger.debug(
+                                f"Skipping non-LoRA cache item for hash {lora_hash}"
+                            )
                             continue
-
-                        lora_entry = populated_entry
-
+                        self._populate_entry_from_cache(lora_entry, local_cached)
+                        # Mirror base.py:150-151 counts for API-path loras
+                        bm = local_cached.get("base_model") or ""
+                        if bm:
+                            base_model_counts[bm] = base_model_counts.get(bm, 0) + 1
                         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}"
-                        )
+                    else:
+                        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)
@@ -663,32 +742,51 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                 # Try to get info from Civitai if hash is available
                 if lora_entry["hash"] and 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:
+                    # local_cache keys are stored lowercase
+                    local_cached = local_cache.get(lora_hash.lower()) if local_cache else None
+                    if local_cached:
+                        cached_type = self._cache_item_model_type(local_cached)
+                        if cached_type and cached_type not in VALID_LORA_TYPES:
+                            logger.debug(
+                                f"Skipping non-LoRA cache item for hash {lora_hash}"
+                            )
                             lora_index += 1
-                            continue  # Skip invalid LoRA types
-
-                        lora_entry = populated_entry
-
+                            continue  # Skip non-LoRA cache items
+                        self._populate_entry_from_cache(lora_entry, local_cached)
+                        # Mirror base.py:150-151 counts for API-path loras
+                        bm = local_cached.get("base_model") or ""
+                        if bm:
+                            base_model_counts[bm] = base_model_counts.get(bm, 0) + 1
                         # If we have a version ID from Civitai, track it for deduplication
                         if "id" in lora_entry and lora_entry["id"]:
                             added_loras[str(lora_entry["id"])] = len(result["loras"])
-                    except Exception as e:
-                        logger.error(
-                            f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
-                        )
+                    else:
+                        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:
+                                lora_index += 1
+                                continue  # Skip invalid LoRA types
+
+                            lora_entry = populated_entry
+
+                            # If we have a version ID from Civitai, track it for deduplication
+                            if "id" in lora_entry and lora_entry["id"]:
+                                added_loras[str(lora_entry["id"])] = len(result["loras"])
+                        except Exception as e:
+                            logger.error(
+                                f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
+                            )
 
                 # Track by hash if we have it
                 if lora_hash:
@@ -747,3 +845,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         base_model = cache_item.get("base_model", "")
         if base_model:
             entry["baseModel"] = base_model
+
+    @staticmethod
+    def _cache_item_model_type(cache_item: dict[str, Any]) -> str:
+        """Lowercased civitai.model.type of a cache item, or '' when unknown."""
+        civ = cache_item.get("civitai")
+        if not isinstance(civ, dict):
+            return ""
+        model_info = civ.get("model")
+        if not isinstance(model_info, dict):
+            return ""
+        return (model_info.get("type") or "").lower()

py/recipes/parsers/meta_format.py

@@ -30,7 +30,7 @@ class MetaFormatParser(RecipeMetadataParser):
             prompt = parts[0].strip()
             
             # Initialize metadata
-            metadata = {"prompt": prompt, "loras": []}
+            metadata: Dict[str, Any] = {"prompt": prompt, "loras": []}
             
             # Extract negative prompt and parameters if available
             if len(parts) > 1:

py/recipes/parsers/recipe_format.py

@@ -91,7 +91,15 @@ class RecipeFormatParser(RecipeMetadataParser):
                         exists_locally = lora_scanner.has_hash(lora['hash'])
                         if exists_locally:
                             lora_cache = await lora_scanner.get_cached_data()
-                            lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                            # Cascade match: full sha256, stored autov3, or autov2 (sha256[:10]).
+                            h = (lora.get('hash') or '').lower()
+                            lora_item = next(
+                                (item for item in lora_cache.raw_data
+                                 if (item.get("sha256") or "").lower() == h
+                                 or (item.get("autov3") or "").lower() == h
+                                 or (item.get("sha256") or "")[:10].lower() == h),
+                                None
+                            )
                             if lora_item:
                                 lora_entry['existsLocally'] = True
                                 lora_entry['inLibrary'] = True
@@ -148,7 +156,7 @@ class RecipeFormatParser(RecipeMetadataParser):
             checkpoint_data = recipe_metadata.get('checkpoint') or {}
             if isinstance(checkpoint_data, dict) and checkpoint_data:
                 version_id = checkpoint_data.get('modelVersionId') or checkpoint_data.get('id')
-                checkpoint_entry = {
+                checkpoint_entry: Dict[str, Any] = {
                     'id': version_id or 0,
                     'modelId': checkpoint_data.get('modelId', 0),
                     'name': checkpoint_data.get('name', 'Unknown Checkpoint'),

py/routes/base_model_routes.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import logging
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Callable, Dict, Mapping
+from typing import TYPE_CHECKING, Awaitable, Callable, Dict, Mapping
 
 import jinja2
 from aiohttp import web
@@ -84,7 +84,7 @@ class BaseModelRoutes(ABC):
         self.metadata_progress_callback = WebSocketBroadcastCallback()
 
         self._handler_set: ModelHandlerSet | None = None
-        self._handler_mapping: Dict[str, Callable[[web.Request], web.StreamResponse]] | None = None
+        self._handler_mapping: Dict[str, Callable[[web.Request], Awaitable[web.Response]]] | None = None
 
         self._preview_service = PreviewAssetService(
             metadata_manager=MetadataManager,
@@ -131,7 +131,7 @@ class BaseModelRoutes(ABC):
         self._handler_set = None
         self._handler_mapping = None
 
-    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         if self._handler_mapping is None:
             handler_set = self._create_handler_set()
             self._handler_set = handler_set
@@ -220,7 +220,7 @@ class BaseModelRoutes(ABC):
         )
 
     @property
-    def route_handlers(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+    def route_handlers(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         return self._ensure_handler_mapping()
 
     def setup_routes(self, app: web.Application, prefix: str) -> None:
@@ -237,7 +237,7 @@ class BaseModelRoutes(ABC):
         """Setup model-specific routes."""
         raise NotImplementedError
 
-    def _parse_specific_params(self, request: web.Request) -> Dict:
+    def _parse_specific_params(self, request: web.Request) -> Dict[str, Any]:
         """Parse model-specific parameters - to be overridden by subclasses."""
         return {}
 
@@ -253,7 +253,7 @@ class BaseModelRoutes(ABC):
         """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") in ("Model", "Diffusion Model") and file.get("primary") is True), None)
 
-    def get_handler(self, name: str) -> Callable[[web.Request], web.StreamResponse]:
+    def get_handler(self, name: str) -> Callable[[web.Request], Awaitable[web.StreamResponse]]:
         """Expose handlers for subclasses or tests."""
         return self._ensure_handler_mapping()[name]
 
@@ -285,7 +285,7 @@ class BaseModelRoutes(ABC):
             )
         return self.model_lifecycle_service
 
-    def _make_handler_proxy(self, name: str) -> Callable[[web.Request], web.StreamResponse]:
+    def _make_handler_proxy(self, name: str) -> Callable[[web.Request], Awaitable[web.StreamResponse]]:
         async def proxy(request: web.Request) -> web.StreamResponse:
             try:
                 handler = self.get_handler(name)

py/routes/base_recipe_routes.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Callable, Mapping
+from typing import Awaitable, Callable, Mapping
 
 import jinja2
 from aiohttp import web
@@ -61,7 +61,9 @@ class BaseRecipeRoutes:
         self._i18n_registered = False
         self._startup_hooks_registered = False
         self._handler_set: RecipeHandlerSet | None = None
-        self._handler_mapping: dict[str, Callable] | None = None
+        self._handler_mapping: Mapping[
+            str, Callable[[web.Request], Awaitable[web.StreamResponse]]
+        ] | None = None
 
     async def attach_dependencies(self, app: web.Application | None = None) -> None:
         """Resolve shared services from the registry."""
@@ -84,7 +86,9 @@ class BaseRecipeRoutes:
         app.on_startup.append(self.attach_dependencies)
         self._startup_hooks_registered = True
 
-    def to_route_mapping(self) -> Mapping[str, Callable]:
+    def to_route_mapping(
+        self,
+    ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         """Return a mapping of handler name to coroutine for registrar binding."""
 
         if self._handler_mapping is None:
@@ -124,17 +128,17 @@ class BaseRecipeRoutes:
             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]
+            from ..metadata_collector import get_metadata  # pyright: ignore[reportMissingImports]
+            from ..metadata_collector.metadata_processor import (  # pyright: ignore[reportMissingImports]
                 MetadataProcessor,
             )
-            from ..metadata_collector.metadata_registry import (  # type: ignore[import-not-found]
+            from ..metadata_collector.metadata_registry import (  # pyright: ignore[reportMissingImports]
                 MetadataRegistry,
             )
         else:  # pragma: no cover - optional dependency path
-            get_metadata = None  # type: ignore[assignment]
-            MetadataProcessor = None  # type: ignore[assignment]
-            MetadataRegistry = None  # type: ignore[assignment]
+            get_metadata = None  # pyright: ignore[reportAssignmentType]
+            MetadataProcessor = None  # pyright: ignore[reportAssignmentType]
+            MetadataRegistry = None  # pyright: ignore[reportAssignmentType]
 
         analysis_service = RecipeAnalysisService(
             exif_utils=ExifUtils,

py/routes/checkpoint_routes.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Set
+from typing import Any, Dict, List, Set
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
@@ -28,13 +28,13 @@ class CheckpointRoutes(BaseModelRoutes):
         # Attach service dependencies
         self.attach_service(self.service)
     
-    def setup_routes(self, app: web.Application):
+    def setup_routes(self, app: web.Application, prefix: str = "checkpoints"):
         """Setup Checkpoint routes"""
         # Schedule service initialization on app startup
         app.on_startup.append(lambda _: self.initialize_services())
-        
+
         # Setup common routes with 'checkpoints' prefix (includes page route)
-        super().setup_routes(app, 'checkpoints')
+        super().setup_routes(app, prefix)
     
     def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup Checkpoint-specific routes"""
@@ -53,9 +53,9 @@ class CheckpointRoutes(BaseModelRoutes):
         """Get expected model types string for error messages"""
         return "Checkpoint"
 
-    def _parse_specific_params(self, request: web.Request) -> Dict:
+    def _parse_specific_params(self, request: web.Request) -> Dict[str, Any]:
         """Parse Checkpoint-specific parameters"""
-        params: Dict = {}
+        params: Dict[str, Any] = {}
 
         if 'checkpoint_hash' in request.query:
             params['hash_filters'] = {'single_hash': request.query['checkpoint_hash'].lower()}
@@ -70,7 +70,7 @@ class CheckpointRoutes(BaseModelRoutes):
         """Get detailed information for a specific checkpoint by name"""
         try:
             name = request.match_info.get('name', '')
-            checkpoint_info = await self.service.get_model_info_by_name(name)
+            checkpoint_info = await self.service.get_model_info_by_name(name)  # pyright: ignore[reportAttributeAccessIssue]
             
             if checkpoint_info:
                 return web.json_response(checkpoint_info)
@@ -89,7 +89,7 @@ class CheckpointRoutes(BaseModelRoutes):
             roots.extend(config.checkpoints_roots or [])
             roots.extend(config.extra_checkpoints_roots or [])
             # Remove duplicates while preserving order
-            seen: set = set()
+            seen: set[str] = set()
             unique_roots: List[str] = []
             for root in roots:
                 if root and root not in seen:
@@ -114,7 +114,7 @@ class CheckpointRoutes(BaseModelRoutes):
             roots.extend(config.unet_roots or [])
             roots.extend(config.extra_unet_roots or [])
             # Remove duplicates while preserving order
-            seen: set = set()
+            seen: set[str] = set()
             unique_roots: List[str] = []
             for root in roots:
                 if root and root not in seen:

py/routes/embedding_routes.py

@@ -26,13 +26,13 @@ class EmbeddingRoutes(BaseModelRoutes):
         # Attach service dependencies
         self.attach_service(self.service)
     
-    def setup_routes(self, app: web.Application):
+    def setup_routes(self, app: web.Application, prefix: str = "embeddings"):
         """Setup Embedding routes"""
         # Schedule service initialization on app startup
         app.on_startup.append(lambda _: self.initialize_services())
-        
+
         # Setup common routes with 'embeddings' prefix (includes page route)
-        super().setup_routes(app, 'embeddings')
+        super().setup_routes(app, prefix)
     
     def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup Embedding-specific routes"""
@@ -51,7 +51,7 @@ class EmbeddingRoutes(BaseModelRoutes):
         """Get detailed information for a specific embedding by name"""
         try:
             name = request.match_info.get('name', '')
-            embedding_info = await self.service.get_model_info_by_name(name)
+            embedding_info = await self.service.get_model_info_by_name(name)  # pyright: ignore[reportAttributeAccessIssue]
             
             if embedding_info:
                 return web.json_response(embedding_info)

py/routes/example_images_routes.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Callable, Mapping
+from typing import Any, Awaitable, Callable, Mapping
 
 from aiohttp import web
 
@@ -35,7 +35,7 @@ class ExampleImagesRoutes:
         *,
         ws_manager,
         download_manager: DownloadManager | None = None,
-        processor=ExampleImagesProcessor,
+        processor: Any = ExampleImagesProcessor,
         file_manager=ExampleImagesFileManager,
         cleanup_service: ExampleImagesCleanupService | None = None,
     ) -> None:
@@ -46,7 +46,9 @@ class ExampleImagesRoutes:
         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
+        self._handler_mapping: Mapping[
+            str, Callable[[web.Request], Awaitable[web.StreamResponse]]
+        ] | None = None
 
     @classmethod
     def setup_routes(cls, app: web.Application, *, ws_manager) -> None:
@@ -61,7 +63,9 @@ class ExampleImagesRoutes:
         registrar = ExampleImagesRouteRegistrar(app)
         registrar.register_routes(self.to_route_mapping())
 
-    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+    def to_route_mapping(
+        self,
+    ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         """Return the registrar-compatible mapping of handler names to callables."""
 
         if self._handler_mapping is None:

py/routes/handlers/agent_handlers.py

@@ -0,0 +1,165 @@
+"""HTTP route handlers for agent skill endpoints.
+
+These handlers expose the :class:`AgentService` via HTTP, allowing the
+frontend to list available skills and execute them on selected models.
+Progress is reported via WebSocket broadcast.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any, Dict
+
+from aiohttp import web
+
+from ...services.agent import AgentService, AgentProgressReporter
+from ...services.llm_service import LLMNotConfiguredError
+
+logger = logging.getLogger(__name__)
+
+
+class AgentHandler:
+    """HTTP handler for agent skill operations."""
+
+    def __init__(self, agent_service: AgentService | None = None) -> None:
+        self._agent_service = agent_service
+
+    async def _ensure_service(self) -> AgentService:
+        if self._agent_service is None:
+            self._agent_service = await AgentService.get_instance()
+        return self._agent_service
+
+    # ------------------------------------------------------------------
+    # GET /api/lm/agent/skills
+    # ------------------------------------------------------------------
+
+    async def get_agent_skills(self, request: web.Request) -> web.Response:
+        """Return a list of available agent skills."""
+
+        service = await self._ensure_service()
+        skills = await service.list_skills()
+        return web.json_response({"skills": skills})
+
+    # ------------------------------------------------------------------
+    # POST /api/lm/agent/execute/{skill_name}
+    # ------------------------------------------------------------------
+
+    async def execute_agent_skill(self, request: web.Request) -> web.Response:
+        """Execute an agent skill on the provided model paths.
+
+        Request body::
+
+            {"model_paths": ["/path/to/model1.safetensors", ...], "options": {}}
+
+        Returns immediately with a task ID.  Execution runs in the
+        background; progress and completion are pushed via WebSocket
+        events of type ``agent_progress``.
+        """
+
+        skill_name = request.match_info.get("skill_name", "")
+        if not skill_name:
+            return web.json_response(
+                {"error": "Skill name is required"}, status=400
+            )
+
+        try:
+            body = await request.json()
+        except Exception:
+            return web.json_response(
+                {"error": "Invalid JSON body"}, status=400
+            )
+
+        model_paths = body.get("model_paths", [])
+        if not model_paths or not isinstance(model_paths, list):
+            return web.json_response(
+                {"error": "model_paths must be a non-empty array"},
+                status=400,
+            )
+
+        service = await self._ensure_service()
+
+        # Validate LLM configuration early for skills that need it
+        # (fail fast rather than after starting background work)
+        try:
+            from ...services.llm_service import LLMService
+
+            llm = await LLMService.get_instance()
+            if not llm.is_configured():
+                return web.json_response(
+                    {
+                        "error": "LLM provider is not configured. "
+                        "Enable it in Settings → AI Provider.",
+                    },
+                    status=400,
+                )
+        except Exception as exc:
+            logger.error("Failed to check LLM configuration: %s", exc)
+
+        # Launch execution in the background
+        progress_reporter = AgentProgressReporter()
+        logger.info(
+            "LLM enrichment '%s' starting for %d model(s)",
+            skill_name, len(model_paths),
+        )
+
+        async def _run() -> None:
+            try:
+                result = await service.execute_skill(
+                    skill_name=skill_name,
+                    input_data={"model_paths": model_paths},
+                    progress_callback=progress_reporter,
+                )
+                logger.info(
+                    "LLM enrichment '%s' finished: success=%s, summary='%s', errors=%s",
+                    skill_name, result.success, result.summary, result.errors,
+                )
+            except LLMNotConfiguredError as exc:
+                logger.warning("LLM enrichment '%s' not configured: %s", skill_name, exc)
+                await progress_reporter.on_progress(
+                    {
+                        "type": "agent_progress",
+                        "skill": skill_name,
+                        "status": "error",
+                        "error": str(exc),
+                    }
+                )
+            except Exception as exc:
+                logger.error("LLM enrichment '%s' failed: %s", skill_name, exc, exc_info=True)
+                await progress_reporter.on_progress(
+                    {
+                        "type": "agent_progress",
+                        "skill": skill_name,
+                        "status": "error",
+                        "error": str(exc),
+                    }
+                )
+
+        # Fire and forget — progress comes via WebSocket
+        asyncio.create_task(_run())
+
+        return web.json_response(
+            {
+                "status": "started",
+                "skill": skill_name,
+                "model_count": len(model_paths),
+            }
+        )
+
+    # ------------------------------------------------------------------
+    # POST /api/lm/agent/cancel
+    # ------------------------------------------------------------------
+
+    async def cancel_agent_skill(self, request: web.Request) -> web.Response:
+        """Cancel a running agent skill.
+
+        NOTE: Cancellation is a stub for now — the AgentService processes
+        models sequentially and does not yet support mid-execution
+        cancellation.  This endpoint exists for API completeness.
+        """
+
+        # TODO: implement cooperative cancellation in AgentService
+        return web.json_response(
+            {"status": "acknowledged", "note": "Cancellation not yet implemented"},
+            status=200,
+        )

py/routes/handlers/example_images_handlers.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from typing import Callable, Mapping
+from typing import Awaitable, Callable, Mapping
 
 from aiohttp import web
 
@@ -170,7 +170,7 @@ class ExampleImagesHandlerSet:
     management: ExampleImagesManagementHandler
     files: ExampleImagesFileHandler
 
-    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], web.StreamResponse]]:
+    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         """Flatten handler methods into the registrar mapping."""
 
         return {

py/routes/handlers/hf_handlers.py

@@ -0,0 +1,508 @@
+"""Handlers for Hugging Face model listing and download.
+
+Minimal MVP implementation — uses direct HTTP to the HF API for file
+listing and the project's existing aiohttp-based Downloader for
+downloading.  No huggingface_hub dependency required.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+from typing import Any
+
+import aiohttp
+from aiohttp import web
+
+from ...config import config
+from ...services.downloader import (
+    DownloadProgress,
+    get_downloader,
+)
+from ...services.aria2_downloader import Aria2Downloader
+from ...services.settings_manager import get_settings_manager
+from ...services.service_registry import ServiceRegistry
+from ...services.websocket_manager import ws_manager
+from ...utils.constants import MODEL_FILE_EXTENSIONS
+from ...utils.metadata_manager import MetadataManager
+from ...utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_MODEL_CLASS = LoraMetadata
+_DEFAULT_SCANNER_GETTER = "get_lora_scanner"
+
+# Shared aiohttp session for HF API calls (created on first use)
+_hf_api_session: aiohttp.ClientSession | None = None
+
+
+async def _get_hf_api_session() -> aiohttp.ClientSession:
+    """Get or create the shared aiohttp session for HF API calls."""
+    global _hf_api_session  # needed because we reassign the module-level name
+    if _hf_api_session is None or _hf_api_session.closed:
+        _hf_api_session = aiohttp.ClientSession(
+            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
+            timeout=aiohttp.ClientTimeout(total=30),
+        )
+    return _hf_api_session
+
+
+async def close_hf_api_session() -> None:
+    """Close the shared HF API session, if it was ever created."""
+    global _hf_api_session
+    if _hf_api_session is not None and not _hf_api_session.closed:
+        await _hf_api_session.close()
+        _hf_api_session = None
+
+
+def _infer_model_type(model_root: str) -> tuple[Any, str]:
+    """Determine model class and scanner by matching ``model_root`` against the
+    configured root paths for each model type (from ``Config``).
+
+    The ``model_root`` value comes from the frontend's model-root dropdown,
+    which is populated from the current page's scanner roots.  By checking
+    which scanner's root list it belongs to, we avoid fragile heuristics
+    like substring-matching path names.
+    """
+    norm = os.path.normpath(model_root).replace(os.sep, "/")
+
+    # LoRA roots
+    for p in (config.loras_roots or []) + (config.extra_loras_roots or []):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return LoraMetadata, "get_lora_scanner"
+
+    # Checkpoint / UNet roots
+    for p in (
+        (config.checkpoints_roots or [])
+        + (config.extra_checkpoints_roots or [])
+        + (config.unet_roots or [])
+        + (config.extra_unet_roots or [])
+    ):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return CheckpointMetadata, "get_checkpoint_scanner"
+
+    # Embedding roots
+    for p in (config.embeddings_roots or []) + (config.extra_embeddings_roots or []):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return EmbeddingMetadata, "get_embedding_scanner"
+
+    # Fallback — should not happen in normal use
+    logger.warning(
+        "Could not determine model type for root '%s'; defaulting to LoRA",
+        model_root,
+    )
+    return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
+
+
+async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
+    """Create a proper .metadata.json and add the model to the scanner cache.
+
+    Uses ``MetadataManager.create_default_metadata()`` which computes the
+    SHA256 hash, extracts safetensors header metadata (base_model), and
+    produces a fully-populated ``LoraMetadata`` (or ``CheckpointMetadata`` /
+    ``EmbeddingMetadata``) object.  We then overlay HF-specific fields and
+    register the model in the in-memory scanner cache so it appears
+    immediately without a full filesystem walk.
+    """
+    try:
+        hf_url = f"https://huggingface.co/{repo}"
+        model_class, scanner_getter_name = _infer_model_type(model_root)
+
+        # 1. Create proper metadata (computes SHA256, reads safetensors headers)
+        metadata = await MetadataManager.create_default_metadata(
+            dest_path, model_class=model_class
+        )
+        if metadata is None:
+            logger.warning("create_default_metadata returned None for %s", dest_path)
+            return
+
+        # 2. Overlay HF-specific fields
+        metadata._unknown_fields["hf_url"] = hf_url
+        metadata.from_civitai = False  # HF models are not from CivitAI
+
+        metadata_dict = metadata.to_dict()
+        if "trainedWords" in metadata_dict and not metadata_dict["trainedWords"]:
+            del metadata_dict["trainedWords"]
+
+        # 3. Save metadata atomically
+        await MetadataManager.save_metadata(dest_path, metadata_dict)
+        logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
+
+        # 4. Determine relative folder path for cache
+        #    model_root is an absolute path; dest_path is under it
+        folder = ""
+        if os.path.isabs(model_root) and dest_path.startswith(model_root):
+            rel = os.path.relpath(os.path.dirname(dest_path), model_root)
+            folder = rel.replace(os.sep, "/") if rel != "." else ""
+
+        # 5. Add to scanner cache (same as CivitAI's _execute_download does)
+        scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
+        if scanner_getter is not None:
+            scanner = await scanner_getter()
+            if scanner is not None:
+                metadata_dict = metadata.to_dict()
+                metadata_dict["hf_url"] = hf_url
+                await scanner.add_model_to_cache(metadata_dict, folder)
+                logger.info("Added %s to scanner cache (folder=%s)", dest_path, folder)
+
+    except Exception as exc:
+        logger.warning("Failed to save HF metadata for %s: %s", dest_path, exc)
+
+
+def _find_matching_root(dest_dir: str) -> str | None:
+    """Walk up *dest_dir* to find which configured scanner root it belongs to."""
+    norm = os.path.normpath(dest_dir).replace(os.sep, "/")
+    all_roots = []
+    for root_list in (
+        config.loras_roots or [],
+        config.extra_loras_roots or [],
+        config.checkpoints_roots or [],
+        config.extra_checkpoints_roots or [],
+        config.unet_roots or [],
+        config.extra_unet_roots or [],
+        config.embeddings_roots or [],
+        config.extra_embeddings_roots or [],
+    ):
+        all_roots.extend([os.path.normpath(p).replace(os.sep, "/") for p in root_list])
+    # Find the longest matching prefix
+    match: str | None = None
+    for root in all_roots:
+        if norm.startswith(root):
+            if match is None or len(root) > len(match):
+                match = root
+    return match
+
+
+async def _add_to_scanner_cache(dest_path: str, metadata: dict[str, Any]) -> None:
+    model_dir = os.path.dirname(dest_path)
+    model_root = _find_matching_root(model_dir)
+    if not model_root:
+        raise ValueError(f"File path {dest_path} is not within any configured scanner root")
+    scanner_getter_name = _infer_model_type(model_root)[1]
+    scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
+    if scanner_getter is None:
+        raise RuntimeError(f"Scanner getter '{scanner_getter_name}' not found in ServiceRegistry")
+    scanner = await scanner_getter()
+    if scanner is None:
+        raise RuntimeError(f"Scanner '{scanner_getter_name}' returned None")
+    await scanner.update_single_model_cache(dest_path, dest_path, metadata)
+
+
+class HfHandler:
+    """Handle Hugging Face model browsing and download."""
+
+    async def set_hf_url(self, request: web.Request) -> web.Response:
+        try:
+            payload: dict[str, Any] = await request.json()
+        except json.JSONDecodeError:
+            return web.json_response({"success": False, "error": "Invalid JSON"}, status=400)
+
+        file_path = (payload.get("file_path") or "").strip()
+        hf_url = (payload.get("hf_url") or "").strip()
+
+        if not file_path or not hf_url:
+            return web.json_response(
+                {"success": False, "error": "Missing required fields: 'file_path' and 'hf_url'"},
+                status=400,
+            )
+
+        m = re.match(r"^https?://huggingface\.co/([^/]+/[^/]+)/?$", hf_url)
+        if not m:
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "Invalid HuggingFace URL. Expected format: https://huggingface.co/user/repo",
+                },
+                status=400,
+            )
+
+        if not os.path.isfile(file_path):
+            return web.json_response(
+                {"success": False, "error": f"File not found: {file_path}"},
+                status=404,
+            )
+
+        model_root = _find_matching_root(os.path.dirname(file_path))
+        if not model_root:
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "File is not within any configured model directory. Cannot link to HuggingFace.",
+                },
+                status=400,
+            )
+
+        try:
+            existing = await MetadataManager.load_metadata_payload(file_path)
+            if existing.get("hf_url") == hf_url:
+                return web.json_response({
+                    "success": True,
+                    "message": "hf_url already set",
+                    "hf_url": hf_url,
+                })
+
+            existing["hf_url"] = hf_url
+            existing["from_civitai"] = False
+            await MetadataManager.save_metadata(file_path, existing)
+
+            await _add_to_scanner_cache(file_path, existing)
+
+            logger.info("Set hf_url=%s for %s", hf_url, file_path)
+            return web.json_response({
+                "success": True,
+                "message": f"hf_url set to {hf_url}",
+                "hf_url": hf_url,
+            })
+        except Exception as exc:
+            logger.error("Failed to set hf_url for %s: %s", file_path, exc)
+            return web.json_response(
+                {"success": False, "error": str(exc)},
+                status=500,
+            )
+
+    async def get_hf_repo_files(self, request: web.Request) -> web.Response:
+        """List model-weight files from a HF repo with real file sizes.
+
+        Uses the HF tree API endpoint which returns accurate file sizes
+        (including LFS-tracked files), unlike the model info endpoint.
+        """
+        repo = request.query.get("repo", "").strip()
+        if not repo or "/" not in repo:
+            return web.json_response(
+                {"error": "Missing or invalid 'repo' parameter (expected user/repo)"},
+                status=400,
+            )
+
+        url = f"https://huggingface.co/api/models/{repo}/tree/main"
+
+        try:
+            session = await _get_hf_api_session()
+            async with session.get(url) as resp:
+                if resp.status == 404:
+                    return web.json_response(
+                        {"error": f"Repo '{repo}' not found"}, status=404
+                    )
+                if resp.status != 200:
+                    text = await resp.text()
+                    return web.json_response(
+                        {"error": f"HF API error {resp.status}: {text[:200]}"},
+                        status=resp.status,
+                    )
+                tree: list[dict[str, Any]] = await resp.json()
+        except Exception as exc:
+            logger.error("Failed to fetch HF repo files: %s", exc)
+            return web.json_response({"error": str(exc)}, status=502)
+
+        files: list[dict[str, Any]] = []
+        for entry in tree:
+            path: str = entry.get("path", "")
+            ext = os.path.splitext(path)[1].lower()
+            if ext not in MODEL_FILE_EXTENSIONS:
+                continue
+            size = entry.get("size", 0) or 0
+            if size == 0 and "lfs" in entry:
+                size = entry["lfs"].get("size", 0) or 0
+            files.append({
+                "filename": path,
+                "size": size,
+            })
+
+        files.sort(key=lambda f: f["size"], reverse=True)
+        return web.json_response(files)
+
+    async def download_hf_model(self, request: web.Request) -> web.Response:
+        """Download a single file from Hugging Face into the model directory.
+
+        POST JSON body::
+
+            {
+              "repo": "dx8152/Flux2-Klein-9B-Consistency",
+              "filename": "Flux2-Klein-9B-consistency-V2.safetensors",
+              "revision": "main",
+              "model_root": "loras",
+              "relative_path": "",
+              "use_default_paths": false,
+              "download_id": "optional-batch-id"
+            }
+
+        If ``download_id`` is provided, real-time progress (bytes, speed,
+        percentage) is broadcast via the WebSocket progress system, matching
+        the CivitAI download experience.
+
+        Respects the ``download_backend`` setting (``aria2`` or ``default``).
+        """
+        try:
+            payload: dict[str, Any] = await request.json()
+        except json.JSONDecodeError:
+            return web.json_response({"error": "Invalid JSON"}, status=400)
+
+        repo = (payload.get("repo") or "").strip()
+        filename = (payload.get("filename") or "").strip()
+        revision = (payload.get("revision") or "main").strip()
+        model_root = (payload.get("model_root") or "").strip()
+        relative_path = (payload.get("relative_path") or "").strip()
+        use_default_paths = bool(payload.get("use_default_paths", False))
+        download_id: str | None = payload.get("download_id")
+
+        logger.info(
+            "download_hf_model: repo=%s file=%s root=%s download_id=%s",
+            repo, filename, model_root, download_id,
+        )
+
+        if not repo or not filename:
+            return web.json_response(
+                {"error": "Missing required fields: 'repo' and 'filename'"}, status=400
+            )
+
+        # Validate repo format — must be user/repo_name
+        if repo.count("/") != 1 or not re.match(r"^[a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+$", repo):
+            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
+        author, repo_name = repo.split("/", 1)
+        if ".." in (author, repo_name) or "." in (author, repo_name):
+            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
+
+        # Validate filename — must not contain path traversal
+        if ".." in filename:
+            return web.json_response({"error": "Invalid filename"}, status=400)
+
+        # Validate relative_path — must not be absolute or escape base directory
+        if relative_path:
+            if os.path.isabs(relative_path):
+                return web.json_response({"error": "relative_path must not be absolute"}, status=400)
+            if ".." in relative_path.split("/") or "\\" in relative_path:
+                return web.json_response({"error": "Invalid relative_path"}, status=400)
+
+        # Use model_root directly as the base directory — same approach as
+        # CivitAI's download path (download_manager.py).  No realpath, no
+        # allowed-roots validation, no path-traversal check; those are
+        # unnecessary when the frontend sends the path from its own dropdown
+        # (populated from scanner roots).  Using the "business path" directly
+        # keeps dest_path consistent with scanner roots so that later folder
+        # derivation (in _save_hf_metadata) works correctly.
+        if os.path.isabs(model_root):
+            base_dir = os.path.normpath(model_root)
+        else:
+            base_dir = os.path.normpath(os.path.join(os.getcwd(), "models", model_root))
+
+        if use_default_paths:
+            target_dir = os.path.join(base_dir, "huggingface", author, repo_name)
+        elif relative_path:
+            target_dir = os.path.join(base_dir, relative_path)
+        else:
+            target_dir = base_dir
+
+        # Strip HF repo subdirectory — "diffusion_models/xxx.safetensors"
+        # is an HF repo convention, not meaningful for local storage.
+        file_base = os.path.basename(filename)
+
+        os.makedirs(target_dir, exist_ok=True)
+        dest_path = os.path.join(target_dir, file_base)
+
+        # Check if already exists (simple skip)
+        if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
+            logger.info("download_hf_model: file already exists, skipping — %s", dest_path)
+            return web.json_response({
+                "success": True,
+                "message": f"File already exists: {dest_path}",
+                "path": dest_path,
+            })
+
+        # Build HF resolve URL
+        resolve_url = (
+            f"https://huggingface.co/{repo}/resolve/{revision}/{filename}"
+        )
+
+        # Set up progress callback if download_id is provided
+        progress_callback = None
+        if download_id:
+
+            async def _progress_callback(
+                progress: float | DownloadProgress,
+                snapshot: DownloadProgress | None = None,
+            ) -> None:
+                percent = 0.0
+                metrics = snapshot if isinstance(snapshot, DownloadProgress) else None
+
+                if isinstance(progress, DownloadProgress):
+                    percent = progress.percent_complete
+                    metrics = progress
+                elif isinstance(snapshot, DownloadProgress):
+                    percent = snapshot.percent_complete
+                else:
+                    percent = float(progress)
+
+                broadcast: dict[str, Any] = {
+                    "status": "progress",
+                    "progress": round(percent),
+                }
+                if metrics:
+                    broadcast["bytes_downloaded"] = metrics.bytes_downloaded
+                    broadcast["total_bytes"] = metrics.total_bytes
+                    broadcast["bytes_per_second"] = metrics.bytes_per_second
+
+                await ws_manager.broadcast_download_progress(download_id, broadcast)
+
+            progress_callback = _progress_callback
+
+        # Respect download backend setting (aria2 vs default)
+        download_backend = (
+            get_settings_manager().get("download_backend", "default")
+        )
+
+        if download_backend == "aria2":
+            aria2 = await Aria2Downloader.get_instance()
+            aid = download_id or f"hf_{repo}_{filename}"
+            try:
+                hf_success, hf_result = await aria2.download_file(
+                    url=resolve_url,
+                    save_path=dest_path,
+                    download_id=aid,
+                    progress_callback=progress_callback,
+                )
+                if hf_success:
+                    await _save_hf_metadata(dest_path, repo, model_root)
+                    return web.json_response({
+                        "success": True,
+                        "message": f"Downloaded to {dest_path}",
+                        "path": dest_path,
+                    })
+                else:
+                    return web.json_response(
+                        {"success": False, "error": hf_result or "aria2 download failed"},
+                        status=500,
+                    )
+            except Exception as exc:
+                logger.error("HF download (aria2) failed: %s", exc)
+                return web.json_response(
+                    {"success": False, "error": str(exc)}, status=500
+                )
+
+        # Default: use built-in aiohttp Downloader
+        downloader = await get_downloader()
+        try:
+            success, result = await downloader.download_file(
+                url=resolve_url,
+                save_path=dest_path,
+                use_auth=False,
+                allow_resume=True,
+                progress_callback=progress_callback,
+            )
+            if success:
+                await _save_hf_metadata(dest_path, repo, model_root)
+                return web.json_response({
+                    "success": True,
+                    "message": f"Downloaded to {result}",
+                    "path": result,
+                })
+            else:
+                return web.json_response(
+                    {"success": False, "error": result or "Download failed"},
+                    status=500,
+                )
+        except Exception as exc:
+            logger.error("HF download failed: %s", exc)
+            return web.json_response(
+                {"success": False, "error": str(exc)}, status=500
+            )

py/routes/handlers/model_handlers.py

@@ -37,6 +37,7 @@ from ...services.use_cases import (
 )
 from ...services.websocket_manager import WebSocketManager
 from ...services.websocket_progress_callback import WebSocketProgressCallback
+from ...services.download_queue_service import DownloadQueueService
 from ...services.errors import RateLimitError, ResourceNotFoundError
 from ...utils.civitai_utils import resolve_license_payload
 from ...utils.file_utils import calculate_sha256
@@ -70,7 +71,7 @@ class ModelPageView:
         self._server_i18n = server_i18n
         self._logger = logger
 
-    def _load_supporters(self) -> dict:
+    def _load_supporters(self) -> dict[str, Any]:
         """Load supporters data from JSON file."""
         try:
             current_file = os.path.abspath(__file__)
@@ -151,7 +152,15 @@ class ModelPageView:
                 self._template_env.filters["t"] = (
                     self._server_i18n.create_template_filter()
                 )
-                self._template_env._i18n_filter_added = True  # type: ignore[attr-defined]
+                self._template_env._i18n_filter_added = True  # pyright: ignore[reportAttributeAccessIssue]
+
+            from ...services.llm_service import PROVIDER_PRESETS
+
+            # Provider presets are embedded directly (local, no await needed).
+            # Provider model catalogs are fetched asynchronously by the
+            # frontend via GET /api/lm/llm/provider-models so page rendering
+            # never blocks on the remote model catalog (which can take up to
+            # 30s on cold cache).
 
             template_context = {
                 "is_initializing": is_initializing,
@@ -160,6 +169,8 @@ class ModelPageView:
                 "folders": [],
                 "t": self._server_i18n.get_translation,
                 "version": self._get_app_version(),
+                "provider_presets_json": json.dumps(PROVIDER_PRESETS),
+                "provider_models_json": "{}",
             }
 
             if not is_initializing:
@@ -188,7 +199,7 @@ class ModelListingHandler:
         self,
         *,
         service,
-        parse_specific_params: Callable[[web.Request], Dict],
+        parse_specific_params: Callable[[web.Request], Dict[str, Any]],
         logger: logging.Logger,
     ) -> None:
         self._service = service
@@ -202,11 +213,17 @@ class ModelListingHandler:
             result = await self._service.get_paginated_data(**params)
 
             format_start = time.perf_counter()
+            formatted_raw = [
+                await self._service.format_response(entry)
+                for entry in result["items"]
+            ]
+            # Filter out None entries returned for corrupted cache rows (issue #730).
+            # Note: "total" intentionally remains the pre-filter count to reflect
+            # the true number of models in the cache; corrupted entries are rare
+            # and adjusting total would cause pagination drift on every page.
+            formatted_items = [item for item in formatted_raw if item is not None]
             formatted_result = {
-                "items": [
-                    await self._service.format_response(item)
-                    for item in result["items"]
-                ],
+                "items": formatted_items,
                 "total": result["total"],
                 "page": result["page"],
                 "page_size": result["page_size"],
@@ -232,14 +249,20 @@ class ModelListingHandler:
         start_time = time.perf_counter()
         try:
             params = self._parse_common_params(request)
+            # group_by_model is meaningless for excluded view; strip it
+            params.pop("group_by_model", None)
             result = await self._service.get_excluded_paginated_data(**params)
 
             format_start = time.perf_counter()
+            formatted_raw = [
+                await self._service.format_response(entry)
+                for entry in result["items"]
+            ]
+            # Filter out None entries returned for corrupted cache rows (issue #730).
+            # "total" stays at the pre-filter count; see get_models for rationale.
+            formatted_items = [item for item in formatted_raw if item is not None]
             formatted_result = {
-                "items": [
-                    await self._service.format_response(item)
-                    for item in result["items"]
-                ],
+                "items": formatted_items,
                 "total": result["total"],
                 "page": result["page"],
                 "page_size": result["page_size"],
@@ -264,7 +287,7 @@ class ModelListingHandler:
             )
             return web.json_response({"error": str(exc)}, status=500)
 
-    def _parse_common_params(self, request: web.Request) -> Dict:
+    def _parse_common_params(self, request: web.Request) -> Dict[str, Any]:
         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")
@@ -365,6 +388,21 @@ class ModelListingHandler:
             request.query.get("name_pattern_use_regex", "false").lower() == "true"
         )
 
+        # Group-by-model flag: deduplicate versions sharing the same civitai modelId
+        group_by_model = (
+            request.query.get("group_by_model", "false").lower() == "true"
+        )
+
+        # View-local-versions filter: show all local versions of a specific model
+        # Accepts either a CivitAI modelId (int) or a HF group key like "hf:user/repo"
+        civitai_model_id = request.query.get("civitai_model_id")
+        if civitai_model_id is not None:
+            try:
+                civitai_model_id = int(civitai_model_id)
+            except (TypeError, ValueError):
+                # Keep as string — could be an HF group key (e.g. "hf:user/repo")
+                pass
+
         return {
             "page": page,
             "page_size": page_size,
@@ -388,6 +426,8 @@ class ModelListingHandler:
             "name_pattern_include": name_pattern_include,
             "name_pattern_exclude": name_pattern_exclude,
             "name_pattern_use_regex": name_pattern_use_regex,
+            "group_by_model": group_by_model,
+            "civitai_model_id": civitai_model_id,
             **self._parse_specific_params(request),
         }
 
@@ -499,6 +539,7 @@ class ModelManagementHandler:
                     # Update model_data with new hash
                     model_data["sha256"] = sha256
                     model_data["hash_status"] = "completed"
+                    hash_status = "completed"
                 else:
                     return web.json_response(
                         {"success": False, "error": "No SHA256 hash found"}, status=400
@@ -506,6 +547,32 @@ class ModelManagementHandler:
 
             await MetadataManager.hydrate_model_data(model_data)
 
+            # hydrate_model_data replaces model_data with .metadata.json content,
+            # which may lack sha256. Restore from cache and persist the fix.
+            if not model_data.get("sha256"):
+                if sha256:
+                    model_data["sha256"] = sha256
+                    model_data["hash_status"] = model_data.get("hash_status", hash_status)
+                    data_to_save = model_data.copy()
+                    data_to_save.pop("folder", None)
+                    await MetadataManager.save_metadata(file_path, data_to_save)
+                else:
+                    sha256 = await calculate_sha256(file_path)
+                    if sha256:
+                        model_data["sha256"] = sha256.lower()
+                        model_data["hash_status"] = "completed"
+                        data_to_save = model_data.copy()
+                        data_to_save.pop("folder", None)
+                        await MetadataManager.save_metadata(file_path, data_to_save)
+                    else:
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": "Failed to compute SHA256 hash for model",
+                            },
+                            status=500,
+                        )
+
             success, error = await self._metadata_sync.fetch_and_update_model(
                 sha256=model_data["sha256"],
                 file_path=file_path,
@@ -515,15 +582,25 @@ class ModelManagementHandler:
             if not success:
                 return web.json_response({"success": False, "error": error})
 
-            formatted_metadata = await self._service.format_response(model_data)
-            return web.json_response({"success": True, "metadata": formatted_metadata})
+            formatted = await self._service.format_response(model_data)
+            if formatted is None:
+                return web.json_response(
+                    {"success": False, "error": "Model entry is corrupted (missing file_path)"},
+                    status=500,
+                )
+            return web.json_response({"success": True, "metadata": formatted})
         except Exception as exc:
             if is_expected_offline_error(str(exc)):
                 return web.json_response(
                     {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
                     status=503,
                 )
-            self._logger.error("Error fetching from CivitAI: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error fetching from CivitAI for %s: %s",
+                locals().get("file_path", "unknown"),
+                exc,
+                exc_info=True,
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def relink_civitai(self, request: web.Request) -> web.Response:
@@ -581,7 +658,7 @@ class ModelManagementHandler:
         try:
             reader = await request.multipart()
 
-            field = await reader.next()
+            field: Any = await reader.next()
             if field is None or field.name != "preview_file":
                 raise ValueError("Expected 'preview_file' field")
             content_type = field.headers.get("Content-Type", "image/png")
@@ -623,7 +700,7 @@ class ModelManagementHandler:
                 {
                     "success": True,
                     "preview_url": config.get_preview_static_url(
-                        result["preview_path"]
+                        str(result["preview_path"])
                     ),
                     "preview_nsfw_level": result["preview_nsfw_level"],
                 }
@@ -704,7 +781,7 @@ class ModelManagementHandler:
 
             result = await self._preview_service.replace_preview(
                 model_path=model_path,
-                preview_data=preview_data,
+                preview_data=preview_bytes,
                 content_type=content_type,
                 original_filename=original_filename,
                 nsfw_level=nsfw_level,
@@ -716,7 +793,7 @@ class ModelManagementHandler:
                 {
                     "success": True,
                     "preview_url": config.get_preview_static_url(
-                        result["preview_path"]
+                        str(result["preview_path"])
                     ),
                     "preview_nsfw_level": result["preview_nsfw_level"],
                 }
@@ -930,6 +1007,8 @@ class ModelQueryHandler:
             limit = int(request.query.get("limit", "20"))
             if limit < 0:
                 limit = 20
+            elif limit > 200:
+                limit = 20
             top_tags = await self._service.get_top_tags(limit)
             return web.json_response({"success": True, "tags": top_tags})
         except Exception as exc:
@@ -938,6 +1017,22 @@ class ModelQueryHandler:
                 {"success": False, "error": "Internal server error"}, status=500
             )
 
+    async def search_tags(self, request: web.Request) -> web.Response:
+        try:
+            query = request.query.get("q", "")
+            limit = int(request.query.get("limit", "20"))
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+            tags = await self._service.search_tags(query, limit)
+            return web.json_response({"success": True, "tags": tags})
+        except Exception as exc:
+            self._logger.error("Error searching tags: %s", exc, 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:
         try:
             limit = int(request.query.get("limit", "20"))
@@ -1073,10 +1168,12 @@ class ModelQueryHandler:
                 # Sort: originals first, copies last
                 sorted_models = self._sort_duplicate_group(filtered)
 
-                # Format response
+                # Format response, filtering out corrupted entries (issue #730)
                 group = {"hash": sha256, "models": []}
                 for model in sorted_models:
-                    group["models"].append(await self._service.format_response(model))
+                    formatted = await self._service.format_response(model)
+                    if formatted is not None:
+                        group["models"].append(formatted)
 
                 # Only include groups with 2+ models after filtering
                 if len(group["models"]) > 1:
@@ -1193,9 +1290,9 @@ class ModelQueryHandler:
                         (m for m in cache.raw_data if m["file_path"] == path), None
                     )
                     if model:
-                        group["models"].append(
-                            await self._service.format_response(model)
-                        )
+                        formatted = await self._service.format_response(model)
+                        if formatted is not None:
+                            group["models"].append(formatted)
                 hash_val = self._service.scanner.get_hash_by_filename(filename)
                 if hash_val:
                     main_path = self._service.get_path_by_hash(hash_val)
@@ -1205,9 +1302,9 @@ class ModelQueryHandler:
                             None,
                         )
                         if main_model:
-                            group["models"].insert(
-                                0, await self._service.format_response(main_model)
-                            )
+                            formatted = await self._service.format_response(main_model)
+                            if formatted is not None:
+                                group["models"].insert(0, formatted)
                 if group["models"]:
                     result.append(group)
             return web.json_response(
@@ -1230,9 +1327,13 @@ class ModelQueryHandler:
                     text=f"{self._service.model_type.capitalize()} file name is required",
                     status=400,
                 )
-            notes = await self._service.get_model_notes(model_name)
-            if notes is not None:
-                return web.json_response({"success": True, "notes": notes})
+            result = await self._service.get_model_notes(model_name)
+            if result is not None:
+                return web.json_response({
+                    "success": True,
+                    "notes": result["notes"],
+                    "file_path": result["file_path"],
+                })
             return web.json_response(
                 {
                     "success": False,
@@ -1268,9 +1369,28 @@ class ModelQueryHandler:
                 }
                 if include_license_flags:
                     model_data = await self._service.get_model_info_by_name(model_name)
-                    license_flags = (model_data or {}).get("license_flags")
-                    if license_flags is not None:
-                        response_payload["license_flags"] = int(license_flags)
+                    # Only return license_flags when real CivitAI model license
+                    # data exists. This mirrors ModelModal's guard
+                    # (modelData?.civitai?.model) so the preview tooltip never
+                    # shows misleading license icons for HF or other models
+                    # without actual license metadata.
+                    civitai_data = (model_data or {}).get("civitai") or {}
+                    has_license_data = (
+                        isinstance(civitai_data, dict)
+                        and isinstance(civitai_data.get("model"), dict)
+                    )
+                    if has_license_data:
+                        license_flags = (model_data or {}).get("license_flags")
+                        if license_flags is not None:
+                            response_payload["license_flags"] = int(license_flags)
+                    # Include the user's license icon style preference so the
+                    # ComfyUI tooltip can pick the right set without a separate
+                    # API call.
+                    try:
+                        settings = get_settings_manager()
+                        response_payload["use_new_license_icons"] = settings.get("use_new_license_icons", True)
+                    except Exception:
+                        pass
                 return web.json_response(response_payload)
             return web.json_response(
                 {
@@ -1368,8 +1488,73 @@ class ModelQueryHandler:
             search = request.query.get("search", "").strip()
             limit = min(int(request.query.get("limit", "15")), 100)
             offset = max(0, int(request.query.get("offset", "0")))
+
+            folder = request.query.get("folder")
+            recursive = request.query.get("recursive", "true").lower() == "true"
+            base_models = list(request.query.getall("base_model", []))
+            model_types = list(request.query.getall("model_type", []))
+
+            tag_filters: Dict[str, str] = {}
+            for tag in request.query.getall("tag_include", []):
+                if tag:
+                    tag_filters[tag] = "include"
+            for tag in request.query.getall("tag_exclude", []):
+                if tag:
+                    tag_filters[tag] = "exclude"
+
+            auto_tag_filters: Dict[str, str] = {}
+            for tag in request.query.getall("auto_tag_include", []):
+                if tag:
+                    auto_tag_filters[tag] = "include"
+            for tag in request.query.getall("auto_tag_exclude", []):
+                if tag:
+                    auto_tag_filters[tag] = "exclude"
+
+            tag_logic = request.query.get("tag_logic", "any").lower()
+            if tag_logic not in ("any", "all"):
+                tag_logic = "any"
+
+            credit_required = request.query.get("credit_required")
+            if credit_required is not None:
+                credit_required = credit_required.lower() not in ("false", "0", "")
+
+            allow_selling_generated_content = request.query.get(
+                "allow_selling_generated_content"
+            )
+            if allow_selling_generated_content is not None:
+                allow_selling_generated_content = (
+                    allow_selling_generated_content.lower() not in ("false", "0", "")
+                )
+
+            # The presence of the recursive param (always sent by the loras
+            # widget when filter mode is on) signals that the filter pipeline
+            # must run even when no concrete filter is set, so global settings
+            # like show_only_sfw stay consistent with the list endpoint.
+            apply_filters = (
+                "recursive" in request.query
+                or folder is not None
+                or bool(base_models)
+                or bool(model_types)
+                or bool(tag_filters)
+                or bool(auto_tag_filters)
+                or credit_required is not None
+                or allow_selling_generated_content is not None
+            )
+
             matching_paths = await self._service.search_relative_paths(
-                search, limit, offset
+                search,
+                limit,
+                offset,
+                folder=folder,
+                recursive=recursive,
+                base_models=base_models,
+                model_types=model_types,
+                tags=tag_filters,
+                auto_tags=auto_tag_filters,
+                tag_logic=tag_logic,
+                credit_required=credit_required,
+                allow_selling_generated_content=allow_selling_generated_content,
+                apply_filters=apply_filters,
             )
             return web.json_response(
                 {"success": True, "relative_paths": matching_paths}
@@ -1567,6 +1752,303 @@ class ModelDownloadHandler:
             )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    # ------------------------------------------------------------------
+    # Download queue / history handlers
+    # ------------------------------------------------------------------
+
+    async def get_download_queue(self, request: web.Request) -> web.Response:
+        try:
+            service = await DownloadQueueService.get_instance()
+            queue = await service.get_queue()
+            stats = await service.get_stats()
+            return web.json_response({"success": True, "queue": queue, "stats": stats})
+        except Exception as exc:
+            self._logger.error(
+                "Error getting download queue: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def add_to_download_queue(self, request: web.Request) -> web.Response:
+        try:
+            import uuid
+
+            download_id = request.query.get("download_id") or str(uuid.uuid4())
+            model_id_str = request.query.get("model_id")
+            model_version_id_str = request.query.get("model_version_id")
+            model_name = request.query.get("model_name", "")
+            version_name = request.query.get("version_name", "")
+            thumbnail_url = request.query.get("thumbnail_url", "")
+            source = request.query.get("source")
+            file_params_json = request.query.get("file_params")
+
+            model_id = int(model_id_str) if model_id_str else None
+            model_version_id = int(model_version_id_str) if model_version_id_str else None
+            file_params = json.loads(file_params_json) if file_params_json else None
+
+            service = await DownloadQueueService.get_instance()
+            item = await service.add_to_queue(
+                download_id=download_id,
+                model_id=model_id,
+                model_version_id=model_version_id,
+                model_name=model_name,
+                version_name=version_name,
+                thumbnail_url=thumbnail_url,
+                source=source,
+                file_params=file_params,
+            )
+            return web.json_response({"success": True, "item": item})
+        except Exception as exc:
+            self._logger.error(
+                "Error adding to download queue: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def remove_from_download_queue(self, request: web.Request) -> web.Response:
+        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
+                )
+
+            service = await DownloadQueueService.get_instance()
+            removed = await service.remove_from_queue(download_id)
+            return web.json_response({"success": removed})
+        except Exception as exc:
+            self._logger.error(
+                "Error removing from download queue: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def move_queue_item_to_top(self, request: web.Request) -> web.Response:
+        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
+                )
+
+            service = await DownloadQueueService.get_instance()
+            moved = await service.move_to_top(download_id)
+            return web.json_response({"success": moved})
+        except Exception as exc:
+            self._logger.error(
+                "Error moving queue item to top: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def move_queue_item_to_end(self, request: web.Request) -> web.Response:
+        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
+                )
+
+            service = await DownloadQueueService.get_instance()
+            moved = await service.move_to_end(download_id)
+            return web.json_response({"success": moved})
+        except Exception as exc:
+            self._logger.error(
+                "Error moving queue item to end: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def clear_download_queue(self, request: web.Request) -> web.Response:
+        try:
+            status_filter = request.query.get("status") or None
+            service = await DownloadQueueService.get_instance()
+            cleared = await service.clear_queue(status_filter=status_filter)
+            return web.json_response({"success": True, "cleared": cleared})
+        except Exception as exc:
+            self._logger.error(
+                "Error clearing download queue: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_download_history(self, request: web.Request) -> web.Response:
+        try:
+            limit = min(int(request.query.get("limit", "50")), 500)
+            offset = int(request.query.get("offset", "0"))
+            status_filter = request.query.get("status") or None
+            service = await DownloadQueueService.get_instance()
+            result = await service.get_history(
+                limit=limit, offset=offset, status_filter=status_filter
+            )
+            return web.json_response(
+                {
+                    "success": True,
+                    "items": result["items"],
+                    "total": result["total"],
+                    "limit": result["limit"],
+                    "offset": result["offset"],
+                }
+            )
+        except Exception as exc:
+            self._logger.error(
+                "Error getting download history: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def clear_download_history(self, request: web.Request) -> web.Response:
+        try:
+            status_filter = request.query.get("status") or None
+            service = await DownloadQueueService.get_instance()
+            cleared = await service.clear_history(status_filter=status_filter)
+            return web.json_response({"success": True, "cleared": cleared})
+        except Exception as exc:
+            self._logger.error(
+                "Error clearing download history: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def delete_download_history_item(self, request: web.Request) -> web.Response:
+        try:
+            download_id = request.query.get("download_id")
+            id_str = request.query.get("id")
+            item_id = int(id_str) if id_str else None
+
+            if not download_id and not item_id:
+                return web.json_response(
+                    {"success": False, "error": "id or download_id is required"},
+                    status=400,
+                )
+
+            service = await DownloadQueueService.get_instance()
+            deleted = await service.delete_history_item(
+                id=item_id, download_id=download_id
+            )
+            return web.json_response({"success": deleted})
+        except Exception as exc:
+            self._logger.error(
+                "Error deleting download history item: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def retry_download_from_history(self, request: web.Request) -> web.Response:
+        try:
+            download_id = request.query.get("download_id")
+            id_str = request.query.get("id")
+            item_id = int(id_str) if id_str else None
+
+            if not download_id and not item_id:
+                return web.json_response(
+                    {"success": False, "error": "id or download_id is required"},
+                    status=400,
+                )
+
+            service = await DownloadQueueService.get_instance()
+            item = await service.retry_from_history(
+                item_id=item_id, download_id=download_id
+            )
+            if item is None:
+                return web.json_response(
+                    {"success": False, "error": "History item not found or not retryable"},
+                    status=404,
+                )
+            return web.json_response({"success": True, "item": item})
+        except Exception as exc:
+            self._logger.error(
+                "Error retrying download from history: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def retry_all_failed_downloads(self, request: web.Request) -> web.Response:
+        try:
+            service = await DownloadQueueService.get_instance()
+            retry_count = await service.retry_all_failed()
+            return web.json_response({"success": True, "retry_count": retry_count})
+        except Exception as exc:
+            self._logger.error(
+                "Error retrying all failed downloads: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def complete_download_in_queue(self, request: web.Request) -> web.Response:
+        """Atomically move a download from queue to history with terminal status."""
+        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
+                )
+            status = request.query.get("status", "completed")
+            error = request.query.get("error")
+            file_path = request.query.get("file_path")
+            try:
+                bytes_downloaded = int(request.query.get("bytes_downloaded", "0"))
+            except (TypeError, ValueError):
+                bytes_downloaded = 0
+            total_bytes_raw = request.query.get("total_bytes")
+            total_bytes = int(total_bytes_raw) if total_bytes_raw else None
+            completed_at_raw = request.query.get("completed_at")
+            completed_at = float(completed_at_raw) if completed_at_raw else None
+
+            service = await DownloadQueueService.get_instance()
+            item = await service.complete_download(
+                download_id=download_id,
+                status=status,
+                error=error,
+                file_path=file_path,
+                bytes_downloaded=bytes_downloaded,
+                total_bytes=total_bytes,
+                completed_at=completed_at,
+            )
+            if item is None:
+                return web.json_response(
+                    {"success": False, "error": "Download not found in queue"}, status=404
+                )
+            return web.json_response({"success": True, "item": item})
+        except Exception as exc:
+            self._logger.error(
+                "Error completing download: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_download_stats(self, request: web.Request) -> web.Response:
+        try:
+            service = await DownloadQueueService.get_instance()
+            stats = await service.get_stats()
+            return web.json_response({"success": True, "stats": stats})
+        except Exception as exc:
+            self._logger.error(
+                "Error getting download stats: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def update_download_queue_status(self, request: web.Request) -> web.Response:
+        """Update the status of a queue item (non-terminal transitions).
+
+        Supported transitions include ``queued → downloading``,
+        ``downloading → paused``, ``paused → downloading``, etc.
+        Terminal transitions (``completed``, ``failed``, ``canceled``)
+        should use ``complete_download_in_queue`` instead.
+        """
+        try:
+            download_id = request.query.get("download_id")
+            status = request.query.get("status")
+            if not download_id or not status:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "download_id and status are required",
+                    },
+                    status=400,
+                )
+            service = await DownloadQueueService.get_instance()
+            updated = await service.update_status(download_id, status)
+            if not updated:
+                return web.json_response(
+                    {"success": False, "error": "Download not found in queue"},
+                    status=404,
+                )
+            return web.json_response({"success": True})
+        except Exception as exc:
+            self._logger.error(
+                "Error updating download queue status: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class ModelCivitaiHandler:
     """CivitAI integration endpoints."""
@@ -1578,7 +2060,7 @@ class ModelCivitaiHandler:
         settings_service: SettingsManager,
         ws_manager: WebSocketManager,
         logger: logging.Logger,
-        metadata_provider_factory: Callable[[], Awaitable],
+        metadata_provider_factory: Callable[[], Awaitable[Any]],
         validate_model_type: Callable[[str], bool],
         expected_model_types: Callable[[], str],
         find_model_file: Callable[
@@ -1608,7 +2090,9 @@ class ModelCivitaiHandler:
             return web.json_response(result)
         except Exception as exc:
             self._logger.error(
-                "Error in fetch_all_civitai for %ss: %s", self._service.model_type, exc
+                "Error in fetch_all_civitai for %ss: %s",
+                self._service.model_type, exc,
+                exc_info=True,
             )
             return web.Response(text=str(exc), status=500)
 
@@ -1641,7 +2125,7 @@ class ModelCivitaiHandler:
                 downloaded_version_ids = set(
                     await history_service.get_downloaded_version_ids(
                         self._service.model_type,
-                        model_id,
+                        int(model_id),
                     )
                 )
             except Exception as exc:  # pragma: no cover - defensive logging
@@ -1918,8 +2402,8 @@ class ModelUpdateHandler:
             self._logger.error("Failed to fetch license info: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
-        updated: List[Dict[str, str]] = []
-        errors: List[Dict[str, str]] = []
+        updated: List[Dict[str, Any]] = []
+        errors: List[Dict[str, Any]] = []
         for model_id in model_ids:
             license_payload = license_map.get(model_id)
             if not license_payload:
@@ -1932,6 +2416,7 @@ class ModelUpdateHandler:
                 model_section = civitai_section.get("model")
                 if not isinstance(model_section, Mapping):
                     model_section = {}
+                model_section = dict(model_section)
                 model_section.update(resolved_payload)
                 civitai_section["model"] = model_section
                 metadata_payload["civitai"] = civitai_section
@@ -1947,7 +2432,7 @@ class ModelUpdateHandler:
                     )
                     errors.append({"filePath": metadata_path, "error": str(exc)})
 
-        response_payload = {"success": True, "updated": updated}
+        response_payload: Dict[str, Any] = {"success": True, "updated": updated}
         missing_model_ids = [mid for mid in model_ids if mid not in license_map]
         if missing_model_ids:
             response_payload["missingModelIds"] = missing_model_ids
@@ -2016,10 +2501,21 @@ class ModelUpdateHandler:
             self._logger.error("Failed to refresh model updates: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+        hide_early_access = False
+        if self._settings is not None:
+            try:
+                hide_early_access = bool(
+                    self._settings.get("hide_early_access_updates", False)
+                )
+            except Exception:
+                pass
+
         serialized_records = []
         for record in records.values():
             has_update_fn = getattr(record, "has_update", None)
-            if callable(has_update_fn) and has_update_fn():
+            if callable(has_update_fn) and has_update_fn(
+                hide_early_access=hide_early_access
+            ):
                 serialized_records.append(self._serialize_record(record))
 
         return web.json_response(
@@ -2285,6 +2781,7 @@ class ModelUpdateHandler:
             civitai_payload = metadata_payload.get("civitai")
             if not isinstance(civitai_payload, Mapping):
                 civitai_payload = {}
+            civitai_payload = dict(civitai_payload)
 
             model_payload = civitai_payload.get("model")
             if not isinstance(model_payload, Mapping):
@@ -2329,7 +2826,7 @@ class ModelUpdateHandler:
 
         return aggregated
 
-    def _extract_target_model_ids(self, payload: Dict) -> Optional[List[int]]:
+    def _extract_target_model_ids(self, payload: Dict[str, Any]) -> Optional[List[int]]:
         if not isinstance(payload, Mapping):
             return None
 
@@ -2357,7 +2854,7 @@ class ModelUpdateHandler:
             return {}
 
         to_dict = getattr(metadata, "to_dict", None)
-        if callable(to_dict):
+        if to_dict:
             try:
                 return to_dict()
             except Exception:
@@ -2368,7 +2865,7 @@ class ModelUpdateHandler:
 
         return {}
 
-    async def _read_json(self, request: web.Request) -> Dict:
+    async def _read_json(self, request: web.Request) -> Dict[str, Any]:
         if not request.can_read_body:
             return {}
         try:
@@ -2400,7 +2897,7 @@ class ModelUpdateHandler:
         record,
         *,
         version_context: Optional[Dict[int, Dict[str, Any]]] = None,
-    ) -> Dict:
+    ) -> Dict[str, Any]:
         context = version_context or {}
         # Check user setting for hiding early access versions
         hide_early_access = False
@@ -2429,7 +2926,7 @@ class ModelUpdateHandler:
     @staticmethod
     def _serialize_version(
         version, context: Optional[Dict[str, Any]]
-    ) -> Dict:
+    ) -> Dict[str, Any]:
         context = context or {}
         preview_override = context.get("preview_override")
         preview_url = (
@@ -2569,6 +3066,7 @@ class ModelHandlerSet:
             "bulk_delete_models": self.management.bulk_delete_models,
             "verify_duplicates": self.management.verify_duplicates,
             "get_top_tags": self.query.get_top_tags,
+            "search_tags": self.query.search_tags,
             "get_base_models": self.query.get_base_models,
             "get_model_types": self.query.get_model_types,
             "scan_models": self.query.scan_models,
@@ -2585,6 +3083,20 @@ class ModelHandlerSet:
             "pause_download_get": self.download.pause_download_get,
             "resume_download_get": self.download.resume_download_get,
             "get_download_progress": self.download.get_download_progress,
+            "get_download_queue": self.download.get_download_queue,
+            "add_to_download_queue": self.download.add_to_download_queue,
+            "remove_from_download_queue": self.download.remove_from_download_queue,
+            "move_queue_item_to_top": self.download.move_queue_item_to_top,
+            "move_queue_item_to_end": self.download.move_queue_item_to_end,
+            "clear_download_queue": self.download.clear_download_queue,
+            "get_download_history": self.download.get_download_history,
+            "clear_download_history": self.download.clear_download_history,
+            "delete_download_history_item": self.download.delete_download_history_item,
+            "retry_download_from_history": self.download.retry_download_from_history,
+            "retry_all_failed_downloads": self.download.retry_all_failed_downloads,
+            "complete_download_in_queue": self.download.complete_download_in_queue,
+            "get_download_stats": self.download.get_download_stats,
+            "update_download_queue_status": self.download.update_download_queue_status,
             "get_civitai_versions": self.civitai.get_civitai_versions,
             "get_civitai_model_by_version": self.civitai.get_civitai_model_by_version,
             "get_civitai_model_by_hash": self.civitai.get_civitai_model_by_hash,

py/routes/handlers/preview_handlers.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import mimetypes
 import urllib.parse
@@ -13,7 +14,7 @@ from ...config import config as global_config
 
 logger = logging.getLogger(__name__)
 
-_CHUNK_SIZE = 256 * 1024  # 256 KB
+_CHUNK_SIZE = 1024 * 1024  # 1 MB — balance between streaming iteration overhead and per-chunk memory
 
 # Video file extensions that bypass native sendfile on Windows
 # to avoid IOCP/ProactorEventLoop crashes during client disconnect.
@@ -53,18 +54,51 @@ class PreviewHandler:
 
         if not resolved.is_file():
             logger.debug("Preview file not found at %s", str(resolved))
+            asyncio.create_task(self._cleanup_stale_preview_url(normalized))
             raise web.HTTPNotFound(text="Preview file not found")
 
-        # Video files: stream manually to avoid Windows native sendfile crash.
-        # aiohttp's FileResponse uses _sendfile_native on Windows (IOCP-based),
-        # which breaks when the client disconnects mid-transfer — this happens
-        # constantly when users scroll through a gallery of animated previews.
-        suffix = resolved.suffix.lower()
-        if suffix in _VIDEO_EXTENSIONS:
-            return await self._stream_file(request, resolved)
+        # aiohttp's FileResponse handles range requests, content headers, and
+        # uses kernel sendfile (zero-copy DMA) on Linux/macOS. On Windows it
+        # uses IOCP-based _sendfile_native which can crash when the client
+        # disconnects mid-transfer during fast scrolling. The _stream_file()
+        # fallback is kept for a future compat toggle.
+        #
+        # Set explicit Cache-Control so the browser can cache video (and image)
+        # previews across VirtualScroller recycling cycles. Without this,
+        # Chrome does not cache 206 Partial Content responses for <video>
+        # elements, causing the same video to be re-downloaded on every scroll.
+        resp = web.FileResponse(path=resolved, chunk_size=_CHUNK_SIZE)
+        resp.headers["Cache-Control"] = "public, max-age=86400"
+        return resp
 
-        # aiohttp's FileResponse handles range requests and content headers for us.
-        return web.FileResponse(path=resolved, chunk_size=_CHUNK_SIZE)
+    async def _cleanup_stale_preview_url(self, normalized_preview_path: str) -> None:
+        """Fire-and-forget: clear stale preview_url from all model caches.
+
+        When a preview file is no longer on disk, remove its reference from
+        every cached entry so subsequent list API responses return an empty
+        ``preview_url``, letting the frontend show the no-preview placeholder.
+        """
+        try:
+            from ...services.service_registry import ServiceRegistry
+
+            for service_name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner"):
+                scanner = ServiceRegistry.get_service_sync(service_name)
+                if scanner is None or not hasattr(scanner, "_cache"):
+                    continue
+                cache = getattr(scanner, "_cache", None)
+                if cache is None or not hasattr(cache, "clear_preview_by_path"):
+                    continue
+                cleared = await cache.clear_preview_by_path(normalized_preview_path)
+                if cleared and hasattr(scanner, "_persist_current_cache"):
+                    await scanner._persist_current_cache()
+                    logger.info(
+                        "Cleared stale preview_url for %d %s entries (%s)",
+                        cleared,
+                        service_name,
+                        normalized_preview_path,
+                    )
+        except Exception as exc:
+            logger.debug("Failed to clean up stale preview_url: %s", exc)
 
     async def _stream_file(
         self, request: web.Request, path: Path
@@ -83,6 +117,10 @@ class PreviewHandler:
         resp.content_type = content_type
         resp.content_length = file_size
 
+        # Allow browser caching: video previews rarely change during a session.
+        # The frontend already appends ?t={version} to bust cache on update.
+        resp.headers["Cache-Control"] = "public, max-age=86400"
+
         await resp.prepare(request)
 
         try:

py/routes/handlers/recipe_handlers.py

@@ -10,7 +10,7 @@ import asyncio
 import tempfile
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional
+from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Tuple
 
 from aiohttp import web
 
@@ -32,6 +32,7 @@ from ...utils.civitai_utils import (
     extract_civitai_image_id_from_cdn_url,
     rewrite_preview_url,
 )
+from ...utils.constants import NSFW_LEVELS
 from ...utils.exif_utils import ExifUtils
 from ...recipes.merger import GenParamsMerger
 from ...recipes.enrichment import RecipeEnricher
@@ -43,6 +44,22 @@ EnsureDependenciesCallable = Callable[[], Awaitable[None]]
 RecipeScannerGetter = Callable[[], Any]
 CivitaiClientGetter = Callable[[], Any]
 
+# Cap concurrent preview-dimension reads across requests. With a cold LRU
+# cache one page can touch up to page_size image files; 16 balances SSD and
+# HDD throughput without starving the event loop.
+_DIMS_READ_SEMAPHORE = asyncio.Semaphore(16)
+
+
+async def _read_preview_dims(path: str) -> Optional[Tuple[int, int]]:
+    """Read preview dimensions off the event loop under the concurrency cap.
+
+    PIL I/O runs in a worker thread so it never blocks the event loop, and the
+    semaphore bounds how many files are opened at once even when many list
+    requests land together.
+    """
+    async with _DIMS_READ_SEMAPHORE:
+        return await asyncio.to_thread(ExifUtils.get_image_dimensions, path)
+
 
 @dataclass(frozen=True)
 class RecipeHandlerSet:
@@ -71,6 +88,7 @@ class RecipeHandlerSet:
             "save_recipe": self.management.save_recipe,
             "delete_recipe": self.management.delete_recipe,
             "get_top_tags": self.query.get_top_tags,
+            "search_tags": self.query.search_tags,
             "get_base_models": self.query.get_base_models,
             "get_roots": self.query.get_roots,
             "get_folders": self.query.get_folders,
@@ -102,6 +120,7 @@ class RecipeHandlerSet:
             "check_image_exists": self.management.check_image_exists,
             "import_from_url": self.management.import_from_url,
             "create_from_example": self.management.create_from_example,
+            "reimport_recipe": self.management.reimport_recipe,
         }
 
 
@@ -243,7 +262,8 @@ class RecipeListingHandler:
                 recursive=recursive,
             )
 
-            for item in result.get("items", []):
+            items = result.get("items", [])
+            for item in items:
                 file_path = item.get("file_path")
                 if file_path:
                     item["file_url"] = self.format_recipe_file_url(file_path)
@@ -252,6 +272,26 @@ class RecipeListingHandler:
                 item.setdefault("loras", [])
                 item.setdefault("base_model", "")
 
+            # Batch preview dimension reads with asyncio.gather. The previous
+            # loop awaited asyncio.to_thread once per item, so a page_size=100
+            # request submitted 100 sequential thread calls (50-300ms cold-page
+            # latency). gather runs them concurrently while the semaphore caps
+            # disk opens; dimensions stay omitted (not null) when a preview has
+            # no readable size (video, missing file).
+            to_read = [
+                (i, item.get("file_path"))
+                for i, item in enumerate(items)
+                if item.get("file_path")
+            ]
+            if to_read:
+                dims_list = await asyncio.gather(
+                    *(_read_preview_dims(path) for _, path in to_read)
+                )
+                for (idx, _), dims in zip(to_read, dims_list):
+                    if dims:
+                        item = items[idx]
+                        item["width"], item["height"] = dims
+
             return web.json_response(result)
         except Exception as exc:
             self._logger.error("Error retrieving recipes: %s", exc, exc_info=True)
@@ -315,12 +355,11 @@ class RecipeQueryHandler:
                 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
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+            tag_counts = await self._get_recipe_tag_counts(recipe_scanner)
 
             sorted_tags = [
                 {"tag": tag, "count": count} for tag, count in tag_counts.items()
@@ -331,6 +370,55 @@ class RecipeQueryHandler:
             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 search_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")
+
+            query = request.query.get("q", "")
+            limit = int(request.query.get("limit", "20"))
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+
+            tag_counts = await self._get_recipe_tag_counts(recipe_scanner)
+            normalized_query = (query or "").strip().lower()
+            if not normalized_query:
+                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 if limit > 0 else 20)]}
+                )
+
+            matched = [
+                {"tag": tag, "count": count}
+                for tag, count in tag_counts.items()
+                if normalized_query in tag.lower()
+            ]
+            matched.sort(key=lambda entry: entry["count"], reverse=True)
+            if limit == 0:
+                result = matched
+            else:
+                result = matched[:limit]
+            return web.json_response({"success": True, "tags": result})
+        except Exception as exc:
+            self._logger.error("Error searching recipe tags: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def _get_recipe_tag_counts(self, recipe_scanner) -> Dict[str, int]:
+        """Compute tag->count mapping from cached recipe data."""
+        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
+        return tag_counts
+
     async def get_base_models(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -799,6 +887,126 @@ class RecipeManagementHandler:
             self._logger.error("Error repairing single recipe: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def reimport_recipe(self, request: web.Request) -> web.Response:
+        """Delete a recipe and re-import it from its source URL.
+
+        This gives the recipe a fresh start — re-downloads the image from
+        CivitAI, re-parses EXIF metadata with the current parser, and
+        re-resolves LoRAs / checkpoint. User edits (title, tags, favorite)
+        are carried over from the old recipe.
+        """
+        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"]
+            old_recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+            if not old_recipe:
+                raise RecipeNotFoundError(f"Recipe {recipe_id} not found")
+
+            source_path = old_recipe.get("source_path")
+            if not source_path:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": (
+                            "Recipe has no source URL — cannot re-import. "
+                            "Use repair or manual import instead."
+                        ),
+                    },
+                    status=400,
+                )
+
+            user_edits: dict[str, Any] = {}
+            for key in ("title", "tags", "favorite", "preview_nsfw_level"):
+                if key in old_recipe and old_recipe[key] is not None:
+                    user_edits[key] = old_recipe[key]
+            if "tags" in user_edits and not isinstance(user_edits["tags"], list):
+                del user_edits["tags"]
+
+            old_file_path = old_recipe.get("file_path", "")
+            old_folder = os.path.dirname(old_file_path) if old_file_path else None
+
+            image_id = extract_civitai_image_id(source_path)
+            is_local_file = not image_id and os.path.isfile(source_path)
+
+            if not image_id and not is_local_file:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": (
+                            "Recipe source is neither a valid CivitAI image URL "
+                            "nor an accessible local file. "
+                            "Use repair or manual import instead."
+                        ),
+                    },
+                    status=400,
+                )
+
+            if is_local_file:
+                return await self._do_reimport_from_local(
+                    source_path,
+                    recipe_scanner,
+                    recipe_id=recipe_id,
+                    target_dir=old_folder,
+                    user_edits=user_edits,
+                    old_title=old_recipe.get("title", ""),
+                )
+
+            async with self._import_semaphore:
+                import_response = await self._do_import_from_url(
+                    source_path,
+                    recipe_scanner,
+                    target_dir=old_folder,
+                )
+
+            await self._persistence_service.delete_recipe(
+                recipe_scanner=recipe_scanner, recipe_id=recipe_id
+            )
+
+            body_bytes = import_response.body
+            if not body_bytes:
+                raise RuntimeError("Re-import returned an empty response")
+            import_body = json.loads(body_bytes.decode())
+            new_recipe_id = import_body.get("recipe_id")
+
+            if new_recipe_id and user_edits:
+                try:
+                    await self._persistence_service.update_recipe(
+                        recipe_scanner=recipe_scanner,
+                        recipe_id=new_recipe_id,
+                        updates=user_edits,
+                    )
+                except Exception as exc:
+                    self._logger.warning(
+                        "Re-import succeeded but failed to carry over "
+                        "user edits for new recipe %s: %s",
+                        new_recipe_id,
+                        exc,
+                    )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "old_recipe_id": recipe_id,
+                    "recipe_id": new_recipe_id,
+                    "source_path": source_path,
+                }
+            )
+        except RecipeNotFoundError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=404)
+        except RecipeValidationError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except RecipeDownloadError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error(
+                "Error reimporting recipe: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def get_repair_progress(self, request: web.Request) -> web.Response:
         try:
             progress = self._ws_manager.get_recipe_repair_progress()
@@ -874,10 +1082,10 @@ class RecipeManagementHandler:
         *,
         image_url: str,
         name: str,
-        lora_entries: list,
-        checkpoint_entry: dict,
-        gen_params_request: dict,
-        tags: list,
+        lora_entries: list[Any],
+        checkpoint_entry: Dict[str, Any] | None,
+        gen_params_request: Dict[str, Any] | None,
+        tags: list[Any],
         base_model: str,
         source_path: str,
     ) -> web.Response:
@@ -907,8 +1115,15 @@ class RecipeManagementHandler:
             extension,
             civitai_meta_raw,
             model_version_id,
+            _original_image_url,
         ) = await self._download_remote_media(image_url)
 
+        # Build a version-cached map of local model hashes to cache items so
+        # CivitaiApiMetadataParser can skip CivitAI API calls for models that
+        # exist on disk. Built once and shared by every parse pass below.
+        local_cache = await recipe_scanner.build_local_hash_cache()
+        from ...recipes.parsers.civitai_image import CivitaiApiMetadataParser
+
         # Extract embedded EXIF metadata (offloaded to thread pool in this call)
         embedded_gen_params = {}
         parsed_embedded = None
@@ -930,9 +1145,16 @@ class RecipeManagementHandler:
                         )
                     )
                     if parser:
-                        parsed_embedded = await parser.parse_metadata(
-                            raw_embedded, recipe_scanner=recipe_scanner
-                        )
+                        if isinstance(parser, CivitaiApiMetadataParser):
+                            parsed_embedded = await parser.parse_metadata(
+                                raw_embedded,
+                                recipe_scanner=recipe_scanner,
+                                local_cache=local_cache,
+                            )
+                        else:
+                            parsed_embedded = await parser.parse_metadata(
+                                raw_embedded, recipe_scanner=recipe_scanner
+                            )
                         if parsed_embedded and "gen_params" in parsed_embedded:
                             embedded_gen_params = parsed_embedded["gen_params"]
                     else:
@@ -963,9 +1185,16 @@ class RecipeManagementHandler:
                     civitai_inner_meta
                 )
                 if parser:
-                    civitai_parsed = await parser.parse_metadata(
-                        civitai_inner_meta, recipe_scanner=recipe_scanner
-                    )
+                    if isinstance(parser, CivitaiApiMetadataParser):
+                        civitai_parsed = await parser.parse_metadata(
+                            civitai_inner_meta,
+                            recipe_scanner=recipe_scanner,
+                            local_cache=local_cache,
+                        )
+                    else:
+                        civitai_parsed = await parser.parse_metadata(
+                            civitai_inner_meta, recipe_scanner=recipe_scanner
+                        )
                     if civitai_parsed and "gen_params" in civitai_parsed:
                         # Merge: API gen_params override EXIF at field level,
                         # EXIF fills in fields the API doesn't have.
@@ -998,6 +1227,13 @@ class RecipeManagementHandler:
             if parsed_embedded.get("base_model") and not metadata.get("base_model"):
                 metadata["base_model"] = parsed_embedded["base_model"]
 
+        # Extract preview_nsfw_level from the CivitAI API response
+        # (injected into civitai_meta_raw by _download_remote_media).
+        if isinstance(civitai_meta_raw, dict):
+            bl = civitai_meta_raw.get("browsingLevel")
+            if isinstance(bl, int) and bl > 0:
+                metadata["preview_nsfw_level"] = bl
+
         civitai_client = self._civitai_client_getter()
         await RecipeEnricher.enrich_recipe(
             recipe=metadata,
@@ -1319,7 +1555,9 @@ class RecipeManagementHandler:
             "exclude": False,
         }
 
-    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any, Any]:
+    async def _download_remote_media(
+        self, image_url: str
+    ) -> tuple[bytes, str, Any, Any, Optional[str]]:
         civitai_client = self._civitai_client_getter()
         downloader = await self._downloader_factory()
         temp_path = None
@@ -1391,14 +1629,42 @@ class RecipeManagementHandler:
                     # CivitAI API returns modelVersionIds at the root level of
                     # the image response, NOT inside the meta object.
                     mvids = image_info.get("modelVersionIds")
-                    if mvids and isinstance(civitai_meta_raw, dict):
-                        civitai_meta_raw["modelVersionIds"] = mvids
+                    if mvids:
+                        if isinstance(civitai_meta_raw, dict):
+                            civitai_meta_raw["modelVersionIds"] = mvids
+                        else:
+                            # meta is null but modelVersionIds exists — create a
+                            # minimal dict so downstream parsers can discover
+                            # LoRAs and checkpoints from the API response.
+                            civitai_meta_raw = {"modelVersionIds": mvids}
+
+                    # Inject browsingLevel (canonical integer) so the recipe's
+                    # preview_nsfw_level can be set, enabling proper NSFW blur
+                    # of the preview image.  Fall back to nsfwLevel (string)
+                    # when browsingLevel is absent.
+                    if isinstance(civitai_meta_raw, dict):
+                        browsing_level = image_info.get("browsingLevel")
+                        nsfw_level_str = image_info.get("nsfwLevel")
+                        if isinstance(browsing_level, int) and browsing_level > 0:
+                            civitai_meta_raw["browsingLevel"] = browsing_level
+                        elif (
+                            isinstance(nsfw_level_str, str)
+                            and nsfw_level_str in NSFW_LEVELS
+                        ):
+                            civitai_meta_raw["browsingLevel"] = NSFW_LEVELS[
+                                nsfw_level_str
+                            ]
+
+                original_url = (
+                    image_info.get("url") if civitai_image_id and image_info else None
+                )
 
                 return (
                     file_obj.read(),
                     extension,
                     civitai_meta_raw,
                     model_ver_id,
+                    original_url,
                 )
         except RecipeDownloadError:
             raise
@@ -1432,7 +1698,7 @@ class RecipeManagementHandler:
             if not provider:
                 return ""
 
-            version_info = await provider.get_model_version_info(version_id)
+            version_info = await provider.get_model_version_info(str(version_id))
             if isinstance(version_info, tuple):
                 version_info = version_info[0]
 
@@ -1468,15 +1734,8 @@ class RecipeManagementHandler:
 
             cache = await recipe_scanner.get_cached_data()
 
-            # Build lookup: image_id -> recipe_id from stored source_path
-            image_to_recipe = {}
-            for recipe in getattr(cache, "raw_data", []):
-                source = recipe.get("source_path")
-                if not source:
-                    continue
-                image_id = extract_civitai_image_id(source)
-                if image_id and image_id not in image_to_recipe:
-                    image_to_recipe[image_id] = recipe.get("id")
+            # Use precomputed image_id_map (built once at cache init)
+            image_to_recipe = getattr(cache, "image_id_map", {})
 
             results = {}
             for img_id in requested_ids:
@@ -1512,20 +1771,22 @@ class RecipeManagementHandler:
                     "Could not extract Civitai image ID from URL"
                 )
 
-            # Check for duplicate (fast, before acquiring semaphore), unless force
             if not force:
                 cache = await recipe_scanner.get_cached_data()
-                for recipe in getattr(cache, "raw_data", []):
-                    source = recipe.get("source_path")
-                    if source:
-                        existing_id = extract_civitai_image_id(source)
-                        if existing_id == image_id:
-                            return web.json_response({
-                                "success": True,
-                                "recipe_id": recipe.get("id"),
-                                "name": recipe.get("title", ""),
-                                "already_exists": True,
-                            })
+                image_to_recipe = getattr(cache, "image_id_map", {})
+                existing_recipe_id = image_to_recipe.get(image_id)
+                if existing_recipe_id:
+                    recipe_name = ""
+                    for recipe in getattr(cache, "raw_data", []):
+                        if str(recipe.get("id", "")) == existing_recipe_id:
+                            recipe_name = recipe.get("title", "") or ""
+                            break
+                    return web.json_response({
+                        "success": True,
+                        "recipe_id": existing_recipe_id,
+                        "name": recipe_name,
+                        "already_exists": True,
+                    })
 
             async with self._import_semaphore:
                 return await self._do_import_from_url(image_url, recipe_scanner)
@@ -1543,6 +1804,9 @@ class RecipeManagementHandler:
         self,
         image_url: str,
         recipe_scanner: Any,
+        *,
+        recipe_id: str | None = None,
+        target_dir: str | None = None,
     ) -> web.Response:
         image_id = extract_civitai_image_id(image_url)
         if not image_id:
@@ -1550,10 +1814,16 @@ class RecipeManagementHandler:
                 "Could not extract Civitai image ID from URL"
             )
 
-        image_bytes, extension, civitai_meta_raw, model_version_id = (
+        image_bytes, extension, civitai_meta_raw, model_version_id, original_image_url = (
             await self._download_remote_media(image_url)
         )
 
+        # Build a version-cached map of local model hashes to cache items so
+        # CivitaiApiMetadataParser can skip CivitAI API calls for models that
+        # exist on disk. Built once and shared by every parse pass below.
+        local_cache = await recipe_scanner.build_local_hash_cache()
+        from ...recipes.parsers.civitai_image import CivitaiApiMetadataParser
+
         # Extract embedded EXIF metadata
         embedded_gen_params = {}
         parsed_embedded = None
@@ -1575,9 +1845,16 @@ class RecipeManagementHandler:
                         )
                     )
                     if parser:
-                        parsed_embedded = await parser.parse_metadata(
-                            raw_embedded, recipe_scanner=recipe_scanner
-                        )
+                        if isinstance(parser, CivitaiApiMetadataParser):
+                            parsed_embedded = await parser.parse_metadata(
+                                raw_embedded,
+                                recipe_scanner=recipe_scanner,
+                                local_cache=local_cache,
+                            )
+                        else:
+                            parsed_embedded = await parser.parse_metadata(
+                                raw_embedded, recipe_scanner=recipe_scanner
+                            )
                         if parsed_embedded and "gen_params" in parsed_embedded:
                             embedded_gen_params = parsed_embedded["gen_params"]
             finally:
@@ -1588,6 +1865,58 @@ class RecipeManagementHandler:
                 "Failed to extract embedded metadata: %s", exc
             )
 
+        if not parsed_embedded and original_image_url:
+            self._logger.debug(
+                "Optimized image has no embedded metadata, "
+                "falling back to original: %s",
+                original_image_url,
+            )
+            try:
+                downloader = await self._downloader_factory()
+                with tempfile.NamedTemporaryFile(
+                    suffix=".png", delete=False
+                ) as tmp:
+                    orig_tmp_path = tmp.name
+                try:
+                    success, _ = await downloader.download_file(
+                        original_image_url, orig_tmp_path, use_auth=False
+                    )
+                    if success:
+                        raw_orig = await asyncio.to_thread(
+                            ExifUtils.extract_image_metadata, orig_tmp_path
+                        )
+                        if raw_orig:
+                            parser = (
+                                self._analysis_service._recipe_parser_factory.create_parser(
+                                    raw_orig
+                                )
+                            )
+                            if parser:
+                                if isinstance(parser, CivitaiApiMetadataParser):
+                                    parsed_embedded = await parser.parse_metadata(
+                                        raw_orig,
+                                        recipe_scanner=recipe_scanner,
+                                        local_cache=local_cache,
+                                    )
+                                else:
+                                    parsed_embedded = await parser.parse_metadata(
+                                        raw_orig, recipe_scanner=recipe_scanner
+                                    )
+                                if (
+                                    parsed_embedded
+                                    and "gen_params" in parsed_embedded
+                                ):
+                                    embedded_gen_params = parsed_embedded[
+                                        "gen_params"
+                                    ]
+                finally:
+                    if os.path.exists(orig_tmp_path):
+                        os.unlink(orig_tmp_path)
+            except Exception as exc:
+                self._logger.warning(
+                    "Failed to extract metadata from original image: %s", exc
+                )
+
         # Parse CivitAI API meta to discover all resources from modelVersionIds.
         # Run unconditionally — EXIF parsing succeeds for gen_params but misses
         # LoRAs (modelVersionIds is NOT in the image EXIF).
@@ -1606,9 +1935,16 @@ class RecipeManagementHandler:
                     civitai_inner_meta
                 )
                 if parser:
-                    civitai_parsed = await parser.parse_metadata(
-                        civitai_inner_meta, recipe_scanner=recipe_scanner
-                    )
+                    if isinstance(parser, CivitaiApiMetadataParser):
+                        civitai_parsed = await parser.parse_metadata(
+                            civitai_inner_meta,
+                            recipe_scanner=recipe_scanner,
+                            local_cache=local_cache,
+                        )
+                    else:
+                        civitai_parsed = await parser.parse_metadata(
+                            civitai_inner_meta, recipe_scanner=recipe_scanner
+                        )
                     if civitai_parsed and "gen_params" in civitai_parsed:
                         # Merge: API gen_params override EXIF at field level,
                         # EXIF fills in fields the API doesn't have.
@@ -1624,6 +1960,13 @@ class RecipeManagementHandler:
             "source_path": image_url,
         }
 
+        # Extract preview_nsfw_level from the CivitAI API response
+        # (injected into civitai_meta_raw by _download_remote_media).
+        if isinstance(civitai_meta_raw, dict):
+            bl = civitai_meta_raw.get("browsingLevel")
+            if isinstance(bl, int) and bl > 0:
+                metadata["preview_nsfw_level"] = bl
+
         if civitai_parsed:
             civitai_loras = civitai_parsed.get("loras", [])
             if civitai_loras and not metadata.get("loras"):
@@ -1671,9 +2014,104 @@ class RecipeManagementHandler:
             tags=[],
             metadata=metadata,
             extension=extension,
+            recipe_id=recipe_id,
+            target_dir=target_dir,
         )
         return web.json_response(result.payload, status=result.status)
 
+    async def _do_reimport_from_local(
+        self,
+        file_path: str,
+        recipe_scanner: Any,
+        *,
+        recipe_id: str,
+        target_dir: str | None,
+        user_edits: dict[str, Any],
+        old_title: str,
+    ) -> web.Response:
+        """Re-import a recipe from a local image file.
+
+        Reads the original source file, re-parses its EXIF metadata, saves a
+        fresh recipe, then deletes the old one.
+        """
+        normalized = os.path.normpath(file_path)
+        if not os.path.isfile(normalized):
+            raise RecipeNotFoundError(
+                f"Source file no longer accessible: {normalized}"
+            )
+
+        with open(normalized, "rb") as fh:
+            image_bytes = fh.read()
+
+        extension = os.path.splitext(normalized)[1].lower() or ".png"
+
+        analysis_result = await self._analysis_service.analyze_local_image(
+            file_path=normalized,
+            recipe_scanner=recipe_scanner,
+        )
+        analysis_payload: dict[str, Any] = analysis_result.payload
+
+        gen_params = analysis_payload.get("gen_params") or {}
+        loras = analysis_payload.get("loras") or []
+        checkpoint = analysis_payload.get("checkpoint")
+        base_model = analysis_payload.get("base_model", "")
+
+        metadata: dict[str, Any] = {
+            "base_model": base_model,
+            "loras": loras,
+            "gen_params": gen_params,
+            "source_path": normalized,
+        }
+        if checkpoint:
+            metadata["checkpoint"] = checkpoint
+
+        prompt = (
+            gen_params.get("prompt")
+            or gen_params.get("positivePrompt")
+            or ""
+        )
+        name = " ".join(str(prompt).split()[:10]) if prompt else old_title
+
+        result = await self._persistence_service.save_recipe(
+            recipe_scanner=recipe_scanner,
+            image_bytes=image_bytes,
+            image_base64=analysis_payload.get("image_base64"),
+            name=name,
+            tags=[],
+            metadata=metadata,
+            extension=extension,
+            target_dir=target_dir,
+        )
+
+        await self._persistence_service.delete_recipe(
+            recipe_scanner=recipe_scanner, recipe_id=recipe_id
+        )
+
+        new_recipe_id = result.payload.get("recipe_id")
+        if new_recipe_id and user_edits:
+            try:
+                await self._persistence_service.update_recipe(
+                    recipe_scanner=recipe_scanner,
+                    recipe_id=new_recipe_id,
+                    updates=user_edits,
+                )
+            except Exception as exc:
+                self._logger.warning(
+                    "Re-import (local) succeeded but failed to carry over "
+                    "user edits for recipe %s: %s",
+                    new_recipe_id,
+                    exc,
+                )
+
+        return web.json_response(
+            {
+                "success": True,
+                "old_recipe_id": recipe_id,
+                "recipe_id": new_recipe_id,
+                "source_path": normalized,
+            }
+        )
+
     async def create_from_example(self, request: web.Request) -> web.Response:
         """Create a recipe from a model's example image using cached metadata.
 
@@ -1718,33 +2156,44 @@ class RecipeManagementHandler:
             parsed_input = {**image_data, **inner_meta}
             parsed_input.pop("meta", None)
 
-            # Build a local cache of {hash → cache_item} so the parser can
-            # skip CivitAI API calls for models that exist on disk.
-            local_cache: Dict[str, Dict[str, Any]] = {}
-            lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
-            if lora_scanner and model_hash:
-                try:
-                    parent_cache_data = await lora_scanner.get_cached_data()
-                    for item in getattr(parent_cache_data, "raw_data", []):
-                        if item.get("sha256", "").lower() == model_hash.lower():
-                            local_cache[model_hash.lower()] = item
-                            # Compute AutoV3 so the parser can also match on
-                            # that hash type (CivitAI metadata resources use
-                            # AutoV3).
-                            file_path = item.get("file_path")
-                            if file_path and os.path.exists(file_path):
-                                try:
-                                    from ...utils.file_utils import (
-                                        calculate_autov3,
-                                    )
-                                    autov3 = calculate_autov3(file_path)
-                                    if autov3:
-                                        local_cache[autov3.lower()] = item
-                                except Exception:
-                                    pass
-                            break
-                except Exception:
-                    pass
+            # Build the shared local hash cache so the parser can skip CivitAI
+            # API calls for models that exist on disk.
+            local_cache: Dict[str, Dict[str, Any]] = (
+                await recipe_scanner.build_local_hash_cache()
+            )
+
+            # Bounded supplement for un-backfilled parents. The shared builder
+            # never computes autov3; when the parent model exists on disk but
+            # its cached entry has no stored AutoV3, compute it for that single
+            # file and register the AutoV3 key so the parser can also match on
+            # that hash type (CivitAI metadata resources use AutoV3). This runs
+            # whenever the parent is found with an empty autov3, independent of
+            # whether the sha256 key is already present in the shared cache.
+            if model_hash:
+                lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
+                if lora_scanner:
+                    try:
+                        parent_cache_data = await lora_scanner.get_cached_data()
+                        for item in getattr(parent_cache_data, "raw_data", []):
+                            if item.get("sha256", "").lower() == model_hash.lower():
+                                autov3 = (item.get("autov3") or "").lower()
+                                if not autov3:
+                                    file_path = item.get("file_path")
+                                    if file_path and os.path.exists(file_path):
+                                        try:
+                                            from ...utils.file_utils import (
+                                                calculate_autov3,
+                                            )
+                                            autov3 = (
+                                                calculate_autov3(file_path) or ""
+                                            ).lower()
+                                        except Exception:
+                                            pass
+                                if autov3:
+                                    local_cache[autov3] = item
+                                break
+                    except Exception:
+                        pass
 
             parser = self._analysis_service._recipe_parser_factory.create_parser(
                 parsed_input
@@ -1776,10 +2225,10 @@ class RecipeManagementHandler:
             parent_model_id: int | None = None
             parent_version_name: str | None = None
             parent_model_name: str | None = None
-            # Prefer sha256 key; fall back to any cached entry.
+            # Resolve the parent strictly by its sha256 key. There is no
+            # arbitrary fallback: with a full-library cache, picking any entry
+            # would corrupt the isDeleted reconciliation below.
             parent_item = local_cache.get(model_hash.lower()) if model_hash else None
-            if parent_item is None and local_cache:
-                parent_item = next(iter(local_cache.values()))
             if parent_item:
                 civ = parent_item.get("civitai") or {}
                 if isinstance(civ, dict):
@@ -1913,6 +2362,31 @@ class RecipeManagementHandler:
                         "Failed to download image for recipe: %s", exc
                     )
 
+            # Fallback: try to locate a custom image on disk using model_hash + image id
+            if image_bytes is None:
+                image_id = image_data.get("id") or ""
+                if image_id and model_hash:
+                    from ...utils.example_images_paths import get_model_folder
+                    model_folder = get_model_folder(model_hash)
+                    if model_folder and os.path.exists(model_folder):
+                        for fname in os.listdir(model_folder):
+                            if f"custom_{image_id}" in fname:
+                                ext = os.path.splitext(fname)[1].lower()
+                                if ext not in (".jpg", ".jpeg", ".png", ".webp", ".gif"):
+                                    continue
+                                fpath = os.path.join(model_folder, fname)
+                                if os.path.isfile(fpath):
+                                    try:
+                                        with open(fpath, "rb") as f:
+                                            image_bytes = f.read()
+                                        extension = ext
+                                    except Exception as exc:
+                                        self._logger.warning(
+                                            "Failed to read custom image file %s: %s",
+                                            fpath, exc,
+                                        )
+                                break
+
             prompt = (
                 (parsed.get("gen_params") or {}).get("prompt") or ""
             )
@@ -1970,7 +2444,7 @@ class RecipeAnalysisHandler:
             content_type = request.headers.get("Content-Type", "")
             if "multipart/form-data" in content_type:
                 reader = await request.multipart()
-                field = await reader.next()
+                field: Any = await reader.next()
                 if field is None or field.name != "image":
                     raise RecipeValidationError("No image field found")
                 image_chunks = bytearray()

py/routes/lora_routes.py

@@ -1,8 +1,8 @@
 import asyncio
 import logging
 from aiohttp import web
-from typing import Dict
-from server import PromptServer  # type: ignore
+from typing import Any, Dict
+from server import PromptServer  # pyright: ignore[reportMissingImports]
 
 from .base_model_routes import BaseModelRoutes
 from .model_route_registrar import ModelRouteRegistrar
@@ -31,13 +31,13 @@ class LoraRoutes(BaseModelRoutes):
         # Attach service dependencies
         self.attach_service(self.service)
 
-    def setup_routes(self, app: web.Application):
+    def setup_routes(self, app: web.Application, prefix: str = "loras"):
         """Setup LoRA routes"""
         # Schedule service initialization on app startup
         app.on_startup.append(lambda _: self.initialize_services())
 
         # Setup common routes with 'loras' prefix (includes page route)
-        super().setup_routes(app, "loras")
+        super().setup_routes(app, prefix)
 
     def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
         """Setup LoRA-specific routes"""
@@ -73,7 +73,7 @@ class LoraRoutes(BaseModelRoutes):
             "POST", "/api/lm/{prefix}/get_trigger_words", prefix, self.get_trigger_words
         )
 
-    def _parse_specific_params(self, request: web.Request) -> Dict:
+    def _parse_specific_params(self, request: web.Request) -> Dict[str, Any]:
         """Parse LoRA-specific parameters"""
         params = {}
 
@@ -119,25 +119,6 @@ class LoraRoutes(BaseModelRoutes):
             logger.error(f"Error getting letter counts: {e}")
             return web.json_response({"success": False, "error": str(e)}, status=500)
 
-    async def get_lora_notes(self, request: web.Request) -> web.Response:
-        """Get notes for a specific LoRA file"""
-        try:
-            lora_name = request.query.get("name")
-            if not lora_name:
-                return web.Response(text="Lora file name is required", status=400)
-
-            notes = await self.service.get_lora_notes(lora_name)
-            if notes is not None:
-                return web.json_response({"success": True, "notes": notes})
-            else:
-                return web.json_response(
-                    {"success": False, "error": "LoRA not found in cache"}, status=404
-                )
-
-        except Exception as e:
-            logger.error(f"Error getting lora notes: {e}", exc_info=True)
-            return web.json_response({"success": False, "error": str(e)}, status=500)
-
     async def get_lora_trigger_words(self, request: web.Request) -> web.Response:
         """Get trigger words for a specific LoRA file"""
         try:
@@ -168,52 +149,6 @@ class LoraRoutes(BaseModelRoutes):
             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:
-            lora_name = request.query.get("name")
-            if not lora_name:
-                return web.Response(text="Lora file name is required", status=400)
-
-            preview_url = await self.service.get_lora_preview_url(lora_name)
-            if preview_url:
-                return web.json_response({"success": True, "preview_url": preview_url})
-            else:
-                return web.json_response(
-                    {
-                        "success": False,
-                        "error": "No preview URL found for the specified lora",
-                    },
-                    status=404,
-                )
-
-        except Exception as e:
-            logger.error(f"Error getting lora preview URL: {e}", exc_info=True)
-            return web.json_response({"success": False, "error": str(e)}, status=500)
-
-    async def get_lora_civitai_url(self, request: web.Request) -> web.Response:
-        """Get the Civitai URL for a LoRA file"""
-        try:
-            lora_name = request.query.get("name")
-            if not lora_name:
-                return web.Response(text="Lora file name is required", status=400)
-
-            result = await self.service.get_lora_civitai_url(lora_name)
-            if result["civitai_url"]:
-                return web.json_response({"success": True, **result})
-            else:
-                return web.json_response(
-                    {
-                        "success": False,
-                        "error": "No Civitai data found for the specified lora",
-                    },
-                    status=404,
-                )
-
-        except Exception as e:
-            logger.error(f"Error getting lora Civitai URL: {e}", exc_info=True)
-            return web.json_response({"success": False, "error": str(e)}, status=500)
-
     async def get_random_loras(self, request: web.Request) -> web.Response:
         """Get random LoRAs based on filters and strength ranges"""
         try:
@@ -337,7 +272,7 @@ class LoraRoutes(BaseModelRoutes):
                     graph_identifier = entry.get("graph_id")
 
                 try:
-                    parsed_node_id = int(node_identifier)
+                    parsed_node_id = int(node_identifier)  # pyright: ignore[reportArgumentType]
                 except (TypeError, ValueError):
                     parsed_node_id = node_identifier
 

py/routes/misc_route_registrar.py

@@ -5,7 +5,7 @@ miscellaneous endpoints share a consistent registration flow.
 """
 
 from dataclasses import dataclass
-from typing import Callable, Iterable, Mapping
+from typing import Any, Callable, Iterable, Mapping
 
 from aiohttp import web
 
@@ -22,6 +22,8 @@ class RouteDefinition:
 MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/settings", "get_settings"),
     RouteDefinition("POST", "/api/lm/settings", "update_settings"),
+    RouteDefinition("GET", "/api/lm/llm/models", "get_llm_models"),
+    RouteDefinition("GET", "/api/lm/llm/provider-models", "get_provider_models"),
     RouteDefinition("GET", "/api/lm/doctor/diagnostics", "get_doctor_diagnostics"),
     RouteDefinition("POST", "/api/lm/doctor/repair-cache", "repair_doctor_cache"),
     RouteDefinition("POST", "/api/lm/doctor/resolve-filename-conflicts", "resolve_doctor_filename_conflicts"),
@@ -37,10 +39,12 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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/update-lora-code", "get_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("POST", "/api/lm/update-node-widget", "update_node_widget"),
+    RouteDefinition("GET", "/api/lm/update-node-widget", "get_update_node_widget"),
     RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
     RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
     RouteDefinition("GET", "/api/lm/check-models-exist", "check_models_exist"),
@@ -94,6 +98,26 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/delete-model-version", "delete_model_version"
     ),
+    # Hugging Face model endpoints
+    RouteDefinition(
+        "GET", "/api/lm/hf-repo-files", "get_hf_repo_files"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/download-hf-model", "download_hf_model"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/set-hf-url", "set_hf_url"
+    ),
+    # Agent skill endpoints
+    RouteDefinition(
+        "GET", "/api/lm/agent/skills", "get_agent_skills"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/agent/execute/{skill_name}", "execute_agent_skill"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/agent/cancel", "cancel_agent_skill"
+    ),
 )
 
 
@@ -123,7 +147,7 @@ class MiscRouteRegistrar:
                 handler_lookup[definition.handler_name],
             )
 
-    def _bind(self, method: str, path: str, handler: Callable) -> None:
+    def _bind(self, method: str, path: str, handler: Callable[..., Any]) -> 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

@@ -7,7 +7,7 @@ import os
 from typing import Awaitable, Callable, Mapping
 
 from aiohttp import web
-from server import PromptServer  # type: ignore
+from server import PromptServer  # pyright: ignore[reportMissingImports]
 
 from ..services.metadata_service import (
     get_metadata_archive_manager,
@@ -39,6 +39,8 @@ from .handlers.misc_handlers import (
     build_service_registry_adapter,
 )
 from .handlers.base_model_handlers import BaseModelHandlerSet
+from .handlers.hf_handlers import HfHandler
+from .handlers.agent_handlers import AgentHandler
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
@@ -136,6 +138,8 @@ class MiscRoutes:
         doctor = DoctorHandler(settings_service=self._settings)
         example_workflows = ExampleWorkflowsHandler()
         base_model = BaseModelHandlerSet()
+        hf_handler = HfHandler()
+        agent_handler = AgentHandler()
 
         return self._handler_set_factory(
             health=health,
@@ -155,6 +159,8 @@ class MiscRoutes:
             doctor=doctor,
             example_workflows=example_workflows,
             base_model=base_model,
+            hf_handler=hf_handler,
+            agent_handler=agent_handler,
         )
 
 

py/routes/model_route_registrar.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Callable, Iterable, Mapping
+from typing import Any, Callable, Iterable, Mapping
 
 from aiohttp import web
 
@@ -46,6 +46,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[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}/search-tags", "search_tags"),
     RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/model-types", "get_model_types"),
     RouteDefinition("GET", "/api/lm/{prefix}/scan", "scan_models"),
@@ -107,6 +108,40 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/download-progress/{download_id}", "get_download_progress"
     ),
+    RouteDefinition("GET", "/api/lm/downloads/queue", "get_download_queue"),
+    RouteDefinition("GET", "/api/lm/downloads/queue/add", "add_to_download_queue"),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/remove", "remove_from_download_queue"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/move-to-top", "move_queue_item_to_top"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/move-to-end", "move_queue_item_to_end"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/clear", "clear_download_queue"
+    ),
+    RouteDefinition("GET", "/api/lm/downloads/history", "get_download_history"),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/history/clear", "clear_download_history"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/history/delete", "delete_download_history_item"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/history/retry", "retry_download_from_history"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/history/retry-all", "retry_all_failed_downloads"
+    ),
+    RouteDefinition("GET", "/api/lm/downloads/stats", "get_download_stats"),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/complete", "complete_download_in_queue"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/status", "update_download_queue_status"
+    ),
     RouteDefinition("POST", "/api/lm/{prefix}/cancel-task", "cancel_task"),
     RouteDefinition("GET", "/{prefix}", "handle_models_page"),
 )
@@ -139,15 +174,15 @@ class ModelRouteRegistrar:
                 handler_lookup[definition.handler_name],
             )
 
-    def add_route(self, method: str, path: str, handler: Callable) -> None:
+    def add_route(self, method: str, path: str, handler: Callable[..., Any]) -> None:
         self._bind_route(method, path, handler)
 
     def add_prefixed_route(
-        self, method: str, path_template: str, prefix: str, handler: Callable
+        self, method: str, path_template: str, prefix: str, handler: Callable[..., Any]
     ) -> None:
         self._bind_route(method, path_template.replace("{prefix}", prefix), handler)
 
-    def _bind_route(self, method: str, path: str, handler: Callable) -> None:
+    def _bind_route(self, method: str, path: str, handler: Callable[..., Any]) -> None:
         add_method_name = self._METHOD_MAP[method.upper()]
         add_method = getattr(self._app.router, add_method_name)
         add_method(path, handler)

py/routes/recipe_route_registrar.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Callable, Mapping
+from typing import Any, Callable, Mapping
 
 from aiohttp import web
 
@@ -29,6 +29,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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/search-tags", "search_tags"),
     RouteDefinition("GET", "/api/lm/recipes/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/recipes/roots", "get_roots"),
     RouteDefinition("GET", "/api/lm/recipes/folders", "get_folders"),
@@ -78,6 +79,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "POST", "/api/lm/recipes/create-from-example", "create_from_example"
     ),
+    RouteDefinition(
+        "POST", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
+    ),
 )
 
 
@@ -101,7 +105,7 @@ class RecipeRouteRegistrar:
             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:
+    def _bind_route(self, method: str, path: str, handler: Callable[..., Any]) -> 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

@@ -11,6 +11,8 @@ from ..config import config
 from ..services.settings_manager import get_settings_manager
 from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
+from ..services.model_query import normalize_sub_type, resolve_sub_type
+from ..utils.constants import VALID_LORA_SUB_TYPES, VALID_CHECKPOINT_SUB_TYPES
 from ..utils.usage_stats import UsageStats
 
 logger = logging.getLogger(__name__)
@@ -38,10 +40,11 @@ class StatsRoutes:
     """Route handlers for Statistics page and API endpoints"""
     
     def __init__(self):
-        self.lora_scanner = None
-        self.checkpoint_scanner = None
-        self.embedding_scanner = None
-        self.usage_stats = None
+        self.lora_scanner: Any = None
+        self.checkpoint_scanner: Any = None
+        self.embedding_scanner: Any = None
+        self.usage_stats: Any = None
+        self._i18n_filter_added = False
         self.template_env = jinja2.Environment(
             loader=jinja2.FileSystemLoader(config.templates_path),
             autoescape=True
@@ -93,9 +96,9 @@ class StatsRoutes:
             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'):
+            if not self._i18n_filter_added:
                 self.template_env.filters['t'] = server_i18n.create_template_filter()
-                self.template_env._i18n_filter_added = True
+                self._i18n_filter_added = True
 
             template = self.template_env.get_template('statistics.html')
             rendered = template.render(
@@ -140,6 +143,21 @@ class StatsRoutes:
             # Get usage statistics
             usage_data = await self.usage_stats.get_stats()
             
+            # CivitAI model type distribution across all model types
+            # Use the same logic as the filter panel: normalize_sub_type(resolve_sub_type(entry))
+            # with sub-type validation per model type
+            model_types_counter: Counter[str] = Counter()
+            for entry in lora_cache.raw_data:
+                ntype = normalize_sub_type(resolve_sub_type(entry))
+                if ntype and ntype in VALID_LORA_SUB_TYPES:
+                    model_types_counter[ntype] += 1
+            for entry in checkpoint_cache.raw_data:
+                ntype = normalize_sub_type(resolve_sub_type(entry))
+                if ntype and ntype in VALID_CHECKPOINT_SUB_TYPES:
+                    model_types_counter[ntype] += 1
+            # Embeddings: always count as "embedding" regardless of CivitAI sub-type
+            model_types_counter['embedding'] = len(embedding_cache.raw_data)
+            
             return web.json_response({
                 'success': True,
                 'data': {
@@ -154,7 +172,8 @@ class StatsRoutes:
                     'total_generations': usage_data.get('total_executions', 0),
                     'unused_loras': self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {})),
                     'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {})),
-                    'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
+                    'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {})),
+                    'model_types_distribution': dict(model_types_counter.most_common())
                 }
             })
             
@@ -459,9 +478,12 @@ class StatsRoutes:
                 if unused_lora_percent > 50:
                     insights.append({
                         'type': 'warning',
-                        'title': 'High Number of Unused LoRAs',
-                        'description': f'{unused_lora_percent:.1f}% of your LoRAs ({unused_loras}/{total_loras}) have never been used.',
-                        'suggestion': 'Consider organizing or archiving unused models to free up storage space.'
+                        'key': 'insights.unusedLoras.high',
+                        'params': {
+                            'percent': f'{unused_lora_percent:.1f}',
+                            'count': str(unused_loras),
+                            'total': str(total_loras)
+                        }
                     })
             
             if total_checkpoints > 0:
@@ -469,9 +491,12 @@ class StatsRoutes:
                 if unused_checkpoint_percent > 30:
                     insights.append({
                         'type': 'warning',
-                        'title': 'Unused Checkpoints Detected',
-                        'description': f'{unused_checkpoint_percent:.1f}% of your checkpoints ({unused_checkpoints}/{total_checkpoints}) have never been used.',
-                        'suggestion': 'Review and consider removing checkpoints you no longer need.'
+                        'key': 'insights.unusedCheckpoints.detected',
+                        'params': {
+                            'percent': f'{unused_checkpoint_percent:.1f}',
+                            'count': str(unused_checkpoints),
+                            'total': str(total_checkpoints)
+                        }
                     })
             
             if total_embeddings > 0:
@@ -479,9 +504,12 @@ class StatsRoutes:
                 if unused_embedding_percent > 50:
                     insights.append({
                         'type': 'warning',
-                        'title': 'High Number of Unused Embeddings',
-                        'description': f'{unused_embedding_percent:.1f}% of your embeddings ({unused_embeddings}/{total_embeddings}) have never been used.',
-                        'suggestion': 'Consider organizing or archiving unused embeddings to optimize your collection.'
+                        'key': 'insights.unusedEmbeddings.high',
+                        'params': {
+                            'percent': f'{unused_embedding_percent:.1f}',
+                            'count': str(unused_embeddings),
+                            'total': str(total_embeddings)
+                        }
                     })
             
             # Storage insights
@@ -492,18 +520,20 @@ class StatsRoutes:
             if total_size > 100 * 1024 * 1024 * 1024:  # 100GB
                 insights.append({
                     'type': 'info',
-                    'title': 'Large Collection Detected',
-                    'description': f'Your model collection is using {self._format_size(total_size)} of storage.',
-                    'suggestion': 'Consider using external storage or cloud solutions for better organization.'
+                    'key': 'insights.collection.large',
+                    'params': {
+                        'size': self._format_size(total_size)
+                    }
                 })
             
             # Recent activity insight
             if usage_data.get('total_executions', 0) > 100:
                 insights.append({
                     'type': 'success',
-                    'title': 'Active User',
-                    'description': f'You\'ve completed {usage_data["total_executions"]} generations so far!',
-                    'suggestion': 'Keep exploring and creating amazing content with your models.'
+                    'key': 'insights.activity.active',
+                    'params': {
+                        'count': str(usage_data['total_executions'])
+                    }
                 })
             
             return web.json_response({
@@ -520,7 +550,7 @@ class StatsRoutes:
                 'error': str(e)
             }, status=500)
 
-    def _count_unused_models(self, models: List[Dict], usage_data: Dict) -> int:
+    def _count_unused_models(self, models: List[Dict[str, Any]], usage_data: Dict[str, Any]) -> int:
         """Count models that have never been used"""
         used_hashes = set(usage_data.keys())
         unused_count = 0
@@ -531,7 +561,7 @@ class StatsRoutes:
                 
         return unused_count
 
-    def _get_top_used_models(self, usage_data: Dict, model_map: Dict, limit: int) -> List[Dict]:
+    def _get_top_used_models(self, usage_data: Dict[str, Any], model_map: Dict[str, Any], limit: int) -> List[Dict[str, Any]]:
         """Get top used models with their metadata"""
         sorted_usage = sorted(usage_data.items(), key=lambda x: x[1].get('total', 0), reverse=True)
         
@@ -549,7 +579,7 @@ class StatsRoutes:
         
         return top_models
 
-    def _get_usage_timeline(self, usage_data: Dict, days: int) -> List[Dict]:
+    def _get_usage_timeline(self, usage_data: Dict[str, Any], days: int) -> List[Dict[str, Any]]:
         """Get usage timeline for the past N days"""
         timeline = []
         today = datetime.now()
@@ -585,7 +615,7 @@ class StatsRoutes:
         
         return list(reversed(timeline))  # Oldest to newest
 
-    def _format_size(self, size_bytes: int) -> str:
+    def _format_size(self, size_bytes: float) -> str:
         """Format file size in human readable format"""
         for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
             if size_bytes < 1024.0:

py/routes/update_routes.py

@@ -1,13 +1,12 @@
 import os
 import logging
 import toml
-import git
 import zipfile
 import shutil
 import tempfile
 import asyncio
 from aiohttp import web, ClientError
-from typing import Dict, List
+from typing import Any, Dict, List, cast
 
 from ..utils.settings_paths import ensure_settings_file
 from ..services.downloader import get_downloader
@@ -17,6 +16,105 @@ logger = logging.getLogger(__name__)
 
 NETWORK_EXCEPTIONS = (ClientError, OSError, asyncio.TimeoutError)
 
+# User-managed directories that live inside the plugin folder (portable
+# mode) and must survive a Git-based update. ``git clean -fd`` would
+# otherwise delete them because they are untracked and, in released tags,
+# not listed in ``.gitignore``. ``-e`` excludes a path from cleaning
+# regardless of whether it is ignored.
+_PRESERVE_DIRS = ('settings.json', 'civitai', 'wildcards', 'backups', 'stats', 'logs', 'cache', 'model_cache')
+
+
+def _clean_excludes() -> List[str]:
+    """Build the ``-e`` arguments for ``git clean`` from :data:`_PRESERVE_DIRS`."""
+    excludes: List[str] = []
+    for name in _PRESERVE_DIRS:
+        excludes.append('-e')
+        excludes.append(name)
+        # For directories, also exclude nested matches explicitly
+        # (``-e dir`` alone matches the dir entry; ``-e dir/**`` guards
+        # contents under all git versions as defense-in-depth).
+        excludes.append('-e')
+        excludes.append(f'{name}/**')
+    return excludes
+
+
+def _stage_preserved_items(plugin_root: str) -> tuple[str, list[str]]:
+    """Move preserved user-data items to a temp directory outside *plugin_root*.
+
+    This ensures that ``git reset --hard``, ``git clean -fd``, and ZIP-based
+    replacement cannot touch these files even when ``-e`` exclusion patterns
+    are mishandled (e.g. on Windows where forward-slash patterns may not
+    match backslash-prefixed paths in some Git builds, or where file locks
+    prevent deletion/recreation).
+
+    Returns:
+        ``(backup_root, staged_names)``: the temp directory path and the
+        list of item names that were successfully moved.
+    """
+    backup_root = tempfile.mkdtemp(prefix='lora_manager_update_')
+    staged: list[str] = []
+    for name in _PRESERVE_DIRS:
+        src = os.path.join(plugin_root, name)
+        if not os.path.lexists(src):
+            continue
+        dst = os.path.join(backup_root, name)
+        try:
+            shutil.move(src, dst)
+            staged.append(name)
+            logger.debug("Staged '%s' for update safety", name)
+        except OSError:
+            # ``shutil.move`` may fail on Windows if a file handle inside
+            # the directory is still open (e.g. a SQLite WAL file). Fall
+            # back to copy-then-remove.
+            logger.debug("Move failed for '%s', falling back to copy", name)
+            try:
+                if os.path.isdir(src) and not os.path.islink(src):
+                    shutil.copytree(src, dst, symlinks=True)
+                    shutil.rmtree(src, ignore_errors=True)
+                else:
+                    shutil.copy2(src, dst)
+                    os.remove(src)
+                staged.append(name)
+                logger.info("Copied (then removed) '%s' for update safety", name)
+            except Exception as exc:
+                logger.warning(
+                    "Could not stage '%s': %s (will rely on git -e / skip lists)", name, exc
+                )
+    return backup_root, staged
+
+
+def _restore_preserved_items(plugin_root: str, backup_root: str, staged: list[str]) -> None:
+    """Move staged items back from *backup_root* into *plugin_root*.
+
+    Any leftover placeholder at the destination (created by git checkout or
+    ZIP extraction) is removed before the move.
+    """
+    for name in staged:
+        src = os.path.join(backup_root, name)
+        dst = os.path.join(plugin_root, name)
+        try:
+            if os.path.lexists(dst):
+                if os.path.isdir(dst) and not os.path.islink(dst):
+                    shutil.rmtree(dst, ignore_errors=True)
+                else:
+                    os.remove(dst)
+            shutil.move(src, dst)
+            logger.debug("Restored '%s' after update", name)
+        except OSError:
+            logger.debug("Move failed restoring '%s', falling back to copy", name)
+            try:
+                if os.path.isdir(src) and not os.path.islink(src):
+                    shutil.copytree(src, dst, symlinks=True, dirs_exist_ok=True)
+                    shutil.rmtree(src, ignore_errors=True)
+                else:
+                    shutil.copy2(src, dst)
+                    os.remove(src)
+                logger.info("Copied '%s' back after update", name)
+            except Exception as exc:
+                logger.error("Failed to restore '%s': %s", name, exc)
+    shutil.rmtree(backup_root, ignore_errors=True)
+
+
 
 class UpdateRoutes:
     """Routes for handling plugin update checks"""
@@ -27,6 +125,7 @@ class UpdateRoutes:
         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)
+        app.router.add_post('/api/lm/switch-channel', UpdateRoutes.switch_channel)
     
     @staticmethod
     async def check_updates(request):
@@ -45,10 +144,17 @@ class UpdateRoutes:
 
             # Fetch remote version from GitHub
             if nightly:
-                remote_version, changelog = await UpdateRoutes._get_nightly_version()
-                releases = None
+                local_hash = git_info.get('short_hash', '')
+                nightly_version, releases_result = await asyncio.gather(
+                    UpdateRoutes._get_nightly_version(local_hash),
+                    UpdateRoutes._get_remote_version()
+                )
+                remote_version, _, behind_by, commit_date = nightly_version
+                _, changelog, releases = releases_result
             else:
                 remote_version, changelog, releases = await UpdateRoutes._get_remote_version()
+                behind_by = 0
+                commit_date = ''
             
             # Compare versions
             if nightly:
@@ -61,6 +167,10 @@ class UpdateRoutes:
                     remote_version.replace('v', '')
                 )
             
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+            has_git = os.path.exists(os.path.join(plugin_root, '.git'))
+
             response_data = {
                 'success': True,
                 'current_version': local_version,
@@ -68,13 +178,13 @@ class UpdateRoutes:
                 'update_available': update_available,
                 'changelog': changelog,
                 'git_info': git_info,
-                'nightly': nightly
+                'nightly': nightly,
+                'has_git': has_git,
+                'releases': releases,
+                'behind_by': behind_by,
+                'commit_date': commit_date
             }
             
-            # Include releases list for stable mode
-            if releases is not None:
-                response_data['releases'] = releases
-            
             return web.json_response(response_data)
             
         except NETWORK_EXCEPTIONS as e:
@@ -106,9 +216,14 @@ class UpdateRoutes:
             # Format: version-short_hash
             version_string = f"{local_version}-{short_hash}"
             
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+            has_git = os.path.exists(os.path.join(plugin_root, '.git'))
+
             return web.json_response({
                 'success': True,
-                'version': version_string
+                'version': version_string,
+                'has_git': has_git
             })
             
         except Exception as e:
@@ -136,20 +251,22 @@ class UpdateRoutes:
             if os.path.exists(settings_path):
                 with open(settings_path, 'r', encoding='utf-8') as f:
                     settings_backup = f.read()
-                logger.info("Backed up settings.json")
+                logger.debug("Backed up settings.json (%d bytes)", len(settings_backup))
 
-            git_folder = os.path.join(plugin_root, '.git')
-            if os.path.exists(git_folder):
-                # Git update
-                success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
-            else:
-                # Fallback: Download ZIP and replace files
-                success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            staged_backup_dir, staged_items = _stage_preserved_items(plugin_root)
+            try:
+                git_folder = os.path.join(plugin_root, '.git')
+                if os.path.exists(git_folder):
+                    success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
+                else:
+                    success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            finally:
+                _restore_preserved_items(plugin_root, staged_backup_dir, staged_items)
 
             if settings_backup and success:
                 with open(settings_path, 'w', encoding='utf-8') as f:
                     f.write(settings_backup)
-                logger.info("Restored settings.json")
+                logger.debug("Restored settings.json content (%d bytes)", len(settings_backup))
 
             if success:
                 return web.json_response({
@@ -170,6 +287,164 @@ class UpdateRoutes:
                 'error': str(e)
             })
 
+    @staticmethod
+    async def switch_channel(request):
+        """
+        Switch between release and nightly update channels.
+
+        ZIP/CNR install → Nightly: git init + checkout main (one-way upgrade)
+        Git install → Release:   git checkout latest tag (.git preserved)
+        ZIP/CNR install → Release: ZIP download (no .git, stays in ZIP mode)
+        Git install → Nightly:   git checkout main + pull
+        """
+        try:
+            body = await request.json() if request.has_body else {}
+            channel = body.get('channel', '')
+
+            if channel not in ('release', 'nightly'):
+                return web.json_response({
+                    'success': False,
+                    'error': f'Invalid channel: {channel}. Must be "release" or "nightly".'
+                })
+
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+
+            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:
+                    settings_backup = f.read()
+                logger.debug("Backed up settings.json before channel switch (%d bytes)", len(settings_backup))
+
+            staged_backup_dir, staged_items = _stage_preserved_items(plugin_root)
+            try:
+                git_folder = os.path.join(plugin_root, '.git')
+
+                if channel == 'nightly':
+                    git_backup = None
+                    if os.path.exists(git_folder):
+                        git_backup = UpdateRoutes._backup_git(git_folder, 'nightly')
+
+                    success = False
+                    new_version = ''
+                    try:
+                        if os.path.exists(git_folder):
+                            success, new_version = await UpdateRoutes._perform_git_update(
+                                plugin_root, nightly=True
+                            )
+                        else:
+                            success, new_version = UpdateRoutes._init_git_repo(plugin_root)
+                    finally:
+                        UpdateRoutes._restore_git(git_backup, git_folder, success, 'nightly')
+                else:
+                    success = False
+                    new_version = ''
+                    if os.path.exists(git_folder):
+                        success, new_version = await UpdateRoutes._perform_git_update(
+                            plugin_root, nightly=False
+                        )
+                    else:
+                        tracking_file = os.path.join(plugin_root, '.tracking')
+                        if os.path.exists(tracking_file):
+                            os.remove(tracking_file)
+                        success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            finally:
+                _restore_preserved_items(plugin_root, staged_backup_dir, staged_items)
+
+            if settings_backup and success:
+                with open(settings_path, 'w', encoding='utf-8') as f:
+                    f.write(settings_backup)
+                logger.debug("Restored settings.json content after channel switch (%d bytes)", len(settings_backup))
+
+            if success:
+                return web.json_response({
+                    'success': True,
+                    'channel': channel,
+                    'new_version': new_version,
+                    'message': f'Switched to {channel} channel'
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'Failed to switch to {channel} channel'
+                })
+
+        except Exception as e:
+            logger.error("Failed to switch channel: %s", e, exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            })
+
+    @staticmethod
+    def _init_git_repo(plugin_root: str) -> tuple[bool, str]:
+        """
+        Initialize a Git repository in a ZIP-installed plugin folder.
+        Clones the remote history and checks out main branch.
+        """
+        try:
+            import git
+        except ImportError:
+            logger.error(
+                "GitPython is not available: cannot initialize git repo. "
+                "Install git or set $GIT_PYTHON_GIT_EXECUTABLE to the git binary path."
+            )
+            return False, ""
+
+        clean_excludes = _clean_excludes()
+
+        try:
+            repo = git.Repo.init(plugin_root)
+            origin = repo.create_remote(
+                'origin',
+                'https://github.com/willmiao/ComfyUI-Lora-Manager.git'
+            )
+            origin.fetch()
+
+            repo.create_head('main', origin.refs.main)
+            repo.git.checkout('main', '--force')
+            repo.git.reset('--hard')
+            repo.git.clean('-fd', *clean_excludes)
+
+            tracking_file = os.path.join(plugin_root, '.tracking')
+            if os.path.exists(tracking_file):
+                os.remove(tracking_file)
+                logger.info("Removed .tracking file (now in git mode)")
+
+            new_version = f"main-{repo.head.commit.hexsha[:7]}"
+            logger.info("Initialized git repo on main branch: %s", new_version)
+            return True, new_version
+
+        except Exception as e:
+            logger.error("Failed to initialize git repo: %s", e, exc_info=True)
+            return False, ""
+
+    @staticmethod
+    def _backup_git(git_folder, label):
+        try:
+            backup_dir = tempfile.mkdtemp()
+            backup = os.path.join(backup_dir, '.git')
+            shutil.copytree(git_folder, backup)
+            logger.info("Backed up .git before switching to %s", label)
+            return backup
+        except Exception as e:
+            logger.error("Failed to backup .git before %s switch: %s", label, e)
+            return None
+
+    @staticmethod
+    def _restore_git(git_backup, git_folder, success, label):
+        if git_backup and not success:
+            try:
+                if os.path.exists(git_folder):
+                    shutil.rmtree(git_folder)
+                shutil.copytree(git_backup, git_folder)
+                logger.info("Restored .git after failed %s switch", label)
+            except Exception as e:
+                logger.error("Failed to restore .git after %s switch: %s", label, e)
+        if git_backup:
+            shutil.rmtree(os.path.dirname(git_backup), ignore_errors=True)
+
     @staticmethod
     async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
         """
@@ -192,9 +467,10 @@ class UpdateRoutes:
             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")
+
+            release_payload = cast(dict[str, Any], data)
+            zip_url = release_payload.get("zipball_url", "")
+            version = release_payload.get("tag_name", "unknown")
 
             # Download ZIP to temporary file
             with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
@@ -224,8 +500,7 @@ class UpdateRoutes:
             except Exception:
                 logger.debug("Could not close downloaded-version history database", exc_info=True)
 
-            # Skip settings.json, civitai, model cache and runtime cache folders
-            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json', 'civitai', 'model_cache', 'cache', 'wildcards', 'backups'])
+            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=list(_PRESERVE_DIRS))
 
             # Extract ZIP to temp dir
             with tempfile.TemporaryDirectory() as tmp_dir:
@@ -235,7 +510,7 @@ class UpdateRoutes:
                     extracted_root = next(os.scandir(tmp_dir)).path
 
                     # Copy files, skipping user data that should be preserved
-                    skip_items = {'settings.json', 'civitai', 'wildcards', 'backups'}
+                    skip_items = set(_PRESERVE_DIRS)
                     for item in os.listdir(extracted_root):
                         if item in skip_items:
                             continue
@@ -252,7 +527,7 @@ class UpdateRoutes:
                     # for ComfyUI Manager to work properly
                     tracking_info_file = os.path.join(plugin_root, '.tracking')
                     tracking_files = []
-                    skip_tracked = {'civitai', 'wildcards', 'backups'}
+                    skip_tracked = set(_PRESERVE_DIRS) - {'settings.json'}
                     for root, dirs, files in os.walk(extracted_root):
                         # Skip user data directories and their contents
                         rel_root = os.path.relpath(root, extracted_root)
@@ -275,7 +550,8 @@ class UpdateRoutes:
         except Exception as e:
             logger.error(f"ZIP update failed: {e}", exc_info=True)
             return False, ""
-        
+
+    @staticmethod
     def _clean_plugin_folder(plugin_root, skip_files=None):
         skip_files = skip_files or []
         for item in os.listdir(plugin_root):
@@ -288,41 +564,56 @@ class UpdateRoutes:
                 os.remove(path)
     
     @staticmethod
-    async def _get_nightly_version() -> tuple[str, List[str]]:
-        """
-        Fetch latest commit from main branch
-        """
+    async def _get_nightly_version(local_hash: str = "") -> tuple[str, List[str], int, str]:
         repo_owner = "willmiao"
         repo_name = "ComfyUI-Lora-Manager"
-        
-        # Use GitHub API to fetch the latest commit from main branch
+
         github_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/commits/main"
-        
+
         try:
             downloader = await get_downloader()
-            success, data = await downloader.make_request('GET', github_url, custom_headers={'Accept': 'application/vnd.github+json'})
-            
+            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}"
+                logger.warning("Failed to fetch GitHub commit: %s", data)
+                return "main", [], 0, ""
+
+            commit_payload = cast(dict[str, Any], data)
+            commit_sha = commit_payload.get('sha', '')[:7]
+            commit_message = commit_payload.get('commit', {}).get('message', '')
+            commit_date = commit_payload.get('commit', {}).get('committer', {}).get('date', '')[:10]
+
             version = f"main-{commit_sha}"
-            
-            # Use commit message as changelog
             changelog = [commit_message] if commit_message else []
-            
-            return version, changelog
-        
+
+            behind_by = 0
+            if local_hash and local_hash not in ('unknown', 'stable'):
+                compare_url = (
+                    f"https://api.github.com/repos/{repo_owner}/{repo_name}"
+                    f"/compare/{local_hash}...main"
+                )
+                c_ok, c_data = await downloader.make_request(
+                    'GET', compare_url,
+                    custom_headers={'Accept': 'application/vnd.github+json'}
+                )
+                if c_ok:
+                    compare_payload = cast(dict[str, Any], c_data)
+                    if compare_payload.get('status') in ('ahead', 'diverged'):
+                        behind_by = compare_payload.get('ahead_by', 0)
+                    else:
+                        behind_by = compare_payload.get('behind_by', 0)
+
+            return version, changelog, behind_by, commit_date
+
         except NETWORK_EXCEPTIONS as e:
             logger.warning("Unable to reach GitHub for nightly version: %s", e)
-            return "main", []
+            return "main", [], 0, ""
         except Exception as e:
-            logger.error(f"Error fetching nightly version: {e}", exc_info=True)
-            return "main", []
+            logger.error("Error fetching nightly version: %s", e, exc_info=True)
+            return "main", [], 0, ""
     
     @staticmethod
     def _compare_nightly_versions(local_git_info: Dict[str, str], remote_version: str) -> bool:
@@ -357,6 +648,17 @@ class UpdateRoutes:
         Returns:
             tuple: (success, new_version)
         """
+        try:
+            import git
+        except ImportError:
+            logger.error(
+                "GitPython is not available: the git executable was not found in PATH. "
+                "Install git or set $GIT_PYTHON_GIT_EXECUTABLE to the git binary path."
+            )
+            return False, ""
+
+        clean_excludes = _clean_excludes()
+
         try:
             # Open the Git repository
             repo = git.Repo(plugin_root)
@@ -368,8 +670,9 @@ class UpdateRoutes:
             if nightly:
                 # Reset to discard any local changes
                 repo.git.reset('--hard')
-                # Clean untracked files
-                repo.git.clean('-fd')
+                # Clean untracked files, but preserve user-managed directories
+                # (wildcards, backups, stats, civitai, caches, settings.json).
+                repo.git.clean('-fd', *clean_excludes)
                 
                 # Switch to main branch and pull latest
                 main_branch = 'main'
@@ -386,8 +689,9 @@ class UpdateRoutes:
             else:
                 # Reset to discard any local changes
                 repo.git.reset('--hard')
-                # Clean untracked files
-                repo.git.clean('-fd')
+                # Clean untracked files, but preserve user-managed directories
+                # (wildcards, backups, stats, civitai, caches, settings.json).
+                repo.git.clean('-fd', *clean_excludes)
                 
                 # Get latest release tag
                 tags = sorted(repo.tags, key=lambda t: t.commit.committed_datetime, reverse=True)
@@ -405,7 +709,7 @@ class UpdateRoutes:
             logger.info(f"Successfully updated to {new_version}")
             return True, new_version
             
-        except git.exc.GitError as e:
+        except git.exc.GitError as e:  # pyright: ignore[reportAttributeAccessIssue]
             logger.error(f"Git error during update: {e}")
             return False, ""
         except Exception as e:
@@ -453,6 +757,7 @@ class UpdateRoutes:
             if not os.path.exists(os.path.join(plugin_root, '.git')):
                 return git_info
 
+            import git
             repo = git.Repo(plugin_root)
             commit = repo.head.commit
             git_info['commit_hash'] = commit.hexsha
@@ -465,7 +770,7 @@ class UpdateRoutes:
         return git_info
     
     @staticmethod
-    async def _get_remote_version() -> tuple[str, List[str], List[Dict]]:
+    async def _get_remote_version() -> tuple[str, List[str], List[Dict[str, Any]]]:
         """
         Fetch remote version from GitHub
         Returns:
@@ -487,7 +792,7 @@ class UpdateRoutes:
             
             # Parse releases
             releases = []
-            for i, release in enumerate(data):
+            for i, release in enumerate(cast(list[dict[str, Any]], data)):
                 version = release.get('tag_name', '')
                 if not version.startswith('v'):
                     version = f"v{version}"

py/services/agent/__init__.py

@@ -0,0 +1,27 @@
+"""LLM-powered metadata enrichment pipeline infrastructure.
+
+This package provides the orchestration layer for LLM-powered features.
+Skills define *what* to do (prompt template).  The :class:`AgentService`
+handles *how* (LLM calls, context gathering, validation, progress).
+
+NOTE: The current implementation is a code-driven pipeline, not a true
+agent loop.  Future agent orchestration (LLM-driven tool selection) will
+live alongside this package with its own namespace.
+"""
+
+from __future__ import annotations
+
+from .skill_definition import SkillDefinition, SkillPermissions
+from .skill_registry import SkillRegistry
+from .agent_service import AgentService, AgentProgressReporter, SkillResult
+from .post_processor import PostProcessor
+
+__all__ = [
+    "AgentProgressReporter",
+    "AgentService",
+    "PostProcessor",
+    "SkillDefinition",
+    "SkillPermissions",
+    "SkillRegistry",
+    "SkillResult",
+]

py/services/agent/agent_service.py

@@ -0,0 +1,489 @@
+"""Pipeline orchestration service.
+
+The :class:`AgentService` coordinates LLM-powered pipeline execution:
+
+1. Look up the pipeline definition in :class:`SkillRegistry`
+2. Validate input against its ``input_schema``
+3. Prepare context via :mod:`~py.metadata_ops` (read metadata, list base models, fetch HF README)
+4. If ``llm_required``: call :class:`LLMService` with the rendered prompt
+5. Post-process via :class:`PostProcessor` (delegates I/O to :mod:`~py.metadata_ops`)
+6. Broadcast progress and completion via :class:`WebSocketManager`
+
+Pipeline definitions (*skills*) describe *what* to do (prompt template).
+The AgentService handles *how* (LLM calls, context gathering, validation,
+progress).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+import aiohttp
+
+import os
+
+from ...config import config
+from ..llm_service import LLMService
+from ..websocket_manager import ws_manager
+from .post_processor import PostProcessor
+from .skill_registry import SkillRegistry
+from .skills.enrich_hf_metadata.readme_processor import (
+    clean_readme_for_llm,
+    extract_relevant_section,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AgentProgressReporter:
+    """Protocol-compatible progress reporter backed by WebSocket broadcast."""
+
+    async def on_progress(self, payload: Dict[str, Any]) -> None:
+        await ws_manager.broadcast(payload)
+
+
+@dataclass
+class SkillResult:
+    """Outcome of a skill execution."""
+
+    success: bool
+    updated_models: List[Dict[str, Any]] = field(default_factory=list)
+    errors: List[str] = field(default_factory=list)
+    summary: str = ""
+
+
+def _validate_schema(data: Any, schema: Dict[str, Any], path: str = "") -> List[str]:
+    """Minimal JSON schema validator.
+
+    Supports a subset of JSON Schema: ``type``, ``properties``, ``required``,
+    ``items``, ``enum``.  Returns a list of error messages (empty = valid).
+    """
+
+    errors: List[str] = []
+    if not schema:
+        return errors
+
+    expected_type = schema.get("type")
+    if expected_type:
+        type_map = {
+            "string": str,
+            "number": (int, float),
+            "integer": int,
+            "boolean": bool,
+            "array": list,
+            "object": dict,
+            "null": type(None),
+        }
+        expected_py = type_map.get(expected_type)
+        if expected_py is not None and not isinstance(data, expected_py):
+            errors.append(f"{path or 'root'}: expected {expected_type}, got {type(data).__name__}")
+            return errors
+
+    if expected_type == "object" and isinstance(data, dict):
+        properties = schema.get("properties", {})
+        required = schema.get("required", [])
+        for req_key in required:
+            if req_key not in data:
+                errors.append(f"{path or 'root'}: missing required property '{req_key}'")
+        for key, value in data.items():
+            if key in properties:
+                errors.extend(_validate_schema(value, properties[key], f"{path}.{key}"))
+
+    if expected_type == "array" and isinstance(data, list):
+        items_schema = schema.get("items")
+        if items_schema:
+            for i, item in enumerate(data):
+                errors.extend(_validate_schema(item, items_schema, f"{path}[{i}]"))
+
+    if "enum" in schema and data not in schema["enum"]:
+        errors.append(f"{path or 'root'}: value '{data}' not in enum {schema['enum']}")
+
+    return errors
+
+
+# ------------------------------------------------------------------
+# Prompt template rendering
+# ------------------------------------------------------------------
+
+
+def _render_prompt(template: str, variables: Dict[str, Any]) -> str:
+    """Render a prompt template with ``{{variable}}`` placeholders.
+
+    Uses simple regex substitution — no Jinja2 dependency needed.
+    """
+
+    def replace(match: re.Match[str]) -> str:
+        key = match.group(1).strip()
+        value = variables.get(key, "")
+        if isinstance(value, (dict, list)):
+            return json.dumps(value, ensure_ascii=False, indent=2)
+        return str(value)
+
+    return re.sub(r"\{\{(\w+)\}\}", replace, template)
+
+
+class AgentService:
+    """Orchestrate agent skill execution.
+
+    Usage::
+
+        service = await AgentService.get_instance()
+        result = await service.execute_skill(
+            skill_name="enrich_hf_metadata",
+            input_data={"model_paths": ["/path/to/model.safetensors"]},
+            progress_callback=AgentProgressReporter(),
+        )
+    """
+
+    _instance: Optional["AgentService"] = None
+    _lock: asyncio.Lock = asyncio.Lock()
+
+    def __init__(
+        self,
+        *,
+        skill_registry: Optional[SkillRegistry] = None,
+        llm_service: Optional[LLMService] = None,
+    ) -> None:
+        self._registry = skill_registry
+        self._llm_service = llm_service
+
+    @classmethod
+    async def get_instance(cls) -> "AgentService":
+        """Return the lazily-initialised global ``AgentService``."""
+
+        if cls._instance is None:
+            async with cls._lock:
+                if cls._instance is None:
+                    cls._instance = cls(
+                        skill_registry=await SkillRegistry.get_instance(),
+                        llm_service=await LLMService.get_instance(),
+                    )
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Reset the cached singleton — primarily for tests."""
+
+        cls._instance = None
+
+    async def _ensure_registry(self) -> SkillRegistry:
+        if self._registry is None:
+            self._registry = await SkillRegistry.get_instance()
+        return self._registry
+
+    async def _ensure_llm(self) -> LLMService:
+        if self._llm_service is None:
+            self._llm_service = await LLMService.get_instance()
+        return self._llm_service
+
+    async def list_skills(self) -> List[Dict[str, Any]]:
+        """Return a JSON-serialisable list of available skills."""
+
+        registry = await self._ensure_registry()
+        return [
+            {
+                "name": s.name,
+                "title": s.title,
+                "description": s.description,
+                "llm_required": s.llm_required,
+                "model_type_filter": s.model_type_filter,
+            }
+            for s in registry.list_skills()
+        ]
+
+    async def execute_skill(
+        self,
+        *,
+        skill_name: str,
+        input_data: Dict[str, Any],
+        progress_callback: Optional[AgentProgressReporter] = None,
+    ) -> SkillResult:
+        """Execute a pipeline (skill) on the given models.
+
+        Args:
+            skill_name: Name of the pipeline to execute
+            input_data: Input validated against the pipeline's ``input_schema``
+            progress_callback: Optional WebSocket progress reporter
+
+        Returns:
+            :class:`SkillResult` with success status and updated model info
+        """
+
+        registry = await self._ensure_registry()
+        skill = registry.get_skill(skill_name)
+        if skill is None:
+            return SkillResult(
+                success=False,
+                errors=[f"Skill not found: {skill_name}"],
+                summary=f"Skill '{skill_name}' does not exist",
+            )
+
+        input_errors = _validate_schema(input_data, skill.input_schema)
+        if input_errors:
+            return SkillResult(
+                success=False,
+                errors=input_errors,
+                summary=f"Invalid input: {'; '.join(input_errors)}",
+            )
+
+        model_paths = input_data.get("model_paths", [])
+        if not model_paths:
+            return SkillResult(
+                success=False,
+                errors=["No model_paths provided"],
+                summary="No models to process",
+            )
+
+        total = len(model_paths)
+        processed = 0
+        success_count = 0
+        skipped_count = 0
+        updated_models: List[Dict[str, Any]] = []
+        errors: List[str] = []
+        post_processor = PostProcessor()
+
+        await self._emit_progress(
+            progress_callback, skill_name, status="started",
+            total=total, processed=0, success=0,
+        )
+
+        llm = await self._ensure_llm()
+        llm_configured = llm.is_configured() if skill.llm_required else True
+
+        for model_path in model_paths:
+            model_filename = os.path.basename(model_path)
+            logger.info(
+                "[%s] [%d/%d] %s",
+                skill_name, processed + 1, total, model_filename,
+            )
+            updated_data: Dict[str, Any] = {}
+            skip_model = False
+            try:
+                from ...metadata_ops import read_metadata
+                metadata = await read_metadata(model_path)
+
+                # Fast-fail: enrich_hf_metadata requires hf_url to have HF README context
+                if skill_name == "enrich_hf_metadata" and not metadata.get("hf_url", ""):
+                    logger.info(
+                        "[%s] SKIP %s — no hf_url in metadata",
+                        skill_name, model_filename,
+                    )
+                    skipped_count += 1
+                    skip_model = True
+
+                if not skip_model:
+                    prompt_vars: Dict[str, Any] = {"model_path": model_path}
+                    if skill.llm_required and llm_configured:
+                        prompt_vars = await self._build_prompt_context(
+                            skill_name, model_path, metadata, registry, llm,
+                        )
+
+                    llm_response: Optional[Dict[str, Any]] = None
+                    if skill.llm_required and llm_configured:
+                        prompt_template = registry.load_prompt(skill_name)
+                        rendered = _render_prompt(prompt_template, prompt_vars)
+                        llm_response = await llm.chat_completion_json(
+                            system_prompt=prompt_vars.get(
+                                "system_prompt",
+                                "You are a helpful assistant that extracts structured metadata.",
+                            ),
+                            user_prompt=rendered,
+                        )
+                        if llm_response:
+                            logger.info(
+                                "[%s] [%d/%d] %s → base_model=%s confidence=%s",
+                                skill_name, processed + 1, total, model_filename,
+                                (llm_response.get("base_model") or "?")[:50],
+                                llm_response.get("confidence", "?"),
+                            )
+
+                    model_result = await post_processor.process(
+                        skill_name=skill_name,
+                        model_path=model_path,
+                        llm_output=llm_response or {},
+                        metadata=metadata,
+                        readme_content=prompt_vars.get("readme_content_full", ""),
+                    )
+
+                    if model_result.get("success", True):
+                        success_count += 1
+                        uf = model_result.get("updated_fields", [])
+                        if uf:
+                            updated_models.append({"path": model_path, "updated_fields": uf})
+                        updated_data = model_result.get("updates", {})
+                        if "preview_url" in updated_data and updated_data["preview_url"]:
+                            updated_data["preview_url"] = config.get_preview_static_url(
+                                updated_data["preview_url"]
+                            )
+                    else:
+                        errors.extend(
+                            model_result.get("errors", [model_result.get("error", "Unknown error")])
+                        )
+
+            except Exception as exc:
+                logger.error("Skill %s failed for %s: %s", skill_name, model_path, exc)
+                errors.append(f"{model_path}: {exc}")
+
+            processed += 1
+            await self._emit_progress(
+                progress_callback, skill_name, status="processing",
+                total=total, processed=processed, success=success_count,
+                skipped=skipped_count,
+                current_path=model_path,
+                updated_data=updated_data,
+            )
+
+        result = SkillResult(
+            success=success_count > 0,
+            updated_models=updated_models,
+            errors=errors,
+            summary=f"Processed {processed}/{total} models, {success_count} succeeded, {skipped_count} skipped",
+        )
+
+        await self._emit_progress(
+            progress_callback, skill_name, status="completed",
+            total=total, processed=processed, success=success_count,
+            skipped=skipped_count,
+            updated_models=updated_models, errors=errors, summary=result.summary,
+        )
+
+        return result
+
+    # ------------------------------------------------------------------
+    # Base model grouping (keeps the prompt compact)
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _format_base_models(models: List[str]) -> str:
+        """Format the base model list as a flat, one-per-line list.
+
+        Attempts to group by family consistently degraded LLM extraction
+        accuracy — the LLM finds individual model names harder to spot
+        in comma-separated groups than in a simple ``- Name`` list.
+        """
+        return "\n".join(f"- {m}" for m in models)
+
+    async def _build_prompt_context(
+        self,
+        skill_name: str,
+        model_path: str,
+        metadata: Dict[str, Any],
+        registry: SkillRegistry,
+        llm: Any,
+    ) -> Dict[str, Any]:
+        """Gather variables for the skill's prompt template.
+
+        Reads metadata, fetches the HF README (if applicable), lists available
+        base models, loads user priority tags, and returns a dict that maps to
+        ``{{variable}}`` placeholders in ``prompt.md``.
+        """
+        from ...metadata_ops import identify_model_type, list_base_models
+        from ..settings_manager import SettingsManager
+
+        context: Dict[str, Any] = {
+            "model_path": model_path,
+            "model_basename": "",
+            "hf_url": "",
+            "repo": "",
+            "readme_content": "",
+            "readme_content_full": "",
+            "current_metadata": {},
+            "base_models": [],
+            "priority_tags": "",
+        }
+
+        # Extract model basename (filename without extension) for the LLM
+        # to use when locating the matching section in collection repos.
+        raw_basename = os.path.splitext(os.path.basename(model_path))[0]
+        context["model_basename"] = raw_basename or ""
+
+        context["current_metadata"] = {
+            "file_name": metadata.get("file_name", ""),
+            "base_model": metadata.get("base_model", ""),
+            "tags": metadata.get("tags", []),
+            "modelDescription": metadata.get("modelDescription", ""),
+            "trainedWords": metadata.get("trainedWords", []),
+            "sha256": (metadata.get("sha256") or "")[:16] + "..." if metadata.get("sha256") else "",
+            "size": metadata.get("size", 0),
+        }
+
+        hf_url = metadata.get("hf_url", "")
+        context["hf_url"] = hf_url
+        repo = self._extract_repo_from_url(hf_url) if hf_url else ""
+        context["repo"] = repo or ""
+        if repo:
+            readme = await self._fetch_readme(repo)
+            # Trim README to the section relevant to this model file
+            # (collection repos often have multiple models in one README).
+            if readme and raw_basename:
+                trimmed = extract_relevant_section(readme, raw_basename)
+                cleaned = clean_readme_for_llm(trimmed) if trimmed else ""
+            else:
+                cleaned = clean_readme_for_llm(readme) if readme else ""
+            context["readme_content"] = cleaned if cleaned else "(README not available)"
+            context["readme_content_full"] = readme or ""
+
+        try:
+            raw_models = await list_base_models()
+            context["base_models"] = self._format_base_models(raw_models)
+        except Exception as exc:
+            logger.debug("Failed to list base models: %s", exc)
+            context["base_models"] = "</not available>"
+
+        # Determine model type and load the corresponding priority_tags
+        try:
+            model_type = await identify_model_type(model_path)
+            context["model_type"] = model_type
+            settings = SettingsManager()
+            priority_config = settings.get_priority_tag_config()
+            context["priority_tags"] = priority_config.get(model_type, "")
+        except Exception as exc:
+            logger.debug("Failed to load priority tags: %s", exc)
+            context["model_type"] = "lora"
+            context["priority_tags"] = ""
+
+        return context
+
+    @staticmethod
+    def _extract_repo_from_url(hf_url: str) -> Optional[str]:
+        """Extract ``user/repo`` from a HuggingFace URL."""
+        if not hf_url:
+            return None
+        m = re.match(r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url)
+        return m.group(1) if m else None
+
+    @staticmethod
+    async def _fetch_readme(repo: str) -> str:
+        """Fetch README.md from HuggingFace (tries ``main``, then ``master``)."""
+        async with aiohttp.ClientSession(
+            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
+            timeout=aiohttp.ClientTimeout(total=30),
+        ) as session:
+            for branch in ("main", "master"):
+                url = f"https://huggingface.co/{repo}/raw/{branch}/README.md"
+                try:
+                    async with session.get(url) as resp:
+                        if resp.status == 200:
+                            return await resp.text()
+                except Exception as exc:
+                    logger.debug("Failed to fetch README from %s: %s", url, exc)
+        return ""
+
+    async def _emit_progress(
+        self,
+        callback: Optional[AgentProgressReporter],
+        skill_name: str,
+        *,
+        status: str,
+        **extra: Any,
+    ) -> None:
+        """Send a progress update via WebSocket (if callback is set)."""
+        payload: Dict[str, Any] = {"type": "agent_progress", "skill": skill_name, "status": status}
+        payload.update(extra)
+        if callback is not None:
+            await callback.on_progress(payload)

py/services/agent/post_processor.py

@@ -0,0 +1,336 @@
+"""Post-processing engine for skill pipeline outputs.
+
+The :class:`PostProcessor` takes the LLM's structured JSON output and applies
+it to a model's on-disk metadata via the :mod:`~py.metadata_ops` functions.
+
+It handles all the skill-specific business logic — conditions, transformations,
+and orchestration of multiple side-effects (write metadata, download preview,
+refresh cache).  All actual I/O is delegated to :mod:`~py.metadata_ops`.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class PostProcessor:
+    """Deterministic post-processor for skill pipeline outputs.
+
+    Usage (called by :class:`~py.services.agent.agent_service.AgentService`)::
+
+        processor = PostProcessor()
+        result = await processor.process(
+            skill_name="enrich_hf_metadata",
+            model_path="/path/to/model.safetensors",
+            llm_output={...},
+            metadata={...},    # from metadata_ops.read_metadata()
+        )
+    """
+
+    async def process(
+        self,
+        *,
+        skill_name: str,
+        model_path: str,
+        llm_output: Dict[str, Any],
+        metadata: Dict[str, Any],
+        readme_content: str = "",
+    ) -> Dict[str, Any]:
+        """Route *llm_output* to the correct skill post-processor.
+
+        *readme_content* is optional raw markdown content (e.g. HF README)
+        that is converted to HTML and stored as ``modelDescription`` for
+        the description tab.
+
+        Returns a dict with keys ``success`` (bool), ``updated_fields`` (list),
+        ``preview_downloaded`` (bool), and ``errors`` (list).
+        """
+        if skill_name == "enrich_hf_metadata":
+            return await self._process_enrich_hf_metadata(
+                model_path, llm_output, metadata, readme_content,
+            )
+        return {
+            "success": False,
+            "updated_fields": [],
+            "errors": [f"No post-processor registered for skill: {skill_name}"],
+        }
+
+    # ------------------------------------------------------------------
+    # enrich_hf_metadata
+    # ------------------------------------------------------------------
+
+    async def _process_enrich_hf_metadata(
+        self,
+        model_path: str,
+        llm_output: Dict[str, Any],
+        metadata: Dict[str, Any],
+        readme_content: str = "",
+    ) -> Dict[str, Any]:
+        from ...metadata_ops import (
+            apply_metadata_updates,
+            download_preview,
+            refresh_cache,
+        )
+        from .skills.enrich_hf_metadata.readme_processor import (
+            convert_readme_to_html,
+            extract_gallery_images,
+            extract_gallery_table_images,
+            extract_relevant_section,
+            extract_simple_markdown_images,
+            extract_html_img_tags,
+            extract_repo_from_hf_url,
+        )
+
+        updated_fields: List[str] = []
+        preview_downloaded = False
+
+        # -- Determine whether this is an HF-sourced model -----------------
+        is_hf_model = not metadata.get("from_civitai", True)
+
+        # -- Collect updates -----------------------------------------------
+        updates: Dict[str, Any] = {}
+
+        # base_model
+        new_base = (llm_output.get("base_model") or "").strip()
+        current_base = metadata.get("base_model", "") or ""
+        if new_base and self._should_overwrite(current_base, is_hf_model):
+            updates["base_model"] = new_base
+
+        # trigger words → civitai.trainedWords
+        new_triggers = llm_output.get("trigger_words", [])
+        trigger_words_empty = True
+        if isinstance(new_triggers, list):
+            cleaned = [t.strip() for t in new_triggers if t.strip()]
+            cleaned = [t for t in cleaned if t.lower() not in ("none", "null", "n/a")]
+            trigger_words_empty = not cleaned
+            current_civitai = metadata.get("civitai") or {}
+            current_triggers = current_civitai.get("trainedWords") or []
+            if self._should_overwrite_list(current_triggers, is_hf_model):
+                trig_civitai = dict(current_civitai)
+                if "civitai" in updates and isinstance(updates["civitai"], dict):
+                    trig_civitai.update(updates["civitai"])
+                trig_civitai["trainedWords"] = cleaned
+                updates["civitai"] = trig_civitai
+
+        # modelDescription — from raw README content (converted to HTML)
+        if readme_content and is_hf_model:
+            converted = convert_readme_to_html(readme_content)
+            if converted:
+                updates["modelDescription"] = converted
+
+        # short_description → civitai.description (for "About this version")
+        short_desc = (llm_output.get("short_description") or "").strip()
+        if short_desc and is_hf_model:
+            current_civitai = metadata.get("civitai") or {}
+            desc_civitai = dict(current_civitai)
+            if "civitai" in updates and isinstance(updates["civitai"], dict):
+                desc_civitai.update(updates["civitai"])
+            desc_civitai["description"] = short_desc
+            updates["civitai"] = desc_civitai
+
+        # gallery images → civitai.images (from YAML frontmatter widget entries
+        # and Sample Gallery markdown tables in the README body)
+        gallery_images: List[Dict[str, Any]] = []
+        if readme_content and is_hf_model:
+            hf_url = metadata.get("hf_url", "") or ""
+            repo = extract_repo_from_hf_url(hf_url)
+            if repo:
+                rec_w = llm_output.get("recommended_width") or 0
+                rec_h = llm_output.get("recommended_height") or 0
+
+                # 1. Widget images (YAML frontmatter)
+                gallery = extract_gallery_images(
+                    readme_content, repo,
+                    default_width=rec_w, default_height=rec_h,
+                )
+
+                # 2. Sample Gallery table images (markdown body), deduplicated
+                existing_urls = {img["url"] for img in gallery if img.get("url")}
+                table_images = extract_gallery_table_images(
+                    readme_content, repo,
+                    existing_urls=existing_urls,
+                    default_width=rec_w, default_height=rec_h,
+                )
+                existing_urls.update(img["url"] for img in table_images if img.get("url"))
+
+                # 3. Simple markdown images `![alt](url)` in the body
+                simple_images = extract_simple_markdown_images(
+                    readme_content, repo,
+                    existing_urls=existing_urls,
+                    default_width=rec_w, default_height=rec_h,
+                )
+                existing_urls.update(img["url"] for img in simple_images if img.get("url"))
+
+                # 4. HTML `<img>` tags (used by many collection repos)
+                html_images = extract_html_img_tags(
+                    readme_content, repo,
+                    existing_urls=existing_urls,
+                    default_width=rec_w, default_height=rec_h,
+                )
+
+                all_images = gallery + table_images + simple_images + html_images
+                if all_images:
+                    gallery_images = all_images
+                    current_civitai = metadata.get("civitai") or {}
+                    gallery_civitai = dict(current_civitai)
+                    if "civitai" in updates and isinstance(updates["civitai"], dict):
+                        gallery_civitai.update(updates["civitai"])
+                    gallery_civitai["images"] = all_images
+                    updates["civitai"] = gallery_civitai
+
+        # tags
+        new_tags = llm_output.get("tags", [])
+        if isinstance(new_tags, list) and new_tags:
+            existing_tags = metadata.get("tags") or []
+            merged = self._merge_tags(existing_tags, new_tags)
+            if len(merged) > len(existing_tags) or is_hf_model:
+                updates["tags"] = merged
+
+        # metadata_source & llm_enriched_at (always set)
+        updates["metadata_source"] = "agent:enrich_hf_metadata"
+        updates["llm_enriched_at"] = datetime.now(timezone.utc).isoformat()
+
+        # Store LLM confidence in metadata so it's accessible for evaluation
+        raw_confidence = (llm_output.get("confidence") or "").strip()
+        if raw_confidence:
+            updates["_llm_confidence"] = raw_confidence
+
+        # Fallback: extract instance_prompt from YAML frontmatter when the LLM
+        # returned empty trigger words but the README has instance_prompt.
+        if trigger_words_empty:
+            instance_prompt = _extract_yaml_instance_prompt(readme_content)
+            if instance_prompt:
+                current_civitai = metadata.get("civitai") or {}
+                trig_civitai = dict(current_civitai)
+                if "civitai" in updates and isinstance(updates["civitai"], dict):
+                    trig_civitai.update(updates["civitai"])
+                trig_civitai["trainedWords"] = [instance_prompt]
+                updates["civitai"] = trig_civitai
+
+        preview_remote_url = (llm_output.get("preview_url") or "").strip()
+        # Fallback: if the LLM couldn't find a preview image in the cleaned
+        # README, find the first gallery image from the *model-specific
+        # section* of the README (not the repo-wide first image, which
+        # belongs to a different model in collection repos).
+        if not preview_remote_url and readme_content and is_hf_model:
+            model_basename = os.path.splitext(os.path.basename(model_path))[0]
+            relevant_section = extract_relevant_section(
+                readme_content, model_basename,
+            )
+            if relevant_section and relevant_section != readme_content:
+                for img in gallery_images:
+                    img_url = img.get("url", "")
+                    if img_url and img_url in relevant_section:
+                        preview_remote_url = img_url
+                        break
+        # Last resort: use the first gallery image from the full README.
+        if not preview_remote_url and gallery_images:
+            preview_remote_url = gallery_images[0].get("url", "")
+        current_preview = metadata.get("preview_url") or ""
+        if preview_remote_url and not (current_preview and os.path.exists(current_preview)):
+            local_path = await download_preview(model_path, preview_remote_url)
+            if local_path:
+                preview_downloaded = True
+                updates["preview_url"] = local_path
+
+        # notes — plain-text summary of usage info from the LLM
+        new_notes = (llm_output.get("notes") or "").strip()
+        if new_notes:
+            updates["notes"] = new_notes
+
+        # usage_tips — JSON string (e.g. {"strength_min":0.85,"strength_max":1.4})
+        raw_tips = (llm_output.get("usage_tips") or "").strip()
+        if raw_tips and raw_tips != "{}":
+            try:
+                json.loads(raw_tips)
+                updates["usage_tips"] = raw_tips
+            except (json.JSONDecodeError, TypeError):
+                logger.warning(
+                    "LLM returned invalid usage_tips JSON: %s", raw_tips[:200]
+                )
+
+        if updates:
+            updated_fields = await apply_metadata_updates(model_path, updates)
+
+        # -- Refresh scanner cache ------------------------------------------
+        if updated_fields or preview_downloaded:
+            await refresh_cache(model_path)
+
+        return {
+            "success": True,
+            "updated_fields": updated_fields,
+            "preview_downloaded": preview_downloaded,
+            "updates": updates,
+            "errors": [],
+        }
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _should_overwrite(current_value: str, is_hf_model: bool) -> bool:
+        """Return ``True`` when a scalar field should be overwritten."""
+        return is_hf_model or not current_value or current_value.lower() in (
+            "", "unknown",
+        )
+
+    @staticmethod
+    def _should_overwrite_list(current_list: List[str], is_hf_model: bool) -> bool:
+        """Return ``True`` when a list field should be overwritten."""
+        return is_hf_model or not current_list
+
+    @staticmethod
+    def _merge_tags(existing: List[str], new: List[str]) -> List[str]:
+        """Merge *new* tags into *existing*, all lowercased.
+
+        This matches the behaviour of :class:`TagUpdateService` which
+        normalises every tag to lowercase for case-insensitive dedup.
+        """
+        merged: List[str] = []
+        seen: set[str] = set()
+        for tag in list(existing) + list(new):
+            t = tag.strip().lower()
+            if t and t not in seen:
+                merged.append(t)
+                seen.add(t)
+        return merged
+
+
+# ------------------------------------------------------------------
+# Module-level helpers
+# ------------------------------------------------------------------
+
+
+def _extract_yaml_instance_prompt(readme_content: str) -> str:
+    """Extract ``instance_prompt`` from the YAML frontmatter of a HF README.
+
+    Returns the prompt text, or empty string if not found.  Handles
+    ``null`` / ``~`` YAML null values by returning empty string.
+    """
+    if not readme_content or not readme_content.startswith("---"):
+        return ""
+
+    # Find end of frontmatter
+    end = readme_content.find("---", 3)
+    if end == -1:
+        return ""
+    frontmatter = readme_content[3:end]
+
+    for line in frontmatter.split("\n"):
+        line = line.strip()
+        m = re.match(r"^instance_prompt:\s*(.*)", line)
+        if m:
+            val = m.group(1).strip().strip('"').strip("'")
+            if val.lower() in ("null", "~", "none", ""):
+                return ""
+            return val
+
+    return ""

py/services/agent/skill_definition.py

@@ -0,0 +1,45 @@
+"""Skill definition data structures.
+
+Each skill is described by a :class:`SkillDefinition` that declares its
+input/output schemas, whether it needs an LLM call, and what permissions
+its post-processor has.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple
+
+
+@dataclass(frozen=True)
+class SkillPermissions:
+    """Declarative permission scope for a skill's post-processor.
+
+    These are auditable constraints — the :class:`AgentService` checks them
+    before invoking the handler.  They are defense-in-depth, not a sandbox.
+    """
+
+    write_metadata: bool = True
+    write_previews: bool = True
+    network_domains: Tuple[str, ...] = ()
+
+
+@dataclass(frozen=True)
+class SkillDefinition:
+    """Immutable description of an agent skill."""
+
+    name: str
+    title: str
+    description: str
+    llm_required: bool
+    input_schema: Dict[str, Any] = field(default_factory=dict)
+    output_schema: Dict[str, Any] = field(default_factory=dict)
+    model_type_filter: Optional[List[str]] = None
+    permissions: SkillPermissions = field(default_factory=SkillPermissions)
+
+    def applies_to_model_type(self, model_type: str) -> bool:
+        """Return ``True`` if this skill can run on the given model type."""
+
+        if self.model_type_filter is None:
+            return True
+        return model_type in self.model_type_filter

py/services/agent/skill_registry.py

@@ -0,0 +1,210 @@
+"""Discovery and loading of prompt-based skills.
+
+Skills live in ``py/services/agent/skills/<name>/`` directories.  Each
+directory must contain a ``prompt.md`` file with YAML frontmatter::
+
+    ---
+    name: my_skill
+    title: "My Skill"
+    description: "What this skill does"
+    llm_required: true
+    ---
+
+    Prompt template with ``{{variable}}`` placeholders.
+
+Legacy ``SKILL.md`` files are also supported for backward compatibility.
+
+The registry scans the skills directory on first access and caches results.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import yaml
+
+from .skill_definition import SkillDefinition, SkillPermissions
+
+logger = logging.getLogger(__name__)
+
+# Directory where built-in skills are stored
+_SKILLS_DIR = Path(__file__).parent / "skills"
+
+#: Preferred file names for prompt definition files (tried in order).
+#: ``prompt.md`` is the current convention; ``SKILL.md`` is the legacy name
+#: kept for backward compatibility.
+_PROMPT_FILE_NAMES: tuple[str, ...] = ("prompt.md", "SKILL.md")
+
+
+# ---------------------------------------------------------------------------
+# Frontmatter parser
+# ---------------------------------------------------------------------------
+
+_FRONTMATTER_RE = re.compile(
+    r"^---\s*\n(.*?\n)---\s*\n?(.*)", re.DOTALL
+)
+
+
+def _parse_skill_file(path: Path) -> tuple[dict[str, Any], str]:
+    """Read a prompt definition file (``prompt.md`` or legacy ``SKILL.md``) and
+    return (frontmatter_dict, body_text).
+
+    Raises ``ValueError`` if the file lacks valid YAML frontmatter.
+    """
+    text = path.read_text(encoding="utf-8")
+    m = _FRONTMATTER_RE.match(text)
+    if not m:
+        raise ValueError(f"Missing or invalid YAML frontmatter in {path}")
+    frontmatter = yaml.safe_load(m.group(1))
+    if not isinstance(frontmatter, dict):
+        raise ValueError(f"Frontmatter in {path} is not a mapping")
+    body = m.group(2).strip()
+    return frontmatter, body
+
+
+class SkillRegistry:
+    """Discover and load agent skills from the filesystem."""
+
+    _instance: Optional["SkillRegistry"] = None
+    _lock: asyncio.Lock = asyncio.Lock()
+
+    def __init__(self, skills_dir: Path = _SKILLS_DIR) -> None:
+        self._skills_dir = skills_dir
+        self._skills: Dict[str, SkillDefinition] = {}
+        self._loaded: bool = False
+
+    # ------------------------------------------------------------------
+    # Singleton access
+    # ------------------------------------------------------------------
+
+    @classmethod
+    async def get_instance(cls) -> "SkillRegistry":
+        """Return the lazily-initialised global ``SkillRegistry``."""
+
+        if cls._instance is None:
+            async with cls._lock:
+                if cls._instance is None:
+                    registry = cls()
+                    registry._discover()
+                    cls._instance = registry
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Reset the cached singleton — primarily for tests."""
+
+        cls._instance = None
+
+    # ------------------------------------------------------------------
+    # Discovery
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _find_prompt_file(skill_dir: Path) -> Path | None:
+        """Return the first prompt definition file that exists in *skill_dir*.
+
+        Tries ``_PROMPT_FILE_NAMES`` in order so that new conventions
+        (``prompt.md``) take precedence while legacy ``SKILL.md`` files
+        still load without changes.
+        """
+        for name in _PROMPT_FILE_NAMES:
+            candidate = skill_dir / name
+            if candidate.exists():
+                return candidate
+        return None
+
+    def _discover(self) -> None:
+        """Scan the skills directory and load all valid skill definitions."""
+
+        self._skills.clear()
+        if not self._skills_dir.is_dir():
+            logger.warning("Skills directory does not exist: %s", self._skills_dir)
+            self._loaded = True
+            return
+
+        for entry in sorted(self._skills_dir.iterdir()):
+            if not entry.is_dir():
+                continue
+            prompt_file = self._find_prompt_file(entry)
+            if prompt_file is None:
+                continue
+            try:
+                definition = self._load_skill_definition(prompt_file)
+                if definition is not None:
+                    self._skills[definition.name] = definition
+                    logger.debug("Loaded skill: %s", definition.name)
+            except Exception as exc:
+                logger.warning("Failed to load skill from %s: %s", prompt_file, exc)
+
+        self._loaded = True
+        logger.info("Discovered %d prompt-based skills", len(self._skills))
+
+    def _load_skill_definition(self, path: Path) -> Optional[SkillDefinition]:
+        """Parse a prompt definition file's frontmatter into a
+        :class:`SkillDefinition`."""
+
+        try:
+            data, _body = _parse_skill_file(path)
+        except (ValueError, yaml.YAMLError) as exc:
+            logger.warning("Failed to parse prompt file %s: %s", path, exc)
+            return None
+
+        if "name" not in data:
+            logger.warning("Prompt file %s missing required 'name' field", path)
+            return None
+
+        perm_data = data.get("permissions", {})
+        permissions = SkillPermissions(
+            write_metadata=perm_data.get("write_metadata", True),
+            write_previews=perm_data.get("write_previews", True),
+            network_domains=tuple(perm_data.get("network_domains", [])),
+        )
+
+        return SkillDefinition(
+            name=data["name"],
+            title=data.get("title", data["name"]),
+            description=data.get("description", ""),
+            llm_required=data.get("llm_required", False),
+            input_schema=data.get("input_schema", {}),
+            output_schema=data.get("output_schema", {}),
+            model_type_filter=data.get("model_type_filter"),
+            permissions=permissions,
+        )
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def list_skills(self) -> List[SkillDefinition]:
+        """Return all discovered skill definitions."""
+
+        if not self._loaded:
+            self._discover()
+        return list(self._skills.values())
+
+    def get_skill(self, name: str) -> Optional[SkillDefinition]:
+        """Return the skill definition for ``name``, or ``None`` if not found."""
+
+        if not self._loaded:
+            self._discover()
+        return self._skills.get(name)
+
+    def load_prompt(self, name: str) -> str:
+        """Load and return the prompt template body for the named skill."""
+
+        skill_dir = self._skills_dir / name
+        skill_path = self._find_prompt_file(skill_dir)
+        if skill_path is None:
+            raise FileNotFoundError(
+                f"Prompt file not found for skill '{name}' in {skill_dir} "
+                f"(tried {list(_PROMPT_FILE_NAMES)})"
+            )
+        try:
+            _frontmatter, body = _parse_skill_file(skill_path)
+            return body
+        except (ValueError, yaml.YAMLError) as exc:
+            raise ValueError(f"Failed to parse prompt from {skill_path}: {exc}") from exc

py/services/agent/skills/enrich_hf_metadata/prompt.md

@@ -0,0 +1,165 @@
+---
+name: enrich_hf_metadata
+title: "Enrich Metadata from HuggingFace"
+description: >
+  Parse the HuggingFace model card via LLM to extract description, trigger
+  words, base model, tags, and preview image URL.
+llm_required: true
+---
+
+You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md).
+
+## Model Information
+
+- **Repository**: {{hf_url}}
+- **Model file path**: {{model_path}}
+- **Model filename**: {{model_basename}}
+- **Repository ID**: {{repo}}
+
+## Current Metadata (may be incomplete)
+
+```json
+{{current_metadata}}
+```
+
+## User Priority Tags Reference
+
+The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`):
+
+```
+{{priority_tags}}
+```
+
+These are the subjects, styles, and concepts the user considers useful for categorization. Use this list as a **reference** when evaluating tags (see the **tags** section below).
+
+## Available Base Models
+
+The following base models are currently valid in this system.  Use the EXACT
+name listed — do not invent aliases or modify variant suffixes.
+
+{{base_models}}
+
+## HuggingFace README Content
+
+```
+{{readme_content}}
+```
+
+## Extraction Instructions
+
+Extract the following information from the README content above:
+
+### base_model
+The base model this model was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
+
+Check the YAML frontmatter for ``base_model:`` first. If the frontmatter has no ``base_model:``, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
+
+### trigger_words
+The trigger words or activation prompts needed to use this LoRA. Look for:
+- `instance_prompt:` in the YAML frontmatter
+- Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
+- In collection repos: the trigger section **specific to this model file** (look near matching download links or anchor IDs)
+- Example prompts at the start (usually the first word or phrase before any description)
+Return as an array of strings. If none found, return an empty array `[]`. **Never** return `["None"]` or any placeholder value — a truly empty list means no trigger words exist.
+
+### short_description
+A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole.  Return empty string if the README is too minimal.
+
+### tags
+3-8 relevant tags for categorizing this model. **Quality over quantity.**
+
+Sources to consider:
+- The YAML frontmatter `tags:` list (filter out technical ones — see below)
+- The subject, style, character, or concept the model represents
+- The model filename itself may give clues (e.g. "pokemon", "anime", "pixelart")
+
+**Critical filtering rules — apply them strictly:**
+
+1. **Exclude technical/generic tags.** Reject any tag that describes the model's **training methodology, framework, architecture, or modality** rather than its content. Examples to exclude: `text-to-image`, `diffusers`, `lora`, `dreambooth`, `diffusers-training`, `flux`, `sdxl`, `checkpoint`, `pytorch`, `safetensors`, `fine-tuning`, `stable-diffusion`, and any variant of these.
+
+2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags.
+
+3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`).
+
+Return empty array if no meaningful content tags remain after filtering.
+
+### recommended_width, recommended_height
+The recommended image generation resolution for this model, in pixels. Look for sections like "Best Dimensions", "Recommended size", "Suggested resolution", or similar phrasing in the README. Prefer the explicitly marked "Best" or default resolution. If the table/list has multiple entries (e.g. "768 x 1024 (Best)" and "1024 x 1024 (Default)"), use the one marked "Best". Return integers. If no resolution can be determined, return 0 for both.
+
+### preview_url
+The URL of the most suitable preview image from the README. Look for:
+- Image tags near the section matching the model filename (`{{model_basename}}`)
+- The YAML frontmatter `widget:` section (which often has `output.url` fields)
+- In collection repos: the sample images listed **under the section** for this specific model version
+- Generic `![alt](url)` in the body
+Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. If no suitable image is found, return an empty string.
+
+### notes
+A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`.  Return empty string if the README has no useful usage info.
+
+### usage_tips
+A JSON string with structured usage recommendations. Extract from the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5"). Possible fields (include only those you can determine):
+
+```json
+{
+  "strength_min": 0.85,
+  "strength_max": 1.4,
+  "strength_range": "0.85-1.4",
+  "strength": 0.6,
+  "clip_strength": 0.5,
+  "clip_skip": 2
+}
+```
+
+Return the JSON string (e.g. `'{"strength_min":0.85,"strength_max":1.4}'`). Return `"{}"` if nothing useful is found.
+
+### confidence
+Your confidence level in the extracted data:
+- "high" — most fields were explicitly stated in the README
+- "medium" — some fields were inferred from context
+- "low" — most fields are guesses based on limited information
+
+## Important: Handling Collection Repos (multiple model files)
+
+Many HuggingFace repos contain **multiple model files** in a single repository
+(e.g. a "LoRA collection" with different styles/characters in separate files).
+
+The model file currently being enriched is: **`{{model_basename}}`**
+
+To find the correct section in the README:
+
+1. **Search for download links** containing the filename — the surrounding paragraph is your section.
+2. **Search for anchor IDs** (`<a id="...">`) or section headings whose text matches words from the filename.
+3. **Search for HTML headings** (`<h1>`, `<h2>`, `<span>`) containing parts of the filename.
+4. If no match is found, use the full README as usual — the model may be the only one in the repo.
+
+When a matching section IS found, prefer metadata from that section.
+When no section matches (e.g. single-model repos or repos without per-file sections),
+extract metadata from the full README normally.  Do not return empty data just
+because the filename doesn't appear in the README.
+
+## Output Format
+
+Return ONLY a JSON object with exactly these fields (no markdown fences, no extra text):
+
+```json
+{
+  "model_path": "{{model_path}}",
+  "base_model": "<canonical name or empty string>",
+  "trigger_words": ["<word1>", "<word2>"],
+  "short_description": "<1-2 sentence summary>",
+  "tags": ["<tag1>", "<tag2>"],
+  "recommended_width": 768,
+  "recommended_height": 1024,
+  "preview_url": "<image URL or empty string>",
+  "notes": "<plain-text usage summary or empty string>",
+  "usage_tips": "<JSON string like '{\"strength_min\":0.85,\"strength_max\":1.4}' or '{}'>",
+  "confidence": "<high|medium|low>"
+}
+```
+
+Important:
+- Only include the JSON object, no other text
+- If a field cannot be determined, use an empty string or empty array
+- Do not fabricate information not supported by the README
+- Never use placeholder values like `"None"` or `"unknown"` for missing data — use empty string or empty array

py/services/aria2_downloader.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 from __future__ import annotations
 
 import asyncio
@@ -23,7 +27,7 @@ logger = logging.getLogger(__name__)
 def _try_certifi_ca_path() -> str | None:
     """Return the certifi CA bundle path if available, else None."""
     try:
-        import certifi  # type: ignore[import-untyped]
+        import certifi  # pyright: ignore[reportMissingTypeStubs]
 
         path = certifi.where()
         if os.path.isfile(path):
@@ -84,6 +88,7 @@ class Aria2Downloader:
         self._transfers: Dict[str, Aria2Transfer] = {}
         self._poll_interval = 0.5
         self._state_store = Aria2TransferStateStore()
+        self._stderr_reader_task: Optional[asyncio.Task[Any]] = None
 
     @property
     def is_running(self) -> bool:
@@ -115,7 +120,7 @@ class Aria2Downloader:
 
         try:
             while True:
-                status = await self.get_status(download_id)
+                status = await self._get_status_with_retry(download_id)
                 if status is None:
                     return False, "aria2 download not found"
 
@@ -136,6 +141,35 @@ class Aria2Downloader:
         finally:
             self._transfers.pop(download_id, None)
 
+    async def _get_status_with_retry(
+        self, download_id: str, *, max_retries: int = 4, retry_delay: float = 3.0
+    ) -> Optional[Dict[str, Any]]:
+        """Call get_status with retry for transient RPC failures.
+
+        Only retries on :exc:`Aria2Error` (RPC-level failure).  Returns
+        ``None`` immediately when the download_id is not tracked (a missing
+        transfer is not a transient condition, so retrying is pointless).
+
+        A single failed RPC call should not immediately fail the download,
+        because aria2 may be temporarily busy (e.g. finalizing multiple
+        concurrent downloads) and a retry will often succeed.
+        """
+        last_exc: Optional[Exception] = None
+        for attempt in range(max_retries):
+            try:
+                return await self.get_status(download_id)
+            except Aria2Error as exc:
+                last_exc = exc
+                if attempt < max_retries - 1:
+                    logger.warning(
+                        "aria2 get_status transient failure (attempt %d/%d) for %s: %s",
+                        attempt + 1, max_retries, download_id, exc,
+                    )
+                    await asyncio.sleep(retry_delay)
+        raise Aria2Error(
+            f"Failed to query aria2 download status after {max_retries} attempts: {last_exc}"
+        ) from last_exc
+
     async def _schedule_download(
         self,
         url: str,
@@ -160,7 +194,7 @@ class Aria2Downloader:
                     download_id,
                 )
 
-        options: Dict[str, str] = {
+        options: Dict[str, Any] = {
             "dir": save_dir,
             "out": out_name,
             "continue": "true",
@@ -171,6 +205,13 @@ class Aria2Downloader:
             "auto-file-renaming": "false",
             "file-allocation": "none",
         }
+
+        # Pass proxy to aria2 so the actual file transfer goes through the
+        # same proxy used by the aiohttp-based URL resolution step above.
+        downloader = await get_downloader()
+        if downloader.proxy_url:
+            options["all-proxy"] = downloader.proxy_url
+
         if request_headers:
             options["header"] = [
                 f"{key}: {value}" for key, value in request_headers.items()
@@ -312,6 +353,16 @@ class Aria2Downloader:
     async def close(self) -> None:
         """Shut down the RPC process and session."""
 
+        # Cancel the background stderr reader first so it stops reading
+        # from the pipe before the subprocess is terminated.
+        if self._stderr_reader_task is not None:
+            self._stderr_reader_task.cancel()
+            try:
+                await asyncio.wait_for(self._stderr_reader_task, timeout=2.0)
+            except (asyncio.CancelledError, asyncio.TimeoutError):
+                pass
+            self._stderr_reader_task = None
+
         if self._rpc_session is not None:
             await self._rpc_session.close()
             self._rpc_session = None
@@ -331,6 +382,23 @@ class Aria2Downloader:
                 process.kill()
                 await process.wait()
 
+    async def _drain_stderr(self) -> None:
+        """Continuously drain aria2's stderr pipe so it never blocks.
+
+        When the 64 KB pipe buffer fills up, aria2's ``write()`` to stderr
+        blocks, which freezes the entire ``aria2c`` process — including its
+        RPC handler.  This background task reads lines from stderr as they
+        arrive and forwards them to Python's logger.
+        """
+        try:
+            assert self._process is not None and self._process.stderr is not None
+            async for line in self._process.stderr:
+                text = line.decode("utf-8", errors="replace").rstrip()
+                if text:
+                    logger.debug("aria2 stderr: %s", text)
+        except Exception:
+            pass
+
     async def _dispatch_progress(self, callback, snapshot: DownloadProgress) -> None:
         try:
             result = callback(snapshot, snapshot)
@@ -465,6 +533,17 @@ class Aria2Downloader:
 
             await self._wait_until_ready()
 
+            # Drain aria2's stderr in a background task so the pipe buffer
+            # never fills up.  If the pipe blocks, aria2 itself freezes and
+            # cannot respond to RPC — this was the root cause of the
+            # "Failed to query aria2 download status" timeout bug.
+            # Must start AFTER _wait_until_ready to avoid a race where the
+            # drain task consumes aria2's early-exit error message before
+            # _wait_until_ready can read it.
+            self._stderr_reader_task = asyncio.create_task(
+                self._drain_stderr()
+            )
+
     def _resolve_executable(self) -> str:
         settings = get_settings_manager()
         configured_path = (settings.get("aria2c_path") or "").strip()
@@ -584,7 +663,9 @@ class Aria2Downloader:
         if self._rpc_session is None or self._rpc_session.closed:
             async with self._rpc_session_lock:
                 if self._rpc_session is None or self._rpc_session.closed:
-                    timeout = aiohttp.ClientTimeout(total=30)
+                    timeout = aiohttp.ClientTimeout(
+                        total=None, sock_connect=10, sock_read=60
+                    )
                     self._rpc_session = aiohttp.ClientSession(timeout=timeout)
         return self._rpc_session
 

py/services/auto_tag_service.py

@@ -8,7 +8,7 @@ from filename, base_model, and CivitAI version name — no manual tagging requir
 from __future__ import annotations
 
 import re
-from typing import Dict, List, Set
+from typing import Any, Dict, List, Set
 
 # ── Tag category definitions ──────────────────────────────────────────
 # Each category maps a display label to a regex pattern.
@@ -52,7 +52,7 @@ AUTO_TAG_GROUPS = {
 DEFAULT_ENABLED_GROUPS = {"mode", "video"}
 
 
-def _collect_sources(model_data: Dict) -> List[str]:
+def _collect_sources(model_data: Dict[str, Any]) -> List[str]:
     """Collect all text sources from model data for tag matching."""
     sources: List[str] = []
 
@@ -73,7 +73,7 @@ def _collect_sources(model_data: Dict) -> List[str]:
     return sources
 
 
-def extract_auto_tags(model_data: Dict) -> List[str]:
+def extract_auto_tags(model_data: Dict[str, Any]) -> List[str]:
     """Extract auto-detected tags from model metadata.
 
     Uses a two-layer approach:

py/services/autov3_backfill_service.py

@@ -0,0 +1,144 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
+"""Backfill the AutoV3 checked state for models loaded from a persisted snapshot.
+
+The SQLite persistent cache predates the AutoV3 feature, so entries hydrated
+from it have a NULL ``autov3`` column (the "not checked yet" state). This
+service computes the embedded AutoV3 hash for each such model — once per
+process — and persists it through the scanner's single write path
+(:meth:`ModelScanner.update_autov3_for_model`), marking every visited row so a
+subsequent run finds nothing left to do.
+
+Three-state contract honored here:
+
+- ``NULL`` (sqlite) / absent (dict)  = not checked yet  → backfill computes it
+- ``''`` (sqlite/dict) / JSON null   = checked, no value available → never recompute
+- 12-char lowercase hex              = value → never recompute
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import threading
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:  # pragma: no cover - type-check only; runtime imports are local
+    from .model_scanner import ModelScanner
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_autov3(file_path: str) -> str:
+    """Resolve the AutoV3 hash for a model file.
+
+    Prefers the Civitai AutoV3 reported for the file whose SHA256 matches
+    (the authoritative value for recipe matching); falls back to the embedded
+    safetensors header hash. Returns ``''`` when neither is available.
+    """
+    try:
+        metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
+        if os.path.exists(metadata_path):
+            with open(metadata_path, "r", encoding="utf-8") as handle:
+                payload = json.load(handle)
+            if isinstance(payload, dict):
+                from ..utils.models import autov3_from_civitai_files  # local import avoids cycles
+
+                sha256 = (payload.get("sha256") or "").lower()
+                civitai_autov3 = autov3_from_civitai_files(payload.get("civitai"), sha256)
+                if civitai_autov3:
+                    return civitai_autov3
+    except Exception:
+        pass
+    from ..utils.file_utils import calculate_autov3  # local import avoids cycles
+
+    return calculate_autov3(file_path) or ""
+
+
+class Autov3BackfillService:
+    """Compute and persist AutoV3 hashes for models missing a checked state."""
+
+    _instance: Optional["Autov3BackfillService"] = None
+    _instance_lock = threading.Lock()
+
+    def __init__(self) -> None:
+        # Re-entrancy guard per model type: scanners for different model types
+        # initialize concurrently (lora_manager.py), so a global guard would
+        # silently skip every type but the first to start. Each model type
+        # runs its own backfill; a duplicate trigger for the same type no-ops.
+        self._running_types: set[str] = set()
+
+    @classmethod
+    def get_instance(cls) -> "Autov3BackfillService":
+        """Return the process-wide singleton instance."""
+        if cls._instance is None:
+            with cls._instance_lock:
+                if cls._instance is None:
+                    cls._instance = cls()
+        return cls._instance
+
+    async def backfill(self, scanner: "ModelScanner") -> int:
+        """Compute AutoV3 for every un-checked model of ``scanner.model_type``.
+
+        Each candidate file is read once via :func:`~py.utils.file_utils.calculate_autov3`
+        (cheap: safetensors header only) and the result is persisted through
+        ``scanner.update_autov3_for_model``. Files that no longer exist on
+        disk are skipped — they are intentionally NOT marked, because scanner
+        cleanup removes the stale row later.
+
+        Returns:
+            The number of models successfully updated. Never raises; on any
+            failure a warning is logged and ``0`` is returned. A duplicate
+            trigger for a model type that is already being backfilled returns
+            ``0`` immediately; different model types run concurrently.
+        """
+        model_type = scanner.model_type
+        if model_type in self._running_types:
+            return 0
+        self._running_types.add(model_type)
+        try:
+            # Local imports avoid import cycles at module load time.
+            from .persistent_model_cache import get_persistent_cache
+            from ..utils.file_utils import calculate_autov3
+
+            persistent = getattr(scanner, "_persistent_cache", None) or get_persistent_cache()
+            paths = persistent.get_models_missing_autov3(model_type)
+
+            loop = asyncio.get_running_loop()
+            count = 0
+            for path in paths:
+                # A file that no longer exists must not be marked; scanner
+                # cleanup removes the stale row later. The existence check and
+                # hash resolution run in the executor so the loop stays
+                # responsive to API requests while the backfill iterates a
+                # large library.
+                if not await loop.run_in_executor(None, os.path.exists, path):
+                    continue
+                autov3 = await loop.run_in_executor(None, _resolve_autov3, path)
+                if await scanner.update_autov3_for_model(model_type, path, autov3):
+                    count += 1
+
+            if paths:
+                logger.info(
+                    "AutoV3 backfill: updated %d/%d models for %s",
+                    count,
+                    len(paths),
+                    model_type,
+                )
+            else:
+                # Steady state after the first run: nothing left to backfill.
+                logger.debug("AutoV3 backfill: nothing to process for %s", model_type)
+            return count
+        except Exception as exc:
+            logger.warning(
+                "AutoV3 backfill failed for %s: %s",
+                getattr(scanner, "model_type", "?"),
+                exc,
+            )
+            return 0
+        finally:
+            self._running_types.discard(model_type)

py/services/backup_service.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 from __future__ import annotations
 
 import asyncio
@@ -141,6 +145,16 @@ class BackupService:
                     )
                 )
 
+        stats_path = os.path.join(get_settings_dir(create=True), "stats", "lora_manager_stats.json")
+        if os.path.exists(stats_path):
+            targets.append(
+                (
+                    "usage_stats",
+                    "stats/lora_manager_stats.json",
+                    stats_path,
+                )
+            )
+
         return targets
 
     @staticmethod
@@ -348,6 +362,8 @@ class BackupService:
         if kind == "model_update":
             filename = os.path.basename(archive_member)
             return str(Path(get_cache_file_path(CacheType.MODEL_UPDATE, create_dir=True)).parent / filename)
+        if kind == "usage_stats":
+            return os.path.join(get_settings_dir(create=True), "stats", "lora_manager_stats.json")
         return None
 
     async def create_auto_snapshot_if_due(self) -> Optional[dict[str, Any]]:

py/services/base_model_service.py

@@ -1,7 +1,8 @@
 from abc import ABC, abstractmethod
 import asyncio
 import re
-from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
+import random
+from typing import Any, Awaitable, Dict, List, Optional, Type, Union, TYPE_CHECKING, cast
 import logging
 import os
 import time
@@ -69,24 +70,24 @@ class BaseModelService(ABC):
         page: int,
         page_size: int,
         sort_by: str = "name",
-        folder: str = None,
-        folder_include: list = None,
-        folder_exclude: list = None,
-        search: str = None,
+        folder: str | None = None,
+        folder_include: list[str] | None = None,
+        folder_exclude: list[str] | None = None,
+        search: str | None = None,
         fuzzy_search: bool = False,
-        base_models: list = None,
-        model_types: list = None,
+        base_models: list[str] | None = None,
+        model_types: list[str] | None = None,
         tags: Optional[Dict[str, str]] = None,
         auto_tags: Optional[Dict[str, str]] = None,
-        search_options: dict = None,
-        hash_filters: dict = None,
+        search_options: dict[str, Any] | None = None,
+        hash_filters: dict[str, Any] | None = None,
         favorites_only: bool = False,
         update_available_only: bool = False,
         credit_required: Optional[bool] = None,
         allow_selling_generated_content: Optional[bool] = None,
         tag_logic: str = "any",
         **kwargs,
-    ) -> Dict:
+    ) -> Dict[str, Any]:
         """Get paginated and filtered model data"""
         overall_start = time.perf_counter()
 
@@ -104,6 +105,109 @@ class BaseModelService(ABC):
         fetch_duration = time.perf_counter() - t0
         initial_count = len(sorted_data)
 
+        # Optionally filter by civitai model ID (shows all local versions of a specific model)
+        civitai_model_id = kwargs.get("civitai_model_id")
+        if civitai_model_id is not None:
+            sorted_data = [
+                item for item in sorted_data
+                if self._extract_group_key(item) == civitai_model_id
+            ]
+            # VLM mode: always sort by version ID descending (newest version first),
+            # regardless of the current sort_by preference.
+            # Fall back to modified timestamp for non-CivitAI sources.
+            sorted_data.sort(
+                key=lambda x: self._extract_version_id(x)
+                or x.get("modified", 0)
+                or 0,
+                reverse=True,
+            )
+
+        # Optionally group by civitai modelId, showing only the latest version per model
+        dedup_lost = 0
+        if kwargs.get("group_by_model") and civitai_model_id is None:
+            # Determine whether to further sub-group by base model
+            # When version_grouping is "same_base", versions with different
+            # base models are effectively different groups — the dedup key
+            # needs to include base_model so the version count and VLM flow
+            # stay consistent (card shows correct count for its base model).
+            ufs = self.settings.get("version_grouping", "same_base")
+            group_by_base = ufs == "same_base"
+
+            dedup_map = {}  # (modelId [,base_model]) -> (item, version_or_modified)
+            version_counter = {}  # same-key -> count
+            standalone = []
+            for item in sorted_data:
+                mid = self._extract_group_key(item)
+                if mid is None:
+                    standalone.append(item)
+                    continue
+                key = (mid, item.get("base_model") or "") if group_by_base else mid
+                # Count all versions per key
+                version_counter[key] = version_counter.get(key, 0) + 1
+                # Prefer CivitAI version_id; fall back to modified timestamp
+                vid = self._extract_version_id(item)
+                if vid is None:
+                    vid = item.get("modified", 0) or 0
+                if key not in dedup_map or vid > dedup_map[key][1]:
+                    dedup_map[key] = (item, vid)
+            # Attach version_count to each surviving grouped item (shallow copy
+            # to avoid mutating cached dicts — the cache is shared across requests)
+            for key, (item, vid) in dedup_map.items():
+                item = dict(item)
+                item["version_count"] = version_counter[key]
+                dedup_map[key] = (item, vid)
+            dedup_lost = len(sorted_data) - (len(dedup_map) + len(standalone))
+            sorted_data = [entry[0] for entry in dedup_map.values()] + standalone
+
+        # Re-sort by version_count (grouped: after dedup; non-grouped: group internally, sort, expand)
+        if sort_params.key == "versions_count" and civitai_model_id is None:
+            reverse = sort_params.order == "desc"
+            if kwargs.get("group_by_model"):
+                # Grouped mode: items are already dedup'd with version_count attached
+                sorted_data.sort(
+                    key=lambda x: (
+                        x.get("version_count", 0),
+                        (x.get("model_name") or x.get("file_name") or "").lower(),
+                        x.get("file_path", "").lower(),
+                    ),
+                    reverse=reverse,
+                )
+            else:
+                # Non-grouped mode: group internally, sort groups by count, expand
+                # Respect the version_grouping setting (same logic as grouped dedup)
+                ufs = self.settings.get("version_grouping", "same_base")
+                group_by_base = ufs == "same_base"
+
+                model_groups: Dict[Any, List[Dict[str, Any]]] = {}
+                ungrouped_standalone: List[Dict[str, Any]] = []
+                for item in sorted_data:
+                    mid = self._extract_group_key(item)
+                    if mid is None:
+                        ungrouped_standalone.append(item)
+                        continue
+                    key = (mid, item.get("base_model") or "") if group_by_base else mid
+                    model_groups.setdefault(key, []).append(item)
+                # Sort versions within each group by version id (descending);
+                # fall back to modified timestamp for non-CivitAI sources.
+                for items in model_groups.values():
+                    items.sort(
+                        key=lambda x: self._extract_version_id(x)
+                        or x.get("modified", 0)
+                        or 0,
+                        reverse=True,
+                    )
+                # Sort groups by version count
+                sorted_groups = sorted(
+                    model_groups.values(),
+                    key=lambda items: len(items),
+                    reverse=reverse,
+                )
+                # Flatten: grouped items first, standalone items last
+                sorted_data = []
+                for items in sorted_groups:
+                    sorted_data.extend(items)
+                sorted_data.extend(ungrouped_standalone)
+
         t1 = time.perf_counter()
         if hash_filters:
             filtered_data = await self._apply_hash_filters(sorted_data, hash_filters)
@@ -145,7 +249,7 @@ class BaseModelService(ABC):
         filter_duration = time.perf_counter() - t1
         post_filter_count = len(filtered_data)
 
-        annotated_for_filter: Optional[List[Dict]] = None
+        annotated_for_filter: Optional[List[Dict[str, Any]]] = None
         t2 = time.perf_counter()
         if update_available_only:
             annotated_for_filter = await self._annotate_update_flags(filtered_data)
@@ -172,7 +276,7 @@ class BaseModelService(ABC):
         overall_duration = time.perf_counter() - overall_start
         logger.debug(
             "%s.get_paginated_data took %.3fs (fetch: %.3fs, filter: %.3fs, update_filter: %.3fs, pagination: %.3fs, annotate: %.3fs). "
-            "Counts: initial=%d, post_filter=%d, final=%d",
+            "Counts: initial=%d, dedup=%d, post_filter=%d, final=%d",
             self.__class__.__name__,
             overall_duration,
             fetch_duration,
@@ -181,6 +285,7 @@ class BaseModelService(ABC):
             pagination_duration,
             annotate_duration,
             initial_count,
+            dedup_lost,
             post_filter_count,
             final_count,
         )
@@ -191,11 +296,11 @@ class BaseModelService(ABC):
         page: int,
         page_size: int,
         sort_by: str = "name",
-        search: str = None,
+        search: str | None = None,
         fuzzy_search: bool = False,
-        search_options: dict = None,
+        search_options: dict[str, Any] | None = None,
         **kwargs,
-    ) -> Dict:
+    ) -> Dict[str, Any]:
         """Get paginated excluded model data."""
         excluded_paths = list(self.scanner.get_excluded_models())
         excluded_entries: List[Dict[str, Any]] = []
@@ -221,7 +326,7 @@ class BaseModelService(ABC):
                 ]
                 persist_current_cache = getattr(self.scanner, "_persist_current_cache", None)
                 if callable(persist_current_cache):
-                    await persist_current_cache()
+                    await cast(Awaitable[Any], persist_current_cache())
 
         excluded_entries = self._sort_entries(excluded_entries, sort_by)
 
@@ -286,6 +391,12 @@ class BaseModelService(ABC):
                 (item.get("model_name") or item.get("file_name") or "").lower(),
                 item.get("file_path", "").lower(),
             )
+        elif key_name == "random":
+            # Seeded random shuffle: same seed -> same order (stable pagination)
+            rng = random.Random(sort_params.seed or "random")
+            result = list(data)
+            rng.shuffle(result)
+            return result
         elif key_name == "size":
             key_fn = lambda item: (
                 int(item.get("size", 0) or 0),
@@ -333,39 +444,50 @@ class BaseModelService(ABC):
         return entry
 
     async def _apply_hash_filters(
-        self, data: List[Dict], hash_filters: Dict
-    ) -> List[Dict]:
-        """Apply hash-based filtering"""
+        self, data: List[Dict[str, Any]], hash_filters: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Apply hash-based filtering (SHA256 and AutoV3)."""
+
+        def matches_hash_set(item: Dict[str, Any], hash_set: set[str]) -> bool:
+            """Check whether an item matches any hash in the set.
+
+            Compares the item's ``sha256`` field and its non-empty ``autov3``
+            field, both case-insensitively.
+            """
+            if item.get("sha256", "").lower() in hash_set:
+                return True
+            autov3 = item.get("autov3", "")
+            return bool(autov3) and autov3.lower() in hash_set
+
         single_hash = hash_filters.get("single_hash")
         multiple_hashes = hash_filters.get("multiple_hashes")
 
         if single_hash:
-            # Filter by single hash
-            single_hash = single_hash.lower()
+            # Filter by single hash (SHA256 or AutoV3)
             return [
-                item for item in data if item.get("sha256", "").lower() == single_hash
+                item for item in data if matches_hash_set(item, {single_hash.lower()})
             ]
         elif multiple_hashes:
-            # Filter by multiple hashes
-            hash_set = set(hash.lower() for hash in multiple_hashes)
-            return [item for item in data if item.get("sha256", "").lower() in hash_set]
+            # Filter by multiple hashes (SHA256 or AutoV3)
+            hash_set = {hash.lower() for hash in multiple_hashes}
+            return [item for item in data if matches_hash_set(item, hash_set)]
 
         return data
 
     async def _apply_common_filters(
         self,
-        data: List[Dict],
-        folder: str = None,
-        folder_include: list = None,
-        folder_exclude: list = None,
-        base_models: list = None,
-        model_types: list = None,
+        data: List[Dict[str, Any]],
+        folder: str | None = None,
+        folder_include: list[str] | None = None,
+        folder_exclude: list[str] | None = None,
+        base_models: list[str] | None = None,
+        model_types: list[str] | None = None,
         tags: Optional[Dict[str, str]] = None,
         auto_tags: Optional[Dict[str, str]] = None,
         favorites_only: bool = False,
-        search_options: dict = None,
+        search_options: dict[str, Any] | None = None,
         tag_logic: str = "any",
-    ) -> List[Dict]:
+    ) -> List[Dict[str, Any]]:
         """Apply common filters that work across all model types"""
         normalized_options = self.search_strategy.normalize_options(search_options)
         criteria = FilterCriteria(
@@ -384,24 +506,24 @@ class BaseModelService(ABC):
 
     async def _apply_search_filters(
         self,
-        data: List[Dict],
+        data: List[Dict[str, Any]],
         search: str,
         fuzzy_search: bool,
-        search_options: dict,
-    ) -> List[Dict]:
+        search_options: dict[str, Any] | None,
+    ) -> List[Dict[str, Any]]:
         """Apply search filtering"""
         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]:
+    async def _apply_specific_filters(self, data: List[Dict[str, Any]], **kwargs) -> List[Dict[str, Any]]:
         """Apply model-specific filters - to be overridden by subclasses if needed"""
         return data
 
     async def _apply_credit_required_filter(
-        self, data: List[Dict], credit_required: bool
-    ) -> List[Dict]:
+        self, data: List[Dict[str, Any]], credit_required: bool
+    ) -> List[Dict[str, Any]]:
         """Apply credit required filtering based on license_flags.
 
         Args:
@@ -431,8 +553,8 @@ class BaseModelService(ABC):
         return filtered_data
 
     async def _apply_allow_selling_filter(
-        self, data: List[Dict], allow_selling: bool
-    ) -> List[Dict]:
+        self, data: List[Dict[str, Any]], allow_selling: bool
+    ) -> List[Dict[str, Any]]:
         """Apply allow selling generated content filtering based on license_flags.
 
         Args:
@@ -464,8 +586,8 @@ class BaseModelService(ABC):
 
     async def _annotate_update_flags(
         self,
-        items: List[Dict],
-    ) -> List[Dict]:
+        items: List[Dict[str, Any]],
+    ) -> List[Dict[str, Any]]:
         """Attach an update_available flag to each response item.
 
         Items without a civitai model id default to False.
@@ -480,7 +602,7 @@ class BaseModelService(ABC):
                 item["update_available"] = False
             return annotated
 
-        id_to_items: Dict[int, List[Dict]] = {}
+        id_to_items: Dict[int, List[Dict[str, Any]]] = {}
         ordered_ids: List[int] = []
         for item in annotated:
             model_id = self._extract_model_id(item)
@@ -495,7 +617,7 @@ class BaseModelService(ABC):
         if not ordered_ids:
             return annotated
 
-        strategy_value = self.settings.get("update_flag_strategy")
+        strategy_value = self.settings.get("version_grouping")
         if isinstance(strategy_value, str) and strategy_value.strip():
             strategy = strategy_value.strip().lower()
         else:
@@ -517,7 +639,7 @@ class BaseModelService(ABC):
             record_method = getattr(self.update_service, "get_records_bulk", None)
             if callable(record_method):
                 try:
-                    records = await record_method(self.model_type, ordered_ids)
+                    records = await cast(Awaitable[Any], record_method(self.model_type, ordered_ids))
                     resolved = {
                         model_id: record.has_update(hide_early_access=hide_early_access)
                         for model_id, record in records.items()
@@ -537,11 +659,11 @@ class BaseModelService(ABC):
             bulk_method = getattr(self.update_service, "has_updates_bulk", None)
             if callable(bulk_method):
                 try:
-                    resolved = await bulk_method(
+                    resolved = await cast(Awaitable[Any], bulk_method(
                         self.model_type,
                         ordered_ids,
                         hide_early_access=hide_early_access,
-                    )
+                    ))
                 except Exception as exc:
                     logger.error(
                         "Failed to resolve update status in bulk for %s models (%s): %s",
@@ -603,7 +725,34 @@ class BaseModelService(ABC):
         return annotated
 
     @staticmethod
-    def _extract_model_id(item: Dict) -> Optional[int]:
+    def _extract_hf_group_key(item: Dict[str, Any]) -> Optional[str]:
+        """Extract `hf:{owner}/{repo}` from item's ``hf_url``, or None."""
+        hf_url = item.get("hf_url") if isinstance(item, dict) else None
+        if not hf_url or not isinstance(hf_url, str):
+            return None
+        m = re.match(
+            r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url.strip()
+        )
+        if not m:
+            return None
+        return f"hf:{m.group(1)}"
+
+    @staticmethod
+    def _extract_group_key(item: Dict[str, Any]) -> Union[int, str, None]:
+        """Return the group identity key: CivitAI modelId (int) or HF repo (str).
+
+        Preference order:
+        1. CivitAI ``modelId`` (int)
+        2. HF repo identity ``hf:{owner}/{repo}`` (str)
+        3. ``None`` (no known grouping source)
+        """
+        mid = BaseModelService._extract_model_id(item)
+        if mid is not None:
+            return mid
+        return BaseModelService._extract_hf_group_key(item)
+
+    @staticmethod
+    def _extract_model_id(item: Dict[str, Any]) -> Optional[int]:
         civitai = item.get("civitai") if isinstance(item, dict) else None
         if not isinstance(civitai, dict):
             return None
@@ -616,7 +765,7 @@ class BaseModelService(ABC):
             return None
 
     @staticmethod
-    def _extract_version_id(item: Dict) -> Optional[int]:
+    def _extract_version_id(item: Dict[str, Any]) -> Optional[int]:
         civitai = item.get("civitai") if isinstance(item, dict) else None
         if not isinstance(civitai, dict):
             return None
@@ -629,7 +778,7 @@ class BaseModelService(ABC):
             return None
 
     @staticmethod
-    def _extract_base_model(item: Dict) -> Optional[str]:
+    def _extract_base_model(item: Dict[str, Any]) -> Optional[str]:
         value = item.get("base_model")
         if value is None:
             return None
@@ -681,7 +830,7 @@ class BaseModelService(ABC):
 
         return highest_by_base
 
-    def _paginate(self, data: List[Dict], page: int, page_size: int) -> Dict:
+    def _paginate(self, data: List[Dict[str, Any]], page: int, page_size: int) -> Dict[str, Any]:
         """Apply pagination to filtered data"""
         total_items = len(data)
         start_idx = (page - 1) * page_size
@@ -696,16 +845,26 @@ class BaseModelService(ABC):
         }
 
     @abstractmethod
-    async def format_response(self, model_data: Dict) -> Dict:
-        """Format model data for API response - must be implemented by subclasses"""
+    async def format_response(self, model_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Format model data for API response - must be implemented by subclasses.
+
+        Subclasses should return None for corrupted entries so the handler
+        layer can filter them out. See issue #730.
+        """
         pass
 
     # Common service methods that delegate to scanner
-    async def get_top_tags(self, limit: int = 20) -> List[Dict]:
+    async def get_top_tags(self, limit: int = 20) -> List[Dict[str, Any]]:
         """Get top tags sorted by frequency"""
         return await self.scanner.get_top_tags(limit)
 
-    async def get_base_models(self, limit: int = 20) -> List[Dict]:
+    async def search_tags(
+        self, query: str, limit: int = 50
+    ) -> List[Dict[str, Any]]:
+        """Search tags by substring, sorted by frequency"""
+        return await self.scanner.search_tags(query, limit)
+
+    async def get_base_models(self, limit: int = 20) -> List[Dict[str, Any]]:
         """Get base models sorted by frequency"""
         return await self.scanner.get_base_models(limit)
 
@@ -772,7 +931,7 @@ class BaseModelService(ABC):
         """Get model root directories"""
         return self.scanner.get_model_roots()
 
-    def filter_civitai_data(self, data: Dict, minimal: bool = False) -> Dict:
+    def filter_civitai_data(self, data: Dict[str, Any], minimal: bool = False) -> Dict[str, Any]:
         """Filter relevant fields from CivitAI data"""
         if not data:
             return {}
@@ -798,7 +957,7 @@ class BaseModelService(ABC):
         )
         return {k: data[k] for k in fields if k in data}
 
-    async def get_folder_tree(self, model_root: str) -> Dict:
+    async def get_folder_tree(self, model_root: str) -> Dict[str, Any]:
         """Get hierarchical folder tree for a specific model root"""
         cache = await self.scanner.get_cached_data()
 
@@ -827,7 +986,7 @@ class BaseModelService(ABC):
 
         return tree
 
-    async def get_unified_folder_tree(self) -> Dict:
+    async def get_unified_folder_tree(self) -> Dict[str, Any]:
         """Get unified folder tree across all model roots"""
         cache = await self.scanner.get_cached_data()
 
@@ -856,13 +1015,21 @@ class BaseModelService(ABC):
 
         return unified_tree
 
-    async def get_model_notes(self, model_name: str) -> Optional[str]:
-        """Get notes for a specific model file"""
+    async def get_model_notes(self, model_name: str) -> Optional[dict[str, Any]]:
+        """Get notes and file_path for a specific model file.
+
+        Supports both simple names (``OWSMianne_ANIMA_V1``) and full-path
+        syntax (``Anima/character/OWSMianne_ANIMA_V1``).
+        """
         cache = await self.scanner.get_cached_data()
 
         for model in cache.raw_data:
-            if model["file_name"] == model_name:
-                return model.get("notes", "")
+            file_name = model.get("file_name", "")
+            if file_name == model_name or model_name.endswith("/" + file_name) or model_name.endswith("\\" + file_name):
+                return {
+                    "notes": model.get("notes", ""),
+                    "file_path": model.get("file_path", ""),
+                }
 
         return None
 
@@ -980,11 +1147,16 @@ class BaseModelService(ABC):
 
         return {"civitai_url": None, "model_id": None, "version_id": None}
 
-    async def get_model_metadata(self, file_path: str) -> Optional[Dict]:
+    async def get_model_metadata(self, file_path: str) -> Optional[Dict[str, Any]]:
         """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.
+
+        As a beneficial side effect, the in-memory and persistent caches are
+        opportunistically synchronised with the on-disk metadata — this keeps the
+        caches fresh even when a ``.metadata.json`` file was edited outside of the
+        normal save path (e.g. manually or by an external script).
         """
         metadata, should_skip = await MetadataManager.load_metadata(
             file_path, self.metadata_class
@@ -1002,6 +1174,19 @@ class BaseModelService(ABC):
                 MetadataManager.save_metadata(file_path, metadata)
             )
 
+        # Opportunistically sync the in-memory + persistent caches.
+        # The .metadata.json disk read is already paid for; the sync only
+        # performs work when the cache is actually stale, and uses targeted,
+        # in-place operations to minimise overhead even with large model sets.
+        #
+        # Fire-and-forget by design: the task is intentionally untracked.
+        # sync_cache_from_metadata handles its own errors internally.
+        asyncio.create_task(
+            self.scanner.sync_cache_from_metadata(
+                file_path, metadata.to_dict()
+            )
+        )
+
         return self.filter_civitai_data(metadata.to_dict().get("civitai", {}))
 
     async def get_model_description(self, file_path: str) -> Optional[str]:
@@ -1058,7 +1243,7 @@ class BaseModelService(ABC):
         return True
 
     @staticmethod
-    def _relative_path_sort_key(relative_path: str, include_terms: List[str]) -> tuple:
+    def _relative_path_sort_key(relative_path: str, include_terms: List[str]) -> tuple[int, int, int, str]:
         """Sort paths by how well they satisfy the include tokens.
 
         Sorts based on path without extension for consistent ordering.
@@ -1085,19 +1270,87 @@ class BaseModelService(ABC):
         )
 
     async def search_relative_paths(
-        self, search_term: str, limit: int = 15, offset: int = 0
+        self,
+        search_term: str,
+        limit: int = 15,
+        offset: int = 0,
+        *,
+        folder: Optional[str] = None,
+        folder_include: Optional[list[str]] = None,
+        folder_exclude: Optional[list[str]] = None,
+        base_models: Optional[list[str]] = None,
+        model_types: Optional[list[str]] = None,
+        tags: Optional[dict[str, str]] = None,
+        auto_tags: Optional[dict[str, str]] = None,
+        tag_logic: str = "any",
+        credit_required: Optional[bool] = None,
+        allow_selling_generated_content: Optional[bool] = None,
+        recursive: bool = True,
+        apply_filters: bool = False,
     ) -> List[str]:
-        """Search model relative file paths for autocomplete functionality"""
+        """Search model relative file paths for autocomplete functionality.
+
+        Optional filter kwargs mirror the filters used by the list endpoint
+        (/api/lm/{prefix}/list). When no filter kwargs are provided the
+        behavior is identical to plain token-based path matching.
+        """
         cache = await self.scanner.get_cached_data()
         include_terms, exclude_terms = self._parse_search_tokens(search_term)
 
+        data = cache.raw_data
+        has_filters = any(
+            [
+                apply_filters,
+                folder is not None,
+                folder_include,
+                folder_exclude,
+                base_models,
+                model_types,
+                tags,
+                auto_tags,
+                credit_required is not None,
+                allow_selling_generated_content is not None,
+            ]
+        )
+        if has_filters:
+            # Auto-tags are not stored in the scanner cache — they are computed
+            # on the fly. Pre-compute them only when an auto-tag filter is
+            # active to avoid mutating cache entries unnecessarily.
+            if auto_tags:
+                from .auto_tag_service import extract_auto_tags
+
+                for item in data:
+                    if not item.get("auto_tags"):
+                        item["auto_tags"] = extract_auto_tags(item)
+
+            criteria = FilterCriteria(
+                folder=folder,
+                folder_include=folder_include,
+                folder_exclude=folder_exclude,
+                base_models=base_models,
+                model_types=model_types,
+                tags=tags,
+                auto_tags=auto_tags,
+                search_options={"recursive": recursive},
+                tag_logic=tag_logic,
+            )
+            data = self.filter_set.apply(data, criteria)
+            if credit_required is not None:
+                data = await self._apply_credit_required_filter(
+                    data, credit_required
+                )
+            if allow_selling_generated_content is not None:
+                data = await self._apply_allow_selling_filter(
+                    data, allow_selling_generated_content
+                )
+
         matching_paths = []
 
         # Get model roots for path calculation
         model_roots = self.scanner.get_model_roots()
 
         # Collect all matching paths first (needed for proper sorting and offset)
-        for model in cache.raw_data:
+        for model in data:
             file_path = model.get("file_path", "")
             if not file_path:
                 continue

py/services/batch_import_service.py

@@ -523,6 +523,10 @@ class BatchImportService:
                 if payload.get("checkpoint"):
                     metadata["checkpoint"] = payload["checkpoint"]
 
+                nsfw = payload.get("preview_nsfw_level")
+                if isinstance(nsfw, int) and nsfw > 0:
+                    metadata["preview_nsfw_level"] = nsfw
+
                 image_bytes = None
                 image_base64 = payload.get("image_base64")
 

py/services/cache_entry_validator.py

@@ -59,6 +59,7 @@ class CacheEntryValidator:
         'notes': ('', False),
         'usage_tips': ('', False),
         'hash_status': ('completed', False),
+        'autov3': (None, False),
     }
 
     @classmethod
@@ -119,8 +120,13 @@ class CacheEntryValidator:
                 if is_required:
                     errors.append(f"Required field '{field_name}' is missing or None")
                 if auto_repair:
-                    working_entry[field_name] = cls._get_default_copy(default_value)
-                    repaired = True
+                    # A missing optional field whose default is None is already
+                    # semantically equal to its default (e.g. autov3: absent
+                    # means "not checked") — writing None back is a no-op, not
+                    # a repair.
+                    if default_value is not None:
+                        working_entry[field_name] = cls._get_default_copy(default_value)
+                        repaired = True
                 continue
 
             # Validate field type and value
@@ -175,6 +181,15 @@ class CacheEntryValidator:
                     # that invalidates the entry, but we also don't mark it repaired.
                     pass
 
+        # Normalize autov3 to lowercase if needed (optional field, never stripped).
+        autov3 = working_entry.get('autov3')
+        if isinstance(autov3, str) and autov3:
+            normalized_autov3 = autov3.lower()
+            if normalized_autov3 != autov3:
+                if auto_repair:
+                    working_entry['autov3'] = normalized_autov3
+                    repaired = True
+
         # Determine if entry is valid
         # Entry is valid if no critical required field errors remain after repair
         # Critical fields are file_path and sha256
@@ -242,6 +257,19 @@ class CacheEntryValidator:
         """
         expected_type = type(default_value)
 
+        # Special case: autov3 is optional with a three-state contract.
+        # None = not checked, "" = checked but unavailable, otherwise a
+        # 12-character hex string (case-insensitive here; normalized to
+        # lowercase separately).
+        if field_name == 'autov3':
+            if value is None or value == "":
+                return None
+            if not isinstance(value, str):
+                return f"Field 'autov3' should be string or None, got {type(value).__name__}"
+            if len(value) != 12 or any(c not in '0123456789abcdefABCDEF' for c in value):
+                return "Field 'autov3' should be a 12-character hex string"
+            return None
+
         # Special handling for numeric types
         if expected_type == int:
             if not isinstance(value, (int, float)):

py/services/checkpoint_scanner.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import asyncio
 import json
 import logging
@@ -6,7 +10,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from ..utils.models import CheckpointMetadata
-from ..utils.file_utils import find_preview_file, normalize_path
+from ..utils.file_utils import find_preview_file, normalize_path, calculate_autov3
 from ..utils.metadata_manager import MetadataManager
 from ..config import config
 from .model_scanner import ModelScanner
@@ -62,6 +66,11 @@ class CheckpointScanner(ModelScanner):
             # Find preview image
             preview_url = find_preview_file(base_name, dir_path)
 
+            # AutoV3 reads only the safetensors header, so it is cheap even for
+            # large checkpoints; record the checked state at creation time ("" =
+            # checked but unavailable).
+            autov3 = calculate_autov3(real_path)
+
             # Create metadata WITHOUT calculating hash
             metadata = CheckpointMetadata(
                 file_name=base_name,
@@ -77,6 +86,7 @@ class CheckpointScanner(ModelScanner):
                 sub_type="checkpoint",
                 from_civitai=False,  # Mark as local model since no hash yet
                 hash_status="pending",  # Mark hash as pending
+                autov3=autov3 or "",
             )
 
             # Save the created metadata
@@ -114,6 +124,17 @@ class CheckpointScanner(ModelScanner):
                 and metadata.hash_status == "completed"
                 and metadata.sha256
             ):
+                # Ensure the in-memory hash index is populated even when
+                # the hash was already computed and persisted to the metadata
+                # file.  Without this, usage tracking (and any other caller
+                # that queries get_hash_by_filename first) will miss on every
+                # lookup and keep calling back into this method, creating a
+                # tight loop that never populates the index.
+                self._hash_index.add_entry(
+                    metadata.sha256.lower(),
+                    file_path,
+                    getattr(metadata, "autov3", None) or None,
+                )
                 return metadata.sha256
 
             async with self._hash_calculation_lock:
@@ -125,6 +146,11 @@ class CheckpointScanner(ModelScanner):
                     and metadata.hash_status == "completed"
                     and metadata.sha256
                 ):
+                    self._hash_index.add_entry(
+                        metadata.sha256.lower(),
+                        file_path,
+                        getattr(metadata, "autov3", None) or None,
+                    )
                     return metadata.sha256
 
                 task = self._hash_calculation_tasks.get(real_path)
@@ -175,6 +201,13 @@ class CheckpointScanner(ModelScanner):
 
             # Check if hash is already calculated
             if metadata.hash_status == "completed" and metadata.sha256:
+                # Populate the in-memory hash index even for pre-computed
+                # hashes, mirroring the fix in calculate_hash_for_model.
+                self._hash_index.add_entry(
+                    metadata.sha256.lower(),
+                    file_path,
+                    getattr(metadata, "autov3", None) or None,
+                )
                 return metadata.sha256
 
             # Update status to calculating
@@ -191,7 +224,26 @@ class CheckpointScanner(ModelScanner):
             await MetadataManager.save_metadata(file_path, metadata)
 
             # Update hash index
-            self._hash_index.add_entry(sha256.lower(), file_path)
+            self._hash_index.add_entry(
+                sha256.lower(),
+                file_path,
+                getattr(metadata, "autov3", None) or None,
+            )
+
+            # Update the in-memory cache entry so that subsequent
+            # _persist_current_cache / _save_persistent_cache calls
+            # write the hash back to the SQLite models table.  Without
+            # this the hash only lives in the metadata file and the
+            # in-memory hash index, both of which are lost across
+            # restarts, causing the same re-computation loop on the
+            # next session.
+            if self._cache is not None and self._cache.raw_data:
+                for entry in self._cache.raw_data:
+                    if entry.get("file_path") == file_path:
+                        entry["sha256"] = sha256.lower()
+                        entry["hash_status"] = "completed"
+                        self.bump_cache_version()
+                        break
 
             logger.info(f"Hash calculated for checkpoint: {file_path}")
             return sha256
@@ -380,7 +432,7 @@ class CheckpointScanner(ModelScanner):
         roots.extend(config.extra_checkpoints_roots or [])
         roots.extend(config.extra_unet_roots or [])
         # Remove duplicates while preserving order
-        seen: set = set()
+        seen: set[str] = set()
         unique_roots: List[str] = []
         for root in roots:
             if root not in seen:

py/services/checkpoint_service.py

@@ -1,6 +1,6 @@
 import os
 import logging
-from typing import Dict
+from typing import Any, Dict, Optional
 
 from .base_model_service import BaseModelService
 from .auto_tag_service import extract_auto_tags
@@ -21,39 +21,58 @@ class CheckpointService(BaseModelService):
         """
         super().__init__("checkpoint", scanner, CheckpointMetadata, update_service=update_service)
     
-    async def format_response(self, checkpoint_data: Dict) -> Dict:
-        """Format Checkpoint data for API response"""
+    async def format_response(self, model_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Format Checkpoint data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = model_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted checkpoint entry (missing file_path): %s",
+                model_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
-        sub_type = checkpoint_data.get("sub_type", "checkpoint")
-        
+        sub_type = model_data.get("sub_type", "checkpoint")
+
+        file_name = model_data.get("file_name") or ""
+        model_name = model_data.get("model_name") or file_name
+        folder = model_data.get("folder") or ""
+
         return {
-            "model_name": checkpoint_data["model_name"],
-            "file_name": checkpoint_data["file_name"],
-            "preview_url": config.get_preview_static_url(checkpoint_data.get("preview_url", "")),
-            "preview_nsfw_level": checkpoint_data.get("preview_nsfw_level", 0),
-            "base_model": checkpoint_data.get("base_model", ""),
-            "folder": checkpoint_data["folder"],
-            "sha256": checkpoint_data.get("sha256", ""),
-            "file_path": checkpoint_data["file_path"].replace(os.sep, "/"),
-            "file_size": checkpoint_data.get("size", 0),
-            "modified": checkpoint_data.get("modified", ""),
-            "tags": checkpoint_data.get("tags", []),
-            "from_civitai": checkpoint_data.get("from_civitai", True),
-            "usage_count": checkpoint_data.get("usage_count", 0),
-            "notes": checkpoint_data.get("notes", ""),
+            "model_name": model_name,
+            "file_name": file_name,
+            "preview_url": config.get_preview_static_url(model_data.get("preview_url", "")),
+            "preview_nsfw_level": model_data.get("preview_nsfw_level", 0),
+            "base_model": model_data.get("base_model", ""),
+            "folder": folder,
+            "sha256": model_data.get("sha256", ""),
+            "file_path": file_path.replace(os.sep, "/"),
+            "file_size": model_data.get("size", 0),
+            "modified": model_data.get("modified", ""),
+            "tags": model_data.get("tags", []),
+            "from_civitai": model_data.get("from_civitai", True),
+            "usage_count": model_data.get("usage_count", 0),
+            "notes": model_data.get("notes", ""),
             "sub_type": sub_type,
-            "favorite": checkpoint_data.get("favorite", False),
-            "exclude": bool(checkpoint_data.get("exclude", False)),
-            "update_available": bool(checkpoint_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(checkpoint_data.get("skip_metadata_refresh", False)),
-            "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True),
-            "auto_tags": checkpoint_data.get("auto_tags") or extract_auto_tags(checkpoint_data),
+            "favorite": model_data.get("favorite", False),
+            "exclude": bool(model_data.get("exclude", False)),
+            "update_available": bool(model_data.get("update_available", False)),
+            "skip_metadata_refresh": bool(model_data.get("skip_metadata_refresh", False)),
+            "civitai": self.filter_civitai_data(model_data.get("civitai", {}), minimal=True),
+            "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
+            "version_count": model_data.get("version_count"),
+            "hf_url": model_data.get("hf_url", ""),
         }
     
-    def find_duplicate_hashes(self) -> Dict:
+    def find_duplicate_hashes(self) -> Dict[str, Any]:
         """Find Checkpoints with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
     
-    def find_duplicate_filenames(self) -> Dict:
+    def find_duplicate_filenames(self) -> Dict[str, Any]:
         """Find Checkpoints with conflicting filenames"""
         return self.scanner._hash_index.get_duplicate_filenames()

py/services/civarchive_client.py

@@ -1,8 +1,12 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import json
 import logging
 import asyncio
 from copy import deepcopy
-from typing import Optional, Dict, Tuple, List
+from typing import Any, Optional, Dict, Tuple, List, cast
 from .model_metadata_provider import CivArchiveModelMetadataProvider, ModelMetadataProviderManager
 from .downloader import get_downloader
 from .errors import RateLimitError
@@ -37,8 +41,8 @@ class CivArchiveClient:
     async def _request_json(
         self,
         path: str,
-        params: Optional[Dict[str, str]] = None
-    ) -> Tuple[Optional[Dict], Optional[str]]:
+        params: Optional[Dict[str, Any]] = None
+    ) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
         """Call CivArchive API and return JSON payload"""
         success, payload = await self._make_request(path, params=params)
         if not success:
@@ -52,12 +56,12 @@ class CivArchiveClient:
         self,
         path: str,
         *,
-        params: Optional[Dict[str, str]] = None,
-    ) -> Tuple[bool, Dict | str]:
+        params: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[bool, Dict[str, Any] | str]:
         """Wrapper around downloader.make_request that surfaces rate limits."""
 
         downloader = await get_downloader()
-        kwargs: Dict[str, Dict[str, str]] = {}
+        kwargs: Dict[str, Dict[str, Any]] = {}
         if params:
             safe_params = {str(key): str(value) for key, value in params.items() if value is not None}
             if safe_params:
@@ -73,10 +77,11 @@ class CivArchiveClient:
             if payload.provider is None:
                 payload.provider = "civarchive_api"
             raise payload
-        return success, payload
+        # RateLimitError is always raised above, so the returned payload is a dict or str.
+        return success, cast(Dict[str, Any] | str, payload)
 
     @staticmethod
-    def _normalize_payload(payload: Dict) -> Dict:
+    def _normalize_payload(payload: Dict[str, Any]) -> Dict[str, Any]:
         """Unwrap CivArchive responses that wrap content under a data key"""
         if not isinstance(payload, dict):
             return {}
@@ -86,12 +91,12 @@ class CivArchiveClient:
         return payload
 
     @staticmethod
-    def _split_context(payload: Dict) -> Tuple[Dict, Dict, List[Dict]]:
+    def _split_context(payload: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any], List[Dict[str, Any]]]:
         """Separate version payload from surrounding model context"""
         data = CivArchiveClient._normalize_payload(payload)
-        context: Dict = {}
-        fallback_files: List[Dict] = []
-        version: Dict = {}
+        context: Dict[str, Any] = {}
+        fallback_files: List[Dict[str, Any]] = []
+        version: Dict[str, Any] = {}
 
         for key, value in data.items():
             if key in {"version", "model"}:
@@ -115,7 +120,7 @@ class CivArchiveClient:
         return context, version, fallback_files
 
     @staticmethod
-    def _ensure_list(value) -> List:
+    def _ensure_list(value: Any) -> List[Any]:
         if isinstance(value, list):
             return value
         if value is None:
@@ -123,7 +128,7 @@ class CivArchiveClient:
         return [value]
 
     @staticmethod
-    def _build_model_info(context: Dict) -> Dict:
+    def _build_model_info(context: Dict[str, Any]) -> Dict[str, Any]:
         tags = context.get("tags")
         if not isinstance(tags, list):
             tags = list(tags) if isinstance(tags, (set, tuple)) else ([] if tags is None else [tags])
@@ -136,7 +141,7 @@ class CivArchiveClient:
         }
 
     @staticmethod
-    def _build_creator_info(context: Dict) -> Dict:
+    def _build_creator_info(context: Dict[str, Any]) -> Dict[str, Any]:
         username = context.get("creator_username") or context.get("username") or ""
         image = context.get("creator_image") or context.get("creator_avatar") or ""
         creator: Dict[str, Optional[str]] = {
@@ -150,7 +155,7 @@ class CivArchiveClient:
         return creator
 
     @staticmethod
-    def _transform_file_entry(file_data: Dict) -> Dict:
+    def _transform_file_entry(file_data: Dict[str, Any]) -> Dict[str, Any]:
         mirrors = file_data.get("mirrors") or []
         if not isinstance(mirrors, list):
             mirrors = [mirrors]
@@ -165,7 +170,7 @@ class CivArchiveClient:
         if not name and available_mirror:
             name = available_mirror.get("filename")
 
-        transformed: Dict = {
+        transformed: Dict[str, Any] = {
             "id": file_data.get("id"),
             "sizeKB": file_data.get("sizeKB"),
             "name": name,
@@ -216,23 +221,23 @@ class CivArchiveClient:
 
     def _transform_files(
         self,
-        files: Optional[List[Dict]],
-        fallback_files: Optional[List[Dict]] = None
-    ) -> List[Dict]:
-        candidates: List[Dict] = []
+        files: Optional[List[Dict[str, Any]]],
+        fallback_files: Optional[List[Dict[str, Any]]] = None
+    ) -> List[Dict[str, Any]]:
+        candidates: List[Dict[str, Any]] = []
         if isinstance(files, list) and files:
             candidates = files
         elif isinstance(fallback_files, list):
             candidates = fallback_files
 
-        transformed_files: List[Dict] = []
+        transformed_files: List[Dict[str, Any]] = []
         for file_data in candidates:
             if isinstance(file_data, dict):
                 transformed_files.append(self._transform_file_entry(file_data))
 
         # Sort: .safetensors first, .ckpt second, others last
         # so the backend fallback (no file_params) prefers safetensors
-        def _sort_key(f: Dict) -> int:
+        def _sort_key(f: Dict[str, Any]) -> int:
             fname = f.get("name") or ""
             if isinstance(fname, str):
                 lower = fname.lower()
@@ -247,10 +252,10 @@ class CivArchiveClient:
 
     def _transform_version(
         self,
-        context: Dict,
-        version: Dict,
-        fallback_files: Optional[List[Dict]] = None
-    ) -> Optional[Dict]:
+        context: Dict[str, Any],
+        version: Dict[str, Any],
+        fallback_files: Optional[List[Dict[str, Any]]] = None
+    ) -> Optional[Dict[str, Any]]:
         if not version:
             return None
 
@@ -291,7 +296,7 @@ class CivArchiveClient:
 
         return version_copy
 
-    async def _resolve_version_from_files(self, payload: Dict) -> Optional[Dict]:
+    async def _resolve_version_from_files(self, payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         """Fallback to fetch version data when only file metadata is available"""
         data = self._normalize_payload(payload)
         files = data.get("files") or payload.get("files") or []
@@ -304,12 +309,26 @@ class CivArchiveClient:
             version_id = file_data.get("model_version_id") or file_data.get("modelVersionId")
             if model_id is None or version_id is None:
                 continue
+            # CivitAI / CivArchive model IDs are small integers (typically ≤ 7
+            # digits).  Reject suspiciously large values that indicate the API
+            # returned a malformed payload (e.g. a hash reinterpreted as an ID)
+            # to avoid pointless HTTP 500 errors from CivArchive.
+            _MAX_VALID_CIVITAI_ID = 100_000_000
+            try:
+                if int(model_id) >= _MAX_VALID_CIVITAI_ID or int(version_id) >= _MAX_VALID_CIVITAI_ID:
+                    logger.debug(
+                        "Skipping implausible CivArchive model_id=%s / version_id=%s",
+                        model_id, version_id,
+                    )
+                    continue
+            except (TypeError, ValueError):
+                continue
             resolved = await self.get_model_version(model_id, version_id)
             if resolved:
                 return resolved
         return None
 
-    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
         """Find model by SHA256 hash value using CivArchive API"""
         try:
             payload, error = await self._request_json(f"/sha256/{model_hash.lower()}")
@@ -318,16 +337,16 @@ class CivArchiveClient:
                     return None, "Model not found"
                 return None, error
 
-            context, version_data, fallback_files = self._split_context(payload)
+            context, version_data, fallback_files = self._split_context(cast(Dict[str, Any], payload))
             transformed = self._transform_version(context, version_data, fallback_files)
             if transformed:
                 return transformed, None
 
-            resolved = await self._resolve_version_from_files(payload)
+            resolved = await self._resolve_version_from_files(cast(Dict[str, Any], payload))
             if resolved:
                 return resolved, None
 
-            logger.error("Error fetching version of CivArchive model by hash %s", model_hash[:10])
+            logger.debug("Error fetching version of CivArchive model by hash %s", model_hash[:10])
             return None, "No version data found"
 
         except RateLimitError:
@@ -336,7 +355,7 @@ class CivArchiveClient:
             logger.error(f"Error fetching CivArchive model by hash {model_hash[:10]}: {e}")
             return None, str(e)
 
-    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+    async def get_model_versions(self, model_id: str) -> Optional[Dict[str, Any]]:
         """Get all versions of a model using CivArchive API"""
         try:
             payload, error = await self._request_json(f"/models/{model_id}")
@@ -350,7 +369,7 @@ class CivArchiveClient:
             context, version_data, fallback_files = self._split_context(payload)
 
             versions_meta = data.get("versions") or []
-            transformed_versions: List[Dict] = []
+            transformed_versions: List[Dict[str, Any]] = []
             for meta in versions_meta:
                 if not isinstance(meta, dict):
                     continue
@@ -367,7 +386,7 @@ class CivArchiveClient:
             if primary_version:
                 transformed_versions.insert(0, primary_version)
 
-            ordered_versions: List[Dict] = []
+            ordered_versions: List[Dict[str, Any]] = []
             seen_ids = set()
             for version in transformed_versions:
                 version_id = version.get("id")
@@ -388,7 +407,7 @@ class CivArchiveClient:
             logger.error(f"Error fetching CivArchive model versions for {model_id}: {e}")
             return None
 
-    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+    async def get_model_version(self, model_id: int | str | None = None, version_id: int | str | None = None) -> Optional[Dict[str, Any]]:
         """Get specific model version using CivArchive API
         
         Args:
@@ -417,7 +436,7 @@ class CivArchiveClient:
 
             if version_id is not None:
                 raw_id = version_data.get("id")
-                if raw_id != version_id:
+                if raw_id is not None and str(raw_id) != str(version_id):
                     logger.warning(
                         "Requested version %s doesn't match default version %s for model %s",
                         version_id,
@@ -445,7 +464,7 @@ class CivArchiveClient:
             logger.error(f"Error fetching CivArchive model version via API {model_id}/{version_id}: {e}")
             return None
 
-    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
         """ Fetch model version metadata using a known bogus model lookup        
         CivArchive lacks a direct version lookup API, this uses a workaround (which we handle in the main model request now)
         

py/services/civitai_base_model_service.py

@@ -196,6 +196,7 @@ class CivitaiBaseModelService:
             "ernie": "ERNI",
             "ernie turbo": "ETRB",
             "nucleus": "NUCL",
+            "krea 2": "KR2",
             "svd": "SVD",
             "ltxv": "LTXV",
             "ltxv2": "LTV2",
@@ -212,6 +213,18 @@ class CivitaiBaseModelService:
             "wan video 2.2 i2v-a14b": "WAN",
             "wan video 2.5 t2v": "WAN",
             "wan video 2.5 i2v": "WAN",
+            "wan video 2.7": "WAN",
+            "wan image 2.7": "WI27",
+            "ace audio": "ACE",
+            "boogu": "BOOG",
+            "grok": "GROK",
+            "happyhorse": "HAPP",
+            "hidream-o1": "HIO1",
+            "lens": "LENS",
+            "mai": "MAI",
+            "upscaler": "UPSC",
+            "ideogram 4.0": "ID40",
+            "qwen 2": "QWN2",
         }
 
         if lower_name in special_cases:
@@ -270,7 +283,7 @@ class CivitaiBaseModelService:
                 return None
 
             if isinstance(result, str):
-                data = json.loads(result)
+                data: Any = json.loads(result)
             else:
                 data = result
 
@@ -391,6 +404,7 @@ class CivitaiBaseModelService:
                 "LTXV2",
                 "LTXV 2.3",
                 "CogVideoX",
+                "HappyHorse",
                 "Mochi",
                 "Hunyuan Video",
                 "Wan Video",
@@ -403,15 +417,25 @@ class CivitaiBaseModelService:
                 "Wan Video 2.2 I2V-A14B",
                 "Wan Video 2.5 T2V",
                 "Wan Video 2.5 I2V",
+                "Wan Image 2.7",
+                "Wan Video 2.7",
             ],
             "Other Models": [
+                "ACE Audio",
                 "Illustrious",
                 "Pony",
                 "Pony V7",
+                "Boogu",
                 "HiDream",
+                "HiDream-O1",
+                "Ideogram 4.0",
                 "Qwen",
+                "Qwen 2",
                 "AuraFlow",
                 "Chroma",
+                "Grok",
+                "Lens",
+                "MAI",
                 "ZImageTurbo",
                 "ZImageBase",
                 "PixArt a",
@@ -424,6 +448,8 @@ class CivitaiBaseModelService:
                 "Ernie",
                 "Ernie Turbo",
                 "Nucleus",
+                "Krea 2",
+                "Upscaler",
             ],
         }
 

py/services/civitai_client.py

@@ -1,9 +1,14 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import asyncio
 import copy
 import logging
 import os
+import time
 from collections import OrderedDict
-from typing import Any, Optional, Dict, Tuple, List, Sequence
+from typing import Any, Optional, Dict, Tuple, List, Sequence, cast
 from .connectivity_guard import (
     OFFLINE_FRIENDLY_MESSAGE,
     is_expected_offline_error,
@@ -19,6 +24,12 @@ from ..utils.civitai_utils import resolve_license_payload
 
 logger = logging.getLogger(__name__)
 
+# Best-effort cache for creator model counts, keyed by lowercase username.
+# Values are (monotonic timestamp, count or None); None results are cached
+# too so repeated failures don't hammer the API.
+_CREATOR_COUNT_CACHE_TTL_SECONDS = 600
+_creator_model_count_cache: Dict[str, Tuple[float, Optional[int]]] = {}
+
 
 class CivitaiClient:
     _instance = None
@@ -51,12 +62,12 @@ class CivitaiClient:
         # Uses OrderedDict with LRU eviction at MAX_CACHE_ENTRIES to prevent
         # unbounded growth in long-running server processes.
         self._version_info_cache: OrderedDict[
-            str, Tuple[Optional[Dict], Optional[str]]
+            str, Tuple[Optional[Dict[str, Any]], Optional[str]]
         ] = OrderedDict()
         self._MAX_CACHE_ENTRIES = 500
 
     def _build_image_info_url(self, image_id: str) -> str:
-        return f"{self.base_url}/images?imageId={image_id}&nsfw=X"
+        return f"{self.base_url}/images?imageId={image_id}&nsfw=X&withMeta=true"
 
     async def _make_request(
         self,
@@ -65,7 +76,7 @@ class CivitaiClient:
         *,
         use_auth: bool = False,
         **kwargs,
-    ) -> Tuple[bool, Dict | str]:
+    ) -> Tuple[bool, Dict[str, Any] | str]:
         """Wrapper around downloader.make_request that surfaces rate limits,
         with retry for transient server errors (5xx, Cloudflare 524, network flakiness)."""
 
@@ -79,7 +90,8 @@ class CivitaiClient:
                 **kwargs,
             )
             if success:
-                return True, result
+                # RateLimitError is raised below; a successful result is dict or str.
+                return True, cast(Dict[str, Any] | str, result)
 
             if isinstance(result, RateLimitError):
                 if result.provider is None:
@@ -119,7 +131,7 @@ class CivitaiClient:
         return False, "Unexpected error in _make_request"
 
     @staticmethod
-    def _remove_comfy_metadata(model_version: Optional[Dict]) -> None:
+    def _remove_comfy_metadata(model_version: Optional[Dict[str, Any]]) -> None:
         """Remove Comfy-specific metadata from model version images."""
         if not isinstance(model_version, dict):
             return
@@ -166,7 +178,7 @@ class CivitaiClient:
 
     async def get_model_by_hash(
         self, model_hash: str
-    ) -> Tuple[Optional[Dict], Optional[str]]:
+    ) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
         try:
             success, version = await self._make_request(
                 "GET",
@@ -213,7 +225,7 @@ class CivitaiClient:
                 # Ensure directory exists
                 os.makedirs(os.path.dirname(save_path), exist_ok=True)
                 with open(save_path, "wb") as f:
-                    f.write(content)
+                    f.write(content if isinstance(content, bytes) else content.encode("utf-8"))
                 return True
             return False
         except Exception as e:
@@ -268,7 +280,7 @@ class CivitaiClient:
             return True
         return False
 
-    async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+    async def get_model_versions(self, model_id: str) -> Optional[Dict[str, Any]]:
         """Get all versions of a model with local availability info"""
         try:
             success, result = await self._make_request(
@@ -276,7 +288,7 @@ class CivitaiClient:
                 f"{self.base_url}/models/{model_id}",
                 use_auth=True,
             )
-            if success:
+            if success and isinstance(result, dict):
                 # Also return model type along with versions
                 return {
                     "modelVersions": result.get("modelVersions", []),
@@ -310,7 +322,7 @@ class CivitaiClient:
 
     async def get_model_versions_bulk(
         self, model_ids: Sequence[int]
-    ) -> Optional[Dict[int, Dict]]:
+    ) -> Optional[Dict[int, Dict[str, Any]]]:
         """Fetch model metadata for multiple ids using the batch API."""
 
         deduped: Dict[int, None] = {}
@@ -340,13 +352,13 @@ class CivitaiClient:
             if not isinstance(items, list):
                 return {}
 
-            payload: Dict[int, Dict] = {}
+            payload: Dict[int, Dict[str, Any]] = {}
             for item in items:
                 if not isinstance(item, dict):
                     continue
                 model_id = item.get("id")
                 try:
-                    normalized_id = int(model_id)
+                    normalized_id = int(cast(Any, model_id))
                 except (TypeError, ValueError):
                     continue
                 payload[normalized_id] = {
@@ -366,8 +378,8 @@ class CivitaiClient:
             return None
 
     async def get_model_version(
-        self, model_id: int = None, version_id: int = None
-    ) -> Optional[Dict]:
+        self, model_id: int | None = None, version_id: int | None = None
+    ) -> Optional[Dict[str, Any]]:
         """Get specific model version with additional metadata."""
         try:
             if model_id is None and version_id is not None:
@@ -385,7 +397,7 @@ class CivitaiClient:
             logger.error(f"Error fetching model version: {e}")
             return None
 
-    async def _get_version_by_id_only(self, version_id: int) -> Optional[Dict]:
+    async def _get_version_by_id_only(self, version_id: int) -> Optional[Dict[str, Any]]:
         version = await self._fetch_version_by_id(version_id)
         if version is None:
             return None
@@ -404,7 +416,7 @@ class CivitaiClient:
 
     async def _get_version_with_model_id(
         self, model_id: int, version_id: Optional[int]
-    ) -> Optional[Dict]:
+    ) -> Optional[Dict[str, Any]]:
         model_data = await self._fetch_model_data(model_id)
         if not model_data:
             return None
@@ -457,20 +469,20 @@ class CivitaiClient:
         self._remove_comfy_metadata(version)
         return version
 
-    async def _fetch_model_data(self, model_id: int) -> Optional[Dict]:
+    async def _fetch_model_data(self, model_id: int) -> Optional[Dict[str, Any]]:
         success, data = await self._make_request(
             "GET",
             f"{self.base_url}/models/{model_id}",
             use_auth=True,
         )
-        if success:
+        if success and isinstance(data, dict):
             return data
         if is_expected_offline_error(data):
             return None
         logger.warning(f"Failed to fetch model data for model {model_id}")
         return None
 
-    async def _fetch_version_by_id(self, version_id: Optional[int]) -> Optional[Dict]:
+    async def _fetch_version_by_id(self, version_id: Optional[int]) -> Optional[Dict[str, Any]]:
         if version_id is None:
             return None
 
@@ -479,7 +491,7 @@ class CivitaiClient:
             f"{self.base_url}/model-versions/{version_id}",
             use_auth=True,
         )
-        if success:
+        if success and isinstance(version, dict):
             return version
         if is_expected_offline_error(version):
             return None
@@ -487,7 +499,7 @@ class CivitaiClient:
         logger.warning(f"Failed to fetch version by id {version_id}")
         return None
 
-    async def _fetch_version_by_hash(self, model_hash: Optional[str]) -> Optional[Dict]:
+    async def _fetch_version_by_hash(self, model_hash: Optional[str]) -> Optional[Dict[str, Any]]:
         if not model_hash:
             return None
 
@@ -496,7 +508,7 @@ class CivitaiClient:
             f"{self.base_url}/model-versions/by-hash/{model_hash}",
             use_auth=True,
         )
-        if success:
+        if success and isinstance(version, dict):
             return version
         if is_expected_offline_error(version):
             return None
@@ -505,8 +517,8 @@ class CivitaiClient:
         return None
 
     def _select_target_version(
-        self, model_data: Dict, model_id: int, version_id: Optional[int]
-    ) -> Optional[Dict]:
+        self, model_data: Dict[str, Any], model_id: int, version_id: Optional[int]
+    ) -> Optional[Dict[str, Any]]:
         model_versions = model_data.get("modelVersions", [])
         if not model_versions:
             logger.warning(f"No model versions found for model {model_id}")
@@ -525,7 +537,7 @@ class CivitaiClient:
 
         return model_versions[0]
 
-    def _extract_primary_model_hash(self, version_entry: Dict) -> Optional[str]:
+    def _extract_primary_model_hash(self, version_entry: Dict[str, Any]) -> Optional[str]:
         for file_info in version_entry.get("files", []):
             if file_info.get("type") == "Model" and file_info.get("primary"):
                 hashes = file_info.get("hashes", {})
@@ -535,8 +547,8 @@ class CivitaiClient:
         return None
 
     def _build_version_from_model_data(
-        self, version_entry: Dict, model_id: int, model_data: Dict
-    ) -> Dict:
+        self, version_entry: Dict[str, Any], model_id: int, model_data: Dict[str, Any]
+    ) -> Dict[str, Any]:
         version = copy.deepcopy(version_entry)
         version.pop("index", None)
         version["modelId"] = model_id
@@ -548,7 +560,7 @@ class CivitaiClient:
         }
         return version
 
-    def _enrich_version_with_model_data(self, version: Dict, model_data: Dict) -> None:
+    def _enrich_version_with_model_data(self, version: Dict[str, Any], model_data: Dict[str, Any]) -> None:
         model_info = version.get("model")
         if not isinstance(model_info, dict):
             model_info = {}
@@ -564,7 +576,7 @@ class CivitaiClient:
 
     async def get_model_version_info(
         self, version_id: str
-    ) -> Tuple[Optional[Dict], Optional[str]]:
+    ) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
         """Fetch model version metadata from Civitai
 
         Args:
@@ -589,7 +601,7 @@ class CivitaiClient:
             logger.debug("Resolving Civitai model version info: %s", url)
             success, result = await self._make_request("GET", url, use_auth=True)
 
-            if success:
+            if success and isinstance(result, dict):
                 logger.debug("Successfully fetched model version info for: %s", version_id)
                 self._remove_comfy_metadata(result)
                 self._version_info_cache[version_id] = (result, None)
@@ -619,7 +631,7 @@ class CivitaiClient:
 
     async def get_image_info(
         self, image_id: str, source_url: str | None = None
-    ) -> Optional[Dict]:
+    ) -> Optional[Dict[str, Any]]:
         """Fetch image information from Civitai API
 
         Args:
@@ -652,7 +664,7 @@ class CivitaiClient:
                 )
                 return None
 
-            if result and "items" in result and isinstance(result["items"], list):
+            if isinstance(result, dict) and "items" in result and isinstance(result["items"], list):
                 items = result["items"]
 
                 for item in items:
@@ -692,7 +704,7 @@ class CivitaiClient:
 
     async def get_model_versions_by_hashes(
         self, hashes: List[str]
-    ) -> Optional[List[Dict]]:
+    ) -> Optional[List[Dict[str, Any]]]:
         """Fetch full version details for up to 100 SHA256 hashes via the batch endpoint.
 
         Uses POST /api/v1/model-versions/by-hash which returns full version
@@ -709,7 +721,7 @@ class CivitaiClient:
             return []
 
         BATCH_SIZE = 100
-        all_versions: List[Dict] = []
+        all_versions: List[Dict[str, Any]] = []
 
         for start in range(0, len(hashes), BATCH_SIZE):
             batch = hashes[start : start + BATCH_SIZE]
@@ -729,7 +741,7 @@ class CivitaiClient:
                     continue
 
                 if isinstance(result, list):
-                    all_versions.extend(result)
+                    all_versions.extend(cast(Any, result))
                 else:
                     logger.debug(
                         "Unexpected by-hash response type: %s", type(result)
@@ -743,17 +755,34 @@ class CivitaiClient:
 
         return all_versions if all_versions else None
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
-        """Fetch all models for a specific Civitai user."""
+    async def get_user_models(
+        self, username: str, cursor: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        """Fetch one page (up to 100 models) for a specific Civitai user.
+
+        Returns ``{"items": [...], "nextCursor": <str|None>}`` on success,
+        or None on failure. Pass ``cursor`` (from a previous response's
+        ``nextCursor``) to fetch subsequent pages.
+        """
         if not username:
             return None
 
+        params: Dict[str, Any] = {
+            "username": username,
+            "nsfw": "true",
+            "limit": 100,
+            "sort": "Newest",
+            "period": "AllTime",
+        }
+        if cursor:
+            params["cursor"] = cursor
+
         try:
             success, result = await self._make_request(
                 "GET",
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={"username": username, "nsfw": "true"},
+                params=params,
             )
 
             if not success:
@@ -765,7 +794,7 @@ class CivitaiClient:
 
             items = result.get("items") if isinstance(result, dict) else None
             if not isinstance(items, list):
-                return []
+                items = []
 
             for model in items:
                 versions = model.get("modelVersions")
@@ -774,9 +803,68 @@ class CivitaiClient:
                 for version in versions:
                     self._remove_comfy_metadata(version)
 
-            return items
+            next_cursor: Optional[str] = None
+            metadata = result.get("metadata") if isinstance(result, dict) else None
+            if isinstance(metadata, dict):
+                raw_cursor = metadata.get("nextCursor")
+                if raw_cursor is not None:
+                    next_cursor = str(raw_cursor)
+
+            return {"items": items, "nextCursor": next_cursor}
         except RateLimitError:
             raise
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Error fetching models for %s: %s", username, exc)
             return None
+
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        """Best-effort lookup of a creator's published model count.
+
+        Uses the ``/creators`` endpoint (a contains-match query), picking the
+        entry whose username matches exactly (case-insensitive). Returns None
+        on any failure; never raises. Results (including None) are cached
+        for ``_CREATOR_COUNT_CACHE_TTL_SECONDS``.
+        """
+        if not username:
+            return None
+
+        cache_key = username.lower()
+        cached = _creator_model_count_cache.get(cache_key)
+        if cached is not None:
+            cached_at, cached_count = cached
+            if time.monotonic() - cached_at < _CREATOR_COUNT_CACHE_TTL_SECONDS:
+                return cached_count
+
+        count: Optional[int] = None
+        try:
+            success, result = await self._make_request(
+                "GET",
+                f"{self.base_url}/creators",
+                use_auth=True,
+                params={"query": username, "limit": 10},
+            )
+
+            if success and isinstance(result, dict):
+                creators = result.get("items")
+                if isinstance(creators, list):
+                    for creator in creators:
+                        if not isinstance(creator, dict):
+                            continue
+                        creator_name = creator.get("username")
+                        if not isinstance(creator_name, str):
+                            continue
+                        if creator_name.lower() != cache_key:
+                            continue
+                        model_count = creator.get("modelCount")
+                        if isinstance(model_count, (int, float)) and not isinstance(
+                            model_count, bool
+                        ):
+                            count = int(model_count)
+                        break
+        except Exception as exc:  # best-effort only, never propagate
+            logger.debug(
+                "Failed to fetch creator model count for %s: %s", username, exc
+            )
+
+        _creator_model_count_cache[cache_key] = (time.monotonic(), count)
+        return count

py/services/download_coordinator.py

@@ -18,7 +18,7 @@ class DownloadCoordinator:
         self,
         *,
         ws_manager,
-        download_manager_factory: Callable[[], Awaitable],
+        download_manager_factory: Callable[[], Awaitable[Any]],
     ) -> None:
         self._ws_manager = ws_manager
         self._download_manager_factory = download_manager_factory

py/services/download_manager.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 import copy
 import logging
 import os
@@ -8,7 +12,7 @@ import zipfile
 from concurrent.futures import ThreadPoolExecutor
 from collections import OrderedDict
 import uuid
-from typing import Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple, cast
 from urllib.parse import urlparse
 from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 from ..utils.constants import (
@@ -18,7 +22,7 @@ from ..utils.constants import (
     VALID_LORA_TYPES,
 )
 from ..utils.civitai_utils import normalize_civitai_download_url, rewrite_preview_url
-from ..utils.file_utils import calculate_sha256
+from ..utils.file_utils import calculate_sha256, calculate_autov3
 from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 from ..utils.utils import sanitize_folder_name
 from ..utils.exif_utils import ExifUtils
@@ -29,6 +33,7 @@ from .metadata_service import get_default_metadata_provider, get_metadata_provid
 from .downloader import get_downloader, DownloadProgress, DownloadStreamControl
 from .aria2_downloader import Aria2Error, get_aria2_downloader
 from .aria2_transfer_state import Aria2TransferStateStore
+from .download_queue_service import DownloadQueueService
 
 # Download to temporary file first
 import tempfile
@@ -120,7 +125,7 @@ class DownloadManager:
                         "delay": 0,
                     }
                 )
-            except DownloadInProgressError:
+            except DownloadInProgressError:  # pyright: ignore[reportPossiblyUnboundVariable]
                 logger.info(
                     "Skipping automatic example images download for %s; another example images download is already running",
                     model_hash,
@@ -169,7 +174,7 @@ class DownloadManager:
                 logger.error("aria2 download failed for %s: %s", download_url, exc)
                 return False, str(exc)
 
-        download_kwargs = {
+        download_kwargs: Dict[str, Any] = {
             "progress_callback": progress_callback,
             "use_auth": use_auth,
         }
@@ -203,16 +208,16 @@ class DownloadManager:
 
     async def download_from_civitai(
         self,
-        model_id: int = None,
-        model_version_id: int = None,
-        save_dir: str = None,
+        model_id: int | None = None,
+        model_version_id: int | None = None,
+        save_dir: str | None = None,
         relative_path: str = "",
         progress_callback=None,
         use_default_paths: bool = False,
-        download_id: str = None,
-        source: str = None,
-        file_params: Dict = None,
-    ) -> Dict:
+        download_id: str | None = None,
+        source: str | None = None,
+        file_params: Dict[str, Any] | None = None,
+    ) -> Dict[str, Any]:
         """Download model from Civitai with task tracking and concurrency control
 
         Args:
@@ -229,6 +234,12 @@ class DownloadManager:
         Returns:
             Dict with download result
         """
+        logger.debug(
+            "[download] download_from_civitai called: model_id=%s, model_version_id=%s, "
+            "source=%s, file_params=%s",
+            model_id, model_version_id, source, file_params,
+        )
+
         # Validate that at least one identifier is provided
         if not model_id and not model_version_id:
             return {
@@ -249,6 +260,7 @@ class DownloadManager:
             "source": source,
             "file_params": copy.deepcopy(file_params) if file_params is not None else None,
             "progress": 0,
+
             "status": "queued",
             "transfer_backend": self._get_model_download_backend(),
             "bytes_downloaded": 0,
@@ -288,8 +300,8 @@ class DownloadManager:
             return result
         except asyncio.CancelledError:
             return {
-                "success": False,
-                "error": "Download was cancelled",
+                "success": True,
+                "cancelled": True,
                 "download_id": task_id,
             }
         finally:
@@ -301,14 +313,14 @@ class DownloadManager:
     async def _download_with_semaphore(
         self,
         task_id: str,
-        model_id: int,
-        model_version_id: int,
-        save_dir: str,
+        model_id: int | None,
+        model_version_id: int | None,
+        save_dir: str | None,
         relative_path: str,
         progress_callback=None,
         use_default_paths: bool = False,
-        source: str = None,
-        file_params: Dict = None,
+        source: str | None = None,
+        file_params: Dict[str, Any] | None = None,
     ):
         """Execute download with semaphore to limit concurrency"""
         # Update status to waiting
@@ -360,10 +372,20 @@ class DownloadManager:
                     if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                         await self._persist_aria2_state(task_id)
 
+                # Update SQLite queue status to 'downloading'
+                try:
+                    queue_service = await DownloadQueueService.get_instance()
+                    await queue_service.update_status(task_id, "downloading")
+                except Exception:
+                    logger.warning(
+                        "Failed to update queue status for %s", task_id, exc_info=True
+                    )
+
                 # Use original download implementation
                 try:
                     # Check for cancellation before starting
-                    if asyncio.current_task().cancelled():
+                    current_task = asyncio.current_task()
+                    if current_task is not None and current_task.cancelled():
                         raise asyncio.CancelledError()
 
                     result = await self._execute_original_download(
@@ -396,6 +418,22 @@ class DownloadManager:
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
 
+                    # Move queue item to history on completion
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status=result.get("status", "completed") if result.get("success") else "failed",
+                            error=result.get("error") if not result.get("success") else None,
+                            file_path=result.get("file_path"),
+                            bytes_downloaded=self._active_downloads.get(task_id, {}).get("bytes_downloaded", 0),
+                            total_bytes=self._active_downloads.get(task_id, {}).get("total_bytes"),
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to complete queue item for %s", task_id, exc_info=True
+                        )
+
                     return result
                 except asyncio.CancelledError:
                     # Handle cancellation
@@ -404,6 +442,19 @@ class DownloadManager:
                         self._active_downloads[task_id]["bytes_per_second"] = 0.0
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
+
+                    # Move queue item to history as canceled
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status="canceled",
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to cancel queue item for %s", task_id, exc_info=True
+                        )
+
                     logger.info(f"Download cancelled for task {task_id}")
                     raise
                 except Exception as e:
@@ -417,16 +468,32 @@ class DownloadManager:
                         self._active_downloads[task_id]["bytes_per_second"] = 0.0
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
+
+                    # Move queue item to history as failed
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status="failed",
+                            error=str(e),
+                            bytes_downloaded=self._active_downloads.get(task_id, {}).get("bytes_downloaded", 0),
+                            total_bytes=self._active_downloads.get(task_id, {}).get("total_bytes"),
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to complete queue item for %s", task_id, exc_info=True
+                        )
+
                     return {"success": False, "error": str(e)}
         finally:
             # Schedule cleanup of download record after delay
             asyncio.create_task(self._cleanup_download_record(task_id))
 
-    def _start_background_download_task(self, download_id: str, coroutine) -> asyncio.Task:
+    def _start_background_download_task(self, download_id: str, coroutine) -> asyncio.Task[Any]:
         task = asyncio.create_task(coroutine)
         self._download_tasks[download_id] = task
 
-        def _cleanup_done_task(done_task: asyncio.Task) -> None:
+        def _cleanup_done_task(done_task: asyncio.Task[Any]) -> None:
             current_task = self._download_tasks.get(download_id)
             if current_task is done_task:
                 self._download_tasks.pop(download_id, None)
@@ -468,7 +535,7 @@ class DownloadManager:
     async def _cleanup_cancelled_download_files(
         self,
         download_id: str,
-        download_info: Optional[Dict],
+        download_info: Optional[Dict[str, Any]],
     ) -> None:
         target_files = set()
         persisted = await self._aria2_state_store.get(download_id)
@@ -541,13 +608,13 @@ class DownloadManager:
         self,
         download_id: str,
         *,
-        extra: Optional[Dict] = None,
+        extra: Optional[Dict[str, Any]] = None,
     ) -> None:
         info = self._active_downloads.get(download_id)
         if not info:
             return
 
-        payload = {
+        payload: Dict[str, Any] = {
             "download_id": download_id,
             "model_id": info.get("model_id"),
             "model_version_id": info.get("model_version_id"),
@@ -569,7 +636,7 @@ class DownloadManager:
 
         await self._aria2_state_store.upsert(download_id, payload)
 
-    def _build_restored_download_info(self, record: Dict, save_path: str) -> Dict:
+    def _build_restored_download_info(self, record: Dict[str, Any], save_path: str) -> Dict[str, Any]:
         return {
             "model_id": record.get("model_id"),
             "model_version_id": record.get("model_version_id"),
@@ -591,8 +658,8 @@ class DownloadManager:
 
     def _is_same_aria2_download_request(
         self,
-        current_info: Optional[Dict],
-        persisted_record: Dict,
+        current_info: Optional[Dict[str, Any]],
+        persisted_record: Dict[str, Any],
     ) -> bool:
         if not isinstance(current_info, dict):
             return False
@@ -604,13 +671,15 @@ class DownloadManager:
 
         return current_version_id == persisted_version_id
 
-    def _build_download_urls_from_file_info(self, file_info: Dict, source: str = None) -> List[str]:
+    def _build_download_urls_from_file_info(self, file_info: Dict[str, Any], source: str | None = None) -> List[str]:
         mirrors = file_info.get("mirrors") or []
         download_urls: List[str] = []
         if mirrors:
             for mirror in mirrors:
                 if mirror.get("deletedAt") is None and mirror.get("url"):
-                    download_urls.append(normalize_civitai_download_url(mirror["url"]))
+                    normalized_url = normalize_civitai_download_url(mirror["url"])
+                    if normalized_url:
+                        download_urls.append(normalized_url)
 
             if source == "civarchive" and len(download_urls) > 1:
                 civitai_urls = [
@@ -620,10 +689,15 @@ class DownloadManager:
                     u for u in download_urls if not u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
                 ]
                 download_urls = non_civitai_urls + civitai_urls
-        else:
+
+        # Fallback: when mirrors is empty or all mirrors have been deleted,
+        # use the file's downloadUrl directly (e.g. CivitAI download endpoint).
+        if not download_urls:
             download_url = file_info.get("downloadUrl")
             if download_url:
-                download_urls.append(normalize_civitai_download_url(download_url))
+                normalized_url = normalize_civitai_download_url(download_url)
+                if normalized_url:
+                    download_urls.append(normalized_url)
 
         return download_urls
 
@@ -631,8 +705,8 @@ class DownloadManager:
         self,
         *,
         model_type: str,
-        version_info: Dict,
-        file_info: Dict,
+        version_info: Dict[str, Any],
+        file_info: Dict[str, Any],
         save_path: str,
     ):
         if model_type == "checkpoint":
@@ -641,7 +715,7 @@ class DownloadManager:
             return EmbeddingMetadata.from_civitai_info(version_info, file_info, save_path)
         return LoraMetadata.from_civitai_info(version_info, file_info, save_path)
 
-    def _resolve_save_path_from_persisted_record(self, record: Dict) -> Optional[str]:
+    def _resolve_save_path_from_persisted_record(self, record: Dict[str, Any]) -> Optional[str]:
         save_path = record.get("save_path") or record.get("file_path")
         if isinstance(save_path, str) and save_path:
             return os.path.abspath(save_path)
@@ -663,7 +737,7 @@ class DownloadManager:
 
         return os.path.abspath(os.path.join(save_dir, file_name))
 
-    async def _resume_restored_aria2_download(self, download_id: str, record: Dict) -> Dict:
+    async def _resume_restored_aria2_download(self, download_id: str, record: Dict[str, Any]) -> Dict[str, Any]:
         try:
             if download_id in self._active_downloads:
                 self._active_downloads[download_id]["status"] = "downloading"
@@ -777,7 +851,7 @@ class DownloadManager:
         self,
         previous_download_id: str,
         new_download_id: str,
-        persisted_record: Dict,
+        persisted_record: Dict[str, Any],
         save_path: str,
     ) -> None:
         aria2_downloader = await get_aria2_downloader()
@@ -873,7 +947,7 @@ class DownloadManager:
                     except Exception:
                         status_payload = None
 
-                if status_payload is not None:
+                if status_payload is not None and isinstance(gid, str):
                     remote_status = status_payload.get("status", "")
                     if remote_status in {"active", "waiting", "paused"}:
                         await aria2_downloader.restore_transfer(download_id, gid, save_path)
@@ -1050,17 +1124,17 @@ class DownloadManager:
 
     async def _execute_original_download(
         self,
-        model_id,
-        model_version_id,
-        save_dir,
-        relative_path,
+        model_id: int | None,
+        model_version_id: int | None,
+        save_dir: str | None,
+        relative_path: str,
         progress_callback,
-        use_default_paths,
-        download_id=None,
-        transfer_backend="python",
-        source=None,
-        file_params=None,
-    ):
+        use_default_paths: bool,
+        download_id: str | None = None,
+        transfer_backend: str = "python",
+        source: str | None = None,
+        file_params: Dict[str, Any] | None = None,
+    ) -> Dict[str, Any]:
         """Wrapper for original download_from_civitai implementation"""
         try:
             # Check if model version already exists in library
@@ -1107,7 +1181,7 @@ class DownloadManager:
 
             # Get version info based on the provided identifier
             version_info = await metadata_provider.get_model_version(
-                model_id, model_version_id
+                cast(int, model_id), cast(int, model_version_id)
             )
 
             if not version_info:
@@ -1118,7 +1192,7 @@ class DownloadManager:
                     )
                     metadata_provider = await get_default_metadata_provider()
                     version_info = await metadata_provider.get_model_version(
-                        model_id, model_version_id
+                        cast(int, model_id), cast(int, model_version_id)
                     )
 
             if not version_info:
@@ -1233,10 +1307,24 @@ class DownloadManager:
                     "download_id": download_id,
                 }
 
-            # Check if this checkpoint should be treated as a diffusion model based on baseModel
+            # Check if this checkpoint should be treated as a diffusion model
+            # Priority: (1) any file has type "UNet" or "Diffusion Model",
+            #            (2) baseModel is in DIFFUSION_MODEL_BASE_MODELS
             is_diffusion_model = False
             if model_type == "checkpoint":
-                if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
+                # Check file types first (more direct signal from CivitAI)
+                version_files = version_info.get("files", [])
+                for f in version_files:
+                    f_type = f.get("type", "")
+                    if f_type in ("UNet", "Diffusion Model"):
+                        is_diffusion_model = True
+                        logger.info(
+                            f"File type '{f_type}' detected, routing checkpoint to unet folder"
+                        )
+                        break
+
+                # Fallback to baseModel name check
+                if not is_diffusion_model and base_model_value in DIFFUSION_MODEL_BASE_MODELS:
                     is_diffusion_model = True
                     logger.info(
                         f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
@@ -1309,8 +1397,20 @@ class DownloadManager:
                 relative_path = self._calculate_relative_path(version_info, model_type)
 
             # Update save directory with relative path if provided
+            if not save_dir:
+                return {"success": False, "error": "No save directory specified"}
             if relative_path:
+                base_save_dir = save_dir
                 save_dir = os.path.join(save_dir, relative_path)
+                # Security: validate path containment after joining
+                resolved_dir = os.path.abspath(os.path.normpath(save_dir))
+                base_dir = os.path.abspath(os.path.normpath(base_save_dir))
+                if not resolved_dir.startswith(base_dir + os.sep) and resolved_dir != base_dir:
+                    logger.warning(
+                        "Path traversal detected: %s escapes %s",
+                        resolved_dir, base_dir,
+                    )
+                    return {"success": False, "error": "Download path is outside allowed directory"}
                 # Create directory if it doesn't exist
                 os.makedirs(save_dir, exist_ok=True)
 
@@ -1352,86 +1452,100 @@ class DownloadManager:
 
             # If file_params is provided, try to find matching file
             if file_params and model_version_id:
+                target_file_id = file_params.get("id")
                 target_type = file_params.get("type", "Model")
-                target_format = file_params.get("format", "SafeTensor")
-                target_size = file_params.get("size", "full")
+                target_format = file_params.get("format")
+                target_size = file_params.get("size")
                 target_fp = file_params.get("fp")
                 is_primary = file_params.get("isPrimary", False)
 
-                if is_primary:
-                    # Find primary file
+                logger.debug(
+                    "[download] file_params received: id=%s, type=%s, format=%s, size=%s, fp=%s, isPrimary=%s, "
+                    "model_version_id=%s, total_files=%d",
+                    target_file_id, target_type, target_format, target_size, target_fp, is_primary,
+                    model_version_id, len(files),
+                )
+
+                if target_file_id:
+                    target_id_str = str(target_file_id)
+                    for f in files:
+                        f_id = f.get("id")
+                        if str(f_id) == target_id_str:
+                            file_info = f
+                            logger.debug(
+                                "[download] MATCH by ID: id=%s name='%s'",
+                                f_id, f.get("name"),
+                            )
+                            break
+                    if not file_info:
+                        logger.debug("[download] No file found with id=%s", target_file_id)
+
+                elif is_primary:
                     file_info = next(
                         (
                             f
                             for f in files
                             if f.get("primary")
-                            and f.get("type") in ("Model", "Negative", "Diffusion Model")
+                            and f.get("type") in ("Model", "Negative", "Diffusion Model", "UNet")
                         ),
                         None,
                     )
                 else:
-                    # Match by metadata
+                    # Lenient metadata match: only compare fields present on both sides
                     for f in files:
                         f_type = f.get("type", "")
-                        f_meta = f.get("metadata", {})
-
-                        # Check type match
                         if f_type != target_type:
                             continue
 
-                        # Check metadata match
-                        if f_meta.get("format") != target_format:
+                        f_meta = f.get("metadata", {})
+                        f_format = f_meta.get("format") or f.get("format")
+                        f_size = f_meta.get("size") or f.get("size")
+                        f_fp = f_meta.get("fp") or f.get("fp")
+
+                        if target_format and f_format != target_format:
                             continue
-                        if f_meta.get("size") != target_size:
+                        if target_size and f_size and f_size != target_size:
                             continue
-                        if target_fp and f_meta.get("fp") != target_fp:
+                        if target_fp and f_fp and f_fp != target_fp:
                             continue
 
                         file_info = f
                         break
 
+                if not file_info:
+                    logger.debug(
+                        "[download] No match found via file_params — falling back to primary file lookup",
+                    )
+            elif not file_params:
+                logger.debug(
+                    "[download] No file_params provided (null/None) — will use primary file lookup. "
+                    "model_version_id=%s, total_files=%d",
+                    model_version_id, len(files),
+                )
+
             # Fallback to primary file if no match found
             if not file_info:
+                logger.debug("[download] Looking for primary file as fallback")
                 file_info = next(
                     (
                         f
                         for f in files
-                        if f.get("primary") and f.get("type") in ("Model", "Negative", "Diffusion Model")
+                        if f.get("primary") and f.get("type") in ("Model", "Negative", "Diffusion Model", "UNet")
                     ),
                     None,
                 )
+                if file_info:
+                    logger.debug(
+                        "[download] Fallback primary file selected: id=%s, name=%s",
+                        file_info.get("id"), file_info.get("name"),
+                    )
+                else:
+                    logger.debug("[download] No primary file found in fallback lookup")
 
             if not file_info:
                 return {"success": False, "error": "No suitable 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(
-                            normalize_civitai_download_url(mirror["url"])
-                        )
 
-                # When source is 'civarchive', prioritize non-Civitai URLs
-                # This avoids failed downloads from deleted Civitai models
-                if source == "civarchive" and len(download_urls) > 1:
-                    civitai_urls = [
-                        u
-                        for u in download_urls
-                        if u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
-                    ]
-                    non_civitai_urls = [
-                        u
-                        for u in download_urls
-                        if not u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
-                    ]
-                    download_urls = non_civitai_urls + civitai_urls
-            else:
-                download_url = file_info.get("downloadUrl")
-                if download_url:
-                    download_urls.append(
-                        normalize_civitai_download_url(download_url)
-                    )
+            download_urls = self._build_download_urls_from_file_info(file_info, source=source)
 
             if not download_urls:
                 return {"success": False, "error": "No mirror URL found"}
@@ -1458,6 +1572,11 @@ class DownloadManager:
                     version_info, file_info, save_path
                 )
                 logger.info(f"Creating EmbeddingMetadata for {file_name}")
+            else:
+                return {
+                    "success": False,
+                    "error": f'Unsupported model type "{model_type}"',
+                }
 
             # 6. Start download process
             if transfer_backend == "aria2" and download_id:
@@ -1477,7 +1596,7 @@ class DownloadManager:
                     },
                 )
 
-            execute_kwargs = {
+            execute_kwargs: Dict[str, Any] = {
                 "download_urls": download_urls,
                 "save_dir": save_dir,
                 "metadata": metadata,
@@ -1524,7 +1643,8 @@ class DownloadManager:
                 )
 
             # If early_access_msg exists and download failed, replace error message
-            if "early_access_msg" in locals() and not result.get("success", False):
+            early_access_msg = locals().get("early_access_msg")
+            if early_access_msg and not result.get("success", False):
                 result["error"] = early_access_msg
 
             return result
@@ -1549,7 +1669,7 @@ class DownloadManager:
         self,
         model_type: str,
         model_id_value,
-        version_info: Dict,
+        version_info: Dict[str, Any],
         fallback_version_id=None,
         file_path: str | None = None,
     ) -> None:
@@ -1580,8 +1700,8 @@ class DownloadManager:
         try:
             await history_service.mark_downloaded(
                 model_type,
-                int(version_id),
-                model_id=int(resolved_model_id) if resolved_model_id is not None else None,
+                int(cast(Any, version_id)),
+                model_id=int(cast(Any, resolved_model_id)) if resolved_model_id is not None else None,
                 source="download",
                 file_path=file_path,
             )
@@ -1598,7 +1718,7 @@ class DownloadManager:
         self,
         model_type: str,
         model_id_value,
-        version_info: Dict,
+        version_info: Dict[str, Any],
         fallback_version_id=None,
     ) -> None:
         """Ensure update tracking reflects a newly downloaded version."""
@@ -1622,7 +1742,7 @@ class DownloadManager:
             if isinstance(model_info, dict):
                 resolved_model_id = model_info.get("id")
         try:
-            resolved_model_id = int(resolved_model_id)
+            resolved_model_id = int(cast(Any, resolved_model_id))
         except (TypeError, ValueError):
             logger.debug(
                 "Skipping update sync; invalid model id: %s", resolved_model_id
@@ -1633,7 +1753,7 @@ class DownloadManager:
         if version_id is None:
             version_id = fallback_version_id
         try:
-            version_id = int(version_id)
+            version_id = int(cast(Any, version_id))
         except (TypeError, ValueError):
             logger.debug(
                 "Skipping update sync; invalid version id for model %s: %s",
@@ -1670,7 +1790,7 @@ class DownloadManager:
                 for entry in local_versions or []:
                     vid = entry.get("versionId")
                     try:
-                        version_ids.add(int(vid))
+                        version_ids.add(int(cast(Any, vid)))
                     except (TypeError, ValueError):
                         continue
 
@@ -1692,7 +1812,7 @@ class DownloadManager:
             )
 
     def _calculate_relative_path(
-        self, version_info: Dict, model_type: str = "lora"
+        self, version_info: Dict[str, Any], model_type: str = "lora"
     ) -> str:
         """Calculate relative path using template from settings
 
@@ -1734,6 +1854,9 @@ class DownloadManager:
             model_tags, model_type
         )
 
+        if not first_tag:
+            first_tag = "no tags"  # Default if no tags available
+
         # Format the template with available data
         formatted_path = path_template
         formatted_path = formatted_path.replace("{base_model}", mapped_base_model)
@@ -1749,6 +1872,15 @@ class DownloadManager:
         if model_type == "embedding":
             formatted_path = formatted_path.replace(" ", "_")
 
+        # Sanitize the resolved path to prevent path traversal:
+        # - Strip leading slashes (prevents os.path.join from treating path as absolute)
+        # - Collapse double slashes from empty placeholder substitutions
+        # - Strip trailing slashes for cleanliness
+        formatted_path = formatted_path.lstrip("/")
+        while "//" in formatted_path:
+            formatted_path = formatted_path.replace("//", "/")
+        formatted_path = formatted_path.rstrip("/")
+
         return formatted_path
 
     async def _execute_download(
@@ -1756,21 +1888,22 @@ class DownloadManager:
         download_urls: List[str],
         save_dir: str,
         metadata,
-        version_info: Dict,
+        version_info: Dict[str, Any],
         relative_path: str,
         progress_callback=None,
         model_type: str = "lora",
-        download_id: str = None,
+        download_id: str | None = None,
         transfer_backend: Optional[str] = None,
-    ) -> Dict:
+    ) -> Dict[str, Any]:
         """Execute the actual download process including preview images and model files"""
-        metadata_entries: List = []
+        metadata_entries: List[Any] = []
         metadata_files_for_cleanup: List[str] = []
         extracted_paths: List[str] = []
         metadata_path = ""
         preview_targets: List[str] = []
         preview_path: str | None = None
         preview_nsfw_level = 0
+        save_path: str | None = None
         transfer_backend = (transfer_backend or self._get_model_download_backend()).lower()
         try:
             resolved, save_path = await self._resolve_download_target_path(
@@ -1818,9 +1951,9 @@ class DownloadManager:
                     mature_threshold=mature_threshold,
                 )
 
-                preview_url = selected_image.get("url") if selected_image else None
+                preview_url = cast(Optional[str], selected_image.get("url")) if selected_image else None
                 media_type = (
-                    (selected_image.get("type") or "").lower() if selected_image else ""
+                    cast(str, selected_image.get("type") or "").lower() if selected_image else ""
                 )
 
                 def _extension_from_url(url: str, fallback: str) -> str:
@@ -1844,9 +1977,10 @@ class DownloadManager:
                             preview_url, media_type="video"
                         )
                         attempt_urls: List[str] = []
-                        if rewritten:
+                        if rewritten and rewritten_url:
                             attempt_urls.append(rewritten_url)
-                        attempt_urls.append(preview_url)
+                        if preview_url:
+                            attempt_urls.append(preview_url)
 
                         seen_attempts = set()
                         for attempt in attempt_urls:
@@ -1863,7 +1997,7 @@ class DownloadManager:
                         rewritten_url, rewritten = rewrite_preview_url(
                             preview_url, media_type="image"
                         )
-                        if rewritten:
+                        if rewritten and rewritten_url:
                             preview_ext = _extension_from_url(preview_url, ".png")
                             preview_path = os.path.splitext(save_path)[0] + preview_ext
                             success, _ = await downloader.download_file(
@@ -1889,7 +2023,9 @@ class DownloadManager:
                                 )
                                 if success:
                                     with open(temp_path, "wb") as temp_file_handle:
-                                        temp_file_handle.write(content)
+                                        temp_file_handle.write(
+                                            content if isinstance(content, bytes) else content.encode("utf-8")
+                                        )
                                     preview_path = (
                                         os.path.splitext(save_path)[0] + ".webp"
                                     )
@@ -1941,6 +2077,8 @@ class DownloadManager:
             last_error = None
             for download_url in download_urls:
                 download_url = normalize_civitai_download_url(download_url)
+                if download_url is None:
+                    continue
                 use_auth = download_url.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
                 if transfer_backend == "aria2" and download_id:
                     await self._persist_aria2_state(
@@ -1974,7 +2112,21 @@ class DownloadManager:
                     break
 
                 last_error = result
-                if os.path.exists(save_path):
+                # For aria2: if the .aria2 control file is missing, aria2 considers
+                # the download complete.  A transient RPC failure may have made us
+                # think the download failed even though the file is fully on disk.
+                # Keep the file so a retry can find it already complete.
+                if (
+                    transfer_backend == "aria2"
+                    and os.path.exists(save_path)
+                    and not os.path.exists(f"{save_path}.aria2")
+                ):
+                    logger.warning(
+                        "aria2 download reported failure but .aria2 file is absent "
+                        "for %s — the file is likely complete.  Preserving it for retry.",
+                        save_path,
+                    )
+                elif os.path.exists(save_path):
                     try:
                         os.remove(save_path)
                     except Exception as e:
@@ -2031,6 +2183,10 @@ class DownloadManager:
                         "error": f"Zip archive does not contain any supported model files ({supported_text})",
                     }
                 actual_file_paths = extracted_paths
+                # The archive entry's AutoV3 (if any) describes the zip itself,
+                # not the extracted models; clear it so per-file header
+                # resolution applies to every extracted model.
+                metadata.autov3 = None
                 try:
                     os.remove(save_path)
                 except OSError as exc:
@@ -2106,7 +2262,7 @@ class DownloadManager:
                             entry, normalized_file_path, adjust_root
                         )
                         if adjusted_entry is not None:
-                            entry = adjusted_entry
+                            entry = cast(Any, adjusted_entry)
                             metadata_entries[index] = entry
 
                 metadata_file_path = (
@@ -2226,11 +2382,11 @@ class DownloadManager:
 
     async def _build_metadata_entries(
         self, base_metadata, file_paths: List[str]
-    ) -> List:
+    ) -> List[Any]:
         if not file_paths:
             return []
 
-        entries: List = []
+        entries: List[Any] = []
         for index, file_path in enumerate(file_paths):
             entry = base_metadata if index == 0 else copy.deepcopy(base_metadata)
             # Update file paths without modifying size and modified timestamps
@@ -2245,6 +2401,16 @@ class DownloadManager:
                 sha256 = await calculate_sha256(file_path)
                 if sha256:
                     entry.sha256 = sha256.lower()
+            # AutoV3: the Civitai-reported value for the downloaded file (set
+            # by from_civitai_info) takes precedence. Only the un-checked
+            # state (None) triggers a header read; '' (checked-unavailable)
+            # is never re-read, honoring the three-state contract so rows
+            # marked at download time stay untouched by later passes.
+            if entry.autov3 is None:
+                autov3 = await asyncio.get_running_loop().run_in_executor(
+                    None, calculate_autov3, file_path
+                )
+                entry.autov3 = (autov3 or "").lower()
             entries.append(entry)
 
         return entries
@@ -2263,7 +2429,7 @@ class DownloadManager:
         return destination
 
     def _distribute_preview_to_entries(
-        self, preview_path: str, entries: List
+        self, preview_path: str, entries: List[Any]
     ) -> List[str]:
         if not preview_path or not entries:
             return []
@@ -2322,7 +2488,7 @@ class DownloadManager:
             progress_callback, normalized_snapshot, rounded_progress
         )
 
-    async def cancel_download(self, download_id: str) -> Dict:
+    async def cancel_download(self, download_id: str) -> Dict[str, Any]:
         """Cancel an active download by download_id
 
         Args:
@@ -2404,7 +2570,7 @@ class DownloadManager:
                 self._download_tasks.pop(download_id, None)
                 await self._aria2_state_store.remove(download_id)
 
-    async def skip_download(self, download_id: str) -> Dict:
+    async def skip_download(self, download_id: str) -> Dict[str, Any]:
         """Skip a download while preserving all partial files on disk.
 
         Removes all in-memory tracking (asyncio task, semaphore, active/pause
@@ -2487,7 +2653,7 @@ class DownloadManager:
             # Preserve aria2 state store entry so the partial download
             # info survives restarts and can be resumed later
 
-    async def pause_download(self, download_id: str) -> Dict:
+    async def pause_download(self, download_id: str) -> Dict[str, Any]:
         """Pause an active download without losing progress."""
 
         await self._restore_persisted_downloads()
@@ -2534,7 +2700,7 @@ class DownloadManager:
 
         return {"success": True, "message": "Download paused successfully"}
 
-    async def resume_download(self, download_id: str) -> Dict:
+    async def resume_download(self, download_id: str) -> Dict[str, Any]:
         """Resume a previously paused download."""
 
         await self._restore_persisted_downloads()
@@ -2551,7 +2717,7 @@ class DownloadManager:
             self._pause_events[download_id] = pause_control
             self._active_downloads[download_id] = self._build_restored_download_info(
                 persisted,
-                os.path.abspath(save_path),
+                os.path.abspath(cast(str, save_path)),
             )
 
         if pause_control.is_set():
@@ -2678,7 +2844,7 @@ class DownloadManager:
         elif asyncio.iscoroutine(result):
             await result
 
-    async def get_active_downloads(self) -> Dict:
+    async def get_active_downloads(self) -> Dict[str, Any]:
         """Get information about all active downloads
 
         Returns:

py/services/download_queue_service.py

@@ -0,0 +1,938 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import sqlite3
+import time
+from typing import Any, Optional
+
+from ..utils.cache_paths import get_cache_base_dir
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_database_path() -> str:
+    base_dir = get_cache_base_dir(create=True)
+    history_dir = os.path.join(base_dir, "download_history")
+    os.makedirs(history_dir, exist_ok=True)
+    return os.path.join(history_dir, "download_queue.sqlite")
+
+
+class DownloadQueueService:
+    """Persistent download queue and history manager backed by SQLite.
+
+    Provides a singleton interface for managing a download queue and
+    corresponding history table, both stored in a single SQLite database
+    under the cache directory.
+    """
+
+    _instance: Optional[DownloadQueueService] = None
+    _class_lock: asyncio.Lock = asyncio.Lock()
+
+    _SCHEMA_TABLES = """
+        CREATE TABLE IF NOT EXISTS download_queue (
+            download_id TEXT PRIMARY KEY,
+            model_id INTEGER,
+            model_version_id INTEGER,
+            model_name TEXT NOT NULL DEFAULT '',
+            version_name TEXT DEFAULT '',
+            thumbnail_url TEXT DEFAULT '',
+            source TEXT,
+            file_params TEXT,
+            status TEXT NOT NULL DEFAULT 'queued',
+            priority INTEGER DEFAULT 0,
+            progress INTEGER DEFAULT 0,
+            bytes_downloaded INTEGER DEFAULT 0,
+            total_bytes INTEGER,
+            bytes_per_second REAL DEFAULT 0.0,
+            error TEXT,
+            file_path TEXT,
+            added_at REAL NOT NULL,
+            started_at REAL,
+            completed_at REAL
+        );
+        CREATE INDEX IF NOT EXISTS idx_dq_status ON download_queue(status);
+        CREATE INDEX IF NOT EXISTS idx_dq_added ON download_queue(added_at);
+
+        CREATE TABLE IF NOT EXISTS download_history (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            download_id TEXT,
+            model_id INTEGER,
+            model_version_id INTEGER,
+            model_name TEXT NOT NULL DEFAULT '',
+            version_name TEXT DEFAULT '',
+            thumbnail_url TEXT DEFAULT '',
+            status TEXT NOT NULL,
+            error TEXT,
+            file_path TEXT,
+            bytes_downloaded INTEGER DEFAULT 0,
+            total_bytes INTEGER,
+            completed_at REAL NOT NULL,
+            is_already_exists INTEGER DEFAULT 0
+        );
+        CREATE INDEX IF NOT EXISTS idx_dh_completed ON download_history(completed_at DESC);
+        CREATE INDEX IF NOT EXISTS idx_dh_status ON download_history(status);
+    """
+
+    _CREATE_UNIQUE_INDEX = """
+        CREATE UNIQUE INDEX IF NOT EXISTS idx_dh_download_id
+            ON download_history(download_id) WHERE download_id IS NOT NULL;
+    """
+
+    @classmethod
+    async def get_instance(cls) -> DownloadQueueService:
+        """Return the singleton instance, creating it if necessary."""
+        async with cls._class_lock:
+            if cls._instance is None:
+                cls._instance = cls()
+                await cls._instance.deduplicate()
+            return cls._instance
+
+    def __init__(self, db_path: Optional[str] = None) -> None:
+        self._db_path = db_path or _resolve_database_path()
+        self._lock = asyncio.Lock()
+        self._conn: Optional[sqlite3.Connection] = None
+        self._schema_initialized = False
+        self._ensure_directory()
+        self._initialize_schema()
+
+    def _ensure_directory(self) -> None:
+        directory = os.path.dirname(self._db_path)
+        if directory:
+            os.makedirs(directory, exist_ok=True)
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(self._db_path, check_same_thread=False)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _get_conn(self) -> sqlite3.Connection:
+        if self._conn is None:
+            self._conn = sqlite3.connect(self._db_path, check_same_thread=False)
+            self._conn.row_factory = sqlite3.Row
+        return self._conn
+
+    def _initialize_schema(self) -> None:
+        if self._schema_initialized:
+            return
+        with self._connect() as conn:
+            conn.executescript(self._SCHEMA_TABLES)
+
+            # Creating the unique index on download_history.download_id can
+            # fail if pre-existing rows have duplicate values (e.g. from a
+            # previous version that lacked the index).  Deduplicate first so
+            # that the migration does not crash on startup.
+            if not self._index_exists(conn, "idx_dh_download_id"):
+                self._remove_duplicate_download_ids(conn)
+                conn.executescript(self._CREATE_UNIQUE_INDEX)
+
+            conn.commit()
+        self._schema_initialized = True
+
+    @staticmethod
+    def _index_exists(conn: sqlite3.Connection, name: str) -> bool:
+        return conn.execute(
+            "SELECT 1 FROM sqlite_master WHERE type='index' AND name=?",
+            (name,),
+        ).fetchone() is not None
+
+    @staticmethod
+    def _remove_duplicate_download_ids(conn: sqlite3.Connection) -> None:
+        conn.execute("""
+            DELETE FROM download_history
+            WHERE id NOT IN (
+                SELECT MIN(id)
+                FROM download_history
+                WHERE download_id IS NOT NULL
+                GROUP BY download_id
+            )
+            AND download_id IS NOT NULL
+        """)
+
+    def get_database_path(self) -> str:
+        """Return the resolved database file path."""
+        return self._db_path
+
+    def close(self) -> None:
+        """Close the persistent SQLite connection, if open.
+
+        This is called before plugin update operations to release the
+        database file lock on Windows, allowing ``shutil.rmtree()`` to
+        succeed when the cache resides inside the plugin directory.
+        """
+        if self._conn is not None:
+            try:
+                self._conn.close()
+            except Exception:
+                pass
+            finally:
+                self._conn = None
+
+    # ------------------------------------------------------------------
+    # Queue methods
+    # ------------------------------------------------------------------
+
+    async def add_to_queue(
+        self,
+        download_id: str,
+        model_id: Optional[int] = None,
+        model_version_id: Optional[int] = None,
+        model_name: str = "",
+        version_name: str = "",
+        thumbnail_url: str = "",
+        source: Optional[str] = None,
+        file_params: Optional[dict[str, Any]] = None,
+    ) -> dict[str, Any]:
+        """Insert a new download into the queue.
+
+        Returns the inserted row as a dict (or an empty dict if the
+        download_id already exists in the queue or has a terminal
+        record in history).
+        """
+        now = time.time()
+        file_params_json = json.dumps(file_params) if file_params is not None else None
+
+        async with self._lock:
+            conn = self._get_conn()
+
+            # Reject download_ids that already have a terminal record in history.
+            history_row = conn.execute(
+                "SELECT 1 FROM download_history WHERE download_id = ? LIMIT 1",
+                (download_id,),
+            ).fetchone()
+            if history_row is not None:
+                return {}
+
+            conn.execute(
+                """
+                INSERT OR IGNORE INTO download_queue (
+                    download_id, model_id, model_version_id, model_name,
+                    version_name, thumbnail_url, source, file_params,
+                    status, priority, added_at
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 'queued', 0, ?)
+                """,
+                (
+                    download_id,
+                    model_id,
+                    model_version_id,
+                    model_name,
+                    version_name,
+                    thumbnail_url,
+                    source,
+                    file_params_json,
+                    now,
+                ),
+            )
+            conn.commit()
+            row = conn.execute(
+                "SELECT * FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            ).fetchone()
+
+        return dict(row) if row else {}
+
+    async def get_queue(self) -> list[dict[str, Any]]:
+        """Return all items in the queue ordered by priority then added time."""
+        async with self._lock:
+            conn = self._get_conn()
+            rows = conn.execute(
+                "SELECT * FROM download_queue ORDER BY priority DESC, added_at ASC"
+            ).fetchall()
+        return [dict(row) for row in rows]
+
+    async def get_queued_count(self) -> int:
+        """Return the number of items with status ``'queued'``."""
+        async with self._lock:
+            conn = self._get_conn()
+            row = conn.execute(
+                "SELECT COUNT(*) AS cnt FROM download_queue WHERE status = 'queued'"
+            ).fetchone()
+        return row["cnt"] if row else 0
+
+    async def update_status(
+        self,
+        download_id: str,
+        status: str,
+        **extra: Any,
+    ) -> bool:
+        """Update the status and/or extra fields of a queue item.
+
+        Accepted extra keyword arguments:
+        ``progress``, ``error``, ``file_path``, ``bytes_downloaded``,
+        ``total_bytes``, ``bytes_per_second``.
+
+        Returns ``True`` if a row was updated.
+        """
+        allowed_extra = {
+            "progress",
+            "error",
+            "file_path",
+            "bytes_downloaded",
+            "total_bytes",
+            "bytes_per_second",
+        }
+
+        set_clauses: list[str] = ["status = ?"]
+        params: list[Any] = [status]
+        now = time.time()
+
+        if status in ("downloading",):
+            set_clauses.append("started_at = COALESCE(started_at, ?)")
+            params.append(now)
+        if status in ("completed", "failed", "canceled"):
+            set_clauses.append("completed_at = ?")
+            params.append(now)
+
+        for key, value in extra.items():
+            if key in allowed_extra:
+                set_clauses.append(f"{key} = ?")
+                params.append(value)
+
+        params.append(download_id)
+
+        async with self._lock:
+            conn = self._get_conn()
+            cursor = conn.execute(
+                f"UPDATE download_queue SET {', '.join(set_clauses)} "
+                "WHERE download_id = ?",
+                params,
+            )
+            conn.commit()
+        return cursor.rowcount > 0
+
+    async def remove_from_queue(self, download_id: str) -> bool:
+        """Remove a single item from the queue by download_id.
+
+        Returns ``True`` if a row was deleted.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            cursor = conn.execute(
+                "DELETE FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            )
+            conn.commit()
+        return cursor.rowcount > 0
+
+    async def move_to_top(self, download_id: str) -> bool:
+        """Move an item to the front of the queue (highest priority).
+
+        Returns ``True`` if the item was found and updated.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            row = conn.execute(
+                "SELECT priority FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            ).fetchone()
+            if row is None:
+                return False
+
+            max_row = conn.execute(
+                "SELECT MAX(priority) AS mx FROM download_queue"
+            ).fetchone()
+            max_priority: int = max_row["mx"] if max_row["mx"] is not None else 0
+
+            conn.execute(
+                "UPDATE download_queue SET priority = ? WHERE download_id = ?",
+                (max_priority + 1, download_id),
+            )
+            conn.commit()
+        return True
+
+    async def move_to_end(self, download_id: str) -> bool:
+        """Move an item to the end of the queue (lowest priority).
+
+        Returns ``True`` if the item was found and updated.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            row = conn.execute(
+                "SELECT priority FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            ).fetchone()
+            if row is None:
+                return False
+
+            min_row = conn.execute(
+                "SELECT MIN(priority) AS mn FROM download_queue"
+            ).fetchone()
+            min_priority: int = min_row["mn"] if min_row["mn"] is not None else 0
+
+            conn.execute(
+                "UPDATE download_queue SET priority = ? WHERE download_id = ?",
+                (min_priority - 1, download_id),
+            )
+            conn.commit()
+        return True
+
+    async def clear_queue(self, status_filter: Optional[str] = None) -> int:
+        """Remove items from the queue.
+
+        When *status_filter* is provided only items with that status are
+        deleted.  Returns the number of deleted rows.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            if status_filter is not None:
+                cursor = conn.execute(
+                    "DELETE FROM download_queue WHERE status = ?",
+                    (status_filter,),
+                )
+            else:
+                cursor = conn.execute("DELETE FROM download_queue")
+            conn.commit()
+        return cursor.rowcount
+
+    async def complete_download(
+        self,
+        download_id: str,
+        status: str = "completed",
+        error: Optional[str] = None,
+        file_path: Optional[str] = None,
+        bytes_downloaded: int = 0,
+        total_bytes: Optional[int] = None,
+        completed_at: Optional[float] = None,
+    ) -> Optional[dict[str, Any]]:
+        """Atomically move a download from the queue into the history table.
+
+        Looks up the queue record by ``download_id``, deletes it from the
+        queue, and inserts a corresponding history entry with the given
+        terminal status (``completed``, ``failed``, or ``canceled``).
+
+        When *completed_at* is provided it is used as the completion
+        timestamp; otherwise ``time.time()`` is used.
+
+        Returns the original queue record (before deletion) on success,
+        or ``None`` if the download was not found in the queue.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            row = conn.execute(
+                "SELECT * FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            ).fetchone()
+            if row is None:
+                return None
+
+            now = completed_at if completed_at is not None else time.time()
+            conn.execute(
+                "DELETE FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            )
+            conn.execute(
+                """
+                INSERT OR IGNORE INTO download_history (
+                    download_id, model_id, model_version_id, model_name,
+                    version_name, thumbnail_url, status, error, file_path,
+                    bytes_downloaded, total_bytes, completed_at
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    row["download_id"],
+                    row["model_id"],
+                    row["model_version_id"],
+                    row["model_name"],
+                    row["version_name"],
+                    row["thumbnail_url"],
+                    status,
+                    error,
+                    file_path,
+                    bytes_downloaded,
+                    total_bytes,
+                    now,
+                ),
+            )
+            conn.commit()
+        return dict(row)
+
+    async def pop_next_download(self) -> Optional[dict[str, Any]]:
+        """Atomically fetch and mark the next queued item as ``downloading``.
+
+        The item with the highest priority (and earliest ``added_at``
+        among ties) whose status is ``'queued'`` is selected, set to
+        ``'downloading'``, and returned as a dict.  Returns ``None`` if
+        the queue is empty.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            row = conn.execute(
+                """
+                SELECT * FROM download_queue
+                WHERE status = 'queued'
+                ORDER BY priority DESC, added_at ASC
+                LIMIT 1
+                """
+            ).fetchone()
+            if row is None:
+                return None
+
+            download_id = row["download_id"]
+            now = time.time()
+            conn.execute(
+                "UPDATE download_queue SET status = 'downloading', "
+                "started_at = COALESCE(started_at, ?) "
+                "WHERE download_id = ?",
+                (now, download_id),
+            )
+            conn.commit()
+            updated = conn.execute(
+                "SELECT * FROM download_queue WHERE download_id = ?",
+                (download_id,),
+            ).fetchone()
+
+        return dict(updated) if updated else None
+
+    # ------------------------------------------------------------------
+    # History methods
+    # ------------------------------------------------------------------
+
+    async def add_to_history(
+        self,
+        download_id: Optional[str] = None,
+        model_id: Optional[int] = None,
+        model_version_id: Optional[int] = None,
+        model_name: str = "",
+        version_name: str = "",
+        thumbnail_url: str = "",
+        status: str = "completed",
+        error: Optional[str] = None,
+        file_path: Optional[str] = None,
+        bytes_downloaded: int = 0,
+        total_bytes: Optional[int] = None,
+        is_already_exists: int = 0,
+    ) -> int:
+        """Insert a record into the download history.
+
+        Returns the ``id`` (AUTOINCREMENT primary key) of the newly
+        inserted row.
+        """
+        now = time.time()
+
+        async with self._lock:
+            conn = self._get_conn()
+            cursor = conn.execute(
+                """
+                INSERT INTO download_history (
+                    download_id, model_id, model_version_id, model_name,
+                    version_name, thumbnail_url, status, error, file_path,
+                    bytes_downloaded, total_bytes, completed_at, is_already_exists
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    download_id,
+                    model_id,
+                    model_version_id,
+                    model_name,
+                    version_name,
+                    thumbnail_url,
+                    status,
+                    error,
+                    file_path,
+                    bytes_downloaded,
+                    total_bytes,
+                    now,
+                    is_already_exists,
+                ),
+            )
+            conn.commit()
+        return cursor.lastrowid or 0
+
+    async def get_history(
+        self,
+        limit: int = 50,
+        offset: int = 0,
+        status_filter: Optional[str] = None,
+    ) -> dict[str, Any]:
+        """Return a page of download history entries.
+
+        Returns a dict with keys ``items``, ``total``, ``limit``, and
+        ``offset``.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+
+            if status_filter is not None:
+                count_row = conn.execute(
+                    "SELECT COUNT(*) AS cnt FROM download_history WHERE status = ?",
+                    (status_filter,),
+                ).fetchone()
+                rows = conn.execute(
+                    "SELECT * FROM download_history WHERE status = ? "
+                    "ORDER BY completed_at DESC LIMIT ? OFFSET ?",
+                    (status_filter, limit, offset),
+                ).fetchall()
+            else:
+                count_row = conn.execute(
+                    "SELECT COUNT(*) AS cnt FROM download_history"
+                ).fetchone()
+                rows = conn.execute(
+                    "SELECT * FROM download_history "
+                    "ORDER BY completed_at DESC LIMIT ? OFFSET ?",
+                    (limit, offset),
+                ).fetchall()
+
+        return {
+            "items": [dict(row) for row in rows],
+            "total": count_row["cnt"] if count_row else 0,
+            "limit": limit,
+            "offset": offset,
+        }
+
+    async def delete_history_item(
+        self, id: Optional[int] = None, download_id: Optional[str] = None
+    ) -> bool:
+        """Delete a single history entry by *download_id* (preferred) or *id*.
+
+        Returns ``True`` if a row was deleted.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            if download_id:
+                cursor = conn.execute(
+                    "DELETE FROM download_history WHERE download_id = ?",
+                    (download_id,),
+                )
+            elif id is not None:
+                cursor = conn.execute(
+                    "DELETE FROM download_history WHERE id = ?",
+                    (id,),
+                )
+            else:
+                return False
+            conn.commit()
+        return cursor.rowcount > 0
+
+    async def clear_history(
+        self,
+        status_filter: Optional[str] = None,
+        before_timestamp: Optional[float] = None,
+    ) -> int:
+        """Remove history entries matching the optional filters.
+
+        Both ``status_filter`` and ``before_timestamp`` can be combined
+        (AND logic).  Returns the number of deleted rows.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+
+            clauses: list[str] = []
+            params: list[Any] = []
+
+            if status_filter is not None:
+                clauses.append("status = ?")
+                params.append(status_filter)
+            if before_timestamp is not None:
+                clauses.append("completed_at < ?")
+                params.append(before_timestamp)
+
+            where = ""
+            if clauses:
+                where = " WHERE " + " AND ".join(clauses)
+
+            cursor = conn.execute(
+                f"DELETE FROM download_history{where}",
+                params,
+            )
+            conn.commit()
+        return cursor.rowcount
+
+    async def get_history_count(self, status_filter: Optional[str] = None) -> int:
+        """Return the number of history entries, optionally filtered by status."""
+        async with self._lock:
+            conn = self._get_conn()
+            if status_filter is not None:
+                row = conn.execute(
+                    "SELECT COUNT(*) AS cnt FROM download_history WHERE status = ?",
+                    (status_filter,),
+                ).fetchone()
+            else:
+                row = conn.execute(
+                    "SELECT COUNT(*) AS cnt FROM download_history"
+                ).fetchone()
+        return row["cnt"] if row else 0
+
+    # ------------------------------------------------------------------
+    # Retry
+    # ------------------------------------------------------------------
+
+    async def retry_from_history(
+        self,
+        item_id: Optional[int] = None,
+        download_id: Optional[str] = None,
+    ) -> Optional[dict[str, Any]]:
+        """Re-queue a failed or canceled download from history.
+
+        Looks up the history record by *download_id* (preferred) or
+        *item_id*.  If the status is ``failed`` or ``canceled`` a new
+        queue entry is created with the same model metadata and a fresh
+        download id, and the original history entry is **deleted** to
+        prevent exponential growth when the retried item is later
+        canceled or fails again and re-retried.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            if download_id:
+                row = conn.execute(
+                    "SELECT * FROM download_history WHERE download_id = ?",
+                    (download_id,),
+                ).fetchone()
+            elif item_id is not None:
+                row = conn.execute(
+                    "SELECT * FROM download_history WHERE id = ?",
+                    (item_id,),
+                ).fetchone()
+            else:
+                return None
+            if row is None:
+                return None
+            status = str(row["status"])
+            if status not in ("failed", "canceled"):
+                return None
+
+            import uuid
+
+            new_id = str(uuid.uuid4())
+            now = time.time()
+            conn.execute(
+                """
+                INSERT INTO download_queue (
+                    download_id, model_id, model_version_id, model_name,
+                    version_name, thumbnail_url, source, file_params,
+                    status, priority, added_at
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, NULL, 'queued', 0, ?)
+                """,
+                (
+                    new_id,
+                    row["model_id"],
+                    row["model_version_id"],
+                    row["model_name"],
+                    row["version_name"],
+                    row["thumbnail_url"],
+                    "retry",
+                    now,
+                ),
+            )
+            conn.execute(
+                "DELETE FROM download_history WHERE id = ?",
+                (row["id"],),
+            )
+            conn.commit()
+            queued = conn.execute(
+                "SELECT * FROM download_queue WHERE download_id = ?",
+                (new_id,),
+            ).fetchone()
+
+        return dict(queued) if queued else None
+
+    async def retry_all_failed(self) -> int:
+        """Re-queue all failed and canceled downloads from history.
+
+        Each history entry is **deleted** after being re-queued so that
+        repeated retry-all calls do not cause exponential growth.
+
+        Returns the number of items that were re-queued.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            rows = conn.execute(
+                "SELECT * FROM download_history WHERE status IN ('failed', 'canceled')"
+            ).fetchall()
+            if not rows:
+                return 0
+
+            import uuid
+
+            now = time.time()
+            count = 0
+            for row in rows:
+                new_id = str(uuid.uuid4())
+                conn.execute(
+                    """
+                    INSERT INTO download_queue (
+                        download_id, model_id, model_version_id, model_name,
+                        version_name, thumbnail_url, source, file_params,
+                        status, priority, added_at
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, NULL, 'queued', 0, ?)
+                    """,
+                    (
+                        new_id,
+                        row["model_id"],
+                        row["model_version_id"],
+                        row["model_name"],
+                        row["version_name"],
+                        row["thumbnail_url"],
+                        "retry",
+                        now,
+                    ),
+                )
+                conn.execute(
+                    "DELETE FROM download_history WHERE id = ?",
+                    (row["id"],),
+                )
+                count += 1
+            conn.commit()
+
+        return count
+
+    # ------------------------------------------------------------------
+    # Stats
+    # ------------------------------------------------------------------
+
+    async def get_stats(self) -> dict[str, int]:
+        """Return aggregate counts across both tables.
+
+        Returns a dict with keys ``queued``, ``downloading``, ``paused``
+        (all from the queue table) and ``completed``, ``failed``,
+        ``canceled`` (all from the history table).
+        """
+        async with self._lock:
+            conn = self._get_conn()
+
+            queue_rows = conn.execute(
+                "SELECT status, COUNT(*) AS cnt FROM download_queue GROUP BY status"
+            ).fetchall()
+            queue_stats: dict[str, int] = {}
+            for row in queue_rows:
+                queue_stats[str(row["status"])] = row["cnt"]
+
+            history_rows = conn.execute(
+                "SELECT status, COUNT(*) AS cnt FROM download_history GROUP BY status"
+            ).fetchall()
+            history_stats: dict[str, int] = {}
+            for row in history_rows:
+                history_stats[str(row["status"])] = row["cnt"]
+
+        return {
+            "queued": queue_stats.get("queued", 0),
+            "downloading": queue_stats.get("downloading", 0),
+            "paused": queue_stats.get("paused", 0),
+            "completed": history_stats.get("completed", 0),
+            "failed": history_stats.get("failed", 0),
+            "canceled": history_stats.get("canceled", 0),
+        }
+
+    # ------------------------------------------------------------------
+    # Deduplication (one-time cleanup for bug #980)
+    # ------------------------------------------------------------------
+
+    async def deduplicate(self) -> dict[str, int]:
+        """Remove duplicate entries caused by the retry-amplification bug.
+
+        The bug (issue #980) caused the same download to appear N times in
+        both the queue and history tables when ``retry_all_failed`` was
+        called repeatedly without deleting the original history rows.
+
+        This method is called **once** when the singleton is first created.
+        It is idempotent — after the first run there will be no duplicates
+        to remove, so subsequent calls are a no-op.
+
+        Returns a dict with the count of removed rows per table.
+        """
+        result: dict[str, int] = {
+            "removed_history": 0,
+            "removed_queue": 0,
+            "removed_orphan_queue": 0,
+        }
+
+        async with self._lock:
+            conn = self._get_conn()
+
+            # 1. History: for each (model_id, model_version_id, status) triplet
+            #    keep only the row with the highest id (most recently inserted).
+            conn.execute("""
+                DELETE FROM download_history
+                WHERE id NOT IN (
+                    SELECT MAX(id)
+                    FROM download_history
+                    GROUP BY model_id, model_version_id, status
+                )
+            """)
+            result["removed_history"] = conn.execute(
+                "SELECT changes()"
+            ).fetchone()[0]
+
+            # 2. Cross-status dedup: for each (model_id, model_version_id),
+            #    keep only the entry with the highest-priority terminal status.
+            #    Priority: completed (3) > failed (2) > canceled (1).
+            #    This prevents the same model version from having both a
+            #    'failed' and a 'canceled' entry (or a 'completed' alongside
+            #    either) after the bug-created duplicates are removed.
+            conn.execute("""
+                DELETE FROM download_history
+                WHERE id NOT IN (
+                    SELECT dh.id
+                    FROM download_history dh
+                    INNER JOIN (
+                        SELECT model_id, model_version_id,
+                               MAX(CASE status
+                                   WHEN 'completed' THEN 3
+                                   WHEN 'failed' THEN 2
+                                   WHEN 'canceled' THEN 1
+                                   ELSE 0
+                               END) AS best_prio
+                        FROM download_history
+                        GROUP BY model_id, model_version_id
+                    ) best
+                    ON dh.model_id = best.model_id
+                   AND dh.model_version_id = best.model_version_id
+                   AND CASE dh.status
+                           WHEN 'completed' THEN 3
+                           WHEN 'failed' THEN 2
+                           WHEN 'canceled' THEN 1
+                           ELSE 0
+                       END = best.best_prio
+                    GROUP BY dh.model_id, dh.model_version_id
+                    HAVING dh.id = MAX(dh.id)
+                )
+            """)
+            result["removed_history"] += conn.execute(
+                "SELECT changes()"
+            ).fetchone()[0]
+
+            # 3. Queue: for each (model_id, model_version_id) keep only the
+            #    row with the latest added_at (most recently enqueued).
+            conn.execute("""
+                DELETE FROM download_queue
+                WHERE rowid NOT IN (
+                    SELECT MAX(rowid)
+                    FROM download_queue
+                    WHERE status IN ('queued', 'downloading', 'paused', 'waiting')
+                    GROUP BY model_id, model_version_id
+                )
+                AND status IN ('queued', 'downloading', 'paused', 'waiting')
+            """)
+            result["removed_queue"] = conn.execute(
+                "SELECT changes()"
+            ).fetchone()[0]
+
+            # 4. Remove orphaned queue entries — items that were re-queued
+            #    (source='retry') but whose model version already has a
+            #    terminal history entry. These are artifacts of the buggy
+            #    retry cycle that were never cleaned up.
+            conn.execute("""
+                DELETE FROM download_queue
+                WHERE source = 'retry'
+                AND (model_id, model_version_id) IN (
+                    SELECT model_id, model_version_id
+                    FROM download_history
+                    WHERE status IN ('failed', 'canceled')
+                )
+                AND status IN ('queued', 'waiting')
+            """)
+            result["removed_orphan_queue"] = conn.execute(
+                "SELECT changes()"
+            ).fetchone()[0]
+
+            conn.commit()
+
+        logger.info(
+            "Deduplicate: removed %s history rows, %s queue rows, "
+            "%s orphaned queue rows",
+            result["removed_history"],
+            result["removed_queue"],
+            result["removed_orphan_queue"],
+        )
+        return result

py/services/downloaded_version_history_service.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 from __future__ import annotations
 
 import asyncio

py/services/downloader.py

@@ -1,3 +1,7 @@
+# pyright: reportImportCycles=false
+# Lazy (function-local) imports still count as static edges in basedpyright's
+# reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
+# import cycles. Breaking them would require an architectural refactor.
 """
 Unified download manager for all HTTP/HTTPS downloads in the application.
 
@@ -20,7 +24,7 @@ from dataclasses import dataclass
 from datetime import datetime, timedelta
 from email.utils import parsedate_to_datetime
 from urllib.parse import urlparse
-from typing import Optional, Dict, Tuple, Callable, Union, Awaitable
+from typing import Optional, Dict, Tuple, Callable, Union, Awaitable, Any, cast
 from ..services.settings_manager import get_settings_manager
 from .connectivity_guard import (
     OFFLINE_COOLDOWN_ERROR,
@@ -46,6 +50,30 @@ def is_ssl_cert_verify_error(exc: BaseException) -> bool:
     return "CERTIFICATE_VERIFY_FAILED" in str(exc)
 
 
+def _parse_retry_after(value: str) -> int:
+    """Parse a Retry-After header value into seconds.
+
+    Supports both integer seconds and HTTP-date formats.
+    Returns a default of 60 seconds on invalid/missing input.
+    """
+    if not value or not value.strip():
+        return 60
+
+    value = value.strip()
+    try:
+        return max(1, int(value))
+    except ValueError:
+        pass
+
+    try:
+        parsed = parsedate_to_datetime(value)
+        now = datetime.now().astimezone()
+        delta = (parsed - now).total_seconds()
+        return max(1, int(delta))
+    except (ValueError, OverflowError, OSError):
+        return 60
+
+
 @dataclass(frozen=True)
 class DownloadProgress:
     """Snapshot of a download transfer at a moment in time."""
@@ -180,6 +208,7 @@ class Downloader:
                 # Double check after acquiring lock
                 if self._session is None or self._should_refresh_session():
                     await self._create_session()
+        assert self._session is not None
         return self._session
 
     @property
@@ -207,7 +236,7 @@ class Downloader:
         )
 
         try:
-            timeout_value = float(raw_value)
+            timeout_value = float(cast(Any, raw_value))
         except (TypeError, ValueError):
             timeout_value = default_timeout
 
@@ -219,7 +248,7 @@ class Downloader:
         raw_value = os.environ.get("COMFYUI_DOWNLOAD_MAX_RETRIES")
 
         try:
-            retries = int(raw_value)
+            retries = int(cast(Any, raw_value))
         except (TypeError, ValueError):
             retries = default_retries
 
@@ -246,17 +275,19 @@ class Downloader:
 
         Note: This is private and caller MUST hold self._session_lock.
         """
-        # Close existing session if any
-        if self._session is not None:
-            try:
-                await self._session.close()
-            except Exception as e:  # pragma: no cover
-                logger.warning(f"Error closing previous session: {e}")
-            finally:
-                self._session = None
+        # Snapshot and clear old session reference before creating the new
+        # one.  This ensures self._session is always valid (or None, which
+        # triggers a fresh creation) and avoids a race where concurrent
+        # requests hold a reference to a session whose connector has been
+        # torn down by a premature close() call — the root cause of the
+        # intermittent "NoneType has no attribute connect" crash.
+        old_session = self._session
+        self._session = None
 
         # Check for app-level proxy settings
-        proxy_url = None
+        proxy_url = None  # http(s) proxy, passed via the per-request `proxy=` kwarg
+        socks_proxy_url = None  # SOCKS proxy, handled via aiohttp-socks connector
+        app_proxy_active = False
         settings_manager = get_settings_manager()
         if settings_manager.get("proxy_enabled", False):
             proxy_host = settings_manager.get("proxy_host", "").strip()
@@ -268,9 +299,19 @@ class Downloader:
             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}"
+                    full_proxy_url = f"{proxy_type}://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}"
                 else:
-                    proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
+                    full_proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
+
+                app_proxy_active = True
+                # aiohttp cannot tunnel SOCKS via the per-request `proxy=` kwarg
+                # (it would send HTTP to the SOCKS port and fail parsing the
+                # SOCKS handshake reply). SOCKS must be handled by an
+                # aiohttp-socks ProxyConnector instead.
+                if proxy_type.startswith("socks"):
+                    socks_proxy_url = full_proxy_url
+                else:
+                    proxy_url = full_proxy_url
 
                 logger.debug(
                     f"Using app-level proxy: {proxy_type}://{proxy_host}:{proxy_port}"
@@ -284,7 +325,7 @@ class Downloader:
         # CA coverage across different Python environments (especially
         # embedded/compatibility Python builds).
         try:
-            import certifi  # type: ignore[import-untyped]
+            import certifi  # pyright: ignore[reportMissingTypeStubs]
 
             ca_path = certifi.where()
             ssl_context = ssl.create_default_context(cafile=ca_path)
@@ -294,13 +335,27 @@ class Downloader:
             logger.debug("SSL: certifi unavailable; using system default CA bundle")
 
         # Optimize TCP connection parameters
-        connector = aiohttp.TCPConnector(
+        connector_kwargs: Dict[str, Any] = dict(
             ssl=ssl_context,
             limit=8,  # Concurrent connections
             ttl_dns_cache=300,  # DNS cache timeout
             force_close=False,  # Keep connections for reuse
             enable_cleanup_closed=True,
         )
+        if socks_proxy_url:
+            # Route all traffic through the SOCKS proxy via aiohttp-socks. The
+            # connector tunnels every connection, so no per-request `proxy=` is
+            # used (and must not be — see self._proxy_url below).
+            try:
+                from aiohttp_socks import ProxyConnector
+            except ImportError as e:  # pragma: no cover
+                raise RuntimeError(
+                    "A SOCKS proxy is configured but the 'aiohttp-socks' package "
+                    "is not installed. Install it with: pip install aiohttp-socks"
+                ) from e
+            connector = ProxyConnector.from_url(socks_proxy_url, **connector_kwargs)
+        else:
+            connector = aiohttp.TCPConnector(**connector_kwargs)
 
         # Configure timeout parameters
         timeout = aiohttp.ClientTimeout(
@@ -311,15 +366,24 @@ class Downloader:
 
         self._session = aiohttp.ClientSession(
             connector=connector,
-            trust_env=proxy_url
-            is None,  # Only use system proxy if no app-level proxy is set
+            # Only fall back to system/env proxy when no app-level proxy is active
+            trust_env=not app_proxy_active,
             timeout=timeout,
         )
 
-        # Store proxy URL for use in requests
+        # Store proxy URL for per-request use. Stays None for SOCKS because the
+        # ProxyConnector already tunnels everything; passing proxy= for SOCKS
+        # would re-trigger the original aiohttp parse error.
         self._proxy_url = proxy_url
         self._session_created_at = datetime.now()
 
+        # Close the previous session now that the replacement is live.
+        if old_session is not None:
+            try:
+                await old_session.close()
+            except Exception as e:  # pragma: no cover
+                logger.warning(f"Error closing previous session: {e}")
+
         logger.debug(
             "Created new HTTP session with proxy settings. App-level proxy: %s, System-level proxy (trust_env): %s",
             bool(proxy_url),
@@ -701,7 +765,8 @@ class Downloader:
                             else:
                                 resume_offset = 0
                                 total_size = 0
-                            await self._create_session()
+                            async with self._session_lock:
+                                await self._create_session()
                             continue
 
                         return False, integrity_error
@@ -791,7 +856,8 @@ class Downloader:
                         logger.info(f"Will resume from byte {resume_offset}")
 
                     # Refresh session to get new connection
-                    await self._create_session()
+                    async with self._session_lock:
+                        await self._create_session()
                     continue
                 else:
                     logger.error(f"Max retries exceeded for download: {e}")
@@ -829,7 +895,7 @@ class Downloader:
         use_auth: bool = False,
         custom_headers: Optional[Dict[str, str]] = None,
         return_headers: bool = False,
-    ) -> Tuple[bool, Union[bytes, str], Optional[Dict]]:
+    ) -> Tuple[bool, Union[bytes, str], Optional[Dict[str, Any]]]:
         """
         Download a file to memory (for small files like preview images)
 
@@ -883,6 +949,19 @@ class Downloader:
                 elif response.status == 404:
                     error_msg = "File not found"
                     return False, error_msg, None
+                elif response.status == 429:
+                    raw_retry_after = response.headers.get("Retry-After")
+                    retry_after = _parse_retry_after(raw_retry_after or "")
+                    if raw_retry_after:
+                        logger.warning(
+                            "Rate limited (429) for %s, Retry-After: %ss", url, retry_after
+                        )
+                    else:
+                        logger.warning(
+                            "Rate limited (429) for %s, no Retry-After header; defaulting to %ss",
+                            url, retry_after,
+                        )
+                    return False, f"Rate limited (429), retry after {retry_after}s", None
                 else:
                     error_msg = f"Download failed with status {response.status}"
                     return False, error_msg, None
@@ -902,7 +981,7 @@ class Downloader:
         url: str,
         use_auth: bool = False,
         custom_headers: Optional[Dict[str, str]] = None,
-    ) -> Tuple[bool, Union[Dict, str]]:
+    ) -> Tuple[bool, Union[Dict[str, Any], str]]:
         """
         Get response headers without downloading the full content
 
@@ -962,7 +1041,7 @@ class Downloader:
         use_auth: bool = False,
         custom_headers: Optional[Dict[str, str]] = None,
         **kwargs,
-    ) -> Tuple[bool, Union[Dict, str]]:
+    ) -> Tuple[bool, Union[Dict[str, Any], str, RateLimitError]]:
         """
         Make a generic HTTP request and return JSON response
 

py/services/embedding_scanner.py

@@ -27,7 +27,7 @@ class EmbeddingScanner(ModelScanner):
         roots.extend(config.embeddings_roots or [])
         roots.extend(config.extra_embeddings_roots or [])
         # Remove duplicates while preserving order
-        seen: set = set()
+        seen: set[str] = set()
         unique_roots: List[str] = []
         for root in roots:
             if root and root not in seen:

py/services/embedding_service.py

@@ -1,6 +1,6 @@
 import os
 import logging
-from typing import Dict
+from typing import Any, Dict, Optional
 
 from .base_model_service import BaseModelService
 from .auto_tag_service import extract_auto_tags
@@ -21,39 +21,58 @@ class EmbeddingService(BaseModelService):
         """
         super().__init__("embedding", scanner, EmbeddingMetadata, update_service=update_service)
     
-    async def format_response(self, embedding_data: Dict) -> Dict:
-        """Format Embedding data for API response"""
+    async def format_response(self, model_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Format Embedding data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = model_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted embedding entry (missing file_path): %s",
+                model_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
-        sub_type = embedding_data.get("sub_type", "embedding")
-        
+        sub_type = model_data.get("sub_type", "embedding")
+
+        file_name = model_data.get("file_name") or ""
+        model_name = model_data.get("model_name") or file_name
+        folder = model_data.get("folder") or ""
+
         return {
-            "model_name": embedding_data["model_name"],
-            "file_name": embedding_data["file_name"],
-            "preview_url": config.get_preview_static_url(embedding_data.get("preview_url", "")),
-            "preview_nsfw_level": embedding_data.get("preview_nsfw_level", 0),
-            "base_model": embedding_data.get("base_model", ""),
-            "folder": embedding_data["folder"],
-            "sha256": embedding_data.get("sha256", ""),
-            "file_path": embedding_data["file_path"].replace(os.sep, "/"),
-            "file_size": embedding_data.get("size", 0),
-            "modified": embedding_data.get("modified", ""),
-            "tags": embedding_data.get("tags", []),
-            "from_civitai": embedding_data.get("from_civitai", True),
-            # "usage_count": embedding_data.get("usage_count", 0), # TODO: Enable when embedding usage tracking is implemented
-            "notes": embedding_data.get("notes", ""),
+            "model_name": model_name,
+            "file_name": file_name,
+            "preview_url": config.get_preview_static_url(model_data.get("preview_url", "")),
+            "preview_nsfw_level": model_data.get("preview_nsfw_level", 0),
+            "base_model": model_data.get("base_model", ""),
+            "folder": folder,
+            "sha256": model_data.get("sha256", ""),
+            "file_path": file_path.replace(os.sep, "/"),
+            "file_size": model_data.get("size", 0),
+            "modified": model_data.get("modified", ""),
+            "tags": model_data.get("tags", []),
+            "from_civitai": model_data.get("from_civitai", True),
+            # "usage_count": model_data.get("usage_count", 0), # TODO: Enable when embedding usage tracking is implemented
+            "notes": model_data.get("notes", ""),
             "sub_type": sub_type,
-            "favorite": embedding_data.get("favorite", False),
-            "exclude": bool(embedding_data.get("exclude", False)),
-            "update_available": bool(embedding_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(embedding_data.get("skip_metadata_refresh", False)),
-            "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True),
-            "auto_tags": embedding_data.get("auto_tags") or extract_auto_tags(embedding_data),
+            "favorite": model_data.get("favorite", False),
+            "exclude": bool(model_data.get("exclude", False)),
+            "update_available": bool(model_data.get("update_available", False)),
+            "skip_metadata_refresh": bool(model_data.get("skip_metadata_refresh", False)),
+            "civitai": self.filter_civitai_data(model_data.get("civitai", {}), minimal=True),
+            "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
+            "version_count": model_data.get("version_count"),
+            "hf_url": model_data.get("hf_url", ""),
         }
     
-    def find_duplicate_hashes(self) -> Dict:
+    def find_duplicate_hashes(self) -> Dict[str, Any]:
         """Find Embeddings with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
     
-    def find_duplicate_filenames(self) -> Dict:
+    def find_duplicate_filenames(self) -> Dict[str, Any]:
         """Find Embeddings with conflicting filenames"""
         return self.scanner._hash_index.get_duplicate_filenames()

py/services/errors.py

@@ -25,3 +25,21 @@ class ResourceNotFoundError(RuntimeError):
 
     pass
 
+
+class LLMNotConfiguredError(RuntimeError):
+    """Raised when an LLM-dependent operation is attempted but no provider is configured."""
+
+    pass
+
+
+class LLMRateLimitError(RateLimitError):
+    """Raised when the LLM provider rejects a request due to rate limiting."""
+
+    pass
+
+
+class LLMResponseError(RuntimeError):
+    """Raised when the LLM returns an unparseable or schema-invalid response."""
+
+    pass
+

py/services/example_images_cleanup_service.py

@@ -35,7 +35,7 @@ class CleanupResult:
     def to_dict(self) -> Dict[str, object]:
         """Convert the dataclass to a serialisable dictionary."""
 
-        data = {
+        data: Dict[str, object] = {
             "success": self.success,
             "checked_folders": self.checked_folders,
             "moved_empty_folders": self.moved_empty_folders,