feat(ui): add View on Hugging Face button, plumb hf_url through full cache pipeline

Security hardening:
- Validate repo format with strict regex (reject .. traversal)
- Validate filename rejects path separators and ..
- Validate relative_path rejects absolute paths and ..
- Verify model_root is within configured scanner roots using
  realpath + os.sep guard to prevent prefix-match bypass
- Add realpath-based escape detection for final dest_path

Bug fixes:
- Fix WebSocket leak in _downloadHfSingle: wrap ws.close() in
  try/finally so it closes even if downloadHfModel() throws
- Same fix for batch HF download per-file WebSocket loop

Frontend hardening:
- Tighten HF repo regex: require huggingface.co for full URLs,
  reject bare .. patterns
- Add 12 unit tests for detectUrlType() covering HF resolve,
  HF repo, CivitAI, CivArchive, direct HTTP, edge cases

.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

.github/ISSUE_TEMPLATE/feature_request.md

@@ -13,8 +13,5 @@ A clear and concise description of what the problem is. Ex. I'm always frustrate
 **Describe the solution you'd like**
 A clear and concise description of what you want to happen.
 
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
 **Additional context**
 Add any other context or screenshots about the feature request here.

.gitignore

@@ -7,15 +7,24 @@ 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/
 
 # Vue widgets development cache (but keep build output)
 vue-widgets/node_modules/
@@ -24,3 +33,6 @@ vue-widgets/dist/
 
 # Hypothesis test cache
 .hypothesis/
+
+# Working/research notes (not committed)
+.docs/

.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 |

locales/en.json

@@ -16,10 +16,13 @@
             "help": "Help",
             "add": "Add",
             "close": "Close",
-            "menu": "Menu"
+            "menu": "Menu",
+            "remove": "Remove",
+            "change": "Change"
         },
         "status": {
             "loading": "Loading...",
+            "cancelling": "Cancelling...",
             "unknown": "Unknown",
             "date": "Date",
             "version": "Version",
@@ -102,6 +105,7 @@
             "removeFromFavorites": "Remove from favorites",
             "viewOnCivitai": "View on Civitai",
             "notAvailableFromCivitai": "Not available from Civitai",
+            "viewOnHuggingFace": "View on Hugging Face",
             "sendToWorkflow": "Send to ComfyUI (Click: Append, Shift+Click: Replace)",
             "copyLoRASyntax": "Copy LoRA Syntax",
             "checkpointNameCopied": "Checkpoint name copied",
@@ -111,6 +115,7 @@
             "replacePreview": "Replace Preview",
             "copyCheckpointName": "Copy checkpoint name",
             "copyEmbeddingName": "Copy embedding name",
+            "embeddingNameCopied": "Embedding syntax copied",
             "sendCheckpointToWorkflow": "Send to ComfyUI",
             "sendEmbeddingToWorkflow": "Send to ComfyUI"
         },
@@ -141,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Times used"
+        },
+        "footer": {
+            "versionCount": "{count} versions",
+            "viewAllVersions": "View all local versions"
         }
     },
     "globalContextMenu": {
@@ -179,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Manage Excluded Models"
+        },
+        "groupByModel": {
+            "label": "Group by Model"
         }
     },
     "header": {
@@ -191,13 +203,7 @@
             "statistics": "Stats"
         },
         "search": {
-            "placeholder": "Search...",
-            "placeholders": {
-                "loras": "Search LoRAs...",
-                "recipes": "Search recipes...",
-                "checkpoints": "Search checkpoints...",
-                "embeddings": "Search embeddings..."
-            },
+            "placeholder": "Search",
             "options": "Search Options",
             "searchIn": "Search In:",
             "notAvailable": "Search not available on statistics page",
@@ -232,7 +238,10 @@
             "license": "License",
             "noCreditRequired": "No Credit Required",
             "allowSellingGeneratedContent": "Allow Selling",
+            "allowSellingGeneratedContentTooltip": "Allow selling generated images",
+            "noCreditRequiredTooltip": "Use the model without crediting the creator",
             "noTags": "No tags",
+            "autoTags": "Auto Tags",
             "noBaseModelMatches": "No base models match the current search.",
             "clearAll": "Clear All Filters",
             "any": "Any",
@@ -244,7 +253,18 @@
             "toggle": "Toggle theme",
             "switchToLight": "Switch to light theme",
             "switchToDark": "Switch to dark theme",
-            "switchToAuto": "Switch to auto theme"
+            "switchToAuto": "Switch to auto theme",
+            "presets": "Theme Presets",
+            "default": "Default",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Mode",
+            "light": "Light",
+            "dark": "Dark",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Check Updates",
@@ -256,6 +276,9 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Enter your Civitai API key",
         "civitaiApiKeyHelp": "Used for authentication when downloading models from Civitai",
+        "civitaiApiKeyConfigured": "Configured",
+        "civitaiApiKeyNotConfigured": "Not configured",
+        "civitaiApiKeySet": "Set up",
         "civitaiHost": {
             "label": "Civitai host",
             "help": "Choose which Civitai site opens when using View on Civitai links.",
@@ -266,10 +289,10 @@
         },
         "downloadBackend": {
             "label": "Download backend",
-            "help": "Choose how model files are downloaded. Python uses the built-in downloader. aria2 uses the experimental external downloader process.",
+            "help": "Choose how model files are downloaded. Python uses the built-in downloader. aria2 uses the recommended external downloader process.",
             "options": {
                 "python": "Python (built-in)",
-                "aria2": "aria2 (experimental)"
+                "aria2": "aria2 (recommended)"
             }
         },
         "aria2cPath": {
@@ -296,6 +319,7 @@
             "downloads": "Downloads",
             "videoSettings": "Video Settings",
             "layoutSettings": "Layout Settings",
+            "licenseIcons": "License Icons",
             "misc": "Miscellaneous",
             "backup": "Backups",
             "folderSettings": "Default Roots",
@@ -303,7 +327,7 @@
             "extraFolderPaths": "Extra Folder Paths",
             "downloadPathTemplates": "Download Path Templates",
             "priorityTags": "Priority Tags",
-            "updateFlags": "Update Flags",
+            "versionScope": "Version Scope",
             "exampleImages": "Example Images",
             "autoOrganize": "Auto-organize",
             "metadata": "Metadata",
@@ -408,6 +432,8 @@
             "help": "When enabled, versions downloaded before will be skipped."
         },
         "layoutSettings": {
+            "groupByModel": "Group by Model",
+            "groupByModelHelp": "When enabled, only the latest version of each Civitai model is shown as a single card. Older versions are hidden.",
             "displayDensity": "Display Density",
             "displayDensityOptions": {
                 "default": "Default",
@@ -442,7 +468,9 @@
                 "modelName": "Model Name",
                 "fileName": "File Name"
             },
-            "modelNameDisplayHelp": "Choose what to display in the model card footer"
+            "modelNameDisplayHelp": "Choose what to display in the model card footer",
+            "cardBlurAmount": "Card Overlay Blur",
+            "cardBlurAmountHelp": "Adjust the blur intensity of the header and footer overlays on model and recipe cards (0 = no blur, 20 = maximum blur)."
         },
         "folderSettings": {
             "activeLibrary": "Active Library",
@@ -562,21 +590,31 @@
             "download": "Download",
             "restartRequired": "Requires restart"
         },
-        "updateFlagStrategy": {
-            "label": "Update Flag Strategy",
-            "help": "Decide whether update badges should only appear when a new release shares the same base model as your local files or whenever any newer version exists for that model.",
+        "versionGrouping": {
+            "label": "Version Grouping",
+            "help": "Decide how versions are grouped for display: by base model or all together. Also controls update badge logic and the VLM version list filtering.",
             "options": {
-                "sameBase": "Match updates by base model",
-                "any": "Flag any available update"
+                "sameBase": "Group by base model (same_base)",
+                "any": "Show all versions (any)"
             }
         },
         "hideEarlyAccessUpdates": {
             "label": "Hide Early Access Updates",
             "help": "When enabled, models with only early access updates will not show 'Update available' badge"
         },
+        "licenseIcons": {
+            "useNewStyle": "Use updated license icons",
+            "useNewStyleHelp": "Display license permissions with colored indicators (new style) or restriction-only icons (classic style). Mirroring the current CivitAI design."
+        },
         "misc": {
             "includeTriggerWords": "Include Trigger Words in LoRA Syntax",
-            "includeTriggerWordsHelp": "Include trained trigger words when copying LoRA syntax to clipboard"
+            "includeTriggerWordsHelp": "Include trained trigger words when copying LoRA syntax to clipboard",
+            "loraSyntaxFormat": "LoRA Syntax Format",
+            "loraSyntaxFormatHelp": "LoRA syntax format. Full includes subfolder path (<lora:style/anime/x:1.0>) for lossless model resolution. Legacy uses filename only (<lora:x:1.0>) — A1111 convention, may be ambiguous with duplicate filenames across folders.",
+            "loraSyntaxFormatOptions": {
+                "full": "Full path (subfolder/name)",
+                "legacy": "Legacy A1111 (name only)"
+            }
         },
         "metadataArchive": {
             "enableArchiveDb": "Enable Metadata Archive Database",
@@ -636,12 +674,14 @@
                 "sizeAsc": "Smallest",
                 "usage": "Use Count",
                 "usageDesc": "Most",
-                "usageAsc": "Least"
+                "usageAsc": "Least",
+                "versionsCount": "Local Versions",
+                "versionsCountDesc": "Most versions first",
+                "versionsCountAsc": "Fewest versions first",
+                "versionIdDesc": "Newest version first"
             },
             "refresh": {
                 "title": "Refresh model list",
-                "quick": "Sync Changes",
-                "quickTooltip": "Scan for new or missing model files so the list stays current.",
                 "full": "Rebuild Cache",
                 "fullTooltip": "Reload all model details from metadata files—use if the library looks out of date or after manual edits."
             },
@@ -682,6 +722,8 @@
             "setContentRating": "Set Content Rating for Selected",
             "copyAll": "Copy Selected Syntax",
             "refreshAll": "Refresh Selected Metadata",
+            "repairMetadata": "Repair Metadata for Selected",
+            "reimportMetadata": "Re-import from Source",
             "checkUpdates": "Check Updates for Selected",
             "moveAll": "Move Selected to Folder",
             "autoOrganize": "Auto-Organize Selected",
@@ -692,9 +734,18 @@
             "unfavorite": "Remove from Favorites",
             "deleteAll": "Delete Selected",
             "downloadMissingLoras": "Download Missing LoRAs",
+            "downloadExamples": "Download Example Images",
             "clear": "Clear Selection",
             "skipMetadataRefreshCount": "Skip ({count} models)",
             "resumeMetadataRefreshCount": "Resume ({count} models)",
+            "sendToWorkflow": "Send to Workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Metadata",
+                "attributes": "Attributes",
+                "organize": "Organize",
+                "download": "Download"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Initializing auto-organize...",
                 "starting": "Starting auto-organize for {type}...",
@@ -720,6 +771,7 @@
             "setContentRating": "Set Content Rating",
             "moveToFolder": "Move to Folder",
             "repairMetadata": "Repair metadata",
+            "reimportMetadata": "Re-import from Source",
             "excludeModel": "Exclude Model",
             "restoreModel": "Restore Model",
             "deleteModel": "Delete Model",
@@ -807,8 +859,6 @@
             },
             "refresh": {
                 "title": "Refresh recipe list",
-                "quick": "Sync Changes",
-                "quickTooltip": "Sync changes - quick refresh without rebuilding cache",
                 "full": "Rebuild Cache",
                 "fullTooltip": "Rebuild cache - full rescan of all recipe files"
             },
@@ -849,6 +899,13 @@
                 "skipped": "Recipe already at latest version, no repair needed",
                 "failed": "Failed to repair recipe: {message}",
                 "missingId": "Cannot repair recipe: Missing recipe ID"
+            },
+            "reimport": {
+                "starting": "Re-importing recipe from source...",
+                "success": "Recipe re-imported successfully",
+                "noSourceUrl": "Recipe has no source URL, cannot re-import",
+                "failed": "Failed to re-import recipe: {message}",
+                "missingId": "Cannot re-import recipe: Missing recipe ID"
             }
         },
         "batchImport": {
@@ -927,8 +984,9 @@
     "sidebar": {
         "modelRoot": "Root",
         "collapseAll": "Collapse All Folders",
-        "pinSidebar": "Pin Sidebar",
-        "unpinSidebar": "Unpin Sidebar",
+        "hideOnThisPage": "Hide sidebar on this page",
+        "showSidebar": "Show sidebar",
+        "sidebarHiddenNotification": "Folder sidebar hidden on {page} page",
         "switchToListView": "Switch to List View",
         "switchToTreeView": "Switch to Tree View",
         "recursiveOn": "Include subfolders",
@@ -948,6 +1006,13 @@
         "empty": {
             "noFolders": "No folders found",
             "dragHint": "Drag items here to create folders"
+        },
+        "folderUpdateCheck": {
+            "label": "Check for updates in this folder",
+            "loading": "Checking {type} updates for this folder...",
+            "success": "Found {count} update(s) for {type}s in this folder",
+            "none": "All {type}s in this folder are up to date",
+            "error": "Failed to check folder for {type} updates: {message}"
         }
     },
     "statistics": {
@@ -959,6 +1024,18 @@
             "storage": "Storage",
             "insights": "Insights"
         },
+        "metrics": {
+            "totalModels": "Total Models",
+            "totalStorage": "Total Storage",
+            "totalGenerations": "Total Generations",
+            "usageRate": "Usage Rate",
+            "loras": "LoRAs",
+            "checkpoints": "Checkpoints",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Unique Tags",
+            "unusedModels": "Unused Models",
+            "avgUsesPerModel": "Avg. Uses/Model"
+        },
         "usage": {
             "mostUsedLoras": "Most Used LoRAs",
             "mostUsedCheckpoints": "Most Used Checkpoints",
@@ -976,13 +1053,77 @@
         },
         "insights": {
             "smartInsights": "Smart Insights",
-            "recommendations": "Recommendations"
+            "recommendations": "Recommendations",
+            "noInsights": "No insights available",
+            "unusedLoras": {
+                "high": {
+                    "title": "High Number of Unused LoRAs",
+                    "description": "{percent}% of your LoRAs ({count}/{total}) have never been used.",
+                    "suggestion": "Consider organizing or archiving unused models to free up storage space."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Unused Checkpoints Detected",
+                    "description": "{percent}% of your checkpoints ({count}/{total}) have never been used.",
+                    "suggestion": "Review and consider removing checkpoints you no longer need."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "High Number of Unused Embeddings",
+                    "description": "{percent}% of your embeddings ({count}/{total}) have never been used.",
+                    "suggestion": "Consider organizing or archiving unused embeddings to optimize your collection."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Large Collection Detected",
+                    "description": "Your model collection is using {size} of storage.",
+                    "suggestion": "Consider using external storage or cloud solutions for better organization."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Active User",
+                    "description": "You've completed {count} generations so far!",
+                    "suggestion": "Keep exploring and creating amazing content with your models."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Collection Overview",
             "baseModelDistribution": "Base Model Distribution",
             "usageTrends": "Usage Trends (Last 30 Days)",
-            "usageDistribution": "Usage Distribution"
+            "usageDistribution": "Usage Distribution",
+            "date": "Date",
+            "usageCount": "Usage Count",
+            "fileSizeBytes": "File Size (bytes)",
+            "models": "Models",
+            "loraUsage": "LoRA Usage",
+            "checkpointUsage": "Checkpoint Usage",
+            "embeddingUsage": "Embedding Usage"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusion Model",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Loading...",
+            "noModels": "No models found",
+            "errorLoading": "Error loading data",
+            "noStorageData": "No storage data available",
+            "rootFolder": "Root",
+            "chartLibraryMissing": "Chart requires Chart.js library"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} models",
+            "chartUsage": "{name}: {size}, {count} uses",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -992,9 +1133,12 @@
         "download": {
             "title": "Download Model from URL",
             "titleWithType": "Download {type} from URL",
-            "url": "Civitai URL",
-            "civitaiUrl": "Civitai URL:",
+            "civitaiUrl": "Civitai URL(s):",
             "placeholder": "https://civitai.com/models/...",
+            "urlHint": "Enter one CivitAI, CivArchive, or Hugging Face URL per line. Supports multiple URLs for batch download.",
+            "selectHfFiles": "Select file(s) to download from this repository:",
+            "selectAll": "Select All",
+            "fetchingRepoFiles": "Fetching repository files...",
             "locationPreview": "Download Location Preview",
             "useDefaultPath": "Use Default Path",
             "useDefaultPathTooltip": "When enabled, files are automatically organized using configured path templates",
@@ -1016,9 +1160,16 @@
             "downloadedTooltip": "Previously downloaded, but it is not currently in your library.",
             "alreadyInLibrary": "Already in Library",
             "autoOrganizedPath": "[Auto-organized by path template]",
+            "fileSelection": {
+                "title": "Select File Format",
+                "files": "files",
+                "select": "Select File"
+            },
             "errors": {
                 "invalidUrl": "Invalid Civitai URL format",
-                "noVersions": "No versions available for this model"
+                "noVersions": "No versions available for this model",
+                "mixedSources": "Cannot mix CivitAI and Hugging Face URLs in the same batch.",
+                "noModelFiles": "No model files found in this repository."
             },
             "status": {
                 "preparing": "Preparing download...",
@@ -1080,6 +1231,12 @@
             "countMessage": "models will be permanently deleted.",
             "action": "Delete All"
         },
+        "bulkDeleteRecipes": {
+            "title": "Delete Multiple Recipes",
+            "message": "Are you sure you want to delete all selected recipes and their associated files?",
+            "countMessage": "recipes will be permanently deleted.",
+            "action": "Delete All"
+        },
         "checkUpdates": {
             "title": "Check updates for all {typePlural}?",
             "message": "This checks every {typePlural} in your library for updates. Large collections may take a little longer.",
@@ -1160,8 +1317,11 @@
                 "editModelName": "Edit model name",
                 "editFileName": "Edit file name",
                 "editBaseModel": "Edit base model",
+                "editVersionName": "Edit version name",
                 "viewOnCivitai": "View on Civitai",
                 "viewOnCivitaiText": "View on Civitai",
+                "viewOnHuggingFace": "View on Hugging Face",
+                "viewOnHuggingFaceText": "View on Hugging Face",
                 "viewCreatorProfile": "View Creator Profile",
                 "openFileLocation": "Open File Location",
                 "sendToWorkflow": "Send to ComfyUI",
@@ -1191,7 +1351,9 @@
             },
             "notes": {
                 "saved": "Notes saved successfully",
-                "saveFailed": "Failed to save notes"
+                "saveFailed": "Failed to save notes",
+                "showMore": "Show more",
+                "showLess": "Show less"
             },
             "usageTips": {
                 "addPresetParameter": "Add preset parameter...",
@@ -1316,7 +1478,7 @@
                     "resumeModelUpdates": "Resume updates for this model",
                     "ignoreModelUpdates": "Ignore updates for this model",
                     "viewLocalVersions": "View all local versions",
-                    "viewLocalTooltip": "Coming soon"
+                    "viewLocalTooltip": "Show all local versions of this model on the main page"
                 },
                 "filters": {
                     "label": "Base filter",
@@ -1344,6 +1506,21 @@
                     "versionDeleted": "Version deleted"
                 }
             }
+        },
+        "metadataFetchSummary": {
+            "title": "Metadata Fetch Summary",
+            "statSuccess": "Success",
+            "statFailed": "Failed",
+            "statSkipped": "Skipped",
+            "statTotal": "Total Scanned",
+            "statDuration": "Duration",
+            "successMessage": "All {count} {type}s updated successfully!",
+            "failedItems": "Failed Items ({count})",
+            "close": "Close",
+            "copyReport": "Copy Report",
+            "downloadCsv": "Download CSV",
+            "columnModelName": "Model Name",
+            "columnError": "Error"
         }
     },
     "modelTags": {
@@ -1357,15 +1534,6 @@
             "duplicate": "This tag already exists"
         }
     },
-    "keyboard": {
-        "navigation": "Keyboard Navigation:",
-        "shortcuts": {
-            "pageUp": "Scroll up one page",
-            "pageDown": "Scroll down one page",
-            "home": "Jump to top",
-            "end": "Jump to bottom"
-        }
-    },
     "initialization": {
         "title": "Initializing",
         "message": "Preparing your workspace...",
@@ -1453,11 +1621,17 @@
             "noMatchingNodes": "No compatible nodes available in the current workflow",
             "noTargetNodeSelected": "No target node selected",
             "modelUpdated": "Model updated in workflow",
-            "modelFailed": "Failed to update model node"
+            "modelFailed": "Failed to update model node",
+            "embeddingAdded": "Embedding added to workflow",
+            "embeddingFailed": "Failed to add embedding",
+            "promptSent": "Prompt sent to workflow",
+            "promptFailed": "Failed to send prompt"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Replace",
             "append": "Append",
             "selectTargetNode": "Select target node",
@@ -1634,12 +1808,17 @@
             "noRecipeId": "No recipe ID available",
             "sendToWorkflowFailed": "Failed to send recipe to workflow: {message}",
             "copyFailed": "Error copying recipe syntax: {message}",
+            "createError": "Error creating recipe: {message}",
+            "createFailed": "Failed to create recipe: {error}",
+            "createMissingData": "Missing required data to create recipe",
+            "created": "Recipe created successfully",
             "noMissingLoras": "No missing LoRAs to download",
             "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
             "preparingForDownloadFailed": "Error preparing LoRAs for download",
             "enterLoraName": "Please enter a LoRA name or syntax",
             "reconnectedSuccessfully": "LoRA reconnected successfully",
             "reconnectFailed": "Error reconnecting LoRA: {message}",
+            "noPromptToSend": "No prompt to send",
             "cannotSend": "Cannot send recipe: Missing recipe ID",
             "sendFailed": "Failed to send recipe to workflow",
             "sendError": "Error sending recipe to workflow",
@@ -1672,6 +1851,13 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "No recipes selected",
+            "repairBulkComplete": "Repair complete: {repaired} repaired, {skipped} skipped (of {total})",
+            "repairBulkSkipped": "No repair needed for any of the {total} selected recipes",
+            "repairBulkFailed": "Failed to repair selected recipes: {message}",
+            "reimporting": "Re-importing recipe from source...",
+            "reimportSuccess": "Recipe re-imported successfully",
+            "reimportBulkComplete": "Re-import complete: {completed} re-imported, {failed} failed (of {total})",
+            "reimportBulkFailed": "Failed to re-import some recipes",
             "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
             "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
         },
@@ -1889,7 +2075,9 @@
             "bulkMoveSuccess": "Successfully moved {successCount} {type}s",
             "exampleImagesDownloadSuccess": "Successfully downloaded example images!",
             "exampleImagesDownloadFailed": "Failed to download example images: {message}",
-            "moveFailed": "Failed to move item: {message}"
+            "moveFailed": "Failed to move item: {message}",
+            "copiedToClipboard": "Copied to clipboard",
+            "downloadStarted": "Download started"
         }
     },
     "doctor": {
@@ -1909,9 +2097,32 @@
             "warning": "Needs Attention",
             "error": "Action Required"
         },
+        "issues": {
+            "civitai_api_key": {
+                "title": "Civitai API Key"
+            },
+            "cache_health": {
+                "title": "Model Cache Health"
+            },
+            "filename_conflicts": {
+                "title": "Duplicate Filename Conflicts"
+            },
+            "ui_version": {
+                "title": "UI Version"
+            }
+        },
         "actions": {
             "runAgain": "Run Again",
-            "exportBundle": "Export Bundle"
+            "exportBundle": "Export Bundle",
+            "open-settings": "Open Settings",
+            "open-settings-syntax-format": "Switch to Full Path Syntax",
+            "repair-cache": "Rebuild Cache",
+            "resolve-filename-conflicts": "Resolve Conflicts",
+            "reload-page": "Reload UI"
+        },
+        "labels": {
+            "conflicts": "Conflicts",
+            "version": "Version"
         },
         "toast": {
             "loadFailed": "Failed to load diagnostics: {message}",
@@ -1923,6 +2134,15 @@
             "conflictsResolveFailed": "Failed to resolve filename conflicts: {message}"
         }
     },
+    "conflictConfirm": {
+        "title": "Resolve Filename Conflicts",
+        "message": "Renaming by appending a 4-character hash to each duplicate filename.",
+        "note": "This operation renames files on disk. Model references in existing workflows may need updating if you use the A1111 syntax format.",
+        "detail": "Example: <code>filename_v1.2</code> → <code>filename_v1.2-ab3c</code>",
+        "impact": "Will rename <strong>{count}</strong> file(s) across <strong>{groups}</strong> duplicate group(s).",
+        "confirm": "Rename Files",
+        "cancel": "Cancel"
+    },
     "banners": {
         "versionMismatch": {
             "title": "Application Update Detected",

py/config.py

@@ -172,6 +172,12 @@ class Config:
         self.extra_unet_roots: List[str] = []
         self.extra_embeddings_roots: List[str] = []
         self.recipes_path: str = ""
+
+        # 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()
+
         # Scan symbolic links during initialization
         self._initialize_symlink_mappings()
 
@@ -179,6 +185,96 @@ class Config:
             # Save the paths to settings.json when running in ComfyUI mode
             self.save_folder_paths_to_settings()
 
+    def _load_extra_paths_from_settings(self) -> None:
+        """Read extra folder paths from the active library and apply them.
+
+        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``.
+        """
+        try:
+            from .services.settings_manager import get_settings_manager
+
+            settings_manager = get_settings_manager()
+            library_name = settings_manager.get_active_library_name()
+            libraries = settings_manager.get_libraries()
+
+            if not library_name or library_name not in libraries:
+                return
+
+            library_config = libraries[library_name]
+            if not isinstance(library_config, dict):
+                return
+
+            extra_folder_paths = library_config.get("extra_folder_paths")
+            if not isinstance(extra_folder_paths, dict):
+                return
+
+            extra_lora = extra_folder_paths.get("loras", []) or []
+            extra_checkpoint = extra_folder_paths.get("checkpoints", []) or []
+            extra_unet = extra_folder_paths.get("unet", []) or []
+            extra_embedding = extra_folder_paths.get("embeddings", []) or []
+
+            if not any([extra_lora, extra_checkpoint, extra_unet, extra_embedding]):
+                return
+
+            filtered_extra_lora = self._filter_overlapping_extra_lora_paths(
+                self.loras_roots, extra_lora
+            )
+            self.extra_loras_roots = self._prepare_lora_paths(filtered_extra_lora)
+            (
+                _,
+                self.extra_checkpoints_roots,
+                self.extra_unet_roots,
+            ) = self._prepare_checkpoint_paths(extra_checkpoint, extra_unet)
+            self.extra_embeddings_roots = self._prepare_embedding_paths(
+                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:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_loras_roots)
+                )
+            if self.extra_checkpoints_roots:
+                logger.info(
+                    "Found extra checkpoint roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_checkpoints_roots)
+                )
+            if self.extra_unet_roots:
+                logger.info(
+                    "Found extra diffusion model roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_unet_roots)
+                )
+            if self.extra_embeddings_roots:
+                logger.info(
+                    "Found extra embedding roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_embeddings_roots)
+                )
+
+            logger.info(
+                "Applied library settings for '%s' with extra paths: loras=%s, "
+                "checkpoints=%s, embeddings=%s",
+                library_name,
+                extra_lora,
+                extra_checkpoint,
+                extra_embedding,
+            )
+
+        except Exception as exc:
+            logger.debug(
+                "Could not load extra paths from library settings: %s", exc
+            )
+
     def save_folder_paths_to_settings(self):
         """Persist ComfyUI-derived folder paths to the multi-library settings."""
         try:

py/lora_manager.py

@@ -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.
@@ -184,44 +190,15 @@ class LoraManager:
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            # Apply library settings to load extra folder paths before scanning
-            # Only apply if extra paths haven't been loaded yet (preserves test mocks)
-            try:
-                from .services.settings_manager import get_settings_manager
-
-                settings_manager = get_settings_manager()
-                library_name = settings_manager.get_active_library_name()
-                libraries = settings_manager.get_libraries()
-                if library_name and library_name in libraries:
-                    library_config = libraries[library_name]
-                    # Only apply settings if extra paths are not already configured
-                    # This preserves values set by tests via monkeypatch
-                    extra_paths = library_config.get("extra_folder_paths", {})
-                    has_extra_paths = (
-                        config.extra_loras_roots
-                        or config.extra_checkpoints_roots
-                        or config.extra_unet_roots
-                        or config.extra_embeddings_roots
-                    )
-                    if not has_extra_paths and any(extra_paths.values()):
-                        config.apply_library_settings(library_config)
-                        logger.info(
-                            "Applied library settings for '%s' with extra paths: loras=%s, checkpoints=%s, embeddings=%s",
-                            library_name,
-                            extra_paths.get("loras", []),
-                            extra_paths.get("checkpoints", []),
-                            extra_paths.get("embeddings", []),
-                        )
-            except Exception as exc:
-                logger.warning(
-                    "Failed to apply library settings during initialization: %s", exc
-                )
-
             # Initialize CivitaiClient first to ensure it's ready for other services
             await ServiceRegistry.get_civitai_client()
 
             # Register DownloadManager with ServiceRegistry
             await ServiceRegistry.get_download_manager()
+
+            # 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
@@ -459,5 +436,14 @@ 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)
+
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/constants.py

@@ -5,9 +5,10 @@ MODELS = "models"
 PROMPTS = "prompts"
 SAMPLING = "sampling"
 LORAS = "loras"
+EMBEDDINGS = "embeddings"
 SIZE = "size"
 IMAGES = "images"
 IS_SAMPLER = "is_sampler"  # New constant to mark sampler nodes
 
 # Complete list of categories to track
-METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES]

py/metadata_collector/node_extractors.py

@@ -901,6 +901,55 @@ 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):
@@ -1146,6 +1195,7 @@ NODE_EXTRACTORS = {
     "UNETLoaderLM": UNETLoaderExtractor,  # LoRA Manager
     "LoraLoader": LoraLoaderExtractor,
     "LoraLoaderLM": LoraLoaderManagerExtractor,
+    "LoraTextLoaderLM": LoraTextLoaderManagerExtractor,
     "RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
     "TensorRTLoader": TensorRTLoaderExtractor,
     # Conditioning

py/middleware/cache_middleware.py

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

py/middleware/error_middleware.py

@@ -0,0 +1,71 @@
+"""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
+
+        logger.warning(
+            "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/lora_loader.py

@@ -9,6 +9,7 @@ from ..utils.utils import get_lora_info_absolute
 from .utils import (
     FlexibleOptionalInputType,
     any_type,
+    apply_lora_syntax_format,
     detect_nunchaku_model_kind,
     extract_lora_name,
     get_loras_list,
@@ -52,7 +53,7 @@ def _collect_widget_entries(kwargs):
     for lora in get_loras_list(kwargs):
         if not lora.get("active", False):
             continue
-        lora_name = lora["name"]
+        lora_name = apply_lora_syntax_format(lora["name"])
         model_strength = float(lora["strength"])
         clip_strength = float(lora.get("clipStrength", model_strength))
         lora_path, trigger_words = get_lora_info_absolute(lora_name)

py/nodes/lora_stacker.py

@@ -1,6 +1,6 @@
 import os
 from ..utils.utils import get_lora_info
-from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list
+from .utils import FlexibleOptionalInputType, any_type, apply_lora_syntax_format, extract_lora_name, get_loras_list
 
 import logging
 
@@ -48,7 +48,7 @@ class LoraStackerLM:
             if not lora.get('active', False):
                 continue
                 
-            lora_name = lora['name']
+            lora_name = apply_lora_syntax_format(lora['name'])
             model_strength = float(lora['strength'])
             # Get clip strength - use model strength as default if not specified
             clip_strength = float(lora.get('clipStrength', model_strength))

py/nodes/save_image.py

@@ -11,7 +11,7 @@ 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 logging
@@ -298,7 +298,12 @@ 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 +314,14 @@ 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 +335,7 @@ 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]
@@ -600,7 +608,7 @@ 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

py/nodes/utils.py

@@ -44,11 +44,29 @@ import folder_paths  # type: ignore
 logger = logging.getLogger(__name__)
 
 
+def get_lora_syntax_format():
+    try:
+        from ..services.settings_manager import get_settings_manager
+        return get_settings_manager().get("lora_syntax_format", "legacy")
+    except Exception:
+        return "legacy"
+
+
+def apply_lora_syntax_format(name):
+    fmt = get_lora_syntax_format()
+    if fmt == "legacy":
+        return name.replace("\\", "/").rstrip("/").split("/")[-1]
+    return name
+
+
 def extract_lora_name(lora_path):
-    """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
-    # Get the basename without extension
-    basename = os.path.basename(lora_path)
-    return os.path.splitext(basename)[0]
+    normalized = lora_path.replace("\\", "/")
+    basename = os.path.basename(normalized)
+    name_no_ext = os.path.splitext(basename)[0]
+    dirname = os.path.dirname(normalized)
+    if dirname and dirname not in (".", "/") and not normalized.startswith("/"):
+        return apply_lora_syntax_format(f"{dirname}/{name_no_ext}")
+    return apply_lora_syntax_format(name_no_ext)
 
 
 def get_loras_list(kwargs):

py/recipes/base.py

@@ -7,7 +7,7 @@ import re
 from typing import Dict, List, Any, Optional, Tuple
 from abc import ABC, abstractmethod
 from ..config import config
-from ..utils.constants import VALID_LORA_TYPES
+from ..utils.constants import VALID_LORA_TYPES, VALID_CHECKPOINT_SUB_TYPES
 from ..utils.civitai_utils import rewrite_preview_url
 
 logger = logging.getLogger(__name__)
@@ -58,9 +58,52 @@ class RecipeMetadataParser(ABC):
             civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
             
             if not civitai_info or error_msg == "Model not found":
-                # Model not found or deleted
-                lora_entry['isDeleted'] = True
-                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                # CivitAI may fail to resolve a hash that is still being
+                # computed (known CivitAI issue). Before marking as deleted,
+                # try to reconcile with a local model that has the same
+                # filename and matching AutoV3 hash.
+                reconciled = False
+                file_name = lora_entry.get("file_name")
+                if file_name and recipe_scanner and hash_value:
+                    lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
+                    if lora_scanner:
+                        try:
+                            # Local import to avoid circular dependency:
+                            # base.py → file_utils → settings_manager → ...
+                            #  → recipe_scanner → enrichment → base.py
+                            from ..utils.file_utils import calculate_autov3  # fmt: skip
+                            cache = await lora_scanner.get_cached_data()
+                            for item in getattr(cache, "raw_data", []):
+                                if item.get("file_name") == file_name:
+                                    local_path = item.get("file_path")
+                                    if local_path and os.path.exists(local_path):
+                                        local_autov3 = calculate_autov3(local_path)
+                                        if local_autov3 and local_autov3 == hash_value:
+                                            lora_entry["existsLocally"] = True
+                                            lora_entry["localPath"] = local_path
+                                            lora_entry["hash"] = item.get("sha256", hash_value)
+                                            if "preview_url" in item:
+                                                lora_entry["thumbnailUrl"] = config.get_preview_static_url(item["preview_url"])
+                                            civ = item.get("civitai") or {}
+                                            if isinstance(civ, dict):
+                                                if civ.get("id") is not None:
+                                                    lora_entry["id"] = civ["id"]
+                                                if civ.get("modelId") is not None:
+                                                    lora_entry["modelId"] = civ["modelId"]
+                                                if civ.get("name"):
+                                                    lora_entry["version"] = civ["name"]
+                                                # model_name is the CivitAI model display
+                                                # name stored directly in the cache column.
+                                                cached_model_name = item.get("model_name")
+                                                if cached_model_name:
+                                                    lora_entry["name"] = cached_model_name
+                                            reconciled = True
+                                            break
+                        except Exception:
+                            pass
+                if not reconciled:
+                    lora_entry['isDeleted'] = True
+                    lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
                 return lora_entry
                 
             # Get model type and validate
@@ -173,6 +216,20 @@ class RecipeMetadataParser(ABC):
                 checkpoint['isDeleted'] = True
                 return checkpoint
 
+            # Validate that the model type is actually a checkpoint.
+            # Unlike populate_lora_from_civitai which has this check,
+            # this function was missing type validation — allowing LoRA
+            # version data to be saved as the recipe's checkpoint when the
+            # wrong version ID was passed downstream (fixed in v2.7+).
+            model_type = civitai_data.get('model', {}).get('type', '').lower()
+            if model_type not in VALID_CHECKPOINT_SUB_TYPES:
+                logger.warning(
+                    f"Cannot populate checkpoint: model version {civitai_data.get('id')} "
+                    f"has type '{model_type}', expected one of {VALID_CHECKPOINT_SUB_TYPES}. "
+                    f"Skipping checkpoint enrichment."
+                )
+                return checkpoint
+
             if 'model' in civitai_data and 'name' in civitai_data['model']:
                 checkpoint['name'] = civitai_data['model']['name']
 

py/recipes/enrichment.py

@@ -16,55 +16,65 @@ class RecipeEnricher:
     async def enrich_recipe(
         recipe: Dict[str, Any],
         civitai_client: Any,
-        request_params: Optional[Dict[str, Any]] = None
+        request_params: Optional[Dict[str, Any]] = None,
+        prefetched_civitai_meta_raw: Optional[Dict[str, Any]] = None,
+        prefetched_model_version_id: Optional[int] = None,
     ) -> bool:
         """
         Enrich a recipe dictionary in-place with metadata from Civitai and embedded params.
-        
+
         Args:
             recipe: The recipe dictionary to enrich. Must have 'gen_params' initialized.
             civitai_client: Authenticated Civitai client instance.
             request_params: (Optional) Parameters from a user request (e.g. import).
-            
+            prefetched_civitai_meta_raw: (Optional) Pre-fetched raw meta from Civitai
+                get_image_info, avoiding a duplicate API call.
+            prefetched_model_version_id: (Optional) Pre-fetched model version ID.
+
         Returns:
             bool: True if the recipe was modified, False otherwise.
         """
         updated = False
         gen_params = recipe.get("gen_params", {})
-        
-        # 1. Fetch Civitai Info if available
+
+        # 1. Obtain Civitai metadata
         civitai_meta = None
-        model_version_id = None
-        
-        source_url = recipe.get("source_url") or recipe.get("source_path", "")
-        
-        # Check if it's a Civitai image URL
-        image_id = extract_civitai_image_id(str(source_url))
-        if image_id:
-            try:
-                image_info = await civitai_client.get_image_info(
-                    image_id, source_url=str(source_url)
-                )
-                if image_info:
-                    # Handle nested meta often found in Civitai API responses
-                    raw_meta = image_info.get("meta")
-                    if isinstance(raw_meta, dict):
-                        if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
-                            civitai_meta = raw_meta["meta"]
-                        else:
-                            civitai_meta = raw_meta
-                    
-                    model_version_id = image_info.get("modelVersionId")
-                    
-                    # If not at top level, check resources in meta
-                    if not model_version_id and civitai_meta:
-                        resources = civitai_meta.get("civitaiResources", [])
-                        for res in resources:
-                            if res.get("type") == "checkpoint":
-                                model_version_id = res.get("modelVersionId")
-                                break
-            except Exception as e:
-                logger.warning(f"Failed to fetch Civitai image info: {e}")
+        model_version_id = prefetched_model_version_id
+
+        source_path = recipe.get("source_path", "")
+
+        if prefetched_civitai_meta_raw is not None:
+            raw_meta = prefetched_civitai_meta_raw
+            if isinstance(raw_meta, dict):
+                if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
+                    civitai_meta = raw_meta["meta"]
+                else:
+                    civitai_meta = raw_meta
+        else:
+            image_id = extract_civitai_image_id(str(source_path))
+            if image_id:
+                try:
+                    image_info = await civitai_client.get_image_info(
+                        image_id, source_url=str(source_path)
+                    )
+                    if image_info:
+                        raw_meta = image_info.get("meta")
+                        if isinstance(raw_meta, dict):
+                            if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
+                                civitai_meta = raw_meta["meta"]
+                            else:
+                                civitai_meta = raw_meta
+
+                        model_version_id = image_info.get("modelVersionId")
+                except Exception as e:
+                    logger.warning(f"Failed to fetch Civitai image info: {e}")
+
+        if not model_version_id and civitai_meta:
+            resources = civitai_meta.get("civitaiResources", [])
+            for res in resources:
+                if res.get("type") == "checkpoint":
+                    model_version_id = res.get("modelVersionId")
+                    break
 
         # 2. Merge Parameters
         # Priority: request_params > civitai_meta > embedded (existing gen_params)
@@ -180,27 +190,42 @@ class RecipeEnricher:
             existing_cp = recipe.get("checkpoint")
             if existing_cp is None:
                 existing_cp = {}
+
+            # Extract baseModel from raw civitai_info before populate_checkpoint_from_civitai
+            # (populate may reject non-checkpoint types and lose this data)
+            base_model_from_civitai: str = ""
+            if isinstance(civitai_info, dict):
+                base_model_from_civitai = civitai_info.get("baseModel", "") or ""
+            elif isinstance(civitai_info, tuple) and len(civitai_info) > 0 and isinstance(civitai_info[0], dict):
+                base_model_from_civitai = civitai_info[0].get("baseModel", "") or ""
+
             checkpoint_data = await RecipeMetadataParser.populate_checkpoint_from_civitai(existing_cp, civitai_info)
-            # 1. First, resolve base_model using full data before we format it away
+
+            # 1. Resolve base_model from checkpoint_data first, then fall back to raw civitai_info
             current_base_model = recipe.get("base_model")
-            resolved_base_model = checkpoint_data.get("baseModel")
+            resolved_base_model = checkpoint_data.get("baseModel") or base_model_from_civitai
             if resolved_base_model:
-                # Update if empty OR if it matches our generic prefix but is less specific
                 is_generic = not current_base_model or current_base_model.lower() in ["flux", "sdxl", "sd15"]
                 if is_generic and resolved_base_model != current_base_model:
                     recipe["base_model"] = resolved_base_model
-            
-            # 2. Format according to requirements: type, modelId, modelVersionId, modelName, modelVersionName
-            formatted_checkpoint = {
-                "type": "checkpoint",
-                "modelId": checkpoint_data.get("modelId"),
-                "modelVersionId": checkpoint_data.get("id") or checkpoint_data.get("modelVersionId"),
-                "modelName": checkpoint_data.get("name"), # In base.py, 'name' is populated from civitai_data['model']['name']
-                "modelVersionName": checkpoint_data.get("version") # In base.py, 'version' is populated from civitai_data['name']
-            }
-            # Remove None values
-            recipe["checkpoint"] = {k: v for k, v in formatted_checkpoint.items() if v is not None}
-            
+
+            # 2. Only format and save checkpoint if it has real data (not just type after type rejection)
+            has_checkpoint_data = any([
+                checkpoint_data.get("modelId"),
+                checkpoint_data.get("id") or checkpoint_data.get("modelVersionId"),
+                checkpoint_data.get("name"),
+                checkpoint_data.get("version"),
+            ])
+            if has_checkpoint_data:
+                formatted_checkpoint = {
+                    "type": "checkpoint",
+                    "modelId": checkpoint_data.get("modelId"),
+                    "modelVersionId": checkpoint_data.get("id") or checkpoint_data.get("modelVersionId"),
+                    "modelName": checkpoint_data.get("name"),
+                    "modelVersionName": checkpoint_data.get("version"),
+                }
+                recipe["checkpoint"] = {k: v for k, v in formatted_checkpoint.items() if v is not None}
+
             return True
         else:
             # Fallback to name extraction if we don't already have one

py/recipes/parsers/automatic.py

@@ -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

@@ -6,6 +6,7 @@ from typing import Dict, Any, Union
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
 from ...services.metadata_service import get_default_metadata_provider
+from ...config import config
 
 logger = logging.getLogger(__name__)
 
@@ -73,7 +74,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         return False
 
     async def parse_metadata(  # type: ignore[override]
-        self, user_comment, recipe_scanner=None, civitai_client=None
+        self, user_comment, recipe_scanner=None, civitai_client=None,
+        local_cache: dict[str, Any] | None = None,
     ) -> Dict[str, Any]:
         """Parse metadata from Civitai image format
 
@@ -81,6 +83,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             user_comment: The metadata from the image (dict)
             recipe_scanner: Optional recipe scanner service
             civitai_client: Optional Civitai API client (deprecated, use metadata_provider instead)
+            local_cache: Optional dict mapping sha256/autov3 hash → scanner cache item.
+                         When provided, matching models skip CivitAI API calls.
 
         Returns:
             Dict containing parsed recipe data
@@ -185,8 +189,77 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Process standard resources array
             if "resources" in metadata and isinstance(metadata["resources"], list):
                 for resource in metadata["resources"]:
+                    resource_type = resource.get("type", "lora")
+
+                    # Track resources with type "model" — these are checkpoint models.
+                    # The resources array is the most reliable source for checkpoint
+                    # 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 = {
+                            "id": 0,
+                            "modelId": 0,
+                            "name": resource.get("name", "Unknown Model"),
+                            "version": "",
+                            "type": resource.get("type", "model"),
+                            "existsLocally": False,
+                            "localPath": None,
+                            "file_name": resource.get("name", ""),
+                            "hash": resource.get("hash", "") or "",
+                            "thumbnailUrl": "/loras_static/images/no-preview.png",
+                            "baseModel": "",
+                            "size": 0,
+                            "downloadUrl": "",
+                            "isDeleted": False,
+                        }
+
+                        # 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
+                            if local_cached:
+                                self._populate_entry_from_cache(
+                                    checkpoint_entry, local_cached
+                                )
+                                bm = checkpoint_entry.get("baseModel", "")
+                                if bm and not result["base_model"]:
+                                    result["base_model"] = bm
+                            else:
+                                try:
+                                    civitai_info = (
+                                        await metadata_provider.get_model_by_hash(
+                                            cp_hash
+                                        )
+                                    )
+                                    civitai_data, error_msg = (
+                                        (civitai_info, None)
+                                        if not isinstance(civitai_info, tuple)
+                                        else civitai_info
+                                    )
+                                    if civitai_data and error_msg != "Model not found":
+                                        if 'model' in civitai_data and 'name' in civitai_data['model']:
+                                            checkpoint_entry['name'] = civitai_data['model']['name']
+                                        checkpoint_entry['id'] = civitai_data.get('id', 0)
+                                        checkpoint_entry['modelId'] = civitai_data.get('modelId', 0)
+                                        if 'name' in civitai_data:
+                                            checkpoint_entry['version'] = civitai_data['name']
+                                        base_model = civitai_data.get('baseModel', '')
+                                        if base_model:
+                                            checkpoint_entry['baseModel'] = base_model
+                                            if not result['base_model']:
+                                                result['base_model'] = base_model
+                                except Exception as e:
+                                    logger.error(
+                                        f"Error fetching checkpoint info for hash "
+                                        f"{cp_hash}: {e}"
+                                    )
+
+                        if result["model"] is None:
+                            result["model"] = checkpoint_entry
+                        continue
+
                     # Modified to process resources without a type field as potential LoRAs
-                    if resource.get("type", "lora") == "lora":
+                    if resource_type == "lora":
                         lora_hash = resource.get("hash", "")
 
                         # Try to get hash from the hashes field if not present in resource
@@ -220,34 +293,45 @@ 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)
+                        if lora_hash and metadata_provider:
+                            local_cached = local_cache.get(lora_hash) if local_cache else None
+                            if local_cached:
+                                self._populate_entry_from_cache(
+                                    lora_entry, local_cached
                                 )
-
-                                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  # 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"]:
+                                # Track by version ID for deduplication
+                                if lora_entry.get("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:
+                                        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:
@@ -430,11 +514,21 @@ 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)
 
@@ -442,6 +536,13 @@ 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,
@@ -475,9 +576,40 @@ 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}"
@@ -625,3 +757,41 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         except Exception as e:
             logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
             return {"error": str(e), "loras": []}
+
+    @staticmethod
+    def _populate_entry_from_cache(
+        entry: dict[str, Any],
+        cache_item: dict[str, Any],
+    ) -> None:
+        """Fill a lora/checkpoint entry from a scanner cache item.
+
+        Avoids CivitAI API calls for models that exist locally.
+        Mirrors the population logic in
+        ``RecipeMetadataParser.populate_lora_from_civitai()`` but operates
+        entirely on cached data.
+        """
+        civ = cache_item.get("civitai") or {}
+        if isinstance(civ, dict):
+            if civ.get("id") is not None:
+                entry["id"] = civ["id"]
+            if civ.get("modelId") is not None:
+                entry["modelId"] = civ["modelId"]
+            if civ.get("name"):
+                entry["version"] = civ["name"]
+            cached_name = cache_item.get("model_name")
+            if cached_name:
+                entry["name"] = cached_name
+        entry["existsLocally"] = True
+        local_path = cache_item.get("file_path")
+        if local_path:
+            entry["localPath"] = local_path
+        sha256 = cache_item.get("sha256")
+        if sha256:
+            entry["hash"] = sha256
+        if "preview_url" in cache_item:
+            entry["thumbnailUrl"] = config.get_preview_static_url(
+                cache_item["preview_url"]
+            )
+        base_model = cache_item.get("base_model", "")
+        if base_model:
+            entry["baseModel"] = base_model

py/routes/handlers/hf_handlers.py

@@ -0,0 +1,409 @@
+"""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
+
+
+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
+
+        # 3. Save metadata atomically
+        await MetadataManager.save_metadata(dest_path, metadata)
+        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)
+
+
+class HfHandler:
+    """Handle Hugging Face model browsing and download."""
+
+    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 separators or ..
+        if "/" in filename or "\\" in filename or ".." 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)
+
+        # Validate model_root — must not contain path traversal
+        if not os.path.isabs(model_root):
+            # For relative model_root, check it doesn't escape
+            resolved_model_root = os.path.realpath(
+                os.path.join(os.getcwd(), "models", model_root)
+            )
+        else:
+            resolved_model_root = os.path.realpath(model_root)
+
+        # Verify model_root is within a configured scanner root
+        allowed_roots = set()
+        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 [],
+        ):
+            for r in root_list:
+                allowed_roots.add(os.path.realpath(r))
+
+        if not any(resolved_model_root == root or resolved_model_root.startswith(root + os.sep) for root in allowed_roots):
+            logger.warning("Invalid model_root rejected: %s", model_root)
+            return web.json_response({"error": f"Invalid model_root: {model_root}"}, status=400)
+
+        base_dir = resolved_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
+
+        os.makedirs(target_dir, exist_ok=True)
+        dest_path = os.path.join(target_dir, filename)
+
+        # Resolve symlinks and check for path traversal escape
+        real_dest = os.path.realpath(dest_path)
+        real_base = os.path.realpath(target_dir)
+        if not real_dest.startswith(real_base + os.sep):
+            logger.warning("Path traversal blocked: %s -> %s", dest_path, real_dest)
+            return web.json_response({"error": "Path traversal detected"}, status=400)
+
+        # 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/misc_handlers.py

@@ -48,8 +48,12 @@ from ...utils.constants import (
     SUPPORTED_MEDIA_EXTENSIONS,
     VALID_LORA_TYPES,
 )
+from .hf_handlers import HfHandler
 from ...utils.civitai_utils import rewrite_preview_url
-from ...utils.example_images_paths import is_valid_example_images_root
+from ...utils.example_images_paths import (
+    find_non_compliant_items_in_example_images_root,
+    is_valid_example_images_root,
+)
 from ...utils.lora_metadata import extract_trained_words
 from ...utils.session_logging import get_standalone_session_log_snapshot
 from ...utils.usage_stats import UsageStats
@@ -411,9 +415,10 @@ class PromptServerProtocol(Protocol):
     """Subset of PromptServer used by the handlers."""
 
     instance: "PromptServerProtocol"
+    sockets: dict  # maps clientId (sid) → WebSocketResponse
 
     def send_sync(
-        self, event: str, payload: dict
+        self, event: str, payload: dict | None = None, sid: str | None = None
     ) -> None:  # pragma: no cover - protocol
         ...
 
@@ -468,89 +473,154 @@ class BackupServiceProtocol(Protocol):
 
 
 class NodeRegistry:
-    """Thread-safe registry for tracking LoRA nodes in active workflows."""
+    """Thread-safe registry for tracking LoRA nodes across ComfyUI tabs.
+
+    Each connected ComfyUI browser tab (identified by its ``sid`` / ``clientId``)
+    registers its own set of workflow nodes.  Queries merge all known tabs into
+    a single result so that the calling LM panel always sees *every* available
+    target node, regardless of which tab responded fastest.
+    """
 
     def __init__(self) -> None:
         self._lock = asyncio.Lock()
-        self._nodes: Dict[str, dict] = {}
-        self._registry_updated = asyncio.Event()
+        # sid → {unique_id → node_info}
+        self._tab_nodes: Dict[str, Dict[str, dict]] = {}
+        self._ready = asyncio.Event()
+        self._waiting_clients: set[str] = set()
+
+    @property
+    def pending_client_count(self) -> int:
+        """Number of clients that have not yet responded in the current refresh cycle."""
+        return len(self._waiting_clients)
+
+    # ------------------------------------------------------------------
+    # Helpers to build one node dict (extracted so it's reused for each tab)
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _build_node_dict(node: dict) -> dict:
+        node_id = node["node_id"]
+        graph_id = str(node["graph_id"])
+        unique_id = f"{graph_id}:{node_id}"
+        node_type = node.get("type", "")
+        type_id = NODE_TYPES.get(node_type, 0)
+        bgcolor = node.get("bgcolor") or DEFAULT_NODE_COLOR
+
+        raw_capabilities = node.get("capabilities")
+        capabilities: dict = {}
+        if isinstance(raw_capabilities, dict):
+            capabilities = dict(raw_capabilities)
+
+        raw_widget_names: list | None = node.get("widget_names")
+        if not isinstance(raw_widget_names, list):
+            capability_widget_names = capabilities.get("widget_names")
+            raw_widget_names = (
+                capability_widget_names
+                if isinstance(capability_widget_names, list)
+                else None
+            )
+
+        widget_names: list[str] = []
+        if isinstance(raw_widget_names, list):
+            widget_names = [
+                str(widget_name)
+                for widget_name in raw_widget_names
+                if isinstance(widget_name, str) and widget_name
+            ]
+
+        if widget_names:
+            capabilities["widget_names"] = widget_names
+        else:
+            capabilities.pop("widget_names", None)
+
+        if "supports_lora" in capabilities:
+            capabilities["supports_lora"] = bool(capabilities["supports_lora"])
+
+        comfy_class = node.get("comfy_class")
+        if not isinstance(comfy_class, str) or not comfy_class:
+            comfy_class = node_type if isinstance(node_type, str) else None
+
+        return {
+            "id": node_id,
+            "graph_id": graph_id,
+            "graph_name": node.get("graph_name"),
+            "unique_id": unique_id,
+            "bgcolor": bgcolor,
+            "title": node.get("title"),
+            "type": type_id,
+            "type_name": node_type,
+            "comfy_class": comfy_class,
+            "capabilities": capabilities,
+            "widget_names": widget_names,
+            "mode": node.get("mode"),
+            "marker_role": node.get("marker_role"),
+        }
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+    async def register_nodes(self, sid: str, nodes: list[dict]) -> None:
+        """Register/replace the node list for a single ComfyUI tab (identified by *sid*)."""
+        tab_nodes: dict[str, dict] = {}
+        for node in nodes:
+            nd = self._build_node_dict(node)
+            tab_nodes[nd["unique_id"]] = nd
 
-    async def register_nodes(self, nodes: list[dict]) -> None:
         async with self._lock:
-            self._nodes.clear()
-            for node in nodes:
-                node_id = node["node_id"]
-                graph_id = str(node["graph_id"])
-                unique_id = f"{graph_id}:{node_id}"
-                node_type = node.get("type", "")
-                type_id = NODE_TYPES.get(node_type, 0)
-                bgcolor = node.get("bgcolor") or DEFAULT_NODE_COLOR
-                raw_capabilities = node.get("capabilities")
-                capabilities: dict = {}
-                if isinstance(raw_capabilities, dict):
-                    capabilities = dict(raw_capabilities)
+            self._tab_nodes[sid] = tab_nodes
+            self._waiting_clients.discard(sid)
+            if not self._waiting_clients:
+                self._ready.set()
 
-                raw_widget_names: list | None = node.get("widget_names")
-                if not isinstance(raw_widget_names, list):
-                    capability_widget_names = capabilities.get("widget_names")
-                    raw_widget_names = (
-                        capability_widget_names
-                        if isinstance(capability_widget_names, list)
-                        else None
-                    )
+        logger.debug("Registered %s nodes from client %s", len(nodes), sid)
 
-                widget_names: list[str] = []
-                if isinstance(raw_widget_names, list):
-                    widget_names = [
-                        str(widget_name)
-                        for widget_name in raw_widget_names
-                        if isinstance(widget_name, str) and widget_name
-                    ]
+    def prepare_for_refresh(self, active_sids: list[str]) -> None:
+        """Set the list of client IDs we expect to hear from during the next refresh cycle."""
+        self._ready.clear()
+        self._waiting_clients = set(active_sids)
 
-                if widget_names:
-                    capabilities["widget_names"] = widget_names
-                else:
-                    capabilities.pop("widget_names", None)
-
-                if "supports_lora" in capabilities:
-                    capabilities["supports_lora"] = bool(capabilities["supports_lora"])
-
-                comfy_class = node.get("comfy_class")
-                if not isinstance(comfy_class, str) or not comfy_class:
-                    comfy_class = node_type if isinstance(node_type, str) else None
-
-                self._nodes[unique_id] = {
-                    "id": node_id,
-                    "graph_id": graph_id,
-                    "graph_name": node.get("graph_name"),
-                    "unique_id": unique_id,
-                    "bgcolor": bgcolor,
-                    "title": node.get("title"),
-                    "type": type_id,
-                    "type_name": node_type,
-                    "comfy_class": comfy_class,
-                    "capabilities": capabilities,
-                    "widget_names": widget_names,
-                    "mode": node.get("mode"),
-                }
-            logger.debug("Registered %s nodes in registry", len(nodes))
-            self._registry_updated.set()
-
-    async def get_registry(self) -> dict:
-        async with self._lock:
-            return {
-                "nodes": dict(self._nodes),
-                "node_count": len(self._nodes),
-            }
-
-    async def wait_for_update(self, timeout: float = 1.0) -> bool:
-        self._registry_updated.clear()
+    async def wait_for_all(self, timeout: float = 2.0) -> bool:
+        """Block until every client in the current waiting set has responded
+        (or *timeout* seconds elapse).  Returns ``True`` if all responded."""
+        if not self._waiting_clients:
+            return True
         try:
-            await asyncio.wait_for(self._registry_updated.wait(), timeout=timeout)
+            await asyncio.wait_for(self._ready.wait(), timeout=timeout)
             return True
         except asyncio.TimeoutError:
             return False
 
+    async def get_merged_registry(self, active_sids: set[str] | None = None) -> dict:
+        """Return the union of all known tab nodes, pruning any tab that is no
+        longer connected."""
+        async with self._lock:
+            # Garbage-collect stale entries (disconnected tabs)
+            if active_sids is not None:
+                for sid in list(self._tab_nodes):
+                    if sid not in active_sids:
+                        del self._tab_nodes[sid]
+
+            merged: dict[str, dict] = {}
+            tab_info: dict[str, dict] = {}
+            for sid, nodes in self._tab_nodes.items():
+                tab_info[sid] = {
+                    "node_count": len(nodes),
+                    "graph_names": list(
+                        {
+                            n.get("graph_name")
+                            for n in nodes.values()
+                            if n.get("graph_name")
+                        }
+                    ),
+                }
+                merged.update(nodes)
+
+            return {
+                "nodes": merged,
+                "node_count": len(merged),
+                "tab_count": len(self._tab_nodes),
+                "tabs": tab_info,
+            }
+
 
 class HealthCheckHandler:
     async def health_check(self, request: web.Request) -> web.Response:
@@ -686,6 +756,9 @@ class DoctorHandler:
         )
 
     async def resolve_filename_conflicts(self, request: web.Request) -> web.Response:
+        if self._settings.get("lora_syntax_format", "legacy") == "full":
+            return web.json_response({"success": True, "renamed": [], "count": 0})
+
         renamed: list[dict[str, Any]] = []
 
         try:
@@ -990,11 +1063,29 @@ class DoctorHandler:
         }
 
     async def _check_filename_conflicts(self) -> dict[str, Any]:
+        # When full path syntax is active, duplicate filenames across subfolders
+        # are not ambiguous (<lora:subfolder/name:strength>), so skip the check.
+        if self._settings.get("lora_syntax_format", "legacy") == "full":
+            return {
+                "id": "filename_conflicts",
+                "title": "Duplicate Filename Conflicts",
+                "status": "ok",
+                "summary": "Full path syntax is active — duplicate filenames across folders are not ambiguous.",
+                "details": [],
+                "actions": [],
+            }
+
         all_conflicts: list[dict[str, Any]] = []
         total_conflict_groups = 0
         total_conflict_files = 0
 
         for model_type, label, factory in self._scanner_factories:
+            # Duplicate filename detection targets LoRAs which use basename-only
+            # syntax (<lora:name:strength>). Checkpoints/embeddings reference
+            # models via relative paths with extensions, so conflicts there would
+            # be false positives.
+            if model_type != "lora":
+                continue
             try:
                 scanner = await factory()
                 hash_index = getattr(scanner, "_hash_index", None)
@@ -1042,12 +1133,22 @@ class DoctorHandler:
                 "total_conflict_files": total_conflict_files,
             }
         ]
-        for conflict in all_conflicts:
+
+        # Show at most 5 conflict groups inline; note any remainder.
+        MAX_VISIBLE_CONFLICTS = 5
+        visible_conflicts = all_conflicts[:MAX_VISIBLE_CONFLICTS]
+        for conflict in visible_conflicts:
             details.append(
-                f"[{conflict['label']}] '{conflict['filename']}' "
+                f"'{conflict['filename']}' "
                 f"found in {len(conflict['paths'])} locations"
             )
 
+        hidden_count = len(all_conflicts) - MAX_VISIBLE_CONFLICTS
+        if hidden_count > 0:
+            details.append(
+                f"...and {hidden_count} more duplicate filename group(s)"
+            )
+
         return {
             "id": "filename_conflicts",
             "title": "Duplicate Filename Conflicts",
@@ -1058,7 +1159,11 @@ class DoctorHandler:
                 {
                     "id": "resolve-filename-conflicts",
                     "label": "Resolve Conflicts",
-                }
+                },
+                {
+                    "id": "open-settings-syntax-format",
+                    "label": "Switch to Full Path Syntax",
+                },
             ],
         }
 
@@ -1293,6 +1398,9 @@ class SettingsHandler:
             "folder_paths",
             "libraries",
             "active_library",
+            # Sensitive — never expose the actual value to the frontend;
+            # frontend receives a boolean instead (civitai_api_key_set).
+            "civitai_api_key",
         }
     )
 
@@ -1347,6 +1455,9 @@ class SettingsHandler:
                     value = self._settings.get(key)
                     if value is not None:
                         response_data[key] = value
+            # Sensitive fields: only expose a boolean indicating whether set
+            raw_key = self._settings.get("civitai_api_key")
+            response_data["civitai_api_key_set"] = bool(raw_key)
             settings_file = getattr(self._settings, "settings_file", None)
             if settings_file:
                 response_data["settings_file"] = settings_file
@@ -1457,6 +1568,16 @@ class SettingsHandler:
         if not os.path.isdir(folder_path):
             return "Please set a dedicated folder for example images."
         if not self._is_dedicated_example_images_folder(folder_path):
+            offending = find_non_compliant_items_in_example_images_root(folder_path)
+            if offending:
+                items_str = ", ".join(repr(item) for item in offending[:5])
+                if len(offending) > 5:
+                    items_str += f" … and {len(offending) - 5} more"
+                return (
+                    f"The folder contains items that are not valid example image "
+                    f"folders: {items_str}. Please use a dedicated, empty folder "
+                    f"for example images to prevent accidental data loss."
+                )
             return "Please set a dedicated folder for example images."
         return None
 
@@ -2065,7 +2186,7 @@ class ModelLibraryHandler:
                     file_path=file_path if isinstance(file_path, str) else None,
                 )
             else:
-                await history_service.mark_not_downloaded(model_type, model_version_id)
+                await history_service.mark_as_deleted(model_type, model_version_id)
 
             return web.json_response(
                 {
@@ -2139,8 +2260,19 @@ class ModelLibraryHandler:
                 ]
                 await found_cache.resort()
 
+            scanner_map = {
+                "lora": lora_scanner,
+                "checkpoint": checkpoint_scanner,
+                "embedding": embedding_scanner,
+            }
+            scanner = scanner_map.get(found_type)
+            if scanner:
+                persist = getattr(scanner, "_persist_current_cache", None)
+                if callable(persist):
+                    await persist()
+
             history_service = await self._get_download_history_service()
-            await history_service.mark_not_downloaded(found_type, model_version_id)
+            await history_service.mark_as_deleted(found_type, model_version_id)
 
             return web.json_response(
                 {
@@ -2929,10 +3061,21 @@ class NodeRegistryHandler:
         try:
             data = await request.json()
             nodes = data.get("nodes", [])
+            client_id = data.get("client_id")
             if not isinstance(nodes, list):
                 return web.json_response(
                     {"success": False, "error": "nodes must be a list"}, status=400
                 )
+
+            if not isinstance(client_id, str) or not client_id:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Missing client_id parameter",
+                    },
+                    status=400,
+                )
+
             for index, node in enumerate(nodes):
                 if not isinstance(node, dict):
                     return web.json_response(
@@ -2976,7 +3119,7 @@ class NodeRegistryHandler:
                 else:
                     node["graph_name"] = str(graph_name)
 
-            await self._node_registry.register_nodes(nodes)
+            await self._node_registry.register_nodes(client_id, nodes)
             return web.json_response(
                 {
                     "success": True,
@@ -3000,9 +3143,15 @@ class NodeRegistryHandler:
                     status=503,
                 )
 
+            # Snapshot of currently-connected ComfyUI tabs
+            active_sids = list(self._prompt_server.instance.sockets.keys())
+            self._node_registry.prepare_for_refresh(active_sids)
+
             try:
                 self._prompt_server.instance.send_sync("lora_registry_refresh", {})
-                logger.debug("Sent registry refresh request to frontend")
+                logger.debug(
+                    "Sent registry refresh request (expecting %s clients)", len(active_sids)
+                )
             except Exception as exc:
                 logger.error("Failed to send registry refresh message: %s", exc)
                 return web.json_response(
@@ -3014,19 +3163,31 @@ class NodeRegistryHandler:
                     status=500,
                 )
 
-            registry_updated = await self._node_registry.wait_for_update(timeout=1.0)
-            if not registry_updated:
-                logger.warning("Registry refresh timeout after 1 second")
+            if not await self._node_registry.wait_for_all(timeout=2.0):
+                logger.warning(
+                    "Registry refresh timeout after 2s (%s/%s clients responded)",
+                    len(active_sids) - self._node_registry.pending_client_count,
+                    len(active_sids),
+                )
+
+            # Re-read current sockets after the wait: a tab may have connected
+            # while we were waiting, and we don't want to garbage-collect it.
+            current_sids = set(self._prompt_server.instance.sockets.keys())
+            registry_info = await self._node_registry.get_merged_registry(
+                active_sids=current_sids
+            )
+
+            if registry_info["node_count"] == 0:
+                logger.warning("No nodes registered after refresh")
                 return web.json_response(
                     {
                         "success": False,
-                        "error": "Timeout Error",
-                        "message": "Registry refresh timeout - ComfyUI frontend may not be responsive",
+                        "error": "Empty Registry",
+                        "message": "No workflow nodes found — ensure ComfyUI is open and the extension is loaded.",
                     },
                     status=408,
                 )
 
-            registry_info = await self._node_registry.get_registry()
             return web.json_response({"success": True, "data": registry_info})
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to get registry: %s", exc, exc_info=True)
@@ -3039,12 +3200,17 @@ class NodeRegistryHandler:
         try:
             data = await request.json()
             widget_name = data.get("widget_name")
+            action = data.get("action")
             value = data.get("value")
+            mode = data.get("mode", "replace")
             node_ids = data.get("node_ids")
 
-            if not isinstance(widget_name, str) or not widget_name:
+            if not action and (not isinstance(widget_name, str) or not widget_name):
                 return web.json_response(
-                    {"success": False, "error": "Missing widget_name parameter"},
+                    {
+                        "success": False,
+                        "error": "Missing parameter: provide either 'action' or 'widget_name'",
+                    },
                     status=400,
                 )
 
@@ -3083,11 +3249,15 @@ class NodeRegistryHandler:
                 except (TypeError, ValueError):
                     parsed_node_id = node_identifier
 
-                payload = {
+                payload: dict = {
                     "id": parsed_node_id,
-                    "widget_name": widget_name,
                     "value": value,
+                    "mode": mode,
                 }
+                if action:
+                    payload["action"] = action
+                if widget_name:
+                    payload["widget_name"] = widget_name
 
                 if graph_identifier is not None:
                     payload["graph_id"] = str(graph_identifier)
@@ -3146,6 +3316,7 @@ class MiscHandlerSet:
         doctor: DoctorHandler,
         example_workflows: ExampleWorkflowsHandler,
         base_model: BaseModelHandlerSet,
+        hf_handler: HfHandler | None = None,
     ) -> None:
         self.health = health
         self.settings = settings
@@ -3164,6 +3335,7 @@ class MiscHandlerSet:
         self.doctor = doctor
         self.example_workflows = example_workflows
         self.base_model = base_model
+        self.hf_handler = hf_handler
 
     def to_route_mapping(
         self,
@@ -3209,6 +3381,9 @@ class MiscHandlerSet:
             "get_supporters": self.supporters.get_supporters,
             "get_example_workflows": self.example_workflows.get_example_workflows,
             "get_example_workflow": self.example_workflows.get_example_workflow,
+            # Hugging Face handlers
+            "get_hf_repo_files": self.hf_handler.get_hf_repo_files,
+            "download_hf_model": self.hf_handler.download_hf_model,
             # Base model handlers
             "get_base_models": self.base_model.get_base_models,
             "refresh_base_models": self.base_model.refresh_base_models,

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
@@ -202,11 +203,17 @@ 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 +239,20 @@ 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"],
@@ -301,6 +314,15 @@ class ModelListingHandler:
         for tag in exclude_tags:
             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"
+
         favorites_only = request.query.get("favorites_only", "false").lower() == "true"
 
         search_options = {
@@ -356,6 +378,19 @@ 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
+        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):
+                civitai_model_id = None
+
         return {
             "page": page,
             "page_size": page_size,
@@ -367,6 +402,7 @@ class ModelListingHandler:
             "fuzzy_search": fuzzy_search,
             "base_models": base_models,
             "tags": tag_filters,
+            "auto_tags": auto_tag_filters,
             "tag_logic": tag_logic,
             "search_options": search_options,
             "hash_filters": hash_filters,
@@ -378,6 +414,8 @@ 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),
         }
 
@@ -505,8 +543,13 @@ 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(
@@ -778,7 +821,7 @@ class ModelManagementHandler:
 
             metadata_updates = {k: v for k, v in data.items() if k != "file_path"}
 
-            await self._metadata_sync.save_metadata_updates(
+            updated_metadata = await self._metadata_sync.save_metadata_updates(
                 file_path=file_path,
                 updates=metadata_updates,
                 metadata_loader=self._metadata_sync.load_local_metadata,
@@ -789,7 +832,12 @@ class ModelManagementHandler:
                 cache = await self._service.scanner.get_cached_data()
                 await cache.resort()
 
-            return web.json_response({"success": True})
+            from ...services.auto_tag_service import extract_auto_tags
+            auto_tags = extract_auto_tags(updated_metadata)
+
+            return web.json_response(
+                {"success": True, "auto_tags": auto_tags}
+            )
         except Exception as exc:
             self._logger.error("Error saving metadata: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
@@ -806,14 +854,16 @@ class ModelManagementHandler:
             if not isinstance(new_tags, list):
                 return web.Response(text="Tags must be a list", status=400)
 
-            tags = await self._tag_update_service.add_tags(
+            tags, auto_tags = await self._tag_update_service.add_tags(
                 file_path=file_path,
                 new_tags=new_tags,
                 metadata_loader=self._metadata_sync.load_local_metadata,
                 update_cache=self._service.scanner.update_single_model_cache,
             )
 
-            return web.json_response({"success": True, "tags": tags})
+            return web.json_response(
+                {"success": True, "tags": tags, "auto_tags": auto_tags}
+            )
         except Exception as exc:
             self._logger.error("Error adding tags: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
@@ -1056,10 +1106,12 @@ 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:
@@ -1160,6 +1212,12 @@ class ModelQueryHandler:
 
     async def find_filename_conflicts(self, request: web.Request) -> web.Response:
         try:
+            settings = get_settings_manager()
+            if settings.get("lora_syntax_format", "legacy") == "full":
+                return web.json_response(
+                    {"success": True, "conflicts": [], "count": 0}
+                )
+
             duplicates = self._service.find_duplicate_filenames()
             result = []
             cache = await self._service.scanner.get_cached_data()
@@ -1170,9 +1228,9 @@ 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)
@@ -1182,9 +1240,9 @@ 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(
@@ -1248,6 +1306,14 @@ class ModelQueryHandler:
                     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(
                 {
@@ -1449,6 +1515,21 @@ class ModelDownloadHandler:
             )
             return web.Response(status=500, text=str(exc))
 
+    async def skip_download_get(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
+                )
+            result = await self._download_coordinator.skip_download(download_id)
+            return web.json_response(result)
+        except Exception as exc:
+            self._logger.error(
+                "Error skipping download via GET: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def cancel_download_get(self, request: web.Request) -> web.Response:
         try:
             download_id = request.query.get("download_id")
@@ -1529,6 +1610,291 @@ 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:
+            item_id = int(request.query.get("id", "0"))
+            if not item_id:
+                return web.json_response(
+                    {"success": False, "error": "id is required"}, status=400
+                )
+
+            service = await DownloadQueueService.get_instance()
+            deleted = await service.delete_history_item(item_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:
+            item_id = int(request.query.get("id", "0"))
+            if not item_id:
+                return web.json_response(
+                    {"success": False, "error": "id is required"}, status=400
+                )
+
+            service = await DownloadQueueService.get_instance()
+            item = await service.retry_from_history(item_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."""
@@ -1570,7 +1936,9 @@ 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)
 
@@ -1937,6 +2305,10 @@ class ModelUpdateHandler:
         if target_model_ids:
             target_model_ids = sorted(set(target_model_ids))
 
+        folder_path: Optional[str] = payload.get("folder_path")
+        if folder_path is not None and not isinstance(folder_path, str):
+            folder_path = None
+
         provider = await self._get_civitai_provider()
         if provider is None:
             return web.json_response(
@@ -1951,6 +2323,7 @@ class ModelUpdateHandler:
                 provider,
                 force_refresh=force_refresh,
                 target_model_ids=target_model_ids or None,
+                folder_path=folder_path,
             )
             if self._service.scanner.is_cancelled():
                 return web.json_response(
@@ -1973,10 +2346,21 @@ 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(
@@ -2538,9 +2922,24 @@ class ModelHandlerSet:
             "download_model": self.download.download_model,
             "download_model_get": self.download.download_model_get,
             "cancel_download_get": self.download.cancel_download_get,
+            "skip_download_get": self.download.skip_download_get,
             "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

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+import mimetypes
 import urllib.parse
 from pathlib import Path
 
@@ -12,6 +13,12 @@ from ...config import config as global_config
 
 logger = logging.getLogger(__name__)
 
+_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.
+_VIDEO_EXTENSIONS = frozenset({".mp4", ".webm", ".mov", ".avi", ".mkv"})
+
 
 class PreviewHandler:
     """Serve preview assets for the active library at request time."""
@@ -48,8 +55,58 @@ class PreviewHandler:
             logger.debug("Preview file not found at %s", str(resolved))
             raise web.HTTPNotFound(text="Preview file not found")
 
-        # aiohttp's FileResponse handles range requests and content headers for us.
-        return web.FileResponse(path=resolved, chunk_size=256 * 1024)
+        # 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
+
+    async def _stream_file(
+        self, request: web.Request, path: Path
+    ) -> web.StreamResponse:
+        """Stream a file chunk-by-chunk, bypassing native sendfile.
+
+        This avoids the Windows IOCP ``_sendfile_native`` crash that occurs
+        when the client disconnects during a large file transfer.
+        """
+        content_type, _ = mimetypes.guess_type(str(path))
+        if content_type is None:
+            content_type = "application/octet-stream"
+
+        file_size = path.stat().st_size
+        resp = web.StreamResponse()
+        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:
+            with open(path, "rb") as f:
+                while True:
+                    chunk = f.read(_CHUNK_SIZE)
+                    if not chunk:
+                        break
+                    await resp.write(chunk)
+        except (ConnectionResetError, ConnectionAbortedError):
+            # Client disconnected during streaming — expected when scrolling
+            # rapidly through a library with animated previews.
+            pass
+        except OSError as exc:
+            logger.debug("I/O error streaming preview %s: %s", path, exc)
+
+        return resp
 
 
 __all__ = ["PreviewHandler"]

py/routes/misc_route_registrar.py

@@ -94,6 +94,13 @@ 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"
+    ),
 )
 
 

py/routes/misc_routes.py

@@ -39,6 +39,7 @@ from .handlers.misc_handlers import (
     build_service_registry_adapter,
 )
 from .handlers.base_model_handlers import BaseModelHandlerSet
+from .handlers.hf_handlers import HfHandler
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
@@ -136,6 +137,7 @@ class MiscRoutes:
         doctor = DoctorHandler(settings_service=self._settings)
         example_workflows = ExampleWorkflowsHandler()
         base_model = BaseModelHandlerSet()
+        hf_handler = HfHandler()
 
         return self._handler_set_factory(
             health=health,
@@ -155,6 +157,7 @@ class MiscRoutes:
             doctor=doctor,
             example_workflows=example_workflows,
             base_model=base_model,
+            hf_handler=hf_handler,
         )
 
 

py/routes/model_route_registrar.py

@@ -101,11 +101,46 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/download-model", "download_model"),
     RouteDefinition("GET", "/api/lm/download-model-get", "download_model_get"),
     RouteDefinition("GET", "/api/lm/cancel-download-get", "cancel_download_get"),
+    RouteDefinition("GET", "/api/lm/skip-download", "skip_download_get"),
     RouteDefinition("GET", "/api/lm/pause-download", "pause_download_get"),
     RouteDefinition("GET", "/api/lm/resume-download", "resume_download_get"),
     RouteDefinition(
         "GET", "/api/lm/download-progress/{download_id}", "get_download_progress"
     ),
+    RouteDefinition("GET", "/api/lm/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"),
 )

py/routes/recipe_route_registrar.py

@@ -58,6 +58,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
     RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
+    RouteDefinition("POST", "/api/lm/recipes/repair-bulk", "repair_recipes_bulk"),
     RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
     RouteDefinition("POST", "/api/lm/recipes/batch-import/start", "start_batch_import"),
     RouteDefinition(
@@ -70,6 +71,16 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "POST", "/api/lm/recipes/batch-import/directory", "start_directory_import"
     ),
     RouteDefinition("POST", "/api/lm/recipes/browse-directory", "browse_directory"),
+    RouteDefinition(
+        "GET", "/api/lm/recipes/check-image-exists", "check_image_exists"
+    ),
+    RouteDefinition("GET", "/api/lm/recipes/import-from-url", "import_from_url"),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/create-from-example", "create_from_example"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
+    ),
 )
 
 

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__)
@@ -140,6 +142,21 @@ 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 +171,8 @@ 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 +477,12 @@ 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 +490,12 @@ 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 +503,12 @@ 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 +519,20 @@ 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({

py/routes/update_routes.py

@@ -1,7 +1,6 @@
 import os
 import logging
 import toml
-import git
 import zipfile
 import shutil
 import tempfile
@@ -11,11 +10,33 @@ from typing import Dict, List
 
 from ..utils.settings_paths import ensure_settings_file
 from ..services.downloader import get_downloader
+from ..services.service_registry import ServiceRegistry
 
 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
+
 
 class UpdateRoutes:
     """Routes for handling plugin update checks"""
@@ -212,8 +233,19 @@ class UpdateRoutes:
 
             zip_path = tmp_zip_path
 
-            # Skip both settings.json, civitai and model cache folder
-            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json', 'civitai', 'model_cache'])
+            # Close the downloaded-versions SQLite connection before cleaning,
+            # so that shutil.rmtree() does not fail on Windows (the process
+            # cannot delete a file with an outstanding open handle).
+            try:
+                history_svc = ServiceRegistry._services.get("downloaded_version_history_service")
+                if history_svc is not None:
+                    history_svc.close()
+                    logger.info("Closed downloaded-version history database connection")
+            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', 'stats'])
 
             # Extract ZIP to temp dir
             with tempfile.TemporaryDirectory() as tmp_dir:
@@ -222,16 +254,17 @@ class UpdateRoutes:
                     # Find extracted folder (GitHub ZIP contains a root folder)
                     extracted_root = next(os.scandir(tmp_dir)).path
 
-                    # Copy files, skipping settings.json and civitai folder
+                    # Copy files, skipping user data that should be preserved
+                    skip_items = {'settings.json', 'civitai', 'wildcards', 'backups', 'stats'}
                     for item in os.listdir(extracted_root):
-                        if item == 'settings.json' or item == 'civitai':
+                        if item in skip_items:
                             continue
                         src = os.path.join(extracted_root, item)
                         dst = os.path.join(plugin_root, item)
                         if os.path.isdir(src):
                             if os.path.exists(dst):
                                 shutil.rmtree(dst)
-                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json', 'civitai'))
+                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns(*skip_items))
                         else:
                             shutil.copy2(src, dst)
 
@@ -239,15 +272,17 @@ class UpdateRoutes:
                     # for ComfyUI Manager to work properly
                     tracking_info_file = os.path.join(plugin_root, '.tracking')
                     tracking_files = []
+                    skip_tracked = {'civitai', 'wildcards', 'backups', 'stats'}
                     for root, dirs, files in os.walk(extracted_root):
-                        # Skip civitai folder and its contents
+                        # Skip user data directories and their contents
                         rel_root = os.path.relpath(root, extracted_root)
-                        if rel_root == 'civitai' or rel_root.startswith('civitai' + os.sep):
+                        top_dir = rel_root.split(os.sep)[0] if rel_root != '.' else ''
+                        if top_dir in skip_tracked:
                             continue
                         for file in files:
                             rel_path = os.path.relpath(os.path.join(root, file), extracted_root)
-                            # Skip settings.json and any file under civitai
-                            if rel_path == 'settings.json' or rel_path.startswith('civitai' + os.sep):
+                            # Skip settings.json and any file under user data dirs
+                            if rel_path == 'settings.json' or rel_path.split(os.sep)[0] in skip_tracked:
                                 continue
                             tracking_files.append(rel_path.replace("\\", "/"))
                     with open(tracking_info_file, "w", encoding='utf-8') as file:
@@ -342,6 +377,17 @@ 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)
@@ -353,8 +399,9 @@ 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'
@@ -371,8 +418,9 @@ 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)
@@ -438,6 +486,7 @@ 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

py/services/aria2_downloader.py

@@ -14,12 +14,30 @@ from typing import Any, Dict, Optional, Tuple
 
 import aiohttp
 
-from .downloader import DownloadProgress, get_downloader
+from .downloader import DownloadProgress, get_downloader, is_ssl_cert_verify_error
 from .aria2_transfer_state import Aria2TransferStateStore
 from .settings_manager import get_settings_manager
 
 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]
+
+        path = certifi.where()
+        if os.path.isfile(path):
+            logger.debug(
+                "aria2 --ca-certificate: using certifi CA bundle at %s", path
+            )
+            return path
+    except ImportError:
+        pass
+
+    logger.debug("aria2 --ca-certificate: certifi not available")
+    return None
+
+
 CIVITAI_DOWNLOAD_URL_PREFIXES = (
     "https://civitai.com/api/download/",
     "https://civitai.red/api/download/",
@@ -39,7 +57,7 @@ class Aria2Transfer:
 
 
 class Aria2Downloader:
-    """Manage an aria2 RPC daemon for experimental model downloads."""
+    """Manage an aria2 RPC daemon for recommended model downloads."""
 
     _instance = None
     _lock = asyncio.Lock()
@@ -66,6 +84,7 @@ class Aria2Downloader:
         self._transfers: Dict[str, Aria2Transfer] = {}
         self._poll_interval = 0.5
         self._state_store = Aria2TransferStateStore()
+        self._stderr_reader_task: Optional[asyncio.Task] = None
 
     @property
     def is_running(self) -> bool:
@@ -97,7 +116,7 @@ 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"
 
@@ -118,6 +137,35 @@ 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,
@@ -294,6 +342,16 @@ 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
@@ -313,6 +371,23 @@ 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)
@@ -391,6 +466,15 @@ class Aria2Downloader:
                     f"Failed to resolve authenticated Civitai redirect: status={response.status} body={body[:300]}"
                 )
         except aiohttp.ClientError as exc:
+            if is_ssl_cert_verify_error(exc):
+                logger.error(
+                    "SSL certificate verification failed during Civitai redirect "
+                    "resolution for %s. This is usually caused by an outdated CA "
+                    "certificate bundle. Recommended fixes:\n"
+                    "  1. pip install --upgrade certifi\n"
+                    "  2. pip install pip-system-certs",
+                    url,
+                )
             raise Aria2Error(
                 f"Failed to resolve authenticated Civitai redirect: {exc}"
             ) from exc
@@ -414,6 +498,11 @@ class Aria2Downloader:
                 f"--rpc-listen-port={self._rpc_port}",
                 f"--rpc-secret={self._rpc_secret}",
                 "--check-certificate=true",
+                # Point aria2 at certifi's CA bundle when available so it uses
+                # the same certificate store as Python downloads.
+                *((
+                    f"--ca-certificate={ca_cert}",
+                ) if (ca_cert := _try_certifi_ca_path()) else ()),
                 "--allow-overwrite=true",
                 "--auto-file-renaming=false",
                 "--file-allocation=none",
@@ -433,6 +522,17 @@ 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()
@@ -552,7 +652,9 @@ 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

@@ -0,0 +1,139 @@
+"""
+Auto-tag extraction service for model cards.
+
+Extracts implicit model attributes (HIGH/LOW, I2V/T2V/TI2V, Lightning, Turbo)
+from filename, base_model, and CivitAI version name — no manual tagging required.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Dict, List, Set
+
+# ── Tag category definitions ──────────────────────────────────────────
+# Each category maps a display label to a regex pattern.
+# Patterns are case-insensitive and matched against filename, base_model,
+# and civitai version name.
+
+# Use (?<![a-zA-Z0-9]) and (?![a-zA-Z0-9]) instead of \b because
+# Python's \b treats underscore as a word character, so \bHIGH\b
+# won't match '_HIGH_' in filenames.
+_B = r"(?<![a-zA-Z0-9])"   # left boundary
+_E = r"(?![a-zA-Z0-9])"    # right boundary
+
+AUTO_TAG_CATEGORIES: Dict[str, str] = {
+    "HIGH":      _B + r"HIGH" + _E,
+    "LOW":       _B + r"(?<!F)LOW" + _E,
+    "I2V":       _B + r"I2V" + _E,
+    "T2V":       _B + r"T2V" + _E,
+    "TI2V":      _B + r"TI2V" + _E,
+    "Lightning": _B + r"Lightning" + _E,
+    "Turbo":     _B + r"Turbo" + _E,
+}
+
+# Tags that belong to the "mode" group (HIGH/LOW)
+MODE_TAGS = {"HIGH", "LOW"}
+
+# Tags that belong to the "video mode" group (I2V/T2V/TI2V)
+VIDEO_MODE_TAGS = {"I2V", "T2V", "TI2V"}
+
+# Tags that belong to the "speed/optimization" group
+SPEED_TAGS = {"Lightning", "Turbo"}
+
+# ── Display category groups (for settings UI) ─────────────────────────
+
+AUTO_TAG_GROUPS = {
+    "mode":   {"HIGH", "LOW"},
+    "video":  {"I2V", "T2V", "TI2V"},
+    "speed":  {"Lightning", "Turbo"},
+}
+
+# Default enabled categories
+DEFAULT_ENABLED_GROUPS = {"mode", "video"}
+
+
+def _collect_sources(model_data: Dict) -> List[str]:
+    """Collect all text sources from model data for tag matching."""
+    sources: List[str] = []
+
+    file_name = model_data.get("file_name", "")
+    if file_name:
+        sources.append(file_name)
+
+    base_model = model_data.get("base_model", "")
+    if base_model:
+        sources.append(base_model)
+
+    civitai = model_data.get("civitai", {})
+    if isinstance(civitai, dict):
+        version_name = civitai.get("name", "")
+        if version_name:
+            sources.append(version_name)
+
+    return sources
+
+
+def extract_auto_tags(model_data: Dict) -> List[str]:
+    """Extract auto-detected tags from model metadata.
+
+    Uses a two-layer approach:
+      Layer 1 — Regex-based detection against filename, base_model, and
+                CivitAI version name.
+      Layer 2 — Merge in any user-defined tags that overlap with known
+                auto-tag categories. This provides a manual fallback when
+                auto-detection fails (e.g. "I2V HN" or unlabeled models).
+
+    HIGH/LOW tags are only returned when the base_model indicates a Wan
+    family model — no other model architecture uses this distinction.
+
+    Args:
+        model_data: Model metadata dict with keys:
+            file_name, base_model, civitai (with optional 'name' field),
+            tags (user-defined tag list, used as fallback).
+
+    Returns:
+        Sorted list of unique auto-tag strings (e.g. ["I2V"]).
+    """
+    sources = _collect_sources(model_data)
+    base_model = model_data.get("base_model", "")
+    is_wan = "wan" in base_model.lower()
+
+    found: Set[str] = set()
+
+    # ── Layer 1: regex-based detection ────────────────────────────
+    if sources:
+        for label, pattern in AUTO_TAG_CATEGORIES.items():
+            # HIGH/LOW are Wan-specific — skip for non-Wan to avoid noise
+            if label in ("HIGH", "LOW"):
+                if not is_wan:
+                    continue
+                # Use case-insensitive character class + case-sensitive boundary,
+                # so "HighNoise" (camelCase) matches but "highlight" doesn't.
+                # Boundary: not followed by lowercase letter (= word has ended).
+                ci = "".join(f"[{c.lower()}{c.upper()}]" for c in label)
+                if label == "LOW":
+                    regex = re.compile(r"(?<![Ff])" + ci + r"(?![a-z])")
+                else:
+                    regex = re.compile(ci + r"(?![a-z])")
+            else:
+                regex = re.compile(pattern, re.IGNORECASE)
+            for source in sources:
+                if regex.search(source):
+                    found.add(label)
+                    break
+
+    # ── Layer 2: user-defined tags as manual fallback ─────────────
+    # When auto-detection fails (abbreviated names like "Hi"/"Lo",
+    # "I2V HN", or unlabeled models), users can add canonical tags
+    # (HIGH, LOW, I2V, etc.) to the model's regular tags for correct
+    # badge display and filtering. Matching is case-insensitive so
+    # "high"/"High"/"HIGH" all resolve to the canonical label.
+    user_tags = model_data.get("tags")
+    if user_tags:
+        label_map = {label.lower(): label for label in AUTO_TAG_CATEGORIES}
+        for t in user_tags:
+            canonical = label_map.get(t.lower())
+            if canonical:
+                found.add(canonical)
+
+    return sorted(found)

py/services/backup_service.py

@@ -141,6 +141,16 @@ 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 +358,8 @@ 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

@@ -77,6 +77,7 @@ class BaseModelService(ABC):
         base_models: list = None,
         model_types: list = None,
         tags: Optional[Dict[str, str]] = None,
+        auto_tags: Optional[Dict[str, str]] = None,
         search_options: dict = None,
         hash_filters: dict = None,
         favorites_only: bool = False,
@@ -95,9 +96,108 @@ class BaseModelService(ABC):
             sorted_data = await self._fetch_with_usage_sort(sort_params)
         else:
             sorted_data = await self.cache_repository.fetch_sorted(sort_params)
+        # Pre-compute auto_tags for every item — needed for both filtering
+        # and display. Computation is cheap (string regex on 2-3 fields).
+        from .auto_tag_service import extract_auto_tags
+        for item in sorted_data:
+            item["auto_tags"] = extract_auto_tags(item)
         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_model_id(item) == civitai_model_id
+            ]
+            # VLM mode: always sort by version ID descending (newest version first),
+            # regardless of the current sort_by preference.
+            sorted_data.sort(
+                key=lambda x: self._extract_version_id(x) 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_id)
+            version_counter = {}  # same-key -> count
+            standalone = []
+            for item in sorted_data:
+                mid = self._extract_model_id(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
+                vid = self._extract_version_id(item) 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]] = {}
+                ungrouped_standalone: List[Dict] = []
+                for item in sorted_data:
+                    mid = self._extract_model_id(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
+                for items in model_groups.values():
+                    items.sort(
+                        key=lambda x: self._extract_version_id(x) 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)
@@ -110,6 +210,7 @@ class BaseModelService(ABC):
                 base_models=base_models,
                 model_types=model_types,
                 tags=tags,
+                auto_tags=auto_tags,
                 favorites_only=favorites_only,
                 search_options=search_options,
                 tag_logic=tag_logic,
@@ -165,7 +266,7 @@ 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,
@@ -174,6 +275,7 @@ class BaseModelService(ABC):
             pagination_duration,
             annotate_duration,
             initial_count,
+            dedup_lost,
             post_filter_count,
             final_count,
         )
@@ -354,6 +456,7 @@ class BaseModelService(ABC):
         base_models: list = None,
         model_types: list = None,
         tags: Optional[Dict[str, str]] = None,
+        auto_tags: Optional[Dict[str, str]] = None,
         favorites_only: bool = False,
         search_options: dict = None,
         tag_logic: str = "any",
@@ -367,6 +470,7 @@ class BaseModelService(ABC):
             base_models=base_models,
             model_types=model_types,
             tags=tags,
+            auto_tags=auto_tags,
             favorites_only=favorites_only,
             search_options=normalized_options,
             tag_logic=tag_logic,
@@ -486,7 +590,7 @@ 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:
@@ -687,8 +791,12 @@ 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) -> Optional[Dict]:
+        """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
@@ -861,22 +969,75 @@ class BaseModelService(ABC):
         """Get the static preview URL for a model file"""
         cache = await self.scanner.get_cached_data()
 
+        name_normalized = model_name.replace("\\", "/")
+        name_no_ext = name_normalized
+        for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+            if name_no_ext.lower().endswith(ext):
+                name_no_ext = name_no_ext[: -len(ext)]
+                break
+
+        has_path = "/" in name_no_ext
+        basename = os.path.basename(name_no_ext) if has_path else name_no_ext
+        best_fallback = None
+
         for model in cache.raw_data:
-            if model["file_name"] == model_name:
+            file_name = model.get("file_name", "")
+            folder = model.get("folder", "")
+            file_name_no_ext = file_name
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if file_name_no_ext.lower().endswith(ext):
+                    file_name_no_ext = file_name_no_ext[: -len(ext)]
+                    break
+            path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+
+            if name_no_ext == file_name_no_ext or name_no_ext == path_name:
                 preview_url = model.get("preview_url")
                 if preview_url:
                     from ..config import config
 
                     return config.get_preview_static_url(preview_url)
 
+            if has_path and file_name_no_ext == basename:
+                if folder and name_no_ext.startswith(folder.replace("\\", "/") + "/"):
+                    best_fallback = model
+                elif best_fallback is None:
+                    best_fallback = model
+
+        if best_fallback:
+            preview_url = best_fallback.get("preview_url")
+            if preview_url:
+                from ..config import config
+
+                return config.get_preview_static_url(preview_url)
+
         return "/loras_static/images/no-preview.png"
 
     async def get_model_civitai_url(self, model_name: str) -> Dict[str, Optional[str]]:
         """Get the Civitai URL for a model file"""
         cache = await self.scanner.get_cached_data()
 
+        name_normalized = model_name.replace("\\", "/")
+        name_no_ext = name_normalized
+        for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+            if name_no_ext.lower().endswith(ext):
+                name_no_ext = name_no_ext[: -len(ext)]
+                break
+
+        has_path = "/" in name_no_ext
+        basename = os.path.basename(name_no_ext) if has_path else name_no_ext
+        best_fallback = None
+
         for model in cache.raw_data:
-            if model["file_name"] == model_name:
+            file_name = model.get("file_name", "")
+            folder = model.get("folder", "")
+            file_name_no_ext = file_name
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if file_name_no_ext.lower().endswith(ext):
+                    file_name_no_ext = file_name_no_ext[: -len(ext)]
+                    break
+            path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+
+            if name_no_ext == file_name_no_ext or name_no_ext == path_name:
                 civitai_data = model.get("civitai", {})
                 model_id = civitai_data.get("modelId")
                 version_id = civitai_data.get("id")
@@ -895,6 +1056,27 @@ class BaseModelService(ABC):
                         "version_id": str(version_id) if version_id else None,
                     }
 
+            if has_path and file_name_no_ext == basename:
+                if folder and name_no_ext.startswith(folder.replace("\\", "/") + "/"):
+                    best_fallback = model
+                elif best_fallback is None:
+                    best_fallback = model
+
+        if best_fallback:
+            civitai_data = best_fallback.get("civitai", {})
+            model_id = civitai_data.get("modelId")
+            if model_id:
+                version_id = civitai_data.get("id")
+                civitai_host = self.settings.get("civitai_host", "civitai.com")
+                civitai_url = build_civitai_model_page_url(
+                    model_id, version_id, host=civitai_host
+                )
+                return {
+                    "civitai_url": civitai_url,
+                    "model_id": str(model_id),
+                    "version_id": str(version_id) if version_id else None,
+                }
+
         return {"civitai_url": None, "model_id": None, "version_id": None}
 
     async def get_model_metadata(self, file_path: str) -> Optional[Dict]:
@@ -908,6 +1090,17 @@ class BaseModelService(ABC):
         )
         if should_skip or metadata is None:
             return None
+
+        # Prune stale example-image metadata entries whose files no longer
+        # exist on disk (e.g. a user deleted the files manually).
+        from ..utils.example_images_metadata import MetadataUpdater
+
+        was_modified = await MetadataUpdater.prune_stale_example_images(metadata)
+        if was_modified:
+            asyncio.create_task(
+                MetadataManager.save_metadata(file_path, metadata)
+            )
+
         return self.filter_civitai_data(metadata.to_dict().get("civitai", {}))
 
     async def get_model_description(self, file_path: str) -> Optional[str]:

py/services/batch_import_service.py

@@ -224,7 +224,7 @@ class BatchImportService:
                 return False
 
             for recipe in getattr(cache, "raw_data", []):
-                source_path = recipe.get("source_path") or recipe.get("source_url")
+                source_path = recipe.get("source_path")
                 if source_path and source_path == source:
                     return True
             return False
@@ -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/checkpoint_service.py

@@ -1,8 +1,9 @@
 import os
 import logging
-from typing import Dict
+from typing import Dict, Optional
 
 from .base_model_service import BaseModelService
+from .auto_tag_service import extract_auto_tags
 from ..utils.models import CheckpointMetadata
 from ..config import config
 
@@ -20,20 +21,37 @@ 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, checkpoint_data: Dict) -> Optional[Dict]:
+        """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 = checkpoint_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted checkpoint entry (missing file_path): %s",
+                checkpoint_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
         sub_type = checkpoint_data.get("sub_type", "checkpoint")
-        
+
+        file_name = checkpoint_data.get("file_name") or ""
+        model_name = checkpoint_data.get("model_name") or file_name
+        folder = checkpoint_data.get("folder") or ""
+
         return {
-            "model_name": checkpoint_data["model_name"],
-            "file_name": checkpoint_data["file_name"],
+            "model_name": model_name,
+            "file_name": 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"],
+            "folder": folder,
             "sha256": checkpoint_data.get("sha256", ""),
-            "file_path": checkpoint_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": checkpoint_data.get("size", 0),
             "modified": checkpoint_data.get("modified", ""),
             "tags": checkpoint_data.get("tags", []),
@@ -45,7 +63,10 @@ class CheckpointService(BaseModelService):
             "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)
+            "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True),
+            "auto_tags": checkpoint_data.get("auto_tags") or extract_auto_tags(checkpoint_data),
+            "version_count": checkpoint_data.get("version_count"),
+            "hf_url": checkpoint_data.get("hf_url", ""),
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/civarchive_client.py

@@ -186,6 +186,22 @@ class CivArchiveClient:
         if "metadata" in file_data:
             transformed["metadata"] = file_data["metadata"]
 
+        # Infer metadata.format from filename extension
+        name = transformed.get("name")
+        if name and isinstance(name, str):
+            lower_name = name.lower()
+            if lower_name.endswith(".safetensors"):
+                inferred_format = "SafeTensor"
+            elif lower_name.endswith(".ckpt"):
+                inferred_format = "PickleTensor"
+            else:
+                inferred_format = None
+            if inferred_format:
+                if "metadata" not in transformed:
+                    transformed["metadata"] = {}
+                if isinstance(transformed["metadata"], dict):
+                    transformed["metadata"].setdefault("format", inferred_format)
+
         if file_data.get("modelVersionId") is not None:
             transformed["modelVersionId"] = file_data.get("modelVersionId")
         elif file_data.get("model_version_id") is not None:
@@ -213,6 +229,20 @@ class CivArchiveClient:
         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:
+            fname = f.get("name") or ""
+            if isinstance(fname, str):
+                lower = fname.lower()
+                if lower.endswith(".safetensors"):
+                    return 0
+                elif lower.endswith(".ckpt"):
+                    return 1
+            return 2
+
+        transformed_files.sort(key=_sort_key)
         return transformed_files
 
     def _transform_version(
@@ -297,7 +327,7 @@ class CivArchiveClient:
             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:
@@ -387,7 +417,7 @@ 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,

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",
@@ -424,6 +425,7 @@ class CivitaiBaseModelService:
                 "Ernie",
                 "Ernie Turbo",
                 "Nucleus",
+                "Krea 2",
             ],
         }
 

py/services/civitai_client.py

@@ -2,6 +2,7 @@ import asyncio
 import copy
 import logging
 import os
+from collections import OrderedDict
 from typing import Any, Optional, Dict, Tuple, List, Sequence
 from .connectivity_guard import (
     OFFLINE_FRIENDLY_MESSAGE,
@@ -45,9 +46,17 @@ class CivitaiClient:
         self._initialized = True
 
         self.base_url = "https://civitai.red/api/v1"
+        # In-memory cache to avoid redundant get_model_version_info calls
+        # within the same import/scan flow. Only successful results are cached.
+        # 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]]
+        ] = 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,
@@ -57,22 +66,57 @@ class CivitaiClient:
         use_auth: bool = False,
         **kwargs,
     ) -> Tuple[bool, Dict | str]:
-        """Wrapper around downloader.make_request that surfaces rate limits."""
+        """Wrapper around downloader.make_request that surfaces rate limits,
+        with retry for transient server errors (5xx, Cloudflare 524, network flakiness)."""
 
-        downloader = await get_downloader()
-        success, result = await downloader.make_request(
-            method,
-            url,
-            use_auth=use_auth,
-            **kwargs,
-        )
-        if not success and isinstance(result, RateLimitError):
-            if result.provider is None:
-                result.provider = "civitai_api"
-            raise result
-        if not success and is_offline_cooldown_error(result):
-            return False, OFFLINE_FRIENDLY_MESSAGE
-        return success, result
+        max_retries = 3
+        for attempt in range(max_retries):
+            downloader = await get_downloader()
+            success, result = await downloader.make_request(
+                method,
+                url,
+                use_auth=use_auth,
+                **kwargs,
+            )
+            if success:
+                return True, result
+
+            if isinstance(result, RateLimitError):
+                if result.provider is None:
+                    result.provider = "civitai_api"
+                raise result
+
+            if is_offline_cooldown_error(result):
+                return False, OFFLINE_FRIENDLY_MESSAGE
+
+            # Transient server error — retry with exponential backoff
+            if self._is_transient_server_error(str(result)):
+                if attempt < max_retries - 1:
+                    wait = 2**attempt  # 1s, 2s, 4s
+                    logger.info(
+                        "Transient error on %s %s, retrying in %ds "
+                        "(attempt %d/%d): %s",
+                        method,
+                        url,
+                        wait,
+                        attempt + 1,
+                        max_retries,
+                        result,
+                    )
+                    await asyncio.sleep(wait)
+                    continue
+                logger.warning(
+                    "All %d retries exhausted for %s %s: %s",
+                    max_retries,
+                    method,
+                    url,
+                    result,
+                )
+                return False, result
+
+            return False, result
+
+        return False, "Unexpected error in _make_request"
 
     @staticmethod
     def _remove_comfy_metadata(model_version: Optional[Dict]) -> None:
@@ -201,6 +245,29 @@ class CivitaiClient:
 
         return _from_value(payload)
 
+    @staticmethod
+    def _is_transient_server_error(message: str) -> bool:
+        """Return True when the message indicates a transient upstream failure.
+
+        Recognises Cloudflare 524, generic 5xx, and connectivity-level flakiness
+        that should not be treated as a permanent failure.
+        """
+        normalized = message.lower()
+        if "status 5" in normalized or "status 524" in normalized:
+            return True
+        if any(
+            keyword in normalized
+            for keyword in (
+                "connection refused",
+                "connection reset",
+                "temporary failure",
+                "name resolution",
+                "connection closed",
+            )
+        ):
+            return True
+        return False
+
     async def get_model_versions(self, model_id: str) -> Optional[Dict]:
         """Get all versions of a model with local availability info"""
         try:
@@ -223,6 +290,13 @@ class CivitaiClient:
                 logger.info("Civitai request skipped: %s", OFFLINE_FRIENDLY_MESSAGE)
                 return None
             if message:
+                if self._is_transient_server_error(message):
+                    logger.info(
+                        "Transient server error for model %s: %s",
+                        model_id,
+                        message,
+                    )
+                    return None
                 raise RuntimeError(message)
             return None
         except RateLimitError:
@@ -257,7 +331,7 @@ class CivitaiClient:
                 "GET",
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={"ids": query},
+                params={"ids": query, "nsfw": "true"},
             )
             if not success:
                 return None
@@ -336,6 +410,25 @@ class CivitaiClient:
             return None
 
         target_version = self._select_target_version(model_data, model_id, version_id)
+
+        # If modelVersions is empty (e.g. CivitAI cache lag for newly published
+        # models) but a specific version_id is known, fall back to fetching the
+        # version directly via the individual model-versions endpoint, then
+        # enrich it with the model-level data we already have.
+        if target_version is None and version_id is not None:
+            logger.info(
+                "modelVersions empty for model %s; falling back to direct "
+                "version lookup for %s",
+                model_id,
+                version_id,
+            )
+            version = await self._fetch_version_by_id(version_id)
+            if version:
+                self._enrich_version_with_model_data(version, model_data)
+                self._remove_comfy_metadata(version)
+                return version
+            return None
+
         if target_version is None:
             return None
 
@@ -482,6 +575,14 @@ class CivitaiClient:
                 - The model version data or None if not found
                 - An error message if there was an error, or None on success
         """
+        # In-memory cache avoids redundant API calls within the same
+        # import/scan flow (e.g. _resolve_base_model_from_checkpoint
+        # followed by _resolve_and_populate_checkpoint with the same id).
+        if version_id in self._version_info_cache:
+            logger.debug("Cache hit for model version info: %s", version_id)
+            self._version_info_cache.move_to_end(version_id)  # LRU bump
+            return self._version_info_cache[version_id]
+
         try:
             url = f"{self.base_url}/model-versions/{version_id}"
 
@@ -491,6 +592,11 @@ class CivitaiClient:
             if success:
                 logger.debug("Successfully fetched model version info for: %s", version_id)
                 self._remove_comfy_metadata(result)
+                self._version_info_cache[version_id] = (result, None)
+                self._version_info_cache.move_to_end(version_id)
+                # Evict oldest entry when over capacity
+                if len(self._version_info_cache) > self._MAX_CACHE_ENTRIES:
+                    self._version_info_cache.popitem(last=False)
                 return result, None
 
             # Handle specific error cases
@@ -532,6 +638,13 @@ class CivitaiClient:
             if not success:
                 if is_expected_offline_error(result):
                     return None
+                if self._is_transient_server_error(str(result)):
+                    logger.info(
+                        "Transient server error fetching image info for ID %s: %s",
+                        image_id,
+                        result,
+                    )
+                    return None
                 logger.error(
                     "Failed to fetch image info for ID %s from civitai.red: %s",
                     image_id,
@@ -640,7 +753,7 @@ class CivitaiClient:
                 "GET",
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={"username": username},
+                params={"username": username, "nsfw": "true"},
             )
 
             if not success:

py/services/download_coordinator.py

@@ -110,6 +110,23 @@ class DownloadCoordinator:
 
         return result
 
+    async def skip_download(self, download_id: str) -> Dict[str, Any]:
+        """Skip a download while preserving all partial files on disk."""
+        download_manager = await self._download_manager_factory()
+        result = await download_manager.skip_download(download_id)
+
+        await self._ws_manager.broadcast_download_progress(
+            download_id,
+            {
+                "status": "skipped",
+                "progress": 0,
+                "download_id": download_id,
+                "message": "Download skipped by user (partial files preserved)",
+            },
+        )
+
+        return result
+
     async def pause_download(self, download_id: str) -> Dict[str, Any]:
         """Pause an active download and notify listeners."""
 

py/services/download_manager.py

@@ -18,6 +18,7 @@ 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.preview_selection import resolve_mature_threshold, select_preview_media
 from ..utils.utils import sanitize_folder_name
 from ..utils.exif_utils import ExifUtils
@@ -28,6 +29,7 @@ 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
@@ -359,6 +361,15 @@ 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
@@ -395,6 +406,22 @@ 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
@@ -403,6 +430,19 @@ 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:
@@ -416,6 +456,22 @@ 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
@@ -1232,10 +1288,24 @@ 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"
@@ -1364,7 +1434,7 @@ class DownloadManager:
                             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,
                     )
@@ -1395,7 +1465,7 @@ class DownloadManager:
                     (
                         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,
                 )
@@ -1973,7 +2043,21 @@ 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:
@@ -2239,8 +2323,11 @@ class DownloadManager:
             entry.file_name = os.path.splitext(os.path.basename(file_path))[0]
             # Update size to actual downloaded file size
             entry.size = os.path.getsize(file_path)
-            # Use SHA256 from API metadata (already set in from_civitai_info)
-            # Do not recalculate to avoid blocking during ComfyUI execution
+            # Compute SHA256 locally when the API response didn't include it
+            if not entry.sha256:
+                sha256 = await calculate_sha256(file_path)
+                if sha256:
+                    entry.sha256 = sha256.lower()
             entries.append(entry)
 
         return entries
@@ -2400,6 +2487,89 @@ 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:
+        """Skip a download while preserving all partial files on disk.
+
+        Removes all in-memory tracking (asyncio task, semaphore, active/pause
+        state) but keeps partial files (.part / .aria2) on disk so that a
+        subsequent download-model-get request for the same save path can
+        auto-resume from the preserved partial download.
+
+        Args:
+            download_id: The unique identifier of the download task
+
+        Returns:
+            Dict: Status of the skip operation
+        """
+        await self._restore_persisted_downloads()
+
+        if download_id not in self._download_tasks and download_id not in self._active_downloads:
+            return {"success": False, "error": "Download task not found"}
+
+        download_info = self._active_downloads.get(download_id)
+        task = self._download_tasks.get(download_id)
+        active_statuses = {"queued", "waiting", "downloading", "paused", "cancelling"}
+        if task is None and (
+            not isinstance(download_info, dict)
+            or download_info.get("status") not in active_statuses
+        ):
+            return {"success": False, "error": "Download task not found"}
+
+        backend = (
+            self._active_downloads.get(download_id, {}).get("transfer_backend")
+            or "python"
+        )
+
+        try:
+            # For aria2: pause the transfer rather than force-removing it, so
+            # the .aria2 control file stays on disk for future resume
+            if backend == "aria2":
+                try:
+                    aria2_downloader = await get_aria2_downloader()
+                    pause_result = await aria2_downloader.pause_download(download_id)
+                    if not pause_result.get("success"):
+                        logger.warning(
+                            "Failed to pause aria2 transfer for %s during skip: %s",
+                            download_id,
+                            pause_result.get("error"),
+                        )
+                except Exception as exc:
+                    logger.warning(
+                        "Failed to pause aria2 transfer for %s during skip: %s",
+                        download_id,
+                        exc,
+                    )
+
+            # Cancel the asyncio task so the semaphore slot is released
+            if task is not None:
+                task.cancel()
+
+            # Resume pause event so the task can exit cleanly
+            pause_control = self._pause_events.get(download_id)
+            if pause_control is not None:
+                pause_control.resume()
+
+            # Wait briefly for task to acknowledge cancellation
+            if task is not None:
+                try:
+                    await asyncio.wait_for(asyncio.shield(task), timeout=2.0)
+                except (asyncio.CancelledError, asyncio.TimeoutError):
+                    pass
+
+            logger.info(f"Download skipped for task {download_id} (partial files preserved)")
+            return {"success": True, "message": "Download skipped successfully"}
+        except Exception as e:
+            logger.error(f"Error skipping download: {e}", exc_info=True)
+            return {"success": False, "error": str(e)}
+        finally:
+            # Clean up local in-memory tracking only - NO file deletion
+            self._pause_events.pop(download_id, None)
+            self._download_tasks.pop(download_id, None)
+            if download_id in self._active_downloads:
+                del self._active_downloads[download_id]
+            # 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:
         """Pause an active download without losing progress."""
 

py/services/download_queue_service.py

@@ -0,0 +1,871 @@
+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 = """
+        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);
+    """
+
+    @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)
+            conn.commit()
+        self._schema_initialized = True
+
+    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).
+        """
+        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()
+            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 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: int) -> bool:
+        """Delete a single history entry by its *id*.
+
+        Returns ``True`` if a row was deleted.
+        """
+        async with self._lock:
+            conn = self._get_conn()
+            cursor = conn.execute(
+                "DELETE FROM download_history WHERE id = ?",
+                (id,),
+            )
+            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: int) -> Optional[dict[str, Any]]:
+        """Re-queue a failed or canceled download from history.
+
+        Looks up the history record by its primary key.  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()
+            row = conn.execute(
+                "SELECT * FROM download_history WHERE id = ?",
+                (item_id,),
+            ).fetchone()
+            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 = ?",
+                (item_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

@@ -96,6 +96,21 @@ class DownloadedVersionHistoryService:
     def get_database_path(self) -> str:
         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
+
     def _get_active_library_name(self) -> str | None:
         try:
             value = self._settings.get_active_library_name()
@@ -206,7 +221,7 @@ class DownloadedVersionHistoryService:
             )
             conn.commit()
 
-    async def mark_not_downloaded(self, model_type: str, version_id: int) -> None:
+    async def mark_as_deleted(self, model_type: str, version_id: int) -> None:
         normalized_type = _normalize_model_type(model_type)
         normalized_version_id = _normalize_int(version_id)
         if normalized_type is None or normalized_version_id is None:

py/services/downloader.py

@@ -13,6 +13,7 @@ This module provides a centralized download service with:
 import os
 import logging
 import asyncio
+import ssl
 import aiohttp
 from collections import deque
 from dataclasses import dataclass
@@ -31,6 +32,20 @@ from .errors import RateLimitError
 logger = logging.getLogger(__name__)
 
 
+def is_ssl_cert_verify_error(exc: BaseException) -> bool:
+    """Check if an exception represents an SSL certificate verification failure.
+
+    Matches ``ssl.SSLCertVerificationError``, ``aiohttp.ClientConnectorCertificateError``
+    (which wraps the former), and falls back to the standard OpenSSL error text.
+    """
+    if isinstance(exc, ssl.SSLCertVerificationError):
+        return True
+    cert_error = getattr(exc, "certificate_error", None)
+    if isinstance(cert_error, ssl.SSLCertVerificationError):
+        return True
+    return "CERTIFICATE_VERIFY_FAILED" in str(exc)
+
+
 @dataclass(frozen=True)
 class DownloadProgress:
     """Snapshot of a download transfer at a moment in time."""
@@ -241,7 +256,9 @@ class Downloader:
                 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()
@@ -253,9 +270,19 @@ 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}"
@@ -265,14 +292,41 @@ class Downloader:
             logger.debug(
                 "Proxy mode: system-level proxy (trust_env) will be used if configured in environment."
             )
+        # Build SSL context: prefer certifi's CA bundle for broader
+        # CA coverage across different Python environments (especially
+        # embedded/compatibility Python builds).
+        try:
+            import certifi  # type: ignore[import-untyped]
+
+            ca_path = certifi.where()
+            ssl_context = ssl.create_default_context(cafile=ca_path)
+            logger.debug("SSL: using certifi CA bundle at %s", ca_path)
+        except (ImportError, FileNotFoundError, ValueError, OSError):
+            ssl_context = ssl.create_default_context()
+            logger.debug("SSL: certifi unavailable; using system default CA bundle")
+
         # Optimize TCP connection parameters
-        connector = aiohttp.TCPConnector(
-            ssl=True,
+        connector_kwargs = 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(
@@ -283,12 +337,14 @@ 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()
 
@@ -736,6 +792,17 @@ class Downloader:
                 DownloadRestartRequested,
             ) as e:
                 retry_count += 1
+
+                if is_ssl_cert_verify_error(e):
+                    logger.error(
+                        "SSL certificate verification failed when connecting to %s. "
+                        "This is usually caused by an outdated CA certificate bundle "
+                        "in the Python environment. Recommended fixes:\n"
+                        "  1. pip install --upgrade certifi\n"
+                        "  2. pip install pip-system-certs",
+                        url,
+                    )
+
                 logger.warning(
                     f"Network error during download (attempt {retry_count}/{self.max_retries + 1}): {e}"
                 )

py/services/embedding_service.py

@@ -1,8 +1,9 @@
 import os
 import logging
-from typing import Dict
+from typing import Dict, Optional
 
 from .base_model_service import BaseModelService
+from .auto_tag_service import extract_auto_tags
 from ..utils.models import EmbeddingMetadata
 from ..config import config
 
@@ -20,20 +21,37 @@ 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, embedding_data: Dict) -> Optional[Dict]:
+        """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 = embedding_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted embedding entry (missing file_path): %s",
+                embedding_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
         sub_type = embedding_data.get("sub_type", "embedding")
-        
+
+        file_name = embedding_data.get("file_name") or ""
+        model_name = embedding_data.get("model_name") or file_name
+        folder = embedding_data.get("folder") or ""
+
         return {
-            "model_name": embedding_data["model_name"],
-            "file_name": embedding_data["file_name"],
+            "model_name": model_name,
+            "file_name": 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"],
+            "folder": folder,
             "sha256": embedding_data.get("sha256", ""),
-            "file_path": embedding_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": embedding_data.get("size", 0),
             "modified": embedding_data.get("modified", ""),
             "tags": embedding_data.get("tags", []),
@@ -45,7 +63,10 @@ class EmbeddingService(BaseModelService):
             "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)
+            "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True),
+            "auto_tags": embedding_data.get("auto_tags") or extract_auto_tags(embedding_data),
+            "version_count": embedding_data.get("version_count"),
+            "hf_url": embedding_data.get("hf_url", ""),
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/lora_service.py

@@ -5,6 +5,7 @@ from typing import Dict, List, Optional
 
 from .base_model_service import BaseModelService
 from .model_query import resolve_sub_type
+from .auto_tag_service import extract_auto_tags
 from ..utils.models import LoraMetadata
 from ..config import config
 
@@ -23,23 +24,41 @@ class LoraService(BaseModelService):
         """
         super().__init__("lora", scanner, LoraMetadata, update_service=update_service)
 
-    async def format_response(self, lora_data: Dict) -> Dict:
-        """Format LoRA data for API response"""
+    async def format_response(self, lora_data: Dict) -> Optional[Dict]:
+        """Format LoRA data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out instead of crashing the
+        whole listing request. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = lora_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted LoRA entry (missing file_path): %s",
+                lora_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Resolve sub_type using priority: sub_type > model_type > civitai.model.type > default
         # Normalize to lowercase for consistent API responses
         sub_type = resolve_sub_type(lora_data).lower()
 
+        file_name = lora_data.get("file_name") or ""
+        model_name = lora_data.get("model_name") or file_name
+        folder = lora_data.get("folder") or ""
+
         return {
-            "model_name": lora_data["model_name"],
-            "file_name": lora_data["file_name"],
+            "model_name": model_name,
+            "file_name": file_name,
             "preview_url": config.get_preview_static_url(
                 lora_data.get("preview_url", "")
             ),
             "preview_nsfw_level": lora_data.get("preview_nsfw_level", 0),
             "base_model": lora_data.get("base_model", ""),
-            "folder": lora_data["folder"],
+            "folder": folder,
             "sha256": lora_data.get("sha256", ""),
-            "file_path": lora_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": lora_data.get("size", 0),
             "modified": lora_data.get("modified", ""),
             "tags": lora_data.get("tags", []),
@@ -57,6 +76,9 @@ class LoraService(BaseModelService):
             "civitai": self.filter_civitai_data(
                 lora_data.get("civitai", {}), minimal=True
             ),
+            "auto_tags": lora_data.get("auto_tags") or extract_auto_tags(lora_data),
+            "version_count": lora_data.get("version_count"),
+            "hf_url": lora_data.get("hf_url", ""),
         }
 
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
@@ -310,8 +332,23 @@ class LoraService(BaseModelService):
         """Return cached raw metadata for a LoRA matching the given filename."""
         cache = await self.scanner.get_cached_data(force_refresh=False)
 
+        fn_normalized = filename.replace("\\", "/")
+        fn_no_ext = fn_normalized
+        for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+            if fn_no_ext.lower().endswith(ext):
+                fn_no_ext = fn_no_ext[: -len(ext)]
+                break
+
         for lora in cache.raw_data if cache else []:
-            if lora.get("file_name") == filename:
+            file_name = lora.get("file_name", "")
+            folder = lora.get("folder", "")
+            file_name_no_ext = file_name
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if file_name_no_ext.lower().endswith(ext):
+                    file_name_no_ext = file_name_no_ext[: -len(ext)]
+                    break
+            path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+            if fn_no_ext in (file_name_no_ext, path_name):
                 return lora
 
         return None
@@ -399,7 +436,10 @@ class LoraService(BaseModelService):
             locked_loras = locked_loras[:target_count]
 
         # Filter out locked LoRAs from available pool
-        locked_names = {lora["name"] for lora in locked_loras}
+        locked_names = {
+            os.path.basename(lora["name"]) if "/" in str(lora.get("name", "")) else lora["name"]
+            for lora in locked_loras
+        }
         available_pool = [
             l for l in available_loras if l["file_name"] not in locked_names
         ]
@@ -454,7 +494,7 @@ class LoraService(BaseModelService):
 
             result_loras.append(
                 {
-                    "name": lora["file_name"],
+                    "name": f"{lora['folder']}/{lora['file_name']}" if lora.get("folder") else lora["file_name"],
                     "strength": model_str,
                     "clipStrength": clip_str,
                     "active": True,
@@ -670,8 +710,9 @@ class LoraService(BaseModelService):
         # Return minimal data needed for cycling
         return [
             {
-                "file_name": lora["file_name"],
+                "file_name": f"{lora['folder']}/{lora['file_name']}" if lora.get("folder") else lora["file_name"],
                 "model_name": lora.get("model_name", lora["file_name"]),
+                "folder": lora.get("folder", ""),
             }
             for lora in available_loras
         ]

py/services/metadata_sync_service.py

@@ -216,13 +216,19 @@ class MetadataSyncService:
             provider_used: Optional[str] = None
             last_error: Optional[str] = None
             civitai_api_not_found = False
+            any_rate_limited = False
 
             for provider_name, provider in provider_attempts:
                 try:
                     civitai_metadata_candidate, error = await provider.get_model_by_hash(sha256)
                 except RateLimitError as exc:
-                    exc.provider = exc.provider or (provider_name or provider.__class__.__name__)
-                    raise
+                    logger.warning(
+                        "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                        provider_name or provider.__class__.__name__,
+                        exc.retry_after or 0,
+                    )
+                    any_rate_limited = True
+                    continue
                 except Exception as exc:  # pragma: no cover - defensive logging
                     logger.error("Provider %s failed for hash %s: %s", provider_name, sha256, exc)
                     civitai_metadata_candidate, error = None, str(exc)
@@ -258,6 +264,14 @@ class MetadataSyncService:
                     model_data["last_checked_at"] = datetime.now().timestamp()
                     needs_save = True
 
+                # When the model was already classified as "not on CivitAI" via
+                # .metadata.json (civitai_deleted=True) but the SQLite cache is
+                # stale (because the pre-fix code never persisted these flags),
+                # ensure the flags are written to the scanner cache + SQLite.
+                if not needs_save and model_data.get("civitai_deleted") is True:
+                    model_data["last_checked_at"] = datetime.now().timestamp()
+                    needs_save = True
+
                 # Save metadata if any state was updated
                 if needs_save:
                     data_to_save = model_data.copy()
@@ -266,6 +280,7 @@ class MetadataSyncService:
                     if "last_checked_at" not in data_to_save:
                         data_to_save["last_checked_at"] = datetime.now().timestamp()
                     await self._metadata_manager.save_metadata(file_path, data_to_save)
+                    await update_cache_func(file_path, file_path, data_to_save)
 
                 default_error = (
                     "CivitAI model is deleted and metadata archive DB is not enabled"
@@ -276,17 +291,18 @@ class MetadataSyncService:
                 )
 
                 resolved_error = last_error or default_error
+                if any_rate_limited and "Rate limited" not in resolved_error:
+                    resolved_error = "Rate limited"
                 if is_expected_offline_error(resolved_error):
                     resolved_error = OFFLINE_FRIENDLY_MESSAGE
 
                 error_msg = (
                     f"Error fetching metadata: {resolved_error} "
-                    f"(model_name={model_data.get('model_name', '')})"
+                    f"(file={os.path.basename(file_path)}, sha256={sha256})"
                 )
-                if is_expected_offline_error(resolved_error):
-                    logger.info(error_msg)
-                else:
-                    logger.error(error_msg)
+                # Use case layer (BulkMetadataRefreshUseCase) logs failed models at WARNING level,
+                # so this level is demoted to DEBUG to avoid duplicate user-visible logging.
+                logger.debug(error_msg)
                 return False, error_msg
 
             model_data["from_civitai"] = True
@@ -411,7 +427,18 @@ class MetadataSyncService:
         metadata = await metadata_loader(metadata_path)
 
         for key, value in updates.items():
-            if isinstance(value, dict) and isinstance(metadata.get(key), dict):
+            if key == "tags" and isinstance(value, list):
+                # Normalize tags: trim, lowercase, deduplicate
+                normalized = []
+                seen = set()
+                for tag in value:
+                    if isinstance(tag, str):
+                        t = tag.strip().lower()
+                        if t and t not in seen:
+                            normalized.append(t)
+                            seen.add(t)
+                metadata[key] = normalized
+            elif isinstance(value, dict) and isinstance(metadata.get(key), dict):
                 metadata[key].update(value)
             else:
                 metadata[key] = value

py/services/model_cache.py

@@ -18,6 +18,8 @@ SUPPORTED_SORT_MODES = [
     ('size', 'desc'),
     ('usage', 'asc'),
     ('usage', 'desc'),
+    ('versions_count', 'asc'),
+    ('versions_count', 'desc'),
 ]
 # Is this in use?
 
@@ -263,6 +265,17 @@ class ModelCache:
                 ),
                 reverse=reverse
             )
+        elif sort_key == 'versions_count':
+            # Pre-dedup sort: fall back to name sort.
+            # Actual re-sort by version_count happens in get_paginated_data after dedup.
+            result = natsorted(
+                data,
+                key=lambda x: (
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
+                ),
+                reverse=reverse
+            )
         else:
             # Fallback: no sort
             result = list(data)

py/services/model_hash_index.py

@@ -7,6 +7,7 @@ class ModelHashIndex:
     def __init__(self):
         self._hash_to_path: Dict[str, str] = {}
         self._filename_to_hash: Dict[str, str] = {}
+        self._autov2_to_path: Dict[str, str] = {}
         # New data structures for tracking duplicates
         self._duplicate_hashes: Dict[str, List[str]] = {}  # sha256 -> list of paths
         self._duplicate_filenames: Dict[str, List[str]] = {}  # filename -> list of paths
@@ -63,6 +64,9 @@ class ModelHashIndex:
         # Add new mappings
         self._hash_to_path[sha256] = file_path
         self._filename_to_hash[filename] = sha256
+        # AutoV2 = first 10 chars of SHA256
+        if len(sha256) >= 10:
+            self._autov2_to_path[sha256[:10]] = file_path
     
     def _get_filename_from_path(self, file_path: str) -> str:
         """Extract filename without extension from path"""
@@ -157,7 +161,12 @@ class ModelHashIndex:
                 del self._duplicate_filenames[filename]
                 if filename in self._filename_to_hash:
                     del self._filename_to_hash[filename]
-    
+
+        # Remove from AutoV2 index
+        autov2_keys_to_remove = [k for k, v in self._autov2_to_path.items() if v == file_path]
+        for k in autov2_keys_to_remove:
+            del self._autov2_to_path[k]
+
     def remove_by_hash(self, sha256: str) -> None:
         """Remove entry by hash"""
         sha256 = sha256.lower()
@@ -177,6 +186,10 @@ class ModelHashIndex:
         # Remove hash-to-path mapping
         del self._hash_to_path[sha256]
         
+        autov2_key = sha256[:10]
+        if autov2_key in self._autov2_to_path:
+            del self._autov2_to_path[autov2_key]
+        
         # Update filename-to-hash and duplicate filenames for all paths
         for path_to_remove in paths_to_remove:
             fname = self._get_filename_from_path(path_to_remove)
@@ -195,13 +208,24 @@ class ModelHashIndex:
                     # If only one entry remains, it's no longer a duplicate
                     del self._duplicate_filenames[fname]
     
-    def has_hash(self, sha256: str) -> bool:
-        """Check if hash exists in index"""
-        return sha256.lower() in self._hash_to_path
-    
-    def get_path(self, sha256: str) -> Optional[str]:
-        """Get file path for a hash"""
-        return self._hash_to_path.get(sha256.lower())
+    def has_hash(self, hash_value: str) -> bool:
+        """Check if hash exists in index (SHA256 or AutoV2)"""
+        normalized = hash_value.lower()
+        if normalized in self._hash_to_path:
+            return True
+        if len(normalized) == 10:
+            return normalized in self._autov2_to_path
+        return False
+
+    def get_path(self, hash_value: str) -> Optional[str]:
+        """Get file path for a hash (SHA256 or AutoV2)"""
+        normalized = hash_value.lower()
+        path = self._hash_to_path.get(normalized)
+        if path is not None:
+            return path
+        if len(normalized) == 10:
+            return self._autov2_to_path.get(normalized)
+        return None
     
     def get_hash(self, file_path: str) -> Optional[str]:
         """Get hash for a file path"""
@@ -209,13 +233,16 @@ class ModelHashIndex:
         return self._filename_to_hash.get(filename)
     
     def get_hash_by_filename(self, filename: str) -> Optional[str]:
-        """Get hash for a filename without extension"""
+        """Get hash for a filename (bare basename or path-prefixed name)"""
+        if "/" in filename or "\\" in filename:
+            filename = os.path.splitext(os.path.basename(filename.replace("\\", "/")))[0]
         return self._filename_to_hash.get(filename)
     
     def clear(self) -> None:
         """Clear all entries"""
         self._hash_to_path.clear()
         self._filename_to_hash.clear()
+        self._autov2_to_path.clear()
         self._duplicate_hashes.clear()
         self._duplicate_filenames.clear()
     

py/services/model_lifecycle_service.py

@@ -111,6 +111,11 @@ class ModelLifecycleService:
             self._scanner._hash_index.remove_by_path(file_path)
 
         await self._sync_update_for_model(model_id)
+
+        persist_current_cache = getattr(self._scanner, "_persist_current_cache", None)
+        if callable(persist_current_cache):
+            await persist_current_cache()
+
         return {"success": True, "deleted_files": deleted_files}
 
     @staticmethod

py/services/model_metadata_provider.py

@@ -5,7 +5,7 @@ import logging
 import random
 from typing import Optional, Dict, Tuple, Any, List, Sequence
 from .downloader import get_downloader
-from .errors import RateLimitError
+from .errors import RateLimitError, ResourceNotFoundError
 
 try:
     from bs4 import BeautifulSoup
@@ -65,7 +65,14 @@ class _RateLimitRetryHelper:
                 return await func(*args, **kwargs)
             except RateLimitError as exc:
                 attempt += 1
-                if attempt >= self._retry_limit:
+
+                # Determine effective retry limit based on rate-limit magnitude
+                effective_retry_limit = self._retry_limit  # default: 3
+                if exc.retry_after is not None and exc.retry_after >= 120.0:
+                    # Long rate-limit window (>=2 min) — retries are futile
+                    effective_retry_limit = 1  # total 1 attempt = 0 retries
+
+                if attempt >= effective_retry_limit:
                     exc.provider = exc.provider or label
                     raise
 
@@ -81,7 +88,11 @@ class _RateLimitRetryHelper:
 
     def _calculate_delay(self, retry_after: Optional[float], attempt: int) -> float:
         if retry_after is not None:
-            return min(self._max_delay, max(0.0, retry_after))
+            # Cap at 1800s (30 min) as a safety ceiling. The old 30s cap was
+            # too low — CivArchive can return retry_after ~1500s, causing all
+            # retries to fail. A generous ceiling protects against pathological
+            # server values while still respecting the server's guidance.
+            return min(1800.0, max(0.0, retry_after))
 
         base_delay = self._base_delay * (2 ** max(0, attempt - 1))
         jitter_span = base_delay * self._jitter_ratio
@@ -474,14 +485,19 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result, error
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_by_hash: %s", label, e)
                 continue
         return None, "Model not found"
 
     async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        not_found_confirmed = False
         for provider, label in self._iter_providers():
             try:
                 result = await self._call_with_rate_limit(
@@ -492,8 +508,20 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
+            except ResourceNotFoundError:
+                not_found_confirmed = True
+                logger.debug(
+                    "Provider %s reports model %s as not found",
+                    label,
+                    model_id,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_versions: %s", label, e)
                 continue
@@ -511,8 +539,12 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_version: %s", label, e)
                 continue
@@ -529,8 +561,12 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result, error
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_version_info: %s", label, e)
                 continue
@@ -551,8 +587,12 @@ class FallbackMetadataProvider(ModelMetadataProvider):
             except NotImplementedError:
                 continue
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
             except Exception as e:
                 logger.debug(
                     "Provider %s failed for get_model_versions_by_hashes: %s",
@@ -573,8 +613,12 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result is not None:
                     return result
             except RateLimitError as exc:
-                exc.provider = exc.provider or label
-                raise exc
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_user_models: %s", label, e)
                 continue

py/services/model_query.py

@@ -96,6 +96,7 @@ class FilterCriteria:
     folder_exclude: Optional[Sequence[str]] = None
     base_models: Optional[Sequence[str]] = None
     tags: Optional[Dict[str, str]] = None
+    auto_tags: Optional[Dict[str, str]] = None
     favorites_only: bool = False
     search_options: Optional[Dict[str, Any]] = None
     model_types: Optional[Sequence[str]] = None
@@ -293,12 +294,14 @@ class ModelFilterSet:
                 for tag, state in tag_filters.items():
                     if not tag:
                         continue
+                    # Normalize to lowercase for case-insensitive matching
+                    normalized = tag.strip().lower()
                     if state == "exclude":
-                        exclude_tags.add(tag)
+                        exclude_tags.add(normalized)
                     else:
-                        include_tags.add(tag)
+                        include_tags.add(normalized)
             else:
-                include_tags = {tag for tag in tag_filters if tag}
+                include_tags = {tag.strip().lower() for tag in tag_filters if tag}
 
             if include_tags:
                 tag_logic = criteria.tag_logic.lower() if criteria.tag_logic else "any"
@@ -317,13 +320,17 @@ class ModelFilterSet:
                                 return True
                             # Otherwise, check if all non-special tags match
                             if non_special_tags:
-                                return all(tag in (item_tags or []) for tag in non_special_tags)
+                                # Case-insensitive: normalize item tags too
+                                normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                                return all(tag in normalized_item_tags for tag in non_special_tags)
                             return True
-                        # Normal case: all tags must match
-                        return all(tag in (item_tags or []) for tag in non_special_tags)
+                        # Normal case: all tags must match (case-insensitive)
+                        normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                        return all(tag in normalized_item_tags for tag in non_special_tags)
                     else:
-                        # OR logic (default): item must have ANY include tag
-                        return any(tag in include_tags for tag in (item_tags or []))
+                        # OR logic (default): item must have ANY include tag (case-insensitive)
+                        normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                        return bool(normalized_item_tags & include_tags)
 
                 items = [item for item in items if matches_include(item.get("tags"))]
 
@@ -332,7 +339,9 @@ class ModelFilterSet:
                 def matches_exclude(item_tags):
                     if not item_tags and "__no_tags__" in exclude_tags:
                         return True
-                    return any(tag in exclude_tags for tag in (item_tags or []))
+                    # Case-insensitive: normalize item tags
+                    normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                    return bool(normalized_item_tags & exclude_tags)
 
                 items = [
                     item for item in items if not matches_exclude(item.get("tags"))
@@ -359,10 +368,37 @@ class ModelFilterSet:
                 ]
             model_types_duration = time.perf_counter() - t0
 
+        auto_tags_duration = 0
+        auto_tag_filters = criteria.auto_tags or {}
+        if auto_tag_filters:
+            t0 = time.perf_counter()
+            include_at = set()
+            exclude_at = set()
+            for tag, state in auto_tag_filters.items():
+                if not tag:
+                    continue
+                if state == "exclude":
+                    exclude_at.add(tag)
+                else:
+                    include_at.add(tag)
+
+            if include_at:
+                items = [
+                    item for item in items
+                    if any(tag in include_at for tag in (item.get("auto_tags") or []))
+                ]
+
+            if exclude_at:
+                items = [
+                    item for item in items
+                    if not any(tag in exclude_at for tag in (item.get("auto_tags") or []))
+                ]
+            auto_tags_duration = time.perf_counter() - t0
+
         duration = time.perf_counter() - overall_start
         if duration > 0.1:  # Only log if it's potentially slow
             logger.debug(
-                "ModelFilterSet.apply took %.3fs (sfw: %.3fs, fav: %.3fs, folder: %.3fs, base: %.3fs, tags: %.3fs, types: %.3fs). "
+                "ModelFilterSet.apply took %.3fs (sfw: %.3fs, fav: %.3fs, folder: %.3fs, base: %.3fs, tags: %.3fs, types: %.3fs, auto_tags: %.3fs). "
                 "Count: %d -> %d",
                 duration,
                 sfw_duration,
@@ -371,6 +407,7 @@ class ModelFilterSet:
                 base_models_duration,
                 tags_duration,
                 model_types_duration,
+                auto_tags_duration,
                 initial_count,
                 len(items),
             )

py/services/model_scanner.py

@@ -9,7 +9,7 @@ from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Set,
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
-from ..utils.file_utils import find_preview_file, get_preview_extension
+from ..utils.file_utils import find_preview_file, get_preview_extension, calculate_sha256
 from ..utils.metadata_manager import MetadataManager
 from ..utils.civitai_utils import resolve_license_info
 from .model_cache import ModelCache
@@ -248,6 +248,7 @@ class ModelScanner:
             'civitai': civitai_slim,
             'civitai_deleted': bool(get_value('civitai_deleted', False)),
             'skip_metadata_refresh': bool(get_value('skip_metadata_refresh', False)),
+            'hf_url': get_value('hf_url', '') or '',
         }
 
         license_source: Dict[str, Any] = {}
@@ -476,11 +477,20 @@ class ModelScanner:
             for tag in adjusted_item.get('tags') or []:
                 tags_count[tag] = tags_count.get(tag, 0) + 1
 
-        # Validate cache entries and check health
+        # Validate cache entries and check health.
+        # Always use the validated/repaired entries — even when there are no
+        # invalid entries, auto_repair may have filled in missing optional
+        # fields (model_name, file_name, folder) with safe defaults on a copied
+        # working_entry.  Without this unconditional replacement the repaired
+        # copies are discarded and None values propagate to format_response.
+        # See issue #730.
         valid_entries, invalid_entries = CacheEntryValidator.validate_batch(
             adjusted_raw_data, auto_repair=True
         )
 
+        # Always use the validated entries (repaired copies)
+        adjusted_raw_data = valid_entries
+
         if invalid_entries:
             monitor = CacheHealthMonitor()
             report = monitor.check_health(adjusted_raw_data, auto_repair=True)
@@ -532,6 +542,13 @@ class ModelScanner:
         if not scan_result or not getattr(self, '_persistent_cache', None):
             return
 
+        if self.is_cancelled():
+            logger.info(
+                f"{self.model_type.capitalize()} Scanner: Skipping _save_persistent_cache "
+                "after cancellation"
+            )
+            return
+
         hash_snapshot = self._build_hash_index_snapshot(scan_result.hash_index)
         loop = asyncio.get_event_loop()
         try:
@@ -705,14 +722,20 @@ class ModelScanner:
             # Determine the page type based on model type
             # Scan for new data
             scan_result = await self._gather_model_data()
-            await self._apply_scan_result(scan_result)
-            await self._save_persistent_cache(scan_result)
-            await self._sync_download_history(scan_result.raw_data, source='scan')
+            if not self.is_cancelled():
+                await self._apply_scan_result(scan_result)
+                await self._save_persistent_cache(scan_result)
+                await self._sync_download_history(scan_result.raw_data, source='scan')
 
-            logger.info(
-                f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
-                f"found {len(scan_result.raw_data)} models"
-            )
+                logger.info(
+                    f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
+                    f"found {len(scan_result.raw_data)} models"
+                )
+            else:
+                logger.info(
+                    f"{self.model_type.capitalize()} Scanner: Cache initialization cancelled "
+                    f"after {time.time() - start_time:.2f} seconds"
+                )
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error initializing cache: {e}")
             # Ensure cache is at least an empty structure on error
@@ -1067,6 +1090,22 @@ class ModelScanner:
 
         model_data = self._build_cache_entry(metadata, folder=normalized_folder)
 
+        # Compute SHA256 hash when metadata provided none (e.g., CivitAI API response has empty hashes).
+        # Respect hash_status='pending' (set by CheckpointScanner for large models) to defer
+        # hash calculation until on-demand — avoids reading entire checkpoint files at startup.
+        hash_status = model_data.get('hash_status', '')
+        if not model_data.get('sha256') and hash_status != 'pending' and file_path:
+            try:
+                logger.info(f"Computing SHA256 hash for {file_path} (was empty from metadata)")
+                sha256 = await calculate_sha256(file_path)
+                if sha256:
+                    model_data['sha256'] = sha256.lower()
+                    if isinstance(metadata, BaseModelMetadata):
+                        metadata.sha256 = sha256.lower()
+                    await MetadataManager.save_metadata(file_path, metadata)
+            except Exception as e:
+                logger.error(f"Failed to compute SHA256 for {file_path}: {e}")
+
         # Skip excluded models
         if model_data.get('exclude', False):
             excluded_models.append(model_data['file_path'])
@@ -1080,6 +1119,13 @@ class ModelScanner:
         if scan_result is None:
             return
 
+        if self.is_cancelled():
+            logger.info(
+                f"{self.model_type.capitalize()} Scanner: Skipping _apply_scan_result "
+                "after cancellation"
+            )
+            return
+
         self._hash_index = scan_result.hash_index
         self._tags_count = dict(scan_result.tags_count)
         self._excluded_models = list(scan_result.excluded_models)
@@ -1101,7 +1147,15 @@ class ModelScanner:
 
     def _log_duplicate_filename_summary(self) -> None:
         """Log a batched summary of duplicate filename conflicts once per scan."""
-        if self._hash_index is None:
+        # Duplicate filename detection is only relevant for LoRAs, which use
+        # basename-only syntax (<lora:name:strength>). Checkpoints and embeddings
+        # use full relative paths for resolution, so conflicts are not ambiguous.
+        if self._hash_index is None or self.model_type != "lora":
+            return
+
+        # When full path syntax is active, duplicate filenames across subfolders
+        # are fully qualified, so there is no ambiguity — skip the warning.
+        if get_settings_manager().get("lora_syntax_format", "legacy") == "full":
             return
 
         duplicates = self._hash_index.get_duplicate_filenames()
@@ -1473,6 +1527,15 @@ class ModelScanner:
                 file_path_override=normalized_new_path,
             )
 
+            # Ensure sha256 is populated even when metadata doesn't have it
+            if not cache_entry.get('sha256') and normalized_new_path and os.path.exists(normalized_new_path):
+                try:
+                    sha256 = await calculate_sha256(normalized_new_path)
+                    if sha256:
+                        cache_entry['sha256'] = sha256.lower()
+                except Exception as e:
+                    logger.error(f"Failed to compute SHA256 for {normalized_new_path}: {e}")
+
             if recalculate_type:
                 cache_entry = self.adjust_cached_entry(cache_entry)
 
@@ -1572,12 +1635,39 @@ class ModelScanner:
         """Get model information by name"""
         try:
             cache = await self.get_cached_data()
-            
+
+            name_normalized = name.replace("\\", "/")
+            name_no_ext = name_normalized
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if name_no_ext.lower().endswith(ext):
+                    name_no_ext = name_no_ext[: -len(ext)]
+                    break
+
+            has_path = "/" in name_no_ext
+            basename = os.path.basename(name_no_ext) if has_path else name_no_ext
+            best_fallback = None
+
             for model in cache.raw_data:
-                if model.get("file_name") == name:
+                file_name = model.get("file_name", "")
+                folder = model.get("folder", "")
+                file_name_no_ext = file_name
+                for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                    if file_name_no_ext.lower().endswith(ext):
+                        file_name_no_ext = file_name_no_ext[: -len(ext)]
+                        break
+                path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+
+                if name_no_ext == file_name_no_ext or name_no_ext == path_name:
                     return model
-                    
-            return None
+
+                if has_path and file_name_no_ext == basename:
+                    if folder and name_no_ext.startswith(folder.replace("\\", "/") + "/"):
+                        best_fallback = model
+                    elif best_fallback is None:
+                        best_fallback = model
+
+            return best_fallback
+
         except Exception as e:
             logger.error(f"Error getting model info by name: {e}", exc_info=True)
             return None
@@ -1704,6 +1794,13 @@ class ModelScanner:
         """
         if not file_paths or self._cache is None:
             return False
+
+        if self.is_cancelled():
+            logger.info(
+                f"{self.model_type.capitalize()} Scanner: Skipping cache update "
+                "after cancelled bulk delete"
+            )
+            return False
             
         try:
             # Get all models that need to be removed from cache

py/services/model_update_service.py

@@ -689,6 +689,7 @@ class ModelUpdateService:
         *,
         force_refresh: bool = False,
         target_model_ids: Optional[Sequence[int]] = None,
+        folder_path: Optional[str] = None,
     ) -> Dict[int, ModelUpdateRecord]:
         """Refresh update information for every model present in the cache."""
         scanner.reset_cancellation()
@@ -703,6 +704,7 @@ class ModelUpdateService:
         local_versions = await self._collect_local_versions(
             scanner,
             target_model_ids=target_filter,
+            folder_path=folder_path,
         )
         total_models = len(local_versions)
         if total_models == 0:
@@ -722,6 +724,16 @@ class ModelUpdateService:
             "Refreshing update metadata for %d %s models", total_models, model_type
         )
 
+        # When filtering by folder, also collect the cross-folder version set
+        # so that versions already present in other folders are not reported
+        # as available updates. See issue #997.
+        all_local_versions: Optional[Dict[int, List[int]]] = None
+        if folder_path is not None:
+            all_local_versions = await self._collect_local_versions(
+                scanner,
+                target_model_ids=target_filter,
+            )
+
         results: Dict[int, ModelUpdateRecord] = {}
         prefetched: Dict[int, Mapping] = {}
 
@@ -760,6 +772,12 @@ class ModelUpdateService:
         for index, (model_id, version_ids) in enumerate(
             local_versions.items(), start=1
         ):
+            # Use cross-folder version IDs for is_in_library if available
+            all_vids: Sequence[int] = (
+                all_local_versions.get(model_id, [])
+                if all_local_versions is not None
+                else version_ids
+            )
             record = await self._refresh_single_model(
                 model_type,
                 model_id,
@@ -767,6 +785,7 @@ class ModelUpdateService:
                 metadata_provider,
                 force_refresh=force_refresh,
                 prefetched_response=prefetched.get(model_id),
+                all_local_version_ids=all_vids,
             )
             if scanner.is_cancelled():
                 logger.info(f"{model_type.capitalize()} Update Service: Refresh cancelled by user")
@@ -962,8 +981,16 @@ class ModelUpdateService:
         *,
         force_refresh: bool = False,
         prefetched_response: Optional[Mapping] = None,
+        all_local_version_ids: Optional[Sequence[int]] = None,
     ) -> Optional[ModelUpdateRecord]:
         normalized_local = self._normalize_sequence(local_versions)
+        # When folder-filtering, this carries the cross-folder version set
+        # for is_in_library; otherwise it falls back to normalized_local.
+        normalized_all = (
+            self._normalize_sequence(all_local_version_ids)
+            if all_local_version_ids is not None
+            else normalized_local
+        )
         now = time.time()
         async with self._lock:
             existing = self._get_record(model_type, model_id)
@@ -971,6 +998,7 @@ class ModelUpdateService:
                 record = self._merge_with_local_versions(
                     existing,
                     normalized_local,
+                    all_local_version_ids=normalized_all,
                 )
                 self._upsert_record(record)
                 return record
@@ -1000,12 +1028,11 @@ class ModelUpdateService:
                     fallback_error_message = str(exc) or "resource not found"
                     mark_model_as_ignored = True
                 except Exception as exc:  # pragma: no cover - defensive log
-                    logger.error(
+                    logger.warning(
                         "Failed to fetch versions for model %s (%s): %s",
                         model_id,
                         model_type,
                         exc,
-                        exc_info=True,
                     )
                     fallback_error_message = str(exc)
             if response is not None:
@@ -1047,6 +1074,7 @@ class ModelUpdateService:
                 record = self._merge_with_local_versions(
                     existing,
                     normalized_local,
+                    all_local_version_ids=normalized_all,
                 )
                 self._upsert_record(record)
                 return record
@@ -1058,6 +1086,7 @@ class ModelUpdateService:
                     model_type=model_type,
                     model_id=model_id,
                     last_checked_at=now,
+                    all_local_version_ids=normalized_all,
                 )
                 record = replace(record, should_ignore_model=True)
                 self._upsert_record(record)
@@ -1076,6 +1105,7 @@ class ModelUpdateService:
                     fetched_versions,
                     existing,
                     now,
+                    all_local_version_ids=normalized_all,
                 )
             else:
                 record = self._merge_with_local_versions(
@@ -1084,6 +1114,7 @@ class ModelUpdateService:
                     model_type=model_type,
                     model_id=model_id,
                     last_checked_at=existing.last_checked_at if existing else None,
+                    all_local_version_ids=normalized_all,
                 )
             self._upsert_record(record)
             return record
@@ -1277,6 +1308,7 @@ class ModelUpdateService:
         scanner,
         *,
         target_model_ids: Optional[Sequence[int]] = None,
+        folder_path: Optional[str] = None,
     ) -> Dict[int, List[int]]:
         cache = await scanner.get_cached_data()
         mapping: Dict[int, set[int]] = {}
@@ -1289,7 +1321,19 @@ class ModelUpdateService:
             if not target_set:
                 return {}
 
+        normalized_folder = None
+        if folder_path is not None:
+            normalized_folder = folder_path.replace("\\", "/").strip("/")
+
         for item in cache.raw_data:
+            # Apply folder filter first (cheapest check)
+            if normalized_folder is not None:
+                if not isinstance(item, dict):
+                    continue
+                item_folder = (item.get("folder") or "").replace("\\", "/").strip("/")
+                if item_folder != normalized_folder and not item_folder.startswith(normalized_folder + "/"):
+                    continue
+
             civitai = item.get("civitai") if isinstance(item, dict) else None
             if not isinstance(civitai, dict):
                 continue
@@ -1308,12 +1352,20 @@ class ModelUpdateService:
         existing: Optional[ModelUpdateRecord],
         normalized_local: Sequence[int],
         *,
+        all_local_version_ids: Optional[Sequence[int]] = None,
         model_type: Optional[str] = None,
         model_id: Optional[int] = None,
         last_checked_at: Optional[float] = None,
         version_info: Optional[Mapping] = None,
     ) -> ModelUpdateRecord:
         local_set = set(normalized_local)
+        # When folder-filtering, also consider versions in other folders
+        # as in-library so they are not reported as available updates.
+        effective_local_set: set[int] = (
+            local_set | set(all_local_version_ids)
+            if all_local_version_ids is not None
+            else local_set
+        )
         versions: List[ModelVersionRecord] = []
         ignore_map: Dict[int, bool] = {}
         if existing:
@@ -1325,7 +1377,7 @@ class ModelUpdateService:
                 versions.append(
                     replace(
                         version,
-                        is_in_library=version.version_id in local_set,
+                        is_in_library=version.version_id in effective_local_set,
                     )
                 )
         elif model_type is None or model_id is None:
@@ -1372,8 +1424,17 @@ class ModelUpdateService:
         remote_versions: Sequence[ModelVersionRecord],
         existing: Optional[ModelUpdateRecord],
         timestamp: float,
+        *,
+        all_local_version_ids: Optional[Sequence[int]] = None,
     ) -> ModelUpdateRecord:
         local_set = set(local_versions)
+        # When folder-filtering, also consider versions in other folders
+        # as in-library so they are not reported as available updates.
+        effective_local_set: set[int] = (
+            local_set | set(all_local_version_ids)
+            if all_local_version_ids is not None
+            else local_set
+        )
         ignore_map = {version.version_id: version.should_ignore for version in existing.versions} if existing else {}
         preview_map = {version.version_id: version.preview_url for version in existing.versions} if existing else {}
         sort_map = {version.version_id: version.sort_index for version in existing.versions} if existing else {}
@@ -1392,7 +1453,7 @@ class ModelUpdateService:
                     released_at=remote_version.released_at,
                     size_bytes=remote_version.size_bytes,
                     preview_url=remote_version.preview_url or preview_map.get(version_id),
-                    is_in_library=version_id in local_set,
+                    is_in_library=version_id in effective_local_set,
                     should_ignore=ignore_map.get(version_id, remote_version.should_ignore),
                     sort_index=sort_map.get(version_id, index),
                     early_access_ends_at=remote_version.early_access_ends_at,

py/services/persistent_model_cache.py

@@ -57,6 +57,7 @@ class PersistentModelCache:
         "db_checked",
         "last_checked_at",
         "hash_status",
+        "hf_url",
     )
     _MODEL_UPDATE_COLUMNS: Tuple[str, ...] = _MODEL_COLUMNS[2:]
     _instances: Dict[str, "PersistentModelCache"] = {}
@@ -165,8 +166,8 @@ class PersistentModelCache:
 
             item = {
                 "file_path": file_path,
-                "file_name": row["file_name"],
-                "model_name": row["model_name"],
+                "file_name": row["file_name"] or "",
+                "model_name": row["model_name"] or "",
                 "folder": row["folder"] or "",
                 "size": row["size"] or 0,
                 "modified": row["modified"] or 0.0,
@@ -188,6 +189,7 @@ class PersistentModelCache:
                 "skip_metadata_refresh": bool(row["skip_metadata_refresh"]),
                 "license_flags": int(license_value),
                 "hash_status": row["hash_status"] or "completed",
+                "hf_url": row["hf_url"] or "",
             }
             raw_data.append(item)
 
@@ -452,6 +454,7 @@ class PersistentModelCache:
                             db_checked INTEGER,
                             last_checked_at REAL,
                             hash_status TEXT,
+                            hf_url TEXT DEFAULT '',
                             PRIMARY KEY (model_type, file_path)
                         );
 
@@ -500,6 +503,7 @@ class PersistentModelCache:
             # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
             "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
             "hash_status": "TEXT DEFAULT 'completed'",
+            "hf_url": "TEXT DEFAULT ''",
         }
 
         for column, definition in required_columns.items():
@@ -548,19 +552,19 @@ class PersistentModelCache:
         return (
             model_type,
             item.get("file_path"),
-            item.get("file_name"),
-            item.get("model_name"),
-            item.get("folder"),
+            item.get("file_name") or "",
+            item.get("model_name") or "",
+            item.get("folder") or "",
             int(item.get("size") or 0),
             float(item.get("modified") or 0.0),
             (item.get("sha256") or "").lower() or None,
-            item.get("base_model"),
-            item.get("preview_url"),
+            item.get("base_model") or "",
+            item.get("preview_url") or "",
             int(item.get("preview_nsfw_level") or 0),
             1 if item.get("from_civitai", True) else 0,
             1 if item.get("favorite") else 0,
-            item.get("notes"),
-            item.get("usage_tips"),
+            item.get("notes") or "",
+            item.get("usage_tips") or "",
             metadata_source,
             civitai.get("id"),
             civitai.get("modelId"),
@@ -575,6 +579,7 @@ class PersistentModelCache:
             1 if item.get("db_checked") else 0,
             float(item.get("last_checked_at") or 0.0),
             item.get("hash_status", "completed"),
+            item.get("hf_url") or "",
         )
 
     def _insert_model_sql(self) -> str:

py/services/persistent_recipe_cache.py

@@ -12,7 +12,7 @@ import logging
 import os
 import sqlite3
 import threading
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Set, Tuple
 
 from ..utils.cache_paths import CacheType, resolve_cache_path_with_migration
@@ -26,6 +26,8 @@ class PersistedRecipeData:
 
     raw_data: List[Dict]
     file_stats: Dict[str, Tuple[float, int]]  # json_path -> (mtime, size)
+    image_id_map: Dict[str, str] = field(default_factory=dict)
+    """Precomputed mapping of civitai image_id → recipe_id."""
 
 
 class PersistentRecipeCache:
@@ -38,6 +40,7 @@ class PersistentRecipeCache:
         "json_path",
         "title",
         "folder",
+        "source_path",
         "base_model",
         "fingerprint",
         "created_date",
@@ -115,6 +118,20 @@ class PersistentRecipeCache:
                     if not rows:
                         return None
 
+                    # Restore precomputed image_id_map if available
+                    image_id_map: Dict[str, str] = {}
+                    try:
+                        meta_row = conn.execute(
+                            "SELECT value FROM cache_metadata WHERE key = ?",
+                            ("image_id_map",),
+                        ).fetchone()
+                        if meta_row:
+                            parsed = json.loads(meta_row["value"])
+                            if isinstance(parsed, dict):
+                                image_id_map = parsed
+                    except Exception:
+                        pass  # missing or corrupt — rebuilt on next cache refresh
+
                 finally:
                     conn.close()
         except FileNotFoundError:
@@ -137,14 +154,24 @@ class PersistentRecipeCache:
                     row["file_size"] or 0,
                 )
 
-        return PersistedRecipeData(raw_data=raw_data, file_stats=file_stats)
+        return PersistedRecipeData(
+            raw_data=raw_data,
+            file_stats=file_stats,
+            image_id_map=image_id_map,
+        )
 
-    def save_cache(self, recipes: List[Dict], json_paths: Optional[Dict[str, str]] = None) -> None:
+    def save_cache(
+        self,
+        recipes: List[Dict],
+        json_paths: Optional[Dict[str, str]] = None,
+        image_id_map: Optional[Dict[str, str]] = None,
+    ) -> None:
         """Save all recipes to SQLite cache.
 
         Args:
             recipes: List of recipe dictionaries to persist.
             json_paths: Optional mapping of recipe_id -> json_path for file stats.
+            image_id_map: Optional precomputed civitai image_id → recipe_id mapping.
         """
         if not self.is_enabled():
             return
@@ -185,6 +212,12 @@ class PersistentRecipeCache:
                             recipe_rows,
                         )
 
+                    # Persist image_id_map for O(1) lookups on cache load
+                    conn.execute(
+                        "INSERT OR REPLACE INTO cache_metadata (key, value) VALUES (?, ?)",
+                        ("image_id_map", json.dumps(image_id_map or {})),
+                    )
+
                     conn.commit()
                     logger.debug("Persisted %d recipes to cache", len(recipe_rows))
                 finally:
@@ -272,6 +305,29 @@ class PersistentRecipeCache:
         except Exception as exc:
             logger.debug("Failed to remove recipe %s from cache: %s", recipe_id, exc)
 
+    def save_image_id_map(self, image_id_map: Dict[str, str]) -> None:
+        """Persist the image_id_map to cache_metadata without rewriting the full cache.
+
+        This is called after ``add_recipe`` / ``remove_recipe`` mutations so
+        the persistent copy does not go stale between full ``save_cache`` calls.
+        """
+        if not self.is_enabled() or not self._schema_initialized:
+            return
+
+        try:
+            with self._db_lock:
+                conn = self._connect()
+                try:
+                    conn.execute(
+                        "INSERT OR REPLACE INTO cache_metadata (key, value) VALUES (?, ?)",
+                        ("image_id_map", json.dumps(image_id_map)),
+                    )
+                    conn.commit()
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("Failed to persist image_id_map: %s", exc)
+
     def get_indexed_recipe_ids(self) -> Set[str]:
         """Return all recipe IDs in the cache.
 
@@ -334,6 +390,7 @@ class PersistentRecipeCache:
                             json_path TEXT,
                             title TEXT,
                             folder TEXT,
+                            source_path TEXT,
                             base_model TEXT,
                             fingerprint TEXT,
                             created_date REAL,
@@ -358,6 +415,13 @@ class PersistentRecipeCache:
                         );
                         """
                     )
+                    # Migration: add source_path column to existing databases
+                    try:
+                        conn.execute(
+                            "ALTER TABLE recipes ADD COLUMN source_path TEXT"
+                        )
+                    except Exception:
+                        pass  # column already exists
                     conn.commit()
                 self._schema_initialized = True
             except Exception as exc:
@@ -406,6 +470,7 @@ class PersistentRecipeCache:
             json_path,
             recipe.get("title"),
             recipe.get("folder"),
+            recipe.get("source_path"),
             recipe.get("base_model"),
             recipe.get("fingerprint"),
             float(recipe.get("created_date") or 0.0),
@@ -456,6 +521,7 @@ class PersistentRecipeCache:
             "file_path": row["file_path"] or "",
             "title": row["title"] or "",
             "folder": row["folder"] or "",
+            "source_path": row["source_path"] or "",
             "base_model": row["base_model"] or "",
             "fingerprint": row["fingerprint"] or "",
             "created_date": row["created_date"] or 0.0,

py/services/recipe_cache.py

@@ -1,6 +1,6 @@
 import asyncio
 from typing import Iterable, List, Dict, Optional
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from operator import itemgetter
 from natsort import natsorted
 
@@ -14,6 +14,15 @@ class RecipeCache:
     sorted_by_date: List[Dict]
     folders: List[str] | None = None
     folder_tree: Dict | None = None
+    image_id_map: Dict[str, str] = field(default_factory=dict)
+    """Mapping of civitai image_id → recipe_id, precomputed at cache build time.
+
+    Built once during cache initialization (O(n)) so that
+    ``check_image_exists`` and ``import_from_url`` duplicate checks
+    can look up image_id in O(1) instead of scanning all recipes.
+    Recipes imported from local files have no valid civitai image_id
+    and are naturally excluded from this map.
+    """
 
     def __post_init__(self):
         self._lock = asyncio.Lock()

py/services/recipe_scanner.py

@@ -20,6 +20,7 @@ from .metadata_service import get_default_metadata_provider
 from .checkpoint_scanner import CheckpointScanner
 from .settings_manager import get_settings_manager
 from .recipes.errors import RecipeNotFoundError
+from ..utils.civitai_utils import extract_civitai_image_id
 from ..utils.utils import calculate_recipe_fingerprint, fuzzy_match
 from natsort import natsorted
 import sys
@@ -65,7 +66,7 @@ class RecipeScanner:
             cls._instance._civitai_client = None  # Will be lazily initialized
         return cls._instance
 
-    REPAIR_VERSION = 3
+    REPAIR_VERSION = 4
 
     def __init__(
         self,
@@ -292,6 +293,32 @@ class RecipeScanner:
         if recipe.get("repair_version", 0) >= self.REPAIR_VERSION:
             return False
 
+        # 1.5 Detect and clear corrupted checkpoint (LoRA data saved as checkpoint).
+        #     A checkpoint whose modelVersionId also appears in a LoRA entry is
+        #     definitely wrong — the CivitAI import code used to pick
+        #     modelVersionIds[0] as the checkpoint, which was often a LoRA.
+        #     Clearing it lets the enrichment flow re-resolve the correct
+        #     checkpoint from CivitAI image metadata.
+        cp = recipe.get("checkpoint")
+        lora_mvids = {
+            l.get("modelVersionId")
+            for l in recipe.get("loras", [])
+            if l.get("modelVersionId")
+        }
+        if cp and cp.get("modelVersionId") and cp["modelVersionId"] in lora_mvids:
+            cp_mvid = cp["modelVersionId"]
+            logger.info(
+                "Recipe %s: checkpoint modelVersionId %s matches a LoRA — "
+                "clearing corrupted checkpoint and removing matching LoRA entry",
+                recipe.get("id"),
+                cp_mvid,
+            )
+            recipe["checkpoint"] = None
+            recipe["loras"] = [
+                l for l in recipe.get("loras", [])
+                if l.get("modelVersionId") != cp_mvid
+            ]
+
         # 2. Identification: Is repair needed?
         has_checkpoint = (
             "checkpoint" in recipe
@@ -504,6 +531,23 @@ class RecipeScanner:
                     self._cache.raw_data = recipes
                     self._update_folder_metadata(self._cache)
                     self._sort_cache_sync()
+                    # Backfill source_path from JSON files if missing (schema migration)
+                    if self._backfill_source_path_if_needed(recipes, json_paths):
+                        self._cache.image_id_map = self._build_image_id_map()
+                        self._persistent_cache.save_cache(
+                            recipes, json_paths, self._cache.image_id_map
+                        )
+                    else:
+                        # Use persisted map, or rebuild if empty (e.g. first startup
+                        # after deploying the image_id_map feature).
+                        if persisted.image_id_map:
+                            self._cache.image_id_map = dict(persisted.image_id_map)
+                        else:
+                            self._cache.image_id_map = self._build_image_id_map()
+                            if self._cache.image_id_map:
+                                self._persistent_cache.save_image_id_map(
+                                    self._cache.image_id_map
+                                )
                     return self._cache
                 else:
                     # Partial update: some files changed
@@ -514,8 +558,13 @@ class RecipeScanner:
                     self._cache.raw_data = recipes
                     self._update_folder_metadata(self._cache)
                     self._sort_cache_sync()
+                    # Backfill source_path from JSON files if missing (schema migration)
+                    self._backfill_source_path_if_needed(recipes, json_paths)
+                    self._cache.image_id_map = self._build_image_id_map()
                     # Persist updated cache
-                    self._persistent_cache.save_cache(recipes, json_paths)
+                    self._persistent_cache.save_cache(
+                        recipes, json_paths, self._cache.image_id_map
+                    )
                     return self._cache
 
             # Fall back to full directory scan
@@ -527,9 +576,12 @@ class RecipeScanner:
             self._cache.raw_data = recipes
             self._update_folder_metadata(self._cache)
             self._sort_cache_sync()
+            self._cache.image_id_map = self._build_image_id_map()
 
             # Persist for next startup
-            self._persistent_cache.save_cache(recipes, json_paths)
+            self._persistent_cache.save_cache(
+                recipes, json_paths, self._cache.image_id_map
+            )
 
             return self._cache
         except Exception as e:
@@ -642,6 +694,34 @@ class RecipeScanner:
 
         return recipes, changed, json_paths
 
+    def _backfill_source_path_if_needed(
+        self,
+        recipes: List[Dict],
+        json_paths: Dict[str, str],
+    ) -> bool:
+        """Backfill source_path from recipe JSON files if missing from cache.
+
+        Returns True if any recipes were updated (caller should persist cache).
+        """
+        updated = False
+        for recipe in recipes:
+            if recipe.get("source_path"):
+                continue
+            recipe_id = str(recipe.get("id", ""))
+            json_path = json_paths.get(recipe_id)
+            if not json_path or not os.path.exists(json_path):
+                continue
+            try:
+                with open(json_path, "r", encoding="utf-8") as f:
+                    json_data = json.load(f)
+                file_source_path = json_data.get("source_path")
+                if file_source_path:
+                    recipe["source_path"] = file_source_path
+                    updated = True
+            except Exception:
+                pass
+        return updated
+
     def _full_directory_scan_sync(
         self, recipes_dir: str
     ) -> Tuple[List[Dict], Dict[str, str]]:
@@ -773,6 +853,28 @@ class RecipeScanner:
         except Exception as e:
             logger.error(f"Error sorting recipe cache: {e}")
 
+    def _build_image_id_map(self) -> Dict[str, str]:
+        """Build civitai image_id → recipe_id mapping from cached recipes.
+
+        Only recipes with a valid CivitAI image URL source_path produce an
+        entry.  Recipes imported from local files are naturally excluded.
+        """
+        mapping: Dict[str, str] = {}
+        if not self._cache:
+            return mapping
+        for recipe in getattr(self._cache, "raw_data", []):
+            if not isinstance(recipe, dict):
+                continue
+            source = recipe.get("source_path")
+            if not source:
+                continue
+            image_id = extract_civitai_image_id(source)
+            if image_id and image_id not in mapping:
+                recipe_id = recipe.get("id")
+                if recipe_id is not None:
+                    mapping[image_id] = str(recipe_id)
+        return mapping
+
     async def _wait_for_lora_scanner(self) -> None:
         """Ensure the LoRA scanner has initialized before recipe enrichment."""
 
@@ -1237,11 +1339,20 @@ class RecipeScanner:
         # Update FTS index
         self._update_fts_index_for_recipe(recipe_data, "add")
 
+        source = recipe_data.get("source_path")
+        if source:
+            image_id = extract_civitai_image_id(source)
+            if image_id:
+                recipe_id_value = recipe_data.get("id")
+                if recipe_id_value is not None:
+                    cache.image_id_map[image_id] = str(recipe_id_value)
+
         # Persist to SQLite cache
         if self._persistent_cache:
             recipe_id = str(recipe_data.get("id", ""))
             json_path = self._json_path_map.get(recipe_id, "")
             self._persistent_cache.update_recipe(recipe_data, json_path)
+            self._persistent_cache.save_image_id_map(cache.image_id_map)
 
     async def remove_recipe(self, recipe_id: str) -> bool:
         """Remove a recipe from the cache by ID."""
@@ -1260,9 +1371,15 @@ class RecipeScanner:
         # Update FTS index
         self._update_fts_index_for_recipe(recipe_id, "remove")
 
+        # Remove any image_id entry pointing to this recipe
+        stale = [k for k, v in cache.image_id_map.items() if v == recipe_id]
+        for k in stale:
+            del cache.image_id_map[k]
+
         # Remove from SQLite cache
         if self._persistent_cache:
             self._persistent_cache.remove_recipe(recipe_id)
+            self._persistent_cache.save_image_id_map(cache.image_id_map)
             self._json_path_map.pop(recipe_id, None)
 
         return True
@@ -1273,14 +1390,21 @@ class RecipeScanner:
         cache = await self.get_cached_data()
         removed = await cache.bulk_remove(recipe_ids, resort=False)
         if removed:
+            removed_ids = {str(r.get("id", "")) for r in removed}
+            stale = [k for k, v in cache.image_id_map.items() if v in removed_ids]
+            for k in stale:
+                del cache.image_id_map[k]
+
             self._schedule_resort()
-            # Update FTS index and persistent cache for each removed recipe
             for recipe in removed:
                 recipe_id = str(recipe.get("id", ""))
                 self._update_fts_index_for_recipe(recipe_id, "remove")
                 if self._persistent_cache:
                     self._persistent_cache.remove_recipe(recipe_id)
                     self._json_path_map.pop(recipe_id, None)
+
+            if self._persistent_cache:
+                self._persistent_cache.save_image_id_map(cache.image_id_map)
         return len(removed)
 
     async def scan_all_recipes(self) -> List[Dict]:
@@ -2484,6 +2608,7 @@ class RecipeScanner:
                 continue
 
             file_name = None
+            folder = ""
             hash_value = (lora.get("hash") or "").lower()
             if (
                 hash_value
@@ -2493,6 +2618,11 @@ class RecipeScanner:
                 file_path = self._lora_scanner._hash_index.get_path(hash_value)
                 if file_path:
                     file_name = os.path.splitext(os.path.basename(file_path))[0]
+                    if lora_cache is not None:
+                        for cached_lora in getattr(lora_cache, "raw_data", []):
+                            if cached_lora.get("file_path") == file_path:
+                                folder = cached_lora.get("folder", "")
+                                break
 
             if not file_name and lora.get("modelVersionId") and lora_cache is not None:
                 for cached_lora in getattr(lora_cache, "raw_data", []):
@@ -2507,13 +2637,16 @@ class RecipeScanner:
                             file_name = os.path.splitext(os.path.basename(cached_path))[
                                 0
                             ]
+                            folder = cached_lora.get("folder", "")
                         break
 
             if not file_name:
                 file_name = lora.get("file_name", "unknown-lora")
+                folder = lora.get("folder", "")
 
+            lora_name = f"{folder}/{file_name}" if folder else file_name
             strength = lora.get("strength", 1.0)
-            syntax_parts.append(f"<lora:{file_name}:{strength}>")
+            syntax_parts.append(f"<lora:{lora_name}:{strength}>")
 
         return syntax_parts
 

py/services/recipes/analysis_service.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import base64
 import io
 import os
@@ -14,6 +15,7 @@ from PIL import Image
 
 from ...utils.utils import calculate_recipe_fingerprint
 from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
+from ...recipes.enrichment import RecipeEnricher
 from .errors import (
     RecipeDownloadError,
     RecipeNotFoundError,
@@ -144,11 +146,38 @@ class RecipeAnalysisService:
                 ):
                     metadata = metadata["meta"]
 
-                # Include modelVersionIds from root level if available
-                # Civitai API returns modelVersionIds at root level, not in meta
+                # Include modelVersionIds from root level if available.
+                # CivitAI API returns modelVersionIds at root level, not in meta.
+                # When meta is null (None), create a minimal dict so downstream
+                # parsers can still discover LoRAs and checkpoints.
                 model_version_ids = image_info.get("modelVersionIds")
-                if model_version_ids and isinstance(metadata, dict):
-                    metadata["modelVersionIds"] = model_version_ids
+                if model_version_ids:
+                    if isinstance(metadata, dict):
+                        metadata["modelVersionIds"] = model_version_ids
+                    else:
+                        metadata = {"modelVersionIds": model_version_ids}
+
+                # 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(metadata, dict):
+                    browsing_level = image_info.get("browsingLevel")
+                    nsfw_level_str = image_info.get("nsfwLevel")
+                    if isinstance(browsing_level, int) and browsing_level > 0:
+                        metadata["browsingLevel"] = browsing_level
+                    elif (
+                        isinstance(nsfw_level_str, str)
+                        and nsfw_level_str
+                        in (
+                            "PG", "PG13", "R", "X", "XXX", "Blocked",
+                        )
+                    ):
+                        from ...utils.constants import NSFW_LEVELS
+
+                        metadata["browsingLevel"] = NSFW_LEVELS.get(
+                            nsfw_level_str, 0
+                        )
 
                 # Validate that metadata contains meaningful recipe fields
                 # If not, treat as None to trigger EXIF extraction from downloaded image
@@ -169,17 +198,116 @@ class RecipeAnalysisService:
                 temp_path = self._create_temp_path(suffix=extension)
                 await self._download_image(url, temp_path)
 
-            if metadata is None and not is_video:
-                metadata = self._exif_utils.extract_image_metadata(temp_path)
+            # Always extract EXIF from the downloaded image for generation
+            # params (prompt, negative prompt, sampler, steps, etc.).
+            # Previously this was gated on ``metadata is None``, but that
+            # skipped EXIF entirely when API metadata (modelVersionIds,
+            # browsingLevel) is present, losing all generation parameters.
+            exif_metadata = None
+            if not is_video:
+                exif_metadata = await asyncio.to_thread(
+                    self._exif_utils.extract_image_metadata, temp_path
+                )
 
-            return await self._parse_metadata(
-                metadata or {},
+            # Fallback: try the original (non-optimized) image for EXIF data
+            if not exif_metadata and civitai_image_id and image_info:
+                original_url = image_info.get("url")
+                if original_url:
+                    self._logger.debug(
+                        "Optimized image lacks embedded metadata, "
+                        "falling back to original image: %s",
+                        original_url,
+                    )
+                    orig_temp_path = self._create_temp_path(suffix=".png")
+                    try:
+                        await self._download_image(original_url, orig_temp_path)
+                        exif_metadata = await asyncio.to_thread(
+                            self._exif_utils.extract_image_metadata,
+                            orig_temp_path,
+                        )
+                    finally:
+                        self._safe_cleanup(orig_temp_path)
+
+            # Parse EXIF data (typically a string like parameters/prompt/workflow)
+            # and API metadata (dict with modelVersionIds, browsingLevel) separately,
+            # then merge: API loras/checkpoint override, EXIF gen_params fill in gaps.
+            # This mirrors the two-pass approach in _do_import_from_url.
+            exif_parsed_result = None
+            if isinstance(exif_metadata, str):
+                exif_parser = self._recipe_parser_factory.create_parser(exif_metadata)
+                if exif_parser:
+                    exif_data = await exif_parser.parse_metadata(
+                        exif_metadata, recipe_scanner=recipe_scanner,
+                    )
+                    if exif_data and not exif_data.get("error"):
+                        exif_parsed_result = exif_data
+
+            # Merge API metadata (dict) with EXIF data (if dict) for the
+            # CivitaiApiMetadataParser.  If EXIF data is a string it was
+            # parsed above — don't try to merge a string into a dict.
+            merged = {}
+            if isinstance(exif_metadata, dict):
+                merged.update(exif_metadata)
+            if isinstance(metadata, dict):
+                merged.update(metadata)
+
+            result = await self._parse_metadata(
+                merged,
                 recipe_scanner=recipe_scanner,
                 image_path=temp_path,
                 include_image_base64=True,
                 is_video=is_video,
                 extension=extension,
             )
+
+            # Merge EXIF string-parsed gen_params into the API result.
+            # API gen_params take priority (they come later via update).
+            if exif_parsed_result and not result.payload.get("error"):
+                exif_gp = exif_parsed_result.get("gen_params") or {}
+                result_gp = result.payload.get("gen_params") or {}
+                merged_gp = {**exif_gp, **result_gp}
+                if merged_gp:
+                    result.payload["gen_params"] = merged_gp
+
+            if civitai_image_id and image_info and not result.payload.get("error"):
+                # Use the metadata dict we built (may contain modelVersionIds
+                # and browsingLevel from the API root level).  Do NOT pass
+                # image_info.get("meta") — it is null for images whose meta
+                # lives at the root level only.  Also do NOT derive
+                # model_version_id from modelVersionIds[0] — that array mixes
+                # checkpoints, LoRAs, and other types without ordering
+                # guarantees; the parser already resolved them correctly.
+                recipe_for_enrich = {
+                    "gen_params": result.payload.get("gen_params", {}),
+                    "loras": result.payload.get("loras", []),
+                    "base_model": result.payload.get("base_model", "") or "",
+                    "checkpoint": result.payload.get("checkpoint") or result.payload.get("model"),
+                    "source_path": url,
+                }
+
+                await RecipeEnricher.enrich_recipe(
+                    recipe=recipe_for_enrich,
+                    civitai_client=civitai_client,
+                    request_params=None,
+                    prefetched_civitai_meta_raw=(
+                        metadata if isinstance(metadata, dict) else None
+                    ),
+                    prefetched_model_version_id=None,
+                )
+
+                result.payload["gen_params"] = recipe_for_enrich["gen_params"]
+                if recipe_for_enrich.get("checkpoint"):
+                    result.payload["checkpoint"] = recipe_for_enrich["checkpoint"]
+                if recipe_for_enrich.get("base_model"):
+                    result.payload["base_model"] = recipe_for_enrich["base_model"]
+
+                # Extract browsingLevel from our constructed metadata for NSFW blur
+                if isinstance(metadata, dict):
+                    bl = metadata.get("browsingLevel")
+                    if isinstance(bl, int) and bl > 0:
+                        result.payload["preview_nsfw_level"] = bl
+
+            return result
         finally:
             if temp_path:
                 self._safe_cleanup(temp_path)
@@ -199,7 +327,9 @@ class RecipeAnalysisService:
         if not os.path.isfile(normalized_path):
             raise RecipeNotFoundError("File not found")
 
-        metadata = self._exif_utils.extract_image_metadata(normalized_path)
+        metadata = await asyncio.to_thread(
+            self._exif_utils.extract_image_metadata, normalized_path
+        )
         if not metadata:
             return self._metadata_not_found_response(normalized_path)
 
@@ -259,6 +389,10 @@ class RecipeAnalysisService:
             "prompt_type",
             "positive",
             "negative",
+            # modelVersionIds is injected at the root level by CivitAI's image
+            # API when meta is null. It carries the version IDs of ALL models
+            # (checkpoint + LoRAs) used to generate the image.
+            "modelVersionIds",
         }
         return any(field in metadata for field in recipe_fields)
 

py/services/recipes/persistence_service.py

@@ -49,8 +49,18 @@ class RecipePersistenceService:
         tags: Iterable[str],
         metadata: Optional[dict[str, Any]],
         extension: str | None = None,
+        recipe_id: str | None = None,
+        target_dir: str | None = None,
     ) -> PersistenceResult:
-        """Persist a user uploaded recipe."""
+        """Persist a user uploaded recipe.
+
+        Args:
+            recipe_id: If provided, reuse this ID instead of generating a new
+                UUID. Used by re-import to preserve the original recipe identity.
+            target_dir: If provided, save recipe files to this directory instead
+                of the default recipes_dir. Used by re-import to preserve the
+                original folder location.
+        """
 
         missing_fields = []
         if not name:
@@ -63,10 +73,10 @@ class RecipePersistenceService:
             )
 
         resolved_image_bytes = self._resolve_image_bytes(image_bytes, image_base64)
-        recipes_dir = recipe_scanner.recipes_dir
+        recipes_dir = target_dir or recipe_scanner.recipes_dir
         os.makedirs(recipes_dir, exist_ok=True)
 
-        recipe_id = str(uuid.uuid4())
+        recipe_id = recipe_id or str(uuid.uuid4())
         
         # Handle video formats by bypassing optimization and metadata embedding
         is_video = extension in [".mp4", ".webm"]
@@ -115,6 +125,22 @@ class RecipePersistenceService:
         if metadata.get("source_path"):
             recipe_data["source_path"] = metadata.get("source_path")
 
+        nsfw_level = metadata.get("preview_nsfw_level")
+        if nsfw_level is not None and isinstance(nsfw_level, int):
+            recipe_data["preview_nsfw_level"] = nsfw_level
+
+        # Compute recipe folder relative to recipes root, mirroring
+        # RecipeScanner._calculate_folder() which is only called during scan/load.
+        if recipe_scanner.recipes_dir:
+            recipe_file_dir = os.path.dirname(normalized_image_path)
+            try:
+                relative_folder = os.path.relpath(recipe_file_dir, recipe_scanner.recipes_dir)
+                if relative_folder in (".", ""):
+                    relative_folder = ""
+                recipe_data["folder"] = relative_folder.replace(os.path.sep, "/")
+            except Exception:
+                recipe_data["folder"] = ""
+
         json_filename = f"{recipe_id}.recipe.json"
         json_path = os.path.join(recipes_dir, json_filename)
         json_path = os.path.normpath(json_path)

py/services/service_registry.py

@@ -188,6 +188,25 @@ class ServiceRegistry:
             logger.debug(f"Created and registered {service_name}")
             return service
 
+    @classmethod
+    async def get_download_queue_service(cls):
+        """Get or create the download queue service."""
+        service_name = "download_queue_service"
+
+        if service_name in cls._services:
+            return cls._services[service_name]
+
+        async with cls._get_lock(service_name):
+            if service_name in cls._services:
+                return cls._services[service_name]
+
+            from .download_queue_service import DownloadQueueService
+
+            service = await DownloadQueueService.get_instance()
+            cls._services[service_name] = service
+            logger.debug(f"Created and registered {service_name}")
+            return service
+
     @classmethod
     async def get_backup_service(cls):
         """Get or create the backup service."""

py/services/settings_manager.py

@@ -91,20 +91,22 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "autoplay_on_hover": False,
     "display_density": "default",
     "card_info_display": "always",
-    "show_folder_sidebar": True,
     "include_trigger_words": False,
     "compact_mode": False,
     "priority_tags": DEFAULT_PRIORITY_TAG_CONFIG.copy(),
     "model_name_display": "model_name",
+    "lora_syntax_format": "legacy",
     "model_card_footer_action": "replace_preview",
     "show_version_on_card": True,
-    "update_flag_strategy": "same_base",
+    "version_grouping": "same_base",
     "auto_organize_exclusions": [],
     "metadata_refresh_skip_paths": [],
     "skip_previously_downloaded_model_versions": False,
     "download_skip_base_models": [],
     "backup_auto_enabled": True,
     "backup_retention_count": 5,
+    "use_new_license_icons": True,
+    "group_by_model": False,
 }
 
 
@@ -133,6 +135,9 @@ class SettingsManager:
         self._template_path = (
             Path(__file__).resolve().parents[2] / "settings.json.example"
         )
+        # Known placeholder value in settings.json.example; any file containing
+        # this value should be treated as "not configured".
+        self._TEMPLATE_PLACEHOLDER_API_KEY = "your_civitai_api_key_here"
         self.settings = self._load_settings()
         self._migrate_setting_keys()
         self._ensure_default_settings()
@@ -164,6 +169,12 @@ class SettingsManager:
                     self._original_disk_payload = copy.deepcopy(data)
                     if self._matches_template_payload(data):
                         self._preserve_disk_template = True
+                    # Clean up the template placeholder so it is not treated
+                    # as a real key (affects both the frontend boolean and
+                    # the downloader's Authorization header).
+                    placeholder = self._TEMPLATE_PLACEHOLDER_API_KEY
+                    if data.get("civitai_api_key") == placeholder:
+                        data["civitai_api_key"] = ""
                 return data
             except json.JSONDecodeError as exc:
                 logger.error("Failed to parse settings.json: %s", exc)
@@ -734,6 +745,7 @@ class SettingsManager:
             "includeTriggerWords": "include_trigger_words",
             "compactMode": "compact_mode",
             "modelCardFooterAction": "model_card_footer_action",
+            "update_flag_strategy": "version_grouping",
         }
 
         updated = False
@@ -1556,7 +1568,7 @@ class SettingsManager:
         previous_dir = os.path.dirname(previous_path) or target_dir
 
         if os.path.abspath(previous_path) != os.path.abspath(target_path):
-            self._copy_model_cache_directory(previous_dir, target_dir)
+            self._migrate_settings_directory_content(previous_dir, target_dir)
             logger.info("Switching settings file to: %s", target_path)
 
         self._pending_portable_switch = {"other_path": other_path}
@@ -1591,46 +1603,52 @@ class SettingsManager:
         finally:
             self._pending_portable_switch = None
 
-    def _copy_model_cache_directory(self, source_dir: str, target_dir: str) -> None:
-        """Copy model_cache artifacts when switching storage locations."""
+    def _migrate_settings_directory_content(
+        self, source_dir: str, target_dir: str
+    ) -> None:
+        """Migrate settings directory subdirectories when switching storage locations.
+
+        Copies the canonical subdirectories (cache, backups, logs, stats, wildcards)
+        from the old settings directory to the new one. Legacy cache artifacts
+        (model_cache, recipe_cache, etc.) are migrated lazily by
+        ``resolve_cache_path_with_migration`` on first access.
+
+        Args:
+            source_dir: The previous settings directory path.
+            target_dir: The new settings directory path.
+        """
 
         if not source_dir or not target_dir:
             return
 
-        source_cache_dir = os.path.join(source_dir, "model_cache")
-        target_cache_dir = os.path.join(target_dir, "model_cache")
-        if os.path.isdir(source_cache_dir) and os.path.abspath(
-            source_cache_dir
-        ) != os.path.abspath(target_cache_dir):
-            try:
-                shutil.copytree(
-                    source_cache_dir,
-                    target_cache_dir,
-                    dirs_exist_ok=True,
-                    ignore=shutil.ignore_patterns("*.sqlite-shm", "*.sqlite-wal"),
-                )
-            except Exception as exc:
-                logger.warning(
-                    "Failed to copy model_cache directory from %s to %s: %s",
-                    source_cache_dir,
-                    target_cache_dir,
-                    exc,
-                )
+        def _copy_dir(name: str) -> None:
+            source = os.path.join(source_dir, name)
+            target = os.path.join(target_dir, name)
+            if os.path.isdir(source) and os.path.abspath(source) != os.path.abspath(
+                target
+            ):
+                try:
+                    shutil.copytree(
+                        source,
+                        target,
+                        dirs_exist_ok=True,
+                        ignore=shutil.ignore_patterns("*.sqlite-shm", "*.sqlite-wal"),
+                    )
+                except Exception as exc:
+                    logger.warning(
+                        "Failed to copy directory %s from %s to %s: %s",
+                        name,
+                        source,
+                        target,
+                        exc,
+                    )
 
-        source_cache_file = os.path.join(source_dir, "model_cache.sqlite")
-        target_cache_file = os.path.join(target_dir, "model_cache.sqlite")
-        if os.path.isfile(source_cache_file) and os.path.abspath(
-            source_cache_file
-        ) != os.path.abspath(target_cache_file):
-            try:
-                shutil.copy2(source_cache_file, target_cache_file)
-            except Exception as exc:
-                logger.warning(
-                    "Failed to copy model_cache.sqlite from %s to %s: %s",
-                    source_cache_file,
-                    target_cache_file,
-                    exc,
-                )
+        # Managed subdirectories under settings_dir
+        _copy_dir("cache")
+        _copy_dir("backups")
+        _copy_dir("logs")
+        _copy_dir("stats")
+        _copy_dir("wildcards")
 
     def _get_user_config_directory(self) -> str:
         """Return the user configuration directory, falling back to ~/.config."""

py/services/tag_update_service.py

@@ -4,7 +4,9 @@ from __future__ import annotations
 
 import os
 
-from typing import Awaitable, Callable, Dict, List, Sequence
+from typing import Awaitable, Callable, Dict, List, Sequence, Tuple
+
+from .auto_tag_service import extract_auto_tags
 
 
 class TagUpdateService:
@@ -20,9 +22,8 @@ class TagUpdateService:
         new_tags: Sequence[str],
         metadata_loader: Callable[[str], Awaitable[Dict[str, object]]],
         update_cache: Callable[[str, str, Dict[str, object]], Awaitable[bool]],
-    ) -> List[str]:
-        """Add tags to a metadata entry while keeping case-insensitive uniqueness."""
-
+    ) -> Tuple[List[str], List[str]]:
+        """Add tags to a metadata entry and return updated tags and auto_tags."""
         base, _ = os.path.splitext(file_path)
         metadata_path = f"{base}.metadata.json"
         metadata = await metadata_loader(metadata_path)
@@ -35,14 +36,15 @@ class TagUpdateService:
             if isinstance(tag, str) and tag.strip():
                 # Convert all tags to lowercase to avoid case sensitivity issues on Windows
                 normalized = tag.strip().lower()
-                if normalized.lower() not in existing_lower:
+                if normalized not in existing_lower:
                     existing_tags.append(normalized)
-                    existing_lower.append(normalized.lower())
+                    existing_lower.append(normalized)
                     tags_added.append(normalized)
 
         metadata["tags"] = existing_tags
         await self._metadata_manager.save_metadata(file_path, metadata)
         await update_cache(file_path, file_path, metadata)
 
-        return existing_tags
+        auto_tags = extract_auto_tags(metadata)
+        return existing_tags, auto_tags
 

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+import time
 from typing import Any, Dict, List, Optional, Protocol, Sequence
 
 from ..metadata_sync_service import MetadataSyncService
@@ -62,26 +63,48 @@ class BulkMetadataRefreshUseCase:
         ]
 
         total_to_process = len(to_process)
+        initial_skipped = total_models - total_to_process  # models excluded from fetch queue
         processed = 0
         success = 0
+        skipped_count = initial_skipped
+        handled_count = initial_skipped
         needs_resort = False
+        start_time = time.monotonic()
+        failures: List[Dict[str, str]] = []
 
         self._service.scanner.reset_cancellation()
 
         async def emit(status: str, **extra: Any) -> None:
             if progress_callback is None:
                 return
-            payload = {"status": status, "total": total_to_process, "processed": processed, "success": success}
+            payload = {
+                "status": status,
+                "total": total_models,
+                "processed": processed,
+                "success": success,
+                "failure_count": len(failures),
+                "skipped_count": skipped_count,
+                "handled": handled_count,
+                "elapsed_seconds": int(time.monotonic() - start_time),
+            }
+            # Only include full failure details in terminal emits (completed,
+            # cancelled, rate_limited) to avoid serializing the list on every
+            # per-model progress update.
+            if failures and status in ("completed", "cancelled", "rate_limited"):
+                payload["failures"] = failures
             payload.update(extra)
             await progress_callback.on_progress(payload)
 
         await emit("started")
 
+        RATE_LIMIT_ABORT_THRESHOLD = 3
+        consecutive_rate_limits = 0
+
         for model in to_process:
             if self._service.scanner.is_cancelled():
                 self._logger.info("Bulk metadata refresh cancelled by user")
                 await emit("cancelled", processed=processed, success=success)
-                return {"success": False, "message": "Operation cancelled", "processed": processed, "updated": success, "total": total_models}
+                return {"success": False, "message": "Operation cancelled", "processed": processed, "updated": success, "total": total_models, "failures": failures, "failure_count": len(failures), "skipped_count": skipped_count, "elapsed_seconds": int(time.monotonic() - start_time)}
             try:
                 original_name = model.get("model_name")
 
@@ -101,31 +124,76 @@ class BulkMetadataRefreshUseCase:
                             model["hash_status"] = "completed"
                         else:
                             self._logger.error(f"Failed to calculate hash for {file_path}")
+                            failures.append({"name": model.get("model_name", file_path or "Unknown"), "error": "Failed to calculate hash"})
                             processed += 1
+                            handled_count += 1
                             continue
                     else:
                         self._logger.warning(f"Scanner does not support lazy hash calculation for {file_path}")
+                        skipped_count += 1
                         processed += 1
+                        handled_count += 1
                         continue
 
                 # Skip models without valid hash
                 if not model.get("sha256"):
                     self._logger.warning(f"Skipping model without hash: {file_path}")
+                    skipped_count += 1
                     processed += 1
+                    handled_count += 1
                     continue
 
                 await MetadataManager.hydrate_model_data(model)
-                result, _ = await self._metadata_sync.fetch_and_update_model(
+                result, error_msg = await self._metadata_sync.fetch_and_update_model(
                     sha256=model["sha256"],
                     file_path=model["file_path"],
                     model_data=model,
                     update_cache_func=self._service.scanner.update_single_model_cache,
                 )
+
+                if not result and error_msg and "Rate limited" in error_msg:
+                    consecutive_rate_limits += 1
+                else:
+                    consecutive_rate_limits = 0
+
+                if not result:
+                    current_name = model.get("model_name", file_path or "Unknown")
+                    failures.append({"name": current_name, "error": error_msg or "Unknown error"})
+                    self._logger.warning("Failed to fetch metadata for %s: %s", current_name, error_msg)
+
+                if consecutive_rate_limits >= RATE_LIMIT_ABORT_THRESHOLD:
+                    # The current model was attempted and failed due to rate limiting;
+                    # count it before aborting so the summary is consistent.
+                    processed += 1
+                    handled_count += 1
+                    self._logger.warning(
+                        "Bulk metadata refresh aborted: %d consecutive rate limits detected. "
+                        "Processed %d/%d models.",
+                        consecutive_rate_limits,
+                        processed,
+                        total_to_process,
+                    )
+                    await emit(
+                        "rate_limited",
+                    )
+                    return {
+                        "success": False,
+                        "message": f"Rate limit detected; {total_to_process - processed} models skipped",
+                        "processed": processed,
+                        "updated": success,
+                        "total": total_models,
+                        "failures": failures,
+                        "failure_count": len(failures),
+                        "skipped_count": skipped_count,
+                        "elapsed_seconds": int(time.monotonic() - start_time),
+                    }
+
                 if result:
                     success += 1
                     if original_name != model.get("model_name"):
                         needs_resort = True
                 processed += 1
+                handled_count += 1
                 await emit(
                     "processing",
                     processed=processed,
@@ -134,6 +202,9 @@ class BulkMetadataRefreshUseCase:
                 )
             except Exception as exc:  # pragma: no cover - logging path
                 processed += 1
+                handled_count += 1
+                current_name = model.get("model_name", model.get("file_path", "Unknown"))
+                failures.append({"name": current_name, "error": str(exc)})
                 self._logger.error(
                     "Error fetching CivitAI data for %s: %s",
                     model.get("file_path"),
@@ -150,7 +221,7 @@ class BulkMetadataRefreshUseCase:
             f"{success} of {processed} processed {self._service.model_type}s (total: {total_models})"
         )
 
-        return {"success": True, "message": message, "processed": processed, "updated": success, "total": total_models}
+        return {"success": True, "message": message, "processed": processed, "updated": success, "total": total_models, "failures": failures, "failure_count": len(failures), "skipped_count": skipped_count, "elapsed_seconds": int(time.monotonic() - start_time)}
 
     @staticmethod
     def _is_in_skip_path(folder: str, skip_paths: List[str]) -> bool:

py/utils/civitai_utils.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, Iterable, Mapping, Sequence
 from urllib.parse import parse_qs, urlparse, urlunparse
 
 
-_SUPPORTED_CIVITAI_PAGE_HOSTS = frozenset({"civitai.com", "civitai.red"})
+_SUPPORTED_CIVITAI_PAGE_HOSTS = frozenset({"civitai.com", "civitai.red", "civitai.green"})
 DEFAULT_CIVITAI_PAGE_HOST = "civitai.com"
 _DEFAULT_ALLOW_COMMERCIAL_USE: Sequence[str] = ("Sell",)
 _LICENSE_DEFAULTS: Dict[str, Any] = {
@@ -66,6 +66,46 @@ def build_civitai_model_page_url(
     return None
 
 
+_RE_CDN_IMAGE_ID = re.compile(r"/(\d+)\.(?:jpeg|jpg|png|webp|gif)(?:\?|#|$)")
+
+
+def extract_civitai_image_id_from_cdn_url(url: str | None) -> str | None:
+    """Extract the numeric image ID from a Cloudflare CDN image URL.
+
+    CivitAI image CDN URLs follow the pattern::
+
+        https://image.civitai.com/{cf_uuid}/{params}/{image_id}.{ext}
+
+    The image database ID is always the last path segment (minus extension)
+    because ``getEdgeUrl(…, name=id.toString())`` embeds it explicitly
+    in the model-versions REST API response.
+    """
+    if not url:
+        return None
+    match = _RE_CDN_IMAGE_ID.search(url)
+    return match.group(1) if match else None
+
+
+def build_civitai_image_page_url(
+    image_id: str | int | None,
+    *,
+    host: str | None = None,
+) -> str | None:
+    """Build a Civitai image page URL.
+
+    Returns something like ``https://civitai.com/images/12345``.
+    The host is resolved through :func:`normalize_civitai_page_host` and
+    therefore respects the user's ``civitai_host`` setting.
+    """
+    if not image_id:
+        return None
+    normalized_host = normalize_civitai_page_host(host)
+    normalized_id = str(image_id).strip()
+    if not normalized_id:
+        return None
+    return urlunparse(("https", normalized_host, f"/images/{normalized_id}", "", "", ""))
+
+
 def _parse_supported_civitai_page_url(url: str | None):
     if not url:
         return None
@@ -239,9 +279,9 @@ def _resolve_commercial_bits(values: Sequence[str]) -> int:
             normalized_values.add(normalized)
 
     has_sell = "sell" in normalized_values
-    has_rent = has_sell or "rent" in normalized_values
-    has_rentcivit = has_rent or "rentcivit" in normalized_values
-    has_image = has_sell or "image" in normalized_values
+    has_rent = "rent" in normalized_values
+    has_rentcivit = "rentcivit" in normalized_values
+    has_image = "image" in normalized_values
 
     commercial_bits = (
         (1 if has_sell else 0) << 3
@@ -328,8 +368,10 @@ def rewrite_preview_url(
 
 
 __all__ = [
+    "build_civitai_image_page_url",
     "build_license_flags",
     "extract_civitai_image_id",
+    "extract_civitai_image_id_from_cdn_url",
     "extract_civitai_page_host",
     "extract_civitai_model_url_parts",
     "is_supported_civitai_page_host",

py/utils/constants.py

@@ -31,6 +31,8 @@ PREVIEW_EXTENSIONS = [
     ".mp4",
     ".gif",
     ".webm",
+    ".avif",
+    ".jxl",
 ]
 
 # Card preview image width
@@ -41,10 +43,24 @@ EXAMPLE_IMAGE_WIDTH = 832
 
 # Supported media extensions for example downloads
 SUPPORTED_MEDIA_EXTENSIONS = {
-    "images": [".jpg", ".jpeg", ".png", ".webp", ".gif"],
+    "images": [".jpg", ".jpeg", ".png", ".webp", ".gif", ".avif", ".jxl"],
     "videos": [".mp4", ".webm"],
 }
 
+# Model weight file extensions recognised by scanners.
+# This is the union of all scanner extensions (lora, checkpoint, embedding).
+MODEL_FILE_EXTENSIONS = {
+    ".safetensors",
+    ".ckpt",
+    ".pt",
+    ".pt2",
+    ".bin",
+    ".pth",
+    ".pkl",
+    ".sft",
+    ".gguf",
+}
+
 # Valid sub-types for each scanner type
 VALID_LORA_SUB_TYPES = ["lora", "locon", "dora"]
 VALID_CHECKPOINT_SUB_TYPES = ["checkpoint", "diffusion_model"]
@@ -101,8 +117,34 @@ DEFAULT_PRIORITY_TAG_CONFIG = {
 DIFFUSION_MODEL_BASE_MODELS = frozenset(
     [
         "Anima",
-        "ZImageTurbo",
-        "ZImageBase",
+        # Flux series — DiT architecture, loaded via UNETLoader in ComfyUI
+        "Flux.1 D",
+        "Flux.1 S",
+        "Flux.1 Krea",
+        "Flux.1 Kontext",
+        "Flux.2 D",
+        "Flux.2 Klein 9B",
+        "Flux.2 Klein 9B-base",
+        "Flux.2 Klein 4B",
+        "Flux.2 Klein 4B-base",
+        # Non-UNet / DiT image diffusion models
+        "AuraFlow",
+        "Chroma",
+        "HiDream",
+        "Hunyuan 1",
+        "Kolors",
+        "Lumina",
+        "PixArt a",
+        "PixArt E",
+        # Video diffusion models
+        "CogVideoX",
+        "Hunyuan Video",
+        "LTXV",
+        "LTXV2",
+        "LTXV 2.3",
+        "Mochi",
+        "SVD",
+        "Wan Video",
         "Wan Video 1.3B t2v",
         "Wan Video 14B t2v",
         "Wan Video 14B i2v 480p",
@@ -112,9 +154,15 @@ DIFFUSION_MODEL_BASE_MODELS = frozenset(
         "Wan Video 2.2 T2V-A14B",
         "Wan Video 2.5 T2V",
         "Wan Video 2.5 I2V",
-        "CogVideoX",
-        "Mochi",
+        # Other diffusion models
+        "Ernie",
+        "Ernie Turbo",
+        "Nucleus",
         "Qwen",
+        "ZImageBase",
+        "ZImageTurbo",
+        # Krea 2 — loaded via UNETLoader in ComfyUI
+        "Krea 2",
     ]
 )
 
@@ -181,5 +229,6 @@ SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
         "Ernie",
         "Ernie Turbo",
         "Nucleus",
+        "Krea 2",
     ]
 )

py/utils/example_images_download_manager.py

@@ -397,13 +397,12 @@ class DownloadManager:
 
             models_with_hash = len(all_models_with_hash)
 
-            # Calculate pending count: check which models actually need processing
-            # A model is pending if it has a hash, is not in processed_models,
-            # and its folder doesn't exist or is empty
+            # Calculate pending count: check which models actually need processing.
+            # A model is pending if it has a hash, is not already processed or known-failed,
+            # and its folder doesn't exist or is empty.
             pending_hashes = set()
             for model_hash, model_name in all_models_with_hash:
-                if model_hash not in processed_models:
-                    # Check if model folder exists with files
+                if model_hash not in processed_models and model_hash not in failed_models:
                     model_dir = ExampleImagePathResolver.get_model_folder(
                         model_hash, active_library
                     )

py/utils/example_images_metadata.py

@@ -452,3 +452,111 @@ class MetadataUpdater:
         except Exception as e:
             logger.error(f"Error parsing image metadata: {e}", exc_info=True)
             return None
+
+    @staticmethod
+    async def prune_stale_example_images(metadata) -> bool:
+        """Remove example-image metadata entries whose files no longer exist on disk.
+
+        Checks ``civitai.customImages`` (by ``id``) and ``civitai.images`` entries
+        that have an empty ``url`` (no remote fallback) against actual files in
+        the model's example-image folder.  Stale entries are removed in-place so
+        the caller can persist the cleaned metadata afterwards.
+
+        Args:
+            metadata: A ``BaseModelMetadata`` instance (modified in place).
+
+        Returns:
+            True if at least one entry was removed.
+        """
+        from ..utils.example_images_paths import get_model_folder
+
+        model_hash = getattr(metadata, "sha256", None)
+        if not model_hash:
+            return False
+
+        model_folder = get_model_folder(model_hash)
+        if not model_folder:
+            return False
+
+        civitai = getattr(metadata, "civitai", None)
+        if not isinstance(civitai, dict):
+            return False
+
+        has_changes = False
+
+        custom_images = civitai.get("customImages")
+        if isinstance(custom_images, list) and custom_images:
+            stale: list[int] = []
+
+            for idx, img in enumerate(custom_images):
+                img_id = img.get("id", "")
+                if not img_id:
+                    continue
+
+                if not os.path.isdir(model_folder):
+                    stale.append(idx)
+                else:
+                    found = False
+                    try:
+                        prefix = f"custom_{img_id}"
+                        for fname in os.listdir(model_folder):
+                            if fname.startswith(prefix) and os.path.isfile(
+                                os.path.join(model_folder, fname)
+                            ):
+                                found = True
+                                break
+                    except OSError:
+                        stale.append(idx)
+                        continue
+
+                    if not found:
+                        stale.append(idx)
+
+            if stale:
+                for idx in reversed(stale):
+                    custom_images.pop(idx)
+                has_changes = True
+                logger.info(
+                    "Pruned %d stale custom image(s) for %s",
+                    len(stale),
+                    getattr(metadata, "model_name", model_hash),
+                )
+
+        images = civitai.get("images")
+        if isinstance(images, list) and images:
+            stale: list[int] = []
+
+            for idx, img in enumerate(images):
+                if img.get("url", ""):
+                    # Has a remote fallback – keep it even if the local copy
+                    # is gone.
+                    continue
+
+                if not os.path.isdir(model_folder):
+                    stale.append(idx)
+                else:
+                    found = False
+                    try:
+                        prefix = f"image_{idx}."
+                        for fname in os.listdir(model_folder):
+                            if fname.startswith(prefix):
+                                found = True
+                                break
+                    except OSError:
+                        stale.append(idx)
+                        continue
+
+                    if not found:
+                        stale.append(idx)
+
+            if stale:
+                for idx in reversed(stale):
+                    images.pop(idx)
+                has_changes = True
+                logger.info(
+                    "Pruned %d stale image entry(ies) for %s",
+                    len(stale),
+                    getattr(metadata, "model_name", model_hash),
+                )
+
+        return has_changes

py/utils/example_images_paths.py

@@ -12,6 +12,18 @@ from ..services.settings_manager import get_settings_manager
 
 _HEX_PATTERN = re.compile(r"[a-fA-F0-9]{64}")
 
+# Filesystem/metadata files that are never created by the example images system
+# and are safe to ignore during validation. The cleanup service only operates on
+# directories, so these files pose no data-loss risk.
+_SAFE_FILENAMES: frozenset[str] = frozenset({
+    ".DS_Store",       # macOS folder metadata
+    "Thumbs.db",       # Windows thumbnail cache
+    "desktop.ini",     # Windows folder customization
+    ".localized",      # macOS folder name localization
+    ".gitkeep",        # Placeholder to keep empty dirs in git
+    ".gitignore",      # Git ignore rules
+})
+
 logger = logging.getLogger(__name__)
 
 
@@ -180,6 +192,22 @@ def is_hash_folder(name: str) -> bool:
     return bool(_HEX_PATTERN.fullmatch(name or ""))
 
 
+def _is_safe_ignorable_entry(item: str, item_path: str) -> bool:
+    """Return True if *item* is a harmless system/hidden file we can skip.
+
+    These files are never created by the example images system and are safe to
+    ignore because the cleanup/delete operations only act on **directories**,
+    never on individual files (other than ``.download_progress.json``).
+    """
+    if item in _SAFE_FILENAMES:
+        return True
+    # Hide Unix hidden files (dotfiles) that are regular files,
+    # since the cleanup system never deletes or moves files.
+    if item.startswith(".") and os.path.isfile(item_path):
+        return True
+    return False
+
+
 def is_valid_example_images_root(folder_path: str) -> bool:
     """Check whether a folder looks like a dedicated example images root."""
 
@@ -190,9 +218,16 @@ def is_valid_example_images_root(folder_path: str) -> bool:
 
     for item in items:
         item_path = os.path.join(folder_path, item)
+
+        # .download_progress.json is an expected metadata file — check before
+        # the generic dotfile rule so it stays explicitly documented.
         if item == ".download_progress.json" and os.path.isfile(item_path):
             continue
 
+        # Skip harmless system/hidden files — cleanup only touches directories
+        if _is_safe_ignorable_entry(item, item_path):
+            continue
+
         if os.path.isdir(item_path):
             if is_hash_folder(item):
                 continue
@@ -211,6 +246,41 @@ def is_valid_example_images_root(folder_path: str) -> bool:
     return True
 
 
+def find_non_compliant_items_in_example_images_root(folder_path: str) -> list[str]:
+    """Return the names of items that prevent *folder_path* from being a valid
+    example images root, or an empty list if the folder is valid.
+
+    This mirrors ``is_valid_example_images_root`` but **returns** the offending
+    names instead of a boolean, so callers can produce actionable error messages.
+    """
+    try:
+        items = os.listdir(folder_path)
+    except OSError as exc:
+        return [f"<cannot list directory: {exc}>"]
+
+    offending: list[str] = []
+
+    for item in items:
+        item_path = os.path.join(folder_path, item)
+
+        # Same skip rules as is_valid_example_images_root
+        if item == ".download_progress.json" and os.path.isfile(item_path):
+            continue
+        if _is_safe_ignorable_entry(item, item_path):
+            continue
+        if os.path.isdir(item_path):
+            if is_hash_folder(item):
+                continue
+            if item == "_deleted":
+                continue
+            if _library_folder_has_only_hash_dirs(item_path):
+                continue
+
+        offending.append(item)
+
+    return offending
+
+
 def _library_folder_has_only_hash_dirs(path: str) -> bool:
     """Return True when a library subfolder only contains hash folders or metadata files."""
 

py/utils/example_images_processor.py

@@ -62,6 +62,10 @@ class ExampleImagesProcessor:
                 return '.gif'
             elif content.startswith(b'RIFF') and b'WEBP' in content[:12]:
                 return '.webp'
+            elif len(content) >= 12 and content[4:8] == b'ftyp' and b'avif' in content[8:24]:
+                return '.avif'
+            elif content.startswith(b'\x00\x00\x00\x0cJXL \x0d\x0a\x87\x0a'):
+                return '.jxl'
             elif content.startswith(b'\x00\x00\x00\x18ftypmp4') or content.startswith(b'\x00\x00\x00\x20ftypmp4'):
                 return '.mp4'
             elif content.startswith(b'\x1A\x45\xDF\xA3'):
@@ -75,6 +79,8 @@ class ExampleImagesProcessor:
                 'image/png': '.png',
                 'image/gif': '.gif',
                 'image/webp': '.webp',
+                'image/avif': '.avif',
+                'image/jxl': '.jxl',
                 'video/mp4': '.mp4',
                 'video/webm': '.webm',
                 'video/quicktime': '.mov'

py/utils/exif_utils.py

@@ -1,17 +1,125 @@
 import json
 import logging
 import os
+import struct
 from io import BytesIO
 from typing import Any, Optional
 
 import piexif
 from PIL import Image, PngImagePlugin
 
+try:
+    import brotli
+    _BROTLI_AVAILABLE = True
+except ImportError:
+    brotli = None
+    _BROTLI_AVAILABLE = False
+
 logger = logging.getLogger(__name__)
 
 class ExifUtils:
     """Utility functions for working with EXIF data in images"""
 
+    @staticmethod
+    def _parse_isobmff_boxes(data: bytes, offset: int = 0) -> list[dict]:
+        boxes = []
+        while offset + 8 <= len(data):
+            size = struct.unpack('>I', data[offset:offset + 4])[0]
+            box_type = data[offset + 4:offset + 8]
+            if size == 0:
+                break
+            if size < 8 or offset + size > len(data):
+                break
+            box_data = data[offset + 8:offset + size]
+            boxes.append({'type': box_type, 'data': box_data, 'size': size})
+            offset += size
+        return boxes
+
+    @staticmethod
+    def _is_jxl_container(data: bytes) -> bool:
+        if len(data) < 32:
+            return False
+        return (
+            struct.unpack('>I', data[:4])[0] == 12
+            and data[4:8] == b'JXL '
+            and data[8:12] == bytes([0x0d, 0x0a, 0x87, 0x0a])
+            and struct.unpack('>I', data[12:16])[0] >= 16
+            and data[16:20] == b'ftyp'
+            and data[20:24] == b'jxl '
+        )
+
+    @staticmethod
+    def _is_avif_container(data: bytes) -> bool:
+        if len(data) < 16:
+            return False
+        for box in ExifUtils._parse_isobmff_boxes(data):
+            if box['type'] == b'ftyp' and b'avif' in box['data']:
+                return True
+        return False
+
+    # Max decompressed size for brotli metadata (2 MB)
+    _BROTLI_MAX_DECOMPRESSED = 2 * 1024 * 1024
+
+    @staticmethod
+    def _extract_isobmff_brotli(image_path: str) -> Optional[dict]:
+        try:
+            with open(image_path, 'rb') as f:
+                data = f.read()
+        except Exception:
+            return None
+
+        if ExifUtils._is_jxl_container(data):
+            boxes = ExifUtils._parse_isobmff_boxes(data, offset=12)
+        elif ExifUtils._is_avif_container(data):
+            boxes = ExifUtils._parse_isobmff_boxes(data)
+        else:
+            return None
+
+        brob = None
+        for box in boxes:
+            if box['type'] == b'brob':
+                brob = box
+                break
+        if brob is None:
+            return None
+
+        payload = brob['data']
+        if payload[:4] != b'comf':
+            return None
+        compressed = payload[4:]
+
+        if _BROTLI_AVAILABLE:
+            try:
+                decompressed = brotli.decompress(compressed)
+                if len(decompressed) > ExifUtils._BROTLI_MAX_DECOMPRESSED:
+                    logger.warning(
+                        "Brotli metadata too large (%d bytes, max %d), ignoring",
+                        len(decompressed),
+                        ExifUtils._BROTLI_MAX_DECOMPRESSED,
+                    )
+                    decompressed = None
+            except Exception:
+                decompressed = None
+        else:
+            decompressed = None
+
+        raw = decompressed if decompressed is not None else compressed
+        try:
+            meta = json.loads(raw.decode('utf-8'))
+        except Exception:
+            return None
+
+        result = {"parameters": None, "prompt": None, "workflow": None, "comment": None}
+        if isinstance(meta.get("prompt"), (dict, list)):
+            result["prompt"] = json.dumps(meta["prompt"])
+        elif isinstance(meta.get("prompt"), str):
+            result["prompt"] = meta["prompt"]
+        if isinstance(meta.get("workflow"), (dict, list)):
+            result["workflow"] = json.dumps(meta["workflow"])
+        elif isinstance(meta.get("workflow"), str):
+            result["workflow"] = meta["workflow"]
+        return result
+
     @staticmethod
     def _decode_user_comment(user_comment: Any) -> Optional[str]:
         if user_comment is None:
@@ -43,6 +151,12 @@ class ExifUtils:
             "comment": None,
         }
 
+        ext = os.path.splitext(image_path)[1].lower()
+        if ext in ('.avif', '.jxl'):
+            brotli_meta = ExifUtils._extract_isobmff_brotli(image_path)
+            if brotli_meta:
+                return brotli_meta
+
         with Image.open(image_path) as img:
             info = getattr(img, "info", {}) or {}
 
@@ -149,7 +263,6 @@ class ExifUtils:
             Optional[str]: Extracted metadata or None if not found
         """
         try:
-            # Skip for video files
             if image_path:
                 ext = os.path.splitext(image_path)[1].lower()
                 if ext in ['.mp4', '.webm']:
@@ -177,10 +290,9 @@ class ExifUtils:
             str: Path to the updated image
         """
         try:
-            # Skip for video files
             if image_path:
                 ext = os.path.splitext(image_path)[1].lower()
-                if ext in ['.mp4', '.webm']:
+                if ext in ['.mp4', '.webm', '.avif', '.jxl']:
                     return image_path
 
             metadata_fields = ExifUtils._load_structured_metadata(image_path)
@@ -212,10 +324,9 @@ class ExifUtils:
     def append_recipe_metadata(image_path, recipe_data) -> str:
         """Append recipe metadata to an image's EXIF data"""
         try:
-            # Skip for video files
             if image_path:
                 ext = os.path.splitext(image_path)[1].lower()
-                if ext in ['.mp4', '.webm']:
+                if ext in ['.mp4', '.webm', '.avif', '.jxl']:
                     return image_path
 
             # First, extract existing metadata
@@ -327,10 +438,9 @@ class ExifUtils:
             Tuple of (optimized_image_data, extension)
         """
         try:
-            # Skip for video files early if it's a file path
             if isinstance(image_data, str) and os.path.exists(image_data):
                 ext = os.path.splitext(image_data)[1].lower()
-                if ext in ['.mp4', '.webm']:
+                if ext in ['.mp4', '.webm', '.avif', '.jxl']:
                     try:
                         with open(image_data, 'rb') as f:
                             return f.read(), ext

py/utils/file_utils.py

@@ -1,7 +1,10 @@
 
 import hashlib
+import json
 import logging
 import os
+import struct
+from typing import Any
 
 from .constants import (
     CARD_PREVIEW_WIDTH,
@@ -31,14 +34,101 @@ def _get_hash_chunk_size_bytes() -> int:
 
 
 async def calculate_sha256(file_path: str) -> str:
-    """Calculate SHA256 hash of a file"""
+    """Calculate SHA256 hash of a file (full file content).
+
+    Uses ``posix_fadvise`` with ``POSIX_FADV_DONTNEED`` to avoid polluting the OS page
+    cache — critical on WSL where cached file pages live inside the VM and are not
+    accounted for in guest ``used`` memory, causing VmmemWSL to balloon.
+
+    On Windows/macOS where ``posix_fadvise`` is not available the hint is silently
+    skipped.
+    """
     sha256_hash = hashlib.sha256()
     chunk_size = _get_hash_chunk_size_bytes()
     with open(file_path, "rb") as f:
+        fd = f.fileno()
         for byte_block in iter(lambda: f.read(chunk_size), b""):
             sha256_hash.update(byte_block)
+        # Evict pages after reading so the data doesn't linger in the kernel page
+        # cache — on WSL this otherwise appears as unreclaimable VmmemWSL growth.
+        # Guard against platforms (Windows, macOS) that lack posix_fadvise.
+        if hasattr(os, "posix_fadvise") and hasattr(os, "POSIX_FADV_DONTNEED"):
+            os.posix_fadvise(fd, 0, 0, os.POSIX_FADV_DONTNEED)
     return sha256_hash.hexdigest()
 
+
+def calculate_autov2(file_path: str) -> str:
+    """Calculate CivitAI AutoV2 hash.
+
+    AutoV2 is the first 10 characters of the full file SHA256.
+    Used by CivitAI as a shortened file identifier.
+
+    Reference: https://developer.civitai.com/site/reference/model-versions
+    """
+    full_hash = hashlib.sha256()
+    chunk_size = _get_hash_chunk_size_bytes()
+    with open(file_path, "rb") as f:
+        for byte_block in iter(lambda: f.read(chunk_size), b""):
+            full_hash.update(byte_block)
+    return full_hash.hexdigest()[:10]
+
+
+def read_safetensors_metadata(file_path: str) -> dict[str, Any]:
+    """Read the ``__metadata__`` dict from a safetensors file header.
+
+    Safetensors file format:
+      - 8 bytes: header length (little-endian 64-bit)
+      - N bytes: UTF-8 JSON header
+      - The header JSON contains a ``__metadata__`` key holding arbitrary metadata.
+
+    Returns an empty dict if the file is not a valid safetensors file or has no
+    metadata.
+    """
+    try:
+        with open(file_path, "rb") as f:
+            header_len_bytes = f.read(8)
+            if len(header_len_bytes) < 8:
+                return {}
+            header_len = struct.unpack("<Q", header_len_bytes)[0]
+            header_bytes = f.read(header_len)
+            if len(header_bytes) < header_len:
+                return {}
+            header = json.loads(header_bytes.decode("utf-8"))
+            return header.get("__metadata__", {})
+    except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error, MemoryError, Exception):
+        return {}
+
+
+def calculate_autov3(file_path: str) -> str | None:
+    """Calculate CivitAI AutoV3 hash from a safetensors file.
+
+    AutoV3 is extracted from the safetensors file's embedded metadata, not
+    computed from the file bytes directly. The orchestrator reads the
+    ``sshs_model_hash`` (kohya-ss format) or ``modelspec.hash_sha256`` field
+    from the safetensors header and stores the first 12 characters.
+
+    The embedded hash itself is the SHA256 of the file after skipping the
+    8-byte header length + JSON header (a.k.a. the addnet hash / tensor-only
+    hash).
+
+    Reference:
+      - CivitAI DB trigger: ``SUBSTRING(NEW.hash FROM 1 FOR 12)``
+      - https://developer.civitai.com/site/reference/model-versions
+
+    Returns ``None`` when no AutoV3 hash can be determined (e.g. the file is
+    not safetensors, or the metadata doesn't contain a recognised hash field).
+    """
+    metadata = read_safetensors_metadata(file_path)
+    if not metadata:
+        return None
+
+    embedded_hash = metadata.get("sshs_model_hash") or metadata.get("modelspec.hash_sha256")
+    if embedded_hash and isinstance(embedded_hash, str) and len(embedded_hash) >= 12:
+        return embedded_hash[:12]
+
+    return None
+
+
 def find_preview_file(base_name: str, dir_path: str) -> str:
     """Find preview file for given base name in directory.
 

py/utils/session_logging.py

@@ -64,6 +64,27 @@ def _build_log_file_path(settings_file: str | None, started_at: datetime) -> str
     return os.path.join(log_dir, f"standalone-session-{timestamp}.log")
 
 
+_KEEP_LOG_COUNT = 3
+
+
+def _prune_old_logs(log_dir: str) -> None:
+    """Remove older session log files, keeping only the ``_KEEP_LOG_COUNT`` newest."""
+    try:
+        files = [
+            os.path.join(log_dir, name)
+            for name in os.listdir(log_dir)
+            if name.startswith("standalone-session-") and name.endswith(".log")
+        ]
+    except OSError:
+        return
+    files.sort(key=os.path.getmtime, reverse=True)
+    for path in files[_KEEP_LOG_COUNT:]:
+        try:
+            os.remove(path)
+        except OSError:
+            pass
+
+
 def setup_standalone_session_logging(settings_file: str | None) -> StandaloneSessionLogState:
     global _session_state
 
@@ -90,6 +111,7 @@ def setup_standalone_session_logging(settings_file: str | None) -> StandaloneSes
             file_handler.set_name(_FILE_HANDLER_NAME)
             file_handler.setFormatter(formatter)
             root_logger.addHandler(file_handler)
+            _prune_old_logs(os.path.dirname(log_file_path))
 
         _session_state = StandaloneSessionLogState(
             started_at=started_at,

py/utils/usage_stats.py

@@ -1,4 +1,5 @@
 import os
+import re
 import json
 import time
 import asyncio
@@ -9,6 +10,7 @@ from typing import Dict, Set
 
 from ..config import config
 from ..services.service_registry import ServiceRegistry
+from ..utils.settings_paths import get_settings_dir
 
 # 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"
@@ -16,14 +18,18 @@ standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.en
 # Define constants locally to avoid dependency on conditional imports
 MODELS = "models"
 LORAS = "loras"
+EMBEDDINGS = "embeddings"
+PROMPTS = "prompts"
 
 if not standalone_mode:
     from ..metadata_collector.metadata_registry import MetadataRegistry
     # Import constants from metadata_collector to ensure consistency, but we have fallbacks defined above
     try:
-        from ..metadata_collector.constants import MODELS as _MODELS, LORAS as _LORAS
+        from ..metadata_collector.constants import MODELS as _MODELS, LORAS as _LORAS, EMBEDDINGS as _EMBEDDINGS, PROMPTS as _PROMPTS
         MODELS = _MODELS
         LORAS = _LORAS
+        EMBEDDINGS = _EMBEDDINGS
+        PROMPTS = _PROMPTS
     except ImportError:
         pass  # Use the local definitions
 
@@ -65,6 +71,7 @@ class UsageStats:
         self.stats = {
             "checkpoints": {},  # sha256 -> { total: count, history: { date: count } }
             "loras": {},        # sha256 -> { total: count, history: { date: count } }
+            "embeddings": {},   # sha256 -> { total: count, history: { date: count } }
             "total_executions": 0,
             "last_save_time": 0
         }
@@ -77,6 +84,7 @@ class UsageStats:
         
         # Load existing stats if available
         self._stats_file_path = self._get_stats_file_path()
+        self._migrate_from_old_location()
         self._load_stats()
         
         # Save interval in seconds
@@ -89,14 +97,38 @@ class UsageStats:
         logger.debug("Usage statistics tracker initialized")
     
     def _get_stats_file_path(self) -> str:
-        """Get the path to the stats JSON file"""
+        """Get the path to the stats JSON file in the settings directory."""
+        settings_dir = get_settings_dir(create=True)
+        return os.path.join(settings_dir, "stats", self.STATS_FILENAME)
+
+    @staticmethod
+    def _get_old_stats_file_path() -> str:
+        """Get the legacy stats file path in the first lora root directory."""
         if not config.loras_roots or len(config.loras_roots) == 0:
-            # If no lora roots are available, we can't save stats
-            # This will be handled by the caller
-            raise RuntimeError("No LoRA root directories configured. Cannot initialize usage statistics.")
-        
-        # Use the first lora root
-        return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
+            return ""
+        return os.path.join(config.loras_roots[0], UsageStats.STATS_FILENAME)
+
+    def _migrate_from_old_location(self) -> None:
+        """Migrate stats file from old location (first lora root) to new location (settings_dir/stats/)."""
+        new_path = self._stats_file_path
+        if os.path.exists(new_path):
+            return
+
+        old_path = self._get_old_stats_file_path()
+        if not old_path or not os.path.exists(old_path):
+            return
+
+        try:
+            os.makedirs(os.path.dirname(new_path), exist_ok=True)
+            shutil.copy2(old_path, new_path)
+            logger.info("Migrated usage stats from %s to %s", old_path, new_path)
+            try:
+                os.remove(old_path)
+                logger.info("Cleaned up old stats file: %s", old_path)
+            except Exception as e:
+                logger.warning("Failed to remove old stats file %s: %s", old_path, e)
+        except Exception as e:
+            logger.error("Failed to migrate usage stats from %s to %s: %s", old_path, new_path, e)
     
     def _backup_old_stats(self):
         """Backup the old stats file before conversion"""
@@ -115,6 +147,7 @@ class UsageStats:
         new_stats = {
             "checkpoints": {},
             "loras": {},
+            "embeddings": {},
             "total_executions": old_stats.get("total_executions", 0),
             "last_save_time": old_stats.get("last_save_time", time.time())
         }
@@ -142,21 +175,27 @@ class UsageStats:
                     }
                 }
         
+        # Convert embedding stats (if present in old format)
+        if "embeddings" in old_stats and isinstance(old_stats["embeddings"], dict):
+            for hash_id, count in old_stats["embeddings"].items():
+                new_stats["embeddings"][hash_id] = {
+                    "total": count,
+                    "history": {
+                        today: count
+                    }
+                }
+        
         logger.info("Successfully converted stats from old format to new format with history")
         return new_stats
     
     def _is_old_format(self, stats):
         """Check if the stats are in the old format (direct count values)"""
         # Check if any lora or checkpoint entry is a direct number instead of an object
-        if "loras" in stats and isinstance(stats["loras"], dict):
-            for hash_id, data in stats["loras"].items():
-                if isinstance(data, (int, float)):
-                    return True
-                
-        if "checkpoints" in stats and isinstance(stats["checkpoints"], dict):
-            for hash_id, data in stats["checkpoints"].items():
-                if isinstance(data, (int, float)):
-                    return True
+        for category in ("loras", "checkpoints", "embeddings"):
+            if category in stats and isinstance(stats[category], dict):
+                for hash_id, data in stats[category].items():
+                    if isinstance(data, (int, float)):
+                        return True
         
         return False
     
@@ -182,6 +221,9 @@ class UsageStats:
                         if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
                             self.stats["loras"] = loaded_stats["loras"]
                         
+                        if "embeddings" in loaded_stats and isinstance(loaded_stats["embeddings"], dict):
+                            self.stats["embeddings"] = loaded_stats["embeddings"]
+                        
                         if "total_executions" in loaded_stats:
                             self.stats["total_executions"] = loaded_stats["total_executions"]
                         
@@ -304,6 +346,10 @@ class UsageStats:
         if LORAS in metadata and isinstance(metadata[LORAS], dict):
             await self._process_loras(metadata[LORAS], today)
 
+        # Process embeddings — parse prompt text for embedding:name references
+        if PROMPTS in metadata and isinstance(metadata[PROMPTS], dict):
+            await self._process_embeddings(metadata[PROMPTS], today)
+
     def _increment_usage_counter(self, category: str, stat_key: str, today_date: str) -> None:
         """Increment usage counters for a resolved stats key."""
         if stat_key not in self.stats[category]:
@@ -510,6 +556,55 @@ class UsageStats:
         except Exception as e:
             logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
     
+    @staticmethod
+    def _extract_embedding_names(prompt_text: str) -> set:
+        """Parse embedding:name references from prompt text.
+
+        ComfyUI's SDTokenizer resolves ``embedding:<name>`` during tokenization
+        (see ``sd1_clip.py _try_get_embedding``).  This mirrors the same pattern
+        to extract embedding file names from the captured prompt strings.
+        """
+        if not prompt_text:
+            return set()
+        # Matches ``embedding:name`` where name is alphanumeric plus _ . - /
+        names = re.findall(r"embedding:([a-zA-Z0-9_.\-/]+)", prompt_text)
+        return set(names)
+
+    async def _process_embeddings(self, prompts_data, today_date):
+        """Extract embedding usage from prompt texts and record it.
+
+        Iterates every prompt node's text field captured by the metadata
+        collector, extracts ``embedding:<name>`` references, resolves each
+        name to its SHA256 hash via the embedding scanner, and increments
+        usage counters.
+        """
+        try:
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+            if not embedding_scanner:
+                logger.warning("Embedding scanner not available for usage tracking")
+                return
+
+            seen_names = set()
+            for _node_id, prompt_data in prompts_data.items():
+                if not isinstance(prompt_data, dict):
+                    continue
+                for text_field in ("text", "positive_text", "negative_text"):
+                    text = prompt_data.get(text_field)
+                    if isinstance(text, str):
+                        seen_names.update(self._extract_embedding_names(text))
+
+            for emb_name in seen_names:
+                emb_hash = embedding_scanner.get_hash_by_filename(emb_name)
+                if emb_hash:
+                    self._increment_usage_counter("embeddings", emb_hash, today_date)
+                else:
+                    logger.debug(
+                        "No hash found for embedding '%s', skipping usage tracking",
+                        emb_name,
+                    )
+        except Exception as e:
+            logger.error("Error processing embedding usage: %s", e, exc_info=True)
+
     async def get_stats(self):
         """Get current usage statistics"""
         return self.stats
@@ -522,6 +617,9 @@ class UsageStats:
         elif model_type == "lora":
             if sha256 in self.stats["loras"]:
                 return self.stats["loras"][sha256]["total"]
+        elif model_type == "embedding":
+            if sha256 in self.stats["embeddings"]:
+                return self.stats["embeddings"][sha256]["total"]
         return 0
     
     async def process_execution(self, prompt_id):

py/utils/utils.py

@@ -15,30 +15,64 @@ def get_lora_info(lora_name):
         scanner = await ServiceRegistry.get_lora_scanner()
         cache = await scanner.get_cached_data()
 
+        lora_name_normalized = lora_name.replace("\\", "/")
+        lora_name_no_ext = lora_name_normalized
+        for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+            if lora_name_no_ext.lower().endswith(ext):
+                lora_name_no_ext = lora_name_no_ext[: -len(ext)]
+                break
+
+        has_path = "/" in lora_name_no_ext
+        basename = os.path.basename(lora_name_no_ext) if has_path else lora_name_no_ext
+        best_fallback = None
+
         for item in cache.raw_data:
-            if item.get("file_name") == lora_name:
-                file_path = item.get("file_path")
-                if file_path:
-                    # Check all lora roots including extra paths
-                    all_roots = list(config.loras_roots or []) + list(
-                        config.extra_loras_roots or []
+            file_name = item.get("file_name", "")
+            folder = item.get("folder", "")
+            file_name_no_ext = file_name
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if file_name_no_ext.lower().endswith(ext):
+                    file_name_no_ext = file_name_no_ext[: -len(ext)]
+                    break
+            path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+
+            if lora_name_no_ext not in (file_name_no_ext, path_name):
+                if has_path and file_name_no_ext == basename:
+                    if folder and lora_name_no_ext.startswith(folder.replace("\\", "/") + "/"):
+                        best_fallback = item
+                    elif best_fallback is None:
+                        best_fallback = item
+                continue
+
+            file_path = item.get("file_path")
+            if not file_path:
+                continue
+
+            all_roots = list(config.loras_roots or []) + list(
+                config.extra_loras_roots or []
+            )
+            for root in all_roots:
+                root = root.replace(os.sep, "/")
+                if file_path.startswith(root):
+                    relative_path = os.path.relpath(file_path, root).replace(
+                        os.sep, "/"
                     )
-                    for root in all_roots:
-                        root = root.replace(os.sep, "/")
-                        if file_path.startswith(root):
-                            relative_path = os.path.relpath(file_path, root).replace(
-                                os.sep, "/"
-                            )
-                            # Get trigger words from civitai metadata
-                            civitai = item.get("civitai", {})
-                            trigger_words = (
-                                civitai.get("trainedWords", []) if civitai else []
-                            )
-                            return relative_path, trigger_words
-                    # If not found in any root, return path with trigger words from cache
                     civitai = item.get("civitai", {})
-                    trigger_words = civitai.get("trainedWords", []) if civitai else []
-                    return file_path, trigger_words
+                    trigger_words = (
+                        civitai.get("trainedWords", []) if civitai else []
+                    )
+                    return relative_path, trigger_words
+            civitai = item.get("civitai", {})
+            trigger_words = civitai.get("trainedWords", []) if civitai else []
+            return file_path, trigger_words
+
+        if best_fallback:
+            file_path = best_fallback.get("file_path")
+            if file_path:
+                civitai = best_fallback.get("civitai", {})
+                trigger_words = civitai.get("trainedWords", []) if civitai else []
+                return file_path, trigger_words
+
         return lora_name, []
 
     try:
@@ -77,15 +111,54 @@ def get_lora_info_absolute(lora_name):
         scanner = await ServiceRegistry.get_lora_scanner()
         cache = await scanner.get_cached_data()
 
+        lora_name_normalized = lora_name.replace("\\", "/")
+        lora_name_no_ext = lora_name_normalized
+        for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+            if lora_name_no_ext.lower().endswith(ext):
+                lora_name_no_ext = lora_name_no_ext[: -len(ext)]
+                break
+
+        has_path = "/" in lora_name_no_ext
+        basename = os.path.basename(lora_name_no_ext) if has_path else lora_name_no_ext
+        best_fallback = None
+
         for item in cache.raw_data:
-            if item.get("file_name") == lora_name:
+            file_name = item.get("file_name", "")
+            folder = item.get("folder", "")
+            file_name_no_ext = file_name
+            for ext in (".safetensors", ".ckpt", ".pt", ".bin"):
+                if file_name_no_ext.lower().endswith(ext):
+                    file_name_no_ext = file_name_no_ext[: -len(ext)]
+                    break
+            path_name = f"{folder}/{file_name_no_ext}".replace("\\", "/") if folder else file_name_no_ext
+
+            if lora_name_no_ext == file_name_no_ext:
                 file_path = item.get("file_path")
                 if file_path:
-                    # Return absolute path directly
-                    # Get trigger words from civitai metadata
                     civitai = item.get("civitai", {})
                     trigger_words = civitai.get("trainedWords", []) if civitai else []
                     return file_path, trigger_words
+
+            if lora_name_no_ext == path_name:
+                file_path = item.get("file_path")
+                if file_path:
+                    civitai = item.get("civitai", {})
+                    trigger_words = civitai.get("trainedWords", []) if civitai else []
+                    return file_path, trigger_words
+
+            if has_path and file_name_no_ext == basename:
+                if folder and lora_name_no_ext.startswith(folder.replace("\\", "/") + "/"):
+                    best_fallback = item
+                elif best_fallback is None:
+                    best_fallback = item
+
+        if best_fallback:
+            file_path = best_fallback.get("file_path")
+            if file_path:
+                civitai = best_fallback.get("civitai", {})
+                trigger_words = civitai.get("trainedWords", []) if civitai else []
+                return file_path, trigger_words
+
         return lora_name, []
 
     try:

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.0.6"
+version = "1.1.6"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

refs/civarc_api_model_data.json

@@ -1,134 +0,0 @@
-{
-    "id": 1746460,
-    "name": "Mixplin Style [Illustrious]",
-    "type": "LORA",
-    "description": "description",
-    "username": "Ty_Lee",
-    "downloadCount": 4207,
-    "favoriteCount": 0,
-    "commentCount": 8,
-    "ratingCount": 0,
-    "rating": 0,
-    "is_nsfw": true,
-    "nsfw_level": 31,
-    "createdAt": "2025-07-06T01:51:42.859Z",
-    "updatedAt": "2025-10-10T23:15:26.714Z",
-    "deletedAt": null,
-    "tags": [
-        "art",
-        "style",
-        "artist style",
-        "styles",
-        "mixplin",
-        "artiststyle"
-    ],
-    "creator_id": "Ty_Lee",
-    "creator_username": "Ty_Lee",
-    "creator_name": "Ty_Lee",
-    "creator_url": "/users/Ty_Lee",
-    "versions": [
-        {
-            "id": 2042594,
-            "name": "v2.0",
-            "href": "/models/1746460?modelVersionId=2042594"
-        },
-        {
-            "id": 1976567,
-            "name": "v1.0",
-            "href": "/models/1746460?modelVersionId=1976567"
-        }
-    ],
-    "version": {
-        "id": 1976567,
-        "modelId": 1746460,
-        "name": "v1.0",
-        "baseModel": "Illustrious",
-        "baseModelType": "Standard",
-        "description": null,
-        "downloadCount": 437,
-        "ratingCount": 0,
-        "rating": 0,
-        "is_nsfw": true,
-        "nsfw_level": 31,
-        "createdAt": "2025-07-05T10:17:28.716Z",
-        "updatedAt": "2025-10-10T23:15:26.756Z",
-        "deletedAt": null,
-        "files": [
-            {
-                "id": 1874043,
-                "name": "mxpln-illustrious-ty_lee.safetensors",
-                "type": "Model",
-                "sizeKB": 223124.37109375,
-                "downloadUrl": "https://civitai.com/api/download/models/1976567",
-                "modelId": 1746460,
-                "modelName": "Mixplin Style [Illustrious]",
-                "modelVersionId": 1976567,
-                "is_nsfw": true,
-                "nsfw_level": 31,
-                "sha256": "e2b7a280d6539556f23f380b3f71e4e22bc4524445c4c96526e117c6005c6ad3",
-                "createdAt": "2025-07-05T10:17:28.716Z",
-                "updatedAt": "2025-10-10T23:15:26.766Z",
-                "is_primary": false,
-                "mirrors": [
-                    {
-                        "filename": "mxpln-illustrious-ty_lee.safetensors",
-                        "url": "https://civitai.com/api/download/models/1976567",
-                        "source": "civitai",
-                        "model_id": 1746460,
-                        "model_version_id": 1976567,
-                        "deletedAt": null,
-                        "is_gated": false,
-                        "is_paid": false
-                    }
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-        ],
-        "images": [
-            {
-                "id": 86403595,
-                "url": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
-                "nsfwLevel": 1,
-                "width": 1560,
-                "height": 2280,
-                "hash": "U7G8Zp0w02%IA6%N00-;D]-W~VNG0nMw-.IV",
-                "type": "image",
-                "minor": false,
-                "poi": false,
-                "hasMeta": true,
-                "hasPositivePrompt": true,
-                "onSite": false,
-                "remixOfId": null,
-                "image_url": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
-                "link": "https://genur.art/posts/86403595"
-            }
-        ],
-        "trigger": [
-            "mxpln"
-        ],
-        "allow_download": true,
-        "download_url": "/api/download/models/1976567",
-        "platform_url": "https://civitai.com/models/1746460?modelVersionId=1976567",
-        "civitai_model_id": 1746460,
-        "civitai_model_version_id": 1976567,
-        "href": "/models/1746460?modelVersionId=1976567",
-        "mirrors": [
-            {
-                "platform": "tensorart",
-                "href": "/tensorart/models/904473536033245448/versions/904473536033245448",
-                "platform_url": "https://tensor.art/models/904473536033245448",
-                "name": "Mixplin Style MXP",
-                "version_name": "Mixplin",
-                "id": "904473536033245448",
-                "version_id": "904473536033245448"
-            }
-        ]
-    },
-    "platform": "civitai",
-    "platform_name": "CivitAI",
-    "meta": {
-        "title": "Mixplin Style [Illustrious] - v1.0 - CivitAI Archive",
-        "description": "Mixplin Style [Illustrious] v1.0 is a Illustrious LORA AI model created by Ty_Lee for generating images of art, style, artist style, styles, mixplin, artiststyle",
-        "image": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
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-    }
-}

refs/civitai.sql

@@ -1,38 +0,0 @@
-CREATE TABLE models (
-            id         INTEGER PRIMARY KEY,
-            name       TEXT NOT NULL,
-            type       TEXT NOT NULL,
-            username   TEXT,
-            data       TEXT NOT NULL,
-            created_at INTEGER NOT NULL,
-            updated_at INTEGER NOT NULL
-        ) STRICT;
-CREATE TABLE model_versions (
-            id           INTEGER PRIMARY KEY,
-            model_id     INTEGER NOT NULL,
-            position     INTEGER NOT NULL,
-            name         TEXT NOT NULL,
-            base_model   TEXT NOT NULL,
-            published_at INTEGER,
-            data         TEXT NOT NULL,
-            created_at   INTEGER NOT NULL,
-            updated_at   INTEGER NOT NULL
-        ) STRICT;
-CREATE INDEX model_versions_model_id_idx ON model_versions (model_id);
-CREATE TABLE model_files (
-            id               INTEGER PRIMARY KEY,
-            model_id         INTEGER NOT NULL,
-            version_id       INTEGER NOT NULL,
-            type             TEXT NOT NULL,
-            sha256           TEXT,
-            data             TEXT NOT NULL,
-            created_at       INTEGER NOT NULL,
-            updated_at       INTEGER NOT NULL
-        ) STRICT;
-CREATE INDEX model_files_model_id_idx ON model_files (model_id);
-CREATE INDEX model_files_version_id_idx ON model_files (version_id);
-CREATE TABLE archived_model_files (
-            file_id INTEGER PRIMARY KEY,
-            model_id INTEGER NOT NULL,
-            version_id INTEGER NOT NULL
-        ) STRICT;

refs/civitai_api_model_by_modelId.json

@@ -1,110 +0,0 @@
-{
-    "id": 1231067,
-    "name": "Vivid Impressions Storybook Style",
-    "description": "<h3 id=\"if-you'd-like-to-support-me-feel-free-to-visit-my-ko-fi-page.-please-share-your-images-using-the-&quot;+add-post&quot;-button-below.-it-supports-the-creators.-thanks!-nnfwkvfly\">If you'd like to support me, feel free to visit my <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/pixelpawsai\">Ko-Fi</a> page. ❤️<br /><br />Please share your images using the \"<span style=\"color:rgb(250, 82, 82)\">+add post</span>\" button below. It supports the creators. Thanks! 💕</h3><h3 id=\"if-you-like-my-lora-please-like-comment-or-donate-some-buzz.-much-appreciated!-vyeqok3go\">If you like my LoRA, please<span style=\"color:rgb(230, 73, 128)\"> </span><span style=\"color:rgb(250, 82, 82)\">like</span>, <span style=\"color:rgb(250, 82, 82)\">comment</span>, or <span style=\"color:#fa5252\">donate some Buzz</span>. Much appreciated! ❤️</h3><h3 id=\"-lo912t8rj\"></h3><h3 id=\"trigger-word:-ppstorybook-wlggllim2\"><strong><span style=\"color:rgb(253, 126, 20)\">Trigger word: </span></strong>ppstorybook</h3><h3 id=\"strength:-0.8-experiment-as-you-like-luvhks6za\"><strong><span style=\"color:rgb(253, 126, 20)\">Strength: </span></strong>0.8, experiment as you like</h3>",
-    "allowNoCredit": true,
-    "allowCommercialUse": [
-        "Image",
-        "RentCivit",
-        "Rent",
-        "Sell"
-    ],
-    "allowDerivatives": true,
-    "allowDifferentLicense": true,
-    "type": "LORA",
-    "minor": false,
-    "sfwOnly": false,
-    "poi": false,
-    "nsfw": false,
-    "nsfwLevel": 1,
-    "availability": "Public",
-    "cosmetic": null,
-    "supportsGeneration": true,
-    "stats": {
-        "downloadCount": 2183,
-        "favoriteCount": 0,
-        "thumbsUpCount": 416,
-        "thumbsDownCount": 0,
-        "commentCount": 12,
-        "ratingCount": 0,
-        "rating": 0,
-        "tippedAmountCount": 360
-    },
-    "creator": {
-        "username": "PixelPawsAI",
-        "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f3a1aa7c-0159-4dd8-884a-1e7ceb350f96/width=96/PixelPawsAI.jpeg"
-    },
-    "tags": [
-        "style",
-        "illustration",
-        "storybook"
-    ],
-    "modelVersions": [
-        {
-            "id": 1387174,
-            "index": 0,
-            "name": "v1.0",
-            "baseModel": "Flux.1 D",
-            "baseModelType": "Standard",
-            "createdAt": "2025-02-08T11:15:47.197Z",
-            "publishedAt": "2025-02-08T11:29:04.487Z",
-            "status": "Published",
-            "availability": "Public",
-            "nsfwLevel": 1,
-            "trainedWords": [
-                "ppstorybook"
-            ],
-            "covered": true,
-            "stats": {
-                "downloadCount": 2183,
-                "ratingCount": 0,
-                "rating": 0,
-                "thumbsUpCount": 416,
-                "thumbsDownCount": 0
-            },
-            "files": [
-                {
-                    "id": 1289799,
-                    "sizeKB": 18829.1484375,
-                    "name": "pp-storybook_rank2_bf16.safetensors",
-                    "type": "Model",
-                    "pickleScanResult": "Success",
-                    "pickleScanMessage": "No Pickle imports",
-                    "virusScanResult": "Success",
-                    "virusScanMessage": null,
-                    "scannedAt": "2025-02-08T11:21:04.247Z",
-                    "metadata": {
-                        "format": "SafeTensor"
-                    },
-                    "hashes": {
-                        "AutoV1": "F414C813",
-                        "AutoV2": "9753338AB6",
-                        "SHA256": "9753338AB693CA82BF89ED77A5D1912879E40051463EC6E330FB9866CE798668",
-                        "CRC32": "A65AE7B3",
-                        "BLAKE3": "A5F8AB95AC2486345E4ACCAE541FF19D97ED53EFB0A7CC9226636975A0437591",
-                        "AutoV3": "34A22376739D"
-                    },
-                    "downloadUrl": "https://civitai.com/api/download/models/1387174",
-                    "primary": true
-                }
-            ],
-            "images": [
-                {
-                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/42b875cf-c62b-41fa-a349-383b7f074351/original=true/56547310.jpeg",
-                    "nsfwLevel": 1,
-                    "width": 832,
-                    "height": 1216,
-                    "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-                    "type": "image",
-                    "minor": false,
-                    "poi": false,
-                    "hasMeta": true,
-                    "hasPositivePrompt": true,
-                    "onSite": false,
-                    "remixOfId": null
-                }
-            ],
-            "downloadUrl": "https://civitai.com/api/download/models/1387174"
-        }
-    ]
-}

refs/civitai_api_model_by_versionId.json

@@ -1,100 +0,0 @@
-{
-    "id": 1387174,
-    "modelId": 1231067,
-    "name": "v1.0",
-    "createdAt": "2025-02-08T11:15:47.197Z",
-    "updatedAt": "2025-02-08T11:29:04.526Z",
-    "status": "Published",
-    "publishedAt": "2025-02-08T11:29:04.487Z",
-    "trainedWords": [
-        "ppstorybook"
-    ],
-    "trainingStatus": null,
-    "trainingDetails": null,
-    "baseModel": "Flux.1 D",
-    "baseModelType": null,
-    "earlyAccessEndsAt": null,
-    "earlyAccessConfig": null,
-    "description": null,
-    "uploadType": "Created",
-    "usageControl": "Download",
-    "air": "urn:air:flux1:lora:civitai:1231067@1387174",
-    "stats": {
-        "downloadCount": 1436,
-        "ratingCount": 0,
-        "rating": 0,
-        "thumbsUpCount": 316
-    },
-    "model": {
-        "name": "Vivid Impressions Storybook Style",
-        "type": "LORA",
-        "nsfw": false,
-        "poi": false
-    },
-    "files": [
-        {
-            "id": 1289799,
-            "sizeKB": 18829.1484375,
-            "name": "pp-storybook_rank2_bf16.safetensors",
-            "type": "Model",
-            "pickleScanResult": "Success",
-            "pickleScanMessage": "No Pickle imports",
-            "virusScanResult": "Success",
-            "virusScanMessage": null,
-            "scannedAt": "2025-02-08T11:21:04.247Z",
-            "metadata": {
-                "format": "SafeTensor",
-                "size": null,
-                "fp": null
-            },
-            "hashes": {
-                "AutoV1": "F414C813",
-                "AutoV2": "9753338AB6",
-                "SHA256": "9753338AB693CA82BF89ED77A5D1912879E40051463EC6E330FB9866CE798668",
-                "CRC32": "A65AE7B3",
-                "BLAKE3": "A5F8AB95AC2486345E4ACCAE541FF19D97ED53EFB0A7CC9226636975A0437591",
-                "AutoV3": "34A22376739D"
-            },
-            "primary": true,
-            "downloadUrl": "https://civitai.com/api/download/models/1387174"
-        }
-    ],
-    "images": [
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/42b875cf-c62b-41fa-a349-383b7f074351/width=832/56547310.jpeg",
-            "nsfwLevel": 1,
-            "width": 832,
-            "height": 1216,
-            "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-            "type": "image",
-            "metadata": {
-                "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-                "size": 1361590,
-                "width": 832,
-                "height": 1216
-            },
-            "meta": {
-                "Size": "832x1216",
-                "seed": 1116375220995209,
-                "Model": "flux_dev_fp8",
-                "steps": 23,
-                "hashes": {
-                    "model": ""
-                },
-                "prompt": "ppstorybook,A dreamy bunny hopping across a rainbow bridge, with fluffy clouds surrounding it and tiny birds flying alongside, rendered in a magical, soft-focus style with pastel hues and glowing accents.",
-                "Version": "ComfyUI",
-                "sampler": "DPM++ 2M",
-                "cfgScale": 3.5,
-                "clipSkip": 1,
-                "resources": [],
-                "Model hash": ""
-            },
-            "availability": "Public",
-            "hasMeta": true,
-            "hasPositivePrompt": true,
-            "onSite": false,
-            "remixOfId": null
-        }
-    ],
-    "downloadUrl": "https://civitai.com/api/download/models/1387174"
-}

refs/civitai_comfy_metadata.json

@@ -1,153 +0,0 @@
-{
-    "resource-stack": {
-      "class_type": "CheckpointLoaderSimple",
-      "inputs": { "ckpt_name": "urn:air:sdxl:checkpoint:civitai:827184@1410435" }
-    },
-    "resource-stack-1": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:1107767@1253442",
-        "strength_model": 1,
-        "strength_clip": 1,
-        "model": ["resource-stack", 0],
-        "clip": ["resource-stack", 1]
-      }
-    },
-    "resource-stack-2": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:1342708@1516344",
-        "strength_model": 1,
-        "strength_clip": 1,
-        "model": ["resource-stack-1", 0],
-        "clip": ["resource-stack-1", 1]
-      }
-    },
-    "resource-stack-3": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:122359@135867",
-        "strength_model": 1.55,
-        "strength_clip": 1,
-        "model": ["resource-stack-2", 0],
-        "clip": ["resource-stack-2", 1]
-      }
-    },
-    "6": {
-      "class_type": "smZ CLIPTextEncode",
-      "inputs": {
-        "text": "masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking",
-        "parser": "comfy",
-        "text_g": "",
-        "text_l": "",
-        "ascore": 2.5,
-        "width": 0,
-        "height": 0,
-        "crop_w": 0,
-        "crop_h": 0,
-        "target_width": 0,
-        "target_height": 0,
-        "smZ_steps": 1,
-        "mean_normalization": true,
-        "multi_conditioning": true,
-        "use_old_emphasis_implementation": false,
-        "with_SDXL": false,
-        "clip": ["resource-stack-3", 1]
-      },
-      "_meta": { "title": "Positive" }
-    },
-    "7": {
-      "class_type": "smZ CLIPTextEncode",
-      "inputs": {
-        "text": "bad quality,worst quality,worst detail,sketch,censor",
-        "parser": "comfy",
-        "text_g": "",
-        "text_l": "",
-        "ascore": 2.5,
-        "width": 0,
-        "height": 0,
-        "crop_w": 0,
-        "crop_h": 0,
-        "target_width": 0,
-        "target_height": 0,
-        "smZ_steps": 1,
-        "mean_normalization": true,
-        "multi_conditioning": true,
-        "use_old_emphasis_implementation": false,
-        "with_SDXL": false,
-        "clip": ["resource-stack-3", 1]
-      },
-      "_meta": { "title": "Negative" }
-    },
-    "20": {
-      "class_type": "UpscaleModelLoader",
-      "inputs": { "model_name": "urn:air:other:upscaler:civitai:147759@164821" },
-      "_meta": { "title": "Load Upscale Model" }
-    },
-    "17": {
-      "class_type": "LoadImage",
-      "inputs": {
-        "image": "https://orchestration.civitai.com/v2/consumer/blobs/5KZ6358TW8CNEGPZKD08NVDB30",
-        "upload": "image"
-      },
-      "_meta": { "title": "Image Load" }
-    },
-    "19": {
-      "class_type": "ImageUpscaleWithModel",
-      "inputs": { "upscale_model": ["20", 0], "image": ["17", 0] },
-      "_meta": { "title": "Upscale Image (using Model)" }
-    },
-    "23": {
-      "class_type": "ImageScale",
-      "inputs": {
-        "upscale_method": "nearest-exact",
-        "crop": "disabled",
-        "width": 1280,
-        "height": 1856,
-        "image": ["19", 0]
-      },
-      "_meta": { "title": "Upscale Image" }
-    },
-    "21": {
-      "class_type": "VAEEncode",
-      "inputs": { "pixels": ["23", 0], "vae": ["resource-stack", 2] },
-      "_meta": { "title": "VAE Encode" }
-    },
-    "11": {
-      "class_type": "KSampler",
-      "inputs": {
-        "sampler_name": "euler_ancestral",
-        "scheduler": "normal",
-        "seed": 2088370631,
-        "steps": 47,
-        "cfg": 6.5,
-        "denoise": 0.3,
-        "model": ["resource-stack-3", 0],
-        "positive": ["6", 0],
-        "negative": ["7", 0],
-        "latent_image": ["21", 0]
-      },
-      "_meta": { "title": "KSampler" }
-    },
-    "13": {
-      "class_type": "VAEDecode",
-      "inputs": { "samples": ["11", 0], "vae": ["resource-stack", 2] },
-      "_meta": { "title": "VAE Decode" }
-    },
-    "12": {
-      "class_type": "SaveImage",
-      "inputs": { "filename_prefix": "ComfyUI", "images": ["13", 0] },
-      "_meta": { "title": "Save Image" }
-    },
-    "extra": {
-      "airs": [
-        "urn:air:other:upscaler:civitai:147759@164821",
-        "urn:air:sdxl:checkpoint:civitai:827184@1410435",
-        "urn:air:sdxl:lora:civitai:1107767@1253442",
-        "urn:air:sdxl:lora:civitai:1342708@1516344",
-        "urn:air:sdxl:lora:civitai:122359@135867"
-      ]
-    },
-    "extraMetadata": "{\u0022prompt\u0022:\u0022masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking\u0022,\u0022negativePrompt\u0022:\u0022bad quality,worst quality,worst detail,sketch,censor\u0022,\u0022steps\u0022:47,\u0022cfgScale\u0022:6.5,\u0022sampler\u0022:\u0022euler_ancestral\u0022,\u0022workflowId\u0022:\u0022img2img-hires\u0022,\u0022resources\u0022:[{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1253442,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1516344,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:135867,\u0022strength\u0022:1.55}],\u0022remixOfId\u0022:32140259}"
-  }
-  

refs/jpeg_civitai_exif_userComment_example

@@ -1,18 +0,0 @@
-a dynamic and dramatic digital artwork featuring a stylized anthropomorphic white tiger with striking yellow eyes. The tiger is depicted in a powerful stance, wielding a katana with one hand raised above its head. Its fur is detailed with black stripes, and its mane flows wildly, blending with the stormy background. The scene is set amidst swirling dark clouds and flashes of lightning, enhancing the sense of movement and energy. The composition is vertical, with the tiger positioned centrally, creating a sense of depth and intensity. The color palette is dominated by shades of blue, gray, and white, with bright highlights from the lightning. The overall style is reminiscent of fantasy or manga art, with a focus on dynamic action and dramatic lighting.
-Negative prompt:
-Steps: 30, Sampler: Undefined, CFG scale: 3.5, Seed: 90300501, Size: 832x1216, Clip skip: 2, Created Date: 2025-03-05T13:51:18.1770234Z, Civitai resources: [{"type":"checkpoint","modelVersionId":691639,"modelName":"FLUX","modelVersionName":"Dev"},{"type":"lora","weight":0.4,"modelVersionId":1202162,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Gothic Lines"},{"type":"lora","weight":0.8,"modelVersionId":1470588,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Retro"},{"type":"lora","weight":0.75,"modelVersionId":746484,"modelName":"Elden Ring - Yoshitaka Amano","modelVersionName":"V1"},{"type":"lora","weight":0.2,"modelVersionId":914935,"modelName":"Ink-style","modelVersionName":"ink-dynamic"},{"type":"lora","weight":0.2,"modelVersionId":1189379,"modelName":"Painterly Fantasy by ChronoKnight - [FLUX \u0026 IL]","modelVersionName":"FLUX"},{"type":"lora","weight":0.2,"modelVersionId":757030,"modelName":"Mezzotint Artstyle for Flux - by Ethanar","modelVersionName":"V1"}], Civitai metadata: {}
-
-masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject,
-dynamic angle, dutch angle, from below, epic half body portrait, gritty, wabi sabi, looking at viewer, woman is a geisha, parted lips,
-holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous
-<lora:ck-shadow-circuit-IL:0.78>, <lora:ck-nc-cyberpunk-IL-000011:0.4>, <lora:ck-neon-retrowave-IL:0.2>, <lora:ck-yoneyama-mai-IL-000014:0.4>
-Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
-Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
-
-Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting.
-Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
-Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
-
-Immerse yourself in the enchanting journey, where harmonious transmutation of Bauhaus art unites photographic precision and contemporary illustration, capturing an enthralling blend between vivid abstract nature and urban landscapes. Let your eyes be captivated by a kaleidoscope of rich, deep reds and yellows, entwined with intriguing shades that beckon a somber atmosphere. As your spirit ventures along this haunting path, witness the mysterious, high-angle perspective dominated by scattered clouds – granting you a mesmerizing glimpse into the ever-transforming realm of metamorphosing environments. ,<lora:flux/fav/ck-charcoal-drawing-000014.safetensors:1.0:1.0>   
-Negative prompt:
-Steps: 20, Sampler: Euler, CFG scale: 3.5, Seed: 885491426361006, Size: 832x1216, Model hash: 4610115bb0, Model: flux_dev, Hashes: {"LORA:flux/fav/ck-charcoal-drawing-000014.safetensors": "34d36c17c1", "model": "4610115bb0"}, Version: ComfyUI

refs/meta_format.txt

@@ -1,3 +0,0 @@
-In this ethereal masterpiece, metallic sculptures juxtapose effortlessly against a subtle backdrop of misty neutral hues. Exquisite curvatures and geometric shapes converge harmoniously, creating an illuminating realm of polished metallic surfaces. Shimmering copper, gleaming silver, and lustrous gold hues dance in perfect balance, highlighting the intricate play of light and shadow cast upon these celestial forms.  A halo of diffused radiance envelops each piece, enhancing their textured depths and metallic brilliance while allowing delicate details to emerge from obscurity. The composition conveys a serene yet mesmerizing atmosphere, as if suspended in a dreamlike limbo between reality and fantasy. The tantalizing interplay of colors within this transcendent realm creates a profound sense of depth and grandeur that invites the viewer into an enchanting voyage through abstract metallic beauty.  This captivating artwork evokes emotions of boundless curiosity and reverence reminiscent of the timeless works by artists such as Giorgio de Chirico or Paul Klee, while asserting a unique, modern artistic sensibility. With every observation, a new nuance unfolds, as if a never-ending story waiting to be discovered through the lens of metallic artistry.
-Negative prompt:
-Steps: 25, Sampler: dpmpp_2m_sgm_uniform, Seed: 471889513588087, Model: Fluxmania V5P.safetensors, Model hash: 8ae0583b06, VAE: ae.sft, VAE hash: afc8e28272, Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, Lora_0 Strength model: 0.65, Lora_0 Strength clip: 0.65, Lora_1 Model name: Kaoru Yamada.safetensors, Lora_1 Model hash: d4893f7202, Lora_1 Strength model: 0.75, Lora_1 Strength clip: 0.75, Hashes: {"model": "8ae0583b06", "vae": "afc8e28272", "lora:ArtVador I": "08f7133a58", "lora:Kaoru Yamada": "d4893f7202"}

refs/recipe.json

@@ -1,33 +0,0 @@
-{
-    "id": "42803a29-02dc-49e1-b798-27da70e8b408",
-    "file_path": "/home/miao/workspace/ComfyUI/models/loras/recipes/test/42803a29-02dc-49e1-b798-27da70e8b408.webp",
-    "title": "masterpiece, best quality, amazing quality, very aesthetic, detailed eyes, perfect",
-    "modified": 1754897325.0507245,
-    "created_date": 1754897325.0507245,
-    "base_model": "Illustrious",
-    "loras": [
-        {
-            "file_name": "",
-            "hash": "1b5b763d83961bb5745f3af8271ba83f1d4fd69c16278dae6d5b4e194bdde97a",
-            "strength": 1.0,
-            "modelVersionId": 2007092,
-            "modelName": "Pony: People's Works +",
-            "modelVersionName": "v8_Illusv1.0",
-            "isDeleted": false,
-            "exclude": false
-        }
-    ],
-    "gen_params": {
-        "prompt": "masterpiece, best quality, amazing quality, very aesthetic, detailed eyes, perfect eyes, realistic eyes,\n(flat colors:1.5), (anime:1.5), (lineart:1.5),\nclose-up, solo, tongue, 1girl, food, (saliva:0.1), open mouth, candy, simple background, blue background, large lollipop, tongue out, fade background, lips, hand up, holding, looking at viewer, licking, seductive, half-closed eyes,",
-        "negative_prompt": "shiny skin,",
-        "steps": 19,
-        "sampler": "Euler a",
-        "cfg_scale": 5,
-        "seed": 1765271748,
-        "size": "832x1216",
-        "clip_skip": 2
-    },
-    "fingerprint": "1b5b763d83961bb5745f3af8271ba83f1d4fd69c16278dae6d5b4e194bdde97a:1.0",
-    "source_path": "https://civitai.com/images/92427432",
-    "folder": "test"
-}

refs/target_version.json

@@ -1,42 +0,0 @@
-{
-  "id": 2269146,
-  "modelId": 2004760,
-  "name": "v1.0 Illustrious",
-  "nsfwLevel": 1,
-  "trainedWords": ["PencilSketchDaal"],
-  "baseModel": "Illustrious",
-  "description": "<p>Illustrious. Your pencil may vary with your checkpoint. </p>",
-  "model": {
-    "name": "Pencil Sketch Anime",
-    "type": "LORA",
-    "nsfw": false,
-    "description": "description",
-    "tags": ["style"],
-    "allowNoCredit": true,
-    "allowCommercialUse": ["Sell"],
-    "allowDerivatives": true,
-    "allowDifferentLicense": true
-  },
-  "files": [
-    {
-      "id": 2161260,
-      "sizeKB": 223106.37890625,
-      "name": "Pencil-Sketch-Illustrious.safetensors",
-      "type": "Model",
-      "hashes": {
-        "SHA256": "2C70479CD673B0FE056EAF4FD97C7F33A39F14853805431AC9AB84226ECE3B82"
-      },
-      "primary": true,
-      "downloadUrl": "https://civitai.com/api/download/models/2269146",
-      "mirrors": {}
-    }
-  ],
-  "images": [
-    {},
-    {}
-  ],
-  "creator": {
-    "username": "Daalis",
-    "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eb245b49-edc8-4ed6-ad7b-6d61eb8c51de/width=96/Daalis.jpeg"
-  }
-}

refs/version.json

@@ -1,91 +0,0 @@
-{
-  "id": 1255556,
-  "modelId": 1117241,
-  "name": "v1.0",
-  "createdAt": "2025-01-08T06:13:08.839Z",
-  "updatedAt": "2025-01-08T06:28:54.156Z",
-  "status": "Published",
-  "publishedAt": "2025-01-08T06:28:54.155Z",
-  "trainedWords": ["in the style of ppWhimsy"],
-  "trainingStatus": null,
-  "trainingDetails": null,
-  "baseModel": "Flux.1 D",
-  "baseModelType": "Standard",
-  "earlyAccessEndsAt": null,
-  "earlyAccessConfig": null,
-  "description": null,
-  "uploadType": "Created",
-  "usageControl": "Download",
-  "air": "urn:air:flux1:lora:civitai:1117241@1255556",
-  "stats": {
-    "downloadCount": 210,
-    "ratingCount": 0,
-    "rating": 0,
-    "thumbsUpCount": 26
-  },
-  "model": {
-    "name": "Enchanted Whimsy style (Flux)",
-    "type": "LORA",
-    "nsfw": false,
-    "poi": false
-  },
-  "files": [
-    {
-      "id": 1160774,
-      "sizeKB": 38828.8125,
-      "name": "pp-enchanted-whimsy.safetensors",
-      "type": "Model",
-      "pickleScanResult": "Success",
-      "pickleScanMessage": "No Pickle imports",
-      "virusScanResult": "Success",
-      "virusScanMessage": null,
-      "scannedAt": "2025-01-08T06:16:27.731Z",
-      "metadata": {
-        "format": "SafeTensor",
-        "size": null,
-        "fp": null
-      },
-      "hashes": {
-        "AutoV1": "40CAF049",
-        "AutoV2": "3202778C3E",
-        "SHA256": "3202778C3EBE5CF7EBE5FC51561DEAE8611F4362036EB7C02EFA033C705E6240",
-        "CRC32": "69DCD953",
-        "BLAKE3": "ED04580DDB1AD36D8B87F4B0800F5930C7E5D4A7269BDC2BE26ED77EA1A34697",
-        "AutoV3": "BF82986F8597"
-      },
-      "primary": true,
-      "downloadUrl": "https://civitai.com/api/download/models/1255556"
-    }
-  ],
-  "images": [
-    {
-      "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/707aef9b-36fb-46c2-ac41-adcab539d3a6/width=832/50270101.jpeg",
-      "nsfwLevel": 1,
-      "width": 832,
-      "height": 1216,
-      "hash": "U7Am@@$^J3%100R;pLR.M]tQ-ps+?wRiVrof",
-      "type": "image",
-      "metadata": {
-        "hash": "U7Am@@$^J3%100R;pLR.M]tQ-ps+?wRiVrof",
-        "size": 702313,
-        "width": 832,
-        "height": 1216
-      },
-      "minor": false,
-      "poi": false,
-      "meta": {
-        "prompt": "in the style of ppWhimsy, a close-up of a boy with a crown of ferns and tiny horns, his eyes wide with wonder as a family of glowing hedgehogs nestle in his hands, their spines shimmering with soft pastel colors"
-      },
-      "availability": "Public",
-      "hasMeta": true,
-      "hasPositivePrompt": true,
-      "onSite": false,
-      "remixOfId": null
-    }
-  ],
-  "downloadUrl": "https://civitai.com/api/download/models/1255556",
-  "creator": {
-    "username": "PixelPawsAI",
-    "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f3a1aa7c-0159-4dd8-884a-1e7ceb350f96/width=96/PixelPawsAI.jpeg"
-  }
-}

requirements.txt

@@ -1,4 +1,5 @@
 aiohttp
+aiohttp-socks
 jinja2
 safetensors
 piexif
@@ -12,3 +13,5 @@ aiosqlite
 beautifulsoup4
 platformdirs
 pyyaml
+# brotli — ISOBMFF (AVIF/JXL) metadata decompression
+brotli>=1.2.0

scripts/migrate_legacy_metadata.py

@@ -34,6 +34,8 @@ import sys
 from pathlib import Path
 from typing import Any
 
+from platformdirs import user_config_dir
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s - %(levelname)s - %(message)s",
@@ -53,10 +55,7 @@ def resolve_settings_path() -> Path:
         if isinstance(payload, dict) and payload.get("use_portable_settings") is True:
             return portable
 
-    config_home = os.environ.get("XDG_CONFIG_HOME")
-    if config_home:
-        return Path(config_home).expanduser() / APP_NAME / "settings.json"
-    return Path.home() / ".config" / APP_NAME / "settings.json"
+    return Path(user_config_dir(APP_NAME, appauthor=False)) / "settings.json"
 
 
 def load_json(path: Path) -> dict[str, Any]: