feat(llm): add Gemini as a preset AI provider

os.path.commonpath raises ValueError for paths on different Windows
drives. Treat that as no common root so cross-drive recipes migrations
succeed instead of failing with 'Invalid recipes path change'.

AGENTS.md

@@ -102,6 +102,7 @@ npm run test:coverage       # Generate coverage report
 - ComfyUI: `app.registerExtension()`, `node.addDOMWidget(name, type, element, options)`
 - Event handlers via `addEventListener` or widget callbacks
 - Shared utilities: `web/comfyui/utils.js`
+- Dual-mode rendering patterns (canvas vs Vue): see `docs/comfyui-dual-mode-widgets.md`
 
 ### Vue Composables Pattern
 
@@ -136,7 +137,13 @@ npm run test:coverage       # Generate coverage report
 - Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
 - Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
 - Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
-- Symlinks require normalized paths
+- Symlinks require normalized paths.
+  **Business paths vs real paths**: All stored paths and operation routing use the
+  original paths as they appear under configured model roots — symlinks are NOT
+  resolved. `os.path.realpath` is only for scanner dedup and the symlink cache.
+  Any path passed to `os.remove`/`os.rename`/`shutil.move` or validated by a
+  containment check MUST use the business path (i.e. `os.path.abspath`, not
+  `realpath`).
 
 ## Git / Commit Messages
 

__init__.py

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

docs/comfyui-dual-mode-widgets.md

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

locales/de.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "Voreinstellungsname...",
             "baseModel": "Basis-Modell",
             "baseModelSearchPlaceholder": "Basismodelle durchsuchen...",
-            "modelTags": "Tags (Top 20)",
+            "modelTags": "Tags",
             "modelTypes": "Modelltypen",
             "license": "Lizenz",
             "noCreditRequired": "Kein Credit erforderlich",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Verkauf generierter Bilder erlauben",
             "noCreditRequiredTooltip": "Modell ohne Nennung des Erstellers verwenden",
             "noTags": "Keine Tags",
+            "tagSearchPlaceholder": "Tags durchsuchen...",
+            "noTagMatches": "Keine Tags entsprechen der aktuellen Suche.",
             "autoTags": "Auto-Tags",
             "noBaseModelMatches": "Keine Basismodelle entsprechen der aktuellen Suche.",
             "clearAll": "Alle Filter löschen",
@@ -505,7 +507,9 @@
             "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert. Neustart erforderlich, um Änderungen anzuwenden.",
             "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
             "validation": {
-                "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
+                "duplicatePath": "Dieser Pfad ist bereits konfiguriert",
+                "checkpointUnetOverlap": "Derselbe Pfad kann nicht für Checkpoints und Diffusionsmodelle verwendet werden: {paths}",
+                "checkpointUnetOverlapInline": "Dieser Pfad wird bereits für einen anderen Modelltyp verwendet. Bitte verwenden Sie separate Ordner für Checkpoints und Diffusionsmodelle."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "Download wird vorbereitet...",
             "connecting": "Verbindung zum Download-Server wird hergestellt...",
             "completed": "Abgeschlossen",
-            "downloadComplete": "Download erfolgreich abgeschlossen"
+            "downloadComplete": "Download erfolgreich abgeschlossen",
+            "enableCivarchiveApi": "CivArchive API als Metadaten-Anbieter aktivieren",
+            "enableCivarchiveApiHelp": "Wenn aktiviert, wird die CivArchive API als alternative Quelle für Modell-Metadaten verwendet (z. B. für von CivitAI gelöschte Modelle). Deaktivieren, um die Ratenbegrenzungen von CivArchive vollständig zu vermeiden.",
+            "providerOrder": "Reihenfolge der Metadaten-Anbieter",
+            "providerOrderHelp": "Die CivitAI API wird immer zuerst versucht. Wählen Sie die Reihenfolge der übrigen Anbieter bei der Metadatensuche.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "App-Proxy aktivieren",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "Benutzerdefiniert (OpenAI-kompatibel)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "Lokale Versionen",
                 "versionsCountDesc": "Meiste Versionen zuerst",
                 "versionsCountAsc": "Wenigste Versionen zuerst",
-                "versionIdDesc": "Neueste Version zuerst"
+                "versionIdDesc": "Neueste Version zuerst",
+                "random": "Zufällig",
+                "randomAction": "Zufällig mischen"
             },
             "refresh": {
                 "title": "Modelliste aktualisieren",
@@ -761,6 +774,8 @@
             "deleteAll": "Ausgewählte löschen",
             "downloadMissingLoras": "Fehlende LoRAs herunterladen",
             "downloadExamples": "Beispielbilder herunterladen",
+            "downloadMissingExamples": "Fehlende herunterladen",
+            "reprocessExamples": "Alle erneut verarbeiten",
             "clear": "Auswahl löschen",
             "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
             "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Civitai-Daten aktualisieren",
             "checkUpdates": "Updates prüfen",
-            "relinkCivitai": "Mit Civitai neu verknüpfen",
+            "linkModel": "Modell verknüpfen",
+            "linkCivitai": "Mit Civitai neu verknüpfen",
+            "linkHuggingFace": "Mit HuggingFace verknüpfen",
             "copySyntax": "LoRA-Syntax kopieren",
             "copyFilename": "Modell-Dateiname kopieren",
             "copyRecipeSyntax": "Rezept-Syntax kopieren",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "An Workflow senden (Ersetzen)",
             "openExamples": "Beispiele-Ordner öffnen",
             "downloadExamples": "Beispielbilder herunterladen",
+            "downloadMissingExamples": "Fehlende herunterladen",
+            "reprocessExamples": "Alle erneut verarbeiten",
             "replacePreview": "Vorschau ersetzen",
             "setContentRating": "Inhaltsbewertung festlegen",
             "moveToFolder": "In Ordner verschieben",
@@ -1203,7 +1222,9 @@
                 "preparing": "Download wird vorbereitet...",
                 "downloadedPreview": "Vorschaubild heruntergeladen",
                 "downloadingFile": "{type}-Datei wird heruntergeladen",
-                "finalizing": "Download wird abgeschlossen..."
+                "finalizing": "Download wird abgeschlossen...",
+                "cancelling": "Download wird abgebrochen...",
+                "cancelled": "Download abgebrochen"
             },
             "progress": {
                 "currentFile": "Aktuelle Datei:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "Ordnerpfad eingeben oder aus Baum unten auswählen...",
             "root": "Stammverzeichnis"
         },
+        "linkHuggingFace": {
+            "title": "Mit HuggingFace verknüpfen",
+            "infoText": "Fügen Sie die HuggingFace-Repository-URL ein, um dieses Modell zuzuordnen. Dies ermöglicht die KI-gestützte Metadatenanreicherung.",
+            "urlLabel": "HuggingFace-Repository-URL:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Geben Sie die vollständige URL des HuggingFace-Repositorys ein.",
+            "confirmAction": "Speichern & Verknüpfen"
+        },
         "relinkCivitai": {
             "title": "Mit Civitai neu verknüpfen",
             "warning": "Warnung:",
@@ -1526,6 +1555,7 @@
                 "empty": "Noch keine Versionshistorie für dieses Modell vorhanden.",
                 "error": "Versionen konnten nicht geladen werden.",
                 "missingModelId": "Für dieses Modell ist keine Civitai-Model-ID vorhanden.",
+                "hfGroupInfo": "Dies ist eine HuggingFace-Modellgruppe. Öffnen Sie die Bibliothek, um alle Versionen im Raster zu sehen.",
                 "confirm": {
                     "delete": "Diese Version aus Ihrer Bibliothek löschen?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "CSV herunterladen",
             "columnModelName": "Modellname",
             "columnError": "Fehler"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1729,6 +1774,12 @@
         "checkingMessage": "Bitte warten Sie, während wir nach der neuesten Version suchen.",
         "showNotifications": "Update-Benachrichtigungen anzeigen",
         "latestBadge": "Neueste",
+        "latestMain": "Main-Branch",
+        "channel": "Update-Kanal",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Update wird vorbereitet...",
             "installing": "Update wird installiert...",
@@ -1749,6 +1800,15 @@
             "warning": "Warnung: Nightly Builds können experimentelle Funktionen enthalten und könnten instabil sein.",
             "enable": "Nightly Updates aktivieren"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Zu Nightly-Kanal wechseln",
+            "nightlyMessage": "Der Wechsel zu Nightly initialisiert ein Git-Repository und verfolgt die neuesten Commits des main-Branches. Updates sind häufiger, können aber instabil sein. Sie können jederzeit zu Release zurückwechseln.",
+            "releaseTitle": "Zu Release-Kanal wechseln",
+            "releaseMessage": "Der Wechsel zu Release checkt den neuesten stabilen Versions-Tag aus. Sie können jederzeit zu Nightly zurückwechseln.",
+            "switching": "Wechsle zu {channel}-Kanal...",
+            "completed": "Erfolgreich zu {channel}-Kanal gewechselt",
+            "failed": "Kanalwechsel fehlgeschlagen"
+        },
         "banners": {
             "recent": "Neueste Mitteilungen",
             "empty": "Keine aktuellen Banner verfügbar.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "Beispielbilder {action} abgeschlossen",
             "imagesFailed": "Beispielbilder {action} fehlgeschlagen",
             "loadError": "Fehler beim Laden der Downloads: {message}",
-            "downloadError": "Download-Fehler: {message}"
+            "downloadError": "Download-Fehler: {message}",
+            "downloadStopped": "Download abgebrochen"
         },
         "import": {
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "Fehler beim Setzen der Inhaltsbewertung: {message}",
             "relinkSuccess": "Modell erfolgreich mit Civitai neu verknüpft",
             "relinkFailed": "Fehler: {message}",
+            "linkHfSuccess": "Modell erfolgreich mit HuggingFace verknüpft",
+            "linkHfFailed": "Fehler: {message}",
             "fetchMetadataFirst": "Bitte rufen Sie zuerst Metadaten von CivitAI ab",
             "noCivitaiInfo": "Keine CivitAI-Informationen verfügbar",
             "missingHash": "Modell-Hash nicht verfügbar"

locales/en.json

@@ -233,7 +233,7 @@
       "presetNamePlaceholder": "Preset name...",
       "baseModel": "Base Model",
       "baseModelSearchPlaceholder": "Search base models...",
-      "modelTags": "Tags (Top 20)",
+      "modelTags": "Tags",
       "modelTypes": "Model Types",
       "license": "License",
       "noCreditRequired": "No Credit Required",
@@ -241,6 +241,8 @@
       "allowSellingGeneratedContentTooltip": "Allow selling generated images",
       "noCreditRequiredTooltip": "Use the model without crediting the creator",
       "noTags": "No tags",
+      "tagSearchPlaceholder": "Search tags...",
+      "noTagMatches": "No tags match the current search.",
       "autoTags": "Auto Tags",
       "noBaseModelMatches": "No base models match the current search.",
       "clearAll": "Clear All Filters",
@@ -505,7 +507,9 @@
       "saveSuccess": "Extra folder paths updated. Restart required to apply changes.",
       "saveError": "Failed to update extra folder paths: {message}",
       "validation": {
-        "duplicatePath": "This path is already configured"
+        "duplicatePath": "This path is already configured",
+        "checkpointUnetOverlap": "Cannot use the same path for both checkpoints and diffusion models: {paths}",
+        "checkpointUnetOverlapInline": "This path is also used for a different model type. Use separate folders for checkpoints and diffusion models."
       }
     },
     "priorityTags": {
@@ -638,7 +642,13 @@
       "preparing": "Preparing download...",
       "connecting": "Connecting to download server...",
       "completed": "Completed",
-      "downloadComplete": "Download completed successfully"
+      "downloadComplete": "Download completed successfully",
+      "enableCivarchiveApi": "Enable CivArchive API as metadata provider",
+      "enableCivarchiveApiHelp": "When on, CivArchive API is used as a fallback source for model metadata (e.g. for models deleted from CivitAI). Turn off to avoid CivArchive rate limits entirely.",
+      "providerOrder": "Metadata provider fallback order",
+      "providerOrderHelp": "CivitAI API is always tried first. Choose the order of the remaining providers when looking up metadata.",
+      "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+      "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
     },
     "proxySettings": {
       "enableProxy": "Enable App-level Proxy",
@@ -668,6 +678,7 @@
         "deepseek": "DeepSeek",
         "groq": "Groq",
         "openrouter": "OpenRouter",
+        "google": "Gemini",
         "opencode-go": "OpenCode Go",
         "custom": "Custom (OpenAI-compatible)"
       },
@@ -704,7 +715,9 @@
         "versionsCount": "Local Versions",
         "versionsCountDesc": "Most versions first",
         "versionsCountAsc": "Fewest versions first",
-        "versionIdDesc": "Newest version first"
+        "versionIdDesc": "Newest version first",
+        "random": "Random",
+        "randomAction": "Randomize (shuffle)"
       },
       "refresh": {
         "title": "Refresh model list",
@@ -761,6 +774,8 @@
       "deleteAll": "Delete Selected",
       "downloadMissingLoras": "Download Missing LoRAs",
       "downloadExamples": "Download Example Images",
+      "downloadMissingExamples": "Download Missing",
+      "reprocessExamples": "Re-process All",
       "clear": "Clear Selection",
       "skipMetadataRefreshCount": "Skip ({count} models)",
       "resumeMetadataRefreshCount": "Resume ({count} models)",
@@ -786,7 +801,9 @@
     "contextMenu": {
       "refreshMetadata": "Refresh Civitai Data",
       "checkUpdates": "Check Updates",
-      "relinkCivitai": "Re-link to Civitai",
+      "linkModel": "Link Model",
+      "linkCivitai": "Link to Civitai",
+      "linkHuggingFace": "Link to HuggingFace",
       "copySyntax": "Copy LoRA Syntax",
       "copyFilename": "Copy Model Filename",
       "copyRecipeSyntax": "Copy Recipe Syntax",
@@ -794,6 +811,8 @@
       "sendToWorkflowReplace": "Send to Workflow (Replace)",
       "openExamples": "Open Examples Folder",
       "downloadExamples": "Download Example Images",
+      "downloadMissingExamples": "Download Missing",
+      "reprocessExamples": "Re-process All",
       "replacePreview": "Replace Preview",
       "setContentRating": "Set Content Rating",
       "moveToFolder": "Move to Folder",
@@ -1203,7 +1222,9 @@
         "preparing": "Preparing download...",
         "downloadedPreview": "Downloaded preview image",
         "downloadingFile": "Downloading {type} file",
-        "finalizing": "Finalizing download..."
+        "finalizing": "Finalizing download...",
+        "cancelling": "Cancelling download...",
+        "cancelled": "Download cancelled"
       },
       "progress": {
         "currentFile": "Current file:",
@@ -1319,6 +1340,14 @@
       "pathPlaceholder": "Type folder path or select from tree below...",
       "root": "Root"
     },
+    "linkHuggingFace": {
+      "title": "Link to HuggingFace",
+      "infoText": "Paste the HuggingFace repository URL to associate this model with its source. This enables AI-powered metadata enrichment.",
+      "urlLabel": "HuggingFace Repository URL:",
+      "urlPlaceholder": "https://huggingface.co/user/repo",
+      "helpText": "Enter the full URL of the HuggingFace repository.",
+      "confirmAction": "Save & Link"
+    },
     "relinkCivitai": {
       "title": "Re-link to Civitai",
       "warning": "Warning:",
@@ -1526,6 +1555,7 @@
         "empty": "No version history available for this model yet.",
         "error": "Failed to load versions.",
         "missingModelId": "This model is missing a Civitai model id.",
+        "hfGroupInfo": "This is a HuggingFace model group. Open the library to see all versions in the grid.",
         "confirm": {
           "delete": "Delete this version from your library?"
         },
@@ -1552,6 +1582,21 @@
       "downloadCsv": "Download CSV",
       "columnModelName": "Model Name",
       "columnError": "Error"
+    },
+    "downloadBatchSummary": {
+      "title": "Batch Download Summary",
+      "statSuccess": "Success",
+      "statFailed": "Failed",
+      "statTotal": "Total",
+      "successMessage": "All {count} models downloaded successfully",
+      "completedWithErrors": "Completed with errors",
+      "failed": "Download failed",
+      "failedItems": "Failed Items ({count})",
+      "columnName": "Model Name",
+      "columnError": "Error",
+      "close": "Close",
+      "copyReport": "Copy Report",
+      "retryFailed": "Retry Failed ({count})"
     }
   },
   "modelTags": {
@@ -1729,6 +1774,12 @@
     "checkingMessage": "Please wait while we check for the latest version.",
     "showNotifications": "Show update notifications",
     "latestBadge": "Latest",
+    "latestMain": "Latest main",
+    "channel": "Update Channel",
+    "channels": {
+      "release": "Release",
+      "nightly": "Nightly"
+    },
     "updateProgress": {
       "preparing": "Preparing update...",
       "installing": "Installing update...",
@@ -1749,6 +1800,15 @@
       "warning": "Warning: Nightly builds may contain experimental features and could be unstable.",
       "enable": "Enable Nightly Updates"
     },
+    "channelSwitch": {
+      "nightlyTitle": "Switch to Nightly Channel",
+      "nightlyMessage": "Switching to Nightly will initialize a Git repository and track the latest main branch commits. Updates will be more frequent but may be unstable. You can switch back to Release at any time.",
+      "releaseTitle": "Switch to Release Channel",
+      "releaseMessage": "Switching to Release will checkout the latest stable release tag. You can switch back to Nightly at any time.",
+      "switching": "Switching to {channel} channel...",
+      "completed": "Successfully switched to {channel} channel",
+      "failed": "Failed to switch channel"
+    },
     "banners": {
       "recent": "Recent messages",
       "empty": "No recent banners yet.",
@@ -2003,7 +2063,8 @@
       "imagesCompleted": "Example images {action} completed",
       "imagesFailed": "Example images {action} failed",
       "loadError": "Error loading downloads: {message}",
-      "downloadError": "Download error: {message}"
+      "downloadError": "Download error: {message}",
+      "downloadStopped": "Download cancelled"
     },
     "import": {
       "folderTreeFailed": "Failed to load folder tree",
@@ -2048,6 +2109,8 @@
       "contentRatingFailed": "Failed to set content rating: {message}",
       "relinkSuccess": "Model successfully re-linked to Civitai",
       "relinkFailed": "Error: {message}",
+      "linkHfSuccess": "Model successfully linked to HuggingFace",
+      "linkHfFailed": "Error: {message}",
       "fetchMetadataFirst": "Please fetch metadata from CivitAI first",
       "noCivitaiInfo": "No CivitAI information available",
       "missingHash": "Model hash not available"

locales/es.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "Nombre del preajuste...",
             "baseModel": "Modelo base",
             "baseModelSearchPlaceholder": "Buscar modelos base...",
-            "modelTags": "Etiquetas (Top 20)",
+            "modelTags": "Etiquetas",
             "modelTypes": "Tipos de modelos",
             "license": "Licencia",
             "noCreditRequired": "Sin crédito requerido",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Permitir la venta de imágenes generadas",
             "noCreditRequiredTooltip": "Usar el modelo sin atribuir al creador",
             "noTags": "Sin etiquetas",
+            "tagSearchPlaceholder": "Buscar etiquetas...",
+            "noTagMatches": "Ninguna etiqueta coincide con la búsqueda actual.",
             "autoTags": "Etiquetas automáticas",
             "noBaseModelMatches": "Ningún modelo base coincide con la búsqueda actual.",
             "clearAll": "Limpiar todos los filtros",
@@ -505,7 +507,9 @@
             "saveSuccess": "Rutas de carpetas adicionales actualizadas. Se requiere reinicio para aplicar los cambios.",
             "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
             "validation": {
-                "duplicatePath": "Esta ruta ya está configurada"
+                "duplicatePath": "Esta ruta ya está configurada",
+                "checkpointUnetOverlap": "No se puede usar la misma ruta para checkpoints y modelos de difusión: {paths}",
+                "checkpointUnetOverlapInline": "Esta ruta ya se usa para otro tipo de modelo. Use carpetas separadas para checkpoints y modelos de difusión."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "Preparando descarga...",
             "connecting": "Conectando al servidor de descarga...",
             "completed": "Completado",
-            "downloadComplete": "Descarga completada exitosamente"
+            "downloadComplete": "Descarga completada exitosamente",
+            "enableCivarchiveApi": "Habilitar CivArchive API como proveedor de metadatos",
+            "enableCivarchiveApiHelp": "Al activarlo, la API de CivArchive se usa como fuente alternativa de metadatos de modelos (p. ej. para modelos eliminados de CivitAI). Desactívelo para evitar por completo los límites de velocidad de CivArchive.",
+            "providerOrder": "Orden de proveedores de metadatos de respaldo",
+            "providerOrderHelp": "La API de CivitAI siempre se intenta primero. Elija el orden de los demás proveedores al buscar metadatos.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Habilitar proxy a nivel de aplicación",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "Personalizado (compatible con OpenAI)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "Versiones locales",
                 "versionsCountDesc": "Más versiones primero",
                 "versionsCountAsc": "Menos versiones primero",
-                "versionIdDesc": "Versión más nueva primero"
+                "versionIdDesc": "Versión más nueva primero",
+                "random": "Aleatorio",
+                "randomAction": "Aleatorizar (barajar)"
             },
             "refresh": {
                 "title": "Actualizar lista de modelos",
@@ -761,6 +774,8 @@
             "deleteAll": "Eliminar seleccionados",
             "downloadMissingLoras": "Descargar LoRAs faltantes",
             "downloadExamples": "Descargar imágenes de ejemplo",
+            "downloadMissingExamples": "Descargar faltantes",
+            "reprocessExamples": "Reprocesar todo",
             "clear": "Limpiar selección",
             "skipMetadataRefreshCount": "Omitir（{count} modelos）",
             "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Actualizar datos de Civitai",
             "checkUpdates": "Comprobar actualizaciones",
-            "relinkCivitai": "Re-vincular a Civitai",
+            "linkModel": "Vincular modelo",
+            "linkCivitai": "Re-vincular a Civitai",
+            "linkHuggingFace": "Vincular a HuggingFace",
             "copySyntax": "Copiar sintaxis de LoRA",
             "copyFilename": "Copiar nombre de archivo del modelo",
             "copyRecipeSyntax": "Copiar sintaxis de receta",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "Enviar al flujo de trabajo (Reemplazar)",
             "openExamples": "Abrir carpeta de ejemplos",
             "downloadExamples": "Descargar imágenes de ejemplo",
+            "downloadMissingExamples": "Descargar faltantes",
+            "reprocessExamples": "Reprocesar todo",
             "replacePreview": "Reemplazar vista previa",
             "setContentRating": "Establecer clasificación de contenido",
             "moveToFolder": "Mover a carpeta",
@@ -1203,7 +1222,9 @@
                 "preparing": "Preparando descarga...",
                 "downloadedPreview": "Imagen de vista previa descargada",
                 "downloadingFile": "Descargando archivo de {type}",
-                "finalizing": "Finalizando descarga..."
+                "finalizing": "Finalizando descarga...",
+                "cancelling": "Cancelando descarga...",
+                "cancelled": "Descarga cancelada"
             },
             "progress": {
                 "currentFile": "Archivo actual:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "Escribe la ruta de la carpeta o selecciona del árbol de abajo...",
             "root": "Raíz"
         },
+        "linkHuggingFace": {
+            "title": "Vincular a HuggingFace",
+            "infoText": "Pegue la URL del repositorio de HuggingFace para asociar este modelo. Esto permite el enriquecimiento de metadatos con IA.",
+            "urlLabel": "URL del repositorio de HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Ingrese la URL completa del repositorio de HuggingFace.",
+            "confirmAction": "Guardar y vincular"
+        },
         "relinkCivitai": {
             "title": "Re-vincular a Civitai",
             "warning": "Advertencia:",
@@ -1526,6 +1555,7 @@
                 "empty": "Aún no hay historial de versiones para este modelo.",
                 "error": "No se pudieron cargar las versiones.",
                 "missingModelId": "Este modelo no tiene un ID de modelo de Civitai.",
+                "hfGroupInfo": "Este es un grupo de modelos de HuggingFace. Abra la biblioteca para ver todas las versiones en la cuadrícula.",
                 "confirm": {
                     "delete": "¿Eliminar esta versión de tu biblioteca?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "Descargar CSV",
             "columnModelName": "Nombre del modelo",
             "columnError": "Error"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1728,7 +1773,13 @@
         "checkingUpdates": "Comprobando actualizaciones...",
         "checkingMessage": "Por favor espera mientras comprobamos la última versión.",
         "showNotifications": "Mostrar notificaciones de actualización",
-        "latestBadge": "Último",
+        "latestBadge": "Última",
+        "latestMain": "Rama main",
+        "channel": "Canal de actualizacion",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Preparando actualización...",
             "installing": "Instalando actualización...",
@@ -1749,6 +1800,15 @@
             "warning": "Advertencia: Las compilaciones nocturnas pueden contener características experimentales y podrían ser inestables.",
             "enable": "Habilitar actualizaciones nocturnas"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Cambiar a canal Nightly",
+            "nightlyMessage": "Cambiar a Nightly inicializara un repositorio Git y seguira los ultimos commits de la rama main. Las actualizaciones son mas frecuentes pero pueden ser inestables. Puede volver a Release en cualquier momento.",
+            "releaseTitle": "Cambiar a canal Release",
+            "releaseMessage": "Cambiar a Release hara checkout de la ultima etiqueta de version estable. Puede volver a Nightly en cualquier momento.",
+            "switching": "Cambiando a canal {channel}...",
+            "completed": "Cambio a canal {channel} exitoso",
+            "failed": "Error al cambiar de canal"
+        },
         "banners": {
             "recent": "Notificaciones recientes",
             "empty": "No hay banners recientes.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "Imágenes de ejemplo {action} completadas",
             "imagesFailed": "Imágenes de ejemplo {action} fallidas",
             "loadError": "Error al cargar descargas: {message}",
-            "downloadError": "Error de descarga: {message}"
+            "downloadError": "Error de descarga: {message}",
+            "downloadStopped": "Descarga cancelada"
         },
         "import": {
             "folderTreeFailed": "Error al cargar árbol de carpetas",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "Error al establecer clasificación de contenido: {message}",
             "relinkSuccess": "Modelo re-vinculado exitosamente a Civitai",
             "relinkFailed": "Error: {message}",
+            "linkHfSuccess": "Modelo vinculado a HuggingFace exitosamente",
+            "linkHfFailed": "Error: {message}",
             "fetchMetadataFirst": "Por favor obtén metadatos de CivitAI primero",
             "noCivitaiInfo": "No hay información de CivitAI disponible",
             "missingHash": "Hash del modelo no disponible"

locales/fr.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "Nom du préréglage...",
             "baseModel": "Modèle de base",
             "baseModelSearchPlaceholder": "Rechercher des modèles de base...",
-            "modelTags": "Tags (Top 20)",
+            "modelTags": "Tags",
             "modelTypes": "Types de modèles",
             "license": "Licence",
             "noCreditRequired": "Crédit non requis",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Autoriser la vente d\"images générées",
             "noCreditRequiredTooltip": "Utiliser le modèle sans créditer le créateur",
             "noTags": "Aucun tag",
+            "tagSearchPlaceholder": "Rechercher des tags...",
+            "noTagMatches": "Aucun tag ne correspond à la recherche actuelle.",
             "autoTags": "Auto-Tags",
             "noBaseModelMatches": "Aucun modèle de base ne correspond à la recherche actuelle.",
             "clearAll": "Effacer tous les filtres",
@@ -505,7 +507,9 @@
             "saveSuccess": "Chemins de dossiers supplémentaires mis à jour. Redémarrage requis pour appliquer les changements.",
             "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
             "validation": {
-                "duplicatePath": "Ce chemin est déjà configuré"
+                "duplicatePath": "Ce chemin est déjà configuré",
+                "checkpointUnetOverlap": "Impossible d'utiliser le même chemin pour les checkpoints et les modèles de diffusion : {paths}",
+                "checkpointUnetOverlapInline": "Ce chemin est déjà utilisé pour un autre type de modèle. Utilisez des dossiers séparés pour les checkpoints et les modèles de diffusion."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "Préparation du téléchargement...",
             "connecting": "Connexion au serveur de téléchargement...",
             "completed": "Terminé",
-            "downloadComplete": "Téléchargement terminé avec succès"
+            "downloadComplete": "Téléchargement terminé avec succès",
+            "enableCivarchiveApi": "Activer l'API CivArchive comme fournisseur de métadonnées",
+            "enableCivarchiveApiHelp": "Lorsqu'elle est activée, l'API CivArchive est utilisée comme source de secours pour les métadonnées des modèles (par ex. pour les modèles supprimés de CivitAI). Désactivez pour éviter entièrement les limites de débit de CivArchive.",
+            "providerOrder": "Ordre de secours des fournisseurs de métadonnées",
+            "providerOrderHelp": "L'API CivitAI est toujours essayée en premier. Choisissez l'ordre des autres fournisseurs lors de la recherche de métadonnées.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Activer le proxy au niveau de l'application",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "Personnalisé (compatible OpenAI)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "Versions locales",
                 "versionsCountDesc": "Plus de versions d'abord",
                 "versionsCountAsc": "Moins de versions d'abord",
-                "versionIdDesc": "Version la plus récente d'abord"
+                "versionIdDesc": "Version la plus récente d'abord",
+                "random": "Aléatoire",
+                "randomAction": "Aléatoire (mélanger)"
             },
             "refresh": {
                 "title": "Actualiser la liste des modèles",
@@ -761,6 +774,8 @@
             "deleteAll": "Supprimer la sélection",
             "downloadMissingLoras": "Télécharger les LoRAs manquants",
             "downloadExamples": "Télécharger les images d'exemple",
+            "downloadMissingExamples": "Télécharger les manquantes",
+            "reprocessExamples": "Tout retraiter",
             "clear": "Effacer la sélection",
             "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
             "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Actualiser les données Civitai",
             "checkUpdates": "Vérifier les mises à jour",
-            "relinkCivitai": "Relier à nouveau à Civitai",
+            "linkModel": "Lier le modèle",
+            "linkCivitai": "Relier à nouveau à Civitai",
+            "linkHuggingFace": "Lier à HuggingFace",
             "copySyntax": "Copier la syntaxe LoRA",
             "copyFilename": "Copier le nom de fichier du modèle",
             "copyRecipeSyntax": "Copier la syntaxe de la recipe",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "Envoyer vers le workflow (Remplacer)",
             "openExamples": "Ouvrir le dossier d'exemples",
             "downloadExamples": "Télécharger les images d'exemple",
+            "downloadMissingExamples": "Télécharger les manquantes",
+            "reprocessExamples": "Tout retraiter",
             "replacePreview": "Remplacer l'aperçu",
             "setContentRating": "Définir la classification du contenu",
             "moveToFolder": "Déplacer vers un dossier",
@@ -1203,7 +1222,9 @@
                 "preparing": "Préparation du téléchargement...",
                 "downloadedPreview": "Image d'aperçu téléchargée",
                 "downloadingFile": "Téléchargement du fichier {type}",
-                "finalizing": "Finalisation du téléchargement..."
+                "finalizing": "Finalisation du téléchargement...",
+                "cancelling": "Annulation du téléchargement...",
+                "cancelled": "Téléchargement annulé"
             },
             "progress": {
                 "currentFile": "Fichier actuel :",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "Tapez le chemin du dossier ou sélectionnez dans l'arbre ci-dessous...",
             "root": "Racine"
         },
+        "linkHuggingFace": {
+            "title": "Lier à HuggingFace",
+            "infoText": "Collez l'URL du dépôt HuggingFace pour associer ce modèle à sa source. Cela permet l'enrichissement des métadonnées par IA.",
+            "urlLabel": "URL du dépôt HuggingFace :",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Entrez l'URL complète du dépôt HuggingFace.",
+            "confirmAction": "Enregistrer & lier"
+        },
         "relinkCivitai": {
             "title": "Relier à nouveau à Civitai",
             "warning": "Attention :",
@@ -1526,6 +1555,7 @@
                 "empty": "Aucun historique de versions n'est disponible pour ce modèle pour le moment.",
                 "error": "Échec du chargement des versions.",
                 "missingModelId": "Ce modèle ne possède pas d'identifiant de modèle Civitai.",
+                "hfGroupInfo": "Ceci est un groupe de modèles HuggingFace. Ouvrez la bibliothèque pour voir toutes les versions dans la grille.",
                 "confirm": {
                     "delete": "Supprimer cette version de votre bibliothèque ?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "Télécharger CSV",
             "columnModelName": "Nom du modèle",
             "columnError": "Erreur"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1728,7 +1773,13 @@
         "checkingUpdates": "Vérification des mises à jour...",
         "checkingMessage": "Veuillez patienter pendant la vérification de la dernière version.",
         "showNotifications": "Afficher les notifications de mise à jour",
-        "latestBadge": "Dernier",
+        "latestBadge": "Dernière",
+        "latestMain": "Branche main",
+        "channel": "Canal de mise a jour",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Préparation de la mise à jour...",
             "installing": "Installation de la mise à jour...",
@@ -1749,6 +1800,15 @@
             "warning": "Attention : Les versions nightly peuvent contenir des fonctionnalités expérimentales et être instables.",
             "enable": "Activer les mises à jour nightly"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Passer au canal Nightly",
+            "nightlyMessage": "Passer a Nightly initialisera un depot Git et suivra les derniers commits de la branche main. Les mises a jour sont plus frequentes mais peuvent etre instables. Vous pouvez revenir a Release a tout moment.",
+            "releaseTitle": "Passer au canal Release",
+            "releaseMessage": "Passer a Release passera au dernier tag de version stable. Vous pouvez revenir a Nightly a tout moment.",
+            "switching": "Passage au canal {channel}...",
+            "completed": "Basculement vers le canal {channel} reussi",
+            "failed": "Echec du changement de canal"
+        },
         "banners": {
             "recent": "Messages récents",
             "empty": "Aucune bannière récente.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "Images d'exemple {action} terminées",
             "imagesFailed": "Images d'exemple {action} échouées",
             "loadError": "Erreur lors du chargement des téléchargements : {message}",
-            "downloadError": "Erreur de téléchargement : {message}"
+            "downloadError": "Erreur de téléchargement : {message}",
+            "downloadStopped": "Téléchargement annulé"
         },
         "import": {
             "folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "Échec de la définition de la classification du contenu : {message}",
             "relinkSuccess": "Modèle relié à Civitai avec succès",
             "relinkFailed": "Erreur : {message}",
+            "linkHfSuccess": "Modèle lié à HuggingFace avec succès",
+            "linkHfFailed": "Erreur : {message}",
             "fetchMetadataFirst": "Veuillez d'abord récupérer les métadonnées depuis CivitAI",
             "noCivitaiInfo": "Aucune information CivitAI disponible",
             "missingHash": "Hash du modèle non disponible"

locales/he.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "שם קביעה מראש...",
             "baseModel": "מודל בסיס",
             "baseModelSearchPlaceholder": "חפש מודלי בסיס...",
-            "modelTags": "תגיות (20 המובילות)",
+            "modelTags": "תגיות",
             "modelTypes": "סוגי מודלים",
             "license": "רישיון",
             "noCreditRequired": "ללא קרדיט נדרש",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "אפשר מכירת תמונות שנוצרו",
             "noCreditRequiredTooltip": "שימוש במודל ללא מתן קרדיט ליוצר",
             "noTags": "ללא תגיות",
+            "tagSearchPlaceholder": "חיפוש תגיות...",
+            "noTagMatches": "אין תגיות שתואמות את החיפוש הנוכחי.",
             "autoTags": "תגיות אוטומטיות",
             "noBaseModelMatches": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
             "clearAll": "נקה את כל המסננים",
@@ -505,7 +507,9 @@
             "saveSuccess": "נתיבי תיקיות נוספים עודכנו. נדרשת הפעלה מחדש כדי להחיל את השינויים.",
             "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
             "validation": {
-                "duplicatePath": "נתיב זה כבר מוגדר"
+                "duplicatePath": "נתיב זה כבר מוגדר",
+                "checkpointUnetOverlap": "לא ניתן להשתמש באותו נתיב עבור checkpoints ומודלי דיפוזיה: {paths}",
+                "checkpointUnetOverlapInline": "הנתיב הזה כבר נמצא בשימוש עבור סוג מודל אחר. יש להשתמש בתיקיות נפרדות עבור checkpoints ומודלי דיפוזיה."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "מכין הורדה...",
             "connecting": "מתחבר לשרת ההורדות...",
             "completed": "הושלם",
-            "downloadComplete": "ההורדה הושלמה בהצלחה"
+            "downloadComplete": "ההורדה הושלמה בהצלחה",
+            "enableCivarchiveApi": "הפעל את CivArchive API כספק מטא-נתונים",
+            "enableCivarchiveApiHelp": "כאשר מופעל, CivArchive API משמש כמקור גיבוי למטא-נתונים של מודלים (למשל עבור מודלים שנמחקו מ-CivitAI). כבה כדי להימנע לחלוטין ממגבלות הקצב של CivArchive.",
+            "providerOrder": "סדר ספקי מטא-נתונים לגיבוי",
+            "providerOrderHelp": "CivitAI API תמיד מנוסה ראשון. בחר את סדר הספקים הנותרים בעת חיפוש מטא-נתונים.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "הפעל פרוקסי ברמת האפליקציה",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "מותאם אישית (תואם OpenAI)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "גרסאות מקומיות",
                 "versionsCountDesc": "הכי הרבה גרסאות ראשונות",
                 "versionsCountAsc": "הכי מעט גרסאות ראשונות",
-                "versionIdDesc": "גרסה חדשה ביותר ראשונה"
+                "versionIdDesc": "גרסה חדשה ביותר ראשונה",
+                "random": "אקראי",
+                "randomAction": "ערבוב אקראי"
             },
             "refresh": {
                 "title": "רענן רשימת מודלים",
@@ -761,6 +774,8 @@
             "deleteAll": "מחק נבחרים",
             "downloadMissingLoras": "הורדת LoRAs חסרים",
             "downloadExamples": "הורד תמונות דוגמה",
+            "downloadMissingExamples": "הורדת חסרים",
+            "reprocessExamples": "עיבוד מחדש של הכול",
             "clear": "נקה בחירה",
             "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
             "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "רענן נתוני Civitai",
             "checkUpdates": "בדוק עדכונים",
-            "relinkCivitai": "קשר מחדש ל-Civitai",
+            "linkModel": "קישור מודל",
+            "linkCivitai": "קשר מחדש ל-Civitai",
+            "linkHuggingFace": "קישור ל-HuggingFace",
             "copySyntax": "העתק תחביר LoRA",
             "copyFilename": "העתק שם קובץ מודל",
             "copyRecipeSyntax": "העתק תחביר מתכון",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "שלח ל-Workflow (החלף)",
             "openExamples": "פתח תיקיית דוגמאות",
             "downloadExamples": "הורד תמונות דוגמה",
+            "downloadMissingExamples": "הורדת חסרים",
+            "reprocessExamples": "עיבוד מחדש של הכול",
             "replacePreview": "החלף תצוגה מקדימה",
             "setContentRating": "הגדר דירוג תוכן",
             "moveToFolder": "העבר לתיקייה",
@@ -1203,7 +1222,9 @@
                 "preparing": "מכין הורדה...",
                 "downloadedPreview": "תמונת תצוגה מקדימה הורדה",
                 "downloadingFile": "מוריד קובץ {type}",
-                "finalizing": "מסיים הורדה..."
+                "finalizing": "מסיים הורדה...",
+                "cancelling": "מבטל הורדה...",
+                "cancelled": "ההורדה בוטלה"
             },
             "progress": {
                 "currentFile": "הקובץ הנוכחי:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "הקלד נתיב תיקייה או בחר מהעץ למטה...",
             "root": "שורש"
         },
+        "linkHuggingFace": {
+            "title": "קישור ל-HuggingFace",
+            "infoText": "הדבק את כתובת ה-URL של מאגר HuggingFace כדי לשייך מודל זה למקורו. פעולה זו מאפשרת העשרת מטא-דאטה באמצעות AI.",
+            "urlLabel": "כתובת URL של מאגר HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "הזן את כתובת ה-URL המלאה של מאגר HuggingFace.",
+            "confirmAction": "שמור וקשר"
+        },
         "relinkCivitai": {
             "title": "קשר מחדש ל-Civitai",
             "warning": "אזהרה:",
@@ -1526,6 +1555,7 @@
                 "empty": "אין עדיין היסטוריית גרסאות למודל זה.",
                 "error": "טעינת הגרסאות נכשלה.",
                 "missingModelId": "למודל זה אין מזהה מודל של Civitai.",
+                "hfGroupInfo": "זוהי קבוצת דגמים של HuggingFace. פתח את הספרייה כדי לראות את כל הגרסאות ברשת.",
                 "confirm": {
                     "delete": "למחוק גרסה זו מהספרייה שלך?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "הורד CSV",
             "columnModelName": "שם המודל",
             "columnError": "שגיאה"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1728,7 +1773,13 @@
         "checkingUpdates": "בודק עדכונים...",
         "checkingMessage": "אנא המתן בזמן שאנו בודקים את הגרסה האחרונה.",
         "showNotifications": "הצג התראות עדכון",
-        "latestBadge": "עדכן",
+        "latestBadge": "אחרון",
+        "latestMain": "ענף main",
+        "channel": "ערוץ עדכון",
+        "channels": {
+            "release": "Release",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "מכין עדכון...",
             "installing": "מתקין עדכון...",
@@ -1749,6 +1800,15 @@
             "warning": "אזהרה: גרסאות ליליות עשויות להכיל תכונות ניסיוניות ועלולות להיות לא יציבות.",
             "enable": "הפעל עדכונים ליליים"
         },
+        "channelSwitch": {
+            "nightlyTitle": "מעבר לערוץ Nightly",
+            "nightlyMessage": "מעבר ל-Nightly יאתחל מאגר Git ויעקוב אחר הקומיטים האחרונים בענף main. העדכונים תכופים יותר אך עשויים להיות לא יציבים. ניתן לחזור ל-Release בכל עת.",
+            "releaseTitle": "מעבר לערוץ Release",
+            "releaseMessage": "מעבר ל-Release יעבור לתגית הגרסה היציבה האחרונה. ניתן לחזור ל-Nightly בכל עת.",
+            "switching": "מעבר לערוץ {channel}...",
+            "completed": "המעבר לערוץ {channel} הושלם",
+            "failed": "החלפת ערוץ נכשלה"
+        },
         "banners": {
             "recent": "הודעות אחרונות",
             "empty": "אין כרגע באנרים אחרונים.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "{action} תמונות הדוגמה הושלם",
             "imagesFailed": "{action} תמונות הדוגמה נכשל",
             "loadError": "שגיאה בטעינת הורדות: {message}",
-            "downloadError": "שגיאת הורדה: {message}"
+            "downloadError": "שגיאת הורדה: {message}",
+            "downloadStopped": "ההורדה בוטלה"
         },
         "import": {
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "הגדרת דירוג התוכן נכשלה: {message}",
             "relinkSuccess": "המודל קושר מחדש ל-Civitai בהצלחה",
             "relinkFailed": "שגיאה: {message}",
+            "linkHfSuccess": "המודל נקשר בהצלחה ל-HuggingFace",
+            "linkHfFailed": "שגיאה: {message}",
             "fetchMetadataFirst": "אנא אחזר מטא-דאטה מ-CivitAI תחילה",
             "noCivitaiInfo": "אין מידע מ-CivitAI זמין",
             "missingHash": "ה-hash של המודל אינו זמין"

locales/ja.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "プリセット名...",
             "baseModel": "ベースモデル",
             "baseModelSearchPlaceholder": "ベースモデルを検索...",
-            "modelTags": "タグ（上位20）",
+            "modelTags": "タグ",
             "modelTypes": "モデルタイプ",
             "license": "ライセンス",
             "noCreditRequired": "クレジット不要",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "生成した画像の販売を許可",
             "noCreditRequiredTooltip": "クレジット表記なしでモデルを使用可能",
             "noTags": "タグなし",
+            "tagSearchPlaceholder": "タグを検索...",
+            "noTagMatches": "現在の検索に一致するタグはありません。",
             "autoTags": "自動タグ",
             "noBaseModelMatches": "現在の検索に一致するベースモデルはありません。",
             "clearAll": "すべてのフィルタをクリア",
@@ -505,7 +507,9 @@
             "saveSuccess": "追加フォルダーパスを更新しました。変更を適用するには再起動が必要です。",
             "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
             "validation": {
-                "duplicatePath": "このパスはすでに設定されています"
+                "duplicatePath": "このパスはすでに設定されています",
+                "checkpointUnetOverlap": "checkpoints と diffusion models に同じパスは使用できません：{paths}",
+                "checkpointUnetOverlapInline": "このパスは別のモデルタイプですでに使用されています。checkpoints と diffusion models には別々のフォルダを使用してください。"
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "ダウンロードを準備中...",
             "connecting": "ダウンロードサーバーに接続中...",
             "completed": "完了",
-            "downloadComplete": "ダウンロードが正常に完了しました"
+            "downloadComplete": "ダウンロードが正常に完了しました",
+            "enableCivarchiveApi": "CivArchive API をメタデータプロバイダーとして有効化",
+            "enableCivarchiveApiHelp": "有効にすると、CivArchive API がモデルメタデータの代替ソースとして使用されます（例：CivitAI から削除されたモデルの場合）。オフにすると、CivArchive のレート制限を完全に回避できます。",
+            "providerOrder": "メタデータプロバイダーのフォールバック順序",
+            "providerOrderHelp": "CivitAI API が常に最初に試行されます。メタデータ検索時の残りのプロバイダーの順序を選択してください。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "アプリレベルのプロキシを有効化",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "カスタム（OpenAI 互換）"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "ローカルバージョン数",
                 "versionsCountDesc": "バージョン数の多い順",
                 "versionsCountAsc": "バージョン数の少ない順",
-                "versionIdDesc": "最新バージョン順"
+                "versionIdDesc": "最新バージョン順",
+                "random": "ランダム",
+                "randomAction": "シャッフル（ランダム）"
             },
             "refresh": {
                 "title": "モデルリストを更新",
@@ -761,6 +774,8 @@
             "deleteAll": "選択したものを削除",
             "downloadMissingLoras": "不足している LoRA をダウンロード",
             "downloadExamples": "例画像をダウンロード",
+            "downloadMissingExamples": "不足分をダウンロード",
+            "reprocessExamples": "すべて再処理",
             "clear": "選択をクリア",
             "skipMetadataRefreshCount": "スキップ（{count}モデル）",
             "resumeMetadataRefreshCount": "再開（{count}モデル）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Civitaiデータを更新",
             "checkUpdates": "更新確認",
-            "relinkCivitai": "Civitaiに再リンク",
+            "linkModel": "モデルをリンク",
+            "linkCivitai": "Civitai にリンク",
+            "linkHuggingFace": "HuggingFace にリンク",
             "copySyntax": "LoRA構文をコピー",
             "copyFilename": "モデルファイル名をコピー",
             "copyRecipeSyntax": "レシピ構文をコピー",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "ワークフローに送信（置換）",
             "openExamples": "例画像フォルダを開く",
             "downloadExamples": "例画像をダウンロード",
+            "downloadMissingExamples": "不足分をダウンロード",
+            "reprocessExamples": "すべて再処理",
             "replacePreview": "プレビューを置換",
             "setContentRating": "コンテンツレーティングを設定",
             "moveToFolder": "フォルダに移動",
@@ -1203,7 +1222,9 @@
                 "preparing": "ダウンロードを準備中...",
                 "downloadedPreview": "プレビュー画像をダウンロードしました",
                 "downloadingFile": "{type}ファイルをダウンロード中",
-                "finalizing": "ダウンロードを完了中..."
+                "finalizing": "ダウンロードを完了中...",
+                "cancelling": "ダウンロードをキャンセル中...",
+                "cancelled": "ダウンロードをキャンセルしました"
             },
             "progress": {
                 "currentFile": "現在のファイル:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "フォルダパスを入力するか、下のツリーから選択...",
             "root": "ルート"
         },
+        "linkHuggingFace": {
+            "title": "HuggingFace にリンク",
+            "infoText": "HuggingFace リポジトリの URL を貼り付けてモデルを関連付けます。AI によるメタデータ補完が有効になります。",
+            "urlLabel": "HuggingFace リポジトリ URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "完全な HuggingFace リポジトリ URL を入力してください。",
+            "confirmAction": "保存＆リンク"
+        },
         "relinkCivitai": {
             "title": "Civitaiに再リンク",
             "warning": "警告：",
@@ -1526,6 +1555,7 @@
                 "empty": "このモデルにはまだバージョン履歴がありません。",
                 "error": "バージョンの読み込みに失敗しました。",
                 "missingModelId": "このモデルにはCivitaiのモデルIDがありません。",
+                "hfGroupInfo": "これは HuggingFace モデルグループです。ライブラリを開いてグリッドですべてのバージョンを表示してください。",
                 "confirm": {
                     "delete": "このバージョンをライブラリから削除しますか？"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "CSVをダウンロード",
             "columnModelName": "モデル名",
             "columnError": "エラー"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1729,6 +1774,12 @@
         "checkingMessage": "最新バージョンを確認しています。お待ちください。",
         "showNotifications": "更新通知を表示",
         "latestBadge": "最新",
+        "latestMain": "Main ブランチ",
+        "channel": "更新チャンネル",
+        "channels": {
+            "release": "リリース",
+            "nightly": "ナイトリー"
+        },
         "updateProgress": {
             "preparing": "更新を準備中...",
             "installing": "更新をインストール中...",
@@ -1749,6 +1800,15 @@
             "warning": "警告：ナイトリービルドには実験的機能が含まれており、不安定な場合があります。",
             "enable": "ナイトリー更新を有効にする"
         },
+        "channelSwitch": {
+            "nightlyTitle": "ナイトリーチャンネルに切り替え",
+            "nightlyMessage": "ナイトリーに切り替えると、Gitリポジトリが初期化され、mainブランチの最新コミットを追跡します。更新頻度は高くなりますが、不安定な場合があります。いつでもリリース版に戻せます。",
+            "releaseTitle": "リリースチャンネルに切り替え",
+            "releaseMessage": "リリースに切り替えると、最新の安定版タグにチェックアウトされます。いつでもNightlyに戻せます。",
+            "switching": "{channel} チャンネルに切り替え中...",
+            "completed": "{channel} チャンネルに切り替えました",
+            "failed": "チャンネルの切り替えに失敗しました"
+        },
         "banners": {
             "recent": "最近の通知",
             "empty": "最近のバナーはありません。",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "例画像 {action} が完了しました",
             "imagesFailed": "例画像 {action} が失敗しました",
             "loadError": "ダウンロード読み込みエラー：{message}",
-            "downloadError": "ダウンロードエラー：{message}"
+            "downloadError": "ダウンロードエラー：{message}",
+            "downloadStopped": "ダウンロードをキャンセルしました"
         },
         "import": {
             "folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "コンテンツレーティングの設定に失敗しました：{message}",
             "relinkSuccess": "モデルがCivitaiに正常に再リンクされました",
             "relinkFailed": "エラー：{message}",
+            "linkHfSuccess": "モデルを HuggingFace にリンクしました",
+            "linkHfFailed": "エラー：{message}",
             "fetchMetadataFirst": "最初にCivitAIからメタデータを取得してください",
             "noCivitaiInfo": "CivitAI情報が利用できません",
             "missingHash": "モデルハッシュが利用できません"

locales/ko.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "프리셋 이름...",
             "baseModel": "베이스 모델",
             "baseModelSearchPlaceholder": "베이스 모델 검색...",
-            "modelTags": "태그 (상위 20개)",
+            "modelTags": "태그",
             "modelTypes": "모델 유형",
             "license": "라이선스",
             "noCreditRequired": "크레딧 표기 없음",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "생성된 이미지 판매 허용",
             "noCreditRequiredTooltip": "크리에이터 저작자 표시 없이 모델 사용 가능",
             "noTags": "태그 없음",
+            "tagSearchPlaceholder": "태그 검색...",
+            "noTagMatches": "현재 검색과 일치하는 태그가 없습니다.",
             "autoTags": "자동 태그",
             "noBaseModelMatches": "현재 검색과 일치하는 베이스 모델이 없습니다.",
             "clearAll": "모든 필터 지우기",
@@ -505,7 +507,9 @@
             "saveSuccess": "추가 폴다 경로가 업데이트되었습니다. 변경 사항을 적용하려면 재시작이 필요합니다.",
             "saveError": "추가 폴다 경로 업데이트 실패: {message}",
             "validation": {
-                "duplicatePath": "이 경로는 이미 구성되어 있습니다"
+                "duplicatePath": "이 경로는 이미 구성되어 있습니다",
+                "checkpointUnetOverlap": "checkpoints와 diffusion models에 동일한 경로를 사용할 수 없습니다: {paths}",
+                "checkpointUnetOverlapInline": "이 경로는 다른 모델 유형에 이미 사용 중입니다. checkpoints와 diffusion models에 별도의 폴더를 사용하세요."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "다운로드 준비 중...",
             "connecting": "다운로드 서버에 연결 중...",
             "completed": "완료됨",
-            "downloadComplete": "다운로드가 성공적으로 완료되었습니다"
+            "downloadComplete": "다운로드가 성공적으로 완료되었습니다",
+            "enableCivarchiveApi": "CivArchive API를 메타데이터 제공자로 활성화",
+            "enableCivarchiveApiHelp": "활성화하면 CivArchive API가 모델 메타데이터의 대체 소스로 사용됩니다 (예: CivitAI에서 삭제된 모델의 경우). 비활성화하면 CivArchive의 속도 제한을 완전히 피할 수 있습니다.",
+            "providerOrder": "메타데이터 제공자 폴백 순서",
+            "providerOrderHelp": "CivitAI API가 항상 먼저 시도됩니다. 메타데이터 조회 시 나머지 제공자의 순서를 선택하세요.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "앱 수준 프록시 활성화",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "사용자 정의 (OpenAI 호환)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "로컬 버전 수",
                 "versionsCountDesc": "버전 수 많은 순",
                 "versionsCountAsc": "버전 수 적은 순",
-                "versionIdDesc": "최신 버전순"
+                "versionIdDesc": "최신 버전순",
+                "random": "랜덤",
+                "randomAction": "셔플 (무작위)"
             },
             "refresh": {
                 "title": "모델 목록 새로고침",
@@ -761,6 +774,8 @@
             "deleteAll": "선택된 항목 삭제",
             "downloadMissingLoras": "누락된 LoRA 다운로드",
             "downloadExamples": "예시 이미지 다운로드",
+            "downloadMissingExamples": "누락된 것만 다운로드",
+            "reprocessExamples": "모두 다시 처리",
             "clear": "선택 지우기",
             "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
             "resumeMetadataRefreshCount": "재개（{count}개 모델）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Civitai 데이터 새로고침",
             "checkUpdates": "업데이트 확인",
-            "relinkCivitai": "Civitai에 다시 연결",
+            "linkModel": "모델 연결",
+            "linkCivitai": "Civitai에 연결",
+            "linkHuggingFace": "HuggingFace에 연결",
             "copySyntax": "LoRA 문법 복사",
             "copyFilename": "모델 파일명 복사",
             "copyRecipeSyntax": "레시피 문법 복사",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "워크플로로 전송 (교체)",
             "openExamples": "예시 폴더 열기",
             "downloadExamples": "예시 이미지 다운로드",
+            "downloadMissingExamples": "누락된 것만 다운로드",
+            "reprocessExamples": "모두 다시 처리",
             "replacePreview": "미리보기 교체",
             "setContentRating": "콘텐츠 등급 설정",
             "moveToFolder": "폴더로 이동",
@@ -1203,7 +1222,9 @@
                 "preparing": "다운로드 준비 중...",
                 "downloadedPreview": "미리보기 이미지 다운로드됨",
                 "downloadingFile": "{type} 파일 다운로드 중",
-                "finalizing": "다운로드 완료 중..."
+                "finalizing": "다운로드 완료 중...",
+                "cancelling": "다운로드 취소 중...",
+                "cancelled": "다운로드가 취소되었습니다"
             },
             "progress": {
                 "currentFile": "현재 파일:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "폴더 경로를 입력하거나 아래 트리에서 선택하세요...",
             "root": "루트"
         },
+        "linkHuggingFace": {
+            "title": "HuggingFace에 연결",
+            "infoText": "HuggingFace 저장소 URL을 붙여넣어 모델을 연결합니다. AI 메타데이터 보강 기능을 사용할 수 있습니다.",
+            "urlLabel": "HuggingFace 저장소 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "전체 HuggingFace 저장소 URL을 입력하세요.",
+            "confirmAction": "저장 및 연결"
+        },
         "relinkCivitai": {
             "title": "Civitai에 다시 연결",
             "warning": "경고:",
@@ -1526,6 +1555,7 @@
                 "empty": "이 모델에는 아직 버전 기록이 없습니다.",
                 "error": "버전을 불러오지 못했습니다.",
                 "missingModelId": "이 모델에는 Civitai 모델 ID가 없습니다.",
+                "hfGroupInfo": "HuggingFace 모델 그룹입니다. 라이브러리를 열어 그리드에서 모든 버전을 확인하세요.",
                 "confirm": {
                     "delete": "이 버전을 라이브러리에서 삭제하시겠습니까?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "CSV 다운로드",
             "columnModelName": "모델 이름",
             "columnError": "오류"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1729,6 +1774,12 @@
         "checkingMessage": "최신 버전을 확인하는 동안 잠시 기다려주세요.",
         "showNotifications": "업데이트 알림 표시",
         "latestBadge": "최신",
+        "latestMain": "Main 브랜치",
+        "channel": "업데이트 채널",
+        "channels": {
+            "release": "릴리스",
+            "nightly": "나이틀리"
+        },
         "updateProgress": {
             "preparing": "업데이트 준비 중...",
             "installing": "업데이트 설치 중...",
@@ -1749,6 +1800,15 @@
             "warning": "경고: 나이틀리 빌드는 실험적 기능을 포함할 수 있으며 불안정할 수 있습니다.",
             "enable": "나이틀리 업데이트 활성화"
         },
+        "channelSwitch": {
+            "nightlyTitle": "나이틀리 채널로 전환",
+            "nightlyMessage": "나이틀리로 전환하면 Git 저장소가 초기화되고 main 브랜치의 최신 커밋을 추적합니다. 업데이트 빈도는 높지만 불안정할 수 있습니다. 언제든지 릴리스로 돌아갈 수 있습니다.",
+            "releaseTitle": "릴리스 채널로 전환",
+            "releaseMessage": "릴리스로 전환하면 최신 안정 버전 태그로 체크아웃됩니다. 언제든지 나이틀리로 돌아갈 수 있습니다.",
+            "switching": "{channel} 채널로 전환 중...",
+            "completed": "{channel} 채널로 전환 완료",
+            "failed": "채널 전환 실패"
+        },
         "banners": {
             "recent": "최근 알림",
             "empty": "최근 배너가 없습니다.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "예시 이미지 {action}이(가) 완료되었습니다",
             "imagesFailed": "예시 이미지 {action}이(가) 실패했습니다",
             "loadError": "다운로드 로딩 오류: {message}",
-            "downloadError": "다운로드 오류: {message}"
+            "downloadError": "다운로드 오류: {message}",
+            "downloadStopped": "다운로드가 취소되었습니다"
         },
         "import": {
             "folderTreeFailed": "폴더 트리 로딩 실패",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "콘텐츠 등급 설정 실패: {message}",
             "relinkSuccess": "모델이 Civitai에 성공적으로 다시 연결되었습니다",
             "relinkFailed": "오류: {message}",
+            "linkHfSuccess": "모델이 HuggingFace에 연결되었습니다",
+            "linkHfFailed": "오류: {message}",
             "fetchMetadataFirst": "먼저 CivitAI에서 메타데이터를 가져와주세요",
             "noCivitaiInfo": "사용 가능한 CivitAI 정보가 없습니다",
             "missingHash": "모델 해시를 사용할 수 없습니다"

locales/ru.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "Имя пресета...",
             "baseModel": "Базовая модель",
             "baseModelSearchPlaceholder": "Поиск базовых моделей...",
-            "modelTags": "Теги (Топ 20)",
+            "modelTags": "Теги",
             "modelTypes": "Типы моделей",
             "license": "Лицензия",
             "noCreditRequired": "Без указания авторства",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "Разрешить продажу сгенерированных изображений",
             "noCreditRequiredTooltip": "Использование модели без указания автора",
             "noTags": "Без тегов",
+            "tagSearchPlaceholder": "Поиск тегов...",
+            "noTagMatches": "Нет тегов, соответствующих текущему поиску.",
             "autoTags": "Авто-теги",
             "noBaseModelMatches": "Нет базовых моделей, соответствующих текущему поиску.",
             "clearAll": "Очистить все фильтры",
@@ -505,7 +507,9 @@
             "saveSuccess": "Дополнительные пути к папкам обновлены. Требуется перезапуск для применения изменений.",
             "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
             "validation": {
-                "duplicatePath": "Этот путь уже настроен"
+                "duplicatePath": "Этот путь уже настроен",
+                "checkpointUnetOverlap": "Нельзя использовать один и тот же путь для checkpoints и diffusion models: {paths}",
+                "checkpointUnetOverlapInline": "Этот путь уже используется для другого типа модели. Используйте отдельные папки для checkpoints и diffusion models."
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "Подготовка к загрузке...",
             "connecting": "Подключение к серверу загрузки...",
             "completed": "Завершено",
-            "downloadComplete": "Загрузка успешно завершена"
+            "downloadComplete": "Загрузка успешно завершена",
+            "enableCivarchiveApi": "Включить CivArchive API как источник метаданных",
+            "enableCivarchiveApiHelp": "При включении CivArchive API используется как резервный источник метаданных моделей (например, для моделей, удалённых с CivitAI). Отключите, чтобы полностью избежать ограничений скорости CivArchive.",
+            "providerOrder": "Порядок резервных источников метаданных",
+            "providerOrderHelp": "CivitAI API всегда проверяется первым. Выберите порядок остальных источников при поиске метаданных.",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "Включить прокси на уровне приложения",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "Пользовательский (совместимый с OpenAI)"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "Локальные версии",
                 "versionsCountDesc": "Сначала больше версий",
                 "versionsCountAsc": "Сначала меньше версий",
-                "versionIdDesc": "Сначала новые версии"
+                "versionIdDesc": "Сначала новые версии",
+                "random": "Случайно",
+                "randomAction": "Перемешать"
             },
             "refresh": {
                 "title": "Обновить список моделей",
@@ -761,6 +774,8 @@
             "deleteAll": "Удалить выбранные",
             "downloadMissingLoras": "Скачать отсутствующие LoRAs",
             "downloadExamples": "Загрузить примеры изображений",
+            "downloadMissingExamples": "Скачать недостающие",
+            "reprocessExamples": "Обработать всё заново",
             "clear": "Очистить выбор",
             "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
             "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "Обновить данные Civitai",
             "checkUpdates": "Проверить обновления",
-            "relinkCivitai": "Пересвязать с Civitai",
+            "linkModel": "Связать модель",
+            "linkCivitai": "Пересвязать с Civitai",
+            "linkHuggingFace": "Связать с HuggingFace",
             "copySyntax": "Копировать синтаксис LoRA",
             "copyFilename": "Копировать имя файла модели",
             "copyRecipeSyntax": "Копировать синтаксис рецепта",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "Отправить в Workflow (Заменить)",
             "openExamples": "Открыть папку примеров",
             "downloadExamples": "Загрузить примеры изображений",
+            "downloadMissingExamples": "Скачать недостающие",
+            "reprocessExamples": "Обработать всё заново",
             "replacePreview": "Заменить превью",
             "setContentRating": "Установить рейтинг контента",
             "moveToFolder": "Переместить в папку",
@@ -1203,7 +1222,9 @@
                 "preparing": "Подготовка загрузки...",
                 "downloadedPreview": "Превью изображение загружено",
                 "downloadingFile": "Загрузка файла {type}",
-                "finalizing": "Завершение загрузки..."
+                "finalizing": "Завершение загрузки...",
+                "cancelling": "Отмена загрузки...",
+                "cancelled": "Загрузка отменена"
             },
             "progress": {
                 "currentFile": "Текущий файл:",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "Введите путь к папке или выберите из дерева ниже...",
             "root": "Корень"
         },
+        "linkHuggingFace": {
+            "title": "Связать с HuggingFace",
+            "infoText": "Вставьте URL репозитория HuggingFace, чтобы связать эту модель с её источником. Это позволит обогащать метаданные с помощью ИИ.",
+            "urlLabel": "URL репозитория HuggingFace:",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "Введите полный URL репозитория HuggingFace.",
+            "confirmAction": "Сохранить и связать"
+        },
         "relinkCivitai": {
             "title": "Пересвязать с Civitai",
             "warning": "Предупреждение:",
@@ -1526,6 +1555,7 @@
                 "empty": "Для этой модели пока нет истории версий.",
                 "error": "Не удалось загрузить версии.",
                 "missingModelId": "У этой модели отсутствует идентификатор модели Civitai.",
+                "hfGroupInfo": "Это группа моделей HuggingFace. Откройте библиотеку, чтобы увидеть все версии в сетке.",
                 "confirm": {
                     "delete": "Удалить эту версию из библиотеки?"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "Скачать CSV",
             "columnModelName": "Имя модели",
             "columnError": "Ошибка"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1728,7 +1773,13 @@
         "checkingUpdates": "Проверка обновлений...",
         "checkingMessage": "Пожалуйста, подождите, пока мы проверяем последнюю версию.",
         "showNotifications": "Показывать уведомления об обновлениях",
-        "latestBadge": "Последний",
+        "latestBadge": "Последняя",
+        "latestMain": "Ветка main",
+        "channel": "Канал обновлений",
+        "channels": {
+            "release": "Релиз",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "Подготовка обновления...",
             "installing": "Установка обновления...",
@@ -1749,6 +1800,15 @@
             "warning": "Предупреждение: Ночные сборки могут содержать экспериментальные функции и могут быть нестабильными.",
             "enable": "Включить ночные обновления"
         },
+        "channelSwitch": {
+            "nightlyTitle": "Переключиться на Nightly",
+            "nightlyMessage": "Переключение на Nightly инициализирует Git-репозиторий и отслеживает последние коммиты ветки main. Обновления чаще, но могут быть нестабильными. Вы можете вернуться к Release в любое время.",
+            "releaseTitle": "Переключиться на Release",
+            "releaseMessage": "Переключение на Release выполнит checkout последнего стабильного тега. Вы можете вернуться к Nightly в любое время.",
+            "switching": "Переключение на канал {channel}...",
+            "completed": "Успешно переключено на канал {channel}",
+            "failed": "Не удалось переключить канал"
+        },
         "banners": {
             "recent": "Недавние уведомления",
             "empty": "Недавних баннеров нет.",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "Примеры изображений {action} завершены",
             "imagesFailed": "Примеры изображений {action} не удались",
             "loadError": "Ошибка загрузки downloads: {message}",
-            "downloadError": "Ошибка загрузки: {message}"
+            "downloadError": "Ошибка загрузки: {message}",
+            "downloadStopped": "Загрузка отменена"
         },
         "import": {
             "folderTreeFailed": "Не удалось загрузить дерево папок",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "Не удалось установить рейтинг контента: {message}",
             "relinkSuccess": "Модель успешно пересвязана с Civitai",
             "relinkFailed": "Ошибка: {message}",
+            "linkHfSuccess": "Модель успешно связана с HuggingFace",
+            "linkHfFailed": "Ошибка: {message}",
             "fetchMetadataFirst": "Пожалуйста, сначала получите метаданные с CivitAI",
             "noCivitaiInfo": "Информация CivitAI недоступна",
             "missingHash": "Хеш модели недоступен"

locales/zh-CN.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "预设名称...",
             "baseModel": "基础模型",
             "baseModelSearchPlaceholder": "搜索基础模型...",
-            "modelTags": "标签（前20）",
+            "modelTags": "标签",
             "modelTypes": "模型类型",
             "license": "许可证",
             "noCreditRequired": "无需署名",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "允许出售生成的图片",
             "noCreditRequiredTooltip": "使用模型时无需注明原作者",
             "noTags": "无标签",
+            "tagSearchPlaceholder": "搜索标签...",
+            "noTagMatches": "没有匹配当前搜索的标签。",
             "autoTags": "自动标签",
             "noBaseModelMatches": "没有基础模型符合当前搜索。",
             "clearAll": "清除所有筛选",
@@ -505,7 +507,9 @@
             "saveSuccess": "额外文件夹路径已更新，需要重启才能生效。",
             "saveError": "更新额外文件夹路径失败：{message}",
             "validation": {
-                "duplicatePath": "此路径已配置"
+                "duplicatePath": "此路径已配置",
+                "checkpointUnetOverlap": "checkpoints 和 diffusion models 不能使用相同的路径：{paths}",
+                "checkpointUnetOverlapInline": "此路径已被用于另一种模型类型。请为 checkpoints 和 diffusion models 使用不同的文件夹。"
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "正在准备下载...",
             "connecting": "正在连接下载服务器...",
             "completed": "已完成",
-            "downloadComplete": "下载成功完成"
+            "downloadComplete": "下载成功完成",
+            "enableCivarchiveApi": "启用 CivArchive API 作为元数据提供者",
+            "enableCivarchiveApiHelp": "开启后，CivArchive API 将作为模型元数据的备用来源（例如用于已从 CivitAI 删除的模型）。关闭可完全避免 CivArchive 的速率限制。",
+            "providerOrder": "元数据提供者回退顺序",
+            "providerOrderHelp": "CivitAI API 始终优先尝试。选择查找元数据时其余提供者的顺序。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "启用应用级代理",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "自定义（OpenAI 兼容）"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "本地版本数",
                 "versionsCountDesc": "版本数从多到少",
                 "versionsCountAsc": "版本数从少到多",
-                "versionIdDesc": "最新版本优先"
+                "versionIdDesc": "最新版本优先",
+                "random": "随机",
+                "randomAction": "随机排序（洗牌）"
             },
             "refresh": {
                 "title": "刷新模型列表",
@@ -761,6 +774,8 @@
             "deleteAll": "删除已选",
             "downloadMissingLoras": "下载缺失的 LoRAs",
             "downloadExamples": "下载示例图片",
+            "downloadMissingExamples": "下载缺失的",
+            "reprocessExamples": "重新处理全部",
             "clear": "清除选择",
             "skipMetadataRefreshCount": "跳过（{count} 个模型）",
             "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 数据",
             "checkUpdates": "检查更新",
-            "relinkCivitai": "重新关联到 Civitai",
+            "linkModel": "链接模型",
+            "linkCivitai": "链接到 Civitai",
+            "linkHuggingFace": "链接到 HuggingFace",
             "copySyntax": "复制 LoRA 语法",
             "copyFilename": "复制模型文件名",
             "copyRecipeSyntax": "复制配方语法",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "发送到工作流（替换）",
             "openExamples": "打开示例文件夹",
             "downloadExamples": "下载示例图片",
+            "downloadMissingExamples": "下载缺失的",
+            "reprocessExamples": "重新处理全部",
             "replacePreview": "替换预览",
             "setContentRating": "设置内容评级",
             "moveToFolder": "移动到文件夹",
@@ -1203,7 +1222,9 @@
                 "preparing": "正在准备下载...",
                 "downloadedPreview": "预览图片已下载",
                 "downloadingFile": "正在下载 {type} 文件",
-                "finalizing": "正在完成下载..."
+                "finalizing": "正在完成下载...",
+                "cancelling": "取消下载中...",
+                "cancelled": "下载已取消"
             },
             "progress": {
                 "currentFile": "当前文件：",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "输入文件夹路径或从下方树中选择...",
             "root": "根目录"
         },
+        "linkHuggingFace": {
+            "title": "链接到 HuggingFace",
+            "infoText": "粘贴 HuggingFace 仓库 URL 以关联此模型。关联后可启用 AI 元数据增强功能。",
+            "urlLabel": "HuggingFace 仓库 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "请输入完整的 HuggingFace 仓库 URL。",
+            "confirmAction": "保存并链接"
+        },
         "relinkCivitai": {
             "title": "重新关联到 Civitai",
             "warning": "警告：",
@@ -1526,6 +1555,7 @@
                 "empty": "该模型还没有版本历史。",
                 "error": "加载版本失败。",
                 "missingModelId": "该模型缺少 Civitai 模型 ID。",
+                "hfGroupInfo": "这是一个 HuggingFace 模型组。打开库页面即可在网格中查看所有版本。",
                 "confirm": {
                     "delete": "从库中删除此版本？"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "下载 CSV",
             "columnModelName": "模型名称",
             "columnError": "错误"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1729,6 +1774,12 @@
         "checkingMessage": "请稍候，正在检查最新版本。",
         "showNotifications": "显示更新通知",
         "latestBadge": "最新",
+        "latestMain": "Main 分支",
+        "channel": "更新频道",
+        "channels": {
+            "release": "稳定版",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "正在准备更新...",
             "installing": "正在安装更新...",
@@ -1749,6 +1800,15 @@
             "warning": "警告：Nightly 版本可能包含实验性功能，可能不稳定。",
             "enable": "启用 Nightly 更新"
         },
+        "channelSwitch": {
+            "nightlyTitle": "切换到 Nightly",
+            "nightlyMessage": "切换到 Nightly 将初始化 Git 仓库并跟踪 main 分支的最新提交。更新更频繁但可能不稳定，可随时切回稳定版。",
+            "releaseTitle": "切换到稳定版",
+            "releaseMessage": "切换到稳定版将检出最新的发布标签。可随时切换回每日构建版。",
+            "switching": "正在切换到 {channel} 频道...",
+            "completed": "已切换到 {channel} 频道",
+            "failed": "切换频道失败"
+        },
         "banners": {
             "recent": "最近的通知",
             "empty": "暂无最近的横幅通知。",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "示例图片{action}完成",
             "imagesFailed": "示例图片{action}失败",
             "loadError": "加载下载项出错：{message}",
-            "downloadError": "下载错误：{message}"
+            "downloadError": "下载错误：{message}",
+            "downloadStopped": "下载已取消"
         },
         "import": {
             "folderTreeFailed": "加载文件夹树失败",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "设置内容评级失败：{message}",
             "relinkSuccess": "模型已成功重新关联到 Civitai",
             "relinkFailed": "错误：{message}",
+            "linkHfSuccess": "模型已成功链接到 HuggingFace",
+            "linkHfFailed": "错误：{message}",
             "fetchMetadataFirst": "请先从 CivitAI 获取元数据",
             "noCivitaiInfo": "无 CivitAI 信息",
             "missingHash": "模型哈希不可用"

locales/zh-TW.json

@@ -233,7 +233,7 @@
             "presetNamePlaceholder": "預設名稱...",
             "baseModel": "基礎模型",
             "baseModelSearchPlaceholder": "搜尋基礎模型...",
-            "modelTags": "標籤（前 20）",
+            "modelTags": "標籤",
             "modelTypes": "模型類型",
             "license": "授權",
             "noCreditRequired": "無需署名",
@@ -241,6 +241,8 @@
             "allowSellingGeneratedContentTooltip": "允許出售生成的圖片",
             "noCreditRequiredTooltip": "使用模型時無需註明原作者",
             "noTags": "無標籤",
+            "tagSearchPlaceholder": "搜尋標籤...",
+            "noTagMatches": "沒有符合目前搜尋的標籤。",
             "autoTags": "自動標籤",
             "noBaseModelMatches": "沒有基礎模型符合目前的搜尋。",
             "clearAll": "清除所有篩選",
@@ -505,7 +507,9 @@
             "saveSuccess": "額外資料夾路徑已更新，需要重啟才能生效。",
             "saveError": "更新額外資料夾路徑失敗：{message}",
             "validation": {
-                "duplicatePath": "此路徑已設定"
+                "duplicatePath": "此路徑已設定",
+                "checkpointUnetOverlap": "checkpoints 和 diffusion models 不能使用相同的路徑：{paths}",
+                "checkpointUnetOverlapInline": "此路徑已被用於另一種模型類型。請為 checkpoints 和 diffusion models 使用不同的資料夾。"
             }
         },
         "priorityTags": {
@@ -638,7 +642,13 @@
             "preparing": "準備下載中...",
             "connecting": "正在連接下載伺服器...",
             "completed": "已完成",
-            "downloadComplete": "下載成功完成"
+            "downloadComplete": "下載成功完成",
+            "enableCivarchiveApi": "啟用 CivArchive API 作為中繼資料提供者",
+            "enableCivarchiveApiHelp": "開啟後，CivArchive API 將作為模型中繼資料的備用來源（例如用於已從 CivitAI 刪除的模型）。關閉可完全避免 CivArchive 的速率限制。",
+            "providerOrder": "中繼資料提供者回退順序",
+            "providerOrderHelp": "CivitAI API 始終優先嘗試。選擇查詢中繼資料時其餘提供者的順序。",
+            "providerOrderCivitaiArchiveSqlite": "CivitAI → CivArchive → Archive DB",
+            "providerOrderCivitaiSqliteArchive": "CivitAI → Archive DB → CivArchive"
         },
         "proxySettings": {
             "enableProxy": "啟用應用程式代理",
@@ -668,6 +678,7 @@
                 "deepseek": "DeepSeek",
                 "groq": "Groq",
                 "openrouter": "OpenRouter",
+                "google": "Gemini",
                 "opencode-go": "OpenCode Go",
                 "custom": "自訂（OpenAI 相容）"
             },
@@ -704,7 +715,9 @@
                 "versionsCount": "本地版本數",
                 "versionsCountDesc": "版本數從多到少",
                 "versionsCountAsc": "版本數從少到多",
-                "versionIdDesc": "最新版本優先"
+                "versionIdDesc": "最新版本優先",
+                "random": "隨機",
+                "randomAction": "隨機排序（洗牌）"
             },
             "refresh": {
                 "title": "重新整理模型列表",
@@ -761,6 +774,8 @@
             "deleteAll": "刪除所選",
             "downloadMissingLoras": "下載缺失的 LoRAs",
             "downloadExamples": "下載範例圖片",
+            "downloadMissingExamples": "下載缺少的",
+            "reprocessExamples": "重新處理全部",
             "clear": "清除選取",
             "skipMetadataRefreshCount": "跳過（{count} 個模型）",
             "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
@@ -786,7 +801,9 @@
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 資料",
             "checkUpdates": "檢查更新",
-            "relinkCivitai": "重新連結 Civitai",
+            "linkModel": "連結模型",
+            "linkCivitai": "連結到 Civitai",
+            "linkHuggingFace": "連結到 HuggingFace",
             "copySyntax": "複製 LoRA 語法",
             "copyFilename": "複製模型檔名",
             "copyRecipeSyntax": "複製配方語法",
@@ -794,6 +811,8 @@
             "sendToWorkflowReplace": "傳送到工作流（取代）",
             "openExamples": "開啟範例資料夾",
             "downloadExamples": "下載範例圖片",
+            "downloadMissingExamples": "下載缺少的",
+            "reprocessExamples": "重新處理全部",
             "replacePreview": "更換預覽圖",
             "setContentRating": "設定內容分級",
             "moveToFolder": "移動到資料夾",
@@ -1203,7 +1222,9 @@
                 "preparing": "準備下載中...",
                 "downloadedPreview": "已下載預覽圖片",
                 "downloadingFile": "正在下載 {type} 檔案",
-                "finalizing": "完成下載中..."
+                "finalizing": "完成下載中...",
+                "cancelling": "取消下載中...",
+                "cancelled": "下載已取消"
             },
             "progress": {
                 "currentFile": "目前檔案：",
@@ -1319,6 +1340,14 @@
             "pathPlaceholder": "輸入資料夾路徑或從下方樹狀結構選擇...",
             "root": "根目錄"
         },
+        "linkHuggingFace": {
+            "title": "連結到 HuggingFace",
+            "infoText": "貼上 HuggingFace 倉庫 URL 以關聯此模型。關聯後可啟用 AI 中繼資料增強功能。",
+            "urlLabel": "HuggingFace 倉庫 URL：",
+            "urlPlaceholder": "https://huggingface.co/user/repo",
+            "helpText": "請輸入完整的 HuggingFace 倉庫 URL。",
+            "confirmAction": "儲存並連結"
+        },
         "relinkCivitai": {
             "title": "重新連結至 Civitai",
             "warning": "警告：",
@@ -1526,6 +1555,7 @@
                 "empty": "此模型尚無版本歷史。",
                 "error": "載入版本失敗。",
                 "missingModelId": "此模型缺少 Civitai 模型 ID。",
+                "hfGroupInfo": "這是一個 HuggingFace 模型組。打開庫頁面即可在網格中查看所有版本。",
                 "confirm": {
                     "delete": "要從庫中刪除此版本嗎？"
                 },
@@ -1552,6 +1582,21 @@
             "downloadCsv": "下載 CSV",
             "columnModelName": "模型名稱",
             "columnError": "錯誤"
+        },
+        "downloadBatchSummary": {
+            "title": "[TODO: Translate] Batch Download Summary",
+            "statSuccess": "[TODO: Translate] Success",
+            "statFailed": "[TODO: Translate] Failed",
+            "statTotal": "[TODO: Translate] Total",
+            "successMessage": "[TODO: Translate] All {count} models downloaded successfully",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "failed": "[TODO: Translate] Download failed",
+            "failedItems": "[TODO: Translate] Failed Items ({count})",
+            "columnName": "[TODO: Translate] Model Name",
+            "columnError": "[TODO: Translate] Error",
+            "close": "[TODO: Translate] Close",
+            "copyReport": "[TODO: Translate] Copy Report",
+            "retryFailed": "[TODO: Translate] Retry Failed ({count})"
         }
     },
     "modelTags": {
@@ -1729,6 +1774,12 @@
         "checkingMessage": "請稍候，正在檢查最新版本。",
         "showNotifications": "顯示更新通知",
         "latestBadge": "最新",
+        "latestMain": "Main 分支",
+        "channel": "更新頻道",
+        "channels": {
+            "release": "稳定版",
+            "nightly": "Nightly"
+        },
         "updateProgress": {
             "preparing": "正在準備更新...",
             "installing": "正在安裝更新...",
@@ -1749,6 +1800,15 @@
             "warning": "警告：Nightly 版本可能包含實驗性功能且可能不穩定。",
             "enable": "啟用 Nightly 更新"
         },
+        "channelSwitch": {
+            "nightlyTitle": "切换到 Nightly",
+            "nightlyMessage": "切换到 Nightly 将初始化 Git 仓库并跟踪 main 分支的最新提交。更新更频繁但可能不稳定，可随时切回稳定版。",
+            "releaseTitle": "切换到稳定版",
+            "releaseMessage": "切換到穩定版將檢出最新的發布標籤。可隨時切換回每日構建版。",
+            "switching": "正在切換到 {channel} 頻道...",
+            "completed": "已切換到 {channel} 頻道",
+            "failed": "切換頻道失敗"
+        },
         "banners": {
             "recent": "最新通知",
             "empty": "目前沒有最近的橫幅通知。",
@@ -2003,7 +2063,8 @@
             "imagesCompleted": "範例圖片{action}完成",
             "imagesFailed": "範例圖片{action}失敗",
             "loadError": "載入下載時發生錯誤：{message}",
-            "downloadError": "下載錯誤：{message}"
+            "downloadError": "下載錯誤：{message}",
+            "downloadStopped": "下載已取消"
         },
         "import": {
             "folderTreeFailed": "載入資料夾樹狀結構失敗",
@@ -2048,6 +2109,8 @@
             "contentRatingFailed": "設定內容分級失敗：{message}",
             "relinkSuccess": "模型已成功重新連結至 Civitai",
             "relinkFailed": "錯誤：{message}",
+            "linkHfSuccess": "模型已成功連結到 HuggingFace",
+            "linkHfFailed": "錯誤：{message}",
             "fetchMetadataFirst": "請先從 CivitAI 取得 metadata",
             "noCivitaiInfo": "無 CivitAI 資訊",
             "missingHash": "模型雜湊不可用"

py/config.py

@@ -208,6 +208,12 @@ class Config:
             if not isinstance(library_config, dict):
                 return
 
+            # Always read recipes_path — it is independent of extra folder paths
+            # and must be set before any early returns below.
+            recipes_path = library_config.get("recipes_path", "")
+            if isinstance(recipes_path, str) and recipes_path:
+                self.recipes_path = recipes_path
+
             extra_folder_paths = library_config.get("extra_folder_paths")
             if not isinstance(extra_folder_paths, dict):
                 return
@@ -233,10 +239,6 @@ class Config:
                 extra_embedding
             )
 
-            recipes_path = library_config.get("recipes_path", "")
-            if isinstance(recipes_path, str) and recipes_path:
-                self.recipes_path = recipes_path
-
             if self.extra_loras_roots:
                 logger.info(
                     "Found extra LoRA roots:"
@@ -357,6 +359,47 @@ class Config:
                         "Failed to rename legacy 'default' library: %s", rename_error
                     )
 
+            # Clean up a stale "default" library entry that has no meaningful
+            # paths configured (e.g. leftover bootstrap artifact).  This only
+            # fires when "comfyui" already exists so we never delete the last
+            # remaining library.
+            if (
+                "default" in libraries
+                and "comfyui" in libraries
+                and isinstance(default_library, Mapping)
+            ):
+                default_folder_paths = _normalize_library_folder_paths(
+                    default_library
+                )
+                default_extra_paths = default_library.get("extra_folder_paths", {})
+                has_meaningful_paths = bool(default_folder_paths) or bool(
+                    default_extra_paths
+                ) or any(
+                    default_library.get(key)
+                    for key in (
+                        "default_lora_root",
+                        "default_checkpoint_root",
+                        "default_unet_root",
+                        "default_embedding_root",
+                        "recipes_path",
+                    )
+                )
+                if not has_meaningful_paths:
+                    try:
+                        settings_service.delete_library("default")
+                        libraries_changed = True
+                        logger.info(
+                            "Removed stale 'default' library entry "
+                            "with no meaningful paths configured"
+                        )
+                        libraries = settings_service.get_libraries()
+                        comfy_library = libraries.get("comfyui", {})
+                    except Exception as delete_error:
+                        logger.debug(
+                            "Failed to remove stale 'default' library: %s",
+                            delete_error,
+                        )
+
             default_lora_root = _resolve_valid_default_root(
                 comfy_library.get("default_lora_root", ""),
                 list(self.loras_roots or []),

py/metadata_collector/constants.py

@@ -1,5 +1,11 @@
 """Constants used by the metadata collector"""
 
+# Sentinel value for clip_skip to distinguish "unconnected / widget default"
+# from "user wired value 0".  Both ComfyUI CLIPSetLastLayer (-24..-1) and
+# A1111 conventions treat 0 as meaningless for clip skipping, but users may
+# explicitly wire 0 to the overwrite node to express "no clip skip / default".
+CLIP_SKIP_SENTINEL = -25
+
 # Metadata categories
 MODELS = "models"
 PROMPTS = "prompts"
@@ -9,6 +15,14 @@ EMBEDDINGS = "embeddings"
 SIZE = "size"
 IMAGES = "images"
 IS_SAMPLER = "is_sampler"  # New constant to mark sampler nodes
+OVERWRITE = "overwrite"  # Manual metadata overwrite from MetadataOverwriteLM node
+
+# Field names that the MetadataOverwriteLM node and its extractor share
+METADATA_OVERWRITE_FIELDS = (
+    "prompt", "negative_prompt", "seed", "steps", "cfg_scale",
+    "sampler", "scheduler", "model", "loras", "size",
+    "clip_skip", "additional_data",
+)
 
 # Complete list of categories to track
-METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES]
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES, OVERWRITE]

py/metadata_collector/metadata_hook.py

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

py/metadata_collector/metadata_processor.py

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

py/metadata_collector/metadata_registry.py

@@ -1,7 +1,7 @@
 import time
 from nodes import NODE_CLASS_MAPPINGS  # type: ignore
 from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
-from .constants import METADATA_CATEGORIES, IMAGES
+from .constants import METADATA_CATEGORIES, IMAGES, OVERWRITE
 
 
 class MetadataRegistry:
@@ -61,6 +61,7 @@ class MetadataRegistry:
             {
                 "execution_order": [],
                 "current_prompt": None,  # Will store the prompt object
+                "extra_data": None,  # Will store the API extra_data for workflow metadata
                 "timestamp": time.time(),
             }
         )
@@ -75,6 +76,11 @@ class MetadataRegistry:
             # Store the prompt in the metadata for later relationship tracing
             self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
 
+    def set_extra_data(self, extra_data):
+        """Store the API extra_data (contains extra_pnginfo.workflow with node properties)"""
+        if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
+            self.prompt_metadata[self.current_prompt_id]["extra_data"] = extra_data
+
     def get_metadata(self, prompt_id=None):
         """Get collected metadata for a prompt"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
@@ -122,20 +128,28 @@ class MetadataRegistry:
             cache_key = f"{node_id}:{class_type}"
 
             # Check if this node type is relevant for metadata collection
-            if class_type in NODE_EXTRACTORS:
+            if class_type in NODE_EXTRACTORS or cache_key in self.node_cache:
                 # Check if we have cached metadata for this node
                 if cache_key in self.node_cache:
                     cached_data = self.node_cache[cache_key]
 
+                    # Detect bypass (mode=4) / mute (mode=2) — these nodes
+                    # were intentionally disabled and should not contribute
+                    # overwrite values from a previous execution's cache.
+                    node_mode = node_data.get("mode", 0)
+                    node_is_disabled = node_mode in (2, 4)
+
                     # Apply cached metadata to the current metadata
                     for category in self.metadata_categories:
+                        if category == OVERWRITE and node_is_disabled:
+                            continue
                         if category in cached_data and node_id in cached_data[category]:
                             if node_id not in metadata[category]:
                                 metadata[category][node_id] = cached_data[category][
                                     node_id
                                 ]
 
-    def record_node_execution(self, node_id, class_type, inputs, outputs):
+    def record_node_execution(self, node_id, class_type, inputs, outputs, return_types=None):
         """Record information about a node's execution"""
         if not self.current_prompt_id:
             return
@@ -158,17 +172,18 @@ class MetadataRegistry:
 
         # Extract node-specific metadata
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
-        extractor.extract(
-            node_id,
-            processed_inputs,
-            outputs,
-            self.prompt_metadata[self.current_prompt_id],
-        )
+        if extractor is GenericNodeExtractor:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id],
+                              return_types=return_types)
+        else:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id])
 
         # Cache this node's metadata
         self._cache_node_metadata(node_id, class_type)
 
-    def update_node_execution(self, node_id, class_type, outputs):
+    def update_node_execution(self, node_id, class_type, outputs, return_types=None):
         """Update node metadata with output information"""
         if not self.current_prompt_id:
             return
@@ -179,9 +194,17 @@ class MetadataRegistry:
         # Use the same extractor to update with outputs
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
         if hasattr(extractor, "update"):
-            extractor.update(
-                node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
-            )
+            if extractor is GenericNodeExtractor:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                    return_types=return_types,
+                )
+            else:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                )
 
         # Update the cached metadata for this node
         self._cache_node_metadata(node_id, class_type)

py/metadata_collector/node_extractors.py

@@ -2,7 +2,8 @@ import json
 import os
 import re
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE
+from .overwrite_utils import collect_overwrite_params
 
 
 def _store_checkpoint_metadata(metadata, node_id, model_name):
@@ -31,11 +32,78 @@ class NodeMetadataExtractor:
         pass
         
 class GenericNodeExtractor(NodeMetadataExtractor):
-    """Default extractor for nodes without specific handling"""
+    """Fallback extractor with type-signature-based detection.
+
+    When a node is not in the NODE_EXTRACTORS registry, the hook layer
+    passes ``return_types`` from ``obj.RETURN_TYPES``:
+
+    * ``MODEL`` output: common input fields (ckpt_name, unet_name, etc.)
+      are checked for a model file name and stored as checkpoint metadata.
+    * ``CONDITIONING`` output: common text input fields are checked for
+      prompt text and stored as prompt metadata.
+    """
+
+    # Input field names that carry a model path in loader-style nodes.
+    _MODEL_NAME_FIELDS = (
+        "ckpt_name", "unet_name", "model_path", "model_name", "gguf_name",
+    )
+
+    # Extensions used by checkpoint_scanner.py — only record values that look
+    # like real model filenames to avoid capturing unrelated string fields.
+    _MODEL_EXTENSIONS = {
+        ".ckpt", ".pt", ".pt2", ".bin", ".pth", ".safetensors", ".pkl", ".sft", ".gguf",
+    }
+
+    # Input field names that may carry prompt text in encoder-style nodes.
+    _TEXT_FIELDS = ("text", "clip_l", "t5xxl", "prompt", "positive", "negative")
+
     @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        pass
-        
+    def extract(node_id, inputs, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+
+        # — MODEL loader detection (checkpoint / UNET / GGUF) —
+        if "MODEL" in return_types or any("MODEL" in str(t) for t in return_types):
+            for field in GenericNodeExtractor._MODEL_NAME_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    name = val.strip()
+                    if not any(name.lower().endswith(ext) for ext in GenericNodeExtractor._MODEL_EXTENSIONS):
+                        continue
+                    _store_checkpoint_metadata(metadata, node_id, name)
+                    return
+
+        # — CONDITIONING encoder detection (CLIPTextEncode, Flux, custom) —
+        if "CONDITIONING" in return_types or any("CONDITIONING" in str(t) for t in return_types):
+            text = None
+            for field in GenericNodeExtractor._TEXT_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    text = val.strip()
+                    break
+            if text:
+                prompt_data = metadata.setdefault(PROMPTS, {})
+                prompt_data[node_id] = {
+                    "text": text,
+                    "node_id": node_id,
+                }
+
+    @staticmethod
+    def update(node_id, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+        if "CONDITIONING" not in return_types and not any(
+            "CONDITIONING" in str(t) for t in return_types
+        ):
+            return
+        if node_id not in metadata.get(PROMPTS, {}):
+            return
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
+                cond = outputs[0][0]
+                if cond is not None:
+                    metadata[PROMPTS][node_id]["conditioning"] = cond
+
 class CheckpointLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1154,6 +1222,28 @@ class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
                 metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
                 metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
 
+class MetadataOverwriteExtractor(NodeMetadataExtractor):
+    """Extract manually specified metadata from MetadataOverwriteLM node.
+
+    Stores truthy input values under the OVERWRITE category so that
+    extract_generation_params can merge them over the inferred params.
+    """
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        overwrite_params = collect_overwrite_params(inputs)
+
+        if overwrite_params:
+            metadata.setdefault(OVERWRITE, {})
+            metadata[OVERWRITE][node_id] = {
+                "parameters": overwrite_params,
+                "node_id": node_id,
+            }
+
+
 # Registry of node-specific extractors
 # Keys are node class names
 NODE_EXTRACTORS = {
@@ -1221,5 +1311,7 @@ NODE_EXTRACTORS = {
     "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider
     # Image
     "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
+    # Metadata overwrite
+    "MetadataOverwriteLM": MetadataOverwriteExtractor,
     # Add other nodes as needed
 }

py/metadata_collector/overwrite_utils.py

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

py/middleware/error_middleware.py

@@ -41,7 +41,12 @@ async def api_json_error(
         if exc.status < 400:
             raise
 
-        logger.warning(
+        # Preview 404 is routine (file deleted from disk) — not worth a warning.
+        logger_method = logger.warning
+        if request.path.startswith("/api/lm/previews") and exc.status == 404:
+            logger_method = logger.debug
+
+        logger_method(
             "API %s %s returned HTTP %d: %s",
             request.method,
             request.path,

py/nodes/create_hook_lora.py

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

py/nodes/lora_info.py

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

py/nodes/lora_loader.py

@@ -1,6 +1,5 @@
 import importlib
 import logging
-import re
 
 import comfy.sd  # type: ignore
 import comfy.utils  # type: ignore
@@ -14,6 +13,7 @@ from .utils import (
     extract_lora_name,
     get_loras_list,
     nunchaku_load_lora,
+    parse_lora_syntax,
 )
 
 logger = logging.getLogger(__name__)
@@ -189,25 +189,10 @@ class LoraTextLoaderLM:
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras_from_text"
 
-    def parse_lora_syntax(self, text):
-        """Parse LoRA syntax from text input."""
-        pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
-        matches = re.findall(pattern, text, re.IGNORECASE)
-
-        loras = []
-        for match in matches:
-            model_strength = float(match[1])
-            loras.append({
-                "name": match[0],
-                "model_strength": model_strength,
-                "clip_strength": float(match[2]) if match[2] else model_strength,
-            })
-        return loras
-
     def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
         """Load LoRAs based on text syntax input."""
         lora_entries = _collect_stack_entries(lora_stack)
-        for lora in self.parse_lora_syntax(lora_syntax):
+        for lora in parse_lora_syntax(lora_syntax):
             lora_path, trigger_words = get_lora_info_absolute(lora["name"])
             lora_entries.append({
                 "name": lora["name"],

py/nodes/lora_stack_combiner.py

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

py/nodes/lora_syntax_to_path.py

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

py/nodes/metadata_overwrite.py

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

py/nodes/save_image.py

@@ -16,6 +16,156 @@ from PIL import Image, PngImagePlugin
 import piexif
 import logging
 
+# Civitai-compatible sampler name mapping: ComfyUI internal → A1111 display name
+CIVITAI_SAMPLER_MAP = {
+    "euler": "Euler",
+    "euler_ancestral": "Euler a",
+    "lms": "LMS",
+    "heun": "Heun",
+    "dpm_2": "DPM2",
+    "dpm_2_ancestral": "DPM2 a",
+    "dpmpp_2s_ancestral": "DPM++ 2S a",
+    "dpmpp_2m": "DPM++ 2M",
+    "dpmpp_sde": "DPM++ SDE",
+    "dpmpp_sde_gpu": "DPM++ SDE",
+    "dpmpp_2m_sde": "DPM++ 2M SDE",
+    "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
+    "dpmpp_3m_sde": "DPM++ 3M SDE",
+    "dpm_fast": "DPM fast",
+    "dpm_adaptive": "DPM adaptive",
+    "ddim": "DDIM",
+    "plms": "PLMS",
+    "uni_pc_bh2": "UniPC",
+    "uni_pc": "UniPC",
+    "lcm": "LCM",
+}
+
+# Base model display name → AIR URN slug
+# Sourced from civitai source: src/shared/constants/basemodel.constants.ts
+BASE_MODEL_AIR_SLUG = {
+    # Stable Diffusion family
+    "SD 1.4": "sd1",
+    "SD 1.5": "sd1",
+    "SD 1.5 LCM": "sd1",
+    "SD 1.5 Hyper": "sd1",
+    "SD 2.0": "sd2",
+    "SD 2.0 768": "sd2",
+    "SD 2.1": "sd2",
+    "SD 2.1 768": "sd2",
+    "SD 2.1 Unclip": "sd2",
+    "SD 3.0": "sd3",
+    "SD 3.5": "sd35",
+    "SD 3.5 Large": "sd35",
+    "SD 3.5 Large Turbo": "sd35",
+    "SD 3.5 Medium": "sd35",
+    "SDXL 0.9": "sdxl",
+    "SDXL 1.0": "sdxl",
+    "SDXL 1.0 LCM": "sdxl",
+    "SDXL Lightning": "sdxl",
+    "SDXL Hyper": "sdxl",
+    "SDXL Turbo": "sdxl",
+    "SDXL Distilled": "sdxldistilled",
+    "Stable Cascade": "scascade",
+    "Stable Video Diffusion": "svd",
+    "SVD": "svd",
+    "SVD XT": "svdxt",
+
+    # SDXL community fine-tunes
+    "Pony": "pony",
+    "Pony Diffusion": "pony",
+    "Illustrious": "illustrious",
+    "NoobAI": "noobai",
+    "Animagine": "illustrious",
+
+    # Flux family
+    "Flux.1": "flux1",
+    "Flux.1 D": "flux1",
+    "Flux.1 S": "flux1",
+    "Flux.1 Krea": "fluxkrea",
+    "Flux.1 Kontext": "flux1kontext",
+    "Flux.2": "flux2",
+    "Flux.2 D": "flux2",
+    "Flux.2 Klein 9B": "flux2klein_9b",
+    "Flux.2 Klein 9B Base": "flux2klein_9b_base",
+    "Flux.2 Klein 4B": "flux2klein_4b",
+    "Flux.2 Klein 4B Base": "flux2klein_4b_base",
+
+    # Other image models (sorted alphabetically)
+    "AuraFlow": "auraflow",
+    "Chroma": "chroma",
+    "HiDream": "hidream",
+    "HiDream-O1": "hidream-o1",
+    "Hunyuan DiT": "hydit1",
+    "Hunyuan Video": "hyv1",
+    "Kolors": "kolors",
+    "Lumina": "lumina",
+    "Mochi": "mochi",
+    "ODOR": "odor",
+    "PixArt Alpha": "pixarta",
+    "PixArt Sigma": "pixarte",
+    "Playground v2": "playgroundv2",
+    "Playground v2.5": "playgroundv2",
+    "Pony Diffusion V7": "ponyv7",
+
+    # Video models
+    "CogVideoX": "cogvideox",
+    "LTX Video": "ltxv",
+    "LTX Video 2": "ltxv2",
+    "LTX Video 2.3": "ltxv23",
+    "Wan Video": "wanvideo",
+    "Wan Video 1.3B T2V": "wanvideo_13b_t2v",
+    "Wan Video 14B T2V": "wanvideo_14b_t2v",
+    "Wan Video 14B I2V 480p": "wanvideo_14b_i2v_480p",
+    "Wan Video 14B I2V 720p": "wanvideo_14b_i2v_720p",
+
+    # Third-party / proprietary image models
+    "Boogu": "boogu",
+    "Ernie": "ernie",
+    "Grok": "grok",
+    "HappyHorse": "happyhorse",
+    "Ideogram": "ideogram",
+    "Ideogram 4.0": "ideogram",
+    "Imagen": "imagen4",
+    "Imagen 4": "imagen4",
+    "Krea": "krea2",
+    "Krea 2": "krea2",
+    "Lens": "lens",
+    "MAI": "mai",
+    "Nano Banana": "nanobanana",
+    "OpenAI": "openai",
+    "Reve": "reve",
+    "Reve 2": "reve",
+    "Reve 2.1": "reve",
+    "Seedream": "seedream",
+    "Sora": "sora2",
+    "Sora 2": "sora2",
+    "Veo": "veo3",
+    "Veo 2": "veo3",
+    "Veo 3": "veo3",
+    "ZImageTurbo": "zimageturbo",
+    "ZImageBase": "zimagebase",
+    "ZImage": "zimagebase",
+
+    # Third-party video models
+    "Hailuo by MiniMax": "minimax",
+    "Haiper": "haiper",
+    "Kling": "kling",
+    "Lightricks": "lightricks",
+    "Seedance": "seedance",
+    "Vidu": "vidu",
+
+    # Qwen family
+    "Qwen": "qwen",
+    "Qwen 2": "qwen2",
+
+    # Anima
+    "Anima": "anima",
+
+    # Special
+    "Upscaler": "upscaler",
+    "Other": "other",
+}
+
 logger = logging.getLogger(__name__)
 
 
@@ -70,11 +220,29 @@ class SaveImageLM:
                         "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files.",
                     },
                 ),
+                "webp_method": (
+                    "INT",
+                    {
+                        "default": 6,
+                        "min": 0,
+                        "max": 6,
+                        "tooltip": "WebP compression method (0-6). 0=fastest/largest, 6=slowest/smallest. Only applies when file_format is 'webp'.",
+                    },
+                ),
+                "jpeg_subsampling": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2,
+                        "tooltip": "JPEG chroma subsampling level. 0=4:4:4 (best quality), 1=4:2:2, 2=4:2:0 (smallest files). Only applies when file_format is 'jpeg'.",
+                    },
+                ),
                 "embed_workflow": (
                     "BOOLEAN",
                     {
                         "default": False,
-                        "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
+                        "tooltip": "When enabled, saved images store the complete workflow. Drag the image back into ComfyUI to restore the original node graph. PNG and WebP only.",
                     },
                 ),
                 "save_with_metadata": (
@@ -84,6 +252,13 @@ class SaveImageLM:
                         "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
                     },
                 ),
+                "add_loras_to_prompt": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "When enabled, appends the LoRA syntax line (e.g. <lora:name:strength>) after the positive prompt in the saved metadata.",
+                    },
+                ),
                 "add_counter_to_filename": (
                     "BOOLEAN",
                     {
@@ -142,148 +317,197 @@ class SaveImageLM:
 
         return None
 
-    def format_metadata(self, metadata_dict):
-        """Format metadata in the requested format similar to userComment example"""
-        if not metadata_dict:
-            return ""
+    def _resolve_model_cache_entry(self, scanner_type: str, name: str):
+        """Resolve model hash, civitai metadata, and base_model from scanner cache.
+        Returns (hash_str, civitai_dict, base_model_str). All values are empty defaults when not found."""
+        scanner = ServiceRegistry.get_service_sync(scanner_type)
+        if scanner is None or not name:
+            return "", {}, ""
 
-        # Helper function to only add parameter if value is not None
-        def add_param_if_not_none(param_list, label, value):
-            if value is not None:
-                param_list.append(f"{label}: {value}")
+        entry = self._get_cached_model_by_name(scanner, name)
+        if entry is None:
+            basename = os.path.splitext(os.path.basename(name))[0]
+            hash_val = scanner.get_hash_by_filename(basename)
+            return (hash_val or "").lower(), {}, ""
+
+        hash_val = (entry.get("sha256") or "").lower()
+        civitai = entry.get("civitai") or {}
+        base_model = entry.get("base_model") or ""
+        return hash_val, civitai, base_model
+
+    @staticmethod
+    def _get_civitai_sampler_name(sampler_name: str, scheduler: str) -> str:
+        if sampler_name in CIVITAI_SAMPLER_MAP:
+            civitai_name = CIVITAI_SAMPLER_MAP[sampler_name]
+            if scheduler == "karras":
+                civitai_name += " Karras"
+            elif scheduler == "exponential":
+                civitai_name += " Exponential"
+            return civitai_name
+        else:
+            if scheduler and scheduler != "normal":
+                return f"{sampler_name}_{scheduler}"
+            return sampler_name
+
+    @staticmethod
+    def _build_air_string(base_model: str, model_type: str, model_id: int, version_id: int) -> str:
+        slug = BASE_MODEL_AIR_SLUG.get(base_model, "other")
+        type_lower = model_type.lower() if model_type else "other"
+        return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
+
+    def format_metadata(self, metadata_dict: dict, add_loras_to_prompt: bool = False) -> str:
+        """Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
+        if not metadata_dict: return ""
 
-        # Extract the prompt and negative prompt
         prompt = metadata_dict.get("prompt", "")
         negative_prompt = metadata_dict.get("negative_prompt", "")
-
-        # Extract loras from the prompt if present
+        steps = metadata_dict.get("steps")
+        cfg = metadata_dict.get("guidance")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg_scale")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg")
+        seed = metadata_dict.get("seed")
+        size = metadata_dict.get("size")
+        sampler = metadata_dict.get("sampler") or ""
+        scheduler = metadata_dict.get("scheduler") or "normal"
+        checkpoint = metadata_dict.get("checkpoint") or ""
         loras_text = metadata_dict.get("loras", "")
-        lora_hashes = {}
+        clip_skip = metadata_dict.get("clip_skip")
 
-        # If loras are found, add them on a new line after the prompt
+        # Parse LoRA entries from <lora:name:strength> format
+        lora_entries: list[tuple[str, float]] = []
         if loras_text:
-            prompt_with_loras = f"{prompt}\n{loras_text}"
+            for match in re.findall(r"<lora:([^:]+):([^>]+)>", loras_text):
+                lora_name, strength_str = match
+                try:
+                    strength = float(strength_str)
+                except (ValueError, TypeError):
+                    strength = 1.0
+                lora_entries.append((lora_name, strength))
 
-            # Extract lora names from the format <lora:name:strength>
-            lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
+        # Resolve checkpoint hash and Civitai data from local cache
+        ckpt_hash, ckpt_civitai, ckpt_base_model = "", {}, ""
+        ckpt_display_name = ""
+        if checkpoint:
+            ckpt_hash, ckpt_civitai, ckpt_base_model = self._resolve_model_cache_entry(
+                "checkpoint_scanner", checkpoint
+            )
+            ckpt_display_name = os.path.splitext(os.path.basename(checkpoint))[0]
 
-            # Get hash for each lora
-            for lora_name, strength in lora_matches:
-                hash_value = self.get_lora_hash(lora_name)
-                if hash_value:
-                    lora_hashes[lora_name] = hash_value
-        else:
-            prompt_with_loras = prompt
+        # Resolve LoRA hash and Civitai data from local cache
+        loras_data: list[dict] = []
+        for lora_name, strength in lora_entries:
+            lora_hash, lora_civitai, lora_base_model = self._resolve_model_cache_entry(
+                "lora_scanner", lora_name
+            )
+            loras_data.append({
+                "name": lora_name,
+                "strength": strength,
+                "hash": lora_hash,
+                "civitai": lora_civitai,
+                "base_model": lora_base_model,
+            })
 
-        # Format the first part (prompt and loras)
-        metadata_parts = [prompt_with_loras]
+        # Build Hashes JSON (A1111 / Civitai standard format)
+        hashes: dict[str, str] = {}
+        if ckpt_hash:
+            hashes["model"] = ckpt_hash[:10].upper()
+        for lora in loras_data:
+            if lora["hash"]:
+                hashes[f"LORA:{lora['name']}"] = lora["hash"][:10].upper()
 
-        # Add negative prompt
+        # Build Civitai resources JSON array
+        civitai_resources: list[dict] = []
+        if ckpt_civitai.get("id", 0) > 0:
+            ckpt_resource: dict = {}
+            ckpt_type = (ckpt_civitai.get("model") or {}).get("type", "Checkpoint")
+            model_id = ckpt_civitai.get("modelId", 0)
+            version_id = ckpt_civitai.get("id", 0)
+            if model_id and version_id:
+                ckpt_resource["air"] = self._build_air_string(
+                    ckpt_base_model, ckpt_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                ckpt_resource["modelVersionId"] = int(version_id)
+            if ckpt_civitai.get("name"):
+                ckpt_resource["versionName"] = ckpt_civitai["name"]
+            if ckpt_resource:
+                civitai_resources.append(ckpt_resource)
+
+        for lora in loras_data:
+            lora_civitai = lora["civitai"]
+            if not lora_civitai or lora_civitai.get("id", 0) <= 0:
+                continue
+            lora_resource: dict = {"weight": lora["strength"]}
+            lora_type = (lora_civitai.get("model") or {}).get("type", "LORA")
+            model_id = lora_civitai.get("modelId", 0)
+            version_id = lora_civitai.get("id", 0)
+            if model_id and version_id:
+                lora_resource["air"] = self._build_air_string(
+                    lora["base_model"], lora_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                lora_resource["modelVersionId"] = int(version_id)
+            if lora_civitai.get("name"):
+                lora_resource["versionName"] = lora_civitai["name"]
+            civitai_resources.append(lora_resource)
+
+        sampler_name = CIVITAI_SAMPLER_MAP.get(sampler, sampler) if sampler else None
+
+        scheduler_mapping = {
+            "normal": "Normal",
+            "karras": "Karras",
+            "exponential": "Exponential",
+            "sgm_uniform": "SGM Uniform",
+            "sgm_quadratic": "SGM Quadratic",
+        }
+        scheduler_name = scheduler_mapping.get(scheduler, scheduler) if scheduler else None
+
+        # Build output lines
+        prompt_line = prompt if prompt else ""
+        if add_loras_to_prompt and loras_text:
+            prompt_line = f"{prompt_line}\n{loras_text}" if prompt_line else loras_text
+        lines = [prompt_line] if prompt_line else [""]
         if negative_prompt:
-            metadata_parts.append(f"Negative prompt: {negative_prompt}")
+            lines.append(f"Negative prompt: {negative_prompt}")
 
-        # Format the second part (generation parameters)
-        params = []
-
-        # Add standard parameters in the correct order
-        if "steps" in metadata_dict:
-            add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
-
-        # Combine sampler and scheduler information
-        sampler_name = None
-        scheduler_name = None
-
-        if "sampler" in metadata_dict:
-            sampler = metadata_dict.get("sampler")
-            # Convert ComfyUI sampler names to user-friendly names
-            sampler_mapping = {
-                "euler": "Euler",
-                "euler_ancestral": "Euler a",
-                "dpm_2": "DPM2",
-                "dpm_2_ancestral": "DPM2 a",
-                "heun": "Heun",
-                "dpm_fast": "DPM fast",
-                "dpm_adaptive": "DPM adaptive",
-                "lms": "LMS",
-                "dpmpp_2s_ancestral": "DPM++ 2S a",
-                "dpmpp_sde": "DPM++ SDE",
-                "dpmpp_sde_gpu": "DPM++ SDE",
-                "dpmpp_2m": "DPM++ 2M",
-                "dpmpp_2m_sde": "DPM++ 2M SDE",
-                "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
-                "ddim": "DDIM",
-            }
-            sampler_name = sampler_mapping.get(sampler, sampler)
-
-        if "scheduler" in metadata_dict:
-            scheduler = metadata_dict.get("scheduler")
-            scheduler_mapping = {
-                "normal": "Simple",
-                "karras": "Karras",
-                "exponential": "Exponential",
-                "sgm_uniform": "SGM Uniform",
-                "sgm_quadratic": "SGM Quadratic",
-            }
-            scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-
-        # Add combined sampler and scheduler information
+        params: list[str] = []
+        if steps is not None:
+            params.append(f"Steps: {steps}")
         if sampler_name:
             if scheduler_name:
                 params.append(f"Sampler: {sampler_name} {scheduler_name}")
             else:
                 params.append(f"Sampler: {sampler_name}")
+        if cfg is not None:
+            params.append(f"CFG scale: {cfg}")
+        if seed is not None:
+            params.append(f"Seed: {seed}")
+        if size:
+            params.append(f"Size: {size}")
+        if clip_skip is not None:
+            try:
+                params.append(f"Clip skip: {abs(int(clip_skip))}")
+            except (ValueError, TypeError):
+                pass
+        additional_data = metadata_dict.get("additional_data", "")
+        if additional_data:
+            params.append(additional_data)
+        if ckpt_hash:
+            params.append(f"Model hash: {ckpt_hash[:10].upper()}")
+        if ckpt_display_name:
+            params.append(f"Model: {ckpt_display_name}")
+        if hashes:
+            params.append(f"Hashes: {json.dumps(hashes, separators=(',', ':'))}")
+        params.append("Version: ComfyUI")
+        if civitai_resources:
+            params.append(
+                f"Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}"
+            )
 
-        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
-        if "guidance" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
-        elif "cfg_scale" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
-        elif "cfg" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
-
-        # Seed
-        if "seed" in metadata_dict:
-            add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
-
-        # Size
-        if "size" in metadata_dict:
-            add_param_if_not_none(params, "Size", metadata_dict.get("size"))
-
-        # Model info
-        if "checkpoint" in metadata_dict:
-            # Ensure checkpoint is a string before processing
-            checkpoint = metadata_dict.get("checkpoint")
-            if checkpoint is not None:
-                # Get model hash
-                model_hash = self.get_checkpoint_hash(checkpoint)
-
-                # Extract basename without path
-                checkpoint_name = os.path.basename(checkpoint)
-                # Remove extension if present
-                checkpoint_name = os.path.splitext(checkpoint_name)[0]
-
-                # Add model hash if available
-                if model_hash:
-                    params.append(
-                        f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
-                    )
-                else:
-                    params.append(f"Model: {checkpoint_name}")
-
-        # Add LoRA hashes if available
-        if lora_hashes:
-            lora_hash_parts = []
-            for lora_name, hash_value in lora_hashes.items():
-                lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
-
-            if lora_hash_parts:
-                params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
-
-        # Combine all parameters with commas
-        metadata_parts.append(", ".join(params))
-
-        # Join all parts with a new line
-        return "\n".join(metadata_parts)
+        lines.append(", ".join(params))
+        return "\n".join(lines)
 
     # credit to nkchocoai
     # Add format_filename method to handle pattern substitution
@@ -573,10 +797,13 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Save images with metadata"""
         results = []
@@ -585,7 +812,7 @@ class SaveImageLM:
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
 
-        metadata = self.format_metadata(metadata_dict)
+        metadata = self.format_metadata(metadata_dict, add_loras_to_prompt)
 
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)
@@ -627,15 +854,14 @@ class SaveImageLM:
             elif file_format == "jpeg":
                 file = base_filename + ".jpg"
                 file_extension = ".jpg"
-                save_kwargs = {"quality": quality, "optimize": True}
+                save_kwargs = {"quality": quality, "optimize": True, "subsampling": jpeg_subsampling}
             elif file_format == "webp":
                 file = base_filename + ".webp"
                 file_extension = ".webp"
-                # Add optimization param to control performance
                 save_kwargs = {
                     "quality": quality,
                     "lossless": lossless_webp,
-                    "method": 0,
+                    "method": webp_method,
                 }
             else:
                 raise ValueError(f"Unsupported file format: {file_format}")
@@ -722,10 +948,13 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -751,10 +980,13 @@ class SaveImageLM:
             extra_pnginfo,
             lossless_webp,
             quality,
+            webp_method,
+            jpeg_subsampling,
             embed_workflow,
             save_with_metadata,
             add_counter_to_filename,
             save_as_recipe,
+            add_loras_to_prompt,
         )
 
         return {

py/nodes/unet_loader.py

@@ -7,6 +7,21 @@ from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_c
 logger = logging.getLogger(__name__)
 
 
+def _reload_gguf_unet(
+    unet_path: str, weight_dtype: str, disable_dynamic: bool = False
+) -> object:
+    """Reload a GGUF diffusion model from disk (cached_patcher_init factory).
+
+    Mirrors the GGUF branch of UNETLoaderLM.load_unet so ModelPatcher
+    deepclone/dynamic machinery can rebuild GGUF models with the correct
+    GGMLOps. ``disable_dynamic`` is accepted for signature compatibility
+    with core ComfyUI loaders.
+    """
+    loader = UNETLoaderLM()
+    model, = loader._load_gguf_unet(unet_path, unet_path, weight_dtype)
+    return model
+
+
 class UNETLoaderLM:
     """UNET Loader with support for extra folder paths
 
@@ -196,6 +211,12 @@ class UNETLoaderLM:
             # Wrap with GGUFModelPatcher
             model = GGUFModelPatcher.clone(model)
 
+            # Register a reload factory so the MODEL carries its source path
+            # (cached_patcher_init) like core ComfyUI loaders do — required
+            # for model-name extraction downstream and for ModelPatcher
+            # deepclone/dynamic machinery.
+            model.cached_patcher_init = (_reload_gguf_unet, (unet_path, weight_dtype))
+
             return (model,)
 
         except Exception as e:

py/nodes/utils.py

@@ -36,6 +36,7 @@ any_type = AnyType("*")
 
 # Common methods extracted from lora_loader.py and lora_stacker.py
 import os
+import re
 import logging
 import copy
 import sys
@@ -69,6 +70,25 @@ def extract_lora_name(lora_path):
     return apply_lora_syntax_format(name_no_ext)
 
 
+def parse_lora_syntax(text: str) -> list[dict]:
+    """Parse <lora:name:strength> syntax from text input into a list of dicts.
+
+    Each entry contains: name, model_strength, clip_strength.
+    Supports both ``<lora:name:strength>`` and ``<lora:name:model_strength:clip_strength>``.
+    """
+    pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
+    matches = re.findall(pattern, text, re.IGNORECASE)
+    loras = []
+    for match in matches:
+        model_strength = float(match[1])
+        loras.append({
+            "name": match[0],
+            "model_strength": model_strength,
+            "clip_strength": float(match[2]) if match[2] else model_strength,
+        })
+    return loras
+
+
 def get_loras_list(kwargs):
     """Helper to extract loras list from either old or new kwargs format"""
     if "loras" not in kwargs:

py/routes/handlers/hf_handlers.py

@@ -96,7 +96,7 @@ def _infer_model_type(model_root: str) -> tuple[Any, str]:
     return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
 
 
-async def _save_hf_metadata(dest_path: str, repo: str, model_root: str, folder: str = "") -> None:
+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
@@ -105,11 +105,6 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str, folder:
     ``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.
-
-    Args:
-        folder: Relative folder path within the model root. Passed by the
-            caller rather than re-derived from file paths to avoid mismatches
-            when ``dest_path`` was realpath-resolved but scanner roots are not.
     """
     try:
         hf_url = f"https://huggingface.co/{repo}"
@@ -135,22 +130,138 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str, folder:
         await MetadataManager.save_metadata(dest_path, metadata_dict)
         logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
 
-        # 4. Add to scanner cache (same as CivitAI's _execute_download does)
+        # 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)
-        scanner = await scanner_getter() if scanner_getter is not None else None
-        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)
+        if scanner_getter is not None:
+            scanner = await scanner_getter()
+            if scanner is not None:
+                metadata_dict = metadata.to_dict()
+                metadata_dict["hf_url"] = hf_url
+                await scanner.add_model_to_cache(metadata_dict, folder)
+                logger.info("Added %s to scanner cache (folder=%s)", dest_path, folder)
 
     except Exception as exc:
         logger.warning("Failed to save HF metadata for %s: %s", dest_path, exc)
 
 
+def _find_matching_root(dest_dir: str) -> str | None:
+    """Walk up *dest_dir* to find which configured scanner root it belongs to."""
+    norm = os.path.normpath(dest_dir).replace(os.sep, "/")
+    all_roots = []
+    for root_list in (
+        config.loras_roots or [],
+        config.extra_loras_roots or [],
+        config.checkpoints_roots or [],
+        config.extra_checkpoints_roots or [],
+        config.unet_roots or [],
+        config.extra_unet_roots or [],
+        config.embeddings_roots or [],
+        config.extra_embeddings_roots or [],
+    ):
+        all_roots.extend([os.path.normpath(p).replace(os.sep, "/") for p in root_list])
+    # Find the longest matching prefix
+    match: str | None = None
+    for root in all_roots:
+        if norm.startswith(root):
+            if match is None or len(root) > len(match):
+                match = root
+    return match
+
+
+async def _add_to_scanner_cache(dest_path: str, metadata: dict[str, Any]) -> None:
+    model_dir = os.path.dirname(dest_path)
+    model_root = _find_matching_root(model_dir)
+    if not model_root:
+        raise ValueError(f"File path {dest_path} is not within any configured scanner root")
+    scanner_getter_name = _infer_model_type(model_root)[1]
+    scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
+    if scanner_getter is None:
+        raise RuntimeError(f"Scanner getter '{scanner_getter_name}' not found in ServiceRegistry")
+    scanner = await scanner_getter()
+    if scanner is None:
+        raise RuntimeError(f"Scanner '{scanner_getter_name}' returned None")
+    await scanner.update_single_model_cache(dest_path, dest_path, metadata)
+
+
 class HfHandler:
     """Handle Hugging Face model browsing and download."""
 
+    async def set_hf_url(self, request: web.Request) -> web.Response:
+        try:
+            payload: dict[str, Any] = await request.json()
+        except json.JSONDecodeError:
+            return web.json_response({"success": False, "error": "Invalid JSON"}, status=400)
+
+        file_path = (payload.get("file_path") or "").strip()
+        hf_url = (payload.get("hf_url") or "").strip()
+
+        if not file_path or not hf_url:
+            return web.json_response(
+                {"success": False, "error": "Missing required fields: 'file_path' and 'hf_url'"},
+                status=400,
+            )
+
+        m = re.match(r"^https?://huggingface\.co/([^/]+/[^/]+)/?$", hf_url)
+        if not m:
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "Invalid HuggingFace URL. Expected format: https://huggingface.co/user/repo",
+                },
+                status=400,
+            )
+
+        if not os.path.isfile(file_path):
+            return web.json_response(
+                {"success": False, "error": f"File not found: {file_path}"},
+                status=404,
+            )
+
+        model_root = _find_matching_root(os.path.dirname(file_path))
+        if not model_root:
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "File is not within any configured model directory. Cannot link to HuggingFace.",
+                },
+                status=400,
+            )
+
+        try:
+            existing = await MetadataManager.load_metadata_payload(file_path)
+            if existing.get("hf_url") == hf_url:
+                return web.json_response({
+                    "success": True,
+                    "message": "hf_url already set",
+                    "hf_url": hf_url,
+                })
+
+            existing["hf_url"] = hf_url
+            existing["from_civitai"] = False
+            await MetadataManager.save_metadata(file_path, existing)
+
+            await _add_to_scanner_cache(file_path, existing)
+
+            logger.info("Set hf_url=%s for %s", hf_url, file_path)
+            return web.json_response({
+                "success": True,
+                "message": f"hf_url set to {hf_url}",
+                "hf_url": hf_url,
+            })
+        except Exception as exc:
+            logger.error("Failed to set hf_url for %s: %s", file_path, exc)
+            return web.json_response(
+                {"success": False, "error": str(exc)},
+                status=500,
+            )
+
     async def get_hf_repo_files(self, request: web.Request) -> web.Response:
         """List model-weight files from a HF repo with real file sizes.
 
@@ -252,8 +363,8 @@ class HfHandler:
         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:
+        # Validate filename — must not contain path traversal
+        if ".." in filename:
             return web.json_response({"error": "Invalid filename"}, status=400)
 
         # Validate relative_path — must not be absolute or escape base directory
@@ -263,54 +374,31 @@ class HfHandler:
             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)
-            )
+        # Use model_root directly as the base directory — same approach as
+        # CivitAI's download path (download_manager.py).  No realpath, no
+        # allowed-roots validation, no path-traversal check; those are
+        # unnecessary when the frontend sends the path from its own dropdown
+        # (populated from scanner roots).  Using the "business path" directly
+        # keeps dest_path consistent with scanner roots so that later folder
+        # derivation (in _save_hf_metadata) works correctly.
+        if os.path.isabs(model_root):
+            base_dir = os.path.normpath(model_root)
         else:
-            resolved_model_root = os.path.realpath(model_root)
+            base_dir = os.path.normpath(os.path.join(os.getcwd(), "models", 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
-        folder: str = ""
         if use_default_paths:
             target_dir = os.path.join(base_dir, "huggingface", author, repo_name)
-            folder = f"huggingface/{author}/{repo_name}"
         elif relative_path:
             target_dir = os.path.join(base_dir, relative_path)
-            folder = relative_path
         else:
             target_dir = base_dir
 
-        os.makedirs(target_dir, exist_ok=True)
-        dest_path = os.path.join(target_dir, filename)
+        # Strip HF repo subdirectory — "diffusion_models/xxx.safetensors"
+        # is an HF repo convention, not meaningful for local storage.
+        file_base = os.path.basename(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)
+        os.makedirs(target_dir, exist_ok=True)
+        dest_path = os.path.join(target_dir, file_base)
 
         # Check if already exists (simple skip)
         if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
@@ -374,7 +462,7 @@ class HfHandler:
                     progress_callback=progress_callback,
                 )
                 if hf_success:
-                    await _save_hf_metadata(dest_path, repo, model_root, folder=folder)
+                    await _save_hf_metadata(dest_path, repo, model_root)
                     return web.json_response({
                         "success": True,
                         "message": f"Downloaded to {dest_path}",
@@ -402,7 +490,7 @@ class HfHandler:
                 progress_callback=progress_callback,
             )
             if success:
-                await _save_hf_metadata(dest_path, repo, model_root, folder=folder)
+                await _save_hf_metadata(dest_path, repo, model_root)
                 return web.json_response({
                     "success": True,
                     "message": f"Downloaded to {result}",

py/routes/handlers/misc_handlers.py

@@ -573,12 +573,18 @@ class NodeRegistry:
             tab_nodes[nd["unique_id"]] = nd
 
         async with self._lock:
+            prev_count = len(self._tab_nodes.get(sid, {}))
             self._tab_nodes[sid] = tab_nodes
             self._waiting_clients.discard(sid)
             if not self._waiting_clients:
                 self._ready.set()
+            total_tabs = len(self._tab_nodes)
 
-        logger.debug("Registered %s nodes from client %s", len(nodes), sid)
+        if len(nodes) != prev_count or len(nodes) > 0:
+            logger.debug(
+                "[LM:Registry] stored %s nodes (was %s) for client %s (total tabs: %s)",
+                len(nodes), prev_count, sid, total_tabs,
+            )
 
     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."""
@@ -601,10 +607,17 @@ class NodeRegistry:
         longer connected."""
         async with self._lock:
             # Garbage-collect stale entries (disconnected tabs)
+            stale_sids = []
             if active_sids is not None:
                 for sid in list(self._tab_nodes):
                     if sid not in active_sids:
+                        stale_sids.append(sid)
                         del self._tab_nodes[sid]
+            if stale_sids:
+                logger.debug(
+                    "[LM:Registry] GC pruned %s disconnected tabs: %s",
+                    len(stale_sids), stale_sids,
+                )
 
             merged: dict[str, dict] = {}
             tab_info: dict[str, dict] = {}
@@ -1549,6 +1562,11 @@ class SettingsHandler:
                             {"success": False, "error": validation_error}
                         )
 
+                if key == "update_channel" and value not in ("release", "nightly"):
+                    return web.json_response(
+                        {"success": False, "error": "update_channel must be 'release' or 'nightly'"}
+                    )
+
                 if value == "__DELETE__" and key in (
                     "proxy_username",
                     "proxy_password",
@@ -1557,7 +1575,11 @@ class SettingsHandler:
                 else:
                     self._settings.set(key, value)
 
-                if key == "enable_metadata_archive_db":
+                if key in (
+                    "enable_metadata_archive_db",
+                    "enable_civarchive_api",
+                    "metadata_provider_order",
+                ):
                     await self._metadata_provider_updater()
 
                 if key in self._PROXY_KEYS:
@@ -1771,6 +1793,124 @@ class LoraCodeHandler:
             logger.error("Failed to update lora code: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_update_lora_code(self, request: web.Request) -> web.Response:
+        """GET version of update_lora_code — reads parameters from query string.
+
+        Query params:
+          lora_code (required)  — the LoRA syntax to send
+          mode     (optional)   — "append" (default) or "replace"
+          node_id  (repeatable) — target node id(s), e.g. node_id=3&node_id=5
+          node_ids (optional)   — JSON-encoded array for complex references with graph_id:
+                                   [{"node_id":3,"graph_id":"g1"}, ...]
+        """
+        try:
+            node_ids_raw = request.query.get("node_ids")
+            node_id_list = request.query.getall("node_id", [])
+            lora_code = request.query.get("lora_code", "")
+            mode = request.query.get("mode", "append")
+
+            if not lora_code:
+                return web.json_response(
+                    {"success": False, "error": "Missing lora_code parameter"},
+                    status=400,
+                )
+
+            node_ids = None
+            if node_ids_raw:
+                try:
+                    node_ids = json.loads(node_ids_raw)
+                except (json.JSONDecodeError, TypeError):
+                    return web.json_response(
+                        {"success": False, "error": "node_ids must be a valid JSON array"},
+                        status=400,
+                    )
+                if not isinstance(node_ids, list) or not node_ids:
+                    return web.json_response(
+                        {"success": False, "error": "node_ids must be a non-empty JSON array"},
+                        status=400,
+                    )
+            elif node_id_list:
+                node_ids = node_id_list
+
+            results = []
+            if node_ids is None:
+                try:
+                    self._prompt_server.instance.send_sync(
+                        "lora_code_update",
+                        {"id": -1, "lora_code": lora_code, "mode": mode},
+                    )
+                    results.append({"node_id": "broadcast", "success": True})
+                except Exception as exc:  # pragma: no cover - defensive logging
+                    logger.error("Error broadcasting lora code: %s", exc)
+                    results.append(
+                        {"node_id": "broadcast", "success": False, "error": str(exc)}
+                    )
+            else:
+                for entry in node_ids:
+                    node_identifier = entry
+                    graph_identifier = None
+                    if isinstance(entry, dict):
+                        node_identifier = entry.get("node_id")
+                        graph_identifier = entry.get("graph_id")
+
+                    if node_identifier is None:
+                        results.append(
+                            {
+                                "node_id": node_identifier,
+                                "graph_id": graph_identifier,
+                                "success": False,
+                                "error": "Missing node_id parameter",
+                            }
+                        )
+                        continue
+
+                    try:
+                        parsed_node_id = int(node_identifier)
+                    except (TypeError, ValueError):
+                        parsed_node_id = node_identifier
+
+                    payload = {
+                        "id": parsed_node_id,
+                        "lora_code": lora_code,
+                        "mode": mode,
+                    }
+
+                    if graph_identifier is not None:
+                        payload["graph_id"] = str(graph_identifier)
+
+                    try:
+                        self._prompt_server.instance.send_sync(
+                            "lora_code_update",
+                            payload,
+                        )
+                        results.append(
+                            {
+                                "node_id": parsed_node_id,
+                                "graph_id": payload.get("graph_id"),
+                                "success": True,
+                            }
+                        )
+                    except Exception as exc:  # pragma: no cover - defensive logging
+                        logger.error(
+                            "Error sending lora code to node %s (graph %s): %s",
+                            parsed_node_id,
+                            graph_identifier,
+                            exc,
+                        )
+                        results.append(
+                            {
+                                "node_id": parsed_node_id,
+                                "graph_id": payload.get("graph_id"),
+                                "success": False,
+                                "error": str(exc),
+                            }
+                        )
+
+            return web.json_response({"success": True, "results": results})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to update lora code (GET): %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class TrainedWordsHandler:
     async def get_trained_words(self, request: web.Request) -> web.Response:
@@ -2450,6 +2590,8 @@ class ModelLibraryHandler:
                     status=400,
                 )
 
+            cursor = request.query.get("cursor")
+
             metadata_provider = await self._metadata_provider_factory()
             if not metadata_provider:
                 return web.json_response(
@@ -2458,7 +2600,7 @@ class ModelLibraryHandler:
                 )
 
             try:
-                models = await metadata_provider.get_user_models(username)
+                result = await metadata_provider.get_user_models(username, cursor)
             except NotImplementedError:
                 return web.json_response(
                     {
@@ -2468,14 +2610,35 @@ class ModelLibraryHandler:
                     status=501,
                 )
 
-            if models is None:
+            if result is None:
                 return web.json_response(
                     {"success": False, "error": "Failed to fetch user models"},
                     status=502,
                 )
 
+            if isinstance(result, dict):
+                models = result.get("items")
+                next_cursor = result.get("nextCursor")
+            else:
+                # Defensive: tolerate providers that still return a raw list
+                models = result
+                next_cursor = None
+
             if not isinstance(models, list):
                 models = []
+            if next_cursor is not None and not isinstance(next_cursor, str):
+                next_cursor = str(next_cursor)
+
+            estimated_total = None
+            if cursor is None:
+                get_count = getattr(metadata_provider, "get_creator_model_count", None)
+                if get_count is not None:
+                    try:
+                        estimated_total = await get_count(username)
+                    except Exception:  # best-effort only
+                        estimated_total = None
+                if not isinstance(estimated_total, int):
+                    estimated_total = None
 
             lora_scanner = await self._service_registry.get_lora_scanner()
             checkpoint_scanner = await self._service_registry.get_checkpoint_scanner()
@@ -2495,6 +2658,7 @@ class ModelLibraryHandler:
             versions: list[dict] = []
             history_service = await self._get_download_history_service()
             model_ids: list[int] = []
+            model_count = 0
             for model in models:
                 try:
                     model_ids.append(int(model.get("id")))
@@ -2528,6 +2692,8 @@ class ModelLibraryHandler:
                 if model_type not in normalized_allowed_types:
                     continue
 
+                model_count += 1
+
                 scanner = type_scanner_map.get(model_type)
                 if scanner is None:
                     return web.json_response(
@@ -2593,7 +2759,15 @@ class ModelLibraryHandler:
                     )
 
             return web.json_response(
-                {"success": True, "username": username, "versions": versions}
+                {
+                    "success": True,
+                    "username": username,
+                    "versions": versions,
+                    "modelCount": model_count,
+                    "nextCursor": next_cursor,
+                    "hasMore": next_cursor is not None,
+                    "estimatedTotal": estimated_total,
+                }
             )
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to get Civitai user models: %s", exc, exc_info=True)
@@ -3116,6 +3290,8 @@ class NodeRegistryHandler:
         self._node_registry = node_registry
         self._prompt_server = prompt_server
         self._standalone_mode = standalone_mode
+        self._refresh_lock = asyncio.Lock()
+        self._last_slow_path_ts: float = 0.0
 
     async def register_nodes(self, request: web.Request) -> web.Response:
         try:
@@ -3162,7 +3338,12 @@ class NodeRegistryHandler:
                     )
                 graph_name = node.get("graph_name")
                 try:
-                    node["node_id"] = int(node_id)
+                    # Handle compound node IDs from expanded group subgraphs,
+                    # e.g. "252:0" → 0 (parent scope is already in graph_id)
+                    if isinstance(node_id, str) and ":" in node_id:
+                        node["node_id"] = int(node_id.rsplit(":", 1)[-1])
+                    else:
+                        node["node_id"] = int(node_id)
                 except (TypeError, ValueError):
                     return web.json_response(
                         {
@@ -3203,42 +3384,101 @@ 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 (expecting %s clients)", len(active_sids)
-                )
-            except Exception as exc:
-                logger.error("Failed to send registry refresh message: %s", exc)
-                return web.json_response(
-                    {
-                        "success": False,
-                        "error": "Communication Error",
-                        "message": f"Failed to communicate with ComfyUI frontend: {exc}",
-                    },
-                    status=500,
-                )
-
-            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())
+
+            # Fast path: if the frontend has already pushed node data (via
+            # afterConfigureGraph / graphChanged hooks), return it immediately
+            # without triggering a WebSocket round-trip.
             registry_info = await self._node_registry.get_merged_registry(
                 active_sids=current_sids
             )
+            if registry_info["tab_count"] > 0:
+                logger.debug(
+                    "[LM:Registry] fast path: %s nodes across %s tabs %s",
+                    registry_info["node_count"],
+                    registry_info["tab_count"],
+                    dict(registry_info.get("tabs", {})),
+                )
+                return web.json_response({"success": True, "data": registry_info})
+
+            # Slow path: registry is empty — trigger refresh via WebSocket.
+            # Serialize with an async lock so concurrent callers don't all
+            # trigger separate WS refresh cycles.  The second caller will
+            # re-check the fast path and (usually) find populated data.
+            async with self._refresh_lock:
+                # Re-check after acquiring the lock — another concurrent call
+                # may have populated the cache while we were waiting.
+                registry_info = await self._node_registry.get_merged_registry(
+                    active_sids=current_sids
+                )
+                if registry_info["tab_count"] > 0:
+                    logger.debug(
+                        "[LM:Registry] fast path after lock wait: %s nodes across %s tabs",
+                        registry_info["node_count"],
+                        registry_info["tab_count"],
+                    )
+                    return web.json_response({"success": True, "data": registry_info})
+
+                # Cooldown: if the slow path ran recently (< 2 s) and
+                # returned empty, skip another WS round-trip.
+                elapsed = time.monotonic() - self._last_slow_path_ts
+                if elapsed < 2.0:
+                    logger.debug(
+                        "[LM:Registry] slow path cooldown (%.1fs since last refresh), returning empty",
+                        elapsed,
+                    )
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": "Empty Registry",
+                            "message": "No workflow nodes found — ensure ComfyUI is open and the extension is loaded.",
+                        },
+                        status=408,
+                    )
+
+                logger.debug(
+                    "[LM:Registry] slow path: cache empty, triggering WS refresh (%s connected tabs: %s)",
+                    len(current_sids), list(current_sids)[:5],
+                )
+                active_sids = list(current_sids)
+                self._node_registry.prepare_for_refresh(active_sids)
+
+                try:
+                    self._prompt_server.instance.send_sync("lora_registry_refresh", {})
+                    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(
+                        {
+                            "success": False,
+                            "error": "Communication Error",
+                            "message": f"Failed to communicate with ComfyUI frontend: {exc}",
+                        },
+                        status=500,
+                    )
+
+                if not await self._node_registry.wait_for_all(timeout=0.5):
+                    logger.warning(
+                        "Registry refresh timeout after 0.5s (%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
+                )
+                self._last_slow_path_ts = time.monotonic()
 
             if registry_info["node_count"] == 0:
-                logger.warning("No nodes registered after refresh")
+                logger.debug(
+                    "[LM:Registry] refresh OK — %s connected tab(s) but 0 compatible nodes found",
+                    registry_info["tab_count"],
+                )
                 return web.json_response(
                     {
                         "success": False,
@@ -3274,7 +3514,7 @@ class NodeRegistryHandler:
                     status=400,
                 )
 
-            if not isinstance(value, str) or not value:
+            if value is None or (isinstance(value, str) and not value):
                 return web.json_response(
                     {"success": False, "error": "Missing value parameter"}, status=400
                 )
@@ -3352,6 +3592,130 @@ class NodeRegistryHandler:
             logger.error("Failed to update node widget: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_update_node_widget(self, request: web.Request) -> web.Response:
+        """GET version of update_node_widget — reads parameters from query string.
+
+        Query params:
+          widget_name  (optional)   — the widget name to update (required unless action is set)
+          action       (optional)   — alternative action, e.g. "inject_text" (required unless widget_name is set)
+          value        (required)   — the value to set
+          mode         (optional)   — "replace" (default) or "append"
+          node_id      (repeatable) — target node id(s), e.g. node_id=3&node_id=5
+          node_ids     (optional)   — JSON-encoded array for complex references:
+                                       [{"node_id":3,"graph_id":"g1"}, ...]
+        """
+        try:
+            widget_name = request.query.get("widget_name")
+            action = request.query.get("action")
+            value = request.query.get("value")
+            mode = request.query.get("mode", "replace")
+            node_ids_raw = request.query.get("node_ids")
+            node_id_list = request.query.getall("node_id", [])
+
+            if not action and (not isinstance(widget_name, str) or not widget_name):
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Missing parameter: provide either 'action' or 'widget_name'",
+                    },
+                    status=400,
+                )
+
+            if value is None or (isinstance(value, str) and not value):
+                return web.json_response(
+                    {"success": False, "error": "Missing value parameter"}, status=400
+                )
+
+            node_ids = None
+            if node_ids_raw:
+                try:
+                    node_ids = json.loads(node_ids_raw)
+                except (json.JSONDecodeError, TypeError):
+                    return web.json_response(
+                        {"success": False, "error": "node_ids must be a valid JSON array"},
+                        status=400,
+                    )
+                if not isinstance(node_ids, list) or not node_ids:
+                    return web.json_response(
+                        {"success": False, "error": "node_ids must be a non-empty JSON array"},
+                        status=400,
+                    )
+            elif node_id_list:
+                node_ids = node_id_list
+
+            if not isinstance(node_ids, list) or not node_ids:
+                return web.json_response(
+                    {"success": False, "error": "node_ids must be a non-empty list"},
+                    status=400,
+                )
+
+            results = []
+            for entry in node_ids:
+                node_identifier = entry
+                graph_identifier = None
+                if isinstance(entry, dict):
+                    node_identifier = entry.get("node_id")
+                    graph_identifier = entry.get("graph_id")
+
+                if node_identifier is None:
+                    results.append(
+                        {
+                            "node_id": node_identifier,
+                            "graph_id": graph_identifier,
+                            "success": False,
+                            "error": "Missing node_id parameter",
+                        }
+                    )
+                    continue
+
+                try:
+                    parsed_node_id = int(node_identifier)
+                except (TypeError, ValueError):
+                    parsed_node_id = node_identifier
+
+                payload: dict = {
+                    "id": parsed_node_id,
+                    "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)
+
+                try:
+                    self._prompt_server.instance.send_sync("lm_widget_update", payload)
+                    results.append(
+                        {
+                            "node_id": parsed_node_id,
+                            "graph_id": payload.get("graph_id"),
+                            "success": True,
+                        }
+                    )
+                except Exception as exc:  # pragma: no cover - defensive logging
+                    logger.error(
+                        "Error sending widget update to node %s (graph %s): %s",
+                        parsed_node_id,
+                        graph_identifier,
+                        exc,
+                    )
+                    results.append(
+                        {
+                            "node_id": parsed_node_id,
+                            "graph_id": payload.get("graph_id"),
+                            "success": False,
+                            "error": str(exc),
+                        }
+                    )
+
+            return web.json_response({"success": True, "results": results})
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error("Failed to update node widget (GET): %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class MiscHandlerSet:
     """Aggregate handlers into a lookup compatible with the registrar."""
@@ -3418,10 +3782,12 @@ class MiscHandlerSet:
             "update_usage_stats": self.usage_stats.update_usage_stats,
             "get_usage_stats": self.usage_stats.get_usage_stats,
             "update_lora_code": self.lora_code.update_lora_code,
+            "get_update_lora_code": self.lora_code.get_update_lora_code,
             "get_trained_words": self.trained_words.get_trained_words,
             "get_model_example_files": self.model_examples.get_model_example_files,
             "register_nodes": self.node_registry.register_nodes,
             "update_node_widget": self.node_registry.update_node_widget,
+            "get_update_node_widget": self.node_registry.get_update_node_widget,
             "get_registry": self.node_registry.get_registry,
             "check_model_exists": self.model_library.check_model_exists,
             "check_models_exist": self.model_library.check_models_exist,
@@ -3448,6 +3814,7 @@ class MiscHandlerSet:
             # Hugging Face handlers
             "get_hf_repo_files": self.hf_handler.get_hf_repo_files,
             "download_hf_model": self.hf_handler.download_hf_model,
+            "set_hf_url": self.hf_handler.set_hf_url,
             # Agent skill handlers
             "get_agent_skills": self.agent_handler.get_agent_skills,
             "execute_agent_skill": self.agent_handler.execute_agent_skill,

py/routes/handlers/model_handlers.py

@@ -394,12 +394,14 @@ class ModelListingHandler:
         )
 
         # View-local-versions filter: show all local versions of a specific model
+        # Accepts either a CivitAI modelId (int) or a HF group key like "hf:user/repo"
         civitai_model_id = request.query.get("civitai_model_id")
         if civitai_model_id is not None:
             try:
                 civitai_model_id = int(civitai_model_id)
             except (TypeError, ValueError):
-                civitai_model_id = None
+                # Keep as string — could be an HF group key (e.g. "hf:user/repo")
+                pass
 
         return {
             "page": page,
@@ -537,6 +539,7 @@ class ModelManagementHandler:
                     # Update model_data with new hash
                     model_data["sha256"] = sha256
                     model_data["hash_status"] = "completed"
+                    hash_status = "completed"
                 else:
                     return web.json_response(
                         {"success": False, "error": "No SHA256 hash found"}, status=400
@@ -544,6 +547,32 @@ class ModelManagementHandler:
 
             await MetadataManager.hydrate_model_data(model_data)
 
+            # hydrate_model_data replaces model_data with .metadata.json content,
+            # which may lack sha256. Restore from cache and persist the fix.
+            if not model_data.get("sha256"):
+                if sha256:
+                    model_data["sha256"] = sha256
+                    model_data["hash_status"] = model_data.get("hash_status", hash_status)
+                    data_to_save = model_data.copy()
+                    data_to_save.pop("folder", None)
+                    await MetadataManager.save_metadata(file_path, data_to_save)
+                else:
+                    sha256 = await calculate_sha256(file_path)
+                    if sha256:
+                        model_data["sha256"] = sha256.lower()
+                        model_data["hash_status"] = "completed"
+                        data_to_save = model_data.copy()
+                        data_to_save.pop("folder", None)
+                        await MetadataManager.save_metadata(file_path, data_to_save)
+                    else:
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": "Failed to compute SHA256 hash for model",
+                            },
+                            status=500,
+                        )
+
             success, error = await self._metadata_sync.fetch_and_update_model(
                 sha256=model_data["sha256"],
                 file_path=file_path,
@@ -566,7 +595,12 @@ class ModelManagementHandler:
                     {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
                     status=503,
                 )
-            self._logger.error("Error fetching from CivitAI: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error fetching from CivitAI for %s: %s",
+                locals().get("file_path", "unknown"),
+                exc,
+                exc_info=True,
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def relink_civitai(self, request: web.Request) -> web.Response:
@@ -973,6 +1007,8 @@ class ModelQueryHandler:
             limit = int(request.query.get("limit", "20"))
             if limit < 0:
                 limit = 20
+            elif limit > 200:
+                limit = 20
             top_tags = await self._service.get_top_tags(limit)
             return web.json_response({"success": True, "tags": top_tags})
         except Exception as exc:
@@ -981,6 +1017,22 @@ class ModelQueryHandler:
                 {"success": False, "error": "Internal server error"}, status=500
             )
 
+    async def search_tags(self, request: web.Request) -> web.Response:
+        try:
+            query = request.query.get("q", "")
+            limit = int(request.query.get("limit", "20"))
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+            tags = await self._service.search_tags(query, limit)
+            return web.json_response({"success": True, "tags": tags})
+        except Exception as exc:
+            self._logger.error("Error searching tags: %s", exc, exc_info=True)
+            return web.json_response(
+                {"success": False, "error": "Internal server error"}, status=500
+            )
+
     async def get_base_models(self, request: web.Request) -> web.Response:
         try:
             limit = int(request.query.get("limit", "20"))
@@ -1275,9 +1327,13 @@ class ModelQueryHandler:
                     text=f"{self._service.model_type.capitalize()} file name is required",
                     status=400,
                 )
-            notes = await self._service.get_model_notes(model_name)
-            if notes is not None:
-                return web.json_response({"success": True, "notes": notes})
+            result = await self._service.get_model_notes(model_name)
+            if result is not None:
+                return web.json_response({
+                    "success": True,
+                    "notes": result["notes"],
+                    "file_path": result["file_path"],
+                })
             return web.json_response(
                 {
                     "success": False,
@@ -1313,9 +1369,20 @@ class ModelQueryHandler:
                 }
                 if include_license_flags:
                     model_data = await self._service.get_model_info_by_name(model_name)
-                    license_flags = (model_data or {}).get("license_flags")
-                    if license_flags is not None:
-                        response_payload["license_flags"] = int(license_flags)
+                    # Only return license_flags when real CivitAI model license
+                    # data exists. This mirrors ModelModal's guard
+                    # (modelData?.civitai?.model) so the preview tooltip never
+                    # shows misleading license icons for HF or other models
+                    # without actual license metadata.
+                    civitai_data = (model_data or {}).get("civitai") or {}
+                    has_license_data = (
+                        isinstance(civitai_data, dict)
+                        and isinstance(civitai_data.get("model"), dict)
+                    )
+                    if has_license_data:
+                        license_flags = (model_data or {}).get("license_flags")
+                        if license_flags is not None:
+                            response_payload["license_flags"] = int(license_flags)
                     # Include the user's license icon style preference so the
                     # ComfyUI tooltip can pick the right set without a separate
                     # API call.
@@ -1772,14 +1839,20 @@ class ModelDownloadHandler:
 
     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:
+            download_id = request.query.get("download_id")
+            id_str = request.query.get("id")
+            item_id = int(id_str) if id_str else None
+
+            if not download_id and not item_id:
                 return web.json_response(
-                    {"success": False, "error": "id is required"}, status=400
+                    {"success": False, "error": "id or download_id is required"},
+                    status=400,
                 )
 
             service = await DownloadQueueService.get_instance()
-            deleted = await service.delete_history_item(item_id)
+            deleted = await service.delete_history_item(
+                id=item_id, download_id=download_id
+            )
             return web.json_response({"success": deleted})
         except Exception as exc:
             self._logger.error(
@@ -1789,14 +1862,20 @@ class ModelDownloadHandler:
 
     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:
+            download_id = request.query.get("download_id")
+            id_str = request.query.get("id")
+            item_id = int(id_str) if id_str else None
+
+            if not download_id and not item_id:
                 return web.json_response(
-                    {"success": False, "error": "id is required"}, status=400
+                    {"success": False, "error": "id or download_id is required"},
+                    status=400,
                 )
 
             service = await DownloadQueueService.get_instance()
-            item = await service.retry_from_history(item_id)
+            item = await service.retry_from_history(
+                item_id=item_id, download_id=download_id
+            )
             if item is None:
                 return web.json_response(
                     {"success": False, "error": "History item not found or not retryable"},
@@ -2920,6 +2999,7 @@ class ModelHandlerSet:
             "bulk_delete_models": self.management.bulk_delete_models,
             "verify_duplicates": self.management.verify_duplicates,
             "get_top_tags": self.query.get_top_tags,
+            "search_tags": self.query.search_tags,
             "get_base_models": self.query.get_base_models,
             "get_model_types": self.query.get_model_types,
             "scan_models": self.query.scan_models,

py/routes/handlers/preview_handlers.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import mimetypes
 import urllib.parse
@@ -53,6 +54,7 @@ class PreviewHandler:
 
         if not resolved.is_file():
             logger.debug("Preview file not found at %s", str(resolved))
+            asyncio.create_task(self._cleanup_stale_preview_url(normalized))
             raise web.HTTPNotFound(text="Preview file not found")
 
         # aiohttp's FileResponse handles range requests, content headers, and
@@ -69,6 +71,35 @@ class PreviewHandler:
         resp.headers["Cache-Control"] = "public, max-age=86400"
         return resp
 
+    async def _cleanup_stale_preview_url(self, normalized_preview_path: str) -> None:
+        """Fire-and-forget: clear stale preview_url from all model caches.
+
+        When a preview file is no longer on disk, remove its reference from
+        every cached entry so subsequent list API responses return an empty
+        ``preview_url``, letting the frontend show the no-preview placeholder.
+        """
+        try:
+            from ...services.service_registry import ServiceRegistry
+
+            for service_name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner"):
+                scanner = ServiceRegistry.get_service_sync(service_name)
+                if scanner is None or not hasattr(scanner, "_cache"):
+                    continue
+                cache = getattr(scanner, "_cache", None)
+                if cache is None or not hasattr(cache, "clear_preview_by_path"):
+                    continue
+                cleared = await cache.clear_preview_by_path(normalized_preview_path)
+                if cleared and hasattr(scanner, "_persist_current_cache"):
+                    await scanner._persist_current_cache()
+                    logger.info(
+                        "Cleared stale preview_url for %d %s entries (%s)",
+                        cleared,
+                        service_name,
+                        normalized_preview_path,
+                    )
+        except Exception as exc:
+            logger.debug("Failed to clean up stale preview_url: %s", exc)
+
     async def _stream_file(
         self, request: web.Request, path: Path
     ) -> web.StreamResponse:

py/routes/handlers/recipe_handlers.py

@@ -72,6 +72,7 @@ class RecipeHandlerSet:
             "save_recipe": self.management.save_recipe,
             "delete_recipe": self.management.delete_recipe,
             "get_top_tags": self.query.get_top_tags,
+            "search_tags": self.query.search_tags,
             "get_base_models": self.query.get_base_models,
             "get_roots": self.query.get_roots,
             "get_folders": self.query.get_folders,
@@ -317,12 +318,11 @@ class RecipeQueryHandler:
                 raise RuntimeError("Recipe scanner unavailable")
 
             limit = int(request.query.get("limit", "20"))
-            cache = await recipe_scanner.get_cached_data()
-
-            tag_counts: Dict[str, int] = {}
-            for recipe in getattr(cache, "raw_data", []):
-                for tag in recipe.get("tags", []) or []:
-                    tag_counts[tag] = tag_counts.get(tag, 0) + 1
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+            tag_counts = await self._get_recipe_tag_counts(recipe_scanner)
 
             sorted_tags = [
                 {"tag": tag, "count": count} for tag, count in tag_counts.items()
@@ -333,6 +333,55 @@ class RecipeQueryHandler:
             self._logger.error("Error retrieving top tags: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def search_tags(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            query = request.query.get("q", "")
+            limit = int(request.query.get("limit", "20"))
+            if limit < 0:
+                limit = 20
+            elif limit > 200:
+                limit = 20
+
+            tag_counts = await self._get_recipe_tag_counts(recipe_scanner)
+            normalized_query = (query or "").strip().lower()
+            if not normalized_query:
+                sorted_tags = [
+                    {"tag": tag, "count": count} for tag, count in tag_counts.items()
+                ]
+                sorted_tags.sort(key=lambda entry: entry["count"], reverse=True)
+                return web.json_response(
+                    {"success": True, "tags": sorted_tags[: (limit if limit > 0 else 20)]}
+                )
+
+            matched = [
+                {"tag": tag, "count": count}
+                for tag, count in tag_counts.items()
+                if normalized_query in tag.lower()
+            ]
+            matched.sort(key=lambda entry: entry["count"], reverse=True)
+            if limit == 0:
+                result = matched
+            else:
+                result = matched[:limit]
+            return web.json_response({"success": True, "tags": result})
+        except Exception as exc:
+            self._logger.error("Error searching recipe tags: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def _get_recipe_tag_counts(self, recipe_scanner) -> Dict[str, int]:
+        """Compute tag->count mapping from cached recipe data."""
+        cache = await recipe_scanner.get_cached_data()
+        tag_counts: Dict[str, int] = {}
+        for recipe in getattr(cache, "raw_data", []):
+            for tag in recipe.get("tags", []) or []:
+                tag_counts[tag] = tag_counts.get(tag, 0) + 1
+        return tag_counts
+
     async def get_base_models(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()

py/routes/misc_route_registrar.py

@@ -39,10 +39,12 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/update-usage-stats", "update_usage_stats"),
     RouteDefinition("GET", "/api/lm/get-usage-stats", "get_usage_stats"),
     RouteDefinition("POST", "/api/lm/update-lora-code", "update_lora_code"),
+    RouteDefinition("GET", "/api/lm/update-lora-code", "get_update_lora_code"),
     RouteDefinition("GET", "/api/lm/trained-words", "get_trained_words"),
     RouteDefinition("GET", "/api/lm/model-example-files", "get_model_example_files"),
     RouteDefinition("POST", "/api/lm/register-nodes", "register_nodes"),
     RouteDefinition("POST", "/api/lm/update-node-widget", "update_node_widget"),
+    RouteDefinition("GET", "/api/lm/update-node-widget", "get_update_node_widget"),
     RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
     RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
     RouteDefinition("GET", "/api/lm/check-models-exist", "check_models_exist"),
@@ -103,6 +105,9 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "POST", "/api/lm/download-hf-model", "download_hf_model"
     ),
+    RouteDefinition(
+        "POST", "/api/lm/set-hf-url", "set_hf_url"
+    ),
     # Agent skill endpoints
     RouteDefinition(
         "GET", "/api/lm/agent/skills", "get_agent_skills"

py/routes/model_route_registrar.py

@@ -46,6 +46,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"
     ),
     RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
+    RouteDefinition("GET", "/api/lm/{prefix}/search-tags", "search_tags"),
     RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/model-types", "get_model_types"),
     RouteDefinition("GET", "/api/lm/{prefix}/scan", "scan_models"),

py/routes/recipe_route_registrar.py

@@ -29,6 +29,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipes/save", "save_recipe"),
     RouteDefinition("DELETE", "/api/lm/recipe/{recipe_id}", "delete_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/top-tags", "get_top_tags"),
+    RouteDefinition("GET", "/api/lm/recipes/search-tags", "search_tags"),
     RouteDefinition("GET", "/api/lm/recipes/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/recipes/roots", "get_roots"),
     RouteDefinition("GET", "/api/lm/recipes/folders", "get_folders"),

py/routes/update_routes.py

@@ -38,6 +38,84 @@ def _clean_excludes() -> List[str]:
     return excludes
 
 
+def _stage_preserved_items(plugin_root: str) -> tuple[str, list[str]]:
+    """Move preserved user-data items to a temp directory outside *plugin_root*.
+
+    This ensures that ``git reset --hard``, ``git clean -fd``, and ZIP-based
+    replacement cannot touch these files even when ``-e`` exclusion patterns
+    are mishandled (e.g. on Windows where forward-slash patterns may not
+    match backslash-prefixed paths in some Git builds, or where file locks
+    prevent deletion/recreation).
+
+    Returns:
+        ``(backup_root, staged_names)``: the temp directory path and the
+        list of item names that were successfully moved.
+    """
+    backup_root = tempfile.mkdtemp(prefix='lora_manager_update_')
+    staged: list[str] = []
+    for name in _PRESERVE_DIRS:
+        src = os.path.join(plugin_root, name)
+        if not os.path.lexists(src):
+            continue
+        dst = os.path.join(backup_root, name)
+        try:
+            shutil.move(src, dst)
+            staged.append(name)
+            logger.debug("Staged '%s' for update safety", name)
+        except OSError:
+            # ``shutil.move`` may fail on Windows if a file handle inside
+            # the directory is still open (e.g. a SQLite WAL file). Fall
+            # back to copy-then-remove.
+            logger.debug("Move failed for '%s', falling back to copy", name)
+            try:
+                if os.path.isdir(src) and not os.path.islink(src):
+                    shutil.copytree(src, dst, symlinks=True)
+                    shutil.rmtree(src, ignore_errors=True)
+                else:
+                    shutil.copy2(src, dst)
+                    os.remove(src)
+                staged.append(name)
+                logger.info("Copied (then removed) '%s' for update safety", name)
+            except Exception as exc:
+                logger.warning(
+                    "Could not stage '%s': %s (will rely on git -e / skip lists)", name, exc
+                )
+    return backup_root, staged
+
+
+def _restore_preserved_items(plugin_root: str, backup_root: str, staged: list[str]) -> None:
+    """Move staged items back from *backup_root* into *plugin_root*.
+
+    Any leftover placeholder at the destination (created by git checkout or
+    ZIP extraction) is removed before the move.
+    """
+    for name in staged:
+        src = os.path.join(backup_root, name)
+        dst = os.path.join(plugin_root, name)
+        try:
+            if os.path.lexists(dst):
+                if os.path.isdir(dst) and not os.path.islink(dst):
+                    shutil.rmtree(dst, ignore_errors=True)
+                else:
+                    os.remove(dst)
+            shutil.move(src, dst)
+            logger.debug("Restored '%s' after update", name)
+        except OSError:
+            logger.debug("Move failed restoring '%s', falling back to copy", name)
+            try:
+                if os.path.isdir(src) and not os.path.islink(src):
+                    shutil.copytree(src, dst, symlinks=True, dirs_exist_ok=True)
+                    shutil.rmtree(src, ignore_errors=True)
+                else:
+                    shutil.copy2(src, dst)
+                    os.remove(src)
+                logger.info("Copied '%s' back after update", name)
+            except Exception as exc:
+                logger.error("Failed to restore '%s': %s", name, exc)
+    shutil.rmtree(backup_root, ignore_errors=True)
+
+
+
 class UpdateRoutes:
     """Routes for handling plugin update checks"""
     
@@ -47,6 +125,7 @@ class UpdateRoutes:
         app.router.add_get('/api/lm/check-updates', UpdateRoutes.check_updates)
         app.router.add_get('/api/lm/version-info', UpdateRoutes.get_version_info)
         app.router.add_post('/api/lm/perform-update', UpdateRoutes.perform_update)
+        app.router.add_post('/api/lm/switch-channel', UpdateRoutes.switch_channel)
     
     @staticmethod
     async def check_updates(request):
@@ -65,10 +144,17 @@ class UpdateRoutes:
 
             # Fetch remote version from GitHub
             if nightly:
-                remote_version, changelog = await UpdateRoutes._get_nightly_version()
-                releases = None
+                local_hash = git_info.get('short_hash', '')
+                nightly_version, releases_result = await asyncio.gather(
+                    UpdateRoutes._get_nightly_version(local_hash),
+                    UpdateRoutes._get_remote_version()
+                )
+                remote_version, _, behind_by, commit_date = nightly_version
+                _, changelog, releases = releases_result
             else:
                 remote_version, changelog, releases = await UpdateRoutes._get_remote_version()
+                behind_by = 0
+                commit_date = ''
             
             # Compare versions
             if nightly:
@@ -81,6 +167,10 @@ class UpdateRoutes:
                     remote_version.replace('v', '')
                 )
             
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+            has_git = os.path.exists(os.path.join(plugin_root, '.git'))
+
             response_data = {
                 'success': True,
                 'current_version': local_version,
@@ -88,13 +178,13 @@ class UpdateRoutes:
                 'update_available': update_available,
                 'changelog': changelog,
                 'git_info': git_info,
-                'nightly': nightly
+                'nightly': nightly,
+                'has_git': has_git,
+                'releases': releases,
+                'behind_by': behind_by,
+                'commit_date': commit_date
             }
             
-            # Include releases list for stable mode
-            if releases is not None:
-                response_data['releases'] = releases
-            
             return web.json_response(response_data)
             
         except NETWORK_EXCEPTIONS as e:
@@ -126,9 +216,14 @@ class UpdateRoutes:
             # Format: version-short_hash
             version_string = f"{local_version}-{short_hash}"
             
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+            has_git = os.path.exists(os.path.join(plugin_root, '.git'))
+
             return web.json_response({
                 'success': True,
-                'version': version_string
+                'version': version_string,
+                'has_git': has_git
             })
             
         except Exception as e:
@@ -156,20 +251,22 @@ class UpdateRoutes:
             if os.path.exists(settings_path):
                 with open(settings_path, 'r', encoding='utf-8') as f:
                     settings_backup = f.read()
-                logger.info("Backed up settings.json")
+                logger.debug("Backed up settings.json (%d bytes)", len(settings_backup))
 
-            git_folder = os.path.join(plugin_root, '.git')
-            if os.path.exists(git_folder):
-                # Git update
-                success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
-            else:
-                # Fallback: Download ZIP and replace files
-                success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            staged_backup_dir, staged_items = _stage_preserved_items(plugin_root)
+            try:
+                git_folder = os.path.join(plugin_root, '.git')
+                if os.path.exists(git_folder):
+                    success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
+                else:
+                    success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            finally:
+                _restore_preserved_items(plugin_root, staged_backup_dir, staged_items)
 
             if settings_backup and success:
                 with open(settings_path, 'w', encoding='utf-8') as f:
                     f.write(settings_backup)
-                logger.info("Restored settings.json")
+                logger.debug("Restored settings.json content (%d bytes)", len(settings_backup))
 
             if success:
                 return web.json_response({
@@ -190,6 +287,164 @@ class UpdateRoutes:
                 'error': str(e)
             })
 
+    @staticmethod
+    async def switch_channel(request):
+        """
+        Switch between release and nightly update channels.
+
+        ZIP/CNR install → Nightly: git init + checkout main (one-way upgrade)
+        Git install → Release:   git checkout latest tag (.git preserved)
+        ZIP/CNR install → Release: ZIP download (no .git, stays in ZIP mode)
+        Git install → Nightly:   git checkout main + pull
+        """
+        try:
+            body = await request.json() if request.has_body else {}
+            channel = body.get('channel', '')
+
+            if channel not in ('release', 'nightly'):
+                return web.json_response({
+                    'success': False,
+                    'error': f'Invalid channel: {channel}. Must be "release" or "nightly".'
+                })
+
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+
+            settings_path = ensure_settings_file(logger)
+            settings_backup = None
+            if os.path.exists(settings_path):
+                with open(settings_path, 'r', encoding='utf-8') as f:
+                    settings_backup = f.read()
+                logger.debug("Backed up settings.json before channel switch (%d bytes)", len(settings_backup))
+
+            staged_backup_dir, staged_items = _stage_preserved_items(plugin_root)
+            try:
+                git_folder = os.path.join(plugin_root, '.git')
+
+                if channel == 'nightly':
+                    git_backup = None
+                    if os.path.exists(git_folder):
+                        git_backup = UpdateRoutes._backup_git(git_folder, 'nightly')
+
+                    success = False
+                    new_version = ''
+                    try:
+                        if os.path.exists(git_folder):
+                            success, new_version = await UpdateRoutes._perform_git_update(
+                                plugin_root, nightly=True
+                            )
+                        else:
+                            success, new_version = UpdateRoutes._init_git_repo(plugin_root)
+                    finally:
+                        UpdateRoutes._restore_git(git_backup, git_folder, success, 'nightly')
+                else:
+                    success = False
+                    new_version = ''
+                    if os.path.exists(git_folder):
+                        success, new_version = await UpdateRoutes._perform_git_update(
+                            plugin_root, nightly=False
+                        )
+                    else:
+                        tracking_file = os.path.join(plugin_root, '.tracking')
+                        if os.path.exists(tracking_file):
+                            os.remove(tracking_file)
+                        success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+            finally:
+                _restore_preserved_items(plugin_root, staged_backup_dir, staged_items)
+
+            if settings_backup and success:
+                with open(settings_path, 'w', encoding='utf-8') as f:
+                    f.write(settings_backup)
+                logger.debug("Restored settings.json content after channel switch (%d bytes)", len(settings_backup))
+
+            if success:
+                return web.json_response({
+                    'success': True,
+                    'channel': channel,
+                    'new_version': new_version,
+                    'message': f'Switched to {channel} channel'
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'Failed to switch to {channel} channel'
+                })
+
+        except Exception as e:
+            logger.error("Failed to switch channel: %s", e, exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            })
+
+    @staticmethod
+    def _init_git_repo(plugin_root: str) -> tuple[bool, str]:
+        """
+        Initialize a Git repository in a ZIP-installed plugin folder.
+        Clones the remote history and checks out main branch.
+        """
+        try:
+            import git
+        except ImportError:
+            logger.error(
+                "GitPython is not available: cannot initialize git repo. "
+                "Install git or set $GIT_PYTHON_GIT_EXECUTABLE to the git binary path."
+            )
+            return False, ""
+
+        clean_excludes = _clean_excludes()
+
+        try:
+            repo = git.Repo.init(plugin_root)
+            origin = repo.create_remote(
+                'origin',
+                'https://github.com/willmiao/ComfyUI-Lora-Manager.git'
+            )
+            origin.fetch()
+
+            repo.create_head('main', origin.refs.main)
+            repo.git.checkout('main', '--force')
+            repo.git.reset('--hard')
+            repo.git.clean('-fd', *clean_excludes)
+
+            tracking_file = os.path.join(plugin_root, '.tracking')
+            if os.path.exists(tracking_file):
+                os.remove(tracking_file)
+                logger.info("Removed .tracking file (now in git mode)")
+
+            new_version = f"main-{repo.head.commit.hexsha[:7]}"
+            logger.info("Initialized git repo on main branch: %s", new_version)
+            return True, new_version
+
+        except Exception as e:
+            logger.error("Failed to initialize git repo: %s", e, exc_info=True)
+            return False, ""
+
+    @staticmethod
+    def _backup_git(git_folder, label):
+        try:
+            backup_dir = tempfile.mkdtemp()
+            backup = os.path.join(backup_dir, '.git')
+            shutil.copytree(git_folder, backup)
+            logger.info("Backed up .git before switching to %s", label)
+            return backup
+        except Exception as e:
+            logger.error("Failed to backup .git before %s switch: %s", label, e)
+            return None
+
+    @staticmethod
+    def _restore_git(git_backup, git_folder, success, label):
+        if git_backup and not success:
+            try:
+                if os.path.exists(git_folder):
+                    shutil.rmtree(git_folder)
+                shutil.copytree(git_backup, git_folder)
+                logger.info("Restored .git after failed %s switch", label)
+            except Exception as e:
+                logger.error("Failed to restore .git after %s switch: %s", label, e)
+        if git_backup:
+            shutil.rmtree(os.path.dirname(git_backup), ignore_errors=True)
+
     @staticmethod
     async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
         """
@@ -244,8 +499,7 @@ class UpdateRoutes:
             except Exception:
                 logger.debug("Could not close downloaded-version history database", exc_info=True)
 
-            # Skip settings.json, civitai, model cache and runtime cache folders
-            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json', 'civitai', 'model_cache', 'cache', 'wildcards', 'backups', 'stats'])
+            UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=list(_PRESERVE_DIRS))
 
             # Extract ZIP to temp dir
             with tempfile.TemporaryDirectory() as tmp_dir:
@@ -255,7 +509,7 @@ class UpdateRoutes:
                     extracted_root = next(os.scandir(tmp_dir)).path
 
                     # Copy files, skipping user data that should be preserved
-                    skip_items = {'settings.json', 'civitai', 'wildcards', 'backups', 'stats'}
+                    skip_items = set(_PRESERVE_DIRS)
                     for item in os.listdir(extracted_root):
                         if item in skip_items:
                             continue
@@ -272,7 +526,7 @@ class UpdateRoutes:
                     # for ComfyUI Manager to work properly
                     tracking_info_file = os.path.join(plugin_root, '.tracking')
                     tracking_files = []
-                    skip_tracked = {'civitai', 'wildcards', 'backups', 'stats'}
+                    skip_tracked = set(_PRESERVE_DIRS) - {'settings.json'}
                     for root, dirs, files in os.walk(extracted_root):
                         # Skip user data directories and their contents
                         rel_root = os.path.relpath(root, extracted_root)
@@ -295,7 +549,8 @@ class UpdateRoutes:
         except Exception as e:
             logger.error(f"ZIP update failed: {e}", exc_info=True)
             return False, ""
-        
+
+    @staticmethod
     def _clean_plugin_folder(plugin_root, skip_files=None):
         skip_files = skip_files or []
         for item in os.listdir(plugin_root):
@@ -308,41 +563,54 @@ class UpdateRoutes:
                 os.remove(path)
     
     @staticmethod
-    async def _get_nightly_version() -> tuple[str, List[str]]:
-        """
-        Fetch latest commit from main branch
-        """
+    async def _get_nightly_version(local_hash: str = "") -> tuple[str, List[str], int, str]:
         repo_owner = "willmiao"
         repo_name = "ComfyUI-Lora-Manager"
-        
-        # Use GitHub API to fetch the latest commit from main branch
+
         github_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/commits/main"
-        
+
         try:
             downloader = await get_downloader()
-            success, data = await downloader.make_request('GET', github_url, custom_headers={'Accept': 'application/vnd.github+json'})
-            
+            success, data = await downloader.make_request(
+                'GET', github_url,
+                custom_headers={'Accept': 'application/vnd.github+json'}
+            )
+
             if not success:
-                logger.warning(f"Failed to fetch GitHub commit: {data}")
-                return "main", []
-            
-            commit_sha = data.get('sha', '')[:7]  # Short hash
+                logger.warning("Failed to fetch GitHub commit: %s", data)
+                return "main", [], 0, ""
+
+            commit_sha = data.get('sha', '')[:7]
             commit_message = data.get('commit', {}).get('message', '')
-            
-            # Format as "main-{short_hash}"
+            commit_date = data.get('commit', {}).get('committer', {}).get('date', '')[:10]
+
             version = f"main-{commit_sha}"
-            
-            # Use commit message as changelog
             changelog = [commit_message] if commit_message else []
-            
-            return version, changelog
-        
+
+            behind_by = 0
+            if local_hash and local_hash not in ('unknown', 'stable'):
+                compare_url = (
+                    f"https://api.github.com/repos/{repo_owner}/{repo_name}"
+                    f"/compare/{local_hash}...main"
+                )
+                c_ok, c_data = await downloader.make_request(
+                    'GET', compare_url,
+                    custom_headers={'Accept': 'application/vnd.github+json'}
+                )
+                if c_ok:
+                    if c_data.get('status') in ('ahead', 'diverged'):
+                        behind_by = c_data.get('ahead_by', 0)
+                    else:
+                        behind_by = c_data.get('behind_by', 0)
+
+            return version, changelog, behind_by, commit_date
+
         except NETWORK_EXCEPTIONS as e:
             logger.warning("Unable to reach GitHub for nightly version: %s", e)
-            return "main", []
+            return "main", [], 0, ""
         except Exception as e:
-            logger.error(f"Error fetching nightly version: {e}", exc_info=True)
-            return "main", []
+            logger.error("Error fetching nightly version: %s", e, exc_info=True)
+            return "main", [], 0, ""
     
     @staticmethod
     def _compare_nightly_versions(local_git_info: Dict[str, str], remote_version: str) -> bool:

py/services/base_model_service.py

@@ -1,7 +1,8 @@
 from abc import ABC, abstractmethod
 import asyncio
 import re
-from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
+import random
+from typing import Any, Dict, List, Optional, Type, Union, TYPE_CHECKING
 import logging
 import os
 import time
@@ -109,12 +110,15 @@ class BaseModelService(ABC):
         if civitai_model_id is not None:
             sorted_data = [
                 item for item in sorted_data
-                if self._extract_model_id(item) == civitai_model_id
+                if self._extract_group_key(item) == civitai_model_id
             ]
             # VLM mode: always sort by version ID descending (newest version first),
             # regardless of the current sort_by preference.
+            # Fall back to modified timestamp for non-CivitAI sources.
             sorted_data.sort(
-                key=lambda x: self._extract_version_id(x) or 0,
+                key=lambda x: self._extract_version_id(x)
+                or x.get("modified", 0)
+                or 0,
                 reverse=True,
             )
 
@@ -129,18 +133,21 @@ class BaseModelService(ABC):
             ufs = self.settings.get("version_grouping", "same_base")
             group_by_base = ufs == "same_base"
 
-            dedup_map = {}  # (modelId [,base_model]) -> (item, version_id)
+            dedup_map = {}  # (modelId [,base_model]) -> (item, version_or_modified)
             version_counter = {}  # same-key -> count
             standalone = []
             for item in sorted_data:
-                mid = self._extract_model_id(item)
+                mid = self._extract_group_key(item)
                 if mid is None:
                     standalone.append(item)
                     continue
                 key = (mid, item.get("base_model") or "") if group_by_base else mid
                 # Count all versions per key
                 version_counter[key] = version_counter.get(key, 0) + 1
-                vid = self._extract_version_id(item) or 0
+                # Prefer CivitAI version_id; fall back to modified timestamp
+                vid = self._extract_version_id(item)
+                if vid is None:
+                    vid = item.get("modified", 0) or 0
                 if key not in dedup_map or vid > dedup_map[key][1]:
                     dedup_map[key] = (item, vid)
             # Attach version_count to each surviving grouped item (shallow copy
@@ -174,16 +181,19 @@ class BaseModelService(ABC):
                 model_groups: Dict[Any, List[Dict]] = {}
                 ungrouped_standalone: List[Dict] = []
                 for item in sorted_data:
-                    mid = self._extract_model_id(item)
+                    mid = self._extract_group_key(item)
                     if mid is None:
                         ungrouped_standalone.append(item)
                         continue
                     key = (mid, item.get("base_model") or "") if group_by_base else mid
                     model_groups.setdefault(key, []).append(item)
-                # Sort versions within each group by version id descending
+                # Sort versions within each group by version id (descending);
+                # fall back to modified timestamp for non-CivitAI sources.
                 for items in model_groups.values():
                     items.sort(
-                        key=lambda x: self._extract_version_id(x) or 0,
+                        key=lambda x: self._extract_version_id(x)
+                        or x.get("modified", 0)
+                        or 0,
                         reverse=True,
                     )
                 # Sort groups by version count
@@ -381,6 +391,12 @@ class BaseModelService(ABC):
                 (item.get("model_name") or item.get("file_name") or "").lower(),
                 item.get("file_path", "").lower(),
             )
+        elif key_name == "random":
+            # Seeded random shuffle: same seed -> same order (stable pagination)
+            rng = random.Random(sort_params.seed or "random")
+            result = list(data)
+            rng.shuffle(result)
+            return result
         elif key_name == "size":
             key_fn = lambda item: (
                 int(item.get("size", 0) or 0),
@@ -697,6 +713,33 @@ class BaseModelService(ABC):
 
         return annotated
 
+    @staticmethod
+    def _extract_hf_group_key(item: Dict) -> Optional[str]:
+        """Extract `hf:{owner}/{repo}` from item's ``hf_url``, or None."""
+        hf_url = item.get("hf_url") if isinstance(item, dict) else None
+        if not hf_url or not isinstance(hf_url, str):
+            return None
+        m = re.match(
+            r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url.strip()
+        )
+        if not m:
+            return None
+        return f"hf:{m.group(1)}"
+
+    @staticmethod
+    def _extract_group_key(item: Dict) -> Union[int, str, None]:
+        """Return the group identity key: CivitAI modelId (int) or HF repo (str).
+
+        Preference order:
+        1. CivitAI ``modelId`` (int)
+        2. HF repo identity ``hf:{owner}/{repo}`` (str)
+        3. ``None`` (no known grouping source)
+        """
+        mid = BaseModelService._extract_model_id(item)
+        if mid is not None:
+            return mid
+        return BaseModelService._extract_hf_group_key(item)
+
     @staticmethod
     def _extract_model_id(item: Dict) -> Optional[int]:
         civitai = item.get("civitai") if isinstance(item, dict) else None
@@ -804,6 +847,12 @@ class BaseModelService(ABC):
         """Get top tags sorted by frequency"""
         return await self.scanner.get_top_tags(limit)
 
+    async def search_tags(
+        self, query: str, limit: int = 50
+    ) -> List[Dict]:
+        """Search tags by substring, sorted by frequency"""
+        return await self.scanner.search_tags(query, limit)
+
     async def get_base_models(self, limit: int = 20) -> List[Dict]:
         """Get base models sorted by frequency"""
         return await self.scanner.get_base_models(limit)
@@ -955,13 +1004,21 @@ class BaseModelService(ABC):
 
         return unified_tree
 
-    async def get_model_notes(self, model_name: str) -> Optional[str]:
-        """Get notes for a specific model file"""
+    async def get_model_notes(self, model_name: str) -> Optional[dict]:
+        """Get notes and file_path for a specific model file.
+
+        Supports both simple names (``OWSMianne_ANIMA_V1``) and full-path
+        syntax (``Anima/character/OWSMianne_ANIMA_V1``).
+        """
         cache = await self.scanner.get_cached_data()
 
         for model in cache.raw_data:
-            if model["file_name"] == model_name:
-                return model.get("notes", "")
+            file_name = model.get("file_name", "")
+            if file_name == model_name or model_name.endswith("/" + file_name) or model_name.endswith("\\" + file_name):
+                return {
+                    "notes": model.get("notes", ""),
+                    "file_path": model.get("file_path", ""),
+                }
 
         return None
 
@@ -1084,6 +1141,11 @@ class BaseModelService(ABC):
 
         Listing/search endpoints return lightweight cache entries; this method performs
         a lazy read of the on-disk metadata snapshot when callers need full detail.
+
+        As a beneficial side effect, the in-memory and persistent caches are
+        opportunistically synchronised with the on-disk metadata — this keeps the
+        caches fresh even when a ``.metadata.json`` file was edited outside of the
+        normal save path (e.g. manually or by an external script).
         """
         metadata, should_skip = await MetadataManager.load_metadata(
             file_path, self.metadata_class
@@ -1101,6 +1163,19 @@ class BaseModelService(ABC):
                 MetadataManager.save_metadata(file_path, metadata)
             )
 
+        # Opportunistically sync the in-memory + persistent caches.
+        # The .metadata.json disk read is already paid for; the sync only
+        # performs work when the cache is actually stale, and uses targeted,
+        # in-place operations to minimise overhead even with large model sets.
+        #
+        # Fire-and-forget by design: the task is intentionally untracked.
+        # sync_cache_from_metadata handles its own errors internally.
+        asyncio.create_task(
+            self.scanner.sync_cache_from_metadata(
+                file_path, metadata.to_dict()
+            )
+        )
+
         return self.filter_civitai_data(metadata.to_dict().get("civitai", {}))
 
     async def get_model_description(self, file_path: str) -> Optional[str]:

py/services/checkpoint_scanner.py

@@ -114,6 +114,13 @@ class CheckpointScanner(ModelScanner):
                 and metadata.hash_status == "completed"
                 and metadata.sha256
             ):
+                # Ensure the in-memory hash index is populated even when
+                # the hash was already computed and persisted to the metadata
+                # file.  Without this, usage tracking (and any other caller
+                # that queries get_hash_by_filename first) will miss on every
+                # lookup and keep calling back into this method, creating a
+                # tight loop that never populates the index.
+                self._hash_index.add_entry(metadata.sha256.lower(), file_path)
                 return metadata.sha256
 
             async with self._hash_calculation_lock:
@@ -125,6 +132,7 @@ class CheckpointScanner(ModelScanner):
                     and metadata.hash_status == "completed"
                     and metadata.sha256
                 ):
+                    self._hash_index.add_entry(metadata.sha256.lower(), file_path)
                     return metadata.sha256
 
                 task = self._hash_calculation_tasks.get(real_path)
@@ -175,6 +183,9 @@ class CheckpointScanner(ModelScanner):
 
             # Check if hash is already calculated
             if metadata.hash_status == "completed" and metadata.sha256:
+                # Populate the in-memory hash index even for pre-computed
+                # hashes, mirroring the fix in calculate_hash_for_model.
+                self._hash_index.add_entry(metadata.sha256.lower(), file_path)
                 return metadata.sha256
 
             # Update status to calculating
@@ -193,6 +204,20 @@ class CheckpointScanner(ModelScanner):
             # Update hash index
             self._hash_index.add_entry(sha256.lower(), file_path)
 
+            # Update the in-memory cache entry so that subsequent
+            # _persist_current_cache / _save_persistent_cache calls
+            # write the hash back to the SQLite models table.  Without
+            # this the hash only lives in the metadata file and the
+            # in-memory hash index, both of which are lost across
+            # restarts, causing the same re-computation loop on the
+            # next session.
+            if self._cache is not None and self._cache.raw_data:
+                for entry in self._cache.raw_data:
+                    if entry.get("file_path") == file_path:
+                        entry["sha256"] = sha256.lower()
+                        entry["hash_status"] = "completed"
+                        break
+
             logger.info(f"Hash calculated for checkpoint: {file_path}")
             return sha256
 

py/services/civarchive_client.py

@@ -304,6 +304,20 @@ class CivArchiveClient:
             version_id = file_data.get("model_version_id") or file_data.get("modelVersionId")
             if model_id is None or version_id is None:
                 continue
+            # CivitAI / CivArchive model IDs are small integers (typically ≤ 7
+            # digits).  Reject suspiciously large values that indicate the API
+            # returned a malformed payload (e.g. a hash reinterpreted as an ID)
+            # to avoid pointless HTTP 500 errors from CivArchive.
+            _MAX_VALID_CIVITAI_ID = 100_000_000
+            try:
+                if int(model_id) >= _MAX_VALID_CIVITAI_ID or int(version_id) >= _MAX_VALID_CIVITAI_ID:
+                    logger.debug(
+                        "Skipping implausible CivArchive model_id=%s / version_id=%s",
+                        model_id, version_id,
+                    )
+                    continue
+            except (TypeError, ValueError):
+                continue
             resolved = await self.get_model_version(model_id, version_id)
             if resolved:
                 return resolved

py/services/civitai_client.py

@@ -2,6 +2,7 @@ import asyncio
 import copy
 import logging
 import os
+import time
 from collections import OrderedDict
 from typing import Any, Optional, Dict, Tuple, List, Sequence
 from .connectivity_guard import (
@@ -19,6 +20,12 @@ from ..utils.civitai_utils import resolve_license_payload
 
 logger = logging.getLogger(__name__)
 
+# Best-effort cache for creator model counts, keyed by lowercase username.
+# Values are (monotonic timestamp, count or None); None results are cached
+# too so repeated failures don't hammer the API.
+_CREATOR_COUNT_CACHE_TTL_SECONDS = 600
+_creator_model_count_cache: Dict[str, Tuple[float, Optional[int]]] = {}
+
 
 class CivitaiClient:
     _instance = None
@@ -743,17 +750,34 @@ class CivitaiClient:
 
         return all_versions if all_versions else None
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
-        """Fetch all models for a specific Civitai user."""
+    async def get_user_models(
+        self, username: str, cursor: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        """Fetch one page (up to 100 models) for a specific Civitai user.
+
+        Returns ``{"items": [...], "nextCursor": <str|None>}`` on success,
+        or None on failure. Pass ``cursor`` (from a previous response's
+        ``nextCursor``) to fetch subsequent pages.
+        """
         if not username:
             return None
 
+        params: Dict[str, Any] = {
+            "username": username,
+            "nsfw": "true",
+            "limit": 100,
+            "sort": "Newest",
+            "period": "AllTime",
+        }
+        if cursor:
+            params["cursor"] = cursor
+
         try:
             success, result = await self._make_request(
                 "GET",
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={"username": username, "nsfw": "true"},
+                params=params,
             )
 
             if not success:
@@ -765,7 +789,7 @@ class CivitaiClient:
 
             items = result.get("items") if isinstance(result, dict) else None
             if not isinstance(items, list):
-                return []
+                items = []
 
             for model in items:
                 versions = model.get("modelVersions")
@@ -774,9 +798,68 @@ class CivitaiClient:
                 for version in versions:
                     self._remove_comfy_metadata(version)
 
-            return items
+            next_cursor: Optional[str] = None
+            metadata = result.get("metadata") if isinstance(result, dict) else None
+            if isinstance(metadata, dict):
+                raw_cursor = metadata.get("nextCursor")
+                if raw_cursor is not None:
+                    next_cursor = str(raw_cursor)
+
+            return {"items": items, "nextCursor": next_cursor}
         except RateLimitError:
             raise
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Error fetching models for %s: %s", username, exc)
             return None
+
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        """Best-effort lookup of a creator's published model count.
+
+        Uses the ``/creators`` endpoint (a contains-match query), picking the
+        entry whose username matches exactly (case-insensitive). Returns None
+        on any failure; never raises. Results (including None) are cached
+        for ``_CREATOR_COUNT_CACHE_TTL_SECONDS``.
+        """
+        if not username:
+            return None
+
+        cache_key = username.lower()
+        cached = _creator_model_count_cache.get(cache_key)
+        if cached is not None:
+            cached_at, cached_count = cached
+            if time.monotonic() - cached_at < _CREATOR_COUNT_CACHE_TTL_SECONDS:
+                return cached_count
+
+        count: Optional[int] = None
+        try:
+            success, result = await self._make_request(
+                "GET",
+                f"{self.base_url}/creators",
+                use_auth=True,
+                params={"query": username, "limit": 10},
+            )
+
+            if success and isinstance(result, dict):
+                creators = result.get("items")
+                if isinstance(creators, list):
+                    for creator in creators:
+                        if not isinstance(creator, dict):
+                            continue
+                        creator_name = creator.get("username")
+                        if not isinstance(creator_name, str):
+                            continue
+                        if creator_name.lower() != cache_key:
+                            continue
+                        model_count = creator.get("modelCount")
+                        if isinstance(model_count, (int, float)) and not isinstance(
+                            model_count, bool
+                        ):
+                            count = int(model_count)
+                        break
+        except Exception as exc:  # best-effort only, never propagate
+            logger.debug(
+                "Failed to fetch creator model count for %s: %s", username, exc
+            )
+
+        _creator_model_count_cache[cache_key] = (time.monotonic(), count)
+        return count

py/services/download_manager.py

@@ -230,6 +230,12 @@ class DownloadManager:
         Returns:
             Dict with download result
         """
+        logger.debug(
+            "[download] download_from_civitai called: model_id=%s, model_version_id=%s, "
+            "source=%s, file_params=%s",
+            model_id, model_version_id, source, file_params,
+        )
+
         # Validate that at least one identifier is provided
         if not model_id and not model_version_id:
             return {
@@ -250,6 +256,7 @@ class DownloadManager:
             "source": source,
             "file_params": copy.deepcopy(file_params) if file_params is not None else None,
             "progress": 0,
+
             "status": "queued",
             "transfer_backend": self._get_model_download_backend(),
             "bytes_downloaded": 0,
@@ -289,8 +296,8 @@ class DownloadManager:
             return result
         except asyncio.CancelledError:
             return {
-                "success": False,
-                "error": "Download was cancelled",
+                "success": True,
+                "cancelled": True,
                 "download_id": task_id,
             }
         finally:
@@ -675,7 +682,10 @@ class DownloadManager:
                     u for u in download_urls if not u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
                 ]
                 download_urls = non_civitai_urls + civitai_urls
-        else:
+
+        # Fallback: when mirrors is empty or all mirrors have been deleted,
+        # use the file's downloadUrl directly (e.g. CivitAI download endpoint).
+        if not download_urls:
             download_url = file_info.get("downloadUrl")
             if download_url:
                 download_urls.append(normalize_civitai_download_url(download_url))
@@ -1379,7 +1389,17 @@ class DownloadManager:
 
             # Update save directory with relative path if provided
             if relative_path:
+                base_save_dir = save_dir
                 save_dir = os.path.join(save_dir, relative_path)
+                # Security: validate path containment after joining
+                resolved_dir = os.path.abspath(os.path.normpath(save_dir))
+                base_dir = os.path.abspath(os.path.normpath(base_save_dir))
+                if not resolved_dir.startswith(base_dir + os.sep) and resolved_dir != base_dir:
+                    logger.warning(
+                        "Path traversal detected: %s escapes %s",
+                        resolved_dir, base_dir,
+                    )
+                    return {"success": False, "error": "Download path is outside allowed directory"}
                 # Create directory if it doesn't exist
                 os.makedirs(save_dir, exist_ok=True)
 
@@ -1421,14 +1441,35 @@ class DownloadManager:
 
             # If file_params is provided, try to find matching file
             if file_params and model_version_id:
+                target_file_id = file_params.get("id")
                 target_type = file_params.get("type", "Model")
-                target_format = file_params.get("format", "SafeTensor")
-                target_size = file_params.get("size", "full")
+                target_format = file_params.get("format")
+                target_size = file_params.get("size")
                 target_fp = file_params.get("fp")
                 is_primary = file_params.get("isPrimary", False)
 
-                if is_primary:
-                    # Find primary file
+                logger.debug(
+                    "[download] file_params received: id=%s, type=%s, format=%s, size=%s, fp=%s, isPrimary=%s, "
+                    "model_version_id=%s, total_files=%d",
+                    target_file_id, target_type, target_format, target_size, target_fp, is_primary,
+                    model_version_id, len(files),
+                )
+
+                if target_file_id:
+                    target_id_str = str(target_file_id)
+                    for f in files:
+                        f_id = f.get("id")
+                        if str(f_id) == target_id_str:
+                            file_info = f
+                            logger.debug(
+                                "[download] MATCH by ID: id=%s name='%s'",
+                                f_id, f.get("name"),
+                            )
+                            break
+                    if not file_info:
+                        logger.debug("[download] No file found with id=%s", target_file_id)
+
+                elif is_primary:
                     file_info = next(
                         (
                             f
@@ -1439,28 +1480,41 @@ class DownloadManager:
                         None,
                     )
                 else:
-                    # Match by metadata
+                    # Lenient metadata match: only compare fields present on both sides
                     for f in files:
                         f_type = f.get("type", "")
-                        f_meta = f.get("metadata", {})
-
-                        # Check type match
                         if f_type != target_type:
                             continue
 
-                        # Check metadata match
-                        if f_meta.get("format") != target_format:
+                        f_meta = f.get("metadata", {})
+                        f_format = f_meta.get("format") or f.get("format")
+                        f_size = f_meta.get("size") or f.get("size")
+                        f_fp = f_meta.get("fp") or f.get("fp")
+
+                        if target_format and f_format != target_format:
                             continue
-                        if f_meta.get("size") != target_size:
+                        if target_size and f_size and f_size != target_size:
                             continue
-                        if target_fp and f_meta.get("fp") != target_fp:
+                        if target_fp and f_fp and f_fp != target_fp:
                             continue
 
                         file_info = f
                         break
 
+                if not file_info:
+                    logger.debug(
+                        "[download] No match found via file_params — falling back to primary file lookup",
+                    )
+            elif not file_params:
+                logger.debug(
+                    "[download] No file_params provided (null/None) — will use primary file lookup. "
+                    "model_version_id=%s, total_files=%d",
+                    model_version_id, len(files),
+                )
+
             # Fallback to primary file if no match found
             if not file_info:
+                logger.debug("[download] Looking for primary file as fallback")
                 file_info = next(
                     (
                         f
@@ -1469,38 +1523,18 @@ class DownloadManager:
                     ),
                     None,
                 )
+                if file_info:
+                    logger.debug(
+                        "[download] Fallback primary file selected: id=%s, name=%s",
+                        file_info.get("id"), file_info.get("name"),
+                    )
+                else:
+                    logger.debug("[download] No primary file found in fallback lookup")
 
             if not file_info:
                 return {"success": False, "error": "No suitable file found in metadata"}
-            mirrors = file_info.get("mirrors") or []
-            download_urls = []
-            if mirrors:
-                for mirror in mirrors:
-                    if mirror.get("deletedAt") is None and mirror.get("url"):
-                        download_urls.append(
-                            normalize_civitai_download_url(mirror["url"])
-                        )
 
-                # When source is 'civarchive', prioritize non-Civitai URLs
-                # This avoids failed downloads from deleted Civitai models
-                if source == "civarchive" and len(download_urls) > 1:
-                    civitai_urls = [
-                        u
-                        for u in download_urls
-                        if u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
-                    ]
-                    non_civitai_urls = [
-                        u
-                        for u in download_urls
-                        if not u.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES)
-                    ]
-                    download_urls = non_civitai_urls + civitai_urls
-            else:
-                download_url = file_info.get("downloadUrl")
-                if download_url:
-                    download_urls.append(
-                        normalize_civitai_download_url(download_url)
-                    )
+            download_urls = self._build_download_urls_from_file_info(file_info, source=source)
 
             if not download_urls:
                 return {"success": False, "error": "No mirror URL found"}
@@ -1803,6 +1837,9 @@ class DownloadManager:
             model_tags, model_type
         )
 
+        if not first_tag:
+            first_tag = "no tags"  # Default if no tags available
+
         # Format the template with available data
         formatted_path = path_template
         formatted_path = formatted_path.replace("{base_model}", mapped_base_model)
@@ -1818,6 +1855,15 @@ class DownloadManager:
         if model_type == "embedding":
             formatted_path = formatted_path.replace(" ", "_")
 
+        # Sanitize the resolved path to prevent path traversal:
+        # - Strip leading slashes (prevents os.path.join from treating path as absolute)
+        # - Collapse double slashes from empty placeholder substitutions
+        # - Strip trailing slashes for cleanliness
+        formatted_path = formatted_path.lstrip("/")
+        while "//" in formatted_path:
+            formatted_path = formatted_path.replace("//", "/")
+        formatted_path = formatted_path.rstrip("/")
+
         return formatted_path
 
     async def _execute_download(

py/services/download_queue_service.py

@@ -31,7 +31,7 @@ class DownloadQueueService:
     _instance: Optional[DownloadQueueService] = None
     _class_lock: asyncio.Lock = asyncio.Lock()
 
-    _SCHEMA = """
+    _SCHEMA_TABLES = """
         CREATE TABLE IF NOT EXISTS download_queue (
             download_id TEXT PRIMARY KEY,
             model_id INTEGER,
@@ -76,6 +76,11 @@ class DownloadQueueService:
         CREATE INDEX IF NOT EXISTS idx_dh_status ON download_history(status);
     """
 
+    _CREATE_UNIQUE_INDEX = """
+        CREATE UNIQUE INDEX IF NOT EXISTS idx_dh_download_id
+            ON download_history(download_id) WHERE download_id IS NOT NULL;
+    """
+
     @classmethod
     async def get_instance(cls) -> DownloadQueueService:
         """Return the singleton instance, creating it if necessary."""
@@ -113,10 +118,39 @@ class DownloadQueueService:
         if self._schema_initialized:
             return
         with self._connect() as conn:
-            conn.executescript(self._SCHEMA)
+            conn.executescript(self._SCHEMA_TABLES)
+
+            # Creating the unique index on download_history.download_id can
+            # fail if pre-existing rows have duplicate values (e.g. from a
+            # previous version that lacked the index).  Deduplicate first so
+            # that the migration does not crash on startup.
+            if not self._index_exists(conn, "idx_dh_download_id"):
+                self._remove_duplicate_download_ids(conn)
+                conn.executescript(self._CREATE_UNIQUE_INDEX)
+
             conn.commit()
         self._schema_initialized = True
 
+    @staticmethod
+    def _index_exists(conn: sqlite3.Connection, name: str) -> bool:
+        return conn.execute(
+            "SELECT 1 FROM sqlite_master WHERE type='index' AND name=?",
+            (name,),
+        ).fetchone() is not None
+
+    @staticmethod
+    def _remove_duplicate_download_ids(conn: sqlite3.Connection) -> None:
+        conn.execute("""
+            DELETE FROM download_history
+            WHERE id NOT IN (
+                SELECT MIN(id)
+                FROM download_history
+                WHERE download_id IS NOT NULL
+                GROUP BY download_id
+            )
+            AND download_id IS NOT NULL
+        """)
+
     def get_database_path(self) -> str:
         """Return the resolved database file path."""
         return self._db_path
@@ -154,13 +188,23 @@ class DownloadQueueService:
         """Insert a new download into the queue.
 
         Returns the inserted row as a dict (or an empty dict if the
-        download_id already exists).
+        download_id already exists in the queue or has a terminal
+        record in history).
         """
         now = time.time()
         file_params_json = json.dumps(file_params) if file_params is not None else None
 
         async with self._lock:
             conn = self._get_conn()
+
+            # Reject download_ids that already have a terminal record in history.
+            history_row = conn.execute(
+                "SELECT 1 FROM download_history WHERE download_id = ? LIMIT 1",
+                (download_id,),
+            ).fetchone()
+            if history_row is not None:
+                return {}
+
             conn.execute(
                 """
                 INSERT OR IGNORE INTO download_queue (
@@ -380,7 +424,7 @@ class DownloadQueueService:
             )
             conn.execute(
                 """
-                INSERT INTO download_history (
+                INSERT OR IGNORE INTO download_history (
                     download_id, model_id, model_version_id, model_name,
                     version_name, thumbnail_url, status, error, file_path,
                     bytes_downloaded, total_bytes, completed_at
@@ -537,17 +581,27 @@ class DownloadQueueService:
             "offset": offset,
         }
 
-    async def delete_history_item(self, id: int) -> bool:
-        """Delete a single history entry by its *id*.
+    async def delete_history_item(
+        self, id: Optional[int] = None, download_id: Optional[str] = None
+    ) -> bool:
+        """Delete a single history entry by *download_id* (preferred) or *id*.
 
         Returns ``True`` if a row was deleted.
         """
         async with self._lock:
             conn = self._get_conn()
-            cursor = conn.execute(
-                "DELETE FROM download_history WHERE id = ?",
-                (id,),
-            )
+            if download_id:
+                cursor = conn.execute(
+                    "DELETE FROM download_history WHERE download_id = ?",
+                    (download_id,),
+                )
+            elif id is not None:
+                cursor = conn.execute(
+                    "DELETE FROM download_history WHERE id = ?",
+                    (id,),
+                )
+            else:
+                return False
             conn.commit()
         return cursor.rowcount > 0
 
@@ -604,21 +658,34 @@ class DownloadQueueService:
     # Retry
     # ------------------------------------------------------------------
 
-    async def retry_from_history(self, item_id: int) -> Optional[dict[str, Any]]:
+    async def retry_from_history(
+        self,
+        item_id: Optional[int] = None,
+        download_id: Optional[str] = None,
+    ) -> Optional[dict[str, Any]]:
         """Re-queue a failed or canceled download from history.
 
-        Looks up the history record by 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.
+        Looks up the history record by *download_id* (preferred) or
+        *item_id*.  If the status is ``failed`` or ``canceled`` a new
+        queue entry is created with the same model metadata and a fresh
+        download id, and the original history entry is **deleted** to
+        prevent exponential growth when the retried item is later
+        canceled or fails again and re-retried.
         """
         async with self._lock:
             conn = self._get_conn()
-            row = conn.execute(
-                "SELECT * FROM download_history WHERE id = ?",
-                (item_id,),
-            ).fetchone()
+            if download_id:
+                row = conn.execute(
+                    "SELECT * FROM download_history WHERE download_id = ?",
+                    (download_id,),
+                ).fetchone()
+            elif item_id is not None:
+                row = conn.execute(
+                    "SELECT * FROM download_history WHERE id = ?",
+                    (item_id,),
+                ).fetchone()
+            else:
+                return None
             if row is None:
                 return None
             status = str(row["status"])
@@ -650,7 +717,7 @@ class DownloadQueueService:
             )
             conn.execute(
                 "DELETE FROM download_history WHERE id = ?",
-                (item_id,),
+                (row["id"],),
             )
             conn.commit()
             queued = conn.execute(

py/services/downloader.py

@@ -270,14 +270,14 @@ class Downloader:
 
         Note: This is private and caller MUST hold self._session_lock.
         """
-        # Close existing session if any
-        if self._session is not None:
-            try:
-                await self._session.close()
-            except Exception as e:  # pragma: no cover
-                logger.warning(f"Error closing previous session: {e}")
-            finally:
-                self._session = None
+        # Snapshot and clear old session reference before creating the new
+        # one.  This ensures self._session is always valid (or None, which
+        # triggers a fresh creation) and avoids a race where concurrent
+        # requests hold a reference to a session whose connector has been
+        # torn down by a premature close() call — the root cause of the
+        # intermittent "NoneType has no attribute connect" crash.
+        old_session = self._session
+        self._session = None
 
         # Check for app-level proxy settings
         proxy_url = None  # http(s) proxy, passed via the per-request `proxy=` kwarg
@@ -372,6 +372,13 @@ class Downloader:
         self._proxy_url = proxy_url
         self._session_created_at = datetime.now()
 
+        # Close the previous session now that the replacement is live.
+        if old_session is not None:
+            try:
+                await old_session.close()
+            except Exception as e:  # pragma: no cover
+                logger.warning(f"Error closing previous session: {e}")
+
         logger.debug(
             "Created new HTTP session with proxy settings. App-level proxy: %s, System-level proxy (trust_env): %s",
             bool(proxy_url),
@@ -753,7 +760,8 @@ class Downloader:
                             else:
                                 resume_offset = 0
                                 total_size = 0
-                            await self._create_session()
+                            async with self._session_lock:
+                                await self._create_session()
                             continue
 
                         return False, integrity_error
@@ -843,7 +851,8 @@ class Downloader:
                         logger.info(f"Will resume from byte {resume_offset}")
 
                     # Refresh session to get new connection
-                    await self._create_session()
+                    async with self._session_lock:
+                        await self._create_session()
                     continue
                 else:
                     logger.error(f"Max retries exceeded for download: {e}")

py/services/llm_service.py

@@ -201,6 +201,11 @@ PROVIDER_PRESETS: Dict[str, Dict[str, Any]] = {
         "api_base": "https://openrouter.ai/api/v1",
         "requires_key": True,
     },
+    "google": {
+        "name": "Gemini",
+        "api_base": "https://generativelanguage.googleapis.com/v1beta/openai",
+        "requires_key": True,
+    },
     "opencode-go": {
         "name": "OpenCode Go",
         "api_base": "https://opencode.ai/zen/go/v1",
@@ -566,18 +571,52 @@ class LLMService:
         if effective_max is None:
             effective_max = 4096
 
-        result = await self.chat_completion(
-            messages=messages,
-            model=model,
-            temperature=temperature,
-            response_format={"type": "json_object"},
-            max_tokens=effective_max,
-        )
+        # Use json_schema (not json_object) for broader provider compatibility:
+        # LM Studio and some other OpenAI-compatible servers reject
+        # json_object but accept json_schema.  {"type": "object"} is
+        # functionally equivalent — it accepts any JSON object without
+        # constraining specific fields.
+        response_format = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "metadata",
+                "schema": {"type": "object"},
+            },
+        }
+
+        try:
+            result = await self.chat_completion(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                response_format=response_format,
+                max_tokens=effective_max,
+            )
+        except LLMResponseError as e:
+            # Only fall back when the provider rejects the response_format
+            # type value (e.g. "'response_format.type' must be...").  Avoid
+            # catching unrelated 400 errors whose body happens to mention
+            # "response_format" (e.g. "model does not support
+            # response_format restrictions on this endpoint").
+            if "'response_format.type'" not in str(e).lower():
+                raise
+            logger.info(
+                "Provider rejected response_format, retrying without it. "
+                "Falling back to prompt-only JSON mode. Error: %s",
+                e,
+            )
+            result = await self.chat_completion(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                response_format=None,
+                max_tokens=effective_max,
+            )
 
         content = result.get("content", "") or ""
         if not content:
             raise LLMResponseError(
-                "LLM returned empty content in json_object mode. "
+                "LLM returned empty content. "
                 f"Raw response: {json.dumps(result)[:500]}"
             )
 

py/services/lora_service.py

@@ -271,12 +271,16 @@ class LoraService(BaseModelService):
         return letters
 
     async def get_lora_trigger_words(self, lora_name: str) -> List[str]:
-        """Get trigger words for a specific LoRA file"""
+        """Get trigger words for a specific LoRA file.
+
+        Supports both simple names and full-path syntax.
+        """
         cache = await self.scanner.get_cached_data()
 
         for lora in cache.raw_data:
-            if lora["file_name"] == lora_name:
-                civitai_data = lora.get("civitai", {})
+            file_name = lora.get("file_name", "")
+            if file_name == lora_name or lora_name.endswith("/" + file_name) or lora_name.endswith("\\" + file_name):
+                civitai_data = lora.get("civitai") or {}
                 return civitai_data.get("trainedWords", [])
 
         return []

py/services/metadata_service.py

@@ -15,6 +15,17 @@ from .service_registry import ServiceRegistry
 
 logger = logging.getLogger(__name__)
 
+_PROVIDER_DISPLAY_NAMES = {
+    "civitai_api": "CivitAI",
+    "civarchive_api": "CivArchive",
+    "sqlite": "Archive DB",
+}
+
+_PRESET_PROVIDER_ORDERS = {
+    "civitai_archive_sqlite": ["civitai_api", "civarchive_api", "sqlite"],
+    "civitai_sqlite_archive": ["civitai_api", "sqlite", "civarchive_api"],
+}
+
 async def initialize_metadata_providers():
     """Initialize and configure all metadata providers based on settings"""
     provider_manager = await ModelMetadataProviderManager.get_instance()
@@ -26,7 +37,9 @@ async def initialize_metadata_providers():
     # Get settings
     settings_manager = get_settings_manager()
     enable_archive_db = settings_manager.get('enable_metadata_archive_db', False)
-    
+    enable_civarchive_api = settings_manager.get('enable_civarchive_api', True)
+    provider_order = settings_manager.get('metadata_provider_order', 'civitai_archive_sqlite')
+
     providers = []
     
     # Initialize archive database provider if enabled
@@ -59,27 +72,48 @@ async def initialize_metadata_providers():
     except Exception as e:
         logger.error(f"Failed to initialize Civitai API metadata provider: {e}")
 
-    # Register CivArchive provider, and all add to fallback providers
-    try:
-        civarchive_client = await ServiceRegistry.get_civarchive_client()
-        civarchive_provider = CivArchiveModelMetadataProvider(civarchive_client)
-        provider_manager.register_provider('civarchive_api', civarchive_provider)
-        providers.append(('civarchive_api', civarchive_provider))
-        logger.debug("CivArchive metadata provider registered (also included in fallback)")
-    except Exception as e:
-        logger.error(f"Failed to initialize CivArchive metadata provider: {e}")
+    # Register CivArchive provider when enabled. Civitai API is always
+    # preferred (better metadata); CivArchive mainly recovers metadata for
+    # models deleted from Civitai, so it can be turned off to avoid its long
+    # rate-limit windows entirely.
+    if enable_civarchive_api:
+        try:
+            civarchive_client = await ServiceRegistry.get_civarchive_client()
+            civarchive_provider = CivArchiveModelMetadataProvider(civarchive_client)
+            provider_manager.register_provider('civarchive_api', civarchive_provider)
+            providers.append(('civarchive_api', civarchive_provider))
+            logger.debug("CivArchive metadata provider registered (also included in fallback)")
+        except Exception as e:
+            logger.error(f"Failed to initialize CivArchive metadata provider: {e}")
+    else:
+        logger.debug("CivArchive metadata provider disabled by setting 'enable_civarchive_api'")
+
+    # Preset fallback orderings (see module-level _PRESET_PROVIDER_ORDERS).
+    # civitai_api is always first (better metadata); the remaining providers
+    # are arranged by the configured preset.  Providers that are not
+    # registered (disabled/unavailable) are simply skipped, so each preset
+    # degrades gracefully.
+    desired_order = _PRESET_PROVIDER_ORDERS.get(
+        provider_order, _PRESET_PROVIDER_ORDERS["civitai_archive_sqlite"]
+    )
 
     # Set up fallback provider based on available providers
     if len(providers) > 1:
-        # Always use Civitai API (it has better metadata), then CivArchive API, then Archive DB
         ordered_providers: list[tuple[str, ModelMetadataProvider]] = []
-        ordered_providers.extend([p for p in providers if p[0] == 'civitai_api'])
-        ordered_providers.extend([p for p in providers if p[0] == 'civarchive_api'])
-        ordered_providers.extend([p for p in providers if p[0] == 'sqlite'])
-        
+        for name in desired_order:
+            ordered_providers.extend([p for p in providers if p[0] == name])
+        # Include any provider not covered by the preset (defensive) at the end
+        for p in providers:
+            if p not in ordered_providers:
+                ordered_providers.append(p)
+
         if ordered_providers:
             fallback_provider = FallbackMetadataProvider(ordered_providers)
             provider_manager.register_provider('fallback', fallback_provider, is_default=True)
+            logger.debug(
+                "Metadata fallback provider order: %s",
+                ", ".join(name for name, _ in ordered_providers),
+            )
     elif len(providers) == 1:
         # Only one provider available, set it as default
         provider_name, provider = providers[0]
@@ -96,11 +130,30 @@ async def update_metadata_providers():
         # Get current settings
         settings_manager = get_settings_manager()
         enable_archive_db = settings_manager.get('enable_metadata_archive_db', False)
+        enable_civarchive_api = settings_manager.get('enable_civarchive_api', True)
+        provider_order = settings_manager.get('metadata_provider_order', 'civitai_archive_sqlite')
         
         # Reinitialize all providers with new settings
         provider_manager = await initialize_metadata_providers()
         
-        logger.info(f"Updated metadata providers, archive_db enabled: {enable_archive_db}")
+        # Build effective provider chain for logging (use actually-registered
+        # providers, not just settings, so a failed init is reflected correctly)
+        registered = set(provider_manager.providers.keys())
+        desired = _PRESET_PROVIDER_ORDERS.get(
+            provider_order, _PRESET_PROVIDER_ORDERS["civitai_archive_sqlite"]
+        )
+        chain = " → ".join(
+            _PROVIDER_DISPLAY_NAMES[p]
+            for p in desired
+            if p in registered and p in _PROVIDER_DISPLAY_NAMES
+        )
+        
+        logger.info(
+            "Updated metadata providers: archive_db=%s, civarchive_api=%s, chain=%s",
+            enable_archive_db,
+            enable_civarchive_api,
+            chain,
+        )
         return provider_manager
     except Exception as e:
         logger.error(f"Failed to update metadata providers: {e}")

py/services/metadata_sync_service.py

@@ -209,7 +209,21 @@ class MetadataSyncService:
                         error_msg = "CivitAI model is deleted and no archive provider is available"
                     return False, error_msg
             else:
-                provider_attempts.append((None, await self._get_default_provider()))
+                is_hf_source = bool(model_data.get("hf_url"))
+                if is_hf_source:
+                    # HF-sourced model: only check CivitAI API directly.
+                    # CivArchive is almost guaranteed to have no record, and
+                    # hitting it wastes rate-limit budget.
+                    # Use a distinct provider name ("civitai_api" not None) so
+                    # downstream code does NOT interpret a "Model not found"
+                    # response as civitai_api_not_found — which would mark the
+                    # model civitai_deleted=True when it was never on CivitAI.
+                    try:
+                        provider_attempts.append(("civitai_api", await self._get_provider("civitai_api")))
+                    except Exception as exc:  # pragma: no cover - provider resolution fault
+                        logger.debug("Unable to resolve civitai_api provider: %s", exc)
+                if not provider_attempts:
+                    provider_attempts.append((None, await self._get_default_provider()))
 
             civitai_metadata: Optional[Dict[str, Any]] = None
             metadata_provider: Optional[MetadataProviderProtocol] = None

py/services/model_cache.py

@@ -1,6 +1,7 @@
 import asyncio
 import time
 import logging
+import random
 
 logger = logging.getLogger(__name__)
 from typing import Any, Dict, List, Optional, Tuple
@@ -38,8 +39,8 @@ class ModelCache:
 
     def __post_init__(self):
         self._lock = asyncio.Lock()
-        # Cache for last sort: (sort_key, order) -> sorted list
-        self._last_sort: Tuple[str, str] = (None, None)
+        # Cache for last sort: (sort_key, order, seed) -> sorted list
+        self._last_sort: Tuple[Optional[str], str, Optional[str]] = (None, "asc", None)
         self._last_sorted_data: List[Dict] = []
         self._normalize_raw_data()
         self.name_display_mode = self._normalize_display_mode(self.name_display_mode)
@@ -203,9 +204,9 @@ class ModelCache:
     async def resort(self):
         """Resort cached data according to last sort mode if set"""
         async with self._lock:
-            if self._last_sort != (None, None):
-                sort_key, order = self._last_sort
-                sorted_data = self._sort_data(self.raw_data, sort_key, order)
+            if self._last_sort[0] is not None:
+                sort_key, order, seed = self._last_sort
+                sorted_data = self._sort_data(self.raw_data, sort_key, order, seed)
                 self._last_sorted_data = sorted_data
                 # Update folder list
             # else: do nothing
@@ -218,7 +219,7 @@ class ModelCache:
             self.folders = sorted(list(all_folders), key=lambda x: x.lower())
             self.rebuild_version_index()
 
-    def _sort_data(self, data: List[Dict], sort_key: str, order: str) -> List[Dict]:
+    def _sort_data(self, data: List[Dict], sort_key: str, order: str, seed: Optional[str] = None) -> List[Dict]:
         """Sort data by sort_key and order"""
         start_time = time.perf_counter()
         reverse = (order == 'desc')
@@ -265,6 +266,13 @@ class ModelCache:
                 ),
                 reverse=reverse
             )
+        elif sort_key == 'random':
+            # Random shuffle seeded for stable pagination: the same seed
+            # always yields the same order, so successive page requests
+            # stay consistent while browsing.
+            rng = random.Random(seed or 'random')
+            result = list(data)
+            rng.shuffle(result)
         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.
@@ -285,15 +293,16 @@ class ModelCache:
             logger.debug("ModelCache._sort_data(%s, %s) for %d items took %.3fs", sort_key, order, len(data), duration)
         return result
 
-    async def get_sorted_data(self, sort_key: str = 'name', order: str = 'asc') -> List[Dict]:
+    async def get_sorted_data(self, sort_key: str = 'name', order: str = 'asc', seed: Optional[str] = None) -> List[Dict]:
         """Get sorted data by sort_key and order, using cache if possible"""
         async with self._lock:
-            if (sort_key, order) == self._last_sort:
+            cache_key = (sort_key, order, seed)
+            if cache_key == self._last_sort:
                 return self._last_sorted_data
             
             start_time = time.perf_counter()
-            sorted_data = self._sort_data(self.raw_data, sort_key, order)
-            self._last_sort = (sort_key, order)
+            sorted_data = self._sort_data(self.raw_data, sort_key, order, seed)
+            self._last_sort = cache_key
             self._last_sorted_data = sorted_data
             
             duration = time.perf_counter() - start_time
@@ -313,8 +322,8 @@ class ModelCache:
             self.name_display_mode = normalized
 
             if self._last_sort[0] == 'name':
-                sort_key, order = self._last_sort
-                self._last_sorted_data = self._sort_data(self.raw_data, sort_key, order)
+                sort_key, order, seed = self._last_sort
+                self._last_sorted_data = self._sort_data(self.raw_data, sort_key, order, seed)
 
     async def update_preview_url(self, file_path: str, preview_url: str, preview_nsfw_level: int) -> bool:
         """Update preview_url for a specific model in all cached data
@@ -337,4 +346,25 @@ class ModelCache:
             else:
                 return False  # Model not found
                     
-            return True
+            return True
+
+    async def clear_preview_by_path(self, preview_file_path: str) -> int:
+        """Clear ``preview_url`` for every cached entry referencing a file path.
+
+        When a preview file has been deleted from disk, this removes its
+        reference from all matching cache entries so the next list-API
+        response returns an empty ``preview_url`` instead of a stale URL
+        that produces 404s.
+
+        Returns the number of entries that were updated.
+        """
+        normalized = preview_file_path.replace("\\", "/")
+        cleared = 0
+        async with self._lock:
+            for item in self.raw_data:
+                cached_url = item.get("preview_url", "")
+                if cached_url.replace("\\", "/") == normalized:
+                    item["preview_url"] = ""
+                    item["preview_nsfw_level"] = 0
+                    cleared += 1
+        return cleared

py/services/model_file_service.py

@@ -8,6 +8,7 @@ from abc import ABC, abstractmethod
 from ..utils.utils import calculate_relative_path_for_model, remove_empty_dirs
 from ..utils.constants import AUTO_ORGANIZE_BATCH_SIZE
 from ..services.settings_manager import get_settings_manager
+from ..services.model_lifecycle_service import _require_path_in_library_roots
 
 logger = logging.getLogger(__name__)
 
@@ -493,6 +494,9 @@ class ModelMoveService:
             Dictionary with move result
         """
         try:
+            _require_path_in_library_roots(file_path, self.scanner, label="Source path")
+            _require_path_in_library_roots(target_path, self.scanner, label="Target path")
+
             if use_default_paths:
                 # Find the model in cache to get metadata
                 cache = await self.scanner.get_cached_data()

py/services/model_lifecycle_service.py

@@ -48,6 +48,36 @@ async def delete_model_artifacts(
     return deleted
 
 
+def _require_path_in_library_roots(file_path: str, scanner, *, label: str = "path") -> None:
+    """Raise ``ValueError`` if *file_path* is not inside a configured model root.
+
+    Uses ``os.path.abspath()`` (NOT ``realpath``) to resolve ``..`` and ``.``
+    while preserving symlinks — this keeps the check in business-path space.
+    Skips when the scanner does not expose ``get_model_roots`` or the list
+    is empty.
+    """
+
+    roots = None
+    if hasattr(scanner, "get_model_roots"):
+        try:
+            roots = scanner.get_model_roots()
+        except NotImplementedError:
+            roots = None
+    if not roots:
+        return
+
+    resolved = os.path.abspath(os.path.normpath(file_path))
+
+    for root in roots:
+        root_resolved = os.path.abspath(os.path.normpath(root))
+        if resolved == root_resolved or resolved.startswith(root_resolved + os.sep):
+            return
+
+    raise ValueError(
+        f"{label} '{file_path}' is outside configured library directories"
+    )
+
+
 class ModelLifecycleService:
     """Co-ordinate destructive and mutating model operations."""
 
@@ -74,6 +104,8 @@ class ModelLifecycleService:
         if not file_path:
             raise ValueError("Model path is required")
 
+        _require_path_in_library_roots(file_path, self._scanner, label="File path")
+
         cache = await self._scanner.get_cached_data()
 
         cached_entry = None
@@ -182,6 +214,8 @@ class ModelLifecycleService:
         if not file_path:
             raise ValueError("Model path is required")
 
+        _require_path_in_library_roots(file_path, self._scanner, label="File path")
+
         metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
         metadata = await self._metadata_loader(metadata_path)
         metadata["exclude"] = True
@@ -229,6 +263,8 @@ class ModelLifecycleService:
         if not file_path:
             raise ValueError("Model path is required")
 
+        _require_path_in_library_roots(file_path, self._scanner, label="File path")
+
         if not os.path.exists(file_path):
             raise ValueError("Model file does not exist")
 
@@ -270,6 +306,9 @@ class ModelLifecycleService:
         if not file_paths:
             raise ValueError("No file paths provided for deletion")
 
+        for path in file_paths:
+            _require_path_in_library_roots(path, self._scanner, label="File path")
+
         return await self._scanner.bulk_delete_models(file_paths)
 
     async def rename_model(
@@ -280,6 +319,8 @@ class ModelLifecycleService:
         if not file_path or not new_file_name:
             raise ValueError("File path and new file name are required")
 
+        _require_path_in_library_roots(file_path, self._scanner, label="File path")
+
         invalid_chars = {"/", "\\", ":", "*", "?", '"', "<", ">", "|"}
         if any(char in new_file_name for char in invalid_chars):
             raise ValueError("Invalid characters in file name")

py/services/model_metadata_provider.py

@@ -143,10 +143,18 @@ class ModelMetadataProvider(ABC):
         pass
 
     @abstractmethod
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
-        """Fetch models owned by the specified user"""
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
+        """Fetch one page of models owned by the specified user.
+
+        Returns ``{"items": [...], "nextCursor": <str|None>}`` on success,
+        or None when unsupported/failed. ``cursor`` continues a previous page.
+        """
         pass
 
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        """Published model count for the user; None when unsupported."""
+        return None
+
 class CivitaiModelMetadataProvider(ModelMetadataProvider):
     """Provider that uses Civitai API for metadata"""
     
@@ -175,8 +183,11 @@ class CivitaiModelMetadataProvider(ModelMetadataProvider):
     async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
         return await self.client.get_model_version_info(version_id)
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
-        return await self.client.get_user_models(username)
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
+        return await self.client.get_user_models(username, cursor)
+
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        return await self.client.get_creator_model_count(username)
 
 class CivArchiveModelMetadataProvider(ModelMetadataProvider):
     """Provider that uses CivArchive API for metadata"""
@@ -196,7 +207,7 @@ class CivArchiveModelMetadataProvider(ModelMetadataProvider):
     async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
         return await self.client.get_model_version_info(version_id)
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
         """Not supported by CivArchive provider"""
         return None
 
@@ -347,7 +358,7 @@ class SQLiteModelMetadataProvider(ModelMetadataProvider):
             version_data = await self._get_version_with_model_data(db, model_id, version_id)
             return version_data, None
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
         """Listing models by username is not supported for archive database"""
         return None
     
@@ -602,13 +613,14 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 continue
         return None
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
         for provider, label in self._iter_providers():
             try:
                 result = await self._call_with_rate_limit(
                     label,
                     provider.get_user_models,
                     username,
+                    cursor=cursor,
                 )
                 if result is not None:
                     return result
@@ -624,6 +636,19 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 continue
         return None
 
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        for provider, label in self._iter_providers():
+            try:
+                result = await provider.get_creator_model_count(username)
+                if result is not None:
+                    return result
+            except Exception as e:
+                logger.debug(
+                    "Provider %s failed for get_creator_model_count: %s", label, e
+                )
+                continue
+        return None
+
     def _iter_providers(self):
         return zip(self.providers, self._provider_labels)
 
@@ -704,13 +729,17 @@ class RateLimitRetryingProvider(ModelMetadataProvider):
             version_id,
         )
 
-    async def get_user_models(self, username: str) -> Optional[List[Dict]]:
+    async def get_user_models(self, username: str, cursor: Optional[str] = None) -> Optional[Dict]:
         return await self._rate_limit_helper.run(
             self._label,
             self._provider.get_user_models,
             username,
+            cursor=cursor,
         )
 
+    async def get_creator_model_count(self, username: str) -> Optional[int]:
+        return await self._provider.get_creator_model_count(username)
+
 class ModelMetadataProviderManager:
     """Manager for selecting and using model metadata providers"""
     
@@ -776,10 +805,20 @@ class ModelMetadataProviderManager:
         except NotImplementedError:
             return None
 
-    async def get_user_models(self, username: str, provider_name: str = None) -> Optional[List[Dict]]:
-        """Fetch models owned by the specified user"""
+    async def get_user_models(
+        self,
+        username: str,
+        provider_name: str = None,
+        cursor: Optional[str] = None,
+    ) -> Optional[Dict]:
+        """Fetch one page of models owned by the specified user"""
         provider = self._get_provider(provider_name)
-        return await provider.get_user_models(username)
+        return await provider.get_user_models(username, cursor)
+
+    async def get_creator_model_count(self, username: str, provider_name: str = None) -> Optional[int]:
+        """Best-effort published model count for the specified user"""
+        provider = self._get_provider(provider_name)
+        return await provider.get_creator_model_count(username)
         
     def _get_provider(self, provider_name: str = None) -> ModelMetadataProvider:
         """Get provider by name or default provider"""

py/services/model_query.py

@@ -85,6 +85,7 @@ class SortParams:
 
     key: str
     order: str
+    seed: Optional[str] = None
 
 
 @dataclass(frozen=True)
@@ -116,7 +117,7 @@ class ModelCacheRepository:
     async def fetch_sorted(self, params: SortParams) -> List[Dict[str, Any]]:
         """Fetch cached data pre-sorted according to ``params``."""
         cache = await self.get_cache()
-        return await cache.get_sorted_data(params.key, params.order)
+        return await cache.get_sorted_data(params.key, params.order, params.seed)
 
     @staticmethod
     def parse_sort(sort_by: str) -> SortParams:
@@ -132,10 +133,17 @@ class ModelCacheRepository:
             sort_key = sort_by.strip().lower() or "name"
             order = "asc"
 
-        if order not in ("asc", "desc"):
+        seed = None
+        if sort_key == "random":
+            # Random sort: the portion after ':' is the shuffle seed.
+            # A stable seed keeps paginated requests consistent; order is
+            # meaningless for a random shuffle.
+            seed = order if order and order not in ("asc", "desc") else None
+            order = "asc"
+        elif order not in ("asc", "desc"):
             order = "asc"
 
-        return SortParams(key=sort_key, order=order)
+        return SortParams(key=sort_key, order=order, seed=seed)
 
 
 class ModelFilterSet:

py/services/model_scanner.py

@@ -14,7 +14,7 @@ from ..utils.metadata_manager import MetadataManager
 from ..utils.civitai_utils import resolve_license_info
 from .model_cache import ModelCache
 from .model_hash_index import ModelHashIndex
-from .model_lifecycle_service import delete_model_artifacts
+from .model_lifecycle_service import delete_model_artifacts, _require_path_in_library_roots
 from .service_registry import ServiceRegistry
 from .websocket_manager import ws_manager
 from .persistent_model_cache import get_persistent_cache
@@ -227,6 +227,11 @@ class ModelScanner:
 
         entry: Dict[str, Any] = {
             'file_path': normalized_path,
+            # file_name is always stored WITHOUT extension (e.g. "OWSMianne_ANIMA_V1",
+            # not "OWSMianne_ANIMA_V1.safetensors"). All upstream population points
+            # (MetadataManager, from_civitai_info, download manager, etc.) strip the
+            # extension via os.path.splitext before writing. Code consuming this field
+            # should match against names that are likewise extension-free.
             'file_name': get_value('file_name', '') or '',
             'model_name': get_value('model_name', '') or '',
             'folder': normalized_folder,
@@ -922,6 +927,25 @@ class ModelScanner:
                 # Update cache data
                 self._cache.raw_data = [item for item in self._cache.raw_data if item['file_path'] not in missing_files]
             
+            dedup_removed = 0
+            seen_paths: set = set()
+            deduped: list = []
+            for item in reversed(self._cache.raw_data):
+                path = item.get('file_path', '')
+                if path not in seen_paths:
+                    seen_paths.add(path)
+                    deduped.append(item)
+                else:
+                    for tag in item.get('tags', []):
+                        if tag in self._tags_count:
+                            self._tags_count[tag] = max(0, self._tags_count[tag] - 1)
+                            if self._tags_count[tag] == 0:
+                                del self._tags_count[tag]
+                    dedup_removed += 1
+            if dedup_removed > 0:
+                self._cache.raw_data = list(reversed(deduped))
+                total_removed += dedup_removed
+            
             # Resort cache if changes were made
             if total_added > 0 or total_removed > 0:
                 # Update folders list
@@ -1347,18 +1371,25 @@ class ModelScanner:
             # Update folder in metadata
             metadata_dict['folder'] = folder
             
-            # Add to cache
-            self._cache.raw_data.append(metadata_dict)
-            self._cache.add_to_version_index(metadata_dict)
+            file_path = metadata_dict.get('file_path', '')
+            if file_path:
+                old_entries = [item for item in self._cache.raw_data if item.get('file_path') == file_path]
+                for old_entry in old_entries:
+                    for tag in old_entry.get('tags', []):
+                        if tag in self._tags_count:
+                            self._tags_count[tag] = max(0, self._tags_count[tag] - 1)
+                            if self._tags_count[tag] == 0:
+                                del self._tags_count[tag]
+                self._hash_index.remove_by_path(file_path)
+                self._cache.raw_data = [item for item in self._cache.raw_data if item.get('file_path') != file_path]
+
+            for tag in metadata_dict.get('tags', []):
+                self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
+
+            self._cache.raw_data.append(metadata_dict)
 
-            # Resort cache data
             await self._cache.resort()
             
-            # Update folders list
-            all_folders = set(self._cache.folders)
-            all_folders.add(folder)
-            self._cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
-            
             # Update the hash index
             self._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
             await self._persist_current_cache()
@@ -1389,6 +1420,9 @@ class ModelScanner:
                 
             base_name = os.path.splitext(os.path.basename(source_path))[0]
             source_dir = os.path.dirname(source_path)
+
+            _require_path_in_library_roots(source_path, self, label="Source path")
+            _require_path_in_library_roots(target_path, self, label="Target path")
             
             os.makedirs(target_path, exist_ok=True)
             
@@ -1561,6 +1595,218 @@ class ModelScanner:
 
         return cache_entry if metadata else True
         
+    async def sync_cache_from_metadata(
+        self, file_path: str, metadata_dict: Dict[str, Any]
+    ) -> bool:
+        """Opportunistically sync in-memory and persistent caches from metadata.
+
+        Builds a prospective cache entry from *metadata_dict* (deserialized
+        ``.metadata.json`` content) and compares it against the current cache
+        entry.  When the two are already identical this method returns
+        ``False`` without touching anything — avoiding the overhead of
+        ``update_single_model_cache``, which always removes and re-inserts
+        the entry, triggers a full resort, and persists via the heavyweight
+        ``save_cache()``.
+
+        When differences are detected the update is applied **in-place** with
+        targeted operations:
+
+        * The existing ``raw_data`` entry is modified rather than removed and
+          re-appended (O(1) instead of O(n)).
+        * Tag counts and the hash index are updated incrementally.
+        * The version index is rebuilt only for the affected entry.
+        * ``resort()`` is called **only** when a sort-relevant field changed
+          (``model_name`` / ``file_name`` for name-sort, ``modified`` for
+          date-sort, ``size`` for size-sort).
+        * The persistent (SQLite) cache receives a targeted single-row update
+          via :meth:`PersistentModelCache.update_single_model` rather than a
+          full-table ``save_cache()``.
+
+        Returns:
+            ``True`` if any cache update was performed, ``False`` if the
+            caches were already in sync.
+
+        .. note::
+
+            This is a **best-effort** operation.  Failures are logged but
+            never propagated — callers should fire-and-forget via
+            :func:`asyncio.create_task`.
+        """
+        try:
+            return await self._sync_cache_from_metadata_impl(
+                file_path, metadata_dict
+            )
+        except Exception:
+            logger.warning(
+                "sync_cache_from_metadata failed for %s",
+                file_path,
+                exc_info=True,
+            )
+            return False
+
+    async def _sync_cache_from_metadata_impl(
+        self, file_path: str, metadata_dict: Dict[str, Any]
+    ) -> bool:
+        cache = await self.get_cached_data()
+
+        # Locate the existing cache entry -----------------------------------
+        existing_idx: Optional[int] = None
+        existing_entry: Optional[Dict[str, Any]] = None
+        for i, item in enumerate(cache.raw_data):
+            if item.get("file_path") == file_path:
+                existing_entry = item
+                existing_idx = i
+                break
+
+        # Build the desired entry from metadata ------------------------------
+        folder_value = (
+            existing_entry.get("folder", "")
+            if existing_entry
+            else self._calculate_folder(file_path)
+        )
+        desired_entry = self._build_cache_entry(
+            metadata_dict,
+            folder=folder_value,
+            file_path_override=file_path,
+        )
+
+        # Ensure sha256 is populated (defensive — metadata should have it)
+        if (
+            not desired_entry.get("sha256")
+            and file_path
+            and os.path.exists(file_path)
+        ):
+            try:
+                sha256 = await calculate_sha256(file_path)
+                if sha256:
+                    desired_entry["sha256"] = sha256.lower()
+            except Exception:
+                pass
+
+        # Not in cache at all — delegate to the full update path ------------
+        if existing_entry is None:
+            result = await self.update_single_model_cache(
+                file_path, file_path, metadata_dict
+            )
+            return bool(result)
+
+        # Compare — skip everything if already in sync -----------------------
+        if not self._cache_entries_differ(existing_entry, desired_entry):
+            return False
+
+        # Re-validate: the cache may have been replaced concurrently
+        # (e.g. by _apply_scan_result).  Use identity check, not equality,
+        # so we detect when the raw_data list was swapped out from under us.
+        if self._cache is None or not any(
+            item is existing_entry for item in self._cache.raw_data
+        ):
+            return False
+
+        # ---- Differences detected: apply targeted, in-place updates --------
+
+        # Snapshot old values for delta computations
+        old_tags = list(existing_entry.get("tags") or [])
+        old_sha256: str = existing_entry.get("sha256", "") or ""
+        old_model_name: str = existing_entry.get("model_name", "") or ""
+        old_file_name: str = existing_entry.get("file_name", "") or ""
+        old_modified: float = float(existing_entry.get("modified", 0.0) or 0.0)
+        old_size: int = int(existing_entry.get("size", 0) or 0)
+        old_civitai = existing_entry.get("civitai")
+
+        # ---- In-place update of the cache entry ----
+        existing_entry.clear()
+        existing_entry.update(desired_entry)
+
+        # ---- Incremental tag count update ----
+        new_tags: set = set(desired_entry.get("tags") or [])
+        old_tag_set: set = set(old_tags)
+        for tag in old_tag_set - new_tags:
+            current = self._tags_count.get(tag, 0)
+            if current <= 1:
+                self._tags_count.pop(tag, None)
+            else:
+                self._tags_count[tag] = current - 1
+        for tag in new_tags - old_tag_set:
+            self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
+
+        # ---- Incremental hash index update ----
+        new_sha = (desired_entry.get("sha256", "") or "").lower()
+        old_sha = (old_sha256 or "").lower()
+        if new_sha != old_sha:
+            if old_sha:
+                self._hash_index.remove_by_path(file_path)
+            if new_sha:
+                self._hash_index.add_entry(new_sha, file_path)
+
+        # ---- Incremental version index update ----
+        new_civitai = desired_entry.get("civitai")
+        if old_civitai != new_civitai:
+            temp_old = {
+                "file_path": file_path,
+                "file_name": old_file_name,
+                "civitai": old_civitai,
+            }
+            cache.remove_from_version_index(temp_old)
+            cache.add_to_version_index(existing_entry)
+
+        # ---- Conditional resort (only when sort-key fields changed) ----
+        need_resort = False
+        _last = cache._last_sort
+        sort_key: Optional[str] = _last[0] if _last[0] is not None else None
+        if sort_key == "name":
+            if (
+                old_model_name != desired_entry.get("model_name", "")
+                or old_file_name != desired_entry.get("file_name", "")
+            ):
+                need_resort = True
+        elif sort_key == "date":
+            if old_modified != float(desired_entry.get("modified", 0.0) or 0.0):
+                need_resort = True
+        elif sort_key == "size":
+            if old_size != int(desired_entry.get("size", 0) or 0):
+                need_resort = True
+
+        if need_resort:
+            await cache.resort()
+
+        # ---- Targeted SQL update (single row, not full save_cache) ----
+        persistent = getattr(self, "_persistent_cache", None)
+        if persistent is not None:
+            old_item_for_sql: Dict[str, Any] = {
+                "file_path": file_path,
+                "tags": old_tags,
+                "sha256": old_sha256,
+            }
+            await asyncio.get_event_loop().run_in_executor(
+                None,
+                persistent.update_single_model,
+                self.model_type,
+                desired_entry,
+                old_item_for_sql,
+            )
+
+        return True
+
+    @staticmethod
+    def _cache_entries_differ(a: Dict[str, Any], b: Dict[str, Any]) -> bool:
+        """Return ``True`` when two cache-entry dicts differ in any field.
+
+        Tag lists are compared order-insensitively; all other keys use
+        standard equality.
+        """
+        a_tags = sorted(a.get("tags") or [])
+        b_tags = sorted(b.get("tags") or [])
+        if a_tags != b_tags:
+            return True
+
+        all_keys = set(a.keys()) | set(b.keys())
+        for key in all_keys:
+            if key == "tags":
+                continue
+            if a.get(key) != b.get(key):
+                return True
+        return False
+
     def has_hash(self, sha256: str) -> bool:
         """Check if a model with given hash exists"""
         return self._hash_index.has_hash(sha256.lower())
@@ -1613,7 +1859,32 @@ class ModelScanner:
         if limit == 0:
             return sorted_tags
         return sorted_tags[:limit]
-        
+
+    async def search_tags(
+        self, query: str, limit: int = 50
+    ) -> List[Dict[str, any]]:
+        """Search tags by case-insensitive substring match, sorted by count.
+
+        If query is empty, behaves like get_top_tags (returns top ``limit``
+        tags). If limit is 0, all matching tags are returned.
+        """
+        await self.get_cached_data()
+
+        normalized_query = (query or "").strip().lower()
+        if not normalized_query:
+            return await self.get_top_tags(limit if limit > 0 else 20)
+
+        matched = [
+            {"tag": tag, "count": count}
+            for tag, count in self._tags_count.items()
+            if normalized_query in tag.lower()
+        ]
+        matched.sort(key=lambda x: x["count"], reverse=True)
+
+        if limit == 0:
+            return matched
+        return matched[:limit]
+
     async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:
         """Get base models sorted by count. If limit is 0, return all."""
         cache = await self.get_cached_data()
@@ -1729,6 +2000,8 @@ class ModelScanner:
                     break
 
                 try:
+                    _require_path_in_library_roots(file_path, self, label="File path")
+
                     target_dir = os.path.dirname(file_path)
                     base_name = os.path.basename(file_path)
                     file_name, main_extension = os.path.splitext(base_name)

py/services/persistent_model_cache.py

@@ -587,6 +587,95 @@ class PersistentModelCache:
         placeholders = ", ".join(["?"] * len(self._MODEL_COLUMNS))
         return f"INSERT INTO models ({columns}) VALUES ({placeholders})"
 
+    def update_single_model(
+        self,
+        model_type: str,
+        new_item: Dict,
+        old_item: Optional[Dict] = None,
+    ) -> None:
+        """Update a single model row in the persistent cache.
+
+        A lightweight alternative to :meth:`save_cache` that performs a targeted
+        DELETE + INSERT for the model row and computes incremental tag / hash-index
+        deltas from *old_item*.  When *old_item* is omitted the previous tags and
+        hash are not cleaned up (callers should only omit it for brand-new entries).
+
+        All operations run inside a single transaction so readers see a consistent
+        view.
+        """
+        if not self.is_enabled():
+            return
+        if not self._schema_initialized:
+            self._initialize_schema()
+        if not self._schema_initialized:
+            return
+
+        file_path: Optional[str] = new_item.get("file_path")
+        if not file_path:
+            return
+
+        try:
+            with self._db_lock:
+                conn = self._connect()
+                try:
+                    conn.execute("PRAGMA foreign_keys = ON")
+                    conn.execute("BEGIN")
+
+                    # --- model row (DELETE + INSERT = upsert) ---
+                    conn.execute(
+                        "DELETE FROM models WHERE model_type = ? AND file_path = ?",
+                        (model_type, file_path),
+                    )
+                    row = self._prepare_model_row(model_type, new_item)
+                    conn.execute(self._insert_model_sql(), row)
+
+                    # --- tags ---
+                    new_tags: set = set(new_item.get("tags") or [])
+                    old_tags: set = set(old_item.get("tags") or []) if old_item else set()
+                    tags_to_delete = old_tags - new_tags
+                    tags_to_insert = new_tags - old_tags
+
+                    if tags_to_delete:
+                        conn.executemany(
+                            "DELETE FROM model_tags WHERE model_type = ? AND file_path = ? AND tag = ?",
+                            [(model_type, file_path, t) for t in tags_to_delete],
+                        )
+                    if tags_to_insert:
+                        conn.executemany(
+                            "INSERT INTO model_tags (model_type, file_path, tag) VALUES (?, ?, ?)",
+                            [(model_type, file_path, t) for t in tags_to_insert],
+                        )
+
+                    # --- hash_index ---
+                    new_sha: Optional[str] = (new_item.get("sha256") or "").lower() or None
+                    old_sha: Optional[str] = (
+                        (old_item.get("sha256") or "").lower() or None
+                    ) if old_item else None
+                    if new_sha != old_sha:
+                        if old_sha:
+                            conn.execute(
+                                "DELETE FROM hash_index WHERE model_type = ? AND sha256 = ? AND file_path = ?",
+                                (model_type, old_sha, file_path),
+                            )
+                        if new_sha:
+                            conn.execute(
+                                "INSERT OR IGNORE INTO hash_index (model_type, sha256, file_path) VALUES (?, ?, ?)",
+                                (model_type, new_sha, file_path),
+                            )
+
+                    conn.execute("COMMIT")
+                except Exception:
+                    conn.execute("ROLLBACK")
+                    raise
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.warning(
+                "Failed to update single model in persistent cache (%s): %s",
+                file_path,
+                exc,
+            )
+
     def _load_tags(self, conn: sqlite3.Connection, model_type: str) -> Dict[str, List[str]]:
         tag_rows = conn.execute(
             "SELECT file_path, tag FROM model_tags WHERE model_type = ?",

py/services/recipe_cache.py

@@ -1,7 +1,6 @@
 import asyncio
 from typing import Iterable, List, Dict, Optional
 from dataclasses import dataclass, field
-from operator import itemgetter
 from natsort import natsorted
 
 
@@ -149,5 +148,10 @@ class RecipeCache:
         )
         if not name_only:
             self.sorted_by_date = sorted(
-                self.raw_data, key=itemgetter("created_date", "file_path"), reverse=True
+                self.raw_data,
+                key=lambda x: (
+                    x.get("modified", x.get("created_date", 0)),
+                    x.get("file_path", ""),
+                ),
+                reverse=True,
             )

py/services/recipe_scanner.py

@@ -21,7 +21,7 @@ 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 ..utils.utils import calculate_recipe_fingerprint
 from natsort import natsorted
 import sys
 import re
@@ -1020,13 +1020,16 @@ class RecipeScanner:
 
         try:
             result = self._fts_index.search(search, fields)
-            # Return None if empty to trigger fuzzy fallback
-            # Empty FTS results may indicate query syntax issues or need for fuzzy matching
+            # Return empty set for empty FTS results — do NOT fall back to
+            # Python fuzzy matching, which freezes the server with 10k+ recipes.
+            # FTS5 prefix matching with unicode61 tokenizer correctly handles
+            # compound tokens (e.g. "illustrious" matches "path/illustrious/model").
+            # If FTS returns nothing, there are genuinely no matching recipes.
             if not result:
-                return None
+                return set()
             return result
         except Exception as exc:
-            logger.debug("FTS search failed, falling back to fuzzy search: %s", exc)
+            logger.debug("FTS search failed, falling back to title-only search: %s", exc)
             return None
 
     def _update_fts_index_for_recipe(
@@ -2079,49 +2082,14 @@ class RecipeScanner:
                         if str(item.get("id", "")) in fts_matching_ids
                     ]
                 else:
-                    # Fallback to fuzzy_match (slower but always available)
-                    # Build the search predicate based on search options
-                    def matches_search(item):
-                        # Search in title if enabled
-                        if search_options.get("title", True):
-                            if fuzzy_match(str(item.get("title", "")), search):
-                                return True
-
-                        # Search in tags if enabled
-                        if search_options.get("tags", True) and "tags" in item:
-                            for tag in item["tags"]:
-                                if fuzzy_match(tag, search):
-                                    return True
-
-                        # Search in lora file names if enabled
-                        if search_options.get("lora_name", True) and "loras" in item:
-                            for lora in item["loras"]:
-                                if fuzzy_match(str(lora.get("file_name", "")), search):
-                                    return True
-
-                        # Search in lora model names if enabled
-                        if search_options.get("lora_model", True) and "loras" in item:
-                            for lora in item["loras"]:
-                                if fuzzy_match(str(lora.get("modelName", "")), search):
-                                    return True
-
-                        # Search in prompt and negative_prompt if enabled
-                        if search_options.get("prompt", True) and "gen_params" in item:
-                            gen_params = item["gen_params"]
-                            if fuzzy_match(str(gen_params.get("prompt", "")), search):
-                                return True
-                            if fuzzy_match(
-                                str(gen_params.get("negative_prompt", "")), search
-                            ):
-                                return True
-
-                        # No match found
-                        return False
-
-                    # Filter the data using the search predicate
-                    filtered_data = [
-                        item for item in filtered_data if matches_search(item)
-                    ]
+                    # FTS index not yet built — return empty rather than
+                    # scanning 42k+ items in Python. The FTS background build
+                    # finishes in seconds; by the time a user navigates here
+                    # and types a search, it is already available.
+                    logger.debug(
+                        "FTS index not ready — search '%s' returning empty", search
+                    )
+                    filtered_data = []
 
             # Apply additional filters
             if filters:

py/services/recipes/persistence_service.py

@@ -216,11 +216,12 @@ class RecipePersistenceService:
             "preview_nsfw_level",
             "favorite",
             "gen_params",
+            "base_model",
         )
 
         if not any(key in updates for key in allowed_fields):
             raise RecipeValidationError(
-                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite or gen_params)"
+                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite or gen_params or base_model)"
             )
 
         if "gen_params" in updates and not isinstance(updates["gen_params"], dict):

py/services/settings_manager.py

@@ -65,6 +65,8 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "onboarding_completed": False,
     "dismissed_banners": [],
     "enable_metadata_archive_db": False,
+    "enable_civarchive_api": True,
+    "metadata_provider_order": "civitai_archive_sqlite",
     "proxy_enabled": False,
     "proxy_host": "",
     "proxy_port": "",
@@ -152,6 +154,11 @@ class SettingsManager:
         self._check_environment_variables()
         self._collect_configuration_warnings()
 
+        if os.environ.get("LORA_MANAGER_PORTABLE", "0") == "1":
+            if not self.settings.get("use_portable_settings"):
+                self.settings["use_portable_settings"] = True
+                self._save_settings()
+
         if self._needs_initial_save:
             self._save_settings()
             self._needs_initial_save = False
@@ -625,12 +632,37 @@ class SettingsManager:
 
         return False
 
+    @staticmethod
+    def _normalize_path_set(paths: Iterable[str]) -> set[str]:
+        """Normalize an iterable of paths for set-based overlap comparison.
+
+        Resolves symlinks via ``os.path.realpath`` when the path exists on disk,
+        then applies ``os.path.normcase`` + ``os.path.normpath`` for consistent
+        cross-platform comparison.  Non-string / empty entries are skipped.
+        """
+        result: set[str] = set()
+        for p in paths:
+            if not isinstance(p, str):
+                continue
+            stripped = p.strip()
+            if not stripped:
+                continue
+            if os.path.exists(stripped):
+                stripped = os.path.normpath(os.path.realpath(stripped))
+            result.add(os.path.normcase(stripped))
+        return result
+
     def _validate_folder_paths(
         self,
         library_name: str,
         folder_paths: Mapping[str, Iterable[str]],
     ) -> None:
-        """Ensure folder paths do not overlap with other libraries."""
+        """Ensure folder paths do not overlap with other libraries.
+
+        Also detects checkpoints ↔ unet path overlap within the same library
+        (including via symlink resolution), which is a configuration error since
+        these model types must use separate physical folders.
+        """
         libraries = self.settings.get("libraries", {})
         normalized_new: Dict[str, Dict[str, str]] = {}
         for key, values in folder_paths.items():
@@ -668,6 +700,22 @@ class SettingsManager:
                         f"Folder path(s) {collisions} already assigned to library '{other_name}'"
                     )
 
+        # Checkpoints ↔ unet overlap within the same library
+        ckpt_paths = folder_paths.get("checkpoints", []) or []
+        unet_paths = folder_paths.get("unet", []) or []
+        if ckpt_paths and unet_paths:
+            ckpt_real = self._normalize_path_set(ckpt_paths)
+            unet_real = self._normalize_path_set(unet_paths)
+            overlap = ckpt_real & unet_real
+            if overlap:
+                collisions = ", ".join(sorted(overlap))
+                raise ValueError(
+                    f"Path(s) {collisions} are configured for both "
+                    f"'checkpoints' and 'unet' (diffusion models). "
+                    f"These model types must use separate physical folders. "
+                    f"Please remove one of the conflicting entries."
+                )
+
     def _update_active_library_entry(
         self,
         *,
@@ -1425,10 +1473,12 @@ class SettingsManager:
 
         try:
             common_root = os.path.commonpath([source, target])
-        except ValueError as exc:
-            raise ValueError("Invalid recipes path change") from exc
+        except ValueError:
+            # Windows: paths on different drives share no common root.
+            # A cross-drive move is valid, so treat it as no common root.
+            common_root = None
 
-        if common_root == source:
+        if common_root is not None and common_root == source:
             raise ValueError("Recipes path cannot be moved into a nested directory")
 
         planned_recipe_updates: Dict[str, Dict[str, Any]] = {}
@@ -1542,8 +1592,12 @@ class SettingsManager:
             portable_switch_pending = True
             self._prepare_portable_switch(value)
         if key == "folder_paths" and isinstance(value, Mapping):
+            active_name = self.get_active_library_name()
+            self._validate_folder_paths(active_name, value)
             self._update_active_library_entry(folder_paths=value)  # type: ignore[arg-type]
         elif key == "extra_folder_paths" and isinstance(value, Mapping):
+            active_name = self.get_active_library_name()
+            self._validate_folder_paths(active_name, value)
             self._update_active_library_entry(extra_folder_paths=value)  # type: ignore[arg-type]
         elif key == "default_lora_root":
             self._update_active_library_entry(default_lora_root=str(value))
@@ -1797,6 +1851,9 @@ class SettingsManager:
                 if key in self.settings:
                     minimal[key] = copy.deepcopy(self.settings[key])
 
+            if self.settings.get("use_portable_settings"):
+                minimal["use_portable_settings"] = True
+
             if self._seed_template:
                 for key, value in self._seed_template.items():
                     minimal.setdefault(key, copy.deepcopy(value))

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -51,6 +51,10 @@ class BulkMetadataRefreshUseCase:
             if not model.get("skip_metadata_refresh", False)
             and not self._is_in_skip_path(model.get("folder", ""), skip_paths)
             and (not model.get("civitai") or not model["civitai"].get("id"))
+            # Skip models downloaded from Hugging Face — they are not on
+            # CivitAI / CivArchive.  Users can still refresh them individually
+            # via the right-click context menu.
+            and not model.get("hf_url", "")
             and not (
                 # Skip models confirmed not on CivitAI when no need to retry
                 model.get("from_civitai") is False
@@ -122,6 +126,7 @@ class BulkMetadataRefreshUseCase:
                         if sha256:
                             model["sha256"] = sha256
                             model["hash_status"] = "completed"
+                            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"})
@@ -144,6 +149,16 @@ class BulkMetadataRefreshUseCase:
                     continue
 
                 await MetadataManager.hydrate_model_data(model)
+
+                # hydrate_model_data replaces model with .metadata.json content,
+                # which may lack sha256. Restore from cache and persist the fix.
+                if not model.get("sha256"):
+                    model["sha256"] = sha256
+                    model["hash_status"] = model.get("hash_status", hash_status)
+                    data_to_save = model.copy()
+                    data_to_save.pop("folder", None)
+                    await MetadataManager.save_metadata(file_path, data_to_save)
+
                 result, error_msg = await self._metadata_sync.fetch_and_update_model(
                     sha256=model["sha256"],
                     file_path=model["file_path"],

py/services/wildcard_service.py

@@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)
 _WILDCARD_PATTERN = re.compile(r"__([\w\s.\-+/*\\]+?)__")
 _OPTION_PATTERN = re.compile(r"{([^{}]*?)}")
 _TRIGGER_WORD_PATTERN = re.compile(r"^trigger_words\d+$")
-_WEIGHTED_OPTION_PATTERN = re.compile(r"^\s*([0-9.]+)::")
+_WEIGHTED_OPTION_PATTERN = re.compile(r"^\s*-?\d+(\.\d+)?::")
 _NUMERIC_PATTERN = re.compile(r"^-?\d+(\.\d+)?$")
 
 
@@ -390,7 +390,7 @@ class WildcardService:
     ) -> str | None:
         keyword = _normalize_wildcard_key(raw_key)
         if keyword in wildcard_dict:
-            return rng.choice(wildcard_dict[keyword])
+            return self._pick_weighted_or_plain(wildcard_dict[keyword], rng)
 
         if "*" in keyword:
             regex_pattern = keyword.replace("*", ".*").replace("+", r"\+")
@@ -400,7 +400,7 @@ class WildcardService:
                 if compiled.match(key):
                     aggregated.extend(values)
             if aggregated:
-                return rng.choice(aggregated)
+                return self._pick_weighted_or_plain(aggregated, rng)
 
         if "/" not in keyword:
             fallback_keyword = _normalize_wildcard_key(f"*/{keyword}")
@@ -409,6 +409,39 @@ class WildcardService:
 
         return None
 
+    def _pick_weighted_or_plain(
+        self, values: list[str], rng: random.Random
+    ) -> str:
+        """Pick a value from the list, respecting N::weight prefix if present.
+
+        When any value in the list uses the ``N::value`` weighted syntax with a
+        weight different from 1, the pick uses weighted random selection.  When
+        no such weighting is present, a plain ``rng.choice`` is used (preserving
+        backward compatibility for unweighted wildcard files).
+
+        In either case the ``N::`` prefix is always stripped from the returned
+        value, matching the behaviour of ``{...}`` option groups.
+        """
+        # Fast path: skip weighting logic entirely when no :: syntax exists
+        if not any("::" in v for v in values):
+            return rng.choice(values)
+
+        weighted_options: list[tuple[float, str]] = []
+        for value in values:
+            weight = 1.0
+            parts = value.split("::", 1)
+            if len(parts) == 2 and _is_numeric_string(parts[0].strip()):
+                weight = float(parts[0].strip())
+            weighted_options.append((weight, value))
+
+        any_weighted = any(w != 1.0 for w, _ in weighted_options)
+        if any_weighted:
+            picked = self._weighted_choice(weighted_options, rng)
+        else:
+            picked = rng.choice(values)
+
+        return self._strip_weight_prefix(picked)
+
 
 def is_trigger_words_input(name: str) -> bool:
     return bool(_TRIGGER_WORD_PATTERN.match(name))

py/utils/constants.py

@@ -12,6 +12,7 @@ NODE_TYPES = {
     "Lora Loader (LoraManager)": 1,
     "Lora Stacker (LoraManager)": 2,
     "WanVideo Lora Select (LoraManager)": 3,
+    "Create Hook LoRA (LoraManager)": 4,
 }
 
 # Default ComfyUI node color when bgcolor is null

py/utils/example_images_download_manager.py

@@ -14,11 +14,16 @@ from ..services.service_registry import ServiceRegistry
 from ..utils.example_images_paths import (
     ExampleImagePathResolver,
     ensure_library_root_exists,
+    get_example_images_root,
+    is_hash_folder,
     uses_library_scoped_folders,
 )
 from ..utils.metadata_manager import MetadataManager
 from .example_images_processor import ExampleImagesProcessor
-from .example_images_metadata import MetadataUpdater
+from .example_images_metadata import (
+    MetadataUpdater,
+    update_cache_from_metadata,
+)
 from ..services.downloader import get_downloader
 from ..services.settings_manager import get_settings_manager
 
@@ -87,6 +92,13 @@ class _DownloadProgress(dict):
         return snapshot
 
 
+# When fewer candidates than this remain in check_pending_models, probe each
+# model folder directly (preserving legacy-folder migration semantics). Above
+# it, build a folder index with a single directory scan so libraries with
+# 100k+ models do not pay one syscall per candidate.
+_BULK_LOOKUP_THRESHOLD = 1000
+
+
 def _model_directory_has_files(path: str) -> bool:
     """Return True when the provided directory exists and contains entries."""
 
@@ -103,6 +115,36 @@ def _model_directory_has_files(path: str) -> bool:
     return False
 
 
+def _build_example_folder_index(output_dir: str) -> dict[str, bool]:
+    """Build a ``{hash: has_files}`` index for a library's example-image folders.
+
+    A single directory scan over the library root replaces ``O(candidates)``
+    per-folder ``os.scandir`` calls, which is required for libraries with
+    100k+ models. Each hash folder is classified by whether it contains any
+    entries, matching the semantics of ``_model_directory_has_files``.
+    """
+
+    index: dict[str, bool] = {}
+    if not output_dir or not os.path.isdir(output_dir):
+        return index
+
+    try:
+        with os.scandir(output_dir) as entries:
+            for entry in entries:
+                name = entry.name
+                if not entry.is_dir() or not is_hash_folder(name):
+                    continue
+                try:
+                    with os.scandir(entry.path) as subentries:
+                        index[name.lower()] = any(subentries)
+                except OSError:
+                    index[name.lower()] = False
+    except OSError:
+        pass
+
+    return index
+
+
 class DownloadManager:
     """Manages downloading example images for models."""
 
@@ -130,6 +172,7 @@ class DownloadManager:
         model_types = data.get("model_types", ["lora", "checkpoint"])
         delay = float(data.get("delay", 0.2))
         force = data.get("force", False)
+        model_hashes = data.get("model_hashes", [])
 
         # Step 2: Validate configuration (fast lookup)
         settings_manager = get_settings_manager()
@@ -199,6 +242,7 @@ class DownloadManager:
                         delay,
                         active_library,
                         force,
+                        model_hashes,
                     )
                 )
 
@@ -410,14 +454,49 @@ class DownloadManager:
             # 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 and model_hash not in failed_models:
+            candidate_hashes = [
+                model_hash
+                for model_hash, _ in all_models_with_hash
+                if model_hash not in processed_models
+                and model_hash not in failed_models
+            ]
+
+            pending_hashes: set[str] = set()
+            # For small candidate counts the existing per-folder check is fine
+            # and handles legacy folder migration.
+            # For large libraries, scan the library root once and do set lookups.
+            if len(candidate_hashes) <= _BULK_LOOKUP_THRESHOLD or not output_dir:
+                for model_hash in candidate_hashes:
                     model_dir = ExampleImagePathResolver.get_model_folder(
                         model_hash, active_library
                     )
                     if not _model_directory_has_files(model_dir):
                         pending_hashes.add(model_hash)
+            else:
+                folder_index = await asyncio.get_event_loop().run_in_executor(
+                    None, _build_example_folder_index, output_dir
+                )
+                # In multi-library mode, folders that have not been consolidated
+                # into the library root yet (startup migration skipped, failed
+                # move, or created at the legacy path afterwards) still live at
+                # the legacy root/<hash> location. Only scan that root when at
+                # least one candidate is missing from the library-root index, so
+                # the fully-consolidated case does not pay an extra directory
+                # pass on every call.
+                if uses_library_scoped_folders() and any(
+                    not folder_index.get(model_hash, False)
+                    for model_hash in candidate_hashes
+                ):
+                    legacy_root = get_example_images_root()
+                    if legacy_root and legacy_root != output_dir:
+                        legacy_index = await asyncio.get_event_loop().run_in_executor(
+                            None, _build_example_folder_index, legacy_root
+                        )
+                        for hash_key, has_files in legacy_index.items():
+                            folder_index.setdefault(hash_key, has_files)
+                for model_hash in candidate_hashes:
+                    if not folder_index.get(model_hash, False):
+                        pending_hashes.add(model_hash)
 
             pending_count = len(pending_hashes)
 
@@ -500,8 +579,9 @@ class DownloadManager:
         delay,
         library_name,
         force: bool = False,
+        model_hashes: list[str] | None = None,
     ):
-        """Download example images for all models."""
+        """Download example images for all models (or only the given hashes)."""
 
         downloader = await get_downloader()
 
@@ -529,6 +609,18 @@ class DownloadManager:
                         if model.get("sha256"):
                             all_models.append((scanner_type, model, scanner))
 
+            # Restrict to the requested hashes when provided (empty = all models).
+            # Explicit targets are a directed user request, so previously failed
+            # models are retried instead of skipped.
+            explicit_targets = bool(model_hashes)
+            if model_hashes:
+                hash_set = {h.lower() for h in model_hashes}
+                all_models = [
+                    (scanner_type, model, scanner)
+                    for scanner_type, model, scanner in all_models
+                    if model.get("sha256", "").lower() in hash_set
+                ]
+
             # Update total count
             self._progress["total"] = len(all_models)
             logger.debug(f"Found {self._progress['total']} models to process")
@@ -552,6 +644,7 @@ class DownloadManager:
                     downloader,
                     library_name,
                     force,
+                    explicit_targets,
                 )
 
                 # Update progress
@@ -648,6 +741,7 @@ class DownloadManager:
         downloader,
         library_name,
         force: bool = False,
+        explicit_targets: bool = False,
     ):
         """Process a single model download."""
 
@@ -670,8 +764,9 @@ class DownloadManager:
             self._progress["current_model"] = f"{model_name} ({model_hash[:8]})"
             await self._broadcast_progress(status="running")
 
-            # Skip if already in failed models (unless force mode is enabled)
-            if not force and model_hash in self._progress["failed_models"]:
+            # Skip if already in failed models (unless force mode is enabled or
+            # the model was explicitly targeted by hash)
+            if not force and not explicit_targets and model_hash in self._progress["failed_models"]:
                 logger.debug(f"Skipping known failed model: {model_name}")
                 return False
 
@@ -680,30 +775,34 @@ class DownloadManager:
             )
             existing_files = _model_directory_has_files(model_dir)
 
-            # Skip if already processed AND directory exists with files
-            if model_hash in self._progress["processed_models"]:
-                if existing_files:
-                    logger.debug(f"Skipping already processed model: {model_name}")
+            # Model-level guard: a populated folder counts as done. Explicitly
+            # targeted models bypass it so the per-image existence pre-check can
+            # fill individual gaps without re-fetching existing files.
+            if not explicit_targets:
+                # Skip if already processed AND directory exists with files
+                if model_hash in self._progress["processed_models"]:
+                    if existing_files:
+                        logger.debug(f"Skipping already processed model: {model_name}")
+                        return False
+
+                    logger.debug(
+                        "Model %s (%s) marked as processed but folder empty or missing, reprocessing triggered",
+                        model_name,
+                        model_hash,
+                    )
+                    # Track that we are reprocessing this model for summary logging
+                    self._progress["reprocessed_models"].add(model_hash)
+                    # Remove from processed models since we need to reprocess
+                    self._progress["processed_models"].discard(model_hash)
+
+                if existing_files and model_hash not in self._progress["processed_models"]:
+                    logger.debug(
+                        "Model folder already populated for %s, marking as processed without download",
+                        model_name,
+                    )
+                    self._progress["processed_models"].add(model_hash)
                     return False
 
-                logger.debug(
-                    "Model %s (%s) marked as processed but folder empty or missing, reprocessing triggered",
-                    model_name,
-                    model_hash,
-                )
-                # Track that we are reprocessing this model for summary logging
-                self._progress["reprocessed_models"].add(model_hash)
-                # Remove from processed models since we need to reprocess
-                self._progress["processed_models"].discard(model_hash)
-
-            if existing_files and model_hash not in self._progress["processed_models"]:
-                logger.debug(
-                    "Model folder already populated for %s, marking as processed without download",
-                    model_name,
-                )
-                self._progress["processed_models"].add(model_hash)
-                return False
-
             if not model_dir:
                 logger.warning(
                     "Unable to resolve example images folder for model %s (%s)",
@@ -807,7 +906,7 @@ class DownloadManager:
                         model_name,
                     )
                     # Clear failed_models so non-force runs can retry
-                    if force and model_hash in self._progress["failed_models"]:
+                    if (force or explicit_targets) and model_hash in self._progress["failed_models"]:
                         self._progress["failed_models"].discard(model_hash)
                         logger.info(
                             f"Removed {model_name} from failed_models after force retry with rate-limited images"
@@ -827,7 +926,7 @@ class DownloadManager:
                     )
                 elif success:
                     self._progress["processed_models"].add(model_hash)
-                    if force and model_hash in self._progress["failed_models"]:
+                    if (force or explicit_targets) and model_hash in self._progress["failed_models"]:
                         self._progress["failed_models"].discard(model_hash)
                         logger.info(
                             f"Removed {model_name} from failed_models after successful force retry"
@@ -1343,8 +1442,8 @@ class DownloadManager:
                 await MetadataManager.save_metadata(file_path, model_copy)
 
                 try:
-                    await scanner.update_single_model_cache(
-                        file_path, file_path, model_data
+                    await update_cache_from_metadata(
+                        scanner, file_path, model_copy
                     )
                 except AttributeError:
                     logger.debug(

py/utils/example_images_metadata.py

@@ -1,3 +1,4 @@
+import inspect
 import logging
 import os
 import re
@@ -28,6 +29,31 @@ if TYPE_CHECKING:  # pragma: no cover - import for type checkers only
     from ..services.settings_manager import SettingsManager
 
 
+async def update_cache_from_metadata(
+    scanner: Any, file_path: str, metadata: Dict[str, Any]
+) -> bool:
+    """Update the scanner cache from a metadata dict using the in-place sync path.
+
+    ``sync_cache_from_metadata`` patches the existing cache entry incrementally
+    (tag/hash/version indexes, targeted single-row SQL update) and only resorts
+    when a sort-key field changed. This avoids the ``O(n)`` full-list resort and
+    full cache rewrite that ``update_single_model_cache`` performs on every call,
+    which is critical for libraries with 100k+ models.
+
+    Falls back to the legacy full update when the scanner does not expose an
+    async ``sync_cache_from_metadata`` method.
+
+    Returns:
+        ``True`` if the cache entry was updated, ``False`` otherwise.
+    """
+
+    sync_method = getattr(scanner, "sync_cache_from_metadata", None)
+    if inspect.iscoroutinefunction(sync_method):
+        return await sync_method(file_path, metadata)
+
+    return await scanner.update_single_model_cache(file_path, file_path, metadata)
+
+
 def _build_metadata_sync_service(settings_manager: "SettingsManager") -> MetadataSyncService:
     """Construct a metadata sync service bound to the provided settings."""
 
@@ -103,8 +129,8 @@ class MetadataUpdater:
                 progress['refreshed_models'].add(model_hash)
             
             async def update_cache_func(old_path, new_path, metadata):
-                return await scanner.update_single_model_cache(old_path, new_path, metadata)
-            
+                return await update_cache_from_metadata(scanner, new_path, metadata)
+
             await MetadataManager.hydrate_model_data(model_data)
             success, error = await _get_metadata_sync_service().fetch_and_update_model(
                 sha256=model_hash,
@@ -234,6 +260,7 @@ class MetadataUpdater:
                 
                 # Save metadata to .metadata.json file
                 file_path = model.get('file_path')
+                model_copy: Optional[Dict[str, Any]] = None
                 try:
                     model_copy = model.copy()
                     model_copy.pop('folder', None)
@@ -241,14 +268,18 @@ class MetadataUpdater:
                     logger.info(f"Saved metadata for {model.get('model_name')}")
                 except Exception as e:
                     logger.error(f"Failed to save metadata for {model.get('model_name')}: {str(e)}")
-                
-                # Save updated metadata to scanner cache
-                success = await scanner.update_single_model_cache(file_path, file_path, model)
-                if success:
+
+                # Save updated metadata to scanner cache. sync_cache_from_metadata
+                # returns False both for "already in sync" and for actual failures,
+                # so the cache sync result is deliberately not treated as an error;
+                # the return value reflects whether the metadata was persisted.
+                if file_path and model_copy is not None:
+                    await update_cache_from_metadata(scanner, file_path, model_copy)
                     logger.info(f"Successfully updated metadata for {model.get('model_name')} with {len(images)} local examples")
                     return True
-                else:
-                    logger.warning(f"Failed to update metadata for {model.get('model_name')}")
+
+                logger.warning(f"Failed to update metadata for {model.get('model_name')}")
+                return False
             
             return False
         except Exception as e:
@@ -336,6 +367,7 @@ class MetadataUpdater:
             
             # Save metadata to .metadata.json file
             file_path = model_data.get('file_path')
+            model_copy: Optional[Dict[str, Any]] = None
             if file_path:
                 try:
                     model_copy = model_data.copy()
@@ -344,11 +376,11 @@ class MetadataUpdater:
                     logger.info(f"Saved metadata for {model_data.get('model_name')}")
                 except Exception as e:
                     logger.error(f"Failed to save metadata: {str(e)}")
-            
+
             # Save updated metadata to scanner cache
-            if file_path:
-                await scanner.update_single_model_cache(file_path, file_path, model_data)
-            
+            if file_path and model_copy is not None:
+                await update_cache_from_metadata(scanner, file_path, model_copy)
+
             # Get regular images array (might be None)
             regular_images = civitai_data.get('images', [])
             
@@ -475,13 +507,19 @@ class MetadataUpdater:
             return False
 
         model_folder = get_model_folder(model_hash)
-        if not model_folder:
+        if not model_folder or not os.path.isdir(model_folder):
             return False
 
         civitai = getattr(metadata, "civitai", None)
         if not isinstance(civitai, dict):
             return False
 
+        # Read the directory listing once so every image entry reuses it.
+        try:
+            dir_entries = os.listdir(model_folder)
+        except OSError:
+            dir_entries = []
+
         has_changes = False
 
         custom_images = civitai.get("customImages")
@@ -493,24 +531,15 @@ class MetadataUpdater:
                 if not img_id:
                     continue
 
-                if not os.path.isdir(model_folder):
+                prefix = f"custom_{img_id}"
+                found = any(
+                    f.startswith(prefix) and os.path.isfile(
+                        os.path.join(model_folder, f)
+                    )
+                    for f in dir_entries
+                )
+                if not found:
                     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):
@@ -532,22 +561,9 @@ class MetadataUpdater:
                     # is gone.
                     continue
 
-                if not os.path.isdir(model_folder):
+                prefix = f"image_{idx}."
+                if not any(f.startswith(prefix) for f in dir_entries):
                     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):

py/utils/example_images_migration.py

@@ -3,11 +3,19 @@ import logging
 import os
 import re
 import json
+import shutil
 from ..services.settings_manager import get_settings_manager
 from ..services.service_registry import ServiceRegistry
-from ..utils.example_images_paths import iter_library_roots
+from ..utils.example_images_paths import (
+    get_example_images_root,
+    is_hash_folder,
+    iter_library_roots,
+    uses_library_scoped_folders,
+    _library_folder_has_only_hash_dirs,
+)
 from ..utils.metadata_manager import MetadataManager
 from ..utils.example_images_processor import ExampleImagesProcessor
+from ..utils.example_images_metadata import update_cache_from_metadata
 from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
 
 logger = logging.getLogger(__name__)
@@ -36,6 +44,90 @@ settings = _SettingsProxy()
 class ExampleImagesMigration:
     """Handles migrations for example images naming conventions"""
     
+    @staticmethod
+    def _consolidate_library_folders():
+        """Move hash folders from library-named subdirectories back to root.
+
+        When a user switches from multi-library mode back to single-library
+        mode, example images previously stored under e.g.
+        ``<root>/default/<hash>/`` need to be moved back to
+        ``<root>/<hash>/``.  Running this once at startup removes the need
+        for ``get_model_folder()`` to perform directory scans on every
+        request.
+        """
+        if uses_library_scoped_folders():
+            return
+
+        root = get_example_images_root()
+        if not root or not os.path.isdir(root):
+            return
+
+        moved: list[str] = []
+        cleaned: list[str] = []
+
+        try:
+            for entry in os.listdir(root):
+                # Fast regex checks first — no filesystem I/O.
+                if is_hash_folder(entry) or entry == "_deleted":
+                    continue
+
+                entry_path = os.path.join(root, entry)
+                if not os.path.isdir(entry_path):
+                    continue
+                if not _library_folder_has_only_hash_dirs(entry_path):
+                    continue
+
+                try:
+                    for hash_entry in os.listdir(entry_path):
+                        hash_path = os.path.join(entry_path, hash_entry)
+                        if not os.path.isdir(hash_path) or not is_hash_folder(hash_entry):
+                            continue
+                        target = os.path.join(root, hash_entry)
+                        if not os.path.exists(target):
+                            try:
+                                shutil.move(hash_path, target)
+                                moved.append(hash_entry)
+                            except (OSError, shutil.Error) as exc:
+                                logger.error(
+                                    "Failed to move '%s' → '%s': %s",
+                                    hash_path, target, exc,
+                                )
+                except OSError as exc:
+                    logger.error(
+                        "Failed to list library subdirectory '%s': %s",
+                        entry_path, exc,
+                    )
+
+                try:
+                    remaining = os.listdir(entry_path)
+                except OSError:
+                    remaining = []
+                if not remaining:
+                    try:
+                        os.rmdir(entry_path)
+                        cleaned.append(entry)
+                    except OSError as exc:
+                        logger.debug(
+                            "Could not remove empty library dir '%s': %s",
+                            entry_path, exc,
+                        )
+        except OSError as exc:
+            logger.error(
+                "Failed to list example images root during consolidation: %s",
+                exc,
+            )
+
+        if moved:
+            logger.info(
+                "Consolidated %d example image folder(s) to root",
+                len(moved),
+            )
+        if cleaned:
+            logger.info(
+                "Removed %d empty library directories",
+                len(cleaned),
+            )
+
     @staticmethod
     async def check_and_run_migrations():
         """Check if migrations are needed and run them in background"""
@@ -44,6 +136,10 @@ class ExampleImagesMigration:
             logger.debug("No example images path configured or path doesn't exist, skipping migrations")
             return
 
+        # Run library-to-root consolidation once at startup so the hot
+        # path (get_model_folder) stays a pure-path computation.
+        ExampleImagesMigration._consolidate_library_folders()
+
         for library_name, library_path in iter_library_roots():
             if not library_path or not os.path.exists(library_path):
                 continue
@@ -326,7 +422,7 @@ class ExampleImagesMigration:
                         await MetadataManager.save_metadata(file_path, model_copy)
                         
                         # Update scanner cache
-                        await scanner.update_single_model_cache(file_path, file_path, model_metadata)
+                        await update_cache_from_metadata(scanner, file_path, model_copy)
                         
                         updated_models += 1
                     except Exception as e:

py/utils/example_images_paths.py

@@ -83,7 +83,12 @@ def ensure_library_root_exists(library_name: Optional[str] = None) -> str:
 
 
 def get_model_folder(model_hash: str, library_name: Optional[str] = None) -> str:
-    """Return the folder path for a model's example images."""
+    """Return the folder path for a model's example images.
+
+    Multi-library ↔ single-library consolidation is handled once at startup by
+    ``ExampleImagesMigration._consolidate_library_folders`` — this function is a
+    pure path computation on the hot path (no directory scans).
+    """
 
     if not model_hash:
         return ""

py/utils/example_images_processor.py

@@ -9,7 +9,7 @@ from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
 from ..services.service_registry import ServiceRegistry
 from ..services.settings_manager import get_settings_manager
 from ..utils.example_images_paths import get_model_folder, get_model_relative_path
-from .example_images_metadata import MetadataUpdater
+from .example_images_metadata import MetadataUpdater, update_cache_from_metadata
 from ..utils.metadata_manager import MetadataManager
 
 logger = logging.getLogger(__name__)
@@ -113,6 +113,26 @@ class ExampleImagesProcessor:
         message = str(error).lower()
         return '404' in message or 'file not found' in message
 
+    @staticmethod
+    def _example_image_file_exists(model_dir: str, index: int, media_type_hint: str | None = None) -> bool:
+        """Return True when the file that would be written for a media index already exists.
+
+        The final filename (``image_{index}{extension}``) depends on the downloaded
+        content, so the extension cannot be known ahead of time. The post-download
+        check skips the write when the exact target file exists; this pre-check
+        approximates that with the candidate extensions for the media type (videos
+        only when the metadata hints at a video) so the network request is avoided
+        for files that already exist on disk.
+        """
+        if media_type_hint == "video":
+            extensions = SUPPORTED_MEDIA_EXTENSIONS['videos']
+        else:
+            extensions = SUPPORTED_MEDIA_EXTENSIONS['images']
+        return any(
+            os.path.exists(os.path.join(model_dir, f"image_{index}{ext}"))
+            for ext in extensions
+        )
+
     @staticmethod
     async def download_model_images(model_hash, model_name, model_images, model_dir, optimize, downloader):
         """Download images for a single model
@@ -139,7 +159,12 @@ class ExampleImagesProcessor:
             original_url = image_url
             if optimize and 'civitai.com' in image_url:
                 image_url = ExampleImagesProcessor.get_civitai_optimized_url(image_url)
-            
+
+            # Skip the download when the file already exists on disk
+            if ExampleImagesProcessor._example_image_file_exists(model_dir, i, image.get("type")):
+                logger.debug("File already exists, skipping download for %s", image_url)
+                continue
+
             # Download the file first to determine the actual file type
             try:
                 logger.debug(f"Downloading media file {i} for {model_name}")
@@ -229,6 +254,11 @@ class ExampleImagesProcessor:
             if optimize and 'civitai.com' in image_url:
                 image_url = ExampleImagesProcessor.get_civitai_optimized_url(image_url)
 
+            # Skip the download when the file already exists on disk
+            if ExampleImagesProcessor._example_image_file_exists(model_dir, i, image.get("type")):
+                logger.debug("File already exists, skipping download for %s", image_url)
+                continue
+
             async def _attempt_download() -> tuple:
                 logger.debug("Downloading media file %s for %s", i, model_name)
                 return await downloader.download_to_memory(
@@ -644,7 +674,7 @@ class ExampleImagesProcessor:
                     }, status=500)
             
                 # Update cache
-                await scanner.update_single_model_cache(file_path, file_path, model_data)
+                await update_cache_from_metadata(scanner, file_path, model_data)
             
             # Get regular images array (might be None)
             regular_images = civitai_data.get('images', [])
@@ -759,7 +789,7 @@ class ExampleImagesProcessor:
                 model_copy = model_data.copy()
                 model_copy.pop('folder', None)
                 await MetadataManager.save_metadata(file_path, model_copy)
-                await scanner.update_single_model_cache(file_path, file_path, model_data)
+                await update_cache_from_metadata(scanner, file_path, model_copy)
 
             return web.json_response({
                 'success': True,

py/utils/settings_paths.py

@@ -12,6 +12,7 @@ from platformdirs import user_config_dir
 
 
 APP_NAME = "ComfyUI-LoRA-Manager"
+_LM_PORTABLE_ENV = "LORA_MANAGER_PORTABLE"
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -100,7 +101,11 @@ def ensure_settings_file(logger: Optional[logging.Logger] = None) -> str:
 
 
 def _should_use_portable_settings(path: str, logger: logging.Logger) -> bool:
-    """Return ``True`` when the repository settings file enables portable mode."""
+    """Return ``True`` when the env var forces it or the settings file enables it."""
+
+    if os.environ.get(_LM_PORTABLE_ENV, "0") == "1":
+        logger.debug("Portable mode enabled via %s", _LM_PORTABLE_ENV)
+        return True
 
     if not os.path.exists(path):
         return False

py/utils/utils.py

@@ -1,7 +1,7 @@
 from difflib import SequenceMatcher
 import os
 import re
-from typing import Dict
+from typing import Any, Dict, List, Optional
 from ..services.service_registry import ServiceRegistry
 from ..config import config
 from ..services.settings_manager import get_settings_manager
@@ -294,6 +294,53 @@ def _format_model_name_for_comfyui(file_path: str, model_roots: list) -> str:
     return os.path.basename(file_path)
 
 
+def model_patcher_to_name(model_patcher: Any) -> Optional[str]:
+    """Extract a ComfyUI-style model name from a MODEL (ModelPatcher) object.
+
+    Core ComfyUI loaders record the absolute weight file path on the patcher's
+    ``cached_patcher_init`` attribute:
+      - load_checkpoint_guess_config -> (fn, (ckpt_path, ...), index)
+      - load_diffusion_model -> (fn, (unet_path, model_options))
+    Patcher clones (LoRA loaders, model merges, ...) preserve the attribute,
+    so the name is recoverable anywhere downstream of a core loader — including
+    from LoRA Manager's own loaders (CheckpointLoaderLM / UNETLoaderLM), which
+    call the same core load functions.
+
+    The absolute path is converted to the ComfyUI-style relative name used by
+    the metadata pipeline (covering standard ComfyUI roots and LoRA Manager
+    extra folder paths).
+
+    Returns None when the path cannot be recovered (e.g. third-party loaders
+    that never set ``cached_patcher_init``).
+    """
+    init = getattr(model_patcher, "cached_patcher_init", None)
+    if not isinstance(init, (tuple, list)) or len(init) < 2:
+        return None
+    args = init[1]
+    abs_path = args[0] if args else None
+    if not isinstance(abs_path, str) or not abs_path:
+        return None
+    return _abs_model_path_to_name(abs_path)
+
+
+def _abs_model_path_to_name(abs_path: str) -> str:
+    """Convert an absolute model path to a ComfyUI-style relative name.
+
+    Tries standard ComfyUI model roots plus LoRA Manager extra folder paths;
+    falls back to the bare filename.
+    """
+    try:
+        roots: List[str] = list(config.base_models_roots or [])
+        roots.extend(config.extra_checkpoints_roots or [])
+        roots.extend(config.extra_unet_roots or [])
+        formatted = _format_model_name_for_comfyui(abs_path, roots)
+        if formatted:
+            return formatted
+    except Exception:
+        pass
+    return os.path.basename(abs_path)
+
+
 def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
     """
     Check if text matches pattern using fuzzy matching.
@@ -488,6 +535,12 @@ def calculate_relative_path_for_model(
     if model_type == "embedding":
         formatted_path = formatted_path.replace(" ", "_")
 
+    # Sanitize the resolved path to prevent path traversal
+    formatted_path = formatted_path.lstrip("/")
+    while "//" in formatted_path:
+        formatted_path = formatted_path.replace("//", "/")
+    formatted_path = formatted_path.rstrip("/")
+
     return formatted_path
 
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.1.6"
+version = "1.2.0"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

standalone.py

@@ -1,6 +1,10 @@
 import os
 import sys
 import json
+# Ensure the script's directory is on sys.path so that py.* imports resolve
+# regardless of the current working directory (e.g. when launched via
+# ComfyUI's python_embeded from the ComfyUI root directory).
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 from py.middleware.cache_middleware import cache_control
 from py.middleware.error_middleware import api_json_error
 from py.utils.settings_paths import ensure_settings_file

static/css/components/download-batch-summary.css

@@ -0,0 +1,67 @@
+/* Batch Download Summary Modal — component styles only.
+   Stat cards and failure table styles are shared with the metadata refresh
+   result modal (metadata-refresh-result.css) and are not redefined here. */
+
+.download-batch-summary-modal {
+    max-width: 700px;
+}
+
+.summary-header {
+    display: flex;
+    align-items: center;
+    gap: var(--space-2);
+    margin: var(--space-2) 0;
+}
+
+.summary-header i {
+    font-size: 1.4em;
+}
+
+.summary-header.success i {
+    color: var(--color-success);
+}
+
+.summary-header.warning i {
+    color: var(--color-warning);
+}
+
+.summary-header.error i {
+    color: var(--color-error);
+}
+
+.summary-title {
+    font-weight: var(--weight-semibold);
+    color: var(--lora-text);
+}
+
+.summary-hint {
+    margin-left: auto;
+    font-size: var(--text-xs);
+    color: var(--text-secondary);
+}
+
+.btn-retry {
+    display: inline-flex;
+    align-items: center;
+    gap: var(--space-1);
+    background: var(--lora-accent, #4f46e5);
+    color: #fff;
+    border: none;
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2) var(--space-3);
+    cursor: pointer;
+    font-weight: var(--weight-semibold);
+}
+
+.btn-retry:hover {
+    background: var(--lora-accent-hover, #4338ca);
+}
+
+.failure-link {
+    color: var(--lora-accent, #4f46e5);
+    text-decoration: none;
+}
+
+.failure-link:hover {
+    text-decoration: underline;
+}

static/css/components/modal/_base.css

@@ -151,6 +151,7 @@ body.modal-open {
 .support-section,
 .changelog-section,
 .update-info,
+.update-channels,
 .info-item,
 .path-preview {
     background: var(--surface-subtle);

static/css/components/modal/relink-civitai-modal.css

@@ -21,18 +21,22 @@
   margin-bottom: 4px;
 }
 
-.input-group {
+#relinkCivitaiModal .input-group,
+#linkHfModal .input-group {
   display: flex;
   flex-direction: column;
   margin-bottom: var(--space-2);
 }
 
-.input-group label {
+#relinkCivitaiModal .input-group label,
+#linkHfModal .input-group label {
   margin-bottom: var(--space-1);
   font-weight: 500;
 }
 
-.input-group input {
+#relinkCivitaiModal .input-group input,
+#linkHfModal .input-group input {
+  width: auto;
   padding: 8px 12px;
   border-radius: var(--border-radius-xs);
   border: 1px solid var(--border-color);

static/css/components/modal/settings-modal.css

@@ -1562,6 +1562,29 @@ input:checked + .toggle-slider:before {
     box-shadow: 0 0 0 2px rgba(var(--lora-accent-rgb, 79, 70, 229), 0.1);
 }
 
+.extra-folder-path-row .path-controls .extra-folder-path-input.has-error {
+    border-color: var(--lora-error);
+    background-color: rgba(220, 53, 69, 0.08);
+    background-color: rgba(from var(--lora-error) r g b / 0.08);
+}
+
+.extra-folder-path-row .path-controls .extra-folder-path-input.has-error:focus {
+    box-shadow: 0 0 0 2px rgba(220, 53, 69, 0.15);
+    box-shadow: 0 0 0 2px rgba(from var(--lora-error) r g b / 0.15);
+}
+
+.extra-folder-path-error {
+    color: var(--lora-error);
+    font-size: 0.8em;
+    margin-top: 4px;
+    line-height: 1.4;
+    display: none;
+}
+
+.extra-folder-path-error.visible {
+    display: block;
+}
+
 .extra-folder-path-row .path-controls .remove-path-btn {
     width: 32px;
     height: 32px;

static/css/components/modal/update-modal.css

@@ -93,15 +93,13 @@
 .update-content {
     display: flex;
     flex-direction: column;
-    gap: var(--space-3);
+    gap: var(--space-2);
 }
 
 .update-info {
     display: flex;
     justify-content: space-between;
     align-items: center;
-    border-radius: var(--border-radius-sm);
-    padding: var(--space-3);
 }
 
 .update-info .version-info {
@@ -175,7 +173,6 @@
     border: 1px solid var(--lora-border);
     border-radius: var(--border-radius-sm);
     padding: var(--space-2);
-    margin: var(--space-2) 0;
 }
 
 [data-theme="dark"] .update-progress {
@@ -233,11 +230,6 @@
 }
 
 /* Changelog section */
-.changelog-section {
-    border-radius: var(--border-radius-sm);
-    padding: var(--space-3);
-}
-
 .changelog-section h3 {
     margin-top: 0;
     margin-bottom: var(--space-2);
@@ -349,6 +341,131 @@
     text-decoration: underline;
 }
 
+/* Channel Toggle */
+.update-channels {
+}
+
+.channels-label {
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.8;
+    margin-bottom: 8px;
+}
+
+.channel-toggle {
+    display: flex;
+    gap: 0;
+    background: var(--lora-surface);
+    border-radius: 8px;
+    padding: 3px;
+    width: fit-content;
+}
+
+.channel-btn {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 8px 20px;
+    border: none;
+    border-radius: 6px;
+    background: transparent;
+    color: var(--text-secondary, #999);
+    cursor: pointer;
+    font-size: 0.9em;
+    font-weight: 500;
+    transition: all 0.2s ease;
+    white-space: nowrap;
+}
+
+.channel-btn:hover {
+    color: var(--text-primary, #ddd);
+    background: rgba(255, 255, 255, 0.04);
+}
+
+.channel-btn.active {
+    background: var(--lora-accent, #4285F4);
+    color: #fff;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.2);
+}
+
+.channel-btn.active i {
+    color: #fff;
+}
+
+.channel-btn i {
+    font-size: 0.85em;
+}
+
+/* Channel Switch Confirmation Overlay */
+.channel-switch-overlay {
+    position: fixed;
+    inset: 0;
+    background: rgba(0, 0, 0, 0.6);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 10000;
+    backdrop-filter: blur(2px);
+}
+
+.channel-switch-dialog {
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color, rgba(255, 255, 255, 0.1));
+    border-radius: 12px;
+    padding: 28px 32px;
+    max-width: 420px;
+    width: 90%;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
+}
+
+.channel-switch-dialog h3 {
+    margin: 0 0 12px;
+    font-size: 1.1em;
+    color: var(--text-primary, #eee);
+}
+
+.channel-switch-dialog p {
+    margin: 0 0 24px;
+    font-size: 0.9em;
+    color: var(--text-secondary, #aaa);
+    line-height: 1.6;
+}
+
+.channel-switch-actions {
+    display: flex;
+    justify-content: flex-end;
+    gap: 10px;
+}
+
+.channel-switch-cancel {
+    padding: 8px 18px;
+    border: 1px solid var(--border-color, rgba(255, 255, 255, 0.1));
+    border-radius: 6px;
+    background: transparent;
+    color: var(--text-secondary, #aaa);
+    cursor: pointer;
+    font-size: 0.9em;
+}
+
+.channel-switch-cancel:hover {
+    background: rgba(255, 255, 255, 0.04);
+}
+
+.channel-switch-confirm {
+    padding: 8px 18px;
+    border: none;
+    border-radius: 6px;
+    background: var(--lora-accent, #4285F4);
+    color: #fff;
+    cursor: pointer;
+    font-size: 0.9em;
+    font-weight: 500;
+}
+
+.channel-switch-confirm:hover {
+    opacity: 0.9;
+}
+
 /* Update preferences section */
 .update-preferences {
     border-top: 1px solid var(--lora-border);

static/css/components/search-filter.css

@@ -274,6 +274,11 @@
     font-style: italic;
 }
 
+/* Inline extra tags (selected but not in top-20/appended after API results) */
+.filter-tag.extra-tag {
+    border-style: dashed;
+}
+
 /* Ensure solid border and full opacity when active or excluded */
 .filter-tag.special-tag.active,
 .filter-tag.special-tag.exclude {

static/css/style.css

@@ -41,6 +41,7 @@
 @import 'components/sidebar.css'; /* Add sidebar component */
 @import 'components/media-viewer.css';
 @import 'components/metadata-refresh-result.css';
+@import 'components/download-batch-summary.css';
 
 .initialization-notice {
     display: flex;

static/js/api/apiConfig.js

@@ -49,10 +49,6 @@ export const MODEL_CONFIG = {
  * @returns {Object} Object containing all API endpoints for the model type
  */
 export function getApiEndpoints(modelType) {
-    if (!Object.values(MODEL_TYPES).includes(modelType)) {
-        throw new Error(`Invalid model type: ${modelType}`);
-    }
-
     return {
         // Base CRUD operations
         list: `/api/lm/${modelType}/list`,
@@ -93,6 +89,7 @@ export function getApiEndpoints(modelType) {
         // Query operations
         scan: `/api/lm/${modelType}/scan`,
         topTags: `/api/lm/${modelType}/top-tags`,
+        searchTags: `/api/lm/${modelType}/search-tags`,
         baseModels: `/api/lm/${modelType}/base-models`,
         roots: `/api/lm/${modelType}/roots`,
         folders: `/api/lm/${modelType}/folders`,
@@ -187,7 +184,8 @@ export const DOWNLOAD_ENDPOINTS = {
     downloadGet: '/api/lm/download-model-get',
     cancelGet: '/api/lm/cancel-download-get',
     progress: '/api/lm/download-progress',
-    exampleImages: '/api/lm/force-download-example-images' // New endpoint for downloading example images
+    exampleImages: '/api/lm/force-download-example-images', // Re-process example images ignoring previous status
+    exampleImagesMissing: '/api/lm/download-example-images' // Download only missing example images
 };
 
 // Hugging Face API endpoints

static/js/api/baseModelApi.js

@@ -112,6 +112,18 @@ export class BaseModelApiClient {
         }
     }
 
+    async cancelDownload(downloadId) {
+        try {
+            const response = await fetch(
+                `${DOWNLOAD_ENDPOINTS.cancelGet}?download_id=${encodeURIComponent(downloadId)}`
+            );
+            return await response.json();
+        } catch (error) {
+            console.error('Error cancelling download:', error);
+            return { success: false, error: error.message };
+        }
+    }
+
     async loadMoreWithVirtualScroll(resetPage = false, updateFolders = false) {
         const pageState = this.getPageState();
 
@@ -1629,7 +1641,7 @@ export class BaseModelApiClient {
         }
     }
 
-    async downloadExampleImages(modelHashes, modelTypes = null) {
+    async downloadExampleImages(modelHashes, modelTypes = null, { force = true } = {}) {
         let ws = null;
 
         await state.loadingManager.showWithProgress(async (loading) => {
@@ -1688,8 +1700,13 @@ export class BaseModelApiClient {
                 // Determine optimize setting
                 const optimize = state.global?.settings?.optimize_example_images ?? true;
 
+                // force=false routes to the regular endpoint, which skips already-processed models
+                const endpoint = force
+                    ? DOWNLOAD_ENDPOINTS.exampleImages
+                    : DOWNLOAD_ENDPOINTS.exampleImagesMissing;
+
                 // Make the API request to start the download process
-                const response = await fetch(DOWNLOAD_ENDPOINTS.exampleImages, {
+                const response = await fetch(endpoint, {
                     method: 'POST',
                     headers: {
                         'Content-Type': 'application/json'
@@ -1698,6 +1715,7 @@ export class BaseModelApiClient {
                         model_hashes: modelHashes,
                         output_dir: outputDir,
                         optimize: optimize,
+                        force: force,
                         model_types: modelTypes || [this.apiConfig.config.singularName]
                     })
                 });

static/js/components/ContextMenu/BulkContextMenu.js

@@ -137,11 +137,10 @@ export class BulkContextMenu extends BaseContextMenu {
             downloadMissingLorasItem.style.display = currentModelType === 'recipes' ? 'flex' : 'none';
         }
 
-        const downloadExampleImagesItem = this.menu.querySelector('[data-action="download-example-images"]');
-        if (downloadExampleImagesItem) {
+        const downloadExampleImagesSubmenu = this.menu.querySelector('[data-has-submenu="download-example-images"]');
+        if (downloadExampleImagesSubmenu) {
             // Show on model pages (loras, checkpoints, embeddings), hide on recipes
-            const modelPages = ['loras', 'checkpoints', 'embeddings'];
-            downloadExampleImagesItem.style.display = modelPages.includes(currentModelType) ? 'flex' : 'none';
+            downloadExampleImagesSubmenu.style.display = ['loras', 'checkpoints', 'embeddings'].includes(currentModelType) ? 'flex' : 'none';
         }
 
         const skipMetadataRefreshItem = this.menu.querySelector('[data-action="skip-metadata-refresh"]');
@@ -294,8 +293,11 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'download-missing-loras':
                 this.handleDownloadMissingLoras();
                 break;
+            case 'download-missing-example-images':
+                this.handleDownloadExampleImages({ force: false });
+                break;
             case 'download-example-images':
-                this.handleDownloadExampleImages();
+                this.handleDownloadExampleImages({ force: true });
                 break;
             case 'clear':
                 bulkManager.clearSelection();
@@ -340,7 +342,7 @@ export class BulkContextMenu extends BaseContextMenu {
         await bulkMissingLoraDownloadManager.downloadMissingLoras(selectedRecipes);
     }
 
-    async handleDownloadExampleImages() {
+    async handleDownloadExampleImages({ force = true } = {}) {
         if (state.selectedModels.size === 0) {
             return;
         }
@@ -361,7 +363,7 @@ export class BulkContextMenu extends BaseContextMenu {
 
         try {
             const apiClient = getModelApiClient();
-            await apiClient.downloadExampleImages([...hashes]);
+            await apiClient.downloadExampleImages([...hashes], null, { force });
         } catch (error) {
             console.error('Bulk download example images failed:', error);
         }
@@ -416,6 +418,7 @@ export class BulkContextMenu extends BaseContextMenu {
             cleanupCallbacks();
 
             if (data.status === 'completed') {
+                if (state.bulkMode) bulkManager.toggleBulkMode();
                 progressUI.complete(data.summary || 'Enrich complete');
                 showToast(
                     'toast.agent.enrichComplete',
@@ -428,6 +431,7 @@ export class BulkContextMenu extends BaseContextMenu {
 
         const onError = (data) => {
             cleanupCallbacks();
+            if (state.bulkMode) bulkManager.toggleBulkMode();
             state.loadingManager.hide();
             showToast(
                 'toast.agent.enrichFailed',
@@ -441,6 +445,7 @@ export class BulkContextMenu extends BaseContextMenu {
             await agentManager.executeSkill('enrich_hf_metadata', modelPaths);
         } catch (error) {
             cleanupCallbacks();
+            if (state.bulkMode) bulkManager.toggleBulkMode();
             state.loadingManager.hide();
             showToast(
                 'toast.agent.enrichFailed',

static/js/components/ContextMenu/LoraContextMenu.js

@@ -32,6 +32,9 @@ export class LoraContextMenu extends BaseContextMenu {
         if (!enrichItem) return;
         const hasHfUrl = !!card.dataset.hf_url;
         enrichItem.classList.toggle('disabled', !hasHfUrl);
+        enrichItem.title = hasHfUrl
+            ? ''
+            : 'Link this model to a HuggingFace repo first (Link Model \u2192 Link to HuggingFace)';
     }
 
     handleMenuAction(action, menuItem) {
@@ -149,7 +152,9 @@ export class LoraContextMenu extends BaseContextMenu {
     sendLoraToWorkflow(replaceMode) {
         const card = this.currentCard;
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
-        const loraSyntax = buildLoraSyntax(card.dataset.file_name, usageTips);
+        const folder = card.dataset.folder || '';
+        const loraName = folder ? `${folder}/${card.dataset.file_name}` : card.dataset.file_name;
+        const loraSyntax = buildLoraSyntax(loraName, usageTips);
 
         sendLoraToWorkflow(loraSyntax, replaceMode, 'lora');
     }

static/js/components/ContextMenu/ModelContextMenuMixin.js

@@ -187,6 +187,74 @@ export const ModelContextMenuMixin = {
         setTimeout(() => urlInput.focus(), 50);
     },
 
+    // HuggingFace linking methods
+    showLinkHfModal() {
+        const filePath = this.currentCard.dataset.filepath;
+        if (!filePath) return;
+
+        const confirmBtn = document.getElementById('confirmLinkHfBtn');
+        const urlInput = document.getElementById('hfModelUrl');
+        const errorDiv = document.getElementById('hfModelUrlError');
+
+        if (this._boundLinkHfHandler) {
+            confirmBtn.removeEventListener('click', this._boundLinkHfHandler);
+        }
+
+        this._boundLinkHfHandler = async () => {
+            const hfUrl = urlInput.value.trim();
+            if (!hfUrl) {
+                errorDiv.textContent = 'Please enter a HuggingFace repository URL.';
+                return;
+            }
+
+            const hfPattern = /^https?:\/\/huggingface\.co\/([^/]+\/[^/]+)\/?$/;
+            if (!hfPattern.test(hfUrl)) {
+                errorDiv.textContent = 'Invalid URL format. Expected: https://huggingface.co/user/repo';
+                return;
+            }
+
+            errorDiv.textContent = '';
+            modalManager.closeModal('linkHfModal');
+
+            try {
+                state.loadingManager.showSimpleLoading('Linking to HuggingFace...');
+
+                const response = await fetch('/api/lm/set-hf-url', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ file_path: filePath, hf_url: hfUrl }),
+                });
+
+                if (!response.ok) {
+                    const errData = await response.json().catch(() => ({}));
+                    throw new Error(errData.error || `Request failed: ${response.statusText}`);
+                }
+
+                const data = await response.json();
+                if (data.success) {
+                    showToast('toast.contextMenu.linkHfSuccess', {}, 'success');
+                    await this.resetAndReload();
+                } else {
+                    throw new Error(data.error || 'Failed to link model');
+                }
+            } catch (error) {
+                console.error('Error linking model to HuggingFace:', error);
+                showToast('toast.contextMenu.linkHfFailed', { message: error.message }, 'error');
+            } finally {
+                state.loadingManager.hide();
+            }
+        };
+
+        confirmBtn.addEventListener('click', this._boundLinkHfHandler);
+
+        urlInput.value = '';
+        errorDiv.textContent = '';
+
+        modalManager.showModal('linkHfModal');
+
+        setTimeout(() => urlInput.focus(), 50);
+    },
+
     extractModelVersionId(url) {
         return extractCivitaiModelUrlParts(url);
     },
@@ -279,7 +347,10 @@ export const ModelContextMenuMixin = {
                 openExampleImagesFolder(this.currentCard.dataset.sha256);
                 return true;
             case 'download-examples':
-                this.downloadExampleImages();
+                this.downloadExampleImages(false);
+                return true;
+            case 'download-examples-force':
+                this.downloadExampleImages(true);
                 return true;
             case 'civitai':
                 if (this.currentCard.dataset.from_civitai === 'true') {
@@ -295,6 +366,9 @@ export const ModelContextMenuMixin = {
             case 'relink-civitai':
                 this.showRelinkCivitaiModal();
                 return true;
+            case 'link-hf':
+                this.showLinkHfModal();
+                return true;
             case 'set-nsfw':
                 this.showNSFWLevelSelector(null, null, this.currentCard);
                 return true;
@@ -307,7 +381,7 @@ export const ModelContextMenuMixin = {
     },
 
     // Download example images method
-    async downloadExampleImages() {
+    async downloadExampleImages(force = false) {
         const modelHash = this.currentCard.dataset.sha256;
         if (!modelHash) {
             showToast('toast.contextMenu.missingHash', {}, 'error');
@@ -316,7 +390,7 @@ export const ModelContextMenuMixin = {
 
         try { 
             const apiClient = getModelApiClient();
-            await apiClient.downloadExampleImages([modelHash]);
+            await apiClient.downloadExampleImages([modelHash], null, { force });
         } catch (error) {
             console.error('Error downloading example images:', error);
         }

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -260,8 +260,9 @@ export class RecipeContextMenu extends BaseContextMenu {
                         strength: lora.strength || 1.0,
 
                         // Model identifiers
+                        modelId: lora.modelId || lora.model_id || civitaiInfo.modelId,
                         hash: modelFile?.hashes?.SHA256?.toLowerCase() || lora.hash,
-                        modelVersionId: civitaiInfo.id || lora.modelVersionId,
+                        id: civitaiInfo.id || lora.modelVersionId,
 
                         // Metadata
                         thumbnailUrl: civitaiInfo.images?.[0]?.url || '',

static/js/components/DownloadBatchSummaryModal.js

@@ -0,0 +1,340 @@
+import { translate } from '../utils/i18nHelpers.js';
+import { showToast, openHuggingFace } from '../utils/uiHelpers.js';
+
+/**
+ * Escape HTML entities in a string to prevent injection when interpolating into innerHTML.
+ * Safe for both text content and attribute values (quotes are escaped too).
+ * @param {string} str - The string to escape
+ * @returns {string} - The escaped string
+ */
+function _escapeHtml(str) {
+    if (!str) return '';
+    const div = document.createElement('div');
+    div.textContent = str;
+    return div.innerHTML.replace(/"/g, '"').replace(/'/g, ''');
+}
+
+/**
+ * Resolve the display name of a failed download entry.
+ * Prefers the resolved name carried on the entry, then known item fields,
+ * then derives a name from the item URL as a last resort.
+ * @param {Object} entry - The failed entry ({ item, error, name? })
+ * @returns {string} - The best available display name
+ */
+function _resolveItemName(entry) {
+    if (entry?.name) {
+        return entry.name;
+    }
+    const item = entry?.item ?? entry;
+    const direct = item?.displayName || item?.name || item?.file_name || item?.filename || item?.selectedVersion?.name;
+    if (direct) {
+        return direct;
+    }
+    if (item?.url) {
+        try {
+            const segments = new URL(item.url).pathname.split('/').filter(Boolean);
+            if (segments.length > 0) {
+                return decodeURIComponent(segments[segments.length - 1]);
+            }
+        } catch (e) {
+            // Unparseable URL — fall through to 'Unknown'
+        }
+    }
+    return 'Unknown';
+}
+
+/**
+ * Resolve the URL to open for a failed item — always the original item URL.
+ * @param {Object} item - The failed item payload
+ * @returns {string|null} - A URL string, or null when nothing is available
+ */
+function _resolveItemUrl(item) {
+    return item?.url || null;
+}
+
+/**
+ * Format a raw failure error into a concise human-readable message.
+ * Unwraps JSON envelopes and extracts HTTP status/body details when present.
+ * @param {*} error - The raw error (usually a string)
+ * @returns {string} - The formatted error message
+ */
+function _formatError(error) {
+    if (!error) {
+        return 'Unknown error';
+    }
+    let base = typeof error === 'string' ? error : String(error);
+
+    // Unwrap JSON envelope: { "success": false, "error": "...", ... }
+    try {
+        const parsed = JSON.parse(base);
+        if (parsed && typeof parsed.error === 'string' && parsed.error) {
+            base = parsed.error;
+        }
+    } catch (e) {
+        // Not a JSON envelope — keep the raw string
+    }
+
+    // Extract HTTP status and JSON body details, e.g. "status=403 body={...}"
+    let result = base;
+    const statusMatch = base.match(/status=(\d{3})/);
+    const bodyMatch = base.match(/body=(\{.*\})/s);
+    if (bodyMatch) {
+        try {
+            const body = JSON.parse(bodyMatch[1]);
+            const detail = (typeof body?.message === 'string' && body.message)
+                || (typeof body?.error === 'string' && body.error)
+                || null;
+            if (detail) {
+                const status = statusMatch ? statusMatch[1] : null;
+                result = `${status ? `HTTP ${status} — ` : ''}${detail}`;
+            }
+        } catch (e) {
+            // Body is not valid JSON — keep the base string
+        }
+    }
+
+    // Truncate overly long messages
+    if (result.length > 220) {
+        result = result.slice(0, 220) + '…';
+    }
+    return result;
+}
+
+/**
+ * Build a plain-text report of the batch download results.
+ * @param {number} total - Total number of models attempted
+ * @param {number} completed - Number of models successfully downloaded
+ * @param {Array} failedItems - Array of failed items ({ item, error })
+ * @returns {string} - The report text
+ */
+function _buildReportText(total, completed, failedItems) {
+    const lines = [
+        '=== Batch Download Report ===',
+        `Date: ${new Date().toLocaleString()}`,
+        `Total: ${total}`,
+        `Successfully downloaded: ${completed}`,
+        `Failed: ${failedItems.length}`,
+        '',
+    ];
+    if (failedItems.length > 0) {
+        lines.push('--- Failed Items ---');
+        failedItems.forEach((entry, i) => {
+            const name = _resolveItemName(entry);
+            const error = _formatError(entry?.error);
+            lines.push(`${i + 1}. ${name} — ${error}`);
+            const itemUrl = _resolveItemUrl(entry?.item ?? entry);
+            if (itemUrl) {
+                lines.push(`   URL: ${itemUrl}`);
+            }
+        });
+        lines.push('');
+    }
+    lines.push('====================');
+    return lines.join('\n');
+}
+
+/**
+ * Handle a successful clipboard write: confirm via toast and briefly swap the
+ * trigger button to a "Copied!" state.
+ * @param {HTMLElement|null} btn - The button that triggered the copy action
+ */
+function _onCopyReportSuccess(btn) {
+    showToast('toast.api.copiedToClipboard', {}, 'success');
+    if (btn) {
+        const origHTML = btn.innerHTML;
+        btn.innerHTML = '<i class="fas fa-check"></i> Copied!';
+        setTimeout(() => { btn.innerHTML = origHTML; }, 2000);
+    }
+}
+
+/**
+ * Fallback for environments without the async Clipboard API (e.g. insecure
+ * contexts over LAN http where `navigator.clipboard` is undefined): copy via a
+ * hidden textarea and `document.execCommand('copy')`.
+ * @param {string} text - The report text to copy
+ */
+function _copyReportWithExecCommand(text) {
+    const textarea = document.createElement('textarea');
+    textarea.value = text;
+    document.body.appendChild(textarea);
+    textarea.select();
+    document.execCommand('copy');
+    document.body.removeChild(textarea);
+    showToast('toast.api.copiedToClipboard', {}, 'success');
+}
+
+/**
+ * Copy the batch download report to the clipboard.
+ * Uses the async Clipboard API when available, otherwise falls back to a hidden
+ * textarea + execCommand so the action still works in insecure contexts.
+ * @param {HTMLElement} btn - The button that triggered the copy action
+ * @param {number} total - Total number of models attempted
+ * @param {number} completed - Number of models successfully downloaded
+ * @param {Array} failedItems - Array of failed items
+ */
+function _copyReport(btn, total, completed, failedItems) {
+    const text = _buildReportText(total, completed, failedItems);
+    if (navigator.clipboard && typeof navigator.clipboard.writeText === 'function') {
+        navigator.clipboard.writeText(text)
+            .then(() => _onCopyReportSuccess(btn))
+            .catch(() => _copyReportWithExecCommand(text));
+    } else {
+        _copyReportWithExecCommand(text);
+    }
+}
+
+/**
+ * Show the batch download summary modal after a batch download completes.
+ * Mirrors the Metadata Fetch Summary modal lifecycle: the modal element is
+ * appended directly to document.body and removed on close; it is not
+ * registered with ModalManager.
+ * @param {Object} options - Summary options
+ * @param {number} options.total - Total number of models attempted
+ * @param {number} options.completed - Number of models successfully downloaded
+ * @param {Array} options.failedItems - Array of failed items ({ item, error })
+ * @param {Function} options.onRetry - Callback invoked with failedItems to retry the failed subset
+ */
+export function showDownloadBatchSummary({ total, completed, failedItems, onRetry }) {
+    const failures = failedItems || [];
+    const failedCount = failures.length;
+
+    // 3-state summary header semantics (mirrors BatchImportManager results header)
+    let headerState;
+    let headerIcon;
+    let headerText;
+    if (completed === 0) {
+        headerState = 'error';
+        headerIcon = 'fa-times-circle';
+        headerText = translate('modals.downloadBatchSummary.failed', {}, 'Download failed');
+    } else if (failedCount > 0) {
+        headerState = 'warning';
+        headerIcon = 'fa-exclamation-circle';
+        headerText = translate('modals.downloadBatchSummary.completedWithErrors', {}, 'Completed with errors');
+    } else {
+        headerState = 'success';
+        headerIcon = 'fa-check-circle';
+        headerText = translate('modals.downloadBatchSummary.successMessage', { count: completed }, 'All ' + completed + ' models downloaded successfully');
+    }
+
+    // Build failure table rows
+    const failureRows = failures.map((entry, i) => {
+        const item = entry?.item ?? entry;
+        const name = _resolveItemName(entry);
+        const itemUrl = _resolveItemUrl(item);
+        const rawError = entry?.error ? String(entry.error) : '';
+        const error = _formatError(entry?.error);
+        const nameCell = itemUrl
+            ? `<td class="failure-name"><a href="#" class="failure-link" data-action="open-model" data-index="${i}" title="${_escapeHtml(itemUrl)}">${_escapeHtml(name)}</a></td>`
+            : `<td class="failure-name" title="${_escapeHtml(name)}">${_escapeHtml(name)}</td>`;
+        return `<tr>
+                <td class="failure-index">${i + 1}</td>
+                ${nameCell}
+                <td class="failure-error" title="${_escapeHtml(rawError)}">${_escapeHtml(error)}</td>
+            </tr>`;
+    }).join('');
+
+    const modalHtml = `
+        <div id="downloadBatchSummaryModal" class="modal" style="display: block;">
+            <div class="modal-content download-batch-summary-modal">
+                <button class="close" data-action="close-modal">&times;</button>
+
+                <h2>${translate('modals.downloadBatchSummary.title', {}, 'Batch Download Summary')}</h2>
+
+                <div class="summary-header ${headerState}">
+                    <i class="fas ${headerIcon}"></i>
+                    <span class="summary-title">${headerText}</span>
+                    <span class="summary-hint">${completed}/${total}</span>
+                </div>
+
+                <div class="refresh-summary-stats">
+                    <div class="stat-card stat-card-success">
+                        <div class="stat-card-body">
+                            <span class="stat-card-label">${translate('modals.downloadBatchSummary.statSuccess', {}, 'Success')}</span>
+                            <span class="stat-card-value">${completed}</span>
+                        </div>
+                    </div>
+                    <div class="stat-card stat-card-failure">
+                        <div class="stat-card-body">
+                            <span class="stat-card-label">${translate('modals.downloadBatchSummary.statFailed', {}, 'Failed')}</span>
+                            <span class="stat-card-value">${failedCount}</span>
+                        </div>
+                    </div>
+                    <div class="stat-card stat-card-total">
+                        <div class="stat-card-body">
+                            <span class="stat-card-label">${translate('modals.downloadBatchSummary.statTotal', {}, 'Total')}</span>
+                            <span class="stat-card-value">${total}</span>
+                        </div>
+                    </div>
+                </div>
+
+                ${failedCount > 0 ? `
+                <div class="refresh-failures-section">
+                    <h4><i class="fas fa-exclamation-triangle"></i> ${translate('modals.downloadBatchSummary.failedItems', { count: failedCount }, 'Failed Items (' + failedCount + ')')}</h4>
+                    <div class="failure-table-wrapper">
+                        <table class="failure-table">
+                            <thead>
+                                <tr>
+                                    <th>#</th>
+                                    <th>${translate('modals.downloadBatchSummary.columnName', {}, 'Model Name')}</th>
+                                    <th>${translate('modals.downloadBatchSummary.columnError', {}, 'Error')}</th>
+                                </tr>
+                            </thead>
+                            <tbody>${failureRows}</tbody>
+                        </table>
+                    </div>
+                </div>
+                ` : `
+                <div class="refresh-success-message">
+                    <i class="fas fa-check-circle"></i> ${translate('modals.downloadBatchSummary.successMessage', { count: completed }, 'All ' + completed + ' models downloaded successfully')}
+                </div>
+                `}
+
+                <div class="modal-actions">
+                    ${failedCount > 0 ? `
+                    <button class="btn-retry" data-action="retry-failed"><i class="fas fa-redo"></i> ${translate('modals.downloadBatchSummary.retryFailed', { count: failedCount }, 'Retry Failed (' + failedCount + ')')}</button>
+                    <button class="secondary-btn" data-action="copy-report"><i class="fas fa-copy"></i> ${translate('modals.downloadBatchSummary.copyReport', {}, 'Copy Report')}</button>
+                    ` : ''}
+                    <button class="cancel-btn" data-action="close-modal">${translate('modals.downloadBatchSummary.close', {}, 'Close')}</button>
+                </div>
+            </div>
+        </div>
+    `;
+
+    const existing = document.getElementById('downloadBatchSummaryModal');
+    if (existing) existing.remove();
+
+    const container = document.createElement('div');
+    container.innerHTML = modalHtml;
+    const modal = container.firstElementChild;
+    document.body.appendChild(modal);
+
+    modal.addEventListener('click', (e) => {
+        const actionEl = e.target.closest('[data-action]');
+        const action = actionEl?.dataset.action;
+        if (!action) return;
+        e.preventDefault();
+
+        switch (action) {
+            case 'close-modal':
+                modal.remove();
+                break;
+            case 'retry-failed':
+                modal.remove();
+                if (typeof onRetry === 'function') {
+                    onRetry(failures);
+                }
+                break;
+            case 'copy-report':
+                _copyReport(actionEl, total, completed, failures);
+                break;
+            case 'open-model': {
+                // Keep the modal open; just open the item's original URL in a new tab
+                const entry = failures[Number(actionEl.dataset.index)];
+                const item = entry?.item;
+                if (!item?.url) break;
+                openHuggingFace(item.url);
+                break;
+            }
+        }
+    });
+}

static/js/components/RecipeCard.js

@@ -358,7 +358,7 @@ class RecipeCard {
                         <div class="delete-preview">
                             ${isVideo ?
                     `<video src="${previewUrl}" controls muted loop playsinline style="max-width: 100%;"></video>` :
-                    `<img src="${previewUrl}" alt="${this.recipe.title}">`
+                `<img src="${previewUrl}" alt="${this.recipe.title}" onerror="this.onerror=null; this.src='/loras_static/images/no-preview.png'">`
                 }
                         </div>
                         <div class="delete-info">

static/js/components/RecipeModal.js

@@ -757,7 +757,7 @@ class RecipeModal {
                     `<video class="thumbnail-video" autoplay loop muted playsinline>
                         <source src="${lora.preview_url}" type="video/mp4">
                      </video>` :
-                    `<img src="${lora.preview_url || '/loras_static/images/no-preview.png'}" alt="LoRA preview">`;
+                    `<img src="${lora.preview_url || '/loras_static/images/no-preview.png'}" alt="LoRA preview" onerror="this.onerror=null; this.src='/loras_static/images/no-preview.png'">`;
 
                 let loraItemClass = 'recipe-lora-item';
                 if (existsLocally) {
@@ -1421,6 +1421,7 @@ class RecipeModal {
                         strength: lora.strength || 1.0,
 
                         // Model identifiers
+                        modelId: lora.modelId || lora.model_id || civitaiInfo.modelId,
                         hash: modelFile?.hashes?.SHA256?.toLowerCase() || lora.hash,
                         id: civitaiInfo.id || lora.modelVersionId,
 
@@ -1606,7 +1607,7 @@ class RecipeModal {
             <video class="thumbnail-video" autoplay loop muted playsinline>
                 <source src="${previewUrl}" type="video/mp4">
             </video>
-        ` : `<img src="${previewUrl}" alt="Checkpoint preview">`;
+        ` : `<img src="${previewUrl}" alt="Checkpoint preview" onerror="this.onerror=null; this.src='/loras_static/images/no-preview.png'">`;
 
         const badge = existsLocally ? `
             <div class="local-badge">

static/js/components/controls/PageControls.js

@@ -108,10 +108,20 @@ export class PageControls {
         const sortSelect = document.getElementById('sortSelect');
         if (sortSelect) {
             initSortDropdown(sortSelect);
-            sortSelect.value = this.pageState.sortBy;
+            this.applySortToSelect(this.pageState.sortBy);
             sortSelect.addEventListener('change', async (e) => {
-                this.pageState.sortBy = e.target.value;
-                this.saveSortPreference(e.target.value);
+                let value = e.target.value;
+                if (value.startsWith('random')) {
+                    // Every pick of Random reshuffles the list: generate a
+                    // fresh seed so the backend keeps a stable order across
+                    // paginated requests.
+                    value = this._randomizeSortValue();
+                }
+                this.pageState.sortBy = value;
+                this.saveSortPreference(value);
+                // Reset the seeded Random option when switching away from
+                // Random, or re-apply the fresh seed when picking it again.
+                this.applySortToSelect(value);
                 await this.resetAndReload();
             });
         }
@@ -312,6 +322,44 @@ export class PageControls {
         }
     }
     
+    /**
+     * Apply a sort value to the native sort <select>, keeping the Random
+     * option's value in sync when the persisted value carries a seed
+     * (e.g. "random:abc123"). Must be used instead of assigning
+     * sortSelect.value directly whenever the value may be a seeded random
+     * sort, otherwise the native select has no matching option.
+     * @param {string} sortValue - Sort value like "name:asc" or "random:<seed>"
+     */
+    applySortToSelect(sortValue) {
+        const sortSelect = document.getElementById('sortSelect');
+        if (!sortSelect) return;
+        const randomOpt = sortSelect.querySelector('option[value="random"], option[value^="random:"]');
+        if (randomOpt) {
+            randomOpt.value = String(sortValue).startsWith('random') ? sortValue : 'random';
+        }
+        sortSelect.value = sortValue;
+    }
+
+    /**
+     * Generate a fresh seeded random sort value ("random:<seed>") and keep
+     * the native <select> in sync so its value matches the persisted sort
+     * string and the dropdown shows the selected label.
+     * @returns {string} The new sort value, e.g. "random:abc123xyz"
+     */
+    _randomizeSortValue() {
+        const seed = Math.random().toString(36).slice(2, 12);
+        const value = `random:${seed}`;
+        const sortSelect = document.getElementById('sortSelect');
+        if (sortSelect) {
+            const randomOpt = sortSelect.querySelector('option[value="random"], option[value^="random:"]');
+            if (randomOpt) {
+                randomOpt.value = value;
+            }
+            sortSelect.value = value;
+        }
+        return value;
+    }
+
     /**
      * Load sort preference from storage
      */
@@ -326,10 +374,7 @@ export class PageControls {
             // Handle legacy format conversion
             const convertedSort = this.convertLegacySortFormat(savedSort);
             this.pageState.sortBy = convertedSort;
-            const sortSelect = document.getElementById('sortSelect');
-            if (sortSelect) {
-                sortSelect.value = convertedSort;
-            }
+            this.applySortToSelect(convertedSort);
         }
     }
     
@@ -523,9 +568,9 @@ export class PageControls {
         this.pageState.sortBy = restoredSort;
         this.saveSortPreference(restoredSort);
         this._removeVlmSortOption();
+        this.applySortToSelect(restoredSort);
         const sortSelect = document.getElementById('sortSelect');
         if (sortSelect) {
-            sortSelect.value = restoredSort;
             sortSelect.disabled = false;
         }
     }
@@ -575,10 +620,7 @@ export class PageControls {
             const savedGroupedSort = getStorageItem(groupedKey);
             if (savedGroupedSort) {
                 this.pageState.sortBy = savedGroupedSort;
-                const sortSelect = document.getElementById('sortSelect');
-                if (sortSelect) {
-                    sortSelect.value = savedGroupedSort;
-                }
+                this.applySortToSelect(savedGroupedSort);
             }
         } else {
             // Leaving group mode: persist current sort for next time, restore non-group sort
@@ -586,10 +628,7 @@ export class PageControls {
             const savedNormalSort = getStorageItem(`${this.pageType}_sort`);
             if (savedNormalSort) {
                 this.pageState.sortBy = savedNormalSort;
-                const sortSelect = document.getElementById('sortSelect');
-                if (sortSelect) {
-                    sortSelect.value = savedNormalSort;
-                }
+                this.applySortToSelect(savedNormalSort);
             }
         }
     }
@@ -874,7 +913,7 @@ export class PageControls {
         }
 
         if (sortSelect) {
-            sortSelect.value = this.pageState.sortBy;
+            this.applySortToSelect(this.pageState.sortBy);
         }
         if (searchInput) {
             searchInput.value = this.pageState.filters?.search || '';

static/js/components/controls/SortDropdown.js

@@ -96,7 +96,16 @@ export function initSortDropdown(select) {
     };
 
     const choose = (value) => {
-        if (select.value === value) return;
+        if (select.value === value) {
+            // Re-picking the already-selected option is normally a no-op,
+            // matching native <select> behavior. The seeded Random sort is
+            // the exception: clicking it again should reshuffle, so let the
+            // change handler (PageControls) generate a fresh seed.
+            if (String(value).startsWith('random')) {
+                select.dispatchEvent(new Event('change', { bubbles: true }));
+            }
+            return;
+        }
         select.value = value;
         select.dispatchEvent(new Event('change', { bubbles: true }));
     };
@@ -277,9 +286,10 @@ export function initSortDropdown(select) {
     }
 
     // Rebuild the menu when <option>s change (VLM adds/removes a temporary
-    // option at runtime).
+    // option at runtime, and the seeded Random sort option gets a new value
+    // attribute each time it is picked).
     const observer = new MutationObserver(() => buildMenu());
-    observer.observe(select, { childList: true });
+    observer.observe(select, { childList: true, subtree: true, attributes: true, attributeFilter: ['value'] });
 
     buildMenu();
     group.dataset.sortReady = '1';

static/js/components/shared/ModelCard.js

@@ -489,6 +489,12 @@ export function createModelCard(model, modelType) {
     const modelId = civitaiData?.modelId ?? civitaiData?.model_id;
     if (modelId !== undefined && modelId !== null && modelId !== '') {
         card.dataset.modelId = modelId;
+    } else if (model.hf_url) {
+        // For HF-only models, derive a group key from hf_url for version grouping
+        const match = model.hf_url.match(/https?:\/\/huggingface\.co\/([^/]+\/[^/]+)/);
+        if (match) {
+            card.dataset.modelId = 'hf:' + match[1];
+        }
     }
 
     // LoRA specific data
@@ -643,7 +649,7 @@ export function createModelCard(model, modelType) {
         <div class="card-preview ${shouldBlur ? 'blurred' : ''}">
             ${isVideo ?
             `<video ${videoAttrs.join(' ')} style="pointer-events: none;"></video>` :
-            `<img src="${versionedPreviewUrl}" alt="${model.model_name}">`
+            `<img src="${versionedPreviewUrl}" alt="${model.model_name}" onerror="this.onerror=null; this.src='/loras_static/images/no-preview.png'">`
         }
             <div class="card-header">
                 ${shouldBlur ?

static/js/components/shared/ModelModal.js

@@ -473,7 +473,14 @@ export async function showModelModal(model, modelType) {
     const loadingExamplesText = translate('modals.model.loading.examples', {}, 'Loading examples...');
 
     const loadingVersionsText = translate('modals.model.loading.versions', {}, 'Loading versions...');
-    const civitaiModelId = modelWithFullData.civitai?.modelId || '';
+    // Use CivitAI modelId, or derive HF group key for HF-only models
+    let civitaiModelId = modelWithFullData.civitai?.modelId || '';
+    if (!civitaiModelId && modelWithFullData.hf_url) {
+        const match = modelWithFullData.hf_url.match(/https?:\/\/huggingface\.co\/([^/]+\/[^/]+)/);
+        if (match) {
+            civitaiModelId = 'hf:' + match[1];
+        }
+    }
     const civitaiVersionId = modelWithFullData.civitai?.id || '';
     const navAriaLabel = translate('modals.model.navigation.label', {}, 'Model navigation');
     const previousTitle = translate('modals.model.navigation.previousWithShortcut', {}, 'Previous model (←)');
@@ -885,7 +892,8 @@ function setupEventHandlers(filePath, modelType) {
             case 'view-creator':
                 const username = target.dataset.username;
                 if (username) {
-                    window.open(`https://civitai.com/user/${username}`, '_blank');
+                    const host = state.global.settings.civitai_host || 'civitai.com';
+                    window.open(`https://${host}/user/${username}`, '_blank');
                 }
                 break;
             case 'open-file-location':

static/js/components/shared/ModelVersionsTab.js

@@ -432,7 +432,7 @@ function renderMediaMarkup(version) {
 
     return `
         <div class="version-media">
-            <img src="${escapeHtml(version.previewUrl)}" alt="${escapeHtml(version.name || 'preview')}">
+            <img src="${escapeHtml(version.previewUrl)}" alt="${escapeHtml(version.name || 'preview')}" onerror="this.onerror=null; this.src='/loras_static/images/no-preview.png'">
         </div>
     `;
 }
@@ -950,6 +950,26 @@ export function initVersionsTab({
             renderErrorState(container, translate('modals.model.versions.missingModelId', {}, 'This model is missing a Civitai model id.'));
             return;
         }
+        // HF group keys (e.g. "hf:user/repo") are not real CivitAI model IDs —
+        // skip the remote API call and show a helpful message instead.
+        const isHfGroupKey = typeof modelId === 'string' && modelId.startsWith('hf:');
+        if (isHfGroupKey) {
+            controller.isLoading = false;
+            controller.hasLoaded = true;
+            controller.record = null;
+            const hfMsg = translate(
+                'modals.model.versions.hfGroupInfo',
+                {},
+                'This is a HuggingFace model group. Open the library to see all versions in the grid.'
+            );
+            container.innerHTML = `
+                <div class="versions-empty-state">
+                    <i class="fas fa-info-circle"></i>
+                    <p>${escapeHtml(hfMsg)}</p>
+                </div>
+            `;
+            return;
+        }
         if (controller.hasLoaded && !forceRefresh) {
             return;
         }

static/js/managers/BulkManager.js

@@ -27,6 +27,8 @@ export class BulkManager {
 
         // Drag detection properties
         this.dragThreshold = 5; // Pixels to move before considering it a drag
+        this.dragDelayMs = 100; // Minimum hold time before a drag is treated as a marquee
+        this.minMarqueeSize = 10; // Minimum drag box (px) before a marquee counts as a selection
         this.mouseDownTime = 0;
         this.mouseDownPosition = { x: 0, y: 0 };
 
@@ -88,7 +90,7 @@ export class BulkManager {
                 moveAll: true,
                 autoOrganize: false,
                 deleteAll: true,
-                setContentRating: false,
+                setContentRating: true,
                 skipMetadataRefresh: false,
                 setFavorite: true,
                 unfavorite: true,
@@ -173,6 +175,19 @@ export class BulkManager {
         });
 
         eventManager.addHandler('mousemove', 'bulkManager-marquee-move', (e) => {
+            // Only track marquee/drag while the left button is physically held.
+            // mouseup can be missed (release outside the window, focus loss, driver quirks),
+            // so mousemove must verify the button state itself instead of relying on it.
+            if (!(e.buttons & 1)) {
+                if (this.isMarqueeActive) {
+                    this.endMarqueeSelection(e);
+                } else {
+                    this.mouseDownTime = 0;
+                    this.isDragging = false;
+                }
+                return false;
+            }
+
             if (this.isMarqueeActive) {
                 this.lastClientX = e.clientX;
                 this.lastClientY = e.clientY;
@@ -184,7 +199,10 @@ export class BulkManager {
                 const dy = e.clientY - this.mouseDownPosition.y;
                 const distance = Math.sqrt(dx * dx + dy * dy);
 
-                if (distance >= this.dragThreshold) {
+                // Require both enough movement AND enough hold time so quick
+                // click jitter from micro-movement input devices is not a marquee.
+                const heldTime = Date.now() - this.mouseDownTime;
+                if (heldTime >= this.dragDelayMs && distance >= this.dragThreshold) {
                     this.isDragging = true;
                     this.startMarqueeSelection(e, true);
                 }
@@ -397,6 +415,7 @@ export class BulkManager {
         const updated = {
             ...existing,
             fileName: card.dataset.file_name ?? existing.fileName,
+            folder: card.dataset.folder ?? existing.folder,
             usageTips: card.dataset.usage_tips ?? existing.usageTips,
             modelName: card.dataset.name ?? existing.modelName,
         };
@@ -494,7 +513,8 @@ export class BulkManager {
 
             if (metadata) {
                 const usageTips = JSON.parse(metadata.usageTips || '{}');
-                loraSyntaxes.push(buildLoraSyntax(metadata.fileName, usageTips));
+                const loraName = metadata.folder ? `${metadata.folder}/${metadata.fileName}` : metadata.fileName;
+                loraSyntaxes.push(buildLoraSyntax(loraName, usageTips));
             } else {
                 missingLoras.push(filepath);
             }
@@ -537,7 +557,8 @@ export class BulkManager {
 
             if (metadata) {
                 const usageTips = JSON.parse(metadata.usageTips || '{}');
-                loraSyntaxes.push(buildLoraSyntax(metadata.fileName, usageTips));
+                const loraName = metadata.folder ? `${metadata.folder}/${metadata.fileName}` : metadata.fileName;
+                loraSyntaxes.push(buildLoraSyntax(loraName, usageTips));
             } else {
                 missingLoras.push(filepath);
             }
@@ -553,7 +574,8 @@ export class BulkManager {
             return;
         }
 
-        await sendLoraToWorkflow(loraSyntaxes.join(', '), replaceMode, 'lora');
+        const exitBulkMode = () => { if (state.bulkMode) this.toggleBulkMode(); };
+        await sendLoraToWorkflow(loraSyntaxes.join(', '), replaceMode, 'lora', exitBulkMode);
     }
 
     async _sendAllEmbeddingsToWorkflow() {
@@ -575,7 +597,8 @@ export class BulkManager {
         }
 
         const joinedCode = embeddingCodes.join(', ');
-        await sendEmbeddingToWorkflow(joinedCode);
+        const exitBulkMode = () => { if (state.bulkMode) this.toggleBulkMode(); };
+        await sendEmbeddingToWorkflow(joinedCode, exitBulkMode);
     }
 
     showBulkDeleteModal() {
@@ -674,6 +697,7 @@ export class BulkManager {
                     const modelId = this.parseModelId(item?.civitai?.modelId);
                     metadataCache.set(item.file_path, {
                         fileName: item.file_name,
+                        folder: item.folder || '',
                         usageTips: item.usage_tips || '{}',
                         modelName: item.name || item.file_name,
                         ...(modelId !== null ? { modelId } : {})
@@ -1504,14 +1528,18 @@ export class BulkManager {
         let failureCount = 0;
 
         try {
-            const apiClient = getModelApiClient();
+            const isRecipesPage = state.currentPageType === 'recipes';
             for (const filePath of targets) {
                 if (cancelled) {
                     showToast('toast.api.operationCancelled', {}, 'info');
                     break;
                 }
                 try {
-                    await apiClient.saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    if (isRecipesPage) {
+                        await updateRecipeMetadata(filePath, { preview_nsfw_level: level });
+                    } else {
+                        await getModelApiClient().saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    }
                     successCount++;
                 } catch (error) {
                     failureCount++;
@@ -1659,13 +1687,19 @@ export class BulkManager {
                 cancelled = true;
             });
 
+            const isRecipesPage = state.currentPageType === 'recipes';
+
             for (const filepath of state.selectedModels) {
                 if (cancelled) {
                     showToast('toast.api.operationCancelled', {}, 'info');
                     break;
                 }
                 try {
-                    await getModelApiClient().saveModelMetadata(filepath, { base_model: newBaseModel });
+                    if (isRecipesPage) {
+                        await updateRecipeMetadata(filepath, { base_model: newBaseModel });
+                    } else {
+                        await getModelApiClient().saveModelMetadata(filepath, { base_model: newBaseModel });
+                    }
                     successCount++;
                 } catch (error) {
                     errorCount++;
@@ -1946,9 +1980,31 @@ export class BulkManager {
         // Remove visual feedback class
         document.body.classList.remove('marquee-selecting');
 
+        // Compute the actual drag box size in document coordinates, matching how
+        // updateMarqueeSelectionFromPosition tracks the rectangle. Client-space
+        // size would wrongly flag auto-scroll marquees (tiny pointer movement,
+        // large document-space box) as accidental clicks.
+        const container = document.querySelector('.page-content');
+        const scrollX = container?.scrollLeft || 0;
+        const scrollY = container?.scrollTop || 0;
+        const dragWidth = Math.abs((e.clientX + scrollX) - this.marqueeStartDoc.x);
+        const dragHeight = Math.abs((e.clientY + scrollY) - this.marqueeStartDoc.y);
+        const isTinyMarquee = dragWidth < this.minMarqueeSize && dragHeight < this.minMarqueeSize;
+
         // Get selection count
         const selectionCount = state.selectedModels.size;
 
+        // A tiny box (e.g. click jitter that happened to graze a card) is treated
+        // as an accidental click: undo any selection and leave bulk mode.
+        if (isTinyMarquee) {
+            this.clearSelection();
+            if (state.bulkMode) {
+                this.toggleBulkMode();
+            }
+            this.initialSelectedModels.clear();
+            return;
+        }
+
         // If no models were selected, exit bulk mode
         if (selectionCount === 0) {
             if (state.bulkMode) {

static/js/managers/BulkMissingLoraDownloadManager.js

@@ -196,6 +196,17 @@ export class BulkMissingLoraDownloadManager {
         let completedDownloads = 0;
         let failedDownloads = 0;
         let currentLoraProgress = 0;
+        let cancelled = false;
+
+        loadingManager.showCancelButton(async () => {
+            if (cancelled) return;
+            cancelled = true;
+            try {
+                await this.loraApiClient.cancelDownload(batchDownloadId);
+            } catch (e) {
+                console.error('Cancel request failed:', e);
+            }
+        });
 
         // Set up WebSocket message handler
         ws.onmessage = (event) => {
@@ -207,6 +218,11 @@ export class BulkMissingLoraDownloadManager {
                 return;
             }
 
+            if (data.status === 'cancelled') {
+                cancelled = true;
+                return;
+            }
+
             // Process progress updates
             if (data.status === 'progress' && data.download_id && data.download_id.startsWith(batchDownloadId)) {
                 currentLoraProgress = data.progress;
@@ -249,6 +265,8 @@ export class BulkMissingLoraDownloadManager {
 
         // Download each LoRA sequentially
         for (let i = 0; i < lorasToDownload.length; i++) {
+            if (cancelled) break;
+
             const lora = lorasToDownload[i];
             
             currentLoraProgress = 0;
@@ -275,11 +293,13 @@ export class BulkMissingLoraDownloadManager {
                     modelId,
                     versionId,
                     loraRoot,
-                    '', // Empty relative path, use default paths
+                    '',
                     useDefaultPaths,
                     batchDownloadId
                 );
 
+                if (cancelled) break;
+
                 if (!response.success) {
                     console.error(`Failed to download LoRA ${lora.name || lora.file_name}: ${response.error}`);
                     failedDownloads++;
@@ -288,8 +308,10 @@ export class BulkMissingLoraDownloadManager {
                     updateProgress(100, completedDownloads, '');
                 }
             } catch (error) {
-                console.error(`Error downloading LoRA ${lora.name || lora.file_name}:`, error);
-                failedDownloads++;
+                if (!cancelled) {
+                    console.error(`Error downloading LoRA ${lora.name || lora.file_name}:`, error);
+                    failedDownloads++;
+                }
             }
         }
 
@@ -300,7 +322,10 @@ export class BulkMissingLoraDownloadManager {
         loadingManager.hide();
 
         // Show completion message
-        if (failedDownloads === 0) {
+        if (cancelled) {
+            showToast('toast.downloads.downloadStopped', {}, 'info',
+                `Download cancelled. ${completedDownloads} item(s) completed.`);
+        } else if (failedDownloads === 0) {
             showToast('toast.loras.allDownloadSuccessful', { count: completedDownloads }, 'success');
         } else {
             showToast('toast.loras.downloadPartialSuccess', {

static/js/managers/DownloadManager.js

@@ -8,6 +8,7 @@ import { FolderTreeManager } from '../components/FolderTreeManager.js';
 import { translate } from '../utils/i18nHelpers.js';
 import { extractCivitaiModelUrlParts } from '../utils/civitaiUtils.js';
 import { formatFileSize } from '../utils/formatters.js';
+import { showDownloadBatchSummary } from '../components/DownloadBatchSummaryModal.js';
 
 export class DownloadManager {
     constructor() {
@@ -158,6 +159,7 @@ export class DownloadManager {
         this.modelVersionId = null;
         this.source = null;
         this.selectedFile = null;
+        this._isDiffusionModel = false;
 
         this.selectedFolder = '';
         this.batchModels = [];
@@ -728,14 +730,23 @@ export class DownloadManager {
 
     confirmFileSelection() {
         const selectedRadio = document.querySelector('#fileSelectionList input[type="radio"]:checked');
-        if (!selectedRadio) return;
+        if (!selectedRadio) {
+            console.warn('[download] confirmFileSelection: no radio button checked');
+            return;
+        }
 
         const version = this.currentVersion;
-        if (!version) return;
+        if (!version) {
+            console.warn('[download] confirmFileSelection: no currentVersion set');
+            return;
+        }
 
         const modelFiles = (version.files || []).filter(f => f.type === 'Model' || f.type === 'UNet' || f.type === 'Diffusion Model');
         this.selectedFile = modelFiles.find(f => f.id.toString() === selectedRadio.value);
 
+        console.log('[download] confirmFileSelection: selected file id=%s, name="%s", type="%s", metadata=%o',
+            this.selectedFile?.id, this.selectedFile?.name, this.selectedFile?.type, this.selectedFile?.metadata);
+
         document.getElementById('fileSelectionStep').style.display = 'none';
         document.getElementById('locationStep').style.display = 'block';
         this.proceedToLocationContent();
@@ -778,24 +789,40 @@ export class DownloadManager {
     async proceedToLocationContent() {
 
         try {
-            // Fetch model roots
-            const rootsData = await this.apiClient.fetchModelRoots();
+            const _isDiffusionModel = this.selectedFile
+                ? (this.selectedFile.type === 'UNet' || this.selectedFile.type === 'Diffusion Model')
+                : (this.currentVersion?.files || []).some(
+                    f => f.type === 'UNet' || f.type === 'Diffusion Model'
+                );
+            this._isDiffusionModel = _isDiffusionModel;
+
+            let rootsData;
+            if (this._isDiffusionModel && this.apiClient.modelType === 'checkpoints') {
+                rootsData = await this.apiClient.fetchModelRoots('diffusion_model');
+            } else {
+                rootsData = await this.apiClient.fetchModelRoots();
+            }
             const modelRoot = document.getElementById('modelRoot');
             modelRoot.innerHTML = rootsData.roots.map(root =>
                 `<option value="${root}">${root}</option>`
             ).join('');
 
-            // Set default root if available
-            const singularType = this.apiClient.modelType.replace(/s$/, '');
+            const singularType = this._isDiffusionModel
+                ? 'unet'
+                : this.apiClient.modelType.replace(/s$/, '');
             const defaultRootKey = `default_${singularType}_root`;
             const defaultRoot = state.global.settings[defaultRootKey];
-            console.log(`Default root for ${this.apiClient.modelType}:`, defaultRoot);
+            console.log(`Default root for ${singularType}:`, defaultRoot);
             console.log('Available roots:', rootsData.roots);
             if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
                 console.log(`Setting default root: ${defaultRoot}`);
                 modelRoot.value = defaultRoot;
             }
 
+            const subtypeDisplay = this._isDiffusionModel ? 'Diffusion Model' : this.apiClient.apiConfig.config.displayName;
+            document.getElementById('modelRootLabel').textContent =
+                translate('modals.download.selectTypeRoot', { type: subtypeDisplay });
+
             // Set autocomplete="off" on folderPath input
             const folderPathInput = document.getElementById('folderPath');
             if (folderPathInput) {
@@ -872,16 +899,26 @@ export class DownloadManager {
         const displayName = versionName || `#${versionId}`;
         let ws = null;
         let updateProgress = () => { };
+        let cancelled = false;
+        const downloadId = Date.now().toString();
 
         try {
             this.loadingManager.restoreProgressBar();
             updateProgress = this.loadingManager.showDownloadProgress(1);
             updateProgress(0, 0, displayName);
-
-            const downloadId = Date.now().toString();
             const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
             ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${downloadId}`);
 
+            this.loadingManager.showCancelButton(async () => {
+                if (cancelled) return;
+                cancelled = true;
+                try {
+                    await this.apiClient.cancelDownload(downloadId);
+                } catch (e) {
+                    console.error('Cancel request failed:', e);
+                }
+            });
+
             ws.onmessage = event => {
                 const data = JSON.parse(event.data);
 
@@ -890,6 +927,12 @@ export class DownloadManager {
                     return;
                 }
 
+                if (data.status === 'cancelled') {
+                    cancelled = true;
+                    this.loadingManager.setStatus(translate('modals.download.status.cancelled', {}, 'Download cancelled'));
+                    return;
+                }
+
                 if (data.status === 'progress' && data.download_id === downloadId) {
                     const metrics = {
                         bytesDownloaded: data.bytes_downloaded,
@@ -928,6 +971,10 @@ export class DownloadManager {
                 fileParams
             );
 
+            if (cancelled) {
+                return false;
+            }
+
             if (response?.skipped) {
                 this.loadingManager.setStatus(translate('modals.download.status.finalizing'));
                 updateProgress(100, 0, displayName);
@@ -968,8 +1015,12 @@ export class DownloadManager {
 
             return true;
         } catch (error) {
-            console.error('Failed to download model version:', error);
-            showToast('toast.downloads.downloadError', { message: error?.message }, 'error');
+            if (cancelled) {
+                console.log('Download cancelled by user:', downloadId);
+            } else {
+                console.error('Failed to download model version:', error);
+                showToast('toast.downloads.downloadError', { message: error?.message }, 'error');
+            }
             return false;
         } finally {
             try {
@@ -989,16 +1040,33 @@ export class DownloadManager {
         const totalFiles = this.hfSelectedFiles.length;
         const updateProgress = this.loadingManager.showDownloadProgress(totalFiles);
 
+        let cancelled = false;
+        let currentDownloadId = null;
+
+        this.loadingManager.showCancelButton(async () => {
+            if (cancelled) return;
+            cancelled = true;
+            if (currentDownloadId) {
+                try {
+                    await this.apiClient.cancelDownload(currentDownloadId);
+                } catch (e) {
+                    console.error('Cancel request failed:', e);
+                }
+            }
+        });
+
         try {
             let completedDownloads = 0;
             for (let i = 0; i < totalFiles; i++) {
+                if (cancelled) break;
+
                 const filename = this.hfSelectedFiles[i];
                 updateProgress(0, completedDownloads, filename);
                 this.loadingManager.setStatus(`Downloading ${filename}...`);
 
-                const downloadId = Date.now().toString() + '_' + i;
+                currentDownloadId = Date.now().toString() + '_' + i;
                 const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
-                const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${downloadId}`);
+                const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${currentDownloadId}`);
 
                 try {
                     await new Promise((resolve, reject) => {
@@ -1006,12 +1074,13 @@ export class DownloadManager {
                         ws.onerror = reject;
                     });
 
-                    // Capture completed count at WS creation time so progress
-                    // updates arriving after completedDownloads increments still
-                    // show the correct "N / total" position.
                     const snapshotCompleted = completedDownloads;
                     ws.onmessage = (event) => {
                         const data = JSON.parse(event.data);
+                        if (data.status === 'cancelled') {
+                            cancelled = true;
+                            return;
+                        }
                         if (data.status === 'progress') {
                             const metrics = {
                                 bytesDownloaded: data.bytes_downloaded,
@@ -1029,9 +1098,11 @@ export class DownloadManager {
                         modelRoot,
                         relativePath: targetFolder,
                         useDefaultPaths,
-                        download_id: downloadId,
+                        download_id: currentDownloadId,
                     });
 
+                    if (cancelled) break;
+
                     if (response?.success) {
                         completedDownloads++;
                         updateProgress(100, completedDownloads, filename);
@@ -1041,13 +1112,19 @@ export class DownloadManager {
                 }
             }
 
-            showToast('toast.loras.downloadCompleted', {}, 'success');
-            // Reload page data — model is already in scanner cache via backend
+            if (cancelled) {
+                showToast('toast.downloads.downloadStopped', {}, 'info',
+                    `Download cancelled. ${completedDownloads} item(s) completed.`);
+            } else {
+                showToast('toast.loras.downloadCompleted', {}, 'success');
+            }
             await resetAndReload(true);
             return true;
         } catch (error) {
-            console.error('Failed to download HF model:', error);
-            showToast('toast.downloads.downloadError', { message: error?.message }, 'error');
+            if (!cancelled) {
+                console.error('Failed to download HF model:', error);
+                showToast('toast.downloads.downloadError', { message: error?.message }, 'error');
+            }
             return false;
         } finally {
             this.loadingManager.hide();
@@ -1426,12 +1503,23 @@ export class DownloadManager {
             }
 
             const fileParams = this.selectedFile ? {
+                id: this.selectedFile.id,
                 type: this.selectedFile.type || 'Model',
-                format: this.selectedFile.metadata?.format || 'SafeTensor',
-                size: this.selectedFile.metadata?.size || 'full',
-                fp: this.selectedFile.metadata?.fp,
+                format: this.selectedFile.metadata?.format || null,
+                size: this.selectedFile.metadata?.size || null,
+                fp: this.selectedFile.metadata?.fp || null,
             } : null;
 
+            if (fileParams) {
+                console.log('[download] startDownload (single): fileParams built from selectedFile — id=%s, type=%s, format=%s, size=%s, fp=%s',
+                    fileParams.id, fileParams.type, fileParams.format, fileParams.size, fileParams.fp);
+            } else {
+                console.log('[download] startDownload (single): this.selectedFile is null — no file selection, will download primary/default file. version=%s has %d files',
+                    this.currentVersion?.id, (this.currentVersion?.files || []).length);
+            }
+
+            modalManager.closeModal('downloadModal');
+
             return this.executeDownloadWithProgress({
                 modelId: this.modelId,
                 versionId: this.currentVersion.id,
@@ -1461,6 +1549,10 @@ export class DownloadManager {
 
         modalManager.closeModal('downloadModal');
 
+        return this.executeBatchDownload(downloadItems, { modelRoot, targetFolder, useDefaultPaths });
+    }
+
+    async executeBatchDownload(downloadItems, { modelRoot, targetFolder, useDefaultPaths }) {
         const batchDownloadId = Date.now().toString();
         const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
         const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${batchDownloadId}`);
@@ -1470,11 +1562,28 @@ export class DownloadManager {
 
         let completedDownloads = 0;
         let failedDownloads = 0;
+        let cancelled = false;
+        const failedItems = [];
+
+        loadingManager.showCancelButton(async () => {
+            if (cancelled) return;
+            cancelled = true;
+            try {
+                await this.apiClient.cancelDownload(batchDownloadId);
+            } catch (e) {
+                console.error('Cancel request failed:', e);
+            }
+        });
 
         ws.onmessage = (event) => {
             const data = JSON.parse(event.data);
             if (data.type === 'download_id') return;
 
+            if (data.status === 'cancelled') {
+                cancelled = true;
+                return;
+            }
+
             if (data.status === 'progress' && data.download_id?.startsWith(batchDownloadId)) {
                 const current = downloadItems[completedDownloads + failedDownloads];
                 const name = current?.selectedVersion?.name || current?.displayName || current?.filename || `#${completedDownloads + failedDownloads + 1}`;
@@ -1493,6 +1602,8 @@ export class DownloadManager {
         });
 
         for (let i = 0; i < downloadItems.length; i++) {
+            if (cancelled) break;
+
             const item = downloadItems[i];
             const name = item.displayName || item.filename || (item.selectedVersion?.name || `Model #${item.modelId}`);
             const isHf = item.source === 'huggingface';
@@ -1503,7 +1614,6 @@ export class DownloadManager {
             try {
                 let response;
                 if (isHf) {
-                    // Per-file WebSocket for real-time progress
                     const downloadId = Date.now().toString() + '_hf_' + i;
                     const wsHf = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${downloadId}`);
                     try {
@@ -1537,6 +1647,8 @@ export class DownloadManager {
                         wsHf.close();
                     }
                 } else {
+                    console.log('[download] batch download: fileParams NOT passed for modelId=%s, versionId=%s — backend will use primary file',
+                        item.modelId, item.selectedVersion?.id);
                     response = await this.apiClient.downloadModel(
                         item.modelId,
                         item.selectedVersion.id,
@@ -1548,28 +1660,42 @@ export class DownloadManager {
                     );
                 }
 
+                if (cancelled) break;
+
                 if (!response.success) {
                     failedDownloads++;
+                    failedItems.push({ item, error: response.error || 'Unknown error', name });
                 } else {
                     completedDownloads++;
                     updateProgress(100, completedDownloads, '');
                 }
             } catch (err) {
-                console.error(`Failed to download ${name}:`, err);
-                failedDownloads++;
+                if (!cancelled) {
+                    console.error(`Failed to download ${name}:`, err);
+                    failedDownloads++;
+                    failedItems.push({ item, error: err?.message || 'Unknown error', name });
+                }
             }
         }
 
         ws.close();
         loadingManager.hide();
 
-        if (failedDownloads === 0) {
+        if (cancelled) {
+            showToast('toast.downloads.downloadStopped', {}, 'info',
+                `Download cancelled. ${completedDownloads} item(s) completed.`);
+        } else if (failedDownloads === 0) {
             showToast('toast.loras.allDownloadSuccessful', { count: completedDownloads }, 'success');
         } else {
-            showToast('toast.loras.downloadPartialSuccess', {
-                completed: completedDownloads,
+            showDownloadBatchSummary({
                 total: downloadItems.length,
-            }, 'warning');
+                completed: completedDownloads,
+                failedItems,
+                onRetry: (failed) => this.executeBatchDownload(
+                    failed.map((f) => f.item),
+                    { modelRoot, targetFolder, useDefaultPaths }
+                ),
+            });
         }
 
         await resetAndReload(true);
@@ -1581,6 +1707,10 @@ export class DownloadManager {
         modelRoot = '',
         targetFolder = ''
     } = {}) {
+        console.warn('[download] downloadVersionWithDefaults: NO fileParams will be sent — backend will always use primary file. '
+            + 'modelType=%s, modelId=%s, versionId=%s, versionName="%s"',
+            modelType, modelId, versionId, versionName);
+
         try {
             this.apiClient = getModelApiClient(modelType);
         } catch (error) {
@@ -1676,13 +1806,15 @@ export class DownloadManager {
         const modelRoot = document.getElementById('modelRoot').value;
         const config = this.apiClient.apiConfig.config;
 
-        let fullPath = modelRoot || translate('modals.download.selectTypeRoot', { type: config.displayName });
+        const subtypeDisplay = this._isDiffusionModel ? 'Diffusion Model' : config.displayName;
+        let fullPath = modelRoot || translate('modals.download.selectTypeRoot', { type: subtypeDisplay });
 
         if (modelRoot) {
             if (this.useDefaultPath) {
-                // Show actual template path
                 try {
-                    const singularType = this.apiClient.modelType.replace(/s$/, '');
+                    const singularType = this._isDiffusionModel
+                        ? 'unet'
+                        : this.apiClient.modelType.replace(/s$/, '');
                     const templates = state.global.settings.download_path_templates;
                     const template = templates[singularType];
                     fullPath += `/${template}`;