fix(autocomplete): never show a stale /activefilters Tip when the toggle is on

The one-time dropdown Tip ('type /activefilters ...') was inserted while the
active-filters search was OFF and then stuck around: _maybeShowFirstRunHint
bailed out while a hint element existed, so toggling the search ON from the
node's filter chip (which can keep the dropdown open in ComfyUI) left the
enable-hint visible even though the feature was now active.

- _maybeShowFirstRunHint now removes any existing hint and recomputes from the
  live setting on every dropdown render, so a Tip is never kept for the
  previous state.
- AutoComplete listens for the lora-manager:setting-toggled window event and
  refreshes an open dropdown's state hints (Tip in plain suggestions, footer
  text in the slash command list) immediately when the toggle changes, instead
  of waiting for the next hide/show cycle.

Regression tests cover: stale Tip removed when toggled ON while the dropdown
stays open (and not resurrected by further typing), and the command-list
footer flipping to ON.

AGENTS.md

@@ -72,6 +72,11 @@ python scripts/sync_translation_keys.py
 
 Locale files are in `locales/` (en, zh-CN, zh-TW, ja, ko, fr, de, es, ru, he).
 
+After adding keys to `en.json` and syncing, **stop**: the `[TODO: Translate]` placeholders in
+the other locales are the expected end state during feature development. Do NOT translate
+proactively — translate only when the feature owner explicitly asks (see
+`docs/i18n-translation-guidelines.md` §7).
+
 **Before translating anything, read `docs/i18n-translation-guidelines.md`** — it defines the
 term conventions (e.g. "Recipe" stays untranslated in French, 配方 in Chinese; model-type and
 brand names are never translated), per-locale preferred renderings, placeholder rules, and

__init__.py

@@ -3,8 +3,6 @@ try:  # pragma: no cover - import fallback for pytest collection
     from .py.nodes.lora_loader import LoraLoaderLM, LoraTextLoaderLM
     from .py.nodes.checkpoint_loader import CheckpointLoaderLM
     from .py.nodes.unet_loader import UNETLoaderLM
-    from .py.nodes.random_checkpoint_loader import RandomCheckpointLoaderLM
-    from .py.nodes.random_unet_loader import RandomUNETLoaderLM
     from .py.nodes.trigger_word_toggle import TriggerWordToggleLM
     from .py.nodes.prompt import PromptLM
     from .py.nodes.text import TextLM
@@ -42,12 +40,6 @@ except (
         "py.nodes.checkpoint_loader"
     ).CheckpointLoaderLM
     UNETLoaderLM = importlib.import_module("py.nodes.unet_loader").UNETLoaderLM
-    RandomCheckpointLoaderLM = importlib.import_module(
-        "py.nodes.random_checkpoint_loader"
-    ).RandomCheckpointLoaderLM
-    RandomUNETLoaderLM = importlib.import_module(
-        "py.nodes.random_unet_loader"
-    ).RandomUNETLoaderLM
     TriggerWordToggleLM = importlib.import_module(
         "py.nodes.trigger_word_toggle"
     ).TriggerWordToggleLM
@@ -87,8 +79,6 @@ NODE_CLASS_MAPPINGS = {
     LoraTextLoaderLM.NAME: LoraTextLoaderLM,
     CheckpointLoaderLM.NAME: CheckpointLoaderLM,
     UNETLoaderLM.NAME: UNETLoaderLM,
-    RandomCheckpointLoaderLM.NAME: RandomCheckpointLoaderLM,
-    RandomUNETLoaderLM.NAME: RandomUNETLoaderLM,
     TriggerWordToggleLM.NAME: TriggerWordToggleLM,
     LoraStackerLM.NAME: LoraStackerLM,
     LoraStackCombinerLM.NAME: LoraStackCombinerLM,

docs/architecture/recipe_routes.md

@@ -54,7 +54,7 @@ The dedicated services encapsulate long-running work so handlers stay thin.
 | Use case | Entry point | Dependencies | Guarantees |
 | --- | --- | --- | --- |
 | `RecipeAnalysisService` | `analyze_uploaded_image`, `analyze_remote_image`, `analyze_local_image`, `analyze_widget_metadata` | `ExifUtils`, `RecipeParserFactory`, downloader factory, optional metadata collector/processor | Normalises missing/invalid payloads into `RecipeValidationError`; generates consistent fingerprint data to keep duplicate detection stable; temporary files are cleaned up after every analysis path. |
-| `RecipePersistenceService` | `save_recipe`, `delete_recipe`, `update_recipe`, `reconnect_lora`, `bulk_delete`, `save_recipe_from_widget` | `ExifUtils`, recipe scanner, card preview sizing constants | Writes images/JSON metadata atomically; updates scanner caches and hash indices before returning; recalculates fingerprints whenever LoRA assignments change. |
+| `RecipePersistenceService` | `save_recipe`, `delete_recipe`, `update_recipe`, `reconnect_lora`, `get_reconnect_suggestions`, `bulk_delete`, `save_recipe_from_widget` | `ExifUtils`, recipe scanner, card preview sizing constants | Writes images/JSON metadata atomically; updates scanner caches and hash indices before returning; recalculates fingerprints whenever LoRA assignments change. |
 | `RecipeSharingService` | `share_recipe`, `prepare_download` | `tempfile`, recipe scanner | Copies originals to TTL-managed temp files; metadata lookups re-use the scanner; expired shares trigger cleanup and `RecipeNotFoundError`. |
 
 ## Maintaining critical invariants

docs/i18n-translation-guidelines.md

@@ -23,7 +23,9 @@ Locales: `en`, `zh-CN`, `zh-TW`, `ja`, `ko`, `fr`, `de`, `es`, `ru`, `he` (RTL).
   same nested key set. `tests/i18n/test_i18n.py` enforces this.
 - When a new UI string is added to `en.json`, run
   `python scripts/sync_translation_keys.py` (adds the missing keys to all locales with
-  placeholder copies), then translate the newly added keys in every locale.
+  `[TODO: Translate]` placeholder copies) — **then stop**. Do NOT translate proactively:
+  placeholders are the expected end state during feature development, and translations are
+  filled in only when the feature owner explicitly asks (workflow details in §7).
 - Never reorder, re-indent, or reformat a locale file "for tidiness". The sync script
   preserves formatting; manual reformatting creates noisy diffs.
 

locales/de.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "{type} werden aktualisiert...",
+            "fullRebuilding": "{type} werden vollständig neu aufgebaut...",
+            "actionRefresh": "Aktualisierung",
+            "actionFullRebuild": "Vollständiger Neuaufbau",
+            "actionRefreshLower": "Aktualisieren",
+            "actionRebuildLower": "Neuaufbau",
+            "stages": {
+                "scan_folders": "Ordner werden gescannt...",
+                "count_models": "{total} Dateien gefunden",
+                "process_models": "Modelle werden verarbeitet",
+                "reconcile_scan": "Änderungen werden geprüft...",
+                "process_new": "Neue Modelle werden verarbeitet",
+                "finalizing": "Abschließen..."
+            },
+            "eta": {
+                "lessThanMinute": "Weniger als eine Minute verbleibend",
+                "minutes": "~{minutes} Min. verbleibend",
+                "hours": "~{hours} Std. {minutes} Min. verbleibend"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "Massenoperationen",
-                "content": "Wechseln Sie in den Massenmodus, indem Sie auf diese Schaltfläche klicken oder <span class=\"onboarding-shortcut\">B</span> drücken. Wählen Sie mehrere Modelle aus und führen Sie Stapeloperationen durch. Mit <span class=\"onboarding-shortcut\">Strg+A</span> können Sie alle sichtbaren Modelle auswählen."
+                "content": "Wechseln Sie in den Massenmodus, indem Sie auf diese Schaltfläche klicken oder <span class=\"onboarding-shortcut\">B</span> drücken, um mehrere Modelle auszuwählen und Stapeloperationen durchzuführen.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> wählt alle sichtbaren Modelle aus, <span class=\"onboarding-shortcut\">Shift+Click</span> wählt einen Bereich aus.<br>• <span class=\"onboarding-shortcut\">Esc</span> oder ein Klick auf einen leeren Bereich verlässt den Massenmodus."
             },
             "searchOptions": {
                 "title": "Suchoptionen",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "Kontextmenü",
-                "content": "<strong>Rechtsklick</strong> auf eine Modellkarte öffnet ein Kontextmenü mit weiteren Aktionen."
+                "content": "<strong>Rechtsklick</strong> auf eine beliebige Modellkarte öffnet ein Kontextmenü mit Kartenaktionen wie Verschieben, Löschen oder Bearbeiten von Metadaten."
+            },
+            "marqueeSelect": {
+                "title": "Durch Ziehen auswählen",
+                "content": "Halten Sie die <strong>linke Maustaste</strong> auf einem leeren Bereich des Rasters gedrückt und ziehen Sie, um einen Auswahlrahmen aufzuziehen, der mehrere Karten gleichzeitig auswählt."
+            },
+            "dragToSidebar": {
+                "title": "Organisieren durch Ziehen",
+                "content": "Ziehen Sie eine Modellkarte auf einen Ordner in der Seitenleiste, um die Datei dorthin zu verschieben. Dies funktioniert auch mit mehreren ausgewählten Karten im Massenmodus."
+            },
+            "contextMenus": {
+                "title": "Weitere Kontextmenüs",
+                "content": "<strong>Rechtsklick auf eine ausgewählte Karte</strong> im Massenmodus öffnet die Massenaktionen. <strong>Rechtsklick auf einen leeren Bereich</strong> der Seite öffnet globale Aktionen wie das Prüfen auf Updates und das Verwalten ausgeschlossener Modelle."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "Vorheriges Rezept (←)",
             "nextWithShortcut": "Nächstes Rezept (→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "Dateispeicherort öffnen",
+                "copyId": "Rezept-ID kopieren"
+            },
+            "openFileLocation": {
+                "success": "Dateispeicherort erfolgreich geöffnet",
+                "failed": "Fehler beim Öffnen des Dateispeicherorts",
+                "copied": "Pfad in die Zwischenablage kopiert: {{path}}",
+                "clipboardFallback": "Pfad: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "Workflow an ComfyUI senden",
             "sent": "Workflow an ComfyUI gesendet",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "Dieses Modell ist nicht in Ihrer Bibliothek",
             "deletedTooltip": "Dieses LoRA wurde an der Quelle gelöscht und kann nicht mehr heruntergeladen werden",
             "hashInvalidTooltip": "Dieser LoRA-Hash kann auf CivitAI nicht aufgelöst werden - das Modell wurde möglicherweise aktualisiert",
+            "noLorasAssociated": "Keine LoRAs mit diesem Rezept verknüpft",
+            "noLorasWhyToggle": "Warum keine LoRAs?",
+            "noLorasImportMethod": "Importmethode",
+            "noLorasInferredNote": "Mögliche Ursache (abgeleitet) — dieses Rezept wurde importiert, bevor Importdiagnosen aufgezeichnet wurden.",
+            "noLorasChannels": {
+                "batch_import_url": "Massenimport (Bild-URL)",
+                "batch_import_local": "Massenimport (lokale Datei)",
+                "url": "Bild-URL-Import",
+                "local": "Import lokaler Datei",
+                "upload": "Bild-Upload",
+                "widget": "Aus Workflow gespeichert",
+                "reimport_url": "Neuimport (Bild-URL)",
+                "reimport_local": "Neuimport (lokale Datei)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "Die Generierungsmetadaten sind vollständig und verweisen auf keine LoRAs.",
+                "api_meta_no_lora_resources": "Die Quell-API hat für dieses Bild keine LoRA-Ressourcendaten zurückgegeben. Auf der CivitAI-Seite angezeigte LoRAs stammen möglicherweise aus internen Daten, die die öffentliche API nicht bereitstellt.",
+                "api_meta_missing": "Die Quell-API hat für dieses Bild keine Generierungsmetadaten zurückgegeben.",
+                "no_embedded_metadata": "Das Bild enthält keine eingebetteten Generierungsmetadaten, sodass LoRA-Informationen nicht wiederhergestellt werden konnten.",
+                "workflow_metadata_limited": "Die eingebetteten Metadaten des Bildes sind ein ComfyUI-Workflow; das Extrahieren von LoRA-Informationen aus Workflows ist eingeschränkt.",
+                "video_no_metadata": "Videodateien enthalten keine eingebetteten Generierungsmetadaten.",
+                "metadata_unsupported": "Das Bild enthält Metadaten in einem Format, das nicht analysiert werden konnte.",
+                "unknown": "Die Ursache konnte aus den gespeicherten Rezeptdaten nicht ermittelt werden."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "API-Metadatenfelder",
+                "modelVersionIds": "Gemeldete Modellversions-IDs",
+                "embeddedMetadata": "Eingebettete Metadaten",
+                "present": "gefunden",
+                "absent": "keine"
+            },
             "download": "Herunterladen",
             "downloadLoraTooltip": "Dieses LoRA herunterladen",
             "preparingDownload": "Download wird vorbereitet...",
             "reconnect": "Neu verknüpfen",
             "reconnectTooltip": "Mit einem lokalen LoRA neu verknüpfen",
+            "reconnectInstructions": "Geben Sie die LoRA-Syntax oder den Namen zum Neuverknüpfen ein:",
+            "reconnectExample": "Beispiel: <lora:name:1> oder nur der Name",
+            "reconnectPlaceholder": "LoRA-Namen oder -Syntax eingeben",
+            "reconnectSuggestionsLoading": "Lokale Bibliothek wird durchsucht...",
+            "reconnectSuggestionsEmpty": "Keine passenden LoRAs in Ihrer lokalen Bibliothek",
+            "reconnectMatchSameHash": "Gleicher Hash",
+            "reconnectMatchSameVersion": "Gleiche Modellversion",
+            "reconnectMatchSimilarFilename": "Ähnlicher Dateiname",
+            "reconnectMatchSimilarName": "Ähnlicher Name",
+            "undoReconnect": "Rückgängig",
+            "undoReconnectTooltip": "Stellt die Verknüpfung wieder her, die dieser Eintrag vor dem Neuverknüpfen hatte",
+            "undoReconnectTooltipNamed": "Stellt {name} wieder her (die Verknüpfung vor dem Neuverknüpfen)",
             "viewOnCivitai": "Auf CivitAI anzeigen",
             "openLoraDetails": "{name} in der LoRA-Bibliothek anzeigen",
-            "openCheckpointDetails": "{name} in der Modellbibliothek anzeigen"
+            "openCheckpointDetails": "{name} in der Modellbibliothek anzeigen",
+            "checkpointDeletedTooltip": "Dieser Checkpoint wurde aus der Quelle gelöscht und kann nicht mehr heruntergeladen werden - verknüpfen Sie ihn mit einem lokalen Modell neu",
+            "checkpointHashInvalidTooltip": "Dieser Checkpoint-Hash kann auf CivitAI nicht aufgelöst werden - das Modell wurde möglicherweise aktualisiert",
+            "reconnectCheckpoint": "Neu verknüpfen",
+            "reconnectCheckpointTooltip": "Mit einem lokalen Checkpoint neu verknüpfen",
+            "checkpointReconnectInstructions": "Geben Sie den Namen des Checkpoints zum Neuverknüpfen ein:",
+            "checkpointReconnectPlaceholder": "Name des Checkpoints eingeben",
+            "checkpointReconnectSuggestionsEmpty": "Keine passenden Checkpoints in Ihrer lokalen Bibliothek"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Wert",
                 "add": "Hinzufügen",
-                "invalidRange": "Ungültiges Bereichsformat. Verwenden Sie x.x-y.y"
+                "invalidRange": "Ungültiges Bereichsformat. Verwenden Sie x.x-y.y",
+                "invalidValue": "Bitte geben Sie eine gültige Zahl ein",
+                "saveFailed": "Fehler beim Speichern des voreingestellten Parameters",
+                "added": "Voreingestellter Parameter hinzugefügt",
+                "updated": "Voreingestellter Parameter aktualisiert"
             },
             "triggerWords": {
                 "label": "Trigger Words",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "Tippen zum Hinzufügen oder klicken Sie auf Vorschläge unten",
                 "editWord": "Trigger Word bearbeiten",
                 "editPlaceholder": "Trigger Word bearbeiten",
-                "copyWord": "Trigger Word kopieren",
+                "copyOrEditWord": "Klicken zum Kopieren, Doppelklick zum Bearbeiten",
                 "deleteWord": "Trigger Word löschen",
                 "suggestions": {
                     "noSuggestions": "Keine Vorschläge verfügbar",
@@ -1599,8 +1701,8 @@
                 "showCount": "Beispiele anzeigen ({count})",
                 "hideExamples": "Beispiele ausblenden",
                 "addExamples": "Beispiele hinzufügen",
-                "previousExample": "Vorheriges Beispiel",
+                "previousExample": "Vorheriges Beispiel ([)",
-                "nextExample": "Nächstes Beispiel",
+                "nextExample": "Nächstes Beispiel (])",
                 "noExamples": "Keine Beispielbilder verfügbar",
                 "addMoreExamples": "Weitere Beispiele hinzufügen",
                 "dragDrop": "Bilder oder Videos hierher ziehen & ablegen",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "Erste Schritte",
             "updateVlogs": "Update-Vlogs",
-            "documentation": "Dokumentation"
+            "documentation": "Dokumentation",
+            "shortcuts": "Tastenkürzel"
         },
         "gettingStarted": {
-            "title": "Erste Schritte mit LoRA Manager"
+            "title": "Erste Schritte mit LoRA Manager",
+            "replayTutorial": "Tutorial erneut abspielen"
+        },
+        "shortcuts": {
+            "title": "Tastatur- & Mauskürzel",
+            "groups": {
+                "general": "Allgemein",
+                "actions": "Aktionen",
+                "selection": "Auswahl & Massenmodus",
+                "navigation": "Navigation",
+                "modelModal": "Modell- / Rezept-Dialog",
+                "mediaViewer": "Medienanzeige / Beispielgalerie"
+            },
+            "keys": {
+                "click": "Klick",
+                "drag": "Ziehen",
+                "rightClick": "Rechtsklick",
+                "letter": "Buchstabe",
+                "swipe": "Wischen"
+            },
+            "entries": {
+                "focusSearch": "Suche fokussieren",
+                "closeModal": "Dialog / Panel schließen",
+                "openShortcuts": "Dieses Tastenkürzel-Panel öffnen",
+                "refresh": "Modellliste aktualisieren",
+                "fetchMetadata": "Metadaten von CivitAI abrufen (nur Modellseiten)",
+                "downloadModel": "Ein Modell herunterladen (nur Modellseiten)",
+                "toggleBulkMode": "Massenmodus umschalten",
+                "selectAll": "Alle sichtbaren Modelle auswählen",
+                "rangeSelect": "Bereich auswählen",
+                "marqueeSelect": "Karten mit Auswahlrahmen auswählen (auf leerem Rasterbereich)",
+                "exitBulkMode": "Massenmodus verlassen",
+                "bulkActions": "Auf ausgewählter Karte: Menü für Massenaktionen",
+                "globalActions": "Auf leerem Seitenbereich: Menü für globale Aktionen (Updates prüfen, ausgeschlossene Modelle verwalten)",
+                "scrollPages": "Seiten scrollen",
+                "jumpAlphabet": "Zur Alphabetleiste springen",
+                "prevNext": "Vorheriges / nächstes Modell",
+                "deleteEntry": "Löschen",
+                "cycleMedia": "Medien durchblättern ([ / ] in der Beispielgalerie)",
+                "swipeTouch": "Medien auf Touch-Geräten durchblättern",
+                "closeViewer": "Medienanzeige schließen"
+            }
         },
         "updateVlogs": {
             "title": "Neueste Updates",
@@ -1884,7 +2028,8 @@
             "settings": "Einstellungen & Konfiguration",
             "extensions": "Erweiterungen",
             "newBadge": "NEU"
-        }
+        },
+        "newContentBadge": "NEU"
     },
     "update": {
         "title": "Nach Updates suchen",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "Fehler beim Vorbereiten der LoRAs für den Download",
             "enterLoraName": "Bitte geben Sie einen LoRA-Namen oder Syntax ein",
             "reconnectedSuccessfully": "LoRA erfolgreich neu verbunden",
+            "reconnectBaseModelMismatch": "Neuverbindung erfolgreich, aber die Basismodelle unterscheiden sich (Rezept: {recipe}, LoRA: {lora}) — sie sind architekturkompatibel",
             "reconnectFailed": "Fehler beim Neuverbinden des LoRA: {message}",
+            "loraRestored": "LoRA auf die vorherige Verknüpfung zurückgesetzt",
+            "loraRestoreFailed": "Fehler beim Wiederherstellen des LoRA: {message}",
             "noPromptToSend": "Kein zu sendender Prompt",
             "cannotSend": "Kann Rezept nicht senden: Fehlende Rezept-ID",
             "sendFailed": "Fehler beim Senden des Rezepts an Workflow",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Checkpoint-Pfad nicht verfügbar",
             "missingCheckpointInfo": "Checkpoint-Informationen fehlen",
             "downloadCheckpointFailed": "Checkpoint-Download fehlgeschlagen: {message}",
+            "enterCheckpointName": "Bitte geben Sie einen Checkpoint-Namen ein",
+            "checkpointReconnectedSuccessfully": "Checkpoint erfolgreich neu verbunden",
+            "reconnectCheckpointBaseModelMismatch": "Neuverbindung erfolgreich, aber die Basismodelle unterscheiden sich (Rezept: {recipe}, Checkpoint: {checkpoint}) — sie sind architekturkompatibel",
+            "checkpointReconnectFailed": "Fehler beim Neuverbinden des Checkpoints: {message}",
+            "checkpointRestored": "Checkpoint auf die vorherige Verknüpfung zurückgesetzt",
+            "checkpointRestoreFailed": "Fehler beim Wiederherstellen des Checkpoints: {message}",
+            "checkpointDownloadUnavailable": "Dieser Checkpoint kann ohne CivitAI-Kennungen nicht heruntergeladen werden - versuchen Sie, ihn mit einem lokalen Checkpoint neu zu verknüpfen",
             "missingLoraDownloadInfo": "Download-Informationen für dieses LoRA fehlen",
             "hashNotFoundOnCivitai": "Dieser LoRA-Hash kann auf CivitAI nicht aufgelöst werden - das Modell wurde möglicherweise aktualisiert oder der Hash ist ungültig",
             "downloadLoraFailed": "LoRA-Download fehlgeschlagen: {message}",

locales/en.json

@@ -50,6 +50,27 @@
       "mb": "MB",
       "gb": "GB",
       "tb": "TB"
+    },
+    "scanProgress": {
+      "refreshing": "Refreshing {type}s...",
+      "fullRebuilding": "Full rebuild {type}s...",
+      "actionRefresh": "Refresh",
+      "actionFullRebuild": "Full rebuild",
+      "actionRefreshLower": "refresh",
+      "actionRebuildLower": "rebuild",
+      "stages": {
+        "scan_folders": "Scanning folders...",
+        "count_models": "Found {total} files",
+        "process_models": "Processing models",
+        "reconcile_scan": "Checking for changes...",
+        "process_new": "Processing new models",
+        "finalizing": "Finalizing..."
+      },
+      "eta": {
+        "lessThanMinute": "Less than a minute remaining",
+        "minutes": "~{minutes} min remaining",
+        "hours": "~{hours} hr {minutes} min remaining"
+      }
     }
   },
   "onboarding": {
@@ -75,7 +96,7 @@
       },
       "bulk": {
         "title": "Bulk Operations",
-        "content": "Enter bulk mode by clicking this button or pressing <span class=\"onboarding-shortcut\">B</span>. Select multiple models and perform batch operations. Use <span class=\"onboarding-shortcut\">Ctrl+A</span> to select all visible models."
+        "content": "Enter bulk mode by clicking this button or pressing <span class=\"onboarding-shortcut\">B</span> to select multiple models and perform batch operations.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> select all visible models, <span class=\"onboarding-shortcut\">Shift+Click</span> select a range.<br>• <span class=\"onboarding-shortcut\">Esc</span> or clicking an empty area exits bulk mode."
       },
       "searchOptions": {
         "title": "Search Options",
@@ -95,7 +116,19 @@
       },
       "contextMenu": {
         "title": "Context Menu",
-        "content": "<strong>Right-click</strong> any model card for a context menu with additional actions."
+        "content": "<strong>Right-click</strong> any model card for a context menu with card actions like moving, deleting, or editing metadata."
+      },
+      "marqueeSelect": {
+        "title": "Drag to Select",
+        "content": "Hold the <strong>left mouse button</strong> on an empty area of the grid and drag to draw a marquee that selects multiple cards at once."
+      },
+      "dragToSidebar": {
+        "title": "Organize by Dragging",
+        "content": "Drag a model card onto a folder in the sidebar to move the file there. This also works with multiple selected cards in bulk mode."
+      },
+      "contextMenus": {
+        "title": "More Context Menus",
+        "content": "In bulk mode, <strong>right-click a selected card</strong> for bulk actions. <strong>Right-click an empty area</strong> of the page for global actions like update checks and managing excluded models."
       }
     }
   },
@@ -868,6 +901,21 @@
       "previousWithShortcut": "Previous recipe (←)",
       "nextWithShortcut": "Next recipe (→)"
     },
+    "modal": {
+      "metadata": {
+        "id": "ID"
+      },
+      "actions": {
+        "openFileLocation": "Open File Location",
+        "copyId": "Copy recipe ID"
+      },
+      "openFileLocation": {
+        "success": "File location opened successfully",
+        "failed": "Failed to open file location",
+        "copied": "Path copied to clipboard: {{path}}",
+        "clipboardFallback": "Path: {{path}}"
+      }
+    },
     "workflow": {
       "sendWorkflow": "Send Workflow to ComfyUI",
       "sent": "Workflow sent to ComfyUI",
@@ -898,14 +946,64 @@
       "notInLibraryTooltip": "This model is not in your library",
       "deletedTooltip": "This LoRA was deleted from the source and is no longer available for download",
       "hashInvalidTooltip": "This LoRA hash cannot be resolved on CivitAI - the model may have been updated",
+      "noLorasAssociated": "No LoRAs associated with this recipe",
+      "noLorasWhyToggle": "Why no LoRAs?",
+      "noLorasImportMethod": "Import method",
+      "noLorasInferredNote": "Possible reason (inferred) — this recipe was imported before import diagnostics were recorded.",
+      "noLorasChannels": {
+        "batch_import_url": "Batch import (image URL)",
+        "batch_import_local": "Batch import (local file)",
+        "url": "Image URL import",
+        "local": "Local file import",
+        "upload": "Image upload",
+        "widget": "Saved from workflow",
+        "reimport_url": "Re-import (image URL)",
+        "reimport_local": "Re-import (local file)"
+      },
+      "noLorasReasons": {
+        "no_loras_used": "The generation metadata is complete and does not reference any LoRAs.",
+        "api_meta_no_lora_resources": "The source API returned no LoRA resource data for this image. LoRAs shown on the CivitAI page may come from internal data that the public API does not expose.",
+        "api_meta_missing": "The source API returned no generation metadata for this image.",
+        "no_embedded_metadata": "The image has no embedded generation metadata, so LoRA information could not be recovered.",
+        "workflow_metadata_limited": "The image's embedded metadata is a ComfyUI workflow; extracting LoRA information from workflows is limited.",
+        "video_no_metadata": "Video files do not carry embedded generation metadata.",
+        "metadata_unsupported": "The image contains metadata in a format that could not be parsed.",
+        "unknown": "The reason could not be determined from the stored recipe data."
+      },
+      "noLorasDetails": {
+        "apiMetaFields": "API metadata fields",
+        "modelVersionIds": "Model version IDs reported",
+        "embeddedMetadata": "Embedded metadata",
+        "present": "found",
+        "absent": "none"
+      },
       "download": "Download",
       "downloadLoraTooltip": "Download this LoRA",
       "preparingDownload": "Preparing download...",
       "reconnect": "Reconnect",
       "reconnectTooltip": "Reconnect with a local LoRA",
+      "reconnectInstructions": "Enter LoRA syntax or name to reconnect:",
+      "reconnectExample": "Example: <lora:name:1> or just the name",
+      "reconnectPlaceholder": "Enter LoRA name or syntax",
+      "reconnectSuggestionsLoading": "Searching local library...",
+      "reconnectSuggestionsEmpty": "No matching LoRAs in your local library",
+      "reconnectMatchSameHash": "Same hash",
+      "reconnectMatchSameVersion": "Same model version",
+      "reconnectMatchSimilarFilename": "Similar filename",
+      "reconnectMatchSimilarName": "Similar name",
+      "undoReconnect": "Undo",
+      "undoReconnectTooltip": "Restore the association this entry had before reconnecting",
+      "undoReconnectTooltipNamed": "Restore to {name} (the association before reconnecting)",
       "viewOnCivitai": "View on CivitAI",
       "openLoraDetails": "View {name} in the LoRA library",
-      "openCheckpointDetails": "View {name} in the model library"
+      "openCheckpointDetails": "View {name} in the model library",
+      "checkpointDeletedTooltip": "This checkpoint was deleted from the source and can no longer be downloaded - reconnect it with a local model",
+      "checkpointHashInvalidTooltip": "This checkpoint hash cannot be resolved on CivitAI - the model may have been updated",
+      "reconnectCheckpoint": "Reconnect",
+      "reconnectCheckpointTooltip": "Reconnect with a local checkpoint",
+      "checkpointReconnectInstructions": "Enter checkpoint name to reconnect:",
+      "checkpointReconnectPlaceholder": "Enter checkpoint name",
+      "checkpointReconnectSuggestionsEmpty": "No matching checkpoints in your local library"
     },
     "controls": {
       "import": {
@@ -1528,7 +1626,11 @@
         "clipSkip": "Clip Skip",
         "valuePlaceholder": "Value",
         "add": "Add",
-        "invalidRange": "Invalid range format. Use x.x-y.y"
+        "invalidRange": "Invalid range format. Use x.x-y.y",
+        "invalidValue": "Please enter a valid number",
+        "saveFailed": "Failed to save preset parameter",
+        "added": "Preset parameter added",
+        "updated": "Preset parameter updated"
       },
       "triggerWords": {
         "label": "Trigger Words",
@@ -1539,7 +1641,7 @@
         "addPlaceholder": "Type to add or click suggestions below",
         "editWord": "Edit trigger word",
         "editPlaceholder": "Edit trigger word",
-        "copyWord": "Copy trigger word",
+        "copyOrEditWord": "Click to copy, double-click to edit",
         "deleteWord": "Delete trigger word",
         "suggestions": {
           "noSuggestions": "No suggestions available",
@@ -1599,8 +1701,8 @@
         "showCount": "Show examples ({count})",
         "hideExamples": "Hide examples",
         "addExamples": "Add examples",
-        "previousExample": "Previous example",
+        "previousExample": "Previous example ([)",
-        "nextExample": "Next example",
+        "nextExample": "Next example (])",
         "noExamples": "No example images available",
         "addMoreExamples": "Add more examples",
         "dragDrop": "Drag & drop images or videos here",
@@ -1864,10 +1966,52 @@
     "tabs": {
       "gettingStarted": "Getting Started",
       "updateVlogs": "Update Vlogs",
-      "documentation": "Documentation"
+      "documentation": "Documentation",
+      "shortcuts": "Shortcuts"
     },
     "gettingStarted": {
-      "title": "Getting Started with LoRA Manager"
+      "title": "Getting Started with LoRA Manager",
+      "replayTutorial": "Replay Tutorial"
+    },
+    "shortcuts": {
+      "title": "Keyboard & Mouse Shortcuts",
+      "groups": {
+        "general": "General",
+        "actions": "Actions",
+        "selection": "Selection & Bulk Mode",
+        "navigation": "Navigation",
+        "modelModal": "Model / Recipe Modal",
+        "mediaViewer": "Media Viewer / Showcase"
+      },
+      "keys": {
+        "click": "Click",
+        "drag": "Drag",
+        "rightClick": "Right-click",
+        "letter": "Letter",
+        "swipe": "Swipe"
+      },
+      "entries": {
+        "focusSearch": "Focus search",
+        "closeModal": "Close modal / panel",
+        "openShortcuts": "Open this shortcuts panel",
+        "refresh": "Refresh model list",
+        "fetchMetadata": "Fetch metadata from CivitAI (model pages only)",
+        "downloadModel": "Download a model (model pages only)",
+        "toggleBulkMode": "Toggle bulk mode",
+        "selectAll": "Select all visible models",
+        "rangeSelect": "Range select",
+        "marqueeSelect": "Marquee-select cards (on empty grid area)",
+        "exitBulkMode": "Exit bulk mode",
+        "bulkActions": "On selected card: bulk actions menu",
+        "globalActions": "On empty page area: global actions menu (update check, manage excluded models)",
+        "scrollPages": "Scroll pages",
+        "jumpAlphabet": "Jump alphabet bar",
+        "prevNext": "Previous / next model",
+        "deleteEntry": "Delete",
+        "cycleMedia": "Cycle media ([ / ] in showcase gallery)",
+        "swipeTouch": "Cycle media on touch devices",
+        "closeViewer": "Close viewer"
+      }
     },
     "updateVlogs": {
       "title": "Latest Updates",
@@ -1884,7 +2028,8 @@
       "settings": "Settings & Configuration",
       "extensions": "Extensions",
       "newBadge": "NEW"
-    }
+    },
+    "newContentBadge": "New"
   },
   "update": {
     "title": "Check for Updates",
@@ -2043,7 +2188,10 @@
       "preparingForDownloadFailed": "Error preparing LoRAs for download",
       "enterLoraName": "Please enter a LoRA name or syntax",
       "reconnectedSuccessfully": "LoRA reconnected successfully",
+      "reconnectBaseModelMismatch": "Reconnected, but base models differ (recipe: {recipe}, LoRA: {lora}) — they are architecture-compatible",
       "reconnectFailed": "Error reconnecting LoRA: {message}",
+      "loraRestored": "LoRA restored to its previous association",
+      "loraRestoreFailed": "Error restoring LoRA: {message}",
       "noPromptToSend": "No prompt to send",
       "cannotSend": "Cannot send recipe: Missing recipe ID",
       "sendFailed": "Failed to send recipe to workflow",
@@ -2051,6 +2199,13 @@
       "missingCheckpointPath": "Checkpoint path not available",
       "missingCheckpointInfo": "Missing checkpoint information",
       "downloadCheckpointFailed": "Failed to download checkpoint: {message}",
+      "enterCheckpointName": "Please enter a checkpoint name",
+      "checkpointReconnectedSuccessfully": "Checkpoint reconnected successfully",
+      "reconnectCheckpointBaseModelMismatch": "Reconnected, but base models differ (recipe: {recipe}, checkpoint: {checkpoint}) — they are architecture-compatible",
+      "checkpointReconnectFailed": "Error reconnecting checkpoint: {message}",
+      "checkpointRestored": "Checkpoint restored to its previous association",
+      "checkpointRestoreFailed": "Error restoring checkpoint: {message}",
+      "checkpointDownloadUnavailable": "This checkpoint cannot be downloaded without CivitAI identifiers - try reconnecting it with a local checkpoint",
       "missingLoraDownloadInfo": "Missing download information for this LoRA",
       "hashNotFoundOnCivitai": "This LoRA hash cannot be resolved on CivitAI - the model may have been updated or the hash is invalid",
       "downloadLoraFailed": "Failed to download LoRA: {message}",

locales/es.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "Actualizando {type}...",
+            "fullRebuilding": "Reconstrucción completa de {type}...",
+            "actionRefresh": "Actualización",
+            "actionFullRebuild": "Reconstrucción completa",
+            "actionRefreshLower": "actualizar",
+            "actionRebuildLower": "reconstruir",
+            "stages": {
+                "scan_folders": "Escaneando carpetas...",
+                "count_models": "Se encontraron {total} archivos",
+                "process_models": "Procesando modelos",
+                "reconcile_scan": "Comprobando cambios...",
+                "process_new": "Procesando modelos nuevos",
+                "finalizing": "Finalizando..."
+            },
+            "eta": {
+                "lessThanMinute": "Queda menos de un minuto",
+                "minutes": "Quedan ~{minutes} min",
+                "hours": "Quedan ~{hours} h {minutes} min"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "Operaciones por lotes",
-                "content": "Entra en el modo por lotes haciendo clic en este botón o presionando <span class=\"onboarding-shortcut\">B</span>. Selecciona varios modelos y realiza operaciones por lotes. Usa <span class=\"onboarding-shortcut\">Ctrl+A</span> para seleccionar todos los modelos visibles."
+                "content": "Entra en el modo por lotes haciendo clic en este botón o presionando <span class=\"onboarding-shortcut\">B</span> para seleccionar varios modelos y realizar operaciones por lotes.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> selecciona todos los modelos visibles, <span class=\"onboarding-shortcut\">Shift+Click</span> selecciona un rango.<br>• <span class=\"onboarding-shortcut\">Esc</span> o hacer clic en un área vacía sale del modo por lotes."
             },
             "searchOptions": {
                 "title": "Opciones de búsqueda",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "Menú contextual",
-                "content": "<strong>Clic derecho</strong> en cualquier tarjeta de modelo para ver un menú contextual con acciones adicionales."
+                "content": "<strong>Clic derecho</strong> en cualquier tarjeta de modelo para ver un menú contextual con acciones de la tarjeta como mover, eliminar o editar metadatos."
+            },
+            "marqueeSelect": {
+                "title": "Arrastrar para seleccionar",
+                "content": "Mantén pulsado el <strong>botón izquierdo del ratón</strong> en un área vacía de la cuadrícula y arrastra para dibujar un rectángulo de selección que selecciona varias tarjetas a la vez."
+            },
+            "dragToSidebar": {
+                "title": "Organizar arrastrando",
+                "content": "Arrastra una tarjeta de modelo hasta una carpeta de la barra lateral para mover el archivo allí. Esto también funciona con varias tarjetas seleccionadas en el modo por lotes."
+            },
+            "contextMenus": {
+                "title": "Más menús contextuales",
+                "content": "En el modo por lotes, <strong>haz clic derecho en una tarjeta seleccionada</strong> para ver las acciones por lotes. <strong>Haz clic derecho en un área vacía</strong> de la página para ver acciones globales como comprobar actualizaciones y gestionar modelos excluidos."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "Receta anterior (←)",
             "nextWithShortcut": "Siguiente receta (→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "Abrir ubicación del archivo",
+                "copyId": "Copiar ID de la receta"
+            },
+            "openFileLocation": {
+                "success": "Ubicación del archivo abierta exitosamente",
+                "failed": "Error al abrir la ubicación del archivo",
+                "copied": "Ruta copiada al portapapeles: {{path}}",
+                "clipboardFallback": "Ruta: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "Enviar workflow a ComfyUI",
             "sent": "Workflow enviado a ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "Este modelo no está en tu biblioteca",
             "deletedTooltip": "Este LoRA fue eliminado de la fuente y ya no se puede descargar",
             "hashInvalidTooltip": "Este hash de LoRA no se puede resolver en CivitAI - el modelo puede haber sido actualizado",
+            "noLorasAssociated": "No hay LoRAs asociados con esta receta",
+            "noLorasWhyToggle": "¿Por qué no hay LoRAs?",
+            "noLorasImportMethod": "Método de importación",
+            "noLorasInferredNote": "Posible motivo (inferido): esta receta se importó antes de que se registraran los diagnósticos de importación.",
+            "noLorasChannels": {
+                "batch_import_url": "Importación por lotes (URL de imagen)",
+                "batch_import_local": "Importación por lotes (archivo local)",
+                "url": "Importación desde URL de imagen",
+                "local": "Importación de archivo local",
+                "upload": "Carga de imagen",
+                "widget": "Guardada desde el workflow",
+                "reimport_url": "Reimportación (URL de imagen)",
+                "reimport_local": "Reimportación (archivo local)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "Los metadatos de generación están completos y no hacen referencia a ningún LoRA.",
+                "api_meta_no_lora_resources": "La API de origen no devolvió datos de recursos LoRA para esta imagen. Los LoRAs que se muestran en la página de CivitAI pueden proceder de datos internos que la API pública no expone.",
+                "api_meta_missing": "La API de origen no devolvió metadatos de generación para esta imagen.",
+                "no_embedded_metadata": "La imagen no tiene metadatos de generación incrustados, por lo que no se pudo recuperar la información de LoRAs.",
+                "workflow_metadata_limited": "Los metadatos incrustados en la imagen son un workflow de ComfyUI; la extracción de información de LoRAs a partir de workflows es limitada.",
+                "video_no_metadata": "Los archivos de vídeo no contienen metadatos de generación incrustados.",
+                "metadata_unsupported": "La imagen contiene metadatos en un formato que no se pudo analizar.",
+                "unknown": "No se pudo determinar el motivo a partir de los datos de la receta almacenados."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "Campos de metadatos de la API",
+                "modelVersionIds": "IDs de versión de modelo informados",
+                "embeddedMetadata": "Metadatos incrustados",
+                "present": "encontrados",
+                "absent": "ninguno"
+            },
             "download": "Descargar",
             "downloadLoraTooltip": "Descargar este LoRA",
             "preparingDownload": "Preparando descarga...",
             "reconnect": "Reconectar",
             "reconnectTooltip": "Reconectar con un LoRA local",
+            "reconnectInstructions": "Introduce la sintaxis o el nombre del LoRA para reconectar:",
+            "reconnectExample": "Ejemplo: <lora:name:1> o solo el nombre",
+            "reconnectPlaceholder": "Introduce el nombre o la sintaxis del LoRA",
+            "reconnectSuggestionsLoading": "Buscando en la biblioteca local...",
+            "reconnectSuggestionsEmpty": "No hay LoRAs coincidentes en tu biblioteca local",
+            "reconnectMatchSameHash": "Mismo hash",
+            "reconnectMatchSameVersion": "Misma versión del modelo",
+            "reconnectMatchSimilarFilename": "Nombre de archivo similar",
+            "reconnectMatchSimilarName": "Nombre similar",
+            "undoReconnect": "Deshacer",
+            "undoReconnectTooltip": "Restaura la asociación que esta entrada tenía antes de reconectar",
+            "undoReconnectTooltipNamed": "Restaurar a {name} (la asociación antes de reconectar)",
             "viewOnCivitai": "Ver en CivitAI",
             "openLoraDetails": "Ver {name} en la biblioteca de LoRAs",
-            "openCheckpointDetails": "Ver {name} en la biblioteca de modelos"
+            "openCheckpointDetails": "Ver {name} en la biblioteca de modelos",
+            "checkpointDeletedTooltip": "Este checkpoint fue eliminado de la fuente y ya no se puede descargar - reconéctalo con un modelo local",
+            "checkpointHashInvalidTooltip": "El hash de este checkpoint no se puede resolver en CivitAI - el modelo puede haber sido actualizado",
+            "reconnectCheckpoint": "Reconectar",
+            "reconnectCheckpointTooltip": "Reconectar con un checkpoint local",
+            "checkpointReconnectInstructions": "Introduce el nombre del checkpoint para reconectar:",
+            "checkpointReconnectPlaceholder": "Introduce el nombre del checkpoint",
+            "checkpointReconnectSuggestionsEmpty": "No hay checkpoints coincidentes en tu biblioteca local"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Valor",
                 "add": "Añadir",
-                "invalidRange": "Formato de rango inválido. Use x.x-y.y"
+                "invalidRange": "Formato de rango inválido. Use x.x-y.y",
+                "invalidValue": "Introduce un número válido",
+                "saveFailed": "Error al guardar el parámetro preajustado",
+                "added": "Parámetro preajustado añadido",
+                "updated": "Parámetro preajustado actualizado"
             },
             "triggerWords": {
                 "label": "Palabras clave",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "Escribe para añadir o haz clic en sugerencias de abajo",
                 "editWord": "Editar palabra de activación",
                 "editPlaceholder": "Editar palabra de activación",
-                "copyWord": "Copiar palabra de activación",
+                "copyOrEditWord": "Haz clic para copiar, doble clic para editar",
                 "deleteWord": "Eliminar palabra de activación",
                 "suggestions": {
                     "noSuggestions": "No hay sugerencias disponibles",
@@ -1599,8 +1701,8 @@
                 "showCount": "Mostrar ejemplos ({count})",
                 "hideExamples": "Ocultar ejemplos",
                 "addExamples": "Añadir ejemplos",
-                "previousExample": "Ejemplo anterior",
+                "previousExample": "Ejemplo anterior ([)",
-                "nextExample": "Ejemplo siguiente",
+                "nextExample": "Ejemplo siguiente (])",
                 "noExamples": "No hay imágenes de ejemplo disponibles",
                 "addMoreExamples": "Añadir más ejemplos",
                 "dragDrop": "Arrastra y suelta imágenes o videos aquí",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "Comenzando",
             "updateVlogs": "Vlogs de actualización",
-            "documentation": "Documentación"
+            "documentation": "Documentación",
+            "shortcuts": "Atajos"
         },
         "gettingStarted": {
-            "title": "Comenzando con el gestor de LoRA"
+            "title": "Comenzando con el gestor de LoRA",
+            "replayTutorial": "Repetir tutorial"
+        },
+        "shortcuts": {
+            "title": "Atajos de teclado y ratón",
+            "groups": {
+                "general": "General",
+                "actions": "Acciones",
+                "selection": "Selección y modo por lotes",
+                "navigation": "Navegación",
+                "modelModal": "Modal de modelo / receta",
+                "mediaViewer": "Visor de medios / Ejemplos"
+            },
+            "keys": {
+                "click": "Clic",
+                "drag": "Arrastrar",
+                "rightClick": "Clic derecho",
+                "letter": "Letra",
+                "swipe": "Deslizar"
+            },
+            "entries": {
+                "focusSearch": "Enfocar la búsqueda",
+                "closeModal": "Cerrar modal / panel",
+                "openShortcuts": "Abrir este panel de atajos",
+                "refresh": "Actualizar la lista de modelos",
+                "fetchMetadata": "Obtener metadatos de CivitAI (solo páginas de modelos)",
+                "downloadModel": "Descargar un modelo (solo páginas de modelos)",
+                "toggleBulkMode": "Activar/desactivar el modo por lotes",
+                "selectAll": "Seleccionar todos los modelos visibles",
+                "rangeSelect": "Selección por rango",
+                "marqueeSelect": "Seleccionar tarjetas con un rectángulo de selección (en un área vacía de la cuadrícula)",
+                "exitBulkMode": "Salir del modo por lotes",
+                "bulkActions": "En una tarjeta seleccionada: menú de acciones por lotes",
+                "globalActions": "En un área vacía de la página: menú de acciones globales (comprobar actualizaciones, gestionar modelos excluidos)",
+                "scrollPages": "Desplazarse por las páginas",
+                "jumpAlphabet": "Saltar con la barra alfabética",
+                "prevNext": "Modelo anterior / siguiente",
+                "deleteEntry": "Eliminar",
+                "cycleMedia": "Cambiar de medio ([ / ] en la galería de ejemplos)",
+                "swipeTouch": "Cambiar de medio en dispositivos táctiles",
+                "closeViewer": "Cerrar el visor"
+            }
         },
         "updateVlogs": {
             "title": "Últimas actualizaciones",
@@ -1884,7 +2028,8 @@
             "settings": "Configuración",
             "extensions": "Extensiones",
             "newBadge": "NUEVO"
-        }
+        },
+        "newContentBadge": "NUEVO"
     },
     "update": {
         "title": "Comprobar actualizaciones",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "Error preparando LoRAs para descarga",
             "enterLoraName": "Por favor introduce un nombre de LoRA o sintaxis",
             "reconnectedSuccessfully": "LoRA reconectado exitosamente",
+            "reconnectBaseModelMismatch": "Reconectado, pero los modelos base difieren (receta: {recipe}, LoRA: {lora}) — son compatibles a nivel de arquitectura",
             "reconnectFailed": "Error reconectando LoRA: {message}",
+            "loraRestored": "LoRA restaurado a su asociación anterior",
+            "loraRestoreFailed": "Error restaurando LoRA: {message}",
             "noPromptToSend": "No hay prompt para enviar",
             "cannotSend": "No se puede enviar receta: Falta ID de receta",
             "sendFailed": "Error al enviar receta al workflow",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Ruta del checkpoint no disponible",
             "missingCheckpointInfo": "Falta información del checkpoint",
             "downloadCheckpointFailed": "Error al descargar el checkpoint: {message}",
+            "enterCheckpointName": "Introduce un nombre de checkpoint",
+            "checkpointReconnectedSuccessfully": "Checkpoint reconectado exitosamente",
+            "reconnectCheckpointBaseModelMismatch": "Reconectado, pero los modelos base difieren (receta: {recipe}, checkpoint: {checkpoint}) — son compatibles a nivel de arquitectura",
+            "checkpointReconnectFailed": "Error reconectando checkpoint: {message}",
+            "checkpointRestored": "Checkpoint restaurado a su asociación anterior",
+            "checkpointRestoreFailed": "Error restaurando checkpoint: {message}",
+            "checkpointDownloadUnavailable": "Este checkpoint no se puede descargar sin identificadores de CivitAI - intenta reconectarlo con un checkpoint local",
             "missingLoraDownloadInfo": "Falta la información de descarga de este LoRA",
             "hashNotFoundOnCivitai": "Este hash de LoRA no se puede resolver en CivitAI - el modelo puede haber sido actualizado o el hash no es válido",
             "downloadLoraFailed": "Error al descargar el LoRA: {message}",

locales/fr.json

@@ -50,6 +50,27 @@
             "mb": "Mo",
             "gb": "Go",
             "tb": "To"
+        },
+        "scanProgress": {
+            "refreshing": "Actualisation des {type}...",
+            "fullRebuilding": "Reconstruction complète des {type}...",
+            "actionRefresh": "Actualisation",
+            "actionFullRebuild": "Reconstruction complète",
+            "actionRefreshLower": "l’actualisation",
+            "actionRebuildLower": "la reconstruction",
+            "stages": {
+                "scan_folders": "Scan des dossiers...",
+                "count_models": "{total} fichiers trouvés",
+                "process_models": "Traitement des modèles",
+                "reconcile_scan": "Vérification des modifications...",
+                "process_new": "Traitement des nouveaux modèles",
+                "finalizing": "Finalisation..."
+            },
+            "eta": {
+                "lessThanMinute": "Moins d’une minute restante",
+                "minutes": "~{minutes} min restantes",
+                "hours": "~{hours} h {minutes} min restantes"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "Opérations groupées",
-                "content": "Activez le mode groupé en cliquant sur ce bouton ou en appuyant sur <span class=\"onboarding-shortcut\">B</span>. Sélectionnez plusieurs modèles et effectuez des opérations groupées. Utilisez <span class=\"onboarding-shortcut\">Ctrl+A</span> pour sélectionner tous les modèles visibles."
+                "content": "Activez le mode groupé en cliquant sur ce bouton ou en appuyant sur <span class=\"onboarding-shortcut\">B</span> pour sélectionner plusieurs modèles et effectuer des opérations groupées.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> sélectionne tous les modèles visibles, <span class=\"onboarding-shortcut\">Shift+Click</span> sélectionne une plage.<br>• <span class=\"onboarding-shortcut\">Esc</span> ou un clic sur une zone vide quitte le mode groupé."
             },
             "searchOptions": {
                 "title": "Options de recherche",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "Menu contextuel",
-                "content": "<strong>Clic droit</strong> sur une carte de modèle pour accéder à un menu contextuel avec des actions supplémentaires."
+                "content": "<strong>Clic droit</strong> sur n'importe quelle carte de modèle pour ouvrir un menu contextuel avec des actions sur la carte comme déplacer, supprimer ou modifier les métadonnées."
+            },
+            "marqueeSelect": {
+                "title": "Glisser pour sélectionner",
+                "content": "Maintenez le <strong>bouton gauche de la souris</strong> enfoncé sur une zone vide de la grille et glissez pour tracer un rectangle de sélection qui sélectionne plusieurs cartes à la fois."
+            },
+            "dragToSidebar": {
+                "title": "Organiser par glisser-déposer",
+                "content": "Glissez une carte de modèle sur un dossier de la barre latérale pour y déplacer le fichier. Cela fonctionne aussi avec plusieurs cartes sélectionnées en mode groupé."
+            },
+            "contextMenus": {
+                "title": "Plus de menus contextuels",
+                "content": "En mode groupé, <strong>faites un clic droit sur une carte sélectionnée</strong> pour accéder aux actions groupées. <strong>Faites un clic droit sur une zone vide</strong> de la page pour accéder aux actions globales comme la vérification des mises à jour et la gestion des modèles exclus."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "Recette précédente (←)",
             "nextWithShortcut": "Recette suivante (→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "Ouvrir l’emplacement du fichier",
+                "copyId": "Copier l’ID de la Recipe"
+            },
+            "openFileLocation": {
+                "success": "Emplacement du fichier ouvert avec succès",
+                "failed": "Échec de l’ouverture de l’emplacement du fichier",
+                "copied": "Chemin copié dans le presse-papiers: {{path}}",
+                "clipboardFallback": "Chemin: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "Envoyer le workflow vers ComfyUI",
             "sent": "Workflow envoyé vers ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "Ce modèle n'est pas dans votre bibliothèque",
             "deletedTooltip": "Ce LoRA a été supprimé de la source et ne peut plus être téléchargé",
             "hashInvalidTooltip": "Ce hash de LoRA ne peut pas être résolu sur CivitAI - le modèle a peut-être été mis à jour",
+            "noLorasAssociated": "Aucune LoRA associée à cette Recipe",
+            "noLorasWhyToggle": "Pourquoi aucune LoRA ?",
+            "noLorasImportMethod": "Méthode d'import",
+            "noLorasInferredNote": "Raison possible (déduite) — cette Recipe a été importée avant l'enregistrement des diagnostics d'import.",
+            "noLorasChannels": {
+                "batch_import_url": "Import groupé (URL d'image)",
+                "batch_import_local": "Import groupé (fichier local)",
+                "url": "Import d'une URL d'image",
+                "local": "Import d'un fichier local",
+                "upload": "Téléversement d'image",
+                "widget": "Enregistrée depuis le Workflow",
+                "reimport_url": "Réimport (URL d'image)",
+                "reimport_local": "Réimport (fichier local)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "Les métadonnées de génération sont complètes et ne référencent aucune LoRA.",
+                "api_meta_no_lora_resources": "L'API source n'a renvoyé aucune donnée de ressource LoRA pour cette image. Les LoRAs affichées sur la page CivitAI peuvent provenir de données internes que l'API publique n'expose pas.",
+                "api_meta_missing": "L'API source n'a renvoyé aucune métadonnée de génération pour cette image.",
+                "no_embedded_metadata": "L'image ne contient aucune métadonnée de génération intégrée ; les informations LoRA n'ont donc pas pu être récupérées.",
+                "workflow_metadata_limited": "Les métadonnées intégrées à l'image sont un Workflow ComfyUI ; l'extraction des informations LoRA à partir des Workflows est limitée.",
+                "video_no_metadata": "Les fichiers vidéo ne contiennent pas de métadonnées de génération intégrées.",
+                "metadata_unsupported": "L'image contient des métadonnées dans un format non analysable.",
+                "unknown": "La raison n'a pas pu être déterminée à partir des données de la Recipe enregistrée."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "Champs de métadonnées de l'API",
+                "modelVersionIds": "IDs de version de modèle signalés",
+                "embeddedMetadata": "Métadonnées intégrées",
+                "present": "trouvées",
+                "absent": "aucune"
+            },
             "download": "Télécharger",
             "downloadLoraTooltip": "Télécharger ce LoRA",
             "preparingDownload": "Préparation du téléchargement...",
             "reconnect": "Reconnecter",
             "reconnectTooltip": "Reconnecter avec un LoRA local",
+            "reconnectInstructions": "Entrez la syntaxe ou le nom du LoRA à reconnecter:",
+            "reconnectExample": "Exemple: <lora:name:1> ou simplement le nom",
+            "reconnectPlaceholder": "Entrez le nom ou la syntaxe du LoRA",
+            "reconnectSuggestionsLoading": "Recherche dans la bibliothèque locale...",
+            "reconnectSuggestionsEmpty": "Aucun LoRA correspondant dans votre bibliothèque locale",
+            "reconnectMatchSameHash": "Hash identique",
+            "reconnectMatchSameVersion": "Même version du modèle",
+            "reconnectMatchSimilarFilename": "Nom de fichier similaire",
+            "reconnectMatchSimilarName": "Nom similaire",
+            "undoReconnect": "Annuler",
+            "undoReconnectTooltip": "Restaurer l'association que cette entrée avait avant la reconnexion",
+            "undoReconnectTooltipNamed": "Restaurer vers {name} (l'association avant la reconnexion)",
             "viewOnCivitai": "Voir sur CivitAI",
             "openLoraDetails": "Voir {name} dans la bibliothèque LoRA",
-            "openCheckpointDetails": "Voir {name} dans la bibliothèque de modèles"
+            "openCheckpointDetails": "Voir {name} dans la bibliothèque de modèles",
+            "checkpointDeletedTooltip": "Ce checkpoint a été supprimé de la source et ne peut plus être téléchargé - reconnectez-le avec un modèle local",
+            "checkpointHashInvalidTooltip": "Le hash de ce checkpoint ne peut pas être résolu sur CivitAI - le modèle a peut-être été mis à jour",
+            "reconnectCheckpoint": "Reconnecter",
+            "reconnectCheckpointTooltip": "Reconnecter avec un checkpoint local",
+            "checkpointReconnectInstructions": "Entrez le nom du checkpoint à reconnecter:",
+            "checkpointReconnectPlaceholder": "Entrez le nom du checkpoint",
+            "checkpointReconnectSuggestionsEmpty": "Aucun checkpoint correspondant dans votre bibliothèque locale"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Valeur",
                 "add": "Ajouter",
-                "invalidRange": "Format de plage invalide. Utilisez x.x-y.y"
+                "invalidRange": "Format de plage invalide. Utilisez x.x-y.y",
+                "invalidValue": "Veuillez saisir un nombre valide",
+                "saveFailed": "Échec de l'enregistrement du paramètre préréglé",
+                "added": "Paramètre préréglé ajouté",
+                "updated": "Paramètre préréglé mis à jour"
             },
             "triggerWords": {
                 "label": "Mots-clés",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "Tapez pour ajouter ou cliquez sur les suggestions ci-dessous",
                 "editWord": "Modifier le mot-clé",
                 "editPlaceholder": "Modifier le mot-clé",
-                "copyWord": "Copier le mot-clé",
+                "copyOrEditWord": "Cliquez pour copier, double-cliquez pour modifier",
                 "deleteWord": "Supprimer le mot-clé",
                 "suggestions": {
                     "noSuggestions": "Aucune suggestion disponible",
@@ -1599,8 +1701,8 @@
                 "showCount": "Afficher les exemples ({count})",
                 "hideExamples": "Masquer les exemples",
                 "addExamples": "Ajouter des exemples",
-                "previousExample": "Exemple précédent",
+                "previousExample": "Exemple précédent ([)",
-                "nextExample": "Exemple suivant",
+                "nextExample": "Exemple suivant (])",
                 "noExamples": "Aucune image d'exemple disponible",
                 "addMoreExamples": "Ajouter d'autres exemples",
                 "dragDrop": "Glissez-déposez des images ou des vidéos ici",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "Commencer",
             "updateVlogs": "Vlogs de mise à jour",
-            "documentation": "Documentation"
+            "documentation": "Documentation",
+            "shortcuts": "Raccourcis"
         },
         "gettingStarted": {
-            "title": "Premiers pas avec le Gestionnaire LoRA"
+            "title": "Premiers pas avec le Gestionnaire LoRA",
+            "replayTutorial": "Rejouer le tutoriel"
+        },
+        "shortcuts": {
+            "title": "Raccourcis clavier et souris",
+            "groups": {
+                "general": "Général",
+                "actions": "Actions",
+                "selection": "Sélection et mode groupé",
+                "navigation": "Navigation",
+                "modelModal": "Modale Modèle / Recipe",
+                "mediaViewer": "Visionneuse de médias / Galerie d'exemples"
+            },
+            "keys": {
+                "click": "Clic",
+                "drag": "Glisser",
+                "rightClick": "Clic droit",
+                "letter": "Lettre",
+                "swipe": "Balayage"
+            },
+            "entries": {
+                "focusSearch": "Donner le focus au champ de recherche",
+                "closeModal": "Fermer la fenêtre modale / le panneau",
+                "openShortcuts": "Ouvrir ce panneau de raccourcis",
+                "refresh": "Actualiser la liste des modèles",
+                "fetchMetadata": "Récupérer les métadonnées depuis CivitAI (pages de modèles uniquement)",
+                "downloadModel": "Télécharger un modèle (pages de modèles uniquement)",
+                "toggleBulkMode": "Activer/désactiver le mode groupé",
+                "selectAll": "Sélectionner tous les modèles visibles",
+                "rangeSelect": "Sélection d'une plage",
+                "marqueeSelect": "Sélection par glisser-déposer des cartes (sur une zone vide de la grille)",
+                "exitBulkMode": "Quitter le mode groupé",
+                "bulkActions": "Sur une carte sélectionnée : menu des actions groupées",
+                "globalActions": "Sur une zone vide de la page : menu des actions globales (vérification des mises à jour, gestion des modèles exclus)",
+                "scrollPages": "Faire défiler les pages",
+                "jumpAlphabet": "Sauter via la barre alphabétique",
+                "prevNext": "Modèle précédent / suivant",
+                "deleteEntry": "Supprimer",
+                "cycleMedia": "Parcourir les médias ([ / ] dans la galerie d'exemples)",
+                "swipeTouch": "Parcourir les médias sur les appareils tactiles",
+                "closeViewer": "Fermer la visionneuse"
+            }
         },
         "updateVlogs": {
             "title": "Dernières mises à jour",
@@ -1884,7 +2028,8 @@
             "settings": "Paramètres & Configuration",
             "extensions": "Extensions",
             "newBadge": "NOUVEAU"
-        }
+        },
+        "newContentBadge": "NOUVEAU"
     },
     "update": {
         "title": "Vérifier les mises à jour",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "Erreur lors de la préparation des LoRAs pour le téléchargement",
             "enterLoraName": "Veuillez entrer un nom ou une syntaxe LoRA",
             "reconnectedSuccessfully": "LoRA reconnecté avec succès",
+            "reconnectBaseModelMismatch": "Reconnexion effectuée, mais les modèles de base diffèrent (Recipe : {recipe}, LoRA : {lora}) — ils sont compatibles au niveau architectural",
             "reconnectFailed": "Erreur lors de la reconnexion du LoRA : {message}",
+            "loraRestored": "LoRA restauré à son association précédente",
+            "loraRestoreFailed": "Erreur lors de la restauration du LoRA : {message}",
             "noPromptToSend": "Aucun prompt à envoyer",
             "cannotSend": "Impossible d'envoyer la recipe : ID de recipe manquant",
             "sendFailed": "Échec de l'envoi de la recipe vers le workflow",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Chemin du checkpoint indisponible",
             "missingCheckpointInfo": "Informations sur le checkpoint manquantes",
             "downloadCheckpointFailed": "Échec du téléchargement du checkpoint : {message}",
+            "enterCheckpointName": "Veuillez saisir un nom de checkpoint",
+            "checkpointReconnectedSuccessfully": "Checkpoint reconnecté avec succès",
+            "reconnectCheckpointBaseModelMismatch": "Reconnexion effectuée, mais les modèles de base diffèrent (Recipe : {recipe}, checkpoint : {checkpoint}) — ils sont compatibles au niveau architectural",
+            "checkpointReconnectFailed": "Erreur lors de la reconnexion du checkpoint : {message}",
+            "checkpointRestored": "Checkpoint restauré à son association précédente",
+            "checkpointRestoreFailed": "Erreur lors de la restauration du checkpoint : {message}",
+            "checkpointDownloadUnavailable": "Ce checkpoint ne peut pas être téléchargé sans identifiants CivitAI - essayez de le reconnecter avec un checkpoint local",
             "missingLoraDownloadInfo": "Informations de téléchargement manquantes pour ce LoRA",
             "hashNotFoundOnCivitai": "Ce hash de LoRA ne peut pas être résolu sur CivitAI - le modèle a peut-être été mis à jour ou le hash est invalide",
             "downloadLoraFailed": "Échec du téléchargement du LoRA : {message}",

locales/he.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "מרענן {type}...",
+            "fullRebuilding": "בונה מחדש את כל ה-{type}...",
+            "actionRefresh": "רענון",
+            "actionFullRebuild": "רענון מלא",
+            "actionRefreshLower": "רענון",
+            "actionRebuildLower": "רענון מלא",
+            "stages": {
+                "scan_folders": "סורק תיקיות...",
+                "count_models": "נמצאו {total} קבצים",
+                "process_models": "מעבד מודלים",
+                "reconcile_scan": "בודק שינויים...",
+                "process_new": "מעבד מודלים חדשים",
+                "finalizing": "מסיים..."
+            },
+            "eta": {
+                "lessThanMinute": "נותרה פחות מדקה",
+                "minutes": "נותרו ~{minutes} דקות",
+                "hours": "נותרו ~{hours} שעות ו-{minutes} דקות"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "פעולות בכמות גדולה",
-                "content": "היכנס למצב פעולות בכמות גדולה על ידי לחיצה על כפתור זה או על <span class=\"onboarding-shortcut\">B</span>. בחר מספר מודלים ובצע פעולות בכמות גדולה. השתמש ב-<span class=\"onboarding-shortcut\">Ctrl+A</span> כדי לבחור את כל המודלים הגלויים."
+                "content": "היכנס למצב פעולות בכמות גדולה על ידי לחיצה על כפתור זה או על <span class=\"onboarding-shortcut\">B</span> כדי לבחור מספר מודלים ולבצע פעולות בכמות גדולה.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> בחר את כל המודלים הגלויים, <span class=\"onboarding-shortcut\">Shift+Click</span> בחר טווח.<br>• <span class=\"onboarding-shortcut\">Esc</span> או לחיצה על אזור ריק מוציאים ממצב בכמות גדולה."
             },
             "searchOptions": {
                 "title": "אפשרויות חיפוש",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "תפריט הקשר",
-                "content": "<strong>לחיצה ימנית</strong> על כל כרטיס מודל לתפריט הקשר עם פעולות נוספות."
+                "content": "<strong>לחיצה ימנית</strong> על כל כרטיס מודל לתפריט הקשר עם פעולות כרטיס כמו העברה, מחיקה או עריכת מטא-נתונים."
+            },
+            "marqueeSelect": {
+                "title": "גרור כדי לבחור",
+                "content": "החזק את <strong>לחצן העכבר השמאלי</strong> לחוץ על אזור ריק של הרשת וגרור כדי לצייר מסגרת בחירה שבוחרת מספר כרטיסים בבת אחת."
+            },
+            "dragToSidebar": {
+                "title": "ארגון באמצעות גרירה",
+                "content": "גרור כרטיס מודל אל תיקייה בסרגל הצד כדי להעביר את הקובץ לשם. פעולה זו עובדת גם עם מספר כרטיסים נבחרים במצב בכמות גדולה."
+            },
+            "contextMenus": {
+                "title": "תפריטי הקשר נוספים",
+                "content": "במצב בכמות גדולה, <strong>לחץ לחיצה ימנית על כרטיס נבחר</strong> לפעולות בכמות גדולה. <strong>לחץ לחיצה ימנית על אזור ריק</strong> בדף לפעולות גלובליות כמו בדיקת עדכונים וניהול מודלים מוחרגים."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "המתכון הקודם (←)",
             "nextWithShortcut": "המתכון הבא (→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "פתח מיקום קובץ",
+                "copyId": "העתק מזהה מתכון"
+            },
+            "openFileLocation": {
+                "success": "מיקום הקובץ נפתח בהצלחה",
+                "failed": "פתיחת מיקום הקובץ נכשלה",
+                "copied": "הנתיב הועתק ללוח העריכה: {{path}}",
+                "clipboardFallback": "נתיב: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "שלח workflow ל-ComfyUI",
             "sent": "ה-workflow נשלח ל-ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "מודל זה לא נמצא בספרייה שלך",
             "deletedTooltip": "LoRA זה נמחק מהמקור ואינו זמין יותר להורדה",
             "hashInvalidTooltip": "לא ניתן לפתור את ה-hash של ה-LoRA ב-CivitAI - ייתכן שהמודל עודכן",
+            "noLorasAssociated": "אין LoRAs המשויכים למתכון זה",
+            "noLorasWhyToggle": "למה אין LoRAs?",
+            "noLorasImportMethod": "שיטת ייבוא",
+            "noLorasInferredNote": "סיבה אפשרית (משוערת) — מתכון זה יובא לפני שנרשמו אבחוני ייבוא.",
+            "noLorasChannels": {
+                "batch_import_url": "ייבוא בכמות גדולה (URL של תמונה)",
+                "batch_import_local": "ייבוא בכמות גדולה (קובץ מקומי)",
+                "url": "ייבוא מ-URL של תמונה",
+                "local": "ייבוא קובץ מקומי",
+                "upload": "העלאת תמונה",
+                "widget": "נשמר מה-workflow",
+                "reimport_url": "ייבוא מחדש (URL של תמונה)",
+                "reimport_local": "ייבוא מחדש (קובץ מקומי)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "מטא-הנתונים של היצירה שלמים ואינם מפנים ל-LoRAs כלשהם.",
+                "api_meta_no_lora_resources": "ה-API של המקור לא החזיר נתוני משאבי LoRA עבור תמונה זו. LoRAs המוצגים בעמוד CivitAI עשויים להגיע מנתונים פנימיים שה-API הציבורי אינו חושף.",
+                "api_meta_missing": "ה-API של המקור לא החזיר מטא-נתוני יצירה עבור תמונה זו.",
+                "no_embedded_metadata": "לתמונה אין מטא-נתוני יצירה מוטבעים, ולכן לא ניתן היה לשחזר את מידע ה-LoRA.",
+                "workflow_metadata_limited": "המטא-נתונים המוטבעים של התמונה הם workflow של ComfyUI; חילוץ מידע LoRA מתוך workflows מוגבל.",
+                "video_no_metadata": "קבצי וידאו אינם נושאים מטא-נתוני יצירה מוטבעים.",
+                "metadata_unsupported": "התמונה מכילה מטא-נתונים בפורמט שלא ניתן לנתח.",
+                "unknown": "לא ניתן היה לקבוע את הסיבה מנתוני המתכון השמורים."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "שדות מטא-נתונים של API",
+                "modelVersionIds": "מספר מזהי גרסת מודל שדווחו",
+                "embeddedMetadata": "מטא-נתונים מוטבעים",
+                "present": "נמצאו",
+                "absent": "אין"
+            },
             "download": "הורדה",
             "downloadLoraTooltip": "הורד את ה-LoRA הזה",
             "preparingDownload": "מכין את ההורדה...",
             "reconnect": "חבר מחדש",
             "reconnectTooltip": "חבר מחדש עם LoRA מקומי",
+            "reconnectInstructions": "הזן תחביר או שם של LoRA לחיבור מחדש:",
+            "reconnectExample": "דוגמה: <lora:name:1> או רק את השם",
+            "reconnectPlaceholder": "הזן שם או תחביר של LoRA",
+            "reconnectSuggestionsLoading": "מחפש בספרייה המקומית...",
+            "reconnectSuggestionsEmpty": "לא נמצאו LoRAs תואמים בספרייה המקומית שלך",
+            "reconnectMatchSameHash": "אותו hash",
+            "reconnectMatchSameVersion": "אותה גרסת מודל",
+            "reconnectMatchSimilarFilename": "שם קובץ דומה",
+            "reconnectMatchSimilarName": "שם דומה",
+            "undoReconnect": "בטל",
+            "undoReconnectTooltip": "שחזר את השיוך שהיה לרשומה זו לפני החיבור מחדש",
+            "undoReconnectTooltipNamed": "שחזר ל-{name} (השיוך לפני החיבור מחדש)",
             "viewOnCivitai": "הצג ב-CivitAI",
             "openLoraDetails": "הצג את {name} בספריית ה-LoRA",
-            "openCheckpointDetails": "הצג את {name} בספריית המודלים"
+            "openCheckpointDetails": "הצג את {name} בספריית המודלים",
+            "checkpointDeletedTooltip": "Checkpoint זה נמחק מהמקור ואינו זמין עוד להורדה - חבר אותו מחדש עם מודל מקומי",
+            "checkpointHashInvalidTooltip": "לא ניתן לפתור את ה-hash של Checkpoint זה ב-CivitAI - ייתכן שהמודל עודכן",
+            "reconnectCheckpoint": "חבר מחדש",
+            "reconnectCheckpointTooltip": "חבר מחדש עם Checkpoint מקומי",
+            "checkpointReconnectInstructions": "הזן שם של Checkpoint לחיבור מחדש:",
+            "checkpointReconnectPlaceholder": "הזן שם של Checkpoint",
+            "checkpointReconnectSuggestionsEmpty": "לא נמצאו Checkpoints תואמים בספרייה המקומית שלך"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "ערך",
                 "add": "הוסף",
-                "invalidRange": "פורמט טווח לא תקין. השתמש ב-x.x-y.y"
+                "invalidRange": "פורמט טווח לא תקין. השתמש ב-x.x-y.y",
+                "invalidValue": "נא להזין מספר תקין",
+                "saveFailed": "שמירת הפרמטר הקבוע מראש נכשלה",
+                "added": "הפרמטר הקבוע מראש נוסף",
+                "updated": "הפרמטר הקבוע מראש עודכן"
             },
             "triggerWords": {
                 "label": "מילות טריגר",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "הקלד להוספה או לחץ על הצעות למטה",
                 "editWord": "עריכת מילת טריגר",
                 "editPlaceholder": "עריכת מילת טריגר",
-                "copyWord": "העתק מילת טריגר",
+                "copyOrEditWord": "לחץ כדי להעתיק, לחץ פעמיים כדי לערוך",
                 "deleteWord": "מחק מילת טריגר",
                 "suggestions": {
                     "noSuggestions": "אין הצעות זמינות",
@@ -1599,8 +1701,8 @@
                 "showCount": "הצג דוגמאות ({count})",
                 "hideExamples": "הסתר דוגמאות",
                 "addExamples": "הוסף דוגמאות",
-                "previousExample": "דוגמה קודמת",
+                "previousExample": "דוגמה קודמת ([)",
-                "nextExample": "דוגמה הבאה",
+                "nextExample": "דוגמה הבאה (])",
                 "noExamples": "אין תמונות דוגמה זמינות",
                 "addMoreExamples": "הוסף עוד דוגמאות",
                 "dragDrop": "גרור ושחרר תמונות או סרטונים כאן",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "תחילת עבודה",
             "updateVlogs": "בלוגי וידאו של עדכונים",
-            "documentation": "תיעוד"
+            "documentation": "תיעוד",
+            "shortcuts": "קיצורי דרך"
         },
         "gettingStarted": {
-            "title": "תחילת עבודה עם מנהל LoRA"
+            "title": "תחילת עבודה עם מנהל LoRA",
+            "replayTutorial": "הפעל את המדריך מחדש"
+        },
+        "shortcuts": {
+            "title": "קיצורי מקלדת ועכבר",
+            "groups": {
+                "general": "כללי",
+                "actions": "פעולות",
+                "selection": "בחירה ומצב בכמות גדולה",
+                "navigation": "ניווט",
+                "modelModal": "חלון מודל / מתכון",
+                "mediaViewer": "מציג מדיה / גלריית דוגמאות"
+            },
+            "keys": {
+                "click": "לחיצה",
+                "drag": "גרירה",
+                "rightClick": "לחיצה ימנית",
+                "letter": "אות",
+                "swipe": "החלקה"
+            },
+            "entries": {
+                "focusSearch": "העבר מיקוד לחיפוש",
+                "closeModal": "סגור חלון / פאנל",
+                "openShortcuts": "פתח את פאנל קיצורי הדרך הזה",
+                "refresh": "רענן את רשימת המודלים",
+                "fetchMetadata": "אחזר מטא-נתונים מ-CivitAI (דפי מודלים בלבד)",
+                "downloadModel": "הורד מודל (דפי מודלים בלבד)",
+                "toggleBulkMode": "הפעל/כבה מצב בכמות גדולה",
+                "selectAll": "בחר את כל המודלים הגלויים",
+                "rangeSelect": "בחר טווח",
+                "marqueeSelect": "בחר כרטיסים במסגרת בחירה (באזור ריק של הרשת)",
+                "exitBulkMode": "צא ממצב בכמות גדולה",
+                "bulkActions": "על כרטיס נבחר: תפריט פעולות בכמות גדולה",
+                "globalActions": "באזור ריק בדף: תפריט פעולות גלובליות (בדיקת עדכונים, ניהול מודלים מוחרגים)",
+                "scrollPages": "גלול בין דפים",
+                "jumpAlphabet": "קפוץ בעזרת סרגל האותיות",
+                "prevNext": "מודל קודם / הבא",
+                "deleteEntry": "מחק",
+                "cycleMedia": "עבור בין פריטי מדיה ([ / ] בגלריית הדוגמאות)",
+                "swipeTouch": "עבור בין פריטי מדיה במכשירי מגע",
+                "closeViewer": "סגור את המציג"
+            }
         },
         "updateVlogs": {
             "title": "עדכונים אחרונים",
@@ -1884,7 +2028,8 @@
             "settings": "הגדרות ותצורה",
             "extensions": "הרחבות",
             "newBadge": "חדש"
-        }
+        },
+        "newContentBadge": "חדש"
     },
     "update": {
         "title": "בדוק עדכונים",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "שגיאה בהכנת LoRAs להורדה",
             "enterLoraName": "אנא הזן שם LoRA או תחביר",
             "reconnectedSuccessfully": "LoRA קושר מחדש בהצלחה",
+            "reconnectBaseModelMismatch": "הקישור מחדש הצליח, אך מודלי הבסיס שונים (מתכון: {recipe}, LoRA: {lora}) — הם תואמים מבחינת הארכיטקטורה",
             "reconnectFailed": "שגיאה בקישור מחדש של LoRA: {message}",
+            "loraRestored": "LoRA שוחזר לשיוך הקודם",
+            "loraRestoreFailed": "שגיאה בשחזור LoRA: {message}",
             "noPromptToSend": "אין פרומפט לשליחה",
             "cannotSend": "לא ניתן לשלוח מתכון: חסר מזהה מתכון",
             "sendFailed": "שליחת המתכון ל-workflow נכשלה",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "נתיב ה-checkpoint אינו זמין",
             "missingCheckpointInfo": "חסרים פרטי checkpoint",
             "downloadCheckpointFailed": "הורדת checkpoint נכשלה: {message}",
+            "enterCheckpointName": "הזן שם של Checkpoint",
+            "checkpointReconnectedSuccessfully": "Checkpoint קושר מחדש בהצלחה",
+            "reconnectCheckpointBaseModelMismatch": "הקישור מחדש הצליח, אך מודלי הבסיס שונים (מתכון: {recipe}, Checkpoint: {checkpoint}) — הם תואמים מבחינת הארכיטקטורה",
+            "checkpointReconnectFailed": "שגיאה בקישור מחדש של Checkpoint: {message}",
+            "checkpointRestored": "Checkpoint שוחזר לשיוך הקודם",
+            "checkpointRestoreFailed": "שגיאה בשחזור Checkpoint: {message}",
+            "checkpointDownloadUnavailable": "לא ניתן להוריד Checkpoint זה ללא מזהי CivitAI - נסה לחבר אותו מחדש עם Checkpoint מקומי",
             "missingLoraDownloadInfo": "חסר מידע הורדה עבור LoRA זה",
             "hashNotFoundOnCivitai": "לא ניתן לפתור את ה-hash של ה-LoRA ב-CivitAI - ייתכן שהמודל עודכן או שה-hash אינו תקין",
             "downloadLoraFailed": "הורדת ה-LoRA נכשלה: {message}",

locales/ja.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "{type}を更新中...",
+            "fullRebuilding": "{type}を完全に再構築中...",
+            "actionRefresh": "更新",
+            "actionFullRebuild": "完全な再構築",
+            "actionRefreshLower": "更新",
+            "actionRebuildLower": "再構築",
+            "stages": {
+                "scan_folders": "フォルダをスキャン中...",
+                "count_models": "{total} 件のファイルが見つかりました",
+                "process_models": "モデルを処理中",
+                "reconcile_scan": "変更を確認中...",
+                "process_new": "新しいモデルを処理中",
+                "finalizing": "最終処理中..."
+            },
+            "eta": {
+                "lessThanMinute": "残り1分未満",
+                "minutes": "残り約 {minutes} 分",
+                "hours": "残り約 {hours} 時間 {minutes} 分"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "一括操作",
-                "content": "このボタンをクリックするか、<span class=\"onboarding-shortcut\">B</span>キーを押して一括モードに入ります。複数のモデルを選択して一括操作が可能です。<span class=\"onboarding-shortcut\">Ctrl+A</span>で表示中のモデルをすべて選択できます。"
+                "content": "このボタンをクリックするか、<span class=\"onboarding-shortcut\">B</span>キーを押して一括モードに入り、複数のモデルを選択して一括操作を実行できます。<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span>で表示中のモデルをすべて選択、<span class=\"onboarding-shortcut\">Shift+Click</span>で範囲選択。<br>• <span class=\"onboarding-shortcut\">Esc</span>キーまたは空白部分をクリックすると一括モードを終了します。"
             },
             "searchOptions": {
                 "title": "検索オプション",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "コンテキストメニュー",
-                "content": "<strong>モデルカードを右クリック</strong>すると追加の操作ができるコンテキストメニューが表示されます。"
+                "content": "<strong>モデルカードを右クリック</strong>すると、移動、削除、メタデータの編集などのカード操作を含むコンテキストメニューが表示されます。"
+            },
+            "marqueeSelect": {
+                "title": "ドラッグで選択",
+                "content": "グリッドの空白部分で<strong>マウスの左ボタン</strong>を押したままドラッグすると、複数のカードを一度に選択する矩形（マーキー）を描画できます。"
+            },
+            "dragToSidebar": {
+                "title": "ドラッグで整理",
+                "content": "モデルカードをサイドバーのフォルダにドラッグすると、ファイルをそこに移動できます。一括モードで複数選択したカードでも同様に機能します。"
+            },
+            "contextMenus": {
+                "title": "その他のコンテキストメニュー",
+                "content": "一括モードでは、<strong>選択したカードを右クリック</strong>すると一括操作メニューが表示されます。<strong>ページの空白部分を右クリック</strong>すると、更新の確認や除外モデルの管理などのグローバル操作メニューが表示されます。"
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "前のレシピ（←）",
             "nextWithShortcut": "次のレシピ（→）"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "ファイルの場所を開く",
+                "copyId": "レシピIDをコピー"
+            },
+            "openFileLocation": {
+                "success": "ファイルの場所を正常に開きました",
+                "failed": "ファイルの場所を開くのに失敗しました",
+                "copied": "パスをクリップボードにコピーしました: {{path}}",
+                "clipboardFallback": "パス: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "ワークフローをComfyUIへ送信",
             "sent": "ワークフローをComfyUIへ送信しました",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "このモデルはライブラリにありません",
             "deletedTooltip": "この LoRA は配信元から削除されたため、ダウンロードできません",
             "hashInvalidTooltip": "このLoRAハッシュはCivitAIで解決できません - モデルが更新された可能性があります",
+            "noLorasAssociated": "このレシピに関連付けられた LoRA はありません",
+            "noLorasWhyToggle": "LoRA がない理由",
+            "noLorasImportMethod": "インポート方法",
+            "noLorasInferredNote": "考えられる理由（推定）— このレシピはインポート診断が記録される前にインポートされました。",
+            "noLorasChannels": {
+                "batch_import_url": "一括インポート（画像 URL）",
+                "batch_import_local": "一括インポート（ローカルファイル）",
+                "url": "画像 URL からのインポート",
+                "local": "ローカルファイルのインポート",
+                "upload": "画像のアップロード",
+                "widget": "ワークフローから保存",
+                "reimport_url": "再インポート（画像 URL）",
+                "reimport_local": "再インポート（ローカルファイル）"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "生成メタデータは完全で、LoRA への参照は含まれていません。",
+                "api_meta_no_lora_resources": "ソース API がこの画像の LoRA リソースデータを返しませんでした。CivitAI ページに表示される LoRA は、公開 API では公開されない内部データに由来する場合があります。",
+                "api_meta_missing": "ソース API がこの画像の生成メタデータを返しませんでした。",
+                "no_embedded_metadata": "画像に埋め込まれた生成メタデータがないため、LoRA 情報を復元できませんでした。",
+                "workflow_metadata_limited": "画像に埋め込まれたメタデータは ComfyUI ワークフローです。ワークフローからの LoRA 情報の抽出には限界があります。",
+                "video_no_metadata": "動画ファイルには埋め込み生成メタデータがありません。",
+                "metadata_unsupported": "画像に解析できない形式のメタデータが含まれています。",
+                "unknown": "保存されたレシピデータから理由を特定できませんでした。"
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "API メタデータフィールド",
+                "modelVersionIds": "報告されたモデルバージョン ID 数",
+                "embeddedMetadata": "埋め込みメタデータ",
+                "present": "あり",
+                "absent": "なし"
+            },
             "download": "ダウンロード",
             "downloadLoraTooltip": "この LoRA をダウンロード",
             "preparingDownload": "ダウンロードを準備中...",
             "reconnect": "再接続",
             "reconnectTooltip": "ローカルの LoRA と再接続",
+            "reconnectInstructions": "再接続する LoRA の構文または名前を入力してください：",
+            "reconnectExample": "例：<lora:name:1> または名前のみ",
+            "reconnectPlaceholder": "LoRA 名または構文を入力",
+            "reconnectSuggestionsLoading": "ローカルライブラリを検索中...",
+            "reconnectSuggestionsEmpty": "ローカルライブラリに一致するLoRAがありません",
+            "reconnectMatchSameHash": "同じハッシュ",
+            "reconnectMatchSameVersion": "同じモデルバージョン",
+            "reconnectMatchSimilarFilename": "類似のファイル名",
+            "reconnectMatchSimilarName": "類似の名前",
+            "undoReconnect": "元に戻す",
+            "undoReconnectTooltip": "このエントリーを再接続前の関連付けに戻します",
+            "undoReconnectTooltipNamed": "{name} に戻す（再接続前の関連付け）",
             "viewOnCivitai": "CivitAI で表示",
             "openLoraDetails": "LoRA ライブラリで {name} を表示",
-            "openCheckpointDetails": "モデルライブラリで {name} を表示"
+            "openCheckpointDetails": "モデルライブラリで {name} を表示",
+            "checkpointDeletedTooltip": "この Checkpoint はソースから削除されたため、ダウンロードできません - ローカルモデルで再接続してください",
+            "checkpointHashInvalidTooltip": "この Checkpoint のハッシュは CivitAI で解決できません - モデルが更新された可能性があります",
+            "reconnectCheckpoint": "再接続",
+            "reconnectCheckpointTooltip": "ローカルの Checkpoint と再接続",
+            "checkpointReconnectInstructions": "再接続する Checkpoint の名前を入力してください：",
+            "checkpointReconnectPlaceholder": "Checkpoint 名を入力",
+            "checkpointReconnectSuggestionsEmpty": "ローカルライブラリに一致するCheckpointがありません"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "値",
                 "add": "追加",
-                "invalidRange": "無効な範囲形式です。x.x-y.y を使用してください"
+                "invalidRange": "無効な範囲形式です。x.x-y.y を使用してください",
+                "invalidValue": "有効な数値を入力してください",
+                "saveFailed": "プリセットパラメータの保存に失敗しました",
+                "added": "プリセットパラメータを追加しました",
+                "updated": "プリセットパラメータを更新しました"
             },
             "triggerWords": {
                 "label": "トリガーワード",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "入力して追加するか、下の提案をクリック",
                 "editWord": "トリガーワードを編集",
                 "editPlaceholder": "トリガーワードを編集",
-                "copyWord": "トリガーワードをコピー",
+                "copyOrEditWord": "クリックでコピー、ダブルクリックで編集",
                 "deleteWord": "トリガーワードを削除",
                 "suggestions": {
                     "noSuggestions": "提案はありません",
@@ -1599,8 +1701,8 @@
                 "showCount": "例を表示（{count}）",
                 "hideExamples": "例を非表示",
                 "addExamples": "例を追加",
-                "previousExample": "前の例",
+                "previousExample": "前の例（[）",
-                "nextExample": "次の例",
+                "nextExample": "次の例（]）",
                 "noExamples": "利用可能な例画像がありません",
                 "addMoreExamples": "さらに例を追加",
                 "dragDrop": "画像または動画をここにドラッグ＆ドロップ",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "はじめに",
             "updateVlogs": "更新Vlog",
-            "documentation": "ドキュメント"
+            "documentation": "ドキュメント",
+            "shortcuts": "ショートカット"
         },
         "gettingStarted": {
-            "title": "LoRA Managerを始める"
+            "title": "LoRA Managerを始める",
+            "replayTutorial": "チュートリアルをもう一度再生"
+        },
+        "shortcuts": {
+            "title": "キーボード & マウスのショートカット",
+            "groups": {
+                "general": "一般",
+                "actions": "操作",
+                "selection": "選択 & 一括モード",
+                "navigation": "ナビゲーション",
+                "modelModal": "モデル / レシピモーダル",
+                "mediaViewer": "メディアビューア / ショーケース"
+            },
+            "keys": {
+                "click": "クリック",
+                "drag": "ドラッグ",
+                "rightClick": "右クリック",
+                "letter": "文字キー",
+                "swipe": "スワイプ"
+            },
+            "entries": {
+                "focusSearch": "検索にフォーカス",
+                "closeModal": "モーダル / パネルを閉じる",
+                "openShortcuts": "このショートカットパネルを開く",
+                "refresh": "モデルリストを更新",
+                "fetchMetadata": "CivitAIからメタデータを取得（モデルページのみ）",
+                "downloadModel": "モデルをダウンロード（モデルページのみ）",
+                "toggleBulkMode": "一括モードを切り替え",
+                "selectAll": "表示中のモデルをすべて選択",
+                "rangeSelect": "範囲選択",
+                "marqueeSelect": "カードを矩形選択（グリッドの空白部分で）",
+                "exitBulkMode": "一括モードを終了",
+                "bulkActions": "選択したカード上：一括操作メニュー",
+                "globalActions": "ページの空白部分：グローバル操作メニュー（更新の確認、除外モデルの管理）",
+                "scrollPages": "ページをスクロール",
+                "jumpAlphabet": "アルファベットバーへジャンプ",
+                "prevNext": "前 / 次のモデル",
+                "deleteEntry": "削除",
+                "cycleMedia": "メディアを切り替え（ショーケースギャラリーでは [ / ]）",
+                "swipeTouch": "タッチデバイスでメディアを切り替え",
+                "closeViewer": "ビューアを閉じる"
+            }
         },
         "updateVlogs": {
             "title": "最新の更新",
@@ -1884,7 +2028,8 @@
             "settings": "設定&構成",
             "extensions": "拡張機能",
             "newBadge": "新着"
-        }
+        },
+        "newContentBadge": "新着"
     },
     "update": {
         "title": "更新確認",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "ダウンロード用LoRAの準備中にエラーが発生しました",
             "enterLoraName": "LoRA名または構文を入力してください",
             "reconnectedSuccessfully": "LoRAが正常に再接続されました",
+            "reconnectBaseModelMismatch": "再接続しましたが、ベースモデルが異なります（レシピ：{recipe}、LoRA：{lora}）— アーキテクチャ互換です",
             "reconnectFailed": "LoRA再接続エラー：{message}",
+            "loraRestored": "LoRAが以前の関連付けに復元されました",
+            "loraRestoreFailed": "LoRA復元エラー：{message}",
             "noPromptToSend": "送信するプロンプトがありません",
             "cannotSend": "レシピを送信できません：レシピIDがありません",
             "sendFailed": "レシピのワークフローへの送信に失敗しました",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Checkpointのパスがありません",
             "missingCheckpointInfo": "Checkpoint情報が不足しています",
             "downloadCheckpointFailed": "Checkpointのダウンロードに失敗しました: {message}",
+            "enterCheckpointName": "Checkpoint 名を入力してください",
+            "checkpointReconnectedSuccessfully": "Checkpointが正常に再接続されました",
+            "reconnectCheckpointBaseModelMismatch": "再接続しましたが、ベースモデルが異なります（レシピ：{recipe}、Checkpoint：{checkpoint}）— アーキテクチャ互換です",
+            "checkpointReconnectFailed": "Checkpoint再接続エラー：{message}",
+            "checkpointRestored": "Checkpoint が以前の関連付けに復元されました",
+            "checkpointRestoreFailed": "Checkpoint復元エラー：{message}",
+            "checkpointDownloadUnavailable": "CivitAI の識別子がないため、この Checkpoint をダウンロードできません - ローカルの Checkpoint と再接続してみてください",
             "missingLoraDownloadInfo": "この LoRA のダウンロード情報がありません",
             "hashNotFoundOnCivitai": "このLoRAハッシュはCivitAIで解決できません - モデルが更新されたか、ハッシュが無効な可能性があります",
             "downloadLoraFailed": "LoRA のダウンロードに失敗しました: {message}",

locales/ko.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "{type} 새로고침 중...",
+            "fullRebuilding": "{type} 전체 재구성 중...",
+            "actionRefresh": "새로고침",
+            "actionFullRebuild": "전체 재구성",
+            "actionRefreshLower": "새로고침",
+            "actionRebuildLower": "재구성",
+            "stages": {
+                "scan_folders": "폴더 스캔 중...",
+                "count_models": "파일 {total}개 발견",
+                "process_models": "모델 처리 중",
+                "reconcile_scan": "변경 사항 확인 중...",
+                "process_new": "새 모델 처리 중",
+                "finalizing": "마무리 중..."
+            },
+            "eta": {
+                "lessThanMinute": "남은 시간 1분 미만",
+                "minutes": "약 {minutes}분 남음",
+                "hours": "약 {hours}시간 {minutes}분 남음"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "일괄 작업",
-                "content": "이 버튼을 클릭하거나 <span class=\"onboarding-shortcut\">B</span> 키를 눌러 일괄 모드로 진입하세요. 여러 모델을 선택하여 일괄 작업을 수행할 수 있습니다. <span class=\"onboarding-shortcut\">Ctrl+A</span>로 모든 표시된 모델을 선택하세요."
+                "content": "이 버튼을 클릭하거나 <span class=\"onboarding-shortcut\">B</span> 키를 눌러 일괄 모드로 진입하여 여러 모델을 선택하고 일괄 작업을 수행하세요.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span>로 모든 표시된 모델을 선택하고, <span class=\"onboarding-shortcut\">Shift+Click</span>으로 범위를 선택할 수 있습니다.<br>• <span class=\"onboarding-shortcut\">Esc</span> 키를 누르거나 빈 영역을 클릭하면 일괄 모드가 종료됩니다."
             },
             "searchOptions": {
                 "title": "검색 옵션",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "컨텍스트 메뉴",
-                "content": "<strong>오른쪽 클릭</strong>으로 모델 카드의 추가 작업 메뉴를 사용할 수 있습니다."
+                "content": "모델 카드를 <strong>오른쪽 클릭</strong>하면 이동, 삭제, 메타데이터 편집 같은 카드 작업이 담긴 컨텍스트 메뉴를 사용할 수 있습니다."
+            },
+            "marqueeSelect": {
+                "title": "드래그로 선택",
+                "content": "그리드의 빈 영역에서 <strong>마우스 왼쪽 버튼</strong>을 누른 채 드래그하여 여러 카드를 한 번에 선택하는 선택 영역을 그리세요."
+            },
+            "dragToSidebar": {
+                "title": "드래그로 정리",
+                "content": "모델 카드를 사이드바의 폴더로 드래그하면 파일이 해당 폴더로 이동합니다. 일괄 모드에서 선택한 여러 카드에도 적용됩니다."
+            },
+            "contextMenus": {
+                "title": "더 많은 컨텍스트 메뉴",
+                "content": "일괄 모드에서는 <strong>선택한 카드를 오른쪽 클릭</strong>하여 일괄 작업을 사용할 수 있습니다. 페이지의 <strong>빈 영역을 오른쪽 클릭</strong>하면 업데이트 확인이나 제외된 모델 관리 같은 전역 작업을 사용할 수 있습니다."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "이전 레시피(←)",
             "nextWithShortcut": "다음 레시피(→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "파일 위치 열기",
+                "copyId": "레시피 ID 복사"
+            },
+            "openFileLocation": {
+                "success": "파일 위치가 성공적으로 열렸습니다",
+                "failed": "파일 위치 열기에 실패했습니다",
+                "copied": "경로가 클립보드에 복사되었습니다: {{path}}",
+                "clipboardFallback": "경로: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "워크플로를 ComfyUI로 보내기",
             "sent": "워크플로를 ComfyUI로 보냈습니다",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "이 모델은 라이브러리에 없습니다",
             "deletedTooltip": "이 LoRA는 소스에서 삭제되어 더 이상 다운로드할 수 없습니다",
             "hashInvalidTooltip": "이 LoRA 해시는 CivitAI에서 해석할 수 없습니다 - 모델이 업데이트되었을 수 있습니다",
+            "noLorasAssociated": "이 레시피에 연결된 LoRA가 없습니다",
+            "noLorasWhyToggle": "LoRA가 없는 이유",
+            "noLorasImportMethod": "가져오기 방법",
+            "noLorasInferredNote": "가능한 이유(추정) — 이 레시피는 가져오기 진단이 기록되기 전에 가져온 것입니다.",
+            "noLorasChannels": {
+                "batch_import_url": "일괄 가져오기(이미지 URL)",
+                "batch_import_local": "일괄 가져오기(로컬 파일)",
+                "url": "이미지 URL 가져오기",
+                "local": "로컬 파일 가져오기",
+                "upload": "이미지 업로드",
+                "widget": "워크플로에서 저장",
+                "reimport_url": "다시 가져오기(이미지 URL)",
+                "reimport_local": "다시 가져오기(로컬 파일)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "생성 메타데이터가 완전하며 LoRA를 참조하지 않습니다.",
+                "api_meta_no_lora_resources": "소스 API가 이 이미지에 대한 LoRA 리소스 데이터를 반환하지 않았습니다. CivitAI 페이지에 표시되는 LoRA는 공개 API가 노출하지 않는 내부 데이터에서 비롯될 수 있습니다.",
+                "api_meta_missing": "소스 API가 이 이미지에 대한 생성 메타데이터를 반환하지 않았습니다.",
+                "no_embedded_metadata": "이미지에 내장된 생성 메타데이터가 없어 LoRA 정보를 복구할 수 없습니다.",
+                "workflow_metadata_limited": "이미지에 내장된 메타데이터는 ComfyUI 워크플로입니다. 워크플로에서 LoRA 정보를 추출하는 것은 제한적입니다.",
+                "video_no_metadata": "동영상 파일에는 내장 생성 메타데이터가 없습니다.",
+                "metadata_unsupported": "이미지에 파싱할 수 없는 형식의 메타데이터가 포함되어 있습니다.",
+                "unknown": "저장된 레시피 데이터에서 이유를 확인할 수 없습니다."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "API 메타데이터 필드",
+                "modelVersionIds": "보고된 모델 버전 ID 수",
+                "embeddedMetadata": "내장 메타데이터",
+                "present": "있음",
+                "absent": "없음"
+            },
             "download": "다운로드",
             "downloadLoraTooltip": "이 LoRA 다운로드",
             "preparingDownload": "다운로드 준비 중...",
             "reconnect": "다시 연결",
             "reconnectTooltip": "로컬 LoRA와 다시 연결",
+            "reconnectInstructions": "다시 연결할 LoRA 구문 또는 이름을 입력하세요：",
+            "reconnectExample": "예：<lora:name:1> 또는 이름만 입력",
+            "reconnectPlaceholder": "LoRA 이름 또는 구문 입력",
+            "reconnectSuggestionsLoading": "로컬 라이브러리 검색 중...",
+            "reconnectSuggestionsEmpty": "로컬 라이브러리에 일치하는 LoRA가 없습니다",
+            "reconnectMatchSameHash": "동일한 해시",
+            "reconnectMatchSameVersion": "동일한 모델 버전",
+            "reconnectMatchSimilarFilename": "유사한 파일 이름",
+            "reconnectMatchSimilarName": "유사한 이름",
+            "undoReconnect": "실행 취소",
+            "undoReconnectTooltip": "이 항목을 다시 연결 전의 연결 상태로 복원",
+            "undoReconnectTooltipNamed": "이전 연결 상태로 복원: {name}",
             "viewOnCivitai": "CivitAI에서 보기",
             "openLoraDetails": "LoRA 라이브러리에서 {name} 보기",
-            "openCheckpointDetails": "모델 라이브러리에서 {name} 보기"
+            "openCheckpointDetails": "모델 라이브러리에서 {name} 보기",
+            "checkpointDeletedTooltip": "이 Checkpoint는 소스에서 삭제되어 더 이상 다운로드할 수 없습니다 - 로컬 모델로 다시 연결하세요",
+            "checkpointHashInvalidTooltip": "이 Checkpoint의 해시를 CivitAI에서 확인할 수 없습니다 - 모델이 업데이트되었을 수 있습니다",
+            "reconnectCheckpoint": "다시 연결",
+            "reconnectCheckpointTooltip": "로컬 Checkpoint와 다시 연결",
+            "checkpointReconnectInstructions": "다시 연결할 Checkpoint 이름을 입력하세요：",
+            "checkpointReconnectPlaceholder": "Checkpoint 이름 입력",
+            "checkpointReconnectSuggestionsEmpty": "로컬 라이브러리에 일치하는 Checkpoint가 없습니다"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "클립 스킵",
                 "valuePlaceholder": "값",
                 "add": "추가",
-                "invalidRange": "잘못된 범위 형식입니다. x.x-y.y를 사용하세요"
+                "invalidRange": "잘못된 범위 형식입니다. x.x-y.y를 사용하세요",
+                "invalidValue": "유효한 숫자를 입력하세요",
+                "saveFailed": "프리셋 매개변수 저장에 실패했습니다",
+                "added": "프리셋 매개변수가 추가되었습니다",
+                "updated": "프리셋 매개변수가 업데이트되었습니다"
             },
             "triggerWords": {
                 "label": "트리거 단어",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "입력하거나 아래 제안을 클릭하세요",
                 "editWord": "트리거 단어 편집",
                 "editPlaceholder": "트리거 단어 편집",
-                "copyWord": "트리거 단어 복사",
+                "copyOrEditWord": "클릭하여 복사, 더블 클릭하여 편집",
                 "deleteWord": "트리거 단어 삭제",
                 "suggestions": {
                     "noSuggestions": "사용 가능한 제안이 없습니다",
@@ -1599,8 +1701,8 @@
                 "showCount": "예시 보기 ({count})",
                 "hideExamples": "예시 숨기기",
                 "addExamples": "예시 추가",
-                "previousExample": "이전 예시",
+                "previousExample": "이전 예시([)",
-                "nextExample": "다음 예시",
+                "nextExample": "다음 예시(])",
                 "noExamples": "사용 가능한 예시 이미지가 없습니다",
                 "addMoreExamples": "예시 더 추가",
                 "dragDrop": "이미지 또는 비디오를 여기로 끌어다 놓으세요",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "시작하기",
             "updateVlogs": "업데이트 영상",
-            "documentation": "문서"
+            "documentation": "문서",
+            "shortcuts": "단축키"
         },
         "gettingStarted": {
-            "title": "LoRA Manager 시작하기"
+            "title": "LoRA Manager 시작하기",
+            "replayTutorial": "튜토리얼 다시 보기"
+        },
+        "shortcuts": {
+            "title": "키보드 & 마우스 단축키",
+            "groups": {
+                "general": "일반",
+                "actions": "작업",
+                "selection": "선택 & 일괄 모드",
+                "navigation": "내비게이션",
+                "modelModal": "모델 / 레시피 모달",
+                "mediaViewer": "미디어 뷰어 / 쇼케이스"
+            },
+            "keys": {
+                "click": "클릭",
+                "drag": "드래그",
+                "rightClick": "오른쪽 클릭",
+                "letter": "문자 키",
+                "swipe": "스와이프"
+            },
+            "entries": {
+                "focusSearch": "검색창으로 포커스 이동",
+                "closeModal": "모달 / 패널 닫기",
+                "openShortcuts": "이 단축키 패널 열기",
+                "refresh": "모델 목록 새로고침",
+                "fetchMetadata": "CivitAI에서 메타데이터 가져오기 (모델 페이지만)",
+                "downloadModel": "모델 다운로드 (모델 페이지만)",
+                "toggleBulkMode": "일괄 모드 전환",
+                "selectAll": "표시된 모든 모델 선택",
+                "rangeSelect": "범위 선택",
+                "marqueeSelect": "드래그로 카드 선택 (빈 그리드 영역에서)",
+                "exitBulkMode": "일괄 모드 종료",
+                "bulkActions": "선택한 카드에서: 일괄 작업 메뉴",
+                "globalActions": "페이지 빈 영역에서: 전역 작업 메뉴 (업데이트 확인, 제외된 모델 관리)",
+                "scrollPages": "페이지 스크롤",
+                "jumpAlphabet": "알파벳 바로 이동",
+                "prevNext": "이전 / 다음 모델",
+                "deleteEntry": "삭제",
+                "cycleMedia": "미디어 전환 (쇼케이스 갤러리에서 [ / ])",
+                "swipeTouch": "터치 기기에서 미디어 전환",
+                "closeViewer": "뷰어 닫기"
+            }
         },
         "updateVlogs": {
             "title": "최신 업데이트",
@@ -1884,7 +2028,8 @@
             "settings": "설정 & 구성",
             "extensions": "확장",
             "newBadge": "신규"
-        }
+        },
+        "newContentBadge": "신규"
     },
     "update": {
         "title": "업데이트 확인",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "LoRA 다운로드 준비 오류",
             "enterLoraName": "LoRA 이름 또는 문법을 입력해주세요",
             "reconnectedSuccessfully": "LoRA가 성공적으로 다시 연결되었습니다",
+            "reconnectBaseModelMismatch": "다시 연결했지만 베이스 모델이 다릅니다(레시피: {recipe}, LoRA: {lora}) — 아키텍처 호환입니다",
             "reconnectFailed": "LoRA 다시 연결 오류: {message}",
+            "loraRestored": "LoRA가 이전 연결 상태로 복원되었습니다",
+            "loraRestoreFailed": "LoRA 복원 오류: {message}",
             "noPromptToSend": "보낼 프롬프트가 없습니다",
             "cannotSend": "레시피를 전송할 수 없습니다: 레시피 ID 누락",
             "sendFailed": "레시피를 워크플로로 전송하는데 실패했습니다",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Checkpoint 경로를 사용할 수 없습니다",
             "missingCheckpointInfo": "Checkpoint 정보가 부족합니다",
             "downloadCheckpointFailed": "Checkpoint 다운로드 실패: {message}",
+            "enterCheckpointName": "Checkpoint 이름을 입력하세요",
+            "checkpointReconnectedSuccessfully": "Checkpoint가 성공적으로 다시 연결되었습니다",
+            "reconnectCheckpointBaseModelMismatch": "다시 연결했지만 베이스 모델이 다릅니다(레시피: {recipe}, Checkpoint: {checkpoint}) — 아키텍처 호환입니다",
+            "checkpointReconnectFailed": "Checkpoint 다시 연결 오류: {message}",
+            "checkpointRestored": "Checkpoint가 이전 연결 상태로 복원되었습니다",
+            "checkpointRestoreFailed": "Checkpoint 복원 오류: {message}",
+            "checkpointDownloadUnavailable": "CivitAI 식별자가 없어 이 Checkpoint를 다운로드할 수 없습니다 - 로컬 Checkpoint로 다시 연결해 보세요",
             "missingLoraDownloadInfo": "이 LoRA의 다운로드 정보가 없습니다",
             "hashNotFoundOnCivitai": "이 LoRA 해시는 CivitAI에서 해석할 수 없습니다 - 모델이 업데이트되었거나 해시가 유효하지 않을 수 있습니다",
             "downloadLoraFailed": "LoRA 다운로드 실패: {message}",

locales/ru.json

@@ -50,6 +50,27 @@
             "mb": "МБ",
             "gb": "ГБ",
             "tb": "ТБ"
+        },
+        "scanProgress": {
+            "refreshing": "Обновление {type}...",
+            "fullRebuilding": "Полная пересборка {type}...",
+            "actionRefresh": "Обновление",
+            "actionFullRebuild": "Полная пересборка",
+            "actionRefreshLower": "обновить",
+            "actionRebuildLower": "пересобрать",
+            "stages": {
+                "scan_folders": "Сканирование папок...",
+                "count_models": "Найдено файлов: {total}",
+                "process_models": "Обработка моделей",
+                "reconcile_scan": "Проверка изменений...",
+                "process_new": "Обработка новых моделей",
+                "finalizing": "Завершение..."
+            },
+            "eta": {
+                "lessThanMinute": "Осталось меньше минуты",
+                "minutes": "Осталось ~{minutes} мин",
+                "hours": "Осталось ~{hours} ч {minutes} мин"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "Массовые операции",
-                "content": "Войдите в массовый режим, нажав эту кнопку или клавишу <span class=\"onboarding-shortcut\">B</span>. Выберите несколько моделей и выполните пакетные операции. Используйте <span class=\"onboarding-shortcut\">Ctrl+A</span> для выбора всех видимых моделей."
+                "content": "Войдите в массовый режим, нажав эту кнопку или клавишу <span class=\"onboarding-shortcut\">B</span>, чтобы выбрать несколько моделей и выполнить пакетные операции.<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> — выбрать все видимые модели, <span class=\"onboarding-shortcut\">Shift+Click</span> — выбрать диапазон.<br>• <span class=\"onboarding-shortcut\">Esc</span> или клик по пустой области выходит из массового режима."
             },
             "searchOptions": {
                 "title": "Опции поиска",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "Контекстное меню",
-                "content": "<strong>Правый клик</strong> по карточке модели откроет контекстное меню с дополнительными действиями."
+                "content": "<strong>Правый клик</strong> по любой карточке модели открывает контекстное меню с действиями над карточкой, такими как перемещение, удаление или редактирование метаданных."
+            },
+            "marqueeSelect": {
+                "title": "Выделение рамкой",
+                "content": "Удерживайте <strong>левую кнопку мыши</strong> на пустой области сетки и перетащите, чтобы нарисовать рамку, выделяющую сразу несколько карточек."
+            },
+            "dragToSidebar": {
+                "title": "Организация перетаскиванием",
+                "content": "Перетащите карточку модели на папку в боковой панели, чтобы переместить туда файл. Это также работает с несколькими выделенными карточками в массовом режиме."
+            },
+            "contextMenus": {
+                "title": "Другие контекстные меню",
+                "content": "В массовом режиме <strong>правый клик по выделенной карточке</strong> открывает меню массовых операций. <strong>Правый клик по пустой области</strong> страницы открывает глобальные действия, такие как проверка обновлений и управление исключёнными моделями."
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "Предыдущий рецепт (←)",
             "nextWithShortcut": "Следующий рецепт (→)"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "Открыть расположение файла",
+                "copyId": "Копировать ID рецепта"
+            },
+            "openFileLocation": {
+                "success": "Расположение файла успешно открыто",
+                "failed": "Не удалось открыть расположение файла",
+                "copied": "Путь скопирован в буфер обмена: {{path}}",
+                "clipboardFallback": "Путь: {{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "Отправить workflow в ComfyUI",
             "sent": "Workflow отправлен в ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "Этой модели нет в вашей библиотеке",
             "deletedTooltip": "Этот LoRA был удалён из источника и больше недоступен для скачивания",
             "hashInvalidTooltip": "Этот хеш LoRA не удаётся распознать на CivitAI - возможно, модель была обновлена",
+            "noLorasAssociated": "С этим рецептом не связаны LoRA",
+            "noLorasWhyToggle": "Почему нет LoRA?",
+            "noLorasImportMethod": "Способ импорта",
+            "noLorasInferredNote": "Возможная причина (выведена) — этот рецепт был импортирован до того, как стала записываться диагностика импорта.",
+            "noLorasChannels": {
+                "batch_import_url": "Пакетный импорт (URL изображения)",
+                "batch_import_local": "Пакетный импорт (локальный файл)",
+                "url": "Импорт по URL изображения",
+                "local": "Импорт локального файла",
+                "upload": "Загрузка изображения",
+                "widget": "Сохранён из Workflow",
+                "reimport_url": "Повторный импорт (URL изображения)",
+                "reimport_local": "Повторный импорт (локальный файл)"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "Метаданные генерации полны и не содержат ссылок на LoRA.",
+                "api_meta_no_lora_resources": "Исходный API не вернул данные о ресурсах LoRA для этого изображения. LoRA, отображаемые на странице CivitAI, могут поступать из внутренних данных, которые публичный API не раскрывает.",
+                "api_meta_missing": "Исходный API не вернул метаданные генерации для этого изображения.",
+                "no_embedded_metadata": "Изображение не содержит встроенных метаданных генерации, поэтому восстановить информацию о LoRA невозможно.",
+                "workflow_metadata_limited": "Встроенные метаданные изображения представляют собой Workflow ComfyUI; извлечение информации о LoRA из Workflow ограничено.",
+                "video_no_metadata": "Видеофайлы не содержат встроенных метаданных генерации.",
+                "metadata_unsupported": "Изображение содержит метаданные в формате, который не удалось разобрать.",
+                "unknown": "Причину не удалось определить по сохранённым данным рецепта."
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "Поля метаданных API",
+                "modelVersionIds": "Сообщено ID версий моделей",
+                "embeddedMetadata": "Встроенные метаданные",
+                "present": "найдены",
+                "absent": "нет"
+            },
             "download": "Скачать",
             "downloadLoraTooltip": "Скачать этот LoRA",
             "preparingDownload": "Подготовка к скачиванию...",
             "reconnect": "Переподключить",
             "reconnectTooltip": "Переподключить к локальному LoRA",
+            "reconnectInstructions": "Введите синтаксис или имя LoRA для переподключения:",
+            "reconnectExample": "Пример: <lora:name:1> или просто имя",
+            "reconnectPlaceholder": "Введите имя или синтаксис LoRA",
+            "reconnectSuggestionsLoading": "Поиск в локальной библиотеке...",
+            "reconnectSuggestionsEmpty": "В локальной библиотеке нет подходящих LoRA",
+            "reconnectMatchSameHash": "Тот же хеш",
+            "reconnectMatchSameVersion": "Та же версия модели",
+            "reconnectMatchSimilarFilename": "Похожее имя файла",
+            "reconnectMatchSimilarName": "Похожее имя",
+            "undoReconnect": "Отменить",
+            "undoReconnectTooltip": "Восстановить привязку, которая была у записи до переподключения",
+            "undoReconnectTooltipNamed": "Восстановить {name} (привязка до переподключения)",
             "viewOnCivitai": "Открыть на CivitAI",
             "openLoraDetails": "Открыть {name} в библиотеке LoRA",
-            "openCheckpointDetails": "Открыть {name} в библиотеке моделей"
+            "openCheckpointDetails": "Открыть {name} в библиотеке моделей",
+            "checkpointDeletedTooltip": "Этот чекпойнт был удалён из источника и больше не может быть скачан - переподключите его к локальной модели",
+            "checkpointHashInvalidTooltip": "Хеш этого чекпойнта не удаётся разрешить на CivitAI - возможно, модель была обновлена",
+            "reconnectCheckpoint": "Переподключить",
+            "reconnectCheckpointTooltip": "Переподключить к локальному чекпойнту",
+            "checkpointReconnectInstructions": "Введите имя чекпойнта для переподключения:",
+            "checkpointReconnectPlaceholder": "Введите имя чекпойнта",
+            "checkpointReconnectSuggestionsEmpty": "В локальной библиотеке нет подходящих чекпойнтов"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "Значение",
                 "add": "Добавить",
-                "invalidRange": "Неверный формат диапазона. Используйте x.x-y.y"
+                "invalidRange": "Неверный формат диапазона. Используйте x.x-y.y",
+                "invalidValue": "Введите корректное число",
+                "saveFailed": "Не удалось сохранить предустановленный параметр",
+                "added": "Предустановленный параметр добавлен",
+                "updated": "Предустановленный параметр обновлён"
             },
             "triggerWords": {
                 "label": "Триггерные слова",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "Введите для добавления или нажмите на предложения ниже",
                 "editWord": "Редактировать триггерное слово",
                 "editPlaceholder": "Редактировать триггерное слово",
-                "copyWord": "Копировать триггерное слово",
+                "copyOrEditWord": "Клик — скопировать, двойной клик — редактировать",
                 "deleteWord": "Удалить триггерное слово",
                 "suggestions": {
                     "noSuggestions": "Предложения недоступны",
@@ -1599,8 +1701,8 @@
                 "showCount": "Показать примеры ({count})",
                 "hideExamples": "Скрыть примеры",
                 "addExamples": "Добавить примеры",
-                "previousExample": "Предыдущий пример",
+                "previousExample": "Предыдущий пример ([)",
-                "nextExample": "Следующий пример",
+                "nextExample": "Следующий пример (])",
                 "noExamples": "Примеры изображений недоступны",
                 "addMoreExamples": "Добавить ещё примеры",
                 "dragDrop": "Перетащите изображения или видео сюда",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "Начало работы",
             "updateVlogs": "Видео обновлений",
-            "documentation": "Документация"
+            "documentation": "Документация",
+            "shortcuts": "Горячие клавиши"
         },
         "gettingStarted": {
-            "title": "Начало работы с LoRA Manager"
+            "title": "Начало работы с LoRA Manager",
+            "replayTutorial": "Повторить обучение"
+        },
+        "shortcuts": {
+            "title": "Горячие клавиши и действия мыши",
+            "groups": {
+                "general": "Общие",
+                "actions": "Действия",
+                "selection": "Выделение и массовый режим",
+                "navigation": "Навигация",
+                "modelModal": "Окно модели / рецепта",
+                "mediaViewer": "Просмотр медиа / Витрина"
+            },
+            "keys": {
+                "click": "Клик",
+                "drag": "Перетаскивание",
+                "rightClick": "Правый клик",
+                "letter": "Буква",
+                "swipe": "Свайп"
+            },
+            "entries": {
+                "focusSearch": "Переход к поиску",
+                "closeModal": "Закрыть модальное окно / панель",
+                "openShortcuts": "Открыть эту панель горячих клавиш",
+                "refresh": "Обновить список моделей",
+                "fetchMetadata": "Получить метаданные с CivitAI (только на страницах моделей)",
+                "downloadModel": "Загрузить модель (только на страницах моделей)",
+                "toggleBulkMode": "Переключить массовый режим",
+                "selectAll": "Выбрать все видимые модели",
+                "rangeSelect": "Выбор диапазона",
+                "marqueeSelect": "Выделение карточек рамкой (на пустой области сетки)",
+                "exitBulkMode": "Выйти из массового режима",
+                "bulkActions": "На выделенной карточке: меню массовых операций",
+                "globalActions": "На пустой области страницы: меню глобальных действий (проверка обновлений, управление исключёнными моделями)",
+                "scrollPages": "Прокрутка страниц",
+                "jumpAlphabet": "Переход по алфавитной панели",
+                "prevNext": "Предыдущая / следующая модель",
+                "deleteEntry": "Удалить",
+                "cycleMedia": "Переключение медиа ([ / ] в галерее витрины)",
+                "swipeTouch": "Переключение медиа на сенсорных устройствах",
+                "closeViewer": "Закрыть окно просмотра"
+            }
         },
         "updateVlogs": {
             "title": "Последние обновления",
@@ -1884,7 +2028,8 @@
             "settings": "Настройки и конфигурация",
             "extensions": "Расширения",
             "newBadge": "НОВОЕ"
-        }
+        },
+        "newContentBadge": "НОВОЕ"
     },
     "update": {
         "title": "Проверить обновления",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "Ошибка подготовки LoRAs для загрузки",
             "enterLoraName": "Пожалуйста, введите название LoRA или синтаксис",
             "reconnectedSuccessfully": "LoRA успешно переподключена",
+            "reconnectBaseModelMismatch": "Переподключение выполнено, но базовые модели различаются (рецепт: {recipe}, LoRA: {lora}) — они совместимы по архитектуре",
             "reconnectFailed": "Ошибка переподключения LoRA: {message}",
+            "loraRestored": "LoRA восстановлена к прежней привязке",
+            "loraRestoreFailed": "Ошибка восстановления LoRA: {message}",
             "noPromptToSend": "Нет промпта для отправки",
             "cannotSend": "Невозможно отправить рецепт: отсутствует ID рецепта",
             "sendFailed": "Не удалось отправить рецепт в workflow",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "Путь к чекпойнту недоступен",
             "missingCheckpointInfo": "Отсутствуют данные о чекпойнте",
             "downloadCheckpointFailed": "Не удалось скачать чекпойнт: {message}",
+            "enterCheckpointName": "Введите имя чекпойнта",
+            "checkpointReconnectedSuccessfully": "Чекпойнт успешно переподключён",
+            "reconnectCheckpointBaseModelMismatch": "Переподключение выполнено, но базовые модели различаются (рецепт: {recipe}, чекпойнт: {checkpoint}) — они совместимы по архитектуре",
+            "checkpointReconnectFailed": "Ошибка переподключения чекпойнта: {message}",
+            "checkpointRestored": "Чекпойнт восстановлен к прежней привязке",
+            "checkpointRestoreFailed": "Ошибка восстановления чекпойнта: {message}",
+            "checkpointDownloadUnavailable": "Этот чекпойнт нельзя скачать без идентификаторов CivitAI - попробуйте переподключить его к локальному чекпойнту",
             "missingLoraDownloadInfo": "Нет информации для скачивания этого LoRA",
             "hashNotFoundOnCivitai": "Этот хеш LoRA не удаётся распознать на CivitAI - возможно, модель была обновлена или хеш недействителен",
             "downloadLoraFailed": "Не удалось скачать LoRA: {message}",

locales/zh-CN.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "正在刷新 {type}...",
+            "fullRebuilding": "正在完全重建 {type}...",
+            "actionRefresh": "刷新",
+            "actionFullRebuild": "完全重建",
+            "actionRefreshLower": "刷新",
+            "actionRebuildLower": "重建",
+            "stages": {
+                "scan_folders": "正在扫描文件夹...",
+                "count_models": "找到 {total} 个文件",
+                "process_models": "正在处理模型",
+                "reconcile_scan": "正在检查变更...",
+                "process_new": "正在处理新模型",
+                "finalizing": "正在收尾..."
+            },
+            "eta": {
+                "lessThanMinute": "剩余时间不到一分钟",
+                "minutes": "剩余约 {minutes} 分钟",
+                "hours": "剩余约 {hours} 小时 {minutes} 分钟"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "批量操作",
-                "content": "点击此按钮或按 <span class=\"onboarding-shortcut\">B</span> 进入批量模式。可多选模型并进行批量操作。使用 <span class=\"onboarding-shortcut\">Ctrl+A</span> 全选所有可见模型。"
+                "content": "点击此按钮或按 <span class=\"onboarding-shortcut\">B</span> 进入批量模式，可多选模型并执行批量操作。<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> 全选所有可见模型，<span class=\"onboarding-shortcut\">Shift+Click</span> 选择一个范围。<br>• 按 <span class=\"onboarding-shortcut\">Esc</span> 或点击空白区域退出批量模式。"
             },
             "searchOptions": {
                 "title": "搜索选项",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "右键菜单",
-                "content": "<strong>右键点击</strong>任意模型卡片可打开更多操作菜单。"
+                "content": "<strong>右键点击</strong>任意模型卡片，可打开包含移动、删除或编辑元数据等卡片操作的菜单。"
+            },
+            "marqueeSelect": {
+                "title": "拖动框选",
+                "content": "在网格的空白区域按住<strong>鼠标左键</strong>并拖动，绘制一个可同时选中多张卡片的框选区域。"
+            },
+            "dragToSidebar": {
+                "title": "拖放整理",
+                "content": "将模型卡片拖到侧边栏的文件夹上，即可把文件移动到该文件夹。批量模式下选中的多张卡片也可如此操作。"
+            },
+            "contextMenus": {
+                "title": "更多右键菜单",
+                "content": "在批量模式下，<strong>右键点击已选中的卡片</strong>可进行批量操作。<strong>右键点击页面空白区域</strong>可使用检查更新、管理已排除的模型等全局操作。"
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "上一个配方（←）",
             "nextWithShortcut": "下一个配方（→）"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "打开文件位置",
+                "copyId": "复制配方 ID"
+            },
+            "openFileLocation": {
+                "success": "文件位置已成功打开",
+                "failed": "打开文件位置失败",
+                "copied": "路径已复制到剪贴板：{{path}}",
+                "clipboardFallback": "路径：{{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "发送工作流到 ComfyUI",
             "sent": "工作流已发送到 ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "该模型不在你的本地库中",
             "deletedTooltip": "该 LoRA 已从来源站删除，无法下载",
             "hashInvalidTooltip": "此 LoRA 哈希无法在 CivitAI 上解析——模型可能已更新",
+            "noLorasAssociated": "此配方没有关联任何 LoRA",
+            "noLorasWhyToggle": "为什么没有 LoRA？",
+            "noLorasImportMethod": "导入方式",
+            "noLorasInferredNote": "可能的原因（推断）——该配方是在记录导入诊断信息之前导入的。",
+            "noLorasChannels": {
+                "batch_import_url": "批量导入（图片 URL）",
+                "batch_import_local": "批量导入（本地文件）",
+                "url": "图片 URL 导入",
+                "local": "本地文件导入",
+                "upload": "图片上传",
+                "widget": "从工作流保存",
+                "reimport_url": "重新导入（图片 URL）",
+                "reimport_local": "重新导入（本地文件）"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "生成元数据完整，且未引用任何 LoRA。",
+                "api_meta_no_lora_resources": "来源 API 未返回此图片的 LoRA 资源数据。CivitAI 页面上显示的 LoRA 可能来自公开 API 未开放的内部数据。",
+                "api_meta_missing": "来源 API 未返回此图片的生成元数据。",
+                "no_embedded_metadata": "图片没有内嵌生成元数据，因此无法恢复 LoRA 信息。",
+                "workflow_metadata_limited": "图片内嵌的元数据是 ComfyUI 工作流；从工作流中提取 LoRA 信息的能力有限。",
+                "video_no_metadata": "视频文件不携带内嵌生成元数据。",
+                "metadata_unsupported": "图片包含的元数据格式无法解析。",
+                "unknown": "无法从存储的配方数据中确定原因。"
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "API 元数据字段",
+                "modelVersionIds": "报告的模型版本 ID 数",
+                "embeddedMetadata": "内嵌元数据",
+                "present": "已找到",
+                "absent": "无"
+            },
             "download": "下载",
             "downloadLoraTooltip": "下载此 LoRA",
             "preparingDownload": "正在准备下载...",
             "reconnect": "重新关联",
             "reconnectTooltip": "与本地 LoRA 重新关联",
+            "reconnectInstructions": "输入 LoRA 语法或名称以重新关联：",
+            "reconnectExample": "示例：<lora:name:1> 或只填名称",
+            "reconnectPlaceholder": "输入 LoRA 名称或语法",
+            "reconnectSuggestionsLoading": "正在搜索本地库...",
+            "reconnectSuggestionsEmpty": "本地库中没有匹配的 LoRA",
+            "reconnectMatchSameHash": "相同哈希",
+            "reconnectMatchSameVersion": "相同模型版本",
+            "reconnectMatchSimilarFilename": "相似文件名",
+            "reconnectMatchSimilarName": "相似名称",
+            "undoReconnect": "撤销",
+            "undoReconnectTooltip": "恢复此条目在重新关联前的关联",
+            "undoReconnectTooltipNamed": "恢复为 {name}（重新关联前的关联）",
             "viewOnCivitai": "在 CivitAI 上查看",
             "openLoraDetails": "在 LoRA 库中查看 {name}",
-            "openCheckpointDetails": "在模型库中查看 {name}"
+            "openCheckpointDetails": "在模型库中查看 {name}",
+            "checkpointDeletedTooltip": "此 Checkpoint 已从来源删除，无法再下载 - 请使用本地模型重新关联",
+            "checkpointHashInvalidTooltip": "此 Checkpoint 的哈希无法在 CivitAI 上解析 - 模型可能已更新",
+            "reconnectCheckpoint": "重新关联",
+            "reconnectCheckpointTooltip": "与本地 Checkpoint 重新关联",
+            "checkpointReconnectInstructions": "输入 Checkpoint 名称以重新关联：",
+            "checkpointReconnectPlaceholder": "输入 Checkpoint 名称",
+            "checkpointReconnectSuggestionsEmpty": "本地库中没有匹配的 Checkpoint"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "数值",
                 "add": "添加",
-                "invalidRange": "无效的范围格式。请使用 x.x-y.y"
+                "invalidRange": "无效的范围格式。请使用 x.x-y.y",
+                "invalidValue": "请输入有效的数值",
+                "saveFailed": "保存预设参数失败",
+                "added": "已添加预设参数",
+                "updated": "已更新预设参数"
             },
             "triggerWords": {
                 "label": "触发词",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "输入或点击下方建议添加",
                 "editWord": "编辑触发词",
                 "editPlaceholder": "编辑触发词",
-                "copyWord": "复制触发词",
+                "copyOrEditWord": "单击复制，双击编辑",
                 "deleteWord": "删除触发词",
                 "suggestions": {
                     "noSuggestions": "暂无建议",
@@ -1599,8 +1701,8 @@
                 "showCount": "显示示例（{count}）",
                 "hideExamples": "隐藏示例",
                 "addExamples": "添加示例",
-                "previousExample": "上一个示例",
+                "previousExample": "上一个示例（[）",
-                "nextExample": "下一个示例",
+                "nextExample": "下一个示例（]）",
                 "noExamples": "暂无示例图片",
                 "addMoreExamples": "添加更多示例",
                 "dragDrop": "将图片或视频拖放到此处",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "新手入门",
             "updateVlogs": "更新日志",
-            "documentation": "文档"
+            "documentation": "文档",
+            "shortcuts": "快捷键"
         },
         "gettingStarted": {
-            "title": "LoRA 管理器新手入门"
+            "title": "LoRA 管理器新手入门",
+            "replayTutorial": "重播教程"
+        },
+        "shortcuts": {
+            "title": "键盘与鼠标快捷键",
+            "groups": {
+                "general": "通用",
+                "actions": "操作",
+                "selection": "选择与批量模式",
+                "navigation": "导航",
+                "modelModal": "模型 / 配方弹窗",
+                "mediaViewer": "媒体查看器 / 示例展示"
+            },
+            "keys": {
+                "click": "单击",
+                "drag": "拖动",
+                "rightClick": "右键点击",
+                "letter": "字母",
+                "swipe": "滑动"
+            },
+            "entries": {
+                "focusSearch": "聚焦搜索框",
+                "closeModal": "关闭弹窗 / 面板",
+                "openShortcuts": "打开本快捷键面板",
+                "refresh": "刷新模型列表",
+                "fetchMetadata": "从 CivitAI 获取元数据（仅模型页面）",
+                "downloadModel": "下载模型（仅模型页面）",
+                "toggleBulkMode": "切换批量模式",
+                "selectAll": "全选所有可见模型",
+                "rangeSelect": "范围选择",
+                "marqueeSelect": "框选卡片（在网格空白区域）",
+                "exitBulkMode": "退出批量模式",
+                "bulkActions": "在已选中的卡片上：批量操作菜单",
+                "globalActions": "在页面空白区域：全局操作菜单（检查更新、管理已排除的模型）",
+                "scrollPages": "滚动页面",
+                "jumpAlphabet": "字母索引栏跳转",
+                "prevNext": "上一个 / 下一个模型",
+                "deleteEntry": "删除",
+                "cycleMedia": "切换媒体（在示例展示中按 [ / ]）",
+                "swipeTouch": "在触屏设备上切换媒体",
+                "closeViewer": "关闭查看器"
+            }
         },
         "updateVlogs": {
             "title": "最新更新",
@@ -1884,7 +2028,8 @@
             "settings": "设置与配置",
             "extensions": "扩展",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "检查更新",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "准备下载 LoRA 时出错",
             "enterLoraName": "请输入 LoRA 名称或语法",
             "reconnectedSuccessfully": "LoRA 重新连接成功",
+            "reconnectBaseModelMismatch": "已重新关联，但基础模型不同（配方：{recipe}，LoRA：{lora}）——两者架构兼容",
             "reconnectFailed": "LoRA 重新连接出错：{message}",
+            "loraRestored": "LoRA 已恢复为重新关联前的关联",
+            "loraRestoreFailed": "LoRA 恢复出错：{message}",
             "noPromptToSend": "没有可发送的提示词",
             "cannotSend": "无法发送配方：缺少配方 ID",
             "sendFailed": "发送配方到工作流失败",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "缺少Checkpoint路径",
             "missingCheckpointInfo": "缺少Checkpoint信息",
             "downloadCheckpointFailed": "下载Checkpoint失败：{message}",
+            "enterCheckpointName": "请输入 Checkpoint 名称",
+            "checkpointReconnectedSuccessfully": "Checkpoint 重新连接成功",
+            "reconnectCheckpointBaseModelMismatch": "已重新关联，但基础模型不同（配方：{recipe}，Checkpoint：{checkpoint}）——两者架构兼容",
+            "checkpointReconnectFailed": "Checkpoint 重新连接出错：{message}",
+            "checkpointRestored": "Checkpoint 已恢复为重新关联前的关联",
+            "checkpointRestoreFailed": "Checkpoint 恢复出错：{message}",
+            "checkpointDownloadUnavailable": "缺少 CivitAI 标识，无法下载此 Checkpoint - 请尝试使用本地 Checkpoint 重新关联",
             "missingLoraDownloadInfo": "缺少此 LoRA 的下载信息",
             "hashNotFoundOnCivitai": "此 LoRA 哈希无法在 CivitAI 上解析——模型可能已更新或哈希无效",
             "downloadLoraFailed": "下载 LoRA 失败：{message}",

locales/zh-TW.json

@@ -50,6 +50,27 @@
             "mb": "MB",
             "gb": "GB",
             "tb": "TB"
+        },
+        "scanProgress": {
+            "refreshing": "正在重新整理 {type}...",
+            "fullRebuilding": "正在完整重建 {type}...",
+            "actionRefresh": "重新整理",
+            "actionFullRebuild": "完整重建",
+            "actionRefreshLower": "重新整理",
+            "actionRebuildLower": "重建",
+            "stages": {
+                "scan_folders": "正在掃描資料夾...",
+                "count_models": "找到 {total} 個檔案",
+                "process_models": "正在處理模型",
+                "reconcile_scan": "正在檢查變更...",
+                "process_new": "正在處理新模型",
+                "finalizing": "正在收尾..."
+            },
+            "eta": {
+                "lessThanMinute": "剩餘時間不到一分鐘",
+                "minutes": "剩餘約 {minutes} 分鐘",
+                "hours": "剩餘約 {hours} 小時 {minutes} 分鐘"
+            }
         }
     },
     "onboarding": {
@@ -75,7 +96,7 @@
             },
             "bulk": {
                 "title": "批次操作",
-                "content": "點擊此按鈕或按下 <span class=\"onboarding-shortcut\">B</span> 進入批次模式。可選取多個模型並執行批量操作。使用 <span class=\"onboarding-shortcut\">Ctrl+A</span> 選取所有可見模型。"
+                "content": "點擊此按鈕或按下 <span class=\"onboarding-shortcut\">B</span> 進入批量模式，選取多個模型並執行批量操作。<br>• <span class=\"onboarding-shortcut\">Ctrl/Cmd+A</span> 選取所有可見模型，<span class=\"onboarding-shortcut\">Shift+Click</span> 選取一段範圍。<br>• <span class=\"onboarding-shortcut\">Esc</span> 或點擊空白處離開批量模式。"
             },
             "searchOptions": {
                 "title": "搜尋選項",
@@ -95,7 +116,19 @@
             },
             "contextMenu": {
                 "title": "右鍵選單",
-                "content": "<strong>右鍵點擊</strong>任一模型卡片可開啟更多操作選單。"
+                "content": "<strong>右鍵點擊</strong>任一模型卡片，可開啟包含移動、刪除或編輯中繼資料等卡片操作的右鍵選單。"
+            },
+            "marqueeSelect": {
+                "title": "拖曳框選",
+                "content": "在網格空白處按住<strong>滑鼠左鍵</strong>並拖曳，畫出框選範圍，一次選取多張卡片。"
+            },
+            "dragToSidebar": {
+                "title": "拖曳整理",
+                "content": "將模型卡片拖曳到側邊欄的資料夾上，即可將檔案移動到該處。在批量模式下選取多張卡片也可一起拖曳。"
+            },
+            "contextMenus": {
+                "title": "更多右鍵選單",
+                "content": "在批量模式下，<strong>右鍵點擊已選取的卡片</strong>可開啟批量操作選單。<strong>右鍵點擊頁面空白處</strong>可開啟全域操作選單，例如檢查更新與管理已排除的模型。"
             }
         }
     },
@@ -868,6 +901,21 @@
             "previousWithShortcut": "上一個配方（←）",
             "nextWithShortcut": "下一個配方（→）"
         },
+        "modal": {
+            "metadata": {
+                "id": "ID"
+            },
+            "actions": {
+                "openFileLocation": "開啟檔案位置",
+                "copyId": "複製配方 ID"
+            },
+            "openFileLocation": {
+                "success": "檔案位置已成功開啟",
+                "failed": "開啟檔案位置失敗",
+                "copied": "路徑已複製到剪貼簿：{{path}}",
+                "clipboardFallback": "路徑：{{path}}"
+            }
+        },
         "workflow": {
             "sendWorkflow": "傳送工作流到 ComfyUI",
             "sent": "工作流已傳送到 ComfyUI",
@@ -898,14 +946,64 @@
             "notInLibraryTooltip": "此模型不在您的本地庫中",
             "deletedTooltip": "此 LoRA 已從來源站刪除，無法下載",
             "hashInvalidTooltip": "此 LoRA 雜湊無法在 CivitAI 上解析——模型可能已更新",
+            "noLorasAssociated": "此配方未關聯任何 LoRA",
+            "noLorasWhyToggle": "為什麼沒有 LoRA？",
+            "noLorasImportMethod": "匯入方式",
+            "noLorasInferredNote": "可能的原因（推斷）——此配方是在記錄匯入診斷資訊之前匯入的。",
+            "noLorasChannels": {
+                "batch_import_url": "批量匯入（圖片 URL）",
+                "batch_import_local": "批量匯入（本機檔案）",
+                "url": "圖片 URL 匯入",
+                "local": "本機檔案匯入",
+                "upload": "圖片上傳",
+                "widget": "從工作流儲存",
+                "reimport_url": "重新匯入（圖片 URL）",
+                "reimport_local": "重新匯入（本機檔案）"
+            },
+            "noLorasReasons": {
+                "no_loras_used": "生成中繼資料完整，且未引用任何 LoRA。",
+                "api_meta_no_lora_resources": "來源 API 未回傳此圖片的 LoRA 資源資料。CivitAI 頁面上顯示的 LoRA 可能來自公開 API 未開放的內部資料。",
+                "api_meta_missing": "來源 API 未回傳此圖片的生成中繼資料。",
+                "no_embedded_metadata": "圖片沒有內嵌生成中繼資料，因此無法復原 LoRA 資訊。",
+                "workflow_metadata_limited": "圖片內嵌的中繼資料是 ComfyUI 工作流；從工作流中提取 LoRA 資訊的能力有限。",
+                "video_no_metadata": "影片檔案不攜帶內嵌生成中繼資料。",
+                "metadata_unsupported": "圖片包含的中繼資料格式無法解析。",
+                "unknown": "無法從儲存的配方資料中確定原因。"
+            },
+            "noLorasDetails": {
+                "apiMetaFields": "API 中繼資料欄位",
+                "modelVersionIds": "回報的模型版本 ID 數",
+                "embeddedMetadata": "內嵌中繼資料",
+                "present": "已找到",
+                "absent": "無"
+            },
             "download": "下載",
             "downloadLoraTooltip": "下載此 LoRA",
             "preparingDownload": "正在準備下載...",
             "reconnect": "重新關聯",
             "reconnectTooltip": "與本地 LoRA 重新關聯",
+            "reconnectInstructions": "輸入 LoRA 語法或名稱以重新關聯：",
+            "reconnectExample": "範例：<lora:name:1> 或只填名稱",
+            "reconnectPlaceholder": "輸入 LoRA 名稱或語法",
+            "reconnectSuggestionsLoading": "正在搜尋本地庫...",
+            "reconnectSuggestionsEmpty": "本地庫中沒有符合的 LoRA",
+            "reconnectMatchSameHash": "相同雜湊",
+            "reconnectMatchSameVersion": "相同模型版本",
+            "reconnectMatchSimilarFilename": "相似檔案名稱",
+            "reconnectMatchSimilarName": "相似名稱",
+            "undoReconnect": "撤銷",
+            "undoReconnectTooltip": "恢復此條目在重新關聯前的關聯",
+            "undoReconnectTooltipNamed": "恢復為 {name}（重新關聯前的關聯）",
             "viewOnCivitai": "在 CivitAI 上檢視",
             "openLoraDetails": "在 LoRA 庫中檢視 {name}",
-            "openCheckpointDetails": "在模型庫中檢視 {name}"
+            "openCheckpointDetails": "在模型庫中檢視 {name}",
+            "checkpointDeletedTooltip": "此 Checkpoint 已從來源刪除，無法再下載 - 請使用本地模型重新關聯",
+            "checkpointHashInvalidTooltip": "此 Checkpoint 的雜湊無法在 CivitAI 上解析 - 模型可能已更新",
+            "reconnectCheckpoint": "重新關聯",
+            "reconnectCheckpointTooltip": "與本地 Checkpoint 重新關聯",
+            "checkpointReconnectInstructions": "輸入 Checkpoint 名稱以重新關聯：",
+            "checkpointReconnectPlaceholder": "輸入 Checkpoint 名稱",
+            "checkpointReconnectSuggestionsEmpty": "本地庫中沒有符合的 Checkpoint"
         },
         "controls": {
             "import": {
@@ -1528,7 +1626,11 @@
                 "clipSkip": "Clip Skip",
                 "valuePlaceholder": "數值",
                 "add": "新增",
-                "invalidRange": "無效的範圍格式。請使用 x.x-y.y"
+                "invalidRange": "無效的範圍格式。請使用 x.x-y.y",
+                "invalidValue": "請輸入有效的數值",
+                "saveFailed": "儲存預設參數失敗",
+                "added": "已新增預設參數",
+                "updated": "已更新預設參數"
             },
             "triggerWords": {
                 "label": "觸發詞",
@@ -1539,7 +1641,7 @@
                 "addPlaceholder": "輸入或點擊下方建議",
                 "editWord": "編輯觸發詞",
                 "editPlaceholder": "編輯觸發詞",
-                "copyWord": "複製觸發詞",
+                "copyOrEditWord": "點擊複製，雙擊編輯",
                 "deleteWord": "刪除觸發詞",
                 "suggestions": {
                     "noSuggestions": "無可用建議",
@@ -1599,8 +1701,8 @@
                 "showCount": "顯示範例（{count}）",
                 "hideExamples": "隱藏範例",
                 "addExamples": "新增範例",
-                "previousExample": "上一個範例",
+                "previousExample": "上一個範例（[）",
-                "nextExample": "下一個範例",
+                "nextExample": "下一個範例（]）",
                 "noExamples": "沒有可用的範例圖片",
                 "addMoreExamples": "新增更多範例",
                 "dragDrop": "拖放圖片或影片到此處",
@@ -1864,10 +1966,52 @@
         "tabs": {
             "gettingStarted": "快速開始",
             "updateVlogs": "更新影片",
-            "documentation": "文件"
+            "documentation": "文件",
+            "shortcuts": "快捷鍵"
         },
         "gettingStarted": {
-            "title": "LoRA 管理器快速開始"
+            "title": "LoRA 管理器快速開始",
+            "replayTutorial": "重新播放教學"
+        },
+        "shortcuts": {
+            "title": "鍵盤與滑鼠快捷鍵",
+            "groups": {
+                "general": "一般",
+                "actions": "操作",
+                "selection": "選取與批量模式",
+                "navigation": "導覽",
+                "modelModal": "模型 / 配方彈窗",
+                "mediaViewer": "媒體檢視器 / 範例展示"
+            },
+            "keys": {
+                "click": "點擊",
+                "drag": "拖曳",
+                "rightClick": "右鍵點擊",
+                "letter": "字母鍵",
+                "swipe": "滑動"
+            },
+            "entries": {
+                "focusSearch": "聚焦搜尋欄",
+                "closeModal": "關閉彈窗 / 面板",
+                "openShortcuts": "開啟此快捷鍵面板",
+                "refresh": "重新整理模型列表",
+                "fetchMetadata": "從 CivitAI 擷取中繼資料（僅限模型頁面）",
+                "downloadModel": "下載模型（僅限模型頁面）",
+                "toggleBulkMode": "切換批量模式",
+                "selectAll": "選取所有可見模型",
+                "rangeSelect": "範圍選取",
+                "marqueeSelect": "框選卡片（在網格空白處拖曳）",
+                "exitBulkMode": "離開批量模式",
+                "bulkActions": "在已選取的卡片上：批量操作選單",
+                "globalActions": "在頁面空白處：全域操作選單（檢查更新、管理已排除的模型）",
+                "scrollPages": "捲動頁面",
+                "jumpAlphabet": "字母列跳轉",
+                "prevNext": "上一個 / 下一個模型",
+                "deleteEntry": "刪除",
+                "cycleMedia": "切換媒體（範例展示中的 [ / ]）",
+                "swipeTouch": "在觸控裝置上滑動切換媒體",
+                "closeViewer": "關閉檢視器"
+            }
         },
         "updateVlogs": {
             "title": "最新更新",
@@ -1884,7 +2028,8 @@
             "settings": "設定與配置",
             "extensions": "擴充功能",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "檢查更新",
@@ -2043,7 +2188,10 @@
             "preparingForDownloadFailed": "準備下載 LoRA 時發生錯誤",
             "enterLoraName": "請輸入 LoRA 名稱或語法",
             "reconnectedSuccessfully": "LoRA 重新連結成功",
+            "reconnectBaseModelMismatch": "已重新關聯，但基礎模型不同（配方：{recipe}，LoRA：{lora}）——兩者架構相容",
             "reconnectFailed": "LoRA 重新連結錯誤：{message}",
+            "loraRestored": "LoRA 已恢復為重新關聯前的關聯",
+            "loraRestoreFailed": "LoRA 恢復錯誤：{message}",
             "noPromptToSend": "沒有可發送的提示詞",
             "cannotSend": "無法傳送配方：缺少配方 ID",
             "sendFailed": "傳送配方到工作流失敗",
@@ -2051,6 +2199,13 @@
             "missingCheckpointPath": "缺少Checkpoint路徑",
             "missingCheckpointInfo": "缺少Checkpoint資訊",
             "downloadCheckpointFailed": "下載Checkpoint失敗：{message}",
+            "enterCheckpointName": "請輸入 Checkpoint 名稱",
+            "checkpointReconnectedSuccessfully": "Checkpoint 重新連結成功",
+            "reconnectCheckpointBaseModelMismatch": "已重新關聯，但基礎模型不同（配方：{recipe}，Checkpoint：{checkpoint}）——兩者架構相容",
+            "checkpointReconnectFailed": "Checkpoint 重新連結錯誤：{message}",
+            "checkpointRestored": "Checkpoint 已恢復為重新關聯前的關聯",
+            "checkpointRestoreFailed": "Checkpoint 恢復錯誤：{message}",
+            "checkpointDownloadUnavailable": "缺少 CivitAI 標識，無法下載此 Checkpoint - 請嘗試使用本地 Checkpoint 重新關聯",
             "missingLoraDownloadInfo": "缺少此 LoRA 的下載資訊",
             "hashNotFoundOnCivitai": "此 LoRA 雜湊無法在 CivitAI 上解析——模型可能已更新或雜湊無效",
             "downloadLoraFailed": "下載 LoRA 失敗：{message}",

py/nodes/random_checkpoint_loader.py

@@ -1,214 +0,0 @@
-import logging
-import os
-import random
-from typing import Any, List, Optional, Tuple
-import comfy.sd  # pyright: ignore[reportMissingImports]
-import folder_paths  # pyright: ignore[reportMissingImports]
-from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
-
-logger = logging.getLogger(__name__)
-
-
-class RandomCheckpointLoaderLM:
-    """Checkpoint Loader that can randomly pick a checkpoint from the pool
-
-    Loads checkpoints from both standard ComfyUI folders and LoRA Manager's
-    extra folder paths. When select_at_random is enabled, ignores ckpt_name
-    and picks a random checkpoint (optionally filtered by base_model) on
-    every run.
-    """
-
-    NAME = "Random Checkpoint Loader (LoraManager)"
-    CATEGORY = "Lora Manager/loaders"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        # Get list of checkpoint names from scanner (includes extra folder paths)
-        checkpoint_names = cls._get_checkpoint_names()
-        base_models = cls._get_available_base_models()
-        return {
-            "required": {
-                "ckpt_name": (
-                    checkpoint_names,
-                    {"tooltip": "The name of the checkpoint (model) to load."},
-                ),
-                "select_at_random": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Ignore ckpt_name and pick a random checkpoint from the "
-                            "pool (optionally filtered by base_model) on every run."
-                        ),
-                    },
-                ),
-                "base_model": (
-                    base_models,
-                    {
-                        "default": "Any",
-                        "tooltip": "Restrict random selection to this base model. 'Any' uses the full pool.",
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING")
-    RETURN_NAMES = ("MODEL", "CLIP", "VAE", "model_name")
-    OUTPUT_TOOLTIPS = (
-        "The model used for denoising latents.",
-        "The CLIP model used for encoding text prompts.",
-        "The VAE model used for encoding and decoding images to and from latent space.",
-        "The name of the checkpoint that was loaded (useful when select_at_random is enabled).",
-    )
-    FUNCTION = "load_checkpoint"
-
-    @classmethod
-    def IS_CHANGED(cls, ckpt_name, select_at_random=False, base_model="Any"):
-        # Force re-execution on every run while randomizing, since the widget
-        # values themselves don't change between queue runs.
-        if select_at_random:
-            return float("nan")
-        return ckpt_name
-
-    @staticmethod
-    def _run_async(coro_fn):
-        """Run an async fetcher, handling the case where an event loop is already running."""
-        import asyncio
-
-        try:
-            asyncio.get_running_loop()
-            import concurrent.futures
-
-            def run_in_thread():
-                new_loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(new_loop)
-                try:
-                    return new_loop.run_until_complete(coro_fn())
-                finally:
-                    new_loop.close()
-
-            with concurrent.futures.ThreadPoolExecutor() as executor:
-                future = executor.submit(run_in_thread)
-                return future.result()
-        except RuntimeError:
-            return asyncio.run(coro_fn())
-
-    @classmethod
-    def _get_checkpoint_names(cls, base_model: Optional[str] = None) -> List[str]:
-        """Get list of checkpoint names from scanner cache in ComfyUI format (relative path with extension)
-
-        Args:
-            base_model: If given (and not "Any"), only include checkpoints matching this base model.
-        """
-        try:
-            from ..services.service_registry import ServiceRegistry
-
-            async def _get_names():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                # Get all model roots for calculating relative paths
-                model_roots = scanner.get_model_roots()
-
-                # Filter only checkpoint type (not diffusion_model) and format names
-                names = []
-                for item in cache.raw_data:
-                    if item.get("sub_type") != "checkpoint":
-                        continue
-                    if (
-                        base_model
-                        and base_model != "Any"
-                        and item.get("base_model") != base_model
-                    ):
-                        continue
-                    file_path = item.get("file_path", "")
-                    # Only offer models that still exist on disk so ComfyUI
-                    # flags missing checkpoints at queue time via
-                    # "value not in list" (the scanner cache can be stale).
-                    if file_path and os.path.exists(file_path):
-                        # Format using relative path with OS-native separator
-                        formatted_name = _format_model_name_for_comfyui(
-                            file_path, model_roots
-                        )
-                        if formatted_name:
-                            names.append(formatted_name)
-
-                return sorted(names)
-
-            return cls._run_async(_get_names)
-        except Exception as e:
-            logger.error(f"Error getting checkpoint names: {e}")
-            return []
-
-    @classmethod
-    def _get_available_base_models(cls) -> List[str]:
-        """Get distinct base_model values present among indexed checkpoints, for the random-selection filter."""
-        try:
-            from ..services.service_registry import ServiceRegistry
-
-            async def _get_base_models():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                base_models = set()
-                for item in cache.raw_data:
-                    if item.get("sub_type") != "checkpoint":
-                        continue
-                    base_model = item.get("base_model")
-                    file_path = item.get("file_path", "")
-                    if base_model and file_path and os.path.exists(file_path):
-                        base_models.add(base_model)
-
-                return sorted(base_models)
-
-            return ["Any"] + cls._run_async(_get_base_models)
-        except Exception as e:
-            logger.error(f"Error getting available base models: {e}")
-            return ["Any"]
-
-    def load_checkpoint(
-        self,
-        ckpt_name: str,
-        select_at_random: bool = False,
-        base_model: str = "Any",
-    ) -> Tuple[Any, Any, Any, str]:
-        """Load a checkpoint by name, supporting extra folder paths
-
-        Args:
-            ckpt_name: The name of the checkpoint to load (relative path with extension)
-            select_at_random: If True, ignore ckpt_name and pick randomly from the pool
-            base_model: Restricts random selection to this base model ("Any" = no filter)
-
-        Returns:
-            Tuple of (MODEL, CLIP, VAE, model_name)
-        """
-        if select_at_random:
-            pool = self._get_checkpoint_names(base_model)
-            if not pool:
-                raise FileNotFoundError(
-                    f"No checkpoints found for base model '{base_model}'. "
-                    "Pick a different base model or disable 'select_at_random'."
-                )
-            ckpt_name = random.choice(pool)
-            logger.info(
-                f"[RandomCheckpointLoaderLM] Randomly selected checkpoint: {ckpt_name}"
-            )
-
-        # Get absolute path from cache using ComfyUI-style name
-        ckpt_path, metadata = get_checkpoint_info_absolute(ckpt_name)
-
-        if metadata is None:
-            raise FileNotFoundError(
-                f"Checkpoint '{ckpt_name}' not found in LoRA Manager cache. "
-                "Make sure the checkpoint is indexed and try again."
-            )
-
-        # Load regular checkpoint using ComfyUI's API
-        logger.info(f"Loading checkpoint from: {ckpt_path}")
-        out = comfy.sd.load_checkpoint_guess_config(
-            ckpt_path,
-            output_vae=True,
-            output_clip=True,
-            embedding_directory=folder_paths.get_folder_paths("embeddings"),
-        )
-        return out[:3] + (ckpt_name,)

py/nodes/random_unet_loader.py

@@ -1,326 +0,0 @@
-import logging
-import os
-import random
-from typing import Any, List, Optional, Tuple
-import comfy.sd  # pyright: ignore[reportMissingImports]
-from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
-
-logger = logging.getLogger(__name__)
-
-
-def _reload_gguf_unet(
-    unet_path: str, weight_dtype: str, disable_dynamic: bool = False
-) -> object:
-    """Reload a GGUF diffusion model from disk (cached_patcher_init factory).
-
-    Mirrors the GGUF branch of RandomUNETLoaderLM.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 = RandomUNETLoaderLM()
-    model, _unet_name = loader._load_gguf_unet(unet_path, unet_path, weight_dtype)
-    return model
-
-
-class RandomUNETLoaderLM:
-    """UNET Loader that can randomly pick a diffusion model from the pool
-
-    Loads diffusion models/UNets from both standard ComfyUI folders and LoRA
-    Manager's extra folder paths. Supports both regular diffusion models and
-    GGUF format models. When select_at_random is enabled, ignores unet_name
-    and picks a random diffusion model (optionally filtered by base_model)
-    on every run.
-    """
-
-    NAME = "Random Unet Loader (LoraManager)"
-    CATEGORY = "Lora Manager/loaders"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        # Get list of unet names from scanner (includes extra folder paths)
-        unet_names = cls._get_unet_names()
-        base_models = cls._get_available_base_models()
-        return {
-            "required": {
-                "unet_name": (
-                    unet_names,
-                    {"tooltip": "The name of the diffusion model to load."},
-                ),
-                "weight_dtype": (
-                    ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
-                    {"tooltip": "The dtype to use for the model weights."},
-                ),
-                "select_at_random": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Ignore unet_name and pick a random diffusion model from "
-                            "the pool (optionally filtered by base_model) on every run."
-                        ),
-                    },
-                ),
-                "base_model": (
-                    base_models,
-                    {
-                        "default": "Any",
-                        "tooltip": "Restrict random selection to this base model. 'Any' uses the full pool.",
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL", "STRING")
-    RETURN_NAMES = ("MODEL", "model_name")
-    OUTPUT_TOOLTIPS = (
-        "The model used for denoising latents.",
-        "The name of the diffusion model that was loaded (useful when select_at_random is enabled).",
-    )
-    FUNCTION = "load_unet"
-
-    @classmethod
-    def IS_CHANGED(
-        cls, unet_name, weight_dtype, select_at_random=False, base_model="Any"
-    ):
-        # Force re-execution on every run while randomizing, since the widget
-        # values themselves don't change between queue runs.
-        if select_at_random:
-            return float("nan")
-        return unet_name
-
-    @staticmethod
-    def _run_async(coro_fn):
-        """Run an async fetcher, handling the case where an event loop is already running."""
-        import asyncio
-
-        try:
-            asyncio.get_running_loop()
-            import concurrent.futures
-
-            def run_in_thread():
-                new_loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(new_loop)
-                try:
-                    return new_loop.run_until_complete(coro_fn())
-                finally:
-                    new_loop.close()
-
-            with concurrent.futures.ThreadPoolExecutor() as executor:
-                future = executor.submit(run_in_thread)
-                return future.result()
-        except RuntimeError:
-            return asyncio.run(coro_fn())
-
-    @classmethod
-    def _get_unet_names(cls, base_model: Optional[str] = None) -> List[str]:
-        """Get list of diffusion model names from scanner cache in ComfyUI format (relative path with extension)
-
-        Args:
-            base_model: If given (and not "Any"), only include models matching this base model.
-        """
-        try:
-            from ..services.service_registry import ServiceRegistry
-
-            async def _get_names():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                # Get all model roots for calculating relative paths
-                model_roots = scanner.get_model_roots()
-
-                # Filter only diffusion_model type and format names
-                names = []
-                for item in cache.raw_data:
-                    if item.get("sub_type") != "diffusion_model":
-                        continue
-                    if (
-                        base_model
-                        and base_model != "Any"
-                        and item.get("base_model") != base_model
-                    ):
-                        continue
-                    file_path = item.get("file_path", "")
-                    # Only offer models that still exist on disk so ComfyUI
-                    # flags missing diffusion models at queue time via
-                    # "value not in list" (the scanner cache can be stale).
-                    if file_path and os.path.exists(file_path):
-                        # Format using relative path with OS-native separator
-                        formatted_name = _format_model_name_for_comfyui(
-                            file_path, model_roots
-                        )
-                        if formatted_name:
-                            names.append(formatted_name)
-
-                return sorted(names)
-
-            return cls._run_async(_get_names)
-        except Exception as e:
-            logger.error(f"Error getting unet names: {e}")
-            return []
-
-    @classmethod
-    def _get_available_base_models(cls) -> List[str]:
-        """Get distinct base_model values present among indexed diffusion models, for the random-selection filter."""
-        try:
-            from ..services.service_registry import ServiceRegistry
-
-            async def _get_base_models():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                base_models = set()
-                for item in cache.raw_data:
-                    if item.get("sub_type") != "diffusion_model":
-                        continue
-                    base_model = item.get("base_model")
-                    file_path = item.get("file_path", "")
-                    if base_model and file_path and os.path.exists(file_path):
-                        base_models.add(base_model)
-
-                return sorted(base_models)
-
-            return ["Any"] + cls._run_async(_get_base_models)
-        except Exception as e:
-            logger.error(f"Error getting available base models: {e}")
-            return ["Any"]
-
-    def load_unet(
-        self,
-        unet_name: str,
-        weight_dtype: str,
-        select_at_random: bool = False,
-        base_model: str = "Any",
-    ) -> Tuple[Any, ...]:
-        """Load a diffusion model by name, supporting extra folder paths
-
-        Args:
-            unet_name: The name of the diffusion model to load (relative path with extension)
-            weight_dtype: The dtype to use for model weights
-            select_at_random: If True, ignore unet_name and pick randomly from the pool
-            base_model: Restricts random selection to this base model ("Any" = no filter)
-
-        Returns:
-            Tuple of (MODEL, model_name)
-        """
-        import torch
-
-        if select_at_random:
-            pool = self._get_unet_names(base_model)
-            if not pool:
-                raise FileNotFoundError(
-                    f"No diffusion models found for base model '{base_model}'. "
-                    "Pick a different base model or disable 'select_at_random'."
-                )
-            unet_name = random.choice(pool)
-            logger.info(
-                f"[RandomUNETLoaderLM] Randomly selected diffusion model: {unet_name}"
-            )
-
-        # Get absolute path from cache using ComfyUI-style name
-        unet_path, metadata = get_checkpoint_info_absolute(unet_name)
-
-        if metadata is None:
-            raise FileNotFoundError(
-                f"Diffusion model '{unet_name}' not found in LoRA Manager cache. "
-                "Make sure the model is indexed and try again."
-            )
-
-        # Check if it's a GGUF model
-        if unet_path.endswith(".gguf"):
-            return self._load_gguf_unet(unet_path, unet_name, weight_dtype)
-
-        # Load regular diffusion model using ComfyUI's API
-        logger.info(f"Loading diffusion model from: {unet_path}")
-
-        # Build model options based on weight_dtype
-        model_options = {}
-        if weight_dtype == "fp8_e4m3fn":
-            model_options["dtype"] = torch.float8_e4m3fn
-        elif weight_dtype == "fp8_e4m3fn_fast":
-            model_options["dtype"] = torch.float8_e4m3fn
-            model_options["fp8_optimizations"] = True
-        elif weight_dtype == "fp8_e5m2":
-            model_options["dtype"] = torch.float8_e5m2
-
-        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
-        return (model, unet_name)
-
-    def _load_gguf_unet(
-        self, unet_path: str, unet_name: str, weight_dtype: str
-    ) -> Tuple[Any, ...]:
-        """Load a GGUF format diffusion model
-
-        Args:
-            unet_path: Absolute path to the GGUF file
-            unet_name: Name of the model for error messages
-            weight_dtype: The dtype to use for model weights
-
-        Returns:
-            Tuple of (MODEL, model_name)
-        """
-        import torch
-        from .gguf_import_helper import get_gguf_modules
-
-        # Get ComfyUI-GGUF modules using helper (handles various import scenarios)
-        try:
-            loader_module, ops_module, nodes_module = get_gguf_modules()
-            gguf_sd_loader = getattr(loader_module, "gguf_sd_loader")
-            GGMLOps = getattr(ops_module, "GGMLOps")
-            GGUFModelPatcher = getattr(nodes_module, "GGUFModelPatcher")
-        except RuntimeError as e:
-            raise RuntimeError(f"Cannot load GGUF model '{unet_name}'. {str(e)}")
-
-        logger.info(f"Loading GGUF diffusion model from: {unet_path}")
-
-        try:
-            # Load GGUF state dict
-            sd, extra = gguf_sd_loader(unet_path)
-
-            # Prepare kwargs for metadata if supported
-            kwargs = {}
-            import inspect
-
-            valid_params = inspect.signature(
-                comfy.sd.load_diffusion_model_state_dict
-            ).parameters
-            if "metadata" in valid_params:
-                kwargs["metadata"] = extra.get("metadata", {})
-
-            # Setup custom operations with GGUF support
-            ops = GGMLOps()
-
-            # Handle weight_dtype for GGUF models
-            if weight_dtype in ("default", None):
-                ops.Linear.dequant_dtype = None
-            elif weight_dtype in ["target"]:
-                ops.Linear.dequant_dtype = weight_dtype
-            else:
-                ops.Linear.dequant_dtype = getattr(torch, weight_dtype, None)
-
-            # Load the model
-            model = comfy.sd.load_diffusion_model_state_dict(
-                sd, model_options={"custom_operations": ops}, **kwargs
-            )
-
-            if model is None:
-                raise RuntimeError(
-                    f"Could not detect model type for GGUF diffusion model: {unet_path}"
-                )
-
-            # 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, unet_name)
-
-        except Exception as e:
-            logger.error(f"Error loading GGUF diffusion model '{unet_name}': {e}")
-            raise RuntimeError(
-                f"Failed to load GGUF diffusion model '{unet_name}': {str(e)}"
-            )

py/recipes/parsers/automatic.py

@@ -8,6 +8,7 @@ from typing import Dict, Any
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
 from ...services.metadata_service import get_default_metadata_provider
+from ...utils.constants import is_empty_placeholder_hash
 
 logger = logging.getLogger(__name__)
 
@@ -524,6 +525,26 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                 weight = prompt_entries[0][1] if len(prompt_entries) == 1 else 1.0
                 lora_entry = make_lora_entry(lora_type, lora_name, weight, lora_hash)
 
+                if is_empty_placeholder_hash(lora_hash):
+                    # The empty-hash placeholder (SHA256 of an empty byte
+                    # string) is not a real hash: never look it up in the
+                    # local hash index or on CivitAI. Match by filename;
+                    # otherwise keep the item as unresolved (no hash, flagged
+                    # hashInvalid so the UI shows the unresolvable-hash state
+                    # and offers reconnect instead of download) rather than
+                    # dropping it.
+                    if recipe_scanner and lora_type == 'lora' and basename_key not in queried_local_basenames:
+                        local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
+                        if local_lora:
+                            local_entry = self.populate_lora_from_local(lora_entry, local_lora)
+                            merge_or_append_local(local_entry)
+                            continue
+                    lora_entry['hash'] = ''
+                    lora_entry['hashInvalid'] = True
+                    if not resource_lora_count:
+                        loras.append(lora_entry)
+                    continue
+
                 if lora_hash and recipe_scanner and lora_type == 'lora':
                     local_lora = await recipe_scanner.get_local_lora_by_hash(lora_hash)
                     if local_lora:

py/recipes/parsers/recipe_format.py

@@ -196,7 +196,7 @@ class RecipeFormatParser(RecipeMetadataParser):
                         filtered_gen_params[key] = value
             
             return {
-                'base_model': checkpoint['baseModel'] if checkpoint and checkpoint.get('baseModel') else recipe_metadata.get('base_model', ''),
+                'base_model': checkpoint['baseModel'] if checkpoint and checkpoint.get('baseModel') else (recipe_metadata.get('base_model') or None),
                 'loras': loras,
                 'gen_params': filtered_gen_params,
                 'tags': recipe_metadata.get('tags', []),
@@ -208,3 +208,24 @@ class RecipeFormatParser(RecipeMetadataParser):
         except Exception as e:
             logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
             return {"error": str(e), "loras": []}
+
+
+def strip_recipe_metadata(metadata_text: str) -> str:
+    """Strip the ``Recipe metadata: {...}`` block appended by LoRA Manager.
+
+    The saved recipe image carries the original generation metadata followed
+    by an appended recipe JSON block (see ``ExifUtils.append_recipe_metadata``).
+    Re-import wants to re-parse the original embedded metadata, so this returns
+    only the text before the appended marker. The input is returned unchanged
+    when no marker is present.
+    """
+    if not metadata_text:
+        return metadata_text
+    match = re.search(
+        RecipeFormatParser.METADATA_MARKER,
+        metadata_text,
+        re.IGNORECASE | re.DOTALL,
+    )
+    if not match:
+        return metadata_text
+    return metadata_text[: match.start()].strip()

py/routes/checkpoint_routes.py

@@ -47,15 +47,16 @@ class CheckpointRoutes(BaseModelRoutes):
         registrar.add_prefixed_route('GET', '/api/lm/{prefix}/checkpoints_roots', prefix, self.get_checkpoints_roots)
         registrar.add_prefixed_route('GET', '/api/lm/{prefix}/unet_roots', prefix, self.get_unet_roots)
 
-        # Name/base_model pool for the Random Checkpoint/Unet Loader nodes
+        # Name/base_model pool for the Checkpoint/Unet Loader nodes' base_model filtering
         registrar.add_prefixed_route('GET', '/api/lm/{prefix}/loader-pool', prefix, self.get_loader_pool)
     
     async def get_loader_pool(self, request: web.Request) -> web.Response:
         """Return ComfyUI-formatted model names with their base_model.
 
-        Backing data for the Random Checkpoint/Unet Loader nodes: the front-end
+        Backing data for the Checkpoint/Unet Loader nodes'
-        filters the ckpt_name/unet_name combo options by base_model using this
+        control_after_generate feature: the front-end filters the
-        pool, so control_after_generate randomizes within the narrowed set.
+        ckpt_name/unet_name combo options by base_model using this pool, so
+        randomize mode picks within the narrowed set.
         """
         try:
             sub_type = request.query.get("sub_type", "checkpoint")

py/routes/handlers/model_handlers.py

@@ -15,6 +15,10 @@ from aiohttp import web
 import jinja2
 
 from ...config import config
+from ...services.active_filters_store import (
+    ActiveFiltersStore,
+    active_filters_to_query_kwargs,
+)
 from ...services.download_coordinator import DownloadCoordinator
 from ...services.connectivity_guard import (
     OFFLINE_FRIENDLY_MESSAGE,
@@ -1595,12 +1599,50 @@ class ModelQueryHandler:
                     allow_selling_generated_content.lower() not in ("false", "0", "")
                 )
 
+            # When requested, merge the manager page's active filters stored
+            # server-side. Explicit query parameters take precedence over the
+            # stored values.
+            use_active_filters = (
+                request.query.get("use_active_filters", "").lower() in ("1", "true")
+            )
+            if use_active_filters:
+                stored = ActiveFiltersStore.get_instance().get_filters(
+                    self._service.model_type
+                )
+                injected = active_filters_to_query_kwargs(stored)
+                if folder is None and "folder" in injected:
+                    folder = injected["folder"]
+                if "recursive" not in request.query and "recursive" in injected:
+                    recursive = injected["recursive"]
+                if not base_models and injected.get("base_models"):
+                    base_models = injected["base_models"]
+                if not model_types and injected.get("model_types"):
+                    model_types = injected["model_types"]
+                if not tag_filters and injected.get("tags"):
+                    tag_filters = injected["tags"]
+                if not auto_tag_filters and injected.get("auto_tags"):
+                    auto_tag_filters = injected["auto_tags"]
+                if "tag_logic" not in request.query and injected.get("tag_logic"):
+                    injected_logic = str(injected["tag_logic"]).lower()
+                    if injected_logic in ("any", "all"):
+                        tag_logic = injected_logic
+                if credit_required is None and "credit_required" in injected:
+                    credit_required = injected["credit_required"]
+                if (
+                    allow_selling_generated_content is None
+                    and "allow_selling_generated_content" in injected
+                ):
+                    allow_selling_generated_content = injected[
+                        "allow_selling_generated_content"
+                    ]
+
             # The presence of the recursive param (always sent by the loras
             # widget when filter mode is on) signals that the filter pipeline
             # must run even when no concrete filter is set, so global settings
             # like show_only_sfw stay consistent with the list endpoint.
             apply_filters = (
-                "recursive" in request.query
+                use_active_filters
+                or "recursive" in request.query
                 or folder is not None
                 or bool(base_models)
                 or bool(model_types)
@@ -1634,6 +1676,50 @@ class ModelQueryHandler:
             )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def update_active_filters(self, request: web.Request) -> web.Response:
+        """Store the manager page's active filters for this model type."""
+        try:
+            payload = await request.json()
+        except Exception:
+            return web.json_response(
+                {"success": False, "error": "Invalid JSON body"}, status=400
+            )
+
+        if not isinstance(payload, dict):
+            return web.json_response(
+                {"success": False, "error": "Body must be a JSON object"}, status=400
+            )
+
+        try:
+            ActiveFiltersStore.get_instance().set_filters(
+                self._service.model_type, payload
+            )
+            return web.json_response({"success": True})
+        except Exception as exc:
+            self._logger.error(
+                "Error updating active filters for %s: %s",
+                self._service.model_type,
+                exc,
+                exc_info=True,
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_active_filters(self, request: web.Request) -> web.Response:
+        """Return the stored active filters for this model type."""
+        try:
+            filters = ActiveFiltersStore.get_instance().get_filters(
+                self._service.model_type
+            )
+            return web.json_response({"success": True, "filters": filters})
+        except Exception as exc:
+            self._logger.error(
+                "Error getting active filters for %s: %s",
+                self._service.model_type,
+                exc,
+                exc_info=True,
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class ModelDownloadHandler:
     """Coordinate downloads and progress reporting."""
@@ -3339,6 +3425,8 @@ class ModelHandlerSet:
             "get_model_metadata": self.query.get_model_metadata,
             "get_model_description": self.query.get_model_description,
             "get_relative_paths": self.query.get_relative_paths,
+            "update_active_filters": self.query.update_active_filters,
+            "get_active_filters": self.query.get_active_filters,
             "refresh_model_updates": self.updates.refresh_model_updates,
             "fetch_missing_civitai_license_data": self.updates.fetch_missing_civitai_license_data,
             "set_model_update_ignore": self.updates.set_model_update_ignore,

py/routes/handlers/recipe_handlers.py

@@ -26,6 +26,7 @@ from ...services.recipes import (
     RecipeValidationError,
 )
 from ...services.metadata_service import get_default_metadata_provider
+from ...services.recipe_scanner import UNKNOWN_BASE_MODEL_FILTER
 from ...utils.civitai_utils import (
     build_civitai_image_page_url,
     extract_civitai_image_id,
@@ -113,7 +114,13 @@ class RecipeHandlerSet:
             "update_recipe": self.management.update_recipe,
             "record_recipe_open": self.management.record_recipe_open,
             "reconnect_lora": self.management.reconnect_lora,
+            "restore_lora": self.management.restore_lora,
+            "get_reconnect_suggestions": self.management.get_reconnect_suggestions,
             "mark_lora_hash_invalid": self.management.mark_lora_hash_invalid,
+            "reconnect_checkpoint": self.management.reconnect_checkpoint,
+            "restore_checkpoint": self.management.restore_checkpoint,
+            "get_checkpoint_reconnect_suggestions": self.management.get_checkpoint_reconnect_suggestions,
+            "mark_checkpoint_hash_invalid": self.management.mark_checkpoint_hash_invalid,
             "find_duplicates": self.query.find_duplicates,
             "move_recipes_bulk": self.management.move_recipes_bulk,
             "bulk_delete": self.management.bulk_delete,
@@ -346,6 +353,17 @@ class RecipeListingHandler:
 
             if not recipe:
                 return web.json_response({"error": "Recipe not found"}, status=404)
+
+            # Expose the on-disk recipe JSON path so the modal can offer
+            # "open file location" without guessing the storage layout.
+            recipe = dict(recipe)
+            try:
+                json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+            except Exception:  # pragma: no cover - details must still load
+                json_path = None
+            if json_path:
+                recipe["recipe_json_path"] = json_path
+
             return web.json_response(recipe)
         except Exception as exc:
             self._logger.error(
@@ -467,17 +485,32 @@ class RecipeQueryHandler:
             cache = await recipe_scanner.get_cached_data()
 
             base_model_counts: Dict[str, int] = {}
+            unknown_count = 0
             for recipe in getattr(cache, "raw_data", []):
                 base_model = recipe.get("base_model")
                 if base_model:
                     base_model_counts[base_model] = (
                         base_model_counts.get(base_model, 0) + 1
                     )
+                else:
+                    unknown_count += 1
 
             sorted_models = [
                 {"name": model, "count": count}
                 for model, count in base_model_counts.items()
             ]
+            if unknown_count:
+                # Synthetic "Unknown" bucket for recipes whose base model could
+                # not be determined. `value` carries the filter marker so the
+                # UI can display "Unknown" without colliding with real base
+                # model strings.
+                sorted_models.append(
+                    {
+                        "name": "Unknown",
+                        "value": UNKNOWN_BASE_MODEL_FILTER,
+                        "count": unknown_count,
+                    }
+                )
             sorted_models.sort(key=lambda entry: entry["count"], reverse=True)
             if limit > 0:
                 sorted_models = sorted_models[:limit]
@@ -1068,12 +1101,14 @@ class RecipeManagementHandler:
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def reimport_recipe(self, request: web.Request) -> web.Response:
-        """Delete a recipe and re-import it from its source URL.
+        """Delete a recipe and re-import it from its source.
 
-        This gives the recipe a fresh start — re-downloads the image from
+        Gives the recipe a fresh start: URL-sourced recipes re-download the
-        CivitAI, re-parses EXIF metadata with the current parser, and
+        image from CivitAI; local ones re-parse the saved recipe image. Both
-        re-resolves LoRAs / checkpoint. User edits (title, tags, favorite)
+        use the original embedded generation metadata (the appended recipe
-        are carried over from the old recipe.
+        metadata block is ignored) with the current parser, and re-resolve
+        LoRAs / checkpoint. User edits (title, tags, favorite) are carried
+        over from the old recipe.
         """
         try:
             await self._ensure_dependencies_ready()
@@ -1086,13 +1121,40 @@ class RecipeManagementHandler:
             if not old_recipe:
                 raise RecipeNotFoundError(f"Recipe {recipe_id} not found")
 
-            source_path = old_recipe.get("source_path")
+            old_file_path = old_recipe.get("file_path", "")
-            if not source_path:
+            old_folder = os.path.dirname(old_file_path) if old_file_path else None
+
+            source_path = old_recipe.get("source_path") or ""
+            image_id = extract_civitai_image_id(source_path) if source_path else None
+
+            # Local re-import sources: an explicit local source_path, or — when
+            # no usable source_path was recorded (drag & drop / file-picker
+            # imports, or a dangling path left by an earlier re-import) — the
+            # recipe's own saved image, which still carries the original
+            # embedded generation metadata next to the recipe metadata block.
+            # In the fallback case nothing is persisted as source_path: the
+            # recipe's own previous preview is not an external source, and it
+            # is deleted together with the old recipe below.
+            local_source = None
+            persisted_source_path = ""
+            if not image_id and source_path and os.path.isfile(source_path):
+                local_source = source_path
+                persisted_source_path = source_path
+            elif (
+                not image_id
+                and not source_path.startswith(("http://", "https://"))
+                and old_file_path
+                and os.path.isfile(old_file_path)
+            ):
+                local_source = old_file_path
+
+            if not image_id and not local_source:
                 return web.json_response(
                     {
                         "success": False,
                         "error": (
-                            "Recipe has no source URL — cannot re-import. "
+                            "Recipe has no re-importable source (no source URL "
+                            "and no accessible local image). "
                             "Use repair or manual import instead."
                         ),
                     },
@@ -1106,33 +1168,15 @@ class RecipeManagementHandler:
             if "tags" in user_edits and not isinstance(user_edits["tags"], list):
                 del user_edits["tags"]
 
-            old_file_path = old_recipe.get("file_path", "")
+            if local_source:
-            old_folder = os.path.dirname(old_file_path) if old_file_path else None
-
-            image_id = extract_civitai_image_id(source_path)
-            is_local_file = not image_id and os.path.isfile(source_path)
-
-            if not image_id and not is_local_file:
-                return web.json_response(
-                    {
-                        "success": False,
-                        "error": (
-                            "Recipe source is neither a valid CivitAI image URL "
-                            "nor an accessible local file. "
-                            "Use repair or manual import instead."
-                        ),
-                    },
-                    status=400,
-                )
-
-            if is_local_file:
                 return await self._do_reimport_from_local(
-                    source_path,
+                    local_source,
                     recipe_scanner,
                     recipe_id=recipe_id,
                     target_dir=old_folder,
                     user_edits=user_edits,
                     old_title=old_recipe.get("title", ""),
+                    persisted_source_path=persisted_source_path,
                 )
 
             async with self._import_semaphore:
@@ -1593,6 +1637,65 @@ class RecipeManagementHandler:
             self._logger.error("Error reconnecting LoRA: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def restore_lora(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            data = await request.json()
+            for field in ("recipe_id", "lora_index"):
+                if field not in data:
+                    raise RecipeValidationError(f"Missing required field: {field}")
+
+            result = await self._persistence_service.restore_lora(
+                recipe_scanner=recipe_scanner,
+                recipe_id=data["recipe_id"],
+                lora_index=int(data["lora_index"]),
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error restoring LoRA: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def get_reconnect_suggestions(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            recipe_id = request.match_info.get("recipe_id")
+            lora_index_raw = request.match_info.get("lora_index")
+            if not recipe_id or lora_index_raw is None:
+                raise RecipeValidationError("recipe_id and lora_index are required")
+            try:
+                lora_index = int(lora_index_raw)
+            except (TypeError, ValueError):
+                raise RecipeValidationError("lora_index must be an integer")
+
+            result = await self._persistence_service.get_reconnect_suggestions(
+                recipe_scanner=recipe_scanner,
+                recipe_id=recipe_id,
+                lora_index=lora_index,
+                query=request.query.get("query") or None,
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error(
+                "Error suggesting reconnect candidates: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
     async def mark_lora_hash_invalid(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -1622,6 +1725,116 @@ class RecipeManagementHandler:
             )
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def reconnect_checkpoint(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            data = await request.json()
+            for field in ("recipe_id", "target_name"):
+                if field not in data:
+                    raise RecipeValidationError(f"Missing required field: {field}")
+
+            result = await self._persistence_service.reconnect_checkpoint(
+                recipe_scanner=recipe_scanner,
+                recipe_id=data["recipe_id"],
+                target_name=data["target_name"],
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error(
+                "Error reconnecting checkpoint: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def restore_checkpoint(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            data = await request.json()
+            if "recipe_id" not in data:
+                raise RecipeValidationError("Missing required field: recipe_id")
+
+            result = await self._persistence_service.restore_checkpoint(
+                recipe_scanner=recipe_scanner,
+                recipe_id=data["recipe_id"],
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error("Error restoring checkpoint: %s", exc, exc_info=True)
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def get_checkpoint_reconnect_suggestions(
+        self, request: web.Request
+    ) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            recipe_id = request.match_info.get("recipe_id")
+            if not recipe_id:
+                raise RecipeValidationError("recipe_id is required")
+
+            result = await self._persistence_service.get_checkpoint_reconnect_suggestions(
+                recipe_scanner=recipe_scanner,
+                recipe_id=recipe_id,
+                query=request.query.get("query") or None,
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error(
+                "Error suggesting checkpoint reconnect candidates: %s",
+                exc,
+                exc_info=True,
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def mark_checkpoint_hash_invalid(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            data = await request.json()
+            if "recipe_id" not in data:
+                raise RecipeValidationError("Missing required field: recipe_id")
+
+            result = await self._persistence_service.mark_checkpoint_hash_invalid(
+                recipe_scanner=recipe_scanner,
+                recipe_id=data["recipe_id"],
+                hash_invalid=bool(data.get("hash_invalid", True)),
+            )
+            return web.json_response(result.payload, status=result.status)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeNotFoundError as exc:
+            return web.json_response({"error": str(exc)}, status=404)
+        except Exception as exc:
+            self._logger.error(
+                "Error marking checkpoint hash invalid: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
     async def bulk_delete(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -2054,6 +2267,23 @@ class RecipeManagementHandler:
             await self._download_remote_media(image_url)
         )
 
+        # Diagnostics for the recipe modal's "Why no LoRAs?" panel. This path
+        # always comes from a CivitAI image URL (import_from_url validates the
+        # image id), so civitai_image is True.
+        diagnostics: Dict[str, Any] = {
+            "civitai_image": True,
+            "is_video": extension in (".mp4", ".webm"),
+        }
+        if isinstance(civitai_meta_raw, dict):
+            raw_mvids = civitai_meta_raw.get("modelVersionIds")
+            diagnostics["api_model_version_ids"] = (
+                len(raw_mvids) if isinstance(raw_mvids, list) else 0
+            )
+            inner_meta_for_diag = civitai_meta_raw.get("meta")
+            if isinstance(inner_meta_for_diag, dict):
+                diagnostics["api_meta_present"] = True
+                diagnostics["api_meta_keys"] = sorted(inner_meta_for_diag.keys())
+
         # Build a version-cached map of local model hashes to cache items so
         # CivitaiApiMetadataParser can skip CivitAI API calls for models that
         # exist on disk. Built once and shared by every parse pass below.
@@ -2074,6 +2304,7 @@ class RecipeManagementHandler:
                 raw_embedded = await asyncio.to_thread(
                     ExifUtils.extract_image_metadata, temp_img_path
                 )
+                diagnostics["exif_present"] = bool(raw_embedded)
                 if raw_embedded:
                     parser = (
                         self._analysis_service._recipe_parser_factory.create_parser(
@@ -2081,6 +2312,7 @@ class RecipeManagementHandler:
                         )
                     )
                     if parser:
+                        diagnostics["exif_parser"] = parser.__class__.__name__
                         if isinstance(parser, CivitaiApiMetadataParser):
                             parsed_embedded = await parser.parse_metadata(
                                 raw_embedded,
@@ -2121,6 +2353,7 @@ class RecipeManagementHandler:
                         raw_orig = await asyncio.to_thread(
                             ExifUtils.extract_image_metadata, orig_tmp_path
                         )
+                        diagnostics["exif_present"] = bool(raw_orig)
                         if raw_orig:
                             parser = (
                                 self._analysis_service._recipe_parser_factory.create_parser(
@@ -2128,6 +2361,7 @@ class RecipeManagementHandler:
                                 )
                             )
                             if parser:
+                                diagnostics["exif_parser"] = parser.__class__.__name__
                                 if isinstance(parser, CivitaiApiMetadataParser):
                                     parsed_embedded = await parser.parse_metadata(
                                         raw_orig,
@@ -2249,6 +2483,20 @@ class RecipeManagementHandler:
         else:
             name = f"Civitai Image {image_id}"
 
+        # Record why this import ended up with no LoRAs so the recipe modal
+        # can explain it (collapsed by default).
+        from ...services.recipes.import_info import (
+            CHANNEL_REIMPORT_URL,
+            CHANNEL_URL,
+            build_import_info,
+        )
+
+        metadata["import_info"] = build_import_info(
+            CHANNEL_REIMPORT_URL if recipe_id else CHANNEL_URL,
+            diagnostics,
+            metadata.get("loras"),
+        )
+
         result = await self._persistence_service.save_recipe(
             recipe_scanner=recipe_scanner,
             image_bytes=image_bytes,
@@ -2271,11 +2519,20 @@ class RecipeManagementHandler:
         target_dir: str | None,
         user_edits: dict[str, Any],
         old_title: str,
+        persisted_source_path: str,
     ) -> web.Response:
         """Re-import a recipe from a local image file.
 
-        Reads the original source file, re-parses its EXIF metadata, saves a
+        Reads the original source file, re-parses its original embedded
-        fresh recipe, then deletes the old one.
+        generation metadata (the appended recipe metadata block is ignored so
+        the current parser gets a fresh pass), saves a new recipe, then deletes
+        the old one.
+
+        ``persisted_source_path`` is the source_path recorded on the new
+        recipe: the external source file when one exists, or empty when the
+        re-import fell back to the recipe's own previous preview image (that
+        file is deleted with the old recipe, so recording it would leave a
+        dangling path that blocks future re-imports).
         """
         normalized = os.path.normpath(file_path)
         if not os.path.isfile(normalized):
@@ -2291,6 +2548,7 @@ class RecipeManagementHandler:
         analysis_result = await self._analysis_service.analyze_local_image(
             file_path=normalized,
             recipe_scanner=recipe_scanner,
+            ignore_recipe_metadata=True,
         )
         analysis_payload: dict[str, Any] = analysis_result.payload
 
@@ -2303,11 +2561,22 @@ class RecipeManagementHandler:
             "base_model": base_model,
             "loras": loras,
             "gen_params": gen_params,
-            "source_path": normalized,
+            "source_path": persisted_source_path,
         }
         if checkpoint:
             metadata["checkpoint"] = checkpoint
 
+        from ...services.recipes.import_info import (
+            CHANNEL_REIMPORT_LOCAL,
+            build_import_info,
+        )
+
+        metadata["import_info"] = build_import_info(
+            CHANNEL_REIMPORT_LOCAL,
+            analysis_payload.get("diagnostics"),
+            loras,
+        )
+
         prompt = (
             gen_params.get("prompt")
             or gen_params.get("positivePrompt")
@@ -2324,6 +2593,10 @@ class RecipeManagementHandler:
             metadata=metadata,
             extension=extension,
             target_dir=target_dir,
+            # The source is the recipe's own already-optimized preview image;
+            # store its bytes verbatim instead of re-compressing (which would
+            # only degrade quality) and skip the metadata re-append.
+            skip_optimize=True,
         )
 
         await self._persistence_service.delete_recipe(
@@ -2351,7 +2624,7 @@ class RecipeManagementHandler:
                 "success": True,
                 "old_recipe_id": recipe_id,
                 "recipe_id": new_recipe_id,
-                "source_path": normalized,
+                "source_path": persisted_source_path,
             }
         )
 

py/routes/model_route_registrar.py

@@ -68,6 +68,8 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "GET", "/api/lm/{prefix}/model-description", "get_model_description"
     ),
     RouteDefinition("GET", "/api/lm/{prefix}/relative-paths", "get_relative_paths"),
+    RouteDefinition("PUT", "/api/lm/{prefix}/active-filters", "update_active_filters"),
+    RouteDefinition("GET", "/api/lm/{prefix}/active-filters", "get_active_filters"),
     RouteDefinition(
         "GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"
     ),

py/routes/recipe_route_registrar.py

@@ -49,9 +49,31 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipe/move", "move_recipe"),
     RouteDefinition("POST", "/api/lm/recipes/move-bulk", "move_recipes_bulk"),
     RouteDefinition("POST", "/api/lm/recipe/lora/reconnect", "reconnect_lora"),
+    RouteDefinition("POST", "/api/lm/recipe/lora/restore", "restore_lora"),
+    RouteDefinition(
+        "GET",
+        "/api/lm/recipe/{recipe_id}/lora/{lora_index}/reconnect-suggestions",
+        "get_reconnect_suggestions",
+    ),
     RouteDefinition(
         "POST", "/api/lm/recipe/lora/mark-hash-invalid", "mark_lora_hash_invalid"
     ),
+    RouteDefinition(
+        "POST", "/api/lm/recipe/checkpoint/reconnect", "reconnect_checkpoint"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/recipe/checkpoint/restore", "restore_checkpoint"
+    ),
+    RouteDefinition(
+        "GET",
+        "/api/lm/recipe/{recipe_id}/checkpoint/reconnect-suggestions",
+        "get_checkpoint_reconnect_suggestions",
+    ),
+    RouteDefinition(
+        "POST",
+        "/api/lm/recipe/checkpoint/mark-hash-invalid",
+        "mark_checkpoint_hash_invalid",
+    ),
     RouteDefinition("GET", "/api/lm/recipes/find-duplicates", "find_duplicates"),
     RouteDefinition("POST", "/api/lm/recipes/bulk-delete", "bulk_delete"),
     RouteDefinition(

py/services/active_filters_store.py

@@ -0,0 +1,135 @@
+"""In-memory store for the LoRA Manager page's active filters.
+
+The manager page keeps its filter state in localStorage for its own
+restoration, but the ComfyUI node autocomplete runs in a potentially
+different browser/origin (or Electron shell) where that storage is not
+shared. This store mirrors the active filters server-side so the
+``/api/lm/{prefix}/relative-paths`` endpoint can inject them into
+autocomplete searches regardless of which client set them.
+
+State is process-local and intentionally not persisted; the manager page
+re-pushes its restored state on load.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+# Keys copied from the manager page's persisted filter snapshot.
+_FILTER_KEYS = (
+    "baseModel",
+    "tags",
+    "autoTags",
+    "modelTypes",
+    "tagLogic",
+    "license",
+)
+
+
+class ActiveFiltersStore:
+    """Process-local store of active filters, keyed by model type."""
+
+    _instance: Optional["ActiveFiltersStore"] = None
+
+    def __init__(self) -> None:
+        self._filters: Dict[str, Dict[str, Any]] = {}
+
+    @classmethod
+    def get_instance(cls) -> "ActiveFiltersStore":
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Drop the singleton (test isolation)."""
+        cls._instance = None
+
+    def set_filters(self, model_type: str, payload: Dict[str, Any]) -> None:
+        """Replace the stored active filters for a model type.
+
+        Only recognized keys are kept; everything else is discarded.
+        """
+        filters = payload.get("filters")
+        sanitized: Dict[str, Any] = {
+            "activeFolder": payload.get("activeFolder"),
+            "recursiveSearch": bool(payload.get("recursiveSearch", True)),
+            "filters": (
+                {key: filters[key] for key in _FILTER_KEYS if key in filters}
+                if isinstance(filters, dict)
+                else None
+            ),
+        }
+        self._filters[model_type] = sanitized
+
+    def get_filters(self, model_type: str) -> Optional[Dict[str, Any]]:
+        """Return the stored payload for a model type, or None if unset."""
+        return self._filters.get(model_type)
+
+    def clear(self, model_type: str) -> None:
+        self._filters.pop(model_type, None)
+
+
+def active_filters_to_query_kwargs(payload: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    """Map a stored active-filters payload to ``search_relative_paths`` kwargs.
+
+    Mirrors the query-param mapping that the ComfyUI autocomplete used to
+    build client-side from localStorage (web/comfyui/autocomplete.js).
+    """
+    kwargs: Dict[str, Any] = {}
+    if not payload:
+        return kwargs
+
+    active_folder = payload.get("activeFolder")
+    recursive = payload.get("recursiveSearch", True)
+
+    if active_folder and active_folder != "null":
+        kwargs["folder"] = active_folder
+    elif not recursive:
+        # Root folder with recursion disabled mirrors the page list,
+        # which matches only root-level files via folder=''.
+        kwargs["folder"] = ""
+
+    filters = payload.get("filters")
+    if isinstance(filters, dict):
+        base_models = filters.get("baseModel")
+        if isinstance(base_models, list):
+            kwargs["base_models"] = [m for m in base_models if m]
+
+        for source_key, target_key in (("tags", "tags"), ("autoTags", "auto_tags")):
+            states = filters.get(source_key)
+            if isinstance(states, dict):
+                mapped = {
+                    tag: state
+                    for tag, state in states.items()
+                    if state in ("include", "exclude")
+                }
+                if mapped:
+                    kwargs[target_key] = mapped
+
+        model_types = filters.get("modelTypes")
+        if isinstance(model_types, list):
+            kwargs["model_types"] = [t for t in model_types if t]
+
+        tag_logic = filters.get("tagLogic")
+        if tag_logic:
+            kwargs["tag_logic"] = tag_logic
+
+        license_filter = filters.get("license")
+        if isinstance(license_filter, dict):
+            no_credit = license_filter.get("noCredit")
+            if no_credit == "include":
+                kwargs["credit_required"] = False
+            elif no_credit == "exclude":
+                kwargs["credit_required"] = True
+            allow_selling = license_filter.get("allowSelling")
+            if allow_selling == "include":
+                kwargs["allow_selling_generated_content"] = True
+            elif allow_selling == "exclude":
+                kwargs["allow_selling_generated_content"] = False
+
+    kwargs["recursive"] = recursive
+    return kwargs

py/services/base_model_service.py

@@ -1295,6 +1295,27 @@ class BaseModelService(ABC):
             path_for_sorting,
         )
 
+    @staticmethod
+    def _relative_path_folder_group_sort_key(
+        relative_path: str, include_terms: List[str]
+    ) -> tuple:
+        """Group paths by folder, then sort by relevance within each group.
+
+        Folders are ordered alphabetically (case-insensitive) by their full
+        folder path, with root-level files (empty folder) first. Within a
+        folder, paths keep the relevance ordering of
+        ``_relative_path_sort_key``. This keeps same-folder entries together
+        in the autocomplete dropdown instead of interleaving them by filename.
+        """
+        path_for_sorting = BaseModelService._remove_model_extension(
+            relative_path.lower()
+        )
+        folder = path_for_sorting.rpartition(os.sep)[0]
+
+        return (folder,) + BaseModelService._relative_path_sort_key(
+            relative_path, include_terms
+        )
+
     async def search_relative_paths(
         self,
         search_term: str,
@@ -1404,9 +1425,13 @@ class BaseModelService(ABC):
             ):
                 matching_paths.append(relative_path)
 
-        # Sort by relevance (prefix and earliest hits first, then by length and alphabetically)
+        # Group by folder (root first, then alphabetically) and sort by
+        # relevance (prefix and earliest hits, then length and alphabetically)
+        # within each folder group.
         matching_paths.sort(
-            key=lambda relative: self._relative_path_sort_key(relative, include_terms)
+            key=lambda relative: self._relative_path_folder_group_sort_key(
+                relative, include_terms
+            )
         )
 
         # Apply offset and limit

py/services/batch_import_service.py

@@ -20,6 +20,11 @@ from .recipes import (
     RecipeDownloadError,
     RecipeNotFoundError,
 )
+from .recipes.import_info import (
+    CHANNEL_BATCH_IMPORT_LOCAL,
+    CHANNEL_BATCH_IMPORT_URL,
+    build_import_info,
+)
 
 
 class ImportItemType(Enum):
@@ -624,6 +629,17 @@ class BatchImportService:
                     "loras": loras,
                     "gen_params": payload.get("gen_params", {}),
                     "source_path": item.source,
+                    # Record why this import ended up with no LoRAs so the
+                    # recipe modal can explain it (collapsed by default).
+                    "import_info": build_import_info(
+                        (
+                            CHANNEL_BATCH_IMPORT_URL
+                            if item.item_type == ImportItemType.URL
+                            else CHANNEL_BATCH_IMPORT_LOCAL
+                        ),
+                        payload.get("diagnostics"),
+                        loras,
+                    ),
                 }
 
                 if payload.get("checkpoint"):

py/services/civitai_client.py

@@ -21,7 +21,7 @@ from .model_metadata_provider import (
 from .downloader import get_downloader
 from .errors import RateLimitError, ResourceNotFoundError
 from ..utils.civitai_utils import resolve_license_payload
-from ..utils.constants import MODEL_WEIGHT_FILE_TYPES
+from ..utils.constants import MODEL_WEIGHT_FILE_TYPES, is_empty_placeholder_hash
 
 logger = logging.getLogger(__name__)
 
@@ -180,6 +180,11 @@ class CivitaiClient:
     async def get_model_by_hash(
         self, model_hash: str
     ) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
+        if is_empty_placeholder_hash(model_hash):
+            # The empty-hash placeholder (SHA256 of an empty byte string)
+            # matches no real file; CivitAI's by-hash index can contain
+            # polluted entries for it, so never resolve it.
+            return None, "Model not found"
         try:
             success, version = await self._make_request(
                 "GET",
@@ -503,6 +508,8 @@ class CivitaiClient:
     async def _fetch_version_by_hash(self, model_hash: Optional[str]) -> Optional[Dict[str, Any]]:
         if not model_hash:
             return None
+        if is_empty_placeholder_hash(model_hash):
+            return None
 
         success, version = await self._make_request(
             "GET",

py/services/model_hash_index.py

@@ -1,6 +1,8 @@
 from typing import Dict, Optional, Set, List
 import os
 
+from ..utils.constants import is_empty_placeholder_hash
+
 class ModelHashIndex:
     """Index for looking up models by hash or filename"""
     
@@ -81,6 +83,8 @@ class ModelHashIndex:
         # mapping. First-time registrations stay O(1).
         if autov3:
             autov3 = autov3.lower()
+            if is_empty_placeholder_hash(autov3):
+                autov3 = None
         if is_re_registration and (existing_hash != sha256 or autov3):
             stale_autov3_keys = [
                 key for key, mapped_path in self._autov3_to_path.items()
@@ -93,7 +97,7 @@ class ModelHashIndex:
 
     def add_autov3(self, autov3: str, file_path: str) -> None:
         """Add or update an AutoV3-only index entry (used when only AutoV3 is known)"""
-        if not autov3:
+        if not autov3 or is_empty_placeholder_hash(autov3):
             return
         autov3 = autov3.lower()
         self._autov3_to_path[autov3] = file_path
@@ -250,6 +254,8 @@ class ModelHashIndex:
     
     def has_hash(self, hash_value: str) -> bool:
         """Check if hash exists in index (SHA256, AutoV2, or AutoV3)"""
+        if is_empty_placeholder_hash(hash_value):
+            return False
         normalized = hash_value.lower()
         if normalized in self._hash_to_path:
             return True
@@ -261,6 +267,8 @@ class ModelHashIndex:
 
     def get_path(self, hash_value: str) -> Optional[str]:
         """Get file path for a hash (SHA256, AutoV2, or AutoV3)"""
+        if is_empty_placeholder_hash(hash_value):
+            return None
         normalized = hash_value.lower()
         path = self._hash_to_path.get(normalized)
         if path is not None:

py/services/model_scanner.py

@@ -66,6 +66,14 @@ def _is_hidden_relative_path(rel_path: str) -> bool:
 # requests (modal open + autocomplete) do not re-walk the model roots.
 ALL_FOLDERS_CACHE_TTL_SECONDS = 5.0
 
+# Maps a scanner model type to the manager page type used in progress
+# broadcasts (e.g. 'lora' -> 'loras').
+PAGE_TYPE_MAP = {
+    'lora': 'loras',
+    'checkpoint': 'checkpoints',
+    'embedding': 'embeddings',
+}
+
 
 def _is_pending_delete_path(path: str) -> bool:
     """Return True when any path component is the pending-delete staging dir."""
@@ -149,6 +157,38 @@ class ModelScanner:
         # Register this service
         asyncio.create_task(self._register_service())
 
+    @property
+    def page_type(self) -> str:
+        """Manager page type used in progress broadcasts (e.g. 'loras')."""
+        return PAGE_TYPE_MAP.get(self.model_type, self.model_type)
+
+    async def _broadcast_scan_progress(
+        self,
+        status: str,
+        stage: str,
+        progress: int,
+        full_rebuild: bool,
+        **extra: Any,
+    ) -> None:
+        """Broadcast manual-refresh scan progress on the generic WS channel.
+
+        Best-effort only: broadcast failures must never affect the scan itself.
+        """
+        payload: Dict[str, Any] = {
+            'type': 'scan_progress',
+            'status': status,
+            'model_type': self.model_type,
+            'pageType': self.page_type,
+            'stage': stage,
+            'full_rebuild': full_rebuild,
+            'progress': progress,
+        }
+        payload.update(extra)
+        try:
+            await ws_manager.broadcast(payload)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error(f"Error broadcasting scan progress for {self.model_type}: {exc}")
+
     @property
     def cache_version(self) -> int:
         """Monotonic version counter for the in-memory cache.
@@ -434,12 +474,7 @@ class ModelScanner:
             self._is_initializing = True
             
             # Determine the page type based on model type
-            page_type_map = {
+            page_type = self.page_type
-                'lora': 'loras',
-                'checkpoint': 'checkpoints',
-                'embedding': 'embeddings'
-            }
-            page_type = page_type_map.get(self.model_type, self.model_type)
             
             # First, try to load from cache
             await ws_manager.broadcast_init_progress({
@@ -804,7 +839,7 @@ class ModelScanner:
             last_progress_time = time.time()
             last_progress_percent = 0
 
-            async def progress_callback(processed_files: int, expected_total: int) -> None:
+            async def progress_callback(processed_files: int, expected_total: int, current_name: str = '') -> None:
                 nonlocal last_progress_time, last_progress_percent
 
                 if expected_total <= 0:
@@ -871,32 +906,84 @@ class ModelScanner:
     async def _initialize_cache(self) -> None:
         """Initialize or refresh the cache"""
         self._is_initializing = True  # Set flag
+        last_progress_percent = 0
         try:
             start_time = time.time()
 
+            await self._broadcast_scan_progress('started', 'scan_folders', 0, True)
+
             # Manually trigger a symlink rescan during a full rebuild.
             # This ensures that any new symlink mappings are correctly picked up.
             config.rebuild_symlink_cache()
 
-            # Determine the page type based on model type
+            # Count files in a thread so the event loop stays responsive
+            loop = asyncio.get_running_loop()
+            total_files = await loop.run_in_executor(None, self._count_model_files)
+            await self._broadcast_scan_progress(
+                'processing', 'count_models', 1, True,
+                processed=0, total=total_files,
+            )
+
+            last_progress_time = time.time()
+
+            async def progress_callback(processed_files: int, expected_total: int, current_name: str = '') -> None:
+                nonlocal last_progress_time, last_progress_percent
+
+                if expected_total <= 0:
+                    return
+
+                current_time = time.time()
+                progress_percent = min(99, int(1 + (processed_files / expected_total) * 98))
+
+                if progress_percent <= last_progress_percent:
+                    return
+
+                if current_time - last_progress_time <= 0.5 and processed_files != expected_total:
+                    return
+
+                last_progress_percent = progress_percent
+                last_progress_time = current_time
+
+                await self._broadcast_scan_progress(
+                    'processing', 'process_models', progress_percent, True,
+                    processed=processed_files, total=expected_total,
+                    current_name=current_name,
+                )
+
             # Scan for new data
-            scan_result = await self._gather_model_data()
+            scan_result = await self._gather_model_data(
+                total_files=total_files,
+                progress_callback=progress_callback,
+            )
             if not self.is_cancelled():
+                await self._broadcast_scan_progress('finalizing', 'finalizing', 99, True)
                 await self._apply_scan_result(scan_result)
                 await self._save_persistent_cache(scan_result)
                 await self._sync_download_history(scan_result.raw_data, source='scan')
+                await self._broadcast_scan_progress(
+                    'completed', 'finalizing', 100, True,
+                    elapsed_seconds=time.time() - start_time,
+                )
 
                 logger.info(
                     f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
                     f"found {len(scan_result.raw_data)} models"
                 )
             else:
+                await self._broadcast_scan_progress(
+                    'cancelled', 'process_models', last_progress_percent, True,
+                    elapsed_seconds=time.time() - start_time,
+                )
                 logger.info(
                     f"{self.model_type.capitalize()} Scanner: Cache initialization cancelled "
                     f"after {time.time() - start_time:.2f} seconds"
                 )
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error initializing cache: {e}")
+            await self._broadcast_scan_progress(
+                'error', 'process_models', last_progress_percent, True,
+                error=str(e),
+            )
             # Ensure cache is at least an empty structure on error
             if self._cache is None:
                 self._cache = ModelCache(
@@ -915,6 +1002,8 @@ class ModelScanner:
             start_time = time.time()
             logger.info(f"{self.model_type.capitalize()} Scanner: Starting fast cache reconciliation...")
 
+            await self._broadcast_scan_progress('started', 'reconcile_scan', 0, False)
+            
             # Get current cached file paths
             cached_paths = {item['file_path'] for item in self._cache.raw_data}
             path_to_item = {item['file_path']: item for item in self._cache.raw_data}
@@ -987,6 +1076,10 @@ class ModelScanner:
                     await asyncio.sleep(0)
                     if self.is_cancelled():
                         logger.info(f"{self.model_type.capitalize()} Scanner: Reconcile scan cancelled")
+                        await self._broadcast_scan_progress(
+                            'cancelled', 'reconcile_scan', 0, False,
+                            elapsed_seconds=time.time() - start_time,
+                        )
                         return
 
             # Process new files in batches
@@ -994,10 +1087,14 @@ class ModelScanner:
             if new_files:
                 logger.info(f"{self.model_type.capitalize()} Scanner: Found {len(new_files)} new files to process")
                 batch_size = 50
-                for i in range(0, len(new_files), batch_size):
+                total_new = len(new_files)
+                processed_new = 0
+                last_progress_time = time.time()
+                for i in range(0, total_new, batch_size):
                     batch = new_files[i:i+batch_size]
                     for path in batch:
                         logger.info(f"{self.model_type.capitalize()} Scanner: Processing {path}")
+                        processed_new += 1
                         try:
                             # Find the appropriate root path for this file
                             root_path = None
@@ -1054,8 +1151,23 @@ class ModelScanner:
                         except Exception as e:
                             logger.error(f"Error adding {path} to cache: {e}")
 
+                        current_time = time.time()
+                        if current_time - last_progress_time > 0.5 or processed_new == total_new:
+                            last_progress_time = current_time
+                            await self._broadcast_scan_progress(
+                                'processing', 'process_new',
+                                min(99, int(1 + (processed_new / total_new) * 98)), False,
+                                processed=processed_new, total=total_new,
+                                current_name=os.path.basename(path),
+                            )
+
                         if self.is_cancelled():
                             logger.info(f"{self.model_type.capitalize()} Scanner: Reconcile processing cancelled")
+                            await self._broadcast_scan_progress(
+                                'cancelled', 'process_new',
+                                min(99, int(1 + (processed_new / total_new) * 98)), False,
+                                elapsed_seconds=time.time() - start_time,
+                            )
                             return
             
             # Find missing files (in cache but not in filesystem)
@@ -1121,8 +1233,17 @@ class ModelScanner:
                 await self._persist_current_cache()
                 
             logger.info(f"{self.model_type.capitalize()} Scanner: Cache reconciliation completed in {time.time() - start_time:.2f} seconds. Added {total_added}, removed {total_removed} models.")
+            await self._broadcast_scan_progress(
+                'completed', 'process_new', 100, False,
+                added=total_added, removed=total_removed,
+                elapsed_seconds=time.time() - start_time,
+            )
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error reconciling cache: {e}", exc_info=True)
+            await self._broadcast_scan_progress(
+                'error', 'reconcile_scan', 0, False,
+                error=str(e),
+            )
         finally:
             self._is_initializing = False # Unset flag
             self.bump_cache_version()
@@ -1498,7 +1619,7 @@ class ModelScanner:
         self,
         *,
         total_files: int = 0,
-        progress_callback: Optional[Callable[[int, int], Awaitable[None]]] = None
+        progress_callback: Optional[Callable[[int, int, str], Awaitable[None]]] = None
     ) -> CacheBuildResult:
         """Collect metadata for all model files."""
 
@@ -1510,11 +1631,11 @@ class ModelScanner:
         processed_real_files: Set[str] = set()
         visited_real_dirs: Set[str] = set()
 
-        async def handle_progress() -> None:
+        async def handle_progress(current_name: str = '') -> None:
             if progress_callback is None:
                 return
             try:
-                await progress_callback(processed_files, total_files)
+                await progress_callback(processed_files, total_files, current_name)
             except Exception as exc:  # pragma: no cover - defensive logging
                 logger.error(f"Error reporting progress for {self.model_type}: {exc}")
 
@@ -1580,7 +1701,7 @@ class ModelScanner:
                                 for tag in result.get('tags') or []:
                                     tags_count[tag] = tags_count.get(tag, 0) + 1
 
-                            await handle_progress()
+                            await handle_progress(entry.name)
                             await asyncio.sleep(0)
                             if self.is_cancelled():
                                 return

py/services/persistent_recipe_cache.py

@@ -59,6 +59,7 @@ class PersistentRecipeCache:
         "gen_params_json",
         "tags_json",
         "has_workflow",
+        "import_info_json",
     )
     _instances: Dict[str, "PersistentRecipeCache"] = {}
     _instance_lock = threading.Lock()
@@ -447,7 +448,8 @@ class PersistentRecipeCache:
                             checkpoint_json TEXT,
                             gen_params_json TEXT,
                             tags_json TEXT,
-                            has_workflow INTEGER DEFAULT 0
+                            has_workflow INTEGER DEFAULT 0,
+                            import_info_json TEXT
                         );
 
                         CREATE INDEX IF NOT EXISTS idx_recipes_json_path ON recipes(json_path);
@@ -473,6 +475,13 @@ class PersistentRecipeCache:
                         )
                     except Exception:
                         pass  # column already exists
+                    # Migration: add import_info_json column to existing databases
+                    try:
+                        conn.execute(
+                            "ALTER TABLE recipes ADD COLUMN import_info_json TEXT"
+                        )
+                    except Exception:
+                        pass  # column already exists
                     conn.commit()
                 self._schema_initialized = True
             except Exception as exc:
@@ -504,6 +513,9 @@ class PersistentRecipeCache:
         tags = recipe.get("tags")
         tags_json = json.dumps(tags) if tags else None
 
+        import_info = recipe.get("import_info")
+        import_info_json = json.dumps(import_info) if import_info else None
+
         # Get file stats if json_path exists
         file_mtime = 0.0
         file_size = 0
@@ -536,6 +548,7 @@ class PersistentRecipeCache:
             gen_params_json,
             tags_json,
             1 if recipe.get("has_workflow") else 0,
+            import_info_json,
         )
 
     def _row_to_recipe(self, row: sqlite3.Row) -> Dict[str, Any]:
@@ -568,6 +581,13 @@ class PersistentRecipeCache:
             except json.JSONDecodeError:
                 pass
 
+        import_info = None
+        if row["import_info_json"]:
+            try:
+                import_info = json.loads(row["import_info_json"])
+            except json.JSONDecodeError:
+                pass
+
         recipe = {
             "id": row["recipe_id"],
             "file_path": row["file_path"] or "",
@@ -592,6 +612,9 @@ class PersistentRecipeCache:
         if checkpoint:
             recipe["checkpoint"] = checkpoint
 
+        if import_info:
+            recipe["import_info"] = import_info
+
         return recipe
 
 

py/services/recipe_scanner.py

@@ -5,6 +5,8 @@
 from __future__ import annotations
 
 import asyncio
+import copy
+import difflib
 import json
 import logging
 import os
@@ -46,6 +48,12 @@ _CHECKPOINT_MODEL_TYPE_ALIASES = {"diffusionmodel": "diffusion_model"}
 # Valid LoRA availability statuses for the recipe listing filter.
 _VALID_LORA_AVAILABILITY_STATUSES = frozenset({"ready", "missing", "deleted"})
 
+# Filter marker for recipes whose base model could not be determined
+# (base_model is None or empty). The UI displays "Unknown" for this bucket;
+# the marker keeps the semantics explicit and disjoint from any real base
+# model string.
+UNKNOWN_BASE_MODEL_FILTER = "__unknown__"
+
 
 class RecipeScanner:
     """Service for scanning and managing recipe images"""
@@ -244,6 +252,239 @@ class RecipeScanner:
             self._local_filename_cache_versions = versions
             return cache
 
+    @staticmethod
+    def _strip_weight_extension(name: str) -> str:
+        """Strip a known weight-file extension, preserving the original case."""
+        lower = name.lower()
+        for ext in sorted(WEIGHT_FILE_EXTENSIONS, key=len, reverse=True):
+            if lower.endswith(ext):
+                return name[: -len(ext)]
+        return name
+
+    async def suggest_reconnect_candidates(
+        self,
+        *,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        query: Optional[str] = None,
+        limit: int = 5,
+    ) -> list[dict[str, Any]]:
+        """Rank local LoRAs as reconnect candidates for a broken recipe entry.
+
+        Thin wrapper over ``_suggest_reconnect_candidates`` scoped to the
+        LoRA library (see it for the ranking contract).
+        """
+        return await self._suggest_reconnect_candidates(
+            entry=entry,
+            recipe_base_model=recipe_base_model,
+            query=query,
+            limit=limit,
+            is_checkpoint=False,
+        )
+
+    async def suggest_checkpoint_reconnect_candidates(
+        self,
+        *,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        query: Optional[str] = None,
+        limit: int = 5,
+    ) -> list[dict[str, Any]]:
+        """Rank local checkpoints as reconnect candidates for a broken entry.
+
+        Thin wrapper over ``_suggest_reconnect_candidates`` scoped to the
+        checkpoint library (see it for the ranking contract).
+        """
+        return await self._suggest_reconnect_candidates(
+            entry=entry,
+            recipe_base_model=recipe_base_model,
+            query=query,
+            limit=limit,
+            is_checkpoint=True,
+        )
+
+    async def _suggest_reconnect_candidates(
+        self,
+        *,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        query: Optional[str] = None,
+        limit: int = 5,
+        is_checkpoint: bool,
+    ) -> list[dict[str, Any]]:
+        """Rank local models as reconnect candidates for a broken recipe entry.
+
+        Identity signals (same hash / same CivitAI model version) outrank
+        similarity signals (filename / model name fuzzy match). A confident
+        base-model mismatch (both sides known and different) is a hard
+        rejection here. This is deliberately stricter than reconnect itself,
+        which tolerates same-architecture-family labels (Pony ↔ Illustrious):
+        suggestions trade recall for a noise-free list, and the input box
+        remains available for deliberate cross-family picks. Unknown on
+        either side stays eligible, matching ``find_matching_models``.
+        When ``query`` is given
+        (search-as-you-type), identity signals are skipped and both
+        similarity signals score against the query, with a substring hit
+        (query of 3+ chars) flooring that signal's ratio at 0.8.
+
+        The name-similarity threshold (0.65) is stricter than the filename
+        one (0.55): long generic names share tokens like "style"/"pony" and
+        score deceptively high (measured 0.638 for unrelated models), while
+        filenames are the authoritative match key and get more slack.
+        """
+        if limit <= 0 or not isinstance(entry, dict):
+            return []
+
+        scanner = self._checkpoint_scanner if is_checkpoint else self._lora_scanner
+        if scanner is None:
+            return []
+
+        data = await scanner.get_cached_data()
+        recipe_bm = (recipe_base_model or "").strip().casefold()
+
+        def _base_model_known_mismatch(item: dict[str, Any]) -> bool:
+            """Confident mismatch only — unknown on either side stays eligible."""
+            if not recipe_bm or recipe_bm == "unknown":
+                return False
+            item_bm = (item.get("base_model") or "").strip().casefold()
+            return bool(item_bm) and item_bm != "unknown" and item_bm != recipe_bm
+
+        def _base_model_adjustment(item: dict[str, Any]) -> float:
+            # Mismatches are already filtered out; this only boosts known-equal.
+            if not recipe_bm or recipe_bm == "unknown":
+                return 0.0
+            item_bm = (item.get("base_model") or "").strip().casefold()
+            return 0.1 if item_bm == recipe_bm else 0.0
+
+        pool: list[dict[str, Any]] = []
+        for item in getattr(data, "raw_data", None) or []:
+            if not isinstance(item, dict):
+                continue
+            # Items without a sha256 (pending/failed downloads) leave the
+            # entry without a usable hash — same rule as the filename cache.
+            if not (item.get("sha256") or "").strip():
+                continue
+            if not self._is_type_compatible(item, is_checkpoint=is_checkpoint):
+                continue
+            if _base_model_known_mismatch(item):
+                continue
+            pool.append(item)
+        if not pool:
+            return []
+
+        # Basename collision counts decide whether target_name needs the
+        # folder-relative path to resolve uniquely in find_matching_models.
+        basename_counts: dict[str, int] = {}
+        for item in pool:
+            key = self._normalize_filename_key(item.get("file_name") or "")
+            if key:
+                basename_counts[key] = basename_counts.get(key, 0) + 1
+
+        best: dict[str, dict[str, Any]] = {}
+
+        def _consider(item: dict[str, Any], score: float, reason: str) -> None:
+            key = item.get("file_path") or item.get("file_name") or ""
+            if not key:
+                return
+            current = best.get(key)
+            if current is None or score > current["score"]:
+                best[key] = {"item": item, "score": score, "reason": reason}
+
+        query_text = (query or "").strip()
+
+        if not query_text:
+            entry_hash = (entry.get("hash") or "").lower()
+            if entry_hash:
+                hash_cache = await self.build_local_hash_cache()
+                hit = hash_cache.get(entry_hash)
+                if (
+                    isinstance(hit, dict)
+                    and (hit.get("sha256") or "").strip()
+                    and self._is_type_compatible(hit, is_checkpoint=is_checkpoint)
+                    and not _base_model_known_mismatch(hit)
+                ):
+                    _consider(hit, 1.0 + _base_model_adjustment(hit), "same_hash")
+
+            version_id = entry.get("modelVersionId") or entry.get("id")
+            if version_id is not None:
+                if is_checkpoint:
+                    hit = self._get_checkpoint_from_version_index(str(version_id))
+                else:
+                    hit = self._get_lora_from_version_index(str(version_id))
+                if (
+                    isinstance(hit, dict)
+                    and (hit.get("sha256") or "").strip()
+                    and not _base_model_known_mismatch(hit)
+                ):
+                    _consider(hit, 0.95 + _base_model_adjustment(hit), "same_version")
+
+        filename_source = query_text or (entry.get("file_name") or "")
+        # Parser-style checkpoint entries carry the model name under ``name``,
+        # widget-style ones under ``modelName`` — try both for checkpoints.
+        if is_checkpoint:
+            name_source = query_text or (entry.get("name") or entry.get("modelName") or "")
+        else:
+            name_source = query_text or (entry.get("modelName") or "")
+        norm_filename_source = self._normalize_filename_key(filename_source)
+        name_source_cf = name_source.casefold()
+        # Substring hits floor the similarity ratio, but only for meaningful
+        # queries — a 1-2 character query is a substring of nearly every
+        # filename and would flood the suggestions with noise.
+        substring_floor = len(query_text) >= 3
+
+        for item in pool:
+            adjustment = _base_model_adjustment(item)
+
+            item_filename = self._normalize_filename_key(item.get("file_name") or "")
+            if norm_filename_source and item_filename:
+                ratio = difflib.SequenceMatcher(
+                    None, norm_filename_source, item_filename
+                ).ratio()
+                if substring_floor and norm_filename_source in item_filename:
+                    ratio = max(ratio, 0.8)
+                if ratio >= 0.55:
+                    _consider(
+                        item, 0.5 + 0.4 * ratio + adjustment, "similar_filename"
+                    )
+
+            item_name = (item.get("model_name") or "").casefold()
+            if name_source_cf and item_name:
+                ratio = difflib.SequenceMatcher(
+                    None, name_source_cf, item_name
+                ).ratio()
+                if substring_floor and name_source_cf in item_name:
+                    ratio = max(ratio, 0.8)
+                if ratio >= 0.65:
+                    _consider(item, 0.4 + 0.35 * ratio + adjustment, "similar_name")
+
+        suggestions = []
+        for record in best.values():
+            item = record["item"]
+            file_name = item.get("file_name") or ""
+            stem = self._strip_weight_extension(file_name)
+            folder = (item.get("folder") or "").replace("\\", "/").strip("/")
+            norm_key = self._normalize_filename_key(file_name)
+            if norm_key and basename_counts.get(norm_key, 0) > 1 and folder:
+                target_name = f"{folder}/{stem}"
+            else:
+                target_name = stem
+            suggestions.append(
+                {
+                    "file_name": file_name,
+                    "file_path": item.get("file_path") or "",
+                    "model_name": item.get("model_name") or "",
+                    "base_model": item.get("base_model") or "",
+                    "preview_url": item.get("preview_url") or "",
+                    "hash": (item.get("sha256") or "").lower(),
+                    "score": round(record["score"], 3),
+                    "match_reason": record["reason"],
+                    "target_name": target_name,
+                }
+            )
+
+        suggestions.sort(key=lambda s: (-s["score"], s["file_name"].lower()))
+        return suggestions[:limit]
+
     def _is_rematch_candidate(self, entry: dict[str, Any]) -> bool:
         """Return True when a recipe entry is eligible for local re-matching.
 
@@ -1312,6 +1553,7 @@ class RecipeScanner:
         identifier key when neither identifier form exists).
         """
         entry["isDeleted"] = False
+        entry["hashInvalid"] = False
 
         new_hash = (item.get("sha256") or "").lower()
         if new_hash:
@@ -1511,7 +1753,36 @@ class RecipeScanner:
             # Mark initialization as complete regardless of outcome
             self._is_initializing = False
 
-    def _initialize_recipe_cache_sync(self):
+    async def _broadcast_scan_progress(
+        self,
+        status: str,
+        stage: str,
+        progress: int,
+        full_rebuild: bool,
+        **extra: Any,
+    ) -> None:
+        """Broadcast manual-refresh scan progress on the generic WS channel.
+
+        Mirrors ``ModelScanner._broadcast_scan_progress`` so the recipes page
+        can reuse the same frontend contract. Best-effort only: broadcast
+        failures must never affect the scan itself.
+        """
+        payload: Dict[str, Any] = {
+            'type': 'scan_progress',
+            'status': status,
+            'model_type': 'recipe',
+            'pageType': 'recipes',
+            'stage': stage,
+            'full_rebuild': full_rebuild,
+            'progress': progress,
+        }
+        payload.update(extra)
+        try:
+            await ws_manager.broadcast(payload)
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.error(f"Error broadcasting scan progress for recipe: {exc}")
+
+    def _initialize_recipe_cache_sync(self, report_progress: bool = False):
         """Synchronous version of recipe cache initialization for thread pool execution.
 
         Uses persistent cache for fast startup when available:
@@ -1519,8 +1790,14 @@ class RecipeScanner:
         2. Reconcile with filesystem (check mtime/size for changes)
         3. Fall back to full directory scan if cache miss or reconciliation fails
         4. Persist results for next startup
+
+        Args:
+            report_progress: When True (manual force-refresh only), broadcast
+                scan_progress messages during the full directory scan. Startup
+                initialization leaves this False and behaves as before.
         """
         loop = None
+        scan_start_time: Optional[float] = None
         try:
             # Ensure cache exists to avoid None reference errors
             if self._cache is None:
@@ -1602,7 +1879,17 @@ class RecipeScanner:
 
             # Fall back to full directory scan
             logger.info("Recipe cache miss: performing full directory scan")
-            recipes, json_paths = self._full_directory_scan_sync(recipes_dir)
+            if report_progress:
+                scan_start_time = time.time()
+                # Broadcast from the worker thread via its own event loop,
+                # mirroring ModelScanner._initialize_cache_sync.
+                loop.run_until_complete(
+                    self._broadcast_scan_progress('started', 'scan_folders', 0, True)
+                )
+            recipes, json_paths = self._full_directory_scan_sync(
+                recipes_dir,
+                progress_loop=loop if report_progress else None,
+            )
             self._json_path_map = json_paths
 
             # Update cache with the collected data
@@ -1616,12 +1903,30 @@ class RecipeScanner:
                 recipes, json_paths, self._cache.image_id_map
             )
 
+            if report_progress:
+                loop.run_until_complete(
+                    self._broadcast_scan_progress(
+                        'completed', 'finalizing', 100, True,
+                        elapsed_seconds=time.time() - (scan_start_time or time.time()),
+                        total=len(recipes),
+                    )
+                )
+
             return self._cache
         except Exception as e:
             logger.error(f"Error in thread-based recipe cache initialization: {e}")
             import traceback
 
             traceback.print_exc(file=sys.stderr)
+            if report_progress and loop is not None:
+                try:
+                    loop.run_until_complete(
+                        self._broadcast_scan_progress(
+                            'error', 'process_models', 0, True, error=str(e)
+                        )
+                    )
+                except Exception:  # pragma: no cover - defensive logging
+                    logger.error("Error broadcasting recipe scan failure", exc_info=True)
             return self._cache if hasattr(self, "_cache") else None
         finally:
             # Clean up the event loop
@@ -1775,12 +2080,16 @@ class RecipeScanner:
         return updated
 
     def _full_directory_scan_sync(
-        self, recipes_dir: str
+        self,
+        recipes_dir: str,
+        progress_loop: Optional[asyncio.AbstractEventLoop] = None,
     ) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
         """Perform a full synchronous directory scan for recipes.
 
         Args:
             recipes_dir: Path to the recipes directory.
+            progress_loop: When set (manual force-refresh only), broadcast
+                scan_progress messages through this thread-local event loop.
 
         Returns:
             Tuple of (recipes list, json_paths dict).
@@ -1795,6 +2104,17 @@ class RecipeScanner:
                 if file.lower().endswith(".recipe.json"):
                     recipe_files.append(os.path.join(root, file))
 
+        total_files = len(recipe_files)
+        if progress_loop is not None:
+            progress_loop.run_until_complete(
+                self._broadcast_scan_progress(
+                    'processing', 'count_models', 1, True,
+                    processed=0, total=total_files,
+                )
+            )
+
+        last_progress_time = time.time()
+
         # Process each recipe file
         for i, recipe_path in enumerate(recipe_files):
             recipe_data = self._load_recipe_file_sync(recipe_path)
@@ -1802,6 +2122,23 @@ class RecipeScanner:
                 recipe_id = str(recipe_data.get("id", ""))
                 recipes.append(recipe_data)
                 json_paths[recipe_id] = recipe_path
+            if progress_loop is not None and total_files > 0:
+                processed = i + 1
+                current_time = time.time()
+                # Throttle to one update per 0.5s; always send the final one.
+                if (
+                    processed == total_files
+                    or current_time - last_progress_time > 0.5
+                ):
+                    last_progress_time = current_time
+                    progress_percent = min(99, int(1 + (processed / total_files) * 98))
+                    progress_loop.run_until_complete(
+                        self._broadcast_scan_progress(
+                            'processing', 'process_models', progress_percent, True,
+                            processed=processed, total=total_files,
+                            current_name=os.path.basename(recipe_path),
+                        )
+                    )
             # Periodically release GIL so the event loop thread can run
             if i % 100 == 0:
                 time.sleep(0)
@@ -2371,11 +2708,14 @@ class RecipeScanner:
                         start_time = time.time()
 
                         # Run the heavy lifting in a thread pool – same path
-                        # used by initialize_in_background().
+                        # used by initialize_in_background(). Pass
+                        # report_progress=True so manual refreshes broadcast
+                        # scan_progress updates; startup init keeps it off.
                         loop = asyncio.get_event_loop()
                         cache = await loop.run_in_executor(
                             None,
                             self._initialize_recipe_cache_sync,
+                            True,
                         )
                         if cache is not None:
                             self._cache = cache
@@ -3106,6 +3446,19 @@ class RecipeScanner:
 
         return await self._lora_scanner.find_models_by_name(name, base_model=base_model)
 
+    async def find_local_checkpoints_by_name(
+        self, name: str, base_model: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
+        """Return every local checkpoint matching ``name`` (used to explain lookup misses)."""
+
+        checkpoint_scanner = getattr(self, "_checkpoint_scanner", None)
+        if not checkpoint_scanner or not name:
+            return []
+
+        return await checkpoint_scanner.find_models_by_name(
+            name, base_model=base_model
+        )
+
     async def get_local_lora_by_hash(self, hash_value: str) -> Optional[Dict[str, Any]]:
         """Lookup a local LoRA through the scanner's hash index."""
 
@@ -3281,11 +3634,23 @@ class RecipeScanner:
             if filters:
                 # Filter by base model
                 if "base_model" in filters and filters["base_model"]:
-                    filtered_data = [
+                    base_model_filter = filters["base_model"]
-                        item
+                    if UNKNOWN_BASE_MODEL_FILTER in base_model_filter:
-                        for item in filtered_data
+                        # The unknown bucket matches recipes whose base model
-                        if item.get("base_model", "") in filters["base_model"]
+                        # could not be determined (None/empty); real base
-                    ]
+                        # models in the list still match by exact name.
+                        filtered_data = [
+                            item
+                            for item in filtered_data
+                            if not item.get("base_model")
+                            or item.get("base_model") in base_model_filter
+                        ]
+                    else:
+                        filtered_data = [
+                            item
+                            for item in filtered_data
+                            if item.get("base_model", "") in base_model_filter
+                        ]
 
                 # Filter by favorite
                 if "favorite" in filters and filters["favorite"]:
@@ -3677,6 +4042,13 @@ class RecipeScanner:
                 raise RecipeNotFoundError("LoRA index out of range in recipe")
 
             lora_entry = loras[lora_index]
+            # Snapshot the pre-update state so the association can be restored
+            # later (undo reconnect). Never nest snapshots.
+            snapshot = {
+                key: copy.deepcopy(value)
+                for key, value in lora_entry.items()
+                if key != "reconnectSnapshot"
+            }
             lora_entry["isDeleted"] = False
             lora_entry["hashInvalid"] = False
             lora_entry["exclude"] = False
@@ -3695,6 +4067,8 @@ class RecipeScanner:
                     lora_entry["modelVersionName"] = civitai_info.get("name", "")
                     lora_entry["modelVersionId"] = civitai_info.get("id")
 
+            lora_entry["reconnectSnapshot"] = snapshot
+
             from ..utils.utils import calculate_recipe_fingerprint
 
             recipe_data["fingerprint"] = calculate_recipe_fingerprint(
@@ -3730,6 +4104,68 @@ class RecipeScanner:
         updated_lora = self._enrich_lora_entry(updated_lora)
         return recipe_data, updated_lora
 
+    async def restore_lora_entry(
+        self,
+        recipe_id: str,
+        lora_index: int,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Restore a LoRA entry to its pre-reconnect snapshot.
+
+        Reverses :meth:`update_lora_entry`: the entry saved under
+        ``reconnectSnapshot`` becomes the entry again and the snapshot is
+        dropped. Returns the updated recipe data and the restored LoRA
+        metadata.
+        """
+
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            loras = recipe_data.get("loras", [])
+            if lora_index < 0 or lora_index >= len(loras):
+                raise RecipeNotFoundError("LoRA index out of range in recipe")
+
+            snapshot = loras[lora_index].get("reconnectSnapshot")
+            if not isinstance(snapshot, dict):
+                raise RecipeValidationError(
+                    "LoRA entry has no reconnect snapshot to restore"
+                )
+
+            restored_entry = copy.deepcopy(snapshot)
+            restored_entry.pop("reconnectSnapshot", None)
+            loras[lora_index] = restored_entry
+
+            from ..utils.utils import calculate_recipe_fingerprint
+
+            recipe_data["fingerprint"] = calculate_recipe_fingerprint(
+                recipe_data.get("loras", [])
+            )
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        # Update FTS index
+        self._update_fts_index_for_recipe(recipe_data, "update")
+
+        # Update persistent SQLite cache
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        restored_lora = self._enrich_lora_entry(dict(restored_entry))
+        return recipe_data, restored_lora
+
     async def set_lora_entry_hash_invalid(
         self,
         recipe_id: str,
@@ -3781,6 +4217,214 @@ class RecipeScanner:
         updated_lora = self._enrich_lora_entry(dict(lora_entry))
         return recipe_data, updated_lora
 
+    async def update_checkpoint_entry(
+        self,
+        recipe_id: str,
+        *,
+        target_name: str,
+        target_checkpoint: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Update the checkpoint entry within a recipe (manual reconnect).
+
+        Mirrors :meth:`update_lora_entry`: the pre-update entry is snapshotted
+        under ``reconnectSnapshot`` so the association can be restored later,
+        then the matched local checkpoint is written back following the same
+        pinned key set as ``_write_rematch_checkpoint_entry``. ``file_name``
+        keeps the user-entered ``target_name`` (the same convention as the
+        LoRA reconnect), while hash/name/version/baseModel/identifier are
+        refreshed from the local item. The fingerprint is untouched — it is
+        computed over LoRAs only.
+
+        Returns:
+            The updated recipe data and the refreshed checkpoint metadata.
+        """
+        if target_name is None:
+            raise ValueError("target_name must be provided")
+
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError(
+                    "Recipe has no checkpoint entry to reconnect"
+                )
+
+            # Snapshot the pre-update state so the association can be restored
+            # later (undo reconnect). Never nest snapshots.
+            snapshot = {
+                key: copy.deepcopy(value)
+                for key, value in checkpoint.items()
+                if key != "reconnectSnapshot"
+            }
+            checkpoint["isDeleted"] = False
+            checkpoint["hashInvalid"] = False
+            checkpoint["file_name"] = target_name
+
+            if target_checkpoint is not None:
+                sha_value = target_checkpoint.get("sha256") or target_checkpoint.get(
+                    "sha"
+                )
+                if sha_value:
+                    checkpoint["hash"] = sha_value.lower()
+
+                self._write_rematch_checkpoint_entry(checkpoint, target_checkpoint)
+
+                # The write-back only refreshes keys the entry already has;
+                # a manual reconnect must also backfill the display keys so a
+                # sparse parser-style entry renders properly after the swap.
+                if not checkpoint.get("name") and target_checkpoint.get("model_name"):
+                    checkpoint["name"] = target_checkpoint["model_name"]
+                civitai = target_checkpoint.get("civitai") or {}
+                civ_name = civitai.get("name")
+                if not checkpoint.get("version") and civ_name:
+                    checkpoint["version"] = civ_name
+                if (
+                    not checkpoint.get("baseModel")
+                    and target_checkpoint.get("base_model")
+                ):
+                    checkpoint["baseModel"] = target_checkpoint["base_model"]
+
+            checkpoint["reconnectSnapshot"] = snapshot
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        # Update FTS index
+        self._update_fts_index_for_recipe(recipe_data, "update")
+
+        # Update persistent SQLite cache
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        updated_checkpoint = dict(checkpoint)
+        if target_checkpoint is not None:
+            preview_url = target_checkpoint.get("preview_url")
+            if preview_url:
+                updated_checkpoint["preview_url"] = config.get_preview_static_url(
+                    preview_url
+                )
+            if target_checkpoint.get("file_path"):
+                updated_checkpoint["localPath"] = target_checkpoint["file_path"]
+
+        updated_checkpoint = self._enrich_checkpoint_entry(updated_checkpoint)
+        return recipe_data, updated_checkpoint
+
+    async def restore_checkpoint_entry(
+        self,
+        recipe_id: str,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Restore the checkpoint entry to its pre-reconnect snapshot.
+
+        Reverses :meth:`update_checkpoint_entry`: the entry saved under
+        ``reconnectSnapshot`` becomes the checkpoint again and the snapshot is
+        dropped. Returns the updated recipe data and the restored checkpoint
+        metadata.
+        """
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError(
+                    "Recipe has no checkpoint entry to restore"
+                )
+
+            snapshot = checkpoint.get("reconnectSnapshot")
+            if not isinstance(snapshot, dict):
+                raise RecipeValidationError(
+                    "Checkpoint entry has no reconnect snapshot to restore"
+                )
+
+            restored_entry = copy.deepcopy(snapshot)
+            restored_entry.pop("reconnectSnapshot", None)
+            recipe_data["checkpoint"] = restored_entry
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        # Update FTS index
+        self._update_fts_index_for_recipe(recipe_data, "update")
+
+        # Update persistent SQLite cache
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        restored_checkpoint = self._enrich_checkpoint_entry(dict(restored_entry))
+        return recipe_data, restored_checkpoint
+
+    async def set_checkpoint_entry_hash_invalid(
+        self,
+        recipe_id: str,
+        hash_invalid: bool,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """Set the ``hashInvalid`` flag on the recipe's checkpoint entry.
+
+        ``hashInvalid`` records that the entry's hash could not be resolved
+        on CivitAI (e.g. a download attempt returned "Model not found").
+        Marking it makes the entry an unresolved rematch candidate without
+        touching its stored hash/file_name.
+
+        Returns:
+            The updated recipe data and the refreshed checkpoint metadata.
+        """
+        recipe_json_path = await self.get_recipe_json_path(recipe_id)
+        if not recipe_json_path or not os.path.exists(recipe_json_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        async with self._mutation_lock:
+            with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
+                recipe_data = json.load(file_obj)
+
+            checkpoint = recipe_data.get("checkpoint")
+            if not isinstance(checkpoint, dict):
+                raise RecipeValidationError("Checkpoint entry is not a dict")
+
+            checkpoint["hashInvalid"] = bool(hash_invalid)
+            recipe_data["modified"] = time.time()
+
+            with open(recipe_json_path, "w", encoding="utf-8") as file_obj:
+                json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        cache = await self.get_cached_data()
+        replaced = await cache.replace_recipe(recipe_id, recipe_data, resort=False)
+        if not replaced:
+            await cache.add_recipe(recipe_data, resort=False)
+        self._schedule_resort()
+
+        if self._persistent_cache:
+            self._persistent_cache.update_recipe(recipe_data, recipe_json_path)
+            self._json_path_map[recipe_id] = recipe_json_path
+
+        updated_checkpoint = self._enrich_checkpoint_entry(dict(checkpoint))
+        return recipe_data, updated_checkpoint
+
     async def get_recipes_for_lora(self, lora_hash: str) -> List[Dict[str, Any]]:
         """Return recipes that reference a given LoRA hash."""
 

py/services/recipes/__init__.py

@@ -1,6 +1,7 @@
 """Recipe service layer implementations."""
 
 from .analysis_service import RecipeAnalysisService
+from .import_info import build_import_info, compute_no_loras_reason
 from .persistence_service import RecipePersistenceService
 from .sharing_service import RecipeSharingService
 from .errors import (
@@ -15,6 +16,8 @@ __all__ = [
     "RecipeAnalysisService",
     "RecipePersistenceService",
     "RecipeSharingService",
+    "build_import_info",
+    "compute_no_loras_reason",
     "RecipeServiceError",
     "RecipeValidationError",
     "RecipeNotFoundError",

py/services/recipes/analysis_service.py

@@ -72,15 +72,28 @@ class RecipeAnalysisService:
             metadata = self._exif_utils.extract_image_metadata(temp_path)
             if not metadata:
                 return AnalysisResult(
-                    {"error": "No metadata found in this image", "loras": []}
+                    {
+                        "error": "No metadata found in this image",
+                        "loras": [],
+                        "diagnostics": {
+                            "channel": "upload",
+                            "exif_present": False,
+                        },
+                    }
                 )
 
-            return await self._parse_metadata(
+            result = await self._parse_metadata(
                 metadata,
                 recipe_scanner=recipe_scanner,
                 image_path=None,
                 include_image_base64=False,
             )
+            result.payload["diagnostics"] = {
+                "channel": "upload",
+                "exif_present": True,
+                "exif_parser": result.payload.get("parser"),
+            }
+            return result
         finally:
             self._safe_cleanup(temp_path)
 
@@ -104,9 +117,13 @@ class RecipeAnalysisService:
         image_info: Optional[dict[str, Any]] = None
         is_video = False
         extension = ".jpg"  # Default
+        # Diagnostics collected during analysis; surfaced in the payload so
+        # callers can persist an import_info block explaining empty LoRA lists.
+        diagnostics: dict[str, Any] = {"channel": "url"}
 
         try:
             civitai_image_id = extract_civitai_image_id(url)
+            diagnostics["civitai_image"] = bool(civitai_image_id)
             if civitai_image_id:
                 image_info = await civitai_client.get_image_info(
                     civitai_image_id, source_url=url
@@ -147,11 +164,23 @@ class RecipeAnalysisService:
                 ):
                     metadata = metadata["meta"]
 
+                # Diagnostics: capture the API meta shape before injecting
+                # modelVersionIds / browsingLevel so the recipe modal can
+                # explain why an import ended up without LoRAs.
+                diagnostics["api_meta_present"] = isinstance(metadata, dict)
+                if isinstance(metadata, dict):
+                    diagnostics["api_meta_keys"] = sorted(metadata.keys())
+
                 # Include modelVersionIds from root level if available.
                 # CivitAI API returns modelVersionIds at root level, not in meta.
                 # When meta is null (None), create a minimal dict so downstream
                 # parsers can still discover LoRAs and checkpoints.
                 model_version_ids = image_info.get("modelVersionIds")
+                diagnostics["api_model_version_ids"] = (
+                    len(model_version_ids)
+                    if isinstance(model_version_ids, list)
+                    else 0
+                )
                 if model_version_ids:
                     if isinstance(metadata, dict):
                         metadata["modelVersionIds"] = model_version_ids
@@ -229,6 +258,8 @@ class RecipeAnalysisService:
                     finally:
                         self._safe_cleanup(orig_temp_path)
 
+            diagnostics["exif_present"] = bool(exif_metadata)
+
             # Parse EXIF data (typically a string like parameters/prompt/workflow)
             # and API metadata (dict with modelVersionIds, browsingLevel) separately,
             # then merge: API loras/checkpoint override, EXIF gen_params fill in gaps.
@@ -237,6 +268,7 @@ class RecipeAnalysisService:
             if isinstance(exif_metadata, str):
                 exif_parser = self._recipe_parser_factory.create_parser(exif_metadata)
                 if exif_parser:
+                    diagnostics["exif_parser"] = exif_parser.__class__.__name__
                     exif_data = await exif_parser.parse_metadata(
                         exif_metadata, recipe_scanner=recipe_scanner,
                     )
@@ -324,6 +356,8 @@ class RecipeAnalysisService:
                     if isinstance(bl, int) and bl > 0:
                         result.payload["preview_nsfw_level"] = bl
 
+            diagnostics["is_video"] = is_video
+            result.payload["diagnostics"] = diagnostics
             return result
         finally:
             if temp_path:
@@ -334,6 +368,7 @@ class RecipeAnalysisService:
         *,
         file_path: str | None,
         recipe_scanner,
+        ignore_recipe_metadata: bool = False,
     ) -> AnalysisResult:
         """Analyze a file already present on disk."""
 
@@ -348,14 +383,41 @@ class RecipeAnalysisService:
             self._exif_utils.extract_image_metadata, normalized_path
         )
         if not metadata:
-            return self._metadata_not_found_response(normalized_path)
+            result = self._metadata_not_found_response(normalized_path)
+            result.payload["diagnostics"] = {
+                "channel": "local",
+                "exif_present": False,
+            }
+            return result
 
-        return await self._parse_metadata(
+        if ignore_recipe_metadata:
+            # Re-import: re-parse the original embedded generation metadata
+            # instead of the recipe JSON block LoRA Manager appended on save.
+            from ...recipes.parsers.recipe_format import strip_recipe_metadata
+
+            metadata = strip_recipe_metadata(metadata)
+            if not metadata:
+                result = self._metadata_not_found_response(normalized_path)
+                result.payload["diagnostics"] = {
+                    "channel": "local",
+                    "exif_present": True,
+                    "ignore_recipe_metadata": True,
+                    "reason": "only_recipe_metadata",
+                }
+                return result
+
+        result = await self._parse_metadata(
             metadata,
             recipe_scanner=recipe_scanner,
             image_path=normalized_path,
             include_image_base64=True,
         )
+        result.payload["diagnostics"] = {
+            "channel": "local",
+            "exif_present": True,
+            "exif_parser": result.payload.get("parser"),
+        }
+        return result
 
     async def analyze_widget_metadata(self, *, recipe_scanner) -> AnalysisResult:
         """Analyse the most recent generation metadata for widget saves."""
@@ -452,6 +514,10 @@ class RecipeAnalysisService:
                 metadata, recipe_scanner=recipe_scanner
             )
 
+        # Record which parser handled the metadata so import diagnostics
+        # can distinguish e.g. ComfyUI workflow sources.
+        result["parser"] = parser.__class__.__name__
+
         if include_image_base64 and image_path:
             result["image_base64"] = self._encode_file(image_path)
 

py/services/recipes/import_info.py

@@ -0,0 +1,129 @@
+"""Import provenance helpers for recipes.
+
+Builds the ``import_info`` block persisted on a recipe: the import channel
+(batch import / single URL / local file / upload / widget) and, when the
+recipe ended up with no LoRAs, a machine-readable reason plus the diagnostic
+details that led to it. The recipe modal renders this block in a collapsed
+"Why no LoRAs?" panel; legacy recipes without ``import_info`` fall back to a
+frontend heuristic.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+# Import channels (how the recipe entered the library).
+CHANNEL_BATCH_IMPORT_URL = "batch_import_url"
+CHANNEL_BATCH_IMPORT_LOCAL = "batch_import_local"
+CHANNEL_URL = "url"
+CHANNEL_LOCAL = "local"
+CHANNEL_UPLOAD = "upload"
+CHANNEL_WIDGET = "widget"
+CHANNEL_REIMPORT_URL = "reimport_url"
+CHANNEL_REIMPORT_LOCAL = "reimport_local"
+
+_URL_CHANNELS = frozenset(
+    {CHANNEL_BATCH_IMPORT_URL, CHANNEL_URL, CHANNEL_REIMPORT_URL}
+)
+
+# No-LoRA reason codes (persisted, consumed by the recipe modal).
+REASON_NO_LORAS_USED = "no_loras_used"
+REASON_API_NO_LORA_RESOURCES = "api_meta_no_lora_resources"
+REASON_API_META_MISSING = "api_meta_missing"
+REASON_NO_EMBEDDED_METADATA = "no_embedded_metadata"
+REASON_WORKFLOW_METADATA_LIMITED = "workflow_metadata_limited"
+REASON_VIDEO_NO_METADATA = "video_no_metadata"
+REASON_METADATA_UNSUPPORTED = "metadata_unsupported"
+REASON_UNKNOWN = "unknown"
+
+_COMFY_PARSER_NAME = "ComfyMetadataParser"
+
+# Cap for api_meta_keys kept in details — enough for the UI bullet without
+# bloating the recipe JSON.
+_MAX_DETAIL_KEYS = 12
+
+
+def compute_no_loras_reason(
+    channel: str, diagnostics: Optional[Dict[str, Any]]
+) -> str:
+    """Classify why an import produced no LoRA entries.
+
+    Args:
+        channel: One of the CHANNEL_* constants.
+        diagnostics: Signals collected during analysis (see
+            ``RecipeAnalysisService``), or None for channels without analysis
+            (e.g. widget saves).
+    """
+    diag = diagnostics or {}
+
+    if diag.get("is_video"):
+        return REASON_VIDEO_NO_METADATA
+
+    # Embedded metadata that is a ComfyUI workflow: LoRA extraction from
+    # workflows is limited, so report that specifically.
+    parser = diag.get("exif_parser") or diag.get("parser")
+    if parser == _COMFY_PARSER_NAME:
+        return REASON_WORKFLOW_METADATA_LIMITED
+
+    if channel in _URL_CHANNELS:
+        if not diag.get("civitai_image"):
+            # Generic (non-CivitAI) URL: only embedded metadata is available.
+            if not diag.get("exif_present"):
+                return REASON_NO_EMBEDDED_METADATA
+            return (
+                REASON_NO_LORAS_USED if parser else REASON_METADATA_UNSUPPORTED
+            )
+        # NOTE: no "parsed EXIF means no LoRAs were used" shortcut here.
+        # CivitAI's onsite generator writes A1111-style EXIF (prompt, seed,
+        # steps, ...) WITHOUT LoRA references — LoRA usage lives only in
+        # CivitAI-internal data — so cleanly parsed EXIF cannot prove the
+        # generation used no LoRAs. Report the API meta shape instead.
+        api_keys = diag.get("api_meta_keys") or []
+        api_mvids = diag.get("api_model_version_ids") or 0
+        if api_keys or api_mvids:
+            return REASON_API_NO_LORA_RESOURCES
+        return REASON_API_META_MISSING
+
+    if channel == CHANNEL_WIDGET:
+        return REASON_NO_LORAS_USED
+
+    # Local file / upload / local re-import: embedded metadata only.
+    if not diag.get("exif_present"):
+        return REASON_NO_EMBEDDED_METADATA
+    return REASON_NO_LORAS_USED if parser else REASON_METADATA_UNSUPPORTED
+
+
+def build_import_info(
+    channel: str,
+    diagnostics: Optional[Dict[str, Any]],
+    loras: Optional[List[Dict[str, Any]]],
+) -> Dict[str, Any]:
+    """Build the ``import_info`` block persisted on a recipe.
+
+    Always records the import channel; adds ``reason`` and ``details`` only
+    when the recipe has no LoRAs.
+    """
+    info: Dict[str, Any] = {"channel": channel}
+    if loras:
+        return info
+
+    info["reason"] = compute_no_loras_reason(channel, diagnostics)
+
+    diag = diagnostics or {}
+    details: Dict[str, Any] = {}
+    api_keys = diag.get("api_meta_keys")
+    if api_keys:
+        details["api_meta_keys"] = list(api_keys)[:_MAX_DETAIL_KEYS]
+    api_mvids = diag.get("api_model_version_ids")
+    if api_mvids is not None:
+        details["api_model_version_ids"] = api_mvids
+    if "exif_present" in diag:
+        details["exif_present"] = bool(diag.get("exif_present"))
+    if diag.get("exif_parser"):
+        details["exif_parser"] = diag["exif_parser"]
+    if diag.get("is_video"):
+        details["is_video"] = True
+    if details:
+        info["details"] = details
+
+    return info

py/services/recipes/persistence_service.py

@@ -13,9 +13,15 @@ from typing import Any, Awaitable, Dict, Iterable, Optional, cast
 
 from ...config import config
 from ...recipes.constants import GEN_PARAM_KEYS
+from ...utils.base_model import (
+    RELATION_COMPATIBLE,
+    RELATION_INCOMPATIBLE,
+    base_model_relation,
+)
 from ...utils.utils import calculate_recipe_fingerprint
 from ..pending_delete_service import get_pending_delete_service
 from .errors import RecipeNotFoundError, RecipeValidationError
+from .import_info import CHANNEL_UPLOAD, CHANNEL_WIDGET, build_import_info
 
 
 @dataclass(frozen=True)
@@ -52,6 +58,7 @@ class RecipePersistenceService:
         extension: str | None = None,
         recipe_id: str | None = None,
         target_dir: str | None = None,
+        skip_optimize: bool = False,
     ) -> PersistenceResult:
         """Persist a user uploaded recipe.
 
@@ -61,6 +68,11 @@ class RecipePersistenceService:
             target_dir: If provided, save recipe files to this directory instead
                 of the default recipes_dir. Used by re-import to preserve the
                 original folder location.
+            skip_optimize: If True, store the image bytes verbatim without
+                resizing/re-encoding (recipe metadata is still embedded via a
+                byte-level EXIF update that leaves the pixels untouched). Used
+                by local re-import, where the source is the recipe's own
+                already-optimized preview image.
         """
 
         missing_fields = []
@@ -81,9 +93,12 @@ class RecipePersistenceService:
 
         recipe_id = recipe_id or str(uuid.uuid4())
         
-        # Handle video formats by bypassing optimization and metadata embedding
+        # Handle video formats by bypassing optimization and metadata embedding.
+        # Local re-import also bypasses optimization: the source is the
+        # recipe's own already-optimized preview image, so re-compressing it
+        # would only degrade quality.
         is_video = extension in [".mp4", ".webm"]
-        if is_video:
+        if is_video or skip_optimize:
             optimized_image = resolved_image_bytes
             # extension is already set
         else:
@@ -129,6 +144,22 @@ class RecipePersistenceService:
         if metadata.get("source_path"):
             recipe_data["source_path"] = metadata.get("source_path")
 
+        # Persist import provenance. Batch import / re-import paths pass a
+        # prebuilt import_info; frontend-driven saves (upload, single URL,
+        # local path) carry the analysis payload's diagnostics, from which
+        # import_info is derived here.
+        import_info = metadata.get("import_info")
+        if not isinstance(import_info, dict):
+            diagnostics = metadata.get("diagnostics")
+            if isinstance(diagnostics, dict):
+                import_info = build_import_info(
+                    diagnostics.get("channel") or CHANNEL_UPLOAD,
+                    diagnostics,
+                    loras_data,
+                )
+        if isinstance(import_info, dict) and import_info:
+            recipe_data["import_info"] = import_info
+
         nsfw_level = metadata.get("preview_nsfw_level")
         if nsfw_level is not None and isinstance(nsfw_level, int):
             recipe_data["preview_nsfw_level"] = nsfw_level
@@ -153,7 +184,11 @@ class RecipePersistenceService:
             json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
 
         if not is_video:
-            self._exif_utils.append_recipe_metadata(normalized_image_path, recipe_data)
+            self._exif_utils.append_recipe_metadata(
+                normalized_image_path,
+                recipe_data,
+                pixel_preserving=skip_optimize,
+            )
 
         matching_recipes = await self._find_matching_recipes(recipe_scanner, fingerprint, exclude_id=recipe_id)
         await recipe_scanner.add_recipe(recipe_data)
@@ -430,20 +465,31 @@ class RecipePersistenceService:
         with open(recipe_path, "r", encoding="utf-8") as file_obj:
             recipe_base_model = json.load(file_obj).get("base_model", "")
 
-        target_lora = await recipe_scanner.get_local_lora(target_name, recipe_base_model)
+        matches = await recipe_scanner.find_local_loras_by_name(target_name)
-        if not target_lora:
+        if not matches:
-            matches = await recipe_scanner.find_local_loras_by_name(target_name)
-            if len(matches) > 1:
-                raise RecipeValidationError(
-                    f"Multiple local LoRAs match '{target_name}'; "
-                    "include the folder path to disambiguate"
-                )
-            if len(matches) == 1:
-                raise RecipeValidationError(
-                    f"Local LoRA '{target_name}' has a different base model than the recipe"
-                )
             raise RecipeNotFoundError(f"Local LoRA not found with name: {target_name}")
 
+        # Three-tier base-model guard: exact/unknown labels pass silently;
+        # labels from the same architecture family (e.g. Pony ↔ Illustrious)
+        # pass but are reported so the UI can warn; confident architecture
+        # mismatches stay hard-rejected because they can never load.
+        eligible: list[tuple[dict, str]] = []
+        for match in matches:
+            relation = base_model_relation(recipe_base_model, match.get("base_model"))
+            if relation != RELATION_INCOMPATIBLE:
+                eligible.append((match, relation))
+
+        if not eligible:
+            raise RecipeValidationError(
+                f"Local LoRA '{target_name}' has a different base model than the recipe"
+            )
+        if len(eligible) > 1:
+            raise RecipeValidationError(
+                f"Multiple local LoRAs match '{target_name}'; "
+                "include the folder path to disambiguate"
+            )
+        target_lora, target_relation = eligible[0]
+
         recipe_data, updated_lora = await recipe_scanner.update_lora_entry(
             recipe_id,
             lora_index,
@@ -451,6 +497,43 @@ class RecipePersistenceService:
             target_lora=target_lora,
         )
 
+        image_path = recipe_data.get("file_path")
+        if image_path and os.path.exists(image_path):
+            self._exif_utils.append_recipe_metadata(image_path, recipe_data)
+
+        matching_recipes = []
+        if "fingerprint" in recipe_data:
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(recipe_data["fingerprint"])
+            if recipe_id in matching_recipes:
+                matching_recipes.remove(recipe_id)
+
+        payload: dict[str, Any] = {
+            "success": True,
+            "recipe_id": recipe_id,
+            "updated_lora": updated_lora,
+            "matching_recipes": matching_recipes,
+        }
+        if target_relation == RELATION_COMPATIBLE:
+            # Structured data, not prose — the frontend localizes the warning.
+            payload["base_model_mismatch"] = {
+                "recipe_base_model": recipe_base_model,
+                "lora_base_model": target_lora.get("base_model") or "",
+            }
+        return PersistenceResult(payload)
+
+    async def restore_lora(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        lora_index: int,
+    ) -> PersistenceResult:
+        """Restore a LoRA entry to the state captured before its reconnect."""
+
+        recipe_data, updated_lora = await recipe_scanner.restore_lora_entry(
+            recipe_id, lora_index
+        )
+
         image_path = recipe_data.get("file_path")
         if image_path and os.path.exists(image_path):
             self._exif_utils.append_recipe_metadata(image_path, recipe_data)
@@ -470,6 +553,35 @@ class RecipePersistenceService:
             }
         )
 
+    async def get_reconnect_suggestions(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        lora_index: int,
+        query: str | None = None,
+    ) -> PersistenceResult:
+        """Return ranked local LoRA candidates for reconnecting a recipe entry."""
+
+        recipe_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_path or not os.path.exists(recipe_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        with open(recipe_path, "r", encoding="utf-8") as file_obj:
+            recipe_data = json.load(file_obj)
+
+        loras = recipe_data.get("loras") or []
+        if lora_index < 0 or lora_index >= len(loras):
+            raise RecipeValidationError(f"Invalid lora_index: {lora_index}")
+
+        suggestions = await recipe_scanner.suggest_reconnect_candidates(
+            entry=loras[lora_index],
+            recipe_base_model=recipe_data.get("base_model"),
+            query=query,
+        )
+
+        return PersistenceResult({"success": True, "suggestions": suggestions})
+
     async def mark_lora_hash_invalid(
         self,
         *,
@@ -500,6 +612,172 @@ class RecipePersistenceService:
             }
         )
 
+    async def reconnect_checkpoint(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        target_name: str,
+    ) -> PersistenceResult:
+        """Reconnect the checkpoint entry within an existing recipe."""
+
+        recipe_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_path or not os.path.exists(recipe_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        with open(recipe_path, "r", encoding="utf-8") as file_obj:
+            recipe_base_model = json.load(file_obj).get("base_model", "")
+
+        matches = await recipe_scanner.find_local_checkpoints_by_name(target_name)
+        if not matches:
+            raise RecipeNotFoundError(
+                f"Local checkpoint not found with name: {target_name}"
+            )
+
+        # Same three-tier base-model guard as reconnect_lora: exact/unknown
+        # labels pass silently; same-architecture-family labels pass but are
+        # reported so the UI can warn; confident mismatches stay hard-rejected.
+        eligible: list[tuple[dict, str]] = []
+        for match in matches:
+            relation = base_model_relation(recipe_base_model, match.get("base_model"))
+            if relation != RELATION_INCOMPATIBLE:
+                eligible.append((match, relation))
+
+        if not eligible:
+            raise RecipeValidationError(
+                f"Local checkpoint '{target_name}' has a different base model "
+                "than the recipe"
+            )
+        if len(eligible) > 1:
+            raise RecipeValidationError(
+                f"Multiple local checkpoints match '{target_name}'; "
+                "include the folder path to disambiguate"
+            )
+        target_checkpoint, target_relation = eligible[0]
+
+        recipe_data, updated_checkpoint = await recipe_scanner.update_checkpoint_entry(
+            recipe_id,
+            target_name=target_name,
+            target_checkpoint=target_checkpoint,
+        )
+
+        image_path = recipe_data.get("file_path")
+        if image_path and os.path.exists(image_path):
+            self._exif_utils.append_recipe_metadata(image_path, recipe_data)
+
+        matching_recipes = []
+        if "fingerprint" in recipe_data:
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(
+                recipe_data["fingerprint"]
+            )
+            if recipe_id in matching_recipes:
+                matching_recipes.remove(recipe_id)
+
+        payload: dict[str, Any] = {
+            "success": True,
+            "recipe_id": recipe_id,
+            "updated_checkpoint": updated_checkpoint,
+            "matching_recipes": matching_recipes,
+        }
+        if target_relation == RELATION_COMPATIBLE:
+            # Structured data, not prose — the frontend localizes the warning.
+            payload["base_model_mismatch"] = {
+                "recipe_base_model": recipe_base_model,
+                "checkpoint_base_model": target_checkpoint.get("base_model") or "",
+            }
+        return PersistenceResult(payload)
+
+    async def restore_checkpoint(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+    ) -> PersistenceResult:
+        """Restore the checkpoint entry to the state captured before its reconnect."""
+
+        recipe_data, updated_checkpoint = await recipe_scanner.restore_checkpoint_entry(
+            recipe_id
+        )
+
+        image_path = recipe_data.get("file_path")
+        if image_path and os.path.exists(image_path):
+            self._exif_utils.append_recipe_metadata(image_path, recipe_data)
+
+        matching_recipes = []
+        if "fingerprint" in recipe_data:
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(
+                recipe_data["fingerprint"]
+            )
+            if recipe_id in matching_recipes:
+                matching_recipes.remove(recipe_id)
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "updated_checkpoint": updated_checkpoint,
+                "matching_recipes": matching_recipes,
+            }
+        )
+
+    async def get_checkpoint_reconnect_suggestions(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        query: str | None = None,
+    ) -> PersistenceResult:
+        """Return ranked local checkpoint candidates for reconnecting a recipe entry."""
+
+        recipe_path = await recipe_scanner.get_recipe_json_path(recipe_id)
+        if not recipe_path or not os.path.exists(recipe_path):
+            raise RecipeNotFoundError("Recipe not found")
+
+        with open(recipe_path, "r", encoding="utf-8") as file_obj:
+            recipe_data = json.load(file_obj)
+
+        checkpoint = recipe_data.get("checkpoint")
+        if not isinstance(checkpoint, dict):
+            raise RecipeValidationError("Recipe has no checkpoint entry")
+
+        suggestions = await recipe_scanner.suggest_checkpoint_reconnect_candidates(
+            entry=checkpoint,
+            recipe_base_model=recipe_data.get("base_model"),
+            query=query,
+        )
+
+        return PersistenceResult({"success": True, "suggestions": suggestions})
+
+    async def mark_checkpoint_hash_invalid(
+        self,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        hash_invalid: bool = True,
+    ) -> PersistenceResult:
+        """Mark the recipe checkpoint entry's hash as unresolvable on CivitAI.
+
+        Called when a download attempt by hash returned "Model not found".
+        The flag makes the entry an unresolved rematch candidate without
+        altering its stored hash/file_name.
+        """
+
+        recipe_data, updated_checkpoint = (
+            await recipe_scanner.set_checkpoint_entry_hash_invalid(
+                recipe_id,
+                hash_invalid=hash_invalid,
+            )
+        )
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "hash_invalid": bool(hash_invalid),
+                "updated_checkpoint": updated_checkpoint,
+            }
+        )
+
     async def bulk_delete(
         self,
         *,
@@ -649,6 +927,9 @@ class RecipePersistenceService:
             # Widget saves re-encode an in-memory tensor to PNG/WebP with no
             # embedded metadata chunks, so a workflow can never be present.
             "has_workflow": False,
+            # Widget saves read LoRAs straight from the current workflow; an
+            # empty list means the workflow used no LoRAs.
+            "import_info": build_import_info(CHANNEL_WIDGET, None, loras_data),
         }
         if checkpoint_entry:
             recipe_data["checkpoint"] = checkpoint_entry

py/utils/base_model.py

@@ -0,0 +1,79 @@
+"""Base-model architecture families and compatibility relations.
+
+CivitAI base-model labels describe fine-tune lineages, not architectures.
+A LoRA physically loads on any checkpoint sharing its tensor architecture,
+so e.g. Pony / Illustrious / NoobAI / SDXL 1.0 LoRAs are interchangeable
+(quality varies, but nothing breaks). Different architectures (SD 1.5 vs
+SDXL vs Flux) are guaranteed failures and must stay hard-rejected.
+
+Only families with high-confidence architecture equivalence are listed.
+Anything not in the table is treated as its own family, i.e. only an exact
+label match is accepted — unknown new labels never get wrongly waved through.
+"""
+
+from __future__ import annotations
+
+from typing import Optional
+
+# Normalized (casefolded, stripped) base-model label -> architecture family.
+_BASE_MODEL_FAMILIES = {
+    # SD 1.x — all share the original 512px latent UNet.
+    "sd 1.4": "sd1",
+    "sd 1.5": "sd1",
+    "sd 1.5 lcm": "sd1",
+    "sd 1.5 hyper": "sd1",
+    # SDXL lineage — Pony / Illustrious / NoobAI are SDXL fine-tunes.
+    # Note: Pony V7 is AuraFlow-based, NOT SDXL, so it is deliberately absent.
+    "sdxl 1.0": "sdxl",
+    "sdxl lightning": "sdxl",
+    "sdxl hyper": "sdxl",
+    "pony": "sdxl",
+    "pony diffusion": "sdxl",
+    "pony diffusion v6 xl": "sdxl",
+    "illustrious": "sdxl",
+    "illustrious 0.1": "sdxl",
+    "illustrious 1.0": "sdxl",
+    "illustrious 1.1": "sdxl",
+    "noobai": "sdxl",
+    # Flux.1 — dev/schnell/Krea share the 12B rectified-flow transformer.
+    "flux.1 d": "flux1",
+    "flux.1 s": "flux1",
+    "flux.1 krea": "flux1",
+    # SD 3.5 Large and its Turbo distill share the 8B MMDiT. SD 3 (2B) and
+    # SD 3.5 Medium (2.5B) have different shapes and stay unlisted.
+    "sd 3.5 large": "sd35-large",
+    "sd 3.5 large turbo": "sd35-large",
+}
+
+_UNKNOWN_TOKENS = {"", "unknown", "other", "none", "null"}
+
+# Relation constants returned by base_model_relation().
+RELATION_UNKNOWN = "unknown"  # at least one side has no usable label
+RELATION_SAME = "same"  # identical labels
+RELATION_COMPATIBLE = "compatible"  # different labels, same architecture family
+RELATION_INCOMPATIBLE = "incompatible"  # different labels, different/unknown family
+
+
+def _normalize(label: Optional[str]) -> str:
+    return (label or "").strip().casefold()
+
+
+def base_model_relation(a: Optional[str], b: Optional[str]) -> str:
+    """Classify how two base-model labels relate for reconnect purposes.
+
+    ``RELATION_UNKNOWN`` when either side has no usable label (callers treat
+    it as lenient-allow), ``RELATION_SAME`` for identical labels,
+    ``RELATION_COMPATIBLE`` when both labels map to the same architecture
+    family, and ``RELATION_INCOMPATIBLE`` otherwise — including when a label
+    is missing from the family table (conservative fallback).
+    """
+    na, nb = _normalize(a), _normalize(b)
+    if na in _UNKNOWN_TOKENS or nb in _UNKNOWN_TOKENS:
+        return RELATION_UNKNOWN
+    if na == nb:
+        return RELATION_SAME
+    fa = _BASE_MODEL_FAMILIES.get(na)
+    fb = _BASE_MODEL_FAMILIES.get(nb)
+    if fa is not None and fa == fb:
+        return RELATION_COMPATIBLE
+    return RELATION_INCOMPATIBLE

py/utils/constants.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 NSFW_LEVELS = {
     "PG": 1,
     "PG13": 2,
@@ -99,11 +101,30 @@ DEFAULT_HASH_CHUNK_SIZE_MB = 4
 # absurd 64-bit header length from forcing a multi-GB allocation during scan.
 MAX_SAFETENSORS_HEADER_BYTES = 64 * 1024 * 1024
 
-# First 12 chars of the SHA256 of an empty byte string. Some (re-packaging)
+# SHA256 of an empty byte string. Some (re-packaging) training tools write a
-# training tools write this placeholder into safetensors metadata instead of a
+# truncated form of this placeholder into safetensors metadata (as
-# real hash; it must never be treated as a valid AutoV3 — several broken
+# ``modelspec.hash_sha256`` / ``sshs_model_hash``), and hashing an empty or
-# models sharing it would collide in the hash index and falsely match recipes.
+# unreadable file produces it directly. It must never be treated as a valid
-INVALID_AUTOV3_EMPTY_HASH = "e3b0c44298fc"
+# hash: several broken models share it, CivitAI's by-hash index can contain
+# such polluted entries, and matching it falsely attributes recipes.
+EMPTY_HASH_SHA256 = "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+INVALID_AUTOV3_EMPTY_HASH = EMPTY_HASH_SHA256[:12]
+INVALID_AUTOV2_EMPTY_HASH = EMPTY_HASH_SHA256[:10]
+
+
+def is_empty_placeholder_hash(value: Any) -> bool:
+    """True for a 10/12/64-hex-char spelling of the empty-hash placeholder.
+
+    These are the AutoV2, AutoV3 and full-SHA256 forms of the placeholder;
+    such values identify no real model and must never be resolved against
+    local files or CivitAI.
+    """
+    if not isinstance(value, str):
+        return False
+    v = value.strip().lower()
+    if len(v) not in (10, 12, 64):
+        return False
+    return v == EMPTY_HASH_SHA256[: len(v)]
 
 # Auto-organize settings
 AUTO_ORGANIZE_BATCH_SIZE = (

py/utils/exif_utils.py

@@ -348,8 +348,14 @@ class ExifUtils:
             return image_path
             
     @staticmethod
-    def append_recipe_metadata(image_path, recipe_data) -> str:
+    def append_recipe_metadata(image_path, recipe_data, pixel_preserving=False) -> str:
-        """Append recipe metadata to an image's EXIF data"""
+        """Append recipe metadata to an image's EXIF data
+
+        When ``pixel_preserving`` is True (and the image is a WebP) only the
+        EXIF container is rewritten at the byte level, so the preview pixels
+        are never re-encoded. Local re-import uses this because its source is
+        the recipe's own already-optimized preview image.
+        """
         try:
             if image_path:
                 ext = os.path.splitext(image_path)[1].lower()
@@ -418,12 +424,70 @@ class ExifUtils:
             # Append to existing metadata or create new one
             new_metadata = f"{metadata} \n {recipe_metadata_marker}" if metadata else recipe_metadata_marker
 
+            # Write back to the image. Re-import keeps the already-optimized
+            # preview pixels untouched and updates only the WebP EXIF chunk
+            # instead of re-encoding the whole image.
+            if pixel_preserving and image_path.lower().endswith(".webp"):
+                metadata_fields = ExifUtils._load_structured_metadata(image_path)
+                metadata_fields["parameters"] = new_metadata
+                exif_bytes = ExifUtils._build_exif_bytes(metadata_fields)
+                with open(image_path, "rb") as file_obj:
+                    image_bytes = file_obj.read()
+                try:
+                    updated = ExifUtils._replace_webp_exif(image_bytes, exif_bytes)
+                except ValueError:
+                    # Container without an EXIF chunk; fall back to re-encoding.
+                    return ExifUtils.update_image_metadata(image_path, new_metadata)
+                with open(image_path, "wb") as file_obj:
+                    file_obj.write(updated)
+                return image_path
+
             # Write back to the image
             return ExifUtils.update_image_metadata(image_path, new_metadata)
         except Exception as e:
             logger.error(f"Error appending recipe metadata: {e}", exc_info=True)
             return image_path
 
+    @staticmethod
+    def _replace_webp_exif(image_bytes: bytes, exif_bytes: bytes) -> bytes:
+        """Replace the EXIF chunk of a WebP file without re-encoding pixels."""
+        if image_bytes[:4] != b"RIFF" or image_bytes[8:12] != b"WEBP":
+            raise ValueError("Not a WebP file")
+        # The WebP EXIF chunk stores raw TIFF data; strip the JPEG-style
+        # "Exif\\0\\0" prefix that piexif.dump may prepend.
+        tiff = exif_bytes[6:] if exif_bytes[:6] == b"Exif\x00\x00" else exif_bytes
+
+        out = bytearray(image_bytes[:12])
+        pos = 12
+        exif_payload = None
+        while pos + 8 <= len(image_bytes):
+            fourcc = image_bytes[pos : pos + 4]
+            size = struct.unpack("<I", image_bytes[pos + 4 : pos + 8])[0]
+            chunk_data = image_bytes[pos + 8 : pos + 8 + size]
+            pad = size % 2
+            if fourcc == b"EXIF":
+                exif_payload = tiff
+            else:
+                out += (
+                    fourcc
+                    + struct.pack("<I", size)
+                    + chunk_data
+                    + (b"\x00" * pad)
+                )
+            pos += 8 + size + pad
+
+        if exif_payload is None:
+            raise ValueError("WebP has no EXIF chunk")
+
+        out += (
+            b"EXIF"
+            + struct.pack("<I", len(exif_payload))
+            + exif_payload
+            + (b"\x00" * (len(exif_payload) % 2))
+        )
+        out[4:8] = struct.pack("<I", len(out) - 8)
+        return bytes(out)
+
     @staticmethod
     def remove_recipe_metadata(user_comment):
         """Remove recipe metadata from user comment"""

static/css/base.css

@@ -31,9 +31,14 @@ body {
     --header-height: 48px;
     --scrollbar-width: 8px;
 
-    --shortcut-bg: var(--color-accent-subtle);
+    /* Neutral "keycap" style for keyboard shortcut hints (GitHub/Linear-like).
-    --shortcut-border: var(--color-accent-border);
+       Derived from --text-muted so it adapts to every theme/preset. */
-    --shortcut-text: var(--text-primary);
+    --shortcut-bg: color-mix(in oklch, var(--text-muted) 10%, transparent);
+    --shortcut-bg-hover: color-mix(in oklch, var(--text-muted) 16%, transparent);
+    --shortcut-border: color-mix(in oklch, var(--text-muted) 30%, transparent);
+    --shortcut-border-hover: color-mix(in oklch, var(--text-muted) 45%, transparent);
+    --shortcut-text: var(--text-muted);
+    --shortcut-shadow: 0 1.5px 0 color-mix(in oklch, var(--text-muted) 30%, transparent);
 
     --lora-accent-transparent: var(--color-accent-transparent);
 

static/css/components/header.css

@@ -249,10 +249,10 @@
   font-family: inherit;
   font-size: 0.68rem;
   font-weight: 500;
-  color: var(--text-muted);
+  color: var(--shortcut-text);
-  /* Subtle tint derived from text color so it adapts to both light & dark themes */
+  background: var(--shortcut-bg);
-  background: color-mix(in oklch, var(--text-muted) 12%, transparent);
+  border: 1px solid var(--shortcut-border);
-  border: 1px solid color-mix(in oklch, var(--text-muted) 25%, transparent);
+  box-shadow: var(--shortcut-shadow);
   border-radius: var(--border-radius-xs, 3px);
   line-height: 1;
 }

static/css/components/lora-modal/preset-tags.css

@@ -66,3 +66,12 @@
 .add-preset-btn:hover {
   opacity: 0.9;
 }
+
+.add-preset-btn:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.add-preset-btn:hover:disabled {
+  opacity: 0.5;
+}

static/css/components/lora-modal/showcase.css

@@ -115,6 +115,9 @@
   position: relative;
   overflow: hidden;
   border-radius: var(--border-radius-sm);
+  /* Horizontal touch pans are claimed for swipe navigation (ShowcaseView);
+     vertical pans still scroll the modal */
+  touch-action: pan-y;
 }
 
 .main-media-container {
@@ -134,6 +137,32 @@
   margin-bottom: 0;
 }
 
+/* Direction-aware slide on example switches (set by updateMainDisplay) */
+.main-media-container.slide-from-right .media-wrapper {
+  animation: gallery-slide-from-right 0.25s ease;
+}
+
+.main-media-container.slide-from-left .media-wrapper {
+  animation: gallery-slide-from-left 0.25s ease;
+}
+
+@keyframes gallery-slide-from-right {
+  from { transform: translateX(32px); opacity: 0; }
+  to { transform: translateX(0); opacity: 1; }
+}
+
+@keyframes gallery-slide-from-left {
+  from { transform: translateX(-32px); opacity: 0; }
+  to { transform: translateX(0); opacity: 1; }
+}
+
+@media (prefers-reduced-motion: reduce) {
+  .main-media-container.slide-from-right .media-wrapper,
+  .main-media-container.slide-from-left .media-wrapper {
+    animation: none;
+  }
+}
+
 .main-media-container .media-wrapper img,
 .main-media-container .media-wrapper video {
   position: absolute;

static/css/components/modal/_base.css

@@ -13,6 +13,16 @@
     overflow: auto; /* Change from hidden to auto to allow scrolling */
 }
 
+/* Software-rendering fallback (set by applyModalBackdropBlurPolicy): a
+   full-viewport backdrop-filter forces per-frame CPU rasterization of
+   everything behind the modal and freezes the browser (issue #1092) */
+html.no-modal-backdrop-blur .modal,
+html.no-modal-backdrop-blur .delete-modal,
+html.no-modal-backdrop-blur .batch-preview-select-all {
+    backdrop-filter: none;
+    -webkit-backdrop-filter: none;
+}
+
 /* Prevent body scroll when modal is open */
 body.modal-open {
     position: fixed;

static/css/components/modal/download-modal.css

@@ -921,8 +921,8 @@
     position: sticky;
     top: 0;
     z-index: 1;
-    backdrop-filter: blur(8px);
+    backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
-    -webkit-backdrop-filter: blur(8px);
+    -webkit-backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
 }
 
 .batch-preview-select-all input[type="checkbox"] {

static/css/components/modal/help-modal.css

@@ -167,6 +167,29 @@
   box-shadow: var(--shadow-lg);
 }
 
+/* Replay Tutorial button: badge hidden until the button is flagged as new content */
+.replay-tutorial-btn .new-content-badge {
+  display: none;
+  background-color: rgba(255, 255, 255, 0.22);
+  color: #fff;
+  box-shadow: none;
+  margin-left: 2px;
+}
+
+.replay-tutorial-btn.has-new-content .new-content-badge {
+  display: inline-flex;
+}
+
+/* One-time attention pulse when the button is flagged as new content */
+@keyframes new-content-glow {
+  0% { box-shadow: 0 0 0 0 oklch(from var(--lora-accent) l c h / 55%); }
+  100% { box-shadow: 0 0 0 16px transparent; }
+}
+
+.replay-tutorial-btn.has-new-content {
+  animation: new-content-glow 1.2s ease-out 3;
+}
+
 /* Update video list styles */
 .video-list {
   display: flex;
@@ -305,3 +328,86 @@
 [data-theme="dark"] .video-container {
   background-color: var(--surface-hover);
 }
+/* Replay tutorial button styles */
+.help-actions {
+  margin-top: var(--space-3);
+}
+
+.replay-tutorial-btn {
+  display: inline-flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 20px;
+  border-radius: var(--border-radius-sm);
+  font-weight: 500;
+  cursor: pointer;
+  transition: var(--transition-base);
+  background-color: var(--lora-accent);
+  color: white;
+  border: none;
+}
+
+.replay-tutorial-btn:hover {
+  background-color: oklch(from var(--lora-accent) l c h / 85%);
+}
+
+/* Shortcuts tab styles */
+.shortcuts-section {
+  margin-bottom: var(--space-3);
+}
+
+.shortcuts-section h4 {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  margin-bottom: var(--space-1);
+}
+
+.shortcuts-list {
+  list-style-type: none;
+  padding-left: var(--space-3);
+}
+
+.shortcuts-list li {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  margin-bottom: var(--space-1);
+}
+
+.shortcut-keys {
+  display: inline-flex;
+  align-items: center;
+  gap: 2px;
+  flex-shrink: 0;
+  min-width: 150px;
+}
+
+.shortcut-sep {
+  color: var(--text-muted);
+  font-size: 0.75rem;
+  margin: 0 1px;
+}
+
+.shortcuts-list kbd {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  min-width: 18px;
+  height: 18px;
+  padding: 0 4px;
+  font-family: inherit;
+  font-size: 0.68rem;
+  font-weight: 500;
+  color: var(--shortcut-text);
+  background: var(--shortcut-bg);
+  border: 1px solid var(--shortcut-border);
+  box-shadow: var(--shortcut-shadow);
+  border-radius: var(--border-radius-xs, 3px);
+  line-height: 1;
+}
+
+.shortcut-description {
+  font-size: 0.9em;
+  opacity: 0.85;
+}

static/css/components/recipe-modal.css

@@ -715,6 +715,72 @@
     min-height: 0;
 }
 
+/* Empty LoRA list + collapsible "Why no LoRAs?" explanation */
+.no-loras {
+    color: var(--text-muted);
+    font-size: 0.9em;
+    padding: var(--space-2) 0;
+}
+
+.no-loras-reason {
+    margin: var(--space-1) 0 var(--space-2);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-xs);
+    background: var(--lora-surface);
+    font-size: 0.85em;
+}
+
+.no-loras-reason summary {
+    display: flex;
+    align-items: center;
+    gap: var(--space-2);
+    padding: var(--space-2) var(--space-3);
+    cursor: pointer;
+    color: var(--text-muted);
+    user-select: none;
+    list-style: none;
+}
+
+/* Hide the native disclosure triangle; rotate the icon instead. */
+.no-loras-reason summary::-webkit-details-marker {
+    display: none;
+}
+
+.no-loras-reason summary i {
+    transition: transform 0.15s ease;
+}
+
+.no-loras-reason[open] summary i {
+    transform: rotate(90deg);
+}
+
+.no-loras-reason summary:hover {
+    color: var(--text-color);
+}
+
+.no-loras-reason-body {
+    padding: 0 var(--space-3) var(--space-3);
+    color: var(--text-muted);
+}
+
+.no-loras-reason-body ul {
+    margin: 0;
+    padding-left: var(--space-5);
+}
+
+.no-loras-reason-body li {
+    margin: var(--space-1) 0;
+}
+
+.no-loras-bullet-label {
+    color: var(--text-color);
+    font-weight: 600;
+}
+
+.no-loras-inferred-note {
+    font-style: italic;
+}
+
 .recipe-checkpoint-container {
     display: flex;
     flex-direction: column;
@@ -729,6 +795,9 @@
 
 .recipe-lora-item {
     display: flex;
+    /* The reconnect panel is a full-width child that wraps below the
+       thumbnail + content row. */
+    flex-wrap: wrap;
     gap: var(--space-2);
     padding: 10px var(--space-2);
     border: 1px solid var(--border-color);
@@ -887,6 +956,33 @@
     color: var(--lora-accent);
 }
 
+/* Restore icon for manually reconnected entries: its presence on the info
+   row doubles as the "was reconnected" marker. Shared by LoRA and
+   checkpoint entries, which use the same info-row flex layout. */
+.lora-undo-reconnect,
+.checkpoint-undo-reconnect {
+    margin-left: auto;
+    background: none;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.55;
+    cursor: pointer;
+    padding: 2px 4px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.95em;
+    line-height: 1;
+    transition: var(--transition-base);
+}
+
+.lora-undo-reconnect:hover,
+.lora-undo-reconnect:focus-visible,
+.checkpoint-undo-reconnect:hover,
+.checkpoint-undo-reconnect:focus-visible {
+    opacity: 1;
+    color: var(--lora-accent);
+    background: var(--lora-surface);
+}
+
 .local-badge, 
 .missing-badge,
 .invalid-hash-badge {
@@ -966,15 +1062,19 @@
 /* Deleted badge is a pure status indicator; the reconnect action lives on
    an explicit ghost button in the item's action row. */
 
-/* LoRA reconnect container */
+/* LoRA reconnect container: an inline extension of the item, not a nested
+   card — a dashed separator reads lighter than another bordered box inside
+   an already bordered item. It is a direct child of .recipe-lora-item and
+   spans the full row (thumbnail column included). */
 .lora-reconnect-container {
     display: none;
     flex-direction: column;
-    background: var(--lora-surface);
+    flex-basis: 100%;
-    border: 1px solid var(--border-color);
+    /* Flex items default to min-width:auto — never let content force the
-    border-radius: var(--border-radius-xs);
+       panel wider than the row. */
-    padding: 12px;
+    min-width: 0;
-    margin-top: 10px;
+    border-top: 1px dashed var(--border-color);
+    padding-top: 10px;
     gap: 10px;
 }
 
@@ -1001,18 +1101,6 @@
     font-size: 0.85em;
 }
 
-.reconnect-instructions code {
-    background: rgba(0, 0, 0, 0.1);
-    padding: 2px 4px;
-    border-radius: 3px;
-    font-family: var(--font-mono);
-    font-size: 0.9em;
-}
-
-[data-theme="dark"] .reconnect-instructions code {
-    background: rgba(255, 255, 255, 0.1);
-}
-
 .reconnect-form {
     display: flex;
     flex-direction: column;
@@ -1020,13 +1108,108 @@
 }
 
 .reconnect-input {
-    width: calc(100% - 20px);
+    box-sizing: border-box;
+    width: 100%;
     padding: 8px 10px;
     border: 1px solid var(--border-color);
     border-radius: var(--border-radius-xs);
     background: var(--bg-color);
     color: var(--text-color);
+    font-size: 0.95em;
+}
+
+.reconnect-error {
+    display: none;
+    margin: 0;
+    color: var(--lora-error);
+    font-size: 0.85em;
+}
+
+.reconnect-error.active {
+    display: block;
+}
+
+.reconnect-suggestions {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.reconnect-suggestions:empty {
+    display: none;
+}
+
+.reconnect-suggestions-loading,
+.reconnect-suggestions-empty {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    padding: 4px 2px;
+}
+
+.reconnect-suggestion {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    width: 100%;
+    /* Buttons default to content-box: without this, width:100% + padding +
+       border overflows the panel by 18px and forces a horizontal scrollbar. */
+    box-sizing: border-box;
+    padding: 6px 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--lora-surface, var(--bg-color));
+    color: var(--text-color);
+    font-size: 0.95em;
+    text-align: left;
+    cursor: pointer;
+    transition: var(--transition-base);
+}
+
+.reconnect-suggestion:hover,
+.reconnect-suggestion:focus-visible {
+    border-color: var(--lora-accent);
+}
+
+.reconnect-suggestion-preview {
+    width: 40px;
+    height: 40px;
+    border-radius: var(--border-radius-xs);
+    object-fit: cover;
+    flex-shrink: 0;
+    background: var(--bg-color);
+}
+
+.reconnect-suggestion-info {
+    display: flex;
+    flex-direction: column;
+    gap: 2px;
+    min-width: 0;
+    flex: 1;
+}
+
+.reconnect-suggestion-name {
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.reconnect-suggestion-secondary {
     font-size: 0.9em;
+    opacity: 0.7;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.reconnect-suggestion-reason {
+    flex-shrink: 0;
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    color: var(--lora-accent);
+    font-size: 0.85em;
+    white-space: nowrap;
 }
 
 .reconnect-actions {
@@ -1364,3 +1547,93 @@
     width: 20px;
     height: calc(1em * 1.3);
 }
+
+/* Meta footer: de-emphasized location + recipe ID line below the modal body,
+   mirroring the hash footnote in the shared model modal. Location sits left
+   (tail of the path survives truncation), ID + copy button sit right. */
+.recipe-meta-footer {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    gap: 12px;
+    padding-top: 6px;
+    margin-top: 8px;
+    border-top: 1px solid var(--border-color);
+    font-size: 0.75em;
+    color: var(--text-muted);
+}
+
+.recipe-meta-footer[hidden] {
+    display: none;
+}
+
+.recipe-meta-location {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    min-width: 0;
+    cursor: pointer;
+}
+
+.recipe-meta-location i {
+    flex-shrink: 0;
+    opacity: 0.6;
+}
+
+.recipe-meta-location-path {
+    font-family: var(--font-mono, monospace);
+    opacity: 0.7;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.recipe-meta-location:hover .recipe-meta-location-path,
+.recipe-meta-location:focus-visible .recipe-meta-location-path {
+    opacity: 1;
+    text-decoration: underline;
+}
+
+.recipe-meta-location:focus-visible {
+    outline: 1px solid var(--lora-accent);
+    outline-offset: 2px;
+    border-radius: var(--border-radius-xs);
+}
+
+.recipe-meta-id {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    flex-shrink: 0;
+}
+
+.recipe-meta-id-label {
+    font-size: 0.9em;
+    opacity: 0.5;
+    text-transform: uppercase;
+    letter-spacing: 0.03em;
+}
+
+.recipe-meta-id-value {
+    font-family: var(--font-mono, monospace);
+    opacity: 0.7;
+    white-space: nowrap;
+}
+
+.recipe-meta-copy-btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    padding: 0 2px;
+    border: none;
+    background: none;
+    color: var(--text-muted);
+    opacity: 0.35;
+    font-size: 0.95em;
+    cursor: pointer;
+    flex-shrink: 0;
+}
+
+.recipe-meta-copy-btn:hover {
+    opacity: 0.9;
+}

static/css/layout.css

@@ -202,15 +202,17 @@
     align-items: center;
     justify-content: center;
     margin-left: 6px;
-    min-width: 16px;
+    min-width: 18px;
-    height: 16px;
+    height: 18px;
-    padding: 0 3px;
+    padding: 0 5px;
     font-size: 11px;
     font-weight: 600;
     line-height: 1;
+    text-transform: uppercase;
     border-radius: var(--border-radius-xs);
     background-color: var(--shortcut-bg);
     border: 1px solid var(--shortcut-border);
+    box-shadow: var(--shortcut-shadow);
     color: var(--shortcut-text);
     vertical-align: middle;
     opacity: 0.8;
@@ -219,12 +221,8 @@
 
 .control-group button:hover .shortcut-key {
     opacity: 1;
-    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.2);
+    background-color: var(--shortcut-bg-hover);
-}
+    border-color: var(--shortcut-border-hover);
-
-[data-theme="dark"] .shortcut-key {
-    --shortcut-bg: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.15);
-    --shortcut-border: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.3);
 }
 
 /* Ensure correct vertical alignment for text+shortcut */

static/css/onboarding.css

@@ -205,6 +205,7 @@
     display: inline-block;
     background: var(--shortcut-bg);
     border: 1px solid var(--shortcut-border);
+    box-shadow: var(--shortcut-shadow);
     border-radius: var(--border-radius-xs);
     padding: 2px 6px;
     font-size: 0.8em;

static/js/api/baseModelApi.js

@@ -12,6 +12,11 @@ import {
 } from './apiConfig.js';
 import { resetAndReload } from './modelApiFactory.js';
 import { sidebarManager } from '../components/SidebarManager.js';
+// Shared scan ETA helpers live in a dependency-light module so pages that do
+// not use BaseModelApiClient (e.g. recipes) can reuse them without pulling
+// this module's import cycle (modelApiFactory -> loraApi -> baseModelApi).
+import { createScanEtaTracker, formatScanRemainingTime } from '../utils/scanEtaUtils.js';
+export { createScanEtaTracker, formatScanRemainingTime };
 
 /**
  * Abstract base class for all model API clients
@@ -507,23 +512,67 @@ export class BaseModelApiClient {
 
     async refreshModels(fullRebuild = false) {
         const abortController = new AbortController();
-        try {
+        const displayName = this.apiConfig.config.displayName;
-            state.loadingManager.show(
+        const singularName = this.apiConfig.config.singularName;
-                `${fullRebuild ? 'Full rebuild' : 'Refreshing'} ${this.apiConfig.config.displayName}s...`,
+        const actionText = translate(
-                0
+            fullRebuild ? 'common.scanProgress.actionFullRebuild' : 'common.scanProgress.actionRefresh',
+            {},
+            fullRebuild ? 'Full rebuild' : 'Refresh'
+        );
+        const actionLowerText = translate(
+            fullRebuild ? 'common.scanProgress.actionRebuildLower' : 'common.scanProgress.actionRefreshLower',
+            {},
+            fullRebuild ? 'rebuild' : 'refresh'
+        );
+        const initialMessage = translate(
+            fullRebuild ? 'common.scanProgress.fullRebuilding' : 'common.scanProgress.refreshing',
+            { type: displayName },
+            `${fullRebuild ? 'Full rebuild' : 'Refreshing'} ${displayName}s...`
+        );
+        const etaTracker = createScanEtaTracker();
+        let ws = null;
+
+        const handleScanProgress = (data) => {
+            if (typeof data.progress === 'number') {
+                state.loadingManager.setProgress(data.progress);
+            }
+            let statusText = translate(
+                `common.scanProgress.stages.${data.stage}`,
+                { total: data.total },
+                data.stage || ''
             );
+            if (data.status === 'processing' && data.total > 0) {
+                statusText += ` (${data.processed}/${data.total})`;
+                if (data.current_name) {
+                    statusText += ` ${data.current_name}`;
+                }
+                const etaText = etaTracker.update(data.processed, data.total);
+                if (etaText) {
+                    statusText += ` | ${etaText}`;
+                }
+            }
+            state.loadingManager.setStatus(statusText);
+        };
+
+        try {
+            state.loadingManager.show(initialMessage, 0);
             state.loadingManager.showCancelButton(() => {
                 this.cancelTask();
                 abortController.abort();
             });
 
+            // Connect to the shared progress channel for live scan updates.
+            // Failure to connect must not block the refresh itself — fall back
+            // to the plain loading indicator.
+            ws = await this._connectScanProgressSocket(handleScanProgress, singularName);
+
             const url = new URL(this.apiConfig.endpoints.scan, window.location.origin);
             url.searchParams.append('full_rebuild', fullRebuild);
 
             const response = await fetch(url, { signal: abortController.signal });
 
             if (!response.ok) {
-                throw new Error(`Failed to refresh ${this.apiConfig.config.displayName}s: ${response.status} ${response.statusText}`);
+                throw new Error(`Failed to refresh ${displayName}s: ${response.status} ${response.statusText}`);
             }
 
             const data = await response.json();
@@ -534,20 +583,69 @@ export class BaseModelApiClient {
 
             resetAndReload(true);
 
-            showToast('toast.api.refreshComplete', { action: fullRebuild ? 'Full rebuild' : 'Refresh' }, 'success');
+            showToast('toast.api.refreshComplete', { action: actionText }, 'success');
         } catch (error) {
             if (error.name === 'AbortError') {
                 showToast('toast.api.operationCancelled', {}, 'info');
                 return;
             }
             console.error('Refresh failed:', error);
-            showToast('toast.api.refreshFailed', { action: fullRebuild ? 'rebuild' : 'refresh', type: this.apiConfig.config.displayName }, 'error');
+            showToast('toast.api.refreshFailed', { action: actionLowerText, type: displayName }, 'error');
         } finally {
+            if (ws) {
+                ws.close();
+            }
             state.loadingManager.hide();
             state.loadingManager.restoreProgressBar();
         }
     }
 
+    /**
+     * Connect to the shared fetch-progress WebSocket for scan progress updates.
+     * Returns null when the connection cannot be established (silent fallback).
+     * @param {Function} onScanProgress - Handler for scan_progress messages
+     * @param {string} singularName - Model type filter (e.g. 'lora')
+     * @returns {Promise<WebSocket|null>}
+     */
+    async _connectScanProgressSocket(onScanProgress, singularName) {
+        let socket = null;
+        try {
+            const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+            socket = new WebSocket(`${wsProtocol}${window.location.host}${WS_ENDPOINTS.fetchProgress}`);
+
+            await new Promise((resolve, reject) => {
+                socket.onopen = resolve;
+                socket.onerror = reject;
+            });
+
+            socket.onmessage = (event) => {
+                let data;
+                try {
+                    data = JSON.parse(event.data);
+                } catch (parseError) {
+                    return;
+                }
+                // Only handle scan progress for this client's model type;
+                // other operations share this channel and must be ignored.
+                if (data.type !== 'scan_progress' || data.model_type !== singularName) {
+                    return;
+                }
+                onScanProgress(data);
+            };
+
+            return socket;
+        } catch (error) {
+            if (socket) {
+                try {
+                    socket.close();
+                } catch (closeError) {
+                    // Ignore close errors during fallback
+                }
+            }
+            return null;
+        }
+    }
+
     async refreshSingleModelMetadata(filePath) {
         try {
             state.loadingManager.showSimpleLoading('Refreshing metadata...');
@@ -605,6 +703,9 @@ export class BaseModelApiClient {
                     ws.onmessage = (event) => {
                         const data = JSON.parse(event.data);
 
+                        // Scan progress shares this channel; it is handled by refreshModels
+                        if (data.type === 'scan_progress') return;
+
                         switch (data.status) {
                             case 'started':
                                 loading.setStatus('Starting metadata fetch...');

static/js/api/recipeApi.js

@@ -1,7 +1,12 @@
 import { RecipeCard } from '../components/RecipeCard.js';
 import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
+import { translate } from '../utils/i18nHelpers.js';
 import { captureScrollPosition, restoreScrollPosition } from '../utils/infiniteScroll.js';
+import { WS_ENDPOINTS } from './apiConfig.js';
+// Import from the dependency-light utils module, not baseModelApi.js, to
+// avoid the baseModelApi <-> modelApiFactory import cycle on this page.
+import { createScanEtaTracker } from '../utils/scanEtaUtils.js';
 
 const RECIPE_ENDPOINTS = {
     list: '/api/lm/recipes',
@@ -333,11 +338,53 @@ export async function syncChanges() {
 }
 
 export async function refreshRecipes(fullRebuild = true) {
-    const actionLabel = fullRebuild ? 'Rebuilding recipe cache' : 'Refreshing recipes';
+    const actionText = translate(
-    const actionToast = fullRebuild ? 'Full rebuild' : 'Refresh';
+        fullRebuild ? 'common.scanProgress.actionFullRebuild' : 'common.scanProgress.actionRefresh',
+        {},
+        fullRebuild ? 'Full rebuild' : 'Refresh'
+    );
+    const actionLowerText = translate(
+        fullRebuild ? 'common.scanProgress.actionRebuildLower' : 'common.scanProgress.actionRefreshLower',
+        {},
+        fullRebuild ? 'rebuild' : 'refresh'
+    );
+    const initialMessage = translate(
+        fullRebuild ? 'common.scanProgress.fullRebuilding' : 'common.scanProgress.refreshing',
+        { type: RECIPE_SIDEBAR_CONFIG.config.displayName },
+        `${fullRebuild ? 'Full rebuild' : 'Refreshing'} Recipes...`
+    );
+    const etaTracker = createScanEtaTracker();
+    let ws = null;
+
+    const handleScanProgress = (data) => {
+        if (typeof data.progress === 'number') {
+            state.loadingManager.setProgress(data.progress);
+        }
+        let statusText = translate(
+            `common.scanProgress.stages.${data.stage}`,
+            { total: data.total },
+            data.stage || ''
+        );
+        if (data.status === 'processing' && data.total > 0) {
+            statusText += ` (${data.processed}/${data.total})`;
+            if (data.current_name) {
+                statusText += ` ${data.current_name}`;
+            }
+            const etaText = etaTracker.update(data.processed, data.total);
+            if (etaText) {
+                statusText += ` | ${etaText}`;
+            }
+        }
+        state.loadingManager.setStatus(statusText);
+    };
 
     try {
-        state.loadingManager.show(`${actionLabel}...`, 0);
+        state.loadingManager.show(initialMessage, 0);
+
+        // Connect to the shared progress channel for live scan updates.
+        // Failure to connect must not block the refresh itself — fall back
+        // to the plain loading indicator.
+        ws = await connectScanProgressSocket(handleScanProgress);
 
         const url = new URL(RECIPE_ENDPOINTS.scan, window.location.origin);
         url.searchParams.append('full_rebuild', fullRebuild);
@@ -356,16 +403,64 @@ export async function refreshRecipes(fullRebuild = true) {
 
         await resetAndReload(false);
 
-        showToast('toast.api.refreshComplete', { action: actionToast }, 'success');
+        showToast('toast.api.refreshComplete', { action: actionText }, 'success');
     } catch (error) {
         console.error('Error refreshing recipes:', error);
-        showToast('toast.api.refreshFailed', { action: fullRebuild ? 'rebuild' : 'refresh', type: 'recipe' }, 'error');
+        showToast('toast.api.refreshFailed', { action: actionLowerText, type: 'recipe' }, 'error');
     } finally {
+        if (ws) {
+            ws.close();
+        }
         state.loadingManager.hide();
         state.loadingManager.restoreProgressBar();
     }
 }
 
+/**
+ * Connect to the shared fetch-progress WebSocket for recipe scan progress.
+ * Returns null when the connection cannot be established (silent fallback).
+ * @param {Function} onScanProgress - Handler for scan_progress messages
+ * @returns {Promise<WebSocket|null>}
+ */
+async function connectScanProgressSocket(onScanProgress) {
+    let socket = null;
+    try {
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+        socket = new WebSocket(`${wsProtocol}${window.location.host}${WS_ENDPOINTS.fetchProgress}`);
+
+        await new Promise((resolve, reject) => {
+            socket.onopen = resolve;
+            socket.onerror = reject;
+        });
+
+        socket.onmessage = (event) => {
+            let data;
+            try {
+                data = JSON.parse(event.data);
+            } catch (parseError) {
+                return;
+            }
+            // Only handle recipe scan progress; other operations share this
+            // channel and must be ignored.
+            if (data.type !== 'scan_progress' || data.model_type !== 'recipe') {
+                return;
+            }
+            onScanProgress(data);
+        };
+
+        return socket;
+    } catch (error) {
+        if (socket) {
+            try {
+                socket.close();
+            } catch (closeError) {
+                // Ignore close errors during fallback
+            }
+        }
+        return null;
+    }
+}
+
 /**
  * Load more recipes with pagination - updated to work with VirtualScroller
  * @param {boolean} resetPage - Whether to reset to the first page

static/js/checkpoints.js

@@ -3,6 +3,7 @@ import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } fr
 import { createPageControls } from './components/controls/index.js';
 import { ModelDuplicatesManager } from './components/ModelDuplicatesManager.js';
 import { MODEL_TYPES } from './api/apiConfig.js';
+import { initActiveFiltersSync } from './utils/activeFiltersSync.js';
 
 // Initialize the Checkpoints page
 export class CheckpointsPageManager {
@@ -32,6 +33,9 @@ export class CheckpointsPageManager {
         // Initialize common page features (including context menus)
         appCore.initializePageFeatures();
 
+        // Mirror active filters to the backend for the ComfyUI-side autocomplete
+        initActiveFiltersSync(MODEL_TYPES.CHECKPOINT);
+
         console.log('Checkpoints Manager initialized');
     }
 }

static/js/components/Combobox.js

@@ -28,8 +28,14 @@ export class Combobox {
      * @param {string[]} [options.presets=[]] Static preset values shown in dropdown.
      * @param {(inputValue: string) => Promise<string[]>} [options.fetchOptions]
      *        Async function returning dynamic suggestions for the current input.
-     * @param {string} [options.placeholder] Placeholder text for the empty state.
+     * @param {string} [options.placeholder] Placeholder text for the input and the
+     *        dropdown empty state (see emptyText to override the latter).
+     * @param {string} [options.emptyText] Text for the dropdown empty state;
+     *        defaults to `placeholder`, then 'No options'. Unlike `placeholder`
+     *        it never touches the input element.
      * @param {(value: string) => void} [options.onSelect] Callback when an option is chosen.
+     * @param {(value: string) => void} [options.onCommit] Callback when Enter is
+     *        pressed without a highlighted option (free-text commit).
      */
     constructor(inputElement, options = {}) {
         if (!inputElement || inputElement.tagName !== 'INPUT') {
@@ -41,7 +47,9 @@ export class Combobox {
         this.presets = Array.isArray(options.presets) ? [...options.presets] : [];
         this.fetchOptions = typeof options.fetchOptions === 'function' ? options.fetchOptions : null;
         this.placeholder = options.placeholder || '';
+        this.emptyText = options.emptyText || '';
         this.onSelect = typeof options.onSelect === 'function' ? options.onSelect : null;
+        this.onCommit = typeof options.onCommit === 'function' ? options.onCommit : null;
 
         // Internal state
         this._isOpen = false;
@@ -109,19 +117,24 @@ export class Combobox {
     // ---- event wiring ----
 
     _bindEvents() {
-        this.input.addEventListener('focus', () => {
+        // Keep references so destroy() can detach input listeners — callers
+        // may destroy a Combobox while its input stays in the DOM.
+        this._focusHandler = () => {
             if (this._suppressInputOpen) return;
             this._open();
-        });
+        };
+        this.input.addEventListener('focus', this._focusHandler);
 
-        this.input.addEventListener('input', () => {
+        this._inputHandler = () => {
             if (this._suppressInputOpen) return;
             this._open();          // no-op if already open
             this._refresh();       // re-filter by current input value
             this._scheduleFetch();
-        });
+        };
+        this.input.addEventListener('input', this._inputHandler);
 
-        this.input.addEventListener('keydown', (event) => this._onKeyDown(event));
+        this._keyDownHandler = (event) => this._onKeyDown(event);
+        this.input.addEventListener('keydown', this._keyDownHandler);
 
         // Click an option (delegated)
         this.panel.addEventListener('click', (event) => {
@@ -167,6 +180,9 @@ export class Combobox {
                 event.preventDefault();
                 this._open();
                 this._setActiveIndex(0);
+            } else if (event.key === 'Enter' && typeof this.onCommit === 'function') {
+                event.preventDefault();
+                this.onCommit(this.input.value);
             }
             return;
         }
@@ -184,11 +200,17 @@ export class Combobox {
 
             case 'Enter':
                 // Only intercept Enter to pick an option when one is actively
-                // highlighted; otherwise let the input's default behavior
+                // highlighted; otherwise commit the free-text value (when an
-                // (form submit / free-text commit) proceed.
+                // onCommit handler is registered) and let the input's default
+                // behavior proceed otherwise.
                 if (this._activeIndex >= 0 && this._activeIndex < this._renderedOptions.length) {
                     event.preventDefault();
                     this._choose(this._renderedOptions[this._activeIndex]);
+                } else if (typeof this.onCommit === 'function') {
+                    event.preventDefault();
+                    const value = this.input.value;
+                    this._close();
+                    this.onCommit(value);
                 }
                 break;
 
@@ -254,7 +276,7 @@ export class Combobox {
         if (items.length === 0) {
             const empty = document.createElement('div');
             empty.className = 'lm-combobox-empty';
-            empty.textContent = this.placeholder ? this.placeholder : 'No options';
+            empty.textContent = this.emptyText || this.placeholder || 'No options';
             this.panel.appendChild(empty);
             this._activeIndex = -1;
             return;
@@ -333,11 +355,19 @@ export class Combobox {
         if (this.panel && this.panel.parentNode) {
             this.panel.parentNode.removeChild(this.panel);
         }
+        this.input.removeEventListener('focus', this._focusHandler);
+        this.input.removeEventListener('input', this._inputHandler);
+        this.input.removeEventListener('keydown', this._keyDownHandler);
         document.removeEventListener('mousedown', this._outsideClickHandler);
         window.removeEventListener('resize', this._resizeHandler);
         window.removeEventListener('scroll', this._resizeHandler, true);
     }
 
+    /** Whether the dropdown panel is currently open. */
+    isOpen() {
+        return this._isOpen;
+    }
+
     _choose(value) {
         this.input.value = value;
         this._close();

static/js/components/controls/PageControls.js

@@ -1,7 +1,8 @@
 // PageControls.js - Manages controls for both LoRAs and Checkpoints pages
 import { state, getCurrentPageState, setCurrentPageType } from '../../state/index.js';
 import { getStorageItem, setStorageItem, removeStorageItem, getSessionItem, setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
-import { showToast, openCivitaiByMetadata } from '../../utils/uiHelpers.js';
+import { showToast, openCivitaiByMetadata, isTypingContext } from '../../utils/uiHelpers.js';
+import { eventManager } from '../../utils/EventManager.js';
 import { performModelUpdateCheck } from '../../utils/updateCheckHelpers.js';
 import { sidebarManager } from '../SidebarManager.js';
 import { initSortDropdown, applySortToSelect, randomizeSortValue } from './SortDropdown.js';
@@ -146,6 +147,62 @@ export class PageControls {
 
         // Page-specific event listeners
         this.initPageSpecificListeners();
+
+        // Keyboard shortcuts for the actions toolbar (R / F / D)
+        this.registerKeyboardShortcuts();
+    }
+
+    /**
+     * Register keyboard shortcuts for the actions toolbar buttons
+     * (R = refresh, F = fetch metadata, D = download)
+     */
+    registerKeyboardShortcuts() {
+        eventManager.addHandler('keydown', 'pageControls-actions', (e) => {
+            return this.handleActionShortcut(e);
+        }, {
+            priority: 90,
+            skipWhenModalOpen: true
+        });
+    }
+
+    /**
+     * Handle a keydown event for the actions toolbar shortcuts
+     * @param {KeyboardEvent} e
+     * @returns {boolean} True when the event was handled and propagation should stop
+     */
+    handleActionShortcut(e) {
+        // Plain letters only — leave modified combos (Ctrl/Cmd/Alt) alone
+        if (e.ctrlKey || e.metaKey || e.altKey) {
+            return false;
+        }
+
+        // Don't hijack keys while typing in a text entry context
+        if (isTypingContext(e.target)) {
+            return false;
+        }
+
+        const actionByKey = {
+            r: 'refresh',
+            f: 'fetch',
+            d: 'download'
+        };
+        const action = actionByKey[e.key.toLowerCase()];
+        if (!action) {
+            return false;
+        }
+
+        // The button may not exist on this page (e.g. recipes has no
+        // fetch/download) — let other handlers run in that case
+        const button = document.querySelector(`[data-action="${action}"]`);
+        if (!button) {
+            return false;
+        }
+
+        e.preventDefault();
+        // Native disabled buttons ignore .click(), so an in-progress
+        // refresh is safe
+        button.click();
+        return true;
     }
 
     initExcludedViewControls() {

static/js/components/shared/ModelCard.js

@@ -607,9 +607,11 @@ export function createModelCard(model, modelType) {
         sendTitle = translate('modelCard.actions.sendToWorkflow', {}, 'Send to ComfyUI (Click: Append, Shift+Click: Replace)');
         copyTitle = translate('modelCard.actions.copyLoRASyntax', {}, 'Copy LoRA Syntax');
     } else if (modelType === MODEL_TYPES.CHECKPOINT) {
+        // Checkpoint send sets the widget value directly; no append/replace modes.
         sendTitle = translate('modelCard.actions.sendCheckpointToWorkflow', {}, 'Send to ComfyUI');
         copyTitle = translate('modelCard.actions.copyCheckpointName', {}, 'Copy checkpoint name');
     } else if (modelType === MODEL_TYPES.EMBEDDING) {
+        // Embedding send always appends to the prompt; no replace mode.
         sendTitle = translate('modelCard.actions.sendEmbeddingToWorkflow', {}, 'Send to ComfyUI');
         copyTitle = translate('modelCard.actions.copyEmbeddingName', {}, 'Copy embedding name');
     } else {

static/js/components/shared/ModelModal.js

@@ -877,8 +877,9 @@ function renderLoraSpecificContent(lora, escapedWords) {
                         <option value="clip_strength">${translate('modals.model.usageTips.clipStrength', {}, 'Clip Strength')}</option>
                         <option value="clip_skip">${translate('modals.model.usageTips.clipSkip', {}, 'Clip Skip')}</option>
                     </select>
-                    <input type="number" id="preset-value" step="0.01" placeholder="${translate('modals.model.usageTips.valuePlaceholder', {}, 'Value')}" style="display:none;">
+                    <!-- autofill opt-out attrs prevent password managers / email-alias extensions from attaching popups -->
-                    <button class="add-preset-btn">${translate('modals.model.usageTips.add', {}, 'Add')}</button>
+                    <input type="number" id="preset-value" step="0.01" placeholder="${translate('modals.model.usageTips.valuePlaceholder', {}, 'Value')}" style="display:none;" autocomplete="off" data-1p-ignore data-lpignore="true" data-bwignore data-form-type="other">
+                    <button class="add-preset-btn" disabled>${translate('modals.model.usageTips.add', {}, 'Add')}</button>
                 </div>
                 <div class="preset-tags">
                     ${renderPresetTags(parsePresets(lora.usage_tips))}
@@ -1086,6 +1087,11 @@ function setupLoraSpecificFields(filePath) {
 
     if (!presetSelector || !presetValue || !addPresetBtn || !presetTags) return;
 
+    // Add button stays disabled until both a parameter and a value are provided
+    const updateAddPresetButtonState = () => {
+        addPresetBtn.disabled = !(presetSelector.value && presetValue.value.trim());
+    };
+
     presetSelector.addEventListener('change', function () {
         const selected = this.value;
         if (selected) {
@@ -1111,12 +1117,16 @@ function setupLoraSpecificFields(filePath) {
         } else {
             presetValue.style.display = 'none';
         }
+        updateAddPresetButtonState();
     });
 
+    presetValue.addEventListener('input', updateAddPresetButtonState);
+
     addPresetBtn.addEventListener('click', async function () {
         const key = presetSelector.value;
-        const value = presetValue.value;
+        const value = presetValue.value.trim();
 
+        // Unreachable via UI while the button is disabled; kept as a safety net
         if (!key || !value) return;
 
         const currentPath = resolveFilePath();
@@ -1131,9 +1141,11 @@ function setupLoraSpecificFields(filePath) {
             document.querySelector(`.model-card[data-filepath="${escapedFilePath}"]`);
         const currentPresets = parsePresets(loraCard?.dataset.usage_tips);
 
+        let isUpdate;
         if (key === 'strength_range') {
             const rangeMatch = value.match(/^(-?\d*\.?\d+)\s*[-~]\s*(-?\d*\.?\d+)$/);
             if (rangeMatch) {
+                isUpdate = 'strength_min' in currentPresets || 'strength_max' in currentPresets;
                 currentPresets['strength_min'] = parseFloat(rangeMatch[1]);
                 currentPresets['strength_max'] = parseFloat(rangeMatch[2]);
             } else {
@@ -1141,17 +1153,36 @@ function setupLoraSpecificFields(filePath) {
                 return;
             }
         } else {
-            currentPresets[key] = parseFloat(value);
+            const numericValue = parseFloat(value);
+            if (!Number.isFinite(numericValue)) {
+                showToast('modals.model.usageTips.invalidValue', {}, 'error', 'Please enter a valid number');
+                return;
+            }
+            isUpdate = key in currentPresets;
+            currentPresets[key] = numericValue;
         }
         const newPresetsJson = JSON.stringify(currentPresets);
 
-        await getModelApiClient().saveModelMetadata(currentPath, { usage_tips: newPresetsJson });
+        try {
+            await getModelApiClient().saveModelMetadata(currentPath, { usage_tips: newPresetsJson });
+        } catch (error) {
+            console.error('Failed to save preset parameter:', error);
+            showToast('modals.model.usageTips.saveFailed', {}, 'error', 'Failed to save preset parameter');
+            return;
+        }
 
         presetTags.innerHTML = renderPresetTags(currentPresets);
+        showToast(
+            isUpdate ? 'modals.model.usageTips.updated' : 'modals.model.usageTips.added',
+            {},
+            'success',
+            isUpdate ? 'Preset parameter updated' : 'Preset parameter added'
+        );
 
         presetSelector.value = '';
         presetValue.value = '';
         presetValue.style.display = 'none';
+        addPresetBtn.disabled = true;
     });
 
     // Add keydown event for preset value

static/js/components/shared/TriggerWords.js

@@ -227,7 +227,7 @@ export function renderTriggerWords(words, filePath) {
         const escapedWord = escapeHtml(word);
         const escapedAttr = escapeAttribute(word);
         return `
-                        <div class="trigger-word-tag" data-word="${escapedAttr}" title="${translate('modals.model.triggerWords.copyWord')}">
+                        <div class="trigger-word-tag" data-word="${escapedAttr}" title="${translate('modals.model.triggerWords.copyOrEditWord')}">
                             <span class="trigger-word-content">${escapedWord}</span>
                             <span class="trigger-word-copy">
                                 <i class="fas fa-copy"></i>
@@ -455,7 +455,7 @@ function resetTriggerWordsUIState(section) {
         // Restore click-to-copy functionality
         tag.removeEventListener('click', startEditTriggerWord);
         setupDisplayTriggerWordTag(tag);
-        tag.title = translate('modals.model.triggerWords.copyWord');
+        tag.title = translate('modals.model.triggerWords.copyOrEditWord');
 
         // Show copy icon, hide delete button
         if (copyIcon) copyIcon.style.display = '';
@@ -503,7 +503,7 @@ function createTriggerWordTag(word, isEditMode = false) {
     const tag = document.createElement('div');
     tag.className = 'trigger-word-tag';
     tag.dataset.word = word;
-    tag.title = translate(isEditMode ? 'modals.model.triggerWords.editWord' : 'modals.model.triggerWords.copyWord');
+    tag.title = translate(isEditMode ? 'modals.model.triggerWords.editWord' : 'modals.model.triggerWords.copyOrEditWord');
 
     const escapedWord = escapeHtml(word);
     tag.innerHTML = `
@@ -537,7 +537,7 @@ function setupDisplayTriggerWordTag(tag) {
 
     tag.addEventListener('click', handleDisplayTriggerWordClick);
     tag.addEventListener('dblclick', handleDisplayTriggerWordDoubleClick);
-    tag.title = translate('modals.model.triggerWords.copyWord');
+    tag.title = translate('modals.model.triggerWords.copyOrEditWord');
 }
 
 /**

static/js/components/shared/showcase/MediaRenderers.js

@@ -76,6 +76,7 @@ export function generateImageWrapper(media, shouldBlur, nsfwText, metadataPanel,
                 alt="Preview" 
                 width="${media.width}"
                 height="${media.height}"
+                fetchpriority="high"
                 class="lazy ${shouldBlur ? 'blurred' : ''}"> 
             ${shouldBlur ? `
                 <div class="nsfw-overlay">

static/js/components/shared/showcase/ShowcaseView.js

@@ -22,8 +22,8 @@ import {
 } from './MediaUtils.js';
 import { generateMetadataPanel } from './MetadataPanel.js';
 import { generateImageWrapper, generateVideoWrapper } from './MediaRenderers.js';
-import { getShowcaseUrl, getThumbnailUrl } from '../../../utils/civitaiUtils.js';
+import { getShowcaseUrl, getDisplayUrl, getGalleryThumbnailUrl } from '../../../utils/civitaiUtils.js';
-import { openMediaViewer } from '../MediaViewer.js';
+import { openMediaViewer, isMediaViewerOpen } from '../MediaViewer.js';
 import { escapeAttribute } from '../utils.js';
 
 /**
@@ -54,6 +54,13 @@ export async function loadExampleImages(images, modelHash, previewUrl = '') {
         const showcaseTab = document.getElementById('showcase-tab');
         if (!showcaseTab) return;
 
+        // Fresh load of a model's examples: reset the gallery position so a
+        // previously viewed model's active index / expansion state never leaks
+        // into this one (the modal is a singleton, state is module-level)
+        galleryState.activeIndex = 0;
+        galleryState.expanded = false;
+        lastNavDirection = 1;
+
         // First fetch local example files
         let localFiles = [];
 
@@ -224,10 +231,10 @@ export function renderShowcaseContent(images, exampleFiles = [], previewUrl = ''
                     ${renderMediaItem(activeImg, galleryState.activeIndex, exampleFiles)}
                     ${renderPositionBadge(positionText)}
                 </div>
-                ${showNav ? `<button class="gallery-nav prev" id="galleryPrevBtn" title="${translate('modals.model.showcase.previousExample', {}, 'Previous example')}">
+                ${showNav ? `<button class="gallery-nav prev" id="galleryPrevBtn" title="${translate('modals.model.showcase.previousExample', {}, 'Previous example ([)')}">
                     <i class="fas fa-chevron-left"></i>
                 </button>
-                <button class="gallery-nav next" id="galleryNextBtn" title="${translate('modals.model.showcase.nextExample', {}, 'Next example')}">
+                <button class="gallery-nav next" id="galleryNextBtn" title="${translate('modals.model.showcase.nextExample', {}, 'Next example (])')}">
                     <i class="fas fa-chevron-right"></i>
                 </button>` : ''}
             </div>
@@ -275,7 +282,7 @@ function renderThumbnail(img, index, exampleFiles) {
         originalRemoteUrl.endsWith('.mp4') || originalRemoteUrl.endsWith('.webm');
     const mediaType = isVideo ? 'video' : 'image';
 
-    const thumbUrl = localFile ? localFile.path : getThumbnailUrl(originalRemoteUrl, mediaType);
+    const thumbUrl = localFile ? localFile.path : getGalleryThumbnailUrl(originalRemoteUrl, mediaType);
 
     const nsfwLevel = img.nsfwLevel !== undefined ? img.nsfwLevel : 0;
     const matureBlurThreshold = getMatureBlurThreshold(state.settings);
@@ -284,9 +291,9 @@ function renderThumbnail(img, index, exampleFiles) {
     const activeClass = index === galleryState.activeIndex ? ' active' : '';
     const blurClass = shouldBlur ? ' blurred' : '';
     const mediaHtml = isVideo ?
-        `<video class="thumb-media${blurClass}" src="${escapeAttribute(thumbUrl)}" muted playsinline preload="metadata"></video>
+        `<video class="thumb-media${blurClass}" src="${escapeAttribute(thumbUrl)}" muted playsinline preload="none" data-lazy-video></video>
          <i class="fas fa-play thumb-video-badge"></i>` :
-        `<img class="thumb-media${blurClass}" src="${escapeAttribute(thumbUrl)}" loading="lazy" alt="">`;
+        `<img class="thumb-media${blurClass}" src="${escapeAttribute(thumbUrl)}" loading="lazy" fetchpriority="low" alt="">`;
     const nsfwBadge = shouldBlur ? '<i class="fas fa-eye-slash thumb-nsfw-badge"></i>' : '';
 
     return `<button class="gallery-thumb${activeClass}" data-index="${index}">${mediaHtml}${nsfwBadge}</button>`;
@@ -311,8 +318,9 @@ function renderMediaItem(img, index, exampleFiles) {
                   originalRemoteUrl.endsWith('.mp4') || originalRemoteUrl.endsWith('.webm');
     const mediaType = isVideo ? 'video' : 'image';
 
-    // Optimize CivitAI URLs for showcase display (full quality)
+    // Optimize CivitAI URLs for in-modal display (images capped at width=2400;
-    const remoteUrl = getShowcaseUrl(originalRemoteUrl, mediaType);
+    // the full-size media viewer uses getShowcaseUrl separately)
+    const remoteUrl = getDisplayUrl(originalRemoteUrl, mediaType);
 
     const localUrl = localFile ? localFile.path : '';
 
@@ -438,6 +446,48 @@ function findLocalFile(img, index, exampleFiles) {
     return localFile;
 }
 
+// URLs already warmed in the HTTP cache, so repeat navigations and re-renders
+// never issue duplicate prefetch requests
+const prefetchedUrls = new Set();
+
+// Direction of the last main-viewer navigation (+1 next / -1 prev); users
+// tend to keep clicking the same arrow, so prefetch reaches one further
+// ahead along it. Defaults to forward (Next is the most common navigation)
+let lastNavDirection = 1;
+
+/**
+ * Warm the HTTP cache for the examples most likely to be shown next: both
+ * indices adjacent to the active one, plus one extra ahead along the last
+ * navigation direction, so prev/next navigation feels instant. Images only:
+ * video payloads are too heavy for speculative prefetch, and locally stored
+ * examples need no network fetch at all.
+ */
+function prefetchAdjacentMedia() {
+    const { images, exampleFiles, activeIndex, expanded } = galleryState;
+    if (!expanded || images.length < 2) return;
+
+    [1, -1, lastNavDirection * 2].forEach(offset => {
+        const index = ((activeIndex + offset) % images.length + images.length) % images.length;
+        const img = images[index];
+        if (!img?.url || findLocalFile(img, index, exampleFiles)) return;
+
+        const isVideo = img.url.endsWith('.mp4') || img.url.endsWith('.webm');
+        if (isVideo) return;
+
+        // Must match the main viewer's URL (display mode) or the warmed
+        // cache entry is never used
+        const url = getDisplayUrl(img.url, 'image');
+        if (prefetchedUrls.has(url)) return;
+        prefetchedUrls.add(url);
+
+        // Off-DOM image: fills the HTTP/memory cache without affecting layout.
+        // Low priority keeps it from competing with the active media's load.
+        const preloader = new Image();
+        preloader.fetchPriority = 'low';
+        preloader.src = url;
+    });
+}
+
 /**
  * Switch the main viewer to another example (wraps around)
  * @param {number} index - Target index in galleryState.images
@@ -446,6 +496,11 @@ export function updateMainDisplay(index) {
     const count = galleryState.images.length;
     if (!count || !galleryState.expanded) return;
 
+    // Remember the navigation direction for direction-aware prefetching
+    // (a raw index of -1 / count means wrap-around prev / next)
+    const delta = index - galleryState.activeIndex;
+    if (delta !== 0) lastNavDirection = delta > 0 ? 1 : -1;
+
     galleryState.activeIndex = ((index % count) + count) % count;
 
     const container = document.getElementById('mainMediaContainer');
@@ -453,6 +508,13 @@ export function updateMainDisplay(index) {
 
     const activeImg = galleryState.images[galleryState.activeIndex];
     container.style.setProperty('--media-aspect', mediaAspectRatio(activeImg));
+    // Direction-aware slide makes every switch (wheel, keys, buttons,
+    // thumbnails) perceivable instead of an instant, unexplained swap
+    container.classList.remove('slide-from-left', 'slide-from-right');
+    if (delta !== 0) {
+        void container.offsetWidth; // restart the animation on rapid switches
+        container.classList.add(delta > 0 ? 'slide-from-right' : 'slide-from-left');
+    }
     // The badge lives inside the container, so rebuild it together with the media
     container.innerHTML = renderMediaItem(
         activeImg,
@@ -470,6 +532,7 @@ export function updateMainDisplay(index) {
     });
 
     initMainMediaInteractions(container);
+    prefetchAdjacentMedia();
 }
 
 /**
@@ -624,6 +687,211 @@ function setupScrollToExpand(gallery) {
     }, { passive: true });
 }
 
+// Wheel-navigation tuning: one gesture = one step. Trackpads emit a stream
+// of small deltas, so deltas accumulate until the threshold; the cooldown
+// keeps the tail of the same gesture from stepping again
+const WHEEL_STEP_THRESHOLD = 50;
+const WHEEL_COOLDOWN_MS = 250;
+const WHEEL_ACCUM_RESET_MS = 200;
+
+/**
+ * Wheel navigation on the main viewer area. Bound to .gallery-main (not the
+ * media element) so it works wherever the cursor rests within the viewer —
+ * including over the nav buttons and the dead zones beside the media, and
+ * regardless of whether the hover-triggered metadata panel is showing.
+ *
+ * - Horizontal-dominant deltas (trackpad two-finger swipe) always navigate;
+ *   the modal never scrolls horizontally, so nothing is hijacked.
+ * - Vertical deltas navigate only when the modal content cannot scroll
+ *   further in that direction (same boundary pass-through pattern as the
+ *   metadata panel's wheel handler), so wheel-scrolling the modal through
+ *   the gallery is never trapped mid-way.
+ * - Once a boundary crossing triggers a vertical switch, a "wheel session"
+ *   starts: while the pointer stays over .gallery-main, vertical wheel in
+ *   BOTH directions switches examples (down = next, up = prev — the reverse
+ *   gesture must undo, not scroll the modal away). The session ends when the
+ *   pointer leaves the area, returning vertical scroll to the modal.
+ * @param {HTMLElement} gallery - The .showcase-gallery element
+ */
+function initWheelNavigation(gallery) {
+    const main = gallery.querySelector('.gallery-main');
+    if (!main || galleryState.images.length < 2) return;
+
+    let accumulated = 0;
+    let lastEventAt = 0;
+    let lastStepAt = 0;
+    let verticalSession = false;
+
+    // Leaving the viewer area releases the vertical wheel back to the modal
+    main.addEventListener('pointerleave', () => {
+        verticalSession = false;
+        accumulated = 0;
+    });
+
+    main.addEventListener('wheel', (event) => {
+        // The metadata panel and media controls keep their own behavior;
+        // the panel passes boundary scrolls through to the modal by itself
+        if (event.target.closest('.image-metadata-panel, .media-controls')) return;
+
+        const horizontal = Math.abs(event.deltaX) > Math.abs(event.deltaY);
+        const delta = horizontal ? event.deltaX : event.deltaY;
+        if (delta === 0) return;
+
+        if (!horizontal && !verticalSession) {
+            const scroller = main.closest('.modal-content');
+            if (scroller) {
+                const atTop = scroller.scrollTop <= 0;
+                const atBottom = scroller.scrollHeight - scroller.scrollTop - scroller.clientHeight <= 1;
+                if ((delta < 0 && !atTop) || (delta > 0 && !atBottom)) return;
+            }
+        }
+
+        event.preventDefault();
+
+        const now = performance.now();
+        if (now - lastEventAt > WHEEL_ACCUM_RESET_MS) accumulated = 0;
+        lastEventAt = now;
+        if (now - lastStepAt < WHEEL_COOLDOWN_MS) return;
+
+        accumulated += delta;
+        if (Math.abs(accumulated) < WHEEL_STEP_THRESHOLD) return;
+
+        const direction = accumulated > 0 ? 1 : -1;
+        accumulated = 0;
+        lastStepAt = now;
+        if (!horizontal) verticalSession = true;
+        updateMainDisplay(galleryState.activeIndex + direction);
+    }, { passive: false });
+}
+
+// Touch/pen swipe tuning. Mouse is excluded: it already has wheel, keys and
+// buttons, and mouse-drag would fight the media's click-to-view gesture
+const SWIPE_THRESHOLD_PX = 50;
+const SWIPE_CLICK_SUPPRESS_MS = 400;
+
+/**
+ * Horizontal swipe navigation on the main viewer area (touch/pen). Requires
+ * `touch-action: pan-y` on .gallery-main so horizontal pans reach these
+ * handlers while vertical pans still scroll the modal.
+ * @param {HTMLElement} gallery - The .showcase-gallery element
+ */
+function initSwipeNavigation(gallery) {
+    const main = gallery.querySelector('.gallery-main');
+    if (!main || galleryState.images.length < 2) return;
+
+    let startX = 0;
+    let startY = 0;
+    let tracking = false;
+    let lastSwipeAt = 0;
+
+    main.addEventListener('pointerdown', (event) => {
+        if (event.pointerType === 'mouse') return;
+        // Native video controls own their pointer gestures (scrubbing etc.)
+        if (event.target.closest('video, .image-metadata-panel, .media-controls, .gallery-nav')) return;
+        startX = event.clientX;
+        startY = event.clientY;
+        tracking = true;
+    });
+
+    main.addEventListener('pointercancel', () => { tracking = false; });
+
+    main.addEventListener('pointerup', (event) => {
+        if (!tracking) return;
+        tracking = false;
+        const dx = event.clientX - startX;
+        const dy = event.clientY - startY;
+        if (Math.abs(dx) < SWIPE_THRESHOLD_PX || Math.abs(dx) < Math.abs(dy) * 1.5) return;
+        lastSwipeAt = performance.now();
+        updateMainDisplay(galleryState.activeIndex + (dx < 0 ? 1 : -1));
+    });
+
+    // A completed swipe still produces a click on the media — swallow it in
+    // the capture phase (beats the media element's own handler) so the
+    // full-size viewer does not open
+    main.addEventListener('click', (event) => {
+        if (performance.now() - lastSwipeAt < SWIPE_CLICK_SUPPRESS_MS) {
+            event.stopPropagation();
+            event.preventDefault();
+        }
+    }, true);
+}
+
+/**
+ * True when the showcase tab is the active pane of an open modal
+ * @returns {boolean}
+ */
+function isShowcaseTabVisible() {
+    const showcaseTab = document.getElementById('showcase-tab');
+    if (!showcaseTab || !showcaseTab.classList.contains('active')) return false;
+    const modalEl = showcaseTab.closest('.modal');
+    // No .modal ancestor: standalone/test rendering, treat as visible
+    if (!modalEl) return true;
+    return modalEl.classList.contains('show') || modalEl.style.display === 'block';
+}
+
+/**
+ * Typing-target guard for the example shortcuts. Unlike the model-level
+ * navigation guard, buttons are NOT excluded: clicking a thumbnail or nav
+ * button leaves focus on it, which would make [ ] feel dead right after the
+ * most common interaction — and buttons consume Space/Enter natively, never
+ * bracket keys.
+ * @param {EventTarget|null} target - keydown event target
+ * @returns {boolean}
+ */
+function isTypingTarget(target) {
+    if (!target) return false;
+    const tagName = target.tagName ? target.tagName.toLowerCase() : '';
+    return target.isContentEditable || ['input', 'textarea', 'select'].includes(tagName);
+}
+
+// '[' / ']' switch examples while the gallery is expanded. ArrowLeft/Right
+// stay reserved for model-level navigation (ModelModal), and the full-size
+// media viewer owns its keys while open.
+document.addEventListener('keydown', (event) => {
+    if (event.key !== '[' && event.key !== ']') return;
+    if (!galleryState.expanded || galleryState.images.length < 2) return;
+    if (isTypingTarget(event.target)) return;
+    if (isMediaViewerOpen()) return;
+    if (!isShowcaseTabVisible()) return;
+
+    event.preventDefault();
+    updateMainDisplay(galleryState.activeIndex + (event.key === ']' ? 1 : -1));
+});
+
+/**
+ * Defer metadata fetches for video thumbnails until they scroll into view:
+ * with preload="metadata" on every strip video, expanding the gallery would
+ * otherwise hit the network for all of them at once
+ * @param {HTMLElement} gallery - The .showcase-gallery element
+ */
+function initStripVideoLazyLoading(gallery) {
+    const videos = gallery.querySelectorAll('.gallery-strip video[data-lazy-video]');
+    if (!videos.length) return;
+
+    const enable = (video) => {
+        video.preload = 'metadata';
+        video.load();
+        video.removeAttribute('data-lazy-video');
+    };
+
+    if (typeof IntersectionObserver === 'undefined') {
+        videos.forEach(enable);
+        return;
+    }
+
+    // No explicit root: intersection accounts for the strip's overflow
+    // clipping, so off-screen thumbnails stay at preload="none"
+    const observer = new IntersectionObserver((entries) => {
+        entries.forEach(entry => {
+            if (entry.isIntersecting) {
+                enable(entry.target);
+                observer.unobserve(entry.target);
+            }
+        });
+    });
+    videos.forEach(video => observer.observe(video));
+}
+
 /**
  * Initialize all gallery interactions
  * @param {HTMLElement} gallery - The .showcase-gallery element
@@ -683,6 +951,13 @@ export function initShowcaseContent(gallery) {
     const container = gallery.querySelector('.main-media-container');
     if (container && galleryState.expanded) {
         initMainMediaInteractions(container);
+        initWheelNavigation(gallery);
+        initSwipeNavigation(gallery);
+        // Gallery just (re)rendered expanded: warm the cache for the
+        // examples adjacent to the active one
+        prefetchAdjacentMedia();
+        // Video thumbnails start at preload="none"; enable them on visibility
+        initStripVideoLazyLoading(gallery);
     }
 
     // Reposition controls on window resize

static/js/core.js

@@ -12,6 +12,7 @@ import { helpManager } from './managers/HelpManager.js';
 import { doctorManager } from './managers/DoctorManager.js';
 import { bannerService } from './managers/BannerService.js';
 import { initTheme, initBackToTop } from './utils/uiHelpers.js';
+import { applyModalBackdropBlurPolicy } from './utils/renderingCapability.js';
 import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
 import { i18n } from './i18n/index.js';
 import { onboardingManager } from './managers/OnboardingManager.js';
@@ -34,6 +35,10 @@ export class AppCore {
 
         console.log('AppCore: Initializing...');
 
+        // Disable full-viewport backdrop blur under software rendering before
+        // anything can open a modal (issue #1092)
+        applyModalBackdropBlurPolicy();
+
         // Initialize i18n first
         window.i18n = i18n;
         // Wait for i18n to be ready

static/js/embeddings.js

@@ -3,6 +3,7 @@ import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } fr
 import { createPageControls } from './components/controls/index.js';
 import { ModelDuplicatesManager } from './components/ModelDuplicatesManager.js';
 import { MODEL_TYPES } from './api/apiConfig.js';
+import { initActiveFiltersSync } from './utils/activeFiltersSync.js';
 
 // Initialize the Embeddings page
 class EmbeddingsPageManager {
@@ -32,6 +33,9 @@ class EmbeddingsPageManager {
         // Initialize common page features (including context menus)
         appCore.initializePageFeatures();
         
+        // Mirror active filters to the backend for the ComfyUI-side autocomplete
+        initActiveFiltersSync(MODEL_TYPES.EMBEDDING);
+
         console.log('Embeddings Manager initialized');
     }
 }

static/js/loras.js

@@ -4,6 +4,7 @@ import { updateCardsForBulkMode } from './components/shared/ModelCard.js';
 import { createPageControls } from './components/controls/index.js';
 import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 import { ModelDuplicatesManager } from './components/ModelDuplicatesManager.js';
+import { initActiveFiltersSync } from './utils/activeFiltersSync.js';
 
 // Initialize the LoRA page
 export class LoraPageManager {
@@ -41,6 +42,9 @@ export class LoraPageManager {
 
         // Initialize common page features (including context menus and virtual scroll)
         appCore.initializePageFeatures();
+
+        // Mirror active filters to the backend for the ComfyUI-side autocomplete
+        initActiveFiltersSync('loras');
     }
 }
 

static/js/managers/DownloadManager.js

@@ -1182,10 +1182,13 @@ export class DownloadManager {
             if (!response?.success) {
                 this.loadingManager.setStatus(translate('modals.download.status.finalizing'));
                 const errorMessage = response?.error || 'Unknown error';
+                // Always record the latest failure so callers can distinguish
+                // an unresolvable model (not found / deleted) from a transient
+                // transport failure; the summary flow below may or may not run.
+                this._lastDownloadError = errorMessage;
                 // When the caller aggregates failures itself (multi-file
                 // loop), just record the error and return (#1058).
                 if (suppressFailureSummary) {
-                    this._lastDownloadError = errorMessage;
                     return false;
                 }
                 // A file-level "already in library" rejection is an expected

static/js/managers/FilterManager.js

@@ -511,18 +511,21 @@ export class FilterManager {
         filteredModels.forEach(model => {
             const tag = document.createElement('div');
             tag.className = 'filter-tag base-model-tag';
-            tag.dataset.baseModel = model.name;
+            // Display name may differ from the filter value (e.g. the "Unknown"
+            // bucket shows "Unknown" but filters via a dedicated marker).
+            const filterValue = model.value ?? model.name;
+            tag.dataset.baseModel = filterValue;
             tag.innerHTML = `${model.name} <span class="tag-count">${model.count}</span>`;
 
             tag.addEventListener('click', async () => {
                 tag.classList.toggle('active');
 
                 if (tag.classList.contains('active')) {
-                    if (!this.filters.baseModel.includes(model.name)) {
+                    if (!this.filters.baseModel.includes(filterValue)) {
-                        this.filters.baseModel.push(model.name);
+                        this.filters.baseModel.push(filterValue);
                     }
                 } else {
-                    this.filters.baseModel = this.filters.baseModel.filter(m => m !== model.name);
+                    this.filters.baseModel = this.filters.baseModel.filter(m => m !== filterValue);
                 }
 
                 this.updateActiveFiltersCount();

static/js/managers/HelpManager.js

@@ -1,23 +1,16 @@
 import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
+import { onboardingManager } from './OnboardingManager.js';
 
 /**
  * Manages help modal functionality and tutorial update notifications
  */
 export class HelpManager {
     constructor() {
-        this.lastViewedTimestamp = getStorageItem('help_last_viewed', 0);
+        // Version of the help content the user has seen. Compared against the
-        this.latestContentTimestamp = new Date('2025-10-11').getTime(); // Will be updated from server or config
+        // data-help-content-version marker rendered into the help modal markup,
+        // so badge state is always derived from the content actually served.
+        this.viewedContentVersion = getStorageItem('help_viewed_content_version', null);
         this.isInitialized = false;
-        
-        // Default latest content data - could be fetched from server
-        this.latestVideoData = {
-            timestamp: new Date('2024-06-09').getTime(), // Default timestamp
-            walkthrough: {
-                id: 'hvKw31YpE-U',
-                title: 'Getting Started with LoRA Manager'
-            },
-            playlistUpdated: true
-        };
     }
 
     /**
@@ -34,9 +27,6 @@ export class HelpManager {
         // Check if we need to show the badge
         this.updateHelpBadge();
         
-        // Fetch latest video data (could be implemented to fetch from remote source)
-        this.fetchLatestVideoData();
-        
         this.isInitialized = true;
         return this;
     }
@@ -55,77 +45,147 @@ export class HelpManager {
         const tabButtons = document.querySelectorAll('.help-tabs .tab-btn');
         tabButtons.forEach(button => {
             button.addEventListener('click', (event) => {
-                // Remove active class from all buttons and panes
+                this.activateHelpTab(event.currentTarget.getAttribute('data-tab'));
-                document.querySelectorAll('.help-tabs .tab-btn').forEach(btn => {
-                    btn.classList.remove('active');
-                });
-                document.querySelectorAll('.help-content .tab-pane').forEach(pane => {
-                    pane.classList.remove('active');
-                });
-                
-                // Add active class to clicked button
-                event.currentTarget.classList.add('active');
-                
-                // Show corresponding tab content
-                const tabId = event.currentTarget.getAttribute('data-tab');
-                document.getElementById(tabId).classList.add('active');
             });
         });
+
+        // Replay tutorial button in the Getting Started tab
+        const replayTutorialBtn = document.getElementById('replayTutorialBtn');
+        if (replayTutorialBtn) {
+            replayTutorialBtn.addEventListener('click', () => {
+                // Close the help modal, then restart the onboarding tutorial
+                if (window.modalManager) {
+                    window.modalManager.closeModal('helpModal');
+                }
+                onboardingManager.reset();
+                onboardingManager.startTutorial();
+            });
+        }
+
+        // Global "?" shortcut opens the help modal on the Shortcuts tab
+        document.addEventListener('keydown', (event) => {
+            if (event.key !== '?') return;
+            if (this.isTypingContext(event.target)) return;
+            if (window.modalManager?.isAnyModalOpen()) return;
+
+            event.preventDefault();
+            this.openHelpModal('shortcuts');
+        });
+    }
+
+    /**
+     * Check if the event target is a text entry context where "?" is literal input
+     */
+    isTypingContext(target) {
+        if (!(target instanceof Element)) return false;
+
+        const tagName = target.tagName?.toLowerCase();
+        return target.isContentEditable || tagName === 'input' || tagName === 'textarea' || tagName === 'select';
+    }
+
+    /**
+     * Activate a specific help modal tab by its data-tab id
+     * @param {string} tabId - The tab id (matches data-tab and pane element id)
+     */
+    activateHelpTab(tabId) {
+        const tabButton = document.querySelector(`.help-tabs .tab-btn[data-tab="${tabId}"]`);
+        const tabPane = document.getElementById(tabId);
+        if (!tabButton || !tabPane) return;
+
+        // Remove active class from all buttons and panes
+        document.querySelectorAll('.help-tabs .tab-btn').forEach(btn => {
+            btn.classList.remove('active');
+        });
+        document.querySelectorAll('.help-content .tab-pane').forEach(pane => {
+            pane.classList.remove('active');
+        });
+
+        // Activate the requested tab
+        tabButton.classList.add('active');
+        tabPane.classList.add('active');
     }
     
     /**
      * Open the help modal
+     * @param {string} [tabId] - Optional tab id to activate after opening
      */
-    openHelpModal() {
+    openHelpModal(tabId) {
         // Use modalManager to open the help modal
-        if (window.modalManager) {
+        if (!window.modalManager) return;
-            window.modalManager.toggleModal('helpModal');
 
-            // Add visual indicator to Documentation tab if there's new content
+        const hadNewContent = this.hasNewContent();
-            this.updateDocumentationTabIndicator();
 
-            // Update the last viewed timestamp
+        window.modalManager.toggleModal('helpModal');
+
+        if (tabId) {
+            this.activateHelpTab(tabId);
+        }
+
+        // Only acknowledge the content as viewed when the user opened the
+        // modal while it actually contained new content. Opening a stale
+        // (pre-upgrade) page must not suppress the badge after a refresh.
+        if (hadNewContent) {
+            this.updateNewContentTabIndicators();
             this.markContentAsViewed();
-            
-            // Hide the badge
-            this.hideHelpBadge();
         }
+
+        // Hide the badge
+        this.hideHelpBadge();
     }
 
     /**
-     * Add visual indicator to Documentation tab for new content
+     * Add visual indicator to tabs that received new content
      */
-    updateDocumentationTabIndicator() {
+    updateNewContentTabIndicators() {
-        const docTab = document.querySelector('.tab-btn[data-tab="documentation"]');
+        if (!this.hasNewContent()) return;
-        if (docTab && this.hasNewContent()) {
+
-            docTab.classList.add('has-new-content');
+        // Tabs updated in the 2026-09-03 discoverability release:
+        // getting-started (Replay Tutorial button) and shortcuts (new cheat-sheet tab)
+        const NEW_CONTENT_TABS = ['getting-started', 'shortcuts'];
+        NEW_CONTENT_TABS.forEach(tabId => {
+            const tab = document.querySelector(`.help-tabs .tab-btn[data-tab="${tabId}"]`);
+            if (tab) {
+                tab.classList.add('has-new-content');
+            }
+        });
+
+        // Point the indicator at the specific new element inside the
+        // Getting Started tab, and scroll it into view so it is not lost
+        // below the fold of the modal body.
+        const replayBtn = document.getElementById('replayTutorialBtn');
+        if (replayBtn) {
+            replayBtn.classList.add('has-new-content');
+            const gettingStartedActive = document.querySelector('#getting-started.tab-pane.active');
+            if (gettingStartedActive && typeof replayBtn.scrollIntoView === 'function') {
+                replayBtn.scrollIntoView({ behavior: 'smooth', block: 'nearest' });
+            }
         }
     }
     
     /**
-     * Mark content as viewed by saving current timestamp
+     * Mark content as viewed by persisting the version rendered in the DOM.
+     * No-op when the served markup carries no version marker (stale assets),
+     * so viewing old content never suppresses the badge for new content.
      */
     markContentAsViewed() {
-        this.lastViewedTimestamp = Date.now();
+        const currentVersion = this.getCurrentContentVersion();
-        setStorageItem('help_last_viewed', this.lastViewedTimestamp);
+        if (!currentVersion) return;
+
+        this.viewedContentVersion = currentVersion;
+        setStorageItem('help_viewed_content_version', this.viewedContentVersion);
     }
 
     /**
-     * Fetch latest video data (could be implemented to actually fetch from a remote source)
+     * Read the help content version from the rendered modal markup
+     * @returns {string|null} Version marker, or null if the served markup has none
      */
-    fetchLatestVideoData() {
+    getCurrentContentVersion() {
-        // In a real implementation, you'd fetch this from your server
+        const marker = document.querySelector('[data-help-content-version]');
-        // For now, we'll just use the hardcoded data from constructor
+        return marker ? marker.getAttribute('data-help-content-version') : null;
-        
-        // Update the timestamp with the latest data
-        this.latestContentTimestamp = Math.max(this.latestContentTimestamp, this.latestVideoData.timestamp);
-        
-        // Check again if we need to show the badge with this new data
-        this.updateHelpBadge();
     }
 
     /**
-     * Update help badge visibility based on timestamps
+     * Update help badge visibility based on viewed vs. served content version
      */
     updateHelpBadge() {
         if (this.hasNewContent()) {
@@ -136,11 +196,11 @@ export class HelpManager {
     }
 
     /**
-     * Check if there's new content the user hasn't seen
+     * Check if the served help content is newer than what the user has viewed
      */
     hasNewContent() {
-        // If user has never viewed the help, or the content is newer than last viewed
+        const currentVersion = this.getCurrentContentVersion();
-        return this.lastViewedTimestamp === 0 || this.latestContentTimestamp > this.lastViewedTimestamp;
+        return Boolean(currentVersion) && currentVersion !== this.viewedContentVersion;
     }
     
     /**

static/js/managers/OnboardingManager.js

@@ -43,7 +43,7 @@ export class OnboardingManager {
             {
                 target: '.controls .action-buttons [data-action="bulk"]',
                 title: () => translate('onboarding.steps.bulk.title', {}, 'Bulk Operations'),
-                content: () => translate('onboarding.steps.bulk.content', {}, 'Enter bulk mode by clicking this button or pressing <span class="onboarding-shortcut">B</span>. Select multiple models and perform batch operations. Use <span class="onboarding-shortcut">Ctrl+A</span> to select all visible models.'),
+                content: () => translate('onboarding.steps.bulk.content', {}, 'Enter bulk mode by clicking this button or pressing <span class="onboarding-shortcut">B</span> to select multiple models and perform batch operations.<br>• <span class="onboarding-shortcut">Ctrl/Cmd+A</span> select all visible models, <span class="onboarding-shortcut">Shift+Click</span> select a range.<br>• <span class="onboarding-shortcut">Esc</span> or clicking an empty area exits bulk mode.'),
                 position: 'bottom'
             },
             {
@@ -71,10 +71,30 @@ export class OnboardingManager {
                 position: 'top',
                 customPosition: { top: '20%', left: '50%' }
             },
+            {
+                target: '.card-grid',
+                title: () => translate('onboarding.steps.marqueeSelect.title', {}, 'Drag to Select'),
+                content: () => translate('onboarding.steps.marqueeSelect.content', {}, 'Hold the <strong>left mouse button</strong> on an empty area of the grid and drag to draw a marquee that selects multiple cards at once.'),
+                position: 'top',
+                customPosition: { top: '20%', left: '50%' }
+            },
+            {
+                target: '#folderSidebar',
+                title: () => translate('onboarding.steps.dragToSidebar.title', {}, 'Organize by Dragging'),
+                content: () => translate('onboarding.steps.dragToSidebar.content', {}, 'Drag a model card onto a folder in the sidebar to move the file there. This also works with multiple selected cards in bulk mode.'),
+                position: 'right'
+            },
             {
                 target: '.card-grid',
                 title: () => translate('onboarding.steps.contextMenu.title', {}, 'Context Menu'),
-                content: () => translate('onboarding.steps.contextMenu.content', {}, '<strong>Right-click</strong> any model card for a context menu with additional actions.'),
+                content: () => translate('onboarding.steps.contextMenu.content', {}, '<strong>Right-click</strong> any model card for a context menu with card actions like moving, deleting, or editing metadata.'),
+                position: 'top',
+                customPosition: { top: '20%', left: '50%' }
+            },
+            {
+                target: '.card-grid',
+                title: () => translate('onboarding.steps.contextMenus.title', {}, 'More Context Menus'),
+                content: () => translate('onboarding.steps.contextMenus.content', {}, 'In bulk mode, <strong>right-click a selected card</strong> for bulk actions. <strong>Right-click an empty area</strong> of the page for global actions like update checks and managing excluded models.'),
                 position: 'top',
                 customPosition: { top: '20%', left: '50%' }
             }

static/js/managers/import/DownloadManager.js

@@ -65,6 +65,13 @@ export class DownloadManager {
                     raw_metadata: this.importManager.recipeData.raw_metadata || {},
                 };
 
+                // Pass analysis diagnostics through so the backend can record
+                // why the recipe ended up with no LoRAs (recipe modal panel).
+                const diagnostics = this.importManager.recipeData.diagnostics;
+                if (diagnostics && typeof diagnostics === 'object') {
+                    completeMetadata.diagnostics = diagnostics;
+                }
+
                 // Preserve preview_nsfw_level from analysis so the saved
                 // recipe applies the correct NSFW blur on the preview image.
                 const nsfwLevel = this.importManager.recipeData.preview_nsfw_level;

static/js/utils/activeFiltersSync.js

@@ -0,0 +1,61 @@
+/**
+ * Mirrors the manager page's active filter state to the backend's in-memory
+ * store, so the ComfyUI-side autocomplete can apply it even when the manager
+ * page and ComfyUI run in different browsers/origins (localStorage is not
+ * shared there).
+ */
+
+import { getStorageItem, setActiveFiltersListener } from './storageHelpers.js';
+import { debounce } from './debounce.js';
+
+const SYNC_DEBOUNCE_MS = 300;
+
+const debouncedPushByPage = {};
+
+function buildActiveFiltersPayload(pageType) {
+    const activeFolder = getStorageItem(`${pageType}_activeFolder`);
+    const recursiveSearch = getStorageItem(`${pageType}_recursiveSearch`, true);
+    const filters = getStorageItem(`${pageType}_filters`);
+
+    return {
+        // null stays null; legacy "null" string is normalized to null
+        activeFolder: activeFolder && activeFolder !== 'null' ? activeFolder : null,
+        recursiveSearch: recursiveSearch !== false,
+        filters: filters && typeof filters === 'object' ? filters : null,
+    };
+}
+
+export async function pushActiveFilters(pageType) {
+    try {
+        const response = await fetch(`/api/lm/${pageType}/active-filters`, {
+            method: 'PUT',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify(buildActiveFiltersPayload(pageType)),
+        });
+        if (!response.ok) {
+            console.warn(`[Lora Manager] Failed to sync active filters for ${pageType}: HTTP ${response.status}`);
+        }
+    } catch (error) {
+        console.warn(`[Lora Manager] Failed to sync active filters for ${pageType}:`, error);
+    }
+}
+
+export function syncActiveFilters(pageType) {
+    if (!debouncedPushByPage[pageType]) {
+        debouncedPushByPage[pageType] = debounce(() => {
+            pushActiveFilters(pageType);
+        }, SYNC_DEBOUNCE_MS);
+    }
+    debouncedPushByPage[pageType]();
+}
+
+/**
+ * Register the storage listener and push the current (restored) state once.
+ * The initial push covers server restarts, where the backend store is empty
+ * until the manager page re-publishes its localStorage-restored filters.
+ * @param {string} pageType - 'loras' | 'checkpoints' | 'embeddings'
+ */
+export function initActiveFiltersSync(pageType) {
+    setActiveFiltersListener((changedPageType) => syncActiveFilters(changedPageType));
+    pushActiveFilters(pageType);
+}

static/js/utils/civitaiUtils.js

@@ -9,8 +9,13 @@
 export const OptimizationMode = {
     /** Full quality for showcase/display - uses /optimized=true only */
     SHOWCASE: 'showcase',
+    /** In-modal display - caps image width at 2400 (covers the ~1200 CSS px
+     * main viewer at DPR 2); videos stay full quality */
+    DISPLAY: 'display',
     /** Thumbnail size for cards - uses /width=450,optimized=true */
     THUMBNAIL: 'thumbnail',
+    /** Small thumbnails for the showcase gallery strip (72px display) - uses /width=160,optimized=true */
+    GALLERY_THUMBNAIL: 'gallery-thumbnail',
 };
 
 export const DEFAULT_CIVITAI_PAGE_HOST = 'civitai.com';
@@ -95,15 +100,21 @@ export function rewriteCivitaiUrl(sourceUrl, mediaType = null, mode = Optimizati
         }
 
         // Determine replacement based on mode and media type
+        const isVideo = Boolean(mediaType && mediaType.toLowerCase() === 'video');
         let replacement;
         if (mode === OptimizationMode.SHOWCASE) {
             // Full quality for showcase - no width restriction
             replacement = '/optimized=true';
+        } else if (mode === OptimizationMode.DISPLAY) {
+            // Display mode caps image width for in-modal viewing; videos stay
+            // full quality (CDN transcoding costs more than it saves here)
+            replacement = isVideo ? '/optimized=true' : '/width=2400,optimized=true';
         } else {
-            // Thumbnail mode with width restriction
+            // Thumbnail modes with width restriction
-            replacement = '/width=450,optimized=true';
+            const width = mode === OptimizationMode.GALLERY_THUMBNAIL ? 160 : 450;
-            if (mediaType && mediaType.toLowerCase() === 'video') {
+            replacement = `/width=${width},optimized=true`;
-                replacement = '/transcode=true,width=450,optimized=true';
+            if (isVideo) {
+                replacement = `/transcode=true,width=${width},optimized=true`;
             }
         }
 
@@ -150,6 +161,19 @@ export function getShowcaseUrl(url, type = 'image') {
     return getOptimizedUrl(url, type, OptimizationMode.SHOWCASE);
 }
 
+/**
+ * Get display-optimized URL for the in-modal main viewer (images capped at
+ * width=2400; videos full quality). Use getShowcaseUrl for full-size viewing
+ * (e.g. the media viewer overlay)
+ *
+ * @param {string} url - Original URL
+ * @param {string} type - Media type ("image" or "video")
+ * @returns {string} - Optimized URL for in-modal display
+ */
+export function getDisplayUrl(url, type = 'image') {
+    return getOptimizedUrl(url, type, OptimizationMode.DISPLAY);
+}
+
 /**
  * Get thumbnail-optimized URL (width=450)
  * 
@@ -161,6 +185,17 @@ export function getThumbnailUrl(url, type = 'image') {
     return getOptimizedUrl(url, type, OptimizationMode.THUMBNAIL);
 }
 
+/**
+ * Get gallery-strip-thumbnail-optimized URL (width=160, for the 72px strip)
+ *
+ * @param {string} url - Original URL
+ * @param {string} type - Media type ("image" or "video")
+ * @returns {string} - Optimized URL for gallery strip thumbnail display
+ */
+export function getGalleryThumbnailUrl(url, type = 'image') {
+    return getOptimizedUrl(url, type, OptimizationMode.GALLERY_THUMBNAIL);
+}
+
 /**
  * Check if a URL is from CivitAI
  *

static/js/utils/renderingCapability.js

@@ -0,0 +1,66 @@
+/**
+ * Software-rendering detection for degrading expensive visual effects.
+ *
+ * With hardware acceleration disabled (or a GPU blocklisted), Chrome rasterizes
+ * in software. A full-viewport `backdrop-filter: blur()` then forces a per-frame
+ * CPU blur over everything painted behind the modal, freezing the entire
+ * browser (issue #1092). When software rendering is detected we add the
+ * `no-modal-backdrop-blur` class to <html>, and CSS drops the backdrop blur.
+ */
+
+const SOFTWARE_RENDERER_PATTERN = /swiftshader|llvmpipe|softpipe|software|basic render/i;
+
+/**
+ * Check a WebGL renderer string against known software rasterizers.
+ * @param {string} renderer - UNMASKED_RENDERER_WEBGL string
+ * @returns {boolean}
+ */
+export function isSoftwareRendererString(renderer) {
+    return SOFTWARE_RENDERER_PATTERN.test(renderer || '');
+}
+
+/**
+ * Read the unmasked WebGL renderer string, or null when unavailable/masked.
+ * @returns {string|null}
+ */
+function getWebGLRendererString() {
+    const canvas = document.createElement('canvas');
+    const gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl');
+    if (!gl) return null;
+
+    const debugInfo = gl.getExtension('WEBGL_debug_renderer_info');
+    const renderer = debugInfo
+        ? String(gl.getParameter(debugInfo.UNMASKED_RENDERER_WEBGL) || '')
+        : '';
+
+    const loseContext = gl.getExtension('WEBGL_lose_context');
+    if (loseContext) loseContext.loseContext();
+
+    return renderer || null;
+}
+
+/**
+ * Heuristic: is the browser rasterizing in software?
+ * - No WebGL at all: no evidence of GPU acceleration, assume software.
+ * - Masked renderer string or detection failure: cannot tell, keep effects on.
+ * @returns {boolean}
+ */
+export function isSoftwareRendering() {
+    try {
+        const renderer = getWebGLRendererString();
+        if (renderer === null) {
+            return true;
+        }
+        return isSoftwareRendererString(renderer);
+    } catch (error) {
+        return true;
+    }
+}
+
+/**
+ * Toggle the blur-disabling class on <html>. Runs once at app startup.
+ * @param {boolean} [isSoftware] - Override for tests; defaults to detection.
+ */
+export function applyModalBackdropBlurPolicy(isSoftware = isSoftwareRendering()) {
+    document.documentElement.classList.toggle('no-modal-backdrop-blur', isSoftware);
+}

static/js/utils/scanEtaUtils.js

@@ -0,0 +1,61 @@
+import { translate } from './i18nHelpers.js';
+
+/**
+ * Format a remaining-time estimate for scan progress display.
+ * @param {number} remainingMs - Estimated remaining time in milliseconds
+ * @returns {string} Localized ETA text
+ */
+export function formatScanRemainingTime(remainingMs) {
+    if (remainingMs < 60000) {
+        return translate('common.scanProgress.eta.lessThanMinute', {}, 'Less than a minute remaining');
+    }
+    if (remainingMs < 3600000) {
+        const minutes = Math.round(remainingMs / 60000);
+        return translate('common.scanProgress.eta.minutes', { minutes }, `~${minutes} min remaining`);
+    }
+    const hours = Math.floor(remainingMs / 3600000);
+    const minutes = Math.round((remainingMs % 3600000) / 60000);
+    return translate('common.scanProgress.eta.hours', { hours, minutes }, `~${hours} hr ${minutes} min remaining`);
+}
+
+/**
+ * Create an ETA tracker for scan progress. Uses an exponential moving
+ * average (0.7/0.3) over the observed per-file processing time, mirroring
+ * the estimator in components/initialization.js.
+ * @returns {{ update: (processed: number, total: number) => (string|null) }}
+ */
+export function createScanEtaTracker() {
+    let startTime = null;
+    let lastProcessed = 0;
+    let averageMsPerFile = null;
+
+    return {
+        /**
+         * Update with the latest counters.
+         * @returns {string|null} Localized ETA text, or null when not applicable
+         */
+        update(processed, total) {
+            if (!total || total <= 0 || processed >= total) {
+                return null;
+            }
+            const now = Date.now();
+            if (startTime === null) {
+                // First sample only anchors the timer; not enough data yet
+                startTime = now;
+                lastProcessed = processed;
+                return translate('initialization.estimatingTime', {}, 'Estimating time...');
+            }
+            if (processed > lastProcessed) {
+                const msPerFile = (now - startTime) / processed;
+                averageMsPerFile = averageMsPerFile === null
+                    ? msPerFile
+                    : averageMsPerFile * 0.7 + msPerFile * 0.3;
+                lastProcessed = processed;
+            }
+            if (averageMsPerFile === null) {
+                return translate('initialization.estimatingTime', {}, 'Estimating time...');
+            }
+            return formatScanRemainingTime((total - lastProcessed) * averageMsPerFile);
+        }
+    };
+}

static/js/utils/storageHelpers.js

@@ -6,6 +6,31 @@
 // Namespace prefix for all localStorage keys
 const STORAGE_PREFIX = 'lora_manager_';
 
+// Matches keys that carry the manager page's active filter state
+// (e.g. 'loras_activeFolder', 'checkpoints_filters').
+const ACTIVE_FILTER_KEY_PATTERN = /^(loras|checkpoints|embeddings)_(activeFolder|recursiveSearch|filters)$/;
+
+let activeFiltersListener = null;
+
+/**
+ * Register a listener invoked with the page type whenever one of the
+ * active-filter storage keys changes. Used to mirror filter state to the
+ * backend so the ComfyUI-side autocomplete can pick it up across
+ * browsers/origins where localStorage is not shared.
+ * @param {function(string): void} listener
+ */
+export function setActiveFiltersListener(listener) {
+    activeFiltersListener = listener;
+}
+
+function notifyActiveFiltersChanged(key) {
+    if (!activeFiltersListener) return;
+    const match = ACTIVE_FILTER_KEY_PATTERN.exec(key);
+    if (match) {
+        activeFiltersListener(match[1]);
+    }
+}
+
 /**
  * Get an item from localStorage with namespace support and fallback to legacy keys
  * @param {string} key - The key without prefix
@@ -58,6 +83,8 @@ export function setStorageItem(key, value) {
     } else {
         localStorage.setItem(prefixedKey, value);
     }
+
+    notifyActiveFiltersChanged(key);
 }
 
 /**
@@ -67,6 +94,8 @@ export function setStorageItem(key, value) {
 export function removeStorageItem(key) {
     localStorage.removeItem(STORAGE_PREFIX + key);
     localStorage.removeItem(key); // Also remove legacy key
+
+    notifyActiveFiltersChanged(key);
 }
 
 /**

static/js/utils/uiHelpers.js

@@ -311,6 +311,20 @@ export function showActionToast(key, params = {}, type = 'info', options = {}) {
   toast.append(closeBtn);
 }
 
+/**
+ * Check whether the event target is a text-entry context (input, textarea,
+ * select, or contenteditable) where single-letter shortcuts should be treated
+ * as literal input.
+ * @param {EventTarget|null} target - The DOM event target
+ * @returns {boolean}
+ */
+export function isTypingContext(target) {
+  if (!(target instanceof Element)) return false;
+
+  const tagName = target.tagName?.toLowerCase();
+  return target.isContentEditable || tagName === 'input' || tagName === 'textarea' || tagName === 'select';
+}
+
 export function restoreFolderFilter() {
   const activeFolder = getStorageItem('activeFolder');
   const folderTag = activeFolder && document.querySelector(`.tag[data-folder="${activeFolder}"]`);

templates/components/controls.html

@@ -65,7 +65,7 @@
                 </select>
             </div>
             <div title="{% if page_id == 'recipes' %}{{ t('recipes.controls.refresh.title') }}{% else %}{{ t('loras.controls.refresh.title') }}{% endif %}" class="control-group dropdown-group">
-                <button data-action="refresh" class="dropdown-main"><i class="fas fa-sync"></i> <span>{{ t('common.actions.refresh') }}</span></button>
+                <button data-action="refresh" class="dropdown-main"><i class="fas fa-sync"></i> <span><span>{{ t('common.actions.refresh') }}</span> <kbd class="shortcut-key">R</kbd></span></button>
                 <button class="dropdown-toggle" aria-label="Show refresh options">
                     <i class="fas fa-caret-down"></i>
                 </button>
@@ -78,11 +78,11 @@
             
             {% if page_id != 'recipes' %}
             <div class="control-group">
-                <button data-action="fetch" title="{{ t('loras.controls.fetch.title') }}"><i class="fas fa-download"></i> <span>{{ t('loras.controls.fetch.action') }}</span></button>
+                <button data-action="fetch" title="{{ t('loras.controls.fetch.title') }}"><i class="fas fa-download"></i> <span><span>{{ t('loras.controls.fetch.action') }}</span> <kbd class="shortcut-key">F</kbd></span></button>
             </div>
             <div class="control-group">
                 <button data-action="download" title="{{ t('loras.controls.download.title') }}">
-                    <i class="fas fa-cloud-download-alt"></i> <span>{{ t('loras.controls.download.action') }}</span>
+                    <i class="fas fa-cloud-download-alt"></i> <span><span>{{ t('loras.controls.download.action') }}</span> <kbd class="shortcut-key">D</kbd></span>
                 </button>
             </div>
             {% endif %}
@@ -96,7 +96,7 @@
             {% endif %}
             <div class="control-group">
                 <button id="bulkOperationsBtn" data-action="bulk" title="{{ t('loras.controls.bulk.title') }}">
-                    <i class="fas fa-th-large"></i> <span><span>{{ t('loras.controls.bulk.action') }}</span> <div class="shortcut-key">B</div></span>
+                    <i class="fas fa-th-large"></i> <span><span>{{ t('loras.controls.bulk.action') }}</span> <kbd class="shortcut-key">B</kbd></span>
                 </button>
             </div>
             <div class="control-group">

templates/components/modals/help_modal.html

@@ -1,5 +1,5 @@
 <!-- Help Modal -->
-<div id="helpModal" class="modal">
+<div id="helpModal" class="modal" data-help-content-version="2026-09-03">
     <div class="modal-content help-modal">
         <button class="close" onclick="modalManager.closeModal('helpModal')">&times;</button>
         <div class="help-header">
@@ -10,6 +10,7 @@
             <button class="tab-btn active" data-tab="getting-started">{{ t('help.tabs.gettingStarted') }}</button>
             <button class="tab-btn" data-tab="update-vlogs">{{ t('help.tabs.updateVlogs') }}</button>
             <button class="tab-btn" data-tab="documentation">{{ t('help.tabs.documentation') }}</button>
+            <button class="tab-btn" data-tab="shortcuts">{{ t('help.tabs.shortcuts') }}</button>
         </div>
         
         <div class="help-content">
@@ -39,6 +40,13 @@
                         <li><strong>Recipe System:</strong> Create, save and share your perfect combinations</li>
                     </ul>
                 </div>
+                <div class="help-actions">
+                    <button id="replayTutorialBtn" class="replay-tutorial-btn">
+                        <i class="fas fa-graduation-cap"></i>
+                        <span>{{ t('help.gettingStarted.replayTutorial') }}</span>
+                        <span class="new-content-badge">{{ t('help.newContentBadge') }}</span>
+                    </button>
+                </div>
             </div>
             
             <!-- Update Vlogs Tab -->
@@ -136,6 +144,126 @@
                     </ul>
                 </div>
             </div>
+            <!-- Shortcuts Tab -->
+            <div class="tab-pane" id="shortcuts">
+                <h3>{{ t('help.shortcuts.title') }}</h3>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-keyboard"></i> {{ t('help.shortcuts.groups.general') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>Ctrl</kbd><span class="shortcut-sep">/</span><kbd>Cmd</kbd><span class="shortcut-sep">+</span><kbd>F</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.focusSearch') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Esc</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.closeModal') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>?</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.openShortcuts') }}</span>
+                        </li>
+                    </ul>
+                </div>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-bolt"></i> {{ t('help.shortcuts.groups.actions') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>R</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.refresh') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>F</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.fetchMetadata') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>D</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.downloadModel') }}</span>
+                        </li>
+                    </ul>
+                </div>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-object-group"></i> {{ t('help.shortcuts.groups.selection') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>B</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.toggleBulkMode') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Ctrl</kbd><span class="shortcut-sep">/</span><kbd>Cmd</kbd><span class="shortcut-sep">+</span><kbd>A</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.selectAll') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Shift</kbd><span class="shortcut-sep">+</span><kbd>{{ t('help.shortcuts.keys.click') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.rangeSelect') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>{{ t('help.shortcuts.keys.drag') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.marqueeSelect') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Esc</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.exitBulkMode') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>{{ t('help.shortcuts.keys.rightClick') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.bulkActions') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>{{ t('help.shortcuts.keys.rightClick') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.globalActions') }}</span>
+                        </li>
+                    </ul>
+                </div>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-arrows-alt-v"></i> {{ t('help.shortcuts.groups.navigation') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>PageUp</kbd><span class="shortcut-sep">/</span><kbd>PageDown</kbd><span class="shortcut-sep">/</span><kbd>Home</kbd><span class="shortcut-sep">/</span><kbd>End</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.scrollPages') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Alt</kbd><span class="shortcut-sep">+</span><kbd>{{ t('help.shortcuts.keys.letter') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.jumpAlphabet') }}</span>
+                        </li>
+                    </ul>
+                </div>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-window-restore"></i> {{ t('help.shortcuts.groups.modelModal') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>&larr;</kbd><span class="shortcut-sep">/</span><kbd>&rarr;</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.prevNext') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Delete</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.deleteEntry') }}</span>
+                        </li>
+                    </ul>
+                </div>
+
+                <div class="shortcuts-section">
+                    <h4><i class="fas fa-images"></i> {{ t('help.shortcuts.groups.mediaViewer') }}</h4>
+                    <ul class="shortcuts-list">
+                        <li>
+                            <span class="shortcut-keys"><kbd>&larr;</kbd><span class="shortcut-sep">/</span><kbd>&rarr;</kbd><span class="shortcut-sep">/</span><kbd>[</kbd><span class="shortcut-sep">/</span><kbd>]</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.cycleMedia') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>{{ t('help.shortcuts.keys.swipe') }}</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.swipeTouch') }}</span>
+                        </li>
+                        <li>
+                            <span class="shortcut-keys"><kbd>Esc</kbd></span>
+                            <span class="shortcut-description">{{ t('help.shortcuts.entries.closeViewer') }}</span>
+                        </li>
+                    </ul>
+                </div>
+            </div>
         </div>
     </div>
 </div>

templates/components/recipe_modal.html

@@ -144,5 +144,8 @@
             </div>
         </div>
     </div>
-</div>
+
+        <!-- Meta footer: file location + recipe ID, populated by RecipeModal.syncMetaFooter() -->
+        <footer class="recipe-meta-footer" id="recipeMetaFooter" hidden></footer>
+    </div>
 </div>

tests/frontend/api/baseModelApi.refresh.test.js

@@ -0,0 +1,348 @@
+import { describe, it, beforeEach, afterEach, expect, vi } from 'vitest';
+
+const {
+  BASE_MODEL_API_MODULE,
+  STATE_MODULE,
+  UI_HELPERS_MODULE,
+  I18N_MODULE,
+  STORAGE_MODULE,
+  API_CONFIG_MODULE,
+  API_FACTORY_MODULE,
+  SIDEBAR_MANAGER_MODULE,
+} = vi.hoisted(() => ({
+  BASE_MODEL_API_MODULE: new URL('../../../static/js/api/baseModelApi.js', import.meta.url).pathname,
+  STATE_MODULE: new URL('../../../static/js/state/index.js', import.meta.url).pathname,
+  UI_HELPERS_MODULE: new URL('../../../static/js/utils/uiHelpers.js', import.meta.url).pathname,
+  I18N_MODULE: new URL('../../../static/js/utils/i18nHelpers.js', import.meta.url).pathname,
+  STORAGE_MODULE: new URL('../../../static/js/utils/storageHelpers.js', import.meta.url).pathname,
+  API_CONFIG_MODULE: new URL('../../../static/js/api/apiConfig.js', import.meta.url).pathname,
+  API_FACTORY_MODULE: new URL('../../../static/js/api/modelApiFactory.js', import.meta.url).pathname,
+  SIDEBAR_MANAGER_MODULE: new URL('../../../static/js/components/SidebarManager.js', import.meta.url).pathname,
+}));
+
+const showToastMock = vi.fn();
+const showMock = vi.fn();
+const showCancelButtonMock = vi.fn();
+const hideMock = vi.fn();
+const restoreProgressBarMock = vi.fn();
+const setProgressMock = vi.fn();
+const setStatusMock = vi.fn();
+const resetAndReloadMock = vi.fn();
+
+vi.mock(STATE_MODULE, () => ({
+  state: {
+    loadingManager: {
+      show: showMock,
+      showCancelButton: showCancelButtonMock,
+      hide: hideMock,
+      restoreProgressBar: restoreProgressBarMock,
+      setProgress: setProgressMock,
+      setStatus: setStatusMock,
+    },
+  },
+  getCurrentPageState: vi.fn(() => ({})),
+}));
+
+vi.mock(UI_HELPERS_MODULE, () => ({
+  showToast: showToastMock,
+}));
+
+vi.mock(I18N_MODULE, () => ({
+  translate: vi.fn((key, params, fallback) => {
+    if (fallback) {
+      return Object.entries(params || {}).reduce(
+        (text, [name, value]) => text.replaceAll(`{${name}}`, value),
+        fallback
+      );
+    }
+    return key;
+  }),
+}));
+
+vi.mock(STORAGE_MODULE, () => ({
+  getStorageItem: vi.fn(),
+  getSessionItem: vi.fn(),
+  removeSessionItem: vi.fn(),
+  saveMapToStorage: vi.fn(),
+}));
+
+vi.mock(API_CONFIG_MODULE, () => ({
+  getCompleteApiConfig: vi.fn(() => ({
+    endpoints: { scan: '/api/lm/loras/scan' },
+    config: { displayName: 'LoRA', singularName: 'lora' },
+  })),
+  getCurrentModelType: vi.fn(() => 'loras'),
+  isValidModelType: vi.fn(() => true),
+  DOWNLOAD_ENDPOINTS: {},
+  HF_ENDPOINTS: {},
+  WS_ENDPOINTS: { fetchProgress: '/ws/fetch-progress' },
+}));
+
+vi.mock(API_FACTORY_MODULE, () => ({
+  resetAndReload: resetAndReloadMock,
+}));
+
+vi.mock(SIDEBAR_MANAGER_MODULE, () => ({
+  sidebarManager: { refresh: vi.fn() },
+}));
+
+class FakeWebSocket {
+  static instances = [];
+  static failNextConnection = false;
+
+  constructor(url) {
+    this.url = url;
+    this.onopen = null;
+    this.onerror = null;
+    this.onmessage = null;
+    this.close = vi.fn();
+    FakeWebSocket.instances.push(this);
+    const shouldFail = FakeWebSocket.failNextConnection;
+    FakeWebSocket.failNextConnection = false;
+    queueMicrotask(() => {
+      if (shouldFail) {
+        this.onerror?.(new Error('connection refused'));
+      } else {
+        this.onopen?.();
+      }
+    });
+  }
+
+  emit(data) {
+    this.onmessage?.({ data: JSON.stringify(data) });
+  }
+}
+
+async function createClient() {
+  const { BaseModelApiClient } = await import(BASE_MODEL_API_MODULE);
+  class TestClient extends BaseModelApiClient {}
+  return new TestClient('loras');
+}
+
+async function flushMicrotasks() {
+  await new Promise((resolve) => setTimeout(resolve, 0));
+}
+
+describe('BaseModelApiClient.refreshModels scan progress', () => {
+  beforeEach(() => {
+    showToastMock.mockReset();
+    showMock.mockReset();
+    showCancelButtonMock.mockReset();
+    hideMock.mockReset();
+    restoreProgressBarMock.mockReset();
+    setProgressMock.mockReset();
+    setStatusMock.mockReset();
+    resetAndReloadMock.mockReset();
+    FakeWebSocket.instances = [];
+    FakeWebSocket.failNextConnection = false;
+    vi.stubGlobal('WebSocket', FakeWebSocket);
+  });
+
+  afterEach(() => {
+    delete global.fetch;
+    vi.unstubAllGlobals();
+    vi.restoreAllMocks();
+  });
+
+  function mockFetchPending() {
+    let resolveFetch;
+    global.fetch = vi.fn(() => new Promise((resolve) => { resolveFetch = resolve; }));
+    return {
+      resolveOk: (payload = { status: 'success' }) =>
+        resolveFetch({ ok: true, json: async () => payload }),
+    };
+  }
+
+  async function startRefresh(client, fullRebuild = false) {
+    const promise = client.refreshModels(fullRebuild);
+    await vi.waitFor(() => {
+      expect(FakeWebSocket.instances.length).toBe(1);
+    });
+    await flushMicrotasks();
+    const socket = FakeWebSocket.instances[0];
+    await vi.waitFor(() => {
+      expect(socket.onmessage).toBeTruthy();
+    });
+    return { promise, socket };
+  }
+
+  it('shows scan progress updates from the WebSocket channel', async () => {
+    const fetchControl = mockFetchPending();
+    const client = await createClient();
+    const { promise, socket } = await startRefresh(client);
+
+    expect(socket.url).toBe(`ws://${window.location.host}/ws/fetch-progress`);
+
+    socket.emit({
+      type: 'scan_progress',
+      status: 'started',
+      stage: 'scan_folders',
+      model_type: 'lora',
+      pageType: 'loras',
+      full_rebuild: false,
+      progress: 0,
+    });
+    socket.emit({
+      type: 'scan_progress',
+      status: 'processing',
+      stage: 'process_models',
+      model_type: 'lora',
+      pageType: 'loras',
+      full_rebuild: false,
+      progress: 50,
+      processed: 5,
+      total: 10,
+      current_name: 'style.safetensors',
+    });
+
+    expect(setProgressMock).toHaveBeenCalledWith(0);
+    expect(setProgressMock).toHaveBeenCalledWith(50);
+    const lastStatus = setStatusMock.mock.calls.at(-1)[0];
+    expect(lastStatus).toContain('(5/10)');
+    expect(lastStatus).toContain('style.safetensors');
+    // First ETA sample only anchors the timer
+    expect(lastStatus).toContain('Estimating time...');
+
+    fetchControl.resolveOk();
+    await promise;
+
+    expect(resetAndReloadMock).toHaveBeenCalledWith(true);
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.api.refreshComplete',
+      { action: 'Refresh' },
+      'success'
+    );
+    expect(socket.close).toHaveBeenCalled();
+    expect(hideMock).toHaveBeenCalled();
+  });
+
+  it('ignores messages for other types or other model types', async () => {
+    const fetchControl = mockFetchPending();
+    const client = await createClient();
+    const { promise, socket } = await startRefresh(client);
+
+    socket.emit({
+      type: 'scan_progress',
+      status: 'processing',
+      stage: 'process_models',
+      model_type: 'checkpoint',
+      progress: 33,
+      processed: 1,
+      total: 3,
+    });
+    socket.emit({
+      type: 'example_images_progress',
+      status: 'running',
+      model_type: 'lora',
+      progress: 66,
+      processed: 2,
+      total: 3,
+    });
+
+    expect(setProgressMock).not.toHaveBeenCalled();
+    expect(setStatusMock).not.toHaveBeenCalled();
+
+    fetchControl.resolveOk();
+    await promise;
+  });
+
+  it('falls back to plain loading when the WebSocket connection fails', async () => {
+    FakeWebSocket.failNextConnection = true;
+    global.fetch = vi.fn().mockResolvedValue({
+      ok: true,
+      json: async () => ({ status: 'success' }),
+    });
+
+    const client = await createClient();
+    await client.refreshModels(true);
+
+    expect(global.fetch).toHaveBeenCalled();
+    const [url] = global.fetch.mock.calls[0];
+    expect(url.searchParams.get('full_rebuild')).toBe('true');
+    expect(showMock).toHaveBeenCalledWith('Full rebuild LoRAs...', 0);
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.api.refreshComplete',
+      { action: 'Full rebuild' },
+      'success'
+    );
+  });
+
+  it('computes an ETA with EMA smoothing once enough samples arrive', async () => {
+    const fetchControl = mockFetchPending();
+    let now = 1000;
+    vi.spyOn(Date, 'now').mockImplementation(() => now);
+
+    const client = await createClient();
+    const { promise, socket } = await startRefresh(client);
+
+    const emitProcessing = (processed, total) => socket.emit({
+      type: 'scan_progress',
+      status: 'processing',
+      stage: 'process_models',
+      model_type: 'lora',
+      progress: Math.floor((processed / total) * 100),
+      processed,
+      total,
+    });
+
+    // First sample anchors the timer
+    emitProcessing(1, 10);
+    expect(setStatusMock.mock.calls.at(-1)[0]).toContain('Estimating time...');
+
+    // 100s elapsed for 2 files -> 50s per file -> 400s remaining -> ~7 min
+    now = 101000;
+    emitProcessing(2, 10);
+    expect(setStatusMock.mock.calls.at(-1)[0]).toContain('~7 min remaining');
+
+    // 110s elapsed for 4 files -> EMA = 50000*0.7 + 27500*0.3 = 43250ms/file
+    // remaining 6 files -> 259.5s -> ~4 min
+    now = 111000;
+    emitProcessing(4, 10);
+    expect(setStatusMock.mock.calls.at(-1)[0]).toContain('~4 min remaining');
+
+    fetchControl.resolveOk();
+    await promise;
+  });
+
+  it('shows the cancelled toast when the server reports cancellation', async () => {
+    const fetchControl = mockFetchPending();
+    const client = await createClient();
+    const { promise } = await startRefresh(client);
+
+    fetchControl.resolveOk({ status: 'cancelled' });
+    await promise;
+
+    expect(showToastMock).toHaveBeenCalledWith('toast.api.operationCancelled', {}, 'info');
+    expect(resetAndReloadMock).not.toHaveBeenCalled();
+  });
+});
+
+describe('createScanEtaTracker / formatScanRemainingTime', () => {
+  it('estimates remaining time from EMA of per-file cost', async () => {
+    const { createScanEtaTracker } = await import(BASE_MODEL_API_MODULE);
+    let now = 0;
+    vi.spyOn(Date, 'now').mockImplementation(() => now);
+
+    const tracker = createScanEtaTracker();
+    expect(tracker.update(1, 10)).toBe('Estimating time...');
+
+    now = 60000; // 60s for 3 files -> 20s/file -> 7 * 20s = 140s -> ~2 min
+    expect(tracker.update(3, 10)).toBe('~2 min remaining');
+
+    now = 61000; // tiny delta keeps EMA near 20s/file
+    expect(tracker.update(4, 10)).toBe('~2 min remaining');
+
+    // Done: no ETA
+    expect(tracker.update(10, 10)).toBeNull();
+    expect(tracker.update(0, 0)).toBeNull();
+
+    vi.restoreAllMocks();
+  });
+
+  it('formats hours and sub-minute remainders', async () => {
+    const { formatScanRemainingTime } = await import(BASE_MODEL_API_MODULE);
+    expect(formatScanRemainingTime(30000)).toBe('Less than a minute remaining');
+    expect(formatScanRemainingTime(5 * 60000)).toBe('~5 min remaining');
+    expect(formatScanRemainingTime(3600000 + 30 * 60000)).toBe('~1 hr 30 min remaining');
+  });
+});

tests/frontend/api/recipeApi.refresh.test.js

@@ -0,0 +1,285 @@
+import { describe, it, beforeEach, afterEach, expect, vi } from 'vitest';
+
+const showToastMock = vi.hoisted(() => vi.fn());
+const loadingManagerMock = vi.hoisted(() => ({
+  show: vi.fn(),
+  hide: vi.fn(),
+  restoreProgressBar: vi.fn(),
+  setProgress: vi.fn(),
+  setStatus: vi.fn(),
+}));
+const virtualScrollerMock = vi.hoisted(() => ({
+  refreshWithData: vi.fn(),
+}));
+const getCurrentPageStateMock = vi.hoisted(() => vi.fn());
+const etaUpdateMock = vi.hoisted(() => vi.fn(() => 'ETA soon'));
+
+vi.mock('../../../static/js/components/RecipeCard.js', () => ({
+  RecipeCard: vi.fn(() => ({ element: document.createElement('div') })),
+}));
+
+vi.mock('../../../static/js/state/index.js', () => ({
+  state: {
+    loadingManager: loadingManagerMock,
+    virtualScroller: virtualScrollerMock,
+  },
+  getCurrentPageState: getCurrentPageStateMock,
+}));
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+  showToast: showToastMock,
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+  translate: vi.fn((key, params, fallback) => {
+    if (fallback) {
+      return Object.entries(params || {}).reduce(
+        (text, [name, value]) => text.replaceAll(`{${name}}`, value),
+        fallback
+      );
+    }
+    return key;
+  }),
+}));
+
+vi.mock('../../../static/js/utils/infiniteScroll.js', () => ({
+  captureScrollPosition: vi.fn(),
+  restoreScrollPosition: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  WS_ENDPOINTS: { fetchProgress: '/ws/fetch-progress' },
+}));
+
+vi.mock('../../../static/js/utils/scanEtaUtils.js', () => ({
+  createScanEtaTracker: () => ({ update: etaUpdateMock }),
+}));
+
+import { refreshRecipes } from '../../../static/js/api/recipeApi.js';
+
+class FakeWebSocket {
+  static instances = [];
+  static failNextConnection = false;
+
+  constructor(url) {
+    this.url = url;
+    this.onopen = null;
+    this.onerror = null;
+    this.onmessage = null;
+    this.close = vi.fn();
+    FakeWebSocket.instances.push(this);
+    const shouldFail = FakeWebSocket.failNextConnection;
+    FakeWebSocket.failNextConnection = false;
+    queueMicrotask(() => {
+      if (shouldFail) {
+        this.onerror?.(new Error('connection refused'));
+      } else {
+        this.onopen?.();
+      }
+    });
+  }
+
+  emit(data) {
+    this.onmessage?.({ data: JSON.stringify(data) });
+  }
+}
+
+async function flushMicrotasks() {
+  await new Promise((resolve) => setTimeout(resolve, 0));
+}
+
+describe('refreshRecipes scan progress', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+    getCurrentPageStateMock.mockReturnValue({
+      pageSize: 50,
+      currentPage: 1,
+      hasMore: true,
+      isLoading: false,
+      sortBy: 'date:desc',
+      showFavoritesOnly: false,
+      activeFolder: null,
+      searchOptions: { recursive: true },
+      customFilter: { active: false },
+      filters: {},
+    });
+    FakeWebSocket.instances = [];
+    FakeWebSocket.failNextConnection = false;
+    vi.stubGlobal('WebSocket', FakeWebSocket);
+  });
+
+  afterEach(() => {
+    delete global.fetch;
+    vi.unstubAllGlobals();
+  });
+
+  function mockFetchPendingScan() {
+    let resolveScan;
+    global.fetch = vi.fn((input) => {
+      const url = String(input);
+      if (url.includes('/scan')) {
+        return new Promise((resolve) => { resolveScan = resolve; });
+      }
+      // Recipe list reload after the scan completes
+      return Promise.resolve({
+        ok: true,
+        json: async () => ({ items: [], total: 0, total_pages: 0 }),
+      });
+    });
+    return {
+      resolveOk: (payload = { status: 'success' }) =>
+        resolveScan({ ok: true, json: async () => payload }),
+      resolveNotOk: () =>
+        resolveScan({ ok: false, status: 500, statusText: 'Server Error' }),
+    };
+  }
+
+  async function startRefresh(fullRebuild = true) {
+    const promise = refreshRecipes(fullRebuild);
+    await vi.waitFor(() => {
+      expect(FakeWebSocket.instances.length).toBe(1);
+    });
+    await flushMicrotasks();
+    const socket = FakeWebSocket.instances[0];
+    await vi.waitFor(() => {
+      expect(socket.onmessage).toBeTruthy();
+    });
+    return { promise, socket };
+  }
+
+  it('shows scan progress updates from the WebSocket channel', async () => {
+    const fetchControl = mockFetchPendingScan();
+    const { promise, socket } = await startRefresh();
+
+    expect(socket.url).toBe(`ws://${window.location.host}/ws/fetch-progress`);
+
+    socket.emit({
+      type: 'scan_progress',
+      status: 'started',
+      stage: 'scan_folders',
+      model_type: 'recipe',
+      pageType: 'recipes',
+      full_rebuild: true,
+      progress: 0,
+    });
+    socket.emit({
+      type: 'scan_progress',
+      status: 'processing',
+      stage: 'process_models',
+      model_type: 'recipe',
+      pageType: 'recipes',
+      full_rebuild: true,
+      progress: 50,
+      processed: 5,
+      total: 10,
+      current_name: 'style.recipe.json',
+    });
+
+    expect(loadingManagerMock.setProgress).toHaveBeenCalledWith(0);
+    expect(loadingManagerMock.setProgress).toHaveBeenCalledWith(50);
+    const lastStatus = loadingManagerMock.setStatus.mock.calls.at(-1)[0];
+    expect(lastStatus).toContain('(5/10)');
+    expect(lastStatus).toContain('style.recipe.json');
+    expect(lastStatus).toContain('ETA soon');
+    expect(etaUpdateMock).toHaveBeenCalledWith(5, 10);
+
+    fetchControl.resolveOk();
+    await promise;
+
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.api.refreshComplete',
+      { action: 'Full rebuild' },
+      'success'
+    );
+    expect(socket.close).toHaveBeenCalled();
+    expect(loadingManagerMock.hide).toHaveBeenCalled();
+  });
+
+  it('ignores messages for other types or other model types', async () => {
+    const fetchControl = mockFetchPendingScan();
+    const { promise, socket } = await startRefresh();
+
+    socket.emit({
+      type: 'scan_progress',
+      status: 'processing',
+      stage: 'process_models',
+      model_type: 'lora',
+      progress: 33,
+      processed: 1,
+      total: 3,
+    });
+    socket.emit({
+      type: 'example_images_progress',
+      status: 'running',
+      model_type: 'recipe',
+      progress: 66,
+      processed: 2,
+      total: 3,
+    });
+
+    expect(loadingManagerMock.setProgress).not.toHaveBeenCalled();
+    expect(loadingManagerMock.setStatus).not.toHaveBeenCalled();
+
+    fetchControl.resolveOk();
+    await promise;
+  });
+
+  it('falls back to plain loading when the WebSocket connection fails', async () => {
+    FakeWebSocket.failNextConnection = true;
+    global.fetch = vi.fn((input) => {
+      const url = String(input);
+      if (url.includes('/scan')) {
+        return Promise.resolve({
+          ok: true,
+          json: async () => ({ status: 'success' }),
+        });
+      }
+      return Promise.resolve({
+        ok: true,
+        json: async () => ({ items: [], total: 0, total_pages: 0 }),
+      });
+    });
+
+    await refreshRecipes(false);
+
+    expect(global.fetch).toHaveBeenCalled();
+    const [url] = global.fetch.mock.calls[0];
+    expect(url.searchParams.get('full_rebuild')).toBe('false');
+    expect(loadingManagerMock.show).toHaveBeenCalledWith('Refreshing Recipes...', 0);
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.api.refreshComplete',
+      { action: 'Refresh' },
+      'success'
+    );
+  });
+
+  it('shows the cancelled toast when the server reports cancellation', async () => {
+    const fetchControl = mockFetchPendingScan();
+    const { promise } = await startRefresh();
+
+    fetchControl.resolveOk({ status: 'cancelled' });
+    await promise;
+
+    expect(showToastMock).toHaveBeenCalledWith('toast.api.operationCancelled', {}, 'info');
+    expect(showToastMock).not.toHaveBeenCalledWith(
+      'toast.api.refreshComplete',
+      expect.anything(),
+      expect.anything()
+    );
+  });
+
+  it('reports refresh failures through the error toast', async () => {
+    const fetchControl = mockFetchPendingScan();
+    const { promise } = await startRefresh();
+
+    fetchControl.resolveNotOk();
+    await promise;
+
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.api.refreshFailed',
+      { action: 'rebuild', type: 'recipe' },
+      'error'
+    );
+    expect(loadingManagerMock.hide).toHaveBeenCalled();
+  });
+});

tests/frontend/components/autocomplete.activeFilters.test.js

@@ -0,0 +1,378 @@
+import { describe, it, beforeEach, afterEach, expect, vi } from 'vitest';
+
+const {
+  API_MODULE,
+  APP_MODULE,
+  CARET_HELPER_MODULE,
+  PREVIEW_COMPONENT_MODULE,
+  AUTOCOMPLETE_MODULE,
+} = vi.hoisted(() => ({
+  API_MODULE: new URL('../../../scripts/api.js', import.meta.url).pathname,
+  APP_MODULE: new URL('../../../scripts/app.js', import.meta.url).pathname,
+  CARET_HELPER_MODULE: new URL('../../../web/comfyui/textarea_caret_helper.js', import.meta.url).pathname,
+  PREVIEW_COMPONENT_MODULE: new URL('../../../web/comfyui/preview_tooltip.js', import.meta.url).pathname,
+  AUTOCOMPLETE_MODULE: new URL('../../../web/comfyui/autocomplete.js', import.meta.url).pathname,
+}));
+
+const fetchApiMock = vi.fn();
+const settingGetMock = vi.fn();
+const caretHelperInstance = {
+  getBeforeCursor: vi.fn(() => ''),
+  getCursorOffset: vi.fn(() => ({ left: 0, top: 0 })),
+};
+
+vi.mock(API_MODULE, () => ({
+  api: {
+    fetchApi: fetchApiMock,
+  },
+}));
+
+vi.mock(APP_MODULE, () => ({
+  app: {
+    canvas: {
+      ds: { scale: 1 },
+    },
+    extensionManager: {
+      setting: {
+        get: settingGetMock,
+        set: vi.fn(),
+      },
+    },
+    registerExtension: vi.fn(),
+  },
+}));
+
+vi.mock(CARET_HELPER_MODULE, () => ({
+  TextAreaCaretHelper: vi.fn(() => caretHelperInstance),
+}));
+
+vi.mock(PREVIEW_COMPONENT_MODULE, () => ({
+  PreviewTooltip: vi.fn(() => ({ show: vi.fn(), hide: vi.fn(), cleanup: vi.fn() })),
+}));
+
+async function createAutoComplete(modelType, activeFiltersEnabled) {
+  settingGetMock.mockImplementation((key) => {
+    if (key === 'loramanager.lora_active_filters_autocomplete') {
+      return activeFiltersEnabled;
+    }
+    if (key === 'loramanager.autocomplete_append_comma') return false;
+    if (key === 'loramanager.autocomplete_auto_format') return false;
+    if (key === 'loramanager.autocomplete_accept_key') return 'both';
+    return undefined;
+  });
+
+  fetchApiMock.mockResolvedValue({
+    json: () => Promise.resolve({ success: true, relative_paths: [] }),
+  });
+
+  const input = document.createElement('textarea');
+  document.body.append(input);
+
+  const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+  const autoComplete = new AutoComplete(input, modelType, { debounceDelay: 0, showPreview: false });
+
+  input.value = 'example';
+  input.dispatchEvent(new Event('input', { bubbles: true }));
+  await vi.runAllTimersAsync();
+  await Promise.resolve();
+
+  return autoComplete;
+}
+
+describe('AutoComplete active-filters flag', () => {
+  beforeEach(() => {
+    vi.useFakeTimers();
+    document.body.innerHTML = '';
+    document.head.querySelectorAll('style').forEach((styleEl) => styleEl.remove());
+    Element.prototype.scrollIntoView = vi.fn();
+    fetchApiMock.mockReset();
+    settingGetMock.mockReset();
+    caretHelperInstance.getBeforeCursor.mockReset();
+    caretHelperInstance.getCursorOffset.mockReset();
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 0, top: 0 });
+  });
+
+  afterEach(() => {
+    vi.useRealTimers();
+  });
+
+  it('sends use_active_filters for loras when the setting is enabled', async () => {
+    await createAutoComplete('loras', true);
+
+    expect(fetchApiMock).toHaveBeenCalledWith(
+      '/lm/loras/relative-paths?search=example&limit=100&use_active_filters=true'
+    );
+  });
+
+  it('omits the flag when the setting is disabled', async () => {
+    await createAutoComplete('loras', false);
+
+    expect(fetchApiMock).toHaveBeenCalledWith('/lm/loras/relative-paths?search=example&limit=100');
+  });
+
+  it('omits the flag for non-lora model types even when enabled', async () => {
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: [] }),
+    });
+    await createAutoComplete('prompt', true);
+
+    for (const call of fetchApiMock.mock.calls) {
+      expect(call[0]).not.toContain('use_active_filters');
+    }
+  });
+
+  it('does not read filter state from localStorage anymore', async () => {
+    localStorage.setItem('lora_manager_loras_activeFolder', 'SD_XL');
+    localStorage.setItem('lora_manager_loras_filters', JSON.stringify({ baseModel: ['SDXL 1.0'] }));
+
+    await createAutoComplete('loras', true);
+
+    for (const call of fetchApiMock.mock.calls) {
+      expect(call[0]).not.toContain('folder=');
+      expect(call[0]).not.toContain('base_model=');
+    }
+  });
+
+  const typeLorasSlashCommand = async () => {
+    const input = document.createElement('textarea');
+    input.value = '/';
+    input.selectionStart = 1;
+    document.body.append(input);
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('/');
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', { showPreview: false, minChars: 1 });
+
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    return autoComplete;
+  };
+
+  it('shows the active-filters state below the loras slash command list', async () => {
+    await typeLorasSlashCommand();
+
+    const footer = document.querySelector('.lm-autocomplete-command-footer');
+    expect(footer).not.toBeNull();
+    expect(footer.textContent).toContain('Active Filters Search: OFF');
+    expect(footer.textContent).toContain('/activefilters to enable');
+  });
+
+  it('shows how to disable active-filters search in the footer when it is on', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.lora_active_filters_autocomplete') {
+        return true;
+      }
+      return undefined;
+    });
+
+    await typeLorasSlashCommand();
+
+    const footer = document.querySelector('.lm-autocomplete-command-footer');
+    expect(footer).not.toBeNull();
+    expect(footer.textContent).toContain('Active Filters Search: ON');
+    expect(footer.textContent).toContain('/noactivefilters to disable');
+  });
+
+  it('shows a dismissible first-run hint on loras suggestions and remembers dismissal', async () => {
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({
+        success: true,
+        relative_paths: ['models/example.safetensors'],
+      }),
+    });
+
+    const triggerSearch = async () => {
+      const input = document.createElement('textarea');
+      input.value = 'example';
+      input.selectionStart = 7;
+      document.body.append(input);
+
+      caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+      const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+      const autoComplete = new AutoComplete(input, 'loras', {
+        debounceDelay: 0,
+        showPreview: false,
+        minChars: 1,
+      });
+
+      input.dispatchEvent(new Event('input', { bubbles: true }));
+      await vi.runOnlyPendingTimersAsync();
+      await vi.runOnlyPendingTimersAsync();
+      await Promise.resolve();
+      return autoComplete;
+    };
+
+    const autoComplete = await triggerSearch();
+    const hint = autoComplete.dropdown.querySelector('.lm-autocomplete-first-run-hint');
+    expect(hint).not.toBeNull();
+    expect(hint.textContent).toContain('/activefilters');
+
+    hint.querySelector('button').click();
+    expect(autoComplete.dropdown.querySelector('.lm-autocomplete-first-run-hint')).toBeNull();
+    expect(localStorage.getItem('lm:activefilters-tip-dismissed')).toBe('1');
+    // A fresh instance no longer shows the hint once dismissed
+    const autoComplete2 = await triggerSearch();
+    expect(autoComplete2.dropdown.querySelector('.lm-autocomplete-first-run-hint')).toBeNull();
+  });
+
+  it('does not show the loras first-run hint when active-filters search is already on', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.lora_active_filters_autocomplete') {
+        return true;
+      }
+      return undefined;
+    });
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({
+        success: true,
+        relative_paths: ['models/example.safetensors'],
+      }),
+    });
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = 7;
+    document.body.append(input);
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    await vi.runOnlyPendingTimersAsync();
+    await vi.runOnlyPendingTimersAsync();
+    await Promise.resolve();
+
+    expect(autoComplete.dropdown.querySelector('.lm-autocomplete-first-run-hint')).toBeNull();
+  });
+
+  it('broadcasts a setting-toggled window event when /activefilters is accepted', async () => {
+    const events = [];
+    const listener = (event) => events.push(event.detail);
+    window.addEventListener('lora-manager:setting-toggled', listener);
+    try {
+      const input = document.createElement('textarea');
+      input.value = '/activefilters';
+      input.selectionStart = input.value.length;
+      input.focus = vi.fn();
+      input.setSelectionRange = vi.fn();
+      document.body.append(input);
+
+      caretHelperInstance.getBeforeCursor.mockReturnValue('/activefilters');
+
+      const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+const autoComplete = new AutoComplete(input, 'loras', { showPreview: false, minChars: 1 });
+      input.dispatchEvent(new Event('input', { bubbles: true }));
+
+      // The command token is cleared after acceptance; simulate the caret
+      // helper seeing the cleared input so the synthetic input event does
+      // not re-trigger command parsing (same pattern as behavior tests).
+      caretHelperInstance.getBeforeCursor.mockReturnValue('');
+      await Promise.resolve();
+      await Promise.resolve();
+      expect(events).toContainEqual({
+        settingId: 'loramanager.lora_active_filters_autocomplete',
+        value: true,
+      });
+    } finally {
+      window.removeEventListener('lora-manager:setting-toggled', listener);
+    }
+  });
+
+  it('removes a stale first-run hint when the toggle is switched on while the dropdown stays open', async () => {
+    localStorage.removeItem('lm:activefilters-tip-dismissed');
+    let enabled = false;
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.lora_active_filters_autocomplete') return enabled;
+      if (key === 'loramanager.autocomplete_append_comma') return false;
+      if (key === 'loramanager.autocomplete_auto_format') return false;
+      if (key === 'loramanager.autocomplete_accept_key') return 'both';
+      return undefined;
+    });
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, relative_paths: ['models/example.safetensors'] }),
+    });
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = 7;
+    document.body.append(input);
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    const triggerShow = async () => {
+      input.dispatchEvent(new Event('input', { bubbles: true }));
+      await vi.runOnlyPendingTimersAsync();
+      await vi.runOnlyPendingTimersAsync();
+      await Promise.resolve();
+      return autoComplete.dropdown.querySelector('.lm-autocomplete-first-run-hint');
+    };
+
+    // OFF → the enable hint is shown in the suggestions dropdown.
+    expect(await triggerShow()).not.toBeNull();
+
+    // The node's filter chip toggles the setting ON while the dropdown is
+    // still open (ComfyUI can keep focus in the textarea, so no blur/hide
+    // fires). settings.js broadcasts the setting-toggled event.
+    enabled = true;
+    window.dispatchEvent(new CustomEvent('lora-manager:setting-toggled', {
+      detail: { settingId: 'loramanager.lora_active_filters_autocomplete', value: true },
+    }));
+
+    // The stale OFF hint must be gone even though the dropdown never closed.
+    expect(autoComplete.dropdown.querySelector('.lm-autocomplete-first-run-hint')).toBeNull();
+
+    // Further typing while ON must not resurrect the enable hint.
+    expect(await triggerShow()).toBeNull();
+  });
+
+  it('updates the command-list footer when the toggle changes while the command list is open', async () => {
+    let enabled = false;
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.lora_active_filters_autocomplete') return enabled;
+      return undefined;
+    });
+
+    const input = document.createElement('textarea');
+    input.value = '/';
+    input.selectionStart = 1;
+    document.body.append(input);
+    caretHelperInstance.getBeforeCursor.mockReturnValue('/');
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', { showPreview: false, minChars: 1 });
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    await vi.runOnlyPendingTimersAsync();
+    await Promise.resolve();
+
+    const footer = () => autoComplete.dropdown.querySelector('.lm-autocomplete-command-footer');
+    expect(footer()).not.toBeNull();
+    expect(footer().textContent).toContain('Active Filters Search: OFF');
+    expect(footer().textContent).toContain('/activefilters to enable');
+
+    enabled = true;
+    window.dispatchEvent(new CustomEvent('lora-manager:setting-toggled', {
+      detail: { settingId: 'loramanager.lora_active_filters_autocomplete', value: true },
+    }));
+
+    expect(footer()).not.toBeNull();
+    expect(footer().textContent).toContain('Active Filters Search: ON');
+    expect(footer().textContent).toContain('/noactivefilters to disable');
+  });
+
+});

tests/frontend/components/autocomplete.behavior.test.js

@@ -1789,7 +1789,7 @@ describe('AutoComplete widget interactions', () => {
     expect(settingSetMock).toHaveBeenCalledWith('loramanager.lora_active_filters_autocomplete', true);
   });
 
-  it('appends active filter params to loras autocomplete requests when enabled', async () => {
+  it('sends only the use_active_filters flag when enabled (filters resolved server-side)', async () => {
     vi.useFakeTimers();
 
     settingGetMock.mockImplementation((key) => {
@@ -1799,12 +1799,11 @@ describe('AutoComplete widget interactions', () => {
       return undefined;
     });
 
+    // Stored manager-page filters must NOT leak into the request URL; the
+    // backend injects them from its server-side store.
     localStorage.setItem('lora_manager_loras_filters', JSON.stringify({
       baseModel: ['SD 1.5'],
-      tags: { anime: 'include', nsfw: 'exclude', __no_tags__: 'exclude' },
+      tags: { anime: 'include', nsfw: 'exclude' },
-      autoTags: { I2V: 'include' },
-      modelTypes: ['standard'],
-      tagLogic: 'all',
       license: { noCredit: 'include', allowSelling: 'exclude' },
     }));
     localStorage.setItem('lora_manager_loras_activeFolder', 'MyLoras');
@@ -1830,19 +1829,7 @@ describe('AutoComplete widget interactions', () => {
     await Promise.resolve();
 
     const calledUrl = fetchApiMock.mock.calls[0][0];
-    expect(calledUrl).toContain('/lm/loras/relative-paths?search=example&limit=100');
+    expect(calledUrl).toBe('/lm/loras/relative-paths?search=example&limit=100&use_active_filters=true');
-    expect(calledUrl).toContain('folder=MyLoras');
-    expect(calledUrl).toContain('recursive=true');
-    expect(calledUrl).toContain('tag_include=anime');
-    expect(calledUrl).toContain('tag_exclude=nsfw');
-    expect(calledUrl).toContain('tag_exclude=__no_tags__');
-    expect(calledUrl).toContain('auto_tag_include=I2V');
-    expect(calledUrl).toContain('tag_logic=all');
-    expect(calledUrl).toContain('credit_required=false');
-    expect(calledUrl).toContain('allow_selling_generated_content=false');
-    const parsed = new URL(calledUrl, 'https://example.com');
-    expect(parsed.searchParams.get('base_model')).toBe('SD 1.5');
-    expect(parsed.searchParams.get('model_type')).toBe('standard');
   });
 
   it('keeps the default loras autocomplete URL when active-filters mode is off', async () => {
@@ -1870,10 +1857,12 @@ describe('AutoComplete widget interactions', () => {
     expect(fetchApiMock).toHaveBeenCalledWith('/lm/loras/relative-paths?search=example&limit=100');
   });
 
-  it('sends the filter-pipeline signal even when no filters are stored', async () => {
+  it('sends the filter-pipeline flag even when no filters are stored', async () => {
     // Regression: with filter mode on but no folder/filters stored, the request
-    // carried no params, so the backend skipped the filter pipeline and global
+    // carried no signal, so the backend skipped the filter pipeline and global
-    // settings like show_only_sfw diverged from the list endpoint.
+    // settings like show_only_sfw diverged from the list endpoint. The flag
+    // makes the backend run the pipeline (injecting nothing when its store
+    // is empty).
     vi.useFakeTimers();
 
     settingGetMock.mockImplementation((key) => {
@@ -1907,10 +1896,13 @@ describe('AutoComplete widget interactions', () => {
     await Promise.resolve();
 
     const calledUrl = fetchApiMock.mock.calls[0][0];
-    expect(calledUrl).toContain('recursive=true');
+    expect(calledUrl).toContain('use_active_filters=true');
   });
 
-  it('omits folder param when active folder is root and recursion is enabled', async () => {
+  it('leaves folder params to the backend when active folder is root with recursion enabled', async () => {
+    // The root-folder/recursion semantics now live server-side (see
+    // active_filters_store.active_filters_to_query_kwargs); the client only
+    // sends the flag.
     vi.useFakeTimers();
 
     settingGetMock.mockImplementation((key) => {
@@ -1948,10 +1940,12 @@ describe('AutoComplete widget interactions', () => {
 
     const calledUrl = fetchApiMock.mock.calls[0][0];
     expect(calledUrl).not.toContain('folder=');
-    expect(calledUrl).toContain('recursive=true');
+    expect(calledUrl).toContain('use_active_filters=true');
   });
 
-  it('sends an empty folder param for root with recursion disabled, mirroring the page list', async () => {
+  it('leaves the root+non-recursive folder mapping to the backend', async () => {
+    // Root with recursion disabled maps to folder='' server-side (mirroring
+    // the page list); the client no longer encodes this in the URL.
     vi.useFakeTimers();
 
     settingGetMock.mockImplementation((key) => {
@@ -1988,15 +1982,14 @@ describe('AutoComplete widget interactions', () => {
     await Promise.resolve();
 
     const calledUrl = fetchApiMock.mock.calls[0][0];
-    expect(calledUrl).toContain('folder=');
+    expect(calledUrl).not.toContain('folder=');
-    expect(calledUrl).toContain('recursive=false');
+    expect(calledUrl).toContain('use_active_filters=true');
-    const parsed = new URL(calledUrl, 'https://example.com');
-    expect(parsed.searchParams.get('folder')).toBe('');
   });
 
-  it('applies the active folder even when no filter-panel filters are set', async () => {
+  it('sends the flag even when only a folder is stored (no filter-panel filters)', async () => {
     // Regression: folder was skipped when lora_manager_loras_filters was
-    // missing because the filters key gate returned early.
+    // missing because the filters key gate returned early. The flag is now
+    // unconditional, and the backend injects the folder from its store.
     vi.useFakeTimers();
 
     settingGetMock.mockImplementation((key) => {
@@ -2029,8 +2022,8 @@ describe('AutoComplete widget interactions', () => {
     await Promise.resolve();
 
     const calledUrl = fetchApiMock.mock.calls[0][0];
-    expect(calledUrl).toContain('folder=Flux.1+D%2Fstyle');
+    expect(calledUrl).toContain('use_active_filters=true');
-    expect(calledUrl).toContain('recursive=true');
+    expect(calledUrl).not.toContain('folder=');
   });
 
   describe('discoverability hints', () => {

tests/frontend/components/autocomplete.export-strip.test.js

@@ -0,0 +1,234 @@
+import { describe, it, expect, vi } from 'vitest';
+
+const {
+    API_MODULE,
+    APP_MODULE,
+    CARET_HELPER_MODULE,
+    PREVIEW_COMPONENT_MODULE,
+    AUTOCOMPLETE_MODULE,
+} = vi.hoisted(() => ({
+    API_MODULE: new URL('../../../scripts/api.js', import.meta.url).pathname,
+    APP_MODULE: new URL('../../../scripts/app.js', import.meta.url).pathname,
+    CARET_HELPER_MODULE: new URL('../../../web/comfyui/textarea_caret_helper.js', import.meta.url).pathname,
+    PREVIEW_COMPONENT_MODULE: new URL('../../../web/comfyui/preview_tooltip.js', import.meta.url).pathname,
+    AUTOCOMPLETE_MODULE: new URL('../../../web/comfyui/autocomplete.js', import.meta.url).pathname,
+}));
+
+vi.mock(API_MODULE, () => ({
+    api: { fetchApi: vi.fn() },
+}));
+
+vi.mock(APP_MODULE, () => ({
+    app: {
+        canvas: { ds: { scale: 1 } },
+        extensionManager: {
+            setting: { get: vi.fn(), set: vi.fn() },
+        },
+        registerExtension: vi.fn(),
+    },
+}));
+
+vi.mock(CARET_HELPER_MODULE, () => ({
+    TextAreaCaretHelper: vi.fn(() => ({
+        getBeforeCursor: vi.fn(() => ''),
+        getCursorOffset: vi.fn(() => ({ left: 0, top: 0 })),
+    })),
+}));
+
+vi.mock(PREVIEW_COMPONENT_MODULE, () => ({
+    PreviewTooltip: vi.fn(() => ({ show: vi.fn(), hide: vi.fn(), cleanup: vi.fn() })),
+}));
+
+const METADATA_NAME = '__lm_autocomplete_meta_text';
+
+function makeMetadataValue() {
+    return {
+        version: 1,
+        textWidgetName: 'text',
+        lastAccepted: {
+            start: 0,
+            end: 6,
+            insertedText: '1girl ',
+            textSnapshot: 'old prompt text, 1girl ',
+        },
+    };
+}
+
+describe('stripAutocompleteLastAccepted', () => {
+    let stripAutocompleteLastAccepted;
+
+    beforeAll(async () => {
+        const module = await import(AUTOCOMPLETE_MODULE);
+        stripAutocompleteLastAccepted = module.stripAutocompleteLastAccepted;
+    });
+
+    it('removes lastAccepted while keeping the metadata base fields', () => {
+        const value = makeMetadataValue();
+        const stripped = stripAutocompleteLastAccepted(value);
+
+        expect(stripped).toEqual({ version: 1, textWidgetName: 'text' });
+        expect('lastAccepted' in stripped).toBe(false);
+        // Original value must not be mutated
+        expect(value.lastAccepted).toBeDefined();
+    });
+
+    it('returns values without lastAccepted as-is (same reference)', () => {
+        const value = { version: 1, textWidgetName: 'text' };
+        expect(stripAutocompleteLastAccepted(value)).toBe(value);
+    });
+
+    it('returns non-object values as-is', () => {
+        expect(stripAutocompleteLastAccepted(null)).toBe(null);
+        expect(stripAutocompleteLastAccepted(undefined)).toBe(undefined);
+        expect(stripAutocompleteLastAccepted('text')).toBe('text');
+        expect(stripAutocompleteLastAccepted([1, 2])).toEqual([1, 2]);
+    });
+});
+
+describe('stripAutocompleteMetadataFromPromptResult', () => {
+    let stripResult;
+
+    beforeAll(async () => {
+        const module = await import(AUTOCOMPLETE_MODULE);
+        stripResult = module.stripAutocompleteMetadataFromPromptResult;
+    });
+
+    function makeWorkflowNode() {
+        const metadataValue = makeMetadataValue();
+        return {
+            properties: { __lm_widget_ids: ['text', METADATA_NAME] },
+            widgets_values: ['current text', metadataValue],
+            widgets_values_named: {
+                text: 'current text',
+                [METADATA_NAME]: metadataValue,
+            },
+        };
+    }
+
+    it('strips lastAccepted from workflow widgets_values using __lm_widget_ids alignment', () => {
+        const result = {
+            workflow: { nodes: [makeWorkflowNode()] },
+            output: {},
+        };
+
+        const returned = stripResult(result);
+
+        expect(returned).toBe(result);
+        expect(result.workflow.nodes[0].widgets_values[1])
+            .toEqual({ version: 1, textWidgetName: 'text' });
+    });
+
+    it('strips lastAccepted from widgets_values_named and leaves other widgets untouched', () => {
+        const result = {
+            workflow: { nodes: [makeWorkflowNode()] },
+            output: {},
+        };
+
+        stripResult(result);
+
+        const node = result.workflow.nodes[0];
+        expect(node.widgets_values_named[METADATA_NAME])
+            .toEqual({ version: 1, textWidgetName: 'text' });
+        expect(node.widgets_values_named.text).toBe('current text');
+        expect(node.widgets_values[0]).toBe('current text');
+    });
+
+    it('handles null entries in widgets_values (bypass compatibility padding)', () => {
+        const node = makeWorkflowNode();
+        node.properties.__lm_widget_ids = ['text', 'seed', METADATA_NAME];
+        node.widgets_values = ['current text', null, makeMetadataValue()];
+        const result = { workflow: { nodes: [node] }, output: {} };
+
+        stripResult(result);
+
+        expect(result.workflow.nodes[0].widgets_values[1]).toBe(null);
+        expect(result.workflow.nodes[0].widgets_values[2])
+            .toEqual({ version: 1, textWidgetName: 'text' });
+    });
+
+    it('still strips widgets_values_named when __lm_widget_ids is missing (legacy files)', () => {
+        const node = makeWorkflowNode();
+        delete node.properties;
+        const arrayValue = node.widgets_values[1];
+        const result = { workflow: { nodes: [node] }, output: {} };
+
+        stripResult(result);
+
+        // Array entries cannot be located without widget ids — left untouched
+        expect(result.workflow.nodes[0].widgets_values[1]).toBe(arrayValue);
+        expect(result.workflow.nodes[0].widgets_values_named[METADATA_NAME])
+            .toEqual({ version: 1, textWidgetName: 'text' });
+    });
+
+    it('strips lastAccepted from output (API prompt) inputs', () => {
+        const result = {
+            workflow: { nodes: [] },
+            output: {
+                '7': {
+                    class_type: 'Prompt (LoraManager)',
+                    inputs: {
+                        text: 'current text',
+                        [METADATA_NAME]: makeMetadataValue(),
+                    },
+                },
+            },
+        };
+
+        stripResult(result);
+
+        const inputs = result.output['7'].inputs;
+        expect(inputs[METADATA_NAME]).toEqual({ version: 1, textWidgetName: 'text' });
+        expect(inputs.text).toBe('current text');
+    });
+
+    it('strips lastAccepted inside subgraph definitions', () => {
+        const result = {
+            workflow: {
+                nodes: [],
+                definitions: {
+                    subgraphs: [{ nodes: [makeWorkflowNode()] }],
+                },
+            },
+            output: {},
+        };
+
+        stripResult(result);
+
+        const subgraphNode = result.workflow.definitions.subgraphs[0].nodes[0];
+        expect(subgraphNode.widgets_values_named[METADATA_NAME])
+            .toEqual({ version: 1, textWidgetName: 'text' });
+    });
+
+    it('leaves results without lastAccepted unchanged', () => {
+        const metadataValue = { version: 1, textWidgetName: 'text' };
+        const result = {
+            workflow: {
+                nodes: [{
+                    properties: { __lm_widget_ids: ['text', METADATA_NAME] },
+                    widgets_values: ['abc', metadataValue],
+                    widgets_values_named: { text: 'abc', [METADATA_NAME]: metadataValue },
+                }],
+            },
+            output: {
+                '1': { inputs: { text: 'abc', [METADATA_NAME]: metadataValue } },
+            },
+        };
+
+        stripResult(result);
+
+        expect(result.workflow.nodes[0].widgets_values[1]).toBe(metadataValue);
+        expect(result.output['1'].inputs[METADATA_NAME]).toBe(metadataValue);
+    });
+
+    it('tolerates malformed results', () => {
+        expect(stripResult(null)).toBe(null);
+        expect(stripResult(undefined)).toBe(undefined);
+        expect(stripResult({})).toEqual({});
+
+        const result = {
+            workflow: { nodes: [null, { widgets_values: null }] },
+            output: { '1': { inputs: null }, '2': {} },
+        };
+        expect(() => stripResult(result)).not.toThrow();
+    });
+});

tests/frontend/components/loraLoader.activeFiltersMenu.test.js

@@ -0,0 +1,141 @@
+import { describe, it, expect, beforeEach, vi } from "vitest";
+
+const {
+  APP_MODULE,
+  API_MODULE,
+  UTILS_MODULE,
+  SETTINGS_MODULE,
+  LORA_LOADER_MODULE,
+} = vi.hoisted(() => ({
+  APP_MODULE: new URL("../../../scripts/app.js", import.meta.url).pathname,
+  API_MODULE: new URL("../../../scripts/api.js", import.meta.url).pathname,
+  UTILS_MODULE: new URL("../../../web/comfyui/utils.js", import.meta.url).pathname,
+  SETTINGS_MODULE: new URL("../../../web/comfyui/settings.js", import.meta.url).pathname,
+  LORA_LOADER_MODULE: new URL("../../../web/comfyui/lora_loader.js", import.meta.url).pathname,
+}));
+
+const extensionState = { current: null };
+const registerExtensionMock = vi.fn((extension) => {
+  extensionState.current = extension;
+});
+
+vi.mock(APP_MODULE, () => ({
+  app: {
+    registerExtension: registerExtensionMock,
+    graph: {},
+  },
+}));
+
+vi.mock(API_MODULE, () => ({
+  api: {
+    addEventListener: vi.fn(),
+  },
+}));
+
+const showToastMock = vi.fn();
+
+vi.mock(UTILS_MODULE, () => ({
+  collectActiveLorasFromChain: vi.fn(),
+  updateConnectedTriggerWords: vi.fn(),
+  mergeLoras: vi.fn(),
+  chainCallback: (proto, property, callback) => {
+    proto[property] = callback;
+  },
+  getAllGraphNodes: vi.fn(),
+  getNodeFromGraph: vi.fn(),
+  getWidgetByName: vi.fn(),
+  getWidgetSerializedValue: vi.fn(),
+  showToast: showToastMock,
+}));
+
+const getActiveFiltersPreferenceMock = vi.fn();
+const setSettingValueMock = vi.fn();
+
+vi.mock(SETTINGS_MODULE, () => ({
+  LORA_ACTIVE_FILTERS_AUTOCOMPLETE_SETTING_ID:
+    "loramanager.lora_active_filters_autocomplete",
+  SETTING_TOGGLED_EVENT_NAME: "lora-manager:setting-toggled",
+  getLoraActiveFiltersAutocompletePreference: getActiveFiltersPreferenceMock,
+  setLoraManagerSettingValue: setSettingValueMock,
+}));
+
+async function registerNodeType(comfyClass) {
+  await import(LORA_LOADER_MODULE);
+  const extension = extensionState.current;
+  expect(extension).toBeDefined();
+  const nodeType = { comfyClass, prototype: {} };
+  await extension.beforeRegisterNodeDef(nodeType, {}, {});
+  return nodeType;
+}
+
+function getMenuOption(nodeType, enabled) {
+  getActiveFiltersPreferenceMock.mockReturnValue(enabled);
+  const options = [];
+  nodeType.prototype.getExtraMenuOptions(null, options);
+  return options.find(
+    (option) =>
+      option &&
+      typeof option.content === "string" &&
+      option.content.startsWith("Active Filters Search:")
+  );
+}
+
+describe("Lora Loader active-filters context menu", () => {
+  beforeEach(() => {
+    vi.resetModules();
+    extensionState.current = null;
+    registerExtensionMock.mockClear();
+    showToastMock.mockClear();
+    getActiveFiltersPreferenceMock.mockReset();
+    setSettingValueMock.mockReset();
+    setSettingValueMock.mockResolvedValue(true);
+  });
+
+  it.each([
+    "Lora Loader (LoraManager)",
+    "Lora Stacker (LoraManager)",
+    "WanVideo Lora Select (LoraManager)",
+    "Create Hook LoRA (LoraManager)",
+  ])("adds the toggle entry to the %s context menu", async (comfyClass) => {
+    const nodeType = await registerNodeType(comfyClass);
+
+    const option = getMenuOption(nodeType, false);
+    expect(option).toBeDefined();
+    expect(option.content).toContain("Active Filters Search: OFF");
+    expect(option.content).toContain("/activefilters to enable");
+  });
+
+  it("shows the disable hint when active-filters search is on", async () => {
+    const nodeType = await registerNodeType("Lora Loader (LoraManager)");
+
+    const option = getMenuOption(nodeType, true);
+    expect(option.content).toContain("Active Filters Search: ON");
+    expect(option.content).toContain("/noactivefilters to disable");
+  });
+
+  it("toggles the setting and toasts feedback", async () => {
+    const nodeType = await registerNodeType("Lora Loader (LoraManager)");
+
+    const enableOption = getMenuOption(nodeType, false);
+    await enableOption.callback();
+
+    expect(setSettingValueMock).toHaveBeenCalledWith(
+      "loramanager.lora_active_filters_autocomplete",
+      true
+    );
+    expect(showToastMock).toHaveBeenCalledWith(
+      expect.objectContaining({ summary: "Active Filters Search Enabled" })
+    );
+
+    const disableOption = getMenuOption(nodeType, true);
+    await disableOption.callback();
+
+    expect(setSettingValueMock).toHaveBeenCalledWith(
+      "loramanager.lora_active_filters_autocomplete",
+      false
+    );
+    expect(showToastMock).toHaveBeenCalledWith(
+      expect.objectContaining({ summary: "Active Filters Search Disabled" })
+    );
+  });
+});

tests/frontend/components/pageControls.filtering.test.js

@@ -43,6 +43,12 @@ vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
   showToast: showToastMock,
   openCivitaiByMetadata: openCivitaiByMetadataMock,
   updatePanelPositions: updatePanelPositionsMock,
+  // Faithful stand-in for the real helper in uiHelpers.js
+  isTypingContext: (target) => {
+    if (!(target instanceof Element)) return false;
+    const tagName = target.tagName?.toLowerCase();
+    return target.isContentEditable || tagName === 'input' || tagName === 'textarea' || tagName === 'select';
+  },
 }));
 
 vi.mock('../../../static/js/managers/DownloadManager.js', () => ({
@@ -350,6 +356,49 @@ describe('FilterManager tag and base model filters', () => {
     expect(baseModelChip.classList.contains('active')).toBe(false);
   });
 
+  it('filters recipes by the unknown base model bucket via its marker value', async () => {
+    global.fetch = vi.fn().mockResolvedValue({
+      ok: true,
+      json: async () => ({
+        success: true,
+        base_models: [
+          { name: 'Unknown', value: '__unknown__', count: 3 },
+          { name: 'SDXL', count: 2 },
+        ],
+      }),
+    });
+
+    renderControlsDom('recipes');
+    const stateModule = await import('../../../static/js/state/index.js');
+    stateModule.initPageState('recipes');
+    const { getCurrentPageState } = stateModule;
+    const { FilterManager } = await import('../../../static/js/managers/FilterManager.js');
+
+    const loadRecipesMock = vi.fn().mockResolvedValue(undefined);
+    window.recipeManager = { loadRecipes: loadRecipesMock };
+
+    new FilterManager({ page: 'recipes' });
+
+    await vi.waitFor(() => {
+      const chip = document.querySelector('[data-base-model="__unknown__"]');
+      expect(chip).not.toBeNull();
+    });
+
+    const unknownChip = document.querySelector('[data-base-model="__unknown__"]');
+    // Display label is "Unknown" even though the filter value is the marker
+    expect(unknownChip.textContent).toContain('Unknown');
+
+    unknownChip.dispatchEvent(new Event('click', { bubbles: true }));
+    await vi.waitFor(() => expect(loadRecipesMock).toHaveBeenCalledTimes(1));
+
+    expect(getCurrentPageState().filters.baseModel).toEqual(['__unknown__']);
+    expect(unknownChip.classList.contains('active')).toBe(true);
+
+    const storageKey = 'lora_manager_recipes_filters';
+    const storedFilters = JSON.parse(localStorage.getItem(storageKey));
+    expect(storedFilters.baseModel).toEqual(['__unknown__']);
+  });
+
   it('filters base model chips locally without changing selected state', async () => {
     global.fetch = vi.fn().mockResolvedValue({
       ok: true,
@@ -1158,3 +1207,86 @@ describe('PageControls favorites, sorting, and duplicates scenarios', () => {
     });
   });
 });
+
+describe('PageControls action keyboard shortcuts', () => {
+  async function setupLorasControls() {
+    renderControlsDom('loras');
+    const stateModule = await import('../../../static/js/state/index.js');
+    stateModule.initPageState('loras');
+    const { LorasControls } = await import('../../../static/js/components/controls/LorasControls.js');
+    return new LorasControls();
+  }
+
+  function keydownEvent(key, { target = document.body, ...init } = {}) {
+    const event = new KeyboardEvent('keydown', { key, bubbles: true, cancelable: true, ...init });
+    Object.defineProperty(event, 'target', { value: target });
+    return event;
+  }
+
+  it('registers a pageControls-actions keydown handler with the event manager', async () => {
+    await setupLorasControls();
+
+    const { eventManager } = await import('../../../static/js/utils/EventManager.js');
+    const keydownHandlers = eventManager.handlers.get('keydown') || [];
+    expect(keydownHandlers.some((h) => h.source === 'pageControls-actions')).toBe(true);
+  });
+
+  it('triggers refresh, fetch, and download via the R / F / D keys', async () => {
+    const controls = await setupLorasControls();
+
+    expect(controls.handleActionShortcut(keydownEvent('r'))).toBe(true);
+    expect(refreshModelsMock).toHaveBeenCalledWith(false);
+
+    expect(controls.handleActionShortcut(keydownEvent('f'))).toBe(true);
+    expect(fetchCivitaiMetadataMock).toHaveBeenCalledTimes(1);
+
+    expect(controls.handleActionShortcut(keydownEvent('d'))).toBe(true);
+    expect(downloadManagerMock.showDownloadModal).toHaveBeenCalledTimes(1);
+  });
+
+  it('handles a real keydown dispatched on the document', async () => {
+    await setupLorasControls();
+
+    const event = new KeyboardEvent('keydown', { key: 'r', bubbles: true, cancelable: true });
+    document.dispatchEvent(event);
+
+    expect(event.defaultPrevented).toBe(true);
+    expect(refreshModelsMock).toHaveBeenCalledWith(false);
+  });
+
+  it('ignores R / F / D while typing in an input', async () => {
+    const controls = await setupLorasControls();
+
+    const input = document.getElementById('searchInput');
+    for (const key of ['r', 'f', 'd']) {
+      const event = keydownEvent(key, { target: input });
+      expect(controls.handleActionShortcut(event)).toBe(false);
+      expect(event.defaultPrevented).toBe(false);
+    }
+
+    expect(refreshModelsMock).not.toHaveBeenCalled();
+    expect(fetchCivitaiMetadataMock).not.toHaveBeenCalled();
+    expect(downloadManagerMock.showDownloadModal).not.toHaveBeenCalled();
+  });
+
+  it('ignores R / F / D when combined with modifier keys', async () => {
+    const controls = await setupLorasControls();
+
+    const event = keydownEvent('r', { ctrlKey: true });
+    expect(controls.handleActionShortcut(event)).toBe(false);
+    expect(event.defaultPrevented).toBe(false);
+    expect(refreshModelsMock).not.toHaveBeenCalled();
+  });
+
+  it('passes the event through when the action button does not exist', async () => {
+    const controls = await setupLorasControls();
+
+    // Recipes page has no fetch/download buttons
+    document.querySelector('[data-action="fetch"]').closest('.control-group').remove();
+
+    const event = keydownEvent('f');
+    expect(controls.handleActionShortcut(event)).toBe(false);
+    expect(event.defaultPrevented).toBe(false);
+    expect(fetchCivitaiMetadataMock).not.toHaveBeenCalled();
+  });
+});

tests/frontend/components/pageControls.randomSort.test.js

@@ -13,6 +13,12 @@ vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
   showToast: vi.fn(),
   openCivitaiByMetadata: vi.fn(),
   updatePanelPositions: vi.fn(),
+  // Faithful stand-in for the real helper in uiHelpers.js
+  isTypingContext: (target) => {
+    if (!(target instanceof Element)) return false;
+    const tagName = target.tagName?.toLowerCase();
+    return target.isContentEditable || tagName === 'input' || tagName === 'textarea' || tagName === 'select';
+  },
 }));
 
 vi.mock('../../../static/js/managers/DownloadManager.js', () => ({

tests/frontend/components/recipeModal.metaFooter.test.js

@@ -0,0 +1,270 @@
+import { describe, it, beforeEach, afterEach, expect, vi } from 'vitest';
+
+const showToastMock = vi.fn();
+const copyToClipboardMock = vi.fn();
+const translateMock = vi.fn((key, params, fallback) => (typeof fallback === 'string' ? fallback : key));
+
+const loadingManagerStub = {
+  showSimpleLoading: vi.fn(),
+  hide: vi.fn(),
+  show: vi.fn(),
+  restoreProgressBar: vi.fn(),
+};
+
+const recipeItem = {
+  id: 'a1b2c3d4-e5f6-7890-abcd-ef1234567890',
+  file_path: '/recipes/a1b2c3d4-e5f6-7890-abcd-ef1234567890.png',
+  title: 'Demo Recipe',
+  tags: [],
+  loras: [],
+};
+
+const virtualScrollerStub = {
+  updateSingleItem: vi.fn(),
+  getNavigationState: vi.fn(() => ({
+    index: 0,
+    hasPrev: false,
+    hasNext: false,
+    loadedItems: 1,
+    totalItems: 1,
+  })),
+  getAdjacentItemByFilePath: vi.fn(async () => null),
+};
+
+const stateStub = {
+  global: { settings: {}, loadingManager: loadingManagerStub },
+  loadingManager: loadingManagerStub,
+  virtualScroller: virtualScrollerStub,
+};
+
+const modalManagerMock = {
+  showModal: vi.fn(),
+  closeModal: vi.fn(),
+};
+
+const fetchRecipeDetailsMock = vi.fn(async () => ({}));
+const updateRecipeMetadataMock = vi.fn(() => Promise.resolve({ success: true }));
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+  showToast: showToastMock,
+  copyToClipboard: copyToClipboardMock,
+  sendLoraToWorkflow: vi.fn(),
+  sendModelPathToWorkflow: vi.fn(),
+  openCivitaiByMetadata: vi.fn(),
+  stripLoraTags: vi.fn((text) => text),
+  sendPromptToWorkflow: vi.fn(),
+  sendGenParamsToWorkflow: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+  translate: translateMock,
+}));
+
+vi.mock('../../../static/js/state/index.js', () => ({
+  state: stateStub,
+}));
+
+vi.mock('../../../static/js/utils/storageHelpers.js', () => ({
+  setSessionItem: vi.fn(),
+  removeSessionItem: vi.fn(),
+  getStorageItem: vi.fn(() => null),
+  setStorageItem: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/recipeApi.js', () => ({
+  fetchRecipeDetails: fetchRecipeDetailsMock,
+  updateRecipeMetadata: updateRecipeMetadataMock,
+  sendRecipeWorkflow: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  MODEL_TYPES: {
+    LORA: 'loras',
+    CHECKPOINT: 'checkpoints',
+    EMBEDDING: 'embeddings',
+  },
+}));
+
+function recipeModalFixture() {
+  return `
+    <div id="recipeModal" class="modal">
+      <div class="modal-content">
+        <header class="recipe-modal-header">
+          <div class="recipe-modal-header-row">
+            <h2 id="recipeModalTitle">Recipe Details</h2>
+            <div class="modal-nav-controls">
+              <button class="modal-nav-btn" id="recipeNavPrevBtn" disabled></button>
+              <button class="modal-nav-btn" id="recipeNavNextBtn" disabled></button>
+            </div>
+          </div>
+          <div class="recipe-header-actions" id="recipeHeaderActions">
+            <button class="modal-send-btn" id="sendRecipeBtn"><i class="fas fa-paper-plane"></i></button>
+            <button class="modal-copy-btn" id="copyRecipeSyntaxBtn"><i class="fas fa-copy"></i></button>
+          </div>
+          <div id="recipeTagsContainer"></div>
+        </header>
+        <div class="modal-body">
+          <div class="recipe-media-column">
+            <div class="recipe-preview-container" id="recipePreviewContainer">
+              <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
+            </div>
+          </div>
+          <div class="info-section recipe-gen-params">
+            <div class="gen-params-container">
+              <div class="param-group info-item">
+                <div class="param-content" id="recipePrompt"></div>
+                <div class="param-editor" id="recipePromptEditor">
+                  <textarea class="param-textarea" id="recipePromptInput"></textarea>
+                </div>
+              </div>
+              <div class="param-group info-item">
+                <div class="param-content" id="recipeNegativePrompt"></div>
+                <div class="param-editor" id="recipeNegativePromptEditor">
+                  <textarea class="param-textarea" id="recipeNegativePromptInput"></textarea>
+                </div>
+              </div>
+              <div class="other-params" id="recipeOtherParams"></div>
+            </div>
+          </div>
+          <div class="info-section recipe-bottom-section">
+            <div class="recipe-section-actions">
+              <span id="recipeLorasCount"></span>
+              <button class="action-btn view-loras-btn" id="viewRecipeLorasBtn"></button>
+            </div>
+            <div class="recipe-loras-list" id="recipeLorasList"></div>
+          </div>
+        </div>
+        <footer class="recipe-meta-footer" id="recipeMetaFooter" hidden></footer>
+      </div>
+    </div>
+  `;
+}
+
+async function flushAsyncTasks() {
+  await Promise.resolve();
+  await new Promise((resolve) => setTimeout(resolve, 0));
+}
+
+const createdModals = [];
+
+async function createRecipeModal() {
+  const { RecipeModal } = await import('../../../static/js/components/RecipeModal.js');
+  const recipeModal = new RecipeModal();
+  createdModals.push(recipeModal);
+  return recipeModal;
+}
+
+function openLocationFetchCalls() {
+  return global.fetch.mock.calls.filter(([url]) => url === '/api/lm/open-file-location');
+}
+
+describe('RecipeModal meta footer', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+    document.body.innerHTML = recipeModalFixture();
+    global.modalManager = modalManagerMock;
+    global.fetch = vi.fn(async () => ({
+      ok: true,
+      json: async () => ({}),
+    }));
+  });
+
+  afterEach(() => {
+    createdModals.forEach(recipeModal => recipeModal.cleanupNavigationShortcuts());
+    createdModals.length = 0;
+    document.body.innerHTML = '';
+    delete global.modalManager;
+    delete global.fetch;
+  });
+
+  it('shows the folder path and a middle-truncated recipe ID', async () => {
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails(recipeItem);
+
+    const footer = document.getElementById('recipeMetaFooter');
+    expect(footer.hidden).toBe(false);
+
+    const location = footer.querySelector('.recipe-meta-location');
+    expect(location.querySelector('.recipe-meta-location-path').textContent).toBe('/recipes/');
+    expect(location.dataset.filepath).toBe(recipeItem.file_path);
+
+    const idValue = footer.querySelector('.recipe-meta-id-value');
+    expect(idValue.textContent).toBe('a1b2c3d4…7890');
+    expect(idValue.getAttribute('title')).toBe(recipeItem.id);
+  });
+
+  it('copies the full recipe ID when the copy button is clicked', async () => {
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails(recipeItem);
+
+    document.querySelector('.recipe-meta-copy-btn').click();
+
+    expect(copyToClipboardMock).toHaveBeenCalledWith(recipeItem.id);
+  });
+
+  it('opens the recipe file location when the path is clicked', async () => {
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails(recipeItem);
+
+    document.querySelector('.recipe-meta-location').click();
+    await flushAsyncTasks();
+
+    const calls = openLocationFetchCalls();
+    expect(calls).toHaveLength(1);
+    expect(JSON.parse(calls[0][1].body)).toEqual({ file_path: recipeItem.file_path });
+    expect(showToastMock).toHaveBeenCalledWith('recipes.modal.openFileLocation.success', {}, 'success');
+  });
+
+  it('opens the recipe JSON path once hydration provides it', async () => {
+    const jsonPath = '/recipes/a1b2c3d4-e5f6-7890-abcd-ef1234567890.recipe.json';
+    fetchRecipeDetailsMock.mockResolvedValueOnce({
+      id: recipeItem.id,
+      file_path: recipeItem.file_path,
+      recipe_json_path: jsonPath,
+    });
+
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails(recipeItem);
+    await flushAsyncTasks();
+
+    const location = document.querySelector('.recipe-meta-location');
+    expect(location.dataset.filepath).toBe(jsonPath);
+  });
+
+  it('copies the path to clipboard when the backend reports clipboard mode', async () => {
+    const writeTextMock = vi.fn(async () => {});
+    Object.defineProperty(window.navigator, 'clipboard', {
+      value: { writeText: writeTextMock },
+      configurable: true,
+    });
+
+    global.fetch = vi.fn(async (url) => ({
+      ok: true,
+      json: async () => (url === '/api/lm/open-file-location'
+        ? { mode: 'clipboard', path: '/recipes' }
+        : {}),
+    }));
+
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails(recipeItem);
+
+    document.querySelector('.recipe-meta-location').click();
+    await flushAsyncTasks();
+
+    expect(writeTextMock).toHaveBeenCalledWith('/recipes');
+    expect(showToastMock).toHaveBeenCalledWith(
+      'recipes.modal.openFileLocation.copied',
+      { path: '/recipes' },
+      'success',
+    );
+  });
+
+  it('hides the footer when neither ID nor file path is available', async () => {
+    const recipeModal = await createRecipeModal();
+    recipeModal.showRecipeDetails({ title: 'Orphan', tags: [], loras: [] });
+
+    const footer = document.getElementById('recipeMetaFooter');
+    expect(footer.hidden).toBe(true);
+    expect(footer.innerHTML).toBe('');
+  });
+});

tests/frontend/components/recipeModal.noLorasReason.test.js

@@ -0,0 +1,228 @@
+import { describe, it, beforeEach, expect, vi } from 'vitest';
+
+const translateMock = vi.fn((key, params, fallback) => (typeof fallback === 'string' ? fallback : key));
+
+const loadingManagerStub = {
+  showSimpleLoading: vi.fn(),
+  hide: vi.fn(),
+  show: vi.fn(),
+  restoreProgressBar: vi.fn(),
+};
+
+const virtualScrollerStub = {
+  updateSingleItem: vi.fn(),
+  getNavigationState: vi.fn(() => ({
+    index: 0,
+    hasPrev: false,
+    hasNext: false,
+    loadedItems: 1,
+    totalItems: 1,
+  })),
+  getAdjacentItemByFilePath: vi.fn(async () => null),
+};
+
+const stateStub = {
+  global: { settings: {}, loadingManager: loadingManagerStub },
+  loadingManager: loadingManagerStub,
+  virtualScroller: virtualScrollerStub,
+};
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+  showToast: vi.fn(),
+  copyToClipboard: vi.fn(),
+  sendLoraToWorkflow: vi.fn(),
+  sendModelPathToWorkflow: vi.fn(),
+  openCivitaiByMetadata: vi.fn(),
+  stripLoraTags: vi.fn((text) => text),
+  sendPromptToWorkflow: vi.fn(),
+  sendGenParamsToWorkflow: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+  translate: translateMock,
+}));
+
+vi.mock('../../../static/js/state/index.js', () => ({
+  state: stateStub,
+}));
+
+vi.mock('../../../static/js/utils/storageHelpers.js', () => ({
+  setSessionItem: vi.fn(),
+  removeSessionItem: vi.fn(),
+  getStorageItem: vi.fn(() => null),
+  setStorageItem: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/recipeApi.js', () => ({
+  fetchRecipeDetails: vi.fn(),
+  updateRecipeMetadata: vi.fn(() => Promise.resolve({ success: true })),
+  sendRecipeWorkflow: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  MODEL_TYPES: {
+    LORA: 'loras',
+    CHECKPOINT: 'checkpoints',
+    EMBEDDING: 'embeddings',
+  },
+}));
+
+function recipeModalFixture() {
+  return `
+    <div id="recipeModal" class="modal">
+      <div class="modal-content">
+        <header class="recipe-modal-header">
+          <h2 id="recipeModalTitle">Recipe Details</h2>
+          <div id="recipeTagsContainer"></div>
+        </header>
+        <div class="modal-body">
+          <div class="recipe-media-column">
+            <div class="recipe-preview-container" id="recipePreviewContainer">
+              <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
+            </div>
+          </div>
+          <div class="info-section recipe-bottom-section">
+            <div class="recipe-section-actions">
+              <span id="recipeLorasCount"></span>
+              <button class="action-btn view-loras-btn" id="viewRecipeLorasBtn"></button>
+            </div>
+            <div class="recipe-loras-list" id="recipeLorasList"></div>
+          </div>
+        </div>
+      </div>
+    </div>
+  `;
+}
+
+describe('RecipeModal no-LoRA reason panel', () => {
+  let recipeModal;
+
+  beforeEach(async () => {
+    vi.clearAllMocks();
+    document.body.innerHTML = recipeModalFixture();
+    const { RecipeModal } = await import('../../../static/js/components/RecipeModal.js');
+    recipeModal = new RecipeModal();
+  });
+
+  function sync(recipe) {
+    recipeModal.syncResourcesSection(recipe);
+    return document.getElementById('recipeLorasList');
+  }
+
+  it('shows the base message only when generation genuinely used no LoRAs', () => {
+    const list = sync({
+      id: 'r1',
+      loras: [],
+      import_info: { channel: 'url', reason: 'no_loras_used' },
+    });
+
+    expect(list.querySelector('.no-loras')).not.toBeNull();
+    expect(list.textContent).toContain('No LoRAs associated with this recipe');
+    expect(list.querySelector('details.no-loras-reason')).toBeNull();
+  });
+
+  it('renders a collapsed reason panel from recorded import_info', () => {
+    const list = sync({
+      id: 'r2',
+      loras: [],
+      import_info: {
+        channel: 'batch_import_url',
+        reason: 'api_meta_no_lora_resources',
+        details: {
+          api_meta_keys: ['prompt'],
+          api_model_version_ids: 0,
+          exif_present: false,
+        },
+      },
+    });
+
+    const details = list.querySelector('details.no-loras-reason');
+    expect(details).not.toBeNull();
+    // Collapsed by default (no `open` attribute).
+    expect(details.hasAttribute('open')).toBe(false);
+    expect(details.querySelector('summary').textContent).toContain('Why no LoRAs?');
+
+    const body = details.querySelector('.no-loras-reason-body');
+    expect(body.textContent).toContain('Batch import (image URL)');
+    expect(body.textContent).toContain('The source API returned no LoRA resource data');
+    expect(body.textContent).toContain('API metadata fields');
+    expect(body.textContent).toContain('prompt');
+    expect(body.textContent).toContain('Model version IDs reported');
+    expect(body.textContent).toContain('Embedded metadata');
+    // Recorded diagnostics are not labeled as inferred.
+    expect(body.querySelector('.no-loras-inferred-note')).toBeNull();
+  });
+
+  it('infers a possible reason for legacy URL recipes without import_info', () => {
+    const list = sync({
+      id: 'r3',
+      loras: [],
+      source_path: 'https://civitai.red/images/139995974',
+      gen_params: { prompt: 'a castle' },
+    });
+
+    const details = list.querySelector('details.no-loras-reason');
+    expect(details).not.toBeNull();
+    const body = details.querySelector('.no-loras-reason-body');
+    expect(body.textContent).toContain('The source API returned no LoRA resource data');
+    // Heuristic results must be labeled as inferred.
+    expect(body.querySelector('.no-loras-inferred-note')).not.toBeNull();
+  });
+
+  it('reports missing embedded metadata for legacy local recipes with no params', () => {
+    const list = sync({
+      id: 'r4',
+      loras: [],
+      source_path: '/data/images/photo.png',
+      gen_params: {},
+    });
+
+    const details = list.querySelector('details.no-loras-reason');
+    expect(details).not.toBeNull();
+    expect(details.querySelector('.no-loras-reason-body').textContent).toContain(
+      'The image has no embedded generation metadata'
+    );
+  });
+
+  it('does not show the panel for legacy local recipes with complete params', () => {
+    const list = sync({
+      id: 'r5',
+      loras: [],
+      source_path: '/data/images/photo.png',
+      gen_params: { prompt: 'a castle', steps: 20, seed: 42 },
+    });
+
+    expect(list.querySelector('details.no-loras-reason')).toBeNull();
+  });
+
+  it('flags ComfyUI workflow sources via has_workflow', () => {
+    const list = sync({
+      id: 'r6',
+      loras: [],
+      has_workflow: true,
+    });
+
+    const details = list.querySelector('details.no-loras-reason');
+    expect(details).not.toBeNull();
+    expect(details.querySelector('.no-loras-reason-body').textContent).toContain(
+      'ComfyUI workflow'
+    );
+  });
+
+  it('escapes HTML in recorded diagnostic values', () => {
+    const list = sync({
+      id: 'r7',
+      loras: [],
+      import_info: {
+        channel: 'url',
+        reason: 'api_meta_no_lora_resources',
+        details: { api_meta_keys: ['<img src=x onerror=alert(1)>'] },
+      },
+    });
+
+    const details = list.querySelector('details.no-loras-reason');
+    expect(details).not.toBeNull();
+    expect(details.innerHTML).not.toContain('<img src=x');
+    expect(details.textContent).toContain('<img src=x onerror=alert(1)>');
+  });
+});

tests/frontend/components/recipeModal.resourceItems.test.js

@@ -92,10 +92,13 @@ vi.mock('../../../static/js/api/apiConfig.js', () => ({
   },
 }));
 
+const downloadManagerMock = {
+  downloadVersionWithDefaults: downloadVersionWithDefaultsMock,
+  _lastDownloadError: '',
+};
+
 vi.mock('../../../static/js/managers/DownloadManager.js', () => ({
-  downloadManager: {
+  downloadManager: downloadManagerMock,
-    downloadVersionWithDefaults: downloadVersionWithDefaultsMock,
-  },
 }));
 
 function recipeModalFixture() {
@@ -192,6 +195,7 @@ describe('RecipeModal resource item interactions', () => {
     // the shared mocks to their defaults explicitly.
     downloadVersionWithDefaultsMock.mockReset();
     downloadVersionWithDefaultsMock.mockResolvedValue(undefined);
+    downloadManagerMock._lastDownloadError = '';
     fetchRecipeDetailsMock.mockReset();
     // Hydration re-fetches the recipe right after render; resolving an empty
     // object would delete currentRecipe.loras and wipe the list, so resolve
@@ -206,7 +210,11 @@ describe('RecipeModal resource item interactions', () => {
   });
 
   afterEach(() => {
-    createdModals.forEach(recipeModal => recipeModal.cleanupNavigationShortcuts());
+    // dispose() marks each modal instance dead: pending deferred timers
+    // (wiring, 500ms reconnect re-renders) are cancelled and in-flight
+    // async chains become no-ops, so nothing from this test can touch the
+    // DOM of the next one.
+    createdModals.forEach(recipeModal => recipeModal.dispose());
     createdModals.length = 0;
     document.body.innerHTML = '';
     delete global.modalManager;
@@ -330,6 +338,42 @@ describe('RecipeModal resource item interactions', () => {
     expect(container.classList.contains('active')).toBe(true);
   });
 
+  it('shows reconnect failures inline in the panel instead of a toast', async () => {
+    const recipeModal = await createRecipeModal();
+    global.fetch = vi.fn(async (url) => {
+      if (String(url).includes('/recipe/lora/reconnect')) {
+        return { ok: true, json: async () => ({ success: false, error: 'LoRA not found locally' }) };
+      }
+      return { ok: true, json: async () => ({}) };
+    });
+    recipeModal.showRecipeDetails(recipeWithResources);
+    await flushWiring();
+
+    const deletedItem = document.querySelector('.recipe-lora-item.is-deleted');
+    deletedItem.querySelector('.lora-reconnect').click();
+    const container = deletedItem.querySelector('.lora-reconnect-container');
+    const input = container.querySelector('.reconnect-input');
+    const error = container.querySelector('.reconnect-error');
+    expect(error).not.toBeNull();
+
+    input.value = 'nonexistent-lora';
+    container.querySelector('.reconnect-confirm-btn').click();
+
+    await vi.waitFor(() => {
+      expect(error.classList.contains('active')).toBe(true);
+    });
+    expect(error.textContent).toContain('LoRA not found locally');
+    expect(showToastMock).not.toHaveBeenCalledWith(
+      'toast.recipes.reconnectFailed',
+      expect.anything(),
+      'error'
+    );
+
+    // Typing again clears the inline error
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    expect(error.classList.contains('active')).toBe(false);
+    expect(error.textContent).toBe('');
+  });
   it('renders hash-invalid LoRAs with a dedicated badge and reconnect instead of download', async () => {
     const recipeModal = await createRecipeModal();
     recipeModal.showRecipeDetails(recipeWithResources);
@@ -392,14 +436,20 @@ describe('RecipeModal resource item interactions', () => {
     expect(downloadVersionWithDefaultsMock).not.toHaveBeenCalled();
   });
 
-  it('renders no action row when neither identifiers nor hash are available', async () => {
+  it('offers reconnect for name-only LoRAs with no CivitAI identifiers', async () => {
     const recipeModal = await createRecipeModal();
     recipeModal.showRecipeDetails(recipeWithResources);
+    await flushWiring();
 
     const mysteryItem = document.querySelector('[data-lora-index="4"]');
     expect(mysteryItem.querySelector('.lora-download')).toBeNull();
-    // No actions at all -> no empty action row taking vertical space
+    const reconnectButton = mysteryItem.querySelector('.lora-reconnect');
-    expect(mysteryItem.querySelector('.recipe-lora-actions')).toBeNull();
+    expect(reconnectButton).not.toBeNull();
+
+    reconnectButton.click();
+    const container = mysteryItem.querySelector('.lora-reconnect-container');
+    expect(container).not.toBeNull();
+    expect(container.classList.contains('active')).toBe(true);
 
     // The name-fallback search link still sits inline in the title
     const link = mysteryItem.querySelector('.recipe-lora-title a.recipe-civitai-link');
@@ -407,6 +457,81 @@ describe('RecipeModal resource item interactions', () => {
     expect(link.href).toContain('query=Mystery%20LoRA');
   });
 
+  it('marks the entry hash-invalid when a direct download fails with an unresolvable error', async () => {
+    const recipeModal = await createRecipeModal();
+    const requests = [];
+    // Deep copy so the mark step mutating loras[1].hashInvalid does not
+    // leak into the shared fixture used by later tests.
+    const isolatedRecipe = JSON.parse(JSON.stringify(recipeWithResources));
+    fetchRecipeDetailsMock.mockResolvedValue(isolatedRecipe);
+    downloadManagerMock._lastDownloadError = 'Model not found';
+    downloadVersionWithDefaultsMock.mockResolvedValue(false);
+    global.fetch = vi.fn(async (url, options) => {
+      requests.push({ url: String(url), options });
+      return { ok: true, json: async () => ({}) };
+    });
+    recipeModal.showRecipeDetails(isolatedRecipe);
+    await flushWiring();
+
+    // missingLora carries direct identifiers, so no hash-resolution round
+    // trip happens before the download attempt.
+    const missingItem = document.querySelector('[data-lora-index="1"]');
+    missingItem.querySelector('.lora-download').click();
+
+    await vi.waitFor(() => {
+      expect(downloadVersionWithDefaultsMock).toHaveBeenCalledTimes(1);
+    });
+    await vi.waitFor(() => {
+      expect(
+        requests.some(r => r.url.includes('/recipe/lora/mark-hash-invalid'))
+      ).toBe(true);
+    });
+
+    const markRequest = requests.find(r => r.url.includes('/mark-hash-invalid'));
+    expect(JSON.parse(markRequest.options.body)).toEqual({
+      recipe_id: 'recipe-resources',
+      lora_index: 1,
+    });
+
+    // The re-rendered entry swaps the download action for the reconnect one
+    await vi.waitFor(() => {
+      const item = document.querySelector('[data-lora-index="1"]');
+      expect(item.querySelector('.lora-reconnect')).not.toBeNull();
+      expect(item.querySelector('.lora-download')).toBeNull();
+      expect(item.querySelector('.invalid-hash-badge')).not.toBeNull();
+    });
+  });
+
+  it('leaves the entry untouched when a direct download fails transiently', async () => {
+    const recipeModal = await createRecipeModal();
+    const requests = [];
+    const isolatedRecipe = JSON.parse(JSON.stringify(recipeWithResources));
+    fetchRecipeDetailsMock.mockResolvedValue(isolatedRecipe);
+    downloadManagerMock._lastDownloadError = 'Connection timed out';
+    downloadVersionWithDefaultsMock.mockResolvedValue(false);
+    global.fetch = vi.fn(async (url, options) => {
+      requests.push({ url: String(url), options });
+      return { ok: true, json: async () => ({}) };
+    });
+    recipeModal.showRecipeDetails(isolatedRecipe);
+    await flushWiring();
+
+    const missingItem = document.querySelector('[data-lora-index="1"]');
+    missingItem.querySelector('.lora-download').click();
+
+    await vi.waitFor(() => {
+      expect(downloadVersionWithDefaultsMock).toHaveBeenCalledTimes(1);
+    });
+    // Give any (unexpected) mark request a chance to fire
+    await new Promise(resolve => setTimeout(resolve, 50));
+    expect(requests.some(r => r.url.includes('mark-hash-invalid'))).toBe(false);
+
+    // The entry keeps the download action and never flips to reconnect
+    const item = document.querySelector('[data-lora-index="1"]');
+    expect(item.querySelector('.lora-download')).not.toBeNull();
+    expect(item.querySelector('.lora-reconnect')).toBeNull();
+  });
+
   it('offers download for hash-only LoRAs and resolves identifiers on demand', async () => {
     const recipeModal = await createRecipeModal();
     global.fetch = vi.fn(async (url) => ({
@@ -503,4 +628,558 @@ describe('RecipeModal resource item interactions', () => {
     checkpointItem.click();
     expect(navigateSpy).not.toHaveBeenCalled();
   });
+
+  describe('reconnect suggestions', () => {
+    const suggestionsPayload = {
+      success: true,
+      suggestions: [
+        {
+          file_name: 'deleted-lora-v1.safetensors',
+          file_path: '/models/loras/deleted-lora-v1.safetensors',
+          model_name: 'Deleted LoRA v1',
+          base_model: 'SD 1.5',
+          preview_url: '/preview/deleted.png',
+          hash: 'abc123',
+          score: 0.95,
+          match_reason: 'same_version',
+          target_name: 'deleted-lora-v1',
+        },
+      ],
+    };
+
+    function mockSuggestionsFetch(payload) {
+      const requests = [];
+      global.fetch = vi.fn(async (url, options) => {
+        requests.push({ url: String(url), options });
+        if (String(url).includes('/reconnect-suggestions')) {
+          return { ok: true, json: async () => payload };
+        }
+        if (String(url).includes('/recipe/lora/reconnect')) {
+          return {
+            ok: true,
+            json: async () => ({
+              success: true,
+              updated_lora: { name: 'deleted-lora-v1', modelName: 'Deleted LoRA v1', inLibrary: true },
+            }),
+          };
+        }
+        return { ok: true, json: async () => ({}) };
+      });
+      return requests;
+    }
+
+    async function openReconnectPanel(recipeModal, loraIndex) {
+      recipeModal.showRecipeDetails(recipeWithResources);
+      await flushWiring();
+      const item = document.querySelector(`[data-lora-index="${loraIndex}"]`);
+      item.querySelector('.lora-reconnect').click();
+      return item.querySelector('.lora-reconnect-container');
+    }
+
+    it('fetches suggestions when the panel opens and renders them as rows', async () => {
+      const recipeModal = await createRecipeModal();
+      mockSuggestionsFetch(suggestionsPayload);
+
+      const container = await openReconnectPanel(recipeModal, 2);
+
+      // The loading state shows synchronously while the fetch is in flight
+      expect(container.querySelector('.reconnect-suggestions-loading')).not.toBeNull();
+
+      await vi.waitFor(() => {
+        expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(1);
+      });
+
+      expect(global.fetch).toHaveBeenCalledWith(
+        '/api/lm/recipe/recipe-resources/lora/2/reconnect-suggestions'
+      );
+
+      const row = container.querySelector('.reconnect-suggestion');
+      // Primary label is the file stem (what the match scored on and what
+      // gets submitted); the secondary line shows only the base model — the
+      // model name is noise and intentionally omitted.
+      expect(row.querySelector('.reconnect-suggestion-name').textContent).toBe('deleted-lora-v1');
+      expect(row.querySelector('.reconnect-suggestion-secondary').textContent).toBe('SD 1.5');
+      expect(row.querySelector('.reconnect-suggestion-reason').textContent).toBe('Same model version');
+      expect(row.title).toBe('deleted-lora-v1');
+      const preview = row.querySelector('.reconnect-suggestion-preview');
+      expect(preview.getAttribute('src')).toBe('/preview/deleted.png');
+    });
+
+    it('reconnects with the suggestion target_name when a row is clicked', async () => {
+      const recipeModal = await createRecipeModal();
+      const requests = mockSuggestionsFetch(suggestionsPayload);
+
+      const container = await openReconnectPanel(recipeModal, 2);
+      await vi.waitFor(() => {
+        expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(1);
+      });
+
+      container.querySelector('.reconnect-suggestion').click();
+
+      await vi.waitFor(() => {
+        expect(requests.some(r => r.url === '/api/lm/recipe/lora/reconnect')).toBe(true);
+      });
+
+      const reconnectRequest = requests.find(r => r.url === '/api/lm/recipe/lora/reconnect');
+      expect(reconnectRequest.options.method).toBe('POST');
+      // lora_index rides as the DOM attribute string, same as the manual form
+      expect(JSON.parse(reconnectRequest.options.body)).toEqual({
+        recipe_id: 'recipe-resources',
+        lora_index: '2',
+        target_name: 'deleted-lora-v1',
+      });
+    });
+
+    it('warns when the reconnect crossed base-model families', async () => {
+      const recipeModal = await createRecipeModal();
+      global.fetch = vi.fn(async (url) => {
+        if (String(url).includes('/reconnect-suggestions')) {
+          return { ok: true, json: async () => suggestionsPayload };
+        }
+        if (String(url).includes('/recipe/lora/reconnect')) {
+          return {
+            ok: true,
+            json: async () => ({
+              success: true,
+              updated_lora: { name: 'deleted-lora-v1', modelName: 'Deleted LoRA v1', inLibrary: true },
+              base_model_mismatch: { recipe_base_model: 'Illustrious', lora_base_model: 'Pony' },
+            }),
+          };
+        }
+        return { ok: true, json: async () => ({}) };
+      });
+
+      const container = await openReconnectPanel(recipeModal, 2);
+      await vi.waitFor(() => {
+        expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(1);
+      });
+
+      container.querySelector('.reconnect-suggestion').click();
+
+      await vi.waitFor(() => {
+        expect(showToastMock).toHaveBeenCalledWith(
+          'toast.recipes.reconnectBaseModelMismatch',
+          { recipe: 'Illustrious', lora: 'Pony' },
+          'warning'
+        );
+      });
+    });
+
+    it('shows an empty state when no suggestions are available', async () => {
+      const recipeModal = await createRecipeModal();
+      mockSuggestionsFetch({ success: true, suggestions: [] });
+
+      const container = await openReconnectPanel(recipeModal, 3);
+
+      await vi.waitFor(() => {
+        expect(container.querySelector('.reconnect-suggestions-empty')).not.toBeNull();
+      });
+      expect(container.querySelector('.reconnect-suggestions-empty').textContent)
+        .toBe('No matching LoRAs in your local library');
+      expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(0);
+    });
+
+    it('submits free text via the combobox onCommit when Enter is pressed', async () => {
+      const recipeModal = await createRecipeModal();
+      const requests = mockSuggestionsFetch({ success: true, suggestions: [] });
+
+      const container = await openReconnectPanel(recipeModal, 2);
+      const input = container.querySelector('.reconnect-input');
+      input.value = 'typed-lora-name';
+      input.dispatchEvent(new KeyboardEvent('keydown', { key: 'Enter', bubbles: true }));
+
+      await vi.waitFor(() => {
+        expect(requests.some(r => r.url === '/api/lm/recipe/lora/reconnect')).toBe(true);
+      });
+      const reconnectRequest = requests.find(r => r.url === '/api/lm/recipe/lora/reconnect');
+      expect(JSON.parse(reconnectRequest.options.body)).toEqual({
+        recipe_id: 'recipe-resources',
+        lora_index: '2',
+        target_name: 'typed-lora-name',
+      });
+    });
+
+    it('keeps the panel open when the combobox dropdown is clicked', async () => {
+      const recipeModal = await createRecipeModal();
+      mockSuggestionsFetch({ success: true, suggestions: [] });
+
+      recipeModal.showRecipeDetails(recipeWithResources);
+      await flushWiring();
+      // Open the panel directly — button wiring races the hydration re-render,
+      // and this test is about the document click handler, not the button.
+      recipeModal.showReconnectInput('2');
+      const container = document.querySelector('.lora-reconnect-container[data-lora-index="2"]');
+      expect(container.classList.contains('active')).toBe(true);
+
+      // The dropdown panel lives on document.body; clicking an option there is
+      // part of the reconnect interaction, not an outside click.
+      const panel = document.createElement('div');
+      panel.className = 'lm-combobox-panel';
+      document.body.appendChild(panel);
+      panel.dispatchEvent(new MouseEvent('click', { bubbles: true }));
+      expect(container.classList.contains('active')).toBe(true);
+      panel.remove();
+
+      // A genuine outside click still closes the panel
+      document.body.dispatchEvent(new MouseEvent('click', { bubbles: true }));
+      expect(container.classList.contains('active')).toBe(false);
+    });
+  });
+
+  it('offers undo for reconnected entries and restores via the API', async () => {
+    const recipeModal = await createRecipeModal();
+    const isolatedRecipe = JSON.parse(JSON.stringify(recipeWithResources));
+    isolatedRecipe.loras[0].reconnectSnapshot = { file_name: 'gone', isDeleted: true };
+    fetchRecipeDetailsMock.mockResolvedValue(isolatedRecipe);
+    const requests = [];
+    global.fetch = vi.fn(async (url, options) => {
+      requests.push({ url: String(url), options });
+      if (String(url).includes('/recipe/lora/restore')) {
+        return {
+          ok: true,
+          json: async () => ({
+            success: true,
+            updated_lora: { name: 'gone', modelName: 'Gone', inLibrary: false, isDeleted: true },
+          }),
+        };
+      }
+      return { ok: true, json: async () => ({}) };
+    });
+    recipeModal.showRecipeDetails(isolatedRecipe);
+    await flushWiring();
+
+    const item = document.querySelector('[data-lora-index="0"]');
+    const undoButton = item.querySelector('.lora-undo-reconnect');
+    expect(undoButton).not.toBeNull();
+
+    undoButton.click();
+    // Wait for the whole restore chain (fetch -> json -> toast), not just the
+    // request itself.
+    await vi.waitFor(() => {
+      expect(showToastMock).toHaveBeenCalledWith('toast.recipes.loraRestored', {}, 'success');
+    });
+    const restoreRequest = requests.find(r => r.url === '/api/lm/recipe/lora/restore');
+    expect(restoreRequest.options.method).toBe('POST');
+    expect(JSON.parse(restoreRequest.options.body)).toEqual({
+      recipe_id: 'recipe-resources',
+      lora_index: '0',
+    });
+  });
+
+  describe('checkpoint reconnect', () => {
+    const brokenCheckpoint = {
+      name: 'gone-checkpoint',
+      file_name: 'gone',
+      inLibrary: false,
+      isDeleted: true,
+      hash: 'a2a12bfa01',
+    };
+    const hashInvalidCheckpoint = {
+      name: 'invalid-checkpoint',
+      file_name: 'invalid',
+      inLibrary: false,
+      hashInvalid: true,
+      hash: 'deadbeefcafe',
+    };
+
+    function recipeWithCheckpoint(checkpoint) {
+      return {
+        ...JSON.parse(JSON.stringify(recipeWithResources)),
+        checkpoint: { ...checkpoint },
+      };
+    }
+
+    // Hydration re-fetches the recipe right after render and re-renders the
+    // modal, so the mock must resolve the SAME broken-checkpoint recipe —
+    // otherwise the fetch wipes isDeleted/hashInvalid back to the fixture.
+    async function renderBrokenCheckpoint(recipeModal, checkpoint) {
+      const isolated = recipeWithCheckpoint(checkpoint);
+      fetchRecipeDetailsMock.mockResolvedValue(isolated);
+      recipeModal.showRecipeDetails(isolated);
+      await flushWiring();
+    }
+
+    function mockCheckpointSuggestionsFetch(payload) {
+      const requests = [];
+      global.fetch = vi.fn(async (url, options) => {
+        requests.push({ url: String(url), options });
+        if (String(url).includes('/checkpoint/reconnect-suggestions')) {
+          return { ok: true, json: async () => payload };
+        }
+        if (String(url).includes('/recipe/checkpoint/reconnect')) {
+          return {
+            ok: true,
+            json: async () => ({
+              success: true,
+              updated_checkpoint: {
+                name: 'main-checkpoint',
+                file_name: 'main',
+                inLibrary: true,
+              },
+            }),
+          };
+        }
+        return { ok: true, json: async () => ({}) };
+      });
+      return requests;
+    }
+
+    it('renders a deleted checkpoint with a badge and reconnect affordance', async () => {
+      const recipeModal = await createRecipeModal();
+      await renderBrokenCheckpoint(recipeModal, brokenCheckpoint);
+
+      const item = document.querySelector('.checkpoint-item');
+      expect(item.classList.contains('is-deleted')).toBe(true);
+      expect(item.querySelector('.deleted-badge')).not.toBeNull();
+      const reconnectButton = item.querySelector('.checkpoint-reconnect');
+      expect(reconnectButton).not.toBeNull();
+      // Deleted checkpoints lose the civitai link (their source page is gone)
+      expect(item.querySelector('.recipe-lora-title a.recipe-civitai-link')).toBeNull();
+      // The inline form is present but hidden until the button is pressed
+      const container = item.querySelector('.lora-reconnect-container[data-lora-index="checkpoint"]');
+      expect(container).not.toBeNull();
+      expect(container.classList.contains('active')).toBe(false);
+    });
+
+    it('renders a hash-invalid checkpoint with the unresolvable hash badge', async () => {
+      const recipeModal = await createRecipeModal();
+      await renderBrokenCheckpoint(recipeModal, hashInvalidCheckpoint);
+
+      const item = document.querySelector('.checkpoint-item');
+      expect(item.querySelector('.invalid-hash-badge')).not.toBeNull();
+      expect(item.querySelector('.checkpoint-reconnect')).not.toBeNull();
+    });
+
+    it('renders reconnect for a name-only checkpoint with no download identifiers', async () => {
+      // Importers can leave a checkpoint entry with nothing but a model name
+      // (no hash / version id, so nothing was ever queryable on CivitAI).
+      // It cannot be downloaded and is not marked deleted — reconnect is the
+      // only remediation, so it must still surface.
+      const recipeModal = await createRecipeModal();
+      await renderBrokenCheckpoint(recipeModal, {
+        type: 'checkpoint',
+        modelName: 'meichidarkMix_meichidarkanimxlV1',
+        inLibrary: false,
+      });
+
+      const item = document.querySelector('.checkpoint-item');
+      expect(item.querySelector('.checkpoint-download')).toBeNull();
+      const reconnectButton = item.querySelector('.checkpoint-reconnect');
+      expect(reconnectButton).not.toBeNull();
+      const container = item.querySelector('.lora-reconnect-container[data-lora-index="checkpoint"]');
+      expect(container).not.toBeNull();
+
+      reconnectButton.click();
+      expect(container.classList.contains('active')).toBe(true);
+    });
+
+    it('fetches checkpoint suggestions against the checkpoint endpoint', async () => {
+      const recipeModal = await createRecipeModal();
+      const suggestionsPayload = {
+        success: true,
+        suggestions: [
+          {
+            file_name: 'main-checkpoint.safetensors',
+            base_model: 'SD 1.5',
+            preview_url: '/preview/main.png',
+            score: 0.95,
+            match_reason: 'same_version',
+            target_name: 'main-checkpoint',
+          },
+        ],
+      };
+      mockCheckpointSuggestionsFetch(suggestionsPayload);
+
+      await renderBrokenCheckpoint(recipeModal, brokenCheckpoint);
+      document.querySelector('.checkpoint-reconnect').click();
+      const container = document.querySelector('.lora-reconnect-container[data-lora-index="checkpoint"]');
+
+      expect(container.classList.contains('active')).toBe(true);
+      expect(global.fetch).toHaveBeenCalledWith(
+        '/api/lm/recipe/recipe-resources/checkpoint/reconnect-suggestions'
+      );
+
+      await vi.waitFor(() => {
+        expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(1);
+      });
+      expect(container.querySelector('.reconnect-suggestion-name').textContent)
+        .toBe('main-checkpoint');
+    });
+
+    it('reconnects the checkpoint via its own endpoint when a suggestion is clicked', async () => {
+      const recipeModal = await createRecipeModal();
+      const requests = mockCheckpointSuggestionsFetch({
+        success: true,
+        suggestions: [
+          {
+            file_name: 'main-checkpoint.safetensors',
+            target_name: 'main-checkpoint',
+            match_reason: 'same_version',
+            score: 0.95,
+          },
+        ],
+      });
+
+      await renderBrokenCheckpoint(recipeModal, brokenCheckpoint);
+      document.querySelector('.checkpoint-reconnect').click();
+      const container = document.querySelector('.lora-reconnect-container[data-lora-index="checkpoint"]');
+
+      await vi.waitFor(() => {
+        expect(container.querySelectorAll('.reconnect-suggestion').length).toBe(1);
+      });
+      container.querySelector('.reconnect-suggestion').click();
+
+      await vi.waitFor(() => {
+        expect(requests.some(r => r.url === '/api/lm/recipe/checkpoint/reconnect')).toBe(true);
+      });
+      const reconnectRequest = requests.find(r => r.url === '/api/lm/recipe/checkpoint/reconnect');
+      expect(reconnectRequest.options.method).toBe('POST');
+      expect(JSON.parse(reconnectRequest.options.body)).toEqual({
+        recipe_id: 'recipe-resources',
+        target_name: 'main-checkpoint',
+      });
+      await vi.waitFor(() => {
+        expect(showToastMock).toHaveBeenCalledWith(
+          'toast.recipes.checkpointReconnectedSuccessfully',
+          {},
+          'success'
+        );
+      });
+      expect(recipeModal.currentRecipe.checkpoint.inLibrary).toBe(true);
+    });
+
+    it('warns when the checkpoint reconnect crossed base-model families', async () => {
+      const recipeModal = await createRecipeModal();
+      global.fetch = vi.fn(async (url) => {
+        if (String(url).includes('/checkpoint/reconnect-suggestions')) {
+          return { ok: true, json: async () => ({ success: true, suggestions: [] }) };
+        }
+        if (String(url).includes('/recipe/checkpoint/reconnect')) {
+          return {
+            ok: true,
+            json: async () => ({
+              success: true,
+              updated_checkpoint: { name: 'main', inLibrary: true },
+              base_model_mismatch: { recipe_base_model: 'Illustrious', checkpoint_base_model: 'Pony' },
+            }),
+          };
+        }
+        return { ok: true, json: async () => ({}) };
+      });
+
+      await renderBrokenCheckpoint(recipeModal, brokenCheckpoint);
+      document.querySelector('.checkpoint-reconnect').click();
+      const container = document.querySelector('.lora-reconnect-container[data-lora-index="checkpoint"]');
+      const input = container.querySelector('.reconnect-input');
+      input.value = 'main';
+      container.querySelector('.reconnect-confirm-btn').click();
+
+      await vi.waitFor(() => {
+        expect(showToastMock).toHaveBeenCalledWith(
+          'toast.recipes.reconnectCheckpointBaseModelMismatch',
+          { recipe: 'Illustrious', checkpoint: 'Pony' },
+          'warning'
+        );
+      });
+    });
+
+    it('offers undo for a reconnected checkpoint and restores via the API', async () => {
+      const recipeModal = await createRecipeModal();
+      const isolatedRecipe = recipeWithCheckpoint(brokenCheckpoint);
+      isolatedRecipe.checkpoint = {
+        name: 'main-checkpoint',
+        file_name: 'main',
+        inLibrary: true,
+        reconnectSnapshot: { name: 'gone-checkpoint', file_name: 'gone', isDeleted: true },
+      };
+      fetchRecipeDetailsMock.mockResolvedValue(isolatedRecipe);
+      const requests = [];
+      global.fetch = vi.fn(async (url, options) => {
+        requests.push({ url: String(url), options });
+        if (String(url).includes('/recipe/checkpoint/restore')) {
+          return {
+            ok: true,
+            json: async () => ({
+              success: true,
+              updated_checkpoint: { name: 'gone', inLibrary: false, isDeleted: true },
+            }),
+          };
+        }
+        return { ok: true, json: async () => ({}) };
+      });
+      recipeModal.showRecipeDetails(isolatedRecipe);
+      await flushWiring();
+
+      const item = document.querySelector('.checkpoint-item');
+      const undoButton = item.querySelector('.checkpoint-undo-reconnect');
+      expect(undoButton).not.toBeNull();
+
+      undoButton.click();
+      await vi.waitFor(() => {
+        expect(showToastMock).toHaveBeenCalledWith('toast.recipes.checkpointRestored', {}, 'success');
+      });
+      const restoreRequest = requests.find(r => r.url === '/api/lm/recipe/checkpoint/restore');
+      expect(restoreRequest.options.method).toBe('POST');
+      expect(JSON.parse(restoreRequest.options.body)).toEqual({
+        recipe_id: 'recipe-resources',
+      });
+    });
+
+    it('marks the checkpoint hash invalid only when the failure is unresolvable', async () => {
+      const recipeModal = await createRecipeModal();
+      const { downloadManager } = await import('../../../static/js/managers/DownloadManager.js');
+      const requests = [];
+      global.fetch = vi.fn(async (url, options) => {
+        requests.push({ url: String(url), options });
+        return { ok: true, json: async () => ({ success: true }) };
+      });
+
+      // Explicit "model removed" signal: the entry becomes a rematch/
+      // reconnect candidate (same rule as the LoRA resolve "not found").
+      // Use an isolated copy so the hashInvalid mutation does not leak into
+      // the shared recipeWithResources fixture used by later tests.
+      const isolatedRecipe = JSON.parse(JSON.stringify(recipeWithResources));
+      fetchRecipeDetailsMock.mockResolvedValue(isolatedRecipe);
+      downloadVersionWithDefaultsMock.mockResolvedValue(false);
+      downloadManager._lastDownloadError = 'Model not found';
+      recipeModal.showRecipeDetails(isolatedRecipe);
+      await flushWiring();
+      document.querySelector('.checkpoint-download').click();
+
+      await vi.waitFor(() => {
+        expect(requests.some(r => r.url === '/api/lm/recipe/checkpoint/mark-hash-invalid')).toBe(true);
+      });
+      const markRequest = requests.find(r => r.url === '/api/lm/recipe/checkpoint/mark-hash-invalid');
+      expect(markRequest.options.method).toBe('POST');
+      expect(JSON.parse(markRequest.options.body)).toEqual({ recipe_id: 'recipe-resources' });
+      expect(recipeModal.currentRecipe.checkpoint.hashInvalid).toBe(true);
+    });
+
+    it('does not mark the checkpoint hash invalid on transient download failures', async () => {
+      const recipeModal = await createRecipeModal();
+      const { downloadManager } = await import('../../../static/js/managers/DownloadManager.js');
+      const requests = [];
+      global.fetch = vi.fn(async (url, options) => {
+        requests.push({ url: String(url), options });
+        return { ok: true, json: async () => ({ success: true }) };
+      });
+
+      // Transport/API exceptions must NOT enroll the entry in the
+      // remediation flow — transient failures are not evidence the model is
+      // unrecoverable (mirrors the LoRA path).
+      downloadVersionWithDefaultsMock.mockRejectedValue(new Error('Network timeout'));
+      recipeModal.showRecipeDetails(recipeWithResources);
+      await flushWiring();
+      document.querySelector('.checkpoint-download').click();
+
+      await new Promise(resolve => setTimeout(resolve, 100));
+      expect(requests.some(r => r.url === '/api/lm/recipe/checkpoint/mark-hash-invalid')).toBe(false);
+
+      // Business failure without an unresolvable signal also stays untouched.
+      downloadVersionWithDefaultsMock.mockResolvedValue(false);
+      downloadManager._lastDownloadError = 'Connection refused';
+      await recipeModal.downloadCheckpoint(recipeModal.currentRecipe.checkpoint);
+      expect(requests.some(r => r.url === '/api/lm/recipe/checkpoint/mark-hash-invalid')).toBe(false);
+    });
+  });
 });

tests/frontend/components/showcase.gallery.test.js

@@ -16,6 +16,7 @@ vi.mock(MEDIA_UTILS_MODULE, () => ({
 
 vi.mock(MEDIA_VIEWER_MODULE, () => ({
   openMediaViewer: vi.fn(),
+  isMediaViewerOpen: vi.fn(() => false),
 }));
 
 const PREVIEW_URL = '/loras_static/preview/abc.png';
@@ -197,4 +198,361 @@ describe('Showcase gallery', () => {
     expect(document.querySelector('.gallery-indicator-bar')).toBeTruthy();
     expect(document.querySelectorAll('.gallery-thumb')).toHaveLength(0);
   });
+
+  it('prefetches adjacent example images (skipping videos) while expanded', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    const prefetched = [];
+    class MockImage {
+      set src(value) { prefetched.push(value); }
+      set fetchPriority(_value) { /* jsdom lacks fetchPriority */ }
+    }
+    vi.stubGlobal('Image', MockImage);
+
+    // Unique URLs: the module-level prefetch dedup set persists across tests
+    const images = [
+      { url: 'https://image.civitai.com/pf/aaa.jpeg', width: 100, height: 100, nsfwLevel: 0 },
+      { url: 'https://image.civitai.com/pf/bbb.jpeg', width: 100, height: 100, nsfwLevel: 0 },
+      { url: 'https://image.civitai.com/pf/ccc.mp4', width: 100, height: 100, nsfwLevel: 0 },
+    ];
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(images, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+
+    // galleryState.activeIndex persists across tests → pin it to 0
+    updateMainDisplay(0);
+
+    // Active index 0 → prefetches index 1; index 2 is a video and is skipped
+    expect(prefetched).toContain('https://image.civitai.com/pf/bbb.jpeg');
+    expect(prefetched).not.toContain('https://image.civitai.com/pf/ccc.mp4');
+
+    // Navigating to 1 prefetches the new neighbor (index 0)
+    updateMainDisplay(1);
+    expect(prefetched).toContain('https://image.civitai.com/pf/aaa.jpeg');
+
+    // Navigating back does not duplicate prefetch requests
+    const count = prefetched.length;
+    updateMainDisplay(0);
+    expect(prefetched).toHaveLength(count);
+
+    vi.unstubAllGlobals();
+  });
+
+  it('does not prefetch while collapsed', async () => {
+    const { renderShowcaseContent, initShowcaseContent } = await import(SHOWCASE_MODULE);
+
+    const prefetched = [];
+    class MockImage {
+      set src(value) { prefetched.push(value); }
+      set fetchPriority(_value) { /* jsdom lacks fetchPriority */ }
+    }
+    vi.stubGlobal('Image', MockImage);
+
+    const images = [
+      { url: 'https://image.civitai.com/pc/ddd.jpeg', width: 100, height: 100, nsfwLevel: 0 },
+      { url: 'https://image.civitai.com/pc/eee.jpeg', width: 100, height: 100, nsfwLevel: 0 },
+    ];
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(images, [], PREVIEW_URL)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+
+    expect(prefetched).toHaveLength(0);
+
+    vi.unstubAllGlobals();
+  });
+
+  it('prefetches one extra example ahead along the navigation direction', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    const prefetched = [];
+    class MockImage {
+      set src(value) { prefetched.push(value); }
+      set fetchPriority(_value) { /* jsdom lacks fetchPriority */ }
+    }
+    vi.stubGlobal('Image', MockImage);
+
+    // Unique URLs: the module-level prefetch dedup set persists across tests
+    const images = [0, 1, 2, 3, 4].map(i => ({
+      url: `https://image.civitai.com/pd/${i}.jpeg`, width: 100, height: 100, nsfwLevel: 0,
+    }));
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(images, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+
+    // Pin position, then step forward: prefetch reaches +2 ahead (index 3)
+    updateMainDisplay(0);
+    updateMainDisplay(1);
+    expect(prefetched).toContain('https://image.civitai.com/pd/2.jpeg');
+    expect(prefetched).toContain('https://image.civitai.com/pd/3.jpeg');
+
+    // Step backward: prefetch reaches -2 ahead (index 4 wrapping around)
+    updateMainDisplay(0);
+    expect(prefetched).toContain('https://image.civitai.com/pd/4.jpeg');
+
+    vi.unstubAllGlobals();
+  });
+
+  it('resets the gallery position when a new model is loaded', async () => {
+    const { renderShowcaseContent, loadExampleImages, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    // Model A: expand and navigate to the third example
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    updateMainDisplay(2);
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('2');
+
+    // Model B opens: loadExampleImages is the per-model entry point
+    const modelBImages = [0, 1, 2, 3].map(i => ({
+      url: `https://image.civitai.com/reset/${i}.jpeg`, width: 100, height: 100, nsfwLevel: 0,
+    }));
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue({
+      json: () => Promise.resolve({ success: true, files: [] }),
+    }));
+    await loadExampleImages(modelBImages, 'model-b-hash', '');
+    vi.unstubAllGlobals();
+
+    // The leaked index (2) must not carry over: model B starts at example 1
+    const gallery = document.querySelector('.showcase-gallery');
+    expect(gallery).toBeTruthy();
+    expect(document.querySelector('.gallery-indicator-bar')).toBeTruthy();
+    // Expand model B's gallery: it renders from index 0, not the leaked 2
+    // (loadExampleImages already bound the controls via initShowcaseContent)
+    document.querySelector('#galleryShowBtn').click();
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+    expect(document.querySelector('#galleryPosition')?.textContent).toBe('1 / 4');
+  });
+
+  it('defers video thumbnail metadata fetches until the strip shows them', async () => {
+    const { renderShowcaseContent, initShowcaseContent } = await import(SHOWCASE_MODULE);
+
+    const images = [
+      { url: 'https://image.civitai.com/lv/fff.jpeg', width: 100, height: 100, nsfwLevel: 0 },
+      { url: 'https://image.civitai.com/lv/ggg.mp4', width: 100, height: 100, nsfwLevel: 0 },
+    ];
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(images, [], PREVIEW_URL, true)}</div>`;
+
+    const video = document.querySelector('.gallery-strip video');
+    expect(video?.getAttribute('preload')).toBe('none');
+    expect(video?.hasAttribute('data-lazy-video')).toBe(true);
+
+    // jsdom's HTMLMediaElement.load() is a not-implemented stub that logs
+    const loadSpy = vi.spyOn(HTMLMediaElement.prototype, 'load').mockImplementation(() => {});
+
+    // jsdom has no IntersectionObserver → fallback enables everything at once
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    expect(video.preload).toBe('metadata');
+    expect(video.hasAttribute('data-lazy-video')).toBe(false);
+    expect(loadSpy).toHaveBeenCalled();
+    loadSpy.mockRestore();
+  });
+
+  it('switches examples on wheel over the main viewer', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    const main = document.querySelector('.gallery-main');
+    main.dispatchEvent(new WheelEvent('wheel', { deltaX: 120, deltaY: 0, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+
+    // The one-step-per-gesture cooldown intentionally blocks an immediate
+    // second step; a fresh gallery (new listener) accepts the next gesture.
+    // No .modal-content ancestor → no boundary guard, vertical also navigates
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+    document.querySelector('.gallery-main')
+      .dispatchEvent(new WheelEvent('wheel', { deltaX: 0, deltaY: 120, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+  });
+
+  it('vertical wheel only hijacks at the modal scroll boundary', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    document.body.innerHTML = `
+      <div class="modal-content">
+        <div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>
+      </div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    const scroller = document.querySelector('.modal-content');
+    // Mid-scroll: the modal owns vertical wheel, the gallery must not navigate
+    Object.defineProperties(scroller, {
+      scrollTop: { value: 100, configurable: true },
+      scrollHeight: { value: 1000, configurable: true },
+      clientHeight: { value: 500, configurable: true },
+    });
+
+    const main = document.querySelector('.gallery-main');
+    main.dispatchEvent(new WheelEvent('wheel', { deltaY: 120, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    // Bottom of the modal: further down-scroll switches to the next example
+    // (and starts a vertical wheel session — covered by the next test)
+    Object.defineProperty(scroller, 'scrollTop', { value: 500, configurable: true });
+    main.dispatchEvent(new WheelEvent('wheel', { deltaY: 120, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+
+    // Leaving the viewer area ends the session: up-scroll away from the top
+    // belongs to the modal again
+    main.dispatchEvent(new Event('pointerleave'));
+    main.dispatchEvent(new WheelEvent('wheel', { deltaY: -120, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+  });
+
+  it('keeps vertical wheel in a sticky session once engaged, until pointer leaves', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    document.body.innerHTML = `
+      <div class="modal-content">
+        <div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>
+      </div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    const scroller = document.querySelector('.modal-content');
+    // Modal sits at its bottom: down-scroll engages the gallery
+    Object.defineProperties(scroller, {
+      scrollTop: { value: 500, configurable: true },
+      scrollHeight: { value: 1000, configurable: true },
+      clientHeight: { value: 500, configurable: true },
+    });
+
+    // The one-step-per-gesture cooldown would block consecutive steps; fake
+    // the clock so each gesture lands after it
+    let now = 10000;
+    const nowSpy = vi.spyOn(performance, 'now').mockImplementation(() => now);
+
+    const main = document.querySelector('.gallery-main');
+    const wheelUp = () => main.dispatchEvent(
+      new WheelEvent('wheel', { deltaY: -120, bubbles: true, cancelable: true }));
+    const wheelDown = () => main.dispatchEvent(
+      new WheelEvent('wheel', { deltaY: 120, bubbles: true, cancelable: true }));
+
+    wheelDown();
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+
+    // Reverse gesture must undo: up-scroll switches back to the previous
+    // example even though the modal is not at its top
+    now += 300;
+    wheelUp();
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    // Pointer leaving the viewer area releases vertical wheel to the modal
+    now += 300;
+    main.dispatchEvent(new Event('pointerleave'));
+    wheelUp();
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    nowSpy.mockRestore();
+  });
+
+  it('ignores wheel events coming from the metadata panel', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    // MediaUtils is mocked, so hoist a panel manually (real code appends it
+    // as a direct child of .gallery-main)
+    const main = document.querySelector('.gallery-main');
+    const panel = document.createElement('div');
+    panel.className = 'image-metadata-panel visible';
+    main.appendChild(panel);
+
+    panel.dispatchEvent(new WheelEvent('wheel', { deltaX: 120, bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+  });
+
+  it('switches examples with [ and ] while expanded, with guards', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+    const { isMediaViewerOpen } = await import(MEDIA_VIEWER_MODULE);
+
+    document.body.innerHTML = `<div id="showcase-tab" class="tab-pane active">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    document.dispatchEvent(new KeyboardEvent('keydown', { key: ']', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+    document.dispatchEvent(new KeyboardEvent('keydown', { key: '[', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    // Typing in a field: the key belongs to the field
+    const input = document.createElement('input');
+    document.body.appendChild(input);
+    input.dispatchEvent(new KeyboardEvent('keydown', { key: ']', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+    input.remove();
+
+    // Focus resting on a button (e.g. right after clicking a thumbnail or nav
+    // button) must NOT deaden the keys — buttons consume Space/Enter natively
+    document.querySelector('#galleryNextBtn')
+      .dispatchEvent(new KeyboardEvent('keydown', { key: ']', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+    document.dispatchEvent(new KeyboardEvent('keydown', { key: '[', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    // Full-size media viewer open: it owns the keys
+    isMediaViewerOpen.mockReturnValueOnce(true);
+    document.dispatchEvent(new KeyboardEvent('keydown', { key: ']', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+
+    // Another tab active: examples must not change behind the scenes
+    document.getElementById('showcase-tab').classList.remove('active');
+    document.dispatchEvent(new KeyboardEvent('keydown', { key: ']', bubbles: true, cancelable: true }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('0');
+  });
+
+  it('switches examples on touch swipe and suppresses the follow-up click', async () => {
+    const { renderShowcaseContent, initShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+    const { openMediaViewer } = await import(MEDIA_VIEWER_MODULE);
+
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    initShowcaseContent(document.querySelector('.showcase-gallery'));
+    updateMainDisplay(0);
+
+    const main = document.querySelector('.gallery-main');
+    const img = main.querySelector('.media-wrapper img');
+
+    // A plain tap still opens the full-size viewer
+    img.click();
+    expect(openMediaViewer).toHaveBeenCalledTimes(1);
+
+    // jsdom lacks PointerEvent; MouseEvent carries clientX/clientY and its
+    // undefined pointerType passes the non-mouse guard
+    main.dispatchEvent(new MouseEvent('pointerdown', { bubbles: true, clientX: 300, clientY: 100 }));
+    main.dispatchEvent(new MouseEvent('pointerup', { bubbles: true, clientX: 100, clientY: 110 }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+
+    // The click synthesized after the swipe must not open the viewer
+    document.querySelector('.gallery-main .media-wrapper img').click();
+    expect(openMediaViewer).toHaveBeenCalledTimes(1);
+
+    // A short drag below the threshold neither navigates nor eats the click
+    main.dispatchEvent(new MouseEvent('pointerdown', { bubbles: true, clientX: 300, clientY: 100 }));
+    main.dispatchEvent(new MouseEvent('pointerup', { bubbles: true, clientX: 280, clientY: 100 }));
+    expect(document.querySelector('.gallery-thumb.active')?.dataset.index).toBe('1');
+  });
+
+  it('marks the main viewer with a direction-aware slide class on switches', async () => {
+    const { renderShowcaseContent, updateMainDisplay } = await import(SHOWCASE_MODULE);
+
+    document.body.innerHTML = `<div id="showcase-tab">${renderShowcaseContent(IMAGES, [], PREVIEW_URL, true)}</div>`;
+    const container = document.getElementById('mainMediaContainer');
+
+    // galleryState.activeIndex leaks across tests → anchor on the actual index
+    const start = Number(document.querySelector('.gallery-thumb.active')?.dataset.index || 0);
+
+    updateMainDisplay(start); // same index: no direction, no slide
+    expect(container.classList.contains('slide-from-right')).toBe(false);
+    expect(container.classList.contains('slide-from-left')).toBe(false);
+
+    updateMainDisplay(start + 1); // forward
+    expect(container.classList.contains('slide-from-right')).toBe(true);
+    expect(container.classList.contains('slide-from-left')).toBe(false);
+
+    updateMainDisplay(start); // backward
+    expect(container.classList.contains('slide-from-left')).toBe(true);
+    expect(container.classList.contains('slide-from-right')).toBe(false);
+  });
 });

tests/frontend/managers/HelpManager.contentVersion.test.js

@@ -0,0 +1,162 @@
+import { describe, it, beforeEach, expect, vi } from 'vitest';
+
+const HELP_MANAGER_MODULE = new URL('../../../static/js/managers/HelpManager.js', import.meta.url).pathname;
+const VIEWED_KEY = 'lora_manager_help_viewed_content_version';
+
+function setupDom({ versionMarker = null } = {}) {
+    const markerAttr = versionMarker ? ` data-help-content-version="${versionMarker}"` : '';
+    document.body.innerHTML = `
+        <div class="help-toggle" id="helpToggleBtn">
+            <span class="update-badge"></span>
+        </div>
+        <div id="helpModal" class="modal"${markerAttr}>
+            <div class="help-tabs">
+                <button class="tab-btn" data-tab="getting-started"></button>
+                <button class="tab-btn" data-tab="shortcuts"></button>
+            </div>
+            <div class="tab-pane active" id="getting-started">
+                <button id="replayTutorialBtn" class="replay-tutorial-btn"></button>
+            </div>
+        </div>
+    `;
+}
+
+describe('HelpManager content-version badge logic', () => {
+    let HelpManager;
+
+    beforeEach(async () => {
+        ({ HelpManager } = await import(HELP_MANAGER_MODULE));
+    });
+
+    function badgeIsVisible() {
+        return document.querySelector('#helpToggleBtn .update-badge').classList.contains('visible');
+    }
+
+    it('has no new content when the served markup carries no version marker', () => {
+        setupDom({ versionMarker: null });
+        const manager = new HelpManager();
+
+        expect(manager.hasNewContent()).toBe(false);
+        manager.updateHelpBadge();
+        expect(badgeIsVisible()).toBe(false);
+    });
+
+    it('has new content when a version marker exists and nothing has been viewed yet', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        const manager = new HelpManager();
+
+        expect(manager.hasNewContent()).toBe(true);
+        manager.updateHelpBadge();
+        expect(badgeIsVisible()).toBe(true);
+    });
+
+    it('has no new content once the stored viewed version matches the marker', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        localStorage.setItem(VIEWED_KEY, '2026-09-03');
+        const manager = new HelpManager();
+
+        expect(manager.hasNewContent()).toBe(false);
+        manager.updateHelpBadge();
+        expect(badgeIsVisible()).toBe(false);
+    });
+
+    it('has new content again when the marker moves to a newer version', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        localStorage.setItem(VIEWED_KEY, '2025-10-11');
+        const manager = new HelpManager();
+
+        expect(manager.hasNewContent()).toBe(true);
+    });
+
+    it('markContentAsViewed persists the DOM marker version', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        const manager = new HelpManager();
+
+        manager.markContentAsViewed();
+
+        expect(localStorage.getItem(VIEWED_KEY)).toBe('2026-09-03');
+        expect(manager.hasNewContent()).toBe(false);
+    });
+
+    it('markContentAsViewed is a no-op without a version marker (stale assets)', () => {
+        setupDom({ versionMarker: null });
+        const manager = new HelpManager();
+
+        manager.markContentAsViewed();
+
+        expect(localStorage.getItem(VIEWED_KEY)).toBeNull();
+    });
+
+    it('opening the help modal without new content does not mark it as viewed', () => {
+        // Regression test: on a stale (pre-upgrade) page the user may open the
+        // help modal before refreshing; that must not suppress the badge for
+        // the new content they have not seen yet.
+        setupDom({ versionMarker: null });
+        window.modalManager = { toggleModal: vi.fn() };
+        const manager = new HelpManager();
+
+        manager.openHelpModal();
+
+        expect(localStorage.getItem(VIEWED_KEY)).toBeNull();
+        expect(manager.hasNewContent()).toBe(false);
+        delete window.modalManager;
+    });
+
+    it('opening the help modal with new content marks it as viewed and hides the badge', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        window.modalManager = { toggleModal: vi.fn() };
+        const manager = new HelpManager();
+        manager.updateHelpBadge();
+        expect(badgeIsVisible()).toBe(true);
+
+        manager.openHelpModal();
+
+        expect(localStorage.getItem(VIEWED_KEY)).toBe('2026-09-03');
+        expect(badgeIsVisible()).toBe(false);
+        delete window.modalManager;
+    });
+
+    it('adds new-content indicators to the getting-started and shortcuts tabs', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        const manager = new HelpManager();
+
+        manager.updateNewContentTabIndicators();
+
+        expect(document.querySelector('.help-tabs .tab-btn[data-tab="getting-started"]').classList.contains('has-new-content')).toBe(true);
+        expect(document.querySelector('.help-tabs .tab-btn[data-tab="shortcuts"]').classList.contains('has-new-content')).toBe(true);
+    });
+
+    it('flags the Replay Tutorial button and scrolls it into view', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        const replayBtn = document.getElementById('replayTutorialBtn');
+        replayBtn.scrollIntoView = vi.fn();
+        const manager = new HelpManager();
+
+        manager.updateNewContentTabIndicators();
+
+        expect(replayBtn.classList.contains('has-new-content')).toBe(true);
+        expect(replayBtn.scrollIntoView).toHaveBeenCalledWith({ behavior: 'smooth', block: 'nearest' });
+    });
+
+    it('does not flag the Replay Tutorial button when the content is not new', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        localStorage.setItem(VIEWED_KEY, '2026-09-03');
+        const manager = new HelpManager();
+
+        manager.updateNewContentTabIndicators();
+
+        expect(document.getElementById('replayTutorialBtn').classList.contains('has-new-content')).toBe(false);
+    });
+
+    it('does not scroll the Replay Tutorial button when the getting-started tab is inactive', () => {
+        setupDom({ versionMarker: '2026-09-03' });
+        document.getElementById('getting-started').classList.remove('active');
+        const replayBtn = document.getElementById('replayTutorialBtn');
+        replayBtn.scrollIntoView = vi.fn();
+        const manager = new HelpManager();
+
+        manager.updateNewContentTabIndicators();
+
+        expect(replayBtn.scrollIntoView).not.toHaveBeenCalled();
+    });
+});

tests/frontend/utils/activeFiltersSync.test.js

@@ -0,0 +1,130 @@
+import { describe, it, beforeEach, afterEach, expect, vi } from 'vitest';
+import { setStorageItem, removeStorageItem, setActiveFiltersListener } from '../../../static/js/utils/storageHelpers.js';
+import { initActiveFiltersSync, pushActiveFilters } from '../../../static/js/utils/activeFiltersSync.js';
+
+const okResponse = () => ({ ok: true, status: 200 });
+
+describe('activeFiltersSync', () => {
+  let fetchMock;
+
+  beforeEach(() => {
+    fetchMock = vi.fn(() => Promise.resolve(okResponse()));
+    vi.stubGlobal('fetch', fetchMock);
+  });
+
+  afterEach(() => {
+    vi.unstubAllGlobals();
+    vi.useRealTimers();
+  });
+
+  it('pushes current state immediately on init', async () => {
+    setStorageItem('loras_activeFolder', 'SD_XL');
+    setStorageItem('loras_recursiveSearch', false);
+    setStorageItem('loras_filters', { baseModel: ['SDXL 1.0'], tags: { anime: 'include' } });
+
+    initActiveFiltersSync('loras');
+    await Promise.resolve();
+
+    expect(fetchMock).toHaveBeenCalledTimes(1);
+    const [url, options] = fetchMock.mock.calls[0];
+    expect(url).toBe('/api/lm/loras/active-filters');
+    expect(options.method).toBe('PUT');
+    expect(JSON.parse(options.body)).toEqual({
+      activeFolder: 'SD_XL',
+      recursiveSearch: false,
+      filters: { baseModel: ['SDXL 1.0'], tags: { anime: 'include' } },
+    });
+  });
+
+  it('syncs with debounce when a filter key changes', async () => {
+    vi.useFakeTimers();
+    initActiveFiltersSync('loras');
+    fetchMock.mockClear();
+
+    setStorageItem('loras_activeFolder', 'anime');
+    setStorageItem('loras_activeFolder', 'anime/sub');
+
+    expect(fetchMock).not.toHaveBeenCalled();
+    await vi.advanceTimersByTimeAsync(400);
+
+    expect(fetchMock).toHaveBeenCalledTimes(1);
+    const body = JSON.parse(fetchMock.mock.calls[0][1].body);
+    expect(body.activeFolder).toBe('anime/sub');
+  });
+
+  it('does not sync for unrelated storage keys', async () => {
+    vi.useFakeTimers();
+    initActiveFiltersSync('loras');
+    fetchMock.mockClear();
+
+    setStorageItem('loras_sort', 'name');
+    setStorageItem('theme', 'dark');
+
+    await vi.advanceTimersByTimeAsync(1000);
+    expect(fetchMock).not.toHaveBeenCalled();
+  });
+
+  it('pushes null filters after the filters key is removed', async () => {
+    vi.useFakeTimers();
+    setStorageItem('loras_filters', { baseModel: ['Pony'] });
+    initActiveFiltersSync('loras');
+    fetchMock.mockClear();
+
+    removeStorageItem('loras_filters');
+    await vi.advanceTimersByTimeAsync(400);
+
+    expect(fetchMock).toHaveBeenCalledTimes(1);
+    const body = JSON.parse(fetchMock.mock.calls[0][1].body);
+    expect(body.filters).toBeNull();
+  });
+
+  it('normalizes the legacy "null" folder string to null', async () => {
+    localStorage.setItem('lora_manager_loras_activeFolder', 'null');
+
+    await pushActiveFilters('loras');
+
+    const body = JSON.parse(fetchMock.mock.calls[0][1].body);
+    expect(body.activeFolder).toBeNull();
+    expect(body.recursiveSearch).toBe(true);
+  });
+
+  it('warns instead of throwing when the request fails', async () => {
+    fetchMock.mockRejectedValue(new Error('network down'));
+    const warnSpy = vi.spyOn(console, 'warn').mockImplementation(() => {});
+
+    await expect(pushActiveFilters('loras')).resolves.toBeUndefined();
+    expect(warnSpy).toHaveBeenCalled();
+    warnSpy.mockRestore();
+  });
+});
+
+describe('storageHelpers active-filter listener', () => {
+  afterEach(() => {
+    setActiveFiltersListener(null);
+  });
+
+  it('notifies with the page type for filter keys', () => {
+    const listener = vi.fn();
+    setActiveFiltersListener(listener);
+
+    setStorageItem('loras_activeFolder', 'a');
+    setStorageItem('checkpoints_recursiveSearch', true);
+    removeStorageItem('embeddings_filters');
+
+    expect(listener.mock.calls.map((call) => call[0])).toEqual([
+      'loras',
+      'checkpoints',
+      'embeddings',
+    ]);
+  });
+
+  it('ignores non-filter keys', () => {
+    const listener = vi.fn();
+    setActiveFiltersListener(listener);
+
+    setStorageItem('loras_sort', 'name');
+    removeStorageItem('version_info');
+
+    expect(listener).not.toHaveBeenCalled();
+  });
+});

tests/frontend/utils/civitaiUtils.test.js

@@ -8,7 +8,9 @@ import {
     rewriteCivitaiUrl,
     getOptimizedUrl,
     getShowcaseUrl,
+    getDisplayUrl,
     getThumbnailUrl,
+    getGalleryThumbnailUrl,
     extractCivitaiImageId,
     extractCivitaiModelUrlParts,
     classifyModelRelinkUrl,
@@ -21,7 +23,9 @@ describe('civitaiUtils', () => {
     describe('OptimizationMode', () => {
         it('should have correct mode values', () => {
             expect(OptimizationMode.SHOWCASE).toBe('showcase');
+            expect(OptimizationMode.DISPLAY).toBe('display');
             expect(OptimizationMode.THUMBNAIL).toBe('thumbnail');
+            expect(OptimizationMode.GALLERY_THUMBNAIL).toBe('gallery-thumbnail');
         });
     });
 
@@ -107,6 +111,38 @@ describe('civitaiUtils', () => {
             expect(rewritten).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=450,optimized=true/12345.jpeg');
         });
 
+        it('should rewrite image URLs with /original=true for gallery-thumbnail mode (width=160)', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.jpeg';
+            const [rewritten, wasRewritten] = rewriteCivitaiUrl(originalUrl, 'image', OptimizationMode.GALLERY_THUMBNAIL);
+
+            expect(wasRewritten).toBe(true);
+            expect(rewritten).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=160,optimized=true/12345.jpeg');
+        });
+
+        it('should rewrite video URLs with /original=true for gallery-thumbnail mode', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.mp4';
+            const [rewritten, wasRewritten] = rewriteCivitaiUrl(originalUrl, 'video', OptimizationMode.GALLERY_THUMBNAIL);
+
+            expect(wasRewritten).toBe(true);
+            expect(rewritten).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/transcode=true,width=160,optimized=true/12345.mp4');
+        });
+
+        it('should rewrite image URLs with /original=true for display mode (width=2400)', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.jpeg';
+            const [rewritten, wasRewritten] = rewriteCivitaiUrl(originalUrl, 'image', OptimizationMode.DISPLAY);
+
+            expect(wasRewritten).toBe(true);
+            expect(rewritten).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=2400,optimized=true/12345.jpeg');
+        });
+
+        it('should keep videos full quality in display mode (no transcode/width)', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.mp4';
+            const [rewritten, wasRewritten] = rewriteCivitaiUrl(originalUrl, 'video', OptimizationMode.DISPLAY);
+
+            expect(wasRewritten).toBe(true);
+            expect(rewritten).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/optimized=true/12345.mp4');
+        });
+
         it('should not rewrite URLs without /original=true', () => {
             const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=450/12345.jpeg';
             const [rewritten, wasRewritten] = rewriteCivitaiUrl(originalUrl, 'image', OptimizationMode.THUMBNAIL);
@@ -232,6 +268,38 @@ describe('civitaiUtils', () => {
         });
     });
 
+    describe('getDisplayUrl', () => {
+        it('should return display-optimized URL (width=2400) for images', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.jpeg';
+            const displayUrl = getDisplayUrl(originalUrl, 'image');
+
+            expect(displayUrl).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=2400,optimized=true/12345.jpeg');
+        });
+
+        it('should keep videos full quality', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.mp4';
+            const displayUrl = getDisplayUrl(originalUrl, 'video');
+
+            expect(displayUrl).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/optimized=true/12345.mp4');
+        });
+    });
+
+    describe('getGalleryThumbnailUrl', () => {
+        it('should return gallery-thumbnail-optimized URL (width=160)', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.jpeg';
+            const thumbnailUrl = getGalleryThumbnailUrl(originalUrl, 'image');
+
+            expect(thumbnailUrl).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/width=160,optimized=true/12345.jpeg');
+        });
+
+        it('should handle videos for gallery thumbnails', () => {
+            const originalUrl = 'https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/original=true/12345.mp4';
+            const thumbnailUrl = getGalleryThumbnailUrl(originalUrl, 'video');
+
+            expect(thumbnailUrl).toBe('https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/abc123/transcode=true,width=160,optimized=true/12345.mp4');
+        });
+    });
+
     describe('isCivitaiUrl', () => {
         it('should return true for CivitAI URLs', () => {
             expect(isCivitaiUrl('https://image.civitai.com/something')).toBe(true);

tests/frontend/utils/renderingCapability.test.js

@@ -0,0 +1,59 @@
+import { describe, it, expect, beforeEach } from 'vitest';
+import {
+    isSoftwareRendererString,
+    applyModalBackdropBlurPolicy,
+} from '../../../static/js/utils/renderingCapability.js';
+
+describe('isSoftwareRendererString', () => {
+    it('detects SwiftShader (Chrome with hardware acceleration disabled)', () => {
+        expect(isSoftwareRendererString(
+            'WebKit WebGL SwiftShader'
+        )).toBe(true);
+        expect(isSoftwareRendererString(
+            'ANGLE (Google, Vulkan 1.3.0 (SwiftShader Device (Subzero) (0x0000C0DE)), SwiftShader driver)'
+        )).toBe(true);
+    });
+
+    it('detects Mesa software rasterizers (Linux)', () => {
+        expect(isSoftwareRendererString('llvmpipe (LLVM 17.0.6, 256 bits)')).toBe(true);
+        expect(isSoftwareRendererString('softpipe')).toBe(true);
+    });
+
+    it('detects generic software renderer strings', () => {
+        expect(isSoftwareRendererString('Software Renderer')).toBe(true);
+        expect(isSoftwareRendererString('Microsoft Basic Render Driver')).toBe(true);
+    });
+
+    it('accepts hardware GPU strings', () => {
+        expect(isSoftwareRendererString(
+            'ANGLE (NVIDIA, NVIDIA GeForce RTX 4090 Direct3D11 vs_5_0 ps_5_0, D3D11)'
+        )).toBe(false);
+        expect(isSoftwareRendererString(
+            'ANGLE (AMD, AMD Radeon RX 7900 XTX (0x0000744C) Direct3D11 vs_5_0 ps_5_0, D3D11)'
+        )).toBe(false);
+        expect(isSoftwareRendererString('Apple M4 Pro')).toBe(false);
+        expect(isSoftwareRendererString('Mesa Intel(R) UHD Graphics 620 (KBL GT2)')).toBe(false);
+    });
+
+    it('handles empty input', () => {
+        expect(isSoftwareRendererString('')).toBe(false);
+        expect(isSoftwareRendererString(null)).toBe(false);
+    });
+});
+
+describe('applyModalBackdropBlurPolicy', () => {
+    beforeEach(() => {
+        document.documentElement.classList.remove('no-modal-backdrop-blur');
+    });
+
+    it('adds the disabling class under software rendering', () => {
+        applyModalBackdropBlurPolicy(true);
+        expect(document.documentElement.classList.contains('no-modal-backdrop-blur')).toBe(true);
+    });
+
+    it('removes the disabling class under hardware rendering', () => {
+        document.documentElement.classList.add('no-modal-backdrop-blur');
+        applyModalBackdropBlurPolicy(false);
+        expect(document.documentElement.classList.contains('no-modal-backdrop-blur')).toBe(false);
+    });
+});

tests/nodes/test_random_checkpoint_unet_loaders.py

@@ -1,179 +0,0 @@
-"""Tests for the Random Checkpoint/Unet Loader nodes' base-model filtering and
-random-selection behavior.
-"""
-
-import pytest
-
-from py.nodes.random_checkpoint_loader import RandomCheckpointLoaderLM
-from py.nodes.random_unet_loader import RandomUNETLoaderLM
-
-
-class _FakeCache:
-    def __init__(self, raw_data):
-        self.raw_data = raw_data
-
-
-class _FakeScanner:
-    def __init__(self, raw_data, model_roots):
-        self._raw_data = raw_data
-        self._model_roots = model_roots
-
-    async def get_cached_data(self, force_refresh=False):
-        return _FakeCache(self._raw_data)
-
-    def get_model_roots(self):
-        return self._model_roots
-
-
-@pytest.fixture
-def base_model_library(tmp_path, monkeypatch):
-    from py.services.service_registry import ServiceRegistry
-
-    illustrious = tmp_path / "illustrious.safetensors"
-    illustrious.write_bytes(b"x")
-    flux = tmp_path / "flux.safetensors"
-    flux.write_bytes(b"x")
-    missing = tmp_path / "missing.safetensors"  # referenced but never created
-
-    raw_data = [
-        {
-            "sub_type": "checkpoint",
-            "file_path": str(illustrious),
-            "base_model": "Illustrious",
-        },
-        {"sub_type": "checkpoint", "file_path": str(flux), "base_model": "Flux.1 D"},
-        {
-            "sub_type": "checkpoint",
-            "file_path": str(missing),
-            "base_model": "SDXL 1.0",
-        },
-        {
-            "sub_type": "diffusion_model",
-            "file_path": str(flux),
-            "base_model": "Flux.1 D",
-        },
-    ]
-
-    async def _fake_scanner():
-        return _FakeScanner(raw_data, [str(tmp_path)])
-
-    monkeypatch.setattr(ServiceRegistry, "get_checkpoint_scanner", _fake_scanner)
-    return tmp_path
-
-
-def test_checkpoint_names_drop_deleted_files(tmp_path, monkeypatch):
-    from py.services.service_registry import ServiceRegistry
-
-    existing = tmp_path / "keep.safetensors"
-    existing.write_bytes(b"x")
-    deleted = tmp_path / "deleted.safetensors"  # referenced but never created
-
-    raw_data = [
-        {"sub_type": "checkpoint", "file_path": str(existing)},
-        {"sub_type": "checkpoint", "file_path": str(deleted)},
-        # Wrong type must stay excluded by the sub_type filter.
-        {"sub_type": "diffusion_model", "file_path": str(existing)},
-    ]
-
-    async def _fake_scanner():
-        return _FakeScanner(raw_data, [str(tmp_path)])
-
-    monkeypatch.setattr(ServiceRegistry, "get_checkpoint_scanner", _fake_scanner)
-    assert RandomCheckpointLoaderLM._get_checkpoint_names() == ["keep.safetensors"]
-
-
-def test_unet_names_drop_deleted_files(tmp_path, monkeypatch):
-    from py.services.service_registry import ServiceRegistry
-
-    existing = tmp_path / "keep.safetensors"
-    existing.write_bytes(b"x")
-    deleted = tmp_path / "deleted.safetensors"
-
-    raw_data = [
-        {"sub_type": "diffusion_model", "file_path": str(existing)},
-        {"sub_type": "diffusion_model", "file_path": str(deleted)},
-        {"sub_type": "checkpoint", "file_path": str(existing)},
-    ]
-
-    async def _fake_scanner():
-        return _FakeScanner(raw_data, [str(tmp_path)])
-
-    monkeypatch.setattr(ServiceRegistry, "get_checkpoint_scanner", _fake_scanner)
-    assert RandomUNETLoaderLM._get_unet_names() == ["keep.safetensors"]
-
-
-def test_checkpoint_names_empty_when_scanner_fails(tmp_path, monkeypatch):
-    from py.services.service_registry import ServiceRegistry
-
-    def _boom():
-        raise RuntimeError("scanner not available")
-
-    monkeypatch.setattr(ServiceRegistry, "get_checkpoint_scanner", _boom)
-    assert RandomCheckpointLoaderLM._get_checkpoint_names() == []
-
-
-def test_checkpoint_available_base_models(base_model_library):
-    # "SDXL 1.0" is excluded because its file no longer exists on disk.
-    assert RandomCheckpointLoaderLM._get_available_base_models() == [
-        "Any",
-        "Flux.1 D",
-        "Illustrious",
-    ]
-
-
-def test_checkpoint_names_filtered_by_base_model(base_model_library):
-    assert RandomCheckpointLoaderLM._get_checkpoint_names("Illustrious") == [
-        "illustrious.safetensors"
-    ]
-    assert RandomCheckpointLoaderLM._get_checkpoint_names("Any") == [
-        "flux.safetensors",
-        "illustrious.safetensors",
-    ]
-
-
-def test_unet_available_base_models(base_model_library):
-    assert RandomUNETLoaderLM._get_available_base_models() == ["Any", "Flux.1 D"]
-
-
-def test_load_checkpoint_random_selection_uses_pool(base_model_library, monkeypatch):
-    from py.nodes import random_checkpoint_loader as random_checkpoint_loader_module
-
-    monkeypatch.setattr(
-        random_checkpoint_loader_module,
-        "get_checkpoint_info_absolute",
-        lambda name: (str(base_model_library / name), {"file_path": name}),
-    )
-    monkeypatch.setattr(
-        random_checkpoint_loader_module.comfy.sd,
-        "load_checkpoint_guess_config",
-        lambda *a, **k: ("MODEL", "CLIP", "VAE", None),
-        raising=False,
-    )
-
-    node = RandomCheckpointLoaderLM()
-    result = node.load_checkpoint(
-        "ignored.safetensors", select_at_random=True, base_model="Illustrious"
-    )
-    # Only one checkpoint matches "Illustrious", so the random pick is deterministic here.
-    assert result[3] == "illustrious.safetensors"
-
-
-def test_load_checkpoint_random_selection_raises_when_pool_empty(base_model_library):
-    node = RandomCheckpointLoaderLM()
-    with pytest.raises(FileNotFoundError, match="No checkpoints found"):
-        node.load_checkpoint(
-            "ignored.safetensors", select_at_random=True, base_model="SDXL 1.0"
-        )
-
-
-def test_checkpoint_is_changed_forces_rerun_when_random():
-    assert RandomCheckpointLoaderLM.IS_CHANGED(
-        "a.safetensors", select_at_random=True, base_model="Any"
-    ) != RandomCheckpointLoaderLM.IS_CHANGED(
-        "a.safetensors", select_at_random=True, base_model="Any"
-    )
-    assert RandomCheckpointLoaderLM.IS_CHANGED(
-        "a.safetensors", select_at_random=False, base_model="Any"
-    ) == RandomCheckpointLoaderLM.IS_CHANGED(
-        "a.safetensors", select_at_random=False, base_model="Any"
-    )

tests/routes/test_active_filters_handlers.py

@@ -0,0 +1,197 @@
+import json
+import logging
+from types import SimpleNamespace
+
+import pytest
+from multidict import MultiDict
+
+from py.routes.handlers.model_handlers import ModelQueryHandler
+from py.services.active_filters_store import ActiveFiltersStore
+
+
+class DummyService:
+    model_type = "loras"
+
+    def __init__(self):
+        self.calls = []
+
+    async def search_relative_paths(self, search, limit, offset, **kwargs):
+        self.calls.append((search, limit, offset, kwargs))
+        return []
+
+
+def make_handler(service=None):
+    return ModelQueryHandler(
+        service=service or DummyService(), logger=logging.getLogger(__name__)
+    )
+
+
+def make_request(query=None, body=None, raise_on_json=False):
+    async def json_body():
+        if raise_on_json:
+            raise ValueError("bad json")
+        return body
+
+    return SimpleNamespace(
+        query=MultiDict(query or {}),
+        json=json_body,
+    )
+
+
+@pytest.fixture(autouse=True)
+def reset_store():
+    ActiveFiltersStore.reset_instance()
+    yield
+    ActiveFiltersStore.reset_instance()
+
+
+@pytest.mark.asyncio
+async def test_update_active_filters_stores_sanitized_payload():
+    handler = make_handler()
+    response = await handler.update_active_filters(
+        make_request(
+            body={
+                "activeFolder": "SD_XL",
+                "recursiveSearch": False,
+                "filters": {"baseModel": ["SDXL 1.0"], "rogue": "dropped"},
+                "rogue": "dropped",
+            }
+        )
+    )
+
+    assert response.status == 200
+    stored = ActiveFiltersStore.get_instance().get_filters("loras")
+    assert stored == {
+        "activeFolder": "SD_XL",
+        "recursiveSearch": False,
+        "filters": {"baseModel": ["SDXL 1.0"]},
+    }
+
+
+@pytest.mark.asyncio
+async def test_update_active_filters_rejects_invalid_json():
+    handler = make_handler()
+    response = await handler.update_active_filters(
+        make_request(raise_on_json=True)
+    )
+    assert response.status == 400
+
+
+@pytest.mark.asyncio
+async def test_update_active_filters_rejects_non_object_body():
+    handler = make_handler()
+    response = await handler.update_active_filters(make_request(body=["not", "dict"]))
+    assert response.status == 400
+
+
+@pytest.mark.asyncio
+async def test_get_active_filters_returns_stored_payload():
+    ActiveFiltersStore.get_instance().set_filters(
+        "loras", {"activeFolder": "anime", "recursiveSearch": True, "filters": None}
+    )
+    handler = make_handler()
+    response = await handler.get_active_filters(make_request())
+
+    payload = json.loads(response.text)
+    assert payload["success"] is True
+    assert payload["filters"]["activeFolder"] == "anime"
+
+
+@pytest.mark.asyncio
+async def test_get_active_filters_returns_null_when_unset():
+    handler = make_handler()
+    response = await handler.get_active_filters(make_request())
+
+    payload = json.loads(response.text)
+    assert payload["success"] is True
+    assert payload["filters"] is None
+
+
+@pytest.mark.asyncio
+async def test_relative_paths_injects_stored_active_filters():
+    ActiveFiltersStore.get_instance().set_filters(
+        "loras",
+        {
+            "activeFolder": "SD_XL",
+            "recursiveSearch": False,
+            "filters": {
+                "baseModel": ["SDXL 1.0"],
+                "tags": {"anime": "include"},
+                "tagLogic": "all",
+            },
+        },
+    )
+    service = DummyService()
+    handler = make_handler(service)
+
+    response = await handler.get_relative_paths(
+        make_request({"search": "cartoon", "use_active_filters": "true"})
+    )
+
+    assert response.status == 200
+    _, _, _, kwargs = service.calls[0]
+    assert kwargs["folder"] == "SD_XL"
+    assert kwargs["recursive"] is False
+    assert kwargs["base_models"] == ["SDXL 1.0"]
+    assert kwargs["tags"] == {"anime": "include"}
+    assert kwargs["tag_logic"] == "all"
+    assert kwargs["apply_filters"] is True
+
+
+@pytest.mark.asyncio
+async def test_relative_paths_explicit_params_take_precedence():
+    ActiveFiltersStore.get_instance().set_filters(
+        "loras",
+        {
+            "activeFolder": "SD_XL",
+            "recursiveSearch": True,
+            "filters": {"baseModel": ["SDXL 1.0"]},
+        },
+    )
+    service = DummyService()
+    handler = make_handler(service)
+
+    await handler.get_relative_paths(
+        make_request(
+            {
+                "search": "cartoon",
+                "use_active_filters": "true",
+                "folder": "pony",
+                "base_model": "Pony",
+            }
+        )
+    )
+
+    _, _, _, kwargs = service.calls[0]
+    assert kwargs["folder"] == "pony"
+    assert kwargs["base_models"] == ["Pony"]
+
+
+@pytest.mark.asyncio
+async def test_relative_paths_empty_store_still_runs_filter_pipeline():
+    service = DummyService()
+    handler = make_handler(service)
+
+    await handler.get_relative_paths(
+        make_request({"search": "cartoon", "use_active_filters": "true"})
+    )
+
+    _, _, _, kwargs = service.calls[0]
+    assert kwargs["apply_filters"] is True
+    assert kwargs["folder"] is None
+    assert kwargs["base_models"] == []
+
+
+@pytest.mark.asyncio
+async def test_relative_paths_without_flag_ignores_store():
+    ActiveFiltersStore.get_instance().set_filters(
+        "loras", {"activeFolder": "SD_XL", "recursiveSearch": True, "filters": None}
+    )
+    service = DummyService()
+    handler = make_handler(service)
+
+    await handler.get_relative_paths(make_request({"search": "cartoon"}))
+
+    _, _, _, kwargs = service.calls[0]
+    assert kwargs["folder"] is None
+    assert kwargs["apply_filters"] is False

tests/routes/test_random_loader_pool.py

@@ -1,5 +1,5 @@
-"""Tests for the loader-pool endpoint backing the Random Checkpoint/Unet
+"""Tests for the loader-pool endpoint backing the Checkpoint/Unet Loader
-Loader nodes' front-end base_model filtering.
+nodes' front-end base_model filtering.
 """
 
 import json

tests/routes/test_recipe_query_handler.py

@@ -5,6 +5,7 @@ from types import SimpleNamespace
 import pytest
 
 from py.routes.handlers.recipe_handlers import RecipeQueryHandler
+from py.services.recipe_scanner import UNKNOWN_BASE_MODEL_FILTER
 
 
 async def _noop():
@@ -46,3 +47,42 @@ async def test_recipe_query_handler_base_models_limit_zero_returns_all():
         {"name": "SDXL", "count": 2},
         {"name": "LTXV 2.3", "count": 1},
     ]
+
+
+@pytest.mark.asyncio
+async def test_recipe_query_handler_base_models_includes_unknown_bucket():
+    cache = SimpleNamespace(
+        raw_data=[
+            {"base_model": "SDXL"},
+            {"base_model": None},
+            {"base_model": ""},
+        ]
+    )
+    scanner = SimpleNamespace(get_cached_data=lambda: None)
+
+    async def get_cached_data():
+        return cache
+
+    scanner.get_cached_data = get_cached_data
+
+    handler = RecipeQueryHandler(
+        ensure_dependencies_ready=_noop,
+        recipe_scanner_getter=lambda: scanner,
+        format_recipe_file_url=lambda value: value,
+        logger=logging.getLogger(__name__),
+    )
+
+    response = await handler.get_base_models(
+        SimpleNamespace(query={"limit": "0"})  # pyright: ignore[reportArgumentType]
+    )
+    text = response.text
+    assert text is not None
+    payload = json.loads(text)
+
+    assert payload["success"] is True
+    # Unknown bucket carries a dedicated marker so the UI can show "Unknown"
+    # without colliding with real base model strings.
+    assert payload["base_models"] == [
+        {"name": "Unknown", "value": UNKNOWN_BASE_MODEL_FILTER, "count": 2},
+        {"name": "SDXL", "count": 1},
+    ]

tests/routes/test_recipe_routes.py

@@ -197,6 +197,7 @@ class StubAnalysisService:
         self.upload_calls: List[bytes] = []
         self.remote_calls: List[Optional[str]] = []
         self.local_calls: List[Optional[str]] = []
+        self.local_ignore_recipe_metadata_calls: List[bool] = []
         self.result = SimpleNamespace(payload={"loras": []}, status=200)
         self._recipe_parser_factory: Any = None
         StubAnalysisService.instances.append(self)
@@ -218,11 +219,16 @@ class StubAnalysisService:
         return self.result
 
     async def analyze_local_image(
-        self, *, file_path: Optional[str], recipe_scanner
+        self,
+        *,
+        file_path: Optional[str],
+        recipe_scanner,
+        ignore_recipe_metadata: bool = False,
     ) -> SimpleNamespace:  # noqa: D401
         if self.raise_for_local:
             raise self.raise_for_local
         self.local_calls.append(file_path)
+        self.local_ignore_recipe_metadata_calls.append(ignore_recipe_metadata)
         return self.result
 
     async def analyze_widget_metadata(self, *, recipe_scanner) -> SimpleNamespace:
@@ -257,6 +263,7 @@ class StubPersistenceService:
         extension=None,
         recipe_id=None,
         target_dir=None,
+        skip_optimize=False,
     ) -> SimpleNamespace:  # noqa: D401
         self.save_calls.append(
             {
@@ -269,6 +276,7 @@ class StubPersistenceService:
                 "extension": extension,
                 "recipe_id": recipe_id,
                 "target_dir": target_dir,
+                "skip_optimize": skip_optimize,
             }
         )
         return self.save_result
@@ -311,6 +319,28 @@ class StubPersistenceService:
     ) -> SimpleNamespace:  # pragma: no cover
         return SimpleNamespace(payload={"success": True}, status=200)
 
+    async def reconnect_checkpoint(
+        self, *, recipe_scanner, recipe_id: str, target_name: str
+    ) -> SimpleNamespace:  # pragma: no cover
+        return SimpleNamespace(payload={"success": True}, status=200)
+
+    async def restore_checkpoint(
+        self, *, recipe_scanner, recipe_id: str
+    ) -> SimpleNamespace:  # pragma: no cover
+        return SimpleNamespace(payload={"success": True}, status=200)
+
+    async def get_checkpoint_reconnect_suggestions(
+        self, *, recipe_scanner, recipe_id: str, query: str | None = None
+    ) -> SimpleNamespace:  # pragma: no cover
+        return SimpleNamespace(
+            payload={"success": True, "suggestions": []}, status=200
+        )
+
+    async def mark_checkpoint_hash_invalid(
+        self, *, recipe_scanner, recipe_id: str, hash_invalid: bool = True
+    ) -> SimpleNamespace:  # pragma: no cover
+        return SimpleNamespace(payload={"success": True}, status=200)
+
     async def bulk_delete(
         self, *, recipe_scanner, recipe_ids: List[str]
     ) -> SimpleNamespace:  # pragma: no cover
@@ -2050,3 +2080,279 @@ async def test_find_duplicates_forwards_include_prompt_and_assigns_unique_keys(
         assert len(groups) == 2
         assert {g["type"] for g in groups} == {"fingerprint", "source_path"}
         assert len({g["key"] for g in groups}) == 2
+
+
+# ---------------------------------------------------------------------------
+# Checkpoint reconnect routes (manual remediation for recipe.checkpoint)
+# ---------------------------------------------------------------------------
+
+
+async def test_checkpoint_reconnect_route(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/reconnect",
+            json={"recipe_id": "r1", "target_name": "main"},
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+
+
+async def test_checkpoint_reconnect_route_requires_target_name(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/reconnect",
+            json={"recipe_id": "r1"},
+        )
+        assert response.status == 400
+
+
+async def test_checkpoint_restore_route(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/restore",
+            json={"recipe_id": "r1"},
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+
+
+async def test_checkpoint_restore_route_requires_recipe_id(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/restore",
+            json={},
+        )
+        assert response.status == 400
+
+
+async def test_checkpoint_reconnect_suggestions_route(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get(
+            "/api/lm/recipe/r1/checkpoint/reconnect-suggestions?query=main"
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+        assert payload["suggestions"] == []
+
+
+async def test_checkpoint_reconnect_suggestions_route_without_query(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get(
+            "/api/lm/recipe/r1/checkpoint/reconnect-suggestions"
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+
+
+async def test_checkpoint_mark_hash_invalid_route(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/mark-hash-invalid",
+            json={"recipe_id": "r1"},
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+
+
+async def test_checkpoint_mark_hash_invalid_route_requires_recipe_id(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipe/checkpoint/mark-hash-invalid",
+            json={},
+        )
+        assert response.status == 400
+
+
+async def test_reimport_without_source_path_falls_back_to_recipe_file(
+    monkeypatch, tmp_path: Path
+) -> None:
+    """Drag & drop imports record no source_path; re-import must fall back to
+    the recipe's own saved image and re-parse ignoring the recipe metadata."""
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        recipe_file = harness.tmp_dir / "recipes" / "rec1.webp"
+        recipe_file.parent.mkdir(parents=True, exist_ok=True)
+        recipe_file.write_bytes(b"fake-image")
+
+        harness.scanner.recipes["rec1"] = {
+            "id": "rec1",
+            "title": "Old title",
+            "file_path": str(recipe_file),
+            "tags": ["tag1"],
+            # no source_path on purpose
+        }
+        harness.analysis.result = SimpleNamespace(
+            payload={
+                "success": True,
+                "recipe_id": "new-rec",
+                "loras": [],
+            },
+            status=200,
+        )
+        harness.persistence.save_result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "new-rec"}, status=200
+        )
+
+        response = await harness.client.post("/api/lm/recipe/rec1/reimport")
+        payload = await response.json()
+
+        assert response.status == 200
+        assert payload["success"] is True
+        assert payload["old_recipe_id"] == "rec1"
+        assert payload["recipe_id"] == "new-rec"
+        # Local analysis is used on the saved image, ignoring recipe metadata.
+        assert harness.analysis.local_calls == [str(recipe_file)]
+        assert harness.analysis.local_ignore_recipe_metadata_calls == [True]
+        # The old recipe is deleted after the fresh save.
+        assert harness.persistence.delete_calls == ["rec1"]
+        # The already-optimized preview image must be stored verbatim.
+        assert harness.persistence.save_calls[-1]["skip_optimize"] is True
+        assert harness.persistence.save_calls[-1]["image_bytes"] == b"fake-image"
+        # The fallback source is the recipe's own previous preview, which gets
+        # deleted with the old recipe — it must not be recorded as source_path.
+        assert harness.persistence.save_calls[-1]["metadata"]["source_path"] == ""
+        # User edits (title, tags) are carried over to the new recipe.
+        assert harness.persistence.update_calls[-1]["recipe_id"] == "new-rec"
+        assert harness.persistence.update_calls[-1]["updates"]["title"] == "Old title"
+        assert harness.persistence.update_calls[-1]["updates"]["tags"] == ["tag1"]
+
+
+async def test_reimport_with_dangling_source_path_falls_back_to_recipe_file(
+    monkeypatch, tmp_path: Path
+) -> None:
+    """A source_path pointing to a deleted file (left by an earlier re-import)
+    must not block re-import: fall back to the recipe's own saved image and
+    clear the dangling source_path."""
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        recipe_file = harness.tmp_dir / "recipes" / "rec3.webp"
+        recipe_file.parent.mkdir(parents=True, exist_ok=True)
+        recipe_file.write_bytes(b"fake-image")
+
+        harness.scanner.recipes["rec3"] = {
+            "id": "rec3",
+            "title": "Dangling source",
+            "file_path": str(recipe_file),
+            "tags": [],
+            # Dangling local path: the file no longer exists.
+            "source_path": str(harness.tmp_dir / "recipes" / "deleted.webp"),
+        }
+        harness.analysis.result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "new-rec-3", "loras": []},
+            status=200,
+        )
+        harness.persistence.save_result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "new-rec-3"}, status=200
+        )
+
+        response = await harness.client.post("/api/lm/recipe/rec3/reimport")
+        payload = await response.json()
+
+        assert response.status == 200
+        assert payload["success"] is True
+        assert payload["recipe_id"] == "new-rec-3"
+        assert harness.analysis.local_calls == [str(recipe_file)]
+        assert harness.persistence.delete_calls == ["rec3"]
+        # The dangling path is not carried over to the new recipe.
+        assert harness.persistence.save_calls[-1]["metadata"]["source_path"] == ""
+
+
+async def test_reimport_with_accessible_local_source_keeps_source_path(
+    monkeypatch, tmp_path: Path
+) -> None:
+    """When the recorded source_path is an existing external file, it remains
+    the source of truth and stays recorded on the new recipe."""
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        source_file = harness.tmp_dir / "imports" / "original.png"
+        source_file.parent.mkdir(parents=True, exist_ok=True)
+        source_file.write_bytes(b"original-image")
+        recipe_file = harness.tmp_dir / "recipes" / "rec4.webp"
+        recipe_file.parent.mkdir(parents=True, exist_ok=True)
+        recipe_file.write_bytes(b"fake-image")
+
+        harness.scanner.recipes["rec4"] = {
+            "id": "rec4",
+            "title": "External source",
+            "file_path": str(recipe_file),
+            "tags": [],
+            "source_path": str(source_file),
+        }
+        harness.analysis.result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "new-rec-4", "loras": []},
+            status=200,
+        )
+        harness.persistence.save_result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "new-rec-4"}, status=200
+        )
+
+        response = await harness.client.post("/api/lm/recipe/rec4/reimport")
+        payload = await response.json()
+
+        assert response.status == 200
+        assert payload["success"] is True
+        # The external source file is re-parsed, not the recipe preview.
+        assert harness.analysis.local_calls == [str(source_file)]
+        assert harness.persistence.save_calls[-1]["metadata"]["source_path"] == str(
+            source_file
+        )
+
+
+async def test_reimport_without_any_source_returns_400(
+    monkeypatch, tmp_path: Path
+) -> None:
+    """Recipes with neither source_path nor an accessible image cannot re-import."""
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        harness.scanner.recipes["rec2"] = {
+            "id": "rec2",
+            "title": "No source",
+            "file_path": str(harness.tmp_dir / "recipes" / "missing.webp"),
+        }
+
+        response = await harness.client.post("/api/lm/recipe/rec2/reimport")
+        payload = await response.json()
+
+        assert response.status == 400
+        assert payload["success"] is False
+        assert harness.analysis.local_calls == []
+        assert harness.persistence.delete_calls == []
+
+
+async def test_get_recipe_detail_includes_recipe_json_path(
+    monkeypatch, tmp_path: Path
+) -> None:
+    """The detail response exposes the recipe JSON path for open-location UI."""
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        recipes_dir = Path(harness.scanner.recipes_dir)
+        recipes_dir.mkdir(parents=True, exist_ok=True)
+        harness.scanner.recipes["recipe-1"] = {
+            "id": "recipe-1",
+            "title": "Demo",
+            "file_path": str(recipes_dir / "recipe-1.png"),
+        }
+        json_file = recipes_dir / "recipe-1.recipe.json"
+        json_file.write_text("{}", encoding="utf-8")
+
+        response = await harness.client.get("/api/lm/recipe/recipe-1")
+        assert response.status == 200
+        payload = await response.json()
+        assert payload["recipe_json_path"] == str(json_file)
+
+        # Without the JSON file on disk the key is omitted entirely.
+        json_file.unlink()
+        response = await harness.client.get("/api/lm/recipe/recipe-1")
+        assert response.status == 200
+        payload = await response.json()
+        assert "recipe_json_path" not in payload

tests/services/test_active_filters_store.py

@@ -0,0 +1,128 @@
+import pytest
+
+from py.services.active_filters_store import (
+    ActiveFiltersStore,
+    active_filters_to_query_kwargs,
+)
+
+
+@pytest.fixture(autouse=True)
+def reset_store():
+    ActiveFiltersStore.reset_instance()
+    yield
+    ActiveFiltersStore.reset_instance()
+
+
+def test_store_roundtrip():
+    store = ActiveFiltersStore.get_instance()
+    payload = {
+        "activeFolder": "SD_XL",
+        "recursiveSearch": False,
+        "filters": {"baseModel": ["SDXL 1.0"], "tags": {"anime": "include"}},
+    }
+    store.set_filters("loras", payload)
+
+    assert store.get_filters("loras") == payload
+    assert store.get_filters("checkpoints") is None
+
+
+def test_store_sanitizes_payload():
+    store = ActiveFiltersStore.get_instance()
+    store.set_filters(
+        "loras",
+        {
+            "activeFolder": "anime",
+            "recursiveSearch": True,
+            "filters": {"baseModel": [], "unexpected": "dropped"},
+            "extra": "dropped",
+        },
+    )
+
+    stored = store.get_filters("loras")
+    assert stored == {
+        "activeFolder": "anime",
+        "recursiveSearch": True,
+        "filters": {"baseModel": []},
+    }
+
+
+def test_store_non_dict_filters_become_none():
+    store = ActiveFiltersStore.get_instance()
+    store.set_filters("loras", {"activeFolder": None, "filters": "garbage"})
+
+    assert store.get_filters("loras")["filters"] is None
+
+
+def test_store_clear():
+    store = ActiveFiltersStore.get_instance()
+    store.set_filters("loras", {"activeFolder": "x"})
+    store.clear("loras")
+
+    assert store.get_filters("loras") is None
+
+
+def test_mapping_empty_payload():
+    assert active_filters_to_query_kwargs(None) == {}
+    assert active_filters_to_query_kwargs({}) == {}
+    assert active_filters_to_query_kwargs({"activeFolder": None}) == {"recursive": True}
+
+
+def test_mapping_folder():
+    assert active_filters_to_query_kwargs(
+        {"activeFolder": "SD_XL", "recursiveSearch": True}
+    ) == {"folder": "SD_XL", "recursive": True}
+
+
+def test_mapping_root_folder_non_recursive():
+    # Root folder with recursion disabled matches only root-level files
+    assert active_filters_to_query_kwargs(
+        {"activeFolder": None, "recursiveSearch": False}
+    ) == {"folder": "", "recursive": False}
+
+
+def test_mapping_legacy_null_string_folder():
+    assert active_filters_to_query_kwargs(
+        {"activeFolder": "null", "recursiveSearch": True}
+    ) == {"recursive": True}
+
+
+def test_mapping_full_filters():
+    kwargs = active_filters_to_query_kwargs(
+        {
+            "activeFolder": "anime",
+            "recursiveSearch": True,
+            "filters": {
+                "baseModel": ["SDXL 1.0", "Pony"],
+                "tags": {"anime": "include", "3d": "exclude", "junk": "ignored"},
+                "autoTags": {"cute": "include"},
+                "modelTypes": ["LoRA"],
+                "tagLogic": "all",
+                "license": {"noCredit": "include", "allowSelling": "exclude"},
+            },
+        }
+    )
+
+    assert kwargs == {
+        "folder": "anime",
+        "recursive": True,
+        "base_models": ["SDXL 1.0", "Pony"],
+        "tags": {"anime": "include", "3d": "exclude"},
+        "auto_tags": {"cute": "include"},
+        "model_types": ["LoRA"],
+        "tag_logic": "all",
+        "credit_required": False,
+        "allow_selling_generated_content": False,
+    }
+
+
+def test_mapping_license_exclude_variants():
+    kwargs = active_filters_to_query_kwargs(
+        {
+            "filters": {
+                "license": {"noCredit": "exclude", "allowSelling": "include"},
+            },
+        }
+    )
+
+    assert kwargs["credit_required"] is True
+    assert kwargs["allow_selling_generated_content"] is True

tests/services/test_automatic_metadata_parser.py

@@ -503,3 +503,64 @@ async def test_parse_metadata_extracts_checkpoint_from_model_hash(monkeypatch):
     assert result["model"] == checkpoint
     assert result["base_model"] == "flux"
     assert result["loras"] == []
+
+
+@pytest.mark.asyncio
+async def test_parse_metadata_keeps_empty_placeholder_hash_lora_unresolved(monkeypatch):
+    """A LoRA hash equal to the SHA256("") placeholder must never be resolved
+    against CivitAI or the local hash index, but the LoRA item itself must be
+    kept: matched by filename locally when present, otherwise kept as an
+    unresolved entry (no hash) instead of being dropped."""
+    queried_hashes = []
+
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_by_hash(self, model_hash):
+                queried_hashes.append(model_hash)
+                return None, "Model not found"
+
+            async def get_model_version_info(self, version_id):
+                raise AssertionError("get_model_version_info should not be called")
+
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.automatic.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = AutomaticMetadataParser()
+    metadata_text = (
+        "photo of a DeLorean DMC12, <lora:dmc12bttf:1.2>, at night\n"
+        "Steps: 20, Sampler: Euler, CFG scale: 1, Seed: 2242760352, Size: 1280x720, "
+        "Model: flux1-dev, Model hash: 3f97fdc57a, "
+        'Lora hashes: "dmc12bttf: e3b0c44298fc"'
+    )
+
+    # Local file with the same name: the item is matched by filename.
+    scanner_with_local = LocalRecipeScanner({"dmc12bttf": local_lora("dmc12bttf")})
+    result = await parser.parse_metadata(metadata_text, recipe_scanner=scanner_with_local)
+
+    assert "e3b0c44298fc" not in queried_hashes
+    assert "e3b0c44298" not in queried_hashes
+    assert scanner_with_local.hash_queries == []
+    assert scanner_with_local.queries == ["dmc12bttf"]
+    assert len(result["loras"]) == 1
+    assert result["loras"][0]["file_name"] == "dmc12bttf"
+    assert result["loras"][0]["weight"] == 1.2
+    assert result["loras"][0]["existsLocally"] is True
+    assert result["loras"][0]["isDeleted"] is False
+
+    # No local file: the item is kept as unresolved (empty hash, flagged
+    # hashInvalid so the UI renders the unresolvable-hash badge).
+    scanner_without_local = LocalRecipeScanner({})
+    result = await parser.parse_metadata(metadata_text, recipe_scanner=scanner_without_local)
+
+    assert len(result["loras"]) == 1
+    lora = result["loras"][0]
+    assert lora["file_name"] == "dmc12bttf"
+    assert lora["weight"] == 1.2
+    assert lora["hash"] == ""
+    assert lora["hashInvalid"] is True
+    assert lora["existsLocally"] is False
+    assert lora["isDeleted"] is False

tests/services/test_civitai_client.py

@@ -789,3 +789,32 @@ async def test_get_creator_model_count_never_raises(downloader):
 
     client = await CivitaiClient.get_instance()
     assert await client.get_creator_model_count("pixel") is None
+
+
+@pytest.mark.parametrize(
+    "placeholder_hash",
+    [
+        "e3b0c44298",  # AutoV2 (10 chars)
+        "e3b0c44298fc",  # AutoV3 (12 chars)
+        "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",  # full SHA256
+    ],
+)
+async def test_get_model_by_hash_rejects_empty_placeholder_without_request(downloader, placeholder_hash):
+    """The empty-hash placeholder must never be resolved via the by-hash API:
+    CivitAI's index can contain polluted entries for it (e.g. a broken SD 1.5
+    LoRA whose AutoV3 equals the placeholder)."""
+    requested = []
+
+    async def fake_make_request(method, url, use_auth=True, **kwargs):
+        requested.append(url)
+        return True, {}
+
+    downloader.make_request = fake_make_request
+
+    client = await CivitaiClient.get_instance()
+
+    result, error = await client.get_model_by_hash(placeholder_hash)
+
+    assert result is None
+    assert error == "Model not found"
+    assert requested == []