chore(release): bump version to v1.2.2

The public REST API rewrites files[].name to "{model}_{version}" for
non-LoRA model types, so every precision variant of a multi-file version
shared one name and landed on disk with a random short-hash suffix.

Fetch the raw stored filename from the model-versions/mini endpoint
(always pinned with modelFileId) and use it for the on-disk name and
metadata when available; fall back silently to the REST name otherwise.
CivArchive already serves raw names and is skipped.

.agents/skills/lora-manager-e2e/SKILL.md

@@ -1,146 +0,0 @@
----
-name: lora-manager-e2e
-description: "End-to-end testing and validation for LoRa Manager features. Use ONLY for sandboxed E2E validation of LoRa Manager standalone mode: start the standalone server on a free port with --settings-path, drive the web UI (http://127.0.0.1:{PORT}/loras) via Chrome DevTools MCP, and verify frontend-to-backend integration. NOT for UI behavior checks that unit tests (Vitest/jsdom) can cover. Trigger keywords: E2E, standalone, Chrome DevTools MCP, lora-manager-e2e, sandbox."
----
-
-# LoRa Manager E2E Testing
-
-End-to-end testing of LoRa Manager standalone mode using Chrome DevTools MCP.
-
-## When to Use — and When NOT To
-
-E2E runs are slow and token-heavy. Reach for them only when the question genuinely
-spans server + browser (routing, scan persistence, websocket updates, EXIF writes).
-
-- **Default to unit/component tests first**: `npm run test:js` (Vitest/jsdom) covers
-  DOM rendering, modal behavior, event handling and API-client calls deterministically
-  in seconds. Backend logic goes through `pytest`. A UI-behavior question answered by
-  jsdom MUST NOT be escalated to E2E.
-- **Use E2E only when** the behavior cannot be observed without a live server and a
-  real browser, e.g. template rendering through the aiohttp server, scanner → SQLite
-  persistence → API → DOM round-trips, or real EXIF/image writes.
-- If you start an E2E and realize a unit test would answer the question, stop and
-  switch.
-
-**Browser driver is fixed: Chrome DevTools MCP.** Do not substitute kimi-webbridge —
-it operates on the user's real browser (real tabs, real sessions, synthetic
-`isTrusted=false` events), which breaks the isolation this skill requires and lacks
-the console/network inspection E2E debugging relies on. kimi-webbridge is for
-interactive browsing with the user's real login sessions, not for sandboxed E2E.
-
-## Conventions
-
-- **`{PORT}`**: default candidate `8188`, but it is **commonly occupied by a live
-  ComfyUI** — always check first (`ss -tlnp | grep ':{PORT}'`) and use a free port
-  (e.g. `8199`). Substitute the chosen port everywhere below. Never kill a process
-  you did not start for this E2E.
-- **`<repo-root>`**: the repository/worktree root; run all commands from there.
-- **`<sandbox>`**: a throwaway dir, e.g. `/tmp/opencode/<plan>-e2e`.
-
-## SANDBOX (MANDATORY)
-
-> Every E2E run MUST target a throwaway sandbox, never real user data.
-
-1. **Explicit settings directory**: always launch with `--settings-path <sandbox>/settings`.
-   This pins ALL runtime data (`settings.json`, `cache/`, `backups/`, `logs/`, `stats/`,
-   `wildcards/`) under the sandbox. **Never** create `<repo-root>/settings.json` — the repo
-   folder is usually the real ComfyUI plugin folder and a portable settings file there is
-   read by the real instance.
-2. **Sandboxed library paths**: point `folder_paths` / `recipes_path` /
-   `example_images_path` at disposable dirs under `<sandbox>` — never the real library,
-   real recipe dir, or real settings:
-
-   ```json
-   {
-     "folder_paths": {
-       "loras": ["<sandbox>/models/loras"],
-       "checkpoints": ["<sandbox>/models/checkpoints"],
-       "unet": ["<sandbox>/models/checkpoints"],
-       "diffusers": []
-     },
-     "recipes_path": "<sandbox>/recipes",
-     "example_images_path": "<sandbox>/example_images"
-   }
-   ```
-
-3. **Real-data protection proof**: before starting and after finishing, snapshot the real
-   config and recipe library and confirm they are byte-identical; also confirm
-   `<repo-root>` gained no `settings.json` or `cache/`:
-
-   ```bash
-   sha256sum ~/.config/ComfyUI-LoRA-Manager/settings.json > <sandbox>/settings.before.sha256
-   ls ~/models/recipes/*.recipe.json 2>/dev/null | wc -l > <sandbox>/recipes-count.before.txt
-   # AFTER the run: record again and diff. Any change = the run leaked into real data.
-   ```
-
-## Quick Start
-
-```bash
-cd <repo-root>
-# 1. Sandbox
-mkdir -p <sandbox>/settings <sandbox>/models/{loras,checkpoints} <sandbox>/{recipes,example_images}
-#    write <sandbox>/settings/settings.json per the SANDBOX example
-# 2. Port
-ss -tlnp | grep ':{PORT}' || echo "port {PORT} is free"
-# 3. Server — MUST be fully detached (a plain background & dies with the shell);
-#    the helper enforces this and manages its own pidfile
-python .agents/skills/lora-manager-e2e/scripts/start_server.py \
-  --port {PORT} --settings-path <sandbox>/settings --wait --timeout 30 --detach
-ss -tlnp | grep ':{PORT}'   # verify listening BEFORE proceeding
-# 4. Chrome with remote debugging, then connect Chrome DevTools MCP (verify via list_pages)
-google-chrome --remote-debugging-port=9222 --user-data-dir=/tmp/chrome-lora-manager http://127.0.0.1:{PORT}/loras
-```
-
-Then drive the UI with the MCP tools (`take_snapshot`, `click`, `fill`, `fill_form`,
-`evaluate_script`, `wait_for`, `list_network_requests`, `list_console_messages`) —
-see [references/mcp-cheatsheet.md](references/mcp-cheatsheet.md) for patterns.
-
-Server restart after config/fixture changes:
-
-```bash
-python .agents/skills/lora-manager-e2e/scripts/start_server.py \
-  --port {PORT} --settings-path <sandbox>/settings --restart --wait --detach
-# then reload the browser page (ignoreCache=True)
-```
-
-`--restart` only kills the E2E server the script itself started (via its pidfile) and
-aborts instead of killing unrelated processes on the port.
-
-## Abort Rule
-
-A sandboxed E2E should finish in well under 30 minutes. If any phase exceeds ~2x its
-expected duration (server readiness > 60 s, MCP connect > 2 min, a single scenario >
-10 min), or any single tool call fails 3+ times in a row, **STOP** — do not retry
-blindly. Report `BLOCKED` with the phase, last observed state (server PID,
-`ss -tlnp` output, page snapshot, last API response) and suspected cause. A clean
-BLOCKED report beats an hour of retries.
-
-## Troubleshooting
-
-- **"browser is already running" / `list_pages` fails**: a stale Chrome holds the
-  profile dir. Find it (`ps -ef | grep -i '[c]hrome.*user-data-dir'`), confirm it is a
-  leftover QA Chrome (not the live ComfyUI, not your current MCP browser), kill only
-  that PID, then retry `list_pages`.
-- **MCP refuses to write screenshots into the worktree**: save to `/tmp` via
-  `take_screenshot(filePath="/tmp/...")` and copy into the evidence dir from the shell.
-
-## Cleanup
-
-1. Stop the standalone server: `kill <recorded-pid>` (only the PID you started), then
-   confirm `ss -tlnp | grep ':{PORT}'` is empty.
-2. Close browser pages (keep at least one open).
-3. `rm -rf <sandbox>`; verify `<repo-root>` gained no `settings.json` or `cache/`.
-4. Re-run the real-data protection check from the SANDBOX section and record the result.
-
-## References & Scripts
-
-- [references/mcp-cheatsheet.md](references/mcp-cheatsheet.md) — Chrome DevTools MCP
-  command patterns (navigation, waiting, snapshots, forms, network, console, performance).
-- [references/test-scenarios.md](references/test-scenarios.md) — detailed test scenarios
-  (list display, metadata editing, recipes, settings, import/export).
-- [references/recipe-rematch-fixtures.md](references/recipe-rematch-fixtures.md) —
-  fixture format, fresh-state reset and known gaps for recipe rematch/repair E2E runs.
-- `scripts/start_server.py` — start/restart the standalone server
-  (`--port --settings-path --restart --wait --timeout --detach`); refuses to touch
-  unrelated processes on the port.
-- `scripts/wait_for_server.py` — poll readiness (`--port --timeout`).

.agents/skills/lora-manager-e2e/references/mcp-cheatsheet.md

@@ -1,360 +0,0 @@
-# Chrome DevTools MCP Cheatsheet for LoRa Manager
-
-Quick reference for common MCP commands used in LoRa Manager E2E testing.
-
-> **Port convention**: `{PORT}` is the port chosen for the E2E run (default candidate `8188`, but only if actually free — see the SKILL.md Port Selection section; use e.g. `8199` when `8188` is occupied by a live ComfyUI). Always run against the **sandboxed** standalone server, never a live instance.
-
-## Navigation
-
-```python
-# Navigate to LoRA list page
-navigate_page(type="url", url="http://127.0.0.1:{PORT}/loras")
-
-# Reload page with cache clear
-navigate_page(type="reload", ignoreCache=True)
-
-# Go back/forward
-navigate_page(type="back")
-navigate_page(type="forward")
-```
-
-## Waiting
-
-```python
-# Wait for text to appear
-wait_for(text="LoRAs", timeout=10000)
-
-# Wait for specific element (via evaluate_script)
-evaluate_script(function="""
-() => {
-    return new Promise((resolve) => {
-        const check = () => {
-            if (document.querySelector('.lora-card')) {
-                resolve(true);
-            } else {
-                setTimeout(check, 100);
-            }
-        };
-        check();
-    });
-}
-""")
-```
-
-## Taking Snapshots
-
-```python
-# Full page snapshot
-snapshot = take_snapshot()
-
-# Verbose snapshot (more details)
-snapshot = take_snapshot(verbose=True)
-
-# Save to file
-take_snapshot(filePath="test-snapshots/page-load.json")
-```
-
-## Element Interaction
-
-```python
-# Click element
-click(uid="element-uid-from-snapshot")
-
-# Double click
-click(uid="element-uid", dblClick=True)
-
-# Fill input
-fill(uid="search-input", value="test query")
-
-# Fill multiple inputs
-fill_form(elements=[
-    {"uid": "input-1", "value": "value 1"},
-    {"uid": "input-2", "value": "value 2"},
-])
-
-# Hover
-hover(uid="lora-card-1")
-
-# Upload file
-upload_file(uid="file-input", filePath="/path/to/file.safetensors")
-```
-
-## Keyboard Input
-
-```python
-# Press key
-press_key(key="Enter")
-press_key(key="Escape")
-press_key(key="Tab")
-
-# Keyboard shortcuts
-press_key(key="Control+A")  # Select all
-press_key(key="Control+F")  # Find
-```
-
-## JavaScript Evaluation
-
-```python
-# Simple evaluation
-result = evaluate_script(function="() => document.title")
-
-# Async evaluation
-result = evaluate_script(function="""
-async () => {
-    const response = await fetch('/loras/api/list');
-    return await response.json();
-}
-""")
-
-# Check element existence
-exists = evaluate_script(function="""
-() => document.querySelector('.lora-card') !== null
-""")
-
-# Get element count
-count = evaluate_script(function="""
-() => document.querySelectorAll('.lora-card').length
-""")
-```
-
-## Network Monitoring
-
-```python
-# List all network requests
-requests = list_network_requests()
-
-# Filter by resource type
-xhr_requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Get specific request details
-details = get_network_request(reqid=123)
-
-# Include preserved requests from previous navigations
-all_requests = list_network_requests(includePreservedRequests=True)
-```
-
-## Console Monitoring
-
-```python
-# List all console messages
-messages = list_console_messages()
-
-# Filter by type
-errors = list_console_messages(types=["error", "warn"])
-
-# Include preserved messages
-all_messages = list_console_messages(includePreservedMessages=True)
-
-# Get specific message
-details = get_console_message(msgid=1)
-```
-
-## Performance Testing
-
-```python
-# Start trace with page reload
-performance_start_trace(reload=True, autoStop=False)
-
-# Start trace without reload
-performance_start_trace(reload=False, autoStop=True, filePath="trace.json.gz")
-
-# Stop trace
-results = performance_stop_trace()
-
-# Stop and save
-performance_stop_trace(filePath="trace-results.json.gz")
-
-# Analyze specific insight
-insight = performance_analyze_insight(
-    insightSetId="results.insightSets[0].id",
-    insightName="LCPBreakdown"
-)
-```
-
-## Page Management
-
-```python
-# List open pages
-pages = list_pages()
-
-# Select a page
-select_page(pageId=0, bringToFront=True)
-
-# Create new page
-new_page(url="http://127.0.0.1:{PORT}/loras")
-
-# Close page (keep at least one open!)
-close_page(pageId=1)
-
-# Resize page
-resize_page(width=1920, height=1080)
-```
-
-## Screenshots
-
-```python
-# Full page screenshot
-take_screenshot(fullPage=True)
-
-# Viewport screenshot
-take_screenshot()
-
-# Element screenshot
-take_screenshot(uid="lora-card-1")
-
-# Save to file
-take_screenshot(filePath="screenshots/page.png", format="png")
-
-# JPEG with quality
-take_screenshot(filePath="screenshots/page.jpg", format="jpeg", quality=90)
-```
-
-## Dialog Handling
-
-```python
-# Accept dialog
-handle_dialog(action="accept")
-
-# Accept with text input
-handle_dialog(action="accept", promptText="user input")
-
-# Dismiss dialog
-handle_dialog(action="dismiss")
-```
-
-## Device Emulation
-
-```python
-# Mobile viewport
-emulate(viewport={"width": 375, "height": 667, "isMobile": True, "hasTouch": True})
-
-# Tablet viewport
-emulate(viewport={"width": 768, "height": 1024, "isMobile": True, "hasTouch": True})
-
-# Desktop viewport
-emulate(viewport={"width": 1920, "height": 1080})
-
-# Network throttling
-emulate(networkConditions="Slow 3G")
-emulate(networkConditions="Fast 4G")
-
-# CPU throttling
-emulate(cpuThrottlingRate=4)  # 4x slowdown
-
-# Geolocation
-emulate(geolocation={"latitude": 37.7749, "longitude": -122.4194})
-
-# User agent
-emulate(userAgent="Mozilla/5.0 (Custom)")
-
-# Reset emulation
-emulate(viewport=None, networkConditions="No emulation", userAgent=None)
-```
-
-## Drag and Drop
-
-```python
-# Drag element to another
-drag(from_uid="draggable-item", to_uid="drop-zone")
-```
-
-## Common LoRa Manager Test Patterns
-
-### Verify LoRA Cards Loaded
-
-```python
-navigate_page(type="url", url="http://127.0.0.1:{PORT}/loras")
-wait_for(text="LoRAs", timeout=10000)
-
-# Check if cards loaded
-result = evaluate_script(function="""
-() => {
-    const cards = document.querySelectorAll('.lora-card');
-    return {
-        count: cards.length,
-        hasData: cards.length > 0
-    };
-}
-""")
-```
-
-### Search and Verify Results
-
-```python
-fill(uid="search-input", value="character")
-press_key(key="Enter")
-wait_for(timeout=2000)  # Wait for debounce
-
-# Check results
-result = evaluate_script(function="""
-() => {
-    const cards = document.querySelectorAll('.lora-card');
-    const names = Array.from(cards).map(c => c.dataset.name || c.textContent);
-    return { count: cards.length, names };
-}
-""")
-```
-
-### Check API Response
-
-```python
-# Trigger API call
-evaluate_script(function="""
-() => window.loraApiCallPromise = fetch('/loras/api/list').then(r => r.json())
-""")
-
-# Wait and get result
-import time
-time.sleep(1)
-
-result = evaluate_script(function="""
-async () => await window.loraApiCallPromise
-""")
-```
-
-### Monitor Console for Errors
-
-```python
-# Before test: clear console (navigate reloads)
-navigate_page(type="reload")
-
-# ... perform actions ...
-
-# Check for errors
-errors = list_console_messages(types=["error"])
-assert len(errors) == 0, f"Console errors: {errors}"
-```
-
-## Troubleshooting
-
-### Stale profile lock ("browser is already running" / `list_pages` fails)
-
-A Chrome profile held by a stale Chrome from a prior MCP session makes `list_pages`
-fail with "browser is already running". Fix:
-
-1. Find the stale Chrome that owns the profile dir (e.g. `~/.config/chrome-dev-profile`):
-   ```bash
-   ps -ef | grep -i '[c]hrome.*user-data-dir'
-   ```
-2. Confirm it is a QA Chrome from a completed task (NOT the live ComfyUI server, NOT
-   your current MCP instance).
-3. Kill ONLY that stale Chrome (`kill <stale-pid>`), then retry `list_pages`.
-
-### Screenshot-write restrictions
-
-The MCP may refuse to write into paths outside its configured workspace roots
-(e.g. `.omo/evidence/screenshots/` under a worktree that canonicalizes to an unmapped
-path). Save the screenshot to `/tmp` via the MCP, then copy it into the evidence dir:
-
-```bash
-# MCP:  take_screenshot(filePath="/tmp/<plan>-e2e/recipe-b-after.png", format="png")
-# Shell:
-mkdir -p <repo-root>/.omo/evidence/screenshots
-cp /tmp/<plan>-e2e/recipe-b-after.png <repo-root>/.omo/evidence/screenshots/
-```
-
-### Time budgets & abort rule
-
-See SKILL.md "Time Budgets & Abort Guidance": if a phase exceeds ~2x its budget or a
-tool call retries 3+ times in a row, STOP and report BLOCKED with the last observed
-state (server PID + `ss -tlnp`, page snapshot, last API response). Do not loop.

.agents/skills/lora-manager-e2e/references/recipe-rematch-fixtures.md

@@ -1,72 +0,0 @@
-# Recipe Rematch/Repair E2E — Fixtures, Fresh State, Known Gaps
-
-Specialized guidance for recipe rematch/repair E2E runs, extracted from the SKILL.md
-main flow. Read the SKILL.md SANDBOX section first — everything here assumes a
-sandboxed run.
-
-## Fixture Rules (validated by the task-8 E2E)
-
-Seed the **sandboxed** `recipes_path` with hand-written fixture recipes:
-
-1. **Filename constraint**: each file MUST be named `f"{id}.recipe.json"` **and** the
-   in-JSON `id` field MUST equal the filename. Discovery accepts any `*.recipe.json`,
-   but persistence resolves the path via `get_recipe_json_path` and
-   `_save_recipe_persistently` returns `False` on a mismatch → the fixture would be
-   counted as an error.
-   - `recipe-a.recipe.json` → in-JSON `"id": "recipe-a"`
-2. **File format**: mirror an existing recipe JSON — top-level `id`, `file_path`,
-   `title`, `loras`, `fingerprint`, `gen_params`; lora entries per the persistence
-   conventions (`hash`, `file_name`, `modelVersionId`, `isDeleted`, ...).
-3. **Companion image**: each recipe needs an image (e.g. a `.webp` generated with PIL)
-   referenced by `file_path`, used for EXIF verification
-   (`ExifUtils.append_recipe_metadata` writes a `"Recipe metadata: ..."` marker; a
-   freshly generated `.webp` with no marker is the clean "untouched" control).
-4. **autov3 three-state contract**: for L3 (autov3-only, renamed-file) fixtures the
-   local model's `.metadata.json` sidecar MUST have the `autov3` key **ABSENT** (the
-   "unchecked" state), NOT `""` — `""` is the TERMINAL "checked but unavailable" state
-   that L3 deliberately skips. The scanner computes + persists `autov3` from the file
-   header during the normal library scan (`model_scanner.py` `_process_model_file`), so
-   the live L3 match resolves through the local autov3/hash cache; the
-   computed-autov3 branch for unchecked items is covered by the unit suite.
-5. **Fixture design for a rematch run** (mirrors the task-8 E2E):
-   - `recipe-a`: lora entry `isDeleted=True`, `hash` = 12-char autov3 computed from the
-     local model (`calculate_autov3`, `py/utils/file_utils.py`), whose local model file
-     was RENAMED after the recipe was written so `file_name` differs (proves L3 match
-     without filename).
-   - `recipe-b`: parser-convention checkpoint entry (uses `id`, no `modelVersionId`)
-     matching a local checkpoint via L2 — the local checkpoint's `.metadata.json` MUST
-     carry civitai version data with that `id` so `version_index` contains it (L2
-     cannot match otherwise).
-   - `recipe-c`: healthy recipe (no deleted entries) → must remain untouched.
-
-The scanner computes and persists model hashes during the library scan, so the sandbox
-model dirs just need the model files + `.metadata.json` sidecars. With
-`--settings-path`, all derived data lands under the sandbox settings dir (`cache/`,
-`backups/`, `logs/`, `stats/`, `wildcards/`), and NO `cache/` appears in the repo root.
-
-## Fresh State Between Entry-Point Runs
-
-Each entry point (global / per-recipe / selection-bulk) must start from the same
-deleted state. Between runs (keep a pristine copy in `<sandbox>/recipes-before/`):
-
-```bash
-# 1. Reset fixtures to the before-state snapshot
-cp <sandbox>/recipes-before/*.recipe.json <sandbox>/recipes/
-# 2. Clear the recipe/FTS caches (with --settings-path these live under the sandbox
-#    settings dir, NOT <repo-root>/cache)
-rm -f <sandbox>/settings/cache/recipe/*.sqlite
-rm -rf <sandbox>/settings/cache/fts/*
-# 3. Restart the server (fresh process, fresh scan)
-python .agents/skills/lora-manager-e2e/scripts/start_server.py \
-  --port {PORT} --settings-path <sandbox>/settings --restart --wait --timeout 30 --detach
-# 4. Re-verify the server is listening + reload the browser page
-```
-
-## Cancellation Testing (KNOWN GAP)
-
-Testing the rematch-cancel path E2E requires a run long enough to cancel mid-flight. A
-tiny 3-recipe fixture set completes in **seconds** — too fast to reliably cancel. The
-cancel path is currently **unit-covered only** (`rematch_all_recipes` cancellation
-tests); do not block an E2E run on cancel-path verification. If you must attempt it,
-you would need an artificially large/deferred fixture set to create a cancellable
-window — treat this as a research task, not part of the standard E2E.

.agents/skills/lora-manager-e2e/references/test-scenarios.md

@@ -1,280 +0,0 @@
-# LoRa Manager E2E Test Scenarios
-
-This document provides detailed test scenarios for end-to-end validation of LoRa Manager features.
-
-> **Run preconditions (from SKILL.md)**: every run uses the **sandboxed** standalone
-> server on a free port `{PORT}` (default candidate `8188`, only if actually free — pick
-> e.g. `8199` when `8188` is occupied by a live ComfyUI). Fixtures live in the sandboxed
-> `recipes_path` as `f"{id}.recipe.json"` files with matching in-JSON `id`; the real user
-> config and real library are never touched (record protection proof before/after).
-> Abort if a phase exceeds ~2x its budget or a tool call retries 3+ times (SKILL.md
-> "Time Budgets & Abort Guidance").
-
-## Table of Contents
-
-1. [LoRA List Page](#lora-list-page)
-2. [Model Details](#model-details)
-3. [Recipes](#recipes)
-4. [Settings](#settings)
-5. [Import/Export](#importexport)
-
----
-
-## LoRA List Page
-
-### Scenario: Page Load and Display
-
-**Objective**: Verify the LoRA list page loads correctly and displays models.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:{PORT}/loras`
-2. Wait for page title "LoRAs" to appear
-3. Take snapshot to verify:
-   - Header with "LoRAs" title is visible
-   - Search/filter controls are present
-   - Grid/list view toggle exists
-   - LoRA cards are displayed (if models exist)
-   - Pagination controls (if applicable)
-
-**Expected Result**: Page loads without errors, UI elements are present.
-
-### Scenario: Search Functionality
-
-**Objective**: Verify search filters LoRA models correctly.
-
-**Steps**:
-1. Ensure at least one LoRA exists with known name (e.g., "test-character")
-2. Navigate to LoRA list page
-3. Enter search term in search box: "test"
-4. Press Enter or click search button
-5. Wait for results to update
-
-**Expected Result**: Only LoRAs matching search term are displayed.
-
-**Verification Script**:
-```python
-# After search, verify filtered results
-evaluate_script(function="""
-() => {
-  const cards = document.querySelectorAll('.lora-card');
-  const names = Array.from(cards).map(c => c.dataset.name);
-  return { count: cards.length, names };
-}
-""")
-```
-
-### Scenario: Filter by Tags
-
-**Objective**: Verify tag filtering works correctly.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click on a tag (e.g., "character", "style")
-3. Wait for filtered results
-
-**Expected Result**: Only LoRAs with selected tag are displayed.
-
-### Scenario: View Mode Toggle
-
-**Objective**: Verify grid/list view toggle works.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click list view button
-3. Verify list layout
-4. Click grid view button
-5. Verify grid layout
-
-**Expected Result**: View mode changes correctly, layout updates.
-
----
-
-## Model Details
-
-### Scenario: Open Model Details
-
-**Objective**: Verify clicking a LoRA opens its details.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click on a LoRA card
-3. Wait for details panel/modal to open
-
-**Expected Result**: Details panel shows:
-- Model name
-- Preview image
-- Metadata (trigger words, tags, etc.)
-- Action buttons (edit, delete, etc.)
-
-### Scenario: Edit Model Metadata
-
-**Objective**: Verify metadata editing works end-to-end.
-
-**Steps**:
-1. Open a LoRA's details
-2. Click "Edit" button
-3. Modify trigger words field
-4. Add/remove tags
-5. Save changes
-6. Refresh page
-7. Reopen the same LoRA
-
-**Expected Result**: Changes persist after refresh.
-
-### Scenario: Delete Model
-
-**Objective**: Verify model deletion works.
-
-**Steps**:
-1. Open a LoRA's details
-2. Click "Delete" button
-3. Confirm deletion in dialog
-4. Wait for removal
-
-**Expected Result**: Model removed from list, success message shown.
-
----
-
-## Recipes
-
-### Scenario: Recipe List Display
-
-**Objective**: Verify recipes page loads and displays recipes.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:{PORT}/recipes`
-2. Wait for "Recipes" title
-3. Take snapshot
-
-**Expected Result**: Recipe list displayed with cards/items.
-
-### Scenario: Create New Recipe
-
-**Objective**: Verify recipe creation workflow.
-
-**Steps**:
-1. Navigate to recipes page
-2. Click "New Recipe" button
-3. Fill recipe form:
-   - Name: "Test Recipe"
-   - Description: "E2E test recipe"
-   - Add LoRA models
-4. Save recipe
-5. Verify recipe appears in list
-
-**Expected Result**: New recipe created and displayed.
-
-### Scenario: Apply Recipe
-
-**Objective**: Verify applying a recipe to ComfyUI.
-
-**Steps**:
-1. Open a recipe
-2. Click "Apply" or "Load in ComfyUI"
-3. Verify action completes
-
-**Expected Result**: Recipe applied successfully.
-
----
-
-## Settings
-
-### Scenario: Settings Page Load
-
-**Objective**: Verify settings page displays correctly.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:{PORT}/settings`
-2. Wait for "Settings" title
-3. Take snapshot
-
-**Expected Result**: Settings form with various options displayed.
-
-### Scenario: Change Setting and Restart
-
-**Objective**: Verify settings persist after restart.
-
-**Steps**:
-1. Navigate to settings page
-2. Change a setting (e.g., default view mode)
-3. Save settings
-4. Restart server: `python scripts/start_server.py --port {PORT} --restart --wait --timeout 30 --detach`
-5. Refresh browser page
-6. Navigate to settings
-
-**Expected Result**: Changed setting value persists.
-
----
-
-## Import/Export
-
-### Scenario: Export Models List
-
-**Objective**: Verify export functionality.
-
-**Steps**:
-1. Navigate to LoRA list
-2. Click "Export" button
-3. Select format (JSON/CSV)
-4. Download file
-
-**Expected Result**: File downloaded with correct data.
-
-### Scenario: Import Models
-
-**Objective**: Verify import functionality.
-
-**Steps**:
-1. Prepare import file
-2. Navigate to import page
-3. Upload file
-4. Verify import results
-
-**Expected Result**: Models imported successfully, confirmation shown.
-
----
-
-## API Integration Tests
-
-### Scenario: Verify API Endpoints
-
-**Objective**: Verify backend API responds correctly.
-
-**Test via browser console**:
-```javascript
-// List LoRAs
-fetch('/loras/api/list').then(r => r.json()).then(console.log)
-
-// Get LoRA details
-fetch('/loras/api/detail/<id>').then(r => r.json()).then(console.log)
-
-// Search LoRAs
-fetch('/loras/api/search?q=test').then(r => r.json()).then(console.log)
-```
-
-**Expected Result**: APIs return valid JSON with expected structure.
-
----
-
-## Console Error Monitoring
-
-During all tests, monitor browser console for errors:
-
-```python
-# Check for JavaScript errors
-messages = list_console_messages(types=["error"])
-assert len(messages) == 0, f"Console errors found: {messages}"
-```
-
-## Network Request Verification
-
-Verify key API calls are made:
-
-```python
-# List XHR requests
-requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Look for specific endpoints
-lora_list_requests = [r for r in requests if "/api/list" in r.get("url", "")]
-assert len(lora_list_requests) > 0, "LoRA list API not called"
-```

.agents/skills/lora-manager-e2e/scripts/example_e2e_test.py

@@ -1,215 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example E2E test demonstrating LoRa Manager testing workflow.
-
-This script shows how to:
-1. Start the standalone server
-2. Use Chrome DevTools MCP to interact with the UI
-3. Verify functionality end-to-end
-
-Note: This is a template. Actual execution requires Chrome DevTools MCP.
-
-Port: pick a FREE port for the run — 8188 is commonly occupied by a live
-ComfyUI (see the skill's Port Selection section). Set PORT below to e.g. 8199
-when 8188 is taken. Always run against a SANDBOXED standalone server.
-"""
-
-import subprocess
-import sys
-
-# Choose the E2E port. 8188 is only the default candidate; use 8199 (or any
-# free port checked with `ss -tlnp`) when 8188 is occupied by a live ComfyUI.
-PORT = "8188"
-
-
-def run_test():
-    """Run example E2E test flow."""
-
-    print("=" * 60)
-    print("LoRa Manager E2E Test Example")
-    print("=" * 60)
-
-    # Step 1: Start server (detached so it survives the shell)
-    print("\n[1/5] Starting LoRa Manager standalone server...")
-    result = subprocess.run(
-        [sys.executable, "start_server.py", "--port", PORT, "--wait", "--timeout", "30", "--detach"],
-        capture_output=True,
-        text=True,
-    )
-    if result.returncode != 0:
-        print(f"Failed to start server: {result.stderr}")
-        return 1
-    print("Server ready!")
-
-    # Step 2: Open Chrome (manual step - show command)
-    print("\n[2/5] Open Chrome with debug mode:")
-    print(
-        f"google-chrome --remote-debugging-port=9222 "
-        f"--user-data-dir=/tmp/chrome-lora-manager http://127.0.0.1:{PORT}/loras"
-    )
-    print("(In actual test, this would be automated via MCP)")
-
-    # Step 3: Navigate and verify page load
-    print("\n[3/5] Page Load Verification:")
-    print(
-        f"""
-    MCP Commands to execute:
-    1. navigate_page(type="url", url="http://127.0.0.1:{PORT}/loras")
-    2. wait_for(text="LoRAs", timeout=10000)
-    3. snapshot = take_snapshot()
-    """
-    )
-
-    # Step 4: Test search functionality
-    print("\n[4/5] Search Functionality Test:")
-    print(
-        """
-    MCP Commands to execute:
-    1. fill(uid="search-input", value="test")
-    2. press_key(key="Enter")
-    3. wait_for(text="Results", timeout=5000)
-    4. result = evaluate_script(function=`
-        () => {
-            const cards = document.querySelectorAll('.lora-card');
-            return { count: cards.length };
-        }
-        `)
-    """
-    )
-
-    # Step 5: Verify API
-    print("\n[5/5] API Verification:")
-    print(
-        """
-    MCP Commands to execute:
-    1. api_result = evaluate_script(function=`
-        async () => {
-            const response = await fetch('/loras/api/list');
-            const data = await response.json();
-            return { count: data.length, status: response.status };
-        }
-        `)
-    2. Verify api_result['status'] == 200
-    """
-    )
-
-    print("\n" + "=" * 60)
-    print("Test flow completed!")
-    print("=" * 60)
-
-    return 0
-
-
-def example_restart_flow():
-    """Example: Testing configuration change that requires restart."""
-
-    print("\n" + "=" * 60)
-    print("Example: Server Restart Flow")
-    print("=" * 60)
-
-    print(
-        f"""
-    Scenario: Change setting and verify after restart
-
-    Steps:
-    1. Navigate to settings page
-       - navigate_page(type="url", url="http://127.0.0.1:{PORT}/settings")
-
-    2. Change a setting (e.g., theme)
-       - fill(uid="theme-select", value="dark")
-       - click(uid="save-settings-button")
-
-    3. Restart server
-       - subprocess.run([python, "start_server.py", "--port", "{PORT}", "--restart", "--wait", "--detach"])
-
-    4. Refresh browser
-       - navigate_page(type="reload", ignoreCache=True)
-       - wait_for(text="LoRAs", timeout=15000)
-
-    5. Verify setting persisted
-       - navigate_page(type="url", url="http://127.0.0.1:{PORT}/settings")
-       - theme = evaluate_script(function="() => document.querySelector('#theme-select').value")
-       - assert theme == "dark"
-    """
-    )
-
-
-def example_modal_interaction():
-    """Example: Testing modal dialog interaction."""
-
-    print("\n" + "=" * 60)
-    print("Example: Modal Dialog Interaction")
-    print("=" * 60)
-
-    print(
-        """
-    Scenario: Add new LoRA via modal
-
-    Steps:
-    1. Open modal
-       - click(uid="add-lora-button")
-       - wait_for(text="Add LoRA", timeout=3000)
-
-    2. Fill form
-       - fill_form(elements=[
-           {"uid": "lora-name", "value": "Test Character"},
-           {"uid": "lora-path", "value": "/models/test.safetensors"},
-       ])
-
-    3. Submit
-       - click(uid="modal-submit-button")
-
-    4. Verify success
-       - wait_for(text="Successfully added", timeout=5000)
-       - snapshot = take_snapshot()
-    """
-    )
-
-
-def example_network_monitoring():
-    """Example: Network request monitoring."""
-
-    print("\n" + "=" * 60)
-    print("Example: Network Request Monitoring")
-    print("=" * 60)
-
-    print(
-        f"""
-    Scenario: Verify API calls during user interaction
-
-    Steps:
-    1. Clear network log (implicit on navigation)
-       - navigate_page(type="url", url="http://127.0.0.1:{PORT}/loras")
-
-    2. Perform action that triggers API call
-       - fill(uid="search-input", value="character")
-       - press_key(key="Enter")
-
-    3. List network requests
-       - requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-    4. Find search API call
-       - search_requests = [r for r in requests if "/api/search" in r.get("url", "")]
-       - assert len(search_requests) > 0, "Search API was not called"
-
-    5. Get request details
-       - if search_requests:
-           details = get_network_request(reqid=search_requests[0]["reqid"])
-           - Verify request method, response status, etc.
-    """
-    )
-
-
-if __name__ == "__main__":
-    print("LoRa Manager E2E Test Examples\n")
-    print("This script demonstrates E2E testing patterns.\n")
-    print("Note: Actual execution requires Chrome DevTools MCP connection.\n")
-
-    run_test()
-    example_restart_flow()
-    example_modal_interaction()
-    example_network_monitoring()
-
-    print("\n" + "=" * 60)
-    print("All examples shown!")
-    print("=" * 60)

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
@@ -165,6 +170,10 @@ The system runs in two modes:
 - Route registrars organize endpoints by domain: `ModelRouteRegistrar`, `RecipeRouteRegistrar`, etc.
 - Request handlers in `py/routes/handlers/` implement route logic
 - All routes use aiohttp, return `web.json_response` or `web.Response`
+- Endpoints consumed by the companion browser extension (lm-civitai-extension)
+  MUST also accept `GET` with query-string params: the extension is GET-only by
+  convention (see its AGENTS.md), even for state-changing operations such as
+  `GET /api/lm/recipe/{recipe_id}/reimport`
 
 ### Recipe System
 
@@ -210,6 +219,26 @@ The system runs in two modes:
 - Vanilla JS tests: `tests/frontend/**/*.test.js` with jsdom; setup in `tests/frontend/setup.js`
 - Vue widget tests: `vue-widgets/tests/**/*.test.ts` with jsdom + `@vue/test-utils`
 
+### UI Verification (manual default)
+
+UI/layout changes are verified by the user by eye — do NOT spin up a sandbox,
+standalone server, or browser automation to "prove" a visual fix. Ask the user to
+look instead. The full browser E2E ceremony (server + Chrome DevTools MCP +
+screenshots) is slow, token-heavy, and fragile; reserve it for genuine
+server+browser integration bugs, and only when the user explicitly agrees.
+
+If a cross-layer issue ever needs a live server, the sandboxed helpers live in
+`scripts/e2e/` (`start_server.py`, `wait_for_server.py`). Non-negotiable rules:
+
+- Always launch with `--settings-path <sandbox>/settings` and sandboxed
+  `folder_paths` under `/tmp` — the repo folder is the real plugin folder and a
+  `settings.json` there is read by the live instance. Never touch real config or
+  real model libraries.
+- Never kill a process you did not start; `start_server.py` tracks its own PIDs
+  via pidfile and refuses to touch unrelated processes on the port.
+- Abort after ~30 minutes or 3 consecutive tool failures; report `BLOCKED` with
+  observed state instead of retrying blindly. Clean up sandbox and server after.
+
 ## Key Integration Points
 
 - **Settings:** Stored in the user config directory (via `platformdirs`) or portable mode (`"use_portable_settings": true`)

__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.
 
@@ -135,7 +137,7 @@ and must be normalized. `en` = keep the English word as-is.
 
 | Term | Use | Fix |
 |---|---|---|
-| recipe | Rezept/Rezepte | 5 leftover English "Recipe" keys → Rezept (e.g. `globalContextMenu.repairRecipes.label`, `toast.recipes.recipeSaved`) |
+| recipe | Rezept/Rezepte | leftover English "Recipe" keys → Rezept (e.g. `toast.recipes.recipeSaved`) |
 | base model | pick Basis-Modell or Basismodell | currently 27× hyphenated vs 15× closed |
 | metadata | Metadaten | 4 keys use "Modelldaten" (`onboarding.steps.fetch.title/content`) → Metadaten |
 | bulk | pick Massen- or Sammelmodus | `loras.controls.bulk.action` = "Massen" reads as "crowds" — use "Massenbearbeitung"/"Mehrfachauswahl" |
@@ -191,7 +193,7 @@ and must be normalized. `en` = keep the English word as-is.
 | Checkpoint | Checkpoint or チェックポイント (pick one) | 3 variants: Checkpoint (~14), checkpoint lowercase (4), チェックポイント (4, e.g. `settings.priorityTags.modelTypes.checkpoint`) |
 | Embedding | Embedding | 4 keys lowercase "embedding" mid-sentence |
 | bulk | 一括 | `modals.checkUpdates.tip` "バルクモード" → 一括モード |
-| recipe counter | 件 or 個 | `repairRecipes.success` uses 件, `.cancelled` uses 個 — unify |
+| recipe counter | 件 or 個 | `globalContextMenu.rematchRecipes.success` uses 件, `.cancelled` uses 個 — unify |
 
 ### ko
 

docs/recipe-civitai-image-no-metadata.md

@@ -0,0 +1,58 @@
+# CivitAI image imports can end up with 0 LoRAs
+
+## Symptom
+
+Importing a CivitAI image URL can produce a recipe with **zero LoRA
+entries**, even though the image page lists LoRAs in its resource panel.
+
+Reported example: `https://civitai.red/images/140818889` was imported as a
+local recipe with 0 LoRAs, while the page shows 3 LoRAs. Some images (e.g.
+NSFW / higher browsing level) additionally require a login to view, so their
+data is not publicly reachable at all.
+
+## Root cause
+
+URL imports use only two data sources:
+
+1. **CivitAI REST image API** — `GET /api/v1/images?imageId=<id>&nsfw=X&withMeta=true` → `meta`
+2. **Embedded image metadata** — EXIF/XMP read from the downloaded bytes
+
+For the same image both sources can be empty, and the one source that does
+contain the data is never queried. Verified for image 140818889:
+
+| Source | What it returned |
+|---|---|
+| REST image API | `meta` holds only a prompt; `modelVersionIds: []`; no `resources`/`hashes`; `baseModel: null` |
+| Downloaded image | PNG with **no EXIF/XMP** (the CDN URL ends in `.jpeg`, the body is PNG) |
+| Image page HTML | `__NEXT_DATA__` embeds the trpc `image.getGenerationData` result → full `resources` list: 3 LoRAs, each with `modelId`, `modelVersionId`, `modelName`, `modelType`, `versionName`, `baseModel` |
+
+Key points:
+
+- The page's resource panel is fed by an **internal, non-public trpc
+  endpoint**, not by the public REST image API.
+- That internal endpoint is **login-gated** for some content — the
+  "requires login" symptom.
+- Even with the version IDs in hand, `/model-versions/{id}` for these
+  (Krea) versions returns **no `sha256`**, so an exact local-file hash match
+  is impossible; only model/version identity is recoverable.
+
+## Conclusion / status
+
+0-LoRA imports are a data-source gap: public REST meta and image EXIF are
+both empty, while the only complete source (page generation data) is
+internal, sometimes login-gated, and not used by the importer.
+
+Such imports **cannot be reliably auto-repaired/completed** by the backend
+alone. The old "Repair Metadata" feature only re-fetched the same incomplete
+REST meta and could not fix them; it was deprecated and has been removed.
+
+**Fixed via the companion browser extension.** When the extension is
+installed with a valid license, it scrapes the image page's internal trpc
+generation data with the user's session and calls the payload-capable
+re-import endpoint (`POST /api/lm/recipe/{recipe_id}/reimport` with
+`image_url`/`name`/`resources`/`gen_params`/`base_model`/`tags` query
+params), which rebuilds the recipe from the caller-supplied metadata. The
+web UI delegates re-import of CivitAI-image-sourced recipes to the extension
+automatically (probe + `lm:reimport*` DOM events); without the extension,
+re-import silently falls back to the native path, which remains limited by
+the data-source gap documented above.

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "Alle {typePlural} verfügen bereits über Lizenzmetadaten",
             "error": "Lizenzmetadaten für {typePlural} konnten nicht aktualisiert werden: {message}"
         },
-        "repairRecipes": {
-            "label": "Rezept-Daten reparieren",
-            "loading": "Rezept-Daten werden repariert...",
-            "success": "{count} Rezepte erfolgreich repariert.",
-            "cancelled": "Reparatur abgebrochen. {count} Rezepte wurden repariert.",
-            "error": "Rezept-Reparatur fehlgeschlagen: {message}"
-        },
         "rematchRecipes": {
             "label": "Rezepte lokalen Modellen neu zuordnen",
             "loading": "Rezepte werden lokalen Modellen neu zugeordnet...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "Nach Modell gruppieren",
             "groupByModelHelp": "Wenn aktiviert, wird nur die neueste Version jedes CivitAI-Modells als einzelne Karte angezeigt. Ältere Versionen werden ausgeblendet.",
+            "stickyControls": "Aktionsleiste sichtbar halten",
+            "stickyControlsHelp": "Wenn aktiviert, bleibt die Aktionsleiste (Aktualisieren, Herunterladen usw.) beim Scrollen zusammen mit der Breadcrumb-Navigation oben angeheftet.",
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
                 "default": "Standard",
@@ -786,7 +814,6 @@
             "setContentRating": "Inhaltsbewertung für alle festlegen",
             "copyAll": "Alle Syntax kopieren",
             "refreshAll": "Alle Metadaten aktualisieren",
-            "repairMetadata": "Metadaten der Auswahl reparieren",
             "rematchMetadata": "Ausgewählte mit lokalen Modellen abgleichen",
             "reimportMetadata": "Aus Quelle neu importieren",
             "checkUpdates": "Auswahl auf Updates prüfen",
@@ -842,7 +869,6 @@
             "replacePreview": "Vorschau ersetzen",
             "setContentRating": "Inhaltsbewertung festlegen",
             "moveToFolder": "In Ordner verschieben",
-            "repairMetadata": "Metadaten reparieren",
             "rematchMetadata": "Mit lokalen Modellen abgleichen",
             "reimportMetadata": "Aus Quelle neu importieren",
             "excludeModel": "Modell ausschließen",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
                 "prepareError": "Fehler beim Vorbereiten der LoRAs für den Download: {message}"
             },
-            "repair": {
-                "starting": "Rezept-Metadaten werden repariert...",
-                "success": "Rezept-Metadaten erfolgreich repariert",
-                "skipped": "Rezept bereits in der neuesten Version, keine Reparatur erforderlich",
-                "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
-                "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
-            },
             "reimport": {
                 "starting": "Rezept wird aus Quelle neu importiert...",
                 "success": "Rezept erfolgreich neu importiert",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "Beispiele anzeigen ({count})",
                 "hideExamples": "Beispiele ausblenden",
                 "addExamples": "Beispiele hinzufügen",
-                "previousExample": "Vorheriges Beispiel",
-                "nextExample": "Nächstes Beispiel",
+                "previousExample": "Vorheriges Beispiel ([)",
+                "nextExample": "Nächstes Beispiel (])",
                 "noExamples": "Keine Beispielbilder verfügbar",
                 "addMoreExamples": "Weitere Beispiele hinzufügen",
                 "dragDrop": "Bilder oder Videos hierher ziehen & ablegen",
@@ -1864,10 +1952,52 @@
         "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 +2014,8 @@
             "settings": "Einstellungen & Konfiguration",
             "extensions": "Erweiterungen",
             "newBadge": "NEU"
-        }
+        },
+        "newContentBadge": "NEU"
     },
     "update": {
         "title": "Nach Updates suchen",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "Ordner konnte nicht durchsucht werden: {message}",
             "batchImportDirectorySelected": "Verzeichnis ausgewählt: {path}",
             "noRecipesSelected": "Keine Rezepte ausgewählt",
-            "repairBulkComplete": "Reparatur abgeschlossen: {repaired} repariert, {skipped} übersprungen (von {total})",
-            "repairBulkSkipped": "Keine Reparatur für die {total} ausgewählten Rezepte erforderlich",
-            "repairBulkFailed": "Reparatur der ausgewählten Rezepte fehlgeschlagen: {message}",
             "rematchComplete": "{entries} Einträge in {recipes} Rezepten zugeordnet",
             "rematchCompleteErrors": "{entries} Einträge in {recipes} Rezepten zugeordnet, {failures} fehlgeschlagen",
             "rematchAllFailed": "Zuordnung fehlgeschlagen für {failures} von {total} ausgewählten Rezepten",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "Keine Zuordnung für die {total} ausgewählten Rezepte erforderlich",
             "rematchFailed": "Zuordnung der ausgewählten Rezepte fehlgeschlagen: {message}",
             "reimporting": "Rezept wird aus Quelle neu importiert...",
+            "reimportingViaExtension": "Rezept {current}/{total} wird über die Browser-Erweiterung neu importiert...",
             "reimportSuccess": "Rezept erfolgreich neu importiert",
             "reimportBulkComplete": "Neuimport abgeschlossen: {completed} importiert, {failed} fehlgeschlagen (von {total})",
             "reimportBulkFailed": "Neuimport einiger Rezepte fehlgeschlagen",

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."
       }
     }
   },
@@ -179,13 +212,6 @@
       "none": "All {typePlural} already have license metadata",
       "error": "Failed to refresh license metadata for {typePlural}: {message}"
     },
-    "repairRecipes": {
-      "label": "Repair recipes data",
-      "loading": "Repairing recipe data...",
-      "success": "Successfully repaired {count} recipes.",
-      "cancelled": "Repair cancelled. {count} recipes were repaired.",
-      "error": "Recipe repair failed: {message}"
-    },
     "rematchRecipes": {
       "label": "Rematch recipes to local models",
       "loading": "Rematching recipes to local models...",
@@ -451,6 +477,8 @@
     "layoutSettings": {
       "groupByModel": "Group by Model",
       "groupByModelHelp": "When enabled, only the latest version of each CivitAI model is shown as a single card. Older versions are hidden.",
+      "stickyControls": "Keep Action Bar Visible",
+      "stickyControlsHelp": "When enabled, the action bar (Refresh, Download, etc.) stays pinned at the top while scrolling, together with the breadcrumb navigation.",
       "displayDensity": "Display Density",
       "displayDensityOptions": {
         "default": "Default",
@@ -786,7 +814,6 @@
       "setContentRating": "Set Content Rating for Selected",
       "copyAll": "Copy Selected Syntax",
       "refreshAll": "Refresh Selected Metadata",
-      "repairMetadata": "Repair Metadata for Selected",
       "rematchMetadata": "Rematch Selected to Local Models",
       "reimportMetadata": "Re-import from Source",
       "checkUpdates": "Check Updates for Selected",
@@ -842,7 +869,6 @@
       "replacePreview": "Replace Preview",
       "setContentRating": "Set Content Rating",
       "moveToFolder": "Move to Folder",
-      "repairMetadata": "Repair metadata",
       "rematchMetadata": "Rematch to local models",
       "reimportMetadata": "Re-import from Source",
       "excludeModel": "Exclude Model",
@@ -868,6 +894,21 @@
       "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 +939,64 @@
       "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": {
@@ -1030,13 +1121,6 @@
         "getInfoFailed": "Failed to get information for missing LoRAs",
         "prepareError": "Error preparing LoRAs for download: {message}"
       },
-      "repair": {
-        "starting": "Repairing recipe metadata...",
-        "success": "Recipe metadata repaired successfully",
-        "skipped": "Recipe already at latest version, no repair needed",
-        "failed": "Failed to repair recipe: {message}",
-        "missingId": "Cannot repair recipe: Missing recipe ID"
-      },
       "reimport": {
         "starting": "Re-importing recipe from source...",
         "success": "Recipe re-imported successfully",
@@ -1528,7 +1612,11 @@
         "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 +1627,7 @@
         "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 +1687,8 @@
         "showCount": "Show examples ({count})",
         "hideExamples": "Hide examples",
         "addExamples": "Add examples",
-        "previousExample": "Previous example",
-        "nextExample": "Next example",
+        "previousExample": "Previous example ([)",
+        "nextExample": "Next example (])",
         "noExamples": "No example images available",
         "addMoreExamples": "Add more examples",
         "dragDrop": "Drag & drop images or videos here",
@@ -1864,10 +1952,52 @@
     "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 +2014,8 @@
       "settings": "Settings & Configuration",
       "extensions": "Extensions",
       "newBadge": "NEW"
-    }
+    },
+    "newContentBadge": "New"
   },
   "update": {
     "title": "Check for Updates",
@@ -2043,7 +2174,10 @@
       "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 +2185,13 @@
       "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}",
@@ -2081,9 +2222,6 @@
       "batchImportBrowseFailed": "Failed to browse directory: {message}",
       "batchImportDirectorySelected": "Directory selected: {path}",
       "noRecipesSelected": "No recipes selected",
-      "repairBulkComplete": "Repair complete: {repaired} repaired, {skipped} skipped (of {total})",
-      "repairBulkSkipped": "No repair needed for any of the {total} selected recipes",
-      "repairBulkFailed": "Failed to repair selected recipes: {message}",
       "rematchComplete": "Matched {entries} entries across {recipes} recipes",
       "rematchCompleteErrors": "Matched {entries} entries across {recipes} recipes, {failures} failed",
       "rematchAllFailed": "Rematch failed for {failures} of {total} selected recipes",
@@ -2091,6 +2229,7 @@
       "rematchSkipped": "No rematch needed for any of the {total} selected recipes",
       "rematchFailed": "Failed to rematch selected recipes: {message}",
       "reimporting": "Re-importing recipe from source...",
+      "reimportingViaExtension": "Re-importing recipe {current}/{total} via browser extension...",
       "reimportSuccess": "Recipe re-imported successfully",
       "reimportBulkComplete": "Re-import complete: {completed} re-imported, {failed} failed (of {total})",
       "reimportBulkFailed": "Failed to re-import some recipes",

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "Todos los {typePlural} ya tienen metadatos de licencia",
             "error": "No se pudieron actualizar los metadatos de licencia de los {typePlural}: {message}"
         },
-        "repairRecipes": {
-            "label": "Reparar datos de recetas",
-            "loading": "Reparando datos de recetas...",
-            "success": "Se repararon con éxito {count} recetas.",
-            "cancelled": "Reparación cancelada. {count} recetas fueron reparadas.",
-            "error": "Error al reparar recetas: {message}"
-        },
         "rematchRecipes": {
             "label": "Reasociar recetas con modelos locales",
             "loading": "Reasociando recetas con modelos locales...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "Agrupar por modelo",
             "groupByModelHelp": "Cuando está activado, solo se muestra la versión más reciente de cada modelo de CivitAI como una tarjeta única. Las versiones anteriores están ocultas.",
+            "stickyControls": "Mantener visible la barra de acciones",
+            "stickyControlsHelp": "Cuando está activado, la barra de acciones (Actualizar, Descargar, etc.) permanece fijada en la parte superior al desplazarse, junto con la navegación por rutas.",
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
                 "default": "Predeterminado",
@@ -786,7 +814,6 @@
             "setContentRating": "Establecer clasificación de contenido para todos",
             "copyAll": "Copiar toda la sintaxis",
             "refreshAll": "Actualizar todos los metadatos",
-            "repairMetadata": "Reparar metadatos de la selección",
             "rematchMetadata": "Reasociar los seleccionados con modelos locales",
             "reimportMetadata": "Reimportar desde origen",
             "checkUpdates": "Comprobar actualizaciones para la selección",
@@ -842,7 +869,6 @@
             "replacePreview": "Reemplazar vista previa",
             "setContentRating": "Establecer clasificación de contenido",
             "moveToFolder": "Mover a carpeta",
-            "repairMetadata": "Reparar metadatos",
             "rematchMetadata": "Reasociar con modelos locales",
             "reimportMetadata": "Reimportar desde origen",
             "excludeModel": "Excluir modelo",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "Error al obtener información de LoRAs faltantes",
                 "prepareError": "Error preparando LoRAs para descarga: {message}"
             },
-            "repair": {
-                "starting": "Reparando metadatos de la receta...",
-                "success": "Metadatos de la receta reparados con éxito",
-                "skipped": "La receta ya está en la última versión, no se necesita reparación",
-                "failed": "Error al reparar la receta: {message}",
-                "missingId": "No se puede reparar la receta: falta el ID de la receta"
-            },
             "reimport": {
                 "starting": "Reimportando receta desde origen...",
                 "success": "Receta reimportada exitosamente",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "Mostrar ejemplos ({count})",
                 "hideExamples": "Ocultar ejemplos",
                 "addExamples": "Añadir ejemplos",
-                "previousExample": "Ejemplo anterior",
-                "nextExample": "Ejemplo siguiente",
+                "previousExample": "Ejemplo anterior ([)",
+                "nextExample": "Ejemplo siguiente (])",
                 "noExamples": "No hay imágenes de ejemplo disponibles",
                 "addMoreExamples": "Añadir más ejemplos",
                 "dragDrop": "Arrastra y suelta imágenes o videos aquí",
@@ -1864,10 +1952,52 @@
         "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 +2014,8 @@
             "settings": "Configuración",
             "extensions": "Extensiones",
             "newBadge": "NUEVO"
-        }
+        },
+        "newContentBadge": "NUEVO"
     },
     "update": {
         "title": "Comprobar actualizaciones",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "No se pudo examinar el directorio: {message}",
             "batchImportDirectorySelected": "Directorio seleccionado: {path}",
             "noRecipesSelected": "No se han seleccionado recetas",
-            "repairBulkComplete": "Reparación completa: {repaired} reparadas, {skipped} omitidas (de {total})",
-            "repairBulkSkipped": "No se necesita reparación para ninguna de las {total} recetas seleccionadas",
-            "repairBulkFailed": "Error al reparar las recetas seleccionadas: {message}",
             "rematchComplete": "{entries} entradas asociadas en {recipes} recetas",
             "rematchCompleteErrors": "{entries} entradas asociadas en {recipes} recetas, {failures} fallidas",
             "rematchAllFailed": "Falló la reasociación de {failures} de {total} recetas seleccionadas",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "Ninguna de las {total} recetas seleccionadas necesita reasociación",
             "rematchFailed": "Falló la reasociación de las recetas seleccionadas: {message}",
             "reimporting": "Reimportando receta desde origen...",
+            "reimportingViaExtension": "Reimportando receta {current}/{total} mediante la extensión del navegador...",
             "reimportSuccess": "Receta reimportada exitosamente",
             "reimportBulkComplete": "Reimportación completa: {completed} reimportadas, {failed} fallidas (de {total})",
             "reimportBulkFailed": "Error al reimportar algunas recetas",

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "Tous les {typePlural} possèdent déjà des métadonnées de licence",
             "error": "Échec de l'actualisation des métadonnées de licence pour les {typePlural} : {message}"
         },
-        "repairRecipes": {
-            "label": "Réparer les données de Recipes",
-            "loading": "Réparation des données de Recipes...",
-            "success": "{count} Recipes réparées avec succès.",
-            "cancelled": "Réparation annulée. {count} Recipes ont été réparées.",
-            "error": "Échec de la réparation des Recipes : {message}"
-        },
         "rematchRecipes": {
             "label": "Réassocier les Recipes aux modèles locaux",
             "loading": "Réassociation des Recipes aux modèles locaux...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "Grouper par modèle",
             "groupByModelHelp": "Lorsque activé, seule la version la plus récente de chaque modèle CivitAI s'affiche sous forme de carte unique. Les versions plus anciennes sont masquées.",
+            "stickyControls": "Garder la barre d'actions visible",
+            "stickyControlsHelp": "Lorsque activé, la barre d'actions (Actualiser, Télécharger, etc.) reste épinglée en haut lors du défilement, avec la navigation par fil d'Ariane.",
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
                 "default": "Par défaut",
@@ -786,7 +814,6 @@
             "setContentRating": "Définir la classification du contenu pour tous",
             "copyAll": "Copier toute la syntaxe",
             "refreshAll": "Actualiser toutes les métadonnées",
-            "repairMetadata": "Réparer les métadonnées de la sélection",
             "rematchMetadata": "Réassocier la sélection aux modèles locaux",
             "reimportMetadata": "Ré-importer depuis la source",
             "checkUpdates": "Vérifier les mises à jour pour la sélection",
@@ -842,7 +869,6 @@
             "replacePreview": "Remplacer l'aperçu",
             "setContentRating": "Définir la classification du contenu",
             "moveToFolder": "Déplacer vers un dossier",
-            "repairMetadata": "Réparer les métadonnées",
             "rematchMetadata": "Réassocier aux modèles locaux",
             "reimportMetadata": "Ré-importer depuis la source",
             "excludeModel": "Exclure le modèle",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
                 "prepareError": "Erreur lors de la préparation des LoRAs pour le téléchargement : {message}"
             },
-            "repair": {
-                "starting": "Réparation des métadonnées de la Recipe...",
-                "success": "Métadonnées de la Recipe réparées avec succès",
-                "skipped": "Recette déjà à la version la plus récente, aucune réparation nécessaire",
-                "failed": "Échec de la réparation de la Recipe : {message}",
-                "missingId": "Impossible de réparer la Recipe : ID de Recipe manquant"
-            },
             "reimport": {
                 "starting": "Ré-import de la Recipe depuis la source...",
                 "success": "Recette ré-importée avec succès",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "Afficher les exemples ({count})",
                 "hideExamples": "Masquer les exemples",
                 "addExamples": "Ajouter des exemples",
-                "previousExample": "Exemple précédent",
-                "nextExample": "Exemple suivant",
+                "previousExample": "Exemple précédent ([)",
+                "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 +1952,52 @@
         "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 +2014,8 @@
             "settings": "Paramètres & Configuration",
             "extensions": "Extensions",
             "newBadge": "NOUVEAU"
-        }
+        },
+        "newContentBadge": "NOUVEAU"
     },
     "update": {
         "title": "Vérifier les mises à jour",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "Échec de la navigation dans le dossier : {message}",
             "batchImportDirectorySelected": "Dossier sélectionné : {path}",
             "noRecipesSelected": "Aucune Recipe sélectionnée",
-            "repairBulkComplete": "Réparation terminée : {repaired} réparée(s), {skipped} ignorée(s) (sur {total})",
-            "repairBulkSkipped": "Aucune réparation nécessaire parmi les {total} Recipes sélectionnées",
-            "repairBulkFailed": "Échec de la réparation des Recipes sélectionnées : {message}",
             "rematchComplete": "{entries} entrées associées dans {recipes} Recipes",
             "rematchCompleteErrors": "{entries} entrées associées dans {recipes} Recipes, {failures} échecs",
             "rematchAllFailed": "Échec de la réassociation de {failures} Recipes sélectionnées sur {total}",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "Aucune des {total} Recipes sélectionnées ne nécessite de réassociation",
             "rematchFailed": "Échec de la réassociation des Recipes sélectionnées : {message}",
             "reimporting": "Ré-import de la Recipe depuis la source...",
+            "reimportingViaExtension": "Ré-import de la Recipe {current}/{total} via l’extension du navigateur...",
             "reimportSuccess": "Recette ré-importée avec succès",
             "reimportBulkComplete": "Ré-import terminé : {completed} ré-importé(s), {failed} échec(s) (sur {total})",
             "reimportBulkFailed": "Échec du ré-import de certaines Recipes",

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "לכל ה-{typePlural} כבר יש מטא-נתוני רישיון",
             "error": "לא ניתן היה לרענן את מטא-נתוני הרישיון עבור {typePlural}: {message}"
         },
-        "repairRecipes": {
-            "label": "תיקון נתוני מתכונים",
-            "loading": "מתקן נתוני מתכונים...",
-            "success": "תוקנו בהצלחה {count} מתכונים.",
-            "cancelled": "תיקון בוטל. {count} מתכונים תוקנו.",
-            "error": "תיקון המתכונים נכשל: {message}"
-        },
         "rematchRecipes": {
             "label": "התאמה מחדש של מתכונים למודלים מקומיים",
             "loading": "מתבצעת התאמה מחדש של מתכונים למודלים מקומיים...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "קיבוץ לפי מודל",
             "groupByModelHelp": "כאשר מופעל, רק הגרסה העדכנית ביותר של כל מודל CivitAI מוצגת ככרטיס בודד. גרסאות ישנות יותר מוסתרות.",
+            "stickyControls": "השארת סרגל הפעולות גלוי",
+            "stickyControlsHelp": "כאשר מופעל, סרגל הפעולות (רענון, הורדה וכו') נשאר מוצמד לחלק העליון בעת גלילה, יחד עם ניווט פירורי הלחם.",
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
                 "default": "ברירת מחדל",
@@ -786,7 +814,6 @@
             "setContentRating": "הגדר דירוג תוכן לכל המודלים",
             "copyAll": "העתק את כל התחבירים",
             "refreshAll": "רענן את כל המטא-נתונים",
-            "repairMetadata": "תקן מטא-נתונים עבור הנבחרים",
             "rematchMetadata": "התאמה מחדש של הנבחרים למודלים מקומיים",
             "reimportMetadata": "ייבא מחדש ממקור",
             "checkUpdates": "בדוק עדכונים לבחירה",
@@ -842,7 +869,6 @@
             "replacePreview": "החלף תצוגה מקדימה",
             "setContentRating": "הגדר דירוג תוכן",
             "moveToFolder": "העבר לתיקייה",
-            "repairMetadata": "תיקון מטא-נתונים",
             "rematchMetadata": "התאמה מחדש למודלים מקומיים",
             "reimportMetadata": "ייבא מחדש ממקור",
             "excludeModel": "החרג מודל",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
                 "prepareError": "שגיאה בהכנת LoRAs להורדה: {message}"
             },
-            "repair": {
-                "starting": "מתקן מטא-נתונים של מתכון...",
-                "success": "מטא-נתונים של מתכון תוקן בהצלחה",
-                "skipped": "המתכון כבר בגרסה העדכנית ביותר, אין צורך בתיקון",
-                "failed": "תיקון המתכון נכשל: {message}",
-                "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
-            },
             "reimport": {
                 "starting": "מייבא מתכון מחדש מהמקור...",
                 "success": "המתכון יובא מחדש בהצלחה",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "הצג דוגמאות ({count})",
                 "hideExamples": "הסתר דוגמאות",
                 "addExamples": "הוסף דוגמאות",
-                "previousExample": "דוגמה קודמת",
-                "nextExample": "דוגמה הבאה",
+                "previousExample": "דוגמה קודמת ([)",
+                "nextExample": "דוגמה הבאה (])",
                 "noExamples": "אין תמונות דוגמה זמינות",
                 "addMoreExamples": "הוסף עוד דוגמאות",
                 "dragDrop": "גרור ושחרר תמונות או סרטונים כאן",
@@ -1864,10 +1952,52 @@
         "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 +2014,8 @@
             "settings": "הגדרות ותצורה",
             "extensions": "הרחבות",
             "newBadge": "חדש"
-        }
+        },
+        "newContentBadge": "חדש"
     },
     "update": {
         "title": "בדוק עדכונים",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "לא ניתן היה לעיין בתיקייה: {message}",
             "batchImportDirectorySelected": "נבחרה תיקייה: {path}",
             "noRecipesSelected": "לא נבחרו מתכונים",
-            "repairBulkComplete": "התיקון הושלם: {repaired} תוקנו, {skipped} דולגו (מתוך {total})",
-            "repairBulkSkipped": "אין צורך בתיקון עבור {total} המתכונים הנבחרים",
-            "repairBulkFailed": "תיקון המתכונים הנבחרים נכשל: {message}",
             "rematchComplete": "הותאמו {entries} פריטים ב־{recipes} מתכונים",
             "rematchCompleteErrors": "הותאמו {entries} פריטים ב־{recipes} מתכונים, {failures} נכשלו",
             "rematchAllFailed": "ההתאמה נכשלה עבור {failures} מתוך {total} מתכונים שנבחרו",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "אין צורך בהתאמה עבור {total} המתכונים שנבחרו",
             "rematchFailed": "ההתאמה מחדש של המתכונים שנבחרו נכשלה: {message}",
             "reimporting": "מייבא מתכון מחדש מהמקור...",
+            "reimportingViaExtension": "מייבא מתכון מחדש {current}/{total} דרך תוסף הדפדפן...",
             "reimportSuccess": "המתכון יובא מחדש בהצלחה",
             "reimportBulkComplete": "ייבוא מחדש הושלם: {completed} יובאו, {failed} נכשלו (מתוך {total})",
             "reimportBulkFailed": "ייבוא מחדש של חלק מהמתכונים נכשל",

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。"
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "すべての{typePlural}には既にライセンスメタデータがあります",
             "error": "{typePlural}のライセンスメタデータを更新できませんでした: {message}"
         },
-        "repairRecipes": {
-            "label": "レシピデータの修復",
-            "loading": "レシピデータを修復中...",
-            "success": "{count} 件のレシピを正常に修復しました。",
-            "cancelled": "修復がキャンセルされました。{count}件のレシピが修復されました。",
-            "error": "レシピの修復に失敗しました: {message}"
-        },
         "rematchRecipes": {
             "label": "レシピをローカルモデルに再マッチング",
             "loading": "レシピをローカルモデルに再マッチングしています...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "モデルでグループ化",
             "groupByModelHelp": "有効にすると、各CivitAIモデルの最新バージョンのみが1枚のカードとして表示され、古いバージョンは非表示になります。",
+            "stickyControls": "アクションバーを常に表示",
+            "stickyControlsHelp": "有効にすると、アクションバー（更新、ダウンロードなど）がスクロール時にパンくずナビゲーションと一緒に画面上部に固定されます。",
             "displayDensity": "表示密度",
             "displayDensityOptions": {
                 "default": "デフォルト",
@@ -786,7 +814,6 @@
             "setContentRating": "すべてのモデルのコンテンツレーティングを設定",
             "copyAll": "すべての構文をコピー",
             "refreshAll": "すべてのメタデータを更新",
-            "repairMetadata": "選択したレシピのメタデータを修復",
             "rematchMetadata": "選択したモデルをローカルモデルに再マッチング",
             "reimportMetadata": "ソースから再インポート",
             "checkUpdates": "選択項目の更新を確認",
@@ -842,7 +869,6 @@
             "replacePreview": "プレビューを置換",
             "setContentRating": "コンテンツレーティングを設定",
             "moveToFolder": "フォルダに移動",
-            "repairMetadata": "メタデータを修復",
             "rematchMetadata": "ローカルモデルに再マッチング",
             "reimportMetadata": "ソースから再インポート",
             "excludeModel": "モデルを除外",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "不足LoRAの情報取得に失敗しました",
                 "prepareError": "ダウンロード用LoRAの準備中にエラー：{message}"
             },
-            "repair": {
-                "starting": "レシピのメタデータを修復中...",
-                "success": "レシピのメタデータが正常に修復されました",
-                "skipped": "レシピはすでに最新バージョンです。修復は不要です",
-                "failed": "レシピの修復に失敗しました: {message}",
-                "missingId": "レシピを修復できません: レシピIDがありません"
-            },
             "reimport": {
                 "starting": "ソースからレシピを再インポート中...",
                 "success": "レシピの再インポートが完了しました",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "例を表示（{count}）",
                 "hideExamples": "例を非表示",
                 "addExamples": "例を追加",
-                "previousExample": "前の例",
-                "nextExample": "次の例",
+                "previousExample": "前の例（[）",
+                "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 +1952,52 @@
         "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 +2014,8 @@
             "settings": "設定&構成",
             "extensions": "拡張機能",
             "newBadge": "新着"
-        }
+        },
+        "newContentBadge": "新着"
     },
     "update": {
         "title": "更新確認",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "フォルダを参照できませんでした: {message}",
             "batchImportDirectorySelected": "選択されたフォルダ: {path}",
             "noRecipesSelected": "レシピが選択されていません",
-            "repairBulkComplete": "修復完了：{repaired} 件修復、{skipped} 件スキップ（合計 {total} 件）",
-            "repairBulkSkipped": "選択した {total} 件のレシピは修復不要です",
-            "repairBulkFailed": "選択したレシピの修復に失敗しました：{message}",
             "rematchComplete": "{recipes} 件のレシピで {entries} エントリをマッチングしました",
             "rematchCompleteErrors": "{recipes} 件のレシピで {entries} エントリをマッチングしました（{failures} 件失敗）",
             "rematchAllFailed": "選択した {total} 件中 {failures} 件のレシピの再マッチングに失敗しました",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "選択した {total} 件のレシピは再マッチングの必要がありませんでした",
             "rematchFailed": "選択したレシピの再マッチングに失敗しました：{message}",
             "reimporting": "ソースからレシピを再インポート中...",
+            "reimportingViaExtension": "ブラウザ拡張機能経由でレシピを再インポート中 ({current}/{total})...",
             "reimportSuccess": "レシピの再インポートが完了しました",
             "reimportBulkComplete": "再インポート完了：{completed} 件成功、{failed} 件失敗（合計 {total} 件）",
             "reimportBulkFailed": "一部のレシピの再インポートに失敗しました",

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "모든 {typePlural}에 이미 라이선스 메타데이터가 있습니다",
             "error": "{typePlural}의 라이선스 메타데이터를 새로고침하지 못했습니다: {message}"
         },
-        "repairRecipes": {
-            "label": "레시피 데이터 복구",
-            "loading": "레시피 데이터 복구 중...",
-            "success": "{count}개의 레시피가 성공적으로 복구되었습니다.",
-            "cancelled": "수리가 취소되었습니다. {count}개의 레시피가 수리되었습니다.",
-            "error": "레시피 복구 실패: {message}"
-        },
         "rematchRecipes": {
             "label": "레시피를 로컬 모델에 다시 매칭",
             "loading": "레시피를 로컬 모델에 다시 매칭하는 중...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "모델별 그룹화",
             "groupByModelHelp": "활성화하면 각 CivitAI 모델의 최신 버전만 단일 카드로 표시되며, 이전 버전은 숨겨집니다.",
+            "stickyControls": "작업 표시줄 항상 표시",
+            "stickyControlsHelp": "활성화하면 작업 표시줄(새로고침, 다운로드 등)이 스크롤 시 브레드크럼 내비게이션과 함께 상단에 고정됩니다.",
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
                 "default": "기본",
@@ -786,7 +814,6 @@
             "setContentRating": "모든 모델에 콘텐츠 등급 설정",
             "copyAll": "모든 문법 복사",
             "refreshAll": "모든 메타데이터 새로고침",
-            "repairMetadata": "선택한 레시피 메타데이터 복구",
             "rematchMetadata": "선택 항목을 로컬 모델에 다시 매칭",
             "reimportMetadata": "소스에서 다시 가져오기",
             "checkUpdates": "선택 항목 업데이트 확인",
@@ -842,7 +869,6 @@
             "replacePreview": "미리보기 교체",
             "setContentRating": "콘텐츠 등급 설정",
             "moveToFolder": "폴더로 이동",
-            "repairMetadata": "메타데이터 복구",
             "rematchMetadata": "로컬 모델에 다시 매칭",
             "reimportMetadata": "소스에서 다시 가져오기",
             "excludeModel": "모델 제외",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
                 "prepareError": "LoRA 다운로드 준비 중 오류: {message}"
             },
-            "repair": {
-                "starting": "레시피 메타데이터 복구 중...",
-                "success": "레시피 메타데이터가 성공적으로 복구되었습니다",
-                "skipped": "레시피가 이미 최신 버전입니다. 복구가 필요하지 않습니다",
-                "failed": "레시피 복구 실패: {message}",
-                "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
-            },
             "reimport": {
                 "starting": "소스에서 레시피를 다시 가져오는 중...",
                 "success": "레시피를 다시 가져왔습니다",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "예시 보기 ({count})",
                 "hideExamples": "예시 숨기기",
                 "addExamples": "예시 추가",
-                "previousExample": "이전 예시",
-                "nextExample": "다음 예시",
+                "previousExample": "이전 예시([)",
+                "nextExample": "다음 예시(])",
                 "noExamples": "사용 가능한 예시 이미지가 없습니다",
                 "addMoreExamples": "예시 더 추가",
                 "dragDrop": "이미지 또는 비디오를 여기로 끌어다 놓으세요",
@@ -1864,10 +1952,52 @@
         "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 +2014,8 @@
             "settings": "설정 & 구성",
             "extensions": "확장",
             "newBadge": "신규"
-        }
+        },
+        "newContentBadge": "신규"
     },
     "update": {
         "title": "업데이트 확인",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "폴더를 찾아보지 못했습니다: {message}",
             "batchImportDirectorySelected": "선택한 폴더: {path}",
             "noRecipesSelected": "선택한 레시피가 없습니다",
-            "repairBulkComplete": "복구 완료: {repaired}개 복구, {skipped}개 건너뜀 (총 {total}개)",
-            "repairBulkSkipped": "선택한 {total}개 레시피는 복구가 필요하지 않습니다",
-            "repairBulkFailed": "선택한 레시피 복구 실패: {message}",
             "rematchComplete": "{recipes}개 레시피에서 {entries}개 항목이 매칭되었습니다",
             "rematchCompleteErrors": "{recipes}개 레시피에서 {entries}개 항목이 매칭되었습니다. {failures}개 실패",
             "rematchAllFailed": "선택한 {total}개 레시피 중 {failures}개 재매칭 실패",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "선택한 {total}개 레시피는 재매칭이 필요하지 않습니다",
             "rematchFailed": "선택한 레시피 재매칭 실패: {message}",
             "reimporting": "소스에서 레시피를 다시 가져오는 중...",
+            "reimportingViaExtension": "브라우저 확장 프로그램을 통해 레시피를 다시 가져오는 중 ({current}/{total})...",
             "reimportSuccess": "레시피를 다시 가져왔습니다",
             "reimportBulkComplete": "다시 가져오기 완료: {completed}개 성공, {failed}개 실패 (총 {total}개)",
             "reimportBulkFailed": "일부 레시피를 다시 가져오지 못했습니다",

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."
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "У всех {typePlural} уже есть метаданные лицензии",
             "error": "Не удалось обновить метаданные лицензии для {typePlural}: {message}"
         },
-        "repairRecipes": {
-            "label": "Восстановить данные рецептов",
-            "loading": "Восстановление данных рецептов...",
-            "success": "Успешно восстановлено {count} рецептов.",
-            "cancelled": "Восстановление отменено. {count} рецептов было восстановлено.",
-            "error": "Ошибка восстановления рецептов: {message}"
-        },
         "rematchRecipes": {
             "label": "Повторное сопоставление рецептов с локальными моделями",
             "loading": "Повторное сопоставление рецептов с локальными моделями...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "Группировать по модели",
             "groupByModelHelp": "При включении отображается только последняя версия каждой модели CivitAI в виде одной карточки. Старые версии скрыты.",
+            "stickyControls": "Держать панель действий видимой",
+            "stickyControlsHelp": "При включении панель действий (Обновить, Загрузить и т. д.) остаётся закреплённой вверху при прокрутке вместе с навигацией по папкам.",
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
                 "default": "По умолчанию",
@@ -786,7 +814,6 @@
             "setContentRating": "Установить рейтинг контента для всех",
             "copyAll": "Копировать весь синтаксис",
             "refreshAll": "Обновить все метаданные",
-            "repairMetadata": "Восстановить метаданные для выбранных",
             "rematchMetadata": "Сопоставить выбранные с локальными моделями",
             "reimportMetadata": "Переимпортировать из источника",
             "checkUpdates": "Проверить обновления для выбранных",
@@ -842,7 +869,6 @@
             "replacePreview": "Заменить превью",
             "setContentRating": "Установить рейтинг контента",
             "moveToFolder": "Переместить в папку",
-            "repairMetadata": "Восстановить метаданные",
             "rematchMetadata": "Сопоставить с локальными моделями",
             "reimportMetadata": "Переимпортировать из источника",
             "excludeModel": "Исключить модель",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
                 "prepareError": "Ошибка подготовки LoRAs для загрузки: {message}"
             },
-            "repair": {
-                "starting": "Восстановление метаданных рецепта...",
-                "success": "Метаданные рецепта успешно восстановлены",
-                "skipped": "Рецепт уже последней версии, восстановление не требуется",
-                "failed": "Не удалось восстановить рецепт: {message}",
-                "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
-            },
             "reimport": {
                 "starting": "Переимпорт рецепта из источника...",
                 "success": "Рецепт успешно переимпортирован",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "Показать примеры ({count})",
                 "hideExamples": "Скрыть примеры",
                 "addExamples": "Добавить примеры",
-                "previousExample": "Предыдущий пример",
-                "nextExample": "Следующий пример",
+                "previousExample": "Предыдущий пример ([)",
+                "nextExample": "Следующий пример (])",
                 "noExamples": "Примеры изображений недоступны",
                 "addMoreExamples": "Добавить ещё примеры",
                 "dragDrop": "Перетащите изображения или видео сюда",
@@ -1864,10 +1952,52 @@
         "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 +2014,8 @@
             "settings": "Настройки и конфигурация",
             "extensions": "Расширения",
             "newBadge": "НОВОЕ"
-        }
+        },
+        "newContentBadge": "НОВОЕ"
     },
     "update": {
         "title": "Проверить обновления",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "Не удалось открыть папку: {message}",
             "batchImportDirectorySelected": "Выбрана папка: {path}",
             "noRecipesSelected": "Рецепты не выбраны",
-            "repairBulkComplete": "Восстановление завершено: {repaired} восстановлено, {skipped} пропущено (из {total})",
-            "repairBulkSkipped": "Ни один из {total} выбранных рецептов не требует восстановления",
-            "repairBulkFailed": "Не удалось восстановить выбранные рецепты: {message}",
             "rematchComplete": "Сопоставлено записей: {entries} в рецептах: {recipes}",
             "rematchCompleteErrors": "Сопоставлено записей: {entries} в рецептах: {recipes}, ошибок: {failures}",
             "rematchAllFailed": "Не удалось сопоставить: {failures} из {total} выбранных рецептов",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "Ни один из {total} выбранных рецептов не требует сопоставления",
             "rematchFailed": "Не удалось сопоставить выбранные рецепты: {message}",
             "reimporting": "Переимпорт рецепта из источника...",
+            "reimportingViaExtension": "Переимпорт рецепта {current}/{total} через расширение браузера...",
             "reimportSuccess": "Рецепт успешно переимпортирован",
             "reimportBulkComplete": "Переимпорт завершён: {completed} переимпортировано, {failed} ошибок (из {total})",
             "reimportBulkFailed": "Не удалось переимпортировать некоторые рецепты",

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。"
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "所有 {typePlural} 都已具备许可证元数据",
             "error": "刷新 {typePlural} 的许可证元数据失败：{message}"
         },
-        "repairRecipes": {
-            "label": "修复配方数据",
-            "loading": "正在修复配方数据...",
-            "success": "成功修复了 {count} 个配方。",
-            "cancelled": "修复已取消。已修复 {count} 个配方。",
-            "error": "配方修复失败：{message}"
-        },
         "rematchRecipes": {
             "label": "将配方重新匹配到本地模型",
             "loading": "正在将配方重新匹配到本地模型...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "按模型分组",
             "groupByModelHelp": "开启后，每个 CivitAI 模型仅显示最新版本的单张卡片，旧版本将被隐藏。",
+            "stickyControls": "保持操作栏可见",
+            "stickyControlsHelp": "开启后，操作栏（刷新、下载等）会在滚动时与路径导航一起固定在页面顶部。",
             "displayDensity": "显示密度",
             "displayDensityOptions": {
                 "default": "默认",
@@ -786,7 +814,6 @@
             "setContentRating": "为所选中设置内容评级",
             "copyAll": "复制所选中语法",
             "refreshAll": "刷新所选中元数据",
-            "repairMetadata": "修复所选中元数据",
             "rematchMetadata": "将所选中重新匹配到本地模型",
             "reimportMetadata": "从源重新导入",
             "checkUpdates": "检查所选更新",
@@ -842,7 +869,6 @@
             "replacePreview": "替换预览",
             "setContentRating": "设置内容评级",
             "moveToFolder": "移动到文件夹",
-            "repairMetadata": "修复元数据",
             "rematchMetadata": "重新匹配到本地模型",
             "reimportMetadata": "从源重新导入",
             "excludeModel": "排除模型",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "获取缺失 LoRA 信息失败",
                 "prepareError": "准备下载 LoRA 时出错：{message}"
             },
-            "repair": {
-                "starting": "正在修复配方元数据...",
-                "success": "配方元数据修复成功",
-                "skipped": "配方已是最新版本，无需修复",
-                "failed": "修复配方失败：{message}",
-                "missingId": "无法修复配方：缺少配方 ID"
-            },
             "reimport": {
                 "starting": "正在从源重新导入配方...",
                 "success": "配方已从源重新导入成功",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "显示示例（{count}）",
                 "hideExamples": "隐藏示例",
                 "addExamples": "添加示例",
-                "previousExample": "上一个示例",
-                "nextExample": "下一个示例",
+                "previousExample": "上一个示例（[）",
+                "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 +1952,52 @@
         "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 +2014,8 @@
             "settings": "设置与配置",
             "extensions": "扩展",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "检查更新",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "浏览目录失败：{message}",
             "batchImportDirectorySelected": "已选择目录：{path}",
             "noRecipesSelected": "未选择任何配方",
-            "repairBulkComplete": "修复完成：{repaired} 个已修复，{skipped} 个已跳过（共 {total} 个）",
-            "repairBulkSkipped": "所选 {total} 个配方无需修复",
-            "repairBulkFailed": "修复所选配方失败：{message}",
             "rematchComplete": "已匹配 {entries} 个条目，涉及 {recipes} 个配方",
             "rematchCompleteErrors": "已匹配 {entries} 个条目，涉及 {recipes} 个配方，{failures} 个失败",
             "rematchAllFailed": "{failures}/{total} 个所选配方重新匹配失败",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "{total} 个所选配方均无需重新匹配",
             "rematchFailed": "重新匹配所选配方失败：{message}",
             "reimporting": "正在从源重新导入配方...",
+            "reimportingViaExtension": "正在通过浏览器扩展重新导入配方 {current}/{total}...",
             "reimportSuccess": "配方已从源重新导入成功",
             "reimportBulkComplete": "重新导入完成：{completed} 个已导入，{failed} 个失败（共 {total} 个）",
             "reimportBulkFailed": "重新导入某些配方失败",

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。"
             }
         }
     },
@@ -179,13 +212,6 @@
             "none": "所有 {typePlural} 已具備授權中繼資料",
             "error": "重新整理 {typePlural} 授權中繼資料失敗：{message}"
         },
-        "repairRecipes": {
-            "label": "修復配方資料",
-            "loading": "正在修復配方資料...",
-            "success": "成功修復 {count} 個配方。",
-            "cancelled": "修復已取消。已修復 {count} 個配方。",
-            "error": "配方修復失敗：{message}"
-        },
         "rematchRecipes": {
             "label": "將配方重新匹配到本地模型",
             "loading": "正在將配方重新匹配到本地模型...",
@@ -451,6 +477,8 @@
         "layoutSettings": {
             "groupByModel": "按模型分組",
             "groupByModelHelp": "啟用後，每個 CivitAI 模型僅顯示最新版本的單張卡片，舊版本將被隱藏。",
+            "stickyControls": "保持操作列可見",
+            "stickyControlsHelp": "啟用後，操作列（重新整理、下載等）會在捲動時與麵包屑導覽一起固定在頁面頂端。",
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
                 "default": "預設",
@@ -786,7 +814,6 @@
             "setContentRating": "為全部設定內容分級",
             "copyAll": "複製全部語法",
             "refreshAll": "刷新全部 metadata",
-            "repairMetadata": "修復所選中元數據",
             "rematchMetadata": "將所選中重新匹配到本地模型",
             "reimportMetadata": "從來源重新匯入",
             "checkUpdates": "檢查所選更新",
@@ -842,7 +869,6 @@
             "replacePreview": "更換預覽圖",
             "setContentRating": "設定內容分級",
             "moveToFolder": "移動到資料夾",
-            "repairMetadata": "修復元數據",
             "rematchMetadata": "重新匹配到本地模型",
             "reimportMetadata": "從來源重新匯入",
             "excludeModel": "排除模型",
@@ -868,6 +894,21 @@
             "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 +939,64 @@
             "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": {
@@ -1030,13 +1121,6 @@
                 "getInfoFailed": "取得缺少 LoRA 資訊失敗",
                 "prepareError": "準備下載 LoRA 時發生錯誤：{message}"
             },
-            "repair": {
-                "starting": "正在修復配方元數據...",
-                "success": "配方元數據修復成功",
-                "skipped": "配方已是最新版本，無需修復",
-                "failed": "修復配方失敗：{message}",
-                "missingId": "無法修復配方：缺少配方 ID"
-            },
             "reimport": {
                 "starting": "正在從來源重新匯入配方...",
                 "success": "配方已從來源重新匯入成功",
@@ -1528,7 +1612,11 @@
                 "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 +1627,7 @@
                 "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 +1687,8 @@
                 "showCount": "顯示範例（{count}）",
                 "hideExamples": "隱藏範例",
                 "addExamples": "新增範例",
-                "previousExample": "上一個範例",
-                "nextExample": "下一個範例",
+                "previousExample": "上一個範例（[）",
+                "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 +1952,52 @@
         "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 +2014,8 @@
             "settings": "設定與配置",
             "extensions": "擴充功能",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "檢查更新",
@@ -2043,7 +2174,10 @@
             "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 +2185,13 @@
             "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}",
@@ -2081,9 +2222,6 @@
             "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
             "batchImportDirectorySelected": "已選擇目錄：{path}",
             "noRecipesSelected": "未選取任何配方",
-            "repairBulkComplete": "修復完成：{repaired} 個已修復，{skipped} 個已跳過（共 {total} 個）",
-            "repairBulkSkipped": "所選 {total} 個配方無需修復",
-            "repairBulkFailed": "修復所選配方失敗：{message}",
             "rematchComplete": "已匹配 {entries} 個條目，涉及 {recipes} 個配方",
             "rematchCompleteErrors": "已匹配 {entries} 個條目，涉及 {recipes} 個配方，{failures} 個失敗",
             "rematchAllFailed": "{failures}/{total} 個所選配方重新匹配失敗",
@@ -2091,6 +2229,7 @@
             "rematchSkipped": "{total} 個所選配方均無需重新匹配",
             "rematchFailed": "重新匹配所選配方失敗：{message}",
             "reimporting": "正在從來源重新匯入配方...",
+            "reimportingViaExtension": "正在透過瀏覽器擴充功能重新匯入配方 {current}/{total}...",
             "reimportSuccess": "配方已從來源重新匯入成功",
             "reimportBulkComplete": "重新匯入完成：{completed} 個已匯入，{failed} 個失敗（共 {total} 個）",
             "reimportBulkFailed": "重新匯入某些配方失敗",

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
-        filters the ckpt_name/unet_name combo options by base_model using this
-        pool, so control_after_generate randomizes within the narrowed set.
+        Backing data for the Checkpoint/Unet Loader nodes'
+        control_after_generate feature: the front-end filters the
+        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,
@@ -122,11 +129,6 @@ class RecipeHandlerSet:
             "get_recipes_for_checkpoint": self.query.get_recipes_for_checkpoint,
             "scan_recipes": self.query.scan_recipes,
             "move_recipe": self.management.move_recipe,
-            "repair_recipes": self.management.repair_recipes,
-            "cancel_repair": self.management.cancel_repair,
-            "repair_recipe": self.management.repair_recipe,
-            "repair_recipes_bulk": self.management.repair_recipes_bulk,
-            "get_repair_progress": self.management.get_repair_progress,
             "rematch_recipes": self.management.rematch_recipes,
             "cancel_rematch": self.management.cancel_rematch,
             "rematch_recipe": self.management.rematch_recipe,
@@ -346,6 +348,17 @@ 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 +480,32 @@ 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]
@@ -763,157 +791,6 @@ class RecipeManagementHandler:
             self._logger.error("Error saving recipe: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
 
-    async def repair_recipes(self, request: web.Request) -> web.Response:
-        try:
-            await self._ensure_dependencies_ready()
-            recipe_scanner = self._recipe_scanner_getter()
-            if recipe_scanner is None:
-                return web.json_response(
-                    {"success": False, "error": "Recipe scanner unavailable"},
-                    status=503,
-                )
-
-            # Check if already running
-            if self._ws_manager.is_recipe_repair_running():
-                return web.json_response(
-                    {"success": False, "error": "Recipe repair already in progress"},
-                    status=409,
-                )
-
-            recipe_scanner.reset_cancellation()
-
-            async def progress_callback(data):
-                await self._ws_manager.broadcast_recipe_repair_progress(data)
-
-            # Run in background to avoid timeout
-            async def run_repair():
-                try:
-                    await recipe_scanner.repair_all_recipes(
-                        progress_callback=progress_callback
-                    )
-                except Exception as e:
-                    self._logger.error(
-                        f"Error in recipe repair task: {e}", exc_info=True
-                    )
-                    await self._ws_manager.broadcast_recipe_repair_progress(
-                        {"status": "error", "error": str(e)}
-                    )
-                finally:
-                    # Keep the final status for a while so the UI can see it
-                    await asyncio.sleep(5)
-                    # Don't cleanup if it was cancelled, let the UI see the cancelled state for a bit?
-                    # Actually cleanup_recipe_repair_progress is fine as long as we waited enough.
-                    self._ws_manager.cleanup_recipe_repair_progress()
-
-            asyncio.create_task(run_repair())
-
-            return web.json_response(
-                {"success": True, "message": "Recipe repair started"}
-            )
-        except Exception as exc:
-            self._logger.error("Error starting recipe repair: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-    async def cancel_repair(self, request: web.Request) -> web.Response:
-        try:
-            await self._ensure_dependencies_ready()
-            recipe_scanner = self._recipe_scanner_getter()
-            if recipe_scanner is None:
-                return web.json_response(
-                    {"success": False, "error": "Recipe scanner unavailable"},
-                    status=503,
-                )
-
-            recipe_scanner.cancel_task()
-            return web.json_response(
-                {"success": True, "message": "Cancellation requested"}
-            )
-        except Exception as exc:
-            self._logger.error("Error cancelling recipe repair: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-    async def repair_recipes_bulk(self, request: web.Request) -> web.Response:
-        """Bulk repair metadata for multiple recipes by their IDs.
-
-        Accepts a JSON body with a "recipe_ids" array and iterates
-        repair_recipe_by_id over each entry, collecting statistics.
-        """
-        try:
-            await self._ensure_dependencies_ready()
-            recipe_scanner = self._recipe_scanner_getter()
-            if recipe_scanner is None:
-                return web.json_response(
-                    {"success": False, "error": "Recipe scanner unavailable"},
-                    status=503,
-                )
-
-            data = await request.json()
-            recipe_ids = data.get("recipe_ids", [])
-            if not recipe_ids:
-                return web.json_response(
-                    {"success": False, "error": "recipe_ids are required"},
-                    status=400,
-                )
-
-            total = len(recipe_ids)
-            repaired = 0
-            skipped = 0
-            errors = 0
-            recipes = []
-
-            for recipe_id in recipe_ids:
-                try:
-                    result = await recipe_scanner.repair_recipe_by_id(recipe_id)
-                    if result.get("success"):
-                        repaired += result.get("repaired", 0)
-                        skipped += result.get("skipped", 0)
-                        if result.get("recipe"):
-                            recipes.append(result["recipe"])
-                    else:
-                        errors += 1
-                except RecipeNotFoundError:
-                    skipped += 1
-                except Exception as exc:
-                    self._logger.error(
-                        "Error repairing recipe %s: %s", recipe_id, exc
-                    )
-                    errors += 1
-
-            return web.json_response({
-                "success": True,
-                "total": total,
-                "repaired": repaired,
-                "skipped": skipped,
-                "errors": errors,
-                "recipes": recipes,
-            })
-        except Exception as exc:
-            self._logger.error(
-                "Error performing bulk repair: %s", exc, exc_info=True
-            )
-            return web.json_response(
-                {"success": False, "error": str(exc)}, status=500
-            )
-
-    async def repair_recipe(self, request: web.Request) -> web.Response:
-        try:
-            await self._ensure_dependencies_ready()
-            recipe_scanner = self._recipe_scanner_getter()
-            if recipe_scanner is None:
-                return web.json_response(
-                    {"success": False, "error": "Recipe scanner unavailable"},
-                    status=503,
-                )
-
-            recipe_id = request.match_info["recipe_id"]
-            result = await recipe_scanner.repair_recipe_by_id(recipe_id)
-            return web.json_response(result)
-        except RecipeNotFoundError as exc:
-            return web.json_response({"success": False, "error": str(exc)}, status=404)
-        except Exception as exc:
-            self._logger.error("Error repairing single recipe: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
     async def rematch_recipes(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -925,12 +802,9 @@ class RecipeManagementHandler:
                 )
 
             # Mutual exclusion: a global rematch cannot start while a rematch
-            # OR a repair is already running — both mutate recipes under the
-            # same mutation lock.
-            if (
-                self._ws_manager.is_recipe_rematch_running()
-                or self._ws_manager.is_recipe_repair_running()
-            ):
+            # is already running — both mutate recipes under the same
+            # mutation lock.
+            if self._ws_manager.is_recipe_rematch_running():
                 return web.json_response(
                     {"success": False, "error": "Recipe rematch already in progress"},
                     status=409,
@@ -1068,12 +942,14 @@ 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
-        CivitAI, re-parses EXIF metadata with the current parser, and
-        re-resolves LoRAs / checkpoint. User edits (title, tags, favorite)
-        are carried over from the old recipe.
+        Gives the recipe a fresh start: URL-sourced recipes re-download the
+        image from CivitAI; local ones re-parse the saved recipe image. Both
+        use the original embedded generation metadata (the appended 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 +962,40 @@ class RecipeManagementHandler:
             if not old_recipe:
                 raise RecipeNotFoundError(f"Recipe {recipe_id} not found")
 
-            source_path = old_recipe.get("source_path")
-            if not source_path:
+            old_file_path = old_recipe.get("file_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."
                         ),
                     },
@@ -1106,41 +1009,66 @@ 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", "")
-            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:
+            if local_source:
                 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:
-                import_response = await self._do_import_from_url(
-                    source_path,
-                    recipe_scanner,
-                    target_dir=old_folder,
-                )
+            # Optional caller-supplied metadata payload (companion browser
+            # extension re-import). Only honored for CivitAI image page
+            # sources; everything else uses the native URL import below.
+            params = request.rel_url.query
+            payload_image_url = params.get("image_url")
+            payload_name = params.get("name")
+            payload_resources = params.get("resources")
+            has_import_payload = bool(
+                payload_image_url and payload_name and payload_resources
+            )
+
+            import_response: web.Response | None = None
+            if has_import_payload and image_id:
+                try:
+                    async with self._import_semaphore:
+                        import_response = await self._import_remote_recipe_impl(
+                            image_url=payload_image_url,
+                            name=payload_name,
+                            resources_raw=payload_resources,
+                            gen_params_raw=params.get("gen_params"),
+                            tags_raw=params.get("tags"),
+                            base_model=params.get("base_model", "") or "",
+                            source_path=source_path,
+                            target_dir=old_folder,
+                        )
+                except RecipeValidationError as exc:
+                    # Malformed resources/gen_params JSON: treat as "no
+                    # payload" and use the legacy URL re-import.
+                    self._logger.warning(
+                        "Ignoring malformed re-import payload for recipe %s "
+                        "(%s); falling back to source URL re-import",
+                        recipe_id,
+                        exc,
+                    )
+                except Exception as exc:
+                    self._logger.warning(
+                        "Payload-based re-import failed for recipe %s: %s; "
+                        "falling back to source URL re-import",
+                        recipe_id,
+                        exc,
+                    )
+
+            if import_response is None:
+                async with self._import_semaphore:
+                    import_response = await self._do_import_from_url(
+                        source_path,
+                        recipe_scanner,
+                        target_dir=old_folder,
+                    )
 
             await self._persistence_service.delete_recipe(
                 recipe_scanner=recipe_scanner, recipe_id=recipe_id
@@ -1167,14 +1095,19 @@ class RecipeManagementHandler:
                         exc,
                     )
 
-            return web.json_response(
-                {
-                    "success": True,
-                    "old_recipe_id": recipe_id,
-                    "recipe_id": new_recipe_id,
-                    "source_path": source_path,
-                }
+            response_body: Dict[str, Any] = {
+                "success": True,
+                "old_recipe_id": recipe_id,
+                "recipe_id": new_recipe_id,
+                "source_path": source_path,
+            }
+            loras_count = await self._count_recipe_loras(
+                recipe_scanner, new_recipe_id
             )
+            if loras_count is not None:
+                response_body["loras_count"] = loras_count
+
+            return web.json_response(response_body)
         except RecipeNotFoundError as exc:
             return web.json_response({"success": False, "error": str(exc)}, status=404)
         except RecipeValidationError as exc:
@@ -1187,18 +1120,6 @@ class RecipeManagementHandler:
             )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
-    async def get_repair_progress(self, request: web.Request) -> web.Response:
-        try:
-            progress = self._ws_manager.get_recipe_repair_progress()
-            if progress:
-                return web.json_response({"success": True, "progress": progress})
-            return web.json_response(
-                {"success": False, "message": "No repair in progress"}, status=404
-            )
-        except Exception as exc:
-            self._logger.error("Error getting repair progress: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
     async def import_remote_recipe(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -1219,31 +1140,14 @@ class RecipeManagementHandler:
             if not resources_raw:
                 raise RecipeValidationError("Missing required field: resources")
 
-            checkpoint_entry, lora_entries = self._parse_resources_payload(
-                resources_raw
-            )
-            gen_params_request = self._parse_gen_params(params.get("gen_params"))
-
-            self._logger.info(
-                "Remote recipe import received: url=%s, lora_count=%d",
-                image_url,
-                len(lora_entries),
-            )
-            self._logger.debug(
-                "  gen_params_keys=%s, checkpoint_keys=%s",
-                sorted(gen_params_request.keys()) if gen_params_request else [],
-                sorted(checkpoint_entry.keys()) if isinstance(checkpoint_entry, dict) else [],
-            )
-
             # Throttle concurrent imports to avoid starving ComfyUI's event loop
             async with self._import_semaphore:
-                return await self._do_import_remote_recipe(
+                return await self._import_remote_recipe_impl(
                     image_url=image_url,
                     name=name,
-                    lora_entries=lora_entries,
-                    checkpoint_entry=checkpoint_entry,
-                    gen_params_request=gen_params_request,
-                    tags=self._parse_tags(params.get("tags")),
+                    resources_raw=resources_raw,
+                    gen_params_raw=params.get("gen_params"),
+                    tags_raw=params.get("tags"),
                     base_model=params.get("base_model", "") or "",
                     source_path=params.get("source_path") or image_url,
                 )
@@ -1257,6 +1161,52 @@ class RecipeManagementHandler:
             )
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def _import_remote_recipe_impl(
+        self,
+        *,
+        image_url: str,
+        name: str,
+        resources_raw: str,
+        gen_params_raw: Optional[str],
+        tags_raw: Optional[str],
+        base_model: str,
+        source_path: str,
+        target_dir: str | None = None,
+    ) -> web.Response:
+        """Payload-based remote import engine shared by import-remote and the
+        extension-driven re-import path.
+
+        Parses the caller-supplied payloads and delegates to
+        :meth:`_do_import_remote_recipe`. Raises ``RecipeValidationError`` on
+        malformed payloads so callers can decide how to handle them (the
+        re-import path falls back to the legacy URL import).
+        """
+        checkpoint_entry, lora_entries = self._parse_resources_payload(resources_raw)
+        gen_params_request = self._parse_gen_params(gen_params_raw)
+
+        self._logger.info(
+            "Remote recipe import received: url=%s, lora_count=%d",
+            image_url,
+            len(lora_entries),
+        )
+        self._logger.debug(
+            "  gen_params_keys=%s, checkpoint_keys=%s",
+            sorted(gen_params_request.keys()) if gen_params_request else [],
+            sorted(checkpoint_entry.keys()) if isinstance(checkpoint_entry, dict) else [],
+        )
+
+        return await self._do_import_remote_recipe(
+            image_url=image_url,
+            name=name,
+            lora_entries=lora_entries,
+            checkpoint_entry=checkpoint_entry,
+            gen_params_request=gen_params_request,
+            tags=self._parse_tags(tags_raw),
+            base_model=base_model,
+            source_path=source_path,
+            target_dir=target_dir,
+        )
+
     async def _do_import_remote_recipe(
         self,
         *,
@@ -1268,6 +1218,7 @@ class RecipeManagementHandler:
         tags: list[Any],
         base_model: str,
         source_path: str,
+        target_dir: str | None = None,
     ) -> web.Response:
         recipe_scanner = self._recipe_scanner_getter()
         if recipe_scanner is None:
@@ -1431,6 +1382,7 @@ class RecipeManagementHandler:
             tags=tags,
             metadata=metadata,
             extension=extension,
+            target_dir=target_dir,
         )
         return web.json_response(result.payload, status=result.status)
 
@@ -1593,6 +1545,65 @@ 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 +1633,116 @@ 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()
@@ -1726,6 +1847,25 @@ class RecipeManagementHandler:
             return []
         return [tag.strip() for tag in tag_text.split(",") if tag.strip()]
 
+    async def _count_recipe_loras(
+        self, recipe_scanner: Any, recipe_id: Optional[str]
+    ) -> Optional[int]:
+        """Best-effort LoRA count for a freshly saved recipe (for the
+        re-import response). Returns None when the recipe cannot be read."""
+        if not recipe_id:
+            return None
+        try:
+            recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
+        except Exception as exc:
+            self._logger.debug(
+                "Could not read new recipe %s for loras_count: %s",
+                recipe_id,
+                exc,
+            )
+            return None
+        loras = (recipe or {}).get("loras")
+        return len(loras) if isinstance(loras, list) else None
+
     def _parse_gen_params(self, payload: Optional[str]) -> Optional[Dict[str, Any]]:
         if payload is None:
             return None
@@ -2054,6 +2194,23 @@ 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 +2231,7 @@ 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 +2239,7 @@ class RecipeManagementHandler:
                         )
                     )
                     if parser:
+                        diagnostics["exif_parser"] = parser.__class__.__name__
                         if isinstance(parser, CivitaiApiMetadataParser):
                             parsed_embedded = await parser.parse_metadata(
                                 raw_embedded,
@@ -2121,6 +2280,7 @@ 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 +2288,7 @@ class RecipeManagementHandler:
                                 )
                             )
                             if parser:
+                                diagnostics["exif_parser"] = parser.__class__.__name__
                                 if isinstance(parser, CivitaiApiMetadataParser):
                                     parsed_embedded = await parser.parse_metadata(
                                         raw_orig,
@@ -2249,6 +2410,20 @@ 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 +2446,20 @@ 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
-        fresh recipe, then deletes the old one.
+        Reads the original source file, re-parses its original embedded
+        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 +2475,7 @@ 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 +2488,22 @@ 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 +2520,10 @@ 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 +2551,7 @@ 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(
@@ -62,11 +84,6 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "GET", "/api/lm/recipes/for-checkpoint", "get_recipes_for_checkpoint"
     ),
     RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
-    RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
-    RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
-    RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
-    RouteDefinition("POST", "/api/lm/recipes/repair-bulk", "repair_recipes_bulk"),
-    RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
     RouteDefinition("POST", "/api/lm/recipes/rematch", "rematch_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/rematch-bulk", "rematch_recipes_bulk"),
     RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/rematch", "rematch_recipe"),
@@ -93,6 +110,11 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "POST", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
     ),
+    # The companion browser extension only ever issues GET requests, so the
+    # payload-based re-import variant must also be reachable via GET.
+    RouteDefinition(
+        "GET", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
+    ),
     RouteDefinition(
         "POST", "/api/lm/recipe/{recipe_id}/send-workflow", "send_recipe_workflow"
     ),

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",
@@ -500,9 +505,55 @@ class CivitaiClient:
         logger.warning(f"Failed to fetch version by id {version_id}")
         return None
 
+    async def get_version_file_mini(
+        self, version_id: int, file_id: int
+    ) -> Optional[Dict[str, Any]]:
+        """Fetch raw stored file info via the model-versions/mini endpoint.
+
+        The public REST API rewrites ``files[].name`` to
+        ``"{model}_{version}"`` for non-LoRA model types, so every
+        precision variant of a multi-file version shares one name (#1100).
+        The mini endpoint returns the raw ``ModelFile.name`` in
+        ``fileName``. ``file_id`` is mandatory: without it mini picks a
+        file via its own primary-file logic, which can disagree with the
+        REST ``primary`` flag.
+
+        Returns the mini payload dict on success, None on any failure.
+        """
+        try:
+            success, data = await self._make_request(
+                "GET",
+                f"{self.base_url}/model-versions/mini/{version_id}",
+                params={"modelFileId": file_id},
+                use_auth=True,
+            )
+            if success and isinstance(data, dict):
+                return data
+            if is_expected_offline_error(data):
+                return None
+            logger.debug(
+                "Mini endpoint lookup failed for version %s file %s: %s",
+                version_id,
+                file_id,
+                data,
+            )
+            return None
+        except RateLimitError:
+            raise
+        except Exception as exc:
+            logger.debug(
+                "Error fetching mini info for version %s file %s: %s",
+                version_id,
+                file_id,
+                exc,
+            )
+            return None
+
     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/download_manager.py

@@ -35,6 +35,7 @@ from .service_registry import ServiceRegistry
 from .settings_manager import get_settings_manager
 from .metadata_service import get_default_metadata_provider, get_metadata_provider
 from .downloader import get_downloader, DownloadProgress, DownloadStreamControl
+from .errors import RateLimitError
 from .aria2_downloader import Aria2Error, get_aria2_downloader
 from .aria2_transfer_state import Aria2TransferStateStore
 from .download_queue_service import DownloadQueueService
@@ -929,6 +930,42 @@ class DownloadManager:
 
         return download_urls
 
+    async def _fetch_raw_file_name(
+        self,
+        metadata_provider,
+        version_id: Optional[int],
+        file_id: Any,
+    ) -> Optional[str]:
+        """Best-effort lookup of the raw stored filename via the CivitAI
+        model-versions/mini endpoint (#1100). Returns None on any failure so
+        the caller can fall back to the (possibly rewritten) REST name."""
+        if version_id is None or file_id is None:
+            return None
+        fetch = getattr(metadata_provider, "get_version_file_mini", None)
+        if fetch is None:
+            return None
+        try:
+            mini_info = await fetch(int(version_id), int(file_id))
+        except (TypeError, ValueError):
+            return None
+        except RateLimitError:
+            raise
+        except Exception as exc:
+            logger.debug(
+                "Mini endpoint lookup failed for version %s file %s: %s",
+                version_id,
+                file_id,
+                exc,
+            )
+            return None
+        if not isinstance(mini_info, dict):
+            return None
+        raw_name = mini_info.get("fileName")
+        if not isinstance(raw_name, str) or not raw_name.strip():
+            return None
+        # Defensive: never let a path component slip into the filename.
+        return os.path.basename(raw_name.strip()) or None
+
     def _build_metadata_for_resume(
         self,
         *,
@@ -1858,6 +1895,24 @@ class DownloadManager:
             if not download_urls:
                 return {"success": False, "error": "No mirror URL found"}
 
+            # The public REST API rewrites files[].name to
+            # "{model}_{version}" for non-LoRA model types, so every
+            # precision variant of a multi-file version shares one name and
+            # lands on disk with a random short-hash suffix. The mini
+            # endpoint returns the raw stored filename (#1100). CivArchive
+            # already serves raw names.
+            if source != "civarchive":
+                raw_file_name = await self._fetch_raw_file_name(
+                    metadata_provider, resolved_version_id, file_info.get("id")
+                )
+                if raw_file_name and raw_file_name != file_info.get("name"):
+                    logger.info(
+                        "[download] Using raw stored filename '%s' instead of REST name '%s'",
+                        raw_file_name,
+                        file_info.get("name"),
+                    )
+                    file_info = {**file_info, "name": raw_file_name}
+
             # 3. Prepare download
             file_name = file_info.get("name", "")
             if not file_name:

py/services/llm_service.py

@@ -58,7 +58,7 @@ async def _load_model_catalog() -> Dict[str, List[str]]:
                     logger.warning("Model catalog returned HTTP %s", resp.status)
                     return _catalog_cache or {}
                 data = await resp.json()
-    except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as exc:
+    except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError, UnicodeDecodeError) as exc:
         logger.warning("Failed to fetch model catalog: %s", exc)
         return _catalog_cache or {}
 
@@ -131,7 +131,7 @@ async def fetch_ollama_models(api_base: str) -> List[str]:
                     logger.debug("Ollama API returned HTTP %s from %s", resp.status, api_base)
                     return []
                 data = await resp.json()
-    except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as exc:
+    except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError, UnicodeDecodeError) as exc:
         logger.debug("Ollama not reachable at %s: %s", api_base, exc)
         return []
 

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_metadata_provider.py

@@ -169,6 +169,17 @@ class ModelMetadataProvider(ABC):
         """Published model count for the user; None when unsupported."""
         return None
 
+    async def get_version_file_mini(
+        self, version_id: int, file_id: int
+    ) -> Optional[Dict[str, Any]]:
+        """Fetch raw stored file info via CivitAI's model-versions/mini endpoint.
+
+        Only the CivitAI provider implements this (#1100); other providers
+        already serve raw file names (CivArchive) or cannot resolve this
+        lookup (SQLite), so the default is None.
+        """
+        return None
+
 class CivitaiModelMetadataProvider(ModelMetadataProvider):
     """Provider that uses Civitai API for metadata"""
     
@@ -203,6 +214,11 @@ class CivitaiModelMetadataProvider(ModelMetadataProvider):
     async def get_creator_model_count(self, username: str) -> Optional[int]:
         return await self.client.get_creator_model_count(username)
 
+    async def get_version_file_mini(
+        self, version_id: int, file_id: int
+    ) -> Optional[Dict[str, Any]]:
+        return await self.client.get_version_file_mini(version_id, file_id)
+
 class CivArchiveModelMetadataProvider(ModelMetadataProvider):
     """Provider that uses CivArchive API for metadata"""
     
@@ -700,6 +716,37 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 continue
         return None
 
+    async def get_version_file_mini(
+        self, version_id: int, file_id: int
+    ) -> Optional[Dict[str, Any]]:
+        rate_limited = False
+        for provider, label in self._iter_providers():
+            if rate_limited and label not in _LOCAL_PROVIDER_LABELS:
+                continue
+            try:
+                result = await self._call_with_rate_limit(
+                    label,
+                    provider.get_version_file_mini,
+                    version_id,
+                    file_id,
+                )
+                if result:
+                    return result
+            except RateLimitError as exc:
+                rate_limited = True
+                logger.warning(
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
+                    label,
+                    exc.retry_after or 0,
+                )
+                continue
+            except Exception as e:
+                logger.debug(
+                    "Provider %s failed for get_version_file_mini: %s", label, e
+                )
+                continue
+        return None
+
     def _iter_providers(self):
         return zip(self.providers, self._provider_labels)
 
@@ -791,6 +838,16 @@ class RateLimitRetryingProvider(ModelMetadataProvider):
     async def get_creator_model_count(self, username: str) -> Optional[int]:
         return await self._provider.get_creator_model_count(username)
 
+    async def get_version_file_mini(
+        self, version_id: int, file_id: int
+    ) -> Optional[Dict[str, Any]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_version_file_mini,
+            version_id,
+            file_id,
+        )
+
 class ModelMetadataProviderManager:
     """Manager for selecting and using model metadata providers"""
     

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 = {
-                'lora': 'loras',
-                'checkpoint': 'checkpoints',
-                'embedding': 'embeddings'
-            }
-            page_type = page_type_map.get(self.model_type, self.model_type)
+            page_type = self.page_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

@@ -52,13 +52,13 @@ class PersistentRecipeCache:
         "file_mtime",
         "file_size",
         "favorite",
-        "repair_version",
         "preview_nsfw_level",
         "loras_json",
         "checkpoint_json",
         "gen_params_json",
         "tags_json",
         "has_workflow",
+        "import_info_json",
     )
     _instances: Dict[str, "PersistentRecipeCache"] = {}
     _instance_lock = threading.Lock()
@@ -441,13 +441,13 @@ class PersistentRecipeCache:
                             file_mtime REAL,
                             file_size INTEGER,
                             favorite INTEGER DEFAULT 0,
-                            repair_version INTEGER DEFAULT 0,
                             preview_nsfw_level INTEGER DEFAULT 0,
                             loras_json TEXT,
                             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 +473,13 @@ 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 +511,9 @@ 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
@@ -529,13 +539,13 @@ class PersistentRecipeCache:
             file_mtime,
             file_size,
             1 if recipe.get("favorite") else 0,
-            int(recipe.get("repair_version") or 0),
             int(recipe.get("preview_nsfw_level") or 0),
             loras_json,
             checkpoint_json,
             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 +578,13 @@ 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 "",
@@ -579,7 +596,6 @@ class PersistentRecipeCache:
             "created_date": row["created_date"] or 0.0,
             "modified": row["modified"] or 0.0,
             "favorite": bool(row["favorite"]),
-            "repair_version": row["repair_version"] or 0,
             "preview_nsfw_level": row["preview_nsfw_level"] or 0,
             "has_workflow": bool(row["has_workflow"]),
             "loras": loras,
@@ -592,6 +608,9 @@ class PersistentRecipeCache:
         if checkpoint:
             recipe["checkpoint"] = checkpoint
 
+        if import_info:
+            recipe["import_info"] = import_info
+
         return recipe
 
 

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)
-        if not target_lora:
-            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"
-                )
+        matches = await recipe_scanner.find_local_loras_by_name(target_name)
+        if not matches:
             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/services/settings_manager.py

@@ -116,6 +116,7 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "backup_retention_count": 5,
     "use_new_license_icons": True,
     "group_by_model": False,
+    "sticky_controls": False,
     # AI / LLM provider configuration (BYOK)
     "llm_provider": "openai",  # "openai" | "ollama" | "custom"
     "llm_api_key": "",

py/services/websocket_manager.py

@@ -20,8 +20,6 @@ class WebSocketManager:
         self._last_init_progress: Dict[str, Dict[str, Any]] = {}
         # Add auto-organize progress tracking
         self._auto_organize_progress: Optional[Dict[str, Any]] = None
-        # Add recipe repair progress tracking
-        self._recipe_repair_progress: Optional[Dict[str, Any]] = None
         # Add recipe rematch progress tracking
         self._recipe_rematch_progress: Optional[Dict[str, Any]] = None
         self._auto_organize_lock = asyncio.Lock()
@@ -193,14 +191,6 @@ class WebSocketManager:
         # Broadcast via WebSocket
         await self.broadcast(data)
     
-    async def broadcast_recipe_repair_progress(self, data: Dict[str, Any]):
-        """Broadcast recipe repair progress to connected clients"""
-        # Store progress data in memory
-        self._recipe_repair_progress = data
-        
-        # Broadcast via WebSocket
-        await self.broadcast(data)
-    
     def get_auto_organize_progress(self) -> Optional[Dict[str, Any]]:
         """Get current auto-organize progress"""
         return self._auto_organize_progress
@@ -209,22 +199,6 @@ class WebSocketManager:
         """Clear auto-organize progress data"""
         self._auto_organize_progress = None
     
-    def get_recipe_repair_progress(self) -> Optional[Dict[str, Any]]:
-        """Get current recipe repair progress"""
-        return self._recipe_repair_progress
-    
-    def cleanup_recipe_repair_progress(self):
-        """Clear recipe repair progress data if it is in a finished state"""
-        if self._recipe_repair_progress and self._recipe_repair_progress.get('status') in ['completed', 'cancelled', 'error']:
-            self._recipe_repair_progress = None
-    
-    def is_recipe_repair_running(self) -> bool:
-        """Check if recipe repair is currently running"""
-        if not self._recipe_repair_progress:
-            return False
-        status = self._recipe_repair_progress.get('status')
-        return status in ['started', 'processing']
-    
     async def broadcast_recipe_rematch_progress(self, data: Dict[str, Any]):
         """Broadcast recipe rematch progress to connected clients"""
         # Store progress data in memory

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)
-# training tools write this placeholder into safetensors metadata instead of a
-# real hash; it must never be treated as a valid AutoV3 — several broken
-# models sharing it would collide in the hash index and falsely match recipes.
-INVALID_AUTOV3_EMPTY_HASH = "e3b0c44298fc"
+# SHA256 of an empty byte string. Some (re-packaging) training tools write a
+# truncated form of this placeholder into safetensors metadata (as
+# ``modelspec.hash_sha256`` / ``sshs_model_hash``), and hashing an empty or
+# unreadable file produces it directly. It must never be treated as a valid
+# 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:
-        """Append recipe metadata to an image's EXIF data"""
+    def append_recipe_metadata(image_path, recipe_data, pixel_preserving=False) -> str:
+        """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"""

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.2.1"
+version = "1.2.2"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

static/css/base.css

@@ -31,9 +31,14 @@ body {
     --header-height: 48px;
     --scrollbar-width: 8px;
 
-    --shortcut-bg: var(--color-accent-subtle);
-    --shortcut-border: var(--color-accent-border);
-    --shortcut-text: var(--text-primary);
+    /* Neutral "keycap" style for keyboard shortcut hints (GitHub/Linear-like).
+       Derived from --text-muted so it adapts to every theme/preset. */
+    --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);
-  /* Subtle tint derived from text color so it adapts to both light & dark themes */
-  background: color-mix(in oklch, var(--text-muted) 12%, transparent);
-  border: 1px solid color-mix(in oklch, var(--text-muted) 25%, transparent);
+  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;
 }

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);
-    -webkit-backdrop-filter: blur(8px);
+    backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
+    -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);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    padding: 12px;
-    margin-top: 10px;
+    flex-basis: 100%;
+    /* Flex items default to min-width:auto — never let content force the
+       panel wider than the row. */
+    min-width: 0;
+    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/components/toast.css

@@ -174,7 +174,7 @@
     z-index: var(--z-toast);
     display: flex;
     flex-direction: column;
-    align-items: flex-end;
+    /* No align-items (defaults to stretch) so every toast shares one equal width */
     gap: 10px;
     padding: 8px 20px 0; /* Small breathing room below the header */
     pointer-events: none; /* Allow clicking through the container */

static/css/layout.css

@@ -25,6 +25,23 @@
     box-shadow: var(--shadow-xs);
 }
 
+/* Wrapper around the controls bar and breadcrumb nav. With the sticky-controls
+   setting off it is transparent to layout (display: contents), preserving the
+   original behavior (only the breadcrumb stays visible). When enabled, the whole
+   wrapper sticks as one unit so the two bars can never drift apart. */
+.sticky-topbar {
+    display: contents;
+}
+
+body.sticky-controls .sticky-topbar {
+    display: block;
+    position: sticky;
+    top: 0;
+    z-index: calc(var(--z-header) - 1);
+    background: var(--bg-color);
+    box-shadow: var(--shadow-xs);
+}
+
 /* Responsive container for larger screens */
 @media (min-width: 2150px) {
     .container {
@@ -201,36 +218,42 @@
     display: inline-flex;
     align-items: center;
     justify-content: center;
-    margin-left: 6px;
     min-width: 16px;
     height: 16px;
-    padding: 0 3px;
-    font-size: 11px;
-    font-weight: 600;
+    padding: 0 4px;
+    font-size: 10px;
+    font-weight: 500;
     line-height: 1;
+    text-transform: uppercase;
     border-radius: var(--border-radius-xs);
     background-color: var(--shortcut-bg);
     border: 1px solid var(--shortcut-border);
     color: var(--shortcut-text);
-    vertical-align: middle;
     opacity: 0.8;
     transition: var(--transition-base);
 }
 
 .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);
+/* Invert the keycap on active (accent-filled) buttons for contrast.
+   Must come after the hover rule above so it wins on active+hover. */
+.control-group button.active .shortcut-key,
+.control-group button.active:hover .shortcut-key {
+    color: var(--lora-accent);
+    background: rgba(255, 255, 255, 0.92);
+    border-color: transparent;
+    opacity: 1;
 }
 
 /* Ensure correct vertical alignment for text+shortcut */
 .control-group button span {
     display: inline-flex;
     align-items: center;
+    gap: 6px;
 }
 
 /* Select dropdown styling */

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 {
-            state.loadingManager.show(
-                `${fullRebuild ? 'Full rebuild' : 'Refreshing'} ${this.apiConfig.config.displayName}s...`,
-                0
+        const displayName = this.apiConfig.config.displayName;
+        const singularName = this.apiConfig.config.singularName;
+        const actionText = translate(
+            fullRebuild ? 'common.scanProgress.actionFullRebuild' : 'common.scanProgress.actionRefresh',
+            {},
+            fullRebuild ? 'Full rebuild' : 'Refresh'
+        );
+        const actionLowerText = translate(
+            fullRebuild ? 'common.scanProgress.actionRebuildLower' : 'common.scanProgress.actionRefreshLower',
+            {},
+            fullRebuild ? 'rebuild' : 'refresh'
+        );
+        const initialMessage = translate(
+            fullRebuild ? 'common.scanProgress.fullRebuilding' : 'common.scanProgress.refreshing',
+            { type: displayName },
+            `${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',
@@ -15,7 +20,6 @@ const RECIPE_ENDPOINTS = {
     move: '/api/lm/recipe/move',
     moveBulk: '/api/lm/recipes/move-bulk',
     bulkDelete: '/api/lm/recipes/bulk-delete',
-    repairBulk: '/api/lm/recipes/repair-bulk',
     rematchBulk: '/api/lm/recipes/rematch-bulk',
     rematchSingle: '/api/lm/recipe/{recipe_id}/rematch',
 };
@@ -333,11 +337,53 @@ export async function syncChanges() {
 }
 
 export async function refreshRecipes(fullRebuild = true) {
-    const actionLabel = fullRebuild ? 'Rebuilding recipe cache' : 'Refreshing recipes';
-    const actionToast = fullRebuild ? 'Full rebuild' : 'Refresh';
+    const actionText = translate(
+        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 +402,64 @@ 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
@@ -583,38 +677,6 @@ export class RecipeSidebarApiClient {
         };
     }
 
-    async repairBulkModels(filePaths) {
-        if (!filePaths || filePaths.length === 0) {
-            throw new Error('No file paths provided');
-        }
-
-        const recipeIds = filePaths
-            .map((path) => extractRecipeId(path))
-            .filter((id) => !!id);
-
-        if (recipeIds.length === 0) {
-            throw new Error('No recipe IDs could be derived from file paths');
-        }
-
-        const response = await fetch(this.apiConfig.endpoints.repairBulk, {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                recipe_ids: recipeIds,
-            }),
-        });
-
-        const result = await response.json();
-
-        if (!response.ok || !result.success) {
-            throw new Error(result.error || 'Failed to repair recipes');
-        }
-
-        return result;
-    }
-
     async rematchBulkModels(filePaths) {
         if (!filePaths || filePaths.length === 0) {
             throw new Error('No file paths provided');

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
-                // (form submit / free-text commit) proceed.
+                // highlighted; otherwise commit the free-text value (when an
+                // 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/ContextMenu/BulkContextMenu.js

@@ -41,13 +41,9 @@ export class BulkContextMenu extends BaseContextMenu {
         const autoOrganizeItem = this.menu.querySelector('[data-action="auto-organize"]');
         const deleteAllItem = this.menu.querySelector('[data-action="delete-all"]');
         const downloadMissingLorasItem = this.menu.querySelector('[data-action="download-missing-loras"]');
-        const repairMetadataItem = this.menu.querySelector('[data-action="repair-metadata"]');
         const reimportMetadataItem = this.menu.querySelector('[data-action="reimport-metadata"]');
         const rematchMetadataItem = this.menu.querySelector('[data-action="rematch-metadata"]');
 
-        if (repairMetadataItem) {
-            repairMetadataItem.style.display = config.repairMetadata ? 'flex' : 'none';
-        }
         if (reimportMetadataItem) {
             reimportMetadataItem.style.display = config.reimportMetadata ? 'flex' : 'none';
         }
@@ -283,9 +279,6 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'delete-all':
                 bulkManager.showBulkDeleteModal();
                 break;
-            case 'repair-metadata':
-                bulkManager.repairSelectedRecipes();
-                break;
             case 'rematch-metadata':
                 bulkManager.rematchSelectedRecipes();
                 break;

static/js/components/ContextMenu/GlobalContextMenu.js

@@ -23,7 +23,6 @@ export class GlobalContextMenu extends BaseContextMenu {
         const downloadExamplesItem = this.menu.querySelector('[data-action="download-example-images"]');
         const cleanupExamplesItem = this.menu.querySelector('[data-action="cleanup-example-images-folders"]');
         const excludedModelsItem = this.menu.querySelector('[data-action="manage-excluded-models"]');
-        const repairRecipesItem = this.menu.querySelector('[data-action="repair-recipes"]');
         const rematchRecipesItem = this.menu.querySelector('[data-action="rematch-recipes"]');
         const groupByModelItem = this.menu.querySelector('[data-action="toggle-group-by-model"]');
         const groupByModelCheck = groupByModelItem?.querySelector('.check-indicator');
@@ -41,7 +40,6 @@ export class GlobalContextMenu extends BaseContextMenu {
             cleanupExamplesItem?.classList.add('hidden');
             excludedModelsItem?.classList.add('hidden');
             groupByModelItem?.classList.add('hidden');
-            repairRecipesItem?.classList.remove('hidden');
             rematchRecipesItem?.classList.remove('hidden');
         } else {
             modelUpdateItem?.classList.remove('hidden');
@@ -50,7 +48,6 @@ export class GlobalContextMenu extends BaseContextMenu {
             cleanupExamplesItem?.classList.remove('hidden');
             excludedModelsItem?.classList.remove('hidden');
             groupByModelItem?.classList.remove('hidden');
-            repairRecipesItem?.classList.add('hidden');
             rematchRecipesItem?.classList.add('hidden');
         }
 
@@ -95,11 +92,6 @@ export class GlobalContextMenu extends BaseContextMenu {
                     console.error('Failed to refresh missing license metadata:', error);
                 });
                 break;
-            case 'repair-recipes':
-                this.repairRecipes(menuItem).catch((error) => {
-                    console.error('Failed to repair recipes:', error);
-                });
-                break;
             case 'rematch-recipes':
                 this.rematchRecipes(menuItem).catch((error) => {
                     console.error('Failed to rematch recipes:', error);
@@ -371,99 +363,6 @@ export class GlobalContextMenu extends BaseContextMenu {
         return `${displayName}s`;
     }
 
-    async repairRecipes(menuItem) {
-        if (this._repairInProgress) {
-            return;
-        }
-
-        this._repairInProgress = true;
-        menuItem?.classList.add('disabled');
-
-        const loadingMessage = translate(
-            'globalContextMenu.repairRecipes.loading',
-            {},
-            'Repairing recipe data...'
-        );
-
-        const progressUI = state.loadingManager?.showEnhancedProgress(loadingMessage);
-        progressUI?.showCancelButton(() => this.cancelRepair());
-
-        try {
-            const response = await fetch('/api/lm/recipes/repair', {
-                method: 'POST',
-                headers: { 'Content-Type': 'application/json' },
-            });
-
-            const result = await response.json();
-            if (!response.ok || !result.success) {
-                throw new Error(result.error || 'Failed to start repair');
-            }
-
-            // Poll for progress (or wait for WebSocket if preferred, but polling is simpler for this implementation)
-            let isComplete = false;
-            while (!isComplete && this._repairInProgress) {
-                const progressResponse = await fetch('/api/lm/recipes/repair-progress');
-                if (progressResponse.ok) {
-                    const progressResult = await progressResponse.json();
-                    if (progressResult.success && progressResult.progress) {
-                        const p = progressResult.progress;
-                        if (p.status === 'processing') {
-                            const percent = (p.current / p.total) * 100;
-                            progressUI?.updateProgress(percent, p.recipe_name, `${loadingMessage} (${p.current}/${p.total})`);
-                        } else if (p.status === 'completed') {
-                            isComplete = true;
-                            progressUI?.complete(translate(
-                                'globalContextMenu.repairRecipes.success',
-                                { count: p.repaired },
-                                `Repaired ${p.repaired} recipes.`
-                            ));
-                            showToast('globalContextMenu.repairRecipes.success', { count: p.repaired }, 'success');
-                            // Refresh recipes page if active
-                            if (window.recipesPage) {
-                                window.recipesPage.refresh();
-                            }
-                        } else if (p.status === 'error') {
-                            throw new Error(p.error || 'Repair failed');
-                        } else if (p.status === 'cancelled') {
-                            isComplete = true;
-                            progressUI?.complete(translate(
-                                'globalContextMenu.repairRecipes.cancelled',
-                                { count: p.repaired },
-                                `Repair cancelled. ${p.repaired} recipes were repaired.`
-                            ));
-                            showToast('globalContextMenu.repairRecipes.cancelled', { count: p.repaired }, 'info');
-                        }
-                    } else if (progressResponse.status === 404) {
-                        // Progress might have finished quickly and been cleaned up
-                        isComplete = true;
-                        progressUI?.complete();
-                    }
-                }
-
-                if (!isComplete) {
-                    await new Promise(resolve => setTimeout(resolve, 1000));
-                }
-            }
-        } catch (error) {
-            console.error('Recipe repair failed:', error);
-            progressUI?.complete(translate('globalContextMenu.repairRecipes.error', { message: error.message }, 'Repair failed: {message}'));
-            showToast('globalContextMenu.repairRecipes.error', { message: error.message }, 'error');
-        } finally {
-            this._repairInProgress = false;
-            menuItem?.classList.remove('disabled');
-        }
-    }
-
-    async cancelRepair() {
-        try {
-            await fetch('/api/lm/recipes/cancel-repair', {
-                method: 'POST',
-            });
-        } catch (error) {
-            console.error('Failed to cancel recipe repair:', error);
-        }
-    }
-
     async rematchRecipes(menuItem) {
         if (this._rematchInProgress) {
             return;

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -6,6 +6,7 @@ import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js
 import { updateRecipeMetadata } from '../../api/recipeApi.js';
 import { state } from '../../state/index.js';
 import { moveManager } from '../../managers/MoveManager.js';
+import { probeExtension, delegateReimport, getCivitaiImageInfo } from '../../utils/extensionReimportBridge.js';
 
 export class RecipeContextMenu extends BaseContextMenu {
     constructor() {
@@ -93,10 +94,6 @@ export class RecipeContextMenu extends BaseContextMenu {
                 // Download missing LoRAs
                 this.downloadMissingLoRAs(recipeId);
                 break;
-            case 'repair':
-                // Repair recipe metadata
-                this.repairRecipe(recipeId);
-                break;
             case 'rematch':
                 // Rematch recipe resources to local models
                 this.rematchRecipe(recipeId);
@@ -297,44 +294,6 @@ export class RecipeContextMenu extends BaseContextMenu {
         }
     }
 
-    // Repair recipe metadata
-    async repairRecipe(recipeId) {
-        if (!recipeId) {
-            showToast('recipes.contextMenu.repair.missingId', {}, 'error');
-            return;
-        }
-
-        try {
-            showToast('recipes.contextMenu.repair.starting', {}, 'info');
-
-            const response = await fetch(`/api/lm/recipe/${recipeId}/repair`, {
-                method: 'POST'
-            });
-            const result = await response.json();
-
-            if (result.success) {
-                if (result.repaired > 0) {
-                    showToast('recipes.contextMenu.repair.success', {}, 'success');
-                    const detailResponse = await fetch(`/api/lm/recipe/${recipeId}`);
-                    if (detailResponse.ok) {
-                        const updatedRecipe = await detailResponse.json();
-                        const filePath = this.currentCard?.dataset?.filepath;
-                        if (filePath && state.virtualScroller) {
-                            state.virtualScroller.updateSingleItem(filePath, updatedRecipe);
-                        }
-                    }
-                } else {
-                    showToast('recipes.contextMenu.repair.skipped', {}, 'info');
-                }
-            } else {
-                throw new Error(result.error || 'Repair failed');
-            }
-        } catch (error) {
-            console.error('Error repairing recipe:', error);
-            showToast('recipes.contextMenu.repair.failed', { message: error.message }, 'error');
-        }
-    }
-
     async rematchRecipe(recipeId) {
         if (!recipeId) {
             showToast('toast.recipes.rematchFailed', { message: 'Missing recipe ID' }, 'error');
@@ -397,6 +356,24 @@ export class RecipeContextMenu extends BaseContextMenu {
             return;
         }
 
+        // Recipes imported from a CivitAI image page can carry incomplete
+        // metadata (0 LoRAs); the companion browser extension can re-import
+        // them with the full page data. Fall back to the native path whenever
+        // the extension is absent, unlicensed, or the delegation fails.
+        const recipeItem = state.virtualScroller?.items?.find(item => item?.id === recipeId);
+        const civitaiImage = getCivitaiImageInfo(recipeItem?.source_path);
+        if (civitaiImage) {
+            try {
+                const probe = await probeExtension();
+                if (probe?.supported && probe?.licenseValid) {
+                    await this.reimportViaExtension(recipeId, civitaiImage, recipeItem?.title || '');
+                    return;
+                }
+            } catch (error) {
+                console.warn('Extension re-import unavailable, using native path:', error);
+            }
+        }
+
         state.loadingManager.showSimpleLoading('Re-importing recipe from source...');
 
         try {
@@ -419,6 +396,34 @@ export class RecipeContextMenu extends BaseContextMenu {
             showToast('recipes.contextMenu.reimport.failed', { message: error.message }, 'error');
         }
     }
+
+    // Re-import a single CivitAI-image recipe through the companion browser
+    // extension. Throws on delegation failure so the caller can fall back to
+    // the native path.
+    async reimportViaExtension(recipeId, civitaiImage, title) {
+        state.loadingManager.showSimpleLoading('Re-importing recipe via browser extension...');
+
+        try {
+            const { failed } = await delegateReimport([{
+                recipeId,
+                imageId: civitaiImage.imageId,
+                imageUrl: civitaiImage.imageUrl,
+                title,
+            }]);
+
+            state.loadingManager.hide();
+            if (failed > 0) {
+                showToast('recipes.contextMenu.reimport.failed', { message: 'Extension re-import failed' }, 'error');
+            } else {
+                showToast('toast.recipes.reimportSuccess', {}, 'success');
+            }
+            const { resetAndReload } = await import('../../api/recipeApi.js');
+            resetAndReload(false, { preserveScroll: false });
+        } catch (error) {
+            state.loadingManager.hide();
+            throw error;
+        }
+    }
 }
 
 // Mix in shared methods from ModelContextMenuMixin

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="${translate('modals.model.usageTips.valuePlaceholder', {}, 'Value')}" style="display:none;">
-                    <button class="add-preset-btn">${translate('modals.model.usageTips.add', {}, 'Add')}</button>
+                    <!-- autofill opt-out attrs prevent password managers / email-alias extensions from attaching popups -->
+                    <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 { openMediaViewer } from '../MediaViewer.js';
+import { getShowcaseUrl, getDisplayUrl, getGalleryThumbnailUrl } from '../../../utils/civitaiUtils.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)
-    const remoteUrl = getShowcaseUrl(originalRemoteUrl, mediaType);
+    // Optimize CivitAI URLs for in-modal display (images capped at width=2400;
+    // 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/BulkManager.js

@@ -10,6 +10,7 @@ import { createBaseModelPicker, inferBaseModelsFromFilepaths } from '../componen
 import { getPriorityTagSuggestions } from '../utils/priorityTagHelpers.js';
 import { eventManager } from '../utils/EventManager.js';
 import { translate } from '../utils/i18nHelpers.js';
+import { probeExtension, delegateReimport, getCivitaiImageInfo } from '../utils/extensionReimportBridge.js';
 import { getNsfwLevelSelector } from '../components/shared/NsfwLevelSelector.js';
 
 export class BulkManager {
@@ -103,7 +104,6 @@ export class BulkManager {
                 skipMetadataRefresh: false,
                 setFavorite: true,
                 unfavorite: true,
-                repairMetadata: true,
                 reimportMetadata: true,
                 rematchMetadata: true
             }
@@ -858,17 +858,74 @@ export class BulkManager {
             `Re-importing recipe 1/${total}...`
         );
 
+        // Partition the selection: recipes sourced from a CivitAI image page
+        // can be delegated to the companion browser extension (which scrapes
+        // the full page metadata); everything else uses the native endpoint.
+        const delegatable = [];
+        const nativeFilePaths = [];
+        for (const filePath of filePaths) {
+            const recipeItem = recipeMap.get(filePath);
+            const civitaiImage = getCivitaiImageInfo(recipeItem?.source_path);
+            if (civitaiImage && recipeItem?.id) {
+                delegatable.push({
+                    filePath,
+                    recipeId: recipeItem.id,
+                    imageId: civitaiImage.imageId,
+                    imageUrl: civitaiImage.imageUrl,
+                    title: recipeItem.title || '',
+                });
+            } else {
+                nativeFilePaths.push(filePath);
+            }
+        }
+
+        // Probe once; on any probe/delegate failure the delegatable recipes
+        // fall back to the native sequential loop below.
+        if (delegatable.length > 0) {
+            try {
+                const probe = await probeExtension();
+                if (probe?.supported && probe?.licenseValid) {
+                    const batchResult = await delegateReimport(
+                        delegatable.map(({ recipeId, imageId, imageUrl, title }) => ({
+                            recipeId, imageId, imageUrl, title,
+                        })),
+                        {
+                            onProgress: (progress) => {
+                                progressUI.updateProgress(
+                                    Math.floor(((progress.current || 0) / total) * 100),
+                                    progress.title || '',
+                                    translate('toast.recipes.reimportingViaExtension', {
+                                        current: progress.current || 0,
+                                        total,
+                                    })
+                                );
+                            },
+                        }
+                    );
+                    completed += batchResult.completed;
+                    failed += batchResult.failed;
+                } else {
+                    nativeFilePaths.push(...delegatable.map(entry => entry.filePath));
+                }
+            } catch (error) {
+                console.warn('[reimportSelectedRecipes] extension delegation failed, using native path:', error);
+                nativeFilePaths.push(...delegatable.map(entry => entry.filePath));
+            }
+        }
+
         try {
-            for (let i = 0; i < filePaths.length; i++) {
-                const filePath = filePaths[i];
+            const processedBeforeNative = completed + failed;
+            for (let i = 0; i < nativeFilePaths.length; i++) {
+                const filePath = nativeFilePaths[i];
                 const recipeItem = recipeMap.get(filePath);
                 const recipeId = recipeItem?.id;
                 const recipeName = recipeItem?.title || recipeId || 'Unknown';
+                const processed = processedBeforeNative + i;
 
                 progressUI.updateProgress(
-                    Math.floor((i / total) * 100),
+                    Math.floor((processed / total) * 100),
                     recipeName,
-                    `Re-importing recipe ${Math.min(i + 1, total)}/${total}...`
+                    `Re-importing recipe ${Math.min(processed + 1, total)}/${total}...`
                 );
 
                 if (!recipeId) {
@@ -910,76 +967,6 @@ export class BulkManager {
         }
     }
 
-    async repairSelectedRecipes() {
-        if (state.selectedModels.size === 0) {
-            showToast('toast.recipes.noRecipesSelected', {}, 'warning');
-            return;
-        }
-
-        if (state.currentPageType !== 'recipes') {
-            showToast('This operation is only available for recipes', {}, 'warning');
-            return;
-        }
-
-        try {
-            const apiClient = this.getActiveApiClient();
-            const filePaths = Array.from(state.selectedModels);
-
-            if (typeof apiClient.repairBulkModels !== 'function') {
-                showToast('Bulk repair is not supported for this model type', {}, 'error');
-                return;
-            }
-
-            state.loadingManager.showSimpleLoading('Repairing recipe metadata...');
-
-            const result = await apiClient.repairBulkModels(filePaths);
-
-            if (result.success) {
-                const total = result.total || filePaths.length;
-                const repaired = result.repaired || 0;
-                const skipped = result.skipped || 0;
-
-                const recipes = result.recipes || [];
-                for (const recipe of recipes) {
-                    if (recipe.file_path) {
-                        state.virtualScroller.updateSingleItem(
-                            recipe.file_path,
-                            recipe
-                        );
-                    }
-                }
-
-                if (repaired > 0) {
-                    showToast(
-                        'toast.recipes.repairBulkComplete',
-                        { repaired, skipped, total },
-                        'success'
-                    );
-                } else {
-                    showToast(
-                        'toast.recipes.repairBulkSkipped',
-                        { total },
-                        'info'
-                    );
-                }
-
-                if (state.bulkMode) this.toggleBulkMode();
-            } else {
-                throw new Error(result.error || 'Bulk repair failed');
-            }
-        } catch (error) {
-            console.error('Error during bulk recipe repair:', error);
-            showToast('toast.recipes.repairBulkFailed', { message: error.message }, 'error');
-        } finally {
-            if (state.loadingManager?.hide) {
-                state.loadingManager.hide();
-            }
-            if (typeof state.loadingManager?.restoreProgressBar === 'function') {
-                state.loadingManager.restoreProgressBar();
-            }
-        }
-    }
-
     async rematchSelectedRecipes() {
         if (state.selectedModels.size === 0) {
             showToast('toast.recipes.noRecipesSelected', {}, 'warning');

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)) {
-                        this.filters.baseModel.push(model.name);
+                    if (!this.filters.baseModel.includes(filterValue)) {
+                        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);
-        this.latestContentTimestamp = new Date('2025-10-11').getTime(); // Will be updated from server or config
+        // Version of the help content the user has seen. Compared against the
+        // 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
-                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');
+                this.activateHelpTab(event.currentTarget.getAttribute('data-tab'));
             });
         });
+
+        // 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) {
-            window.modalManager.toggleModal('helpModal');
+        if (!window.modalManager) return;
 
-            // Add visual indicator to Documentation tab if there's new content
-            this.updateDocumentationTabIndicator();
+        const hadNewContent = this.hasNewContent();
 
-            // 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() {
-        const docTab = document.querySelector('.tab-btn[data-tab="documentation"]');
-        if (docTab && this.hasNewContent()) {
-            docTab.classList.add('has-new-content');
+    updateNewContentTabIndicators() {
+        if (!this.hasNewContent()) return;
+
+        // 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();
-        setStorageItem('help_last_viewed', this.lastViewedTimestamp);
+        const currentVersion = this.getCurrentContentVersion();
+        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() {
-        // In a real implementation, you'd fetch this from your server
-        // For now, we'll just use the hardcoded data from constructor
-        
-        // 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();
+    getCurrentContentVersion() {
+        const marker = document.querySelector('[data-help-content-version]');
+        return marker ? marker.getAttribute('data-help-content-version') : null;
     }
 
     /**
-     * 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
-        return this.lastViewedTimestamp === 0 || this.latestContentTimestamp > this.lastViewedTimestamp;
+        const currentVersion = this.getCurrentContentVersion();
+        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/SettingsManager.js

@@ -1047,6 +1047,12 @@ export class SettingsManager {
             groupByModelCheckbox.checked = !!state.global.settings.group_by_model;
         }
 
+        // Set sticky controls
+        const stickyControlsCheckbox = document.getElementById('stickyControls');
+        if (stickyControlsCheckbox) {
+            stickyControlsCheckbox.checked = !!state.global.settings.sticky_controls;
+        }
+
         // Set model name display setting
         const modelNameDisplaySelect = document.getElementById('modelNameDisplay');
         if (modelNameDisplaySelect) {
@@ -3396,6 +3402,10 @@ export class SettingsManager {
         const groupByModel = !!state.global.settings.group_by_model;
         document.body.classList.toggle('group-by-model', groupByModel);
 
+        // Apply sticky controls mode (keeps the action bar visible while scrolling)
+        const stickyControls = !!state.global.settings.sticky_controls;
+        document.body.classList.toggle('sticky-controls', stickyControls);
+
     }
 }
 

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/state/index.js

@@ -59,6 +59,7 @@ const DEFAULT_SETTINGS_BASE = Object.freeze({
     strip_lora_on_copy: false,
     use_new_license_icons: true,
     group_by_model: false,
+    sticky_controls: false,
     llm_provider: 'openai',
     llm_api_key: '',
     llm_api_base: '',

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
-            replacement = '/width=450,optimized=true';
-            if (mediaType && mediaType.toLowerCase() === 'video') {
-                replacement = '/transcode=true,width=450,optimized=true';
+            // Thumbnail modes with width restriction
+            const width = mode === OptimizationMode.GALLERY_THUMBNAIL ? 160 : 450;
+            replacement = `/width=${width},optimized=true`;
+            if (isVideo) {
+                replacement = `/transcode=true,width=${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/extensionReimportBridge.js

@@ -0,0 +1,220 @@
+/**
+ * Bridge to the companion LoRA Manager browser extension.
+ *
+ * The extension can re-import recipes sourced from CivitAI image pages with
+ * the complete page metadata (internal trpc data scraped with the user's
+ * session), fixing recipes that the native import (REST API + EXIF only)
+ * saved with 0 LoRAs.
+ *
+ * Protocol: DOM CustomEvents on `document`; `detail` is ALWAYS a JSON
+ * string on both sides.
+ *
+ *   LM page -> extension: `lm:reimportProbe`, detail `{}`.
+ *   extension -> LM page: `lm:reimportProbeResult`,
+ *     detail `{supported, licenseValid, extensionVersion?, reason?}`.
+ *   LM page -> extension: `lm:reimportViaExtension`,
+ *     detail `{requestId, recipes: [{recipeId, imageId, imageUrl, title}]}`.
+ *   extension -> LM page: `lm:reimportProgress`,
+ *     detail `{requestId, current, total, recipeId, title, status, message?}`.
+ *   extension -> LM page: `lm:reimportBatchDone`,
+ *     detail `{requestId, completed, failed}`.
+ */
+
+const PROBE_EVENT = 'lm:reimportProbe';
+const PROBE_RESULT_EVENT = 'lm:reimportProbeResult';
+const REIMPORT_EVENT = 'lm:reimportViaExtension';
+const PROGRESS_EVENT = 'lm:reimportProgress';
+const BATCH_DONE_EVENT = 'lm:reimportBatchDone';
+
+const DEFAULT_PROBE_TIMEOUT_MS = 500;
+// Generous batch timeout; any progress event resets it (heartbeat).
+const DEFAULT_REIMPORT_TIMEOUT_MS = 3 * 60 * 1000;
+
+// Mirrors py/utils/civitai_utils.py (_SUPPORTED_CIVITAI_PAGE_HOSTS).
+const SUPPORTED_CIVITAI_PAGE_HOSTS = new Set([
+    'civitai.com',
+    'civitai.red',
+    'civitai.green',
+]);
+
+/**
+ * Parse the JSON-string `detail` of a protocol event.
+ * @param {CustomEvent} event
+ * @returns {object|null} Parsed detail object, or null when absent/invalid.
+ */
+function parseDetail(event) {
+    try {
+        const detail = JSON.parse(event?.detail ?? 'null');
+        return detail && typeof detail === 'object' ? detail : null;
+    } catch {
+        return null;
+    }
+}
+
+/**
+ * Dispatch a protocol event with a JSON-stringified detail.
+ * @param {string} type - Event name.
+ * @param {object} payload - Detail payload (JSON-stringified).
+ */
+function dispatchProtocolEvent(type, payload) {
+    document.dispatchEvent(
+        new CustomEvent(type, { detail: JSON.stringify(payload ?? {}) })
+    );
+}
+
+/**
+ * Generate a correlation id for a re-import batch.
+ * @returns {string}
+ */
+function generateRequestId() {
+    if (globalThis.crypto?.randomUUID) {
+        return globalThis.crypto.randomUUID();
+    }
+    return `lm-${Date.now()}-${Math.random().toString(36).slice(2, 10)}`;
+}
+
+/**
+ * Probe whether the companion extension is installed and usable.
+ *
+ * @param {{timeoutMs?: number}} [options]
+ * @returns {Promise<{supported: boolean, licenseValid: boolean, extensionVersion?: string, reason?: string}|null>}
+ *   Resolves with the probe result, or null when the extension is absent or
+ *   too old to answer (timeout).
+ */
+export function probeExtension({ timeoutMs = DEFAULT_PROBE_TIMEOUT_MS } = {}) {
+    return new Promise((resolve) => {
+        let settled = false;
+        const timer = setTimeout(() => finish(null), timeoutMs);
+
+        const finish = (value) => {
+            if (settled) return;
+            settled = true;
+            clearTimeout(timer);
+            document.removeEventListener(PROBE_RESULT_EVENT, onResult);
+            resolve(value);
+        };
+
+        const onResult = (event) => {
+            const detail = parseDetail(event);
+            if (!detail) return;
+            finish({
+                supported: Boolean(detail.supported),
+                licenseValid: Boolean(detail.licenseValid),
+                extensionVersion: detail.extensionVersion,
+                reason: detail.reason,
+            });
+        };
+
+        document.addEventListener(PROBE_RESULT_EVENT, onResult);
+        dispatchProtocolEvent(PROBE_EVENT, {});
+    });
+}
+
+/**
+ * Delegate a batch of recipe re-imports to the companion extension.
+ *
+ * @param {Array<{recipeId: string, imageId: number, imageUrl: string, title: string}>} recipes
+ * @param {{onProgress?: (progress: object) => void, timeoutMs?: number}} [options]
+ * @returns {Promise<{completed: number, failed: number}>} Resolves on
+ *   `lm:reimportBatchDone`; rejects on timeout. Listeners are cleaned up in
+ *   all outcomes.
+ */
+export function delegateReimport(recipes, { onProgress, timeoutMs = DEFAULT_REIMPORT_TIMEOUT_MS } = {}) {
+    return new Promise((resolve, reject) => {
+        if (!Array.isArray(recipes) || recipes.length === 0) {
+            reject(new Error('delegateReimport requires a non-empty recipe list'));
+            return;
+        }
+
+        const requestId = generateRequestId();
+        let settled = false;
+        let timer = null;
+
+        const cleanup = () => {
+            clearTimeout(timer);
+            document.removeEventListener(PROGRESS_EVENT, onProgressEvent);
+            document.removeEventListener(BATCH_DONE_EVENT, onBatchDone);
+        };
+        const succeed = (value) => {
+            if (settled) return;
+            settled = true;
+            cleanup();
+            resolve(value);
+        };
+        const fail = (error) => {
+            if (settled) return;
+            settled = true;
+            cleanup();
+            reject(error);
+        };
+        const armTimer = () => {
+            clearTimeout(timer);
+            timer = setTimeout(
+                () => fail(new Error('Extension re-import timed out')),
+                timeoutMs
+            );
+        };
+
+        const onProgressEvent = (event) => {
+            const detail = parseDetail(event);
+            if (!detail || detail.requestId !== requestId) return;
+            // Heartbeat: any progress for this batch resets the timeout.
+            armTimer();
+            if (typeof onProgress === 'function') {
+                try {
+                    onProgress(detail);
+                } catch (error) {
+                    console.error('[extensionReimportBridge] onProgress callback failed:', error);
+                }
+            }
+        };
+        const onBatchDone = (event) => {
+            const detail = parseDetail(event);
+            if (!detail || detail.requestId !== requestId) return;
+            succeed({
+                completed: Number.isInteger(detail.completed) ? detail.completed : 0,
+                failed: Number.isInteger(detail.failed) ? detail.failed : 0,
+            });
+        };
+
+        document.addEventListener(PROGRESS_EVENT, onProgressEvent);
+        document.addEventListener(BATCH_DONE_EVENT, onBatchDone);
+        armTimer();
+        dispatchProtocolEvent(REIMPORT_EVENT, { requestId, recipes });
+    });
+}
+
+/**
+ * Extract CivitAI image page info from a recipe source_path.
+ * Mirrors py/utils/civitai_utils.py `extract_civitai_image_id`.
+ *
+ * @param {string|null} sourcePath - Recipe source_path.
+ * @returns {{imageId: number, imageUrl: string}|null} Null when the path is
+ *   not a `/images/<id>` URL on civitai.com/.red/.green.
+ */
+export function getCivitaiImageInfo(sourcePath) {
+    if (!sourcePath || typeof sourcePath !== 'string') {
+        return null;
+    }
+
+    let parsed;
+    try {
+        parsed = new URL(sourcePath);
+    } catch {
+        return null;
+    }
+
+    if (parsed.protocol !== 'http:' && parsed.protocol !== 'https:') {
+        return null;
+    }
+    if (!SUPPORTED_CIVITAI_PAGE_HOSTS.has(parsed.hostname.toLowerCase())) {
+        return null;
+    }
+
+    const pathMatch = parsed.pathname.match(/\/images\/(\d+)/);
+    if (!pathMatch) {
+        return null;
+    }
+
+    return { imageId: Number(pathMatch[1]), imageUrl: sourcePath };
+}

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/checkpoints.html

@@ -54,8 +54,10 @@
 {% endblock %}
 
 {% block content %}
+    <div class="sticky-topbar">
     {% include 'components/controls.html' %}
     {% include 'components/breadcrumb.html' %}
+    </div>
     {% include 'components/duplicates_banner.html' %}
     {% include 'components/folder_sidebar.html' %}
     

templates/components/context_menu.html

@@ -94,9 +94,6 @@
         <div class="context-menu-item" data-action="check-updates">
             <i class="fas fa-bell"></i> <span>{{ t('loras.bulkOperations.checkUpdates') }}</span>
         </div>
-        <div class="context-menu-item" data-action="repair-metadata">
-            <i class="fas fa-tools"></i> <span>{{ t('loras.bulkOperations.repairMetadata') }}</span> (Deprecated)
-        </div>
         <div class="context-menu-item" data-action="rematch-metadata">
             <i class="fas fa-link"></i> <span>{{ t('loras.bulkOperations.rematchMetadata') }}</span>
         </div>
@@ -202,9 +199,6 @@
         <i class="fas fa-layer-group"></i> <span>{{ t('globalContextMenu.groupByModel.label') }}</span>
         <i class="fas fa-check check-indicator" style="margin-left:auto;display:none"></i>
     </div>
-    <div class="context-menu-item" data-action="repair-recipes">
-        <i class="fas fa-tools"></i> <span>{{ t('globalContextMenu.repairRecipes.label') }}</span> (Deprecated)
-    </div>
     <div class="context-menu-item" data-action="rematch-recipes">
         <i class="fas fa-link"></i> <span>{{ t('globalContextMenu.rematchRecipes.label') }}</span>
     </div>

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>