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

@@ -1,373 +0,0 @@
----
-name: lora-manager-e2e
-description: End-to-end testing and validation for LoRa Manager features. Use when performing automated E2E validation of LoRa Manager standalone mode in a SANDBOXED, disposable configuration: check the port, start/restart the standalone server on a free port, use Chrome DevTools MCP to interact with the web UI (http://127.0.0.1:{PORT}/loras), and verify frontend-to-backend functionality. Covers workflow validation, UI interaction testing, and integration testing between the standalone Python backend and the browser frontend. Trigger keywords: E2E, standalone, Chrome DevTools MCP, lora-manager-e2e, sandbox.
----
-
-# LoRa Manager E2E Testing
-
-This skill provides workflows and utilities for end-to-end testing of LoRa Manager using Chrome DevTools MCP.
-
-## Conventions Used in This Document
-
-- **`{PORT}`**: The server port. The default candidate is `8188`, but **`8188` is commonly occupied by a live ComfyUI process** and MUST NOT be assumed to be free. Always check availability first (see [Port Selection](#port-selection)) and use a free port (e.g. `8199`) for the E2E run. Substitute the actual port for every `{PORT}` in the commands below.
-- **`<repo-root>`**: The repository/worktree root. Always run commands from the repo or worktree root; never assume a specific absolute path (paths such as `/home/<user>/...` differ per machine). The E2E scripts resolve the project root themselves, but fixture/settings paths are relative to `<repo-root>`.
-
-## SANDBOX (MANDATORY)
-
-> **Read this section before running anything.** Every E2E run MUST target a throwaway sandbox, never the real user data. A fresh subagent that skips this section WILL permanently mutate real user recipes.
-
-1. **Portable settings**: create `<repo-root>/settings.json` (gitignored) with `"use_portable_settings": true` plus sandboxed `folder_paths` (lora/checkpoint roots) and `recipes_path`. This keeps the configuration inside the repo instead of the real user config dir (`~/.config/ComfyUI-LoRA-Manager/settings.json`).
-2. **Sandboxed paths**: point `folder_paths` / `recipes_path` / `example_images_path` at disposable dirs — e.g. under `/tmp/opencode/<plan-name>-e2e/` (or worktree-local dirs). NEVER point the E2E at the real library (`~/models/...`), real recipe dir, or real settings.
-3. **Never touch the real config**: the real user config at `~/.config/ComfyUI-LoRA-Manager/settings.json` and the real recipe dir must remain byte-identical before and after the run.
-4. **Record real-data protection proof** before starting and after finishing:
-   ```bash
-   # BEFORE: snapshot real config + recipe library state
-   sha256sum ~/.config/ComfyUI-LoRA-Manager/settings.json > /tmp/opencode/<plan>-e2e/settings.before.sha256
-   ls ~/models/recipes/*.recipe.json 2>/dev/null | wc -l > /tmp/opencode/<plan>-e2e/recipes-count.before.txt
-   find ~/models/recipes -name '*.recipe.json' -newermt "$(date -Iseconds)" | head   # expect empty after run
-   # AFTER: record again, then diff the two snapshots. Any change = the run leaked into real data.
-   ```
-   Also confirm `<repo-root>/git status` stays clean for `settings.json`/`cache/` (both are gitignored).
-
-### Portable Settings Example
-
-```json
-{
-  "use_portable_settings": true,
-  "folder_paths": {
-    "loras": ["/tmp/opencode/<plan>-e2e/models/loras"],
-    "checkpoints": ["/tmp/opencode/<plan>-e2e/models/checkpoints"],
-    "unet": ["/tmp/opencode/<plan>-e2e/models/checkpoints"],
-    "diffusers": []
-  },
-  "recipes_path": "/tmp/opencode/<plan>-e2e/recipes",
-  "example_images_path": "/tmp/opencode/<plan>-e2e/example_images"
-}
-```
-
-The scanner computes and persists model hashes during the library scan, so the sandbox model dirs just need the model files + `.metadata.json` sidecars (see [Fixture + Fresh-State Guidance](#fixture--fresh-state-guidance)).
-
-## Time Budgets & Abort Guidance
-
-A fresh subagent should complete a sandboxed standalone E2E **in well under 30 minutes**. Budget each phase:
-
-| Phase | Expected duration | Abort if |
-| --- | --- | --- |
-| Port check + sandbox setup | < 2 min | — |
-| Server start (detached) + readiness | < 30 s | > 60 s (2x) → stop |
-| Chrome DevTools MCP connect | < 1 min | > 2 min → stop |
-| Per entry-point run (after fixtures ready) | < 5 min | > 10 min (2x) → stop |
-| Fixture reset + cache clear between runs | < 1 min | > 2 min → stop |
-
-**Abort rule**: if a phase exceeds ~2x its budget, OR any single tool call fails/retries 3+ times in a row, **STOP**. Do not loop or retry blindly. Report `BLOCKED` with: the phase, the last observed state (server PID + `ss -tlnp` output, page snapshot, last API response), and the suspected cause. Record the partial state as evidence; a clean BLOCKED report is more valuable than an hour of retries.
-
-## Prerequisites
-
-- LoRa Manager project cloned and dependencies installed (`pip install -r requirements.txt`) — run everything from `<repo-root>`
-- Chrome browser available for debugging
-- Chrome DevTools MCP connected
-- `ss` (or `lsof`/`netstat`) available for port checks: `ss -tlnp`
-
-## Port Selection
-
-`8188` is only the *default candidate*. Verify it is actually free before every run:
-
-```bash
-# Is anything listening on 8188?
-ss -tlnp | grep ':8188' || echo "8188 is free"
-```
-
-- If a process holds `8188` (e.g. a live ComfyUI — pid 6575 on this machine), pick a different free port, e.g. `8199`:
-  ```bash
-  ss -tlnp | grep ':8199' || echo "8199 is free"
-  ```
-- **Never** kill a process you did not start for this E2E. The live ComfyUI is off-limits. Pick a free port instead.
-- Use your chosen port for **all** subsequent commands (server, Chrome launch, browser URLs).
-
-## Quick Start Workflow (sandboxed)
-
-### 1. Prepare the sandbox
-
-```bash
-cd <repo-root>                       # ALWAYS run from the repo/worktree root
-mkdir -p /tmp/opencode/<plan>-e2e/models/{loras,checkpoints}
-mkdir -p /tmp/opencode/<plan>-e2e/{recipes,example_images,recipes-before}
-# write <repo-root>/settings.json per the portable-settings example above
-# record real-data protection proof (see SANDBOX section)
-```
-
-### 2. Check port availability
-
-```bash
-ss -tlnp | grep ':{PORT}' || echo "port {PORT} is free"
-```
-
-If `{PORT}` is occupied by an unrelated process, pick a free one and use it everywhere below. When in doubt use `8199`.
-
-### 3. Start LoRa Manager Standalone (detached)
-
-The standalone server **dies with the shell unless launched fully detached** — a plain background `&` from the bash tool is killed when the tool call returns. Launch via the helper script:
-
-```bash
-python .agents/skills/lora-manager-e2e/scripts/start_server.py --port {PORT} --wait --timeout 30 --detach
-```
-
-Or manually (equivalent detached form):
-
-```bash
-setsid nohup python standalone.py --port {PORT} --host 127.0.0.1 < /dev/null \
-  >> /tmp/opencode/<plan>-e2e/server.log 2>&1 &
-echo "started"   # record the printed/pidfile PID for cleanup
-```
-
-Verify it is listening **before** proceeding (readiness poll is not a substitute for this):
-
-```bash
-ss -tlnp | grep ':{PORT}'
-```
-
-Record the server PID for cleanup: the helper script writes it to `/tmp/lora-manager-e2e-server-{PORT}.pid`; a manual `setsid` launch has no pidfile, so capture it explicitly (e.g. from `ss -tlnp`).
-
-### 4. Open Chrome Debug Mode
-
-```bash
-# Chrome with remote debugging on port 9222 (note the {PORT} URL)
-google-chrome --remote-debugging-port=9222 --user-data-dir=/tmp/chrome-lora-manager http://127.0.0.1:{PORT}/loras
-```
-
-### 5. Connect Chrome DevTools MCP
-
-Ensure the MCP server is connected to Chrome at `http://localhost:9222`. Verify with `list_pages` — if it fails with "browser is already running", see [Chrome DevTools MCP Troubleshooting](#chrome-devtools-mcp-troubleshooting).
-
-### 6. Navigate and Interact
-
-Use Chrome DevTools MCP tools to:
-- Take snapshots: `take_snapshot`
-- Click elements: `click`
-- Fill forms: `fill` or `fill_form`
-- Evaluate scripts: `evaluate_script`
-- Wait for elements: `wait_for`
-
-## Common E2E Test Patterns
-
-### Pattern: Full Page Load Verification
-
-```python
-# Navigate to LoRA list page
-navigate_page(type="url", url="http://127.0.0.1:{PORT}/loras")
-
-# Wait for page to load
-wait_for(text="LoRAs", timeout=10000)
-
-# Take snapshot to verify UI state
-snapshot = take_snapshot()
-```
-
-### Pattern: Restart Server for Configuration Changes
-
-```python
-# Stop current server (if running), start with new configuration.
-# --restart only kills the E2E server this script started before (via its pidfile);
-# it refuses to blindly kill unrelated processes on the port.
-python .agents/skills/lora-manager-e2e/scripts/start_server.py --port {PORT} --restart --wait --detach
-
-# Wait and refresh browser
-navigate_page(type="reload", ignoreCache=True)
-wait_for(text="LoRAs", timeout=15000)
-```
-
-### Pattern: Verify Backend API via Frontend
-
-```python
-# Execute script in browser to call backend API
-result = evaluate_script(function="""
-async () => {
-  const response = await fetch('/loras/api/list');
-  const data = await response.json();
-  return { count: data.length, firstItem: data[0]?.name };
-}
-""")
-```
-
-### Pattern: Form Submission Flow
-
-```python
-# Fill a form (e.g., search or filter)
-fill_form(elements=[
-    {"uid": "search-input", "value": "character"},
-])
-
-# Click submit button
-click(uid="search-button")
-
-# Wait for results
-wait_for(text="Results", timeout=5000)
-
-# Verify results via snapshot
-snapshot = take_snapshot()
-```
-
-### Pattern: Modal Dialog Interaction
-
-```python
-# Open modal (e.g., add LoRA)
-click(uid="add-lora-button")
-
-# Wait for modal to appear
-wait_for(text="Add LoRA", timeout=3000)
-
-# Fill modal form
-fill_form(elements=[
-    {"uid": "lora-name", "value": "Test LoRA"},
-    {"uid": "lora-path", "value": "/path/to/lora.safetensors"},
-])
-
-# Submit
-click(uid="modal-submit-button")
-
-# Wait for success message or close
-wait_for(text="Success", timeout=5000)
-```
-
-## Fixture + Fresh-State Guidance
-
-For rematch/repair E2E runs, seed the **sandboxed** `recipes_path` with hand-written fixture recipes. Rules (validated by the task-8 E2E):
-
-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.
-
-### Fresh state between entry-point runs
-
-Each entry point (global / per-recipe / selection-bulk) must start from the same deleted state. Between runs:
-
-```bash
-# 1. Reset fixtures to the before-state snapshot (copy back from recipes-before/)
-cp /tmp/opencode/<plan>-e2e/recipes-before/*.recipe.json /tmp/opencode/<plan>-e2e/recipes/
-# 2. Clear the recipe/FTS caches so the stale in-memory/library state is gone
-rm -f <repo-root>/cache/recipe/*.sqlite
-rm -rf <repo-root>/cache/fts/*
-# 3. Restart the server (fresh process, fresh scan)
-python .agents/skills/lora-manager-e2e/scripts/start_server.py --port {PORT} --restart --wait --timeout 30 --detach
-# 4. Re-verify server listening + reload the browser page
-```
-
-## Server Lifecycle
-
-- **Detached launch is mandatory**: the standalone server dies with the shell unless launched via `setsid` (or the helper script's `--detach`). Use `setsid nohup python standalone.py --port {PORT} --host 127.0.0.1 ... < /dev/null &`.
-- **Verify with `ss -tlnp`** after every (re)start; do not proceed on a blind "server starting" message.
-- **Never kill pre-existing processes** — only kill the E2E server PID you started (`start_server.py --restart` kills only PIDs it manages via its pidfile). The live ComfyUI or a stale QA Chrome must never be killed as part of cleanup unless explicitly identified as such (see Chrome troubleshooting).
-- **Record your PID for cleanup**: note the PID printed/pidfile, and stop exactly that PID at the end (`kill <PID>`, then confirm with `ss -tlnp` that `{PORT}` is released).
-
-## Chrome DevTools MCP Troubleshooting
-
-### Stale profile lock ("browser is already running" / `list_pages` fails)
-
-A Chrome profile can be held by a stale Chrome from a prior MCP session, which makes `list_pages` fail with "browser is already running":
-
-1. Identify the stale Chrome — it owns the profile dir in `--user-data-dir` (e.g. `~/.config/chrome-dev-profile`). Find its process:
-   ```bash
-   ps -ef | grep -i '[c]hrome.*user-data-dir'
-   ```
-2. Confirm it is a QA Chrome from a completed task (its parent is an old MCP/browser process, it is NOT the live ComfyUI server, and it is NOT your current MCP instance).
-3. Kill ONLY that stale Chrome:
-   ```bash
-   kill <stale-chrome-pid>
-   ```
-   Never kill the live server or unrelated processes.
-4. Retry `list_pages`. The current MCP will spawn a fresh browser.
-
-### Screenshot-write restrictions
-
-The chrome-devtools MCP may refuse to write into paths outside its configured workspace roots (e.g. the worktree `.omo/evidence/...` canonicalizing to an unmapped path). Workaround:
-
-```bash
-# 1. Save the screenshot to /tmp via the MCP
-#    take_screenshot(filePath="/tmp/<plan>-e2e/recipe-b-after.png", format="png")
-# 2. Copy it into the evidence dir from the shell
-mkdir -p <repo-root>/.omo/evidence/screenshots
-cp /tmp/<plan>-e2e/recipe-b-after.png <repo-root>/.omo/evidence/screenshots/
-```
-
-## 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.
-
-## Available Scripts
-
-### scripts/start_server.py
-
-Starts or restarts the LoRa Manager standalone server for E2E testing.
-
-```bash
-python scripts/start_server.py [--port PORT] [--restart] [--wait] [--timeout SECONDS] [--detach]
-```
-
-Options:
-- `--port`: Server port (default: 8188). The script exits early with a clear message if the port is already in use by an unrelated process.
-- `--restart`: Kill the E2E server this script previously managed (tracked via `/tmp/lora-manager-e2e-server-{PORT}.pid`) before starting. If unrelated processes still hold the port after that, the script reports them and aborts instead of killing them.
-- `--wait`: Wait for the server to be ready before exiting.
-- `--timeout`: Readiness wait timeout in seconds (default: 30).
-- `--detach`: Launch the server fully detached (`setsid`-style, survives shell death — REQUIRED for E2E). Default off: a normal background process that dies with the shell.
-
-### scripts/wait_for_server.py
-
-Polls the server until ready or timeout.
-
-```bash
-python scripts/wait_for_server.py [--port PORT] [--timeout SECONDS]
-```
-
-## Test Scenarios Reference
-
-See [references/test-scenarios.md](references/test-scenarios.md) for detailed test scenarios including:
-- LoRA list display and filtering
-- Model metadata editing
-- Recipe creation and management
-- Settings configuration
-- Import/export functionality
-
-## Network Request Verification
-
-Use `list_network_requests` and `get_network_request` to verify API calls:
-
-```python
-# List recent XHR/fetch requests
-requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Get details of specific request
-details = get_network_request(reqid=123)
-```
-
-## Console Message Monitoring
-
-```python
-# Check for errors or warnings
-messages = list_console_messages(types=["error", "warn"])
-```
-
-## Performance Testing
-
-```python
-# Start performance trace
-performance_start_trace(reload=True, autoStop=False)
-
-# Perform actions...
-
-# Stop and analyze
-results = performance_stop_trace()
-```
-
-## Cleanup
-
-Always ensure proper cleanup after tests:
-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. Remove the sandbox: `rm -rf /tmp/opencode/<plan>-e2e` and `<repo-root>/settings.json` + `<repo-root>/cache` (both gitignored).
-4. Re-run the real-data protection check from the SANDBOX section and record the result in your evidence.

.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/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/skills/lora-manager-runtime-context/SKILL.md

@@ -9,7 +9,10 @@ description: Inspect ComfyUI LoRA Manager runtime configuration and local diagno
 
 - Treat runtime state as local user data. Prefer read-only inspection unless the user explicitly asks for mutation.
 - Never print secret-like settings values. Redact keys containing `key`, `token`, `secret`, `password`, `auth`, or `credential`, including `civitai_api_key`.
-- Resolve paths from the runtime configuration before guessing. In this environment the settings file is normally `/home/miao/.config/ComfyUI-LoRA-Manager/settings.json`, but portable settings can override this through the repository `settings.json`.
+- Resolve paths from the runtime configuration before guessing. Settings-directory precedence (highest first):
+  1. **Explicit override** — env `LORA_MANAGER_SETTINGS_DIR` or standalone `--settings-path` (also accepted by the inspect script as `--settings-path DIR`). Pins EVERYTHING (`settings.json`, `cache/`, `wildcards/`, `backups/`, `logs/`, `stats/`) under the given directory; bypasses portable mode and the user config dir. Common when inspecting a sandboxed/E2E instance.
+  2. **Portable** — repository `<repo-root>/settings.json` with `"use_portable_settings": true` (or `LORA_MANAGER_PORTABLE=1`): settings dir = `<repo-root>`.
+  3. **Default** — `~/.config/ComfyUI-LoRA-Manager` on this machine (`platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`).
 - Use the active library when selecting per-library caches and paths. Read `active_library` from settings; fall back to `default` if missing.
 - Normalize and expand `~` before comparing paths. Symlinks are common in this repo.
 
@@ -32,9 +35,17 @@ python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_conte
 python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py sqlite --db /path/to/cache.sqlite --limit 3
 ```
 
+To inspect a sandboxed/E2E instance that pins its settings directory:
+
+```bash
+# --settings-path DIR (or LORA_MANAGER_SETTINGS_DIR) works with every subcommand:
+python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py \
+  --settings-path /tmp/opencode/<plan>-e2e/settings summary
+```
+
 ## Runtime Path Rules
 
-- Settings directory: use `py/utils/settings_paths.py`. Default platform path is `platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`.
+- Settings directory: resolve via `py/utils/settings_paths.py` — `get_settings_dir()` honors the `LORA_MANAGER_SETTINGS_DIR` / programmatic override first, then portable mode, then `platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`. The inspect script mirrors this precedence in `resolve_settings_path()`.
 - Settings file: `<settings_dir>/settings.json`.
 - Cache root: `<settings_dir>/cache`.
 - Canonical cache files:

.agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py

@@ -14,6 +14,7 @@ from typing import Any
 
 SECRET_PATTERN = re.compile(r"(key|token|secret|password|auth|credential)", re.IGNORECASE)
 APP_NAME = "ComfyUI-LoRA-Manager"
+SETTINGS_DIR_ENV = "LORA_MANAGER_SETTINGS_DIR"
 CACHE_SQLITE = {
     "model": ("model", "{library}.sqlite"),
     "recipe": ("recipe", "{library}.sqlite"),
@@ -30,6 +31,15 @@ CACHE_JSON = {
 
 def main() -> int:
     parser = argparse.ArgumentParser(description="Inspect LoRA Manager runtime state read-only.")
+    parser.add_argument(
+        "--settings-path",
+        type=str,
+        default=None,
+        metavar="DIR",
+        help="Explicit settings directory (same as LORA_MANAGER_SETTINGS_DIR / "
+        "standalone --settings-path). Overrides portable mode and the default "
+        "user config dir.",
+    )
     subparsers = parser.add_subparsers(dest="command", required=True)
 
     subparsers.add_parser("summary", help="Print redacted settings and resolved paths.")
@@ -44,6 +54,8 @@ def main() -> int:
     sqlite_parser.add_argument("--limit", type=int, default=3, help="Rows to sample from each user table.")
 
     args = parser.parse_args()
+    if args.settings_path:
+        os.environ[SETTINGS_DIR_ENV] = args.settings_path
     context = build_context()
 
     if args.command == "summary":
@@ -78,6 +90,11 @@ def build_context() -> dict[str, Any]:
 
 
 def resolve_settings_path() -> Path:
+    # Explicit override: LORA_MANAGER_SETTINGS_DIR env or --settings-path.
+    explicit = os.environ.get(SETTINGS_DIR_ENV)
+    if explicit:
+        return Path(explicit).expanduser() / "settings.json"
+
     repo_root = find_repo_root()
     portable = repo_root / "settings.json"
     if portable.exists():

AGENTS.md

@@ -2,6 +2,10 @@
 
 This file provides guidance for agentic coding assistants working in this repository.
 
+## Overview
+
+ComfyUI LoRA Manager is a comprehensive LoRA management system for ComfyUI that combines a Python backend with browser-based widgets. It provides model organization, downloading from CivitAI/CivArchive, recipe management, and one-click workflow integration.
+
 ## Development Commands
 
 ### Backend Development
@@ -28,16 +32,21 @@ COVERAGE_FILE=coverage/backend/.coverage pytest \
   --cov=py --cov=standalone \
   --cov-report=term-missing \
   --cov-report=html:coverage/backend/html \
-  --cov-report=xml:coverage/backend/coverage.xml
+  --cov-report=xml:coverage/backend/coverage.xml \
+  --cov-report=json:coverage/backend/coverage.json
 ```
 
 ### Frontend Development (LoRA Manager Web UI)
 
 ```bash
+# Install dependencies (root and Vue widgets)
 npm install
+cd vue-widgets && npm install && cd ..
+
 npm test                    # Run all tests (JS + Vue)
 npm run test:js             # Run JS tests only
-npm run test:watch          # Watch mode
+npm run test:vue            # Run Vue widget tests only
+npm run test:watch          # Watch mode (JS tests only)
 npm run test:coverage       # Generate coverage report
 ```
 
@@ -54,88 +63,193 @@ npm run test:watch          # Watch mode
 npm run test:coverage       # Generate coverage report
 ```
 
-## Python Code Style
+### Localization
 
-### Imports & Formatting
+```bash
+# Sync translation keys after UI string updates
+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
+the known confusion hot-spots.
+
+## Code Style
+
+### Python
+
+#### Imports & Formatting
 
 - Use `from __future__ import annotations` for forward references
 - Group imports: standard library, third-party, local (blank line separated)
+- Use `TYPE_CHECKING` guard for type-checking-only imports
 - Absolute imports within `py/`: `from ..services import X`
 - PEP 8 with 4-space indentation, type hints required
 
-### Naming Conventions
+#### Naming Conventions
 
 - Files: `snake_case.py`, Classes: `PascalCase`, Functions/vars: `snake_case`
 - Constants: `UPPER_SNAKE_CASE`, Private: `_protected`, `__mangled`
 
-### Error Handling & Async
+#### Error Handling & Async
 
 - Use `logging.getLogger(__name__)`, define custom exceptions in `py/services/errors.py`
 - `async def` for I/O, `@pytest.mark.asyncio` for async tests
 - Singleton with `asyncio.Lock`: see `ModelScanner.get_instance()`
 - Return `aiohttp.web.json_response` or `web.Response`
 
-### Testing
+### JavaScript/TypeScript
 
-- `pytest` with `--import-mode=importlib`
-- Fixtures in `tests/conftest.py`, use `tmp_path_factory` for isolation
-- Mark tests needing real paths: `@pytest.mark.no_settings_dir_isolation`
-- Mock ComfyUI dependencies via conftest patterns
-
-## JavaScript/TypeScript Code Style
-
-### Imports & Modules
+#### Imports & Modules
 
 - ES modules: `import { app } from "../../scripts/app.js"` for ComfyUI
 - Vue: `import { ref, computed } from 'vue'`, type imports: `import type { Foo }`
 - Export named functions: `export function foo() {}`
 
-### Naming & Formatting
+#### Naming & Formatting
 
 - camelCase for functions/vars/props, PascalCase for classes
 - Constants: `UPPER_SNAKE_CASE`, Files: `snake_case.js` or `kebab-case.js`
 - 2-space indentation preferred (follow existing file conventions)
 - Vue Single File Components: `<script setup lang="ts">` preferred
 
-### Widget Development
+#### Widget Development
 
+- Prefer vanilla JS for `web/comfyui/` widgets; avoid framework dependencies (except the Vue widgets in `vue-widgets/`)
 - ComfyUI: `app.registerExtension()`, `node.addDOMWidget(name, type, element, options)`
 - Event handlers via `addEventListener` or widget callbacks
 - Shared utilities: `web/comfyui/utils.js`
 - Dual-mode rendering patterns (canvas vs Vue): see `docs/comfyui-dual-mode-widgets.md`
 
-### Vue Composables Pattern
+#### Vue Composables Pattern
 
 - Use composition API: `useXxxState(widget)`, return reactive refs and methods
 - Guard restoration loops with flag: `let isRestoring = false`
 - Build config from state: `const buildConfig = (): Config => { ... }`
 
-## Architecture Patterns
+## Architecture
+
+### Dual Mode Operation
+
+The system runs in two modes:
+- **ComfyUI plugin mode**: Integrates with ComfyUI's PromptServer, uses `folder_paths` for model discovery
+- **Standalone mode**: `standalone.py` mocks ComfyUI dependencies, reads paths from `settings.json`
+- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+
+### Backend Entry Points
+
+- `__init__.py` — ComfyUI plugin entry: registers nodes via `NODE_CLASS_MAPPINGS`, sets `WEB_DIRECTORY`, calls `LoraManager.add_routes()`
+- `standalone.py` — Standalone server: mocks `folder_paths` and node modules, starts aiohttp server
+- `py/lora_manager.py` — Main `LoraManager` class that registers all HTTP routes
 
 ### Service Layer
 
 - `ServiceRegistry` singleton for DI, services use `get_instance()` classmethod
+- `BaseModelService` abstract base → `LoraService`, `CheckpointService`, `EmbeddingService`
+- `ModelScanner` base → `LoraScanner`, `CheckpointScanner`, `EmbeddingScanner` for file discovery with hash-based deduplication
+- `PersistentModelCache` (SQLite) for metadata persistence
+- `MetadataSyncService` — background sync from CivitAI/CivArchive APIs
+- `SettingsManager` — settings with schema migration support
+- `WebSocketManager` — real-time progress broadcasting
+- `ModelServiceFactory` — creates the right service for each model type
+- Use cases in `py/services/use_cases/` orchestrate complex business logic (auto-organize, bulk refresh, downloads)
 - Separate scanners (discovery) from services (business logic)
 - Handlers in `py/routes/handlers/` are pure functions with deps as params
 
 ### Model Types & Routes
 
-- `BaseModelService` base for LoRA, Checkpoint, Embedding
-- `ModelScanner` for file discovery, hash deduplication
-- `PersistentModelCache` (SQLite) for persistence
-- Route registrars: `ModelRouteRegistrar`, endpoints: `/loras/*`, `/checkpoints/*`, `/embeddings/*`
-- WebSocket via `WebSocketManager` for real-time updates
+- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*` patterns
+- 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
 
-- Base: `py/recipes/base.py`, Enrichment: `RecipeEnrichmentService`
-- Parsers: `py/recipes/parsers/`
+- Base: `py/recipes/base.py`, Enrichment: `RecipeEnrichmentService` in `py/recipes/enrichment.py`
+- Parsers: `py/recipes/parsers/` for PNG metadata, JSON, and workflow formats
+
+### Custom Nodes
+
+- Location: `py/nodes/`, all nodes registered in `__init__.py`
+- Each node class has a `NAME` class attribute used as key in `NODE_CLASS_MAPPINGS`
+- Standard ComfyUI node pattern: `INPUT_TYPES()` classmethod, `RETURN_TYPES`, `FUNCTION`
+
+### Configuration
+
+- `py/config.py` manages folder paths for models and handles symlink mappings
+- Auto-saves paths to `settings.json` in ComfyUI mode
+
+### Frontend UI Architecture
+
+#### 1. LoRA Manager Web UI
+- Location: `./static/` (JS/CSS) and `./templates/` (HTML)
+- Tech: Vanilla JS + CSS, served by the hosting server (ComfyUI app in plugin mode, `standalone.py` in standalone mode)
+- Tests: `tests/frontend/**/*.test.js` (vitest + jsdom)
+
+#### 2. ComfyUI Custom Node Widgets
+- Location: `./web/comfyui/` (Vanilla JS) + `./vue-widgets/` (Vue)
+- Primary styles: `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
+- Vue widgets: Vue 3 + TypeScript + PrimeVue + vue-i18n, e.g. `LoraPoolWidget`, `LoraRandomizerWidget`, `LoraCyclerWidget`, `AutocompleteTextWidget`
+- Vue builds to `./web/comfyui/vue-widgets/`; auto-built on ComfyUI startup via `py/vue_widget_builder.py`, typecheck via `vue-tsc`
+- Widget registration: `app.registerExtension()` and `getCustomWidgets` hooks; `node.addDOMWidget(...)` embeds HTML in LiteGraph nodes
+- See `docs/dom_widget_dev_guide.md` for the DOMWidget development guide
+
+## Testing
+
+### Backend (pytest)
+
+- Config in `pytest.ini`: `--import-mode=importlib`, testpaths=`tests`
+- Fixtures in `tests/conftest.py` mock ComfyUI dependencies; use `tmp_path_factory` for isolation
+- Markers: `@pytest.mark.asyncio`, `@pytest.mark.no_settings_dir_isolation` (tests needing real settings paths)
+
+### Frontend (vitest)
+
+- 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`)
+- **CivitAI/CivArchive:** API clients for metadata sync and model downloads; CivitAI API key stored in settings
+- **Symlinks:** Config scans symlinks to map virtual→physical paths; fingerprinting prevents redundant rescans
+- **WebSocket:** Broadcasts real-time progress for downloads, scans, and metadata sync
+- **Model scanning flow:** Walk folders → compute hashes → deduplicate → extract safetensors metadata → cache in SQLite → background CivitAI sync → WebSocket broadcast
 
 ## Important Notes
 
 - ALWAYS use English for comments (per copilot-instructions.md)
-- Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
-- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
 - Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
 - Symlinks require normalized paths.
   **Business paths vs real paths**: All stored paths and operation routing use the
@@ -143,23 +257,4 @@ npm run test:coverage       # Generate coverage report
   resolved. `os.path.realpath` is only for scanner dedup and the symlink cache.
   Any path passed to `os.remove`/`os.rename`/`shutil.move` or validated by a
   containment check MUST use the business path (i.e. `os.path.abspath`, not
-  `realpath`).
-
-## Git / Commit Messages
-
-- Follow the style of recent repository commits when writing commit messages
-- Prefer the repo's existing `feat(...)`, `fix(...)`, `chore:` style where applicable
-- If the user has provided a GitHub issue link or issue ID for the task, mention that issue in the commit message, for example `(#871)`
-- When unrelated local changes exist, stage and commit only the files relevant to the requested task
-
-## Frontend UI Architecture
-
-### 1. LoRA Manager Web UI
-- Location: `./static/` and `./templates/`
-- Tech: Vanilla JS + CSS, served by the hosting server (ComfyUI app in plugin mode, `standalone.py` in standalone mode)
-- Tests via npm in root directory
-
-### 2. ComfyUI Custom Node Widgets
-- Location: `./web/comfyui/` (Vanilla JS) + `./vue-widgets/` (Vue)
-- Primary styles: `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
-- Vue builds to `./web/comfyui/vue-widgets/`, typecheck via `vue-tsc`
+  `realpath`).

CLAUDE.md

@@ -1,189 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Overview
-
-ComfyUI LoRA Manager is a comprehensive LoRA management system for ComfyUI that combines a Python backend with browser-based widgets. It provides model organization, downloading from CivitAI/CivArchive, recipe management, and one-click workflow integration.
-
-## Development Commands
-
-### Backend
-
-```bash
-pip install -r requirements.txt
-pip install -r requirements-dev.txt
-
-# Run standalone server (port 8188 by default)
-python standalone.py --port 8188
-
-# Run all backend tests
-pytest
-
-# Run specific test file or function
-pytest tests/test_recipes.py
-pytest tests/test_recipes.py::test_function_name
-
-# Run backend tests with coverage
-COVERAGE_FILE=coverage/backend/.coverage pytest \
-  --cov=py \
-  --cov=standalone \
-  --cov-report=term-missing \
-  --cov-report=html:coverage/backend/html \
-  --cov-report=xml:coverage/backend/coverage.xml \
-  --cov-report=json:coverage/backend/coverage.json
-```
-
-### Frontend
-
-There are three test suites run by `npm test`: vanilla JS tests (vitest at root) and Vue widget tests (`vue-widgets/` vitest).
-
-```bash
-npm install
-cd vue-widgets && npm install && cd ..
-
-# Run all frontend tests (JS + Vue)
-npm test
-
-# Run only vanilla JS tests
-npm run test:js
-
-# Run only Vue widget tests
-npm run test:vue
-
-# Watch mode (JS tests only)
-npm run test:watch
-
-# Frontend coverage
-npm run test:coverage
-
-# Build Vue widgets (output to web/comfyui/vue-widgets/)
-cd vue-widgets && npm run build
-
-# Vue widget dev mode (watch + rebuild)
-cd vue-widgets && npm run dev
-
-# Typecheck Vue widgets
-cd vue-widgets && npm run typecheck
-```
-
-### Localization
-
-```bash
-# Sync translation keys after UI string updates
-python scripts/sync_translation_keys.py
-```
-
-Locale files are in `locales/` (en, zh-CN, zh-TW, ja, ko, fr, de, es, ru, he).
-
-## Architecture
-
-### Dual Mode Operation
-
-The system runs in two modes:
-- **ComfyUI plugin mode**: Integrates with ComfyUI's PromptServer, uses `folder_paths` for model discovery
-- **Standalone mode**: `standalone.py` mocks ComfyUI dependencies, reads paths from `settings.json`
-- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
-
-### Backend (Python)
-
-**Entry points:**
-- `__init__.py` — ComfyUI plugin entry: registers nodes via `NODE_CLASS_MAPPINGS`, sets `WEB_DIRECTORY`, calls `LoraManager.add_routes()`
-- `standalone.py` — Standalone server: mocks `folder_paths` and node modules, starts aiohttp server
-- `py/lora_manager.py` — Main `LoraManager` class that registers all HTTP routes
-
-**Service layer** (`py/services/`):
-- `ServiceRegistry` singleton for dependency injection; services follow `get_instance()` singleton pattern
-- `BaseModelService` abstract base → `LoraService`, `CheckpointService`, `EmbeddingService`
-- `ModelScanner` base → `LoraScanner`, `CheckpointScanner`, `EmbeddingScanner` for file discovery with hash-based deduplication
-- `PersistentModelCache` — SQLite-based metadata cache
-- `MetadataSyncService` — Background sync from CivitAI/CivArchive APIs
-- `SettingsManager` — Settings with schema migration support
-- `WebSocketManager` — Real-time progress broadcasting
-- `ModelServiceFactory` — Creates the right service for each model type
-- Use cases in `py/services/use_cases/` orchestrate complex business logic (auto-organize, bulk refresh, downloads)
-
-**Routes** (`py/routes/`):
-- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, `RecipeRouteRegistrar`, etc.
-- Request handlers in `py/routes/handlers/` implement route logic
-- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*` patterns
-- All routes use aiohttp, return `web.json_response` or `web.Response`
-
-**Recipe system** (`py/recipes/`):
-- `base.py` — Recipe metadata structure
-- `enrichment.py` — Enriches recipes with model metadata
-- `parsers/` — Parsers for PNG metadata, JSON, and workflow formats
-
-**Custom nodes** (`py/nodes/`):
-- Each node class has a `NAME` class attribute used as key in `NODE_CLASS_MAPPINGS`
-- Standard ComfyUI node pattern: `INPUT_TYPES()` classmethod, `RETURN_TYPES`, `FUNCTION`
-- All nodes registered in `__init__.py`
-
-**Configuration** (`py/config.py`):
-- Manages folder paths for models, handles symlink mappings
-- Auto-saves paths to settings.json in ComfyUI mode
-
-### Frontend — Two Distinct UI Systems
-
-#### 1. Standalone Manager Web UI
-- **Location:** `static/` (JS/CSS) and `templates/` (HTML)
-- **Tech:** Vanilla JS + CSS, served by standalone server
-- **Structure:** `static/js/core.js` (shared), `loras.js`, `checkpoints.js`, `embeddings.js`, `recipes.js`, `statistics.js`
-- **Tests:** `tests/frontend/**/*.test.js` (vitest + jsdom)
-
-#### 2. ComfyUI Custom Node Widgets
-- **Vanilla JS widgets:** `web/comfyui/*.js` — ES modules extending ComfyUI's LiteGraph UI
-  - `loras_widget.js` / `loras_widget_events.js` — Main LoRA selection widget
-  - `autocomplete.js` — Trigger word and embedding autocomplete
-  - `preview_tooltip.js` — Model card preview tooltips
-  - `top_menu_extension.js` — "Launch LoRA Manager" menu item
-  - `utils.js` — Shared utilities and API helpers
-  - Widget styling in `web/comfyui/lm_styles.css` (NOT `static/css/`)
-- **Vue widgets:** `vue-widgets/src/` → built to `web/comfyui/vue-widgets/`
-  - Vue 3 + TypeScript + PrimeVue + vue-i18n
-  - Vite build with CSS-injected-by-JS plugin
-  - Components: `LoraPoolWidget`, `LoraRandomizerWidget`, `LoraCyclerWidget`, `AutocompleteTextWidget`
-  - Auto-built on ComfyUI startup via `py/vue_widget_builder.py`
-  - Tests: `vue-widgets/tests/**/*.test.ts` (vitest)
-
-**Widget registration pattern:**
-- Widgets use `app.registerExtension()` and `getCustomWidgets` hooks
-- `node.addDOMWidget(name, type, element, options)` embeds HTML in LiteGraph nodes
-- See `docs/dom_widget_dev_guide.md` for DOMWidget development guide
-
-## Code Style
-
-**Python:**
-- PEP 8, 4-space indentation, English comments only
-- Use `from __future__ import annotations` for forward references
-- Use `TYPE_CHECKING` guard for type-checking-only imports
-- Loggers via `logging.getLogger(__name__)`
-- Custom exceptions in `py/services/errors.py`
-- Async patterns: `async def` for I/O, `@pytest.mark.asyncio` for async tests
-- Singleton pattern with class-level `asyncio.Lock` (see `ModelScanner.get_instance()`)
-
-**JavaScript:**
-- ES modules, camelCase functions/variables, PascalCase classes
-- Widget files use `*_widget.js` suffix
-- Prefer vanilla JS for `web/comfyui/` widgets, avoid framework dependencies (except Vue widgets)
-
-## Testing
-
-**Backend (pytest):**
-- Config in `pytest.ini`: `--import-mode=importlib`, testpaths=`tests`
-- Fixtures in `tests/conftest.py` handle ComfyUI dependency mocking
-- Markers: `@pytest.mark.asyncio`, `@pytest.mark.no_settings_dir_isolation`
-- Uses `tmp_path_factory` for directory isolation
-
-**Frontend (vitest):**
-- Vanilla JS tests: `tests/frontend/**/*.test.js` with jsdom
-- Vue widget tests: `vue-widgets/tests/**/*.test.ts` with jsdom + @vue/test-utils
-- Setup in `tests/frontend/setup.js`
-
-## Key Integration Points
-
-- **Settings:** Stored in user directory (via `platformdirs`) or portable mode (`"use_portable_settings": true`)
-- **CivitAI/CivArchive:** API clients for metadata sync and model downloads; CivitAI API key in settings
-- **Symlink handling:** Config scans symlinks to map virtual→physical paths; fingerprinting prevents redundant rescans
-- **WebSocket:** Broadcasts real-time progress for downloads, scans, and metadata sync
-- **Model scanning flow:** Walk folders → compute hashes → deduplicate → extract safetensors metadata → cache in SQLite → background CivitAI sync → WebSocket broadcast

README.md

@@ -346,8 +346,3 @@ WeChat: [Click to view QR code](https://raw.githubusercontent.com/willmiao/Comfy
 
 Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
-
----
-## Star History
-
-[![Star History Chart](https://api.star-history.com/chart?repos=willmiao/ComfyUI-Lora-Manager&type=date&legend=top-left&sealed_token=Ly6HDvUHnRrM7jWpL67I1aYGnmRiLyxEMcrPz8tNEHpaIAzxVsJVO6trhjWB2gG333EyqRL8ygW0pETjKgRHzoePaQXtaw3xIt6ODjo_5zER74xU51xLVruGOi9kkb16G7Mv8Unq5PkBtQW88tPa53b2jzwZYbDa7GP_k3K8aiA49uk1Vd4yU4uG1q-0)](https://www.star-history.com/?repos=willmiao%2FComfyUI-Lora-Manager&type=date&legend=top-left)

__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

@@ -0,0 +1,370 @@
+# i18n Translation Guidelines
+
+This document is the canonical set of conventions for translating LoRA Manager UI strings.
+It applies to **human translators and AI agents** alike. Read it before editing anything in
+`locales/`.
+
+Source of truth: `locales/en.json` (10 locales, 1810 leaf keys; all locales share the exact
+same key structure).
+
+Locales: `en`, `zh-CN`, `zh-TW`, `ja`, `ko`, `fr`, `de`, `es`, `ru`, `he` (RTL).
+
+> **Status (2026-08 sweep):** a full audit was executed and the terminology, placeholder,
+> stale-text, and untranslated-block fixes described in §2–§6 were applied across all locales
+> (commits `3c3ac49f` … `fd1227d3`). The tables below are now the **normative target state**,
+> not a to-do list — future edits should preserve these renderings and only add what is new.
+
+---
+
+## 1. Hard rules (do not violate)
+
+### R1 — Key structure is sacred
+- Only `locales/en.json` may add/remove/rename keys. All other locales must keep the exact
+  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
+  `[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.
+
+### R2 — Placeholders and HTML must be preserved verbatim
+- `{name}`-style placeholders must appear in the translation exactly as in `en.json`.
+  Do not invent placeholders the source string does not have — the caller may not pass them
+  (example bug: `zh-CN recipes.controls.import.downloadLocationPreview` added `{path}`; the
+  template renders this key with no parameters, so the literal text `{path}` shows in the UI).
+- `{{...}}` in a locale value is an escaped literal brace — keep it identical.
+- Keep embedded HTML tags (e.g. `<strong>...</strong>`, `<code>...</code>`) intact.
+  You may move the tag around the sentence if the target language needs different word order.
+
+### R3 — Never translate or transliterate these
+- Model types: **LoRA, Checkpoint, Embedding, Diffusion Model**
+- Products/brands: **LoRA Manager, ComfyUI, CivitAI, CivArchive, HuggingFace, Ko-fi**
+- Ecosystem names: **LyCORIS, DoRA**, trigger-adjacent jargon **Prompt, Workflow**
+  (these are used as-is in the target-language SD community; see §2 per-language policy)
+- Theme names: **Nord, Midnight, Monokai, Dracula, Solarized**
+
+### R4 — The "Recipe" convention (the most important domain term)
+Product intent: a *Recipe* records a **LoRA combination + generation parameters**
+(prompt, seed, sampler, …) that reproduces an image style. The metaphor is a **cooking
+recipe** — "follow it and you get a similar dish". It is **not** a menu, not a dish list,
+not a prescription.
+
+Decision per language — translate only into a word whose everyday primary meaning is a
+cooking recipe; where that word would mislead users, **keep the English "Recipe(s)"**:
+
+| Locale | Use | Never use |
+|---|---|---|
+| fr | **Recipe / Recipes** (keep English) | recette(s) — cooking reading is secondary and it was explicitly judged misleading |
+| zh-CN / zh-TW | 配方 | 食谱 (reads as "food cookbook") |
+| ja | レシピ | — (leftover English "Recipe" in `initialization.recipes.title` / `toast.recipes.recipeSaved` → translate) |
+| ko | 레시피 | — |
+| de | Rezept / Rezepte | — (cooking meaning dominant; prescription reading acceptable) |
+| es | receta / recetas | — (cooking meaning dominant) |
+| ru | рецепт / рецепты | — (leftover English "Recipe" in `initialization.recipes.title` / `toast.recipes.recipeSaved` → translate) |
+| he | מתכון / מתכונים | — (cooking meaning dominant) |
+
+Whatever the choice, **one concept = one noun within a locale**. Currently violated in:
+- `fr` — "Recipe" (~97 keys, incl. nav) mixed with "recette" (~58 keys)
+- `zh-CN` / `zh-TW` — 配方 (126/122 keys) mixed with 食谱 / 食譜 (14/17 keys, all in the
+  *rematch* flow: `globalContextMenu.rematchRecipes.*`, `toast.recipes.rematch*`)
+- `de` — "Rezept" (136 keys) mixed with leftover English "Recipe" (5 keys)
+- `ja` / `ru` — leftover English "Recipe" in `initialization.recipes.title` ("Recipe Manager
+  zu initialisieren" / «Инициализация Recipe Manager») and `toast.recipes.recipeSaved`
+
+### R5 — One term, one rendering (within each locale)
+Same source word must not be translated several ways in one file. Known offender areas
+(see §5 for the full fix list): recipe, Checkpoint, Embedding, prompt, base model, preset,
+workflow, hash, metadata, tags, bulk. Every locale currently mixes variants of at least one
+of these — pick the preferred form in the §2 tables and normalize.
+
+### R6 — Register consistency
+- `zh-CN` / `zh-TW`: pick 你 or 您 once. Do not mix (zh-CN has 44×你 + 5×您; zh-TW has
+  27×您 + 18×你).
+- `de`: pick "du" or "Sie" once (currently 143×Sie + ~7×du).
+- `es`: pick "tú" or "usted" once.
+
+### R7 — Punctuation per script
+- Full-width punctuation `：（）` is correct **only in CJK locales** (zh-CN, zh-TW, ja, ko).
+- Latin/Cyrillic/Hebrew locales must use ASCII `: ()` — full-width colons leaked in there
+  are machine-translation artifacts. Known: `fr toast.recipes.createError/createFailed`,
+  `es toast.recipes.createError/createFailed` (e.g. "…de la receta：" should be "…de la receta:").
+- `fr` apostrophes must be U+2019 `'` / ASCII `'`, never a straight double quote:
+  `fr header.filter.allowSellingGeneratedContentTooltip` currently reads
+  `vendre d"images` → fix to `d'images`. Do not mix `'` and `'` in one file (fr has 299 vs 15).
+- Ellipsis: use ASCII `...` (project style). Don't introduce `…`.
+- Keep the sentence-ending period/omission consistent with the source string where the
+  language allows it.
+- `he` is RTL: mix of Hebrew and Latin scripts is normal; keep Latin term ordering natural.
+
+### R8 — No untranslated English leftovers
+Full sentences left byte-identical to `en.json` are bugs (brand names and URL placeholders
+are the exception). Every locale has them; see §6 for the per-locale checklist.
+`[TODO: Translate]` placeholders are the sanctioned intermediate state during feature
+development (see §7) — do not "fix" them unless the feature owner asked for translations.
+
+### R9 — Mirror the source even when the source is wrong
+If `en.json` itself contains an inconsistency (e.g. the `Civitai` vs `CivitAI` casing split,
+or the `CivitArchive` typo in `modals.relinkCivitai.helpText.format4`), translate/transcribe
+it as-is in your locale and instead **fix the source** in `en.json` (then propagate by
+re-syncing and re-translating affected keys). Do not silently diverge in one locale only.
+
+---
+
+## 2. Per-language term maps
+
+Preferred rendering per term. "Fix" means the locale currently contains the wrong variant
+and must be normalized. `en` = keep the English word as-is.
+
+### fr
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | Recipe(s) | Replace all "recette(s)" (58 keys, e.g. `recipes.actions.deleteRecipeWithShortcut`, `toast.recipes.rematchComplete`) with "Recipe(s)" |
+| Checkpoint | Checkpoint | `statistics.modelTypes.checkpoint` = "Point de contrôle" → "Checkpoint" |
+| trigger words | mot(s)-clé(s) | unify: `modals.model.triggerWords.editWord` uses "mot déclencheur" — pick one |
+| prompt / negative prompt | Prompt / prompt négatif | — |
+| base model | modèle(s) de base | — |
+| preset | préréglage | unify: `modals.model.usageTips.addPresetParameter` "prédéfini", `toast.presets.restored` "par défaut" |
+| hash | hash | `conflictConfirm.message` "hachage" → "hash" |
+| tags | tags | `settings.sections.priorityTags` "Étiquettes" → "Tags" |
+| metadata | métadonnées | `loras.controls.refresh.fullTooltip` keeps English "metadata" |
+| duplicates | doublon(s) | unify with "dupliqué(e)s" |
+| bulk | groupé(e) | unify with "par lot / mode lot" variants |
+
+### de
+
+| Term | Use | Fix |
+|---|---|---|
+| 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" |
+| register | Sie (formal) | 7 keys use "du/dein" (`settings.backup.managementHelp`, `modals.checkUpdates.message/tip`, `doctor.footer`, …) |
+
+### es
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | receta(s) | — |
+| Checkpoint | Checkpoint | 5 statistics keys "Punto(s) de control" → "Checkpoints" (`statistics.metrics.checkpoints`, `statistics.insights.unusedCheckpoints.*`, `statistics.modelTypes.checkpoint`) |
+| trigger words | palabra(s) de activación | 2 keys already use it; ~15 keys "palabra(s) clave" (reads as search keyword) → unify |
+| base model | modelo base | — |
+| preset | preajuste | 3 keys keep English "preset", 1 "preestablecido" → preajuste |
+| workflow | pick flujo de trabajo or workflow | currently 21× "flujo de trabajo" vs 10× "workflow" |
+| bulk | masivo / por lotes | unify; "Batch Import" → traducción |
+| tags | etiquetas | — |
+
+### ru
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | рецепт(ы) | English leftovers: `initialization.recipes.title`, `recipes.batchImport.*`, `toast.recipes.recipeSaved` → translate |
+| Checkpoint | Checkpoint (recommended) | 3 variants today: "Checkpoint" (17 keys), «Чекпойнт», «Контрольная точка» (statistics, 6 keys) — statistics MUST drop «Контрольная точка» |
+| Embedding | Embedding | «Эмбеддинг» variant exists in `settings.priorityTags.modelTypes.embedding` — unify |
+| prompt | промпт | 8 keys use «запрос» (reads as "database/HTTP request") → «промпт» |
+| base model | базовая модель | — |
+| preset | пресет | `header.theme.presets` "Предустановки" → пресеты |
+| workflow | Workflow (recommended) | «рабочий процесс» used in 4 keys — unify |
+| hash | pick хеш or хэш | both spellings co-occur |
+| tag(s) | тег(и) | — |
+| typos | — | `settings.misc.loraSyntaxFormatHelp`: «безпотерьного» → «беспотерьного» |
+
+### he
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | מתכון / מתכונים | — |
+| Checkpoint | Checkpoint | 5 statistics keys «נקודת/נקודות ביקורת» (road/security checkpoint) → "Checkpoint(s)" (`statistics.metrics.checkpoints`, `statistics.modelTypes.checkpoint`, `statistics.insights.unusedCheckpoints.*`) |
+| Embedding | Embedding | `statistics` keys use הטמעות → Embedding |
+| prompt | pick הנחיה or פרומפט | 9 keys הנחיה vs 3 פרומפט — unify (recommend פרומפט, SD-community loanword) |
+| preset | קביעה מראש | `header.filter.presetOverwriteConfirm` uses פריסט → unify |
+| hash | pick one of האש / גיבוב / hash | 3 variants co-occur — unify (recommend hash or גיבוב) |
+| metadata | pick מטא-דאטה or מטא-נתונים | 38 vs 17 keys — unify |
+| model | מודל | 13 keys use דגם/דגמים — unify |
+| bulk | pick one of 5 variants | 5 different renderings ("כמות גדולה", "המוני", "קבוצתי", "אצווה", …) — unify; `loras.controls.bulk.action` "כמות גדולה" reads as "large quantity" |
+
+### ja
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | レシピ | `initialization.recipes.title` keeps English "Recipe Manager" — translate to レシピマネージャー |
+| 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 | `globalContextMenu.rematchRecipes.success` uses 件, `.cancelled` uses 個 — unify |
+
+### ko
+
+| Term | Use | Fix |
+|---|---|---|
+| recipe | 레시피 | — |
+| Checkpoint | Checkpoint (recommended) | 4 keys transliterate 체크포인트 (`settings.priorityTags.modelTypes.checkpoint`, `toast.recipes.missingCheckpointPath/missingCheckpointInfo/downloadCheckpointFailed`) |
+| Embedding | Embedding | 3 keys 임베딩 (`settings.priorityTags.modelTypes.embedding`, `uiHelpers.nodeSelector.embedding`) |
+| base model | 베이스 모델 | 6 keys «기본 모델» read as "default model" → 베이스 모델 (`settings.downloadSkipBaseModels.*`, `toast.loras.downloadSkippedByBaseModel`) |
+| workflow | pick 워크플로 or 워크플로우 | 26 vs 6 keys — unify |
+| bulk | 일괄 | `modals.checkUpdates.tip` "벌크 모드" → 일괄 모드 |
+| tag logic | — | `header.filter.tagLogicAny` = "모든 태그 일치 (OR)" is **inverted** (should be "하나 이상의 태그 일치") and identical to `tagLogicAll` |
+| particle | — | `modelCard.sendToWorkflow.checkpointNotImplemented`: "Checkpoint을" → "Checkpoint를" |
+
+### zh-CN / zh-TW
+
+| Term | zh-CN | zh-TW |
+|---|---|---|
+| recipe | 配方 (fix 食谱 → 配方, 14 keys in rematch flow) | 配方 (fix 食譜 → 配方, 17 keys in rematch flow) |
+| Checkpoint | Checkpoint (fix 检查点 → Checkpoint, 5 keys: `toast.recipes.missingCheckpointPath/missingCheckpointInfo/downloadCheckpointFailed`, `modelCard.actions.checkpointNameCopied`, `modelCard.sendToWorkflow.checkpointNotImplemented`) | Checkpoint (fix 檢查點 → Checkpoint, 4 keys: `modelCard.actions.copyCheckpointName`, `toast.recipes.missing*`×2, `toast.recipes.downloadCheckpointFailed`) |
+| base model | 基础模型 (fix 基模型 → 基础模型, 3 keys in `modals.model.versions.filters.*`) | 基礎模型 ✓ consistent |
+| prompt | 提示词 ✓ | 提示詞 ✓ |
+| preset | 预设 ✓ | 預設 ✓ |
+| workflow | 工作流 ✓ | 工作流 ✓ |
+| trigger words | 触发词 ✓ | 觸發詞 ✓ |
+| hash | 哈希 (哈希值 variant OK) | 雜湊 ✓ |
+| register | 你 (fix 5×您 → 你) | 您 (fix 18×你 → 您) |
+
+---
+
+## 3. Cross-cutting confusion hot-spots (must-fix list)
+
+All items below were **resolved** in the 2026-08 sweep — treat them as a regression
+watch-list: do not reintroduce these renderings.
+
+1. **Checkpoint rendered as a literal security/road checkpoint** — fr, es, ru, he, zh-CN,
+   zh-TW all had 4–6 keys in the `statistics.*` domain reading as "control point"; reverted
+   to "Checkpoint".
+2. **"recipe" variants that break the one-noun rule** — fr "recette" → "Recipe", zh
+   食谱/食譜 → 配方, de/ja/ru leftover English "Recipe" translated.
+3. **ko `header.filter.tagLogicAny`** — was inverted ("모든 태그 일치 (OR)") and identical
+   to `tagLogicAll`; now "어느 하나의 태그와 일치 (OR)".
+4. **ja `modals.model.versions.actions.viewLocalTooltip`** — was the stale "近日対応予定"
+   ("coming soon"); all 9 locales now describe the actual action.
+5. **Stale help texts** — `settings.downloadSkipBaseModels.help`,
+   `settings.aiProvider.apiBaseHelp`, `settings.hideEarlyAccessUpdates.help` retranslated
+   in all locales to the current `en.json` wording.
+6. **en.json source bugs** (fixed in source, then mirrored):
+   - "Civitai" → "CivitAI" brand casing (values only; key names `relinkCivitai` etc. keep
+     their lowercase form and must not be renamed)
+   - `modals.relinkCivitai.helpText.format4` "CivitArchive" typo → "CivArchive"
+   - `zh-CN recipes.controls.import.downloadLocationPreview` invented `{path}` removed
+
+---
+
+## 4. Placeholder contract deviations (current)
+
+`{...}` token sets must match `en.json` per key. All deviations found in the 2026-08 sweep
+were fixed, with one *intentional* exception:
+
+**`toast.settings.mappingsUpdated`** — the caller passes a hardcoded English inflection
+(`plural: count !== 1 ? 's' : ''`). Languages that cannot build a plural by appending that
+`s` (zh-CN/zh-TW, ja, ko, de, ru, he) **drop `{plural}`** and render a count-friendly form
+(`({count})` or a measure word); fr and es keep it (`mappage{plural}`, `mapeo{plural}`).
+
+```python
+# keep a copy of this rule next to the key if it ever moves:
+#   fr/es:  "... ({count} mappage{plural})"
+#   de/ru/he: "... ({count})"
+#   zh-CN: "（{count} 条映射）" / zh-TW: "（{count} 個對應）" / ja: "（{count} マッピング）"
+```
+
+Do NOT add `{...}` tokens the source lacks (the caller will not supply them, and the literal
+text renders in the UI), and do NOT rename source tokens (`{typePlural}` stays `{typePlural}`).
+
+---
+
+## 5. One term, one rendering — offender matrix
+
+Cross-locale summary of §2 inconsistencies. "✓" = already consistent. All ✗ cells were
+resolved in the 2026-08 sweep; the row shows the single rendering now in force per locale.
+
+| Term | fr | de | es | ru | he | ja | ko | zh-CN | zh-TW |
+|---|---|---|---|---|---|---|---|---|---|
+| recipe | Recipe | Rezept | receta | рецепт | מתכון | レシピ | 레시피 | 配方 | 配方 |
+| Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint |
+| Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding |
+| prompt | Prompt | Prompt | prompt | промпт | פרומפט | プロンプト | 프롬프트 | 提示词 | 提示詞 |
+| base model | modèle de base | Basismodell | modelo base | базовая модель | מודל בסיס | ベースモデル | 베이스 모델 | 基础模型 | 基礎模型 |
+| preset | préréglage | Voreinstellung | preajuste | пресет | קביעה מראש | プリセット | 프리셋 | 预设 | 預設 |
+| workflow | Workflow | Workflow | workflow | Workflow | workflow | ワークフロー | 워크플로 | 工作流 | 工作流 |
+| hash | hash | Hash | hash | хеш | hash | ハッシュ | 해시 | 哈希 | 雜湊 |
+| metadata | métadonnées | Metadaten | metadatos | метаданные | מטא-נתונים | メタデータ | 메타데이터 | 元数据 | 中繼資料 |
+| tags | Tags | Tags | etiquetas | теги | תגיות | タグ | 태그 | 标签 | 標籤 |
+| duplicates | en double | Duplikate | duplicados | дубликаты | כפילויות | 重複 | 중복 | 重复项 | 重複項 |
+| bulk | groupé | Massen- | por lotes | пакетный | בכמות גדולה | 一括 | 일괄 | 批量 | 批量 |
+
+Watch: ja/ko keep the model-type names **Checkpoint/Embedding** and `Diffusion Model` in
+Latin (consistent with their model-type sections) — do not transliterate them as
+チェックポイント/체크포인트.
+
+---
+
+## 6. Untranslated English leftovers (status)
+
+Values byte-identical to `en.json` that are actual UI sentences are bugs (brand names and
+URL placeholders are the exception). As of the 2026-08 sweep, **all previously untranslated
+blocks are translated** in every locale: `recipes.batchImport.*` + `toast.recipes.batchImport*`
+(fr/de/es/ru/he/ja/ko), `banners.communitySupport.*`, `modals.model.license.*`,
+`globalContextMenu.fetchMissingLicenses.*`, the `doctor.*` issue/action/label subset,
+`toast.settings.libraryLoadFailed` / `libraryActivateFailed`, `toast.api.moveFailed`,
+`settings.extraFolderPaths.restartRequired`, `toast.recipes.recipeSaved`,
+`sidebar.dragDrop.moveUnsupported`, `checkpoints.modelTypes.diffusion_model`
+(ja/ko keep the English loanword), `initialization.recipes.title`.
+
+The only values that remain intentionally identical to `en.json` are non-translatable:
+URL/path placeholders (`https://…`, `C:/…`), numeric presets (`5 (1080p), 6 (2K), 8 (4K)`),
+example token lists (`character, concept, style(toon|toon_style)`), service/provider names
+(`CivitAI → CivArchive → Archive DB`), and the external playlist title
+(`help.updateVlogs.playlistTitle`, de: translated to "LoRA Manager-Update-Playlist").
+
+Rule for `uiHelpers.workflow.noPromptTargets`: the second line (`Mark as → Send Prompt
+Target`) quotes literal ComfyUI context-menu items — keep those menu labels in English in
+every locale because that is what the user actually sees in ComfyUI.
+
+License labels (`modals.model.license.*`): the restriction labels are now translated in all
+locales (the sibling `creditRequired` has always been translated).
+
+---
+
+## 7. Workflow for agents and translators
+
+### Adding a new UI string
+1. Add the key to `locales/en.json` only.
+2. Run `python scripts/sync_translation_keys.py` — it inserts the key into the other 9
+   locales (as a `[TODO: Translate]` placeholder) preserving formatting.
+3. **During feature development, stop here.** While the UI copy is still in flux, leave the
+   `[TODO: Translate]` placeholders as-is — translating churning strings into 9 locales is
+   wasted work. Placeholders are a normal intermediate state, not a bug.
+4. Once the wording is final and the feature owner explicitly asks for translations,
+   translate **all** pending `[TODO: Translate]` keys in every locale (not just the latest
+   feature's), applying §1–§3 (placeholders verbatim, Recipe rule, term maps, register).
+   Find pending keys with: `grep -c "TODO: Translate" locales/*.json`
+5. If the new string contains new terminology, extend §2 tables.
+
+### Fixing a translation bug
+1. Locate the key (dotted path) in the relevant locale file.
+2. Check the corresponding `en.json` value and the actual caller (grep `static/js` or
+   `web/comfyui` for the key) to learn which placeholders are passed.
+3. Fix trivially; for normalization sweeps (e.g. "recette" → "Recipe"), do it file-wide for
+   the offending keys only — do not touch unrelated lines.
+4. If the bug is in `en.json` itself (R9), fix the source first, then re-sync and update all
+   locales.
+
+### Verification
+```bash
+pytest tests/i18n/test_i18n.py     # key parity + JSON validity + JS key references
+python scripts/sync_translation_keys.py --dry-run   # shows which keys would change; add --verbose for per-key detail
+npm test                           # frontend tests incl. i18n helpers
+```
+
+`pytest tests/i18n` only checks structure. Quality conventions in this document are not
+machine-enforced — a human/agent review pass is required.
+
+### Anti-patterns checklist
+- [ ] Placeholders `{x}` / `{{x}}` differ from `en.json`
+- [ ] Same source term translated 2+ ways in the same file (see §5)
+- [ ] "Checkpoint" became a literal checkpoint; "recipe" became menu/prescription/food-cookbook
+- [ ] Brand names translated or transliterated (LoRA, CivitAI, ComfyUI, …)
+- [ ] Latin locale using full-width `：（）`; fr using `"` as apostrophe
+- [ ] Mixed 你/您, du/Sie, tú/usted
+- [ ] Full English sentences left behind (see §6)
+- [ ] Register/typos/mojibake; source string is stale vs `en.json` (compare semantics, not
+  just words)

docs/plans/issue-1085-rate-limit-design.md

@@ -0,0 +1,337 @@
+# Plan: Global Rate-Limit Abidance for Recipe Ingest & Metadata Fetching
+
+**Issue:** [#1085 — Large Recipe Ingest Appears to not abide by vendor rate limits, possibly a few other errors?](https://github.com/willmiao/ComfyUI-Lora-Manager/issues/1085)
+**Status:** v2 — reviewed; decisions recorded in §10. **Phase 1 implemented**
+(2026-08-27, commit `c2a2048c`): coordinator + downloader gate + Fix C
+failover semantics + helper double-wait fix + settings. **Phase 2
+implemented** (2026-08-27): batch-import rate-limit failures map to
+`SKIPPED` + `rate_limited` WebSocket flag + UI slowdown hint (toast + status
+text, i18n keys synced); `download_to_memory` / `get_response_headers` /
+`download_file` register 429 cooldowns. Changes vs v1: Fix C moved to
+Phase 1, helper double-wait resolved in Phase 1, gate/guard ordering
+specified.
+**Scope:** HTTP API traffic to CivitAI (`civitai.red`) and CivArchive (`civarchive.com`) from metadata fetching (bulk refresh, metadata sync, recipe analysis/enrichment, usage-control lookups). Large binary downloads (model files / preview images via `download_file`) are out of scope for *pacing* (they are already single-connection transfers) but their 429 responses should still be *registered*.
+
+> Context: a first batch of fixes for this issue was already committed as
+> `ee233548` ("fix(recipes): enforce batch-import concurrency bound and harden
+> ingest errors (#1085)"): the batch-import concurrency controller now shares a
+> real semaphore (bounds 1–5 actually apply), the Comfy parser tolerates
+> list/`None` `ckpt_name`, CivArchive treats empty error payloads as failures,
+> and offline-cooldown short-circuits log at DEBUG. This plan covers the two
+> remaining orchestration-level fixes:
+> **Fix 2** — slow down globally when a vendor rate limit is hit (respect
+> `Retry-After`, queue instead of hammering); **Fix 3** — stop immediately
+> failing over to CivArchive when CivitAI is rate-limited.
+
+---
+
+## 1. Problem Statement
+
+During a large recipe ingest (e.g. importing the example-images directory,
+which can be thousands of images), the manager fires one metadata request per
+checkpoint + per LoRA per image through the fallback provider chain
+(`civitai_api → civarchive_api → sqlite`). Consequences observed in #1085:
+
+1. **CivitAI gets hammered** → 429s. The consumer then *immediately* tries
+   CivArchive for the same lookup, so **CivArchive gets hammered too** before
+   it was ever naturally needed (its only real job is recovering metadata for
+   models deleted from CivitAI).
+2. Requests are retried per-call after `Retry-After`, but **each concurrent
+   call sleeps independently** → thundering herd: thousands of coroutines wake
+   at the same moment and re-flood the vendor.
+3. While CivArchive is in the `ConnectivityGuard` cooldown, every batch item
+   short-circuits and is marked `FAILED` — the batch import's success/failure
+   accounting is polluted by a transient vendor state (log spam was fixed in
+   `ee233548`; the item-failure accounting is not).
+4. `ConnectivityGuard` (`py/services/connectivity_guard.py`) only treats
+   transport-level unreachability as offline; **HTTP 429 is invisible to it**,
+   so nothing ever intentionally paces request rate.
+
+User expectation from the issue: *"once a vendor rate limit time out is hit,
+you should trigger a slow down with intentional reduction in request rate"*.
+
+## 2. Current State (verified against code)
+
+### 2.1 Where 429s are surfaced
+
+- `Downloader.make_request` (`py/services/downloader.py:1120-1132`): HTTP 429 →
+  returns `RateLimitError(message, retry_after=…)` parsed from `Retry-After`
+  (missing header defaults to `None`).
+- `CivitaiClient._make_request` (`py/services/civitai_client.py:97-100`):
+  converts `RateLimitError` to a raise immediately; no waiting. Transient
+  5xx/connection errors are retried 3× with 1s/2s/4s backoff.
+- `CivArchiveClient._make_request` (`py/services/civarchive_client.py`):
+  raises `RateLimitError` with `provider="civarchive_api"` when not set.
+- `_RateLimitRetryHelper` (`py/services/model_metadata_provider.py:45-102`):
+  per-call retry loop — sleeps `retry_after` (capped at 1800 s; `≥120 s` ⇒ no
+  retry), then re-raises. Because every concurrent call runs its own helper,
+  they sleep in parallel and re-fire in parallel.
+- `FallbackMetadataProvider` (`py/services/model_metadata_provider.py:488-508,
+  564-584` etc.): on a final `RateLimitError` from one provider it logs
+  "skipping to next provider" and **continues to the next network provider** —
+  this is the direct cause of the CivArchive flood.
+- `MetadataSyncService.fetch_and_update_model`
+  (`py/services/metadata_sync_service.py:248-333`): manually iterates
+  `provider_attempts`; on `RateLimitError` it `continue`s to the next provider
+  (same failover problem), then reports `"Rate limited"` when nothing
+  succeeded.
+- `Downloader.make_request` has a per-destination scope already available:
+  `_guard_destination(url)` returns the hostname (`downloader.py:1194-1199`),
+  used by `ConnectivityGuard`.
+
+### 2.2 What pacing exists today
+
+- `ConnectivityGuard`: per-destination cooldown (30 s base, ×2 per extra
+  failure batch, 300 s cap) triggered only by transport errors
+  (`connectivity_guard.py:168-197`).
+- `AdaptiveConcurrencyController` (batch import, fixed in `ee233548`): shared
+  semaphore enforces 1–5 concurrent items; *duration*-based adjustment only —
+  it never sees HTTP statuses, so it cannot distinguish "slow because rate
+  limited" from "slow because big image".
+- No token bucket, no minimum inter-request interval, no shared
+  `Retry-After` gate anywhere (`grep` for throttle/token-bucket/rate-limiter:
+  0 hits).
+
+## 3. Requirements & Constraints
+
+R1. **Respect `Retry-After`.** After a 429, no further request to that
+    destination may be sent before the vendor's retry window elapses.
+R2. **No thundering herd.** Concurrent waiters must share one wake-up (gate),
+    not sleep independently.
+R3. **No double load.** A CivitAI 429 must not trigger a CivArchive request
+    for the same lookup. CivArchive should only be consulted when CivitAI
+    legitimately has no answer (404 / "not found"), or when CivitAI is
+    unreachable long-term.
+R4. **No spurious item failures.** A rate-limited request must not turn a
+    batch-import item into `FAILED`; it should wait (bounded) and retry, or at
+    worst be `SKIPPED` with a clear "rate limited" reason (re-runnable import).
+R5. **Never hang forever.** All waiting is bounded by a configurable cap; on
+    expiry the caller receives the `RateLimitError` and can decide.
+R6. **Keep legitimate failover.** Deleted-model recovery via CivArchive/sqlite
+    must keep working (404 paths unchanged).
+R7. **Single choke point.** The pacing gate should live where every API call
+    passes (the `Downloader`), so bulk refresh, metadata sync, recipe
+    analysis, and usage-control lookups all benefit without per-feature work.
+
+## 4. Approach Comparison
+
+### A. Reactive gate — shared `Retry-After` deadman clock (recommended core)
+
+A process-wide, per-destination coordinator records the *next-allowed-send*
+timestamp from each 429 (`now + max(retry_after, backoff)`). Every request
+through `Downloader.make_request` consults the gate *before sending* and *when
+a 429 arrives*; waiters block on a shared `asyncio.Event` that fires when the
+cooldown expires.
+
+- Pros: single choke point (R7); herd-free (R2); honors server guidance (R1);
+  no guessing at vendor limits; covers all providers automatically; reuses
+  existing per-destination scoping.
+- Cons: still experiences 429s before slowing down (reactive); long
+  `Retry-After` windows (CivArchive has been observed at ~1500 s) need a sane
+  wait cap + skip/retry UX.
+
+### B. Preemptive pacing — minimum inter-request interval (recommended companion)
+
+Per-destination token bucket (simplest form: capacity 1 — at least `N` seconds
+between consecutive API requests; `N` configurable, default ~0.75 s ≈ 80
+r/min ceiling).
+
+- Pros: prevents most 429s before they happen — exactly the "intentional
+  reduction in request rate" the issue asks for; trivial to implement on top
+  of A's coordinator.
+- Cons: adds latency to bulk operations (thousands of models × `N`); the *exact*
+  vendor limits are unknown (CivitAI anonymous vs keyed vs `civitai.red`
+  mirror differ), so the default must be conservative-but-not-crippling and
+  settings-tunable.
+
+### C. Fallback semantics change — stop network→network failover on 429 (must-do, low risk)
+
+`FallbackMetadataProvider` (and `MetadataSyncService.fetch_and_update_model`'s
+manual loop) must treat a final `RateLimitError` as a **terminal, non-failover
+result** for network providers. Local-only providers (sqlite archive DB) may
+stay as a last resort (no vendor cost).
+
+- Pros: directly removes the CivArchive flood; small, surgical change.
+- Cons: none significant; requires care to keep 404-failover intact (R6).
+
+### Rejected / deferred
+
+- **Per-feature retry queues** (batch import pauses & resumes whole batches):
+  richer UX but much larger change (batch state machine, WebSocket states);
+  unnecessary once A+B make requests wait at the choke point. Defer unless
+  review finds the bounded-wait UX insufficient.
+- **Full token bucket with burst credit**: overkill; capacity-1 interval is
+  enough given the shared semaphore already caps concurrency at 5.
+- **Retrying in `connectivity_guard`**: wrong layer — the guard is about
+  transport reachability, not vendor quota.
+
+## 5. Recommended Architecture
+
+New singleton **`RateLimitCoordinator`** (`py/services/rate_limit_coordinator.py`,
+mirroring `ConnectivityGuard`'s singleton + per-destination patterns):
+
+```
+state per destination (hostname):
+  next_allowed_send: float (monotonic)   # from 429 Retry-After + backoff
+  consecutive_429: int                   # for backoff growth
+  last_send_at: float                    # for min-interval pacing
+  waiters: list[Future] | asyncio.Event  # shared wake-up per cooldown cycle
+```
+
+API:
+
+- `async wait_for_slot(destination, request_started_within_window: bool)`
+  — called by `Downloader.make_request` *before* sending (blocks until
+  `min(now >= next_allowed_send)` and inter-request interval elapses) and
+  re-armable after a 429.
+- `register_rate_limit(destination, retry_after: float | None)`
+  — called on 429: `next_allowed_send = max(now + retry_after_or_backoff, current)`;
+  `consecutive_429 += 1`; backoff = `retry_after` honored, else exponential
+  `30 · 2^(n-1)` capped at 1800 s; creates/re-arms the shared wake-up event.
+- `register_success(destination)` — resets `consecutive_429` (called from the
+  existing 200 path in `make_request`).
+- `remaining_seconds(destination)`, `in_cooldown(destination)` — for tests and
+  diagnostics.
+
+Enforcement points:
+
+1. **`Downloader.make_request`** (`downloader.py:1102-1132`): ordering inside
+   the method is **connectivity-guard fail-fast first** (offline short-circuit
+   costs nothing to check), **then** `await coordinator.wait_for_slot(destination)`
+   before `session.request`. On 429: `coordinator.register_rate_limit(...)`,
+   then *wait for the gate and re-send* (loop, bounded by
+   `rate_limit_max_wait_seconds`, default 300; `retry_after ≥ cap` ⇒ fail
+   immediately). After the loop, return the `RateLimitError` to the caller
+   (unchanged contract) **with `exc.gate_handled = True` set** so downstream
+   retry helpers know the wait already happened. 200 path calls
+   `register_success`.
+2. **`Downloader.download_to_memory` / `get_response_headers`** (phase 2):
+   register 429s (so API calls queue); waiting only in `make_request`
+   initially.
+3. **`FallbackMetadataProvider`** (`model_metadata_provider.py`): remove
+   network→network failover on `RateLimitError` — re-raise; only sqlite stays
+   as a local last resort (implementation: per-method `except RateLimitError`
+   handler that marks the chain rate-limited and stops iterating).
+4. **`MetadataSyncService.fetch_and_update_model`**
+   (`metadata_sync_service.py:248-333`): on `RateLimitError` from the default
+   provider, stop appending further network providers (sqlite may remain);
+   the existing `any_rate_limited` merge already produces `"Rate limited"`.
+5. **Batch import** (`batch_import_service.py`): no structural change needed —
+   items now wait inside `make_request`; optionally (phase 2) map residual
+   rate-limit failures (after the wait cap) to `SKIPPED` with
+   `"rate limited (retry_after=…s); re-run the import later"` instead of
+   `FAILED`, and surface a `rate_limited` flag in the WebSocket progress
+   broadcast.
+6. **`_RateLimitRetryHelper` retries** (`model_metadata_provider.py`):
+   **Phase 1** — when the raised `RateLimitError` carries `gate_handled = True`
+   (set by the downloader after honoring the gate), the helper skips its own
+   `retry_after` sleep and re-raises immediately, eliminating the double wait.
+   The wiring stays so a `RateLimitError` still propagates cleanly; full
+   demotion/removal can follow once the gate proves out.
+
+Settings (`settings.json`, schema extension in `SettingsManager`):
+
+| key | default | meaning |
+|---|---|---|
+| `rate_limit_gate_enabled` | `true` | master switch for the coordinator |
+| `rate_limit_max_wait_seconds` | `300` | how long `make_request` waits on a 429 gate before returning the error |
+| `rate_limit_min_interval_seconds` | `0.75` | minimum seconds between API requests per destination (pacing, R6-friendly conservative default) |
+
+## 6. Changes by File
+
+| File | Change |
+|---|---|
+| `py/services/rate_limit_coordinator.py` (new) | coordinator singleton + per-destination state + tests seam |
+| `py/services/downloader.py` | gate pre-check + 429 register/wait/retry loop + `register_success`; log the 429 notice at INFO once per cooldown, then DEBUG |
+| `py/services/model_metadata_provider.py` | `FallbackMetadataProvider`: stop network failover on `RateLimitError`; helper skips its sleep when the error is marked `gate_handled` |
+| `py/services/metadata_sync_service.py` | `fetch_and_update_model`: same failover semantics; keep sqlite last resort |
+| `py/services/batch_import_service.py` | (phase 2) rate-limit failures → `SKIPPED` + `rate_limited` progress flag |
+| `py/services/settings_manager.py` | new settings keys + defaults |
+| `tests/services/test_rate_limit_coordinator.py` (new) | gate unit tests |
+| `tests/services/test_civitai_client.py` / `test_civarchive_client.py` | provider-level 429 behavior |
+| `tests/services/test_metadata_service.py` | failover-chain tests |
+| `tests/services/test_batch_import_service.py` | SKIPPED-on-rate-limit |
+
+## 7. Impact, Risks, Open Questions
+
+- **Behavior change**: with the gate in `make_request`, any request can block
+  up to the wait cap — UI actions that call the API (e.g. a model-details
+  fetch) may take longer during cooldowns. Mitigation: bounded cap + INFO log
+  + the existing async request handling already tolerates slow responses.
+  **Decided (§10): interactive requests take the same bounded wait** — one
+  behavior, no call-source plumbing; cooldowns are usually short.
+- **Gate waits occupy batch slots**: with the 1–5 batch semaphore, all slots
+  can park on a gate simultaneously, freezing visible progress for up to one
+  wait cap per wave. Bounded and acceptable; the phase-2 `SKIPPED` mapping +
+  WebSocket `rate_limited` flag (both confirmed in scope, §10) make the stall
+  visible and recoverable.
+- **Rate limit reality check**: CivitAI anonymous vs keyed limits, and whether
+  `civitai.red` differs, is unverified. Default pacing `0.75 s/req` is a
+  conservative guess (R6). Open question for maintainer: preferred default
+  and whether an API-keyed ceiling should be higher.
+- **Long CivArchive windows**: `Retry-After ~1500 s` observed in code
+  comments. **Decided (§10): keep the 300 s default cap** — such lookups
+  fail/skip rather than park a request path for 25 minutes; batch import maps
+  them to `SKIPPED` (phase 2) so the user can re-run later.
+- **Double waiting**: `_RateLimitRetryHelper` + gate could stack waits.
+  **Resolved in Phase 1**: the downloader marks gate-honored errors with
+  `gate_handled = True` and the helper skips its own sleep for those.
+- **Downloads**: `download_file` 429s return an error to download managers
+  unchanged (already handled); only *registration* is proposed, so future
+  API calls queue behind a large `Retry-After` from a download burst.
+
+## 8. Test Plan
+
+1. **Coordinator unit tests** (new file):
+   - 429 with `retry_after` → `wait_for_slot` blocks ~that long, then passes.
+   - N concurrent waiters all wake together (herd test, wall-clock ≈ one
+     window, not N windows).
+   - Consecutive 429s grow backoff; `register_success` resets.
+   - Missing `Retry-After` → default backoff path.
+   - Wait cap: request fails after `rate_limit_max_wait_seconds` with
+     `RateLimitError`.
+2. **Downloader tests** (mock aiohttp session): 429 then 200 → `make_request`
+   returns success after gate delay; two back-to-back calls to the same
+   destination are spaced ≥ `min_interval`; different destinations are not
+   spaced.
+3. **Provider tests**: `FallbackMetadataProvider.get_model_version_info` —
+   Civitai raises `RateLimitError` → CivArchive mock **not called**; 404 still
+   falls through to CivArchive; sqlite still tried after network 429.
+4. **Sync-service test**: `fetch_and_update_model` with a rate-limited default
+   provider → result error contains `"Rate limited"` and sqlite attempt state
+   unchanged.
+5. **Batch-import test**: analysis provider 429s first, then succeeds →
+   item ends `SUCCESS` (wait path), and post-cap 429 → `SKIPPED` with
+   rate-limit reason (phase 2).
+6. Full regression: `pytest tests/services tests/routes tests/standalone`
+   (currently 1582 passing).
+
+## 9. Implementation Phases
+
+- **Phase 1 (this plan, reviewed):** `RateLimitCoordinator` +
+  `Downloader.make_request` integration (guard fail-fast → gate pre-check
+  pacing → 429 register/wait/retry loop with cap → `gate_handled` marking) +
+  settings + **Fix C failover semantics** (`FallbackMetadataProvider`,
+  `fetch_and_update_model` — moved up from phase 2: smallest diff, kills the
+  CivArchive flood immediately, independent of coordinator correctness) +
+  `_RateLimitRetryHelper` double-wait fix + coordinator/downloader/provider/
+  sync tests.
+- **Phase 2:** batch-import `SKIPPED`-on-rate-limit + `rate_limited` WebSocket
+  progress flag + slowdown hint (confirmed, §10),
+  `download_to_memory`/HEAD 429 registration, batch tests.
+- **Phase 3:** full regression + docs + commit referencing `(#1085)`.
+
+## 10. Review Checklist — Decisions (2026-08-27)
+
+- [x] Default pacing interval `0.75 s` — **accepted** as conservative default;
+      tunable via `rate_limit_min_interval_seconds`. Revisit if CivitAI
+      publishes keyed/anonymous ceilings.
+- [x] Wait cap `300 s` — **accepted**; long-window CivArchive lookups fail →
+      batch import marks them `SKIPPED` with a rate-limit reason (phase 2).
+- [x] Interactive API calls also wait (bounded) — **yes**, same behavior for
+      all callers.
+- [x] Keep sqlite as last resort behind a network rate limit — **yes**
+      (local-only, no vendor cost).
+- [x] UI hint — **yes**: WebSocket `rate_limited` flag + "rate limited —
+      slowing down" hint in batch-import progress (phase 2); INFO logging
+      regardless.

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/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": {
@@ -67,15 +88,15 @@
     "steps": {
       "fetch": {
         "title": "Fetch Models Metadata",
-        "content": "Click the <strong>Fetch</strong> button to download model metadata and preview images from Civitai."
+        "content": "Click the <strong>Fetch</strong> button to download model metadata and preview images from CivitAI."
       },
       "download": {
         "title": "Download New Models",
-        "content": "Use the <strong>Download</strong> button to download models directly from Civitai URLs."
+        "content": "Use the <strong>Download</strong> button to download models directly from CivitAI URLs."
       },
       "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."
       }
     }
   },
@@ -103,8 +136,8 @@
     "actions": {
       "addToFavorites": "Add to favorites",
       "removeFromFavorites": "Remove from favorites",
-      "viewOnCivitai": "View on Civitai",
-      "notAvailableFromCivitai": "Not available from Civitai",
+      "viewOnCivitai": "View on CivitAI",
+      "notAvailableFromCivitai": "Not available from CivitAI",
       "viewOnHuggingFace": "View on Hugging Face",
       "sendToWorkflow": "Send to ComfyUI (Click: Append, Shift+Click: Replace)",
       "copyLoRASyntax": "Copy LoRA Syntax",
@@ -137,7 +170,7 @@
     "exampleImages": {
       "checkError": "Error checking for example images",
       "missingHash": "Missing model hash information.",
-      "noRemoteImagesAvailable": "No remote example images available for this model on Civitai"
+      "noRemoteImagesAvailable": "No remote example images available for this model on CivitAI"
     },
     "badges": {
       "update": "Update",
@@ -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...",
@@ -222,6 +248,7 @@
         "modelname": "Model Name",
         "tags": "Tags",
         "creator": "Creator",
+        "hash": "Hash",
         "title": "Recipe Title",
         "loraName": "LoRA Filename",
         "loraModel": "LoRA Model Name",
@@ -259,7 +286,11 @@
       "any": "Any",
       "all": "All",
       "tagLogicAny": "Match any tag (OR)",
-      "tagLogicAll": "Match all tags (AND)"
+      "tagLogicAll": "Match all tags (AND)",
+      "loraAvailability": "Lora Availability",
+      "availabilityReady": "Ready to use",
+      "availabilityMissing": "Has missing",
+      "availabilityDeleted": "Has deleted"
     },
     "theme": {
       "toggle": "Toggle theme",
@@ -285,15 +316,15 @@
     }
   },
   "settings": {
-    "civitaiApiKey": "Civitai API Key",
-    "civitaiApiKeyPlaceholder": "Enter your Civitai API key",
-    "civitaiApiKeyHelp": "Used for authentication when downloading models from Civitai",
+    "civitaiApiKey": "CivitAI API Key",
+    "civitaiApiKeyPlaceholder": "Enter your CivitAI API key",
+    "civitaiApiKeyHelp": "Used for authentication when downloading models from CivitAI",
     "civitaiApiKeyConfigured": "Configured",
     "civitaiApiKeyNotConfigured": "Not configured",
     "civitaiApiKeySet": "Set up",
     "civitaiHost": {
-      "label": "Civitai host",
-      "help": "Choose which Civitai site opens when using View on Civitai links.",
+      "label": "CivitAI host",
+      "help": "Choose which CivitAI site opens when using View on CivitAI links.",
       "options": {
         "com": "civitai.com (SFW)",
         "red": "civitai.red (unrestricted)"
@@ -314,8 +345,8 @@
     },
     "aria2HelpLink": "Learn how to set up the aria2 download backend",
     "civitaiHostBanner": {
-      "title": "Civitai host preference available",
-      "content": "Civitai now uses civitai.com for SFW content and civitai.red for unrestricted content. You can change which site opens by default in Settings.",
+      "title": "CivitAI host preference available",
+      "content": "CivitAI now uses civitai.com for SFW content and civitai.red for unrestricted content. You can change which site opens by default in Settings.",
       "openSettings": "Open Settings"
     },
     "openSettingsFileLocation": {
@@ -445,7 +476,9 @@
     },
     "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.",
+      "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",
@@ -550,7 +583,7 @@
     },
     "downloadPathTemplates": {
       "title": "Download Path Templates",
-      "help": "Configure folder structures for different model types when downloading from Civitai.",
+      "help": "Configure folder structures for different model types when downloading from CivitAI.",
       "availablePlaceholders": "Available placeholders:",
       "templateOptions": {
         "flatStructure": "Flat Structure",
@@ -587,7 +620,7 @@
     "exampleImages": {
       "downloadLocation": "Download Location",
       "downloadLocationPlaceholder": "Enter folder path for example images",
-      "downloadLocationHelp": "Enter the folder path where example images from Civitai will be saved",
+      "downloadLocationHelp": "Enter the folder path where example images from CivitAI will be saved",
       "autoDownload": "Auto Download Example Images",
       "autoDownloadHelp": "Automatically download example images for models that don't have them (requires download location to be set)",
       "openMode": "Open Example Images Action",
@@ -642,7 +675,7 @@
     },
     "metadataArchive": {
       "enableArchiveDb": "Enable Metadata Archive Database",
-      "enableArchiveDbHelp": "Use a local database to access metadata for models that have been deleted from Civitai.",
+      "enableArchiveDbHelp": "Use a local database to access metadata for models that have been deleted from CivitAI.",
       "status": "Status",
       "statusAvailable": "Available",
       "statusUnavailable": "Not Available",
@@ -745,7 +778,7 @@
         "fullTooltip": "Reload all model details from metadata files—use if the library looks out of date or after manual edits."
       },
       "fetch": {
-        "title": "Fetch metadata from Civitai",
+        "title": "Fetch metadata from CivitAI",
         "action": "Fetch"
       },
       "download": {
@@ -781,7 +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",
@@ -820,10 +852,10 @@
       "enrichHfAgent": "Enrich HF Metadata (AI)"
     },
     "contextMenu": {
-      "refreshMetadata": "Refresh Civitai Data",
+      "refreshMetadata": "Refresh CivitAI Data",
       "checkUpdates": "Check Updates",
       "linkModel": "Link Model",
-      "linkCivitai": "Link to Civitai",
+      "linkCivitai": "Link to CivitAI",
       "linkHuggingFace": "Link to HuggingFace",
       "copySyntax": "Copy LoRA Syntax",
       "copyFilename": "Copy Model Filename",
@@ -837,7 +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",
@@ -854,20 +885,129 @@
     "title": "LoRA Recipes",
     "actions": {
       "sendCheckpoint": "Send to ComfyUI",
-      "sendRecipe": "Send to ComfyUI"
+      "sendRecipe": "Send to ComfyUI",
+      "copyRecipeSyntax": "Copy Recipe Syntax",
+      "deleteRecipeWithShortcut": "Delete recipe (Del)"
+    },
+    "navigation": {
+      "label": "Recipe navigation",
+      "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",
+      "sendFailed": "Failed to send workflow to ComfyUI",
+      "noWorkflow": "No embedded workflow found in this recipe"
+    },
+    "status": {
+      "ready": "Ready to use",
+      "missingCount": "{count} missing",
+      "deletedCount": "{count} deleted",
+      "downloadMissing": "Download {count} missing LoRAs",
+      "downloadMissingTooltip": "Click to download missing LoRAs"
+    },
+    "loraStatus": {
+      "none": "No LoRAs in this recipe",
+      "allAvailable": "All LoRAs available - Ready to use",
+      "missing": "{missing} of {total} LoRAs missing",
+      "missingAndUnavailable": "{missing} of {total} LoRAs missing, {unavailable} unavailable (deleted from source or unresolvable hash)",
+      "partial": "{unavailable} of {total} LoRAs unavailable (deleted from source or unresolvable hash) - skipped when recipe is used",
+      "noneUsable": "No usable LoRAs - {unavailable} of {total} deleted from source or unresolvable hash"
+    },
+    "resources": {
+      "inLibrary": "In Library",
+      "notInLibrary": "Not in Library",
+      "deleted": "Deleted",
+      "hashInvalid": "Unresolvable Hash",
+      "inLibraryTooltip": "This model exists in your local library",
+      "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",
+      "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": {
         "action": "Import",
         "title": "Import a recipe from image or URL",
-        "urlLocalPath": "URL / Local Path",
-        "uploadImage": "Upload Image",
-        "urlSectionDescription": "Input a Civitai image URL from civitai.com or civitai.red, or a local file path, to import as a recipe.",
+        "dropZoneLabel": "Upload image",
+        "dropZoneHint": "Drag & drop an image here, paste from clipboard, or click to browse",
+        "orDivider": "or drag & drop / paste an image",
         "imageUrlOrPath": "Image URL or File Path:",
         "urlPlaceholder": "https://civitai.com/images/... or https://civitai.red/images/... or C:/path/to/image.png",
         "fetchImage": "Fetch Image",
-        "uploadSectionDescription": "Upload an image with LoRA metadata to import as a recipe.",
-        "selectImage": "Select Image",
         "recipeName": "Recipe Name",
         "recipeNamePlaceholder": "Enter recipe name",
         "tagsOptional": "Tags (optional)",
@@ -895,7 +1035,7 @@
         "downloadingLoras": "Downloading LoRAs...",
         "savingRecipe": "Saving recipe...",
         "startingDownload": "Starting download for LoRA {current}/{total}",
-        "deletedFromCivitai": "Deleted from Civitai",
+        "deletedFromCivitai": "Deleted from CivitAI",
         "inLibrary": "In Library",
         "notInLibrary": "Not in Library",
         "earlyAccessRequired": "This LoRA requires early access payment to download.",
@@ -912,6 +1052,8 @@
         "errors": {
           "selectImageFile": "Please select an image file",
           "enterUrlOrPath": "Please enter a URL or file path",
+          "invalidUrl": "Please enter a valid URL",
+          "invalidInputFormat": "Please enter an image URL or a local image file path",
           "selectLoraRoot": "Please select a LoRA root directory"
         }
       },
@@ -946,6 +1088,7 @@
       }
     },
     "duplicates": {
+      "finding": "Scanning for duplicate recipes...",
       "found": "Found {count} duplicate groups",
       "noGroups": "No duplicate groups found with the current matching basis",
       "keepLatest": "Keep Latest Versions",
@@ -978,13 +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",
@@ -1015,6 +1151,8 @@
       "start": "Start Import",
       "startImport": "Start Import",
       "importing": "Importing...",
+      "rateLimitedSlowdown": "Rate limited — slowing down...",
+      "rateLimitedHint": "Some items were skipped due to metadata provider rate limits. Re-run the import later to retry them.",
       "progress": "Progress",
       "total": "Total",
       "success": "Success",
@@ -1218,7 +1356,7 @@
     "download": {
       "title": "Download Model from URL",
       "titleWithType": "Download {type} from URL",
-      "civitaiUrl": "Civitai URL(s):",
+      "civitaiUrl": "CivitAI URL(s):",
       "placeholder": "https://civitai.com/models/...",
       "urlHint": "Enter one CivitAI, CivArchive, or Hugging Face URL per line. Supports multiple URLs for batch download.",
       "selectHfFiles": "Select file(s) to download from this repository:",
@@ -1253,7 +1391,7 @@
         "inLibrary": "In Library"
       },
       "errors": {
-        "invalidUrl": "Invalid Civitai URL format",
+        "invalidUrl": "Invalid CivitAI URL format",
         "noVersions": "No versions available for this model",
         "mixedSources": "Cannot mix CivitAI and Hugging Face URLs in the same batch.",
         "noModelFiles": "No model files found in this repository."
@@ -1367,7 +1505,7 @@
       "title": "Local Example Images",
       "message": "No local example images found for this model. View options:",
       "downloadOption": {
-        "title": "Download from Civitai",
+        "title": "Download from CivitAI",
         "description": "Save remote examples locally for offline use and faster loading"
       },
       "importOption": {
@@ -1394,7 +1532,7 @@
       "confirmAction": "Save & Link"
     },
     "relinkCivitai": {
-      "title": "Re-link to Civitai",
+      "title": "Re-link to CivitAI",
       "warning": "Warning:",
       "warningText": "This is a potentially destructive operation. Re-linking will:",
       "warningList": {
@@ -1403,14 +1541,15 @@
         "unintendedConsequences": "May have other unintended consequences"
       },
       "proceedText": "Only proceed if you're sure this is what you want.",
-      "urlLabel": "Civitai Model URL:",
-      "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 or https://civitai.red/models/649516/model-name?modelVersionId=726676",
+      "urlLabel": "CivitAI Model URL:",
+      "urlPlaceholder": "https://civitai.com/models/12345/model-name?modelVersionId=67890 or https://civitai.red/models/12345/model-name?modelVersionId=67890",
       "helpText": {
-        "title": "Paste any Civitai model URL from civitai.com or civitai.red. Supported formats:",
-        "format1": "https://civitai.com/models/649516",
-        "format2": "https://civitai.com/models/649516?modelVersionId=726676",
-        "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
-        "note": "Note: If no modelVersionId is provided, the latest version will be used."
+        "title": "Paste any CivitAI or CivitArchive model URL. Supported formats:",
+        "format1": "https://civitai.com/models/12345",
+        "format2": "https://civitai.com/models/12345?modelVersionId=67890",
+        "format3": "https://civitai.com/models/12345/model-name?modelVersionId=67890",
+        "note": "Note: If no modelVersionId is provided, the latest version will be used.",
+        "format4": "https://civarchive.com/models/12345 (CivArchive)"
       },
       "confirmAction": "Confirm Re-link"
     },
@@ -1420,14 +1559,16 @@
         "editFileName": "Edit file name",
         "editBaseModel": "Edit base model",
         "editVersionName": "Edit version name",
-        "viewOnCivitai": "View on Civitai",
-        "viewOnCivitaiText": "View on Civitai",
+        "viewOnCivitai": "View on CivitAI",
+        "viewOnCivitaiText": "View on CivitAI",
         "viewOnHuggingFace": "View on Hugging Face",
         "viewOnHuggingFaceText": "View on Hugging Face",
         "viewCreatorProfile": "View Creator Profile",
         "openFileLocation": "Open File Location",
         "sendToWorkflow": "Send to ComfyUI",
-        "sendToWorkflowText": "Send to ComfyUI"
+        "sendToWorkflowText": "Send to ComfyUI",
+        "copyHash": "Copy hash",
+        "deleteModelWithShortcut": "Delete model (Del)"
       },
       "openFileLocation": {
         "success": "File location opened successfully",
@@ -1444,6 +1585,7 @@
         "location": "Location",
         "baseModel": "Base Model",
         "size": "Size",
+        "hashes": "Hashes",
         "unknown": "Unknown",
         "usageTips": "Usage Tips",
         "additionalNotes": "Additional Notes",
@@ -1470,7 +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",
@@ -1481,7 +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",
@@ -1520,7 +1666,7 @@
       },
       "license": {
         "noImageSell": "No selling generated content",
-        "noRentCivit": "No Civitai generation",
+        "noRentCivit": "No CivitAI generation",
         "noRent": "No generation services",
         "noSell": "No selling models",
         "creditRequired": "Creator credit required",
@@ -1541,8 +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",
@@ -1585,28 +1731,28 @@
           "newer": "Newer Version",
           "newerTooltip": "This version is newer than your latest local version",
           "earlyAccess": "Early Access",
-          "earlyAccessTooltip": "This version currently requires Civitai early access",
+          "earlyAccessTooltip": "This version currently requires CivitAI early access",
           "paid": "Paid",
           "paidTooltip": "This version requires payment to download",
           "ignored": "Ignored",
           "ignoredTooltip": "Update notifications are disabled for this version",
           "onSiteOnly": "On-Site Only",
-          "onSiteOnlyTooltip": "This version is only available for on-site generation on Civitai"
+          "onSiteOnlyTooltip": "This version is only available for on-site generation on CivitAI"
         },
         "actions": {
           "download": "Download",
           "downloadTooltip": "Download this version",
-          "downloadRemainingTooltip": "Download remaining files of this version",
-          "downloadEarlyAccessTooltip": "Download this early access version from Civitai",
-          "downloadPaidTooltip": "Download this paid version from Civitai",
-          "downloadNotAllowedTooltip": "This version is only available for on-site generation on Civitai",
+          "downloadChooseFilesTooltip": "Choose which files to download",
+          "downloadEarlyAccessTooltip": "Download this early access version from CivitAI",
+          "downloadPaidTooltip": "Download this paid version from CivitAI",
+          "downloadNotAllowedTooltip": "This version is only available for on-site generation on CivitAI",
           "delete": "Delete",
           "deleteTooltip": "Delete this local version",
           "ignore": "Ignore",
           "unignore": "Unignore",
           "ignoreTooltip": "Ignore update notifications for this version",
           "unignoreTooltip": "Resume update notifications for this version",
-          "viewVersionOnCivitai": "View version on Civitai",
+          "viewVersionOnCivitai": "View version on CivitAI",
           "earlyAccessTooltip": "Requires early access purchase",
           "resumeModelUpdates": "Resume updates for this model",
           "ignoreModelUpdates": "Ignore updates for this model",
@@ -1627,7 +1773,7 @@
         },
         "empty": "No version history available for this model yet.",
         "error": "Failed to load versions.",
-        "missingModelId": "This model is missing a Civitai model id.",
+        "missingModelId": "This model is missing a CivitAI model id.",
         "hfGroupInfo": "This is a HuggingFace model group. Open the library to see all versions in the grid.",
         "confirm": {
           "delete": "Delete this version from your library?"
@@ -1711,14 +1857,14 @@
     "tips": {
       "title": "Tips & Tricks",
       "civitai": {
-        "title": "Civitai Integration",
-        "description": "Connect your Civitai account: Visit Profile Avatar → Settings → API Keys → Add API Key, then paste it in Lora Manager settings.",
-        "alt": "Civitai API Setup"
+        "title": "CivitAI Integration",
+        "description": "Connect your CivitAI account: Visit Profile Avatar → Settings → API Keys → Add API Key, then paste it in Lora Manager settings.",
+        "alt": "CivitAI API Setup"
       },
       "download": {
         "title": "Easy Download",
-        "description": "Use Civitai URLs to quickly download and install new models.",
-        "alt": "Civitai Download"
+        "description": "Use CivitAI URLs to quickly download and install new models.",
+        "alt": "CivitAI Download"
       },
       "recipes": {
         "title": "Save Recipes",
@@ -1806,10 +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",
@@ -1826,7 +2014,8 @@
       "settings": "Settings & Configuration",
       "extensions": "Extensions",
       "newBadge": "NEW"
-    }
+    },
+    "newContentBadge": "New"
   },
   "update": {
     "title": "Check for Updates",
@@ -1902,7 +2091,7 @@
       "submitGithubIssue": "Submit GitHub Issue",
       "joinDiscord": "Join Discord",
       "youtubeChannel": "YouTube Channel",
-      "civitaiProfile": "Civitai Profile",
+      "civitaiProfile": "CivitAI Profile",
       "supportKofi": "Support on Ko-fi",
       "supportPatreon": "Support on Patreon"
     },
@@ -1979,11 +2168,16 @@
       "createMissingData": "Missing required data to create recipe",
       "created": "Recipe created successfully",
       "noMissingLoras": "No missing LoRAs to download",
+      "noPreviousRecipe": "No previous recipe available",
+      "noNextRecipe": "No next recipe available",
       "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
       "preparingForDownloadFailed": "Error preparing LoRAs for download",
       "enterLoraName": "Please enter a LoRA name or syntax",
       "reconnectedSuccessfully": "LoRA reconnected successfully",
+      "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",
@@ -1991,6 +2185,16 @@
       "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}",
       "cannotDelete": "Cannot delete recipe: Missing recipe ID",
       "deleteConfirmationError": "Error showing delete confirmation",
       "deletedSuccessfully": "Recipe deleted successfully",
@@ -2014,12 +2218,10 @@
       "batchImportCancelFailed": "Failed to cancel batch import: {message}",
       "batchImportNoUrls": "Please enter at least one URL or file path",
       "batchImportNoDirectory": "Please enter a directory path",
+      "batchImportRateLimited": "Metadata provider rate limit reached — requests are being slowed and some items may be skipped. You can re-run the import later.",
       "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",
@@ -2027,11 +2229,15 @@
       "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",
       "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
-      "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
+      "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings.",
+      "workflowSent": "Workflow sent to ComfyUI",
+      "workflowSendFailed": "Failed to send workflow to ComfyUI: {error}",
+      "workflowNoWorkflow": "No embedded workflow found in this recipe"
     },
     "models": {
       "noModelsSelected": "No models selected",
@@ -2068,8 +2274,8 @@
       "bulkUpdatesChecking": "Checking selected {type}(s) for updates...",
       "bulkUpdatesSuccess": "Updates available for {count} selected {type}(s)",
       "bulkUpdatesNone": "No updates found for selected {type}(s)",
-      "bulkUpdatesMissing": "Selected {type}(s) are not linked to Civitai updates",
-      "bulkUpdatesPartialMissing": "Skipped {missing} selected {type}(s) without Civitai links",
+      "bulkUpdatesMissing": "Selected {type}(s) are not linked to CivitAI updates",
+      "bulkUpdatesPartialMissing": "Skipped {missing} selected {type}(s) without CivitAI links",
       "bulkUpdatesFailed": "Failed to check updates for selected {type}(s): {message}",
       "invalidCharactersRemoved": "Invalid characters removed from filename",
       "filenameCannotBeEmpty": "File name cannot be empty",
@@ -2195,10 +2401,11 @@
     "contextMenu": {
       "contentRatingSet": "Content rating set to {level}",
       "contentRatingFailed": "Failed to set content rating: {message}",
-      "relinkSuccess": "Model successfully re-linked to Civitai",
+      "relinkSuccess": "Model successfully re-linked to CivitAI",
       "relinkFailed": "Error: {message}",
       "linkHfSuccess": "Model successfully linked to HuggingFace",
       "linkHfFailed": "Error: {message}",
+      "linkCivArchSuccess": "Model successfully re-linked via CivitArchive",
       "fetchMetadataFirst": "Please fetch metadata from CivitAI first",
       "noCivitaiInfo": "No CivitAI information available",
       "missingHash": "Model hash not available"
@@ -2288,7 +2495,7 @@
     },
     "issues": {
       "civitai_api_key": {
-        "title": "Civitai API Key"
+        "title": "CivitAI API Key"
       },
       "cache_health": {
         "title": "Model Cache Health"
@@ -2342,9 +2549,9 @@
     },
     "communitySupport": {
       "title": "Keep LoRA Manager Thriving with Your Support ❤️",
-      "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
+      "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM CivitAI Extension as a thank-you gift. Every contribution truly makes a difference.",
       "supportCta": "Support on Ko-fi",
-      "learnMore": "LM Civitai Extension Tutorial"
+      "learnMore": "LM CivitAI Extension Tutorial"
     },
     "cacheHealth": {
       "corrupted": {

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": {
@@ -67,15 +88,15 @@
         "steps": {
             "fetch": {
                 "title": "获取模型元数据",
-                "content": "点击 <strong>获取</strong> 按钮，从 Civitai 下载模型元数据和预览图片。"
+                "content": "点击 <strong>获取</strong> 按钮，从 CivitAI 下载模型元数据和预览图片。"
             },
             "download": {
                 "title": "下载新模型",
-                "content": "使用 <strong>下载</strong> 按钮，可直接通过 Civitai URL 下载模型。"
+                "content": "使用 <strong>下载</strong> 按钮，可直接通过 CivitAI URL 下载模型。"
             },
             "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。"
             }
         }
     },
@@ -103,12 +136,12 @@
         "actions": {
             "addToFavorites": "添加到收藏",
             "removeFromFavorites": "从收藏移除",
-            "viewOnCivitai": "在 Civitai 查看",
-            "notAvailableFromCivitai": "Civitai 上不可用",
+            "viewOnCivitai": "在 CivitAI 查看",
+            "notAvailableFromCivitai": "CivitAI 上不可用",
             "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "发送到 ComfyUI（点击：追加，Shift+点击：替换）",
             "copyLoRASyntax": "复制 LoRA 语法",
-            "checkpointNameCopied": "检查点名称已复制",
+            "checkpointNameCopied": "Checkpoint 名称已复制",
             "toggleBlur": "切换模糊",
             "show": "显示",
             "openExampleImages": "打开示例图片文件夹",
@@ -131,13 +164,13 @@
             "updateFailed": "收藏状态更新失败"
         },
         "sendToWorkflow": {
-            "checkpointNotImplemented": "发送检查点到工作流 - 功能待实现",
+            "checkpointNotImplemented": "发送Checkpoint到工作流 - 功能待实现",
             "missingPath": "无法确定此卡片的模型路径"
         },
         "exampleImages": {
             "checkError": "检查示例图片时出错",
             "missingHash": "缺少模型哈希信息。",
-            "noRemoteImagesAvailable": "此模型在 Civitai 上没有远程示例图片"
+            "noRemoteImagesAvailable": "此模型在 CivitAI 上没有远程示例图片"
         },
         "badges": {
             "update": "更新",
@@ -179,22 +212,15 @@
             "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": "将食谱重新匹配到本地模型",
-            "loading": "正在将食谱重新匹配到本地模型...",
-            "success": "已匹配 {entries} 个条目，涉及 {recipes} 个食谱",
-            "successErrors": "已匹配 {entries} 个条目，涉及 {recipes} 个食谱，{failures} 个失败",
-            "allFailed": "{failures}/{total} 个食谱重新匹配失败",
-            "noMatch": "在 {recipes} 个食谱中未找到 {entries} 个条目的本地匹配",
-            "cancelled": "已取消重新匹配。{recipes} 个食谱已更新（{entries} 个条目）。",
-            "error": "食谱重新匹配失败：{message}"
+            "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...",
+            "success": "已匹配 {entries} 个条目，涉及 {recipes} 个配方",
+            "successErrors": "已匹配 {entries} 个条目，涉及 {recipes} 个配方，{failures} 个失败",
+            "allFailed": "{failures}/{total} 个配方重新匹配失败",
+            "noMatch": "在 {recipes} 个配方中未找到 {entries} 个条目的本地匹配",
+            "cancelled": "已取消重新匹配。{recipes} 个配方已更新（{entries} 个条目）。",
+            "error": "配方重新匹配失败：{message}"
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
@@ -222,6 +248,7 @@
                 "modelname": "模型名称",
                 "tags": "标签",
                 "creator": "创作者",
+                "hash": "哈希",
                 "title": "配方标题",
                 "loraName": "LoRA 文件名",
                 "loraModel": "LoRA 模型名称",
@@ -259,7 +286,11 @@
             "any": "任一",
             "all": "全部",
             "tagLogicAny": "匹配任一标签 (或)",
-            "tagLogicAll": "匹配所有标签 (与)"
+            "tagLogicAll": "匹配所有标签 (与)",
+            "loraAvailability": "LoRA 可用性",
+            "availabilityReady": "可直接使用",
+            "availabilityMissing": "包含缺失 LoRA",
+            "availabilityDeleted": "包含已删除 LoRA"
         },
         "theme": {
             "toggle": "切换主题",
@@ -285,15 +316,15 @@
         }
     },
     "settings": {
-        "civitaiApiKey": "Civitai API 密钥",
-        "civitaiApiKeyPlaceholder": "请输入你的 Civitai API 密钥",
-        "civitaiApiKeyHelp": "用于从 Civitai 下载模型时的身份验证",
+        "civitaiApiKey": "CivitAI API 密钥",
+        "civitaiApiKeyPlaceholder": "请输入你的 CivitAI API 密钥",
+        "civitaiApiKeyHelp": "用于从 CivitAI 下载模型时的身份验证",
         "civitaiApiKeyConfigured": "已配置",
         "civitaiApiKeyNotConfigured": "未配置",
         "civitaiApiKeySet": "设置",
         "civitaiHost": {
-            "label": "Civitai 站点",
-            "help": "选择使用“在 Civitai 中查看”时默认打开的 Civitai 站点。",
+            "label": "CivitAI 站点",
+            "help": "选择使用“在 CivitAI 中查看”时默认打开的 CivitAI 站点。",
             "options": {
                 "com": "civitai.com（仅 SFW）",
                 "red": "civitai.red（无限制）"
@@ -314,8 +345,8 @@
         },
         "aria2HelpLink": "了解如何配置 aria2 下载后端",
         "civitaiHostBanner": {
-            "title": "已提供 Civitai 站点偏好设置",
-            "content": "Civitai 现在使用 civitai.com 提供 SFW 内容，使用 civitai.red 提供无限制内容。你可以在设置中更改默认打开的站点。",
+            "title": "已提供 CivitAI 站点偏好设置",
+            "content": "CivitAI 现在使用 civitai.com 提供 SFW 内容，使用 civitai.red 提供无限制内容。你可以在设置中更改默认打开的站点。",
             "openSettings": "打开设置"
         },
         "openSettingsFileLocation": {
@@ -423,7 +454,7 @@
         },
         "downloadSkipBaseModels": {
             "label": "跳过这些基础模型的下载",
-            "help": "适用于所有下载流程。这里只能选择受支持的基础模型。",
+            "help": "启用后，使用所选基础模型的版本将被跳过。",
             "searchPlaceholder": "筛选基础模型...",
             "empty": "没有与当前搜索匹配的基础模型。",
             "summary": {
@@ -445,7 +476,9 @@
         },
         "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。",
+            "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",
@@ -550,7 +583,7 @@
         },
         "downloadPathTemplates": {
             "title": "下载路径模板",
-            "help": "配置从 Civitai 下载不同模型类型的文件夹结构。",
+            "help": "配置从 CivitAI 下载不同模型类型的文件夹结构。",
             "availablePlaceholders": "可用占位符：",
             "templateOptions": {
                 "flatStructure": "扁平结构",
@@ -587,7 +620,7 @@
         "exampleImages": {
             "downloadLocation": "下载位置",
             "downloadLocationPlaceholder": "输入示例图片文件夹路径",
-            "downloadLocationHelp": "输入保存从 Civitai 下载的示例图片的文件夹路径",
+            "downloadLocationHelp": "输入保存从 CivitAI 下载的示例图片的文件夹路径",
             "autoDownload": "自动下载示例图片",
             "autoDownloadHelp": "自动为没有示例图片的模型下载示例图片（需设置下载位置）",
             "openMode": "打开示例图片操作",
@@ -620,7 +653,7 @@
         },
         "hideEarlyAccessUpdates": {
             "label": "隐藏抢先体验更新",
-            "help": "抢先体验更新"
+            "help": "启用后，仅有抢先体验更新的模型将不显示“可更新”徽章。"
         },
         "hidePaidUpdates": {
             "label": "隐藏付费更新",
@@ -642,7 +675,7 @@
         },
         "metadataArchive": {
             "enableArchiveDb": "启用元数据归档数据库",
-            "enableArchiveDbHelp": "使用本地数据库访问已从 Civitai 删除的模型元数据。",
+            "enableArchiveDbHelp": "使用本地数据库访问已从 CivitAI 删除的模型元数据。",
             "status": "状态",
             "statusAvailable": "可用",
             "statusUnavailable": "不可用",
@@ -691,7 +724,7 @@
         "aiProvider": {
             "title": "AI 提供商",
             "provider": "提供商",
-            "providerHelp": "选择您的 LLM 提供商。OpenAI 和 Ollama 使用预设的 API 端点。自定义允许您指定任何兼容 OpenAI 的端点。",
+            "providerHelp": "选择你的 LLM 提供商。OpenAI 和 Ollama 使用预设的 API 端点。自定义允许你指定任何兼容 OpenAI 的端点。",
             "providerOptions": {
                 "openai": "OpenAI",
                 "ollama": "Ollama（本地）",
@@ -706,7 +739,7 @@
             "apiBaseHelp": "LLM API 的基础地址。选择预设或输入自定义地址，下拉框显示所有支持的提供商预设。",
             "apiBasePlaceholder": "https://api.openai.com/v1",
             "apiKey": "API 密钥",
-            "apiKeyHelp": "LLM 提供商的 API 密钥。本地存储，除您选择的 LLM 提供商外不会发送到任何服务器。",
+            "apiKeyHelp": "LLM 提供商的 API 密钥。本地存储，除你选择的 LLM 提供商外不会发送到任何服务器。",
             "apiKeyPlaceholder": "sk-...",
             "apiKeyNotSet": "未设置",
             "apiKeyConfigured": "已配置",
@@ -745,7 +778,7 @@
                 "fullTooltip": "从元数据文件重新加载所有模型信息；用于列表过时或手动编辑后。"
             },
             "fetch": {
-                "title": "从 Civitai 获取元数据",
+                "title": "从 CivitAI 获取元数据",
                 "action": "获取"
             },
             "download": {
@@ -781,7 +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",
@@ -820,10 +852,10 @@
             "enrichHfAgent": "AI HF 元数据增强"
         },
         "contextMenu": {
-            "refreshMetadata": "刷新 Civitai 数据",
+            "refreshMetadata": "刷新 CivitAI 数据",
             "checkUpdates": "检查更新",
             "linkModel": "链接模型",
-            "linkCivitai": "链接到 Civitai",
+            "linkCivitai": "链接到 CivitAI",
             "linkHuggingFace": "链接到 HuggingFace",
             "copySyntax": "复制 LoRA 语法",
             "copyFilename": "复制模型文件名",
@@ -837,7 +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",
@@ -854,20 +885,129 @@
         "title": "LoRA 配方",
         "actions": {
             "sendCheckpoint": "发送到 ComfyUI",
-            "sendRecipe": "发送到 ComfyUI"
+            "sendRecipe": "发送到 ComfyUI",
+            "copyRecipeSyntax": "复制配方语法",
+            "deleteRecipeWithShortcut": "删除配方（Del）"
+        },
+        "navigation": {
+            "label": "配方导航",
+            "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",
+            "sendFailed": "发送工作流到 ComfyUI 失败",
+            "noWorkflow": "此配方中未找到内嵌工作流"
+        },
+        "status": {
+            "ready": "可直接使用",
+            "missingCount": "缺失 {count} 个",
+            "deletedCount": "已删除 {count} 个",
+            "downloadMissing": "下载 {count} 个缺失的 LoRA",
+            "downloadMissingTooltip": "点击下载缺失的 LoRA"
+        },
+        "loraStatus": {
+            "none": "此配方不包含 LoRA",
+            "allAvailable": "所有 LoRA 均已就绪 - 可直接使用",
+            "missing": "{total} 个 LoRA 中缺失 {missing} 个",
+            "missingAndUnavailable": "{total} 个 LoRA 中缺失 {missing} 个，{unavailable} 个不可用（已从源站删除或哈希无法解析）",
+            "partial": "{total} 个 LoRA 中 {unavailable} 个不可用（已从源站删除或哈希无法解析）- 使用配方时将被跳过",
+            "noneUsable": "没有可用的 LoRA - {total} 个中 {unavailable} 个已从源站删除或哈希无法解析"
+        },
+        "resources": {
+            "inLibrary": "在库中",
+            "notInLibrary": "不在库中",
+            "deleted": "已删除",
+            "hashInvalid": "无法解析的哈希",
+            "inLibraryTooltip": "该模型已存在于本地库中",
+            "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}",
+            "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": {
                 "action": "导入",
                 "title": "从图片或 URL 导入配方",
-                "urlLocalPath": "URL / 本地路径",
-                "uploadImage": "上传图片",
-                "urlSectionDescription": "输入来自 civitai.com 或 civitai.red 的 Civitai 图片 URL，或本地文件路径以导入为配方。",
+                "dropZoneLabel": "上传图片",
+                "dropZoneHint": "将图片拖拽到此处、从剪贴板粘贴，或点击浏览",
+                "orDivider": "或拖拽 / 粘贴图片",
                 "imageUrlOrPath": "图片 URL 或文件路径：",
                 "urlPlaceholder": "https://civitai.com/images/... 或 https://civitai.red/images/... 或 C:/path/to/image.png",
                 "fetchImage": "获取图片",
-                "uploadSectionDescription": "上传带有 LoRA 元数据的图片以导入为配方。",
-                "selectImage": "选择图片",
                 "recipeName": "配方名称",
                 "recipeNamePlaceholder": "输入配方名称",
                 "tagsOptional": "标签（可选）",
@@ -875,7 +1015,7 @@
                 "addTag": "添加",
                 "noTagsAdded": "未添加标签",
                 "lorasInRecipe": "此配方中的 LoRA",
-                "downloadLocationPreview": "下载位置预览：{path}",
+                "downloadLocationPreview": "下载位置预览：",
                 "useDefaultPath": "使用默认路径",
                 "useDefaultPathTooltip": "启用后，文件将自动使用配置的路径模板进行组织",
                 "selectLoraRoot": "选择 LoRA 根目录",
@@ -889,20 +1029,20 @@
                 "importAndDownload": "导入并下载",
                 "downloadMissingLoras": "下载缺失的 LoRA",
                 "saveRecipe": "保存配方",
-                "loraCountInfo": "({existing}/{total} in library)",
+                "loraCountInfo": "（库中 {existing}/{total}）",
                 "processingInput": "处理输入...",
                 "analyzingMetadata": "分析图像元数据...",
                 "downloadingLoras": "下载 LoRA...",
                 "savingRecipe": "保存配方...",
                 "startingDownload": "开始下载 LoRA {current}/{total}",
-                "deletedFromCivitai": "从 Civitai 中删除",
+                "deletedFromCivitai": "从 CivitAI 中删除",
                 "inLibrary": "在库中",
                 "notInLibrary": "不在库中",
                 "earlyAccessRequired": "此 LoRA 需要提前访问权限才能下载。",
                 "earlyAccessEnds": "提前访问权限将于 {date} 结束。",
                 "earlyAccess": "提前访问",
-                "verifyEarlyAccess": "在下载之前，请验证您是否已购买提前访问权限。",
-                "duplicateRecipesFound": "在您的库中找到 {count} 个相同的配方。",
+                "verifyEarlyAccess": "在下载之前，请确认你已购买提前访问权限。",
+                "duplicateRecipesFound": "在你的库中找到 {count} 个相同的配方。",
                 "duplicateRecipesDescription": "这些配方包含相同的 LoRA，权重完全相同。",
                 "showDuplicates": "显示重复项",
                 "hideDuplicates": "隐藏重复项",
@@ -912,6 +1052,8 @@
                 "errors": {
                     "selectImageFile": "请选择一个图像文件",
                     "enterUrlOrPath": "请输入 URL 或文件路径",
+                    "invalidUrl": "请输入有效的 URL",
+                    "invalidInputFormat": "请输入图片 URL 或本地图片文件路径",
                     "selectLoraRoot": "请选择 LoRA 根目录"
                 }
             },
@@ -946,6 +1088,7 @@
             }
         },
         "duplicates": {
+            "finding": "正在扫描重复配方...",
             "found": "发现 {count} 个重复组",
             "noGroups": "按当前判重依据未找到重复组",
             "keepLatest": "保留最新版本",
@@ -978,13 +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",
@@ -1015,6 +1151,8 @@
             "start": "开始导入",
             "startImport": "开始导入",
             "importing": "正在导入配方...",
+            "rateLimitedSlowdown": "触发速率限制 — 正在减速...",
+            "rateLimitedHint": "部分条目因元数据提供方的速率限制而被跳过。稍后重新运行导入即可重试这些条目。",
             "progress": "进度",
             "total": "总计",
             "success": "成功",
@@ -1056,7 +1194,7 @@
         "title": "Checkpoint 模型",
         "modelTypes": {
             "checkpoint": "Checkpoint",
-            "diffusion_model": "Diffusion Model"
+            "diffusion_model": "扩散模型"
         },
         "contextMenu": {
             "moveToOtherTypeFolder": "移动到 {otherType} 文件夹",
@@ -1080,7 +1218,7 @@
         "collapseAllDisabled": "列表视图下不可用",
         "dragDrop": {
             "unableToResolveRoot": "无法确定移动的目标路径。",
-            "moveUnsupported": "Move is not supported for this item.",
+            "moveUnsupported": "此条目不支持移动。",
             "createFolderHint": "释放以创建新文件夹",
             "newFolderName": "新文件夹名称",
             "folderNameHint": "按 Enter 确认，Escape 取消",
@@ -1218,7 +1356,7 @@
         "download": {
             "title": "从 URL 下载模型",
             "titleWithType": "从 URL 下载 {type}",
-            "civitaiUrl": "Civitai URL:",
+            "civitaiUrl": "CivitAI URL:",
             "placeholder": "https://civitai.com/models/...",
             "urlHint": "每行输入一个 CivitAI、CivArchive 或 Hugging Face URL。支持批量下载多个 URL。",
             "selectHfFiles": "选择从此仓库下载的文件：",
@@ -1253,7 +1391,7 @@
                 "inLibrary": "已在库中"
             },
             "errors": {
-                "invalidUrl": "无效的 Civitai URL 格式",
+                "invalidUrl": "无效的 CivitAI URL 格式",
                 "noVersions": "此模型没有可用版本",
                 "mixedSources": "无法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
                 "noModelFiles": "在此仓库中未找到模型文件。"
@@ -1332,8 +1470,8 @@
             "action": "全部删除"
         },
         "checkUpdates": {
-            "title": "检查所有 {type} 的更新？",
-            "message": "这会为库中的每个 {type} 检查更新，大型集合可能需要一些时间。",
+            "title": "检查所有 {typePlural} 的更新？",
+            "message": "这会检查库中的每个 {typePlural} 的更新，大型集合可能需要一些时间。",
             "tip": "想分批进行？切换到批量模式，选中需要的模型，然后使用“检查所选更新”。",
             "action": "检查全部"
         },
@@ -1367,7 +1505,7 @@
             "title": "本地示例图片",
             "message": "未找到此模型的本地示例图片。可选操作：",
             "downloadOption": {
-                "title": "从 Civitai 下载",
+                "title": "从 CivitAI 下载",
                 "description": "将远程示例保存到本地，便于离线使用和更快加载"
             },
             "importOption": {
@@ -1394,7 +1532,7 @@
             "confirmAction": "保存并链接"
         },
         "relinkCivitai": {
-            "title": "重新关联到 Civitai",
+            "title": "重新关联到 CivitAI",
             "warning": "警告：",
             "warningText": "这是一个有潜在风险的操作。重新关联将：",
             "warningList": {
@@ -1403,14 +1541,15 @@
                 "unintendedConsequences": "可能有其他不可预期的后果"
             },
             "proceedText": "仅在你确定需要此操作时继续。",
-            "urlLabel": "Civitai 模型 URL：",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 或 https://civitai.red/models/649516/model-name?modelVersionId=726676",
+            "urlLabel": "CivitAI 模型 URL：",
+            "urlPlaceholder": "https://civitai.com/models/12345/model-name?modelVersionId=67890 或 https://civitai.red/models/12345/model-name?modelVersionId=67890",
             "helpText": {
-                "title": "粘贴任意来自 civitai.com 或 civitai.red 的 Civitai 模型 URL。支持格式：",
-                "format1": "https://civitai.com/models/649516",
-                "format2": "https://civitai.com/models/649516?modelVersionId=726676",
-                "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
-                "note": "注意：如果未提供 modelVersionId，将使用最新版本。"
+                "title": "粘贴任意 CivitAI 或 CivitArchive 模型 URL。支持格式：",
+                "format1": "https://civitai.com/models/12345",
+                "format2": "https://civitai.com/models/12345?modelVersionId=67890",
+                "format3": "https://civitai.com/models/12345/model-name?modelVersionId=67890",
+                "note": "注意：如果未提供 modelVersionId，将使用最新版本。",
+                "format4": "https://civarchive.com/models/12345 (CivArchive)"
             },
             "confirmAction": "确认重新关联"
         },
@@ -1420,14 +1559,16 @@
                 "editFileName": "编辑文件名",
                 "editBaseModel": "编辑基础模型",
                 "editVersionName": "编辑版本名称",
-                "viewOnCivitai": "在 Civitai 查看",
-                "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnCivitai": "在 CivitAI 查看",
+                "viewOnCivitaiText": "在 CivitAI 查看",
                 "viewOnHuggingFace": "在 Hugging Face 查看",
                 "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看创作者主页",
                 "openFileLocation": "打开文件位置",
                 "sendToWorkflow": "发送到 ComfyUI",
-                "sendToWorkflowText": "发送到 ComfyUI"
+                "sendToWorkflowText": "发送到 ComfyUI",
+                "copyHash": "复制哈希值",
+                "deleteModelWithShortcut": "删除模型（Del）"
             },
             "openFileLocation": {
                 "success": "文件位置已成功打开",
@@ -1444,13 +1585,14 @@
                 "location": "位置",
                 "baseModel": "基础模型",
                 "size": "大小",
+                "hashes": "哈希值",
                 "unknown": "未知",
                 "usageTips": "使用提示",
                 "additionalNotes": "附加备注",
                 "notesHint": "回车保存，Shift+回车换行",
                 "addNotesPlaceholder": "在此添加你的备注...",
                 "aboutThisVersion": "关于此版本",
-                "baseModelSearchPlaceholder": "搜索基础模型…",
+                "baseModelSearchPlaceholder": "搜索基础模型...",
                 "baseModelSuggested": "推荐",
                 "baseModelNoMatch": "没有匹配的基础模型"
             },
@@ -1470,7 +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",
@@ -1481,7 +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",
@@ -1519,10 +1665,10 @@
                 "noNext": "没有下一个模型"
             },
             "license": {
-                "noImageSell": "No selling generated content",
-                "noRentCivit": "No Civitai generation",
-                "noRent": "No generation services",
-                "noSell": "No selling models",
+                "noImageSell": "禁止出售生成的图片",
+                "noRentCivit": "禁止在 CivitAI 上生成",
+                "noRent": "禁止生成服务",
+                "noSell": "禁止出售模型",
                 "creditRequired": "需要创作者署名",
                 "noDerivatives": "禁止分享合并作品",
                 "noReLicense": "需要相同权限",
@@ -1541,8 +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",
@@ -1585,49 +1731,49 @@
                     "newer": "较新的版本",
                     "newerTooltip": "此版本比你本地的最新版本更新",
                     "earlyAccess": "抢先体验",
-                    "earlyAccessTooltip": "此版本当前需要 Civitai 抢先体验权限",
+                    "earlyAccessTooltip": "此版本当前需要 CivitAI 抢先体验权限",
                     "paid": "付费",
                     "paidTooltip": "此版本需要付费后才能下载",
                     "ignored": "已忽略",
                     "ignoredTooltip": "此版本已关闭更新通知",
                     "onSiteOnly": "仅站内生成",
-                    "onSiteOnlyTooltip": "此版本仅在 Civitai 站内可用，无法下载"
+                    "onSiteOnlyTooltip": "此版本仅在 CivitAI 站内可用，无法下载"
                 },
                 "actions": {
                     "download": "下载",
                     "downloadTooltip": "下载此版本",
-                    "downloadRemainingTooltip": "下载此版本的剩余文件",
-                    "downloadEarlyAccessTooltip": "从 Civitai 下载此抢先体验版本",
-                    "downloadPaidTooltip": "从 Civitai 下载此付费版本",
-                    "downloadNotAllowedTooltip": "此版本仅在 Civitai 站内可用，无法下载",
+                    "downloadChooseFilesTooltip": "选择要下载的文件",
+                    "downloadEarlyAccessTooltip": "从 CivitAI 下载此抢先体验版本",
+                    "downloadPaidTooltip": "从 CivitAI 下载此付费版本",
+                    "downloadNotAllowedTooltip": "此版本仅在 CivitAI 站内可用，无法下载",
                     "delete": "删除",
                     "deleteTooltip": "删除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
                     "ignoreTooltip": "忽略此版本的更新通知",
                     "unignoreTooltip": "恢复此版本的更新通知",
-                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
+                    "viewVersionOnCivitai": "在 CivitAI 上查看版本",
                     "earlyAccessTooltip": "需要购买抢先体验",
                     "resumeModelUpdates": "继续跟踪该模型的更新",
                     "ignoreModelUpdates": "忽略该模型的更新",
                     "viewLocalVersions": "查看所有本地版本",
-                    "viewLocalTooltip": "敬请期待"
+                    "viewLocalTooltip": "在主页面上显示该模型的所有本地版本"
                 },
                 "filters": {
                     "label": "基础筛选",
                     "state": {
                         "showAll": "全部版本",
-                        "showSameBase": "相同基模型"
+                        "showSameBase": "相同基础模型"
                     },
                     "tooltip": {
                         "showAllVersions": "切换为显示所有版本",
-                        "showSameBaseVersions": "仅显示与当前基模型匹配的版本"
+                        "showSameBaseVersions": "仅显示与当前基础模型匹配的版本"
                     },
-                    "empty": "没有与当前基模型筛选匹配的版本。"
+                    "empty": "没有与当前基础模型筛选匹配的版本。"
                 },
                 "empty": "该模型还没有版本历史。",
                 "error": "加载版本失败。",
-                "missingModelId": "该模型缺少 Civitai 模型 ID。",
+                "missingModelId": "该模型缺少 CivitAI 模型 ID。",
                 "hfGroupInfo": "这是一个 HuggingFace 模型组。打开库页面即可在网格中查看所有版本。",
                 "confirm": {
                     "delete": "从库中删除此版本？"
@@ -1711,14 +1857,14 @@
         "tips": {
             "title": "技巧与提示",
             "civitai": {
-                "title": "Civitai 集成",
-                "description": "连接你的 Civitai 账号：访问头像 → 设置 → API 密钥 → 添加密钥，然后粘贴到 LoRA 管理器设置中。",
-                "alt": "Civitai API 设置"
+                "title": "CivitAI 集成",
+                "description": "连接你的 CivitAI 账号：访问头像 → 设置 → API 密钥 → 添加密钥，然后粘贴到 LoRA 管理器设置中。",
+                "alt": "CivitAI API 设置"
             },
             "download": {
                 "title": "便捷下载",
-                "description": "使用 Civitai URL 快速下载和安装新模型。",
-                "alt": "Civitai 下载"
+                "description": "使用 CivitAI URL 快速下载和安装新模型。",
+                "alt": "CivitAI 下载"
             },
             "recipes": {
                 "title": "保存配方",
@@ -1796,7 +1942,7 @@
             "copiedUri": "链接已复制到剪贴板：{{uri}}",
             "uriClipboardFallback": "链接：{{uri}}",
             "setupRequired": "示例图片存储",
-            "setupDescription": "要添加自定义示例图片，您需要先设置下载位置。",
+            "setupDescription": "要添加自定义示例图片，你需要先设置下载位置。",
             "setupUsage": "此路径用于存储下载的示例图片和自定义图片。",
             "openSettings": "打开设置"
         }
@@ -1806,10 +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",
@@ -1826,7 +2014,8 @@
             "settings": "设置与配置",
             "extensions": "扩展",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "检查更新",
@@ -1902,7 +2091,7 @@
             "submitGithubIssue": "提交 GitHub 问题",
             "joinDiscord": "加入 Discord",
             "youtubeChannel": "YouTube 频道",
-            "civitaiProfile": "Civitai 个人资料",
+            "civitaiProfile": "CivitAI 个人资料",
             "supportKofi": "支持 Ko-fi",
             "supportPatreon": "支持 Patreon"
         },
@@ -1979,18 +2168,33 @@
             "createMissingData": "缺少创建配方所需的数据",
             "created": "配方创建成功",
             "noMissingLoras": "没有缺失的 LoRA 可下载",
+            "noPreviousRecipe": "没有上一个配方",
+            "noNextRecipe": "没有下一个配方",
             "missingLorasInfoFailed": "获取缺失 LoRA 信息失败",
             "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",
             "sendError": "发送配方到工作流出错",
-            "missingCheckpointPath": "缺少检查点路径",
-            "missingCheckpointInfo": "缺少检查点信息",
-            "downloadCheckpointFailed": "下载检查点失败：{message}",
+            "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}",
             "cannotDelete": "无法删除配方：缺少配方 ID",
             "deleteConfirmationError": "显示删除确认出错",
             "deletedSuccessfully": "配方删除成功",
@@ -2014,24 +2218,26 @@
             "batchImportCancelFailed": "取消批量导入失败：{message}",
             "batchImportNoUrls": "请输入至少一个 URL 或文件路径",
             "batchImportNoDirectory": "请输入目录路径",
+            "batchImportRateLimited": "已达到元数据提供方的速率限制 — 请求正在放缓，部分条目可能被跳过。你可以稍后重新运行导入。",
             "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} 个食谱",
-            "rematchCompleteErrors": "已匹配 {entries} 个条目，涉及 {recipes} 个食谱，{failures} 个失败",
-            "rematchAllFailed": "{failures}/{total} 个所选食谱重新匹配失败",
-            "rematchUnmatched": "在 {recipes} 个食谱中未找到 {entries} 个条目的本地匹配",
-            "rematchSkipped": "{total} 个所选食谱均无需重新匹配",
-            "rematchFailed": "重新匹配所选食谱失败：{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",
+            "rematchUnmatched": "在 {recipes} 个配方中未找到 {entries} 个条目的本地匹配",
+            "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",
             "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
-            "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
+            "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。",
+            "workflowSent": "工作流已发送到 ComfyUI",
+            "workflowSendFailed": "发送工作流到 ComfyUI 失败: {error}",
+            "workflowNoWorkflow": "此配方中未找到内嵌工作流"
         },
         "models": {
             "noModelsSelected": "未选中模型",
@@ -2068,8 +2274,8 @@
             "bulkUpdatesChecking": "正在检查所选 {type} 的更新...",
             "bulkUpdatesSuccess": "{count} 个所选 {type} 有可用更新",
             "bulkUpdatesNone": "所选 {type} 未发现更新",
-            "bulkUpdatesMissing": "所选 {type} 未关联 Civitai 更新",
-            "bulkUpdatesPartialMissing": "已跳过 {missing} 个未关联 Civitai 的所选 {type}",
+            "bulkUpdatesMissing": "所选 {type} 未关联 CivitAI 更新",
+            "bulkUpdatesPartialMissing": "已跳过 {missing} 个未关联 CivitAI 的所选 {type}",
             "bulkUpdatesFailed": "检查所选 {type} 的更新失败：{message}",
             "invalidCharactersRemoved": "文件名中的无效字符已移除",
             "filenameCannotBeEmpty": "文件名不能为空",
@@ -2098,7 +2304,7 @@
             "checkpointRootsFailed": "加载 Checkpoint 根目录失败：{message}",
             "unetRootsFailed": "加载 Diffusion Model 根目录失败：{message}",
             "embeddingRootsFailed": "加载 Embedding 根目录失败：{message}",
-            "mappingsUpdated": "基础模型路径映射已更新（{count} 条映射{plural}）",
+            "mappingsUpdated": "基础模型路径映射已更新（{count} 条映射）",
             "mappingsCleared": "基础模型路径映射已清除",
             "mappingSaveFailed": "保存基础模型映射失败：{message}",
             "downloadTemplatesUpdated": "下载路径模板已更新",
@@ -2109,8 +2315,8 @@
             "compactModeToggled": "紧凑模式 {state}",
             "settingSaveFailed": "保存设置失败：{message}",
             "displayDensitySet": "显示密度已设置为 {density}",
-            "libraryLoadFailed": "Failed to load libraries: {message}",
-            "libraryActivateFailed": "Failed to activate library: {message}",
+            "libraryLoadFailed": "加载模型库失败：{message}",
+            "libraryActivateFailed": "激活模型库失败：{message}",
             "languageChangeFailed": "切换语言失败：{message}",
             "cacheCleared": "缓存文件已成功清除。下次操作将重建缓存。",
             "cacheClearFailed": "清除缓存失败：{error}",
@@ -2195,10 +2401,11 @@
         "contextMenu": {
             "contentRatingSet": "内容评级已设置为 {level}",
             "contentRatingFailed": "设置内容评级失败：{message}",
-            "relinkSuccess": "模型已成功重新关联到 Civitai",
+            "relinkSuccess": "模型已成功重新关联到 CivitAI",
             "relinkFailed": "错误：{message}",
             "linkHfSuccess": "模型已成功链接到 HuggingFace",
             "linkHfFailed": "错误：{message}",
+            "linkCivArchSuccess": "模型已成功通过 CivitArchive 重新关联",
             "fetchMetadataFirst": "请先从 CivitAI 获取元数据",
             "noCivitaiInfo": "无 CivitAI 信息",
             "missingHash": "模型哈希不可用"
@@ -2258,7 +2465,7 @@
             "bulkMoveSuccess": "成功移动 {successCount} 个 {type}",
             "exampleImagesDownloadSuccess": "示例图片下载成功！",
             "exampleImagesDownloadFailed": "示例图片下载失败：{message}",
-            "moveFailed": "Failed to move item: {message}",
+            "moveFailed": "移动条目失败：{message}",
             "copiedToClipboard": "已复制到剪贴板",
             "downloadStarted": "下载已开始"
         },
@@ -2288,7 +2495,7 @@
         },
         "issues": {
             "civitai_api_key": {
-                "title": "Civitai API 密钥"
+                "title": "CivitAI API 密钥"
             },
             "cache_health": {
                 "title": "模型缓存健康状态"

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": {
@@ -67,15 +88,15 @@
         "steps": {
             "fetch": {
                 "title": "取得模型 metadata",
-                "content": "點擊 <strong>取得</strong> 按鈕，從 Civitai 下載模型 metadata 與預覽圖片。"
+                "content": "點擊 <strong>取得</strong> 按鈕，從 CivitAI 下載模型 metadata 與預覽圖片。"
             },
             "download": {
                 "title": "下載新模型",
-                "content": "使用 <strong>下載</strong> 按鈕，直接從 Civitai 網址下載模型。"
+                "content": "使用 <strong>下載</strong> 按鈕，直接從 CivitAI 網址下載模型。"
             },
             "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。"
             }
         }
     },
@@ -103,8 +136,8 @@
         "actions": {
             "addToFavorites": "加入收藏",
             "removeFromFavorites": "移除收藏",
-            "viewOnCivitai": "在 Civitai 查看",
-            "notAvailableFromCivitai": "Civitai 不提供",
+            "viewOnCivitai": "在 CivitAI 查看",
+            "notAvailableFromCivitai": "CivitAI 不提供",
             "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "傳送到 ComfyUI（點擊：附加，Shift+點擊：取代）",
             "copyLoRASyntax": "複製 LoRA 語法",
@@ -113,7 +146,7 @@
             "show": "顯示",
             "openExampleImages": "開啟範例圖片資料夾",
             "replacePreview": "更換預覽圖",
-            "copyCheckpointName": "複製檢查點名稱",
+            "copyCheckpointName": "複製 Checkpoint 名稱",
             "copyEmbeddingName": "複製嵌入名稱",
             "embeddingNameCopied": "已複製 Embedding 語法",
             "sendCheckpointToWorkflow": "傳送到 ComfyUI",
@@ -137,7 +170,7 @@
         "exampleImages": {
             "checkError": "檢查範例圖片時發生錯誤",
             "missingHash": "缺少模型雜湊資訊。",
-            "noRemoteImagesAvailable": "此模型在 Civitai 上無遠端範例圖片"
+            "noRemoteImagesAvailable": "此模型在 CivitAI 上無遠端範例圖片"
         },
         "badges": {
             "update": "更新",
@@ -179,22 +212,15 @@
             "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": "將食譜重新匹配到本地模型",
-            "loading": "正在將食譜重新匹配到本地模型...",
-            "success": "已匹配 {entries} 個條目，涉及 {recipes} 個食譜",
-            "successErrors": "已匹配 {entries} 個條目，涉及 {recipes} 個食譜，{failures} 個失敗",
-            "allFailed": "{failures}/{total} 個食譜重新匹配失敗",
-            "noMatch": "在 {recipes} 個食譜中找不到 {entries} 個條目的本地匹配",
-            "cancelled": "已取消重新匹配。{recipes} 個食譜已更新（{entries} 個條目）。",
-            "error": "食譜重新匹配失敗：{message}"
+            "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...",
+            "success": "已匹配 {entries} 個條目，涉及 {recipes} 個配方",
+            "successErrors": "已匹配 {entries} 個條目，涉及 {recipes} 個配方，{failures} 個失敗",
+            "allFailed": "{failures}/{total} 個配方重新匹配失敗",
+            "noMatch": "在 {recipes} 個配方中找不到 {entries} 個條目的本地匹配",
+            "cancelled": "已取消重新匹配。{recipes} 個配方已更新（{entries} 個條目）。",
+            "error": "配方重新匹配失敗：{message}"
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
@@ -222,6 +248,7 @@
                 "modelname": "模型名稱",
                 "tags": "標籤",
                 "creator": "創作者",
+                "hash": "雜湊",
                 "title": "配方標題",
                 "loraName": "LoRA 檔案名稱",
                 "loraModel": "LoRA 模型名稱",
@@ -258,8 +285,12 @@
             "clearAll": "清除所有篩選",
             "any": "任一",
             "all": "全部",
-            "tagLogicAny": "符合任一票籤 (或)",
-            "tagLogicAll": "符合所有標籤 (與)"
+            "tagLogicAny": "符合任一標籤 (或)",
+            "tagLogicAll": "符合所有標籤 (與)",
+            "loraAvailability": "LoRA 可用性",
+            "availabilityReady": "可直接使用",
+            "availabilityMissing": "包含缺少的 LoRA",
+            "availabilityDeleted": "包含已刪除的 LoRA"
         },
         "theme": {
             "toggle": "切換主題",
@@ -285,15 +316,15 @@
         }
     },
     "settings": {
-        "civitaiApiKey": "Civitai API 金鑰",
-        "civitaiApiKeyPlaceholder": "請輸入您的 Civitai API 金鑰",
-        "civitaiApiKeyHelp": "用於從 Civitai 下載模型時的身份驗證",
+        "civitaiApiKey": "CivitAI API 金鑰",
+        "civitaiApiKeyPlaceholder": "請輸入您的 CivitAI API 金鑰",
+        "civitaiApiKeyHelp": "用於從 CivitAI 下載模型時的身份驗證",
         "civitaiApiKeyConfigured": "已設定",
         "civitaiApiKeyNotConfigured": "未設定",
         "civitaiApiKeySet": "設定",
         "civitaiHost": {
-            "label": "Civitai 站點",
-            "help": "選擇使用「在 Civitai 中查看」時預設開啟的 Civitai 站點。",
+            "label": "CivitAI 站點",
+            "help": "選擇使用「在 CivitAI 中查看」時預設開啟的 CivitAI 站點。",
             "options": {
                 "com": "civitai.com（僅 SFW）",
                 "red": "civitai.red（無限制）"
@@ -314,8 +345,8 @@
         },
         "aria2HelpLink": "了解如何設定 aria2 下載後端",
         "civitaiHostBanner": {
-            "title": "已提供 Civitai 站點偏好設定",
-            "content": "Civitai 現在使用 civitai.com 提供 SFW 內容，使用 civitai.red 提供無限制內容。你可以在設定中變更預設開啟的站點。",
+            "title": "已提供 CivitAI 站點偏好設定",
+            "content": "CivitAI 現在使用 civitai.com 提供 SFW 內容，使用 civitai.red 提供無限制內容。您可以在設定中變更預設開啟的站點。",
             "openSettings": "開啟設定"
         },
         "openSettingsFileLocation": {
@@ -402,7 +433,7 @@
             "retentionHelp": "在刪除舊快照之前，要保留多少自動快照。",
             "management": "備份管理",
             "managementHelp": "匯出目前的使用者狀態，或從備份封存中還原。",
-            "scopeHelp": "備份你的設定、下載歷史與模型更新狀態。不包含模型檔案或可重建的快取。",
+            "scopeHelp": "備份您的設定、下載歷史與模型更新狀態。不包含模型檔案或可重建的快取。",
             "locationSummary": "目前備份位置",
             "openFolderButton": "開啟備份資料夾",
             "openFolderSuccess": "已開啟備份資料夾",
@@ -423,7 +454,7 @@
         },
         "downloadSkipBaseModels": {
             "label": "跳過這些基礎模型的下載",
-            "help": "適用於所有下載流程。這裡只能選擇受支援的基礎模型。",
+            "help": "啟用後，使用所選基礎模型的版本將被略過。",
             "searchPlaceholder": "篩選基礎模型...",
             "empty": "沒有符合目前搜尋條件的基礎模型。",
             "summary": {
@@ -445,7 +476,9 @@
         },
         "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。",
+            "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",
@@ -512,7 +545,7 @@
         "extraFolderPaths": {
             "title": "額外資料夾路徑",
             "description": "LoRA Manager 專屬的額外模型根目錄。從 ComfyUI 標準資料夾之外的位置載入模型，特別適合管理大型模型庫，避免影響 ComfyUI 效能。",
-            "restartRequired": "Requires restart to take effect",
+            "restartRequired": "需要重新啟動才能生效",
             "modelTypes": {
                 "lora": "LoRA 路徑",
                 "checkpoint": "Checkpoint 路徑",
@@ -550,7 +583,7 @@
         },
         "downloadPathTemplates": {
             "title": "下載路徑範本",
-            "help": "設定從 Civitai 下載時不同模型類型的資料夾結構。",
+            "help": "設定從 CivitAI 下載時不同模型類型的資料夾結構。",
             "availablePlaceholders": "可用佔位符：",
             "templateOptions": {
                 "flatStructure": "扁平結構",
@@ -587,7 +620,7 @@
         "exampleImages": {
             "downloadLocation": "下載位置",
             "downloadLocationPlaceholder": "輸入範例圖片的資料夾路徑",
-            "downloadLocationHelp": "輸入從 Civitai 下載範例圖片要儲存的資料夾路徑",
+            "downloadLocationHelp": "輸入從 CivitAI 下載範例圖片要儲存的資料夾路徑",
             "autoDownload": "自動下載範例圖片",
             "autoDownloadHelp": "自動為沒有範例圖片的模型下載範例圖片（需設定下載位置）",
             "openMode": "開啟範例圖片動作",
@@ -620,7 +653,7 @@
         },
         "hideEarlyAccessUpdates": {
             "label": "隱藏搶先體驗更新",
-            "help": "搶先體驗更新"
+            "help": "啟用後，只有搶先體驗更新的模型將不顯示「可更新」徽章。"
         },
         "hidePaidUpdates": {
             "label": "隱藏付費更新",
@@ -642,7 +675,7 @@
         },
         "metadataArchive": {
             "enableArchiveDb": "啟用中繼資料封存資料庫",
-            "enableArchiveDbHelp": "使用本機資料庫以存取已從 Civitai 刪除模型的中繼資料。",
+            "enableArchiveDbHelp": "使用本機資料庫以存取已從 CivitAI 刪除模型的中繼資料。",
             "status": "狀態",
             "statusAvailable": "可用",
             "statusUnavailable": "不可用",
@@ -745,7 +778,7 @@
                 "fullTooltip": "從中繼資料檔重新載入所有模型資訊；適用於清單過時或手動編輯後。"
             },
             "fetch": {
-                "title": "從 Civitai 取得 metadata",
+                "title": "從 CivitAI 取得 metadata",
                 "action": "取得"
             },
             "download": {
@@ -781,7 +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",
@@ -820,10 +852,10 @@
             "enrichHfAgent": "AI HF 中繼資料增強"
         },
         "contextMenu": {
-            "refreshMetadata": "刷新 Civitai 資料",
+            "refreshMetadata": "刷新 CivitAI 資料",
             "checkUpdates": "檢查更新",
             "linkModel": "連結模型",
-            "linkCivitai": "連結到 Civitai",
+            "linkCivitai": "連結到 CivitAI",
             "linkHuggingFace": "連結到 HuggingFace",
             "copySyntax": "複製 LoRA 語法",
             "copyFilename": "複製模型檔名",
@@ -837,7 +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",
@@ -854,20 +885,129 @@
         "title": "LoRA 配方",
         "actions": {
             "sendCheckpoint": "傳送到 ComfyUI",
-            "sendRecipe": "傳送到 ComfyUI"
+            "sendRecipe": "傳送到 ComfyUI",
+            "copyRecipeSyntax": "複製配方語法",
+            "deleteRecipeWithShortcut": "刪除配方（Del）"
+        },
+        "navigation": {
+            "label": "配方導覽",
+            "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",
+            "sendFailed": "傳送工作流到 ComfyUI 失敗",
+            "noWorkflow": "此配方中未找到內嵌工作流"
+        },
+        "status": {
+            "ready": "可直接使用",
+            "missingCount": "缺少 {count} 個",
+            "deletedCount": "已刪除 {count} 個",
+            "downloadMissing": "下載 {count} 個缺少的 LoRA",
+            "downloadMissingTooltip": "點擊下載缺少的 LoRA"
+        },
+        "loraStatus": {
+            "none": "此配方不含 LoRA",
+            "allAvailable": "所有 LoRA 皆已就緒 - 可直接使用",
+            "missing": "{total} 個 LoRA 中缺少 {missing} 個",
+            "missingAndUnavailable": "{total} 個 LoRA 中缺少 {missing} 個，{unavailable} 個不可用（已從來源刪除或雜湊無法解析）",
+            "partial": "{total} 個 LoRA 中 {unavailable} 個不可用（已從來源刪除或雜湊無法解析）- 使用配方時將被略過",
+            "noneUsable": "沒有可用的 LoRA - {total} 個中 {unavailable} 個已從來源刪除或雜湊無法解析"
+        },
+        "resources": {
+            "inLibrary": "已在庫存",
+            "notInLibrary": "不在庫存",
+            "deleted": "已刪除",
+            "hashInvalid": "無法解析的雜湊",
+            "inLibraryTooltip": "此模型已存在於本地庫",
+            "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}",
+            "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": {
                 "action": "匯入",
                 "title": "從圖片或網址匯入配方",
-                "urlLocalPath": "網址 / 本機路徑",
-                "uploadImage": "上傳圖片",
-                "urlSectionDescription": "輸入 Civitai 圖片網址或本機檔案路徑以匯入配方。",
+                "dropZoneLabel": "上傳圖片",
+                "dropZoneHint": "將圖片拖曳至此處、從剪貼簿貼上，或點擊瀏覽",
+                "orDivider": "或拖曳 / 貼上圖片",
                 "imageUrlOrPath": "圖片網址或檔案路徑：",
-                "urlPlaceholder": "https://civitai.com/images/... 或 C:/path/to/image.png",
+                "urlPlaceholder": "https://civitai.com/images/... 或 https://civitai.red/images/... 或 C:/path/to/image.png",
                 "fetchImage": "取得圖片",
-                "uploadSectionDescription": "上傳含 LoRA metadata 的圖片以匯入配方。",
-                "selectImage": "選擇圖片",
                 "recipeName": "配方名稱",
                 "recipeNamePlaceholder": "輸入配方名稱",
                 "tagsOptional": "標籤（選填）",
@@ -895,7 +1035,7 @@
                 "downloadingLoras": "下載 LoRA 中...",
                 "savingRecipe": "儲存配方中...",
                 "startingDownload": "開始下載 LoRA {current}/{total}",
-                "deletedFromCivitai": "已從 Civitai 刪除",
+                "deletedFromCivitai": "已從 CivitAI 刪除",
                 "inLibrary": "已在庫存",
                 "notInLibrary": "不在庫存",
                 "earlyAccessRequired": "此 LoRA 需購買早期存取才能下載。",
@@ -912,6 +1052,8 @@
                 "errors": {
                     "selectImageFile": "請選擇圖片檔案",
                     "enterUrlOrPath": "請輸入網址或檔案路徑",
+                    "invalidUrl": "請輸入有效的 URL",
+                    "invalidInputFormat": "請輸入圖片 URL 或本機圖片檔案路徑",
                     "selectLoraRoot": "請選擇 LoRA 根目錄"
                 }
             },
@@ -946,6 +1088,7 @@
             }
         },
         "duplicates": {
+            "finding": "正在掃描重複配方...",
             "found": "發現 {count} 組重複項",
             "noGroups": "按目前判重依據未找到重複組",
             "keepLatest": "保留最新版本",
@@ -978,13 +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",
@@ -1015,6 +1151,8 @@
             "start": "開始匯入",
             "startImport": "開始匯入",
             "importing": "匯入中...",
+            "rateLimitedSlowdown": "觸發速率限制 — 正在減速...",
+            "rateLimitedHint": "部分項目因元數據提供方的速率限制而被略過。稍後重新執行匯入即可重試這些項目。",
             "progress": "進度",
             "total": "總計",
             "success": "成功",
@@ -1056,7 +1194,7 @@
         "title": "Checkpoint 模型",
         "modelTypes": {
             "checkpoint": "Checkpoint",
-            "diffusion_model": "Diffusion Model"
+            "diffusion_model": "擴散模型"
         },
         "contextMenu": {
             "moveToOtherTypeFolder": "移動到 {otherType} 資料夾",
@@ -1080,7 +1218,7 @@
         "collapseAllDisabled": "列表檢視下不可用",
         "dragDrop": {
             "unableToResolveRoot": "無法確定移動的目標路徑。",
-            "moveUnsupported": "Move is not supported for this item.",
+            "moveUnsupported": "此項目不支援移動。",
             "createFolderHint": "放開以建立新資料夾",
             "newFolderName": "新資料夾名稱",
             "folderNameHint": "按 Enter 確認，Escape 取消",
@@ -1143,36 +1281,36 @@
             "unusedLoras": {
                 "high": {
                     "title": "大量未使用的 LoRA",
-                    "description": "你的 LoRA 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "description": "您的 LoRA 中有 {percent}%（{count}/{total}）從未被使用過。",
                     "suggestion": "考慮整理或封存未使用的模型以釋放儲存空間。"
                 }
             },
             "unusedCheckpoints": {
                 "detected": {
                     "title": "檢測到未使用的 Checkpoint",
-                    "description": "你的 Checkpoint 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "description": "您的 Checkpoint 中有 {percent}%（{count}/{total}）從未被使用過。",
                     "suggestion": "審查並考慮刪除不再需要的 Checkpoint。"
                 }
             },
             "unusedEmbeddings": {
                 "high": {
                     "title": "大量未使用的 Embedding",
-                    "description": "你的 Embedding 中有 {percent}%（{count}/{total}）從未被使用過。",
-                    "suggestion": "考慮整理或封存未使用的 Embedding 以優化你的收藏。"
+                    "description": "您的 Embedding 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "考慮整理或封存未使用的 Embedding 以優化您的收藏。"
                 }
             },
             "collection": {
                 "large": {
                     "title": "檢測到大型收藏",
-                    "description": "你的模型收藏正在使用 {size} 的儲存空間。",
+                    "description": "您的模型收藏正在使用 {size} 的儲存空間。",
                     "suggestion": "考慮使用外部儲存或雲端解決方案以獲得更好的組織。"
                 }
             },
             "activity": {
                 "active": {
                     "title": "活躍用戶",
-                    "description": "你已經完成了 {count} 次生成！",
-                    "suggestion": "繼續探索並用你的模型創作精彩內容。"
+                    "description": "您已經完成了 {count} 次生成！",
+                    "suggestion": "繼續探索並用您的模型創作精彩內容。"
                 }
             }
         },
@@ -1218,7 +1356,7 @@
         "download": {
             "title": "從網址下載模型",
             "titleWithType": "從網址下載 {type}",
-            "civitaiUrl": "Civitai 網址：",
+            "civitaiUrl": "CivitAI 網址：",
             "placeholder": "https://civitai.com/models/...",
             "urlHint": "每行輸入一個 CivitAI、CivArchive 或 Hugging Face URL。支援批量下載多個 URL。",
             "selectHfFiles": "選擇從此倉庫下載的檔案：",
@@ -1242,7 +1380,7 @@
             "earlyAccessTooltip": "需要早期存取",
             "inLibrary": "已在庫存",
             "downloaded": "已下載",
-            "downloadedTooltip": "先前已下載，但目前不在你的庫中。",
+            "downloadedTooltip": "先前已下載，但目前不在您的庫中。",
             "alreadyInLibrary": "已在庫存",
             "partiallyDownloaded": "部分已下載",
             "autoOrganizedPath": "[依路徑範本自動整理]",
@@ -1253,7 +1391,7 @@
                 "inLibrary": "已在庫中"
             },
             "errors": {
-                "invalidUrl": "Civitai 網址格式無效",
+                "invalidUrl": "CivitAI 網址格式無效",
                 "noVersions": "此模型無可用版本",
                 "mixedSources": "無法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
                 "noModelFiles": "在此倉庫中未找到模型檔案。"
@@ -1332,8 +1470,8 @@
             "action": "全部刪除"
         },
         "checkUpdates": {
-            "title": "要檢查所有 {type} 的更新嗎？",
-            "message": "這會為資料庫中的每個 {type} 檢查更新，大型收藏可能會花上一些時間。",
+            "title": "要檢查所有 {typePlural} 的更新嗎？",
+            "message": "這會檢查資料庫中的每個 {typePlural} 的更新，大型收藏可能會花上一些時間。",
             "tip": "想分批處理？切換到批次模式，選擇需要的模型，然後使用「檢查所選更新」。",
             "action": "全部檢查"
         },
@@ -1357,7 +1495,7 @@
         },
         "bulkDownloadMissingLoras": {
             "title": "下載缺失的 LoRAs",
-            "message": "發現 {uniqueCount} 個獨特的缺失 LoRAs（從選取食譜中的 {totalCount} 個總數）。",
+            "message": "發現 {uniqueCount} 個獨特的缺失 LoRAs（從選取配方中的 {totalCount} 個總數）。",
             "previewTitle": "要下載的 LoRAs：",
             "moreItems": "...還有 {count} 個",
             "note": "檔案將使用預設路徑模板下載。根據 LoRAs 的數量，這可能需要一些時間。",
@@ -1367,7 +1505,7 @@
             "title": "本機範例圖片",
             "message": "此模型未找到本機範例圖片。可選擇：",
             "downloadOption": {
-                "title": "從 Civitai 下載",
+                "title": "從 CivitAI 下載",
                 "description": "將遠端範例儲存到本機以便離線使用及加快載入"
             },
             "importOption": {
@@ -1394,7 +1532,7 @@
             "confirmAction": "儲存並連結"
         },
         "relinkCivitai": {
-            "title": "重新連結至 Civitai",
+            "title": "重新連結至 CivitAI",
             "warning": "警告：",
             "warningText": "這是可能造成破壞性的操作。重新連結將會：",
             "warningList": {
@@ -1403,14 +1541,15 @@
                 "unintendedConsequences": "可能產生其他非預期後果"
             },
             "proceedText": "僅在確定需要執行時才繼續。",
-            "urlLabel": "Civitai 模型網址：",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
+            "urlLabel": "CivitAI 模型網址：",
+            "urlPlaceholder": "https://civitai.com/models/12345/model-name?modelVersionId=67890 或 https://civitai.red/models/12345/model-name?modelVersionId=67890",
             "helpText": {
-                "title": "貼上任意 Civitai 模型網址。支援格式：",
-                "format1": "https://civitai.com/models/649516",
-                "format2": "https://civitai.com/models/649516?modelVersionId=726676",
-                "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
-                "note": "注意：若未提供 modelVersionId，將使用最新版本。"
+                "title": "貼上任意 CivitAI 或 CivitArchive 模型網址。支援格式：",
+                "format1": "https://civitai.com/models/12345",
+                "format2": "https://civitai.com/models/12345?modelVersionId=67890",
+                "format3": "https://civitai.com/models/12345/model-name?modelVersionId=67890",
+                "note": "注意：若未提供 modelVersionId，將使用最新版本。",
+                "format4": "https://civarchive.com/models/12345 (CivArchive)"
             },
             "confirmAction": "確認重新連結"
         },
@@ -1420,14 +1559,16 @@
                 "editFileName": "編輯檔案名稱",
                 "editBaseModel": "編輯基礎模型",
                 "editVersionName": "編輯版本名稱",
-                "viewOnCivitai": "在 Civitai 查看",
-                "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnCivitai": "在 CivitAI 查看",
+                "viewOnCivitaiText": "在 CivitAI 查看",
                 "viewOnHuggingFace": "在 Hugging Face 查看",
                 "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
                 "openFileLocation": "開啟檔案位置",
                 "sendToWorkflow": "傳送到 ComfyUI",
-                "sendToWorkflowText": "傳送到 ComfyUI"
+                "sendToWorkflowText": "傳送到 ComfyUI",
+                "copyHash": "複製雜湊值",
+                "deleteModelWithShortcut": "刪除模型（Del）"
             },
             "openFileLocation": {
                 "success": "檔案位置已成功開啟",
@@ -1444,13 +1585,14 @@
                 "location": "位置",
                 "baseModel": "基礎模型",
                 "size": "大小",
+                "hashes": "雜湊值",
                 "unknown": "未知",
                 "usageTips": "使用提示",
                 "additionalNotes": "附加備註",
                 "notesHint": "按 Enter 儲存，Shift+Enter 換行",
                 "addNotesPlaceholder": "在此新增備註...",
                 "aboutThisVersion": "關於此版本",
-                "baseModelSearchPlaceholder": "搜尋基礎模型…",
+                "baseModelSearchPlaceholder": "搜尋基礎模型...",
                 "baseModelSuggested": "推薦",
                 "baseModelNoMatch": "沒有符合的基礎模型"
             },
@@ -1470,7 +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",
@@ -1481,7 +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",
@@ -1519,10 +1665,10 @@
                 "noNext": "沒有下一個模型"
             },
             "license": {
-                "noImageSell": "No selling generated content",
-                "noRentCivit": "No Civitai generation",
-                "noRent": "No generation services",
-                "noSell": "No selling models",
+                "noImageSell": "禁止出售生成的圖片",
+                "noRentCivit": "禁止在 CivitAI 上生成",
+                "noRent": "禁止生成服務",
+                "noSell": "禁止出售模型",
                 "creditRequired": "需要創作者標示",
                 "noDerivatives": "禁止分享合併作品",
                 "noReLicense": "需要相同授權",
@@ -1541,8 +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",
@@ -1552,8 +1698,8 @@
                 "importing": "正在匯入檔案...",
                 "noSupportedFiles": "未選擇支援的檔案。請選擇圖片或影片檔案。",
                 "allFiltered": "所有範例圖片都因 NSFW 內容設定而被過濾",
-                "sfwOnlyEnabled": "你目前的設定為僅顯示安全（SFW）內容",
-                "changeInSettings": "你可以在設定中變更此選項",
+                "sfwOnlyEnabled": "您目前的設定為僅顯示安全（SFW）內容",
+                "changeInSettings": "您可以在設定中變更此選項",
                 "nsfwMature": "成熟內容",
                 "nsfwR": "R 級內容",
                 "nsfwX": "X 級內容",
@@ -1577,41 +1723,41 @@
                 },
                 "badges": {
                     "current": "已開啟版本",
-                    "currentTooltip": "這是你用來開啟此彈窗的版本",
+                    "currentTooltip": "這是您用來開啟此彈窗的版本",
                     "inLibrary": "已在庫中",
-                    "inLibraryTooltip": "此版本已存在於你的本地庫中",
+                    "inLibraryTooltip": "此版本已存在於您的本地庫中",
                     "downloaded": "已下載",
-                    "downloadedTooltip": "此版本之前下載過，但目前不在你的本地庫中",
+                    "downloadedTooltip": "此版本之前下載過，但目前不在您的本地庫中",
                     "newer": "較新版本",
-                    "newerTooltip": "此版本比你本地的最新版本更新",
+                    "newerTooltip": "此版本比您本地的最新版本更新",
                     "earlyAccess": "搶先體驗",
-                    "earlyAccessTooltip": "此版本目前需要 Civitai 搶先體驗權限",
+                    "earlyAccessTooltip": "此版本目前需要 CivitAI 搶先體驗權限",
                     "paid": "付費",
                     "paidTooltip": "此版本需要付費才能下載",
                     "ignored": "已忽略",
                     "ignoredTooltip": "此版本已關閉更新通知",
                     "onSiteOnly": "僅站內生成",
-                    "onSiteOnlyTooltip": "此版本僅在 Civitai 站內可用，無法下載"
+                    "onSiteOnlyTooltip": "此版本僅在 CivitAI 站內可用，無法下載"
                 },
                 "actions": {
                     "download": "下載",
                     "downloadTooltip": "下載此版本",
-                    "downloadRemainingTooltip": "下載此版本的剩餘檔案",
-                    "downloadEarlyAccessTooltip": "從 Civitai 下載此搶先體驗版本",
-                    "downloadPaidTooltip": "從 Civitai 下載此付費版本",
-                    "downloadNotAllowedTooltip": "此版本僅在 Civitai 站內可用，無法下載",
+                    "downloadChooseFilesTooltip": "選擇要下載的檔案",
+                    "downloadEarlyAccessTooltip": "從 CivitAI 下載此搶先體驗版本",
+                    "downloadPaidTooltip": "從 CivitAI 下載此付費版本",
+                    "downloadNotAllowedTooltip": "此版本僅在 CivitAI 站內可用，無法下載",
                     "delete": "刪除",
                     "deleteTooltip": "刪除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
                     "ignoreTooltip": "忽略此版本的更新通知",
                     "unignoreTooltip": "恢復此版本的更新通知",
-                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
+                    "viewVersionOnCivitai": "在 CivitAI 上查看版本",
                     "earlyAccessTooltip": "需要購買搶先體驗",
                     "resumeModelUpdates": "恢復追蹤此模型的更新",
                     "ignoreModelUpdates": "忽略此模型的更新",
                     "viewLocalVersions": "檢視所有本地版本",
-                    "viewLocalTooltip": "敬請期待"
+                    "viewLocalTooltip": "在主頁面上顯示此模型的所有本機版本"
                 },
                 "filters": {
                     "label": "基礎篩選",
@@ -1627,7 +1773,7 @@
                 },
                 "empty": "此模型尚無版本歷史。",
                 "error": "載入版本失敗。",
-                "missingModelId": "此模型缺少 Civitai 模型 ID。",
+                "missingModelId": "此模型缺少 CivitAI 模型 ID。",
                 "hfGroupInfo": "這是一個 HuggingFace 模型組。打開庫頁面即可在網格中查看所有版本。",
                 "confirm": {
                     "delete": "要從庫中刪除此版本嗎？"
@@ -1711,14 +1857,14 @@
         "tips": {
             "title": "小技巧",
             "civitai": {
-                "title": "Civitai 整合",
-                "description": "連結您的 Civitai 帳號：前往個人頭像 → 設定 → API 金鑰 → 新增 API 金鑰，然後貼到 LoRA 管理器設定中。",
-                "alt": "Civitai API 設定"
+                "title": "CivitAI 整合",
+                "description": "連結您的 CivitAI 帳號：前往個人頭像 → 設定 → API 金鑰 → 新增 API 金鑰，然後貼到 LoRA 管理器設定中。",
+                "alt": "CivitAI API 設定"
             },
             "download": {
                 "title": "快速下載",
-                "description": "使用 Civitai 網址即可快速下載並安裝新模型。",
-                "alt": "Civitai 下載"
+                "description": "使用 CivitAI 網址即可快速下載並安裝新模型。",
+                "alt": "CivitAI 下載"
             },
             "recipes": {
                 "title": "儲存配方",
@@ -1806,10 +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",
@@ -1826,7 +2014,8 @@
             "settings": "設定與配置",
             "extensions": "擴充功能",
             "newBadge": "新"
-        }
+        },
+        "newContentBadge": "新"
     },
     "update": {
         "title": "檢查更新",
@@ -1902,7 +2091,7 @@
             "submitGithubIssue": "提交 GitHub 問題",
             "joinDiscord": "加入 Discord",
             "youtubeChannel": "YouTube 頻道",
-            "civitaiProfile": "Civitai 個人檔案",
+            "civitaiProfile": "CivitAI 個人檔案",
             "supportKofi": "在 Ko-fi 支持",
             "supportPatreon": "在 Patreon 支持"
         },
@@ -1979,18 +2168,33 @@
             "createMissingData": "缺少建立配方所需的資料",
             "created": "配方建立成功",
             "noMissingLoras": "無缺少的 LoRA 可下載",
+            "noPreviousRecipe": "沒有上一個配方",
+            "noNextRecipe": "沒有下一個配方",
             "missingLorasInfoFailed": "取得缺少 LoRA 資訊失敗",
             "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",
             "sendError": "傳送配方到工作流錯誤",
-            "missingCheckpointPath": "缺少檢查點路徑",
-            "missingCheckpointInfo": "缺少檢查點資訊",
-            "downloadCheckpointFailed": "下載檢查點失敗：{message}",
+            "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}",
             "cannotDelete": "無法刪除配方：缺少配方 ID",
             "deleteConfirmationError": "顯示刪除確認時發生錯誤",
             "deletedSuccessfully": "配方已成功刪除",
@@ -2014,24 +2218,26 @@
             "batchImportCancelFailed": "取消批量匯入失敗：{message}",
             "batchImportNoUrls": "請輸入至少一個 URL 或檔案路徑",
             "batchImportNoDirectory": "請輸入目錄路徑",
+            "batchImportRateLimited": "已達到元數據提供方的速率限制 — 請求正在放緩，部分項目可能被略過。您可以稍後重新執行匯入。",
             "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
             "batchImportDirectorySelected": "已選擇目錄：{path}",
-            "noRecipesSelected": "未選取任何食譜",
-            "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} 個食譜",
-            "rematchCompleteErrors": "已匹配 {entries} 個條目，涉及 {recipes} 個食譜，{failures} 個失敗",
-            "rematchAllFailed": "{failures}/{total} 個所選食譜重新匹配失敗",
-            "rematchUnmatched": "在 {recipes} 個食譜中找不到 {entries} 個條目的本地匹配",
-            "rematchSkipped": "{total} 個所選食譜均無需重新匹配",
-            "rematchFailed": "重新匹配所選食譜失敗：{message}",
+            "noRecipesSelected": "未選取任何配方",
+            "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",
+            "rematchUnmatched": "在 {recipes} 個配方中找不到 {entries} 個條目的本地匹配",
+            "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",
-            "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
-            "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
+            "noMissingLorasInSelection": "在選取的配方中未找到缺失的 LoRAs",
+            "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。",
+            "workflowSent": "工作流已傳送到 ComfyUI",
+            "workflowSendFailed": "傳送工作流到 ComfyUI 失敗: {error}",
+            "workflowNoWorkflow": "此配方中未找到內嵌工作流"
         },
         "models": {
             "noModelsSelected": "未選擇模型",
@@ -2068,8 +2274,8 @@
             "bulkUpdatesChecking": "正在檢查所選 {type} 的更新...",
             "bulkUpdatesSuccess": "{count} 個所選 {type} 有可用更新",
             "bulkUpdatesNone": "所選 {type} 未找到更新",
-            "bulkUpdatesMissing": "所選 {type} 未連結 Civitai 更新",
-            "bulkUpdatesPartialMissing": "已略過 {missing} 個未連結 Civitai 的所選 {type}",
+            "bulkUpdatesMissing": "所選 {type} 未連結 CivitAI 更新",
+            "bulkUpdatesPartialMissing": "已略過 {missing} 個未連結 CivitAI 的所選 {type}",
             "bulkUpdatesFailed": "檢查所選 {type} 更新失敗：{message}",
             "invalidCharactersRemoved": "已移除檔名中的無效字元",
             "filenameCannotBeEmpty": "檔案名稱不可為空",
@@ -2109,8 +2315,8 @@
             "compactModeToggled": "緊湊模式已{state}",
             "settingSaveFailed": "儲存設定失敗：{message}",
             "displayDensitySet": "顯示密度已設為 {density}",
-            "libraryLoadFailed": "Failed to load libraries: {message}",
-            "libraryActivateFailed": "Failed to activate library: {message}",
+            "libraryLoadFailed": "載入模型庫失敗：{message}",
+            "libraryActivateFailed": "啟動模型庫失敗：{message}",
             "languageChangeFailed": "切換語言失敗：{message}",
             "cacheCleared": "快取檔案已成功清除。快取將於下次操作時重建。",
             "cacheClearFailed": "清除快取失敗：{error}",
@@ -2188,17 +2394,18 @@
         },
         "controls": {
             "reloadFailed": "重新載入 {pageType} 失敗：{message}",
-            "refreshFailed": "刷新 {pageType} 失敗：{message}",
+            "refreshFailed": "{action} {pageType} 失敗：{message}",
             "fetchMetadataFailed": "取得 metadata 失敗：{message}",
             "clearFilterFailed": "清除自訂篩選失敗：{message}"
         },
         "contextMenu": {
             "contentRatingSet": "內容分級已設為 {level}",
             "contentRatingFailed": "設定內容分級失敗：{message}",
-            "relinkSuccess": "模型已成功重新連結至 Civitai",
+            "relinkSuccess": "模型已成功重新連結至 CivitAI",
             "relinkFailed": "錯誤：{message}",
             "linkHfSuccess": "模型已成功連結到 HuggingFace",
             "linkHfFailed": "錯誤：{message}",
+            "linkCivArchSuccess": "模型已成功透過 CivitArchive 重新連結",
             "fetchMetadataFirst": "請先從 CivitAI 取得 metadata",
             "noCivitaiInfo": "無 CivitAI 資訊",
             "missingHash": "模型雜湊不可用"
@@ -2258,7 +2465,7 @@
             "bulkMoveSuccess": "已成功移動 {successCount} 個 {type}",
             "exampleImagesDownloadSuccess": "範例圖片下載成功！",
             "exampleImagesDownloadFailed": "下載範例圖片失敗：{message}",
-            "moveFailed": "Failed to move item: {message}",
+            "moveFailed": "移動項目失敗：{message}",
             "copiedToClipboard": "已複製到剪貼簿",
             "downloadStarted": "下載已開始"
         },
@@ -2288,7 +2495,7 @@
         },
         "issues": {
             "civitai_api_key": {
-                "title": "Civitai API 金鑰"
+                "title": "CivitAI API 金鑰"
             },
             "cache_health": {
                 "title": "模型快取健康狀態"
@@ -2341,10 +2548,10 @@
             "seconds": "秒後重新整理"
         },
         "communitySupport": {
-            "title": "Keep LoRA Manager Thriving with Your Support ❤️",
-            "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
-            "supportCta": "Support on Ko-fi",
-            "learnMore": "LM Civitai Extension Tutorial"
+            "title": "用您的支持讓 LoRA Manager 持續茁壯 ❤️",
+            "content": "LoRA Manager 是由一位獨立開發者全職維護的熱情項目。您在 Ko-fi 上的支持有助於支付開發成本、持續推出新更新，並將贈送 LM CivitAI 擴充功能的授權金鑰作為感謝之禮。每一份貢獻都意義重大。",
+            "supportCta": "在 Ko-fi 上支持",
+            "learnMore": "LM CivitAI 擴充功能教學"
         },
         "cacheHealth": {
             "corrupted": {

py/middleware/error_middleware.py

@@ -46,6 +46,16 @@ async def api_json_error(
         if request.path.startswith("/api/lm/previews") and exc.status == 404:
             logger_method = logger.debug
 
+        # Download-progress 404 is routine too: in-memory tracking is removed
+        # once a download finishes/fails, so the extension's final polls 404.
+        # The extension relies on the 404 status itself (failure detection),
+        # so only the log level is lowered.
+        if (
+            request.path.startswith("/api/lm/download-progress/")
+            and exc.status == 404
+        ):
+            logger_method = logger.debug
+
         logger_method(
             "API %s %s returned HTTP %d: %s",
             request.method,

py/nodes/create_hook_lora.py

@@ -39,6 +39,7 @@ class CreateHookLoraLM:
                         ),
                     },
                 ),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -52,7 +53,7 @@ class CreateHookLoraLM:
     RETURN_NAMES = ("HOOKS", "trigger_words", "active_loras")
     FUNCTION = "create_hook"
 
-    def create_hook(self, text: str, **kwargs):
+    def create_hook(self, text: str, loras, **kwargs):
         """Create a HookGroup from the selected LoRAs, chained with prev_hooks.
 
         Each active LoRA from the widget is loaded and wrapped in a WeightHook
@@ -73,7 +74,7 @@ class CreateHookLoraLM:
         all_trigger_words: list[str] = []
         active_loras: list[tuple[str, float, float]] = []
 
-        for lora in get_loras_list(kwargs):
+        for lora in get_loras_list({"loras": loras}):
             if not lora.get("active", False):
                 continue
 

py/nodes/lora_loader.py

@@ -49,9 +49,9 @@ def _collect_stack_entries(lora_stack):
     return entries
 
 
-def _collect_widget_entries(kwargs):
+def _collect_widget_entries(loras):
     entries = []
-    for lora in get_loras_list(kwargs):
+    for lora in get_loras_list({"loras": loras}):
         if not lora.get("active", False):
             continue
         lora_name = apply_lora_syntax_format(lora["name"])
@@ -139,6 +139,7 @@ class LoraLoaderLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -152,12 +153,12 @@ class LoraLoaderLM:
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
 
-    def load_loras(self, model, text, **kwargs):
-        """Loads multiple LoRAs based on the kwargs input and lora_stack."""
+    def load_loras(self, model, text, loras, **kwargs):
+        """Loads multiple LoRAs based on the widget input and lora_stack."""
         del text
         clip = kwargs.get("clip", None)
         lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
-        lora_entries.extend(_collect_widget_entries(kwargs))
+        lora_entries.extend(_collect_widget_entries(loras))
 
         nunchaku_model_kind = detect_nunchaku_model_kind(model)
         if nunchaku_model_kind == "flux":

py/nodes/lora_stacker.py

@@ -18,6 +18,7 @@ class LoraStackerLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -31,8 +32,8 @@ class LoraStackerLM:
     RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
     FUNCTION = "stack_loras"
     
-    def stack_loras(self, text, **kwargs):
-        """Stacks multiple LoRAs based on the kwargs input without loading them."""
+    def stack_loras(self, text, loras, **kwargs):
+        """Stacks multiple LoRAs based on the widget input without loading them."""
         stack = []
         active_loras = []
         all_trigger_words = []
@@ -47,8 +48,8 @@ class LoraStackerLM:
                 _, trigger_words = get_lora_info(lora_name)
                 all_trigger_words.extend(trigger_words)
         
-        # Process loras from kwargs with support for both old and new formats
-        loras_list = get_loras_list(kwargs)
+        # Process loras from the widget with support for both old and new formats
+        loras_list = get_loras_list({"loras": loras})
         for lora in loras_list:
             if not lora.get('active', False):
                 continue

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/nodes/save_image.py

@@ -778,6 +778,14 @@ class SaveImageLM:
         if checkpoint_entry:
             recipe_data["checkpoint"] = checkpoint_entry
 
+        # The recipe image is the WebP produced above from the output file;
+        # reuse the same metadata extraction to record workflow presence.
+        try:
+            metadata = ExifUtils._load_structured_metadata(image_path)
+            recipe_data["has_workflow"] = bool(metadata.get("workflow"))
+        except Exception:
+            recipe_data["has_workflow"] = False
+
         json_path = os.path.normpath(
             os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
         )

py/nodes/wanvideo_lora_select.py

@@ -31,6 +31,7 @@ class WanVideoLoraSelectLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -44,7 +45,7 @@ class WanVideoLoraSelectLM:
     RETURN_NAMES = ("lora", "trigger_words", "active_loras")
     FUNCTION = "process_loras"
     
-    def process_loras(self, text, low_mem_load=False, merge_loras=True, **kwargs):
+    def process_loras(self, text, loras, low_mem_load=False, merge_loras=True, **kwargs):
         loras_list = []
         all_trigger_words = []
         active_loras = []
@@ -62,8 +63,8 @@ class WanVideoLoraSelectLM:
         selected_blocks = blocks.get("selected_blocks", {})
         layer_filter = blocks.get("layer_filter", "")
         
-        # Process loras from kwargs with support for both old and new formats
-        loras_from_widget = get_loras_list(kwargs)
+        # Process loras from the widget with support for both old and new formats
+        loras_from_widget = get_loras_list({"loras": loras})
         for lora in loras_from_widget:
             if not lora.get('active', False):
                 continue

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__)
 
@@ -146,15 +147,13 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                                     # Initialize hashes dict if it doesn't exist
                                     if "hashes" not in metadata:
                                         metadata["hashes"] = {}
-                                    # Add as lora type in the same format as
-                                    # regular hashes.  Only override an
-                                    # existing entry if its value is empty
-                                    # (Lora hashes is the more reliable
-                                    # source when Hashes JSON has blanks).
+                                    # Lora hashes carries the 12-char AutoV3
+                                    # hash (resolvable on CivitAI and the local
+                                    # autov3 index); the Hashes JSON value is
+                                    # only the 10-char AutoV2 prefix, so on
+                                    # conflict the Lora hashes value wins.
                                     key = f"lora:{lora_name}"
-                                    existing = metadata["hashes"].get(key, "")
-                                    if not existing:
-                                        metadata["hashes"][key] = lora_hash
+                                    metadata["hashes"][key] = lora_hash
 
                             # Remove lora hashes from params section
                             params_section = params_section.replace(lora_hashes_match.group(0), '')
@@ -526,6 +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/civitai_image.py

@@ -115,6 +115,27 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 ):
                     metadata = inner_meta
 
+            # Civitai's image API meta parser mangles the A1111 "Lora hashes"
+            # text field into a quote-wrapped dict entry:
+            #   '"Daphne Blake Cosplay_v1": "e67ebd5e315f"'
+            # The 12-char AutoV3 it carries is more reliable than the stale
+            # 10-char AutoV2 value in the "hashes" dict, so recover it and
+            # let it override the conflicting entry.
+            if isinstance(metadata, dict):
+                for key, hash_value in list(metadata.items()):
+                    if (
+                        isinstance(key, str)
+                        and key.startswith('"')
+                        and isinstance(hash_value, str)
+                        and hash_value.endswith('"')
+                    ):
+                        clean_name = key.strip('"').strip()
+                        clean_hash = hash_value.strip('"').strip()
+                        if clean_name and clean_hash:
+                            hashes_dict = metadata.get("hashes")
+                            if isinstance(hashes_dict, dict):
+                                hashes_dict[f"lora:{clean_name}"] = clean_hash
+
             # Initialize result structure
             result: Dict[str, Any] = {
                 "base_model": None,

py/recipes/parsers/comfy.py

@@ -40,24 +40,34 @@ class ComfyMetadataParser(RecipeMetadataParser):
                 checkpoint_node = next(iter(checkpoint_nodes.values()))
                 if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
                     checkpoint_name = checkpoint_node['inputs']['ckpt_name']
-                    checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
-                    if checkpoint_match:
-                        checkpoint_id = checkpoint_match.group(1)
-                        checkpoint_version_id = checkpoint_match.group(2)
-                        checkpoint = {
-                            'id': checkpoint_version_id,
-                            'modelId': checkpoint_id,
-                            'name': f"Checkpoint {checkpoint_id}",
-                            'version': '',
-                            'type': 'checkpoint'
-                        }
-                        if metadata_provider:
-                            try:
-                                civitai_info_tuple = await metadata_provider.get_model_version_info(checkpoint_version_id)
-                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
-                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
-                            except Exception as e:
-                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
+                    # Some ComfyUI workflows serialize ckpt_name as a
+                    # single-element list (e.g. ["model.safetensors"]) or leave
+                    # the value unset (None). Neither is a string, so skip the
+                    # CivitAI-URN lookup instead of crashing re.search with a
+                    # TypeError that fails the whole image import.
+                    if isinstance(checkpoint_name, list):
+                        checkpoint_name = (
+                            checkpoint_name[0] if checkpoint_name else None
+                        )
+                    if isinstance(checkpoint_name, str):
+                        checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
+                        if checkpoint_match:
+                            checkpoint_id = checkpoint_match.group(1)
+                            checkpoint_version_id = checkpoint_match.group(2)
+                            checkpoint = {
+                                'id': checkpoint_version_id,
+                                'modelId': checkpoint_id,
+                                'name': f"Checkpoint {checkpoint_id}",
+                                'version': '',
+                                'type': 'checkpoint'
+                            }
+                            if metadata_provider:
+                                try:
+                                    civitai_info_tuple = await metadata_provider.get_model_version_info(checkpoint_version_id)
+                                    civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+                                    checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                                except Exception as e:
+                                    logger.error(f"Error fetching Civitai info for checkpoint: {e}")
 
             recipe_base_model = checkpoint.get('baseModel') if checkpoint else None
             loras = []

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/base_recipe_routes.py

@@ -32,6 +32,7 @@ from .handlers.recipe_handlers import (
     RecipePageView,
     RecipeQueryHandler,
     RecipeSharingHandler,
+    RecipeWorkflowHandler,
 )
 from .recipe_route_registrar import ROUTE_DEFINITIONS
 
@@ -200,6 +201,18 @@ class BaseRecipeRoutes:
             sharing_service=sharing_service,
         )
 
+        # Lazy import: standalone mode replaces the ``server`` module with a
+        # mock, so resolve PromptServer at handler-set build time instead of
+        # module import time. The handler's standalone check guards UX.
+        from server import PromptServer  # pyright: ignore[reportMissingImports]
+
+        workflow = RecipeWorkflowHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            prompt_server=PromptServer,
+            logger=logger,
+        )
+
         from ..services.websocket_manager import ws_manager
 
         batch_import_service = BatchImportService(
@@ -224,4 +237,5 @@ class BaseRecipeRoutes:
             analysis=analysis,
             sharing=sharing,
             batch_import=batch_import,
+            workflow=workflow,
         )

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/misc_handlers.py

@@ -56,6 +56,7 @@ from ...utils.constants import (
 )
 from .hf_handlers import HfHandler
 from .agent_handlers import AgentHandler
+from .model_handlers import ModelCivitaiHandler
 from ...utils.civitai_utils import rewrite_preview_url
 from ...utils.example_images_paths import (
     find_non_compliant_items_in_example_images_root,
@@ -648,9 +649,60 @@ class NodeRegistry:
 
 
 class HealthCheckHandler:
+    def __init__(
+        self,
+        scanner_getters: Mapping[str, Callable[[], Awaitable[Any]]] | None = None,
+    ) -> None:
+        self._scanner_getters = scanner_getters or {
+            "lora": ServiceRegistry.get_lora_scanner,
+            "checkpoint": ServiceRegistry.get_checkpoint_scanner,
+            "embedding": ServiceRegistry.get_embedding_scanner,
+            "recipe": ServiceRegistry.get_recipe_scanner,
+        }
+
     async def health_check(self, request: web.Request) -> web.Response:
         return web.json_response({"status": "ok"})
 
+    async def get_init_status(self, request: web.Request) -> web.Response:
+        """Report aggregate scanner initialization status.
+
+        Used by the initialization page's polling fallback when the
+        /ws/init-progress WebSocket is unavailable. Omits pageType so every
+        page accepts the update and only reloads once all scanners are done.
+        """
+        pending: list[str] = []
+        for name, getter in self._scanner_getters.items():
+            try:
+                scanner = await getter()
+            except Exception:
+                pending.append(name)
+                continue
+            cache_ready = getattr(scanner, "_cache", None) is not None
+            is_initializing = getattr(scanner, "is_initializing", None)
+            busy = (
+                is_initializing()
+                if callable(is_initializing)
+                else bool(getattr(scanner, "_is_initializing", False))
+            )
+            if busy or not cache_ready:
+                pending.append(name)
+
+        if pending:
+            return web.json_response(
+                {
+                    "status": "initializing",
+                    "stage": "processing",
+                    "details": "Initializing: " + ", ".join(pending),
+                }
+            )
+        return web.json_response(
+            {
+                "status": "complete",
+                "progress": 100,
+                "details": "Initialization complete",
+            }
+        )
+
 
 class SupportersHandler:
     """Handler for supporters data."""
@@ -2061,6 +2113,63 @@ class ModelLibraryHandler:
             enriched.append(entry)
         return enriched
 
+    @staticmethod
+    async def _get_downloaded_files(
+        scanner: Any, model_version_id: int
+    ) -> list[dict[str, Any]]:
+        """Return per-file downloaded state for a version in the library.
+
+        This handler has no CivitAI version payload, so the remote file list
+        is taken from the local entries' cached ``civitai`` metadata (the
+        full version payload persisted at download time, see
+        ``BaseModelMetadata.from_civitai_info``) and matched with the same
+        D2 rule used by ``get_civitai_versions`` (#1058). Local entries that
+        cannot be matched to a known remote file (e.g. missing metadata or
+        renamed files) are still reported with ``fileId`` set to None.
+        Returns ``[{fileId, fileName, filePath}]``.
+        """
+        try:
+            cache = await scanner.get_cached_data()
+        except Exception:  # pragma: no cover - defensive fallback
+            logger.debug(
+                "Failed to read cache for downloaded files of version %s",
+                model_version_id,
+                exc_info=True,
+            )
+            return []
+
+        files_getter = getattr(cache, "get_files_by_version_id", None)
+        local_entries = files_getter(model_version_id) if files_getter else []
+        if not local_entries:
+            return []
+
+        version_payload: Mapping[str, Any] = {}
+        for entry in local_entries:
+            civitai = entry.get("civitai") if isinstance(entry, Mapping) else None
+            if isinstance(civitai, Mapping) and isinstance(civitai.get("files"), list):
+                version_payload = civitai
+                break
+
+        downloaded = ModelCivitaiHandler._match_downloaded_files(
+            version_payload, local_entries
+        )
+
+        # Surface local files that D2 could not map to a known remote file
+        matched_paths = {item.get("filePath") for item in downloaded}
+        for entry in local_entries:
+            if not isinstance(entry, Mapping):
+                continue
+            if entry.get("file_path") in matched_paths:
+                continue
+            downloaded.append(
+                {
+                    "fileId": None,
+                    "fileName": entry.get("file_name"),
+                    "filePath": entry.get("file_path"),
+                }
+            )
+        return downloaded
+
     async def check_model_exists(self, request: web.Request) -> web.Response:
         try:
             model_id_str = request.query.get("modelId")
@@ -2096,9 +2205,11 @@ class ModelLibraryHandler:
 
                 exists = False
                 model_type = None
+                matched_scanner = None
                 if await lora_scanner.check_model_version_exists(model_version_id):
                     exists = True
                     model_type = "lora"
+                    matched_scanner = lora_scanner
                 elif (
                     checkpoint_scanner
                     and await checkpoint_scanner.check_model_version_exists(
@@ -2107,6 +2218,7 @@ class ModelLibraryHandler:
                 ):
                     exists = True
                     model_type = "checkpoint"
+                    matched_scanner = checkpoint_scanner
                 elif (
                     embedding_scanner
                     and await embedding_scanner.check_model_version_exists(
@@ -2115,6 +2227,7 @@ class ModelLibraryHandler:
                 ):
                     exists = True
                     model_type = "embedding"
+                    matched_scanner = embedding_scanner
 
                 if exists:
                     return web.json_response(
@@ -2123,6 +2236,9 @@ class ModelLibraryHandler:
                             "exists": True,
                             "modelType": model_type,
                             "hasBeenDownloaded": False,
+                            "downloadedFiles": await self._get_downloaded_files(
+                                matched_scanner, model_version_id
+                            ),
                         }
                     )
 
@@ -2144,6 +2260,7 @@ class ModelLibraryHandler:
                         "exists": False,
                         "modelType": history_type,
                         "hasBeenDownloaded": has_been_downloaded,
+                        "downloadedFiles": [],
                     }
                 )
 
@@ -3793,6 +3910,7 @@ class MiscHandlerSet:
     ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         return {
             "health_check": self.health.health_check,
+            "get_init_status": self.health.get_init_status,
             "get_settings": self.settings.get_settings,
             "update_settings": self.settings.update_settings,
             "get_doctor_diagnostics": self.doctor.get_doctor_diagnostics,

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,
@@ -364,6 +368,7 @@ class ModelListingHandler:
             == "true",
             "tags": request.query.get("search_tags", "false").lower() == "true",
             "creator": request.query.get("search_creator", "false").lower() == "true",
+            "hash": request.query.get("search_hash", "false").lower() == "true",
             "recursive": request.query.get("recursive", "true").lower() == "true",
         }
 
@@ -633,6 +638,16 @@ class ModelManagementHandler:
             file_path = data.get("file_path")
             model_id = data.get("model_id")
             model_version_id = data.get("model_version_id")
+            source = data.get("source")
+
+            if source not in (None, "", "civarchive"):
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": f"Unsupported relink source: {source}",
+                    },
+                    status=400,
+                )
 
             if not file_path or model_id is None:
                 return web.json_response(
@@ -648,20 +663,33 @@ class ModelManagementHandler:
                 metadata_path
             )
 
+            relink_kwargs = {
+                "file_path": file_path,
+                "metadata": local_metadata,
+                "model_id": int(model_id),
+                "model_version_id": int(model_version_id) if model_version_id else None,
+            }
+            if source == "civarchive":
+                relink_kwargs["provider_name"] = "civarchive_api"
+
             updated_metadata = await self._metadata_sync.relink_metadata(
-                file_path=file_path,
-                metadata=local_metadata,
-                model_id=int(model_id),
-                model_version_id=int(model_version_id) if model_version_id else None,
+                **relink_kwargs
             )
 
             await self._service.scanner.update_single_model_cache(
                 file_path, file_path, updated_metadata
             )
 
-            message = f"Model successfully re-linked to Civitai model {model_id}" + (
-                f" version {model_version_id}" if model_version_id else ""
-            )
+            if source == "civarchive":
+                message = (
+                    f"Model successfully re-linked to CivArchive model {model_id}"
+                    + (f" version {model_version_id}" if model_version_id else "")
+                )
+            else:
+                message = (
+                    f"Model successfully re-linked to Civitai model {model_id}"
+                    + (f" version {model_version_id}" if model_version_id else "")
+                )
             return web.json_response(
                 {
                     "success": True,
@@ -669,6 +697,8 @@ class ModelManagementHandler:
                     "hash": updated_metadata.get("sha256", ""),
                 }
             )
+        except ValueError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
         except Exception as exc:
             if is_expected_offline_error(str(exc)):
                 return web.json_response(
@@ -1029,6 +1059,11 @@ class ModelQueryHandler:
         self._service = service
         self._logger = logger
 
+    @staticmethod
+    def _parse_include_empty(request: web.Request) -> bool:
+        """Parse the include_empty query flag (``1``/``true``)."""
+        return request.query.get("include_empty", "").lower() in ("1", "true")
+
     async def get_top_tags(self, request: web.Request) -> web.Response:
         try:
             limit = int(request.query.get("limit", "20"))
@@ -1123,8 +1158,14 @@ class ModelQueryHandler:
 
     async def get_folders(self, request: web.Request) -> web.Response:
         try:
-            cache = await self._service.scanner.get_cached_data()
-            return web.json_response({"folders": cache.folders})
+            include_empty = self._parse_include_empty(request)
+            if include_empty:
+                # Live enumeration includes empty OS-created directories.
+                folders = await self._service.scanner.get_all_folders()
+            else:
+                cache = await self._service.scanner.get_cached_data()
+                folders = cache.folders
+            return web.json_response({"folders": folders})
         except Exception as exc:
             self._logger.error("Error getting folders: %s", exc)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -1149,7 +1190,9 @@ class ModelQueryHandler:
                     {"success": False, "error": "model_root parameter is required"},
                     status=400,
                 )
-            folder_tree = await self._service.get_folder_tree(model_root)
+            folder_tree = await self._service.get_folder_tree(
+                model_root, include_empty=self._parse_include_empty(request)
+            )
             return web.json_response({"success": True, "tree": folder_tree})
         except Exception as exc:
             self._logger.error("Error getting folder tree: %s", exc)
@@ -1157,7 +1200,9 @@ class ModelQueryHandler:
 
     async def get_unified_folder_tree(self, request: web.Request) -> web.Response:
         try:
-            unified_tree = await self._service.get_unified_folder_tree()
+            unified_tree = await self._service.get_unified_folder_tree(
+                include_empty=self._parse_include_empty(request)
+            )
             return web.json_response({"success": True, "tree": unified_tree})
         except Exception as exc:
             self._logger.error("Error getting unified folder tree: %s", exc)
@@ -1554,12 +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)
@@ -1593,6 +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."""
@@ -1888,8 +2015,18 @@ class ModelDownloadHandler:
         try:
             status_filter = request.query.get("status") or None
             service = await DownloadQueueService.get_instance()
-            cleared = await service.clear_queue(status_filter=status_filter)
-            return web.json_response({"success": True, "cleared": cleared})
+            cleared_ids = await service.clear_queue(status_filter=status_filter)
+            # Clearing the queue rows alone would orphan any in-memory tasks
+            # and persisted aria2 state for those downloads, leaving them
+            # polling the daemon invisibly.  Tear that tracking down too.
+            try:
+                await self._download_coordinator.discard_cleared_downloads(cleared_ids)
+            except Exception:
+                self._logger.warning(
+                    "Failed to discard in-memory state for cleared downloads",
+                    exc_info=True,
+                )
+            return web.json_response({"success": True, "cleared": len(cleared_ids)})
         except Exception as exc:
             self._logger.error(
                 "Error clearing download queue: %s", exc, exc_info=True
@@ -1972,9 +2109,11 @@ class ModelDownloadHandler:
                 item_id=item_id, download_id=download_id
             )
             if item is None:
+                # Missing or non-retryable history entry is a business
+                # outcome, not a routing error: 200 lets the extension's
+                # apiFetch 404-fallback and error middleware stay quiet.
                 return web.json_response(
-                    {"success": False, "error": "History item not found or not retryable"},
-                    status=404,
+                    {"success": False, "error": "History item not found or not retryable"}
                 )
             return web.json_response({"success": True, "item": item})
         except Exception as exc:
@@ -2025,8 +2164,12 @@ class ModelDownloadHandler:
                 completed_at=completed_at,
             )
             if item is None:
+                # A missing queue item (already completed, or never queued) is
+                # a normal business outcome, not a routing error. Return 200
+                # so the browser extension's apiFetch 404-fallback and the
+                # error middleware stay quiet.
                 return web.json_response(
-                    {"success": False, "error": "Download not found in queue"}, status=404
+                    {"success": False, "error": "Download not found in queue"}
                 )
             return web.json_response({"success": True, "item": item})
         except Exception as exc:
@@ -2068,9 +2211,10 @@ class ModelDownloadHandler:
             service = await DownloadQueueService.get_instance()
             updated = await service.update_status(download_id, status)
             if not updated:
+                # Same rationale as complete_download_in_queue: a missing
+                # queue item is a business outcome, not a routing error.
                 return web.json_response(
-                    {"success": False, "error": "Download not found in queue"},
-                    status=404,
+                    {"success": False, "error": "Download not found in queue"}
                 )
             return web.json_response({"success": True})
         except Exception as exc:
@@ -2621,10 +2765,20 @@ class ModelUpdateHandler:
             except Exception:
                 pass
 
+        same_base_scope = self._uses_same_base_update_scope()
+
         serialized_records = []
         for record in records.values():
             has_update_fn = getattr(record, "has_update", None)
-            if callable(has_update_fn) and has_update_fn(
+            if not callable(has_update_fn):
+                continue
+            scoped_fn = (
+                getattr(record, "has_update_for_local_bases", None)
+                if same_base_scope
+                else None
+            )
+            qualifies_fn = scoped_fn if callable(scoped_fn) else has_update_fn
+            if qualifies_fn(
                 hide_early_access=hide_early_access,
                 hide_paid=hide_paid,
             ):
@@ -2637,6 +2791,26 @@ class ModelUpdateHandler:
             }
         )
 
+    def _uses_same_base_update_scope(self) -> bool:
+        """Return True when update reporting must honor same-base scoping.
+
+        Mirrors ``BaseModelService._annotate_update_flags``: the Updates filter
+        evaluates updates per local base model when ``version_grouping`` is
+        ``same_base`` (its default). The refresh summary counts with the same
+        scope so the "Found N update(s)" toast matches what the filter
+        displays. See issue #1083.
+        """
+
+        if self._settings is None:
+            return True
+        try:
+            strategy_value = self._settings.get("version_grouping")
+        except Exception:
+            return True
+        if isinstance(strategy_value, str) and strategy_value.strip():
+            return strategy_value.strip().lower() == "same_base"
+        return True
+
     async def set_model_update_ignore(self, request: web.Request) -> web.Response:
         payload = await self._read_json(request)
         model_id = self._normalize_model_id(payload.get("modelId"))
@@ -3104,6 +3278,9 @@ class ModelUpdateHandler:
             "paidAccess": paid_access_payload,
             "filePath": context.get("file_path"),
             "fileName": context.get("file_name"),
+            # Weight-file variant count (None when unknown); lets the UI hide
+            # the download affordance for single-file in-library versions.
+            "fileCount": getattr(version, "file_count", None),
         }
 
     async def _build_version_context(
@@ -3248,6 +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/misc_route_registrar.py

@@ -32,6 +32,7 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/settings/libraries", "get_settings_libraries"),
     RouteDefinition("POST", "/api/lm/settings/libraries/activate", "activate_library"),
     RouteDefinition("GET", "/api/lm/health-check", "health_check"),
+    RouteDefinition("GET", "/api/lm/init-status", "get_init_status"),
     RouteDefinition("GET", "/api/lm/supporters", "get_supporters"),
     RouteDefinition("GET", "/api/lm/wildcards/search", "search_wildcards"),
     RouteDefinition("POST", "/api/lm/wildcards/open-location", "open_wildcards_location"),

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

@@ -49,6 +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(
@@ -59,11 +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"),
@@ -90,6 +110,14 @@ 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/aria2_downloader.py

@@ -82,6 +82,17 @@ CIVITAI_DOWNLOAD_URL_PREFIXES = (
 )
 
 
+def _is_no_uri_available_error(message: str) -> bool:
+    """Return True for aria2's "No URI available" transfer failure.
+
+    aria2 reports this when every URI for the transfer has become unusable.
+    For CivitAI downloads this typically means the temporary signed URL
+    expired mid-download; the transfer can be recovered by resolving a fresh
+    signed URL and re-scheduling with ``continue=true``.
+    """
+    return "no uri available" in message.lower()
+
+
 class Aria2Error(RuntimeError):
     """Raised when aria2 integration fails."""
 
@@ -145,8 +156,11 @@ class Aria2Downloader:
         disappears (e.g. another download restarted the daemon and
         ``close()`` cleared ``_transfers``) or the RPC becomes unreachable,
         the transfer is re-scheduled with ``continue=true`` so the download
-        resumes from the on-disk ``.aria2`` control file. Recovery is bounded
-        by ``MAX_TRANSFER_RECOVERY_ATTEMPTS``.
+        resumes from the on-disk ``.aria2`` control file.  The same
+        re-scheduling happens when aria2 fails with "No URI available"
+        (typically an expired CivitAI signed URL): a fresh URL is resolved
+        and the partial download continues.  Recovery is bounded by
+        ``MAX_TRANSFER_RECOVERY_ATTEMPTS``.
         """
 
         await self._ensure_process()
@@ -201,7 +215,36 @@ class Aria2Downloader:
                     completed_path = self._resolve_completed_path(status, save_path)
                     return True, completed_path
                 if state == "error":
-                    return False, status.get("errorMessage") or "aria2 download failed"
+                    error_message = status.get("errorMessage") or "aria2 download failed"
+                    if (
+                        _is_no_uri_available_error(error_message)
+                        and recovery_attempts < MAX_TRANSFER_RECOVERY_ATTEMPTS
+                    ):
+                        # The signed URL (e.g. CivitAI's) expired before the
+                        # transfer finished.  Re-registering resolves a fresh
+                        # URL and resumes from the on-disk partial payload and
+                        # .aria2 control file via ``continue=true``.
+                        recovery_attempts += 1
+                        logger.warning(
+                            "aria2 transfer %s failed with %r; refreshing the "
+                            "URL and resuming the partial download "
+                            "(attempt %d/%d)",
+                            download_id,
+                            error_message,
+                            recovery_attempts,
+                            MAX_TRANSFER_RECOVERY_ATTEMPTS,
+                        )
+                        await asyncio.sleep(1.0)
+                        await self._ensure_process()
+                        async with self._register_lock:
+                            transfer = await self._register_transfer(
+                                url,
+                                save_path,
+                                download_id=download_id,
+                                headers=headers,
+                            )
+                        continue
+                    return False, error_message
                 if state == "removed":
                     return False, "Download was cancelled"
 
@@ -217,8 +260,9 @@ class Aria2Downloader:
         """Call get_status with retry for transient RPC failures.
 
         Only retries on :exc:`Aria2Error` (RPC-level failure).  Returns
-        ``None`` immediately when the download_id is not tracked (a missing
-        transfer is not a transient condition, so retrying is pointless).
+        ``None`` immediately when the transfer is not tracked or its GID is
+        gone from the daemon (a missing transfer is not a transient
+        condition, so retrying is pointless).
 
         A single failed RPC call should not immediately fail the download,
         because aria2 may be temporarily busy (e.g. finalizing multiple
@@ -332,7 +376,13 @@ class Aria2Downloader:
         return transfer
 
     async def get_status(self, download_id: str) -> Optional[Dict[str, Any]]:
-        """Return the raw aria2 status payload for a known download."""
+        """Return the raw aria2 status payload for a known download.
+
+        Returns ``None`` when the download_id is not tracked or the daemon no
+        longer knows the transfer's GID (daemon restart / forceRemove).  A
+        forgotten GID is permanent, not transient, so the caller's recovery
+        path handles it instead of burning retry attempts on a dead GID.
+        """
 
         transfer = self._transfers.get(download_id)
         if transfer is None:
@@ -348,8 +398,17 @@ class Aria2Downloader:
             "files",
         ]
         try:
-            status = await self._rpc_call("aria2.tellStatus", [transfer.gid, keys])
+            status = await self._rpc_call(
+                "aria2.tellStatus", [transfer.gid, keys], log_errors=False
+            )
         except Exception as exc:
+            if "not found" in str(exc).lower():
+                logger.debug(
+                    "aria2 GID %s for download %s is gone; treating as lost transfer",
+                    transfer.gid,
+                    download_id,
+                )
+                return None
             raise Aria2Error(f"Failed to query aria2 download status: {exc}") from exc
 
         if isinstance(status, dict):
@@ -367,7 +426,9 @@ class Aria2Downloader:
             "files",
         ]
         try:
-            status = await self._rpc_call("aria2.tellStatus", [gid, keys])
+            status = await self._rpc_call(
+                "aria2.tellStatus", [gid, keys], log_errors=False
+            )
         except Exception as exc:
             message = str(exc)
             if "cannot be found" in message.lower() or "not found" in message.lower():
@@ -434,8 +495,19 @@ class Aria2Downloader:
         try:
             await self._rpc_call("aria2.forceRemove", [transfer.gid])
         except Exception as exc:
-            return {"success": False, "error": str(exc)}
+            if "not found" not in str(exc).lower():
+                return {"success": False, "error": str(exc)}
+            # The daemon already forgot this GID (restart / prior removal),
+            # so the transfer is effectively cancelled.
+            logger.debug(
+                "aria2 GID %s for download %s already gone during cancel",
+                transfer.gid,
+                download_id,
+            )
 
+        # Drop the in-memory entry as well so a concurrent poll loop does
+        # not mistake the removal for a lost transfer and re-register it.
+        self._transfers.pop(download_id, None)
         await self._state_store.remove(download_id)
         return {"success": True, "message": "Download cancelled successfully"}
 
@@ -725,7 +797,9 @@ class Aria2Downloader:
 
         return isinstance(result, dict)
 
-    async def _rpc_call(self, method: str, params: list[Any]) -> Any:
+    async def _rpc_call(
+        self, method: str, params: list[Any], *, log_errors: bool = True
+    ) -> Any:
         if not self._rpc_url:
             raise Aria2Error("aria2 RPC endpoint is not initialized")
 
@@ -756,7 +830,10 @@ class Aria2Downloader:
             error = body["error"] or {}
             code = error.get("code") if isinstance(error, dict) else None
             message = error.get("message") if isinstance(error, dict) else str(error)
-            logger.error(
+            # Probing calls (e.g. tellStatus for a GID the daemon may have
+            # forgotten) pass log_errors=False: an expected "not found" must
+            # not spam the log at ERROR level.
+            (logger.error if log_errors else logger.debug)(
                 "aria2 RPC %s failed with HTTP %s, code=%s, message=%s",
                 method,
                 response.status,
@@ -771,7 +848,7 @@ class Aria2Downloader:
             raise Aria2Error(status_message or "Unknown aria2 RPC error")
 
         if response.status != 200:
-            logger.error(
+            (logger.error if log_errors else logger.debug)(
                 "aria2 RPC %s returned unexpected HTTP status %s without error payload: %s",
                 method,
                 response.status,

py/services/base_model_service.py

@@ -972,14 +972,25 @@ class BaseModelService(ABC):
         )
         return {k: data[k] for k in fields if k in data}
 
-    async def get_folder_tree(self, model_root: str) -> Dict[str, Any]:
+    async def _get_tree_folders(self, cache, include_empty: bool) -> List[str]:
+        """Return the folder list backing folder tree responses.
+
+        With ``include_empty`` the directories are enumerated live from the
+        filesystem (including empty ones) via the scanner; otherwise the
+        models-only ``cache.folders`` list is used unchanged.
+        """
+        if include_empty:
+            return await self.scanner.get_all_folders()
+        return cache.folders
+
+    async def get_folder_tree(self, model_root: str, include_empty: bool = False) -> Dict[str, Any]:
         """Get hierarchical folder tree for a specific model root"""
         cache = await self.scanner.get_cached_data()
 
         # Build tree structure from folders
         tree = {}
 
-        for folder in cache.folders:
+        for folder in await self._get_tree_folders(cache, include_empty):
             # Check if this folder belongs to the specified model root
             folder_belongs_to_root = False
             for root in self.scanner.get_model_roots():
@@ -1001,7 +1012,7 @@ class BaseModelService(ABC):
 
         return tree
 
-    async def get_unified_folder_tree(self) -> Dict[str, Any]:
+    async def get_unified_folder_tree(self, include_empty: bool = False) -> Dict[str, Any]:
         """Get unified folder tree across all model roots"""
         cache = await self.scanner.get_cached_data()
 
@@ -1011,7 +1022,7 @@ class BaseModelService(ABC):
         # Get all model roots for path normalization
         model_roots = self.scanner.get_model_roots()
 
-        for folder in cache.folders:
+        for folder in await self._get_tree_folders(cache, include_empty):
             if not folder:  # Skip empty folders
                 continue
 
@@ -1284,6 +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,
@@ -1393,9 +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):
@@ -71,6 +76,9 @@ class BatchImportProgress:
     tags: List[str] = field(default_factory=list)
     skip_no_metadata: bool = False
     skip_duplicates: bool = False
+    # Set once any item is skipped due to vendor rate limiting (#1085); lets
+    # the UI surface a "slowing down / try again later" hint.
+    rate_limited: bool = False
 
     def to_dict(self) -> Dict[str, Any]:
         return {
@@ -82,6 +90,7 @@ class BatchImportProgress:
             "skipped": self.skipped,
             "current_item": self.current_item,
             "status": self.status,
+            "rate_limited": self.rate_limited,
             "started_at": self.started_at,
             "finished_at": self.finished_at,
             "progress_percent": round((self.completed / self.total) * 100, 1)
@@ -118,6 +127,10 @@ class AdaptiveConcurrencyController:
         self._task_durations: List[float] = []
         self._recent_errors = 0
         self._recent_successes = 0
+        # Batch-wide shared semaphore; created lazily on first use so the
+        # controller can also be constructed outside a running event loop.
+        self._semaphore: Optional[asyncio.Semaphore] = None
+        self._semaphore_capacity = initial_concurrency
 
     def record_result(self, duration: float, success: bool) -> None:
         self._task_durations.append(duration)
@@ -146,7 +159,37 @@ class AdaptiveConcurrencyController:
         self._recent_successes = 0
 
     def get_semaphore(self) -> asyncio.Semaphore:
-        return asyncio.Semaphore(self.current_concurrency)
+        """Return the batch-wide shared semaphore.
+
+        The same semaphore instance is returned for every item of a batch so
+        the configured concurrency bounds are actually enforced. Previously a
+        fresh semaphore was created per call, letting every item run
+        concurrently and hammering remote metadata providers without any
+        limit.
+        """
+        if self._semaphore is None:
+            self._semaphore = asyncio.Semaphore(self.current_concurrency)
+            self._semaphore_capacity = self.current_concurrency
+        return self._semaphore
+
+    async def apply_concurrency(self) -> None:
+        """Synchronize the shared semaphore capacity with ``current_concurrency``.
+
+        Call after ``record_result`` (once per completed item). Growing the
+        capacity is immediate (release). Shrinking requires acquiring a permit
+        and holding it, which is best-effort while other tasks are still
+        running — the capacity converges on subsequent calls.
+        """
+        semaphore = self.get_semaphore()
+        while self._semaphore_capacity < self.current_concurrency:
+            semaphore.release()
+            self._semaphore_capacity += 1
+        while self._semaphore_capacity > self.current_concurrency:
+            try:
+                await asyncio.wait_for(semaphore.acquire(), timeout=0.01)
+            except (asyncio.TimeoutError, asyncio.CancelledError):
+                break
+            self._semaphore_capacity -= 1
 
 
 class BatchImportService:
@@ -184,6 +227,7 @@ class BatchImportService:
     def cancel_import(self, operation_id: str) -> bool:
         if operation_id in self._active_operations:
             self._cancellation_flags[operation_id] = True
+            self._logger.info("Cancel requested for batch import operation %s", operation_id)
             return True
         return False
 
@@ -273,6 +317,14 @@ class BatchImportService:
         self._active_operations[operation_id] = progress
         self._cancellation_flags[operation_id] = False
 
+        self._logger.info(
+            "Starting batch import operation %s: %d item(s) (%d URL(s), %d local path(s))",
+            operation_id,
+            len(import_items),
+            sum(1 for it in import_items if it.item_type == ImportItemType.URL),
+            sum(1 for it in import_items if it.item_type == ImportItemType.LOCAL_PATH),
+        )
+
         asyncio.create_task(
             self._run_batch_import(
                 operation_id=operation_id,
@@ -295,6 +347,12 @@ class BatchImportService:
         skip_duplicates: bool = False,
     ) -> str:
         image_paths = await self._discover_images(directory, recursive)
+        self._logger.info(
+            "Batch import directory scan: %d image(s) discovered in %s (recursive=%s)",
+            len(image_paths),
+            directory,
+            recursive,
+        )
 
         items = [{"source": path, "type": "local_path"} for path in image_paths]
 
@@ -334,6 +392,13 @@ class BatchImportService:
         ext = os.path.splitext(filename)[1].lower()
         return ext in self.SUPPORTED_EXTENSIONS
 
+    @staticmethod
+    def _is_rate_limit_error(error: Optional[str]) -> bool:
+        """Return True when an error payload represents vendor rate limiting."""
+        if not error:
+            return False
+        return "rate limit" in error.lower()
+
     async def _run_batch_import(
         self,
         *,
@@ -379,6 +444,9 @@ class BatchImportService:
                 self._concurrency_controller.record_result(
                     duration, result.get("success", False)
                 )
+                # Keep the shared batch semaphore in sync with the adaptively
+                # adjusted concurrency so the bounds actually take effect.
+                await self._concurrency_controller.apply_concurrency()
 
                 if result.get("success"):
                     item.status = ImportStatus.SUCCESS
@@ -389,6 +457,17 @@ class BatchImportService:
                     item.status = ImportStatus.SKIPPED
                     item.error_message = result.get("error")
                     progress.skipped += 1
+                elif self._is_rate_limit_error(result.get("error")):
+                    # Vendor rate limit is a transient, external condition —
+                    # do not pollute the failure count with it (#1085). The
+                    # import can simply be re-run later.
+                    item.status = ImportStatus.SKIPPED
+                    item.error_message = (
+                        f"Rate limited by metadata provider; "
+                        f"re-run the import later ({result.get('error')})"
+                    )
+                    progress.skipped += 1
+                    progress.rate_limited = True
                 else:
                     item.status = ImportStatus.FAILED
                     item.error_message = result.get("error")
@@ -396,13 +475,36 @@ class BatchImportService:
 
             except Exception as e:
                 self._logger.error(f"Error importing {item.source}: {e}")
-                item.status = ImportStatus.FAILED
-                item.error_message = str(e)
                 item.duration = time.time() - start_time
-                progress.failed += 1
+                if self._is_rate_limit_error(str(e)):
+                    item.status = ImportStatus.SKIPPED
+                    item.error_message = (
+                        f"Rate limited by metadata provider; "
+                        f"re-run the import later ({e})"
+                    )
+                    progress.skipped += 1
+                    progress.rate_limited = True
+                else:
+                    item.status = ImportStatus.FAILED
+                    item.error_message = str(e)
+                    progress.failed += 1
                 self._concurrency_controller.record_result(item.duration, False)
+                await self._concurrency_controller.apply_concurrency()
 
             progress.completed += 1
+            self._logger.info(
+                "Batch import %s: item %d/%d status=%s source=%s%s",
+                operation_id,
+                progress.completed,
+                progress.total,
+                item.status.value,
+                (
+                    os.path.basename(item.source)
+                    if item.item_type == ImportItemType.LOCAL_PATH
+                    else item.source[:50]
+                ),
+                (f" error={item.error_message}" if item.error_message else ""),
+            )
             await self._broadcast_progress(progress)
 
         tasks = [process_item(item) for item in progress.items]
@@ -415,6 +517,15 @@ class BatchImportService:
 
         progress.finished_at = time.time()
         progress.current_item = ""
+        self._logger.info(
+            "Batch import %s finished: status=%s total=%d success=%d failed=%d skipped=%d",
+            operation_id,
+            progress.status,
+            progress.total,
+            progress.success,
+            progress.failed,
+            progress.skipped,
+        )
         await self._broadcast_progress(progress)
 
         await asyncio.sleep(5)
@@ -518,6 +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"):
@@ -595,3 +717,6 @@ class BatchImportService:
     def _cleanup_operation(self, operation_id: str) -> None:
         if operation_id in self._cancellation_flags:
             del self._cancellation_flags[operation_id]
+        if operation_id in self._active_operations:
+            del self._active_operations[operation_id]
+            self._logger.info("Batch import operation %s cleaned up", operation_id)

py/services/checkpoint_service.py

@@ -51,6 +51,7 @@ class CheckpointService(BaseModelService):
             "base_model": model_data.get("base_model", ""),
             "folder": folder,
             "sha256": model_data.get("sha256", ""),
+            "autov3": model_data.get("autov3"),
             "file_path": file_path.replace(os.sep, "/"),
             "file_size": model_data.get("size", 0),
             "modified": model_data.get("modified", ""),

py/services/civarchive_client.py

@@ -7,6 +7,7 @@ import logging
 import asyncio
 from copy import deepcopy
 from typing import Any, Optional, Dict, Tuple, List, cast
+from .connectivity_guard import is_expected_offline_error
 from .model_metadata_provider import CivArchiveModelMetadataProvider, ModelMetadataProviderManager
 from .downloader import get_downloader
 from .errors import RateLimitError
@@ -46,7 +47,11 @@ class CivArchiveClient:
         """Call CivArchive API and return JSON payload"""
         success, payload = await self._make_request(path, params=params)
         if not success:
-            error = payload if isinstance(payload, str) else "Request failed"
+            # Normalize empty-string failure payloads (e.g. a throttled
+            # connection dropped without a message) so callers never see a
+            # falsy error alongside a None payload — that combination used to
+            # crash downstream None.get() calls.
+            error = payload if isinstance(payload, str) and payload else "Request failed"
             return None, error
         if not isinstance(payload, dict):
             return None, "Invalid response structure"
@@ -298,6 +303,8 @@ class CivArchiveClient:
 
     async def _resolve_version_from_files(self, payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         """Fallback to fetch version data when only file metadata is available"""
+        if not isinstance(payload, dict):
+            return None
         data = self._normalize_payload(payload)
         files = data.get("files") or payload.get("files") or []
         if not isinstance(files, list):
@@ -332,10 +339,13 @@ class CivArchiveClient:
         """Find model by SHA256 hash value using CivArchive API"""
         try:
             payload, error = await self._request_json(f"/sha256/{model_hash.lower()}")
-            if error:
-                if "not found" in error.lower():
+            # Treat a missing payload as an error even when the error string is
+            # falsy; passing None into the split/transform helpers below used to
+            # crash with "'NoneType' object has no attribute 'get'".
+            if error is not None or payload is None:
+                if error and "not found" in error.lower():
                     return None, "Model not found"
-                return None, error
+                return None, error or "Request failed"
 
             context, version_data, fallback_files = self._split_context(cast(Dict[str, Any], payload))
             transformed = self._transform_version(context, version_data, fallback_files)
@@ -352,7 +362,14 @@ class CivArchiveClient:
         except RateLimitError:
             raise
         except Exception as e:
-            logger.error(f"Error fetching CivArchive model by hash {model_hash[:10]}: {e}")
+            if is_expected_offline_error(str(e)):
+                logger.debug(
+                    "Skipping CivArchive model by hash %s while offline: %s",
+                    model_hash[:10],
+                    e,
+                )
+            else:
+                logger.error(f"Error fetching CivArchive model by hash {model_hash[:10]}: {e}")
             return None, str(e)
 
     async def get_model_versions(self, model_id: str) -> Optional[Dict[str, Any]]:
@@ -362,7 +379,14 @@ class CivArchiveClient:
             if error or payload is None:
                 if error and "not found" in error.lower():
                     return None
-                logger.error(f"Error fetching CivArchive model versions for {model_id}: {error}")
+                if is_expected_offline_error(error):
+                    logger.debug(
+                        "Skipping CivArchive model versions fetch for %s while offline: %s",
+                        model_id,
+                        error,
+                    )
+                else:
+                    logger.error(f"Error fetching CivArchive model versions for {model_id}: {error}")
                 return None
 
             data = self._normalize_payload(payload)
@@ -426,7 +450,19 @@ class CivArchiveClient:
             if error or payload is None:
                 if error and "not found" in error.lower():
                     return None
-                logger.error(f"Error fetching CivArchive model version via API {model_id}/{version_id}: {error}")
+                # The connectivity guard short-circuits requests during its
+                # offline cooldown; that is an expected, transient state, so
+                # log it as DEBUG instead of spamming one ERROR per request
+                # (batch imports can hit this thousands of times).
+                if is_expected_offline_error(error):
+                    logger.debug(
+                        "Skipping CivArchive model version fetch %s/%s while offline: %s",
+                        model_id,
+                        version_id,
+                        error,
+                    )
+                else:
+                    logger.error(f"Error fetching CivArchive model version via API {model_id}/{version_id}: {error}")
                 return None
 
             context, version_data, fallback_files = self._split_context(payload)

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_coordinator.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Awaitable, Callable, Dict, Optional
+from typing import Any, Awaitable, Callable, Dict, Iterable, Optional
 
 from .downloader import DownloadProgress
 
@@ -186,6 +186,14 @@ class DownloadCoordinator:
         download_manager = await self._download_manager_factory()
         return await download_manager.get_active_downloads()
 
+    async def discard_cleared_downloads(self, download_ids: Iterable[str]) -> int:
+        """Tear down in-memory/aria2 tracking for queue-cleared downloads."""
+
+        if not download_ids:
+            return 0
+        download_manager = await self._download_manager_factory()
+        return await download_manager.discard_cleared_downloads(download_ids)
+
     def _parse_optional_int(self, value: Any, field: str) -> Optional[int]:
         """Parse an optional integer from user input."""
 

py/services/download_manager.py

@@ -2,6 +2,7 @@
 # Lazy (function-local) imports still count as static edges in basedpyright's
 # reportImportCycles, so the ServiceRegistry singleton pattern necessarily forms
 # import cycles. Breaking them would require an architectural refactor.
+import contextlib
 import copy
 import json
 import logging
@@ -12,8 +13,9 @@ import shutil
 import zipfile
 from concurrent.futures import ThreadPoolExecutor
 from collections import OrderedDict
+from dataclasses import dataclass, field
 import uuid
-from typing import Any, Dict, List, Optional, Set, Tuple, cast
+from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, cast
 from urllib.parse import urlparse
 from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 from ..utils.constants import (
@@ -33,6 +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
@@ -53,6 +56,12 @@ CIVITAI_DOWNLOAD_URL_PREFIXES = (
 NON_DOWNLOADABLE_PRIMARY_TYPES = ("Config", "Archive", "Workflow", "Training Data")
 
 
+@dataclass
+class _PathSlot:
+    lock: asyncio.Lock = field(default_factory=asyncio.Lock)
+    refs: int = 0
+
+
 class DownloadManager:
     _instance = None
     _lock = asyncio.Lock()
@@ -82,6 +91,11 @@ class DownloadManager:
         self._aria2_state_store = Aria2TransferStateStore()
         self._restored_persisted_downloads = False
         self._restore_lock = asyncio.Lock()
+        # Refcounted per-target-path locks: two downloads resolving to the
+        # same save_path (e.g. model versions sharing one filename) must not
+        # overlap, or one task's failure cleanup can delete the other's file.
+        self._path_slot_guard: asyncio.Lock = asyncio.Lock()
+        self._path_slots: dict[str, _PathSlot] = {}
 
     @staticmethod
     def _get_model_download_backend() -> str:
@@ -704,6 +718,47 @@ class DownloadManager:
                 await asyncio.sleep(delay)
         return False
 
+    @staticmethod
+    def _reconcile_failed_aria2_partial(save_path: str) -> None:
+        """Reconcile on-disk partial state after a failed aria2 transfer.
+
+        The payload and its ``.aria2`` control file form a resumable pair and
+        are preserved together so a retry (with a refreshed URL when needed)
+        can resume via aria2's ``continue=true``.  A control file without its
+        payload cannot resume anything, so the orphan is reported and removed.
+        """
+        control_path = f"{save_path}.aria2"
+        payload_exists = os.path.exists(save_path)
+        control_exists = os.path.exists(control_path)
+
+        if payload_exists and not control_exists:
+            # If the .aria2 control file is missing, aria2 considers the
+            # download complete.  A transient RPC failure may have made us
+            # think the download failed even though the file is fully on disk.
+            # Keep the file so a retry can find it already complete.
+            logger.warning(
+                "aria2 download reported failure but .aria2 file is absent "
+                "for %s — the file is likely complete.  Preserving it for retry.",
+                save_path,
+            )
+        elif payload_exists and control_exists:
+            logger.info(
+                "Preserving aria2 partial download for resume: %s", save_path
+            )
+        elif control_exists:
+            logger.warning(
+                "Orphaned aria2 control file without payload: %s — removing it",
+                control_path,
+            )
+            try:
+                os.remove(control_path)
+            except OSError as exc:
+                logger.warning(
+                    "Failed to remove orphaned aria2 control file %s: %s",
+                    control_path,
+                    exc,
+                )
+
     async def _cleanup_cancelled_download_files(
         self,
         download_id: str,
@@ -875,6 +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,
         *,
@@ -1100,6 +1191,11 @@ class DownloadManager:
 
                 save_path = self._resolve_save_path_from_persisted_record(record)
                 if save_path is None:
+                    # No resolvable target path (e.g. a queued download whose
+                    # paths were never resolved before shutdown): the record
+                    # can never be restored, so drop it instead of letting it
+                    # accumulate in the state store forever.
+                    await self._aria2_state_store.remove(download_id)
                     continue
 
                 if (
@@ -1208,6 +1304,24 @@ class DownloadManager:
                     )
                     continue
 
+                if not os.path.exists(save_path) and os.path.exists(control_path):
+                    # A control file without its payload cannot resume
+                    # anything; report it and clean up the orphan.
+                    logger.warning(
+                        "Orphaned aria2 control file without payload for %s: "
+                        "%s — removing it",
+                        download_id,
+                        control_path,
+                    )
+                    try:
+                        os.remove(control_path)
+                    except OSError as exc:
+                        logger.warning(
+                            "Failed to remove orphaned aria2 control file %s: %s",
+                            control_path,
+                            exc,
+                        )
+
                 await self._aria2_state_store.remove(download_id)
 
             self._restored_persisted_downloads = True
@@ -1781,6 +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:
@@ -2127,6 +2259,28 @@ class DownloadManager:
 
         return formatted_path
 
+    @contextlib.asynccontextmanager
+    async def _exclusive_target_slot(self, target_key: str):
+        async with self._path_slot_guard:
+            slot = self._path_slots.get(target_key)
+            if slot is None:
+                slot = _PathSlot()
+                self._path_slots[target_key] = slot
+            slot.refs += 1
+        try:
+            async with slot.lock:
+                yield
+        finally:
+            async with self._path_slot_guard:
+                slot.refs -= 1
+                if slot.refs <= 0:
+                    _ = self._path_slots.pop(target_key, None)
+
+    def _target_slot_key(self, save_dir: str, metadata) -> str:
+        return os.path.abspath(
+            os.path.join(save_dir, os.path.basename(metadata.file_path))
+        )
+
     async def _execute_download(
         self,
         download_urls: List[str],
@@ -2138,6 +2292,33 @@ class DownloadManager:
         model_type: str = "lora",
         download_id: str | None = None,
         transfer_backend: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Execute the download serialized against other downloads targeting the same path."""
+        target_key = self._target_slot_key(save_dir, metadata)
+        async with self._exclusive_target_slot(target_key):
+            return await self._execute_download_pipeline(
+                download_urls=download_urls,
+                save_dir=save_dir,
+                metadata=metadata,
+                version_info=version_info,
+                relative_path=relative_path,
+                progress_callback=progress_callback,
+                model_type=model_type,
+                download_id=download_id,
+                transfer_backend=transfer_backend,
+            )
+
+    async def _execute_download_pipeline(
+        self,
+        download_urls: List[str],
+        save_dir: str,
+        metadata,
+        version_info: Dict[str, Any],
+        relative_path: str,
+        progress_callback=None,
+        model_type: str = "lora",
+        download_id: str | None = None,
+        transfer_backend: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Execute the actual download process including preview images and model files"""
         metadata_entries: List[Any] = []
@@ -2356,20 +2537,8 @@ class DownloadManager:
                     break
 
                 last_error = result
-                # For aria2: if the .aria2 control file is missing, aria2 considers
-                # the download complete.  A transient RPC failure may have made us
-                # think the download failed even though the file is fully on disk.
-                # Keep the file so a retry can find it already complete.
-                if (
-                    transfer_backend == "aria2"
-                    and os.path.exists(save_path)
-                    and not os.path.exists(f"{save_path}.aria2")
-                ):
-                    logger.warning(
-                        "aria2 download reported failure but .aria2 file is absent "
-                        "for %s — the file is likely complete.  Preserving it for retry.",
-                        save_path,
-                    )
+                if transfer_backend == "aria2":
+                    self._reconcile_failed_aria2_partial(save_path)
                 elif os.path.exists(save_path):
                     try:
                         os.remove(save_path)
@@ -2897,6 +3066,64 @@ class DownloadManager:
             # Preserve aria2 state store entry so the partial download
             # info survives restarts and can be resumed later
 
+    async def discard_cleared_downloads(self, download_ids: Iterable[str]) -> int:
+        """Stop in-memory tracking for downloads cleared from the queue.
+
+        Cancels asyncio tasks, removes live aria2 transfers and drops the
+        persisted aria2 state so cleared downloads cannot keep polling the
+        daemon or be resurrected as ghost entries on the next restart.
+        Partial files on disk are preserved; unlike ``cancel_download`` no
+        files are deleted.
+
+        Returns the number of downloads that had any in-memory or persisted
+        tracking removed.
+        """
+        discarded = 0
+        aria2_downloader = None
+
+        for download_id in download_ids:
+            task = self._download_tasks.get(download_id)
+            info = self._active_downloads.get(download_id)
+            persisted = await self._aria2_state_store.get(download_id)
+            if task is None and info is None and persisted is None:
+                continue
+
+            discarded += 1
+
+            if task is not None:
+                task.cancel()
+
+            pause_control = self._pause_events.pop(download_id, None)
+            if pause_control is not None:
+                pause_control.resume()
+
+            if task is not None:
+                try:
+                    await asyncio.wait_for(asyncio.shield(task), timeout=2.0)
+                except (asyncio.CancelledError, asyncio.TimeoutError):
+                    pass
+
+            self._download_tasks.pop(download_id, None)
+            self._active_downloads.pop(download_id, None)
+
+            backend = (info or persisted or {}).get("transfer_backend") or "python"
+            if backend == "aria2":
+                if aria2_downloader is None:
+                    aria2_downloader = await get_aria2_downloader()
+                if await aria2_downloader.has_transfer(download_id):
+                    try:
+                        await aria2_downloader.cancel_download(download_id)
+                    except Exception as exc:
+                        logger.warning(
+                            "Failed to remove aria2 transfer for cleared download %s: %s",
+                            download_id,
+                            exc,
+                        )
+
+            await self._aria2_state_store.remove(download_id)
+
+        return discarded
+
     async def pause_download(self, download_id: str) -> Dict[str, Any]:
         """Pause an active download without losing progress."""
 

py/services/download_queue_service.py

@@ -6,12 +6,21 @@ import logging
 import os
 import sqlite3
 import time
-from typing import Any, Optional
+from typing import Any, List, Optional
 
 from ..utils.cache_paths import get_cache_base_dir
 
 logger = logging.getLogger(__name__)
 
+# SQL fragment extracting the CivitAI file id from the JSON ``file_params``
+# column (#1058). ``json_valid`` guards against NULL and legacy/unparseable
+# values, yielding NULL for rows without a file identity; NULL keys group
+# together so such rows keep the old version-level dedup behavior.
+_FILE_ID_SQL = (
+    "CASE WHEN json_valid(file_params) "
+    "THEN json_extract(file_params, '$.id') END"
+)
+
 
 def _resolve_database_path() -> str:
     base_dir = get_cache_base_dir(create=True)
@@ -381,23 +390,31 @@ class DownloadQueueService:
             conn.commit()
         return True
 
-    async def clear_queue(self, status_filter: Optional[str] = None) -> int:
+    async def clear_queue(self, status_filter: Optional[str] = None) -> List[str]:
         """Remove items from the queue.
 
         When *status_filter* is provided only items with that status are
-        deleted.  Returns the number of deleted rows.
+        deleted.  Returns the ``download_id`` values of the deleted rows so
+        callers can also tear down any in-memory tracking for them.
         """
         async with self._lock:
             conn = self._get_conn()
             if status_filter is not None:
-                cursor = conn.execute(
+                rows = conn.execute(
+                    "SELECT download_id FROM download_queue WHERE status = ?",
+                    (status_filter,),
+                ).fetchall()
+                conn.execute(
                     "DELETE FROM download_queue WHERE status = ?",
                     (status_filter,),
                 )
             else:
-                cursor = conn.execute("DELETE FROM download_queue")
+                rows = conn.execute(
+                    "SELECT download_id FROM download_queue"
+                ).fetchall()
+                conn.execute("DELETE FROM download_queue")
             conn.commit()
-        return cursor.rowcount
+        return [row["download_id"] for row in rows]
 
     async def complete_download(
         self,
@@ -866,33 +883,44 @@ class DownloadQueueService:
         async with self._lock:
             conn = self._get_conn()
 
-            # 1. History: for each (model_id, model_version_id, status) triplet
-            #    keep only the row with the highest id (most recently inserted).
-            conn.execute("""
+            # 1. History: for each (model_id, model_version_id, file_id,
+            #    status) group keep only the row with the highest id (most
+            #    recently inserted). file_id comes from file_params (#1058)
+            #    so distinct files of the same version never collapse.
+            conn.execute(f"""
                 DELETE FROM download_history
                 WHERE id NOT IN (
                     SELECT MAX(id)
                     FROM download_history
-                    GROUP BY model_id, model_version_id, status
+                    GROUP BY model_id, model_version_id, status,
+                             {_FILE_ID_SQL}
                 )
             """)
             result["removed_history"] = conn.execute(
                 "SELECT changes()"
             ).fetchone()[0]
 
-            # 2. Cross-status dedup: for each (model_id, model_version_id),
-            #    keep only the entry with the highest-priority terminal status.
+            # 2. Cross-status dedup: for each (model_id, model_version_id,
+            #    file_id), keep only the entry with the highest-priority
+            #    terminal status.
             #    Priority: completed (3) > failed (2) > canceled (1).
-            #    This prevents the same model version from having both a
-            #    'failed' and a 'canceled' entry (or a 'completed' alongside
-            #    either) after the bug-created duplicates are removed.
-            conn.execute("""
+            #    This prevents the same file of a model version from having
+            #    both a 'failed' and a 'canceled' entry (or a 'completed'
+            #    alongside either) after the bug-created duplicates are
+            #    removed. ``IS`` matches NULL file ids against each other so
+            #    rows without file identity keep the old behavior.
+            conn.execute(f"""
                 DELETE FROM download_history
                 WHERE id NOT IN (
                     SELECT dh.id
-                    FROM download_history dh
+                    FROM (
+                        SELECT id, model_id, model_version_id, status,
+                               {_FILE_ID_SQL} AS file_id
+                        FROM download_history
+                    ) dh
                     INNER JOIN (
                         SELECT model_id, model_version_id,
+                               {_FILE_ID_SQL} AS file_id,
                                MAX(CASE status
                                    WHEN 'completed' THEN 3
                                    WHEN 'failed' THEN 2
@@ -900,17 +928,18 @@ class DownloadQueueService:
                                    ELSE 0
                                END) AS best_prio
                         FROM download_history
-                        GROUP BY model_id, model_version_id
+                        GROUP BY model_id, model_version_id, {_FILE_ID_SQL}
                     ) best
                     ON dh.model_id = best.model_id
                    AND dh.model_version_id = best.model_version_id
+                   AND dh.file_id IS best.file_id
                    AND CASE dh.status
                            WHEN 'completed' THEN 3
                            WHEN 'failed' THEN 2
                            WHEN 'canceled' THEN 1
                            ELSE 0
                        END = best.best_prio
-                    GROUP BY dh.model_id, dh.model_version_id
+                    GROUP BY dh.model_id, dh.model_version_id, dh.file_id
                     HAVING dh.id = MAX(dh.id)
                 )
             """)
@@ -918,15 +947,17 @@ class DownloadQueueService:
                 "SELECT changes()"
             ).fetchone()[0]
 
-            # 3. Queue: for each (model_id, model_version_id) keep only the
-            #    row with the latest added_at (most recently enqueued).
-            conn.execute("""
+            # 3. Queue: for each (model_id, model_version_id, file_id) keep
+            #    only the row with the latest added_at (most recently
+            #    enqueued). file_id comes from file_params (#1058) so
+            #    distinct files of the same version never collapse.
+            conn.execute(f"""
                 DELETE FROM download_queue
                 WHERE rowid NOT IN (
                     SELECT MAX(rowid)
                     FROM download_queue
                     WHERE status IN ('queued', 'downloading', 'paused', 'waiting')
-                    GROUP BY model_id, model_version_id
+                    GROUP BY model_id, model_version_id, {_FILE_ID_SQL}
                 )
                 AND status IN ('queued', 'downloading', 'paused', 'waiting')
             """)

py/services/downloaded_version_history_service.py

@@ -236,24 +236,33 @@ class DownloadedVersionHistoryService:
             return
 
         async with self._lock:
-            conn = self._get_conn()
-            conn.executemany(
-                """
-                INSERT INTO downloaded_model_versions (
-                    model_type, version_id, model_id, first_seen_at, last_seen_at,
-                    source, last_file_path, last_library_name, is_deleted_override
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
-                ON CONFLICT(model_type, version_id) DO UPDATE SET
-                    model_id = COALESCE(excluded.model_id, downloaded_model_versions.model_id),
-                    last_seen_at = excluded.last_seen_at,
-                    source = excluded.source,
-                    last_file_path = COALESCE(excluded.last_file_path, downloaded_model_versions.last_file_path),
-                    last_library_name = COALESCE(excluded.last_library_name, downloaded_model_versions.last_library_name),
-                    is_deleted_override = 0
-                """,
-                payload,
-            )
-            conn.commit()
+            # The connection is created with check_same_thread=False and all
+            # access is serialized by self._lock, so the executemany upsert +
+            # commit can run in the default executor without blocking the
+            # event loop on large hydration payloads.
+            loop = asyncio.get_running_loop()
+            await loop.run_in_executor(None, self._mark_downloaded_bulk_sync, payload)
+
+    def _mark_downloaded_bulk_sync(self, payload: Sequence[tuple[object, ...]]) -> None:
+        """Synchronous executemany upsert + commit; runs in a worker thread."""
+        conn = self._get_conn()
+        conn.executemany(
+            """
+            INSERT INTO downloaded_model_versions (
+                model_type, version_id, model_id, first_seen_at, last_seen_at,
+                source, last_file_path, last_library_name, is_deleted_override
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
+            ON CONFLICT(model_type, version_id) DO UPDATE SET
+                model_id = COALESCE(excluded.model_id, downloaded_model_versions.model_id),
+                last_seen_at = excluded.last_seen_at,
+                source = excluded.source,
+                last_file_path = COALESCE(excluded.last_file_path, downloaded_model_versions.last_file_path),
+                last_library_name = COALESCE(excluded.last_library_name, downloaded_model_versions.last_library_name),
+                is_deleted_override = 0
+            """,
+            payload,
+        )
+        conn.commit()
 
     async def mark_as_deleted(self, model_type: str, version_id: int) -> None:
         normalized_type = _normalize_model_type(model_type)

py/services/downloader.py

@@ -32,6 +32,7 @@ from .connectivity_guard import (
     ConnectivityGuard,
 )
 from .errors import RateLimitError
+from .rate_limit_coordinator import RateLimitCoordinator
 
 logger = logging.getLogger(__name__)
 
@@ -156,6 +157,25 @@ class DownloadStalledError(Exception):
     """Raised when download progress stalls beyond the configured timeout."""
 
 
+def _disable_netrc_auth(session: aiohttp.ClientSession) -> None:
+    """Prevent the session from loading credentials from netrc files.
+
+    ``trust_env=True`` is kept so system-level proxies still work, but aiohttp
+    would also auto-apply netrc entries (e.g. ``machine civitai.red``) as
+    BasicAuth. aiohttp refuses to combine those with the explicit
+    ``Authorization: Bearer`` header set for CivitAI requests, raising
+    "Cannot combine AUTHORIZATION header with AUTH argument or credentials
+    encoded in URL" before the request is even sent. Subclassing ClientSession
+    is discouraged by aiohttp (emits a DeprecationWarning), so the private
+    hook is patched on the instance instead.
+    """
+
+    def _no_netrc_auth(*args: Any, **kwargs: Any) -> Optional[aiohttp.BasicAuth]:
+        return None
+
+    setattr(session, "_get_netrc_auth", _no_netrc_auth)
+
+
 class Downloader:
     """Unified downloader for all HTTP/HTTPS downloads in the application."""
 
@@ -370,6 +390,7 @@ class Downloader:
             trust_env=not app_proxy_active,
             timeout=timeout,
         )
+        _disable_netrc_auth(self._session)
 
         # Store proxy URL for per-request use. Stays None for SOCKS because the
         # ProxyConnector already tunnels everything; passing proxy= for SOCKS
@@ -575,6 +596,21 @@ class Downloader:
                             False,
                             "File not found - the download link may be invalid or expired.",
                         )
+                    elif response.status == 429:
+                        # Register the vendor's cooldown so API calls through
+                        # make_request queue behind it (#1085). The download
+                        # itself fails as before; retry policy stays with the
+                        # caller (download manager).
+                        retry_after = self._extract_retry_after(response.headers)
+                        coordinator = await RateLimitCoordinator.get_instance()
+                        if coordinator.enabled:
+                            coordinator.register_rate_limit(
+                                self._guard_destination(url), retry_after
+                            )
+                        logger.warning(
+                            f"Rate limited (429) for {url}, retry_after={retry_after}"
+                        )
+                        return False, f"Download rate limited (429), retry after {retry_after}s"
                     else:
                         logger.error(
                             f"Download failed for {url} with status {response.status}"
@@ -952,6 +988,11 @@ class Downloader:
                 elif response.status == 429:
                     raw_retry_after = response.headers.get("Retry-After")
                     retry_after = _parse_retry_after(raw_retry_after or "")
+                    # Register the vendor's cooldown so API calls through
+                    # make_request queue behind it (#1085).
+                    coordinator = await RateLimitCoordinator.get_instance()
+                    if coordinator.enabled:
+                        coordinator.register_rate_limit(destination, retry_after)
                     if raw_retry_after:
                         logger.warning(
                             "Rate limited (429) for %s, Retry-After: %ss", url, retry_after
@@ -1021,6 +1062,14 @@ class Downloader:
                 if response.status == 200:
                     guard.register_success(destination)
                     return True, dict(response.headers)
+                elif response.status == 429:
+                    # Register the vendor's cooldown so API calls through
+                    # make_request queue behind it (#1085).
+                    retry_after = self._extract_retry_after(response.headers)
+                    coordinator = await RateLimitCoordinator.get_instance()
+                    if coordinator.enabled:
+                        coordinator.register_rate_limit(destination, retry_after)
+                    return False, f"Head request rate limited (429), retry after {retry_after}s"
                 else:
                     return False, f"Head request failed with status {response.status}"
 
@@ -1054,74 +1103,113 @@ class Downloader:
 
         Returns:
             Tuple[bool, Union[Dict, str]]: (success, response data or error message)
+
+        When the rate-limit gate is enabled (``rate_limit_gate_enabled``),
+        requests are paced per destination and 429 responses are honored by
+        waiting out the ``Retry-After`` window (bounded by
+        ``rate_limit_max_wait_seconds``) before re-sending. A ``RateLimitError``
+        returned after gate involvement is marked with ``gate_handled = True``
+        so downstream retry helpers do not wait a second time.
         """
         guard = await ConnectivityGuard.get_instance()
         destination = self._guard_destination(url)
+        # Fail fast on transport-level outages before pacing: there is no
+        # point waiting out a vendor cooldown while the network is down.
         if guard.should_block_request(destination):
             return False, OFFLINE_COOLDOWN_ERROR
 
-        try:
-            session = await self.session
-            # Debug log for proxy mode at request time
-            if self.proxy_url:
-                logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
-            else:
-                logger.debug(
-                    "[make_request] Using system-level proxy (trust_env) if configured."
-                )
+        coordinator = await RateLimitCoordinator.get_instance()
+        gate_enabled = coordinator.enabled
+        # Safety bound on the wait-and-resend loop; each 429 normally exits
+        # via the wait cap in wait_for_slot, this covers pathological 429s
+        # with tiny Retry-After values.
+        max_resend_attempts = 5
+        attempt = 0
 
-            # Prepare headers
-            headers = self._get_auth_headers(use_auth)
-            if custom_headers:
-                headers.update(custom_headers)
+        while True:
+            if gate_enabled:
+                try:
+                    await coordinator.wait_for_slot(destination)
+                except RateLimitError as exc:
+                    exc.gate_handled = True
+                    return False, exc
 
-            # Add proxy to kwargs if not already present
-            if "proxy" not in kwargs:
-                kwargs["proxy"] = self.proxy_url
-
-            async with session.request(
-                method, url, headers=headers, **kwargs
-            ) as response:
-                if response.status == 200:
-                    guard.register_success(destination)
-                    # Try to parse as JSON, fall back to text
-                    try:
-                        data = await response.json()
-                        return True, data
-                    except:
-                        text = await response.text()
-                        return True, text
-                elif response.status == 401:
-                    return False, "Unauthorized access - invalid or missing API key"
-                elif response.status == 403:
-                    return False, "Access forbidden"
-                elif response.status == 404:
-                    return False, "Resource not found"
-                elif response.status == 429:
-                    retry_after = self._extract_retry_after(response.headers)
-                    error_msg = "Request rate limited"
-                    logger.warning(
-                        "Rate limit encountered for %s %s; retry_after=%s",
-                        method,
-                        url,
-                        retry_after,
-                    )
-                    return False, RateLimitError(
-                        error_msg,
-                        retry_after=retry_after,
-                    )
+            try:
+                session = await self.session
+                # Debug log for proxy mode at request time
+                if self.proxy_url:
+                    logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
                 else:
-                    return False, f"Request failed with status {response.status}"
+                    logger.debug(
+                        "[make_request] Using system-level proxy (trust_env) if configured."
+                    )
 
-        except Exception as e:
-            if guard.is_network_unreachable_error(e):
-                guard.register_network_failure(e, destination)
-                if guard.should_block_request(destination):
-                    return False, OFFLINE_COOLDOWN_ERROR
-                logger.debug("Network unavailable for %s %s: %s", method, url, e)
+                # Prepare headers
+                headers = self._get_auth_headers(use_auth)
+                if custom_headers:
+                    headers.update(custom_headers)
+
+                # Add proxy to kwargs if not already present
+                if "proxy" not in kwargs:
+                    kwargs["proxy"] = self.proxy_url
+
+                async with session.request(
+                    method, url, headers=headers, **kwargs
+                ) as response:
+                    if response.status == 200:
+                        guard.register_success(destination)
+                        if gate_enabled:
+                            coordinator.register_success(destination)
+                        # Try to parse as JSON, fall back to text
+                        try:
+                            data = await response.json()
+                            return True, data
+                        except:
+                            text = await response.text()
+                            return True, text
+                    elif response.status == 401:
+                        return False, "Unauthorized access - invalid or missing API key"
+                    elif response.status == 403:
+                        return False, "Access forbidden"
+                    elif response.status == 404:
+                        return False, "Resource not found"
+                    elif response.status == 429:
+                        retry_after = self._extract_retry_after(response.headers)
+                        error_msg = "Request rate limited"
+                        if not gate_enabled:
+                            logger.warning(
+                                "Rate limit encountered for %s %s; retry_after=%s",
+                                method,
+                                url,
+                                retry_after,
+                            )
+                            return False, RateLimitError(
+                                error_msg,
+                                retry_after=retry_after,
+                            )
+                        # The coordinator logs the cooldown notice (INFO once
+                        # per window, DEBUG on extension).
+                        coordinator.register_rate_limit(destination, retry_after)
+                        attempt += 1
+                        if attempt >= max_resend_attempts:
+                            error = RateLimitError(error_msg, retry_after=retry_after)
+                            error.gate_handled = True
+                            return False, error
+                        # Loop back: wait_for_slot blocks until the cooldown
+                        # elapses (or raises once the wait exceeds the cap).
+                        continue
+                    else:
+                        return False, f"Request failed with status {response.status}"
+
+            except Exception as e:
+                if guard.is_network_unreachable_error(e):
+                    guard.register_network_failure(e, destination)
+                    if guard.should_block_request(destination):
+                        return False, OFFLINE_COOLDOWN_ERROR
+                    logger.debug("Network unavailable for %s %s: %s", method, url, e)
+                    return False, str(e)
+                logger.error(f"Error making {method} request to {url}: {e}")
                 return False, str(e)
-            logger.error(f"Error making {method} request to {url}: {e}")
-            return False, str(e)
 
     async def close(self):
         """Close the HTTP session"""

py/services/embedding_service.py

@@ -51,6 +51,7 @@ class EmbeddingService(BaseModelService):
             "base_model": model_data.get("base_model", ""),
             "folder": folder,
             "sha256": model_data.get("sha256", ""),
+            "autov3": model_data.get("autov3"),
             "file_path": file_path.replace(os.sep, "/"),
             "file_size": model_data.get("size", 0),
             "modified": model_data.get("modified", ""),

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/lora_service.py

@@ -58,6 +58,7 @@ class LoraService(BaseModelService):
             "base_model": model_data.get("base_model", ""),
             "folder": folder,
             "sha256": model_data.get("sha256", ""),
+            "autov3": model_data.get("autov3"),
             "file_path": file_path.replace(os.sep, "/"),
             "file_size": model_data.get("size", 0),
             "modified": model_data.get("modified", ""),

py/services/metadata_sync_service.py

@@ -245,16 +245,23 @@ class MetadataSyncService:
             civitai_api_not_found = False
             any_rate_limited = False
 
+            skip_network_providers = False
             for provider_name, provider in provider_attempts:
+                if skip_network_providers and provider_name != "sqlite":
+                    # A network provider was already rate-limited; failing
+                    # over to another network provider just spreads the flood
+                    # (#1085). The local sqlite archive stays as last resort.
+                    continue
                 try:
                     civitai_metadata_candidate, error = await provider.get_model_by_hash(sha256)
                 except RateLimitError as exc:
                     logger.warning(
-                        "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                        "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                         provider_name or provider.__class__.__name__,
                         exc.retry_after or 0,
                     )
                     any_rate_limited = True
+                    skip_network_providers = True
                     continue
                 except Exception as exc:  # pragma: no cover - defensive logging
                     logger.error("Provider %s failed for hash %s: %s", provider_name, sha256, exc)
@@ -419,14 +426,37 @@ class MetadataSyncService:
         metadata: Dict[str, Any],
         model_id: int,
         model_version_id: Optional[int],
+        provider_name: Optional[str] = None,
     ) -> Dict[str, Any]:
-        """Relink a local metadata record to a specific CivitAI model version."""
+        """Relink a local metadata record to a specific CivitAI model version.
+
+        When ``provider_name`` is given, the named provider is resolved via the
+        metadata provider selector instead of the default fallback chain. A
+        missing/disabled provider surfaces a user-friendly error instead of the
+        raw selector exception.
+        """
+
+        if provider_name:
+            try:
+                provider = await self._get_provider(provider_name)
+            except ValueError as exc:
+                logger.warning(
+                    "Unable to resolve metadata provider %s: %s", provider_name, exc
+                )
+                raise ValueError(
+                    "CivitArchive is not available or not enabled. "
+                    "Enable the CivitArchive API in settings to relink via CivArchive."
+                ) from exc
+        else:
+            provider = await self._get_default_provider()
 
-        provider = await self._get_default_provider()
         civitai_metadata = await provider.get_model_version(model_id, model_version_id)
         if not civitai_metadata:
+            provider_label = (
+                "CivitArchive" if provider_name == "civarchive_api" else "CivitAI"
+            )
             raise ValueError(
-                f"Model version not found on CivitAI for ID: {model_id}"
+                f"Model version not found on {provider_label} for ID: {model_id}"
                 + (f" with version: {model_version_id}" if model_version_id else "")
             )
 

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

@@ -66,6 +66,14 @@ class _RateLimitRetryHelper:
             except RateLimitError as exc:
                 attempt += 1
 
+                # The downloader's rate-limit gate already applied the wait
+                # policy for this request (waited out the vendor window or
+                # deliberately refused because it exceeds the cap). Sleeping
+                # again here would double the wait — just propagate.
+                if getattr(exc, "gate_handled", False):
+                    exc.provider = exc.provider or label
+                    raise
+
                 # Determine effective retry limit based on rate-limit magnitude
                 effective_retry_limit = self._retry_limit  # default: 3
                 if exc.retry_after is not None and exc.retry_after >= 120.0:
@@ -101,6 +109,12 @@ class _RateLimitRetryHelper:
 
         return min(self._max_delay, max(0.0, base_delay))
 
+
+# Labels of providers that are free to consult even while a network provider
+# is rate-limited (local lookups, no vendor cost).
+_LOCAL_PROVIDER_LABELS = frozenset({"sqlite"})
+
+
 class ModelMetadataProvider(ABC):
     """Base abstract class for all model metadata providers"""
     
@@ -155,6 +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"""
     
@@ -189,6 +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"""
     
@@ -451,7 +481,14 @@ class SQLiteModelMetadataProvider(ModelMetadataProvider):
             return None
         
 class FallbackMetadataProvider(ModelMetadataProvider):
-    """Try providers in order, return first successful result."""
+    """Try providers in order, return first successful result.
+
+    Rate-limit policy (#1085): once a *network* provider raises
+    ``RateLimitError``, the chain stops consulting further network providers —
+    failing over would just spread the flood to the next vendor. Local-only
+    providers (see ``_LOCAL_PROVIDER_LABELS``) are still allowed as a last
+    resort because they cost the vendor nothing.
+    """
 
     def __init__(
         self,
@@ -486,7 +523,10 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         )
 
     async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
+        rate_limited = False
         for provider, label in self._iter_providers():
+            if rate_limited and label not in _LOCAL_PROVIDER_LABELS:
+                continue
             try:
                 result, error = await self._call_with_rate_limit(
                     label,
@@ -496,8 +536,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result, error
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -505,11 +546,18 @@ class FallbackMetadataProvider(ModelMetadataProvider):
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_by_hash: %s", label, e)
                 continue
+        if rate_limited:
+            # Distinct from "Model not found": callers must not mistake a
+            # rate-limited lookup for a confirmed deletion.
+            return None, "Rate limited"
         return None, "Model not found"
 
     async def get_model_versions(self, model_id: str) -> Optional[Dict[str, Any]]:
         not_found_confirmed = False
+        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,
@@ -519,8 +567,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -539,7 +588,10 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         return None
 
     async def get_model_version(self, model_id: Optional[int] = None, version_id: Optional[int] = None) -> 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,
@@ -550,8 +602,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -562,7 +615,10 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         return None
 
     async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
+        rate_limited = False
         for provider, label in self._iter_providers():
+            if rate_limited and label not in _LOCAL_PROVIDER_LABELS:
+                continue
             try:
                 result, error = await self._call_with_rate_limit(
                     label,
@@ -572,8 +628,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result, error
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -581,12 +638,17 @@ class FallbackMetadataProvider(ModelMetadataProvider):
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_version_info: %s", label, e)
                 continue
+        if rate_limited:
+            return None, "Rate limited"
         return None, "No provider could retrieve the data"
 
     async def get_model_versions_by_hashes(
         self, hashes: List[str]
     ) -> Optional[List[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,
@@ -598,8 +660,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
             except NotImplementedError:
                 continue
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -614,7 +677,10 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         return None
 
     async def get_user_models(self, username: str, cursor: Optional[str] = None) -> 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,
@@ -625,8 +691,9 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result is not None:
                     return result
             except RateLimitError as exc:
+                rate_limited = True
                 logger.warning(
-                    "Provider %s is rate-limited (retry_after=%.0fs); skipping to next provider",
+                    "Provider %s is rate-limited (retry_after=%.0fs); not failing over to other network providers",
                     label,
                     exc.retry_after or 0,
                 )
@@ -649,6 +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)
 
@@ -740,6 +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_query.py

@@ -432,6 +432,7 @@ class SearchStrategy:
         "tags": False,
         "recursive": True,
         "creator": False,
+        "hash": False,
     }
 
     def __init__(
@@ -494,8 +495,28 @@ class SearchStrategy:
                     results.append(item)
                     continue
 
+            # Hash search is always exact (never fuzzy): match the full
+            # sha256, its autov2 prefix (first 10 chars), or the autov3 hash.
+            if options.get("hash", False):
+                hash_query = search_lower.strip()
+                if hash_query and self._matches_hash(item, hash_query):
+                    results.append(item)
+                    continue
+
         return results
 
+    def _matches_hash(self, item: Dict[str, Any], hash_query: str) -> bool:
+        """Exact-match the normalized query against the item's known hashes."""
+        sha256 = item.get("sha256")
+        sha256_lower = sha256.lower() if isinstance(sha256, str) else ""
+        if sha256_lower and hash_query in (sha256_lower, sha256_lower[:10]):
+            return True
+        # autov3 is None when unchecked and "" when checked but unavailable
+        autov3 = item.get("autov3")
+        if isinstance(autov3, str) and autov3 and hash_query == autov3.lower():
+            return True
+        return False
+
     def _matches(
         self, candidate: str, search_term: str, search_lower: str, fuzzy: bool
     ) -> bool:

py/services/model_scanner.py

@@ -5,7 +5,7 @@ import asyncio
 import time
 import shutil
 from dataclasses import dataclass
-from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Sequence, Set, Type, Union, cast
+from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Type, Union, cast
 
 from ..utils.models import BaseModelMetadata, autov3_from_civitai_files
 from ..config import config
@@ -57,6 +57,24 @@ def _is_excluded_dir(name: str) -> bool:
     return name == PENDING_DELETE_DIR_NAME
 
 
+def _is_hidden_relative_path(rel_path: str) -> bool:
+    """Return True when any segment of a relative path is a hidden directory."""
+    return any(part.startswith(".") for part in rel_path.replace(os.sep, "/").split("/"))
+
+
+# TTL (seconds) for the get_all_folders() live-walk cache, so rapid repeated
+# 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."""
     normalized = str(path).replace(os.sep, "/")
@@ -126,6 +144,8 @@ class ModelScanner:
         self._name_display_mode = self._resolve_name_display_mode()
         self._cancel_requested = False  # Flag for cancellation
         self._autov3_backfill_scheduled = False  # One-time AutoV3 backfill trigger per process
+        # Short-lived cache for get_all_folders(): (timestamp, folders) or None
+        self._all_folders_ttl_cache: Optional[Tuple[float, List[str]]] = None
         try:
             loop = asyncio.get_running_loop()
         except RuntimeError:
@@ -137,6 +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.
@@ -165,6 +217,7 @@ class ModelScanner:
         self._excluded_models = []
         self._is_initializing = False
         self._name_display_mode = self._resolve_name_display_mode()
+        self.invalidate_all_folders_cache()
         self.bump_cache_version()
 
         try:
@@ -421,12 +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({
@@ -522,16 +570,21 @@ class ModelScanner:
             self._is_initializing = False
     
     async def _load_persisted_cache(self, page_type: str) -> bool:
-        """Attempt to hydrate the in-memory cache from the SQLite snapshot."""
+        """Attempt to hydrate the in-memory cache from the SQLite snapshot.
+
+        The SQLite read and the per-model rebuild (entry adjustment, tag
+        counting, validation/repair, hash index reconstruction) run in the
+        default executor so the event loop stays responsive; only applying
+        the result to shared cache state happens on the loop.
+        """
         if not getattr(self, '_persistent_cache', None):
             return False
 
         loop = asyncio.get_event_loop()
         try:
-            persisted = await loop.run_in_executor(
+            rebuilt = await loop.run_in_executor(
                 None,
-                self._persistent_cache.load_cache,
-                self.model_type
+                self._rebuild_persisted_cache
             )
         except FileNotFoundError:
             return False
@@ -539,47 +592,14 @@ class ModelScanner:
             logger.debug("%s Scanner: Could not load persisted cache: %s", self.model_type.capitalize(), exc)
             return False
 
-        if not persisted or not persisted.raw_data:
+        if rebuilt is None:
             return False
 
-        hash_index = ModelHashIndex()
-        for sha_value, path in persisted.hash_rows:
-            if sha_value and path:
-                hash_index.add_entry(sha_value.lower(), path)
-
-        # Rebuild the AutoV3 index from the persisted autov3_index rows. These
-        # cover every known autov3 -> path mapping regardless of whether a
-        # sha256 row also exists for the same file.
-        for autov3_value, path in persisted.autov3_hash_rows:
-            if autov3_value and path:
-                hash_index.add_autov3(autov3_value.lower(), path)
-
-        tags_count: Dict[str, int] = {}
-        adjusted_raw_data: List[Dict[str, Any]] = []
-        for item in persisted.raw_data:
-            adjusted_item = self.adjust_cached_entry(dict(item))
-            adjusted_raw_data.append(adjusted_item)
-
-            for tag in adjusted_item.get('tags') or []:
-                tags_count[tag] = tags_count.get(tag, 0) + 1
-
-        # Validate cache entries and check health.
-        # Always use the validated/repaired entries — even when there are no
-        # invalid entries, auto_repair may have filled in missing optional
-        # fields (model_name, file_name, folder) with safe defaults on a copied
-        # working_entry.  Without this unconditional replacement the repaired
-        # copies are discarded and None values propagate to format_response.
-        # See issue #730.
-        valid_entries, invalid_entries = CacheEntryValidator.validate_batch(
-            adjusted_raw_data, auto_repair=True
-        )
-
-        # Always use the validated entries (repaired copies)
-        adjusted_raw_data = valid_entries
+        scan_result, invalid_entries = rebuilt
 
         if invalid_entries:
             monitor = CacheHealthMonitor()
-            report = monitor.check_health(adjusted_raw_data, auto_repair=True)
+            report = monitor.check_health(scan_result.raw_data, auto_repair=True)
 
             if report.status != CacheHealthStatus.HEALTHY:
                 # Broadcast health warning to frontend
@@ -589,31 +609,22 @@ class ModelScanner:
                     f"{report.invalid_entries} invalid entries, {report.repaired_entries} repaired"
                 )
 
-            # Use only valid entries
-            adjusted_raw_data = valid_entries
-
             # Rebuild tags count from valid entries only
             tags_count = {}
-            for item in adjusted_raw_data:
+            for item in scan_result.raw_data:
                 for tag in item.get('tags') or []:
                     tags_count[tag] = tags_count.get(tag, 0) + 1
+            scan_result.tags_count = tags_count
 
             # Remove invalid entries from hash index
             for invalid_entry in invalid_entries:
                 file_path = CacheEntryValidator.get_file_path_safe(invalid_entry)
                 sha256 = CacheEntryValidator.get_sha256_safe(invalid_entry)
                 if file_path:
-                    hash_index.remove_by_path(file_path, sha256)
-
-        scan_result = CacheBuildResult(
-            raw_data=adjusted_raw_data,
-            hash_index=hash_index,
-            tags_count=tags_count,
-            excluded_models=list(persisted.excluded_models)
-        )
+                    scan_result.hash_index.remove_by_path(file_path, sha256)
 
         await self._apply_scan_result(scan_result)
-        await self._sync_download_history(adjusted_raw_data, source='scan')
+        await self._sync_download_history(scan_result.raw_data, source='scan')
 
         await ws_manager.broadcast_init_progress({
             'stage': 'loading_cache',
@@ -638,6 +649,63 @@ class ModelScanner:
 
         return True
 
+    def _rebuild_persisted_cache(self) -> Optional[Tuple[CacheBuildResult, List[Dict[str, Any]]]]:
+        """Load the SQLite snapshot and rebuild a ready-to-apply scan result.
+
+        Runs entirely in a worker thread: it must not touch ``self._cache``,
+        the websocket manager, or any asyncio primitives. Returns ``None``
+        when no usable snapshot exists, otherwise a tuple of the scan result
+        (built from validated/repaired entries) and the invalid entries.
+        """
+        persisted = self._persistent_cache.load_cache(self.model_type)
+
+        if not persisted or not persisted.raw_data:
+            return None
+
+        hash_index = ModelHashIndex()
+        for sha_value, path in persisted.hash_rows:
+            if sha_value and path:
+                hash_index.add_entry(sha_value.lower(), path)
+
+        # Rebuild the AutoV3 index from the persisted autov3_index rows. These
+        # cover every known autov3 -> path mapping regardless of whether a
+        # sha256 row also exists for the same file.
+        for autov3_value, path in persisted.autov3_hash_rows:
+            if autov3_value and path:
+                hash_index.add_autov3(autov3_value.lower(), path)
+
+        tags_count: Dict[str, int] = {}
+        adjusted_raw_data: List[Dict[str, Any]] = []
+        for item in persisted.raw_data:
+            # load_cache builds a fresh dict per row, and validate_batch below
+            # works on its own per-entry copy when auto_repair=True, so no
+            # additional dict copy is needed here.
+            adjusted_item = self.adjust_cached_entry(item)
+            adjusted_raw_data.append(adjusted_item)
+
+            for tag in adjusted_item.get('tags') or []:
+                tags_count[tag] = tags_count.get(tag, 0) + 1
+
+        # Validate cache entries and check health.
+        # Always use the validated/repaired entries — even when there are no
+        # invalid entries, auto_repair may have filled in missing optional
+        # fields (model_name, file_name, folder) with safe defaults on a copied
+        # working_entry.  Without this unconditional replacement the repaired
+        # copies are discarded and None values propagate to format_response.
+        # See issue #730.
+        valid_entries, invalid_entries = CacheEntryValidator.validate_batch(
+            adjusted_raw_data, auto_repair=True
+        )
+
+        # Always use the validated entries (repaired copies)
+        scan_result = CacheBuildResult(
+            raw_data=valid_entries,
+            hash_index=hash_index,
+            tags_count=tags_count,
+            excluded_models=list(persisted.excluded_models)
+        )
+        return scan_result, invalid_entries
+
     async def _run_autov3_backfill(self) -> None:
         """Backfill autov3 for entries loaded from the persisted cache that lack it."""
         try:
@@ -771,7 +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:
@@ -838,32 +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(
@@ -881,6 +1001,8 @@ class ModelScanner:
         try:
             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}
@@ -897,12 +1019,12 @@ class ModelScanner:
             new_files = []
             visited_real_paths = set()
             discovered_real_files = set()
-            
+
             # Scan all model roots
             for root_path in self.get_model_roots():
                 if not os.path.exists(root_path):
                     continue
-                
+
                 # Recursively scan directory
                 for root, dirnames, files in os.walk(root_path, followlinks=True):
                     dirnames[:] = [d for d in dirnames if not _is_excluded_dir(d)]
@@ -910,7 +1032,7 @@ class ModelScanner:
                     if real_root in visited_real_paths:
                         continue
                     visited_real_paths.add(real_root)
-                    
+
                     for file in files:
                         ext = os.path.splitext(file)[1].lower()
                         if ext in self.file_extensions:
@@ -954,17 +1076,25 @@ 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
             total_added = 0
             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
@@ -1020,9 +1150,24 @@ class ModelScanner:
                                 logger.error(f"Could not determine root path for {path}")
                         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)
@@ -1088,8 +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()
@@ -1114,6 +1268,56 @@ class ModelScanner:
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
         raise NotImplementedError("Subclasses must implement get_model_roots")
+
+    async def get_all_folders(self) -> List[str]:
+        """Enumerate every directory under the model roots, live from disk.
+
+        Unlike the models-only ``cache.folders``, this includes empty
+        directories, so it stays accurate even when the in-memory cache was
+        hydrated from a persisted snapshot without a filesystem walk. Hidden
+        directories (any segment starting with '.') and the pending-delete
+        staging dir are excluded. The result is unioned with the model-derived
+        folders so it is always a superset of ``cache.folders``, and cached
+        for ``ALL_FOLDERS_CACHE_TTL_SECONDS`` to avoid repeated walks.
+        """
+        now = time.monotonic()
+        if self._all_folders_ttl_cache is not None:
+            cached_at, cached_folders = self._all_folders_ttl_cache
+            if now - cached_at < ALL_FOLDERS_CACHE_TTL_SECONDS:
+                return cached_folders
+
+        discovered: Set[str] = set()
+        visited_real_paths: Set[str] = set()
+
+        for root_path in self.get_model_roots():
+            if not os.path.exists(root_path):
+                continue
+
+            for root, dirnames, _files in os.walk(root_path, followlinks=True):
+                dirnames[:] = [d for d in dirnames if not _is_excluded_dir(d)]
+                # realpath is used only for symlink dedup, never for the
+                # recorded path (business paths stay unresolved).
+                real_root = os.path.realpath(root)
+                if real_root in visited_real_paths:
+                    continue
+                visited_real_paths.add(real_root)
+
+                rel_dir = os.path.relpath(os.path.abspath(root), os.path.abspath(root_path))
+                rel_dir = rel_dir.replace(os.path.sep, "/")
+                if rel_dir != "." and not _is_hidden_relative_path(rel_dir):
+                    discovered.add(rel_dir)
+
+        folders = set(discovered)
+        if self._cache is not None:
+            folders |= {item.get('folder', '') for item in self._cache.raw_data}
+
+        result = sorted(folders, key=lambda x: x.lower())
+        self._all_folders_ttl_cache = (now, result)
+        return result
+
+    def invalidate_all_folders_cache(self) -> None:
+        """Drop the cached get_all_folders() result (e.g. after a move)."""
+        self._all_folders_ttl_cache = None
     
     async def _create_default_metadata(self, file_path: str) -> Optional[BaseModelMetadata]:
         """Get model file info and metadata (extensible for different model types)"""
@@ -1329,8 +1533,8 @@ class ModelScanner:
         else:
             self._cache.raw_data = list(scan_result.raw_data)
 
-        self._cache.rebuild_version_index()
-
+        # resort() rebuilds folders and the version index on every path, so a
+        # separate rebuild_version_index() call here would be redundant.
         await self._cache.resort()
 
         self._log_duplicate_filename_summary()
@@ -1415,7 +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."""
 
@@ -1427,11 +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}")
 
@@ -1497,7 +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
@@ -1773,6 +1977,10 @@ class ModelScanner:
 
         await cache.resort()
 
+        # A move may have created new directories; drop the cached live-walk
+        # result so the next include_empty request sees them.
+        self.invalidate_all_folders_cache()
+
         if cache_modified:
             await self._persist_current_cache()
             self.bump_cache_version()
@@ -2414,8 +2622,8 @@ class ModelScanner:
                     })
             
             # Merge every staged per-file batch into ONE undoable batch. On a
-            # merge failure (cross-volume EXDEV etc.) the response falls back
-            # to the constituent batch_ids array so the frontend can undo them
+            # merge failure (defensive) the response falls back to the
+            # constituent batch_ids array so the frontend can undo them
             # sequentially.
             batch_field: Dict[str, Any] = {}
             if batch_ids:

py/services/model_update_service.py

@@ -13,11 +13,12 @@ import sqlite3
 import time
 from dataclasses import dataclass, replace
 from datetime import datetime, timezone
-from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence
+from typing import Any, Dict, Iterable, Iterator, List, Mapping, Optional, Sequence
 
 from .errors import RateLimitError, ResourceNotFoundError
 from .settings_manager import get_settings_manager
 from ..utils.cache_paths import CacheType, resolve_cache_path_with_migration
+from ..utils.constants import MODEL_WEIGHT_FILE_TYPES
 from ..utils.civitai_utils import rewrite_preview_url
 from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 
@@ -77,6 +78,10 @@ class ModelVersionRecord:
     usage_control: Optional[str] = None  # "Download", "Generation", "InternalGeneration"
     paid_access: Optional[str] = None  # JSON string of the CivitAI paidAccess DTO
     is_paid: bool = False  # True when paidAccess.permanent is True (permanent paid gate)
+    # Number of downloadable weight files for the version (None when unknown,
+    # e.g. records persisted before this field existed or locally-synthesized
+    # entries). Mirrors the frontend isModelWeightFile() filter.
+    file_count: Optional[int] = None
 
 
 @dataclass
@@ -245,6 +250,51 @@ class ModelUpdateRecord:
 
         return False
 
+    def has_update_for_local_bases(
+        self,
+        hide_early_access: bool = False,
+        hide_non_downloadable: bool = True,
+        hide_paid: bool = False,
+    ) -> bool:
+        """Return True when any locally-held base model scope has an update.
+
+        Aggregates :meth:`has_update_for_base` across every distinct base model
+        present among in-library versions. This mirrors the per-item evaluation
+        performed by ``BaseModelService._annotate_update_flags`` when the
+        ``version_grouping`` setting is ``same_base``, so callers reporting
+        "how many models have updates" stay aligned with what the Updates
+        filter displays. Use this instead of :meth:`has_update` for such
+        summaries; see issue #1083.
+
+        When no local base model is known (nothing held locally, or versions
+        never seen in any remote listing), falls back to :meth:`has_update` so
+        a model the item-level filter may still flag is not silently dropped
+        from summaries.
+        """
+
+        bases = {
+            _normalize_base_model(version.base_model)
+            for version in self.versions
+            if version.is_in_library
+        }
+        bases.discard(None)
+        if not bases:
+            return self.has_update(
+                hide_early_access=hide_early_access,
+                hide_non_downloadable=hide_non_downloadable,
+                hide_paid=hide_paid,
+            )
+        return any(
+            self.has_update_for_base(
+                None,
+                base,
+                hide_early_access=hide_early_access,
+                hide_non_downloadable=hide_non_downloadable,
+                hide_paid=hide_paid,
+            )
+            for base in bases
+        )
+
 
 class ModelUpdateService:
     """Persist and query remote model version metadata."""
@@ -273,6 +323,7 @@ class ModelUpdateService:
             usage_control TEXT,
             paid_access TEXT,
             is_paid INTEGER NOT NULL DEFAULT 0,
+            file_count INTEGER,
             PRIMARY KEY (model_id, version_id),
             FOREIGN KEY(model_id) REFERENCES model_update_status(model_id) ON DELETE CASCADE
         );
@@ -520,6 +571,10 @@ class ModelUpdateService:
                 "ALTER TABLE model_update_versions "
                 "ADD COLUMN is_paid INTEGER NOT NULL DEFAULT 0"
             ),
+            "file_count": (
+                "ALTER TABLE model_update_versions "
+                "ADD COLUMN file_count INTEGER"
+            ),
         }
 
         for column, statement in migrations.items():
@@ -623,6 +678,7 @@ class ModelUpdateService:
                 is_early_access INTEGER NOT NULL DEFAULT 0,
                 paid_access TEXT,
                 is_paid INTEGER NOT NULL DEFAULT 0,
+                file_count INTEGER,
                 PRIMARY KEY (model_id, version_id),
                 FOREIGN KEY(model_id) REFERENCES model_update_status(model_id) ON DELETE CASCADE
             )
@@ -644,6 +700,7 @@ class ModelUpdateService:
             "is_early_access",
             "paid_access",
             "is_paid",
+            "file_count",
         ]
         defaults = {
             "sort_index": "0",
@@ -658,6 +715,7 @@ class ModelUpdateService:
             "is_early_access": "0",
             "paid_access": "NULL",
             "is_paid": "0",
+            "file_count": "NULL",
         }
 
         select_parts = []
@@ -773,6 +831,11 @@ class ModelUpdateService:
                 target_model_ids=target_filter,
             )
 
+        local_base_models = await self._collect_local_version_bases(
+            scanner,
+            target_model_ids=target_filter,
+        )
+
         results: Dict[int, ModelUpdateRecord] = {}
         prefetched: Dict[int, Mapping[Any, Any]] = {}
 
@@ -825,6 +888,7 @@ class ModelUpdateService:
                 force_refresh=force_refresh,
                 prefetched_response=prefetched.get(model_id),
                 all_local_version_ids=all_vids,
+                local_base_models=local_base_models,
             )
             if scanner.is_cancelled():
                 logger.info(f"{model_type.capitalize()} Update Service: Refresh cancelled by user")
@@ -859,12 +923,14 @@ class ModelUpdateService:
 
         local_versions = await self._collect_local_versions(scanner)
         version_ids = local_versions.get(model_id, [])
+        local_base_models = await self._collect_local_version_bases(scanner)
         return await self._refresh_single_model(
             model_type,
             model_id,
             version_ids,
             metadata_provider,
             force_refresh=force_refresh,
+            local_base_models=local_base_models,
         )
 
     async def update_in_library_versions(
@@ -1040,6 +1106,7 @@ class ModelUpdateService:
         force_refresh: bool = False,
         prefetched_response: Optional[Mapping[str, Any]] = None,
         all_local_version_ids: Optional[Sequence[int]] = None,
+        local_base_models: Optional[Mapping[int, str]] = None,
     ) -> Optional[ModelUpdateRecord]:
         normalized_local = self._normalize_sequence(local_versions)
         # When folder-filtering, this carries the cross-folder version set
@@ -1164,6 +1231,7 @@ class ModelUpdateService:
                     existing,
                     now,
                     all_local_version_ids=normalized_all,
+                    local_base_models=local_base_models,
                 )
             else:
                 record = self._merge_with_local_versions(
@@ -1370,27 +1438,17 @@ class ModelUpdateService:
         await self._enrich_version_entries(metadata_provider, aggregated)
         return aggregated
 
-    async def _collect_local_versions(
-        self,
-        scanner,
+    @staticmethod
+    def _iter_local_civitai_items(
+        cache,
         *,
-        target_model_ids: Optional[Sequence[int]] = None,
-        folder_path: Optional[str] = None,
-    ) -> Dict[int, List[int]]:
-        cache = await scanner.get_cached_data()
-        mapping: Dict[int, set[int]] = {}
+        target_set: Optional[set[int]] = None,
+        normalized_folder: Optional[str] = None,
+    ) -> Iterator[tuple[int, int, Any]]:
+        """Yield ``(modelId, versionId, base_model)`` for each scannable item."""
+
         if not cache or not getattr(cache, "raw_data", None):
-            return {}
-
-        target_set = None
-        if target_model_ids:
-            target_set = set(target_model_ids)
-            if not target_set:
-                return {}
-
-        normalized_folder = None
-        if folder_path is not None:
-            normalized_folder = folder_path.replace("\\", "/").strip("/")
+            return
 
         for item in cache.raw_data:
             # Apply folder filter first (cheapest check)
@@ -1410,10 +1468,75 @@ class ModelUpdateService:
                 continue
             if target_set is not None and model_id not in target_set:
                 continue
+            yield model_id, version_id, item.get("base_model")
+
+    def _prepare_collection_filters(
+        self,
+        target_model_ids: Optional[Sequence[int]],
+        folder_path: Optional[str],
+    ) -> tuple[Optional[set[int]], Optional[str]]:
+        target_set: Optional[set[int]] = None
+        if target_model_ids:
+            target_set = set(target_model_ids)
+
+        normalized_folder = None
+        if folder_path is not None:
+            normalized_folder = folder_path.replace("\\", "/").strip("/")
+        return target_set, normalized_folder
+
+    async def _collect_local_versions(
+        self,
+        scanner,
+        *,
+        target_model_ids: Optional[Sequence[int]] = None,
+        folder_path: Optional[str] = None,
+    ) -> Dict[int, List[int]]:
+        cache = await scanner.get_cached_data()
+        mapping: Dict[int, set[int]] = {}
+        target_set, normalized_folder = self._prepare_collection_filters(
+            target_model_ids, folder_path
+        )
+
+        if target_model_ids and not target_set:
+            return {}
+
+        for model_id, version_id, _base_model in self._iter_local_civitai_items(
+            cache, target_set=target_set, normalized_folder=normalized_folder
+        ):
             mapping.setdefault(model_id, set()).add(version_id)
 
         return {model_id: sorted(ids) for model_id, ids in mapping.items()}
 
+    async def _collect_local_version_bases(
+        self,
+        scanner,
+        *,
+        target_model_ids: Optional[Sequence[int]] = None,
+    ) -> Dict[int, str]:
+        """Map version id -> base model from cache items.
+
+        Deliberately unfiltered by folder: synthesized in-library entries must
+        carry a base regardless of which folder triggered the refresh.
+        """
+
+        cache = await scanner.get_cached_data()
+        bases: Dict[int, str] = {}
+        target_set, _normalized_folder = self._prepare_collection_filters(
+            target_model_ids, None
+        )
+
+        if target_model_ids and not target_set:
+            return {}
+
+        for _model_id, version_id, base_model in self._iter_local_civitai_items(
+            cache, target_set=target_set
+        ):
+            normalized_base = _normalize_string(base_model)
+            if normalized_base:
+                bases[version_id] = normalized_base
+
+        return bases
+
     def _merge_with_local_versions(
         self,
         existing: Optional[ModelUpdateRecord],
@@ -1493,6 +1616,7 @@ class ModelUpdateService:
         timestamp: float,
         *,
         all_local_version_ids: Optional[Sequence[int]] = None,
+        local_base_models: Optional[Mapping[int, str]] = None,
     ) -> ModelUpdateRecord:
         local_set = set(local_versions)
         # When folder-filtering, also consider versions in other folders
@@ -1504,6 +1628,7 @@ class ModelUpdateService:
         )
         ignore_map = {version.version_id: version.should_ignore for version in existing.versions} if existing else {}
         preview_map = {version.version_id: version.preview_url for version in existing.versions} if existing else {}
+        file_count_map = {version.version_id: version.file_count for version in existing.versions} if existing else {}
         sort_map = {version.version_id: version.sort_index for version in existing.versions} if existing else {}
         existing_map = {version.version_id: version for version in existing.versions} if existing else {}
 
@@ -1528,11 +1653,17 @@ class ModelUpdateService:
                     usage_control=remote_version.usage_control,
                     paid_access=remote_version.paid_access,
                     is_paid=remote_version.is_paid,
+                    file_count=(
+                        remote_version.file_count
+                        if remote_version.file_count is not None
+                        else file_count_map.get(version_id)
+                    ),
                 )
             )
 
         missing_local = local_set - seen_ids
         if missing_local:
+            item_base_models = local_base_models or {}
             for version_id in sorted(missing_local):
                 existing_version = existing_map.get(version_id)
                 if existing_version:
@@ -1547,7 +1678,7 @@ class ModelUpdateService:
                         ModelVersionRecord(
                             version_id=version_id,
                             name=None,
-                            base_model=None,
+                            base_model=item_base_models.get(version_id),
                             released_at=None,
                             size_bytes=None,
                             preview_url=None,
@@ -1620,6 +1751,7 @@ class ModelUpdateService:
         base_model = _normalize_string(entry.get("baseModel"))
         released_at = _normalize_string(entry.get("publishedAt") or entry.get("createdAt"))
         size_bytes = self._extract_size_bytes(entry.get("files"))
+        file_count = self._extract_file_count(entry.get("files"))
         preview_url = self._extract_preview_url(entry.get("images"))
         early_access_ends_at = _normalize_string(entry.get("earlyAccessEndsAt"))
 
@@ -1655,6 +1787,7 @@ class ModelUpdateService:
             usage_control=usage_control,
             paid_access=paid_access_json,
             is_paid=is_paid,
+            file_count=file_count,
         )
 
     @staticmethod
@@ -1683,6 +1816,25 @@ class ModelUpdateService:
             return None
         return {"permanent": permanent, "endsAt": ends_at}
 
+    @staticmethod
+    def _extract_file_count(files) -> Optional[int]:
+        """Count downloadable weight files in a version entry's ``files`` list.
+
+        Returns None when the payload carries no files array (unknown), so
+        callers can distinguish "no weight files" from "no data".
+        """
+
+        if not isinstance(files, list):
+            return None
+        count = 0
+        for entry in files:
+            if not isinstance(entry, Mapping):
+                continue
+            entry_type = entry.get("type")
+            if isinstance(entry_type, str) and entry_type in MODEL_WEIGHT_FILE_TYPES:
+                count += 1
+        return count
+
     def _extract_size_bytes(self, files) -> Optional[int]:
         if not isinstance(files, Iterable):
             return None
@@ -1795,7 +1947,7 @@ class ModelUpdateService:
                     f"""
                     SELECT model_id, version_id, sort_index, name, base_model, released_at,
                            size_bytes, preview_url, is_in_library, should_ignore, early_access_ends_at,
-                           is_early_access, usage_control, paid_access, is_paid
+                           is_early_access, usage_control, paid_access, is_paid, file_count
                     FROM model_update_versions
                     WHERE model_id IN ({placeholders})
                     ORDER BY model_id ASC, sort_index ASC, version_id ASC
@@ -1826,6 +1978,7 @@ class ModelUpdateService:
                     usage_control=row["usage_control"],
                     paid_access=row["paid_access"],
                     is_paid=bool(row["is_paid"]),
+                    file_count=_normalize_int(row["file_count"]),
                 )
             )
 
@@ -1888,8 +2041,8 @@ class ModelUpdateService:
                     INSERT INTO model_update_versions (
                         version_id, model_id, sort_index, name, base_model, released_at,
                         size_bytes, preview_url, is_in_library, should_ignore, early_access_ends_at,
-                        is_early_access, usage_control, paid_access, is_paid
-                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                        is_early_access, usage_control, paid_access, is_paid, file_count
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                     """,
                     (
                         version.version_id,
@@ -1907,6 +2060,7 @@ class ModelUpdateService:
                         version.usage_control,
                         paid_access_value,
                         1 if version.is_paid else 0,
+                        version.file_count,
                     ),
                 )
             conn.commit()

py/services/pending_delete_service.py

@@ -274,17 +274,21 @@ class PendingDeleteService:
     async def merge_batches(self, batch_ids: Sequence[str]) -> Optional[str]:
         """Merge several batches into the first batch's manifest.
 
-        Winner is ``batch_ids[0]``. The staged files of losing batches are
-        MOVED (os.rename) into the winner's batch dir and their ``staged``
-        paths rewritten in the merged manifest BEFORE any loser dir is
-        removed. ``expires_at`` is re-anchored to ``now + TTL`` at merge time
-        and a FRESH purge timer is armed for the winner.
+        Winner is ``batch_ids[0]``. Merging is MANIFEST-ONLY: staged files
+        are NEVER moved, so the merge is a pure metadata operation with zero
+        data IO and is inherently cross-volume safe (no EXDEV, no rollback).
+        Every loser's entries are appended to the winner's manifest with
+        their ``staged`` paths unchanged (files keep living in the loser's
+        own batch dir - the sibling-of-model staging location), each loser
+        dir is recorded in the winner manifest's ``merged_sources``, and each
+        loser manifest is stamped ``merged_into`` so its own purge timer, a
+        post-restart sweep or a direct undo call no-op. ``expires_at`` is
+        re-anchored to ``now + TTL`` at merge time and a FRESH purge timer is
+        armed for the winner.
 
-        On any move failure every already-moved file is moved BACK and the
-        original batch dirs/manifests are left intact; ``None`` is returned so
-        callers fall back to the ``batch_ids`` array contract. Cross-volume
-        merges hit EXDEV here - expected and fine (the fallback is the normal
-        path for those bulks).
+        Returns the winner id, or ``None`` when the winner batch cannot be
+        resolved (callers then fall back to the ``batch_ids`` array
+        contract).
         """
         if not batch_ids:
             return None
@@ -298,77 +302,68 @@ class PendingDeleteService:
             if winner_manifest is None:
                 return None
 
-            # Track (entry, original_staged_path, loser_dir) for rollback.
-            moved: List[Tuple[Dict[str, Any], str, str]] = []
-            processed_losers: List[Tuple[str, str]] = []  # (loser_id, loser_dir)
-
-            try:
-                for loser_id in batch_ids[1:]:
-                    loser_dir = await self._find_batch_dir(loser_id)
-                    if not loser_dir or os.path.normpath(loser_dir) == os.path.normpath(
-                        winner_dir
-                    ):
+            # Build the merged manifest in memory: loser entries are appended
+            # with their staged paths UNCHANGED - no file moves, no IO, no
+            # EXDEV. Loser dirs remain as physical storage until the merged
+            # batch is undone or purged.
+            merged_sources: List[str] = []
+            seen_loser_dirs: Set[str] = set()
+            for loser_id in batch_ids[1:]:
+                loser_dir = await self._find_batch_dir(loser_id)
+                if not loser_dir or os.path.normpath(loser_dir) == os.path.normpath(
+                    winner_dir
+                ):
+                    continue
+                loser_abs = os.path.abspath(loser_dir)
+                if loser_abs in seen_loser_dirs:
+                    continue
+                seen_loser_dirs.add(loser_abs)
+                loser_manifest = self._read_manifest(loser_dir)
+                if loser_manifest is None:
+                    # Corrupted loser: leave it for the sweep to quarantine.
+                    continue
+                for entry in loser_manifest.get("entries") or []:
+                    if entry.get("restored"):
                         continue
-                    loser_manifest = self._read_manifest(loser_dir)
-                    if loser_manifest is None:
-                        # Corrupted loser: leave it for the sweep to quarantine.
+                    staged_path = entry.get("staged")
+                    if not staged_path or not os.path.exists(staged_path):
                         continue
-                    for entry in loser_manifest.get("entries") or []:
-                        if entry.get("restored"):
-                            continue
-                        staged_path = entry.get("staged")
-                        if not staged_path or not os.path.exists(staged_path):
-                            continue
-                        new_staged = os.path.join(
-                            winner_dir, os.path.basename(staged_path)
-                        )
-                        if os.path.exists(new_staged):
-                            # os.rename would silently overwrite the existing
-                            # staged file on POSIX - never drop a staged file.
-                            # Abort the merge so callers fall back to the
-                            # batch_ids array contract.
-                            raise OSError(
-                                f"Merge collision: {os.path.basename(staged_path)} "
-                                "already staged in winner batch"
-                            )
-                        os.rename(staged_path, new_staged)
-                        original_staged = entry["staged"]
-                        entry["staged"] = os.path.abspath(new_staged)
-                        winner_manifest["entries"].append(entry)
-                        moved.append((entry, original_staged, loser_dir))
-                    processed_losers.append((loser_id, loser_dir))
-            except OSError as exc:
-                logger.warning(
-                    "Merge of %s failed after moving files: %s; rolling back",
-                    list(batch_ids),
-                    exc,
-                )
-                self._rollback_merge_moves(moved)
-                return None
+                    winner_manifest["entries"].append(entry)
+                merged_sources.append(loser_abs)
 
-            # Re-anchor expiry and persist the merged manifest atomically.
+            # Re-anchor expiry and persist the merged manifest atomically - it
+            # becomes the ONLY source of truth for every merged file, wherever
+            # it physically lives.
             winner_manifest["expires_at"] = (
                 int(time.time()) + PENDING_DELETE_TTL_SECONDS
             )
+            if merged_sources:
+                winner_manifest["merged_sources"] = merged_sources
             try:
                 self._write_manifest_atomic(winner_dir, winner_manifest)
             except OSError as exc:
                 logger.warning(
-                    "Failed to write merged manifest for %s: %s; rolling back",
+                    "Failed to write merged manifest for %s: %s",
                     winner_id,
                     exc,
                 )
-                self._rollback_merge_moves(moved)
                 return None
 
-            # All moves committed: remove loser dirs (must be empty by now)
-            # and drop them from the registry. Skipped losers (missing /
-            # corrupted / same-dir) stay registered so the sweep still
-            # quarantines them, exactly as before the registry existed.
-            for loser_id, loser_dir in processed_losers:
-                self._remove_manifest(loser_dir)
-                self._remove_empty_dir(loser_dir)
-                await self._forget_batch(loser_id)
+            # Stamp each loser manifest so its own purge timer / a later sweep
+            # / a direct undo call no-op: the winner owns those files from
+            # here on. Best-effort coordination; a failed stamp only risks the
+            # loser being swept at its own (earlier) expiry after a restart.
+            for loser_dir in merged_sources:
+                try:
+                    self._mark_merged(loser_dir, winner_id)
+                except OSError as exc:  # pragma: no cover - best-effort
+                    logger.warning(
+                        "Failed to mark merged loser %s: %s", loser_dir, exc
+                    )
+
+            # Losers are no longer independently managed.
+            for loser_dir in merged_sources:
+                await self._forget_batch(os.path.basename(loser_dir))
             await self._remember_batch(winner_id, winner_dir)
 
             # Arm a fresh purge timer for the winner with the re-anchored
@@ -397,6 +392,16 @@ class PendingDeleteService:
             if manifest is None:
                 raise ValueError(f"Manifest missing for batch {batch_id}")
 
+            merged_into = manifest.get("merged_into")
+            if merged_into:
+                # The batch was merged into another batch: its staged files
+                # are owned by the winner's manifest. Undo via the winner so
+                # the whole merged batch stays consistent.
+                raise ValueError(
+                    f"Batch {batch_id} was merged into batch {merged_into}; "
+                    "undo that batch instead"
+                )
+
             if manifest.get("state") == "restored":
                 return self._undo_result(manifest)
 
@@ -448,6 +453,10 @@ class PendingDeleteService:
             self._remove_manifest(batch_dir)
             self._remove_empty_dir(batch_dir)
             await self._forget_batch(batch_id)
+            # Clean up merged loser dirs (their staged files were restored
+            # above) and drop them from the registry too.
+            for loser_id in self._remove_merged_batch_dirs(manifest):
+                await self._forget_batch(loser_id)
 
             logger.info("Restored pending-delete batch %s", batch_id)
             return self._undo_result(manifest)
@@ -500,10 +509,36 @@ class PendingDeleteService:
         QUARANTINE them (preserving the pre-registry sweep semantics). The
         walk only descends into dirs literally named ``.lm-pending-delete``,
         so false positives are structurally limited.
+
+        The filesystem walk itself runs in a worker thread so a large or slow
+        library cannot block the event loop at startup; only the (rare) batch
+        registration awaits run on the loop.
+        """
+        roots = await self._get_all_model_roots()
+        loop = asyncio.get_event_loop()
+        staging_parents = await loop.run_in_executor(
+            None,  # Use default thread pool
+            self._collect_staging_parents,  # Run the tree walk off the loop
+            roots,
+        )
+        for staging_parent in staging_parents:
+            await self._register_batch_candidates(staging_parent)
+
+    def _collect_staging_parents(self, roots: Sequence[str]) -> List[str]:
+        """Walk every model root and return its staging-parent dirs.
+
+        Pure synchronous filesystem discovery with no awaits: walks with
+        ``followlinks=True, topdown=True``, prunes symlink cycles via a
+        per-root ``visited`` realpath set (realpath is used ONLY for this
+        dedup set - the returned paths are the unresolved business paths),
+        filters out :func:`_is_excluded_dir` dirs, and collects every dir
+        named ``.lm-pending-delete`` (including the case where a model root
+        itself is one). Results are returned in walk order.
         """
         from .model_scanner import _is_excluded_dir
 
-        for root in await self._get_all_model_roots():
+        staging_parents: List[str] = []
+        for root in roots:
             if not os.path.isdir(root):
                 continue
             visited: Set[str] = set()
@@ -518,21 +553,20 @@ class PendingDeleteService:
                 visited.add(real_dir)
                 if os.path.basename(dirpath) == PENDING_DELETE_DIR_NAME:
                     # The current dir IS a staging parent (reachable only when
-                    # a model root itself is one): register its batches.
-                    await self._register_batch_candidates(dirpath)
+                    # a model root itself is one): collect its batches.
+                    staging_parents.append(dirpath)
                     dirnames[:] = []
                     continue
                 next_dirs: List[str] = []
                 for name in dirnames:
                     if name == PENDING_DELETE_DIR_NAME:
-                        await self._register_batch_candidates(
-                            os.path.join(dirpath, name)
-                        )
+                        staging_parents.append(os.path.join(dirpath, name))
                     elif _is_excluded_dir(name):
                         continue
                     else:
                         next_dirs.append(name)
                 dirnames[:] = next_dirs
+        return staging_parents
 
     async def _register_batch_candidates(self, staging_parent: str) -> None:
         """Register every non-orphaned batch subdir of a staging parent."""
@@ -754,25 +788,35 @@ class PendingDeleteService:
                     "Failed to remove staged copy %s: %s", staged_path, exc
                 )
 
-    def _rollback_merge_moves(
-        self, moved: Sequence[Tuple[Dict[str, Any], str, str]]
-    ) -> None:
-        """Move already-merged files back to their original loser batch dirs."""
-        for _entry, original_staged, _loser_dir in reversed(list(moved)):
-            current = _entry.get("staged")
-            if not current or not original_staged:
+    def _mark_merged(self, loser_dir: str, winner_id: str) -> None:
+        """Stamp ``merged_into`` on a loser manifest (best-effort).
+
+        The stamp makes the loser's own purge timer, post-restart sweeps and
+        direct undo calls no-op, so the winner's merged batch stays the only
+        owner of the loser's staged files until it is undone or purged.
+        """
+        loser_manifest = self._read_manifest(loser_dir)
+        if loser_manifest is None:
+            return
+        loser_manifest["merged_into"] = winner_id
+        self._write_manifest_atomic(loser_dir, loser_manifest)
+
+    def _remove_merged_batch_dirs(self, manifest: Dict[str, Any]) -> List[str]:
+        """Remove merged loser batch dirs once their files were handled.
+
+        Called after a merged batch has been fully undone or purged: each
+        loser manifest (stamped ``merged_into``) and its now-empty dir are
+        removed so the sweep never quarantines an orphaned staging dir.
+        Best-effort - returns the removed batch ids for registry cleanup.
+        """
+        removed: List[str] = []
+        for src in manifest.get("merged_sources") or []:
+            if not isinstance(src, str) or not src:
                 continue
-            if not os.path.exists(current):
-                continue
-            try:
-                os.rename(current, original_staged)
-            except OSError as exc:  # pragma: no cover - best-effort rollback
-                logger.warning(
-                    "Failed to roll back merge move %s -> %s: %s",
-                    current,
-                    original_staged,
-                    exc,
-                )
+            self._remove_manifest(src)
+            self._remove_empty_dir(src)
+            removed.append(os.path.basename(src))
+        return removed
 
     def _purge_batch_dir(self, batch_dir: str) -> bool:
         """Purge one batch dir. Returns True when the batch was purged/removed."""
@@ -786,6 +830,13 @@ class PendingDeleteService:
             self._quarantine_batch_dir(batch_dir)
             return True
 
+        if manifest.get("merged_into"):
+            # Merged into another batch: the winner owns these staged files.
+            # The loser's own purge timer / post-restart sweep must not remove
+            # them early (the winner re-anchored the merged expiry to give the
+            # whole bulk one undo window).
+            return False
+
         if manifest.get("state") == "restored":
             return False
 
@@ -817,6 +868,7 @@ class PendingDeleteService:
 
         self._remove_manifest(batch_dir)
         self._remove_empty_dir(batch_dir)
+        self._remove_merged_batch_dirs(manifest)
         return True
 
     def _quarantine_batch_dir(self, batch_dir: str) -> str:

py/services/persistent_recipe_cache.py

@@ -52,12 +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()
@@ -332,6 +333,44 @@ class PersistentRecipeCache:
         except Exception as exc:
             logger.debug("Failed to persist image_id_map: %s", exc)
 
+    def get_metadata_value(self, key: str) -> Optional[str]:
+        """Return a value from cache_metadata, or None if missing."""
+        if not self.is_enabled() or not self._schema_initialized:
+            return None
+
+        try:
+            with self._db_lock:
+                conn = self._connect(readonly=True)
+                try:
+                    row = conn.execute(
+                        "SELECT value FROM cache_metadata WHERE key = ?",
+                        (key,),
+                    ).fetchone()
+                    return row["value"] if row else None
+                finally:
+                    conn.close()
+        except Exception:
+            return None
+
+    def set_metadata_value(self, key: str, value: str) -> None:
+        """Store a value in cache_metadata without rewriting the full cache."""
+        if not self.is_enabled() or not self._schema_initialized:
+            return
+
+        try:
+            with self._db_lock:
+                conn = self._connect()
+                try:
+                    conn.execute(
+                        "INSERT OR REPLACE INTO cache_metadata (key, value) VALUES (?, ?)",
+                        (key, value),
+                    )
+                    conn.commit()
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("Failed to persist cache metadata %s: %s", key, exc)
+
     def get_indexed_recipe_ids(self) -> Set[str]:
         """Return all recipe IDs in the cache.
 
@@ -402,12 +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
+                            tags_json TEXT,
+                            has_workflow INTEGER DEFAULT 0,
+                            import_info_json TEXT
                         );
 
                         CREATE INDEX IF NOT EXISTS idx_recipes_json_path ON recipes(json_path);
@@ -426,6 +466,20 @@ class PersistentRecipeCache:
                         )
                     except Exception:
                         pass  # column already exists
+                    # Migration: add has_workflow column to existing databases
+                    try:
+                        conn.execute(
+                            "ALTER TABLE recipes ADD COLUMN has_workflow INTEGER DEFAULT 0"
+                        )
+                    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:
@@ -457,6 +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
@@ -482,12 +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]:
@@ -520,6 +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 "",
@@ -531,8 +596,8 @@ 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,
             "gen_params": gen_params,
         }
@@ -543,6 +608,9 @@ class PersistentRecipeCache:
         if checkpoint:
             recipe["checkpoint"] = checkpoint
 
+        if import_info:
+            recipe["import_info"] = import_info
+
         return recipe
 
 

py/services/rate_limit_coordinator.py

@@ -0,0 +1,213 @@
+"""Process-wide, per-destination rate-limit gate for outbound API traffic.
+
+Implements the pacing/gating layer designed in
+``docs/plans/issue-1085-rate-limit-design.md``:
+
+- **Reactive gate**: a 429 response arms ``next_allowed_send`` from the
+  vendor's ``Retry-After`` (or exponential backoff when the header is
+  missing); subsequent requests to the same destination wait out the window.
+- **Preemptive pacing**: a minimum inter-request interval per destination
+  spaces consecutive sends so bursts never form in the first place.
+- **Herd-free**: waiters are serialized through a per-destination lock, so
+  each one claims a distinct send slot instead of thousands of coroutines
+  waking up together.
+- **Bounded**: waits longer than ``rate_limit_max_wait_seconds`` are refused
+  by raising :class:`RateLimitError`, leaving the final decision to callers.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Dict, Optional
+
+from .errors import RateLimitError
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MIN_INTERVAL_SECONDS = 0.75
+DEFAULT_MAX_WAIT_SECONDS = 300.0
+BASE_BACKOFF_SECONDS = 30.0
+MAX_BACKOFF_SECONDS = 1800.0
+
+
+@dataclass
+class _DestinationState:
+    """Rate-limit bookkeeping for one destination (hostname)."""
+
+    next_allowed_send: float = 0.0  # time.monotonic() timestamp
+    consecutive_429: int = 0
+    last_send_at: float = 0.0  # time.monotonic() timestamp
+    lock: asyncio.Lock = field(default_factory=asyncio.Lock)
+
+
+class RateLimitCoordinator:
+    """Coordinates outbound request pacing per destination.
+
+    Singleton mirroring :class:`ConnectivityGuard`'s pattern. All waits are
+    bounded by the ``rate_limit_max_wait_seconds`` setting; when the required
+    wait exceeds the cap, :meth:`wait_for_slot` raises :class:`RateLimitError`
+    instead of parking the caller.
+    """
+
+    _instance: "RateLimitCoordinator | None" = None
+    _instance_lock = asyncio.Lock()
+
+    @classmethod
+    async def get_instance(cls) -> "RateLimitCoordinator":
+        async with cls._instance_lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+
+    def __init__(self) -> None:
+        if hasattr(self, "_initialized"):
+            return
+        self._initialized = True
+        self._states: Dict[str, _DestinationState] = {}
+
+    # ------------------------------------------------------------------
+    # Settings (read live so settings edits apply without a restart)
+
+    @staticmethod
+    def _setting(key: str, default):
+        try:
+            from .settings_manager import get_settings_manager
+
+            return get_settings_manager().get(key, default)
+        except Exception:  # pragma: no cover - defensive: settings unavailable
+            return default
+
+    @property
+    def enabled(self) -> bool:
+        return bool(self._setting("rate_limit_gate_enabled", True))
+
+    @property
+    def min_interval_seconds(self) -> float:
+        try:
+            return max(0.0, float(self._setting("rate_limit_min_interval_seconds", DEFAULT_MIN_INTERVAL_SECONDS)))
+        except (TypeError, ValueError):
+            return DEFAULT_MIN_INTERVAL_SECONDS
+
+    @property
+    def max_wait_seconds(self) -> float:
+        try:
+            return max(0.0, float(self._setting("rate_limit_max_wait_seconds", DEFAULT_MAX_WAIT_SECONDS)))
+        except (TypeError, ValueError):
+            return DEFAULT_MAX_WAIT_SECONDS
+
+    # ------------------------------------------------------------------
+    # State helpers
+
+    @staticmethod
+    def _normalize(destination: Optional[str]) -> str:
+        if destination is None or not destination.strip():
+            return "__global__"
+        return destination.lower().strip()
+
+    def _state_for(self, destination: Optional[str]) -> _DestinationState:
+        key = self._normalize(destination)
+        if key not in self._states:
+            self._states[key] = _DestinationState()
+        return self._states[key]
+
+    def reset(self) -> None:
+        """Drop all per-destination state. Test seam."""
+        self._states.clear()
+
+    def in_cooldown(self, destination: Optional[str] = None) -> bool:
+        return self.remaining_seconds(destination) > 0
+
+    def remaining_seconds(self, destination: Optional[str] = None) -> float:
+        state = self._state_for(destination)
+        return max(0.0, state.next_allowed_send - time.monotonic())
+
+    # ------------------------------------------------------------------
+    # Gate operations
+
+    async def wait_for_slot(self, destination: Optional[str] = None) -> None:
+        """Block until this caller may send the next request to *destination*.
+
+        Waits for both the rate-limit cooldown (``next_allowed_send``) and the
+        minimum inter-request interval (``last_send_at + min_interval``).
+        Waiters queue on the per-destination lock, so concurrent callers are
+        spaced out instead of stampeding when a cooldown expires.
+
+        Raises:
+            RateLimitError: when the required wait exceeds
+                ``rate_limit_max_wait_seconds``.
+        """
+        state = self._state_for(destination)
+        deadline = time.monotonic() + self.max_wait_seconds
+        async with state.lock:
+            now = time.monotonic()
+            wake_at = max(
+                state.next_allowed_send,
+                state.last_send_at + self.min_interval_seconds,
+            )
+            if wake_at > deadline:
+                raise RateLimitError(
+                    f"Rate limit wait for '{self._normalize(destination)}' "
+                    f"exceeds the {self.max_wait_seconds:.0f}s cap",
+                    retry_after=wake_at - now,
+                )
+            delay = wake_at - now
+            if delay > 0:
+                logger.debug(
+                    "Rate-limit gate: pacing request to '%s' by %.2fs",
+                    self._normalize(destination),
+                    delay,
+                )
+                await asyncio.sleep(delay)
+            state.last_send_at = time.monotonic()
+
+    def register_rate_limit(
+        self,
+        destination: Optional[str],
+        retry_after: Optional[float] = None,
+    ) -> float:
+        """Record a 429 for *destination* and arm the cooldown window.
+
+        Honors the vendor's ``Retry-After`` when present; otherwise grows an
+        exponential backoff (30s base, doubling per consecutive 429, capped at
+        1800s). Returns the cooldown duration in seconds.
+        """
+        state = self._state_for(destination)
+        state.consecutive_429 += 1
+        if retry_after is not None and retry_after > 0:
+            backoff = min(MAX_BACKOFF_SECONDS, float(retry_after))
+        else:
+            backoff = min(
+                MAX_BACKOFF_SECONDS,
+                BASE_BACKOFF_SECONDS * (2 ** (state.consecutive_429 - 1)),
+            )
+        now = time.monotonic()
+        already_cooling = state.next_allowed_send > now
+        state.next_allowed_send = max(state.next_allowed_send, now + backoff)
+        if already_cooling:
+            logger.debug(
+                "Rate-limit cooldown for '%s' extended by %.0fs (consecutive_429=%d)",
+                self._normalize(destination),
+                backoff,
+                state.consecutive_429,
+            )
+        else:
+            logger.info(
+                "Rate limited by '%s'; pausing requests for %.0fs",
+                self._normalize(destination),
+                backoff,
+            )
+        return backoff
+
+    def register_success(self, destination: Optional[str]) -> None:
+        """Reset rate-limit state after a successful request.
+
+        A 200 proves the vendor is accepting traffic again, so any armed
+        cooldown window is cleared alongside the backoff counter (mirrors
+        ``ConnectivityGuard.register_success`` semantics).
+        """
+        state = self._state_for(destination)
+        state.consecutive_429 = 0
+        state.next_allowed_send = 0.0

py/services/recipe_fts_index.py

@@ -7,13 +7,14 @@ enabling sub-100ms search times even with 20k+ recipes.
 from __future__ import annotations
 
 import asyncio
+import hashlib
 import logging
 import os
 import re
 import sqlite3
 import threading
 import time
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional, Set, Tuple
 
 from ..utils.cache_paths import CacheType, resolve_cache_path_with_migration
 
@@ -165,6 +166,7 @@ class RecipeFTSIndex:
                     batch_size = 500
                     total = len(recipes)
                     inserted = 0
+                    indexed_ids: Set[str] = set()
 
                     for i in range(0, total, batch_size):
                         batch = recipes[i:i + batch_size]
@@ -179,6 +181,7 @@ class RecipeFTSIndex:
                             row = self._prepare_fts_row(recipe)
                             rows.append(row)
                             inserted += 1
+                            indexed_ids.add(recipe_id)
 
                         if rows:
                             # Insert into FTS table
@@ -213,7 +216,11 @@ class RecipeFTSIndex:
                     )
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ('recipe_count', str(inserted))
+                        (self._COUNT_METADATA_KEY, str(inserted))
+                    )
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        (self._FINGERPRINT_METADATA_KEY, self._compute_ids_fingerprint(indexed_ids))
                     )
 
                     conn.commit()
@@ -288,6 +295,12 @@ class RecipeFTSIndex:
             with self._lock:
                 conn = self._connect()
                 try:
+                    # Check existence via the rowid mapping (fast PK lookup)
+                    existed = conn.execute(
+                        "SELECT 1 FROM recipe_rowid WHERE recipe_id = ?",
+                        (recipe_id,)
+                    ).fetchone() is not None
+
                     # Remove existing entry if present
                     self._remove_recipe_locked(conn, recipe_id)
 
@@ -312,6 +325,10 @@ class RecipeFTSIndex:
                             (recipe_id, result[0])
                         )
 
+                    # Keep validation metadata in sync (only a new id changes it)
+                    if not existed:
+                        self._update_mutation_metadata_locked(conn, recipe_id, delta=1)
+
                     conn.commit()
                     return True
                 finally:
@@ -339,7 +356,13 @@ class RecipeFTSIndex:
             with self._lock:
                 conn = self._connect()
                 try:
+                    existed = conn.execute(
+                        "SELECT 1 FROM recipe_rowid WHERE recipe_id = ?",
+                        (recipe_id,)
+                    ).fetchone() is not None
                     self._remove_recipe_locked(conn, recipe_id)
+                    if existed:
+                        self._update_mutation_metadata_locked(conn, recipe_id, delta=-1)
                     conn.commit()
                     return True
                 finally:
@@ -371,6 +394,15 @@ class RecipeFTSIndex:
                 try:
                     conn.execute("DELETE FROM recipe_fts")
                     conn.execute("DELETE FROM recipe_rowid")
+                    # Reset validation metadata to the empty index state
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        (self._COUNT_METADATA_KEY, '0')
+                    )
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        (self._FINGERPRINT_METADATA_KEY, self._compute_ids_fingerprint(set()))
+                    )
                     conn.commit()
                     self._ready.clear()
                     return True
@@ -427,10 +459,12 @@ class RecipeFTSIndex:
         """Check if the FTS index matches the expected recipes.
 
         This method validates whether the existing FTS index can be reused
-        without a full rebuild. It checks:
-        1. The index has been initialized
-        2. The count matches
-        3. The recipe IDs match
+        without a full rebuild. It compares the expected count and recipe ID
+        fingerprint against metadata recorded when the index was (re)built,
+        so it does not scan the FTS content table. Indexes built by older
+        versions lack this metadata; for those the validation falls back to
+        a one-time scan of the content table and records the metadata so
+        subsequent startups are cheap.
 
         Args:
             recipe_count: Expected number of recipes.
@@ -446,7 +480,28 @@ class RecipeFTSIndex:
             return False
 
         try:
+            metadata = self._read_validation_metadata()
+            if metadata is not None:
+                stored_count, stored_fingerprint = metadata
+                if stored_count != recipe_count:
+                    logger.debug(
+                        "FTS index count mismatch: indexed=%d, expected=%d",
+                        stored_count, recipe_count
+                    )
+                    return False
+
+                if stored_fingerprint != self._compute_ids_fingerprint(recipe_ids):
+                    logger.debug("FTS index recipe ID fingerprint mismatch")
+                    return False
+
+                return True
+
+            # Legacy fallback: no stored metadata, scan the content table once
+            # and persist the metadata so later validations are cheap.
             indexed_count = self.get_indexed_count()
+            indexed_ids = self.get_indexed_recipe_ids()
+            self._store_validation_metadata(indexed_count, indexed_ids)
+
             if indexed_count != recipe_count:
                 logger.debug(
                     "FTS index count mismatch: indexed=%d, expected=%d",
@@ -454,7 +509,6 @@ class RecipeFTSIndex:
                 )
                 return False
 
-            indexed_ids = self.get_indexed_recipe_ids()
             if indexed_ids != recipe_ids:
                 missing = recipe_ids - indexed_ids
                 extra = indexed_ids - recipe_ids
@@ -471,6 +525,112 @@ class RecipeFTSIndex:
 
     # Internal helpers
 
+    _FINGERPRINT_METADATA_KEY = 'recipe_ids_fingerprint'
+    _COUNT_METADATA_KEY = 'recipe_count'
+
+    @staticmethod
+    def _fingerprint_recipe_id(recipe_id: str) -> int:
+        """Return a stable 64-bit fingerprint contribution for a recipe ID."""
+        digest = hashlib.sha256(recipe_id.encode("utf-8")).digest()
+        return int.from_bytes(digest[:8], "big")
+
+    @classmethod
+    def _compute_ids_fingerprint(cls, recipe_ids: Set[str]) -> str:
+        """Order-independent fingerprint of a recipe ID set (XOR of per-id hashes)."""
+        fingerprint = 0
+        for recipe_id in recipe_ids:
+            fingerprint ^= cls._fingerprint_recipe_id(str(recipe_id))
+        return f"{fingerprint:016x}"
+
+    def _read_validation_metadata(self) -> Optional[Tuple[int, str]]:
+        """Return stored (recipe count, ID fingerprint), or None if absent."""
+        try:
+            with self._lock:
+                conn = self._connect(readonly=True)
+                try:
+                    rows = conn.execute(
+                        "SELECT key, value FROM fts_metadata WHERE key IN (?, ?)",
+                        (self._COUNT_METADATA_KEY, self._FINGERPRINT_METADATA_KEY)
+                    ).fetchall()
+                    values = {row[0]: row[1] for row in rows}
+                    fingerprint = values.get(self._FINGERPRINT_METADATA_KEY)
+                    if fingerprint is None:
+                        return None
+                    try:
+                        count = int(values.get(self._COUNT_METADATA_KEY) or 0)
+                    except (TypeError, ValueError):
+                        return None
+                    return count, fingerprint
+                finally:
+                    conn.close()
+        except FileNotFoundError:
+            return None
+        except Exception as exc:
+            logger.debug("Failed to read FTS validation metadata: %s", exc)
+            return None
+
+    def _store_validation_metadata(self, recipe_count: int, recipe_ids: Set[str]) -> None:
+        """Persist recipe count and ID fingerprint for cheap future validation."""
+        try:
+            with self._lock:
+                conn = self._connect()
+                try:
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        (self._COUNT_METADATA_KEY, str(recipe_count))
+                    )
+                    conn.execute(
+                        "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                        (self._FINGERPRINT_METADATA_KEY, self._compute_ids_fingerprint(recipe_ids))
+                    )
+                    conn.commit()
+                finally:
+                    conn.close()
+        except Exception as exc:
+            logger.debug("Failed to store FTS validation metadata: %s", exc)
+
+    def _update_mutation_metadata_locked(
+        self,
+        conn: sqlite3.Connection,
+        recipe_id: str,
+        delta: int,
+    ) -> None:
+        """Incrementally maintain validation metadata after add/remove.
+
+        Caller must hold the lock. The fingerprint is only updated when it
+        already exists; without it, validation falls back to a one-time scan
+        that records fresh metadata.
+        """
+        fingerprint_row = conn.execute(
+            "SELECT value FROM fts_metadata WHERE key = ?",
+            (self._FINGERPRINT_METADATA_KEY,)
+        ).fetchone()
+        if fingerprint_row and fingerprint_row[0]:
+            try:
+                fingerprint = int(fingerprint_row[0], 16)
+            except ValueError:
+                fingerprint = None
+            if fingerprint is not None:
+                fingerprint ^= self._fingerprint_recipe_id(recipe_id)
+                conn.execute(
+                    "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                    (self._FINGERPRINT_METADATA_KEY, f"{fingerprint & 0xFFFFFFFFFFFFFFFF:016x}")
+                )
+
+        count_row = conn.execute(
+            "SELECT value FROM fts_metadata WHERE key = ?",
+            (self._COUNT_METADATA_KEY,)
+        ).fetchone()
+        if count_row:
+            try:
+                count = max(0, int(count_row[0] or 0) + delta)
+            except (TypeError, ValueError):
+                return
+            conn.execute(
+                "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
+                (self._COUNT_METADATA_KEY, str(count))
+            )
+
     def _connect(self, readonly: bool = False) -> sqlite3.Connection:
         """Create a database connection."""
         uri = False

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,
                     )
@@ -270,6 +302,22 @@ class RecipeAnalysisService:
                 if merged_gp:
                     result.payload["gen_params"] = merged_gp
 
+                # The API-only parse (meta=null with only modelVersionIds)
+                # yields a checkpoint but no LoRAs; the image EXIF carries the
+                # full resource list. Fill the gaps the API parse left open.
+                if not result.payload.get("loras"):
+                    exif_loras = exif_parsed_result.get("loras") or []
+                    if exif_loras:
+                        result.payload["loras"] = exif_loras
+                if not result.payload.get("checkpoint") and not result.payload.get("model"):
+                    exif_checkpoint = exif_parsed_result.get("model") or exif_parsed_result.get(
+                        "checkpoint"
+                    )
+                    if exif_checkpoint:
+                        result.payload["checkpoint"] = exif_checkpoint
+                if not result.payload.get("base_model") and exif_parsed_result.get("base_model"):
+                    result.payload["base_model"] = exif_parsed_result["base_model"]
+
             if civitai_image_id and image_info and not result.payload.get("error"):
                 # Use the metadata dict we built (may contain modelVersionIds
                 # and browsingLevel from the API root level).  Do NOT pass
@@ -308,6 +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:
@@ -318,6 +368,7 @@ class RecipeAnalysisService:
         *,
         file_path: str | None,
         recipe_scanner,
+        ignore_recipe_metadata: bool = False,
     ) -> AnalysisResult:
         """Analyze a file already present on disk."""
 
@@ -332,14 +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."""
@@ -436,6 +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:
@@ -117,6 +132,7 @@ class RecipePersistenceService:
             "loras": loras_data,
             "gen_params": gen_params,
             "fingerprint": fingerprint,
+            "has_workflow": self._detect_has_workflow(normalized_image_path),
         }
         if checkpoint_entry:
             recipe_data["checkpoint"] = checkpoint_entry
@@ -128,6 +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
@@ -152,7 +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)
@@ -429,20 +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,
@@ -450,6 +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)
@@ -469,6 +553,231 @@ 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,
+        *,
+        recipe_scanner,
+        recipe_id: str,
+        lora_index: int,
+        hash_invalid: bool = True,
+    ) -> PersistenceResult:
+        """Mark a recipe LoRA 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_lora = await recipe_scanner.set_lora_entry_hash_invalid(
+            recipe_id,
+            lora_index,
+            hash_invalid=hash_invalid,
+        )
+
+        return PersistenceResult(
+            {
+                "success": True,
+                "recipe_id": recipe_id,
+                "hash_invalid": bool(hash_invalid),
+                "updated_lora": updated_lora,
+            }
+        )
+
+    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,
         *,
@@ -532,7 +841,7 @@ class RecipePersistenceService:
                 # Merge succeeded: one undo action covers the whole bulk.
                 payload["batch_id"] = merged_batch_id
             else:
-                # Merge failure (e.g. cross-volume move): expose the constituent
+                # Merge unresolvable (defensive): expose the constituent
                 # batches so the caller can undo them one at a time.
                 payload["batch_ids"] = batch_ids
         else:
@@ -615,6 +924,12 @@ class RecipePersistenceService:
                 if key not in ["checkpoint", "loras"]
             },
             "loras_stack": lora_stack,
+            # 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
@@ -639,6 +954,20 @@ class RecipePersistenceService:
 
     # Helper methods ---------------------------------------------------
 
+    def _detect_has_workflow(self, image_path: str) -> bool:
+        """Detect whether the saved recipe image embeds a ComfyUI workflow.
+
+        Extraction failures (missing file, corrupt image, unsupported format)
+        map to ``False`` and never propagate, mirroring the scanner's behavior.
+        """
+        if not image_path or not os.path.exists(image_path):
+            return False
+        try:
+            metadata = self._exif_utils._load_structured_metadata(image_path)
+            return bool(metadata.get("workflow"))
+        except Exception:
+            return False
+
     async def _build_widget_checkpoint_entry(
         self,
         recipe_scanner,
@@ -775,6 +1104,7 @@ class RecipePersistenceService:
             "modelName": lora.get("name", ""),
             "modelVersionName": lora.get("version", ""),
             "isDeleted": lora.get("isDeleted", False),
+            "hashInvalid": lora.get("hashInvalid", False),
             "exclude": lora.get("exclude", False),
         }
 

py/services/settings_manager.py

@@ -34,6 +34,8 @@ from ..utils.settings_paths import (
     APP_NAME,
     ensure_settings_file,
     get_legacy_settings_path,
+    get_settings_dir_override,
+    is_settings_dir_pinned,
 )
 from ..utils.tag_priorities import (
     PriorityTagEntry,
@@ -68,6 +70,9 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "enable_metadata_archive_db": False,
     "enable_civarchive_api": True,
     "metadata_provider_order": "civitai_archive_sqlite",
+    "rate_limit_gate_enabled": True,
+    "rate_limit_max_wait_seconds": 300,
+    "rate_limit_min_interval_seconds": 0.75,
     "proxy_enabled": False,
     "proxy_host": "",
     "proxy_port": "",
@@ -111,6 +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": "",
@@ -156,7 +162,10 @@ class SettingsManager:
         self._check_environment_variables()
         self._collect_configuration_warnings()
 
-        if os.environ.get("LORA_MANAGER_PORTABLE", "0") == "1":
+        if (
+            os.environ.get("LORA_MANAGER_PORTABLE", "0") == "1"
+            and not is_settings_dir_pinned()
+        ):
             if not self.settings.get("use_portable_settings"):
                 self.settings["use_portable_settings"] = True
                 self._save_settings()
@@ -1641,6 +1650,15 @@ class SettingsManager:
     def _prepare_portable_switch(self, use_portable: bool) -> None:
         """Prepare switching the settings storage location."""
 
+        if is_settings_dir_pinned():
+            logger.info(
+                "Portable-mode switch ignored: settings directory is pinned via "
+                "%s/--settings-path (%s)",
+                "LORA_MANAGER_SETTINGS_DIR",
+                get_settings_dir_override(),
+            )
+            return
+
         legacy_path = get_legacy_settings_path()
         user_dir = self._get_user_config_directory()
         user_settings_path = os.path.join(user_dir, "settings.json")

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()
@@ -417,13 +423,71 @@ 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"""

py/utils/settings_paths.py

@@ -13,6 +13,15 @@ from platformdirs import user_config_dir
 
 APP_NAME = "ComfyUI-LoRA-Manager"
 _LM_PORTABLE_ENV = "LORA_MANAGER_PORTABLE"
+
+# Explicit settings-directory override. Setting this (env var, or standalone's
+# ``--settings-path`` which publishes it) pins the settings location: settings.json,
+# cache/, wildcards/, backups/, logs/, stats/ all resolve under this directory,
+# bypassing portable mode and the platform user config dir. Useful for sandboxed
+# development/E2E runs that must not touch the real user data or the project root.
+SETTINGS_DIR_ENV = "LORA_MANAGER_SETTINGS_DIR"
+_settings_dir_override: Optional[str] = None
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -22,6 +31,51 @@ def get_project_root() -> str:
     return os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
 
 
+def _normalize_settings_dir(path: str) -> str:
+    """Expand ``~`` and absolutize a user-supplied settings directory."""
+
+    return os.path.abspath(os.path.expanduser(path))
+
+
+def set_settings_dir_override(path: Optional[str]) -> Optional[str]:
+    """Set or clear the programmatic settings-directory override.
+
+    Args:
+        path: Absolute/relative directory to pin, or ``None`` to clear the
+            override. ``~`` is expanded and the path absolutized.
+
+    Returns:
+        The previous override value (``None`` when none was active).
+    """
+
+    global _settings_dir_override
+    previous = _settings_dir_override
+    _settings_dir_override = (
+        _normalize_settings_dir(path) if path else None
+    )
+    return previous
+
+
+def get_settings_dir_override() -> Optional[str]:
+    """Return the active explicit settings-directory override, if any.
+
+    The ``LORA_MANAGER_SETTINGS_DIR`` environment variable takes precedence over
+    the programmatic override so that standalone's ``--settings-path`` (which
+    publishes itself through the environment) wins over embedded callers.
+    """
+
+    env_path = os.environ.get(SETTINGS_DIR_ENV)
+    if env_path:
+        return _normalize_settings_dir(env_path)
+    return _settings_dir_override
+
+
+def is_settings_dir_pinned() -> bool:
+    """Return ``True`` when an explicit settings-directory override is active."""
+
+    return get_settings_dir_override() is not None
+
+
 def get_legacy_settings_path() -> str:
     """Return the legacy location of ``settings.json`` within the project tree."""
 
@@ -31,6 +85,11 @@ def get_legacy_settings_path() -> str:
 def get_settings_dir(create: bool = True) -> str:
     """Return the user configuration directory for the application.
 
+    An explicit override (``LORA_MANAGER_SETTINGS_DIR`` or
+    :func:`set_settings_dir_override`) takes precedence. Otherwise the portable
+    project-root ``settings.json`` is used when enabled, falling back to the
+    platform-specific user configuration directory.
+
     Args:
         create: Whether to create the directory if it does not already exist.
 
@@ -38,11 +97,15 @@ def get_settings_dir(create: bool = True) -> str:
         The absolute path to the user configuration directory.
     """
 
-    legacy_path = get_legacy_settings_path()
-    if _should_use_portable_settings(legacy_path, _LOGGER):
-        config_dir = os.path.dirname(legacy_path)
+    override = get_settings_dir_override()
+    if override:
+        config_dir = override
     else:
-        config_dir = user_config_dir(APP_NAME, appauthor=False)
+        legacy_path = get_legacy_settings_path()
+        if _should_use_portable_settings(legacy_path, _LOGGER):
+            config_dir = os.path.dirname(legacy_path)
+        else:
+            config_dir = user_config_dir(APP_NAME, appauthor=False)
 
     if create and config_dir:
         os.makedirs(config_dir, exist_ok=True)
@@ -58,9 +121,14 @@ def get_settings_file_path(create_dir: bool = True) -> str:
 def ensure_settings_file(logger: Optional[logging.Logger] = None) -> str:
     """Ensure the settings file resides in the user configuration directory.
 
-    If a legacy ``settings.json`` is detected in the project root it is migrated to
-    the platform-specific user configuration folder. The caller receives the path
-    to the settings file irrespective of whether a migration was needed.
+    An explicit override (``LORA_MANAGER_SETTINGS_DIR`` or
+    :func:`set_settings_dir_override`) pins the settings file to
+    ``<override>/settings.json`` and skips legacy migration entirely.
+
+    Otherwise, if a legacy ``settings.json`` is detected in the project root it is
+    migrated to the platform-specific user configuration folder. The caller
+    receives the path to the settings file irrespective of whether a migration was
+    needed.
 
     Args:
         logger: Optional logger used for migration messages. Falls back to a
@@ -71,6 +139,12 @@ def ensure_settings_file(logger: Optional[logging.Logger] = None) -> str:
     """
 
     logger = logger or _LOGGER
+
+    override = get_settings_dir_override()
+    if override:
+        os.makedirs(override, exist_ok=True)
+        return os.path.join(override, "settings.json")
+
     legacy_path = get_legacy_settings_path()
 
     if _should_use_portable_settings(legacy_path, logger):

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",

scripts/e2e/start_server.py

@@ -211,6 +211,17 @@ def main() -> int:
         help="Launch the server fully detached (setsid-style) so it survives shell "
         "death. REQUIRED for E2E: a plain background process dies with the shell",
     )
+    parser.add_argument(
+        "--settings-path",
+        type=str,
+        default=None,
+        metavar="DIR",
+        help="Explicit settings directory passed to standalone.py (--settings-path, "
+        "equivalent to LORA_MANAGER_SETTINGS_DIR). settings.json, cache/, "
+        "wildcards/, backups/, logs/, stats/ all live under this directory instead "
+        "of the project root or the user config dir. Recommended for sandboxed E2E "
+        "so the real instance and the repo stay untouched",
+    )
 
     args = parser.parse_args()
 
@@ -283,6 +294,16 @@ def main() -> int:
         "--port",
         str(args.port),
     ]
+    if args.settings_path:
+        settings_dir = os.path.abspath(os.path.expanduser(args.settings_path))
+        if os.path.exists(settings_dir) and not os.path.isdir(settings_dir):
+            print(
+                f"ERROR: --settings-path '{settings_dir}' exists but is not a directory."
+            )
+            return 2
+        os.makedirs(settings_dir, exist_ok=True)
+        cmd.extend(["--settings-path", settings_dir])
+        print(f"Settings directory: {settings_dir}")
 
     if args.detach:
         # Fully detached launch: new session (setsid), no controlling terminal,

standalone.py

@@ -8,12 +8,36 @@ from typing import Any, cast
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 from py.middleware.cache_middleware import cache_control
 from py.middleware.error_middleware import api_json_error
-from py.utils.settings_paths import ensure_settings_file
+from py.utils.settings_paths import SETTINGS_DIR_ENV, ensure_settings_file
 
 # Set environment variable to indicate standalone mode
 os.environ["LORA_MANAGER_STANDALONE"] = "1"
 
 
+def _apply_settings_dir_from_argv(argv=None):
+    """Apply ``--settings-path`` from argv before any settings resolution runs.
+
+    Standalone resolves the settings location at import time (session logging and
+    the settings manager run before ``main()`` parses arguments), so pre-scan
+    argv and publish the explicit directory through ``LORA_MANAGER_SETTINGS_DIR``,
+    which ``py.utils.settings_paths`` honors in both standalone and plugin modes.
+
+    Args:
+        argv: Argument list to scan; defaults to ``sys.argv[1:]``.
+    """
+    args = list(sys.argv[1:] if argv is None else argv)
+    for index, arg in enumerate(args):
+        if arg == "--settings-path" and index + 1 < len(args):
+            os.environ[SETTINGS_DIR_ENV] = args[index + 1]
+            return
+        if arg.startswith("--settings-path="):
+            os.environ[SETTINGS_DIR_ENV] = arg.split("=", 1)[1]
+            return
+
+
+_apply_settings_dir_from_argv()
+
+
 # Create mock modules for py/nodes directory - add this before any other imports
 def mock_nodes_directory():
     """Create mock modules for all Python files in the py/nodes directory"""
@@ -395,6 +419,16 @@ def parse_args():
     #                     help="Additional paths to LoRA model directories (optional if settings.json has paths)")
     # parser.add_argument("--checkpoints", type=str, nargs="+",
     #                     help="Additional paths to checkpoint model directories (optional if settings.json has paths)")
+    parser.add_argument(
+        "--settings-path",
+        type=str,
+        default=None,
+        metavar="DIR",
+        help="Explicit settings directory: settings.json, cache/, wildcards/, "
+        "backups/, logs/, stats/ all live under this directory. Overrides portable "
+        "mode and the default user config dir. Equivalent to the "
+        "LORA_MANAGER_SETTINGS_DIR environment variable.",
+    )
     parser.add_argument(
         "--log-level",
         type=str,
@@ -414,6 +448,18 @@ async def main():
     """Main entry point for standalone mode"""
     args = parse_args()
 
+    # Normalize and validate the explicit settings directory (the pre-import
+    # argv scan already applied it; re-derive so --settings-path wins over any
+    # pre-existing LORA_MANAGER_SETTINGS_DIR and is canonicalized the same way).
+    if args.settings_path:
+        settings_dir = os.path.abspath(os.path.expanduser(args.settings_path))
+        if os.path.exists(settings_dir) and not os.path.isdir(settings_dir):
+            logger.error(
+                "--settings-path '%s' exists but is not a directory.", settings_dir
+            )
+            return
+        os.environ[SETTINGS_DIR_ENV] = settings_dir
+
     # Set log level (verbose flag overrides to DEBUG)
     log_level = "DEBUG" if args.verbose else args.log_level
     logging.getLogger().setLevel(getattr(logging, log_level))

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/bulk.css

@@ -49,34 +49,113 @@
     -ms-user-select: none;
 }
 
-/* Remove bulk base model modal specific styles - now using shared components */
-/* Use shared metadata editing styles instead */
+/* ── Bulk base model modal — dedicated inline-list layout ───────────────
+   Unlike the single-model modal (overlay dropdown), the bulk modal renders
+   the option list inline so it never covers the footer buttons and only the
+   list itself scrolls. Dropdown internals reuse the shared .base-model-*
+   styles from lora-modal.css. */
 
-/* Override for bulk base model select to ensure proper width */
-.bulk-base-model-select {
+#bulkBaseModelModal .modal-content {
+    width: min(720px, calc(100vw - 2rem));
+    height: min(640px, calc(100vh - var(--header-height, 48px) - 5.5rem));
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+#bulkBaseModelModal .modal-header {
+    flex-shrink: 0;
+}
+
+#bulkBaseModelModal .modal-body {
+    flex: 1;
+    min-height: 0;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+#bulkBaseModelModal .bulk-add-tags-info {
+    flex-shrink: 0;
+}
+
+#bulkBaseModelModal .bulk-base-model-label {
+    flex-shrink: 0;
+    display: block;
+    font-weight: 500;
+    margin-bottom: var(--space-1);
+    color: var(--text-color);
+}
+
+.bulk-base-model-picker {
     width: 100%;
-    max-width: 100%;
-    padding: 6px 10px;
+}
+
+#bulkBaseModelModal .bulk-base-model-picker {
+    flex: 1;
+    min-height: 0;
+    display: flex;
+    flex-direction: column;
+}
+
+#bulkBaseModelModal .bulk-base-model-picker .base-model-search-wrapper {
+    flex: 1;
+    min-height: 0;
+    display: flex;
+    flex-direction: column;
+    width: 100%;
+    z-index: auto;
+}
+
+#bulkBaseModelModal .base-model-search-input-wrapper {
+    flex-shrink: 0;
+}
+
+/* Inline list instead of overlay dropdown */
+#bulkBaseModelModal .base-model-dropdown {
+    position: relative;
+    top: auto;
+    left: auto;
+    right: auto;
+    flex: 1;
+    min-height: 0;
+    max-height: none;
+    overflow-y: auto;
+    margin-top: var(--space-1);
+    border: 1px solid var(--lora-border);
     border-radius: var(--border-radius-xs);
-    border: 1px solid var(--border-color);
-    background-color: var(--lora-surface);
-    color: var(--text-color);
-    font-size: 0.95em;
-    height: 32px;
+    box-shadow: none;
+    z-index: auto;
+    overscroll-behavior: contain;
+    scrollbar-gutter: stable;
+    scrollbar-width: thin;
+    scrollbar-color: var(--lora-border) transparent;
 }
 
-.bulk-base-model-select:focus {
-    border-color: var(--lora-accent);
-    outline: none;
+#bulkBaseModelModal .base-model-dropdown::-webkit-scrollbar {
+    width: 8px;
 }
 
-/* Dark theme support for bulk base model select */
-[data-theme="dark"] .bulk-base-model-select {
-    background-color: rgba(30, 30, 30, 0.9);
-    color: var(--text-color);
+#bulkBaseModelModal .base-model-dropdown::-webkit-scrollbar-thumb {
+    background: var(--lora-border);
+    border-radius: 4px;
 }
 
-[data-theme="dark"] .bulk-base-model-select option {
-    background-color: #2d2d2d;
-    color: var(--text-color);
+#bulkBaseModelModal .base-model-dropdown::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+/* The shared dropdown header uses opacity for its muted look, which makes the
+   sticky background translucent — scrolled items bleed through. Keep the
+   muted text color but restore a fully opaque background (bulk scope only). */
+#bulkBaseModelModal .base-model-dropdown-header {
+    opacity: 1;
+    color: var(--text-muted, var(--text-color));
+}
+
+#bulkBaseModelModal .bulk-base-model-footer {
+    flex-shrink: 0;
+    padding-top: var(--space-2);
+    margin-top: var(--space-2);
+    border-top: 1px solid var(--lora-border);
 }

static/css/components/card.css

@@ -671,23 +671,46 @@ body.hide-card-version .hl-badge {
     user-select: none;
 }
 
+/* Compact LoRA status pill: state icon + available/total fraction (e.g. "2/3").
+   The icon switches by state (warning/check/layers) so status never relies on
+   color alone; the tooltip spells out the full details. */
 .lora-count {
     display: flex;
     align-items: center;
     gap: 4px;
-    background: rgba(255, 255, 255, 0.2);
-    padding: 2px 8px;
-    border-radius: var(--border-radius-xs);
+    flex-shrink: 0;
+    /* Pin to the bottom-right corner of the footer, matching how model card
+       footer .card-actions behave when the title wraps to multiple lines */
+    align-self: flex-end;
     font-size: 0.85em;
-    position: relative;
+    padding: 2px 8px;
+    background: rgba(255, 255, 255, 0.2);
+    border: 1px solid rgba(255, 255, 255, 0.25);
+    border-radius: var(--border-radius-xs);
 }
 
 .lora-count.ready {
     background: rgba(46, 204, 113, 0.3);
+    border-color: rgba(46, 204, 113, 0.6);
 }
 
 .lora-count.missing {
-    background: rgba(231, 76, 60, 0.3);
+    background: rgba(231, 76, 60, 0.35);
+    border-color: rgba(231, 76, 60, 0.65);
+}
+
+/* Partial: usable but degraded — some LoRAs are unobtainable (deleted from
+   the source or unresolvable hash) and are skipped when the recipe is used.
+   Amber sits between ready green and missing red. */
+.lora-count.partial {
+    background: rgba(243, 156, 18, 0.35);
+    border-color: rgba(243, 156, 18, 0.65);
+}
+
+/* Unavailable: no usable LoRA at all — gray marks the recipe as dead. */
+.lora-count.unavailable {
+    background: rgba(149, 165, 166, 0.35);
+    border-color: rgba(149, 165, 166, 0.65);
 }
 
 .placeholder-message {

static/css/components/header.css

@@ -4,7 +4,7 @@
   position: fixed;
   top: 0;
   z-index: var(--z-header);
-  height: 48px;
+  height: var(--header-height, 48px);
   /* Reduced height */
   width: 100%;
   box-shadow: var(--shadow-md);
@@ -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/import-modal.css

@@ -77,41 +77,84 @@
     margin-bottom: var(--space-3);
 }
 
-/* File Input Styles */
-.file-input-wrapper {
-    position: relative;
-    margin-bottom: var(--space-1);
+.import-description {
+    margin-top: 0;
 }
 
-.file-input-wrapper input[type="file"] {
-    position: absolute;
-    width: 100%;
-    height: 100%;
-    opacity: 0;
-    cursor: pointer;
-    z-index: 2;
-}
-
-.file-input-button {
+/* Unified Drop Zone */
+.import-drop-zone {
     display: flex;
+    flex-direction: column;
     align-items: center;
     justify-content: center;
-    gap: 8px;
-    padding: 10px 16px;
-    background: var(--lora-accent);
-    color: var(--lora-text);
-    border-radius: var(--border-radius-xs);
-    font-weight: 500;
+    gap: var(--space-1);
+    padding: var(--space-4) var(--space-3);
+    border: 2px dashed var(--border-color);
+    border-radius: var(--border-radius-sm);
+    background: var(--bg-color);
+    color: var(--text-color);
+    text-align: center;
     cursor: pointer;
-    transition: background-color 0.2s;
+    transition: border-color 0.2s, background-color 0.2s;
 }
 
-.file-input-button:hover {
-    background: oklch(from var(--lora-accent) l c h / 0.9);
+.import-drop-zone:hover,
+.import-drop-zone:focus-visible {
+    border-color: var(--lora-accent);
+    outline: none;
 }
 
-.file-input-wrapper:hover .file-input-button {
-    background: oklch(from var(--lora-accent) l c h / 0.9);
+.import-drop-zone.drag-over {
+    border-color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.08);
+}
+
+.drop-zone-icon {
+    font-size: 1.8em;
+    color: var(--lora-accent);
+}
+
+.drop-zone-primary {
+    margin: 0;
+    opacity: 0.8;
+}
+
+.drop-zone-filename {
+    margin: 0;
+    font-weight: 500;
+    word-break: break-all;
+}
+
+/* Divider between drop zone and URL input */
+.import-divider {
+    display: flex;
+    align-items: center;
+    gap: var(--space-2);
+    margin: var(--space-3) 0;
+    color: var(--text-color);
+    opacity: 0.6;
+    font-size: 0.9em;
+}
+
+.import-divider::before,
+.import-divider::after {
+    content: '';
+    flex: 1;
+    border-top: 1px solid var(--border-color);
+}
+
+/* Loading state for the fetch button */
+#fetchImageBtn.loading {
+    opacity: 0.8;
+    cursor: wait;
+}
+
+/* Inputs sit flush against the scrollable step's content edge; an outset
+   outline (global offset: 2px) gets clipped by overflow-x. Draw the focus
+   outline inset instead so the full ring stays visible. */
+#importModal input:focus-visible,
+#importModal select:focus-visible {
+    outline-offset: -2px;
 }
 
 /* Recipe Details Layout */
@@ -626,25 +669,6 @@
     /* Hide the old style */
 }
 
-/* Update deleted badge to be more prominent */
-.deleted-badge {
-    display: inline-flex;
-    align-items: center;
-    background: var(--lora-warning);
-    color: white;
-    padding: 4px 8px;
-    border-radius: var(--border-radius-xs);
-    font-size: 0.8em;
-    font-weight: 500;
-    white-space: nowrap;
-    flex-shrink: 0;
-}
-
-.deleted-badge i {
-    margin-right: 4px;
-    font-size: 0.9em;
-}
-
 /* Error message styling */
 .error-message {
     color: var(--lora-error);

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

@@ -1,10 +1,3 @@
-/* Blurred backdrop for the model modal to match the recipe modal */
-#modelModal {
-    background: rgba(0, 0, 0, 0.5);
-    backdrop-filter: blur(6px);
-    -webkit-backdrop-filter: blur(6px);
-}
-
 /* Lora Modal Header */
 .modal-header {
     display: flex;
@@ -75,6 +68,39 @@
     font-size: 14px;
 }
 
+/* Destructive modal action: ghost icon button right-anchored by its own auto
+   margin, revealing the danger color only on hover/focus. Shared by the model
+   modal and the recipe modal. */
+.modal-delete-btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    width: 36px;
+    height: 36px;
+    padding: 0;
+    margin-left: auto;
+    background: transparent;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-secondary);
+    cursor: pointer;
+    transition: color 0.2s ease, border-color 0.2s ease, background-color 0.2s ease;
+}
+
+.modal-delete-btn:hover,
+.modal-delete-btn:focus-visible {
+    color: var(--lora-error);
+    border-color: var(--lora-error);
+    background: oklch(from var(--lora-error) l c h / 0.08);
+}
+
+.modal-delete-btn i {
+    font-size: 14px;
+}
+
+/* When license icons directly precede the delete button, they carry the auto
+   margin instead, so the [license][delete] cluster stays right-anchored as
+   one group with the delete button flush at the right edge and no split gap. */
 .modal-header-actions .license-restrictions {
     margin-left: auto;
 }
@@ -83,6 +109,11 @@
     margin-left: auto;
 }
 
+.modal-header-actions .license-restrictions + .modal-delete-btn,
+.modal-header-actions .license-permissions + .modal-delete-btn {
+    margin-left: 0;
+}
+
 .license-restrictions {
     display: flex;
     align-items: center;
@@ -223,6 +254,62 @@
     justify-content: space-between;
 }
 
+/* Hashes footnote — borderless full-width muted line; reads as a footnote
+   to the file info grid rather than a peer field */
+.hash-footnote {
+    grid-column: 1 / -1;
+    display: flex;
+    align-items: baseline;
+    flex-wrap: wrap;
+    gap: 4px 8px;
+    padding: 0 var(--space-1);
+    color: var(--text-color);
+}
+
+.hash-footnote .hash-entry {
+    display: inline-flex;
+    align-items: baseline;
+    gap: 6px;
+}
+
+.hash-footnote .hash-kind {
+    font-size: 0.7em;
+    opacity: 0.5;
+    text-transform: uppercase;
+    letter-spacing: 0.03em;
+    flex-shrink: 0;
+}
+
+.hash-footnote .model-hash-value {
+    font-family: monospace;
+    font-size: 0.8em;
+    opacity: 0.6;
+    white-space: nowrap;
+}
+
+.hash-footnote .hash-sep {
+    opacity: 0.3;
+    font-size: 0.8em;
+}
+
+.hash-footnote .hash-copy-btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    padding: 0 2px;
+    border: none;
+    background: none;
+    color: var(--text-color);
+    opacity: 0.35;
+    font-size: 0.7em;
+    cursor: pointer;
+    flex-shrink: 0;
+}
+
+.hash-footnote .hash-copy-btn:hover {
+    opacity: 0.9;
+}
+
 /* Toggle button — icon only, inline with the label */
 .notes-toggle-btn {
     display: none;  /* shown by JS when content exceeds threshold */
@@ -991,6 +1078,19 @@
     color: #facc15;
 }
 
+/* Partial: usable but degraded — some LoRAs are unobtainable and skipped.
+   Orange sits between ready green and missing amber. */
+.recipe-card__badge--partial {
+    background: rgba(249, 115, 22, 0.2);
+    color: #fb923c;
+}
+
+/* Unavailable: no usable LoRA at all — red marks the recipe as dead. */
+.recipe-card__badge--unavailable {
+    background: rgba(239, 68, 68, 0.2);
+    color: #f87171;
+}
+
 .recipe-card__badge--empty {
     background: rgba(148, 163, 184, 0.18);
     color: #e2e8f0;
@@ -1006,6 +1106,16 @@
     background: rgba(245, 199, 43, 0.22);
 }
 
+[data-theme="light"] .recipe-card__badge--partial {
+    color: #c2410c;
+    background: rgba(249, 115, 22, 0.18);
+}
+
+[data-theme="light"] .recipe-card__badge--unavailable {
+    color: #b91c1c;
+    background: rgba(239, 68, 68, 0.16);
+}
+
 [data-theme="light"] .recipe-card__badge--empty {
     color: rgba(71, 85, 105, 0.9);
     background: rgba(148, 163, 184, 0.2);

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

@@ -65,4 +65,13 @@
 
 .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

@@ -6,11 +6,23 @@
     left: 0;
     width: 100%;
     height: calc(100% - var(--header-height, 48px)); /* Adjust height to exclude header */
-    background: rgba(0, 0, 0, 0.2);
+    background: var(--modal-backdrop-bg, rgba(0, 0, 0, 0.5));
+    backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
+    -webkit-backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
     z-index: var(--z-modal);
     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/delete-modal.css

@@ -13,7 +13,10 @@
     left: 0;
     width: 100%;
     height: 100%;
+    /* Darker than --modal-backdrop-bg to stress destructive actions, but keeps the shared blur */
     background: rgba(0, 0, 0, 0.8);
+    backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
+    -webkit-backdrop-filter: blur(var(--modal-backdrop-blur, 6px));
     z-index: var(--z-overlay);
 }
 

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

@@ -514,6 +514,7 @@
     background: oklch(var(--lora-accent) / 0.18);
     color: var(--lora-accent);
     font-size: inherit;
+    font-family: inherit;
     font-weight: 600;
     cursor: pointer;
     transition: var(--transition-base);
@@ -920,8 +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;
@@ -304,4 +327,87 @@
 /* Dark theme adjustments */
 [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

@@ -9,6 +9,21 @@
     position: relative;
 }
 
+/* Header row: title + nav controls. Padding reserves space for the
+   absolutely positioned nav buttons (see .modal-nav-controls in lora-modal.css). */
+.recipe-modal-header-row {
+    box-sizing: border-box;
+    width: 100%;
+    position: relative;
+    padding-right: 152px;
+}
+
+/* 56px right offset keeps the nav buttons clear of the close (x) button,
+   which is absolutely positioned at the modal-content top-right corner. */
+.recipe-modal-header-row .modal-nav-controls {
+    right: 56px;
+}
+
 #recipeTagsContainer {
     width: 100%;
 }
@@ -104,13 +119,6 @@
     display: none;
 }
 
-/* Darker, blurred backdrop keeps the busy page behind the modal from bleeding through */
-#recipeModal {
-    background: rgba(0, 0, 0, 0.5);
-    backdrop-filter: blur(6px);
-    -webkit-backdrop-filter: blur(6px);
-}
-
 #recipeModal .modal-content {
     display: flex;
     flex-direction: column;
@@ -182,6 +190,44 @@
     justify-content: center;
 }
 
+/* Icon-only companion to the Send button: same pill style as its neighbors,
+   just without the text label. */
+.modal-copy-btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    padding: 6px 10px;
+    background: var(--surface-subtle);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 0.9em;
+    transition: var(--transition-base);
+}
+
+[data-theme="dark"] .modal-copy-btn {
+    background: var(--surface-subtle);
+    border: 1px solid var(--lora-border);
+}
+
+.modal-copy-btn:hover {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+}
+
+.modal-copy-btn:active {
+    transform: translateY(0);
+}
+
+.modal-copy-btn i {
+    font-size: 14px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
 @media (max-height: 860px) {
     .recipe-header-actions {
         padding-bottom: 4px;
@@ -669,6 +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;
@@ -683,6 +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);
@@ -692,26 +807,42 @@
     will-change: transform;
     /* Create a new containing block for absolutely positioned descendants */
     transform: translateZ(0);
-    cursor: pointer; /* Make it clear the item is clickable */
+    /* Rows are not clickable by default; only in-library rows navigate */
+    cursor: default;
     transition: transform 0.2s ease, box-shadow 0.2s ease, border-color 0.2s ease;
 }
 
-.recipe-lora-item:hover {
+/* Click affordance (pointer + hover lift) is reserved for rows that
+   actually navigate: in-library items open the local detail view. */
+.recipe-lora-item.exists-locally {
+    cursor: pointer;
+}
+
+.recipe-lora-item.exists-locally:hover {
     transform: translateY(-1px);
     box-shadow: var(--shadow-header);
     border-color: var(--lora-accent);
 }
 
+.recipe-lora-item.exists-locally:focus-visible {
+    outline: 2px solid var(--lora-accent);
+    outline-offset: 2px;
+}
+
 .recipe-lora-item.exists-locally {
     background: oklch(var(--lora-accent) / 0.05);
     border-left: 4px solid var(--lora-accent);
 }
 
 .recipe-lora-item.checkpoint-item {
-    cursor: pointer;
+    cursor: default;
     padding-top: 8px;
     padding-bottom: 8px;
-    align-items: center;
+    align-items: flex-start;
+}
+
+.recipe-lora-item.checkpoint-item.exists-locally {
+    cursor: pointer;
 }
 
 .recipe-lora-item.missing-locally {
@@ -768,12 +899,24 @@
     transform: translateZ(0);
 }
 
+.recipe-lora-title {
+    display: flex;
+    /* Top-align so the inline Civitai link stays glued to the FIRST line
+       even when a long model name wraps to two lines. */
+    align-items: flex-start;
+    gap: 6px;
+    flex: 1;
+    min-width: 0; /* Allow the clamped title to shrink next to the badge */
+}
+
 .recipe-lora-content h4 {
     margin: 0;
     font-size: 1em;
     color: var(--text-color);
-    flex: 1;
-    max-width: calc(100% - 120px); /* Make room for the badge */
+    /* Shrink (for the 2-line clamp) but don't grow: the inline Civitai link
+       should sit right after the name, not pushed to the far edge. */
+    flex: 0 1 auto;
+    min-width: 0;
     overflow: hidden;
     text-overflow: ellipsis;
     display: -webkit-box;
@@ -813,8 +956,36 @@
     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 {
+.missing-badge,
+.invalid-hash-badge {
     position: absolute;
     right: 0;
     top: 0;
@@ -825,21 +996,12 @@
 
 /* Specific styles for recipe modal badges - update z-index */
 .recipe-lora-header .local-badge,
-.recipe-lora-header .missing-badge {
+.recipe-lora-header .missing-badge,
+.recipe-lora-header .invalid-hash-badge {
     z-index: 2; /* Ensure the badge is above other elements */
     backface-visibility: hidden;
 }
 
-/* Ensure local-path tooltip is properly positioned and won't move during scroll */
-.recipe-lora-header .local-badge .local-path {
-    z-index: 3;
-    top: calc(100% + 4px); /* Position tooltip below the badge */
-    right: -4px; /* Align with the badge */
-    max-width: 250px;
-    /* Force hardware acceleration for Chrome */
-    transform: translateZ(0);
-}
-
 .missing-badge {
     display: inline-flex;
     align-items: center;
@@ -877,49 +1039,42 @@
     font-size: 0.9em;
 }
 
-/* Add reconnect functionality styles */
-.deleted-badge.reconnectable {
-    position: relative;
-    cursor: pointer;
-    transition: background-color 0.2s ease;
-}
-
-.deleted-badge.reconnectable:hover {
-    background-color: var(--lora-accent);
-}
-
-.deleted-badge .reconnect-tooltip {
-    position: absolute;
-    display: none;
-    background-color: var(--card-bg);
-    color: var(--text-color);
-    padding: 8px 12px;
+/* Unresolvable-hash badge: the entry has identity fields, but its hash is
+   not registered on CivitAI (stale or invalid). */
+.invalid-hash-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-warning);
+    color: white;
+    padding: 3px 6px;
     border-radius: var(--border-radius-xs);
-    border: 1px solid var(--border-color);
-    box-shadow: var(--shadow-header);
-    z-index: var(--z-overlay);
-    width: max-content;
-    max-width: 200px;
-    font-size: 0.85rem;
-    font-weight: normal;
-    top: calc(100% + 5px);
-    left: 0;
-    margin-left: -100px;
+    font-size: 0.75em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
 }
 
-.deleted-badge.reconnectable:hover .reconnect-tooltip {
-    display: block;
+.invalid-hash-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
 }
 
-/* LoRA reconnect container */
+/* 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: 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;
 }
 
@@ -946,18 +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;
@@ -965,13 +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 {
@@ -1123,69 +1361,78 @@
     align-items: center;
     justify-content: flex-end;
     flex-shrink: 0;
-    min-width: 110px;
     z-index: 2;
 }
 
-/* Update the local-badge and missing-badge to be positioned within the badge-container */
+/* Badges are pure status indicators; actions live in .recipe-lora-actions */
 .badge-container .local-badge, 
 .badge-container .missing-badge,
-.badge-container .deleted-badge {
+.badge-container .deleted-badge,
+.badge-container .invalid-hash-badge {
     position: static; /* Override absolute positioning */
     transform: none; /* Remove the transform */
 }
 
-/* Ensure the tooltip is still properly positioned */
-.badge-container .local-badge .local-path {
-    position: fixed; /* Keep as fixed for Chrome */
-    z-index: 100;
+/* Tonal (soft) status badges: a tinted fill + colored text reads calmer
+   than solid blocks when several rows stack, and matches the tonal
+   "N missing" summary pill above the list. */
+.badge-container .local-badge {
+    background: oklch(var(--lora-accent) / 0.12);
+    color: var(--lora-accent);
+    border: 1px solid oklch(var(--lora-accent) / 0.35);
 }
 
-.badge-container .resource-action {
-    margin-left: auto;
+.badge-container .missing-badge {
+    background: oklch(var(--lora-error) / 0.14);
+    color: var(--lora-error);
+    border: 1px solid oklch(var(--lora-error) / 0.35);
 }
 
-/* Add styles for missing LoRAs download feature */
-.recipe-status.missing {
+.badge-container .deleted-badge {
+    background: rgba(127, 127, 127, 0.15);
+    color: var(--text-muted);
+    border: 1px solid rgba(127, 127, 127, 0.35);
+}
+
+.badge-container .invalid-hash-badge {
+    background: oklch(var(--lora-warning) / 0.14);
+    color: var(--lora-warning);
+    border: 1px solid oklch(var(--lora-warning) / 0.35);
+}
+
+/* Pin the recipe status-badge family (missing / deleted / invalid-hash) to its
+   compact size. import-modal.css defines unscoped .missing-badge/.deleted-badge
+   and is loaded AFTER this file, so without this higher-specificity rule its
+   padding/font-size would clobber recipe modal's, leaving invalid-hash-badge
+   (which has no import counterpart) at a different size. local-badge
+   intentionally keeps the global shared.css size. */
+#recipeModal .badge-container .missing-badge,
+#recipeModal .badge-container .deleted-badge,
+#recipeModal .badge-container .invalid-hash-badge {
+    padding: 3px 6px;
+    font-size: 0.75em;
+}
+
+/* Missing LoRAs status is a real button: the affordance must be visible at
+   rest (persistent border), not only on hover. */
+.recipe-status.missing.clickable {
     position: relative;
     cursor: pointer;
-    transition: background-color 0.2s ease;
+    font: inherit;
+    background: oklch(var(--lora-error) / 0.12);
+    color: var(--lora-error);
+    border: 1px solid oklch(var(--lora-error) / 0.45);
+    transition: background-color 0.2s ease, box-shadow 0.2s ease;
 }
 
-.recipe-status.missing:hover {
-    background-color: rgba(var(--lora-warning-rgb, 255, 165, 0), 0.2);
+.recipe-status.missing.clickable:hover {
+    background: oklch(var(--lora-error) / 0.22);
+    box-shadow: 0 0 0 2px oklch(var(--lora-error) / 0.25);
 }
 
-.recipe-status.missing .missing-tooltip {
-    position: absolute;
-    display: none;
-    background-color: var(--card-bg);
-    color: var(--text-color);
-    padding: 8px 12px;
-    border-radius: var(--border-radius-xs);
-    border: 1px solid var(--border-color);
-    box-shadow: var(--shadow-header);
-    z-index: var(--z-overlay);
-    width: max-content;
-    max-width: 200px;
-    font-size: 0.85rem;
-    font-weight: normal;
-    margin-left: -100px;
-    margin-top: -65px;
-}
-
-.recipe-status.missing:hover .missing-tooltip {
-    display: block;
-}
-
-.recipe-status.clickable {
-    cursor: pointer;
-    padding: 4px 8px;
-    border-radius: var(--border-radius-xs);
-}
-
-.recipe-status.clickable:hover {
-    background-color: rgba(var(--lora-warning-rgb, 255, 165, 0), 0.2);
+.recipe-status.missing.clickable:focus-visible {
+    outline: 2px solid var(--lora-error);
+    outline-offset: 2px;
 }
 
 .recipe-checkpoint-meta {
@@ -1197,11 +1444,11 @@
     margin-bottom: 2px;
 }
 
-.recipe-checkpoint-meta .checkpoint-type {
-    background: var(--lora-surface);
-    padding: 2px 8px;
-    border-radius: var(--border-radius-xs);
-    color: var(--text-color);
+/* Checkpoint type is low-information text (the entry's position above the
+   divider already implies "checkpoint"), so it renders as plain muted text
+   instead of a chip competing with the base-model chip. */
+.recipe-checkpoint-meta .checkpoint-type-text {
+    color: var(--text-muted);
 }
 
 .recipe-resource-actions {
@@ -1245,3 +1492,148 @@
 .resource-action.primary:hover {
     background: color-mix(in oklch, var(--lora-accent), black 10%);
 }
+
+/* Ghost variant: secondary remediation actions (e.g. Reconnect), matching
+   the ghost action pattern used in the versions tab. */
+.resource-action.ghost {
+    background: transparent;
+    color: var(--lora-accent);
+    border-color: oklch(var(--lora-accent) / 0.4);
+}
+
+.resource-action.ghost:hover {
+    background: oklch(var(--lora-accent) / 0.1);
+    border-color: var(--lora-accent);
+}
+
+/* Per-item action row: remediation lives next to the status badge that
+   surfaced the problem (download / reconnect / external link). Right-aligned
+   so the reading order stays: name → status → meta → actions. */
+.recipe-lora-actions {
+    display: flex;
+    align-items: center;
+    justify-content: flex-end;
+    flex-wrap: wrap;
+    gap: 8px;
+    margin-top: 6px;
+}
+
+/* External-link affordance, mirroring .version-civitai-link in the
+   versions tab: leaving the app is always an explicit, signposted action. */
+.recipe-civitai-link {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    width: 24px;
+    height: 24px;
+    border-radius: 999px;
+    color: var(--text-muted);
+    text-decoration: none;
+    flex: 0 0 auto;
+    transition: color 0.2s ease, background-color 0.2s ease, transform 0.2s ease;
+}
+
+.recipe-civitai-link:hover,
+.recipe-civitai-link:focus-visible {
+    color: var(--lora-accent);
+    background: color-mix(in oklch, var(--lora-accent) 12%, transparent);
+    transform: translateY(-1px);
+    outline: none;
+}
+
+/* In titles, size the icon box to the first line box (1em * 1.3 line-height)
+   so it aligns with the first line of both short and wrapped names. */
+.recipe-lora-title .recipe-civitai-link {
+    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

@@ -5,11 +5,11 @@
     border: none;
     padding: 8px 16px;
     font-size: 0.9em;
-    transform: translateX(-50%) translateY(20px);
+    transform: translateY(20px);
 }
 
 .toast.toast-copy.show {
-    transform: translateX(-50%) translateY(0);
+    transform: translateY(0);
 }
 
 /* Toast Notifications */
@@ -19,14 +19,15 @@
     right: 20px;
     left: auto;
     transform: translateX(120%);
-    min-width: 300px;
+    box-sizing: border-box;
+    min-width: 200px;
     max-width: 400px;
     background: var(--lora-surface);
     color: var(--text-color);
     padding: 12px 16px;
     border-radius: var(--border-radius-sm);
     box-shadow: var(--shadow-toast);
-    z-index: calc(var(--z-overlay) + 10);
+    z-index: var(--z-toast);
     opacity: 0;
     transition: transform 0.3s cubic-bezier(0.4, 0, 0.2, 1), 
                 opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1);
@@ -130,7 +131,6 @@
     .toast {
         width: calc(100% - 40px);
         max-width: none;
-        right: 20px;
     }
 }
 
@@ -166,16 +166,17 @@
     opacity: 1;
 }
 
-/* Toast Container for stacked notifications */
+/* Toast Container for stacked notifications (top-right, flush below the header) */
 .toast-container {
     position: fixed;
-    top: 0;
+    top: var(--header-height, 48px); /* Start right below the fixed header */
     right: 0;
-    z-index: calc(var(--z-overlay) + 10);
+    z-index: var(--z-toast);
     display: flex;
     flex-direction: column;
+    /* No align-items (defaults to stretch) so every toast shares one equal width */
     gap: 10px;
-    padding: 20px;
+    padding: 8px 20px 0; /* Small breathing room below the header */
     pointer-events: none; /* Allow clicking through the container */
     width: 400px;
     max-width: 100%;
@@ -215,8 +216,7 @@
 /* Responsive adjustments */
 @media (max-width: 480px) {
     .toast-container {
-        width: 100%;
-        padding: 10px;
+        padding: 0 10px;
     }
     
     .toast {

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 */