fix(nodes): snapshot scanner cache before iterating on executor thread

Node code reads cache.raw_data while MetadataSyncService may mutate it
from a background thread; iterate over a list() snapshot to avoid a
possible 'list changed size during iteration' RuntimeError.

.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 +63,201 @@ 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/*`, `/other/*` 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
+- `settings.json.example` is intentionally minimal (see Important Notes); all
+  other defaults live in `DEFAULT_SETTINGS` (`py/services/settings_manager.py`)
+
+### 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"`
+- **`settings.json.example` must stay minimal**: only `use_portable_settings`,
+  `civitai_api_key`, and the four core `folder_paths` keys (`loras`,
+  `checkpoints`, `unet`, `embeddings`). Do NOT add optional/default keys
+  (model-category folders, `default_*_root`, `auto_organize_exclusions`, etc.)
+  to this file unless the user explicitly asks for it. Defaults belong in
+  `DEFAULT_SETTINGS` in `py/services/settings_manager.py`.
 - 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 +265,4 @@ 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

__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/agent_skills.md

@@ -62,13 +62,23 @@ Environment variable overrides: `LLM_API_KEY`, `LLM_MODEL`, `LLM_API_BASE`, `LLM
 
 ### enrich_hf_metadata
 
-Enriches HuggingFace-downloaded models with metadata extracted by an LLM from the HF model card.
+Enriches models linked to an external model site with metadata extracted by an LLM from the site's model card (README).
 
-**Entry point**: Right-click context menu → "Enrich Metadata (Agent)"
+**Entry point**: Right-click context menu → "Enrich Metadata with AI"
+
+**Supported model sources**:
+
+| Platform | Link | AI enrichment | Direct download |
+| --- | --- | --- | --- |
+| Hugging Face | yes | yes | yes |
+| ModelScope | yes | yes | yes |
+| TensorArt | yes | no (see below) | no |
+
+TensorArt is link-only: `tensor.art` sits behind a Cloudflare managed challenge and its internal API requires session authorization, so the backend cannot read its model pages. Linking still stores the canonical page URL and the "View on TensorArt" link works.
 
 **What it does**:
-1. Reads the model's `.metadata.json` to get the `hf_url`
-2. Fetches the README.md from the HuggingFace repository
+1. Reads the model's `.metadata.json` to get the source (`source_platform` + `source_url`, or the legacy `hf_url`)
+2. Fetches the model card through the provider in `py/services/model_sources/`
 3. Sends the README + local metadata to the LLM for structured extraction
 4. Writes extracted fields to `.metadata.json`:
    - `base_model` — only if current value is empty
@@ -81,6 +91,8 @@ Enriches HuggingFace-downloaded models with metadata extracted by an LLM from th
 6. Updates the scanner cache
 7. Broadcasts WebSocket progress events
 
+Models with no source, an unknown source, or a source without model-card access (TensorArt) are skipped with an explicit reason and counted in the run summary.
+
 **Model types**: LoRA, Checkpoint, Embedding
 
 ## Adding a New Skill
@@ -129,7 +141,7 @@ Use `{{variable}}` placeholders that will be replaced with data from the `prepar
 ```markdown
 You are an expert assistant...
 
-Model URL: {{hf_url}}
+Model URL: {{source_url}}
 README content:
 {{readme_content}}
 

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 +1,479 @@
+# 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, 1982 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.
+>
+> **Status (2026-09, Other Models):** the `other` model type (VAE / Upscaler / Text Encoder /
+> CLIP Vision / ControlNet) and the Other Models opt-in toggles added 36 new keys; all of them
+> are now translated in all 9 locales (terminology in §2 "Other Models feature"). There are no
+> remaining `[TODO: Translate]` placeholders in any locale.
+>
+> **Status (2026-09, revision):** `other.disabled.description`, `banners.otherModels.content` and
+> `settings.folderSettings.enableOtherModelsHelp` were refreshed in `en.json` to name all five
+> sub_types (they had listed four, which read as "these are what enabling manages") and
+> re-translated in all 9 locales in the same pass. `clip_vision` and `controlnet` are now both
+> opt-in, so the first two describe **capability** and the third the **master switch**, not the
+> default set — keep all three enumerating the full five (`VAE / upscaler / text encoder /
+> CLIP vision / ControlNet` in `en`; locale slash-list casing follows each file's existing
+> `VAE / Upscaler / Text Encoder / …` style, de compounds as `CLIP-Vision- und ControlNet-Ordner`).
+>
+> **Status (2026-09, "no folders found" state):** the Other Models page gained an *enabled but
+> nothing to scan* empty state with 6 new keys (`other.noPaths.*`); translated in all 9 locales
+> in the same pass. The `folder_paths` JSON snippet shown in that state lives in
+> `templates/other.html`, **not** in the locale files, so it is never translated — only the
+> surrounding prose is. Terminology added in §2.
+>
+> **Status (2026-09, model sources):** models can now be linked to ModelScope and TensorArt
+> alongside Hugging Face, which added 15 keys (`modelCard.actions.viewOnSource`,
+> `loras.contextMenu.linkModelSource`, `modals.linkModelSource.*`,
+> `modals.model.versions.sourceGroupInfo`, `toast.contextMenu.enrichNeedsSource`,
+> `toast.contextMenu.enrichUnsupportedSource`) and refreshed the two `enrichHfAgent` labels,
+> which had hardcoded "HF" for a button that now also enriches ModelScope models. The
+> `modals.linkModelSource.urlPlaceholder` value stays byte-identical to `en.json` (it is a URL,
+> the §6 exception). Terminology in §2, "Model source feature".
+
+---
+
+## 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) |
+
+### Other Models feature (VAE / Upscaler / Text Encoder / CLIP Vision / ControlNet)
+
+The `other` model type exposes five sub_types. They are **model-type names**, so they follow
+R3 and stay in Latin in every locale. The `settings.folderSettings.subType*` values are
+therefore **intentionally byte-identical to `en.json`** (same precedent as
+`settings.priorityTags.modelTypes` / `checkpoints.modelTypes.checkpoint`) — a §6 sweep must
+not "fix" them.
+
+| Term | Rendering | Note |
+|---|---|---|
+| VAE | `VAE` everywhere | acronym, always upper-case |
+| Upscaler | `Upscaler` everywhere | CivitAI `ModelType` name |
+| Text Encoder | `Text Encoder` everywhere | de compounds as `Text-Encoder-Stammordner` |
+| CLIP Vision | `CLIP Vision` everywhere | de compounds as `CLIP-Vision-Stammordner` |
+| ControlNet | `ControlNet` everywhere | brand casing, capital N |
+
+In prose these names sit next to localized nouns the same way `Diffusion Model` does
+(zh `VAE 根目录`, ja `VAEルート`, ko `VAE 루트`, ru `Корневая папка VAE`).
+
+**"Other Models" is the page/feature name, not a model type — translate it:**
+
+| Locale | `other.title` | `header.navigation.other` |
+|---|---|---|
+| fr | Autres modèles | Autres |
+| zh-CN | 其他模型 | 其他 |
+| zh-TW | 其他模型 | 其他 |
+| ja | その他のモデル | その他 |
+| ko | 기타 모델 | 기타 |
+| de | Weitere Modelle | Andere |
+| es | Otros modelos | Otros |
+| ru | Другие модели | Другое |
+| he | מודלים אחרים | אחרים |
+
+`settings.folderSettings.otherSubTypes` ("Managed Types") must name **model** types, matching
+each locale's `header.filter.modelTypes` rendering (zh `管理的模型类型`, ja `管理するモデルタイプ`,
+de `Verwaltete Modelltypen`, …).
+
+The "no folders found" empty state (`other.noPaths.*`) uses two phrases that must stay
+consistent whenever that copy is edited. `folder key` means the `folder_paths` key name
+(`vae`, `upscale_models`, … — Latin per the table above); `on disk` means the folder must
+physically exist:
+
+| Phrase | Rendering |
+|---|---|
+| folder key | zh-CN 文件夹键 · zh-TW 資料夾鍵 · ja フォルダーキー · ko 폴더 키 · fr clé de dossier · de Ordnerschlüssel · es clave de carpeta · ru ключ папки · he מפתח תיקייה |
+| on disk | zh-CN 在磁盘上 · zh-TW 在磁碟上 · ja ディスク上 · ko 디스크에 · fr sur le disque · de auf dem Datenträger · es en el disco · ru на диске · he בדיסק |
+
+`settings.json` and `ComfyUI` stay verbatim in every locale; "reload this page" / "restart
+LoRA Manager" reuse each locale's existing restart wording (`settings.extraFolderPaths.*`).
+
+### Model source feature (Hugging Face / ModelScope / TensorArt)
+
+A model file can be linked to the page of an external model site. **Hugging Face**,
+**ModelScope** and **TensorArt** are brand names and stay Latin in every locale (R3); the
+generic nouns around them are translated:
+
+| Term | Rendering |
+|---|---|
+| model source | zh-CN 模型来源 · zh-TW 模型來源 · ja モデルソース · ko 모델 소스 · fr source de modèle · de Modellquelle · es fuente de modelo · ru источник модели · he מקור מודל |
+| model page | zh-CN 模型页面 · zh-TW 模型頁面 · ja モデルページ · ko 모델 페이지 · fr page du modèle · de Modellseite · es página del modelo · ru страница модели · he עמוד המודל |
+| model card | zh-CN 模型卡 · zh-TW 模型卡 · ja モデルカード · ko 모델 카드 · fr fiche de modèle · de Modellkarte · es ficha de modelo · ru карточка модели · he כרטיס מודל |
+| AI enrichment (noun) | reuse the existing pair per locale: zh-CN 增强 · zh-TW 增強 · ja 補完 · ko 보강 · fr enrichissement (par IA) · de Anreicherung (KI-) · es enriquecimiento (con IA) · ru обогащение (с помощью ИИ) · he העשרה (AI) |
+
+`modelCard.actions.viewOnSource` ("View on {source}") follows each locale's existing
+`viewOnHuggingFace` pattern — de `Auf … ansehen`, ru `Открыть …`, he `צפייה ב-…`,
+ja `… で見る`, ko `…에서 보기`, zh `在 … 查看`, fr `Voir sur …`, es `Ver en …`. `{source}` is
+replaced at runtime with the untranslated platform name, so the brand never appears inside the
+translated text.
+
+`modals.linkModelSource.enrichNote` states the rule that only sites exposing a readable model
+card can be enriched and names TensorArt as the current exception. Keep the parenthetical
+exception in sync if another link-only source is ever added — the sentence is deliberately
+phrased as a rule, not as an apology for one site.
+
+The context-menu and bulk-operation enrichment entry points read **"Enrich Metadata with AI"**
+in `en`, not "Enrich HF Metadata": they cover ModelScope as well, so no locale may reintroduce
+an `HF` qualifier in `loras.contextMenu.enrichHfAgent` / `loras.bulkOperations.enrichHfAgent`
+(the key names keep the historical `Hf`; only the values changed).
+
+---
+
+## 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`), model-type names (`settings.priorityTags.modelTypes.*`,
+`settings.folderSettings.subTypeVae` … `subTypeControlnet` — see §2), 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-1058-multi-file-version-downloads.md

@@ -0,0 +1,206 @@
+# Plan: Multi-File Downloads Within a Single CivitAI Model Version
+
+**Issue:** [#1058 — Cannot download multiple file variants from the same model version](https://github.com/willmiao/ComfyUI-Lora-Manager/issues/1058)
+**Status:** v2 — revised after adversarial review (backend correctness + frontend/tests)
+**Scope:** CivitAI/CivArchive downloads of `lora`, `checkpoint`, `embedding` model types. HuggingFace downloads are out of scope (already per-file).
+
+> v2 changelog: incorporated 18 review findings. Key changes vs v1:
+> shared file resolver + `resolved_version_id` for the gate (R1); `file_params` normalization at API boundary (R2); D2 hash-matching rule fixed for empty-hash cases (R6/R7); D3 extended to re-point `version_index` on removal (R4); D4 replaced with a child table (R3); `delete_model_version` interaction documented (R5); `ModelVersionsTab` surface added to phase 2 (F6); phase-2 multi-file loop requires a reload-deferred download variant (F7); queue-retry `file_params=NULL` known issue recorded (R9); test-fixture gaps and revised estimates (F10).
+
+---
+
+## 1. Problem Statement
+
+A CivitAI model version can contain multiple downloadable weight files (e.g. fp16/fp32, safetensors/ckpt, different sizes). LoRA Manager already has a working file-selection pipeline (frontend file dialog → `fileParams` → backend file matching), but downloaded state is tracked at the **model-version** level. After any single file of a version is downloaded:
+
+1. The version is marked **In Library** and the file-selection entry point disappears.
+2. The backend rejects further download attempts for that version.
+
+There is no way to download the remaining files of the same version through LoRA Manager.
+
+## 2. Current State (verified against code; all references confirmed by review)
+
+### 2.1 Download gating — backend (`py/services/download_manager.py`)
+
+`_execute_original_download` enforces two version-level gates:
+
+- **Library gate, early** (lines 1157–1184, before metadata fetch, fires when `model_version_id` given) and **late** (lines 1350–1376, fires only when `model_version_id is None`): `scanner.check_model_version_exists(version_id)` across lora/checkpoint/embedding scanners → hard error `"Model version already exists in ... library"`.
+- **History gate** (lines 1238–1279): when `skip_previously_downloaded_model_versions` setting is on, `_has_been_downloaded(model_type, version_id)` → silent skip. History DB primary key is `(model_type, version_id)` (`py/services/downloaded_version_history_service.py:61`).
+
+File selection works: `file_params {id, type, format, size, fp}` is matched against `version_info.files` (lines 1498–1569), **but only under `if file_params and model_version_id:` (line 1499)** — with `model_id`-only requests the selection silently falls back to the primary file (1571–1619). `file_params` currently carries no file `name` or hash.
+
+### 2.2 Downloaded-state surfacing — backend (`py/routes/handlers/model_handlers.py`)
+
+`get_civitai_versions` (lines 2148–2188) sets per-version `existsLocally` via `cache.version_index.get(version_id)` (plus a single `localPath` from that entry) and `hasBeenDownloaded` via the history service. No per-file granularity.
+
+### 2.3 Frontend blockers (`static/js/managers/DownloadManager.js`)
+
+Three independent gates prevent re-entering the file dialog:
+
+1. **Line 598:** file-select badge rendered only when `modelFiles.length > 1 && !existsLocally`.
+2. **Lines 666–681 (`updateNextButtonState`):** Next button disabled with "Already in Library" when `currentVersion.existsLocally`.
+3. **Lines 784–787 (`proceedToLocation`):** toast + abort when `currentVersion.existsLocally`.
+
+The badge path (`confirmFileSelection` lines 737–759 → `proceedToLocationContent` → `startDownload` single mode → `executeDownloadWithProgress` → POST `file_params`, `static/js/api/baseModelApi.js:1236–1250`) has **zero** `existsLocally` guards (all 12 occurrences enumerated; none on this path; `import/DownloadManager.js` has none either). The `.exists-locally` CSS class is purely visual (`download-modal.css:496–499`). **Making the badge visible again is sufficient to unlock the flow** for phase 1.
+
+Post-download refresh is clean: the modal closes and `resetAndReload(true)` performs a full library refetch (`DownloadManager.js:1063`); dialog reopen resets state and refetches versions with no client-side cache. No same-session staleness.
+
+### 2.4 Local identity of the downloaded file
+
+`LoraMetadata/CheckpointMetadata/EmbeddingMetadata.from_civitai_info(version_info, file_info, ...)` (`py/utils/models.py:245–369`) persists:
+
+- `sha256` = `file_info.hashes.SHA256` (lowercased, defaults to `""`) — a stable per-file identity;
+- `civitai` = the full `version_info` payload (including the `files` list).
+
+Metadata refresh (`metadata_sync_service.py:104–105`) replaces the `civitai` blob wholesale but never overwrites top-level `sha256`; `verify_duplicate_hashes` (481–526) corrects it to the on-disk hash. Top-level-sha256 matching is refresh-robust.
+
+**Caveats (review R6/R7):**
+- SHA256 is not guaranteed: CivArchive's transform only sets `hashes` when source data carries it (`civarchive_client.py:185–189`); `from_civitai_info` defaults to `""`.
+- Name fallback is unreliable exactly when it matters: local `file_name` is extension-less (`models.py:264`) and `generate_unique_filename` rewrites it with a hash suffix on conflict (`download_manager.py:1125–1136`); checkpoints with `hash_status='pending'` keep empty sha256 until on-demand hashing (`model_scanner.py:1232–1240`).
+
+### 2.5 Version index collision (pre-existing hazard)
+
+`ModelCache.version_index` is single-valued (`model_cache.py:133`: `version_index[version_id] = item`). Two files of the same version in the library → second entry overwrites the first; `remove_from_version_index` (lines 151–181) drops the whole version key when the indexed entry is removed, even if a sibling file remains. ~10 read sites depend on this index (48 grep touch points total; readers include `recipe_scanner.py:2682–2726`, `recipe_format.py:37–40`, `misc_handlers.py:2440–2444`, `model_handlers.py`, `model_scanner.check_model_version_exists:2444`).
+
+Review correction (F3): bulk paths `remove_models` (`model_scanner.py:2376`) and `update_single_model_cache` (`:1689`) call `rebuild_version_index()` right after, so a sibling re-enters the index in those flows — the hazard is narrower than v1 stated, but direct `remove_from_version_index` callers (e.g. `model_scanner.py:1018`) still drop the key, and the user-visible artifact in phase 1 is real: `localPath` in the dialog flips to whichever file was indexed last.
+
+### 2.6 Entry points that send / don't send `file_params` (fully enumerated by review)
+
+**Send `file_params` (user-initiated dialog flows only):** `DownloadManager.js:1611–1639` (single mode). API surface accepting arbitrary JSON `file_params`: GET `/api/lm/download-model-get` (`model_handlers.py:1634–1686`), POST `/api/lm/downloads/queue/add` (`model_handlers.py:1799–1832`).
+
+**Never send `file_params` (keep version-level semantics):** batch download (`DownloadManager.js:1756–1766`; batch also filters out in-library versions at `:1648`), `downloadVersionWithDefaults` (`:1810–1830`), recipe import (`import/DownloadManager.js:269–276`), bulk missing-LoRA (`BulkMissingLoraDownloadManager.js:292–299`), `RecipeModal.js:1728–1736`, `ModelVersionsTab.js:1427`. `web/comfyui/` and `vue-widgets/src` contain **no** download triggers at all (grep-verified). `py/services/use_cases/` has only `download_model_use_case.py` (pass-through).
+
+### 2.7 Paths that do NOT need changes (verified)
+
+- **aria2 pause/resume** (`_resume_restored_aria2_download`, line 754+): resumes from persisted `resume_context`; never re-runs existence gates.
+- **`download_coordinator.py:90`**: pure pass-through of `file_params`.
+- **Update checker / plugin self-update** (`update_routes.py:496–501`): only closes the history DB handle.
+- **History delete semantics**: `mark_as_deleted` sets `is_deleted_override=1` and `has_been_downloaded` then returns False (`downloaded_version_history_service.py:276`) — LM-initiated deletes already reset the history skip.
+
+### 2.8 Related pre-existing issues (record, not necessarily fix)
+
+- **Queue retry drops file selection** (R9): `download_queue_service.retry_from_history` / `retry_all_failed` re-queue with `file_params=NULL` (`download_queue_service.py:705, 758`) although the queue table has a `file_params` column (`:43`) — a retried non-primary download silently reverts to the primary file. Fix alongside phase 1 (small: persist and reuse the column).
+- **`delete_model_version`** (`misc_handlers.py:2410–2487`): resolves the file via the single-valued `version_index` (2440–2444), deletes only that one file, and `mark_as_deleted` flags the **entire version** as deleted in history (2479) even when a sibling file remains in the library. See phase 2 item 6.1.5.
+
+## 3. Goals / Non-Goals
+
+**Goals**
+
+- G1: A user can download any not-yet-downloaded file of a version already partially in the library (issue repro steps 6–8).
+- G2: True duplicates stay blocked: downloading the *same* file of the same version twice is rejected.
+- G3: Per-file downloaded state visible in the file dialog; multiple files selectable and downloadable in one pass.
+- G4: No regression for version-level semantics relied on by batch download, recipe missing-LoRA detection, and `skip_previously_downloaded_model_versions`.
+
+**Non-Goals**
+
+- No change to recipe `inLibrary` semantics ("any file of the version present" remains sufficient).
+- No change to the update-checker (version-level comparison).
+- No primary-key rebuild of the history database.
+- HuggingFace download flow untouched.
+
+## 4. Design Decisions
+
+- **D1 — Explicit file selection bypasses the history gate, version-level gates stay for everyone else.** The history skip exists to dedupe automated flows. A user explicitly picking a file is unambiguous intent; the file-level library gate (G2) still prevents real duplicates. **Guard conditions use normalized truthiness** (see D1a). All confirmed `file_params` senders are user-initiated dialog flows (2.6), and LM-initiated deletes already reset history (2.7), so the bypass only affects "downloaded but not LM-deleted" versions with the setting on — intended.
+- **D1a — `file_params` normalization at the boundary (R2).** `download-model-get` and `downloads/queue/add` accept arbitrary JSON; `{}` is `not None` but falsy and would bypass gates while downloading the primary file. Normalize `file_params = file_params or None` in the coordinator/handlers, and treat the bypass as active only when a target file id is resolvable.
+- **D2 — File identity matching rule (R6/R7):** hash-compare **only when both sides are non-empty** (lowercase SHA256 equality); name-compare when either side is empty. Never let `"" == ""` match. Name fallback caveats from 2.4 apply (renamed files, pending checkpoint hashes) — acceptable residual risk, worst case is a blocked re-download the user can retry after hashing completes.
+- **D3 — Cache indexes: additive multi-index + removal re-pointing (R4).** Add `version_files_index: Dict[int, List[dict]]` maintained alongside `version_index` by the same add/remove/rebuild methods; existing readers of `version_index` untouched. Additionally fix `remove_from_version_index`: when the popped entry has a surviving sibling (per the multi-index), re-point `version_index[version_id]` to the sibling instead of dropping the key; same for the `model_id_index` descriptor. This closes the 2.5 hazard for existing readers (`check_model_version_exists`, `existsLocally`, recipe matching) without restructuring anything.
+- **D4 — Per-file history via a child table (R3).** v1's additive-column approach is structurally impossible on a `(model_type, version_id)` PK (`ON CONFLICT DO UPDATE` would keep only the last file). Instead add `downloaded_version_files(model_type, version_id, file_id, file_name, downloaded_at, PRIMARY KEY(model_type, version_id, file_id))` — additive, no PK rebuild, honors the Non-Goal. Existing version-level table and queries unchanged. New per-file queries are opt-in. `_initialize_schema` uses `CREATE TABLE IF NOT EXISTS`, so the new table is created for existing DBs without any ALTER.
+- **D5 — UI flow reuse, with an extracted inner download function for multi-file (F7).** Phase 1 unlocks the existing badge → file dialog → location → download pipeline. Phase 2 upgrades the dialog to multi-select; iterating `executeDownloadWithProgress` as-is would produce N full library reloads, N toasts, and competing failure-summary modals — so phase 2 extracts a reload-deferred, failure-aggregating inner variant and runs one reload + one summary at the end.
+
+## 5. Implementation — Phase 1 (fix the issue; independently shippable)
+
+### 5.1 Backend — `py/services/download_manager.py`
+
+1. **Normalize `file_params`** at the boundary (D1a): `download_coordinator.schedule_download` and the two API handlers (`model_handlers.py:1649–1666`, `1810–1832`) apply `file_params = file_params or None`.
+2. **Extract a shared file resolver** (R1): pull the matching logic at 1498–1569 into `_resolve_target_file(version_info, file_params) -> Optional[dict]`, used by **both** the new gate and the download-selection path. The selection path's condition (line 1499) switches from `model_version_id` to `resolved_version_id` (already computed at 1230–1236 from `version_info.id`), so gate and download always agree on the target file — including the `model_id`-only case.
+3. **New helper** `_find_local_file_entry(version_id, target_file) -> Optional[dict]`: iterate the three scanners' cached `raw_data` (NOT `version_index` — single-valued); candidates = entries whose `civitai.id` normalizes to `version_id`; match per D2.
+4. **Gate restructure in `_execute_original_download`**:
+   - Early scanner gate (1157–1184): add `file_params is None` guard; with normalized `file_params`, defer (file identity not resolvable before metadata fetch).
+   - After `version_info` fetch + `resolved_version_id` (~1229): when `file_params` present, resolve target file via the shared resolver; unresolvable → hard error "No matching file" (fail closed, prevents empty-dict bypass). Resolvable → `_find_local_file_entry`; hit → same hard error shape as today with the file name in the message.
+   - History gate (1238–1279): add `file_params is None` (D1). Base-model skip (1281–1324) unchanged — still applies.
+   - Late gate (1350–1376): add `file_params is None` guard (F2) — the post-fetch file-level check above already covers this case.
+   - Nothing between the early gate and the post-fetch point assumes the version is absent (review task 6: only provider selection + metadata fetch; no DB writes; `_persist_aria2_state` runs only when actually downloading at 1659).
+5. **Queue retry fix** (2.8, small): persist `file_params` into the queue table on enqueue and reuse it in `retry_from_history` / `retry_all_failed`.
+6. Logging: `[download]` lines for file-level allow/block, consistent with existing style.
+
+**Estimated:** ~150–220 LOC + resolver extraction.
+
+### 5.2 Frontend — `static/js/managers/DownloadManager.js`
+
+1. Line 598: drop `&& !existsLocally` from the badge condition (badge shows whenever `modelFiles.length > 1`).
+2. `fileParams` construction (1611–1616): add `name: this.selectedFile.name`.
+3. Surface the backend "file already in library" hard error as a toast instead of only the batch-summary modal (R10/F12 nit; reuse existing error message field).
+4. No changes to `updateNextButtonState` / `proceedToLocation` in phase 1; no template or CSS changes.
+
+**Known phase-1 UX limitations (acknowledged, fixed in phase 2):** with all files downloaded the badge still renders and re-picking a downloaded file fails late (backend error after the location step); `localPath` may point at a sibling file; batch-preview "In Library" badge stays version-level and gives no hint of remaining files.
+
+**Estimated:** ~10–30 LOC (confirmed realistic by review).
+
+### 5.3 Phase 1 tests
+
+Backend — extend `tests/services/test_download_manager_basic.py` (1694 lines; all fixture patterns exist):
+
+- **Fixture gaps to add (F10):** `DummyScanner.get_cached_data()`/`raw_data` stub (~10 lines); `hashes.SHA256` in the metadata-provider payload's `files`.
+- Cases: same version + different SHA256 in library + `file_params` → proceeds; same SHA256 → hard error; `file_params=None` + version in library → hard error (unchanged); history-skip on + `file_params` → not skipped; without → skipped (unchanged); empty-dict `file_params` normalized → version-level behavior; `model_id`-only + `file_params` → gate and selection resolve the same file; legacy metadata (empty local sha256) matched by name; target file with empty SHA256 → name fallback, no `""==""` false positive.
+- Queue retry: `file_params` survives retry.
+- Assert proceed/abort via the existing `_execute_download` mock pattern.
+
+Frontend (`tests/frontend/`): badge renders for multi-file version with `existsLocally=true` (pattern from `downloadManager.history.test.js`).
+
+**Estimated:** ~150–250 LOC (confirmed realistic).
+
+## 6. Implementation — Phase 2 (per-file status + multi-select + index hardening)
+
+### 6.1 Backend
+
+1. **`py/services/model_cache.py`** (D3): add `version_files_index`; maintain in `add_to_version_index` / `remove_from_version_index` / `rebuild_version_index`; removal re-points `version_index[version_id]` (and the `model_id_index` descriptor) to a surviving sibling instead of dropping the key.
+2. **`py/services/model_scanner.py`**: expose `get_files_for_version(version_id) -> List[dict]`.
+3. **`py/routes/handlers/model_handlers.py` `get_civitai_versions`**: annotate each version with `downloadedFiles: [{fileId, fileName, filePath}]` via `version_files_index` + D2 matching against `version.files`.
+4. **`py/services/downloaded_version_history_service.py`** (D4): new child table `downloaded_version_files`; `mark_downloaded` also upserts the child row when `file_id` known; `mark_as_deleted` clears the version's child rows only when no sibling remains in the library; new `get_downloaded_file_ids(model_type, version_id) -> set[int]`. `_record_downloaded_version_history` passes `file_info` through.
+5. **`delete_model_version`** (`misc_handlers.py:2410–2487`, R5): resolve **all** local files of the version via `version_files_index`; delete all (current endpoint semantics are version-level) or — if kept per-file — only `mark_as_deleted` when no sibling remains. Decide at implementation time; minimum is documenting current behavior.
+6. **`ModelVersionsTab` backend support**: none needed beyond item 3 (`downloadedFiles`); the tab consumes the same versions payload.
+
+### 6.2 Frontend
+
+1. **File dialog multi-select** — change surface (F8): option markup (`DownloadManager.js:712–724`), the single-select click handler (`727–734`), the `input[type="radio"]:checked` selector in `confirmFileSelection` (`738`); template `templates/components/modals/download_modal.html:48–60` (confirm-button label only); CSS `download-modal.css` — checkbox variant of `.file-option-radio input` (595–604) and a **new** `.file-option.disabled` style (does not exist). Files whose id ∈ `downloadedFiles` render disabled with an "In Library" tag.
+2. **Mixed-type guard (F8):** multi-select is restricted to files sharing the same routing target (`_isDiffusionModel` is computed once from a single `selectedFile` at 798–803; e.g. "Model" + "UNet" files route to different roots). Disallow mixed-type multi-select (simplest, predictable); single-file selection unchanged.
+3. **Multi-file download loop (D5/F7):** extract from `executeDownloadWithProgress` a reload-deferred, no-toast inner function; iterate per selected file with per-file progress; one `resetAndReload(true)` + one aggregated success/failure summary at the end (reuse `showDownloadBatchSummary`).
+4. **`updateNextButtonState` / `proceedToLocation`:** for multi-file versions, Next routes into the file dialog; hard block only when *every* weight file is downloaded.
+5. **`ModelVersionsTab.js` (F6):** the Download action (`:576` hidden when `isInLibrary`) — for multi-file versions with remaining files, show it and route into the download modal's file dialog; keep hidden when all files present.
+6. **Batch preview (F5):** `batch-preview-local-badge` (`:1320`) gains a "partially downloaded" hint for multi-file versions with remaining files.
+7. New i18n keys (`modals.download.fileSelection.inLibrary`, `downloadSelected`, partial-download tooltip, etc.) → run `python scripts/sync_translation_keys.py`.
+
+### 6.3 Phase 2 tests
+
+- `model_cache` (`tests/services/test_model_cache.py` already covers add/remove at 44–55): multi-valued index; sibling re-point on removal; rebuild.
+- `get_civitai_versions`: `downloadedFiles` correctness (hash match, name fallback, no match, CivArchive no-hash payload).
+- History service (`tests/services/test_downloaded_version_history_service.py` uses real SQLite on tmp_path): child-table creation on a legacy DB; per-file record/query; `mark_as_deleted` sibling semantics.
+- Frontend: dialog checkbox rendering/disabled state and multi-file confirm — **greenfield behavior coverage** (F10: no existing test exercises `showFileSelectionStep`/`confirmFileSelection`; infra exists, patterns must be built).
+
+## 7. Risks and Mitigations
+
+| Risk | Impact | Mitigation |
+|---|---|---|
+| History-gate bypass (D1) causes unwanted re-downloads in automated flows | Large checkpoint files re-downloaded | Bypass only with normalized, resolvable `file_params` (D1a); all such senders are user-initiated dialog flows (2.6, verified); tests pin batch/recipe/bulk behavior. |
+| Empty-hash matching edge cases (R6) | Duplicate download of the same file, or false block | D2 rule: hash only when both non-empty; name otherwise; never `""==""`. Residual risk documented (2.4). |
+| Phase-1 late-failure UX (F12) | User picks a downloaded file, fails only after location step | Toast surfacing (5.2.3); phase 2 disables downloaded files up front. |
+| Phase-2 index change corrupts existing behavior | Recipe matching, delete flows | Additive index + re-point only; `version_index` read semantics unchanged; `remove_models`/`update_single_model_cache` already rebuild (F3); tests. |
+| `delete_model_version` marks whole version deleted while sibling remains (R5) | History wrongly suppresses re-download of the surviving sibling's version | Phase 2 item 6.1.5; documented until then. |
+| History child-table migration failure on user installs | Service init crash | `CREATE TABLE IF NOT EXISTS` in `_initialize_schema`; failure degrades to version-level behavior (per-file queries return empty). |
+| Batch-preview badge misleading for partial versions (F5) | Minor UX confusion | Acknowledged in phase 1; fixed in phase 2 item 6.2.6. |
+| UI confusion: version shows "In Library" while files remain downloadable | Support burden | Phase 2: per-file disabled state + partial-download tooltip. |
+| Hash-identical sibling files (repacked content) | Second file blocked | Acceptable: scanner hash dedup already collapses them. |
+
+## 8. Rollout
+
+1. **Commit 1** — `fix(download): allow downloading additional files of an in-library model version (#1058)` → Phase 1 (5.1–5.3).
+2. **Commit 2** — `feat(download): per-file download status and multi-file selection (#1058)` → Phase 2 (6.1–6.3).
+
+Phase 1 alone resolves the issue as reported; phase 2 can ship in a later release if review prefers smaller increments.
+
+## 9. Effort Estimate (revised after review)
+
+| Phase | Backend | Frontend | Tests | Risk |
+|---|---|---|---|---|
+| 1 | ~150–220 LOC (+ queue-retry fix ~30) | ~10–30 LOC | ~150–250 LOC | Low |
+| 2 | ~250–350 LOC | ~250–350 LOC (multi-file loop refactor + ModelVersionsTab + batch badge) | ~250–350 LOC (dialog tests greenfield) | Medium |

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/plans/other-models-page.md

@@ -0,0 +1,363 @@
+# Plan: "Other Models" Page — Unified Management for VAE / Upscaler / Text Encoder / etc.
+
+**Status:** v2 — **Phase 1 implemented** (2026-09-12, commits `27da7b3c` backend + `fa7ce725` frontend; verified live against a running ComfyUI instance: scan/hash/sub_type-derivation/fetch/previews all green). **Phase 2 implemented** (2026-09-12, per §9 design; full pytest + vitest green). **Phase 3 implemented** (§11: opt-in management toggles; default off). **i18n done** (2026-09-13): all 36 new keys translated in the 9 non-English locales — the `[TODO: Translate]` placeholders left by the sync script during development are gone (see `docs/i18n-translation-guidelines.md` §2, "Other Models feature"). **Default set revised (pre-release):** only `vae` / `upscaler` / `text_encoder` are managed by default — `clip_vision` and `controlnet` are both opt-in (§2, §11.1.1).
+**Scope (Phase 1):** scan + manage (list, search, filter, tags, folders, preview, rename, move, delete/exclude, CivitAI metadata fetch) for a new model type `other`, exposed as a new web page. **Phase 2 (§9):** one-click download from CivitAI for these types.
+
+## 1. Goal
+
+Today the manager supports three model types:
+
+| page | model_type | sub_types |
+|---|---|---|
+| `/loras` | `lora` | `lora`, `locon`, `dora` |
+| `/checkpoints` | `checkpoint` | `checkpoint`, `diffusion_model` |
+| `/embeddings` | `embedding` | `embedding` |
+
+Add a fourth page that manages "everything else" — VAE, upscalers, text encoders / CLIP, CLIP vision, optionally ControlNet — with a folder→sub_type mapping table so new ComfyUI folder categories can be added later by configuration, not code.
+
+## 2. Locked Decisions
+
+1. **Architecture: one scanner + one service + one page, sub_type derived by location.**
+   Replicates the checkpoint pattern (`CheckpointScanner` aggregates `checkpoints` + `unet` roots and derives `checkpoint` vs `diffusion_model` from the root containing the file, `py/services/checkpoint_scanner.py:384-415`). One `OtherScanner` aggregates all enabled folder roots; `resolve_sub_type_for_path()` maps each root to a sub_type. No per-category scanners.
+
+2. **Naming: internal `model_type = "other"`, route prefix `/other`, page id `other`.**
+   - `misc` is rejected: `py/routes/misc_routes.py` already owns that name for system/settings routes (`/api/lm/settings`, `/api/lm/doctor/*`).
+   - `components` is rejected: `templates/components/` and `static/js/components/` directories would make `components.html` / `components.js` confusing neighbors.
+   - `other` matches CivitAI's `Other` fallback type semantics. The **display name** is an i18n string (`other.title`, e.g. "Other Models") and can be renamed later without touching code.
+
+3. **sub_type values:** snake_case, aligned with CivitAI `ModelType` semantics:
+
+   | sub_type | ComfyUI `folder_paths` key(s) | CivitAI ModelType | enabled by default |
+   |---|---|---|---|
+   | `vae` | `vae` | `VAE` | yes |
+   | `upscaler` | `upscale_models` | `Upscaler` | yes |
+   | `text_encoder` | `text_encoders`, `clip` (legacy) | `TextEncoder` (CLIP is retired upstream) | yes |
+   | `clip_vision` | `clip_vision` | `CLIPVision` | no (mapping present, opt-in) |
+   | `controlnet` | `controlnet` | `Controlnet` | no (mapping present, opt-in) |
+
+   New folder categories = one line in the mapping table (see §4.1).
+
+   **Why only three are on by default** (revised in Phase 3, before release):
+   VAE, upscalers and text encoders are dependency-style assets every pipeline
+   needs, and "which one am I actually using" is the recurring problem they
+   solve. `clip_vision` and `controlnet` are workflow-driven instead
+   (IPAdapter/SVD image conditioning; per-workflow ControlNet variants), and
+   ControlNet libraries routinely run to dozens of files, so both are treated
+   symmetrically as opt-in. Enumerating all five as "the default set" was not
+   defensible on demand breadth alone.
+
+4. **Phase 1 = scan/manage only.** Downloads from CivitAI (`download_manager.py` type mapping, default-root settings keys, download routing) are Phase 2 (§9). CivitAI **metadata fetch** for existing files IS in Phase 1 (hash-based lookup is type-agnostic; only the type-validation hook needs new values).
+
+5. **Out of scope (default off, revisit later):** usage statistics buckets, recipe matching (`recipe_scanner.py` only merges lora+checkpoint scanners), statistics page, embeddings re-classification (stays its own page — merging would be a breaking change).
+
+## 3. Why This Works With Minimal Churn
+
+- `ModelScanner` (`py/services/model_scanner.py:93`) is specialized entirely via constructor params (`model_type`, `model_class`, `file_extensions`) + optional hooks (`adjust_metadata`, `adjust_cached_entry`, `resolve_sub_type_for_path`, `model_scanner.py:1429-1443`).
+- `BaseModelService` subclasses can be one method (`EmbeddingService` implements only `format_response`, `py/services/embedding_service.py:12`).
+- Routes: `ModelServiceFactory.register_model_type()` (`py/services/model_service_factory.py:120-136`) + `COMMON_ROUTE_DEFINITIONS` (`py/routes/model_route_registrar.py:23-149`) generate the full `/api/lm/{prefix}/*` surface (~50 endpoints) plus the `GET /{prefix}` page route.
+- `PersistentModelCache` (`py/services/persistent_model_cache.py:526-606`) is a single `models` table keyed `(model_type, file_path)` with `model_type` as free text — **zero schema change**.
+- Frontend `apiConfig.js` (`static/js/api/apiConfig.js:51`) generates all endpoints from the model-type string; `ModelCard.js:670-675` renders the sub_type badge from data; the checkpoints page already demonstrates the "one page, multiple sub_types" filter (`header.html:298`).
+
+## 4. Backend Changes
+
+### 4.1 New constants — `py/utils/constants.py`
+
+```python
+# folder_paths key -> sub_type; single source of truth for extensibility
+OTHER_MODEL_FOLDER_SUBTYPES = {
+    "vae": "vae",
+    "upscale_models": "upscaler",
+    "text_encoders": "text_encoder",
+    "clip": "text_encoder",        # legacy ComfyUI key
+    "clip_vision": "clip_vision",
+    "controlnet": "controlnet",
+}
+DEFAULT_OTHER_MODEL_FOLDERS = ("vae", "upscale_models", "text_encoders", "clip", "clip_vision")
+VALID_OTHER_SUB_TYPES = ["vae", "upscaler", "text_encoder", "clip_vision", "controlnet"]
+# CivitAI model.type values accepted for this page (fetch-metadata validation)
+VALID_OTHER_CIVITAI_TYPES = {"vae", "upscaler", "textencoder", "clipvision", "controlnet", "other"}
+```
+
+Also extend `CIVITAI_USER_MODEL_TYPES` (`constants.py:90`) if user-model queries should include these types.
+
+### 4.2 New files (mirror the embedding/checkpoint implementations)
+
+1. **`py/utils/models.py`** — add `OtherModelMetadata(BaseModelMetadata)`: default `sub_type="vae"` placeholder overridden by scanner hook; `from_civitai_info` mapping CivitAI types → our sub_types (`TextEncoder`→`text_encoder`, `CLIPVision`→`clip_vision`, `Upscaler`→`upscaler`, `VAE`→`vae`, `Controlnet`→`controlnet`, else `other`-ish fallback to folder-derived sub_type).
+2. **`py/services/other_scanner.py`** — `OtherScanner(ModelScanner)`:
+   - `model_type="other"`, extensions: reuse the checkpoint set (`safetensors/pt/pt2/bin/pth/pkl/sft/gguf`).
+   - `get_model_roots()`: iterate `OTHER_MODEL_FOLDER_SUBTYPES` ∩ enabled keys, pull each from `config` (§4.3); dedupe; build `root → sub_type` map (normalized abspaths; multiple keys may share a sub_type).
+   - Implement all three hooks like `CheckpointScanner` (`checkpoint_scanner.py:384-415`): `resolve_sub_type_for_path` by longest-prefix root match, `adjust_metadata`, `adjust_cached_entry` (sub_type is re-derived on cache load, never persisted).
+   - **Lazy hashing, checkpoint-style**: text encoders (T5-XXL ≈ 10 GB) make eager sha256 painful. Copy the `hash_status="pending"` + singleflight `calculate_hash_for_model` pattern from `CheckpointScanner`.
+3. **`py/services/other_model_service.py`** — `OtherModelService(BaseModelService)`, `format_response` only (no usage_count, like `EmbeddingService`).
+4. **`py/routes/other_routes.py`** — `OtherRoutes(BaseModelRoutes)`, `template_name="other.html"`, hooks:
+   - `_validate_civitai_model_type` → `VALID_OTHER_CIVITAI_TYPES`
+   - `_get_expected_model_types`, `_parse_specific_params` (no type-specific download params in Phase 1)
+   - `initialize_services()` on `app.on_startup` pulling `ServiceRegistry.get_other_scanner()`.
+
+### 4.3 `py/config.py`
+
+- New `other_roots` property: for each enabled key in `OTHER_MODEL_FOLDER_SUBTYPES`, `folder_paths.get_folder_paths(key)` (plugin mode) — standalone mode needs nothing new: `MockFolderPaths` (`standalone.py:66-105`) already serves arbitrary keys from `settings.json.folder_paths`.
+- Follow the existing per-type recipe: an `_prepare_other_paths()` (dedupe + symlink registration; also **cross-scanner overlap detection** — warn if an `other` root is already covered by checkpoints/unet/embedding roots, mirroring the checkpoint/unet overlap check).
+- Wire into: `_apply_library_paths`, `_symlink_roots()`, `_rebuild_preview_roots()` (hard requirement — preview images are served per registered root), `save_folder_paths_to_settings()`.
+
+### 4.4 Existing-file edits (the "type string scatter" — each is a small branch/entry)
+
+| file | change |
+|---|---|
+| `py/services/model_service_factory.py:120` | register `("other", OtherModelService, OtherRoutes)` in `register_default_model_types()` |
+| `py/services/service_registry.py` | add `get_other_scanner()` (mirror `:297` `get_embedding_scanner`) |
+| `py/services/model_scanner.py:67` | `PAGE_TYPE_MAP['other'] = 'other'` (WebSocket progress) |
+| `py/services/base_model_service.py:896-906` | `get_model_types()` branch → `VALID_OTHER_SUB_TYPES` |
+| `py/lora_manager.py` | `_initialize_services` scanner task list (`:219-242`), `_cleanup` cancel list (`:463`), `_cleanup_backup_files` roots (`:327-330`) |
+| `py/routes/handlers/misc_handlers.py` | `scanner_getters` (`:657-661`) + `scanner_factories` (`:757-759`) so Doctor / init-status / refresh-all see the new scanner |
+| `py/services/pending_delete_service.py` | `_PAGE_TYPE` map (`:57-61`) + scanner getter list (`:983-985`) |
+| `py/metadata_ops/__init__.py:36-38` | `SCANNER_TYPE_MAP['other']` |
+| `settings.json.example` | document optional `folder_paths` keys: `vae`, `upscale_models`, `text_encoders`, `clip_vision` |
+
+**Explicitly NOT touched in Phase 1:** `py/services/download_manager.py`, `py/services/download_routing.py`, `py/services/settings_manager.py` default-root keys, `py/routes/stats_routes.py`, `py/utils/usage_stats.py`, `py/services/recipe_scanner.py`, `py/metadata_collector/`, `py/nodes/`.
+
+**Zero-change confirmations (verified):** `PersistentModelCache`, `ModelUpdateService`, `DownloadedVersionHistoryService`, `MetadataSyncService` + provider chain (type-agnostic hash lookups), `ModelFileService` / `ModelMoveService` / `ModelLifecycleService` (scanner + model_type injected), `ModelCache` / `ModelHashIndex`, `AutoV3BackfillService`.
+
+## 5. Frontend Changes
+
+1. **`static/js/api/apiConfig.js`** — `MODEL_TYPES.OTHER = 'other'`; `MODEL_CONFIG.other` entry (displayName, singularName, `supportsMove`, `supportsBulkOperations`; no letter filter); endpoints come free from `getApiEndpoints()` (`:51`).
+2. **`static/js/api/otherApi.js`** — thin `OtherApiClient extends BaseModelApiClient` (mirror `embeddingApi.js`); register in `modelApiFactory.js`.
+3. **`static/js/other.js`** — page entry (mirror `embeddings.js`): `appCore.initialize()` + `createPageControls('other')` + `initializePageFeatures()` + `ModelDuplicatesManager` + `initActiveFiltersSync('other')`.
+4. **Controls & context menu** — `OtherControls extends PageControls` and `OtherContextMenu` (start from the embedding variants — the smallest); add branches in the two factories (`components/controls/index.js:15`, `components/ContextMenu/index.js:15`). Context-menu template block lives in `templates/other.html` (`{% block additional_components %}`, the checkpoints/embeddings pattern — do NOT touch the shared `context_menu.html`).
+5. **`templates/other.html`** — copy `embeddings.html`: same content blocks (controls + breadcrumb + duplicates banner + folder sidebar + `#modelGrid`), `data-page="other"`, main script `/loras_static/js/other.js`.
+6. **`templates/components/header.html`** — nav entry (`:23-43`, active when `request.path.startswith('/other')`); enable the `modelTypes` sub_type filter panel for `other` (`:298-305` pattern from checkpoints); check search-options panel conditions (`:199-224`).
+7. **`static/js/utils/constants.js`** — `MODEL_SUBTYPE_ABBREVIATIONS` (`:115`): `vae→VAE`, `upscaler→UPS`, `text_encoder→TE`, `clip_vision→CV`, `controlnet→CN`; matching `MODEL_SUBTYPE_DISPLAY_NAMES` (`:99`). (Unknown fallback already uppercases 4 chars, but explicit mappings read better.)
+8. **`static/js/core.js:110` `getPageType()`** — verify `data-page="other"` flows through `state.pages` generically; add only if the page list is enumerated anywhere.
+9. No change to `web/comfyui/top_menu_extension.js` (it opens `/loras`; page-to-page nav is the header bar).
+
+## 6. i18n
+
+- `locales/en.json`: add `other.title` (e.g. "Other Models") + minimal `other.contextMenu.*` / `other.modelTypes.*` keys; reuse `modelCard.*`, `loras.contextMenu.*`, `common.*` wherever possible (the established pattern — checkpoints/embeddings already reuse lora keys).
+- Run `python scripts/sync_translation_keys.py`; leave `[TODO: Translate]` placeholders in other locales (per `docs/i18n-translation-guidelines.md` §7 — do not translate proactively).
+
+## 7. Testing
+
+Follow existing conventions (`pytest.ini`, `tests/frontend/` vitest):
+
+1. **Backend (pytest, async where needed):**
+   - `OtherScanner` root aggregation + `resolve_sub_type_for_path` (file under `vae/` root → `vae`; `text_encoders` and legacy `clip` both → `text_encoder`; disabled `controlnet` root not scanned).
+   - Cache round-trip: sub_type re-derived via `adjust_cached_entry` (not persisted).
+   - Lazy hash: `hash_status="pending"` default; `calculate_hash_for_model` singleflight.
+   - `OtherRoutes` registration smoke test: `/api/lm/other/...` endpoints exist; `_validate_civitai_model_type` accepts `vae`/`upscaler`/`textencoder`, rejects `lora`.
+   - Config: `other_roots` in both modes (mock `folder_paths`, and standalone `settings.json.folder_paths`).
+2. **Frontend (vitest + jsdom, `tests/frontend/`):**
+   - `apiConfig`: `getApiEndpoints('other')` URL shapes; `modelApiFactory` returns the Other client.
+   - `ModelCard` badge rendering for new sub_types.
+   - `createPageControls('other')` / `createPageContextMenu('other')` factories.
+3. **Manual UI verification by the user** (per AGENTS.md — no sandbox/browser automation): page loads, scans a real library, sub_type filter + badges, context menu actions.
+
+## 8. Execution Order
+
+1. `constants.py` + `OtherModelMetadata` + `config.py` roots
+2. `OtherScanner` (+ registry, factory, `PAGE_TYPE_MAP`) → scanner unit tests green
+3. `OtherModelService` + `OtherRoutes` + handler/registrar wiring + `lora_manager.py` lifecycle → route tests green
+4. Doctor/pending-delete/metadata-ops scatter entries
+5. Template + header nav + frontend API/controls/context-menu/card badges → vitest green
+6. i18n keys + sync script
+7. `pytest` + `npm test` full runs; hand to user for manual UI check
+
+## 9. Phase 2 Detailed Design — CivitAI Downloads for `other`
+
+Designed 2026-09-12 against the Phase-1 code on this branch; decisions marked **[locked]** follow the same recommendations the feature owner approved for Phase 1.
+
+### 9.1 Download pipeline touch points
+
+Flow: `POST /api/lm/download-model` (`py/routes/model_route_registrar.py:104`; GET variant `:105` for the browser extension) → `ModelDownloadHandler.download_model` (`model_handlers.py:1740`) → `DownloadModelUseCase.execute` → `DownloadCoordinator.schedule_download` → `DownloadManager.download_from_civitai` (`download_manager.py:386`) → `_execute_original_download` (`:1415`). Inside, seven scatter points need an `other` branch:
+
+1. **Type map** (`:1496-1507`): accept `model.type.lower() in VALID_OTHER_CIVITAI_TYPES` → `model_type = "other"` (reuses the Phase-1 set, incl. `"other"` itself).
+2. **Early version-exists gate** (`:1436-1463`): add `other_scanner.check_model_version_exists`.
+3. **File-level exists gate** (`:1640-1655` → `_find_local_file_entry` `:320-346` → `_get_scanner_for_model_type` `:230-236`): add explicit `other` branch. **Trap**: the function currently falls through to the lora scanner for unknown types — `"other"` would silently dedupe against loras. Also narrow the fall-through to `"lora"` only / raise on unknown.
+4. **Version-level fallback gate** (`:1656-1688`): add `elif model_type == "other"`.
+5. **Default-root selection** (`:1690-1727`): for `other`, first resolve sub_type (§9.2), then read `default_other_roots[sub_type]` (§9.3); if sub_type is undecidable or no default root configured → error guiding the user to pick a folder explicitly.
+6. **Metadata class selection** (`:1909-1928`) + `_build_metadata_for_resume` (`:969-981`): add `OtherModelMetadata.from_civitai_info` branches.
+7. **Post-download cache write** (`_execute_download_pipeline` `:2622-2679`): add `other` scanner branch; `adjust_metadata` re-derives sub_type from the on-disk root automatically. `_get_supported_extensions_for_type` (`:2720-2744`): `other` reuses the checkpoint extension set.
+
+Hooks: `_record_downloaded_version_history` (model_type is free text — zero change); `_sync_downloaded_version` (`:1984` → scanner dispatch `:2130-2135`) add `other`; `py/utils/example_images_download_manager.py` scanner dispatch at `:411-421`, `:591-601`, `:1089+` — add `other` at all three (silent no-scanner otherwise).
+
+Path templates: `get_download_path_template("other")` is unset, so `other` resolves to a **flat** layout (empty template) — downloads land directly under the resolved sub_type root. This is deliberate: other-model roots are already split per sub_type (`default_other_roots`), and `priority_tags` has no `other` entry, so `{first_tag}` would fall back to an arbitrary CivitAI tag and scatter files into unstable folders. Users who want nesting can still set `download_path_templates["other"]` in `settings.json`. See `DEFAULT_DOWNLOAD_PATH_TEMPLATES` (`py/utils/constants.py`) and `DEFAULT_PATH_TEMPLATES` (`static/js/utils/constants.js`).
+
+### 9.2 File-level routing (model.type / file.type → sub_type) **[locked]**
+
+Table-driven, mirroring Phase 1. New in `py/utils/constants.py`:
+
+```python
+CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE = {
+    "VAE": "vae", "Upscaler": "upscaler", "Text Encoder": "text_encoder",
+    "Vision Encoder": "clip_vision", "CLIPVision": "clip_vision",
+    "ControlNet": "controlnet",
+}
+```
+
+`download_routing.py` gains `resolve_other_download_sub_type(civitai_model_type, file_types, selected_file_type=None)` with fixed priority:
+
+1. **Explicit user file pick** (`file_params` from #1058's `_resolve_target_file`) — if the picked file's type maps, it wins even when model.type is `Checkpoint`.
+2. **model.type** via the existing `CIVITAI_TYPE_TO_OTHER_SUB_TYPE` (`constants.py:120-127`).
+3. **file.type fallback** — only when model.type maps to nothing (e.g. model.type `Other` or retired `CLIP`). MUST NOT override a mapped model.type: checkpoint models routinely bundle VAE/Text Encoder component files, and unconditional file-type routing would misroute them.
+4. Still undecidable → `None`; `use_default_paths` errors and the UI offers all other roots for manual selection.
+
+HTTP: extend `DownloadRoutingHandler.get_download_routing` (`download_routing_handlers.py:23`) with an `other` branch returning `{root_kind: "other", sub_type: ...}`; add `GET /api/lm/other/roots_by_subtype` in `OtherRoutes.setup_specific_routes` (data from `config._prepare_other_paths`'s per-key roots, aggregating `text_encoders` + legacy `clip` under `text_encoder`).
+
+### 9.3 Settings: single dict key `default_other_roots` **[locked]**
+
+Rejected: four flat keys (`default_vae_root`…) — each flat key costs ~13 touch points in `settings_manager.py` (defaults `:82-85`, `_check_and_auto_set` `:890-895`, `set()` `:1621-1628`, `_update_active_library_entry` `:738-805`, upsert/create signatures `:1953-2132`, `_build_library_payload` `:552-612`, `_sync_active_library_to_root` `:519-547`, three library constructors, frontend `DEFAULT_SETTINGS_BASE`), repeated per future sub_type.
+
+Chosen: one mapping key `default_other_roots: {sub_type: path}`, copying the `extra_folder_paths` precedent (generic Mapping handling at `:533-535`, `:573-578`, `:763-767`). `_check_and_auto_set` generalizes to per-sub_type candidates (union over that sub_type's folder keys — `text_encoder` → `text_encoders` + `clip`). `set()` validates keys against `VALID_OTHER_SUB_TYPES`.
+
+Also fix the Phase-1 omission: add `"other_scanner"` to `_notify_library_change` (`:2150-2156`) and `_notify_model_name_display_change` (`:1795-1800`) — otherwise switching libraries leaves the other page stale.
+
+### 9.4 Settings UI
+
+- `templates/components/modals/settings/library.html:34-40`: sub_type selectors after the existing four `setting_select`s (Jinja loop; controlnet selector only when `enabled_other_folders` includes it). Dict-subkey save helper `saveOtherRootSetting(subType, value)` alongside the flat `saveSelectSetting`.
+- `static/js/managers/SettingsManager.js:1547-1697`: `loadOtherRoots()` mirroring `loadUnetRoots()`, fed by `/api/lm/other/roots_by_subtype`; current values from `state.global.settings.default_other_roots`. `state/index.js:24` `DEFAULT_SETTINGS_BASE` += `default_other_roots: {}`.
+- Optional: one `other` row in the download-path-template block (`library.html:153-211`).
+- i18n: `settings.folderSettings.*` keys into `locales/en.json` + sync script; other locales keep `[TODO: Translate]`.
+- Settings GET (`misc_handlers.py:1528-1536`) already returns all non-sensitive keys — new key reaches the frontend for free.
+
+### 9.5 Frontend download entry
+
+- `templates/components/controls.html:83`: drop the `page_id != 'other'` exclusion on the download button (keyboard shortcut D self-enables via `PageControls.js:196-198`).
+- `OtherControls.js:22-55`: add `showDownloadModal: () => downloadManager.showDownloadModal()` (mirror `EmbeddingsControls.js:43-45`).
+- `DownloadManager.js` `proceedToLocationContent` (`:955-1017`): add `_resolveOtherSubType()` (mirror `_resolveIsDiffusionModel` `:1026`): selected file type → `/api/lm/download/routing` → `otherApiClient.fetchModelRoots(subType)` (new); default-root preselect reads `default_other_roots[subType]` instead of `` `default_${singularType}_root` `` (`:974`). Undecidable → list all other roots (`/api/lm/other/roots`) for manual pick; an explicit save_dir skips backend default-root logic, so the two paths cannot disagree.
+- `ModelVersionsTab` download buttons are modelType-generic and already work via `getModelApiClient('other')`; context menu has no CivitAI download entry — no change.
+- Version-list type validation (`get_civitai_versions` → `_validate_civitai_model_type`) already accepts `VALID_OTHER_CIVITAI_TYPES` from Phase 1.
+
+### 9.6 CivitAI type mapping decisions **[locked]**
+
+- Download accepts exactly `VALID_OTHER_CIVITAI_TYPES` (`VAE, Upscaler, TextEncoder, CLIP, CLIPVision, Controlnet, Other`) — reuse the Phase-1 tables; do NOT create new ones.
+- Extend `CIVITAI_USER_MODEL_TYPES` (`constants.py:133-137`) with the 7 aliases, and point them at the other scanner / `"other"` history bucket in `misc_handlers.py` (`type_scanner_map` `:2793-2797`, `downloaded_version_map` `:2821-2827`) — otherwise creator pages silently filter these models while downloads claim support.
+- Fix (small Phase-1 bug): `OtherModelMetadata.from_civitai_info` (`py/utils/models.py:343`) reads `version_info.get("type")`, but the type lives at `version["model"]["type"]` — the mapping never fires and always degrades to the placeholder. Read `version_info.get("model", {}).get("type")` instead. (`CheckpointMetadata:290` has the same shape; leave it alone here.)
+
+### 9.7 Tests
+
+Existing base: `tests/services/test_download_manager_basic.py` (incl. `test_download_rejects_unsupported_model_type` `:1336`), `test_download_manager_error.py`, `test_download_manager_concurrent.py`, `tests/integration/test_download_flow.py`, `tests/services/test_settings_manager.py`; frontend `tests/frontend/managers/downloadManager.routing.test.js`, `settingsManager.library.test.js`.
+
+Add: (1) `resolve_other_download_sub_type` unit tests — every priority tier, bundled-component anti-misrouting, undecidable → None, civarchive-shaped payload; (2) download_manager — six model.types accepted → other scanner (mock), unknown still rejected, no lora-scanner fall-through, per-sub_type default roots + unconfigured error, resume metadata, extension set; (3) settings_manager — `default_other_roots` defaults/auto-set (incl. text_encoder dual-key union)/library sync/upsert passthrough/illegal sub_type rejection; (4) routes — `/api/lm/download/routing` other branch, `roots_by_subtype` shape; (5) example-images dispatch accepts `other` (3 sites); (6) vitest — `_resolveOtherSubType` + root select + default preselect, `loadOtherRoots`; (7) user-models existsLocally for VAE.
+
+### 9.8 Phase 2 file list
+
+Backend: `py/utils/constants.py`, `py/services/download_routing.py`, `py/routes/handlers/download_routing_handlers.py`, `py/services/download_manager.py`, `py/utils/example_images_download_manager.py`, `py/services/settings_manager.py`, `py/utils/models.py`, `py/routes/other_routes.py`, `py/routes/handlers/misc_handlers.py`, `settings.json.example`.
+Frontend/templates: `templates/components/controls.html`, `static/js/components/controls/OtherControls.js`, `static/js/managers/DownloadManager.js`, `static/js/api/otherApi.js`, `templates/components/modals/settings/library.html`, `static/js/managers/SettingsManager.js`, `static/js/state/index.js`, `locales/en.json` + sync.
+
+## 10. Risks / Open Questions
+
+- **Root overlap**: a user may point `text_encoders` at a directory already scanned as checkpoints/unet. Realpath dedup inside one scanner won't catch cross-scanner overlap → the `_prepare_other_paths` overlap warning (§4.3) is the mitigation; duplicate cards across pages are cosmetic, not corrupting (cache keyed by `(model_type, file_path)`).
+- **Huge text encoders + lazy hash**: CivitAI fetch for a pending-hash model must trigger on-demand hash like checkpoints do — verify that flow (`calculate_hash_for_model`) is reachable from the `other` routes' fetch-metadata handler.
+- **Retired CivitAI types**: `CLIP`/`CLIPVision` are retired upstream (grandfathered for existing models); metadata fetch must tolerate both retired and current types — `VALID_OTHER_CIVITAI_TYPES` includes them deliberately.
+- **Standalone users** must add the new `folder_paths` keys to `settings.json` themselves; document in `settings.json.example` and the feature doc.
+- **Page display name** is i18n-only; if "Other Models" tests poorly, rename `other.title` without code changes.
+
+### Phase 2 risks
+
+- **Bundled component files**: checkpoint models routinely ship VAE/Text Encoder component files — file.type routing must stay a fallback (or explicit user pick), never an override (§9.2 priority is load-bearing; test it).
+- **`_get_scanner_for_model_type` lora fall-through** (`download_manager.py:236`): without an explicit `other` branch, dedupe checks run against the lora scanner — the most insidious trap in Phase 2.
+- **text_encoder dual folder keys** (`text_encoders` + legacy `clip`): default-root candidates, `roots_by_subtype`, and auto-set must all merge both keys; miss one and the default-root dropdown comes up empty.
+- **Undecidable sub_type** (model.type `Other` + unknown file types): must error and ask, never silently default to the vae folder.
+- **Lazy hash after download**: downloads carry CivitAI SHA256 (no recompute needed) — ensure the post-download cache write doesn't leave `hash_status="pending"`, or the next metadata fetch re-hashes a 10 GB file.
+- **CivArchive source**: same `_execute_original_download` path, same payload shape — cover it once in tests.
+
+## 11. Phase 3 — Opt-in Management Toggles (implemented)
+
+Designed 2026-09-13 against the Phase-1/2 code. Other Models is **opt-in**: after
+Phase 3 the feature ships disabled, so no other-model folder is scanned and the
+page shows an "enable" empty state until the user turns it on.
+
+### 11.1 Settings (global, not per-library)
+
+| key | type | default | meaning |
+|---|---|---|---|
+| `enable_other_models` | bool | `false` | master switch |
+| `enabled_other_sub_types` | list[str] | `["vae","upscaler","text_encoder"]` | allow-list; `clip_vision` and `controlnet` are opt-in (see §2) |
+
+`enabled_other_folders` (the unreleased, additive, no-UI backend key) was removed
+and replaced by the sub_type-level allow-list; there is no migration because the
+feature never shipped. `text_encoder` expands to `text_encoders` + legacy `clip`
+via `OTHER_SUB_TYPE_FOLDER_KEYS`.
+
+The default allow-list lives on five surfaces that must stay in sync:
+`DEFAULT_ENABLED_OTHER_SUB_TYPES` (`py/utils/constants.py`), `DEFAULT_SETTINGS`
+(`py/services/settings_manager.py`), the two `DEFAULT_SETTINGS_BASE` /
+`createDefaultSettings` lists (`static/js/state/index.js`), the
+`updateOtherModelsControls()` fallback (`static/js/managers/SettingsManager.js`)
+and the server-rendered Jinja fallback
+(`templates/components/modals/settings/library.html`).
+
+### 11.1.1 Legacy key handling in `Config._init_other_paths`
+
+ComfyUI's `folder_paths` rewrites legacy names before every access (`clip` →
+`text_encoders`, `unet` → `diffusion_models`) and registers both legacy
+directories under the canonical key, so `get_folder_paths("clip")` returns
+exactly the same list as `get_folder_paths("text_encoders")`. Querying both keys
+made the overlap guard fire twice with `please fix your path configuration` for a
+configuration the user cannot fix. `Config._collapse_legacy_folder_keys()` now
+drops a key when the host exposes `map_legacy` and resolves it to another queried
+key, and `_prepare_other_paths()` downgrades a same-`sub_type` duplicate to
+`debug` (a cross-`sub_type` collision still warns). In standalone mode
+`MockFolderPaths` has no `map_legacy` and its keys are independent
+`settings.json` entries, so every key is still queried there.
+
+`settings.json.example` intentionally stays minimal (only `use_portable_settings`,
+`civitai_api_key`, and the four core `folder_paths` keys: `loras`, `checkpoints`,
+`unet`, `embeddings`). Optional keys — including the other-model folder paths and
+`enable_other_models` — are NOT documented there; they live in `DEFAULT_SETTINGS`
+and reach the user's `settings.json` on demand. This supersedes the Phase-1/Phase-2
+notes that proposed adding the other-model folder keys to the example.
+
+### 11.2 Behaviour matrix
+
+| state | scan | nav / `/other` | other downloads | `default_other_roots` | Doctor / refresh-all |
+|---|---|---|---|---|---|
+| master off | nothing (`other_roots == []`) | nav entry hidden (`nav-item--hidden`); `/other` still renders the disabled empty state + Enable button; one-time dismissible announcement banner on first visit | rejected | preserved, never auto-set | scanner skipped |
+| sub_type off | that sub_type's folder keys excluded | page keeps working, type disappears from data | auto-routing refused (manual folder still allowed) | preserved, not preselected | normal |
+| all on (after enabling) | Phase-1/2 behaviour | normal | normal | normal | normal |
+
+### 11.3 Backend touch points
+
+- `py/utils/constants.py` — `DEFAULT_ENABLED_OTHER_SUB_TYPES`, `OTHER_SUB_TYPE_FOLDER_KEYS`, `normalize_other_sub_types`.
+- `py/config.py` — `_get_enabled_other_folder_keys()` is the single scan gate (master switch + allow-list); new `refresh_other_roots()` rebuilds roots + preview roots on toggle.
+- `py/services/settings_manager.py` — new defaults, `set()` normalization, `is_other_models_enabled()` / `get_enabled_other_sub_types()` / `is_other_sub_type_enabled()`, and `_apply_other_model_settings_change()` which reapplies config and calls `other_scanner.on_library_changed(reconcile=True)`.
+- `py/services/model_scanner.py` — `_should_keep_cached_entry()` hydration hook (default keep) plus `on_library_changed(reconcile=...)` / `initialize_in_background(reconcile=...)`; the hook filters `raw_data` and the hash/autov3 index rows.
+- `py/services/other_scanner.py` — drops persisted entries whose folder is no longer a managed root (sub_type is location-derived, so config is the source of truth).
+- `py/routes/other_routes.py` — `_validate_civitai_model_type` rejects everything while off / mapped-but-disabled sub_types; `_get_page_context_provider()` injects `other_disabled` into the template.
+- `py/routes/handlers/model_handlers.py` + `base_model_routes.py` — optional `page_context_provider` hook on `ModelPageView`.
+- `py/routes/handlers/download_routing_handlers.py` — returns `{sub_type: None, disabled: true, reason}` instead of guessing.
+- `py/services/download_manager.py` — rejects other-type downloads while off; disabled sub_type refuses default-path routing with a "pick a folder" error.
+- `py/routes/handlers/misc_handlers.py` — Doctor / init-status / refresh-all skip the other scanner while off (`_active_scanner_factories` / `_active_scanner_getters`).
+- `py/services/pending_delete_service.py` — deliberately untouched: the scanner stays registered so staged deletes still merge.
+
+### 11.4 Frontend
+
+Discoverability: the nav entry is hidden while the feature is off, and three
+lightweight surfaces replace it — a one-time announcement banner, the download
+toast, and the settings toggle itself.
+
+- `templates/components/header.html` + `static/css/components/header.css` — `nav-item--hidden` class (server-rendered when off, client-toggled after enabling) and the `fa-shapes` icon.
+- `templates/other.html` — `other_disabled` branch in `content` + `main_script`; page-scoped CSS for the empty state.
+- `static/js/other_disabled.js` — boots `appCore` (shared header) and delegates to the shared enable helper.
+- `static/js/utils/otherModels.js` — shared `enableOtherModels()` (POST settings + reload) and `openOtherModelsSettings()` (settings modal on the Library section); used by the disabled page, the banner and the download modal.
+- `static/js/managers/BannerService.js` — `other-models-announcement` banner (only when off and not dismissed; `priority: 0`, dismissal persisted via `dismissed_banners`) with Enable / Open Settings actions; `removeOtherModelsAnnouncement()` drops it without persisting a dismissal.
+- `templates/components/modals/settings/library.html` + `SettingsManager.updateOtherModelsControls()` / `saveEnabledOtherSubTypes()` / `updateOtherModelsNavVisibility()` — master toggle + five sub_type checkboxes; unchecked/disabled sub_types have their default-root select disabled.
+- `static/js/managers/DownloadManager.js` — a disabled routing answer surfaces a `showActionToast` with an "Enable Other Models" action (opening settings) and falls back to manual selection.
+- i18n: `settings.folderSettings.*`, `other.disabled.*` and `banners.otherModels.*` keys in `locales/en.json` + `scripts/sync_translation_keys.py` (other locales keep `[TODO: Translate]`).
+
+### 11.5 Cache consistency
+
+- Disabling purges rows from the in-memory view at hydration time (the
+  `_should_keep_cached_entry` hook) and from SQLite on the reconcile triggered by
+  the toggle; the `.metadata.json` sidecars survive, so re-enabling rescans
+  without recomputing hashes (critical for multi-GB text encoders).
+- Enabling triggers a reconcile so newly managed roots are scanned immediately.
+- Editing `settings.json` while the server is stopped is still covered by the
+  hydration hook, so disabled types never appear after a restart.
+
+### 11.6 Tests
+
+Backend: opt-in fixtures added to the other-related suites; new coverage for
+"default off scans nothing", per-sub_type gating, routing/download rejection,
+`_should_keep_cached_entry`, settings normalization and `other_disabled` page
+context. Frontend: `updateOtherModelsControls` / `saveEnabledOtherSubTypes` and
+the disabled-page enable flow.

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.

docs/technical/reconcile_windows_case_fallback_todo.md

@@ -0,0 +1,92 @@
+# Reconcile 的 Windows 大小写回退分支 - 待验证清单
+
+> **状态**: 待 Windows 环境验证 | **创建日期**: 2026-09-11
+> **相关文件**: `py/services/model_scanner.py` (`ModelScanner._reconcile_cache`)
+> **相关历史**: #871 (`76ee59cd`, 路径重叠去重)、#1108 (按文件夹扫描的需求)
+
+---
+
+## 背景
+
+Refresh 按钮走的是 `_reconcile_cache()`（快速增量对账）。2026-09-11 做了一轮性能优化，把两处"预防性"的
+realpath 全量遍历改成按需触发（详见下方"已完成"）。优化后，一次零变更 Refresh 在 5 万文件库上从
+~1400 ms 降到 ~120 ms。
+
+清理过程中发现**唯一一处遗留的可疑点**：Windows 专属的大小写不敏感回退分支。它无法在 Linux 上验证，
+因此单独记录，留待 Windows 机器上确认。
+
+---
+
+## 待验证分支（现状）
+
+`py/services/model_scanner.py` 中 `_reconcile_cache()` 的 walk 循环内：
+
+```python
+# Try case-insensitive match on Windows
+if os.name == 'nt':
+    lower_path = file_path.lower()
+    matched = False
+    for cached_path in cached_paths:          # 每个未命中文件都全量扫一遍缓存
+        if cached_path.lower() == lower_path:
+            found_paths.add(cached_path)
+            matched = True
+            break
+    if matched:
+        continue
+```
+
+它排在精确匹配（`file_path in cached_paths`）和 realpath 别名匹配之后，只有**未命中**的文件才会走到。
+
+### 为什么可疑
+
+1. **可能不可达**：Windows 上 `os.path.realpath()` 会返回磁盘上的真实大小写，因此"缓存路径大小写与磁盘
+   不一致"的情形，理论上已经被上一步的 realpath 别名匹配覆盖。若如此，这段就是纯冗余代码。
+2. **一旦可达就是 O(N×M)**：每个未命中文件都要遍历全部 `cached_paths` 做小写比较。若某种路径写法让
+   整个库都变成"未命中"（例如缓存里的盘符/大小写形式与 walk 结果系统性不一致），一次 Refresh 会退化
+   成 文件数 × 缓存条目数 次字符串比较，比真实 IO 还贵。
+3. **没有测试覆盖**：`tests/services/test_model_scanner.py` 没有任何针对该分支的用例（它在 Linux 上
+   被 `os.name == 'nt'` 短路，无法覆盖）。
+
+---
+
+## 待办
+
+- [ ] **验证可达性**：在 Windows 上构造"缓存路径与磁盘真实大小写不一致"的场景，确认 realpath 别名匹配
+      是否已经命中，即上面的 `if os.name == 'nt'` 分支是否还有进入的必要。
+- [ ] **若不可达 / 冗余**：删除该分支，并在删除处留注释说明 realpath 已覆盖大小写归一（附验证记录）。
+- [ ] **若可达**：保留语义但改成 O(1)——预先构建一次 `lower_path -> cached_path` 映射（与
+      `cached_real_paths` 同样按需、懒构建），把内层全量扫描换成一次字典查询。
+- [ ] **补一个 Windows-only 的回归测试**（`pytest.mark.skipif(os.name != "nt", ...)`），锁定最终结论。
+- [ ] 把验证结论回填到本文件，并同步更新状态行。
+
+---
+
+## 验证方法（Windows）
+
+1. **构造不一致的大小写**：让缓存里的 `file_path` 与磁盘实际路径大小写不同（例如改过盘符/目录大小写，
+   或从另一台机器迁移了 `settings.json` 与持久化缓存），然后在 UI 点 Refresh。
+2. **看后端日志判据**：
+   - 若 realpath 已覆盖 → 日志应显示 `Cache reconciliation completed in X seconds. Added 0, removed 0 models.`，
+     且**没有** `Found N new files to process` / `Processing <path>`。
+   - 若回退分支在起作用 → 同样应该是 `Added 0, removed 0`（因为 `found_paths` 被补上），这是"分支可达"
+     的证据；反之若出现大量 `Processing ...` 并重新 hash，说明连回退分支也没命中，问题更严重
+     （缓存路径被当成了新文件 + 旧条目被删）。
+3. **跑测试**：`python -m pytest tests/services/test_model_scanner.py -k reconcile`（该文件在 Windows 上会
+   真实执行 `os.name == 'nt'` 分支）。
+4. **量化**：如果需要，可在 `_reconcile_cache` 里临时插桩统计该分支的进入次数与内层迭代次数，确认是否为 0。
+
+---
+
+## 已完成（本轮优化，供对照）
+
+同一次清理里已经落地并验证的部分（Linux，5 万文件库）：
+
+- `cached_real_paths` 别名映射改为**首次未命中时**懒构建（原来每次 Refresh 都对全部缓存条目算一次 realpath）。
+- 每个文件的 `realpath` 移到精确命中检查**之后**（原来对每个文件都算，命中即丢弃）。
+- `get_model_roots()` 在新增文件处理阶段只快照一次（原来每个新文件重读一次）。
+- 全量去重 pass 加了 O(1) 前置判断（`cached_size_before != len(cached_paths) or total_added > 0`），
+  零变更且缓存干净时跳过；快照本身含重复路径时仍会自愈。
+
+结果：零变更 Refresh 5 万文件 **~1400 ms → ~120 ms**；根目录顺序/符号链接别名翻转场景仍是
+`re-processed=0`（不重新读 metadata、不重新 hash）。测试：`tests/services/test_model_scanner.py`
+47 项、全量后端 2567 项全部通过。

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."
       }
     }
   },
@@ -103,9 +136,10 @@
     "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",
+      "viewOnSource": "View on {source}",
       "sendToWorkflow": "Send to ComfyUI (Click: Append, Shift+Click: Replace)",
       "copyLoRASyntax": "Copy LoRA Syntax",
       "checkpointNameCopied": "Checkpoint name copied",
@@ -116,6 +150,7 @@
       "copyCheckpointName": "Copy checkpoint name",
       "copyEmbeddingName": "Copy embedding name",
       "embeddingNameCopied": "Embedding syntax copied",
+      "modelNameCopied": "Model name copied",
       "sendCheckpointToWorkflow": "Send to ComfyUI",
       "sendEmbeddingToWorkflow": "Send to ComfyUI"
     },
@@ -137,7 +172,7 @@
     "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",
@@ -179,20 +214,10 @@
       "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...",
       "success": "Matched {entries} entries across {recipes} recipes",
-      "successErrors": "Matched {entries} entries across {recipes} recipes, {failures} failed",
-      "allFailed": "Rematch failed for {failures} of {total} recipes",
-      "noMatch": "No local match found for {entries} entries in {recipes} recipes",
       "cancelled": "Rematch cancelled. {recipes} recipes updated ({entries} entries).",
       "error": "Recipe rematch failed: {message}"
     },
@@ -210,6 +235,7 @@
       "recipes": "Recipes",
       "checkpoints": "Checkpoints",
       "embeddings": "Embeddings",
+      "other": "Other",
       "statistics": "Stats"
     },
     "search": {
@@ -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",
@@ -503,6 +536,25 @@
       "defaultUnetRootHelp": "Set default diffusion model (UNET) root directory for downloads, imports and moves",
       "defaultEmbeddingRoot": "Embedding Root",
       "defaultEmbeddingRootHelp": "Set default embedding root directory for downloads, imports and moves",
+      "defaultVaeRoot": "VAE Root",
+      "defaultVaeRootHelp": "Set default VAE root directory for downloads, imports and moves",
+      "defaultUpscalerRoot": "Upscaler Root",
+      "defaultUpscalerRootHelp": "Set default upscaler root directory for downloads, imports and moves",
+      "defaultTextEncoderRoot": "Text Encoder Root",
+      "defaultTextEncoderRootHelp": "Set default text encoder root directory for downloads, imports and moves",
+      "defaultClipVisionRoot": "CLIP Vision Root",
+      "defaultClipVisionRootHelp": "Set default CLIP vision root directory for downloads, imports and moves",
+      "defaultControlnetRoot": "ControlNet Root",
+      "defaultControlnetRootHelp": "Set default ControlNet root directory for downloads, imports and moves",
+      "enableOtherModels": "Other Models Management",
+      "enableOtherModelsHelp": "When off, VAE / upscaler / text encoder / CLIP vision / ControlNet folders are not scanned, the Other Models page stays disabled, and these model types cannot be downloaded.",
+      "otherSubTypes": "Managed Types",
+      "otherSubTypesHelp": "Choose which other-model categories are scanned and shown on the Other Models page.",
+      "subTypeVae": "VAE",
+      "subTypeUpscaler": "Upscaler",
+      "subTypeTextEncoder": "Text Encoder",
+      "subTypeClipVision": "CLIP Vision",
+      "subTypeControlnet": "ControlNet",
       "recipesPath": "Recipes Storage Path",
       "recipesPathHelp": "Optional custom directory for stored recipes. Leave empty to use the first LoRA root's recipes folder.",
       "recipesPathPlaceholder": "/path/to/recipes",
@@ -550,7 +602,7 @@
     },
     "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 +639,7 @@
     "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 +694,7 @@
     },
     "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 +797,7 @@
         "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 +833,6 @@
       "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",
@@ -817,14 +868,14 @@
         "complete": "Auto-organize complete",
         "error": "Error: {error}"
       },
-      "enrichHfAgent": "Enrich HF Metadata (AI)"
+      "enrichHfAgent": "Enrich Metadata with AI"
     },
     "contextMenu": {
-      "refreshMetadata": "Refresh Civitai Data",
+      "refreshMetadata": "Refresh CivitAI Data",
       "checkUpdates": "Check Updates",
       "linkModel": "Link Model",
-      "linkCivitai": "Link to Civitai",
-      "linkHuggingFace": "Link to HuggingFace",
+      "linkCivitai": "Link to CivitAI",
+      "linkModelSource": "Link to Model Source",
       "copySyntax": "Copy LoRA Syntax",
       "copyFilename": "Copy Model Filename",
       "copyRecipeSyntax": "Copy Recipe Syntax",
@@ -837,7 +888,6 @@
       "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",
@@ -847,26 +897,136 @@
       "viewAllLoras": "View All LoRAs",
       "downloadMissingLoras": "Download Missing LoRAs",
       "deleteRecipe": "Delete Recipe",
-      "enrichHfAgent": "Enrich HF Metadata (AI)"
+      "enrichHfAgent": "Enrich Metadata with AI"
     }
   },
   "recipes": {
     "title": "LoRA Recipes",
     "actions": {
-      "sendCheckpoint": "Send to ComfyUI"
+      "sendCheckpoint": "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)",
@@ -894,7 +1054,7 @@
         "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.",
@@ -911,6 +1071,8 @@
         "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"
         }
       },
@@ -945,6 +1107,7 @@
       }
     },
     "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",
@@ -977,13 +1140,6 @@
         "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",
@@ -1014,6 +1170,8 @@
       "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",
@@ -1065,6 +1223,26 @@
   "embeddings": {
     "title": "Embedding Models"
   },
+  "other": {
+    "title": "Other Models",
+    "disabled": {
+      "title": "Other Models management is off",
+      "description": "Enable it to scan and manage VAE, upscaler, text encoder, CLIP vision and ControlNet files, and to download them from CivitAI.",
+      "enableButton": "Enable Other Models",
+      "hint": "You can change the managed model types later in Settings > Library.",
+      "enableFailed": "Failed to enable Other Models",
+      "downloadBlocked": "Other Models management is disabled for this model type. Enable it in Settings > Library to download this file.",
+      "enableAction": "Enable Other Models"
+    },
+    "noPaths": {
+      "title": "No other-model folders found",
+      "descriptionStandalone": "Other Models management is on, but none of the configured model folders exist on disk. Add the folder paths below to settings.json and restart LoRA Manager.",
+      "hintStandalone": "Only the folder keys listed above are scanned; keys you do not need can be omitted.",
+      "descriptionComfyUI": "Other Models management is on, but none of the configured model folders exist on disk. Add the matching model folders to your ComfyUI model paths, then reload this page.",
+      "hintComfyUI": "Other models are read from ComfyUI's vae, upscale_models, text_encoders, clip_vision and controlnet folders.",
+      "openSettings": "Open Settings"
+    }
+  },
   "sidebar": {
     "modelRoot": "Root",
     "collapseAll": "Collapse All Folders",
@@ -1217,9 +1395,9 @@
     "download": {
       "title": "Download Model from URL",
       "titleWithType": "Download {type} from URL",
-      "civitaiUrl": "Civitai URL(s):",
+      "civitaiUrl": "Model URL(s):",
       "placeholder": "https://civitai.com/models/...",
-      "urlHint": "Enter one CivitAI, CivArchive, or Hugging Face URL per line. Supports multiple URLs for batch download.",
+      "urlHint": "Enter one CivitAI, CivArchive, Hugging Face, or ModelScope URL per line. Supports multiple URLs for batch download.",
       "selectHfFiles": "Select file(s) to download from this repository:",
       "selectAll": "Select All",
       "fetchingRepoFiles": "Fetching repository files...",
@@ -1243,16 +1421,18 @@
       "downloaded": "Downloaded",
       "downloadedTooltip": "Previously downloaded, but it is not currently in your library.",
       "alreadyInLibrary": "Already in Library",
+      "partiallyDownloaded": "Partially downloaded",
       "autoOrganizedPath": "[Auto-organized by path template]",
       "fileSelection": {
         "title": "Select File Format",
         "files": "files",
-        "select": "Select File"
+        "select": "Select File",
+        "inLibrary": "In Library"
       },
       "errors": {
-        "invalidUrl": "Invalid Civitai URL format",
+        "invalidUrl": "Invalid model URL format",
         "noVersions": "No versions available for this model",
-        "mixedSources": "Cannot mix CivitAI and Hugging Face URLs in the same batch.",
+        "mixedSources": "Cannot mix CivitAI and Hugging Face / ModelScope URLs in the same batch.",
         "noModelFiles": "No model files found in this repository."
       },
       "status": {
@@ -1360,11 +1540,46 @@
       "note": "Files will be downloaded using default path templates. This may take a while depending on the number of LoRAs.",
       "downloadButton": "Download {count} LoRA(s)"
     },
+    "rematchOptions": {
+      "title": "Rematch Recipes",
+      "messageGlobal": "All recipes will be scanned against your local model library.",
+      "messageSingle": "This recipe will be scanned against your local model library.",
+      "messageBulk": "{count} selected recipe(s) will be scanned against your local model library.",
+      "relaxedLabel": "Also reconnect missing models by file name",
+      "relaxedDescription": "These models could also be fixed by downloading — download is more accurate. Matches may link a different version; they'll be listed for review and can be undone.",
+      "confirmButton": "Rematch"
+    },
+    "rematchResults": {
+      "undo": "Undo",
+      "undone": "Undone",
+      "undoFailed": "Failed to undo rematch: {message}"
+    },
+    "rematchSummary": {
+      "title": "Rematch Summary",
+      "successMessage": "Matched {entries} entries",
+      "failed": "Rematch failed",
+      "completedWithWarnings": "Rematch completed — review recommended",
+      "cancelledNote": "Run cancelled before completion — counts are partial.",
+      "statMatched": "Matched entries",
+      "statReview": "Needs review",
+      "statUnresolved": "Unresolved",
+      "statErrors": "Errors",
+      "reviewSection": "Filename matches to review ({count})",
+      "columnRecipe": "Recipe",
+      "columnEntry": "Entry",
+      "columnFile": "Matched file",
+      "columnUndo": "Undo",
+      "copyReport": "Copy Report",
+      "close": "Close",
+      "scope_global": "All recipes",
+      "scope_bulk": "Selected recipes",
+      "scope_single": "Single recipe"
+    },
     "exampleAccess": {
       "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": {
@@ -1382,16 +1597,20 @@
       "pathPlaceholder": "Type folder path or select from tree below...",
       "root": "Root"
     },
-    "linkHuggingFace": {
-      "title": "Link to HuggingFace",
-      "infoText": "Paste the HuggingFace repository URL to associate this model with its source. This enables AI-powered metadata enrichment.",
-      "urlLabel": "HuggingFace Repository URL:",
+    "linkModelSource": {
+      "title": "Link to Model Source",
+      "infoText": "Paste the model page URL to associate this model with its source. Linking enables AI-powered metadata enrichment for Hugging Face and ModelScope models.",
+      "urlLabel": "Model Page URL:",
       "urlPlaceholder": "https://huggingface.co/user/repo",
-      "helpText": "Enter the full URL of the HuggingFace repository.",
+      "helpText": "Enter the full URL of the model page. Supported sites:",
+      "enrichNote": "AI enrichment needs a readable model card. Sites that don't expose one (currently TensorArt) can only be linked.",
+      "urlRequired": "Please enter a model page URL.",
+      "invalidUrl": "Unsupported URL. Supported sites: Hugging Face, ModelScope, TensorArt.",
+      "linking": "Linking model source...",
       "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": {
@@ -1400,14 +1619,15 @@
         "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"
     },
@@ -1417,14 +1637,16 @@
         "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",
@@ -1441,6 +1663,7 @@
         "location": "Location",
         "baseModel": "Base Model",
         "size": "Size",
+        "hashes": "Hashes",
         "unknown": "Unknown",
         "usageTips": "Usage Tips",
         "additionalNotes": "Additional Notes",
@@ -1467,7 +1690,11 @@
         "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",
@@ -1478,7 +1705,7 @@
         "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",
@@ -1517,7 +1744,7 @@
       },
       "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",
@@ -1532,6 +1759,30 @@
         "examples": "Loading examples...",
         "versions": "Loading versions..."
       },
+      "showcase": {
+        "hiddenBySfw": "{count} hidden by SFW-only setting",
+        "showExamples": "Show examples",
+        "showCount": "Show examples ({count})",
+        "hideExamples": "Hide examples",
+        "addExamples": "Add examples",
+        "previousExample": "Previous example ([)",
+        "nextExample": "Next example (])",
+        "noExamples": "No example images available",
+        "addMoreExamples": "Add more examples",
+        "dragDrop": "Drag & drop images or videos here",
+        "or": "or",
+        "selectFiles": "Select Files",
+        "supportedFormats": "Supported formats: jpg, png, gif, webp, avif, jxl, mp4, webm",
+        "importing": "Importing files...",
+        "noSupportedFiles": "No supported files selected. Please select image or video files.",
+        "allFiltered": "All example images are filtered due to NSFW content settings",
+        "sfwOnlyEnabled": "Your settings are currently set to show only safe-for-work content",
+        "changeInSettings": "You can change this in Settings",
+        "nsfwMature": "Mature Content",
+        "nsfwR": "R-rated Content",
+        "nsfwX": "X-rated Content",
+        "nsfwXxx": "XXX-rated Content"
+      },
       "versions": {
         "heading": "Model versions",
         "copy": "Track and manage every version of this model in one place.",
@@ -1558,27 +1809,28 @@
           "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",
-          "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",
@@ -1599,8 +1851,8 @@
         },
         "empty": "No version history available for this model yet.",
         "error": "Failed to load versions.",
-        "missingModelId": "This model is missing a Civitai model id.",
-        "hfGroupInfo": "This is a HuggingFace model group. Open the library to see all versions in the grid.",
+        "missingModelId": "This model is missing a CivitAI model id.",
+        "sourceGroupInfo": "This is a {source} model group. Open the library to see all versions in the grid.",
         "confirm": {
           "delete": "Delete this version from your library?"
         },
@@ -1672,6 +1924,10 @@
       "title": "Initializing Embedding Manager",
       "message": "Scanning and building embedding cache. This may take a few minutes..."
     },
+    "other": {
+      "title": "Initializing Other Models Manager",
+      "message": "Scanning and building model cache. This may take a few minutes..."
+    },
     "recipes": {
       "title": "Initializing Recipe Manager",
       "message": "Loading and processing recipes. This may take a few minutes..."
@@ -1683,14 +1939,14 @@
     "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",
@@ -1778,10 +2034,52 @@
     "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",
@@ -1798,7 +2096,8 @@
       "settings": "Settings & Configuration",
       "extensions": "Extensions",
       "newBadge": "NEW"
-    }
+    },
+    "newContentBadge": "New"
   },
   "update": {
     "title": "Check for Updates",
@@ -1874,7 +2173,7 @@
       "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"
     },
@@ -1917,6 +2216,7 @@
       "downloadPartialSuccess": "Downloaded {completed} of {total} LoRAs",
       "downloadPartialWithAccess": "Downloaded {completed} of {total} LoRAs. {accessFailures} failed due to access restrictions. Check your API key in settings or early access status.",
       "pleaseSelectVersion": "Please select a version",
+      "pleaseSelectFile": "Please select at least one file",
       "versionExists": "This version already exists in your library",
       "downloadCompleted": "Download completed successfully",
       "downloadSkippedByBaseModel": "Skipped download because base model {baseModel} is excluded",
@@ -1950,11 +2250,17 @@
       "createMissingData": "Missing required data to create recipe",
       "created": "Recipe created successfully",
       "noMissingLoras": "No missing LoRAs to download",
+      "unresolvableMarkedForReconnect": "{count} unresolvable entr(ies) marked — they can now be reconnected to a local LoRA.",
+      "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",
@@ -1962,6 +2268,16 @@
       "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",
@@ -1985,24 +2301,22 @@
       "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",
-      "rematchUnmatched": "No local match found for {entries} entries in {recipes} recipes",
       "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",
@@ -2039,8 +2353,8 @@
       "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",
@@ -2069,6 +2383,7 @@
       "checkpointRootsFailed": "Failed to load checkpoint roots: {message}",
       "unetRootsFailed": "Failed to load diffusion model roots: {message}",
       "embeddingRootsFailed": "Failed to load embedding roots: {message}",
+      "otherRootsFailed": "Failed to load other model roots: {message}",
       "mappingsUpdated": "Base model path mappings updated ({count} mapping{plural})",
       "mappingsCleared": "Base model path mappings cleared",
       "mappingSaveFailed": "Failed to save base model mappings: {message}",
@@ -2166,13 +2481,16 @@
     "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",
+      "linkHfSuccess": "Model successfully linked to its model source",
       "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"
+      "missingHash": "Model hash not available",
+      "enrichNeedsSource": "Link this model to a model source first (Link Model → Link to Model Source)",
+      "enrichUnsupportedSource": "AI enrichment is not available for {source} models"
     },
     "exampleImages": {
       "pathUpdated": "Example images path updated successfully",
@@ -2259,7 +2577,7 @@
     },
     "issues": {
       "civitai_api_key": {
-        "title": "Civitai API Key"
+        "title": "CivitAI API Key"
       },
       "cache_health": {
         "title": "Model Cache Health"
@@ -2313,9 +2631,9 @@
     },
     "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": {
@@ -2330,6 +2648,12 @@
       "rebuilding": "Rebuilding cache...",
       "rebuildFailed": "Failed to rebuild cache: {error}",
       "retry": "Retry"
+    },
+    "otherModels": {
+      "title": "Other Models Management is available",
+      "content": "Scan and manage VAE, upscaler, text encoder, CLIP vision and ControlNet files — and download them from CivitAI — from one dedicated page.",
+      "enable": "Enable Other Models",
+      "openSettings": "Open Settings"
     }
   }
-}
+}

py/config.py

@@ -17,6 +17,9 @@ import types as _types
 import time
 
 from .utils.cache_paths import CacheType, get_cache_file_path, get_legacy_cache_paths
+from .utils.constants import (
+    OTHER_MODEL_FOLDER_SUBTYPES,
+)
 from .utils.settings_paths import (
     ensure_settings_file,
     get_settings_dir,
@@ -172,6 +175,13 @@ class Config:
         self.embeddings_roots = None
         self.base_models_roots = self._init_checkpoint_paths()
         self.embeddings_roots = self._init_embedding_paths()
+        # Other-model roots (VAE, upscalers, text encoders, ...): flat deduped
+        # list plus a normalized root -> sub_type map and per-folder_paths-key
+        # roots for settings persistence.
+        self.other_roots: Optional[List[str]] = None
+        self.other_root_subtypes: Dict[str, str] = {}
+        self.other_folder_roots: Dict[str, List[str]] = {}
+        self.other_roots = self._init_other_paths()
         # Extra paths (only for LoRA Manager, not shared with ComfyUI)
         self.extra_loras_roots: List[str] = []
         self.extra_checkpoints_roots: List[str] = []
@@ -336,6 +346,10 @@ class Config:
                 "unet": list(self.unet_roots or []),
                 "embeddings": list(self.embeddings_roots or []),
             }
+            # Persist the other-model roots under their original folder_paths
+            # keys so library switching round-trips them.
+            for key, roots in (self.other_folder_roots or {}).items():
+                target_folder_paths[key] = list(roots)
 
             normalized_target_paths = _normalize_folder_paths_for_comparison(
                 target_folder_paths
@@ -522,6 +536,7 @@ class Config:
         roots.extend(self.loras_roots or [])
         roots.extend(self.base_models_roots or [])
         roots.extend(self.embeddings_roots or [])
+        roots.extend(self.other_roots or [])
         # Include extra paths for scanning symlinks
         roots.extend(self.extra_loras_roots or [])
         roots.extend(self.extra_checkpoints_roots or [])
@@ -862,6 +877,8 @@ class Config:
             preview_roots.update(self._expand_preview_root(root))
         for root in self.embeddings_roots or []:
             preview_roots.update(self._expand_preview_root(root))
+        for root in self.other_roots or []:
+            preview_roots.update(self._expand_preview_root(root))
         # Include extra paths for preview access
         for root in self.extra_loras_roots or []:
             preview_roots.update(self._expand_preview_root(root))
@@ -882,7 +899,7 @@ class Config:
             path for path in preview_roots if path.is_absolute()
         }
         logger.debug(
-            "Preview roots rebuilt: %d paths from %d lora roots (%d extra), %d checkpoint roots (%d extra), %d embedding roots (%d extra), %d symlink mappings",
+            "Preview roots rebuilt: %d paths from %d lora roots (%d extra), %d checkpoint roots (%d extra), %d embedding roots (%d extra), %d other roots, %d symlink mappings",
             len(self._preview_root_paths),
             len(self.loras_roots or []),
             len(self.extra_loras_roots or []),
@@ -890,6 +907,7 @@ class Config:
             len(self.extra_checkpoints_roots or []),
             len(self.embeddings_roots or []),
             len(self.extra_embeddings_roots or []),
+            len(self.other_roots or []),
             len(self._path_mappings),
         )
 
@@ -1128,6 +1146,155 @@ class Config:
 
         return unique_paths
 
+    def _get_enabled_other_folder_keys(self) -> List[str]:
+        """Return the OTHER_MODEL_FOLDER_SUBTYPES keys that are enabled.
+
+        Other Models management is opt-in: while ``enable_other_models`` is
+        off (the default) no other-model folder is scanned at all. When it is
+        on, only the folder keys of the enabled sub_types are scanned
+        (text_encoder merges ``text_encoders`` with the legacy ``clip`` key).
+        """
+        try:
+            from .services.settings_manager import get_settings_manager
+
+            enabled_sub_types = get_settings_manager().get_enabled_other_sub_types()
+        except Exception:
+            enabled_sub_types = []
+        if not enabled_sub_types:
+            return []
+        allowed = set(enabled_sub_types)
+        return [
+            key
+            for key, sub_type in OTHER_MODEL_FOLDER_SUBTYPES.items()
+            if sub_type in allowed
+        ]
+
+    @staticmethod
+    def _collapse_legacy_folder_keys(keys: List[str]) -> List[str]:
+        """Drop folder keys the host already normalizes onto another queried key.
+
+        ComfyUI's ``folder_paths`` rewrites legacy names before every access
+        (``clip`` -> ``text_encoders``, ``unet`` -> ``diffusion_models``), and
+        registers both legacy directories under the canonical key, so
+        ``get_folder_paths("clip")`` returns exactly the same list as
+        ``get_folder_paths("text_encoders")``. Querying both therefore reports
+        every text-encoder folder twice and trips the overlap guard with a
+        conflict the user cannot fix.
+
+        When the host exposes ``map_legacy`` the alias is provably redundant and
+        is skipped (an empty canonical list implies an empty alias list).
+        Without it - the standalone mock, whose keys are independent
+        ``settings.json`` entries - every key is kept, because a ``clip``-only
+        configuration is then genuinely distinct.
+        """
+        map_legacy = getattr(folder_paths, "map_legacy", None)
+        if not callable(map_legacy):
+            return list(keys)
+
+        queried = set(keys)
+        collapsed: List[str] = []
+        for key in keys:
+            try:
+                canonical = map_legacy(key)
+            except Exception:
+                canonical = key
+            if canonical != key and canonical in queried:
+                logger.debug(
+                    "Skipping legacy folder key '%s'; the host resolves it to "
+                    "'%s', which is queried as well.",
+                    key,
+                    canonical,
+                )
+                continue
+            collapsed.append(key)
+        return collapsed
+
+    def _prepare_other_paths(
+        self, folder_path_map: Mapping[str, Iterable[str]]
+    ) -> Tuple[List[str], Dict[str, str], Dict[str, List[str]]]:
+        """Prepare other-model paths from a folder_paths-key -> raw paths map.
+
+        Returns:
+            Tuple of (all_unique_roots, business_root -> sub_type map,
+            folder_paths key -> business roots). This method does NOT modify
+            instance variables - callers must set them.
+        """
+        unique_paths: List[str] = []
+        sub_type_map: Dict[str, str] = {}
+        per_key_roots: Dict[str, List[str]] = {}
+        # real path -> (business path, sub_type) of the category that claimed it
+        seen_real_paths: Dict[str, Tuple[str, str]] = {}
+
+        # Cross-scanner overlap detection: warn when an "other" root is
+        # already covered by the checkpoints/unet or embeddings scanners.
+        # Kept (not dropped) on purpose - duplicate cards across pages are
+        # cosmetic, while dropping would silently unmanage the files.
+        covered_real_paths = {
+            os.path.normpath(os.path.realpath(path)).replace(os.sep, "/"): path
+            for path in [
+                *(self.base_models_roots or []),
+                *(self.embeddings_roots or []),
+            ]
+            if isinstance(path, str) and path.strip() and os.path.exists(path)
+        }
+
+        for key, sub_type in OTHER_MODEL_FOLDER_SUBTYPES.items():
+            raw_paths = folder_path_map.get(key)
+            if not raw_paths:
+                continue
+            path_map = self._dedupe_existing_paths(raw_paths)
+            key_roots: List[str] = []
+            for real_path, business_path in sorted(
+                path_map.items(), key=lambda item: item[1].lower()
+            ):
+                seen = seen_real_paths.get(real_path)
+                if seen is not None:
+                    seen_business_path, seen_sub_type = seen
+                    if seen_sub_type == sub_type:
+                        # Same category reached through a second folder_paths
+                        # key (legacy alias, or a sub_type spanning two keys).
+                        # Expected, so never a "fix your configuration" warning.
+                        logger.debug(
+                            "Ignoring duplicate folder '%s' for category '%s' "
+                            "(already covered by '%s').",
+                            business_path,
+                            sub_type,
+                            seen_business_path,
+                        )
+                    else:
+                        logger.warning(
+                            "Detected the same folder '%s' under multiple other-model "
+                            "categories ('%s' is already mapped as '%s'). Keeping the "
+                            "first category; please fix your path configuration.",
+                            business_path,
+                            seen_business_path,
+                            seen_sub_type,
+                        )
+                    continue
+                seen_real_paths[real_path] = (business_path, sub_type)
+                unique_paths.append(business_path)
+                key_roots.append(business_path)
+                sub_type_map[business_path] = sub_type
+
+                if real_path != business_path:
+                    self.add_path_mapping(business_path, real_path)
+
+                covered_by = covered_real_paths.get(real_path)
+                if covered_by:
+                    logger.warning(
+                        "Detected an other-model root ('%s', category '%s') that "
+                        "overlaps an existing checkpoints/embeddings root ('%s'). "
+                        "The same files will appear on both pages; please review "
+                        "your path configuration.",
+                        business_path,
+                        key,
+                        covered_by,
+                    )
+            if key_roots:
+                per_key_roots[key] = key_roots
+
+        return unique_paths, sub_type_map, per_key_roots
+
     def _apply_library_paths(
         self,
         folder_paths: Mapping[str, Any],
@@ -1151,6 +1318,16 @@ class Config:
         ) = self._prepare_checkpoint_paths(checkpoint_paths, unet_paths)
         self.embeddings_roots = self._prepare_embedding_paths(embedding_paths)
 
+        other_path_map = {
+            key: folder_paths.get(key, []) or []
+            for key in self._get_enabled_other_folder_keys()
+        }
+        (
+            self.other_roots,
+            self.other_root_subtypes,
+            self.other_folder_roots,
+        ) = self._prepare_other_paths(other_path_map)
+
         # Process extra paths (only for LoRA Manager, not shared with ComfyUI)
         extra_paths = extra_folder_paths or {}
         extra_lora_paths = extra_paths.get("loras", []) or []
@@ -1267,6 +1444,104 @@ class Config:
             logger.warning(f"Error initializing embedding paths: {e}")
             return []
 
+    def _init_other_paths(self) -> List[str]:
+        """Initialize and validate other-model paths from ComfyUI settings.
+
+        Iterates the enabled OTHER_MODEL_FOLDER_SUBTYPES keys and pulls each
+        from ``folder_paths.get_folder_paths(key)`` (in standalone mode the
+        mock serves arbitrary keys from ``settings.json.folder_paths``).
+        Legacy aliases the host normalizes onto a canonical key (``clip`` ->
+        ``text_encoders``) are collapsed first so the same folders are not
+        reported twice.
+        """
+        try:
+            folder_path_map: Dict[str, List[str]] = {}
+            for key in self._collapse_legacy_folder_keys(
+                self._get_enabled_other_folder_keys()
+            ):
+                try:
+                    folder_path_map[key] = folder_paths.get_folder_paths(key)
+                except Exception as exc:
+                    logger.debug("Error reading folder paths for '%s': %s", key, exc)
+
+            (
+                unique_paths,
+                self.other_root_subtypes,
+                self.other_folder_roots,
+            ) = self._prepare_other_paths(folder_path_map)
+
+            logger.info(
+                "Found other model roots:"
+                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
+            )
+
+            if not unique_paths:
+                logger.info("No valid other-model folders found in configuration")
+                return []
+
+            return unique_paths
+        except Exception as e:
+            logger.warning(f"Error initializing other model paths: {e}")
+            return []
+
+    def refresh_other_roots(self) -> None:
+        """Rebuild other-model roots after the management toggles changed.
+
+        Called when ``enable_other_models`` / ``enabled_other_sub_types`` are
+        updated so the scanner immediately reflects the new folder set without
+        a full application restart.
+        """
+        self.other_roots = self._init_other_paths()
+        self._rebuild_preview_roots()
+
+    def get_other_models_availability(self) -> Dict[str, Any]:
+        """Report the other-model folders the host can actually expose.
+
+        Independent of the opt-in ``enable_other_models`` toggle: this answers
+        "could Other Models management work here at all?". ComfyUI mode almost
+        always has these folder keys registered, while standalone mode only
+        knows the keys present in ``settings.json.folder_paths`` - so the UI
+        uses this to decide whether announcing the feature would be actionable.
+
+        Returns:
+            ``{"available": bool, "sub_types": {sub_type: [existing roots]}}``.
+            A folder only counts when it exists on disk; an empty folder still
+            counts because CivitAI downloads can target it.
+        """
+        sub_types: Dict[str, List[str]] = {}
+        try:
+            keys = self._collapse_legacy_folder_keys(
+                list(OTHER_MODEL_FOLDER_SUBTYPES.keys())
+            )
+        except Exception:  # pragma: no cover - defensive
+            keys = list(OTHER_MODEL_FOLDER_SUBTYPES.keys())
+
+        for key in keys:
+            sub_type = OTHER_MODEL_FOLDER_SUBTYPES.get(key)
+            if not sub_type:
+                continue
+            try:
+                raw_paths = folder_paths.get_folder_paths(key)
+            except Exception as exc:
+                logger.debug("Error probing folder paths for '%s': %s", key, exc)
+                continue
+
+            bucket = sub_types.setdefault(sub_type, [])
+            for root in sorted(
+                self._dedupe_existing_paths(raw_paths or []).values(),
+                key=lambda path: path.lower(),
+            ):
+                if root not in bucket:
+                    bucket.append(root)
+
+        available_sub_types = {
+            sub_type: roots for sub_type, roots in sub_types.items() if roots
+        }
+        return {
+            "available": bool(available_sub_types),
+            "sub_types": available_sub_types,
+        }
+
     def get_preview_static_url(self, preview_path: str) -> str:
         if not preview_path:
             return ""

py/lora_manager.py

@@ -219,6 +219,7 @@ class LoraManager:
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
             embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+            other_scanner = await ServiceRegistry.get_other_scanner()
 
             # Initialize recipe scanner if needed
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
@@ -236,6 +237,10 @@ class LoraManager:
                     embedding_scanner.initialize_in_background(),
                     name="embedding_cache_init",
                 ),
+                asyncio.create_task(
+                    other_scanner.initialize_in_background(),
+                    name="other_cache_init",
+                ),
                 asyncio.create_task(
                     recipe_scanner.initialize_in_background(), name="recipe_cache_init"
                 ),
@@ -328,6 +333,7 @@ class LoraManager:
             all_roots.update(config.loras_roots)
             all_roots.update(config.base_models_roots or [])
             all_roots.update(config.embeddings_roots or [])
+            all_roots.update(config.other_roots or [])
 
             total_deleted = 0
             total_size_freed = 0
@@ -460,18 +466,11 @@ class LoraManager:
             # Cancel any in-flight scanner initialization tasks so thread-pool
             # workers (e.g. _initialize_cache_sync) can break out of their loops
             # when the server shuts down (e.g. Ctrl+C on WSL).
-            for name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner"):
+            for name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner", "other_scanner"):
                 scanner = ServiceRegistry.get_service_sync(name)
                 if scanner is not None and hasattr(scanner, "cancel_task"):
                     scanner.cancel_task()
                     logger.debug("LoRA Manager: Cancelled %s", name)
 
-            # Close shared aiohttp sessions to avoid "Unclosed client session" warnings
-            try:
-                from py.routes.handlers.hf_handlers import close_hf_api_session
-                await close_hf_api_session()
-            except Exception as exc:
-                logger.debug("Error closing HF API session: %s", exc)
-
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_ops/__init__.py

@@ -36,6 +36,7 @@ SCANNER_TYPE_MAP: dict[str, str] = {
     "get_lora_scanner": "lora",
     "get_checkpoint_scanner": "checkpoint",
     "get_embedding_scanner": "embedding",
+    "get_other_scanner": "other",
 }
 
 SCANNER_GETTER_NAMES = tuple(SCANNER_TYPE_MAP.keys())
@@ -80,8 +81,8 @@ async def _find_scanner_for_model(
 
 
 async def identify_model_type(model_path: str) -> str:
-    """Determine the model type (``\"lora\"``, ``\"checkpoint\"``, or
-    ``\"embedding\"``) for *model_path*.
+    """Determine the model type (``\"lora\"``, ``\"checkpoint\"``,
+    ``\"embedding\"``, or ``\"other\"``) for *model_path*.
 
     Falls back to ``\"lora\"`` when unknown.
     """

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/checkpoint_loader.py

@@ -13,6 +13,10 @@ class CheckpointLoaderLM:
 
     Loads checkpoints from both standard ComfyUI folders and LoRA Manager's
     extra folder paths, providing a unified interface for checkpoint loading.
+    The ckpt_name combo supports ComfyUI's control_after_generate, letting
+    users pick a random checkpoint on every run; the base_model input narrows
+    the random pool through a front-end extension that filters the combo
+    options.
     """
 
     NAME = "Checkpoint Loader (LoraManager)"
@@ -22,11 +26,29 @@ class CheckpointLoaderLM:
     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."},
+                    {
+                        "tooltip": (
+                            "The name of the checkpoint (model) to load. Use "
+                            "control_after_generate to pick a random model on "
+                            "every run."
+                        ),
+                        "control_after_generate": "fixed",
+                    },
+                ),
+                "base_model": (
+                    base_models,
+                    {
+                        "default": "Any",
+                        "tooltip": (
+                            "Restrict the random selection pool to this base "
+                            "model. 'Any' uses the full pool."
+                        ),
+                    },
                 ),
             }
         }
@@ -56,7 +78,7 @@ class CheckpointLoaderLM:
 
                 # Filter only checkpoint type (not diffusion_model) and format names
                 names = []
-                for item in cache.raw_data:
+                for item in list(cache.raw_data):
                     if item.get("sub_type") == "checkpoint":
                         file_path = item.get("file_path", "")
                         # Only offer models that still exist on disk so ComfyUI
@@ -93,15 +115,68 @@ class CheckpointLoaderLM:
             logger.error(f"Error getting checkpoint names: {e}")
             return []
 
-    def load_checkpoint(self, ckpt_name: str) -> Tuple[Any, Any, Any]:
+    @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 list(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"]
+
+    @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())
+
+    def load_checkpoint(
+        self, ckpt_name: str, base_model: str = "Any"
+    ) -> Tuple[Any, Any, Any]:
         """Load a checkpoint by name, supporting extra folder paths
 
         Args:
             ckpt_name: The name of the checkpoint to load (relative path with extension)
+            base_model: Only used by the front-end to filter the random pool
 
         Returns:
             Tuple of (MODEL, CLIP, VAE)
         """
+        del base_model
         # Get absolute path from cache using ComfyUI-style name
         ckpt_path, metadata = get_checkpoint_info_absolute(ckpt_name)
 

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

@@ -601,7 +601,7 @@ class SaveImageLM:
             os.path.basename(name),
             os.path.splitext(os.path.basename(name))[0],
         ]
-        for model in getattr(cache, "raw_data", []):
+        for model in list(getattr(cache, "raw_data", [])):
             file_name = model.get("file_name")
             if file_name in candidates:
                 return model
@@ -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/unet_loader.py

@@ -28,6 +28,10 @@ class UNETLoaderLM:
     Loads diffusion models/UNets from both standard ComfyUI folders and LoRA Manager's
     extra folder paths, providing a unified interface for UNET loading.
     Supports both regular diffusion models and GGUF format models.
+    The unet_name combo supports ComfyUI's control_after_generate, letting
+    users pick a random diffusion model on every run; the base_model input
+    narrows the random pool through a front-end extension that filters the
+    combo options.
     """
 
     NAME = "Unet Loader (LoraManager)"
@@ -37,16 +41,34 @@ class UNETLoaderLM:
     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."},
+                    {
+                        "tooltip": (
+                            "The name of the diffusion model to load. Use "
+                            "control_after_generate to pick a random model on "
+                            "every run."
+                        ),
+                        "control_after_generate": "fixed",
+                    },
                 ),
                 "weight_dtype": (
                     ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
                     {"tooltip": "The dtype to use for the model weights."},
                 ),
+                "base_model": (
+                    base_models,
+                    {
+                        "default": "Any",
+                        "tooltip": (
+                            "Restrict the random selection pool to this base "
+                            "model. 'Any' uses the full pool."
+                        ),
+                    },
+                ),
             }
         }
 
@@ -71,7 +93,7 @@ class UNETLoaderLM:
 
                 # Filter only diffusion_model type and format names
                 names = []
-                for item in cache.raw_data:
+                for item in list(cache.raw_data):
                     if item.get("sub_type") == "diffusion_model":
                         file_path = item.get("file_path", "")
                         # Only offer models that still exist on disk so ComfyUI
@@ -108,16 +130,69 @@ class UNETLoaderLM:
             logger.error(f"Error getting unet names: {e}")
             return []
 
-    def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple[Any, ...]:
+    @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 list(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"]
+
+    @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())
+
+    def load_unet(
+        self, unet_name: str, weight_dtype: str, 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
+            base_model: Only used by the front-end to filter the random pool
 
         Returns:
             Tuple of (MODEL,)
         """
+        del base_model
         import torch
 
         # Get absolute path from cache using ComfyUI-style name

py/nodes/utils.py

@@ -156,7 +156,7 @@ def _find_missing_loras(names: list[str]) -> list[str]:
 
         lookup = {}
         basename_candidates = {}
-        for item in cache.raw_data:
+        for item in list(cache.raw_data):
             file_path = item.get("file_path")
             if not file_path:
                 continue

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/base.py

@@ -41,6 +41,40 @@ class RecipeMetadataParser(ABC):
         """
         pass
     
+    @staticmethod
+    def populate_lora_from_local(lora_entry: Dict[str, Any], local_lora: Dict[str, Any], base_model_counts=None) -> Dict[str, Any]:
+        """Populate a recipe LoRA entry from the local scanner cache."""
+        local_path = local_lora.get('file_path') or ''
+        file_name = local_lora.get('file_name') or os.path.splitext(os.path.basename(local_path))[0]
+        base_model = local_lora.get('base_model') or ''
+
+        lora_entry['name'] = local_lora.get('model_name') or file_name or lora_entry.get('name', '')
+        lora_entry['file_name'] = file_name
+        lora_entry['hash'] = (local_lora.get('sha256') or lora_entry.get('hash') or '').lower()
+        lora_entry['localPath'] = local_path or None
+        lora_entry['size'] = local_lora.get('size', 0) or 0
+        lora_entry['baseModel'] = base_model
+        lora_entry['existsLocally'] = True
+        lora_entry['isDeleted'] = False
+
+        preview_url = local_lora.get('preview_url')
+        if preview_url:
+            lora_entry['thumbnailUrl'] = config.get_preview_static_url(preview_url)
+
+        civitai_info = local_lora.get('civitai') or {}
+        if isinstance(civitai_info, dict):
+            if civitai_info.get('id') is not None:
+                lora_entry['id'] = civitai_info['id']
+            if civitai_info.get('modelId') is not None:
+                lora_entry['modelId'] = civitai_info['modelId']
+            if civitai_info.get('name'):
+                lora_entry['version'] = civitai_info['name']
+
+        if base_model_counts is not None and base_model:
+            base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+
+        return lora_entry
+
     @staticmethod
     async def populate_lora_from_civitai(lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any] | None, str | None] | Dict[str, Any],
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:

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), '')
@@ -362,68 +361,228 @@ class AutomaticMetadataParser(RecipeMetadataParser):
 
                     checkpoint = checkpoint_entry
 
-            # If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
-            if not loras or len(loras) == 0:
-                # Extract lora weights from extranet tags in prompt (for later use)
-                lora_weights = {}
-                lora_matches = re.findall(self.EXTRANETS_REGEX, prompt)
-                for lora_type, lora_name, lora_weight in lora_matches:
-                    key = f"{lora_type}:{lora_name}"
-                    lora_weights[key] = round(float(lora_weight), 2)
-                
-                # Use hashes from metadata as the primary source
-                if metadata.get("hashes"):
-                    for hash_key, lora_hash in metadata.get("hashes", {}).items():
-                        # Only process lora or hypernet types
-                        if not hash_key.startswith(("lora:", "hypernet:")):
+            def normalize_lora_name(name, basename=False):
+                normalized = str(name or '').replace('\\', '/')
+                if normalized.casefold().endswith('.safetensors'):
+                    normalized = normalized[:-12]
+                if basename:
+                    normalized = normalized.rsplit('/', 1)[-1]
+                return normalized.casefold()
+
+            def get_version_id(lora):
+                version_id = lora.get('id')
+                if version_id in (None, '', 0, '0'):
+                    version_id = lora.get('modelVersionId')
+                if version_id in (None, '', 0, '0'):
+                    return None
+                return str(version_id)
+
+            prompt_loras = {}
+            for match in re.findall(self.EXTRANETS_REGEX, prompt):
+                lora_type, lora_name, _ = match
+                prompt_loras[(lora_type, normalize_lora_name(lora_name))] = match
+
+            prompt_by_basename = {}
+            for lora_type, lora_name, lora_weight in prompt_loras.values():
+                key = (lora_type, normalize_lora_name(lora_name, True))
+                prompt_by_basename.setdefault(key, []).append((lora_name, round(float(lora_weight), 2)))
+
+            hash_basenames = {
+                (hash_key.split(':', 1)[0], normalize_lora_name(hash_key.split(':', 1)[1], True))
+                for hash_key, hash_value in metadata.get("hashes", {}).items()
+                if hash_value and hash_key.startswith(("lora:", "hypernet:"))
+            }
+            recipe_base_model = checkpoint.get("baseModel") if checkpoint else None
+            if not recipe_base_model and len(base_model_counts) == 1:
+                recipe_base_model = next(iter(base_model_counts))
+
+            resource_lora_count = len(loras)
+
+            def make_lora_entry(lora_type, lora_name, weight, lora_hash=''):
+                return {
+                    'name': lora_name,
+                    'type': lora_type,
+                    'weight': weight,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+
+            def merge_or_append_civitai(civitai_entry, preserve_existing_weight=False):
+                civitai_id = get_version_id(civitai_entry)
+                civitai_hash = (civitai_entry.get('hash') or '').lower()
+                for index, existing in enumerate(loras):
+                    existing_id = get_version_id(existing)
+                    existing_hash = (existing.get('hash') or '').lower()
+                    if not (
+                        (civitai_id and existing_id == civitai_id)
+                        or (civitai_hash and existing_hash == civitai_hash)
+                    ):
+                        continue
+
+                    if preserve_existing_weight:
+                        civitai_entry['weight'] = existing.get('weight', civitai_entry['weight'])
+                    existing_base = existing.get('baseModel')
+                    if not civitai_entry.get('baseModel'):
+                        civitai_entry['baseModel'] = existing_base or ''
+                    elif existing_base:
+                        remaining = base_model_counts.get(existing_base, 0) - 1
+                        if remaining > 0:
+                            base_model_counts[existing_base] = remaining
+                        else:
+                            base_model_counts.pop(existing_base, None)
+                    loras[index] = civitai_entry
+                    return
+                loras.append(civitai_entry)
+
+            def merge_or_append_local(local_entry):
+                local_id = get_version_id(local_entry)
+                local_hash = (local_entry.get('hash') or '').lower()
+                for existing in loras:
+                    existing_id = get_version_id(existing)
+                    existing_hash = (existing.get('hash') or '').lower()
+                    if not (
+                        (local_id and existing_id == local_id)
+                        or (local_hash and existing_hash == local_hash)
+                    ):
+                        continue
+
+                    existing['weight'] = local_entry['weight']
+                    existing['hash'] = local_entry['hash']
+                    existing['file_name'] = local_entry['file_name']
+                    existing['existsLocally'] = True
+                    existing['localPath'] = local_entry['localPath']
+                    existing['size'] = local_entry['size']
+                    existing['isDeleted'] = False
+                    if not existing.get('modelId') and local_entry.get('modelId'):
+                        existing['modelId'] = local_entry['modelId']
+                    if not existing.get('baseModel') and local_entry.get('baseModel'):
+                        existing['baseModel'] = local_entry['baseModel']
+                        base_model_counts[local_entry['baseModel']] = base_model_counts.get(local_entry['baseModel'], 0) + 1
+                    thumbnail_url = local_entry.get('thumbnailUrl')
+                    if thumbnail_url and not thumbnail_url.endswith('/images/no-preview.png'):
+                        existing['thumbnailUrl'] = thumbnail_url
+                    return
+
+                if local_entry.get('baseModel'):
+                    base_model = local_entry['baseModel']
+                    base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+                loras.append(local_entry)
+
+            resolved_prompt_basenames = set()
+            queried_local_basenames = set()
+            for lora_type, lora_name, lora_weight in prompt_loras.values():
+                weight = round(float(lora_weight), 2)
+                basename_key = (lora_type, normalize_lora_name(lora_name, True))
+                matching_resources = [
+                    lora
+                    for lora in loras[:resource_lora_count]
+                    if lora.get('file_name')
+                    and normalize_lora_name(lora['file_name'], True) == basename_key[1]
+                    and (
+                        (lora_type == 'hypernet' and str(lora.get('type', '')).casefold() in ('hypernet', 'hypernetwork'))
+                        or (lora_type == 'lora' and str(lora.get('type', '')).casefold() not in ('hypernet', 'hypernetwork'))
+                    )
+                ]
+                if len(prompt_by_basename[basename_key]) == 1 and len(matching_resources) == 1:
+                    matching_resources[0]['weight'] = weight
+                    if basename_key not in hash_basenames:
+                        resolved_prompt_basenames.add(basename_key)
+                    continue
+
+                if basename_key in hash_basenames:
+                    continue
+
+                if not recipe_scanner or lora_type != 'lora':
+                    continue
+                queried_local_basenames.add(basename_key)
+                local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
+                if not local_lora:
+                    continue
+
+                local_entry = self.populate_lora_from_local(
+                    make_lora_entry(lora_type, lora_name, weight),
+                    local_lora,
+                )
+                merge_or_append_local(local_entry)
+                resolved_prompt_basenames.add(basename_key)
+
+            for hash_key, lora_hash in metadata.get("hashes", {}).items():
+                if not hash_key.startswith(("lora:", "hypernet:")):
+                    continue
+                lora_type, lora_name = hash_key.split(':', 1)
+                basename_key = (lora_type, normalize_lora_name(lora_name, True))
+                if basename_key in resolved_prompt_basenames:
+                    continue
+
+                prompt_entries = prompt_by_basename.get(basename_key, [])
+                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
-                        
-                        # Skip entries without a hash value — they can't be
-                        # resolved via CivitAI and would only produce a
-                        # useless "Deleted" entry in the recipe.
-                        if not lora_hash:
-                            continue
-                            
-                        lora_type, lora_name = hash_key.split(':', 1)
-                        
-                        # Get weight from extranet tags if available, else default to 1.0
-                        weight = lora_weights.get(hash_key, 1.0)
-                        
-                        # Initialize lora entry
-                        lora_entry = {
-                            'name': lora_name,
-                            'type': lora_type,  # 'lora' or 'hypernet'
-                            'weight': weight,
-                            'hash': lora_hash,
-                            'existsLocally': False,
-                            'localPath': None,
-                            'file_name': lora_name,
-                            'thumbnailUrl': '/loras_static/images/no-preview.png',
-                            'baseModel': '',
-                            'size': 0,
-                            'downloadUrl': '',
-                            'isDeleted': False
-                        }
-                        
-                        # Try to get info from Civitai
-                        if metadata_provider:
-                            try:
-                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                                
-                                populated_entry = await self.populate_lora_from_civitai(
-                                    lora_entry, 
-                                    civitai_info, 
-                                    recipe_scanner,
-                                    base_model_counts,
-                                    lora_hash
-                                )
-                                if populated_entry is None:
-                                    continue  # Skip invalid LoRA types
-                                lora_entry = populated_entry
-                            except Exception as e:
-                                logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
-                        
+                    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:
+                        local_entry = self.populate_lora_from_local(lora_entry, local_lora)
+                        merge_or_append_local(local_entry)
+                        continue
+
+                hash_resolved = False
+                if lora_hash and metadata_provider:
+                    try:
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry,
+                            civitai_info,
+                            recipe_scanner,
+                            base_model_counts,
+                            lora_hash,
+                        )
+                        if populated_entry is None:
+                            continue
+                        lora_entry = populated_entry
+                        hash_resolved = not lora_entry.get('isDeleted')
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
+
+                if hash_resolved:
+                    merge_or_append_civitai(lora_entry, preserve_existing_weight=not prompt_entries)
+                    continue
+
+                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
+
+                if lora_hash and not resource_lora_count:
+                    loras.append(lora_entry)
                 
             # Try to get base model from resources or make educated guess
             base_model = None

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

@@ -31,41 +31,106 @@ class ComfyMetadataParser(RecipeMetadataParser):
             metadata_provider = await get_default_metadata_provider()
             
             data = json.loads(user_comment)
+
+            checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
+            checkpoint = None
+            checkpoint_id = None
+            checkpoint_version_id = None
+            if checkpoint_nodes:
+                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']
+                    # 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 = []
-            
-            # Find all LoraLoader nodes
-            lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
-            
-            # Process each LoraLoader node
-            for node_id, node in lora_nodes.items():
-                if 'inputs' not in node or 'lora_name' not in node['inputs']:
+            lora_candidates = []
+            for node in data.values():
+                if not isinstance(node, dict):
                     continue
-                    
-                lora_name = node['inputs'].get('lora_name', '')
-                
-                # Parse the URN to extract model ID and version ID
-                # Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
+
+                inputs = node.get('inputs')
+                if not isinstance(inputs, dict):
+                    continue
+
+                if node.get('class_type') == 'LoraLoader':
+                    lora_name = inputs.get('lora_name', '')
+                    if isinstance(lora_name, str) and lora_name:
+                        lora_candidates.append((lora_name, inputs.get('strength_model', 1.0)))
+                    continue
+
+                if node.get('class_type') != 'LoraLoaderLM':
+                    continue
+
+                loras_data = inputs.get('loras', [])
+                if isinstance(loras_data, dict):
+                    loras_data = loras_data.get('__value__', [])
+                if isinstance(loras_data, list) and len(loras_data) == 1 and isinstance(loras_data[0], list):
+                    loras_data = loras_data[0]
+                if not isinstance(loras_data, list):
+                    continue
+
+                for lora in loras_data:
+                    if not isinstance(lora, dict) or not lora.get('active', False) or lora.get('_isDummy', False):
+                        continue
+                    lora_name = lora.get('name', '')
+                    if isinstance(lora_name, str) and lora_name:
+                        lora_candidates.append((lora_name, lora.get('strength', 1.0)))
+
+            for lora_name, weight in lora_candidates:
+                if isinstance(weight, str):
+                    try:
+                        weight = float(weight)
+                    except ValueError:
+                        weight = 1.0
                 lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
-                if not lora_id_match:
-                    continue
-                    
-                model_id = lora_id_match.group(1)
-                model_version_id = lora_id_match.group(2)
-                
-                # Get strength from node inputs
-                weight = node['inputs'].get('strength_model', 1.0)
-                
-                # Initialize lora entry with default values
+                if lora_id_match:
+                    model_id = lora_id_match.group(1)
+                    model_version_id = lora_id_match.group(2)
+                    entry_name = f"Lora {model_id}"
+                else:
+                    model_id = 0
+                    model_version_id = 0
+                    entry_name = re.split(r'[\\/]', lora_name)[-1]
+                    entry_name = re.sub(r'\.[^.]+$', '', entry_name)
+
                 lora_entry = {
                     'id': model_version_id,
                     'modelId': model_id,
-                    'name': f"Lora {model_id}",  # Default name
+                    'name': entry_name,
                     'version': '',
                     'type': 'lora',
                     'weight': weight,
                     'existsLocally': False,
                     'localPath': None,
-                    'file_name': '',
+                    'file_name': entry_name,
                     'hash': '',
                     'thumbnailUrl': '/loras_static/images/no-preview.png',
                     'baseModel': '',
@@ -73,59 +138,31 @@ class ComfyMetadataParser(RecipeMetadataParser):
                     'downloadUrl': '',
                     'isDeleted': False
                 }
-                
-                # Get additional info from Civitai if metadata provider is available
-                if metadata_provider:
-                    try:
-                        civitai_info_tuple = await metadata_provider.get_model_version_info(model_version_id)
-                        # Populate lora entry with Civitai info
-                        populated_entry = await self.populate_lora_from_civitai(
-                            lora_entry, 
-                            civitai_info_tuple, 
-                            recipe_scanner
-                        )
-                        if populated_entry is None:
-                            continue  # Skip invalid LoRA types
-                        lora_entry = populated_entry
-                    except Exception as e:
-                        logger.error(f"Error fetching Civitai info for LoRA: {e}")
-                
+
+                if lora_id_match:
+                    if metadata_provider:
+                        try:
+                            civitai_info_tuple = await metadata_provider.get_model_version_info(model_version_id)
+                            populated_entry = await self.populate_lora_from_civitai(
+                                lora_entry,
+                                civitai_info_tuple,
+                                recipe_scanner
+                            )
+                            if populated_entry is None:
+                                continue
+                            lora_entry = populated_entry
+                        except Exception as e:
+                            logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                else:
+                    if not recipe_scanner:
+                        continue
+                    local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
+                    if not local_lora:
+                        continue
+                    lora_entry = self.populate_lora_from_local(lora_entry, local_lora)
+
                 loras.append(lora_entry)
             
-            # Find checkpoint info
-            checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
-            checkpoint = None
-            checkpoint_id = None
-            checkpoint_version_id = None
-            
-            if checkpoint_nodes:
-                # Get the first checkpoint node
-                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']
-                    # Parse checkpoint URN
-                    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'
-                        }
-                        
-                        # Get additional checkpoint info from Civitai
-                        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)
-                                # Populate checkpoint with Civitai info
-                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
-                            except Exception as e:
-                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
-            
             # Extract generation parameters
             gen_params = {}
             

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_model_routes.py

@@ -149,6 +149,7 @@ class BaseModelRoutes(ABC):
             settings_service=self._settings,
             server_i18n=self._server_i18n,
             logger=logger,
+            page_context_provider=self._get_page_context_provider(),
         )
         listing = ModelListingHandler(
             service=service,
@@ -250,6 +251,10 @@ class BaseModelRoutes(ABC):
         """Get expected model types string for error messages - to be overridden by subclasses."""
         return "any model type"
 
+    def _get_page_context_provider(self):
+        """Optional hook returning extra template context for the page view."""
+        return None
+
     def _find_model_file(self, files):
         """Find the appropriate model file from the files list - can be overridden by subclasses."""
         return next((file for file in files if file.get("type") in MODEL_WEIGHT_FILE_TYPES and file.get("primary") is True), None)

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

@@ -1,4 +1,5 @@
 import logging
+import os
 from typing import Any, Dict, List, Set
 from aiohttp import web
 
@@ -7,6 +8,7 @@ from .model_route_registrar import ModelRouteRegistrar
 from ..services.checkpoint_service import CheckpointService
 from ..services.service_registry import ServiceRegistry
 from ..config import config
+from ..utils.utils import _format_model_name_for_comfyui
 
 logger = logging.getLogger(__name__)
 
@@ -44,7 +46,46 @@ class CheckpointRoutes(BaseModelRoutes):
         # Checkpoint roots and Unet roots
         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 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 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")
+            if sub_type not in ("checkpoint", "diffusion_model"):
+                return web.json_response({"error": "invalid sub_type"}, status=400)
+            scanner = await ServiceRegistry.get_checkpoint_scanner()
+            cache = await scanner.get_cached_data()
+            model_roots = scanner.get_model_roots()
+            items: List[Dict[str, str]] = []
+            for item in cache.raw_data:
+                if item.get("sub_type") != sub_type:
+                    continue
+                file_path = item.get("file_path", "")
+                if not file_path or not os.path.exists(file_path):
+                    continue
+                formatted_name = _format_model_name_for_comfyui(file_path, model_roots)
+                if formatted_name:
+                    items.append(
+                        {
+                            "name": formatted_name,
+                            "base_model": item.get("base_model", "") or "",
+                        }
+                    )
+            items.sort(key=lambda x: x["name"])
+            return web.json_response({"items": items})
+        except Exception as e:
+            logger.error(f"Error getting loader pool: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
     def _validate_civitai_model_type(self, model_type: str) -> bool:
         """Validate CivitAI model type for Checkpoint"""
         return model_type.lower() == 'checkpoint'

py/routes/handlers/download_routing_handlers.py

@@ -0,0 +1,110 @@
+"""HTTP handler for download target routing decisions."""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from aiohttp import web
+
+from ...services.download_routing import (
+    is_diffusion_model_download,
+    resolve_other_download_sub_type,
+)
+from ...utils.constants import VALID_OTHER_CIVITAI_TYPES
+
+logger = logging.getLogger(__name__)
+
+
+class DownloadRoutingHandler:
+    """Expose the download-time checkpoint/diffusion-model routing decision.
+
+    The web UI calls this when the user reaches the download location step
+    so the root dropdown offers the same root set (checkpoint vs unet) that
+    the download manager would pick for ``use_default_paths``.
+    """
+
+    async def get_download_routing(self, request: web.Request) -> web.Response:
+        try:
+            payload = await request.json()
+        except json.JSONDecodeError:
+            return web.json_response(
+                {"success": False, "error": "Invalid JSON payload"}, status=400
+            )
+
+        model_type = payload.get("model_type", "")
+        base_model = payload.get("base_model") or ""
+        file_types = payload.get("file_types") or []
+        selected_file_type = payload.get("selected_file_type")
+
+        if not isinstance(model_type, str) or not model_type:
+            return web.json_response(
+                {"success": False, "error": "model_type is required"}, status=400
+            )
+        if not isinstance(base_model, str) or not isinstance(file_types, list):
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": "base_model must be a string and file_types a list",
+                },
+                status=400,
+            )
+        if selected_file_type is not None and not isinstance(selected_file_type, str):
+            return web.json_response(
+                {"success": False, "error": "selected_file_type must be a string"},
+                status=400,
+            )
+
+        if model_type.lower() in VALID_OTHER_CIVITAI_TYPES:
+            from ...services.settings_manager import get_settings_manager
+
+            settings = get_settings_manager()
+            if not settings.is_other_models_enabled():
+                # Opt-in feature is off: never auto-route, the UI falls back to
+                # manual folder selection and the download manager rejects it.
+                return web.json_response(
+                    {
+                        "success": True,
+                        "root_kind": "other",
+                        "sub_type": None,
+                        "disabled": True,
+                        "reason": "other_models_disabled",
+                    }
+                )
+
+            sub_type = resolve_other_download_sub_type(
+                model_type,
+                file_types=(str(t) for t in file_types),
+                selected_file_type=selected_file_type,
+            )
+            if sub_type and not settings.is_other_sub_type_enabled(sub_type):
+                return web.json_response(
+                    {
+                        "success": True,
+                        "root_kind": "other",
+                        "sub_type": None,
+                        "disabled": True,
+                        "reason": "other_sub_type_disabled",
+                        "requested_sub_type": sub_type,
+                    }
+                )
+            return web.json_response(
+                {
+                    "success": True,
+                    "root_kind": "other",
+                    "sub_type": sub_type,
+                }
+            )
+
+        is_diffusion = is_diffusion_model_download(
+            model_type,
+            file_types=(str(t) for t in file_types),
+            base_model=base_model,
+        )
+        return web.json_response(
+            {
+                "success": True,
+                "is_diffusion_model": is_diffusion,
+                "root_kind": "unet" if is_diffusion else model_type,
+            }
+        )

py/routes/handlers/misc_handlers.py

@@ -53,9 +53,12 @@ from ...utils.constants import (
     PREVIEW_EXTENSIONS,
     SUPPORTED_MEDIA_EXTENSIONS,
     VALID_LORA_TYPES,
+    VALID_OTHER_CIVITAI_TYPES,
 )
-from .hf_handlers import HfHandler
+from .model_source_handlers import ModelSourceHandler
 from .agent_handlers import AgentHandler
+from .download_routing_handlers import DownloadRoutingHandler
+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 +651,72 @@ 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,
+            "other": ServiceRegistry.get_other_scanner,
+            "recipe": ServiceRegistry.get_recipe_scanner,
+        }
+
+    def _active_scanner_getters(
+        self,
+    ) -> Mapping[str, Callable[[], Awaitable[Any]]]:
+        """Drop the opt-in other scanner while Other Models is disabled."""
+        getters = self._scanner_getters
+        if "other" not in getters:
+            return getters
+        if get_settings_manager().is_other_models_enabled():
+            return getters
+        return {name: getter for name, getter in getters.items() if name != "other"}
+
     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._active_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."""
@@ -704,10 +770,19 @@ class DoctorHandler:
                 ("lora", "LoRAs", ServiceRegistry.get_lora_scanner),
                 ("checkpoint", "Checkpoints", ServiceRegistry.get_checkpoint_scanner),
                 ("embedding", "Embeddings", ServiceRegistry.get_embedding_scanner),
+                ("other", "Other Models", ServiceRegistry.get_other_scanner),
             )
         )
         self._app_version_getter = app_version_getter
 
+    def _active_scanner_factories(
+        self,
+    ) -> Sequence[tuple[str, str, Callable[[], Awaitable[Any]]]]:
+        """Drop the opt-in other scanner while Other Models is disabled."""
+        if self._settings.is_other_models_enabled():
+            return self._scanner_factories
+        return tuple(entry for entry in self._scanner_factories if entry[0] != "other")
+
     async def get_doctor_diagnostics(self, request: web.Request) -> web.Response:
         try:
             client_version = (request.query.get("clientVersion") or "").strip()
@@ -755,7 +830,7 @@ class DoctorHandler:
         repaired: list[dict[str, Any]] = []
         failures: list[dict[str, str]] = []
 
-        for model_type, label, factory in self._scanner_factories:
+        for model_type, label, factory in self._active_scanner_factories():
             try:
                 scanner = await factory()
                 await scanner.get_cached_data(force_refresh=True, rebuild_cache=True)
@@ -787,7 +862,7 @@ class DoctorHandler:
         renamed: list[dict[str, Any]] = []
 
         try:
-            for model_type, label, factory in self._scanner_factories:
+            for model_type, label, factory in self._active_scanner_factories():
                 try:
                     scanner = await factory()
                     hash_index = getattr(scanner, "_hash_index", None)
@@ -1019,7 +1094,7 @@ class DoctorHandler:
         overall_status = "ok"
         summary = "All model caches look healthy."
 
-        for model_type, label, factory in self._scanner_factories:
+        for model_type, label, factory in self._active_scanner_factories():
             try:
                 scanner = await factory()
                 persisted = None
@@ -1104,7 +1179,7 @@ class DoctorHandler:
         total_conflict_groups = 0
         total_conflict_files = 0
 
-        for model_type, label, factory in self._scanner_factories:
+        for model_type, label, factory in self._active_scanner_factories():
             # Duplicate filename detection targets LoRAs which use basename-only
             # syntax (<lora:name:strength>). Checkpoints/embeddings reference
             # models via relative paths with extensions, so conflicts there would
@@ -1484,6 +1559,22 @@ class SettingsHandler:
             response_data["civitai_api_key_set"] = bool(raw_key)
             raw_llm_key = self._settings.get("llm_api_key")
             response_data["llm_api_key_set"] = bool(raw_llm_key)
+            # Derived capability flag (not persisted): whether the host exposes
+            # any other-model folder at all. Standalone installs only know the
+            # folder_paths keys present in settings.json, so the announcement
+            # banner uses this to avoid promising a page that cannot list
+            # anything.
+            try:
+                availability = config.get_other_models_availability()
+                response_data["other_models_paths_available"] = bool(
+                    availability.get("available")
+                )
+            except Exception as availability_error:  # pragma: no cover - defensive
+                logger.debug(
+                    "Could not resolve Other Models availability: %s",
+                    availability_error,
+                )
+                response_data["other_models_paths_available"] = None
             settings_file = getattr(self._settings, "settings_file", None)
             if settings_file:
                 response_data["settings_file"] = settings_file
@@ -2013,6 +2104,7 @@ class ServiceRegistryAdapter:
     get_embedding_scanner: Callable[[], Awaitable[Any]]
     get_downloaded_version_history_service: Callable[[], Awaitable[Any]]
     get_backup_service: Callable[[], Awaitable[Any]] = _noop_backup_service
+    get_other_scanner: Callable[[], Awaitable[Any]] = ServiceRegistry.get_other_scanner
 
 
 class ModelLibraryHandler:
@@ -2037,6 +2129,8 @@ class ModelLibraryHandler:
             return "checkpoint"
         if normalized in {"embedding", "textualinversion"}:
             return "embedding"
+        if normalized in VALID_OTHER_CIVITAI_TYPES:
+            return "other"
         return None
 
     async def _get_scanner_for_type(self, model_type: str | None):
@@ -2047,6 +2141,13 @@ class ModelLibraryHandler:
             return normalized_type, await self._service_registry.get_checkpoint_scanner()
         if normalized_type == "embedding":
             return normalized_type, await self._service_registry.get_embedding_scanner()
+        if normalized_type == "other":
+            # Opt-in feature: the other scanner only resolves while the master
+            # switch is on, so callers keep returning the legacy "required"
+            # error (400) when it is off.
+            if not get_settings_manager().is_other_models_enabled():
+                return None, None
+            return normalized_type, await self._service_registry.get_other_scanner()
         return None, None
 
     async def _get_download_history_service(self):
@@ -2061,6 +2162,63 @@ 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")
@@ -2081,6 +2239,11 @@ class ModelLibraryHandler:
             lora_scanner = await self._service_registry.get_lora_scanner()
             checkpoint_scanner = await self._service_registry.get_checkpoint_scanner()
             embedding_scanner = await self._service_registry.get_embedding_scanner()
+            # Opt-in: probe the other scanner only while Other Models is enabled,
+            # so the disabled behaviour stays byte-identical to the legacy one.
+            other_scanner = None
+            if get_settings_manager().is_other_models_enabled():
+                other_scanner = await self._service_registry.get_other_scanner()
 
             if model_version_id_str:
                 try:
@@ -2096,9 +2259,11 @@ 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 +2272,7 @@ class ModelLibraryHandler:
                 ):
                     exists = True
                     model_type = "checkpoint"
+                    matched_scanner = checkpoint_scanner
                 elif (
                     embedding_scanner
                     and await embedding_scanner.check_model_version_exists(
@@ -2115,6 +2281,14 @@ class ModelLibraryHandler:
                 ):
                     exists = True
                     model_type = "embedding"
+                    matched_scanner = embedding_scanner
+                elif (
+                    other_scanner
+                    and await other_scanner.check_model_version_exists(model_version_id)
+                ):
+                    exists = True
+                    model_type = "other"
+                    matched_scanner = other_scanner
 
                 if exists:
                     return web.json_response(
@@ -2123,13 +2297,16 @@ class ModelLibraryHandler:
                             "exists": True,
                             "modelType": model_type,
                             "hasBeenDownloaded": False,
+                            "downloadedFiles": await self._get_downloaded_files(
+                                matched_scanner, model_version_id
+                            ),
                         }
                     )
 
                 history_service = await self._get_download_history_service()
                 has_been_downloaded = False
                 history_type = None
-                for candidate_type in ("lora", "checkpoint", "embedding"):
+                for candidate_type in ("lora", "checkpoint", "embedding", "other"):
                     if await history_service.has_been_downloaded(
                         candidate_type,
                         model_version_id,
@@ -2144,12 +2321,14 @@ class ModelLibraryHandler:
                         "exists": False,
                         "modelType": history_type,
                         "hasBeenDownloaded": has_been_downloaded,
+                        "downloadedFiles": [],
                     }
                 )
 
             lora_versions = await lora_scanner.get_model_versions_by_id(model_id)
             checkpoint_versions = []
             embedding_versions = []
+            other_versions = []
             if not lora_versions and checkpoint_scanner:
                 checkpoint_versions = await checkpoint_scanner.get_model_versions_by_id(
                     model_id
@@ -2158,6 +2337,13 @@ class ModelLibraryHandler:
                 embedding_versions = await embedding_scanner.get_model_versions_by_id(
                     model_id
                 )
+            if (
+                not lora_versions
+                and not checkpoint_versions
+                and not embedding_versions
+                and other_scanner
+            ):
+                other_versions = await other_scanner.get_model_versions_by_id(model_id)
 
             model_type = None
             versions = []
@@ -2189,9 +2375,18 @@ class ModelLibraryHandler:
                         "downloadedVersionIds": [],
                     }
                 )
+            if other_versions:
+                return web.json_response(
+                    {
+                        "success": True,
+                        "modelType": "other",
+                        "versions": self._with_downloaded_flag(other_versions),
+                        "downloadedVersionIds": [],
+                    }
+                )
 
             history_service = await self._get_download_history_service()
-            for candidate_type in ("lora", "checkpoint", "embedding"):
+            for candidate_type in ("lora", "checkpoint", "embedding", "other"):
                 candidate_downloaded_version_ids = (
                     await history_service.get_downloaded_version_ids(
                         candidate_type,
@@ -2246,6 +2441,11 @@ class ModelLibraryHandler:
             lora_scanner = await self._service_registry.get_lora_scanner()
             checkpoint_scanner = await self._service_registry.get_checkpoint_scanner()
             embedding_scanner = await self._service_registry.get_embedding_scanner()
+            # Opt-in: keep the other probe last so model cards for lora /
+            # checkpoint / embedding ids are unaffected by the extra scanner.
+            other_scanner = None
+            if get_settings_manager().is_other_models_enabled():
+                other_scanner = await self._service_registry.get_other_scanner()
 
             results: list[dict[str, Any]] = []
             for model_id in model_ids:
@@ -2281,6 +2481,17 @@ class ModelLibraryHandler:
                         })
                         continue
 
+                if other_scanner:
+                    other_versions = await other_scanner.get_model_versions_by_id(model_id)
+                    if other_versions:
+                        results.append({
+                            "modelId": model_id,
+                            "modelType": "other",
+                            "versions": self._with_downloaded_flag(other_versions),
+                            "downloadedVersionIds": [],
+                        })
+                        continue
+
                 results.append({
                     "modelId": model_id,
                     "modelType": None,
@@ -2428,8 +2639,8 @@ class ModelLibraryHandler:
             embedding_scanner = await self._service_registry.get_embedding_scanner()
 
             found_type = None
-            file_path = None
             found_cache = None
+            entries: list = []
 
             for model_type, scanner in (
                 ("lora", lora_scanner),
@@ -2440,27 +2651,43 @@ class ModelLibraryHandler:
                 if cache and model_version_id in cache.version_index:
                     found_type = model_type
                     found_cache = cache
-                    entry = cache.version_index[model_version_id]
-                    file_path = entry.get("file_path")
+                    # A version can have several local files (#1058); collect
+                    # them all so the delete below covers every file.
+                    files_getter = getattr(cache, "get_files_by_version_id", None)
+                    if files_getter is not None:
+                        entries = files_getter(model_version_id)
+                    else:
+                        entries = [cache.version_index[model_version_id]]
                     break
 
-            if not file_path:
+            file_paths = [
+                entry.get("file_path")
+                for entry in entries
+                if isinstance(entry, dict) and entry.get("file_path")
+            ]
+
+            if not file_paths:
                 return web.json_response(
                     {"success": False, "error": "Model version not found in any scanner cache"},
                     status=404,
                 )
 
-            target_dir = os.path.dirname(file_path)
-            base_name = os.path.basename(file_path)
-            file_name, extension = os.path.splitext(base_name)
-            await delete_model_artifacts(target_dir, file_name, main_extension=extension)
+            for file_path in file_paths:
+                target_dir = os.path.dirname(file_path)
+                base_name = os.path.basename(file_path)
+                file_name, extension = os.path.splitext(base_name)
+                await delete_model_artifacts(target_dir, file_name, main_extension=extension)
 
             if found_cache:
+                removed_paths = set(file_paths)
                 found_cache.raw_data = [
                     item
                     for item in found_cache.raw_data
-                    if item.get("file_path") != file_path
+                    if item.get("file_path") not in removed_paths
                 ]
+                rebuild = getattr(found_cache, "rebuild_version_index", None)
+                if rebuild is not None:
+                    rebuild()
                 await found_cache.resort()
 
             scanner_map = {
@@ -2483,6 +2710,7 @@ class ModelLibraryHandler:
                     "success": True,
                     "modelType": found_type,
                     "modelVersionId": model_version_id,
+                    "deletedFiles": len(file_paths),
                 }
             )
         except Exception as exc:
@@ -2652,12 +2880,32 @@ class ModelLibraryHandler:
                 model_type.lower() for model_type in CIVITAI_USER_MODEL_TYPES
             }
             lora_type_aliases = {model_type.lower() for model_type in VALID_LORA_TYPES}
+            other_type_aliases = {
+                model_type.lower() for model_type in VALID_OTHER_CIVITAI_TYPES
+            }
+
+            # Acquire the other scanner lazily so adapters without it only
+            # fail when the payload actually contains other-type models.
+            # While the opt-in feature is off the scanner still exists (its
+            # cache is empty), so other types simply report inLibrary=False.
+            needs_other_scanner = any(
+                isinstance(model, dict)
+                and str(model.get("type", "")).lower() in other_type_aliases
+                for model in models
+            )
+            other_scanner = None
+            if needs_other_scanner:
+                other_scanner = await self._service_registry.get_other_scanner()
 
             type_scanner_map: Dict[str, Any] = {
                 **{alias: lora_scanner for alias in lora_type_aliases},
                 "checkpoint": checkpoint_scanner,
                 "textualinversion": embedding_scanner,
             }
+            if other_scanner is not None:
+                type_scanner_map.update(
+                    {alias: other_scanner for alias in other_type_aliases}
+                )
 
             versions: list[dict[str, Any]] = []
             history_service = await self._get_download_history_service()
@@ -2681,12 +2929,17 @@ class ModelLibraryHandler:
                 "embedding",
                 model_ids,
             )
+            other_downloaded = await history_service.get_downloaded_version_ids_bulk(
+                "other",
+                model_ids,
+            )
             downloaded_version_map: Dict[str, Dict[int, set[int]]] = {
                 "lora": lora_downloaded,
                 "locon": lora_downloaded,
                 "dora": lora_downloaded,
                 "checkpoint": checkpoint_downloaded,
                 "textualinversion": embedding_downloaded,
+                **{alias: other_downloaded for alias in VALID_OTHER_CIVITAI_TYPES},
             }
             for model in models:
                 if not isinstance(model, dict):
@@ -3748,8 +4001,9 @@ class MiscHandlerSet:
         doctor: DoctorHandler,
         example_workflows: ExampleWorkflowsHandler,
         base_model: BaseModelHandlerSet,
-        hf_handler: Any = None,
+        model_source_handler: Any = None,
         agent_handler: Any = None,
+        download_routing: Any = None,
     ) -> None:
         self.health = health
         self.settings = settings
@@ -3768,14 +4022,16 @@ class MiscHandlerSet:
         self.doctor = doctor
         self.example_workflows = example_workflows
         self.base_model = base_model
-        self.hf_handler = hf_handler
+        self.model_source_handler = model_source_handler
         self.agent_handler = agent_handler
+        self.download_routing = download_routing
 
     def to_route_mapping(
         self,
     ) -> 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,
@@ -3820,13 +4076,19 @@ class MiscHandlerSet:
             "get_example_workflows": self.example_workflows.get_example_workflows,
             "get_example_workflow": self.example_workflows.get_example_workflow,
             # Hugging Face handlers
-            "get_hf_repo_files": self.hf_handler.get_hf_repo_files,
-            "download_hf_model": self.hf_handler.download_hf_model,
-            "set_hf_url": self.hf_handler.set_hf_url,
+            # External model sources (Hugging Face / ModelScope)
+            "list_model_source_files": self.model_source_handler.list_model_source_files,
+            "download_model_source": self.model_source_handler.download_model_source,
+            "get_hf_repo_files": self.model_source_handler.list_model_source_files,
+            "download_hf_model": self.model_source_handler.download_model_source,
+            "set_hf_url": self.model_source_handler.set_hf_url,
+            "get_model_sources": self.model_source_handler.get_model_sources,
             # Agent skill handlers
             "get_agent_skills": self.agent_handler.get_agent_skills,
             "execute_agent_skill": self.agent_handler.execute_agent_skill,
             "cancel_agent_skill": self.agent_handler.cancel_agent_skill,
+            # Download routing handler
+            "get_download_routing": self.download_routing.get_download_routing,
             # Base model handlers
             "get_base_models": self.base_model.get_base_models,
             "refresh_base_models": self.base_model.refresh_base_models,
@@ -3840,6 +4102,7 @@ def build_service_registry_adapter() -> ServiceRegistryAdapter:
         get_lora_scanner=ServiceRegistry.get_lora_scanner,
         get_checkpoint_scanner=ServiceRegistry.get_checkpoint_scanner,
         get_embedding_scanner=ServiceRegistry.get_embedding_scanner,
+        get_other_scanner=ServiceRegistry.get_other_scanner,
         get_downloaded_version_history_service=ServiceRegistry.get_downloaded_version_history_service,
         get_backup_service=ServiceRegistry.get_backup_service,
     )

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,
@@ -86,6 +90,7 @@ class ModelPageView:
         settings_service: SettingsManager,
         server_i18n,
         logger: logging.Logger,
+        page_context_provider: Callable[[web.Request], Dict[str, Any]] | None = None,
     ) -> None:
         self._template_env = template_env
         self._template_name = template_name
@@ -93,6 +98,7 @@ class ModelPageView:
         self._settings = settings_service
         self._server_i18n = server_i18n
         self._logger = logger
+        self._page_context_provider = page_context_provider
 
     def _load_supporters(self) -> dict[str, Any]:
         """Load supporters data from JSON file."""
@@ -206,6 +212,16 @@ class ModelPageView:
                     self._logger.error("Error loading cache data: %s", cache_error)
                     template_context["is_initializing"] = True
 
+            if self._page_context_provider is not None:
+                try:
+                    extra_context = self._page_context_provider(request)
+                    if isinstance(extra_context, dict):
+                        template_context.update(extra_context)
+                except Exception as context_error:  # pragma: no cover - logging path
+                    self._logger.error(
+                        "Error building page context: %s", context_error
+                    )
+
             rendered = self._template_env.get_template(self._template_name).render(
                 **template_context
             )
@@ -364,6 +380,7 @@ 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 +650,16 @@ 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 +675,33 @@ 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 +709,8 @@ 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 +1071,11 @@ 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 +1170,14 @@ 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 +1202,9 @@ 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 +1212,9 @@ 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 +1611,50 @@ 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 +1688,50 @@ 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."""
@@ -1659,7 +1798,8 @@ class ModelDownloadHandler:
                 import json
 
                 try:
-                    data["file_params"] = json.loads(file_params_json)
+                    # Normalize falsy payloads (e.g. {}) to None (#1058)
+                    data["file_params"] = json.loads(file_params_json) or None
                 except json.JSONDecodeError:
                     self._logger.warning(
                         "Invalid file_params JSON: %s", file_params_json
@@ -1811,7 +1951,8 @@ class ModelDownloadHandler:
 
             model_id = int(model_id_str) if model_id_str else None
             model_version_id = int(model_version_id_str) if model_version_id_str else None
-            file_params = json.loads(file_params_json) if file_params_json else None
+            # Normalize falsy payloads (e.g. {}) to None (#1058)
+            file_params = (json.loads(file_params_json) if file_params_json else None) or None
 
             service = await DownloadQueueService.get_instance()
             item = await service.add_to_queue(
@@ -1886,8 +2027,18 @@ 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
@@ -1970,9 +2121,11 @@ 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:
@@ -2023,8 +2176,12 @@ 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:
@@ -2066,9 +2223,10 @@ 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:
@@ -2187,6 +2345,19 @@ class ModelCivitaiHandler:
                 else:
                     version.pop("localPath", None)
 
+                # Per-file downloaded state so multi-file versions can show
+                # which individual files are already in the library (#1058)
+                local_entries: List[Any] = []
+                if version_id is not None and cache:
+                    files_getter = getattr(cache, "get_files_by_version_id", None)
+                    if files_getter is not None:
+                        local_entries = files_getter(version_id)
+                    elif cache_entry is not None:
+                        local_entries = [cache_entry]
+                version["downloadedFiles"] = self._match_downloaded_files(
+                    version, local_entries
+                )
+
                 model_file = (
                     self._find_model_file(version.get("files", []))
                     if isinstance(version.get("files"), Iterable)
@@ -2201,6 +2372,64 @@ class ModelCivitaiHandler:
             )
             return web.Response(status=500, text=str(exc))
 
+    @staticmethod
+    def _match_downloaded_files(
+        version: Mapping[str, Any], local_entries: List[Any]
+    ) -> List[Dict[str, Any]]:
+        """Map local library entries back to individual files of a version.
+
+        Matching follows rule D2 (#1058): SHA256 is authoritative when the
+        local entry carries one; otherwise fall back to extension-less file
+        name equality. Returns ``[{fileId, fileName, filePath}]``.
+        """
+        files = version.get("files")
+        if not isinstance(files, list) or not local_entries:
+            return []
+
+        by_hash: Dict[str, Mapping[str, Any]] = {}
+        by_name: Dict[str, Mapping[str, Any]] = {}
+        for file_info in files:
+            if not isinstance(file_info, Mapping):
+                continue
+            sha = str(
+                (file_info.get("hashes") or {}).get("SHA256") or ""
+            ).strip().lower()
+            if sha:
+                by_hash.setdefault(sha, file_info)
+            name = str(file_info.get("name") or "").strip()
+            if name:
+                by_name.setdefault(os.path.splitext(name)[0], file_info)
+
+        downloaded: List[Dict[str, Any]] = []
+        seen_keys: set = set()
+        for entry in local_entries:
+            if not isinstance(entry, Mapping):
+                continue
+            matched: Optional[Mapping[str, Any]] = None
+            local_hash = str(entry.get("sha256") or "").strip().lower()
+            if local_hash:
+                matched = by_hash.get(local_hash)
+            if matched is None:
+                local_name = str(entry.get("file_name") or "").strip()
+                if local_name:
+                    matched = by_name.get(local_name)
+            if matched is None:
+                continue
+
+            file_id = matched.get("id")
+            dedupe_key = file_id if file_id is not None else matched.get("name")
+            if dedupe_key in seen_keys:
+                continue
+            seen_keys.add(dedupe_key)
+            downloaded.append(
+                {
+                    "fileId": file_id,
+                    "fileName": matched.get("name"),
+                    "filePath": entry.get("file_path"),
+                }
+            )
+        return downloaded
+
     async def get_civitai_model_by_version(self, request: web.Request) -> web.Response:
         try:
             model_version_id = request.match_info.get("modelVersionId")
@@ -2548,10 +2777,20 @@ 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,
             ):
@@ -2564,6 +2803,26 @@ 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"))
@@ -3031,6 +3290,9 @@ 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(
@@ -3175,6 +3437,8 @@ class ModelHandlerSet:
             "get_model_metadata": self.query.get_model_metadata,
             "get_model_description": self.query.get_model_description,
             "get_relative_paths": self.query.get_relative_paths,
+            "update_active_filters": self.query.update_active_filters,
+            "get_active_filters": self.query.get_active_filters,
             "refresh_model_updates": self.updates.refresh_model_updates,
             "fetch_missing_civitai_license_data": self.updates.fetch_missing_civitai_license_data,
             "set_model_update_ignore": self.updates.set_model_update_ignore,

py/routes/handlers/model_source_handlers.py

@@ -1,8 +1,13 @@
-"""Handlers for Hugging Face model listing and download.
+"""Handlers for external model sources: linking, file listing and downloads.
 
-Minimal MVP implementation — uses direct HTTP to the HF API for file
-listing and the project's existing aiohttp-based Downloader for
-downloading.  No huggingface_hub dependency required.
+Covers every site registered in :mod:`py.services.model_sources`.  The module
+was Hugging Face only (``hf_handlers.py`` / ``HfHandler``) until ModelScope
+downloads were added; the per-site differences now live in the providers, so
+this file has no platform branches beyond the capability lookups.
+
+The historical route paths (``/api/lm/set-hf-url``, ``/api/lm/hf-repo-files``,
+``/api/lm/download-hf-model``) are still registered as aliases of the generic
+handlers, so existing callers keep working.
 """
 
 from __future__ import annotations
@@ -10,10 +15,8 @@ from __future__ import annotations
 import json
 import logging
 import os
-import re
 from typing import Any
 
-import aiohttp
 from aiohttp import web
 
 from ...config import config
@@ -22,10 +25,18 @@ from ...services.downloader import (
     get_downloader,
 )
 from ...services.aria2_downloader import Aria2Downloader
+from ...services.model_sources import (
+    ModelSourceError,
+    SourceRef,
+    detect_source,
+    get_download_source,
+    is_valid_source_id,
+    list_sources,
+    normalize_metadata_source,
+)
 from ...services.settings_manager import get_settings_manager
 from ...services.service_registry import ServiceRegistry
 from ...services.websocket_manager import ws_manager
-from ...utils.constants import MODEL_FILE_EXTENSIONS
 from ...utils.metadata_manager import MetadataManager
 from ...utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 
@@ -34,28 +45,6 @@ logger = logging.getLogger(__name__)
 _DEFAULT_MODEL_CLASS = LoraMetadata
 _DEFAULT_SCANNER_GETTER = "get_lora_scanner"
 
-# Shared aiohttp session for HF API calls (created on first use)
-_hf_api_session: aiohttp.ClientSession | None = None
-
-
-async def _get_hf_api_session() -> aiohttp.ClientSession:
-    """Get or create the shared aiohttp session for HF API calls."""
-    global _hf_api_session  # needed because we reassign the module-level name
-    if _hf_api_session is None or _hf_api_session.closed:
-        _hf_api_session = aiohttp.ClientSession(
-            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
-            timeout=aiohttp.ClientTimeout(total=30),
-        )
-    return _hf_api_session
-
-
-async def close_hf_api_session() -> None:
-    """Close the shared HF API session, if it was ever created."""
-    global _hf_api_session
-    if _hf_api_session is not None and not _hf_api_session.closed:
-        await _hf_api_session.close()
-        _hf_api_session = None
-
 
 def _infer_model_type(model_root: str) -> tuple[Any, str]:
     """Determine model class and scanner by matching ``model_root`` against the
@@ -96,18 +85,19 @@ def _infer_model_type(model_root: str) -> tuple[Any, str]:
     return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
 
 
-async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
+async def _save_source_metadata(
+    dest_path: str, ref: SourceRef, model_root: str
+) -> None:
     """Create a proper .metadata.json and add the model to the scanner cache.
 
     Uses ``MetadataManager.create_default_metadata()`` which computes the
     SHA256 hash, extracts safetensors header metadata (base_model), and
     produces a fully-populated ``LoraMetadata`` (or ``CheckpointMetadata`` /
-    ``EmbeddingMetadata``) object.  We then overlay HF-specific fields and
-    register the model in the in-memory scanner cache so it appears
+    ``EmbeddingMetadata``) object.  We then overlay the external-source fields
+    and register the model in the in-memory scanner cache so it appears
     immediately without a full filesystem walk.
     """
     try:
-        hf_url = f"https://huggingface.co/{repo}"
         model_class, scanner_getter_name = _infer_model_type(model_root)
 
         # 1. Create proper metadata (computes SHA256, reads safetensors headers)
@@ -118,17 +108,21 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
             logger.warning("create_default_metadata returned None for %s", dest_path)
             return
 
-        # 2. Overlay HF-specific fields
-        metadata._unknown_fields["hf_url"] = hf_url
-        metadata.from_civitai = False  # HF models are not from CivitAI
-
-        metadata_dict = metadata.to_dict()
-        if "trainedWords" in metadata_dict and not metadata_dict["trainedWords"]:
-            del metadata_dict["trainedWords"]
+        # 2. Overlay the external-source fields (`hf_url` is written by
+        #    normalisation for Hugging Face only)
+        fields = metadata._unknown_fields
+        fields["source_url"] = ref.url
+        fields["source_platform"] = ref.platform
+        if ref.platform == "huggingface":
+            fields["hf_url"] = ref.url
+        metadata.from_civitai = False  # externally-sourced models are not from CivitAI
 
         # 3. Save metadata atomically
-        await MetadataManager.save_metadata(dest_path, metadata_dict)
-        logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
+        await MetadataManager.save_metadata(dest_path, metadata)
+        logger.info(
+            "Saved %s metadata (source=%s) for %s",
+            ref.platform, ref.url, dest_path,
+        )
 
         # 4. Determine relative folder path for cache
         #    model_root is an absolute path; dest_path is under it
@@ -142,13 +136,12 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
         if scanner_getter is not None:
             scanner = await scanner_getter()
             if scanner is not None:
-                metadata_dict = metadata.to_dict()
-                metadata_dict["hf_url"] = hf_url
+                metadata_dict = normalize_metadata_source(metadata.to_dict())
                 await scanner.add_model_to_cache(metadata_dict, folder)
                 logger.info("Added %s to scanner cache (folder=%s)", dest_path, folder)
 
     except Exception as exc:
-        logger.warning("Failed to save HF metadata for %s: %s", dest_path, exc)
+        logger.warning("Failed to save source metadata for %s: %s", dest_path, exc)
 
 
 def _find_matching_root(dest_dir: str) -> str | None:
@@ -190,30 +183,87 @@ async def _add_to_scanner_cache(dest_path: str, metadata: dict[str, Any]) -> Non
     await scanner.update_single_model_cache(dest_path, dest_path, metadata)
 
 
-class HfHandler:
-    """Handle Hugging Face model browsing and download."""
+def _unsupported_platform_error(platform: str) -> web.Response:
+    supported = ", ".join(source.label for source in list_sources() if source.supports_download)
+    return web.json_response(
+        {"error": f"'{platform}' does not support downloads. Supported: {supported}"},
+        status=400,
+    )
+
+
+class ModelSourceHandler:
+    """Handle external model browsing, linking and downloads."""
+
+    async def get_model_sources(self, request: web.Request) -> web.Response:
+        """List the external model sites the UI can link a model to.
+
+        Used by the "Link Model" dialog to validate URLs client-side, to
+        explain which sites support AI metadata enrichment, and to pick the
+        right download endpoint/revision.
+        """
+
+        return web.json_response([
+            {
+                "platform": source.platform,
+                "label": source.label,
+                "supports_enrichment": source.supports_enrichment,
+                "supports_download": source.supports_download,
+                "default_revision": source.default_revision,
+                "example_url": source.canonical_url(
+                    "user/repo" if source.platform != "tensorart" else "827823520299086029"
+                ),
+            }
+            for source in list_sources()
+        ])
 
     async def set_hf_url(self, request: web.Request) -> web.Response:
+        """Link a model file to its page on an external model site.
+
+        Accepts ``source_url`` (preferred) or the legacy ``hf_url`` / ``url``
+        payload key.  Every registered site is recognised and the platform is
+        stored alongside the canonical URL.  TensorArt models can be linked and
+        browsed, but not AI-enriched.
+
+        The route path keeps its historical ``set-hf-url`` name.
+        """
+
         try:
             payload: dict[str, Any] = await request.json()
         except json.JSONDecodeError:
             return web.json_response({"success": False, "error": "Invalid JSON"}, status=400)
 
         file_path = (payload.get("file_path") or "").strip()
-        hf_url = (payload.get("hf_url") or "").strip()
+        raw_url = (
+            payload.get("source_url")
+            or payload.get("hf_url")
+            or payload.get("url")
+            or ""
+        )
+        source_url = raw_url.strip() if isinstance(raw_url, str) else ""
 
-        if not file_path or not hf_url:
-            return web.json_response(
-                {"success": False, "error": "Missing required fields: 'file_path' and 'hf_url'"},
-                status=400,
-            )
-
-        m = re.match(r"^https?://huggingface\.co/([^/]+/[^/]+)/?$", hf_url)
-        if not m:
+        if not file_path or not source_url:
             return web.json_response(
                 {
                     "success": False,
-                    "error": "Invalid HuggingFace URL. Expected format: https://huggingface.co/user/repo",
+                    "error": "Missing required fields: 'file_path' and 'source_url'",
+                },
+                status=400,
+            )
+
+        ref = detect_source(source_url, strict=True)
+        if ref is None:
+            return web.json_response(
+                {
+                    "success": False,
+                    "error": (
+                        "Unsupported model URL. Supported formats: "
+                        + ", ".join(
+                            f"{s.label} ({s.canonical_url('user/repo')})"
+                            if s.platform != "tensorart"
+                            else f"{s.label} (https://tensor.art/models/<id>)"
+                            for s in list_sources()
+                        )
+                    ),
                 },
                 status=400,
             )
@@ -229,107 +279,120 @@ class HfHandler:
             return web.json_response(
                 {
                     "success": False,
-                    "error": "File is not within any configured model directory. Cannot link to HuggingFace.",
+                    "error": "File is not within any configured model directory. Cannot link to a model source.",
                 },
                 status=400,
             )
 
         try:
             existing = await MetadataManager.load_metadata_payload(file_path)
-            if existing.get("hf_url") == hf_url:
+
+            already_linked = (
+                (existing.get("source_url") or "").strip() == ref.url
+                and (existing.get("source_platform") or "").strip().lower()
+                == ref.platform
+            ) or (
+                not existing.get("source_url")
+                and ref.platform == "huggingface"
+                and (existing.get("hf_url") or "").strip() == ref.url
+            )
+            if already_linked:
                 return web.json_response({
                     "success": True,
-                    "message": "hf_url already set",
-                    "hf_url": hf_url,
+                    "message": "source_url already set",
+                    "source_url": ref.url,
+                    "source_platform": ref.platform,
+                    "hf_url": ref.url if ref.platform == "huggingface" else "",
                 })
 
-            existing["hf_url"] = hf_url
-            existing["from_civitai"] = False
+            existing["source_url"] = ref.url
+            existing["source_platform"] = ref.platform
+            if ref.platform == "huggingface":
+                existing["hf_url"] = ref.url
+            else:
+                existing.pop("hf_url", None)
+            normalize_metadata_source(existing)
+
+            # NOTE: deliberately do NOT touch `from_civitai` here. It records
+            # where the metadata came from, and the UI must show the CivitAI
+            # link whenever CivitAI data is present — linking an external
+            # source must not hide it (#1094). Source provenance is tracked
+            # via `source_platform` / `source_url`.
             await MetadataManager.save_metadata(file_path, existing)
 
             await _add_to_scanner_cache(file_path, existing)
 
-            logger.info("Set hf_url=%s for %s", hf_url, file_path)
+            logger.info(
+                "Linked %s to %s source (%s)", file_path, ref.platform, ref.url
+            )
             return web.json_response({
                 "success": True,
-                "message": f"hf_url set to {hf_url}",
-                "hf_url": hf_url,
+                "message": f"Linked to {ref.url}",
+                "source_url": ref.url,
+                "source_platform": ref.platform,
+                "hf_url": existing.get("hf_url", ""),
             })
         except Exception as exc:
-            logger.error("Failed to set hf_url for %s: %s", file_path, exc)
+            logger.error("Failed to link %s to a model source: %s", file_path, exc)
             return web.json_response(
                 {"success": False, "error": str(exc)},
                 status=500,
             )
 
-    async def get_hf_repo_files(self, request: web.Request) -> web.Response:
-        """List model-weight files from a HF repo with real file sizes.
+    async def list_model_source_files(self, request: web.Request) -> web.Response:
+        """List the downloadable weight files of an external repository.
 
-        Uses the HF tree API endpoint which returns accurate file sizes
-        (including LFS-tracked files), unlike the model info endpoint.
+        Query params: ``platform``, ``repo`` (``owner/name``), ``revision``
+        (optional; each site has its own default branch).
+
+        Returns a JSON array of ``{"filename", "size"}``, largest first —
+        the same shape the Hugging Face endpoint has always returned.
         """
-        repo = request.query.get("repo", "").strip()
-        if not repo or "/" not in repo:
+
+        platform = (request.query.get("platform") or "").strip()
+        repo = (request.query.get("repo") or "").strip()
+        revision = (request.query.get("revision") or "").strip()
+
+        source = get_download_source(platform)
+        if source is None:
+            return _unsupported_platform_error(platform)
+        if not is_valid_source_id(repo):
             return web.json_response(
-                {"error": "Missing or invalid 'repo' parameter (expected user/repo)"},
+                {"error": "Missing or invalid 'repo' parameter (expected owner/name)"},
                 status=400,
             )
 
-        url = f"https://huggingface.co/api/models/{repo}/tree/main"
-
         try:
-            session = await _get_hf_api_session()
-            async with session.get(url) as resp:
-                if resp.status == 404:
-                    return web.json_response(
-                        {"error": f"Repo '{repo}' not found"}, status=404
-                    )
-                if resp.status != 200:
-                    text = await resp.text()
-                    return web.json_response(
-                        {"error": f"HF API error {resp.status}: {text[:200]}"},
-                        status=resp.status,
-                    )
-                tree: list[dict[str, Any]] = await resp.json()
+            files = await source.list_files(repo, revision)
+        except ModelSourceError as exc:
+            return web.json_response({"error": str(exc)}, status=exc.status)
         except Exception as exc:
-            logger.error("Failed to fetch HF repo files: %s", exc)
+            logger.error("Failed to list %s files in %s: %s", platform, repo, exc)
             return web.json_response({"error": str(exc)}, status=502)
 
-        files: list[dict[str, Any]] = []
-        for entry in tree:
-            path: str = entry.get("path", "")
-            ext = os.path.splitext(path)[1].lower()
-            if ext not in MODEL_FILE_EXTENSIONS:
-                continue
-            size = entry.get("size", 0) or 0
-            if size == 0 and "lfs" in entry:
-                size = entry["lfs"].get("size", 0) or 0
-            files.append({
-                "filename": path,
-                "size": size,
-            })
-
-        files.sort(key=lambda f: f["size"], reverse=True)
         return web.json_response(files)
 
-    async def download_hf_model(self, request: web.Request) -> web.Response:
-        """Download a single file from Hugging Face into the model directory.
+    async def download_model_source(self, request: web.Request) -> web.Response:
+        """Download a single file from an external repository.
 
         POST JSON body::
 
             {
-              "repo": "dx8152/Flux2-Klein-9B-Consistency",
-              "filename": "Flux2-Klein-9B-consistency-V2.safetensors",
-              "revision": "main",
+              "platform": "modelscope",
+              "repo": "owner/name",
+              "filename": "subdir/model.safetensors",
+              "revision": "master",
               "model_root": "loras",
               "relative_path": "",
               "use_default_paths": false,
               "download_id": "optional-batch-id"
             }
 
+        ``platform`` defaults to ``huggingface`` when omitted, which keeps the
+        legacy ``/api/lm/download-hf-model`` payload working unchanged.
+
         If ``download_id`` is provided, real-time progress (bytes, speed,
-        percentage) is broadcast via the WebSocket progress system, matching
-        the CivitAI download experience.
+        percentage) is broadcast via the WebSocket progress system.
 
         Respects the ``download_backend`` setting (``aria2`` or ``default``).
         """
@@ -338,30 +401,33 @@ class HfHandler:
         except json.JSONDecodeError:
             return web.json_response({"error": "Invalid JSON"}, status=400)
 
+        platform = (payload.get("platform") or "huggingface").strip()
         repo = (payload.get("repo") or "").strip()
         filename = (payload.get("filename") or "").strip()
-        revision = (payload.get("revision") or "main").strip()
+        revision = (payload.get("revision") or "").strip()
         model_root = (payload.get("model_root") or "").strip()
         relative_path = (payload.get("relative_path") or "").strip()
         use_default_paths = bool(payload.get("use_default_paths", False))
         download_id: str | None = payload.get("download_id")
 
         logger.info(
-            "download_hf_model: repo=%s file=%s root=%s download_id=%s",
-            repo, filename, model_root, download_id,
+            "download_model_source: platform=%s repo=%s file=%s root=%s download_id=%s",
+            platform, repo, filename, model_root, download_id,
         )
 
+        source = get_download_source(platform)
+        if source is None:
+            return _unsupported_platform_error(platform)
+
         if not repo or not filename:
             return web.json_response(
                 {"error": "Missing required fields: 'repo' and 'filename'"}, status=400
             )
 
-        # Validate repo format — must be user/repo_name
-        if repo.count("/") != 1 or not re.match(r"^[a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+$", repo):
-            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
-        author, repo_name = repo.split("/", 1)
-        if ".." in (author, repo_name) or "." in (author, repo_name):
+        # `owner/name` only; the components become path segments below.
+        if not is_valid_source_id(repo):
             return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
+        owner, repo_name = repo.split("/", 1)
 
         # Validate filename — must not contain path traversal
         if ".." in filename:
@@ -380,21 +446,21 @@ class HfHandler:
         # unnecessary when the frontend sends the path from its own dropdown
         # (populated from scanner roots).  Using the "business path" directly
         # keeps dest_path consistent with scanner roots so that later folder
-        # derivation (in _save_hf_metadata) works correctly.
+        # derivation (in _save_source_metadata) works correctly.
         if os.path.isabs(model_root):
             base_dir = os.path.normpath(model_root)
         else:
             base_dir = os.path.normpath(os.path.join(os.getcwd(), "models", model_root))
 
         if use_default_paths:
-            target_dir = os.path.join(base_dir, "huggingface", author, repo_name)
+            target_dir = os.path.join(base_dir, source.default_subdir, owner, repo_name)
         elif relative_path:
             target_dir = os.path.join(base_dir, relative_path)
         else:
             target_dir = base_dir
 
-        # Strip HF repo subdirectory — "diffusion_models/xxx.safetensors"
-        # is an HF repo convention, not meaningful for local storage.
+        # Strip the repository sub-directory — "diffusion_models/xxx.safetensors"
+        # is a repository convention, not meaningful for local storage.
         file_base = os.path.basename(filename)
 
         os.makedirs(target_dir, exist_ok=True)
@@ -402,16 +468,18 @@ class HfHandler:
 
         # Check if already exists (simple skip)
         if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
-            logger.info("download_hf_model: file already exists, skipping — %s", dest_path)
+            logger.info("download_model_source: file already exists, skipping — %s", dest_path)
             return web.json_response({
                 "success": True,
                 "message": f"File already exists: {dest_path}",
                 "path": dest_path,
             })
 
-        # Build HF resolve URL
-        resolve_url = (
-            f"https://huggingface.co/{repo}/resolve/{revision}/{filename}"
+        # Built per request: sites that redirect to a CDN hand out a
+        # time-limited token in the redirect, so the URL must never be cached.
+        resolve_url = source.file_download_url(repo, filename, revision)
+        ref = SourceRef(
+            platform=source.platform, source_id=repo, url=source.canonical_url(repo)
         )
 
         # Set up progress callback if download_id is provided
@@ -453,28 +521,27 @@ class HfHandler:
 
         if download_backend == "aria2":
             aria2 = await Aria2Downloader.get_instance()
-            aid = download_id or f"hf_{repo}_{filename}"
+            aid = download_id or f"{source.platform}_{repo}_{filename}"
             try:
-                hf_success, hf_result = await aria2.download_file(
+                ok, result = await aria2.download_file(
                     url=resolve_url,
                     save_path=dest_path,
                     download_id=aid,
                     progress_callback=progress_callback,
                 )
-                if hf_success:
-                    await _save_hf_metadata(dest_path, repo, model_root)
+                if ok:
+                    await _save_source_metadata(dest_path, ref, model_root)
                     return web.json_response({
                         "success": True,
                         "message": f"Downloaded to {dest_path}",
                         "path": dest_path,
                     })
-                else:
-                    return web.json_response(
-                        {"success": False, "error": hf_result or "aria2 download failed"},
-                        status=500,
-                    )
+                return web.json_response(
+                    {"success": False, "error": result or "aria2 download failed"},
+                    status=500,
+                )
             except Exception as exc:
-                logger.error("HF download (aria2) failed: %s", exc)
+                logger.error("%s download (aria2) failed: %s", platform, exc)
                 return web.json_response(
                     {"success": False, "error": str(exc)}, status=500
                 )
@@ -490,19 +557,18 @@ class HfHandler:
                 progress_callback=progress_callback,
             )
             if success:
-                await _save_hf_metadata(dest_path, repo, model_root)
+                await _save_source_metadata(dest_path, ref, model_root)
                 return web.json_response({
                     "success": True,
                     "message": f"Downloaded to {result}",
                     "path": result,
                 })
-            else:
-                return web.json_response(
-                    {"success": False, "error": result or "Download failed"},
-                    status=500,
-                )
+            return web.json_response(
+                {"success": False, "error": result or "Download failed"},
+                status=500,
+            )
         except Exception as exc:
-            logger.error("HF download failed: %s", exc)
+            logger.error("%s download failed: %s", platform, exc)
             return web.json_response(
                 {"success": False, "error": str(exc)}, status=500
             )

py/routes/handlers/pending_delete_handler.py

@@ -35,6 +35,7 @@ _MODEL_TYPE_GETTER_NAMES: Dict[str, str] = {
     "loras": "get_lora_scanner",
     "checkpoints": "get_checkpoint_scanner",
     "embeddings": "get_embedding_scanner",
+    "other": "get_other_scanner",
 }
 
 # Staged batch ids are ``uuid.uuid4().hex`` (32 lowercase hex chars). The id is

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"),
@@ -98,16 +99,31 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/delete-model-version", "delete_model_version"
     ),
-    # Hugging Face model endpoints
+    # External model source endpoints (Hugging Face / ModelScope).
+    # The hf-* paths are the historical names, kept as aliases.
+    RouteDefinition(
+        "GET", "/api/lm/model-source-files", "list_model_source_files"
+    ),
     RouteDefinition(
         "GET", "/api/lm/hf-repo-files", "get_hf_repo_files"
     ),
+    # Download target routing decision (checkpoint vs diffusion model roots)
+    RouteDefinition(
+        "POST", "/api/lm/download/routing", "get_download_routing"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/download-model-source", "download_model_source"
+    ),
     RouteDefinition(
         "POST", "/api/lm/download-hf-model", "download_hf_model"
     ),
     RouteDefinition(
         "POST", "/api/lm/set-hf-url", "set_hf_url"
     ),
+    # Supported external model sites (Hugging Face / ModelScope / TensorArt)
+    RouteDefinition(
+        "GET", "/api/lm/model-sources", "get_model_sources"
+    ),
     # Agent skill endpoints
     RouteDefinition(
         "GET", "/api/lm/agent/skills", "get_agent_skills"

py/routes/misc_routes.py

@@ -39,8 +39,9 @@ from .handlers.misc_handlers import (
     build_service_registry_adapter,
 )
 from .handlers.base_model_handlers import BaseModelHandlerSet
-from .handlers.hf_handlers import HfHandler
+from .handlers.model_source_handlers import ModelSourceHandler
 from .handlers.agent_handlers import AgentHandler
+from .handlers.download_routing_handlers import DownloadRoutingHandler
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
@@ -138,8 +139,9 @@ class MiscRoutes:
         doctor = DoctorHandler(settings_service=self._settings)
         example_workflows = ExampleWorkflowsHandler()
         base_model = BaseModelHandlerSet()
-        hf_handler = HfHandler()
+        model_source_handler = ModelSourceHandler()
         agent_handler = AgentHandler()
+        download_routing = DownloadRoutingHandler()
 
         return self._handler_set_factory(
             health=health,
@@ -159,8 +161,9 @@ class MiscRoutes:
             doctor=doctor,
             example_workflows=example_workflows,
             base_model=base_model,
-            hf_handler=hf_handler,
+            model_source_handler=model_source_handler,
             agent_handler=agent_handler,
+            download_routing=download_routing,
         )
 
 

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/other_routes.py

@@ -0,0 +1,139 @@
+import logging
+import os
+from typing import Any, Dict, List
+from aiohttp import web
+
+from .base_model_routes import BaseModelRoutes
+from .model_route_registrar import ModelRouteRegistrar
+from ..config import config
+from ..services.other_model_service import OtherModelService
+from ..services.service_registry import ServiceRegistry
+from ..utils.constants import (
+    CIVITAI_TYPE_TO_OTHER_SUB_TYPE,
+    OTHER_MODEL_FOLDER_SUBTYPES,
+    VALID_OTHER_CIVITAI_TYPES,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class OtherRoutes(BaseModelRoutes):
+    """Other-model-specific route controller (VAE, upscaler, text encoder, ...)"""
+
+    def __init__(self):
+        """Initialize Other-model routes with OtherModel service"""
+        super().__init__()
+        self.template_name = "other.html"
+
+    async def initialize_services(self):
+        """Initialize services from ServiceRegistry"""
+        other_scanner = await ServiceRegistry.get_other_scanner()
+        update_service = await ServiceRegistry.get_model_update_service()
+        self.service = OtherModelService(other_scanner, update_service=update_service)
+        self.set_model_update_service(update_service)
+
+        # Attach service dependencies
+        self.attach_service(self.service)
+
+    def setup_routes(self, app: web.Application, prefix: str = "other"):
+        """Setup Other-model routes"""
+        # Schedule service initialization on app startup
+        app.on_startup.append(lambda _: self.initialize_services())
+
+        # Setup common routes with 'other' prefix (includes page route)
+        super().setup_routes(app, prefix)
+
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
+        """Setup Other-model-specific routes"""
+        # Other-model info by name
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/info/{name}', prefix, self.get_other_model_info)
+        # Other-model roots grouped by sub_type (text_encoders + legacy clip
+        # are aggregated under text_encoder)
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/roots_by_subtype', prefix, self.get_roots_by_subtype)
+
+    def _validate_civitai_model_type(self, model_type: str) -> bool:
+        """Validate CivitAI model type for other models.
+
+        Accepts retired CivitAI types (CLIP, CLIPVision) as well — grandfathered
+        models on CivitAI still carry them. Types whose sub_type is currently
+        disabled (or every type while the opt-in feature is off) are rejected.
+        """
+        normalized = (model_type or "").strip().lower()
+        if normalized not in VALID_OTHER_CIVITAI_TYPES:
+            return False
+        if not self._settings.is_other_models_enabled():
+            return False
+
+        sub_type = CIVITAI_TYPE_TO_OTHER_SUB_TYPE.get(normalized)
+        if sub_type is None:
+            # CivitAI "Other" has no sub_type of its own; it is only usable
+            # while at least one sub_type is enabled.
+            return bool(self._settings.get_enabled_other_sub_types())
+        return self._settings.is_other_sub_type_enabled(sub_type)
+
+    def _get_page_context_provider(self):
+        """Expose the opt-in feature state to the Other Models page template."""
+        return self._page_context_for_other
+
+    def _page_context_for_other(self, request: web.Request) -> Dict[str, Any]:
+        if not self._settings.is_other_models_enabled():
+            return {"other_disabled": True, "other_no_paths": False}
+
+        # Enabled but nothing to scan: folder paths for the managed sub_types
+        # resolved to no existing folder. Render an actionable empty state
+        # instead of an apparently broken empty grid.
+        standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
+        return {
+            "other_disabled": False,
+            "other_no_paths": not bool(config.other_roots),
+            "standalone_mode": standalone_mode,
+        }
+
+    def _get_expected_model_types(self) -> str:
+        """Get expected model types string for error messages"""
+        return "VAE, Upscaler, TextEncoder, CLIPVision, Controlnet, or Other"
+
+    def _parse_specific_params(self, request: web.Request) -> Dict[str, Any]:
+        """Parse other-model-specific parameters (none in Phase 1)."""
+        return {}
+
+    async def get_roots_by_subtype(self, request: web.Request) -> web.Response:
+        """Return other-model roots grouped by sub_type.
+
+        Aggregates the per-folder_paths-key roots from config
+        (``text_encoders`` and the legacy ``clip`` key both land under
+        ``text_encoder``).
+        """
+        try:
+            roots_by_subtype: Dict[str, List[str]] = {}
+            for key, roots in (config.other_folder_roots or {}).items():
+                sub_type = OTHER_MODEL_FOLDER_SUBTYPES.get(key)
+                if not sub_type:
+                    continue
+                bucket = roots_by_subtype.setdefault(sub_type, [])
+                for root in roots:
+                    if root and root not in bucket:
+                        bucket.append(root)
+            return web.json_response(
+                {"success": True, "roots_by_subtype": roots_by_subtype}
+            )
+        except Exception as e:
+            logger.error(f"Error getting other roots by sub_type: {e}", exc_info=True)
+            return web.json_response(
+                {"success": False, "error": str(e)}, status=500
+            )
+
+    async def get_other_model_info(self, request: web.Request) -> web.Response:
+        """Get detailed information for a specific other model by name"""
+        try:
+            name = request.match_info.get('name', '')
+            model_info = await self.service.get_model_info_by_name(name)  # pyright: ignore[reportAttributeAccessIssue]
+
+            if model_info:
+                return web.json_response(model_info)
+            else:
+                return web.json_response({"error": "Model not found"}, status=404)
+
+        except Exception as e:
+            logger.error(f"Error in get_other_model_info: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)

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/agent/agent_service.py

@@ -19,16 +19,18 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
+import os
 import re
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
-import aiohttp
-
-import os
-
 from ...config import config
 from ..llm_service import LLMService
+from ..model_sources import (
+    get_source,
+    resolve_source_ref,
+    source_label,
+)
 from ..websocket_manager import ws_manager
 from .post_processor import PostProcessor
 from .skill_registry import SkillRegistry
@@ -267,14 +269,17 @@ class AgentService:
                 from ...metadata_ops import read_metadata
                 metadata = await read_metadata(model_path)
 
-                # Fast-fail: enrich_hf_metadata requires hf_url to have HF README context
-                if skill_name == "enrich_hf_metadata" and not metadata.get("hf_url", ""):
-                    logger.info(
-                        "[%s] SKIP %s — no hf_url in metadata",
-                        skill_name, model_filename,
-                    )
-                    skipped_count += 1
-                    skip_model = True
+                # Fast-fail: enrich_hf_metadata needs an external model source
+                # that exposes an accessible model card.
+                if skill_name == "enrich_hf_metadata":
+                    skip_reason = self._enrichment_skip_reason(metadata)
+                    if skip_reason:
+                        logger.info(
+                            "[%s] SKIP %s — %s",
+                            skill_name, model_filename, skip_reason,
+                        )
+                        skipped_count += 1
+                        skip_model = True
 
                 if not skip_model:
                     prompt_vars: Dict[str, Any] = {"model_path": model_path}
@@ -358,6 +363,28 @@ class AgentService:
     # Base model grouping (keeps the prompt compact)
     # ------------------------------------------------------------------
 
+    @staticmethod
+    def _enrichment_skip_reason(metadata: Dict[str, Any]) -> str:
+        """Return why ``enrich_hf_metadata`` cannot run, or ``""`` if it can.
+
+        Distinguishes the three cases the user can act on: no source linked,
+        a source we don't know, and a known source whose model card is not
+        reachable from the backend (TensorArt).
+        """
+
+        ref = resolve_source_ref(metadata)
+        if ref is None:
+            return "no model source linked (source_url missing)"
+        source = get_source(ref.platform)
+        if source is None:
+            return f"unsupported model source platform '{ref.platform}'"
+        if not source.supports_enrichment:
+            return (
+                f"{source.label} does not expose a model card to the backend; "
+                "AI metadata enrichment is not available for this source"
+            )
+        return ""
+
     @staticmethod
     def _format_base_models(models: List[str]) -> str:
         """Format the base model list as a flat, one-per-line list.
@@ -388,6 +415,14 @@ class AgentService:
         context: Dict[str, Any] = {
             "model_path": model_path,
             "model_basename": "",
+            # Canonical external-source variables
+            "source_url": "",
+            "source_id": "",
+            "source_platform": "",
+            "source_label": "",
+            "asset_base_url": "",
+            # Legacy Hugging Face aliases (kept so older prompt templates and
+            # third-party skills keep rendering)
             "hf_url": "",
             "repo": "",
             "readme_content": "",
@@ -407,17 +442,24 @@ class AgentService:
             "base_model": metadata.get("base_model", ""),
             "tags": metadata.get("tags", []),
             "modelDescription": metadata.get("modelDescription", ""),
-            "trainedWords": metadata.get("trainedWords", []),
             "sha256": (metadata.get("sha256") or "")[:16] + "..." if metadata.get("sha256") else "",
             "size": metadata.get("size", 0),
         }
 
-        hf_url = metadata.get("hf_url", "")
-        context["hf_url"] = hf_url
-        repo = self._extract_repo_from_url(hf_url) if hf_url else ""
-        context["repo"] = repo or ""
-        if repo:
-            readme = await self._fetch_readme(repo)
+        ref = resolve_source_ref(metadata)
+        if ref is not None:
+            context["source_url"] = ref.url
+            context["source_id"] = ref.source_id
+            context["source_platform"] = ref.platform
+            context["source_label"] = source_label(ref.platform, ref.platform)
+            if ref.platform == "huggingface":
+                context["hf_url"] = ref.url
+            context["repo"] = ref.source_id
+
+        source = get_source(ref.platform) if ref is not None else None
+        if ref is not None and source is not None and source.supports_enrichment:
+            context["asset_base_url"] = source.asset_base_url(ref.source_id)
+            readme = await source.fetch_model_card(ref.source_id)
             # Trim README to the section relevant to this model file
             # (collection repos often have multiple models in one README).
             if readme and raw_basename:
@@ -459,20 +501,14 @@ class AgentService:
 
     @staticmethod
     async def _fetch_readme(repo: str) -> str:
-        """Fetch README.md from HuggingFace (tries ``main``, then ``master``)."""
-        async with aiohttp.ClientSession(
-            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
-            timeout=aiohttp.ClientTimeout(total=30),
-        ) as session:
-            for branch in ("main", "master"):
-                url = f"https://huggingface.co/{repo}/raw/{branch}/README.md"
-                try:
-                    async with session.get(url) as resp:
-                        if resp.status == 200:
-                            return await resp.text()
-                except Exception as exc:
-                    logger.debug("Failed to fetch README from %s: %s", url, exc)
-        return ""
+        """Fetch a Hugging Face README (tries ``main``, then ``master``).
+
+        Kept for backward compatibility; new code should go through the
+        model-source registry so every supported site works.
+        """
+        from ..model_sources import HuggingFaceSource
+
+        return await HuggingFaceSource().fetch_model_card(repo)
 
     async def _emit_progress(
         self,

py/services/agent/post_processor.py

@@ -78,6 +78,7 @@ class PostProcessor:
             download_preview,
             refresh_cache,
         )
+        from ..model_sources import get_source, has_external_source, resolve_source_ref
         from .skills.enrich_hf_metadata.readme_processor import (
             convert_readme_to_html,
             extract_gallery_images,
@@ -85,14 +86,25 @@ class PostProcessor:
             extract_relevant_section,
             extract_simple_markdown_images,
             extract_html_img_tags,
-            extract_repo_from_hf_url,
         )
 
         updated_fields: List[str] = []
         preview_downloaded = False
 
-        # -- Determine whether this is an HF-sourced model -----------------
-        is_hf_model = not metadata.get("from_civitai", True)
+        # -- Determine whether this is an externally-sourced model ---------
+        # Key off the source fields directly: `from_civitai` records provenance
+        # and can be true for a model that is also linked to an external site
+        # (both sources coexist, see #1094), so it must not gate enrichment.
+        is_source_model = has_external_source(metadata)
+
+        source_ref = resolve_source_ref(metadata)
+        source = get_source(source_ref.platform) if source_ref else None
+        source_id = source_ref.source_id if source_ref else ""
+        asset_base_url = (
+            source.asset_base_url(source_id)
+            if source is not None and source_id
+            else None
+        )
 
         # -- Collect updates -----------------------------------------------
         updates: Dict[str, Any] = {}
@@ -100,7 +112,7 @@ class PostProcessor:
         # base_model
         new_base = (llm_output.get("base_model") or "").strip()
         current_base = metadata.get("base_model", "") or ""
-        if new_base and self._should_overwrite(current_base, is_hf_model):
+        if new_base and self._should_overwrite(current_base, is_source_model):
             updates["base_model"] = new_base
 
         # trigger words → civitai.trainedWords
@@ -112,7 +124,7 @@ class PostProcessor:
             trigger_words_empty = not cleaned
             current_civitai = metadata.get("civitai") or {}
             current_triggers = current_civitai.get("trainedWords") or []
-            if self._should_overwrite_list(current_triggers, is_hf_model):
+            if self._should_overwrite_list(current_triggers, is_source_model):
                 trig_civitai = dict(current_civitai)
                 if "civitai" in updates and isinstance(updates["civitai"], dict):
                     trig_civitai.update(updates["civitai"])
@@ -120,14 +132,14 @@ class PostProcessor:
                 updates["civitai"] = trig_civitai
 
         # modelDescription — from raw README content (converted to HTML)
-        if readme_content and is_hf_model:
+        if readme_content and is_source_model:
             converted = convert_readme_to_html(readme_content)
             if converted:
                 updates["modelDescription"] = converted
 
         # short_description → civitai.description (for "About this version")
         short_desc = (llm_output.get("short_description") or "").strip()
-        if short_desc and is_hf_model:
+        if short_desc and is_source_model:
             current_civitai = metadata.get("civitai") or {}
             desc_civitai = dict(current_civitai)
             if "civitai" in updates and isinstance(updates["civitai"], dict):
@@ -138,9 +150,8 @@ class PostProcessor:
         # gallery images → civitai.images (from YAML frontmatter widget entries
         # and Sample Gallery markdown tables in the README body)
         gallery_images: List[Dict[str, Any]] = []
-        if readme_content and is_hf_model:
-            hf_url = metadata.get("hf_url", "") or ""
-            repo = extract_repo_from_hf_url(hf_url)
+        if readme_content and is_source_model:
+            repo = source_id
             if repo:
                 rec_w = llm_output.get("recommended_width") or 0
                 rec_h = llm_output.get("recommended_height") or 0
@@ -149,6 +160,7 @@ class PostProcessor:
                 gallery = extract_gallery_images(
                     readme_content, repo,
                     default_width=rec_w, default_height=rec_h,
+                    base_url=asset_base_url,
                 )
 
                 # 2. Sample Gallery table images (markdown body), deduplicated
@@ -157,6 +169,7 @@ class PostProcessor:
                     readme_content, repo,
                     existing_urls=existing_urls,
                     default_width=rec_w, default_height=rec_h,
+                    base_url=asset_base_url,
                 )
                 existing_urls.update(img["url"] for img in table_images if img.get("url"))
 
@@ -165,6 +178,7 @@ class PostProcessor:
                     readme_content, repo,
                     existing_urls=existing_urls,
                     default_width=rec_w, default_height=rec_h,
+                    base_url=asset_base_url,
                 )
                 existing_urls.update(img["url"] for img in simple_images if img.get("url"))
 
@@ -173,6 +187,7 @@ class PostProcessor:
                     readme_content, repo,
                     existing_urls=existing_urls,
                     default_width=rec_w, default_height=rec_h,
+                    base_url=asset_base_url,
                 )
 
                 all_images = gallery + table_images + simple_images + html_images
@@ -190,7 +205,7 @@ class PostProcessor:
         if isinstance(new_tags, list) and new_tags:
             existing_tags = metadata.get("tags") or []
             merged = self._merge_tags(existing_tags, new_tags)
-            if len(merged) > len(existing_tags) or is_hf_model:
+            if len(merged) > len(existing_tags) or is_source_model:
                 updates["tags"] = merged
 
         # metadata_source & llm_enriched_at (always set)
@@ -219,7 +234,7 @@ class PostProcessor:
         # README, find the first gallery image from the *model-specific
         # section* of the README (not the repo-wide first image, which
         # belongs to a different model in collection repos).
-        if not preview_remote_url and readme_content and is_hf_model:
+        if not preview_remote_url and readme_content and is_source_model:
             model_basename = os.path.splitext(os.path.basename(model_path))[0]
             relevant_section = extract_relevant_section(
                 readme_content, model_basename,
@@ -276,16 +291,16 @@ class PostProcessor:
     # ------------------------------------------------------------------
 
     @staticmethod
-    def _should_overwrite(current_value: str, is_hf_model: bool) -> bool:
+    def _should_overwrite(current_value: str, is_source_model: bool) -> bool:
         """Return ``True`` when a scalar field should be overwritten."""
-        return is_hf_model or not current_value or current_value.lower() in (
+        return is_source_model or not current_value or current_value.lower() in (
             "", "unknown",
         )
 
     @staticmethod
-    def _should_overwrite_list(current_list: List[str], is_hf_model: bool) -> bool:
+    def _should_overwrite_list(current_list: List[str], is_source_model: bool) -> bool:
         """Return ``True`` when a list field should be overwritten."""
-        return is_hf_model or not current_list
+        return is_source_model or not current_list
 
     @staticmethod
     def _merge_tags(existing: List[str], new: List[str]) -> List[str]:

py/services/agent/skills/enrich_hf_metadata/prompt.md

@@ -1,20 +1,23 @@
 ---
 name: enrich_hf_metadata
-title: "Enrich Metadata from HuggingFace"
+title: "Enrich Metadata from Model Card"
 description: >
-  Parse the HuggingFace model card via LLM to extract description, trigger
-  words, base model, tags, and preview image URL.
+  Parse the model card (README) from HuggingFace, ModelScope, or any other
+  supported model site via LLM to extract description, trigger words, base
+  model, tags, and preview image URL.
 llm_required: true
 ---
 
-You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md).
+You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a model card (README).
 
 ## Model Information
 
-- **Repository**: {{hf_url}}
+- **Source site**: {{source_label}} ({{source_platform}})
+- **Model page**: {{source_url}}
 - **Model file path**: {{model_path}}
 - **Model filename**: {{model_basename}}
-- **Repository ID**: {{repo}}
+- **Repository ID**: {{source_id}}
+- **Repository raw-file base URL**: {{asset_base_url}}
 
 ## Current Metadata (may be incomplete)
 
@@ -39,7 +42,7 @@ name listed — do not invent aliases or modify variant suffixes.
 
 {{base_models}}
 
-## HuggingFace README Content
+## Model Card Content
 
 ```
 {{readme_content}}
@@ -92,7 +95,7 @@ The URL of the most suitable preview image from the README. Look for:
 - The YAML frontmatter `widget:` section (which often has `output.url` fields)
 - In collection repos: the sample images listed **under the section** for this specific model version
 - Generic `![alt](url)` in the body
-Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. If no suitable image is found, return an empty string.
+Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL from the repository raw-file base URL (`{{asset_base_url}}`) plus the relative path. If no suitable image is found, return an empty string.
 
 ### notes
 A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`.  Return empty string if the README has no useful usage info.
@@ -121,7 +124,7 @@ Your confidence level in the extracted data:
 
 ## Important: Handling Collection Repos (multiple model files)
 
-Many HuggingFace repos contain **multiple model files** in a single repository
+Many model repositories contain **multiple model files** in a single repository
 (e.g. a "LoRA collection" with different styles/characters in separate files).
 
 The model file currently being enriched is: **`{{model_basename}}`**

py/services/agent/skills/enrich_hf_metadata/readme_processor.py

@@ -1,8 +1,15 @@
-"""HF README processing for the ``enrich_hf_metadata`` skill.
+"""Model card (README) processing for the ``enrich_hf_metadata`` skill.
 
 Provides README cleaning for LLM injection, gallery/image extraction from
 multiple formats (YAML widget, markdown, HTML ``<img>``, gallery tables),
 and section-based README trimming for collection repos.
+
+The extractors default to Hugging Face asset URLs, but every one of them
+accepts an explicit ``base_url`` so the same parsing works for any model
+source (ModelScope, ...).  See :mod:`py.services.model_sources`.
+
+This module deliberately has no package-relative imports: it is also loaded
+standalone by the README-processing test harness.
 """
 
 from __future__ import annotations
@@ -15,12 +22,25 @@ from typing import Any, List, Tuple
 _REPO_URL_PATTERN = re.compile(r"https?://huggingface\.co/([^/]+/[^/]+)")
 
 
+def resolve_asset_base_url(repo: str, base_url: str | None = None) -> str:
+    """Return the base URL used to resolve repository-relative assets.
+
+    Falls back to the historical Hugging Face layout when *base_url* is not
+    supplied, so existing callers keep their behaviour.
+    """
+
+    if base_url:
+        return base_url.rstrip("/")
+    return f"https://huggingface.co/{repo}/resolve/main"
+
+
 def extract_simple_markdown_images(
     markdown_text: str,
     repo: str,
     existing_urls: set[str] | None = None,
     default_width: int = 512,
     default_height: int = 512,
+    base_url: str | None = None,
 ) -> list[dict[str, Any]]:
     """Extract standalone markdown images from the README body.
 
@@ -32,10 +52,10 @@ def extract_simple_markdown_images(
     Returns a list of dicts in the same ``civitai.images`` format as
     :func:`extract_gallery_images`.
     """
-    if not markdown_text or not repo:
+    if not markdown_text or not (repo or base_url):
         return []
 
-    base_url = f"https://huggingface.co/{repo}/resolve/main"
+    base_url = resolve_asset_base_url(repo, base_url)
     images: list[dict[str, Any]] = []
     seen_urls: set[str] = set(existing_urls) if existing_urls else set()
 
@@ -89,20 +109,21 @@ def extract_html_img_tags(
     existing_urls: set[str] | None = None,
     default_width: int = 512,
     default_height: int = 512,
+    base_url: str | None = None,
 ) -> list[dict[str, Any]]:
     """Extract image URLs from HTML ``<img src=\"...\">`` tags in the README.
 
     Many HF collection repos (e.g. ``deadman44/Z-Image_LoRA``) use raw HTML
     ``<img>`` tags exclusively for their sample images, with no markdown
     ``![]()`` equivalents.  This function finds those tags and constructs
-    resolvable HF URLs.
+    resolvable URLs.
 
     Returns a list of dicts in the ``civitai.images`` format.
     """
-    if not markdown_text or not repo:
+    if not markdown_text or not (repo or base_url):
         return []
 
-    base_url = f"https://huggingface.co/{repo}/resolve/main"
+    base_url = resolve_asset_base_url(repo, base_url)
     images: list[dict[str, Any]] = []
     seen_urls: set[str] = set(existing_urls) if existing_urls else set()
 
@@ -166,7 +187,7 @@ def extract_html_img_tags(
 
 def extract_repo_from_hf_url(hf_url: str) -> str:
     """Extract ``user/repo`` from a HuggingFace URL."""
-    m = _REPO_URL_PATTERN.match(hf_url)
+    m = _REPO_URL_PATTERN.match(hf_url or "")
     return m.group(1) if m else ""
 
 
@@ -175,21 +196,23 @@ def extract_gallery_images(
     repo: str,
     default_width: int = 512,
     default_height: int = 512,
+    base_url: str | None = None,
 ) -> List[dict[str, Any]]:
-    """Extract widget/gallery images from the YAML frontmatter of a HF README.
+    """Extract widget/gallery images from the YAML frontmatter of a README.
 
     Args:
         markdown_text: Raw README content.
-        repo: HF repo identifier (``user/repo``).
+        repo: Repository identifier (``user/repo``).
         default_width: Fallback width when the README provides no dimension.
         default_height: Fallback height when the README provides no dimension.
+        base_url: Overrides the asset base URL (defaults to Hugging Face).
 
     Returns a list of dicts compatible with the ``civitai.images`` metadata
-    format, each containing ``url`` (absolute HF URL), ``meta.prompt``,
+    format, each containing ``url`` (absolute), ``meta.prompt``,
     ``width``, ``height``, and ``type``.  Returns an empty list when no
     widget entries are found or when *repo* is empty.
     """
-    if not markdown_text or not repo:
+    if not markdown_text or not (repo or base_url):
         return []
 
     frontmatter = _extract_frontmatter(markdown_text)
@@ -197,7 +220,7 @@ def extract_gallery_images(
         return []
 
     images: List[dict[str, Any]] = []
-    base_url = f"https://huggingface.co/{repo}/resolve/main"
+    base_url = resolve_asset_base_url(repo, base_url)
     w = default_width or 512
     h = default_height or 512
 
@@ -279,10 +302,11 @@ def extract_gallery_table_images(
     existing_urls: set[str] | None = None,
     default_width: int = 512,
     default_height: int = 512,
+    base_url: str | None = None,
 ) -> list[dict[str, Any]]:
     """Extract images from ``| Preview | Prompt |`` markdown gallery tables.
 
-    Many HF READMEs include a sample-gallery table in the body (outside
+    Many READMEs include a sample-gallery table in the body (outside
     the YAML frontmatter) that shows generation examples with their
     prompts.  This function parses those tables and merges results with
     the widget-sourced images from :func:`extract_gallery_images`.
@@ -291,10 +315,10 @@ def extract_gallery_table_images(
     :func:`extract_gallery_images`.  Already-seen URLs (from *existing_urls*)
     are skipped.
     """
-    if not markdown_text or not repo:
+    if not markdown_text or not (repo or base_url):
         return []
 
-    base_url = f"https://huggingface.co/{repo}/resolve/main"
+    base_url = resolve_asset_base_url(repo, base_url)
     images: list[dict[str, Any]] = []
     seen_urls: set[str] = set(existing_urls) if existing_urls else set()
     lines = markdown_text.split("\n")

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 +156,16 @@ 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``.
+
+        Cancellation never leaks daemon transfers: the gid is tracked in
+        ``_transfers`` before any post-``addUri`` await, and a gid accepted
+        by the daemon while the caller is being cancelled is removed again
+        before the ``CancelledError`` propagates.
         """
 
         await self._ensure_process()
@@ -201,14 +220,47 @@ 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"
 
                 await asyncio.sleep(self._poll_interval)
         finally:
             current = self._transfers.get(download_id)
-            if current is not None and current.gid == transfer.gid:
+            if (
+                transfer is not None
+                and current is not None
+                and current.gid == transfer.gid
+            ):
                 self._transfers.pop(download_id, None)
 
     async def _get_status_with_retry(
@@ -217,8 +269,9 @@ 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
@@ -295,21 +348,43 @@ class Aria2Downloader:
             resolved_url != url,
         )
 
+        # Shield the addUri RPC from cancellation: the daemon may accept the
+        # download even when the caller is cancelled while the request is in
+        # flight.  On cancellation, wait for the RPC result so the freshly
+        # created gid can be removed instead of leaking an untracked
+        # download that keeps running in the daemon.
+        add_task = asyncio.ensure_future(
+            self._rpc_call("aria2.addUri", [[resolved_url], options])
+        )
         try:
-            gid = await self._rpc_call("aria2.addUri", [[resolved_url], options])
+            gid = await asyncio.shield(add_task)
+        except asyncio.CancelledError:
+            leaked_gid: Any = None
+            try:
+                leaked_gid = await add_task
+            except Exception:
+                leaked_gid = None
+            if isinstance(leaked_gid, str) and leaked_gid:
+                logger.info(
+                    "Removing aria2 gid %s accepted while download %s was "
+                    "being cancelled",
+                    leaked_gid,
+                    download_id,
+                )
+                try:
+                    await self._rpc_call("aria2.forceRemove", [leaked_gid])
+                except Exception as exc:
+                    logger.warning(
+                        "Failed to remove leaked aria2 gid %s for download %s: %s",
+                        leaked_gid,
+                        download_id,
+                        exc,
+                    )
+            raise
         except Exception as exc:
             raise Aria2Error(f"Failed to schedule aria2 download: {exc}") from exc
 
         logger.debug("aria2 accepted download %s with gid %s", download_id, gid)
-        await self._state_store.upsert(
-            download_id,
-            {
-                "gid": gid,
-                "save_path": save_path,
-                "status": "downloading",
-                "url": url,
-            },
-        )
         return gid
 
     async def _register_transfer(
@@ -328,11 +403,56 @@ class Aria2Downloader:
             headers=headers,
         )
         transfer = Aria2Transfer(gid=gid, save_path=os.path.abspath(save_path))
+        # Register the transfer before any further await: once the daemon
+        # holds the gid, cancel_download() must be able to find it.  An await
+        # in between would open a window where a concurrent cancel reports
+        # "Download task not found" and the daemon keeps downloading
+        # untracked.
         self._transfers[download_id] = transfer
+        try:
+            await self._state_store.upsert(
+                download_id,
+                {
+                    "gid": gid,
+                    "save_path": transfer.save_path,
+                    "status": "downloading",
+                    "url": url,
+                },
+            )
+        except asyncio.CancelledError:
+            # The task was cancelled while persisting state and the
+            # coordinator's cancel ran before the transfer was registered
+            # above.  Remove the daemon transfer unless it was deliberately
+            # paused (skip_download preserves paused transfers for resume).
+            status = None
+            try:
+                status = await self.get_status(download_id)
+            except Exception:
+                status = None
+            if status is not None and status.get("status") != "paused":
+                try:
+                    await self._rpc_call("aria2.forceRemove", [gid])
+                except Exception as exc:
+                    logger.warning(
+                        "Failed to remove aria2 gid %s for cancelled download %s: %s",
+                        gid,
+                        download_id,
+                        exc,
+                    )
+                current = self._transfers.get(download_id)
+                if current is not None and current.gid == gid:
+                    self._transfers.pop(download_id, None)
+            raise
         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 +468,17 @@ 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 +496,9 @@ 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 +565,19 @@ 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 +867,9 @@ 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 +900,10 @@ 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 +918,7 @@ 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

@@ -7,7 +7,7 @@ import logging
 import os
 import time
 
-from ..utils.constants import VALID_LORA_SUB_TYPES, VALID_CHECKPOINT_SUB_TYPES
+from ..utils.constants import VALID_LORA_SUB_TYPES, VALID_CHECKPOINT_SUB_TYPES, VALID_OTHER_SUB_TYPES
 from ..utils.models import BaseModelMetadata
 from ..utils.metadata_manager import MetadataManager
 from ..utils.usage_stats import UsageStats
@@ -21,6 +21,7 @@ from .model_query import (
     resolve_sub_type,
 )
 from .settings_manager import get_settings_manager
+from .model_sources import source_group_key
 from ..utils.civitai_utils import build_civitai_model_page_url
 
 logger = logging.getLogger(__name__)
@@ -742,29 +743,32 @@ class BaseModelService(ABC):
     @staticmethod
     def _extract_hf_group_key(item: Dict[str, Any]) -> Optional[str]:
         """Extract `hf:{owner}/{repo}` from item's ``hf_url``, or None."""
-        hf_url = item.get("hf_url") if isinstance(item, dict) else None
-        if not hf_url or not isinstance(hf_url, str):
-            return None
-        m = re.match(
-            r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url.strip()
-        )
-        if not m:
-            return None
-        return f"hf:{m.group(1)}"
+        key = BaseModelService._extract_source_group_key(item)
+        return key if key and key.startswith("hf:") else None
+
+    @staticmethod
+    def _extract_source_group_key(item: Dict[str, Any]) -> Optional[str]:
+        """Return the external-source group key for *item*, or None.
+
+        Hugging Face keeps the historical ``hf:{owner}/{repo}`` shape; other
+        platforms use their own short prefix (``ms:`` / ``ta:``).
+        """
+        return source_group_key(item)
 
     @staticmethod
     def _extract_group_key(item: Dict[str, Any]) -> Union[int, str, None]:
-        """Return the group identity key: CivitAI modelId (int) or HF repo (str).
+        """Return the group identity key.
 
         Preference order:
         1. CivitAI ``modelId`` (int)
-        2. HF repo identity ``hf:{owner}/{repo}`` (str)
+        2. External model source identity, e.g. ``hf:{owner}/{repo}``,
+           ``ms:{owner}/{repo}``, ``ta:{model_id}`` (str)
         3. ``None`` (no known grouping source)
         """
         mid = BaseModelService._extract_model_id(item)
         if mid is not None:
             return mid
-        return BaseModelService._extract_hf_group_key(item)
+        return BaseModelService._extract_source_group_key(item)
 
     @staticmethod
     def _extract_model_id(item: Dict[str, Any]) -> Optional[int]:
@@ -904,6 +908,11 @@ class BaseModelService(ABC):
                 and normalized_type not in VALID_CHECKPOINT_SUB_TYPES
             ):
                 continue
+            if (
+                self.model_type == "other"
+                and normalized_type not in VALID_OTHER_SUB_TYPES
+            ):
+                continue
 
             type_counts[normalized_type] = type_counts.get(normalized_type, 0) + 1
 
@@ -972,14 +981,25 @@ 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 +1021,7 @@ 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 +1031,7 @@ 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 +1304,27 @@ 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 +1434,13 @@ 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_scanner.py

@@ -410,6 +410,10 @@ class CheckpointScanner(ModelScanner):
 
         return None
 
+    def resolve_sub_type_for_path(self, file_path: Optional[str]) -> Optional[str]:
+        """Resolve sub_type from the configured root that contains the file."""
+        return self._resolve_sub_type(self._find_root_for_file(file_path))
+
     def adjust_metadata(self, metadata, file_path, root_path):
         """Adjust metadata during scanning to set sub_type."""
         sub_type = self._resolve_sub_type(root_path)
@@ -419,9 +423,7 @@ class CheckpointScanner(ModelScanner):
 
     def adjust_cached_entry(self, entry: Dict[str, Any]) -> Dict[str, Any]:
         """Adjust entries loaded from the persisted cache to ensure sub_type is set."""
-        sub_type = self._resolve_sub_type(
-            self._find_root_for_file(entry.get("file_path"))
-        )
+        sub_type = self.resolve_sub_type_for_path(entry.get("file_path"))
         if sub_type:
             entry["sub_type"] = sub_type
         return entry

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", ""),
@@ -66,6 +67,8 @@ class CheckpointService(BaseModelService):
             "civitai": self.filter_civitai_data(model_data.get("civitai", {}), minimal=True),
             "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
             "version_count": model_data.get("version_count"),
+            "source_platform": model_data.get("source_platform", ""),
+            "source_url": model_data.get("source_url", ""),
             "hf_url": model_data.get("hf_url", ""),
         }
     

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
 
@@ -87,7 +87,9 @@ class DownloadCoordinator:
             progress_callback=progress_callback,
             download_id=download_id,
             source=payload.get("source"),
-            file_params=payload.get("file_params"),
+            # Normalize falsy file_params (e.g. {}) to None so download gates
+            # treat it as "no explicit file selection" (#1058).
+            file_params=payload.get("file_params") or None,
         )
 
         result["download_id"] = download_id
@@ -184,6 +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
@@ -12,16 +13,22 @@ 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.models import (
+    LoraMetadata,
+    CheckpointMetadata,
+    EmbeddingMetadata,
+    OtherModelMetadata,
+)
 from ..utils.constants import (
     CARD_PREVIEW_WIDTH,
-    DIFFUSION_MODEL_BASE_MODELS,
     MODEL_WEIGHT_FILE_TYPES,
     SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS,
     VALID_LORA_TYPES,
+    VALID_OTHER_CIVITAI_TYPES,
 )
 from ..utils.civitai_utils import normalize_civitai_download_url, rewrite_preview_url
 from ..utils.file_utils import calculate_sha256, calculate_autov3
@@ -30,9 +37,11 @@ from ..utils.utils import sanitize_folder_name
 from ..utils.exif_utils import ExifUtils
 from ..utils.metadata_manager import MetadataManager
 from .service_registry import ServiceRegistry
+from .download_routing import is_diffusion_model_download, resolve_other_download_sub_type
 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 +62,12 @@ 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 +97,11 @@ 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:
@@ -213,6 +233,171 @@ class DownloadManager:
             )
             return False
 
+    async def _get_scanner_for_model_type(self, model_type: str):
+        """Return the scanner responsible for the given model type.
+
+        Every supported type resolves explicitly — an unknown type must never
+        fall through to the lora scanner (an "other" download would silently
+        dedupe against the lora library).
+        """
+        if model_type == "checkpoint":
+            return await self._get_checkpoint_scanner()
+        if model_type == "embedding":
+            return await ServiceRegistry.get_embedding_scanner()
+        if model_type == "other":
+            return await ServiceRegistry.get_other_scanner()
+        if model_type == "lora":
+            return await self._get_lora_scanner()
+        raise ValueError(f'Unknown model type "{model_type}"')
+
+    @staticmethod
+    def _resolve_target_file(
+        files: Any, file_params: Dict[str, Any] | None
+    ) -> Optional[Dict[str, Any]]:
+        """Resolve the target file within a version's file list from file_params.
+
+        Shared by the existence gate and the actual file selection so both
+        always agree on which file a download refers to (#1058). Returns None
+        when file_params is None or no file matches.
+        """
+        if not file_params or not isinstance(files, list):
+            return None
+
+        target_file_id = file_params.get("id")
+        target_type = file_params.get("type", "Model")
+        target_format = file_params.get("format")
+        target_size = file_params.get("size")
+        target_fp = file_params.get("fp")
+        is_primary = file_params.get("isPrimary", False)
+
+        logger.debug(
+            "[download] file_params received: id=%s, type=%s, format=%s, size=%s, fp=%s, "
+            "isPrimary=%s, total_files=%d",
+            target_file_id, target_type, target_format, target_size, target_fp,
+            is_primary, len(files),
+        )
+
+        file_info: Optional[Dict[str, Any]] = None
+
+        if target_file_id:
+            target_id_str = str(target_file_id)
+            for f in files:
+                if not isinstance(f, dict):
+                    continue
+                f_id = f.get("id")
+                if str(f_id) == target_id_str:
+                    file_info = f
+                    logger.debug(
+                        "[download] MATCH by ID: id=%s name='%s'",
+                        f_id, f.get("name"),
+                    )
+                    break
+            if not file_info:
+                logger.debug("[download] No file found with id=%s", target_file_id)
+
+        elif is_primary:
+            file_info = next(
+                (
+                    f
+                    for f in files
+                    if isinstance(f, dict)
+                    and f.get("primary")
+                    and f.get("type") in MODEL_WEIGHT_FILE_TYPES
+                ),
+                None,
+            )
+        else:
+            # Lenient metadata match: only compare fields present on both sides
+            for f in files:
+                if not isinstance(f, dict):
+                    continue
+                f_type = f.get("type", "")
+                if f_type != target_type:
+                    continue
+
+                f_meta = f.get("metadata", {})
+                f_format = f_meta.get("format") or f.get("format")
+                f_size = f_meta.get("size") or f.get("size")
+                f_fp = f_meta.get("fp") or f.get("fp")
+
+                if target_format and f_format != target_format:
+                    continue
+                if target_size and f_size and f_size != target_size:
+                    continue
+                if target_fp and f_fp and f_fp != target_fp:
+                    continue
+
+                file_info = f
+                break
+
+        return file_info
+
+    async def _find_local_file_entry(
+        self,
+        model_type: str,
+        model_version_id: int,
+        target_file: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        """Find a local library entry for a specific file of a model version.
+
+        Matches per design rule D2 (#1058): SHA256 is only compared when both
+        sides carry a non-empty hash; otherwise fall back to (extension-less)
+        file name equality. Never let two empty hashes compare equal.
+        """
+        try:
+            normalized_version_id = int(model_version_id)
+        except (TypeError, ValueError):
+            return None
+
+        try:
+            scanner = await self._get_scanner_for_model_type(model_type)
+            cache = await scanner.get_cached_data()
+        except Exception as exc:
+            logger.debug(
+                "Failed to scan local entries for version %s file check: %s",
+                model_version_id,
+                exc,
+            )
+            return None
+
+        raw_data = getattr(cache, "raw_data", None) if cache else None
+        if not raw_data:
+            return None
+
+        target_hash = str(
+            (target_file.get("hashes") or {}).get("SHA256") or ""
+        ).strip().lower()
+        target_name = str(target_file.get("name") or "").strip()
+        target_base = os.path.splitext(target_name)[0] if target_name else ""
+
+        for item in raw_data:
+            if not isinstance(item, dict):
+                continue
+            civitai_data = item.get("civitai")
+            if not isinstance(civitai_data, dict):
+                continue
+            try:
+                item_version_id = int(civitai_data.get("id"))
+            except (TypeError, ValueError):
+                continue
+            if item_version_id != normalized_version_id:
+                continue
+
+            local_hash = str(item.get("sha256") or "").strip().lower()
+            if target_hash and local_hash:
+                if local_hash == target_hash:
+                    return item
+                # Both sides carry hashes that differ: this is a different
+                # file of the same version — do not fall back to name match.
+                continue
+
+            if target_base:
+                local_name = str(item.get("file_name") or "").strip()
+                if local_name == target_base:
+                    return item
+
+        return None
+
     async def download_from_civitai(
         self,
         model_id: int | None = None,
@@ -242,6 +427,10 @@ class DownloadManager:
         Returns:
             Dict with download result
         """
+        # Normalize falsy file_params (e.g. an empty dict from API JSON
+        # parsing) to None so gate conditions behave consistently (#1058).
+        file_params = file_params or None
+
         logger.debug(
             "[download] download_from_civitai called: model_id=%s, model_version_id=%s, "
             "source=%s, file_params=%s",
@@ -544,6 +733,47 @@ 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,
@@ -715,6 +945,42 @@ 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,
         *,
@@ -727,6 +993,8 @@ class DownloadManager:
             return CheckpointMetadata.from_civitai_info(version_info, file_info, save_path)
         if model_type == "embedding":
             return EmbeddingMetadata.from_civitai_info(version_info, file_info, save_path)
+        if model_type == "other":
+            return OtherModelMetadata.from_civitai_info(version_info, file_info, save_path)
         return LoraMetadata.from_civitai_info(version_info, file_info, save_path)
 
     def _resolve_save_path_from_persisted_record(self, record: Dict[str, Any]) -> Optional[str]:
@@ -816,6 +1084,7 @@ class DownloadManager:
                                 version_info,
                                 record.get("model_version_id"),
                                 record.get("save_path") or record.get("file_path"),
+                                file_info=file_info,
                             )
                             await self._sync_downloaded_version(
                                 model_type,
@@ -939,6 +1208,11 @@ 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 (
@@ -1047,6 +1321,24 @@ 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
@@ -1152,13 +1444,18 @@ class DownloadManager:
         use_save_dir_as_root: bool = False,
     ) -> Dict[str, Any]:
         """Wrapper for original download_from_civitai implementation"""
+        file_params = file_params or None
         try:
-            # Check if model version already exists in library
-            if model_version_id is not None:
+            # Check if model version already exists in library.
+            # With an explicit file selection (file_params) the version-level
+            # check is deferred until after the metadata fetch, when the target
+            # file can be resolved and checked individually (#1058).
+            if model_version_id is not None and file_params is None:
                 # Check both scanners
                 lora_scanner = await self._get_lora_scanner()
                 checkpoint_scanner = await self._get_checkpoint_scanner()
                 embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+                other_scanner = await ServiceRegistry.get_other_scanner()
 
                 # Check lora scanner first
                 if await lora_scanner.check_model_version_exists(model_version_id):
@@ -1183,6 +1480,13 @@ class DownloadManager:
                         "error": "Model version already exists in embedding library",
                     }
 
+                # Check other scanner
+                if await other_scanner.check_model_version_exists(model_version_id):
+                    return {
+                        "success": False,
+                        "error": "Model version already exists in other library",
+                    }
+
             # Use CivArchive provider directly when source is 'civarchive'
             # This prioritizes CivArchive metadata (with mirror availability info) over Civitai
             if source == "civarchive":
@@ -1221,6 +1525,20 @@ class DownloadManager:
                 model_type = "lora"
             elif model_type_from_info == "textualinversion":
                 model_type = "embedding"
+            elif model_type_from_info in VALID_OTHER_CIVITAI_TYPES:
+                if not get_settings_manager().is_other_models_enabled():
+                    return {
+                        "success": False,
+                        "error": (
+                            "Other Models management is disabled. Enable it in "
+                            "Settings > Library before downloading VAE, upscaler, "
+                            "text encoder or CLIP files."
+                        ),
+                        # Machine-readable failure code consumed by the companion
+                        # browser extension (docs/other-models-support.md C4).
+                        "reason": "other_models_disabled",
+                    }
+                model_type = "other"
             else:
                 return {
                     "success": False,
@@ -1235,8 +1553,26 @@ class DownloadManager:
                 except (TypeError, ValueError):
                     resolved_version_id = None
 
+            # Resolve the explicitly selected file (if any) up front so the
+            # existence gates and the actual file selection below always agree
+            # on the target file (#1058).
+            target_file: Optional[Dict[str, Any]] = None
+            if file_params is not None:
+                target_file = self._resolve_target_file(
+                    version_info.get("files") or [], file_params
+                )
+                if target_file is None:
+                    logger.warning(
+                        "[download] file_params provided but no file matched; "
+                        "falling back to version-level checks and primary file "
+                        "selection (model_version_id=%s)",
+                        resolved_version_id,
+                    )
+            explicit_file = target_file is not None
+
             if (
-                get_settings_manager().get_skip_previously_downloaded_model_versions()
+                not explicit_file
+                and get_settings_manager().get_skip_previously_downloaded_model_versions()
                 and resolved_version_id is not None
                 and await self._has_been_downloaded(model_type, resolved_version_id)
             ):
@@ -1324,31 +1660,46 @@ class DownloadManager:
                 }
 
             # Check if this checkpoint should be treated as a diffusion model
-            # Priority: (1) any file has type "UNet" or "Diffusion Model",
-            #            (2) baseModel is in DIFFUSION_MODEL_BASE_MODELS
-            is_diffusion_model = False
-            if model_type == "checkpoint":
-                # Check file types first (more direct signal from CivitAI)
-                version_files = version_info.get("files", [])
-                for f in version_files:
-                    f_type = f.get("type", "")
-                    if f_type in ("UNet", "Diffusion Model"):
-                        is_diffusion_model = True
-                        logger.info(
-                            f"File type '{f_type}' detected, routing checkpoint to unet folder"
-                        )
-                        break
+            # (shared with the download routing endpoint so the UI location
+            # step and the actual download agree on the target roots).
+            is_diffusion_model = is_diffusion_model_download(
+                model_type,
+                file_types=(f.get("type", "") for f in version_info.get("files", [])),
+                base_model=base_model_value,
+            )
 
-                # Fallback to baseModel name check
-                if not is_diffusion_model and base_model_value in DIFFUSION_MODEL_BASE_MODELS:
-                    is_diffusion_model = True
-                    logger.info(
-                        f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
+            # Existence check after the metadata fetch (#1058):
+            # - An explicit file selection only blocks when THIS file is
+            #   already in the library; other files of the same version
+            #   remain downloadable.
+            # - Without file_params (or when file_params failed to resolve),
+            #   keep version-level protection. The case "model_version_id
+            #   given + no file_params" was already covered by the early
+            #   gate above.
+            if explicit_file and resolved_version_id is not None:
+                existing_entry = await self._find_local_file_entry(
+                    model_type, resolved_version_id, target_file
+                )
+                if existing_entry is not None:
+                    error_message = (
+                        f"File '{target_file.get('name')}' from model version "
+                        f"{resolved_version_id} already exists in {model_type} library"
                     )
-
-            # Case 2: model_version_id was None, check after getting version_info
-            if model_version_id is None:
-                version_id = version_info.get("id")
+                    logger.info("[download] %s", error_message)
+                    return {"success": False, "error": error_message}
+                logger.info(
+                    "[download] File '%s' of model version %s not in %s library — "
+                    "download allowed (other files of this version may exist locally)",
+                    target_file.get("name"), resolved_version_id, model_type,
+                )
+            elif file_params is not None or model_version_id is None:
+                # Case 2: model_version_id was None, or file_params did not
+                # resolve to a concrete file — check at version level.
+                version_id = (
+                    resolved_version_id
+                    if resolved_version_id is not None
+                    else version_info.get("id")
+                )
 
                 if model_type == "lora":
                     # Check lora scanner
@@ -1374,6 +1725,13 @@ class DownloadManager:
                             "success": False,
                             "error": "Model version already exists in embedding library",
                         }
+                elif model_type == "other":
+                    other_scanner = await ServiceRegistry.get_other_scanner()
+                    if await other_scanner.check_model_version_exists(version_id):
+                        return {
+                            "success": False,
+                            "error": "Model version already exists in other library",
+                        }
 
             # Handle use_default_paths
             if use_default_paths:
@@ -1413,6 +1771,60 @@ class DownloadManager:
                                 "error": "Default embedding root path not set in settings",
                             }
                         save_dir = default_path
+                    elif model_type == "other":
+                        other_sub_type = resolve_other_download_sub_type(
+                            model_type_from_info,
+                            file_types=(
+                                f.get("type", "")
+                                for f in version_info.get("files", [])
+                                if isinstance(f, dict)
+                            ),
+                            selected_file_type=(
+                                target_file.get("type") if explicit_file else None
+                            ),
+                        )
+                        default_other_roots = (
+                            settings_manager.get("default_other_roots") or {}
+                        )
+                        if other_sub_type and not settings_manager.is_other_sub_type_enabled(
+                            other_sub_type
+                        ):
+                            return {
+                                "success": False,
+                                "error": (
+                                    f"Other-model sub-type '{other_sub_type}' is "
+                                    f"disabled in settings. Please pick a destination "
+                                    f"folder explicitly instead of using default paths."
+                                ),
+                                "reason": "other_sub_type_disabled",
+                            }
+                        default_path = (
+                            default_other_roots.get(other_sub_type)
+                            if other_sub_type
+                            else None
+                        )
+                        if not isinstance(default_path, str) or not default_path:
+                            if other_sub_type:
+                                detail = (
+                                    f"No default root configured for other-model "
+                                    f"sub-type '{other_sub_type}'"
+                                )
+                                reason = "other_no_default_root"
+                            else:
+                                detail = (
+                                    "Could not determine the other-model sub-type "
+                                    "from the model metadata"
+                                )
+                                reason = "other_sub_type_undecidable"
+                            return {
+                                "success": False,
+                                "error": (
+                                    f"{detail}. Please pick a destination folder "
+                                    f"explicitly instead of using default paths."
+                                ),
+                                "reason": reason,
+                            }
+                        save_dir = default_path
 
                 # Calculate relative path using template
                 relative_path = self._calculate_relative_path(version_info, model_type)
@@ -1495,73 +1907,16 @@ class DownloadManager:
             files = version_info.get("files", [])
             file_info = None
 
-            # If file_params is provided, try to find matching file
-            if file_params and model_version_id:
-                target_file_id = file_params.get("id")
-                target_type = file_params.get("type", "Model")
-                target_format = file_params.get("format")
-                target_size = file_params.get("size")
-                target_fp = file_params.get("fp")
-                is_primary = file_params.get("isPrimary", False)
-
-                logger.debug(
-                    "[download] file_params received: id=%s, type=%s, format=%s, size=%s, fp=%s, isPrimary=%s, "
-                    "model_version_id=%s, total_files=%d",
-                    target_file_id, target_type, target_format, target_size, target_fp, is_primary,
-                    model_version_id, len(files),
-                )
-
-                if target_file_id:
-                    target_id_str = str(target_file_id)
-                    for f in files:
-                        f_id = f.get("id")
-                        if str(f_id) == target_id_str:
-                            file_info = f
-                            logger.debug(
-                                "[download] MATCH by ID: id=%s name='%s'",
-                                f_id, f.get("name"),
-                            )
-                            break
-                    if not file_info:
-                        logger.debug("[download] No file found with id=%s", target_file_id)
-
-                elif is_primary:
-                    file_info = next(
-                        (
-                            f
-                            for f in files
-                            if f.get("primary")
-                            and f.get("type") in MODEL_WEIGHT_FILE_TYPES
-                        ),
-                        None,
-                    )
-                else:
-                    # Lenient metadata match: only compare fields present on both sides
-                    for f in files:
-                        f_type = f.get("type", "")
-                        if f_type != target_type:
-                            continue
-
-                        f_meta = f.get("metadata", {})
-                        f_format = f_meta.get("format") or f.get("format")
-                        f_size = f_meta.get("size") or f.get("size")
-                        f_fp = f_meta.get("fp") or f.get("fp")
-
-                        if target_format and f_format != target_format:
-                            continue
-                        if target_size and f_size and f_size != target_size:
-                            continue
-                        if target_fp and f_fp and f_fp != target_fp:
-                            continue
-
-                        file_info = f
-                        break
-
+            # If file_params is provided, reuse the file resolved right after
+            # the metadata fetch so the existence gate and this selection
+            # always agree on the target file (#1058).
+            if file_params is not None:
+                file_info = target_file
                 if not file_info:
                     logger.debug(
                         "[download] No match found via file_params — falling back to primary file lookup",
                     )
-            elif not file_params:
+            else:
                 logger.debug(
                     "[download] No file_params provided (null/None) — will use primary file lookup. "
                     "model_version_id=%s, total_files=%d",
@@ -1626,6 +1981,24 @@ 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:
@@ -1648,6 +2021,11 @@ class DownloadManager:
                     version_info, file_info, save_path
                 )
                 logger.info(f"Creating EmbeddingMetadata for {file_name}")
+            elif model_type == "other":
+                metadata = OtherModelMetadata.from_civitai_info(
+                    version_info, file_info, save_path
+                )
+                logger.info(f"Creating OtherModelMetadata for {file_name}")
             else:
                 return {
                     "success": False,
@@ -1706,6 +2084,7 @@ class DownloadManager:
                     version_info,
                     model_version_id,
                     save_path,
+                    file_info=file_info,
                 )
                 await self._sync_downloaded_version(
                     model_type,
@@ -1748,6 +2127,7 @@ class DownloadManager:
         version_info: Dict[str, Any],
         fallback_version_id=None,
         file_path: str | None = None,
+        file_info: Dict[str, Any] | None = None,
     ) -> None:
         try:
             history_service = await ServiceRegistry.get_downloaded_version_history_service()
@@ -1773,6 +2153,15 @@ class DownloadManager:
         if version_id is None:
             version_id = fallback_version_id
 
+        # Per-file identity for multi-file versions (#1058)
+        file_id = None
+        file_name = None
+        if isinstance(file_info, dict):
+            file_id = file_info.get("id")
+            raw_file_name = file_info.get("name")
+            if isinstance(raw_file_name, str) and raw_file_name.strip():
+                file_name = raw_file_name.strip()
+
         try:
             await history_service.mark_downloaded(
                 model_type,
@@ -1780,6 +2169,8 @@ class DownloadManager:
                 model_id=int(cast(Any, resolved_model_id)) if resolved_model_id is not None else None,
                 source="download",
                 file_path=file_path,
+                file_id=file_id,
+                file_name=file_name,
             )
         except (TypeError, ValueError):
             logger.debug(
@@ -1847,6 +2238,8 @@ class DownloadManager:
                 scanner = await self._get_checkpoint_scanner()
             elif model_type == "embedding":
                 scanner = await ServiceRegistry.get_embedding_scanner()
+            elif model_type == "other":
+                scanner = await ServiceRegistry.get_other_scanner()
         except Exception as exc:
             logger.debug("Failed to acquire scanner for %s models: %s", model_type, exc)
 
@@ -1959,6 +2352,28 @@ 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],
@@ -1970,6 +2385,33 @@ 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] = []
@@ -2188,20 +2630,8 @@ 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)
@@ -2306,6 +2736,9 @@ class DownloadManager:
             elif model_type == "embedding":
                 scanner = await ServiceRegistry.get_embedding_scanner()
                 logger.info(f"Updating embedding cache for {actual_file_paths[0]}")
+            elif model_type == "other":
+                scanner = await ServiceRegistry.get_other_scanner()
+                logger.info(f"Updating other-model cache for {actual_file_paths[0]}")
 
             adjust_cached_entry = (
                 getattr(scanner, "adjust_cached_entry", None)
@@ -2395,7 +2828,7 @@ class DownloadManager:
             return {"success": False, "error": str(e)}
 
     def _get_supported_extensions_for_type(self, model_type: str) -> Set[str]:
-        if model_type == "checkpoint":
+        if model_type in ("checkpoint", "other"):
             return {
                 ".ckpt",
                 ".pt",
@@ -2729,6 +3162,64 @@ 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)
@@ -64,6 +73,7 @@ class DownloadQueueService:
             model_name TEXT NOT NULL DEFAULT '',
             version_name TEXT DEFAULT '',
             thumbnail_url TEXT DEFAULT '',
+            file_params TEXT,
             status TEXT NOT NULL,
             error TEXT,
             file_path TEXT,
@@ -120,6 +130,18 @@ class DownloadQueueService:
         with self._connect() as conn:
             conn.executescript(self._SCHEMA_TABLES)
 
+            # Databases created by older versions lack
+            # download_history.file_params; add it so retry-from-history can
+            # restore the originally selected file (#1058).
+            history_columns = {
+                row["name"]
+                for row in conn.execute("PRAGMA table_info(download_history)")
+            }
+            if "file_params" not in history_columns:
+                conn.execute(
+                    "ALTER TABLE download_history ADD COLUMN file_params TEXT"
+                )
+
             # Creating the unique index on download_history.download_id can
             # fail if pre-existing rows have duplicate values (e.g. from a
             # previous version that lacked the index).  Deduplicate first so
@@ -368,23 +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,
@@ -418,6 +448,12 @@ class DownloadQueueService:
                 return None
 
             now = completed_at if completed_at is not None else time.time()
+            # Guard against legacy databases whose download_queue table
+            # predates the file_params column.
+            queue_columns = set(row.keys())
+            file_params_json = (
+                row["file_params"] if "file_params" in queue_columns else None
+            )
             conn.execute(
                 "DELETE FROM download_queue WHERE download_id = ?",
                 (download_id,),
@@ -426,9 +462,9 @@ class DownloadQueueService:
                 """
                 INSERT OR IGNORE INTO download_history (
                     download_id, model_id, model_version_id, model_name,
-                    version_name, thumbnail_url, status, error, file_path,
-                    bytes_downloaded, total_bytes, completed_at
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    version_name, thumbnail_url, file_params, status, error,
+                    file_path, bytes_downloaded, total_bytes, completed_at
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                 """,
                 (
                     row["download_id"],
@@ -437,6 +473,7 @@ class DownloadQueueService:
                     row["model_name"],
                     row["version_name"],
                     row["thumbnail_url"],
+                    file_params_json,
                     status,
                     error,
                     file_path,
@@ -503,6 +540,7 @@ class DownloadQueueService:
         bytes_downloaded: int = 0,
         total_bytes: Optional[int] = None,
         is_already_exists: int = 0,
+        file_params: Optional[dict[str, Any]] = None,
     ) -> int:
         """Insert a record into the download history.
 
@@ -510,6 +548,7 @@ class DownloadQueueService:
         inserted row.
         """
         now = time.time()
+        file_params_json = json.dumps(file_params) if file_params is not None else None
 
         async with self._lock:
             conn = self._get_conn()
@@ -517,9 +556,10 @@ class DownloadQueueService:
                 """
                 INSERT INTO download_history (
                     download_id, model_id, model_version_id, model_name,
-                    version_name, thumbnail_url, status, error, file_path,
-                    bytes_downloaded, total_bytes, completed_at, is_already_exists
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    version_name, thumbnail_url, file_params, status, error,
+                    file_path, bytes_downloaded, total_bytes, completed_at,
+                    is_already_exists
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                 """,
                 (
                     download_id,
@@ -528,6 +568,7 @@ class DownloadQueueService:
                     model_name,
                     version_name,
                     thumbnail_url,
+                    file_params_json,
                     status,
                     error,
                     file_path,
@@ -702,7 +743,7 @@ class DownloadQueueService:
                     download_id, model_id, model_version_id, model_name,
                     version_name, thumbnail_url, source, file_params,
                     status, priority, added_at
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, NULL, 'queued', 0, ?)
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 'queued', 0, ?)
                 """,
                 (
                     new_id,
@@ -712,6 +753,7 @@ class DownloadQueueService:
                     row["version_name"],
                     row["thumbnail_url"],
                     "retry",
+                    row["file_params"],
                     now,
                 ),
             )
@@ -755,7 +797,7 @@ class DownloadQueueService:
                         download_id, model_id, model_version_id, model_name,
                         version_name, thumbnail_url, source, file_params,
                         status, priority, added_at
-                    ) VALUES (?, ?, ?, ?, ?, ?, ?, NULL, 'queued', 0, ?)
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 'queued', 0, ?)
                     """,
                     (
                         new_id,
@@ -765,6 +807,7 @@ class DownloadQueueService:
                         row["version_name"],
                         row["thumbnail_url"],
                         "retry",
+                        row["file_params"],
                         now,
                     ),
                 )
@@ -840,33 +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
@@ -874,17 +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)
                 )
             """)
@@ -892,15 +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/download_routing.py

@@ -0,0 +1,103 @@
+"""Shared download routing logic.
+
+Decides whether a download initiated from the checkpoint library should be
+routed to the unet/diffusion-model roots instead of the checkpoint roots.
+Used by both the download manager (at download time) and the download
+routing HTTP endpoint (when the user picks a location in the UI), so the
+two can never disagree.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Iterable, Optional
+
+from ..utils.constants import (
+    CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE,
+    CIVITAI_TYPE_TO_OTHER_SUB_TYPE,
+    DIFFUSION_MODEL_BASE_MODELS,
+)
+
+logger = logging.getLogger(__name__)
+
+# File types reported by the CivitAI API that indicate a raw diffusion
+# model (loaded via UNETLoader in ComfyUI) rather than a full checkpoint.
+DIFFUSION_FILE_TYPES = frozenset({"UNet", "Diffusion Model"})
+
+
+def is_diffusion_model_download(
+    model_type: str,
+    file_types: Iterable[str] = (),
+    base_model: str = "",
+) -> bool:
+    """Return True when a download should be routed to the unet roots.
+
+    Only applies to downloads initiated from the checkpoint library.
+    Priority: (1) any file has type "UNet" or "Diffusion Model" (the more
+    direct signal from CivitAI), (2) baseModel is a known diffusion model.
+    """
+    if model_type != "checkpoint":
+        return False
+
+    for file_type in file_types:
+        if file_type in DIFFUSION_FILE_TYPES:
+            logger.info(
+                "File type '%s' detected, routing checkpoint to unet folder",
+                file_type,
+            )
+            return True
+
+    if base_model in DIFFUSION_MODEL_BASE_MODELS:
+        logger.info(
+            "baseModel '%s' is a known diffusion model, routing to unet folder",
+            base_model,
+        )
+        return True
+
+    return False
+
+
+def resolve_other_download_sub_type(
+    civitai_model_type: str,
+    file_types: Iterable[str] = (),
+    selected_file_type: Optional[str] = None,
+) -> Optional[str]:
+    """Resolve the "other"-page sub_type for a download.
+
+    Fixed priority (locked design, docs/plans/other-models-page.md §9.2):
+
+    1. Explicit user file pick — when the picked file's type maps, it wins
+       even when model.type maps to something else.
+    2. model.type via CIVITAI_TYPE_TO_OTHER_SUB_TYPE.
+    3. file.type fallback — only when model.type maps to nothing. Must NOT
+       override a mapped model.type: checkpoint models routinely bundle
+       VAE/Text Encoder component files.
+    4. Still undecidable -> None (caller must ask the user for a folder).
+    """
+    if selected_file_type:
+        mapped = CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE.get(selected_file_type)
+        if mapped:
+            logger.info(
+                "Explicit file pick type '%s' routes other download to '%s'",
+                selected_file_type,
+                mapped,
+            )
+            return mapped
+
+    normalized_model_type = (civitai_model_type or "").strip().lower()
+    mapped = CIVITAI_TYPE_TO_OTHER_SUB_TYPE.get(normalized_model_type)
+    if mapped:
+        return mapped
+
+    for file_type in file_types:
+        mapped = CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE.get(file_type)
+        if mapped:
+            logger.info(
+                "model.type '%s' unmapped; file type '%s' routes other download to '%s'",
+                civitai_model_type,
+                file_type,
+                mapped,
+            )
+            return mapped
+
+    return None

py/services/downloaded_version_history_service.py

@@ -62,6 +62,14 @@ class DownloadedVersionHistoryService:
         );
         CREATE INDEX IF NOT EXISTS idx_downloaded_model_versions_model
             ON downloaded_model_versions(model_type, model_id);
+        CREATE TABLE IF NOT EXISTS downloaded_version_files (
+            model_type TEXT NOT NULL,
+            version_id INTEGER NOT NULL,
+            file_id INTEGER NOT NULL,
+            file_name TEXT,
+            downloaded_at REAL NOT NULL,
+            PRIMARY KEY (model_type, version_id, file_id)
+        );
     """
 
     def __init__(self, db_path: str | None = None, *, settings_manager=None) -> None:
@@ -131,10 +139,13 @@ class DownloadedVersionHistoryService:
         source: str = "manual",
         file_path: str | None = None,
         library_name: str | None = None,
+        file_id: int | None = None,
+        file_name: str | None = None,
     ) -> None:
         normalized_type = _normalize_model_type(model_type)
         normalized_version_id = _normalize_int(version_id)
         normalized_model_id = _normalize_int(model_id)
+        normalized_file_id = _normalize_int(file_id)
         if normalized_type is None or normalized_version_id is None:
             return
 
@@ -168,6 +179,25 @@ class DownloadedVersionHistoryService:
                     active_library_name,
                 ),
             )
+            if normalized_file_id is not None:
+                # Per-file history for multi-file versions (#1058)
+                conn.execute(
+                    """
+                    INSERT INTO downloaded_version_files (
+                        model_type, version_id, file_id, file_name, downloaded_at
+                    ) VALUES (?, ?, ?, ?, ?)
+                    ON CONFLICT(model_type, version_id, file_id) DO UPDATE SET
+                        file_name = COALESCE(excluded.file_name, downloaded_version_files.file_name),
+                        downloaded_at = excluded.downloaded_at
+                    """,
+                    (
+                        normalized_type,
+                        normalized_version_id,
+                        normalized_file_id,
+                        file_name,
+                        timestamp,
+                    ),
+                )
             conn.commit()
 
     async def mark_downloaded_bulk(
@@ -206,24 +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)
@@ -255,8 +294,63 @@ class DownloadedVersionHistoryService:
                     self._get_active_library_name(),
                 ),
             )
+            # Whole-version deletion also clears the per-file records (#1058)
+            conn.execute(
+                """
+                DELETE FROM downloaded_version_files
+                WHERE model_type = ? AND version_id = ?
+                """,
+                (normalized_type, normalized_version_id),
+            )
             conn.commit()
 
+    async def mark_file_deleted(
+        self, model_type: str, version_id: int, file_id: int
+    ) -> None:
+        """Drop a single file record of a version, keeping siblings (#1058)."""
+        normalized_type = _normalize_model_type(model_type)
+        normalized_version_id = _normalize_int(version_id)
+        normalized_file_id = _normalize_int(file_id)
+        if (
+            normalized_type is None
+            or normalized_version_id is None
+            or normalized_file_id is None
+        ):
+            return
+
+        async with self._lock:
+            conn = self._get_conn()
+            conn.execute(
+                """
+                DELETE FROM downloaded_version_files
+                WHERE model_type = ? AND version_id = ? AND file_id = ?
+                """,
+                (normalized_type, normalized_version_id, normalized_file_id),
+            )
+            conn.commit()
+
+    async def get_downloaded_file_ids(
+        self, model_type: str, version_id: int
+    ) -> list[int]:
+        """Return the CivitAI file ids recorded as downloaded for a version."""
+        normalized_type = _normalize_model_type(model_type)
+        normalized_version_id = _normalize_int(version_id)
+        if normalized_type is None or normalized_version_id is None:
+            return []
+
+        async with self._lock:
+            conn = self._get_conn()
+            rows = conn.execute(
+                """
+                SELECT file_id
+                FROM downloaded_version_files
+                WHERE model_type = ? AND version_id = ?
+                ORDER BY file_id ASC
+                """,
+                (normalized_type, normalized_version_id),
+            ).fetchall()
+        return [int(row["file_id"]) for row in rows]
+
     async def has_been_downloaded(self, model_type: str, version_id: int) -> bool:
         normalized_type = _normalize_model_type(model_type)
         normalized_version_id = _normalize_int(version_id)

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", ""),
@@ -66,6 +67,8 @@ class EmbeddingService(BaseModelService):
             "civitai": self.filter_civitai_data(model_data.get("civitai", {}), minimal=True),
             "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
             "version_count": model_data.get("version_count"),
+            "source_platform": model_data.get("source_platform", ""),
+            "source_url": model_data.get("source_url", ""),
             "hf_url": model_data.get("hf_url", ""),
         }
     

py/services/llm_service.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
+import time
 from typing import Any, Dict, List, Optional
 
 import aiohttp
@@ -32,8 +33,26 @@ _catalog_cache: Optional[Dict[str, List[str]]] = None
 # ``{provider_id: {model_id: max_output_tokens}}``.
 _model_output_limits: Dict[str, Dict[str, int]] = {}
 
+# Monotonic timestamp of the last failed catalog fetch (None = no failure
+# yet).  Failed fetches are negatively cached: further calls return the
+# empty fallback without hitting the network until the cooldown elapses,
+# so users on broken networks don't stall on every settings-modal open.
+_catalog_last_failure: Optional[float] = None
+_CATALOG_FAILURE_COOLDOWN = 600.0  # seconds
+
+# Serializes catalog fetches so concurrent callers don't duplicate requests.
+_catalog_lock = asyncio.Lock()
+
 _CATALOG_TIMEOUT = aiohttp.ClientTimeout(total=30)
 
+# Cloudflare serves brotli when the client advertises it, and brotli is a
+# required dependency here — a corrupted br stream can crash the native
+# decoder with a Windows access violation (issue #1099).  Request gzip
+# instead; zlib decompression is not affected and corrupt gzip data only
+# raises ContentEncodingError (an aiohttp.ClientError subclass), which the
+# exception handlers below already catch.
+_NO_BROTLI_HEADERS = {"Accept-Encoding": "gzip, deflate"}
+
 
 async def _load_model_catalog() -> Dict[str, List[str]]:
     """Fetch and parse the model catalog.
@@ -46,61 +65,85 @@ async def _load_model_catalog() -> Dict[str, List[str]]:
     value has a ``models`` sub-dict keyed by model ID.  The result is cached
     in memory after the first successful fetch.
     Subsequent calls return the cached data immediately.
+
+    Failed fetches are negatively cached: further calls return an empty
+    dict without hitting the network until ``_CATALOG_FAILURE_COOLDOWN``
+    has elapsed, so a broken network does not stall every settings-modal
+    open.  Concurrent callers are serialized behind :data:`_catalog_lock`
+    so only one request is ever in flight.
     """
-    global _catalog_cache, _model_output_limits
+    global _catalog_cache, _model_output_limits, _catalog_last_failure
     if _catalog_cache is not None:
         return _catalog_cache
 
-    try:
-        async with aiohttp.ClientSession(timeout=_CATALOG_TIMEOUT) as session:
-            async with session.get(_MODEL_CATALOG_URL) as resp:
-                if resp.status != 200:
-                    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:
-        logger.warning("Failed to fetch model catalog: %s", exc)
-        return _catalog_cache or {}
+    async with _catalog_lock:
+        # Re-check under the lock: another caller may have fetched (or
+        # failed) while we were waiting.
+        if _catalog_cache is not None:
+            return _catalog_cache
+        if (
+            _catalog_last_failure is not None
+            and time.monotonic() - _catalog_last_failure < _CATALOG_FAILURE_COOLDOWN
+        ):
+            logger.debug(
+                "Skipping model catalog fetch: last attempt failed %.0fs ago",
+                time.monotonic() - _catalog_last_failure,
+            )
+            return {}
 
-    if not isinstance(data, dict):
-        logger.warning("Model catalog is not a dict, got %s", type(data).__name__)
-        return _catalog_cache or {}
+        try:
+            async with aiohttp.ClientSession(timeout=_CATALOG_TIMEOUT) as session:
+                async with session.get(_MODEL_CATALOG_URL, headers=_NO_BROTLI_HEADERS) as resp:
+                    if resp.status != 200:
+                        logger.warning("Model catalog returned HTTP %s", resp.status)
+                        _catalog_last_failure = time.monotonic()
+                        return {}
+                    data = await resp.json()
+        except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError, UnicodeDecodeError) as exc:
+            logger.warning("Failed to fetch model catalog: %s", exc)
+            _catalog_last_failure = time.monotonic()
+            return {}
 
-    result: Dict[str, List[str]] = {}
-    output_limits: Dict[str, Dict[str, int]] = {}
-    for provider_id, provider_info in data.items():
-        if not isinstance(provider_info, dict):
-            continue
-        models_dict = provider_info.get("models")
-        if not isinstance(models_dict, dict):
-            continue
-        model_ids: List[str] = []
-        provider_limits: Dict[str, int] = {}
-        for mid, model_info in models_dict.items():
-            if not isinstance(mid, str):
+        if not isinstance(data, dict):
+            logger.warning("Model catalog is not a dict, got %s", type(data).__name__)
+            _catalog_last_failure = time.monotonic()
+            return {}
+
+        result: Dict[str, List[str]] = {}
+        output_limits: Dict[str, Dict[str, int]] = {}
+        for provider_id, provider_info in data.items():
+            if not isinstance(provider_info, dict):
                 continue
-            model_ids.append(mid)
-            if isinstance(model_info, dict):
-                limit = model_info.get("limit")
-                if isinstance(limit, dict):
-                    output = limit.get("output")
-                    if isinstance(output, (int, float)) and output > 0:
-                        provider_limits[mid] = int(output)
-        if model_ids:
-            result[provider_id] = model_ids
-        if provider_limits:
-            output_limits[provider_id] = provider_limits
+            models_dict = provider_info.get("models")
+            if not isinstance(models_dict, dict):
+                continue
+            model_ids: List[str] = []
+            provider_limits: Dict[str, int] = {}
+            for mid, model_info in models_dict.items():
+                if not isinstance(mid, str):
+                    continue
+                model_ids.append(mid)
+                if isinstance(model_info, dict):
+                    limit = model_info.get("limit")
+                    if isinstance(limit, dict):
+                        output = limit.get("output")
+                        if isinstance(output, (int, float)) and output > 0:
+                            provider_limits[mid] = int(output)
+            if model_ids:
+                result[provider_id] = model_ids
+            if provider_limits:
+                output_limits[provider_id] = provider_limits
 
-    _catalog_cache = result
-    _model_output_limits = output_limits
-    logger.debug(
-        "Loaded model catalog: %d providers, %d total models "
-        "(%d providers have output limits)",
-        len(result),
-        sum(len(m) for m in result.values()),
-        len(output_limits),
-    )
-    return result
+        _catalog_cache = result
+        _model_output_limits = output_limits
+        logger.debug(
+            "Loaded model catalog: %d providers, %d total models "
+            "(%d providers have output limits)",
+            len(result),
+            sum(len(m) for m in result.values()),
+            len(output_limits),
+        )
+        return result
 
 
 def _get_model_max_output(provider: str, model: str) -> Optional[int]:
@@ -126,12 +169,12 @@ async def fetch_ollama_models(api_base: str) -> List[str]:
     url = f"{api_base.rstrip('/')}/models"
     try:
         async with aiohttp.ClientSession(timeout=_OLLAMA_API_TIMEOUT) as session:
-            async with session.get(url) as resp:
+            async with session.get(url, headers=_NO_BROTLI_HEADERS) as resp:
                 if resp.status != 200:
                     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 []
 
@@ -224,6 +267,16 @@ _PROVIDER_DEFAULTS: Dict[str, str] = {
 # Request timeout for LLM calls (seconds)
 _LLM_TIMEOUT = aiohttp.ClientTimeout(total=120)
 
+# Providers that do NOT implement ``response_format: {"type": "json_schema"}``
+# and reject it with HTTP 400.  For these the weaker, widely supported
+# ``json_object`` mode is used instead (the prompt already specifies the
+# expected JSON shape, and ``_try_salvage_json`` repairs imperfect output).
+# DeepSeek answers a json_schema request with
+# ``{"error":{"message":"This response_format type is unavailable now"}}``.
+# LM Studio and some other local OpenAI-compatible servers reject
+# ``json_object`` but accept ``json_schema``, so they are not listed here.
+_JSON_OBJECT_ONLY_PROVIDERS = frozenset({"deepseek"})
+
 
 class LLMService:
     """Centralized LLM API client.
@@ -571,47 +624,61 @@ class LLMService:
         if effective_max is None:
             effective_max = 4096
 
-        # Use json_schema (not json_object) for broader provider compatibility:
-        # LM Studio and some other OpenAI-compatible servers reject
-        # json_object but accept json_schema.  {"type": "object"} is
-        # functionally equivalent — it accepts any JSON object without
-        # constraining specific fields.
-        response_format = {
+        # Structured-output format.  ``json_schema`` is preferred because LM
+        # Studio and other local OpenAI-compatible servers reject
+        # ``json_object`` but accept ``json_schema``; ``{"type": "object"}``
+        # accepts any JSON object without constraining specific fields, so the
+        # two modes are functionally equivalent here.  Providers known to
+        # reject json_schema (see _JSON_OBJECT_ONLY_PROVIDERS) get
+        # ``json_object`` instead.
+        schema_format: Dict[str, Any] = {
             "type": "json_schema",
             "json_schema": {
                 "name": "metadata",
                 "schema": {"type": "object"},
             },
         }
+        json_object_format: Dict[str, Any] = {"type": "json_object"}
 
-        try:
-            result = await self.chat_completion(
-                messages=messages,
-                model=model,
-                temperature=temperature,
-                response_format=response_format,
-                max_tokens=effective_max,
-            )
-        except LLMResponseError as e:
-            # Only fall back when the provider rejects the response_format
-            # type value (e.g. "'response_format.type' must be...").  Avoid
-            # catching unrelated 400 errors whose body happens to mention
-            # "response_format" (e.g. "model does not support
-            # response_format restrictions on this endpoint").
-            if "'response_format.type'" not in str(e).lower():
-                raise
-            logger.info(
-                "Provider rejected response_format, retrying without it. "
-                "Falling back to prompt-only JSON mode. Error: %s",
-                e,
-            )
-            result = await self.chat_completion(
-                messages=messages,
-                model=model,
-                temperature=temperature,
-                response_format=None,
-                max_tokens=effective_max,
-            )
+        if self._get_config()["provider"] in _JSON_OBJECT_ONLY_PROVIDERS:
+            format_chain: List[Optional[Dict[str, Any]]] = [
+                json_object_format,
+                None,
+            ]
+        else:
+            format_chain = [schema_format, json_object_format, None]
+
+        result: Optional[Dict[str, Any]] = None
+        for index, fmt in enumerate(format_chain):
+            try:
+                result = await self.chat_completion(
+                    messages=messages,
+                    model=model,
+                    temperature=temperature,
+                    response_format=fmt,
+                    max_tokens=effective_max,
+                )
+                break
+            except LLMResponseError as e:
+                message = str(e).lower()
+                if index + 1 >= len(format_chain):
+                    raise
+                # Only downgrade when the failure is about ``response_format``.
+                # Everything else (auth, unknown model, rate limits) must
+                # surface unchanged.  Matching on the bare parameter name also
+                # covers variants such as DeepSeek's "This response_format
+                # type is unavailable now" without swallowing unrelated 400s.
+                if "response_format" not in message:
+                    raise
+                logger.info(
+                    "Provider rejected response_format=%s, retrying with %s. "
+                    "Error: %s",
+                    (fmt or {}).get("type", "none"),
+                    (format_chain[index + 1] or {}).get("type", "none"),
+                    e,
+                )
+
+        assert result is not None  # non-empty chain always sets or raises
 
         content = result.get("content", "") or ""
         if not content:

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", ""),
@@ -78,6 +79,8 @@ class LoraService(BaseModelService):
             ),
             "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
             "version_count": model_data.get("version_count"),
+            "source_platform": model_data.get("source_platform", ""),
+            "source_url": model_data.get("source_url", ""),
             "hf_url": model_data.get("hf_url", ""),
         }
 

py/services/metadata_sync_service.py

@@ -14,6 +14,7 @@ from ..utils.model_utils import determine_base_model
 from ..utils.models import autov3_from_civitai_files
 from .connectivity_guard import OFFLINE_FRIENDLY_MESSAGE, is_expected_offline_error
 from .errors import RateLimitError
+from .model_sources import has_external_source
 
 logger = logging.getLogger(__name__)
 
@@ -222,9 +223,10 @@ class MetadataSyncService:
                         error_msg = "CivitAI model is deleted and no archive provider is available"
                     return False, error_msg
             else:
-                is_hf_source = bool(model_data.get("hf_url"))
+                is_hf_source = has_external_source(model_data)
                 if is_hf_source:
-                    # HF-sourced model: only check CivitAI API directly.
+                    # External-source model (Hugging Face / ModelScope /
+                    # TensorArt): only check CivitAI API directly.
                     # CivArchive is almost guaranteed to have no record, and
                     # hitting it wastes rate-limit budget.
                     # Use a distinct provider name ("civitai_api" not None) so
@@ -245,16 +247,23 @@ 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 +428,37 @@ 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_cache.py

@@ -33,8 +33,17 @@ class ModelCache:
 
     raw_data: List[Dict[str, Any]]
     folders: List[str]
+    # Every directory under the model roots (including empty ones), as
+    # recorded by the last scan/hydration. ``None`` means "never recorded"
+    # (e.g. a persisted snapshot predating this field) and triggers a
+    # background filesystem backfill in the scanner.
+    all_folders: Optional[List[str]] = None
     version_index: Dict[int, Dict[str, Any]] = field(default_factory=dict)
     model_id_index: Dict[int, List[Dict[str, Any]]] = field(default_factory=dict)
+    # Multi-valued companion to version_index: every local file entry of a
+    # CivitAI model version, so versions with several downloaded files stay
+    # consistent (#1058).
+    version_files_index: Dict[int, List[Dict[str, Any]]] = field(default_factory=dict)
     name_display_mode: str = "model_name"
     _lock: Any = field(init=False, repr=False, default=None)
     # Cache for last sort: (sort_key, order, seed) -> sorted list
@@ -116,6 +125,7 @@ class ModelCache:
 
         self.version_index = {}
         self.model_id_index = {}
+        self.version_files_index = {}
         for item in self.raw_data:
             self.add_to_version_index(item)
 
@@ -132,6 +142,17 @@ class ModelCache:
 
         self.version_index[version_id] = item
 
+        # Register in the multi-valued index, deduplicated by file_path (#1058)
+        files = self.version_files_index.setdefault(version_id, [])
+        for entry in files:
+            if entry is item or (
+                isinstance(entry, dict)
+                and entry.get('file_path') == item.get('file_path')
+            ):
+                break
+        else:
+            files.append(item)
+
         model_id = self._normalize_version_id(civitai_data.get('modelId'))
         if model_id is None:
             return
@@ -159,12 +180,37 @@ class ModelCache:
         if version_id is None:
             return
 
+        # Drop only this file's entry from the multi-valued index (#1058)
+        files = self.version_files_index.get(version_id)
+        if files:
+            remaining = [
+                entry
+                for entry in files
+                if not (
+                    entry is item
+                    or (
+                        isinstance(entry, dict)
+                        and entry.get('file_path') == item.get('file_path')
+                    )
+                )
+            ]
+            if remaining:
+                self.version_files_index[version_id] = remaining
+            else:
+                self.version_files_index.pop(version_id, None)
+
+        # A surviving sibling file keeps the version present in the indexes
+        sibling = (self.version_files_index.get(version_id) or [None])[0]
+
         existing = self.version_index.get(version_id)
         if existing is item or (
             isinstance(existing, dict)
             and existing.get('file_path') == item.get('file_path')
         ):
-            self.version_index.pop(version_id, None)
+            if sibling is not None:
+                self.version_index[version_id] = sibling
+            else:
+                self.version_index.pop(version_id, None)
 
         model_id = self._normalize_version_id(civitai_data.get('modelId'))
         if model_id is None:
@@ -174,6 +220,20 @@ class ModelCache:
         if not versions:
             return
 
+        if sibling is not None:
+            # Update the descriptor to reflect the surviving sibling file
+            descriptor = self._build_version_descriptor(
+                sibling,
+                sibling.get('civitai') if isinstance(sibling, dict) else {},
+                version_id,
+            )
+            for index, existing_desc in enumerate(versions):
+                if existing_desc.get('versionId') == version_id:
+                    if descriptor is not None:
+                        versions[index] = descriptor
+                    break
+            return
+
         filtered = [v for v in versions if v.get('versionId') != version_id]
         if filtered:
             self.model_id_index[model_id] = filtered
@@ -206,6 +266,15 @@ class ModelCache:
         versions = self.model_id_index.get(normalized_id, [])
         return [dict(version) for version in versions]
 
+    def get_files_by_version_id(self, version_id: Any) -> List[Dict[str, Any]]:
+        """Return every local file entry for a CivitAI model version (#1058)."""
+
+        normalized_id = self._normalize_version_id(version_id)
+        if normalized_id is None:
+            return []
+
+        return list(self.version_files_index.get(normalized_id, []))
+
     async def resort(self):
         """Resort cached data according to last sort mode if set"""
         async with self._lock:

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_service_factory.py

@@ -118,19 +118,24 @@ class ModelServiceFactory:
 
 
 def register_default_model_types():
-    """Register the default model types (LoRA, Checkpoint, and Embedding)"""
+    """Register the default model types (LoRA, Checkpoint, Embedding, and Other)"""
     from ..services.lora_service import LoraService
     from ..services.checkpoint_service import CheckpointService
     from ..services.embedding_service import EmbeddingService
+    from ..services.other_model_service import OtherModelService
     from ..routes.lora_routes import LoraRoutes
     from ..routes.checkpoint_routes import CheckpointRoutes
     from ..routes.embedding_routes import EmbeddingRoutes
-    
+    from ..routes.other_routes import OtherRoutes
+
     # Register LoRA model type
     ModelServiceFactory.register_model_type('lora', LoraService, LoraRoutes)
-    
+
     # Register Checkpoint model type
     ModelServiceFactory.register_model_type('checkpoint', CheckpointService, CheckpointRoutes)
-    
+
     # Register Embedding model type
-    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
+    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
+
+    # Register Other model type (VAE, upscaler, text encoder, ...)
+    ModelServiceFactory.register_model_type('other', OtherModelService, OtherRoutes)

py/services/model_sources/__init__.py

@@ -0,0 +1,73 @@
+"""External model-source providers (Hugging Face, ModelScope, TensorArt).
+
+This package is the single abstraction over "a site that hosts models and
+a model card".  See :mod:`py.services.model_sources.base` for the provider
+protocol and :mod:`py.services.model_sources.registry` for the lookup and
+metadata-normalisation helpers used across the codebase.
+"""
+
+from __future__ import annotations
+
+from .base import (
+    GROUP_PREFIXES,
+    HTTP_TIMEOUT,
+    ModelSource,
+    ModelSourceError,
+    SourceRef,
+    USER_AGENT,
+    clean_source_url,
+    fetch_json,
+    fetch_text,
+    filter_weight_files,
+    is_valid_source_id,
+)
+from .huggingface import HuggingFaceSource
+from .modelscope import ModelScopeSource
+from .registry import (
+    LEGACY_HF_URL_FIELD,
+    SOURCE_PLATFORM_FIELD,
+    SOURCE_URL_FIELD,
+    detect_source,
+    downloadable_sources,
+    get_download_source,
+    get_source,
+    get_source_platform,
+    has_external_source,
+    list_sources,
+    normalize_metadata_source,
+    resolve_source_ref,
+    source_group_key,
+    source_label,
+)
+from .tensorart import TensorArtSource
+
+__all__ = [
+    "GROUP_PREFIXES",
+    "HTTP_TIMEOUT",
+    "LEGACY_HF_URL_FIELD",
+    "ModelSource",
+    "ModelSourceError",
+    "HuggingFaceSource",
+    "ModelScopeSource",
+    "SOURCE_PLATFORM_FIELD",
+    "SOURCE_URL_FIELD",
+    "SourceRef",
+    "TensorArtSource",
+    "USER_AGENT",
+    "clean_source_url",
+    "detect_source",
+    "downloadable_sources",
+    "fetch_json",
+    "fetch_text",
+    "filter_weight_files",
+    "get_download_source",
+    "get_source",
+    "get_source_platform",
+    "has_external_source",
+    "is_valid_source_id",
+    "list_sources",
+    "normalize_metadata_source",
+    "resolve_source_ref",
+    "source_group_key",
+    "source_label",
+]

py/services/model_sources/base.py

@@ -0,0 +1,313 @@
+"""Base types for the external model-source provider abstraction.
+
+A *model source* is a third-party site that hosts model files and a model
+card (README) describing them — Hugging Face, ModelScope, TensorArt, and
+whatever gets added later.  Everything the rest of the codebase needs to
+know about such a site is expressed by :class:`ModelSource`:
+
+* how to recognise one of its URLs (:meth:`ModelSource.parse`)
+* the canonical page URL for a source id (:meth:`ModelSource.canonical_url`)
+* how to fetch the model card (:meth:`ModelSource.fetch_model_card`)
+* how to turn repository-relative asset paths into absolute URLs
+  (:meth:`ModelSource.asset_base_url`)
+* which capabilities the site actually supports
+  (``supports_enrichment`` / ``supports_download``)
+
+Keeping this in one place means the agent pipeline, the scanners, and the
+HTTP handlers never need site-specific branching.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+from dataclasses import dataclass
+from typing import Any, Iterable, Optional
+
+import aiohttp
+
+from ...utils.constants import MODEL_FILE_EXTENSIONS
+
+logger = logging.getLogger(__name__)
+
+#: Shared HTTP timeout for model-card fetches.
+HTTP_TIMEOUT = 30
+
+#: User agent used for all model-source HTTP requests.
+USER_AGENT = "ComfyUI-LoRA-Manager/1.0"
+
+#: Platform → short prefix used when building version-group keys.
+#: ``huggingface`` keeps the historical ``hf:`` prefix for backward
+#: compatibility with already-cached group keys.
+GROUP_PREFIXES: dict[str, str] = {
+    "huggingface": "hf",
+    "modelscope": "ms",
+    "tensorart": "ta",
+}
+
+
+@dataclass(frozen=True)
+class SourceRef:
+    """A parsed reference to a model hosted on an external site."""
+
+    platform: str
+    """Canonical platform id, e.g. ``"huggingface"``."""
+
+    source_id: str
+    """Site-specific identity, e.g. ``"user/repo"`` or ``"827823520299086029"``."""
+
+    url: str
+    """Canonical URL of the model page."""
+
+
+class ModelSourceError(Exception):
+    """Raised when a model source cannot satisfy a request.
+
+    Carries the HTTP status the API handler should answer with, so the
+    handlers stay free of per-site error mapping.
+    """
+
+    def __init__(self, message: str, status: int = 502) -> None:
+        super().__init__(message)
+        self.status = status
+
+
+#: Repository ids are always exactly ``owner/name``. Components may contain
+#: dots (``black-forest-labs/FLUX.1-dev``) but must not be empty, ``.`` / ``..``,
+#: or start with a dot - the id is used as a path segment on disk.
+_SOURCE_ID_COMPONENT = re.compile(r"^[A-Za-z0-9_][A-Za-z0-9_.\-]*$")
+
+
+def is_valid_source_id(source_id: str) -> bool:
+    """Return ``True`` when *source_id* is a safe ``owner/name`` repository id."""
+
+    if not source_id or not isinstance(source_id, str) or source_id.count("/") != 1:
+        return False
+    owner, name = source_id.split("/", 1)
+    return all(
+        part and part not in (".", "..") and _SOURCE_ID_COMPONENT.match(part)
+        for part in (owner, name)
+    )
+
+
+async def fetch_text(url: str, *, timeout: int = HTTP_TIMEOUT) -> str:
+    """Fetch *url* and return its body as text, or ``""`` on any failure.
+
+    Network problems are expected (offline installs, rate limits, dead
+    repos) and must never bubble up into the pipeline, so every error is
+    logged at debug level and normalised to an empty string.
+    """
+
+    try:
+        async with aiohttp.ClientSession(
+            headers={"User-Agent": USER_AGENT},
+            timeout=aiohttp.ClientTimeout(total=timeout),
+        ) as session:
+            async with session.get(url) as resp:
+                if resp.status == 200:
+                    return await resp.text()
+                logger.debug("Fetch %s returned HTTP %s", url, resp.status)
+    except Exception as exc:  # pragma: no cover - network dependent
+        logger.debug("Failed to fetch %s: %s", url, exc)
+    return ""
+
+
+async def fetch_json(
+    url: str, *, timeout: int = HTTP_TIMEOUT
+) -> tuple[int, Any]:
+    """Fetch *url* and return ``(status, parsed_body)``.
+
+    Unlike :func:`fetch_text` this reports the status, because callers such as
+    the file-listing endpoints need to distinguish "repo not found" (404) from
+    a transport failure.  ``parsed_body`` is ``None`` when the response is not
+    JSON or the request failed outright (status ``0``).
+    """
+
+    try:
+        async with aiohttp.ClientSession(
+            headers={"User-Agent": USER_AGENT},
+            timeout=aiohttp.ClientTimeout(total=timeout),
+        ) as session:
+            async with session.get(url) as resp:
+                if resp.status != 200:
+                    return resp.status, None
+                try:
+                    return resp.status, await resp.json(content_type=None)
+                except Exception:
+                    return resp.status, None
+    except Exception as exc:  # pragma: no cover - network dependent
+        logger.debug("Failed to fetch %s: %s", url, exc)
+        return 0, None
+
+
+class ModelSource:
+    """Description and I/O for one external model hosting site."""
+
+    #: Canonical platform id stored in metadata.
+    platform: str = ""
+
+    #: Human-readable name used in UI copy and prompts.
+    label: str = ""
+
+    #: Whether the agent skill can fetch a model card and run AI extraction.
+    supports_enrichment: bool = False
+
+    #: Whether models can be downloaded directly from this site.
+    supports_download: bool = False
+
+    #: Branch used when the caller does not pass an explicit revision.
+    default_revision: str = ""
+
+    #: Sub-directory the "use default paths" template places downloads in.
+    default_subdir: str = ""
+
+    #: Lenient pattern used to recognise URLs already stored in metadata.
+    #: Captures the site-specific source id in group ``id``.
+    url_pattern: re.Pattern[str] | None = None
+
+    #: Strict pattern used to validate user input.  Must match the whole URL.
+    strict_url_pattern: re.Pattern[str] | None = None
+
+    # ------------------------------------------------------------------
+    # Parsing
+    # ------------------------------------------------------------------
+
+    def parse(self, url: str, *, strict: bool = False) -> Optional[str]:
+        """Return the source id contained in *url*, or ``None``.
+
+        With ``strict=True`` the URL must match this site's canonical shape
+        exactly (used when validating what a user pasted); with
+        ``strict=False`` sub-paths such as ``/resolve/main/file.bin`` are
+        tolerated (used when normalising already-stored values).
+        """
+
+        if not url or not isinstance(url, str):
+            return None
+        candidate = url.strip()
+        if not candidate:
+            return None
+        pattern = self.strict_url_pattern if strict else self.url_pattern
+        if pattern is None:
+            return None
+        match = pattern.match(candidate)
+        return match.group("id") if match else None
+
+    def ref(self, url: str, *, strict: bool = False) -> Optional[SourceRef]:
+        """Return a :class:`SourceRef` for *url*, or ``None`` if not ours."""
+
+        source_id = self.parse(url, strict=strict)
+        if not source_id:
+            return None
+        return SourceRef(
+            platform=self.platform,
+            source_id=source_id,
+            url=self.canonical_url(source_id),
+        )
+
+    # ------------------------------------------------------------------
+    # URLs and content
+    # ------------------------------------------------------------------
+
+    def canonical_url(self, source_id: str) -> str:
+        """Return the canonical model-page URL for *source_id*."""
+
+        raise NotImplementedError
+
+    def asset_base_url(self, source_id: str, revision: str = "") -> str:
+        """Base URL used to resolve repository-relative asset paths."""
+
+        return ""
+
+    def group_key(self, source_id: str) -> str:
+        """Return the version-group key for *source_id*."""
+
+        prefix = GROUP_PREFIXES.get(self.platform, self.platform)
+        return f"{prefix}:{source_id}"
+
+    async def fetch_model_card(self, source_id: str) -> str:
+        """Fetch the raw model card (README) markdown for *source_id*."""
+
+        return ""
+
+    # ------------------------------------------------------------------
+    # Download support
+    # ------------------------------------------------------------------
+
+    async def list_files(
+        self, source_id: str, revision: str = ""
+    ) -> list[dict[str, Any]]:
+        """List downloadable weight files in *source_id*.
+
+        Returns ``[{"filename": <repo-relative path>, "size": <bytes>}]``,
+        largest first, filtered to :data:`MODEL_FILE_EXTENSIONS`.  Sites
+        without download support return an empty list.
+
+        Raises :class:`ModelSourceError` when the repository cannot be read,
+        so the handler can surface "not found" separately from a transport
+        failure.
+        """
+
+        return []
+
+    def file_download_url(
+        self, source_id: str, filename: str, revision: str = ""
+    ) -> str:
+        """Return the direct (redirecting) download URL for one file."""
+
+        raise ModelSourceError(
+            f"{self.label or self.platform} does not support downloads", status=400
+        )
+
+    def resolve_revision(self, revision: str = "") -> str:
+        """Return *revision*, falling back to this site's default branch."""
+
+        return revision or self.default_revision
+
+    def page_url_for_file(self, source_id: str, filename: str) -> str:
+        """Return the human-facing page for *filename* inside *source_id*."""
+
+        return self.canonical_url(source_id)
+
+    def __repr__(self) -> str:  # pragma: no cover - debugging aid
+        return f"<ModelSource {self.platform}>"
+
+
+def clean_source_url(url: Any) -> str:
+    """Normalise a stored source URL value into a stripped string."""
+
+    if not isinstance(url, str):
+        return ""
+    return url.strip()
+
+
+def filter_weight_files(entries: Iterable[tuple[str, int]]) -> list[dict[str, Any]]:
+    """Keep model-weight files from ``(path, size)`` pairs, largest first.
+
+    Every site lists a lot more than weights (READMEs, configs, tokenizers,
+    …); the download picker only ever wants the files ComfyUI can load, which
+    is exactly :data:`MODEL_FILE_EXTENSIONS`.
+    """
+
+    files = [
+        {"filename": path, "size": int(size or 0)}
+        for path, size in entries
+        if path and os.path.splitext(path)[1].lower() in MODEL_FILE_EXTENSIONS
+    ]
+    files.sort(key=lambda entry: entry["size"], reverse=True)
+    return files
+
+
+__all__ = [
+    "GROUP_PREFIXES",
+    "HTTP_TIMEOUT",
+    "ModelSource",
+    "ModelSourceError",
+    "SourceRef",
+    "USER_AGENT",
+    "clean_source_url",
+    "fetch_json",
+    "fetch_text",
+    "filter_weight_files",
+    "is_valid_source_id",
+]

py/services/model_sources/huggingface.py

@@ -0,0 +1,106 @@
+"""Hugging Face model source."""
+
+from __future__ import annotations
+
+import logging
+import re
+
+from .base import (
+    ModelSource,
+    ModelSourceError,
+    fetch_json,
+    fetch_text,
+    filter_weight_files,
+)
+
+logger = logging.getLogger(__name__)
+
+#: Lenient — used to normalise URLs already stored in metadata; tolerates
+#: sub-paths such as ``/resolve/main/model.safetensors``.
+_URL_PATTERN = re.compile(
+    r"https?://(?:www\.)?huggingface\.co/(?P<id>[^/?#\s]+/[^/?#\s]+)"
+)
+
+#: Strict — validates what the user pasted into the "link model" dialog.
+_STRICT_URL_PATTERN = re.compile(
+    r"https?://(?:www\.)?huggingface\.co/(?P<id>[^/?#\s]+/[^/?#\s]+)/?$"
+)
+
+
+class HuggingFaceSource(ModelSource):
+    """Hugging Face Hub (``huggingface.co``)."""
+
+    platform = "huggingface"
+    label = "Hugging Face"
+    supports_enrichment = True
+    supports_download = True
+    default_revision = "main"
+    default_subdir = "huggingface"
+    url_pattern = _URL_PATTERN
+    strict_url_pattern = _STRICT_URL_PATTERN
+
+    def canonical_url(self, source_id: str) -> str:
+        return f"https://huggingface.co/{source_id}"
+
+    def asset_base_url(self, source_id: str, revision: str = "") -> str:
+        return f"https://huggingface.co/{source_id}/resolve/{self.resolve_revision(revision)}"
+
+    async def fetch_model_card(self, source_id: str) -> str:
+        """Fetch ``README.md`` from Hugging Face (tries ``main``, then ``master``)."""
+
+        for branch in ("main", "master"):
+            text = await fetch_text(
+                f"https://huggingface.co/{source_id}/raw/{branch}/README.md"
+            )
+            if text:
+                return text
+        return ""
+
+    async def list_files(
+        self, source_id: str, revision: str = ""
+    ) -> list[dict]:
+        """List weight files via the Hub tree API.
+
+        The tree endpoint (rather than the model-info endpoint) is used
+        because it reports accurate sizes for LFS-tracked files.
+        """
+
+        revision = self.resolve_revision(revision)
+        status, payload = await fetch_json(
+            f"https://huggingface.co/api/models/{source_id}/tree/{revision}"
+        )
+
+        if status == 404:
+            raise ModelSourceError(f"Repository '{source_id}' not found", status=404)
+        if status != 200 or not isinstance(payload, list):
+            raise ModelSourceError(
+                f"Hugging Face API error while listing '{source_id}' (HTTP {status})"
+            )
+
+        entries = []
+        for entry in payload:
+            if not isinstance(entry, dict):
+                continue
+            path = entry.get("path", "")
+            size = entry.get("size", 0) or 0
+            if not size and isinstance(entry.get("lfs"), dict):
+                size = entry["lfs"].get("size", 0) or 0
+            entries.append((path, size))
+
+        return filter_weight_files(entries)
+
+    def file_download_url(
+        self, source_id: str, filename: str, revision: str = ""
+    ) -> str:
+        return (
+            f"https://huggingface.co/{source_id}/resolve/"
+            f"{self.resolve_revision(revision)}/{filename}"
+        )
+
+    def page_url_for_file(self, source_id: str, filename: str) -> str:
+        return (
+            f"https://huggingface.co/{source_id}/blob/{self.default_revision}/{filename}"
+        )
+
+
+__all__ = ["HuggingFaceSource"]

py/services/model_sources/modelscope.py

@@ -0,0 +1,144 @@
+"""ModelScope (魔搭社区) model source.
+
+ModelScope exposes the same "model card as README.md" convention as
+Hugging Face, including a YAML frontmatter block that often carries
+``base_model:`` and ``trigger_words:``.  Three public endpoints are used,
+none of which requires an API key for public models:
+
+* ``/models/{owner}/{name}/resolve/{revision}/README.md`` — raw model card
+* ``/api/v1/models/{owner}/{name}/repo?Revision=..&FilePath=README.md`` —
+  the same content through the API, used as a fallback when the resolve
+  URL is unavailable.
+* ``/api/v1/models/{owner}/{name}/repo/files?Revision=..`` — the file
+  listing backing the download picker.  It reports real sizes for LFS
+  files (not the pointer size), so no extra HEAD request is needed.
+
+Downloads go through ``/models/{owner}/{name}/resolve/{revision}/{path}``,
+which redirects to a CDN URL carrying a time-limited ``auth_key``.
+Requesting the resolve URL fresh on every attempt (which the shared
+downloader does, including for resumable Range requests) keeps that key
+valid; the CDN URL must never be cached.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+
+from .base import (
+    ModelSource,
+    ModelSourceError,
+    fetch_json,
+    fetch_text,
+    filter_weight_files,
+)
+
+logger = logging.getLogger(__name__)
+
+_URL_PATTERN = re.compile(
+    r"https?://(?:www\.)?modelscope\.(?:cn|com)/models/(?P<id>[^/?#\s]+/[^/?#\s]+)"
+)
+
+#: Trailing view segments the site appends to a model URL; accepted verbatim
+#: when the user pastes a browser tab URL.
+_VIEW_SEGMENTS = r"(?:summary|files|model-file|readme|community|evaluation)?"
+
+_STRICT_URL_PATTERN = re.compile(
+    r"https?://(?:www\.)?modelscope\.(?:cn|com)/models/(?P<id>[^/?#\s]+/[^/?#\s]+)"
+    rf"/?{_VIEW_SEGMENTS}/?$"
+)
+
+#: ``master`` is ModelScope's default branch; ``main`` is tried as a fallback
+#: for repos imported from Hugging Face.
+_REVISIONS = ("master", "main")
+
+
+class ModelScopeSource(ModelSource):
+    """ModelScope (``modelscope.cn``)."""
+
+    platform = "modelscope"
+    label = "ModelScope"
+    supports_enrichment = True
+    supports_download = True
+    default_revision = "master"
+    default_subdir = "modelscope"
+    url_pattern = _URL_PATTERN
+    strict_url_pattern = _STRICT_URL_PATTERN
+
+    def canonical_url(self, source_id: str) -> str:
+        return f"https://modelscope.cn/models/{source_id}"
+
+    def asset_base_url(self, source_id: str, revision: str = "") -> str:
+        return (
+            f"https://modelscope.cn/models/{source_id}/resolve/"
+            f"{self.resolve_revision(revision)}"
+        )
+
+    async def fetch_model_card(self, source_id: str) -> str:
+        """Fetch the model card, preferring the raw resolve URL."""
+
+        for revision in _REVISIONS:
+            text = await fetch_text(
+                f"https://modelscope.cn/models/{source_id}/resolve/{revision}/README.md"
+            )
+            if text:
+                return text
+
+        # Fallback: the repo API proxies the same file and is reachable in
+        # environments where the CDN resolve host is blocked.
+        for revision in _REVISIONS:
+            text = await fetch_text(
+                "https://modelscope.cn/api/v1/models/"
+                f"{source_id}/repo?Revision={revision}&FilePath=README.md"
+            )
+            if text:
+                return text
+        return ""
+
+    async def list_files(
+        self, source_id: str, revision: str = ""
+    ) -> list[dict]:
+        """List weight files via the repo files API.
+
+        ``master`` is the only branch name the API accepts — even repos
+        imported from Hugging Face are addressed as ``master`` (``main``
+        returns 404) — so no fallback probing is done here.
+        """
+
+        revision = self.resolve_revision(revision)
+        status, payload = await fetch_json(
+            "https://modelscope.cn/api/v1/models/"
+            f"{source_id}/repo/files?Revision={revision}"
+        )
+
+        if status == 404:
+            raise ModelSourceError(f"Repository '{source_id}' not found", status=404)
+        if status != 200 or not isinstance(payload, dict):
+            raise ModelSourceError(
+                f"ModelScope API error while listing '{source_id}' (HTTP {status})"
+            )
+
+        entries = []
+        for entry in (payload.get("Data") or {}).get("Files") or []:
+            if not isinstance(entry, dict) or entry.get("Type") != "blob":
+                continue
+            entries.append((entry.get("Path", ""), entry.get("Size", 0) or 0))
+
+        return filter_weight_files(entries)
+
+    def file_download_url(
+        self, source_id: str, filename: str, revision: str = ""
+    ) -> str:
+        return (
+            f"https://modelscope.cn/models/{source_id}/resolve/"
+            f"{self.resolve_revision(revision)}/{filename}"
+        )
+
+    def page_url_for_file(self, source_id: str, filename: str) -> str:
+        return (
+            f"https://modelscope.cn/models/{source_id}/file/view/"
+            f"{self.default_revision}/{filename}"
+        )
+
+
+__all__ = ["ModelScopeSource"]

py/services/model_sources/registry.py

@@ -0,0 +1,225 @@
+"""Registry and metadata helpers for external model sources.
+
+The registry is the single place the rest of the codebase asks "which site
+is this URL from?", "what is this model's source?", and "can we enrich it?".
+Import from :mod:`py.services.model_sources` rather than this module
+directly.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Mapping, Optional
+
+from .base import GROUP_PREFIXES, ModelSource, SourceRef, clean_source_url
+from .huggingface import HuggingFaceSource
+from .modelscope import ModelScopeSource
+from .tensorart import TensorArtSource
+
+logger = logging.getLogger(__name__)
+
+#: Order matters only for disambiguation; the URL patterns are disjoint.
+_SOURCES: tuple[ModelSource, ...] = (
+    HuggingFaceSource(),
+    ModelScopeSource(),
+    TensorArtSource(),
+)
+
+_BY_PLATFORM: Dict[str, ModelSource] = {s.platform: s for s in _SOURCES}
+
+#: Metadata keys that carry the canonical external-source identity.
+SOURCE_PLATFORM_FIELD = "source_platform"
+SOURCE_URL_FIELD = "source_url"
+#: Legacy field kept as a read/write alias for Hugging Face models so that
+#: older sidecars, cached rows, and third-party consumers keep working.
+LEGACY_HF_URL_FIELD = "hf_url"
+
+
+def list_sources() -> list[ModelSource]:
+    """Return every known model source."""
+
+    return list(_SOURCES)
+
+
+def get_source(platform: Optional[str]) -> Optional[ModelSource]:
+    """Return the source registered for *platform*, or ``None``."""
+
+    if not platform or not isinstance(platform, str):
+        return None
+    return _BY_PLATFORM.get(platform.strip().lower())
+
+
+def source_label(platform: Optional[str], default: str = "") -> str:
+    """Return the human-readable label for *platform*."""
+
+    source = get_source(platform)
+    return source.label if source else default
+
+
+def downloadable_sources() -> list[ModelSource]:
+    """Return the sources whose repositories can be downloaded directly."""
+
+    return [source for source in _SOURCES if source.supports_download]
+
+
+def get_download_source(platform: Optional[str]) -> Optional[ModelSource]:
+    """Return the source for *platform*, but only when it supports downloads."""
+
+    source = get_source(platform)
+    if source is None or not source.supports_download:
+        return None
+    return source
+
+
+def detect_source(url: Optional[str], *, strict: bool = False) -> Optional[SourceRef]:
+    """Return the :class:`SourceRef` for *url*, or ``None`` if unsupported."""
+
+    if not url or not isinstance(url, str):
+        return None
+    for source in _SOURCES:
+        ref = source.ref(url, strict=strict)
+        if ref is not None:
+            return ref
+    return None
+
+
+def resolve_source_ref(metadata: Mapping[str, Any]) -> Optional[SourceRef]:
+    """Return the source reference described by a model's metadata.
+
+    Handles all three storage states found in the wild:
+
+    1. ``source_url`` + ``source_platform`` (current format)
+    2. ``hf_url`` only (legacy Hugging Face storage)
+    3. ``hf_url`` plus a newer ``source_url`` (both written by older builds)
+    """
+
+    if not isinstance(metadata, Mapping):
+        return None
+
+    platform = clean_source_url(metadata.get(SOURCE_PLATFORM_FIELD)).lower()
+    url = clean_source_url(metadata.get(SOURCE_URL_FIELD))
+    legacy = clean_source_url(metadata.get(LEGACY_HF_URL_FIELD))
+
+    source = get_source(platform)
+    if url:
+        if source is not None:
+            ref = source.ref(url)
+            if ref is not None:
+                return ref
+        ref = detect_source(url)
+        if ref is not None:
+            return ref
+        # Unknown platform but a URL is present: keep it addressable.
+        return SourceRef(platform=platform or "unknown", source_id="", url=url)
+
+    if legacy:
+        return detect_source(legacy)
+    return None
+
+
+def normalize_metadata_source(metadata: Dict[str, Any]) -> Dict[str, Any]:
+    """Normalise the external-source fields on *metadata* in place.
+
+    Guarantees that ``source_url``/``source_platform`` are present and
+    consistent, and that ``hf_url`` mirrors ``source_url`` for Hugging Face
+    models (never for other platforms, so a stale alias can't make a
+    ModelScope model look like a Hugging Face one).
+
+    Returns the same dict for convenient chaining.
+    """
+
+    if not isinstance(metadata, dict):
+        return metadata
+
+    platform = clean_source_url(metadata.get(SOURCE_PLATFORM_FIELD)).lower()
+    url = clean_source_url(metadata.get(SOURCE_URL_FIELD))
+    legacy = clean_source_url(metadata.get(LEGACY_HF_URL_FIELD))
+
+    source = get_source(platform)
+    ref: Optional[SourceRef] = None
+
+    if url:
+        ref = source.ref(url) if source is not None else None
+        if ref is None:
+            ref = detect_source(url)
+    elif legacy:
+        ref = detect_source(legacy)
+
+    if ref is not None and ref.source_id:
+        platform = ref.platform
+        url = ref.url or url
+
+    if platform:
+        metadata[SOURCE_PLATFORM_FIELD] = platform
+    else:
+        metadata.setdefault(SOURCE_PLATFORM_FIELD, "")
+
+    metadata[SOURCE_URL_FIELD] = url
+
+    # Keep the legacy alias in sync, but only for Hugging Face.
+    if url and platform == "huggingface":
+        metadata[LEGACY_HF_URL_FIELD] = url
+    elif LEGACY_HF_URL_FIELD in metadata and platform and platform != "huggingface":
+        metadata[LEGACY_HF_URL_FIELD] = ""
+    elif legacy and not url:
+        metadata[LEGACY_HF_URL_FIELD] = legacy
+
+    return metadata
+
+
+def has_external_source(item: Mapping[str, Any]) -> bool:
+    """Return ``True`` when *item* is linked to any external model site."""
+
+    if not isinstance(item, Mapping):
+        return False
+    return bool(
+        clean_source_url(item.get(SOURCE_URL_FIELD))
+        or clean_source_url(item.get(LEGACY_HF_URL_FIELD))
+    )
+
+
+def get_source_platform(item: Mapping[str, Any]) -> str:
+    """Return the platform id stored on *item* (may be empty)."""
+
+    if not isinstance(item, Mapping):
+        return ""
+    platform = clean_source_url(item.get(SOURCE_PLATFORM_FIELD)).lower()
+    if platform:
+        return platform
+    ref = resolve_source_ref(item)
+    return ref.platform if ref else ""
+
+
+def source_group_key(item: Mapping[str, Any]) -> Optional[str]:
+    """Return the version-group key for *item*, or ``None``.
+
+    Hugging Face keeps the historical ``hf:{owner}/{repo}`` shape; other
+    platforms use their own short prefix (see :data:`GROUP_PREFIXES`).
+    """
+
+    ref = resolve_source_ref(item)
+    if ref is None or not ref.source_id:
+        return None
+    source = get_source(ref.platform)
+    if source is None:
+        return None
+    return source.group_key(ref.source_id)
+
+
+__all__ = [
+    "GROUP_PREFIXES",
+    "LEGACY_HF_URL_FIELD",
+    "SOURCE_PLATFORM_FIELD",
+    "SOURCE_URL_FIELD",
+    "detect_source",
+    "downloadable_sources",
+    "get_download_source",
+    "get_source",
+    "get_source_platform",
+    "has_external_source",
+    "list_sources",
+    "normalize_metadata_source",
+    "resolve_source_ref",
+    "source_group_key",
+    "source_label",
+]

py/services/model_sources/tensorart.py

@@ -0,0 +1,56 @@
+"""TensorArt model source (link / provenance only).
+
+TensorArt support is intentionally limited to *linking* a model to its
+TensorArt page.  Automatic metadata extraction is not possible without a
+user session:
+
+* ``tensor.art`` sits behind a Cloudflare managed challenge, so plain
+  HTTP clients (aiohttp, requests, curl) receive ``403 "Just a moment..."``.
+* Its internal API (``ap-east-1.tensorart.cloud`` / ``cn.tensorart.net``)
+  answers every ``/v1/model/*`` route with
+  ``{"code":100002,"message":"invalid authorization header"}``.
+* The official TAMS API requires an AccessKey/SecretKey pair and request
+  signatures, which is a poor fit for a "paste a URL" workflow.
+
+``supports_enrichment`` is therefore ``False``: the agent pipeline skips
+these models with an explicit reason instead of failing silently, and the
+UI keeps showing the "View on TensorArt" link.  ``tusi.cn`` is TensorArt's
+Chinese mirror and is accepted as the same platform.
+"""
+
+from __future__ import annotations
+
+import re
+
+from .base import ModelSource
+
+_DOMAINS = r"(?:tensor\.art|tusi\.cn)"
+
+_URL_PATTERN = re.compile(
+    rf"https?://(?:www\.)?{_DOMAINS}/models/(?P<id>\d+)"
+)
+
+_STRICT_URL_PATTERN = re.compile(
+    rf"https?://(?:www\.)?{_DOMAINS}/models/(?P<id>\d+)(?:/[^/?#\s]+)?/?$"
+)
+
+
+class TensorArtSource(ModelSource):
+    """TensorArt (``tensor.art``)."""
+
+    platform = "tensorart"
+    label = "TensorArt"
+    supports_enrichment = False
+    supports_download = False
+    url_pattern = _URL_PATTERN
+    strict_url_pattern = _STRICT_URL_PATTERN
+
+    def canonical_url(self, source_id: str) -> str:
+        return f"https://tensor.art/models/{source_id}"
+
+    def asset_base_url(self, source_id: str, revision: str = "") -> str:
+        # Unreachable today: enrichment is disabled for this platform.
+        return f"https://tensor.art/models/{source_id}"
+
+
+__all__ = ["TensorArtSource"]

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/other_model_service.py

@@ -0,0 +1,81 @@
+import os
+import logging
+from typing import Any, Dict, Optional
+
+from .base_model_service import BaseModelService
+from .auto_tag_service import extract_auto_tags
+from ..utils.models import OtherModelMetadata
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+
+class OtherModelService(BaseModelService):
+    """Other-model-specific service implementation (VAE, upscaler, text encoder, ...)"""
+
+    def __init__(self, scanner, update_service=None):
+        """Initialize Other-model service
+
+        Args:
+            scanner: Other-model scanner instance
+            update_service: Optional service for remote update tracking.
+        """
+        super().__init__("other", scanner, OtherModelMetadata, update_service=update_service)
+
+    async def format_response(self, model_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Format other-model data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = model_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted other-model entry (missing file_path): %s",
+                model_data.get("file_name", "<unknown>"),
+            )
+            return None
+
+        # Get sub_type from cache entry (new canonical field)
+        sub_type = model_data.get("sub_type", "vae")
+
+        file_name = model_data.get("file_name") or ""
+        model_name = model_data.get("model_name") or file_name
+        folder = model_data.get("folder") or ""
+
+        return {
+            "model_name": model_name,
+            "file_name": file_name,
+            "preview_url": config.get_preview_static_url(model_data.get("preview_url", "")),
+            "preview_nsfw_level": model_data.get("preview_nsfw_level", 0),
+            "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", ""),
+            "tags": model_data.get("tags", []),
+            "from_civitai": model_data.get("from_civitai", True),
+            "notes": model_data.get("notes", ""),
+            "sub_type": sub_type,
+            "favorite": model_data.get("favorite", False),
+            "exclude": bool(model_data.get("exclude", False)),
+            "update_available": bool(model_data.get("update_available", False)),
+            "skip_metadata_refresh": bool(model_data.get("skip_metadata_refresh", False)),
+            "civitai": self.filter_civitai_data(model_data.get("civitai", {}), minimal=True),
+            "auto_tags": model_data.get("auto_tags") or extract_auto_tags(model_data),
+            "version_count": model_data.get("version_count"),
+            "source_platform": model_data.get("source_platform", ""),
+            "source_url": model_data.get("source_url", ""),
+            "hf_url": model_data.get("hf_url", ""),
+        }
+
+    def find_duplicate_hashes(self) -> Dict[str, Any]:
+        """Find other models with duplicate SHA256 hashes"""
+        return self.scanner._hash_index.get_duplicate_hashes()
+
+    def find_duplicate_filenames(self) -> Dict[str, Any]:
+        """Find other models with conflicting filenames"""
+        return self.scanner._hash_index.get_duplicate_filenames()

py/services/other_scanner.py

@@ -0,0 +1,478 @@
+# pyright: reportImportCycles=false
+# 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 asyncio
+import json
+import logging
+import os
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from ..utils.models import OtherModelMetadata
+from ..utils.file_utils import find_preview_file, normalize_path, calculate_autov3
+from ..utils.metadata_manager import MetadataManager
+from ..config import config
+from .model_scanner import ModelScanner, _is_excluded_dir
+from .model_hash_index import ModelHashIndex
+
+logger = logging.getLogger(__name__)
+
+
+class OtherScanner(ModelScanner):
+    """Service for scanning and managing "other" model files.
+
+    Aggregates every enabled folder_paths category from
+    OTHER_MODEL_FOLDER_SUBTYPES (VAE, upscalers, text encoders, CLIP vision,
+    opt-in ControlNet) into one scanner; sub_type is derived from the root
+    containing the file (mirrors CheckpointScanner's checkpoints/unet split).
+
+    Hashing is lazy (checkpoint-style): text encoders can be ~10 GB, so the
+    initial scan records hash_status="pending" and the SHA256 is computed
+    on-demand via calculate_hash_for_model (e.g. when fetching CivitAI
+    metadata).
+    """
+
+    def __init__(self):
+        # Same extension set as CheckpointScanner (ComfyUI's
+        # supported_pt_extensions plus ".gguf").
+        file_extensions = {
+            ".ckpt",
+            ".pt",
+            ".pt2",
+            ".bin",
+            ".pth",
+            ".safetensors",
+            ".pkl",
+            ".sft",
+            ".gguf",
+        }
+        super().__init__(
+            model_type="other",
+            model_class=OtherModelMetadata,
+            file_extensions=file_extensions,
+            hash_index=ModelHashIndex(),
+        )
+        if not hasattr(self, "_hash_calculation_lock"):
+            self._hash_calculation_lock = asyncio.Lock()
+            self._hash_calculation_tasks: dict[str, asyncio.Task[Optional[str]]] = {}
+
+    async def _create_default_metadata(
+        self, file_path: str
+    ) -> Optional[OtherModelMetadata]:
+        """Create default metadata without calculating hash (lazy hash).
+
+        Other models include multi-GB text encoders, so hash calculation is
+        deferred until on-demand (e.g. CivitAI metadata fetch).
+        """
+        try:
+            real_path = os.path.realpath(file_path)
+            if not os.path.exists(real_path):
+                logger.error(f"File not found: {file_path}")
+                return None
+
+            base_name = os.path.splitext(os.path.basename(file_path))[0]
+            dir_path = os.path.dirname(file_path)
+
+            # Find preview image
+            preview_url = find_preview_file(base_name, dir_path)
+
+            # AutoV3 reads only the safetensors header, so it is cheap even for
+            # large files; record the checked state at creation time ("" =
+            # checked but unavailable).
+            autov3 = calculate_autov3(real_path)
+
+            # Create metadata WITHOUT calculating hash
+            metadata = OtherModelMetadata(
+                file_name=base_name,
+                model_name=base_name,
+                file_path=normalize_path(file_path),
+                size=os.path.getsize(real_path),
+                modified=datetime.now().timestamp(),
+                sha256="",  # Empty hash - will be calculated on-demand
+                base_model="Unknown",
+                preview_url=normalize_path(preview_url),
+                tags=[],
+                modelDescription="",
+                sub_type=self.resolve_sub_type_for_path(file_path) or "vae",
+                from_civitai=False,  # Mark as local model since no hash yet
+                hash_status="pending",  # Mark hash as pending
+                autov3=autov3 or "",
+            )
+
+            # Save the created metadata
+            logger.info(f"Creating other-model metadata (hash pending) for {file_path}")
+            await MetadataManager.save_metadata(file_path, metadata)
+
+            return metadata
+
+        except Exception as e:
+            logger.error(
+                f"Error creating default other-model metadata for {file_path}: {e}"
+            )
+            return None
+
+    async def calculate_hash_for_model(self, file_path: str) -> Optional[str]:
+        """Calculate hash for a model on-demand with per-file singleflight.
+
+        Args:
+            file_path: Path to the model file
+
+        Returns:
+            SHA256 hash string, or None if calculation failed
+        """
+        try:
+            real_path = os.path.realpath(file_path)
+            if not os.path.exists(real_path):
+                logger.error(f"File not found for hash calculation: {file_path}")
+                return None
+
+            metadata, _ = await MetadataManager.load_metadata(
+                file_path, self.model_class
+            )
+            if (
+                metadata is not None
+                and metadata.hash_status == "completed"
+                and metadata.sha256
+            ):
+                # Ensure the in-memory hash index is populated even when
+                # the hash was already computed and persisted to the metadata
+                # file.  Without this, usage tracking (and any other caller
+                # that queries get_hash_by_filename first) will miss on every
+                # lookup and keep calling back into this method, creating a
+                # tight loop that never populates the index.
+                self._hash_index.add_entry(
+                    metadata.sha256.lower(),
+                    file_path,
+                    getattr(metadata, "autov3", None) or None,
+                )
+                return metadata.sha256
+
+            async with self._hash_calculation_lock:
+                metadata, _ = await MetadataManager.load_metadata(
+                    file_path, self.model_class
+                )
+                if (
+                    metadata is not None
+                    and metadata.hash_status == "completed"
+                    and metadata.sha256
+                ):
+                    self._hash_index.add_entry(
+                        metadata.sha256.lower(),
+                        file_path,
+                        getattr(metadata, "autov3", None) or None,
+                    )
+                    return metadata.sha256
+
+                task = self._hash_calculation_tasks.get(real_path)
+                if task is None:
+                    task = asyncio.create_task(
+                        self._run_hash_calculation_task(file_path, real_path)
+                    )
+                    self._hash_calculation_tasks[real_path] = task
+
+            return await asyncio.shield(task)
+
+        except Exception as e:
+            logger.error(f"Error calculating hash for {file_path}: {e}")
+            return None
+
+    async def _run_hash_calculation_task(
+        self, file_path: str, real_path: str
+    ) -> Optional[str]:
+        """Run a hash calculation task and remove it from the in-flight map."""
+        try:
+            return await self._calculate_hash_for_model_uncached(file_path, real_path)
+        finally:
+            task = asyncio.current_task()
+            async with self._hash_calculation_lock:
+                if self._hash_calculation_tasks.get(real_path) is task:
+                    del self._hash_calculation_tasks[real_path]
+
+    async def _calculate_hash_for_model_uncached(
+        self, file_path: str, real_path: str
+    ) -> Optional[str]:
+        """Calculate hash for a model without checking in-flight tasks."""
+        from ..utils.file_utils import calculate_sha256
+
+        try:
+            # Load current metadata
+            metadata, should_skip = await MetadataManager.load_metadata(
+                file_path, self.model_class
+            )
+            if metadata is None:
+                if should_skip:
+                    logger.error(f"Invalid metadata found for {file_path}")
+                    return None
+                created_metadata = await self._create_default_metadata(file_path)
+                if created_metadata is None:
+                    logger.error(f"No metadata found for {file_path}")
+                    return None
+                metadata = created_metadata
+
+            # Check if hash is already calculated
+            if metadata.hash_status == "completed" and metadata.sha256:
+                # Populate the in-memory hash index even for pre-computed
+                # hashes, mirroring the fix in calculate_hash_for_model.
+                self._hash_index.add_entry(
+                    metadata.sha256.lower(),
+                    file_path,
+                    getattr(metadata, "autov3", None) or None,
+                )
+                return metadata.sha256
+
+            # Update status to calculating
+            metadata.hash_status = "calculating"
+            await MetadataManager.save_metadata(file_path, metadata)
+
+            # Calculate hash
+            logger.info(f"Calculating hash for other model: {file_path}")
+            sha256 = await calculate_sha256(real_path)
+
+            # Update metadata with hash
+            metadata.sha256 = sha256
+            metadata.hash_status = "completed"
+            await MetadataManager.save_metadata(file_path, metadata)
+
+            # Update hash index
+            self._hash_index.add_entry(
+                sha256.lower(),
+                file_path,
+                getattr(metadata, "autov3", None) or None,
+            )
+
+            # Update the in-memory cache entry so that subsequent
+            # _persist_current_cache / _save_persistent_cache calls
+            # write the hash back to the SQLite models table.  Without
+            # this the hash only lives in the metadata file and the
+            # in-memory hash index, both of which are lost across
+            # restarts, causing the same re-computation loop on the
+            # next session.
+            if self._cache is not None and self._cache.raw_data:
+                for entry in self._cache.raw_data:
+                    if entry.get("file_path") == file_path:
+                        entry["sha256"] = sha256.lower()
+                        entry["hash_status"] = "completed"
+                        self.bump_cache_version()
+                        break
+
+            logger.info(f"Hash calculated for other model: {file_path}")
+            return sha256
+
+        except Exception as e:
+            logger.error(f"Error calculating hash for {file_path}: {e}")
+            # Update status to failed
+            try:
+                metadata, _ = await MetadataManager.load_metadata(
+                    file_path, self.model_class
+                )
+                if metadata:
+                    metadata.hash_status = "failed"
+                    await MetadataManager.save_metadata(file_path, metadata)
+            except Exception:
+                pass
+            return None
+
+    async def calculate_all_pending_hashes(
+        self, progress_callback=None
+    ) -> Dict[str, int]:
+        """Calculate hashes for all other models with pending hash status.
+
+        If cache is not initialized, scans filesystem directly for metadata files
+        with hash_status != 'completed'.
+
+        Args:
+            progress_callback: Optional callback(progress, total, current_file)
+
+        Returns:
+            Dict with 'completed', 'failed', 'total' counts
+        """
+        # Try to get from cache first
+        cache = await self.get_cached_data()
+
+        if cache and cache.raw_data:
+            # Use cache if available
+            pending_models = [
+                item
+                for item in cache.raw_data
+                if item.get("hash_status") != "completed" or not item.get("sha256")
+            ]
+        else:
+            # Cache not initialized, scan filesystem directly
+            pending_models = await self._find_pending_models_from_filesystem()
+
+        if not pending_models:
+            return {"completed": 0, "failed": 0, "total": 0}
+
+        total = len(pending_models)
+        completed = 0
+        failed = 0
+
+        for i, model_data in enumerate(pending_models):
+            file_path = model_data.get("file_path")
+            if not file_path:
+                continue
+
+            try:
+                sha256 = await self.calculate_hash_for_model(file_path)
+                if sha256:
+                    completed += 1
+                else:
+                    failed += 1
+            except Exception as e:
+                logger.error(f"Error calculating hash for {file_path}: {e}")
+                failed += 1
+
+            if progress_callback:
+                try:
+                    await progress_callback(i + 1, total, file_path)
+                except Exception:
+                    pass
+
+        return {"completed": completed, "failed": failed, "total": total}
+
+    async def _find_pending_models_from_filesystem(self) -> List[Dict[str, Any]]:
+        """Scan filesystem for other-model metadata files with pending hash status."""
+        pending_models = []
+
+        for root_path in self.get_model_roots():
+            if not os.path.exists(root_path):
+                continue
+
+            for dirpath, dirnames, filenames in os.walk(root_path):
+                dirnames[:] = [d for d in dirnames if not _is_excluded_dir(d)]
+                for filename in filenames:
+                    if not filename.endswith(".metadata.json"):
+                        continue
+
+                    metadata_path = os.path.join(dirpath, filename)
+                    try:
+                        with open(metadata_path, "r", encoding="utf-8") as f:
+                            data = json.load(f)
+
+                        # Check if hash is pending
+                        hash_status = data.get("hash_status", "completed")
+                        sha256 = data.get("sha256", "")
+
+                        if hash_status != "completed" or not sha256:
+                            # Find corresponding model file
+                            model_name = filename.replace(".metadata.json", "")
+                            model_path = None
+
+                            # Look for model file with matching name
+                            for ext in self.file_extensions:
+                                potential_path = os.path.join(dirpath, model_name + ext)
+                                if os.path.exists(potential_path):
+                                    model_path = potential_path
+                                    break
+
+                            if model_path:
+                                pending_models.append(
+                                    {
+                                        "file_path": model_path.replace(os.sep, "/"),
+                                        "hash_status": hash_status,
+                                        "sha256": sha256,
+                                        **{
+                                            k: v
+                                            for k, v in data.items()
+                                            if k
+                                            not in [
+                                                "file_path",
+                                                "hash_status",
+                                                "sha256",
+                                            ]
+                                        },
+                                    }
+                                )
+                    except (json.JSONDecodeError, Exception) as e:
+                        logger.debug(
+                            f"Error reading metadata file {metadata_path}: {e}"
+                        )
+                        continue
+
+        return pending_models
+
+    def _root_sub_type_map(self) -> Dict[str, str]:
+        """Return the configured business root -> sub_type map."""
+        root_map = getattr(config, "other_root_subtypes", None)
+        return root_map if isinstance(root_map, dict) else {}
+
+    def _resolve_sub_type(self, root_path: Optional[str]) -> Optional[str]:
+        """Resolve the sub_type for a configured root path."""
+        if not root_path:
+            return None
+
+        normalized_root = self._normalize_path_value(root_path)
+        for root, sub_type in self._root_sub_type_map().items():
+            if self._normalize_path_value(root) == normalized_root:
+                return sub_type
+
+        return None
+
+    def resolve_sub_type_for_path(self, file_path: Optional[str]) -> Optional[str]:
+        """Resolve sub_type from the configured root that contains the file.
+
+        Uses the longest-prefix match so nested roots (e.g. a controlnet root
+        inside a vae root) resolve to the most specific category.
+        """
+        normalized_path = self._normalize_path_value(file_path)
+        if not normalized_path:
+            return None
+
+        best_length = 0
+        best_sub_type: Optional[str] = None
+        for root, sub_type in self._root_sub_type_map().items():
+            normalized_root = self._normalize_path_value(root)
+            if not normalized_root:
+                continue
+            if (
+                normalized_path == normalized_root
+                or normalized_path.startswith(f"{normalized_root}/")
+            ) and len(normalized_root) > best_length:
+                best_length = len(normalized_root)
+                best_sub_type = sub_type
+
+        return best_sub_type
+
+    def adjust_metadata(self, metadata, file_path, root_path):
+        """Adjust metadata during scanning to set sub_type."""
+        sub_type = self._resolve_sub_type(root_path) or self.resolve_sub_type_for_path(
+            file_path
+        )
+        if sub_type:
+            metadata.sub_type = sub_type
+        return metadata
+
+    def adjust_cached_entry(self, entry: Dict[str, Any]) -> Dict[str, Any]:
+        """Adjust entries loaded from the persisted cache to ensure sub_type is set.
+
+        sub_type is location-derived: it is re-derived on cache load, never
+        trusted from the persisted snapshot.
+        """
+        sub_type = self.resolve_sub_type_for_path(entry.get("file_path"))
+        if sub_type:
+            entry["sub_type"] = sub_type
+        return entry
+
+    def _should_keep_cached_entry(self, entry: Dict[str, Any]) -> bool:
+        """Drop persisted entries whose folder is no longer a managed root.
+
+        sub_type is location-derived and config only maps enabled roots, so a
+        file under a disabled sub_type - or under any other root while the
+        feature is off - resolves to None here and is filtered out while the
+        persisted cache is hydrated.
+        """
+        return self.resolve_sub_type_for_path(entry.get("file_path")) is not None
+
+    def get_model_roots(self) -> List[str]:
+        """Get other-model root directories"""
+        roots: List[str] = []
+        roots.extend(config.other_roots or [])
+        # Remove duplicates while preserving order
+        seen: set[str] = set()
+        unique_roots: List[str] = []
+        for root in roots:
+            if root and root not in seen:
+                seen.add(root)
+                unique_roots.append(root)
+        return unique_roots

py/services/pending_delete_service.py

@@ -59,6 +59,7 @@ _MODEL_TYPE_PAGE_MAP = {
     "lora": "loras",
     "checkpoint": "checkpoints",
     "embedding": "embeddings",
+    "other": "other",
 }
 
 # Module-level alias so tests can spy on timer task creation without patching
@@ -274,17 +275,21 @@ 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 +303,68 @@ 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 +393,16 @@ 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 +454,10 @@ 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 +510,36 @@ 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 +554,20 @@ 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 +789,35 @@ 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 +831,13 @@ 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 +869,7 @@ 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:
@@ -931,6 +984,7 @@ class PendingDeleteService:
             "get_lora_scanner",
             "get_checkpoint_scanner",
             "get_embedding_scanner",
+            "get_other_scanner",
         ):
             getter = getattr(ServiceRegistry, getter_name, None)
             if not callable(getter):