i18n: translate the Filename Templates feature into all locales

26 keys (settings.filenameTemplates.*, filenameTemplateProgress, modals.filenameTemplateConfirm, related toasts) translated into the 9 non-English locales, reusing each locale's autoOrganizeProgress / downloadPathTemplates renderings. Terminology recorded in docs/i18n-translation-guidelines.md.
feat(settings): empty filename template reverts to recorded original filename (#1071 )
2026-09-21 11:11:26 -03:00 · 2026-09-19 11:00:39 +08:00 · 2026-09-19 10:44:43 +08:00 · 2026-09-19 09:05:39 +08:00 · 2026-09-19 09:04:24 +08:00 · 2026-09-19 07:38:07 +08:00
439 changed files with 70557 additions and 10857 deletions
@@ -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.
@@ -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.
@@ -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"
 ```
@@ -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)
@@ -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:
@@ -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():
@@ -15,6 +15,7 @@ node_modules/
 coverage/
 .coverage
 model_cache/
 recipe_cache/
 # agent / dev tooling
 .opencode/
@@ -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 +63,210 @@ 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`
+#### Imports & Modules
 - 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
 - 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
+- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*`, `/other/*` patterns
- `ModelScanner` for file discovery, hash deduplication
+- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, `RecipeRouteRegistrar`, etc.
- `PersistentModelCache` (SQLite) for persistence
+- Request handlers in `py/routes/handlers/` implement route logic
- Route registrars: `ModelRouteRegistrar`, endpoints: `/loras/*`, `/checkpoints/*`, `/embeddings/*`
+- All routes use aiohttp, return `web.json_response` or `web.Response`
- WebSocket via `WebSocketManager` for real-time updates
+- 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`
+- Base: `py/recipes/base.py`, Enrichment: `RecipeEnrichmentService` in `py/recipes/enrichment.py`
- Parsers: `py/recipes/parsers/`
+- 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`)
 - **`folder_paths` vs `extra_folder_paths` — different purposes, do not conflate:**
  - `folder_paths` (primary model roots): in ComfyUI plugin mode these come
    from the ComfyUI host; in standalone mode they are the ONLY source of
    model library paths and are currently edited by hand in `settings.json`.
  - `extra_folder_paths` is a **ComfyUI-plugin-mode feature**: paths visible
    ONLY to LoRA Manager, not to ComfyUI. Its motivation is that a very large
    model library slows ComfyUI itself down, while LoRA Manager handles large
    libraries without performance issues — so users keep ComfyUI's library
    small and add the bulk via `extra_folder_paths`.
 ### 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)
+- **`settings.json.example` must stay minimal**: only `use_portable_settings`,
- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+  `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 +274,4 @@ 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`).
+  `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`
@@ -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
@@ -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,
@@ -62,27 +62,143 @@ 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 (`modelscope.cn`) | yes | yes | yes |
 | ModelScope International (`modelscope.ai`) | yes | yes | yes |
 | TensorArt | yes | no (see below) | no |
 `modelscope.cn` and `modelscope.ai` are **separate catalogues, not mirrors** — a
 repository published on one is routinely absent from the other — so each is
 registered as its own source (`ModelScopeSource` / `ModelScopeIntlSource` in
 `py/services/model_sources/modelscope.py`). The host therefore decides which
 API and CDN a model resolves against, and the two deployments get separate
 version groups (`ms:` / `msai:`) and default download directories. Keep the two
 tables in `modelSourceHelpers.js` and `registry.py` in step when adding a site.
 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`
+1. Reads the model's `.metadata.json` to get the source (`source_platform` + `source_url`, or the legacy `hf_url`)
-2. Fetches the README.md from the HuggingFace repository
+2. Fetches the model card through the provider in `py/services/model_sources/` — the README via `fetch_model_card()`, plus any extras the site keeps outside it via `fetch_model_card_context()`
-3. Sends the README + local metadata to the LLM for structured extraction
+3. Sends the README + site-provided extras + local metadata to the LLM for structured extraction
 4. Writes extracted fields to `.metadata.json`:
   - `base_model` — only if current value is empty
   - `trainedWords` — trigger words (LoRA only, if none exist)
-   - `modelDescription` — concise summary (if none exists)
+   - `modelDescription` — the site's author description (if any) followed by the README rendered as HTML
   - `tags` — merged with existing tags, deduplicated
   - `civitai.images` — example images
   - `metadata_source` — audit trail: `agent:enrich_hf_metadata`
   - `llm_enriched_at` — ISO timestamp
-5. Downloads and optimizes preview image (if LLM found one in the README)
+5. Downloads and optimizes a preview image, using the per-file example image the
   site publishes when the README has none
 6. Updates the scanner cache
 7. Broadcasts WebSocket progress events
 #### Site-provided card extras (`fetch_model_card_context`)
 A model card is not always just `README.md`. ModelScope keeps the author's
 summary (`Description`), the site-curated tags (`OfficialTags`), and — per
 published version — the model filenames together with that file's example
 images (`MuseInfo.versions[].coverImages`) and trigger words in its
 model-detail API. AIGC repositories there often ship an auto-generated
 boilerplate README and put everything useful in `Description`, so reading only
 the README yields almost nothing.
 Providers opt in by overriding `ModelSource.fetch_model_card_context()`, which
 returns a `ModelCardContext`. The wanted file is identified by its sha256 when
 the caller knows it (the scanner already records one) and by **basename**
 otherwise, so each checkpoint in a collection repo gets its own images — and
 keeps getting them after the user renames the weights, which is the only
 identifier a rename cannot invalidate. Sites with no such extras inherit an
 empty context, and the pipeline behaves exactly as before.
 The README and the repository metadata describe the whole repository, not one
 file, so `execute_skill()` creates a `ModelSourceCache` for the duration of a
 run and passes it down. Enriching the eight checkpoints of one ModelScope
 repository costs two HTTP requests instead of sixteen; only the per-file
 selection is redone for each file. Nothing is cached across runs, and download
 URLs never go through it.
 #### Deterministic data is applied whether or not an LLM is configured
 `AgentService._load_source_card()` runs for every source-backed enrichment, and
 the post-processor applies what it returns before the LLM output is merged. A
 user with **no** provider configured therefore still gets the author summary,
 the example images, the preview, the site-curated tags, the trigger words and
 the README rendered as the model description.
 The LLM is always consulted when one is configured — invoking **Enrich Metadata
 with AI** must call the provider every time, and the site data is never treated
 as a reason to skip it. The deterministic values act as fallbacks that fill
 gaps the LLM leaves behind:
 | Field | Deterministic source | LLM role |
 | --- | --- | --- |
 | `model_name` | site display name (`Name`), written only while the value is still the file stem | — |
 | `modelDescription` | author summary + README as HTML | — |
 | `civitai.name` | the matched version's label (`modelVersion.showName`) | — |
 | `civitai.images` | site example images, then README images | — |
 | `preview_url` | first available example image | may propose one from the README |
 | `tags` | site-curated tags, always merged in | proposes additional content tags |
 | `civitai.description` | author summary | richer 1-2 sentence summary wins |
 | `base_model` | site hints resolved against the canonical vocabulary (`py/services/agent/base_model_resolver.py`) | mapping it is the LLM's job; the resolver only fills in when the LLM returns nothing |
 | `trainedWords` | per-file site trigger words, then YAML `instance_prompt` | primary extraction |
 | `usage_tips` | regex over an explicitly stated strength range | primary extraction |
 | `notes` | — | LLM-only |
 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
 ### Download-time hydration
 The same deterministic mapping runs automatically when a model is downloaded
 from a model source, so a ModelScope or Hugging Face download lands with the
 populated card a CivitAI download produces instead of a bare filename and
 hash. Nothing needs to be triggered by hand and no provider is called.
 `py/services/model_sources/hydration.py` owns this path:
 * `_save_source_metadata()` in `py/routes/handlers/model_source_handlers.py`
  creates the sidecar (hash, source link, scanner-cache entry) and then calls
  `hydrate_from_source()`. It also runs for a file that was already on disk, so
  models downloaded before this existed get topped up on the next attempt.
 * Metadata is created through the **owning scanner**
  (`scanner._create_default_metadata()`) rather than
  `MetadataManager.create_default_metadata()`, so the per-type lazy-hash rule
  applies: `CheckpointScanner` and `OtherScanner` store
  `hash_status="pending"` with an empty `sha256` for their multi-GB files, and
  the generic helper would read a 10 GB checkpoint end to end inside the
  download request. Hydration copes with the empty hash — `_matching_versions()`
  falls back to the repository basename, which the download just wrote.
 * Hydration reuses `PostProcessor` with an empty `llm_output`, so the two paths
  cannot drift apart. It reports `metadata_source = "source:<platform>"` rather
  than the skill's `agent:enrich_hf_metadata`, and — because no provider ran —
  it does not stamp `llm_enriched_at`.
 * `model_name` is only written while it still equals the file stem: once a user
  renames a model, that choice is kept.
 * Only a model whose stored `source_platform`/`source_url` match the repository
  being downloaded is updated; a local file that merely shares a name must not
  receive another model's card.
 * The README and repository payload describe the *repository*, so a short-lived
  process-wide `ModelSourceCache` (`shared_source_cache`, 300 s, 32 entries)
  keeps a batch over one repository to two HTTP requests.
 * Every failure — unreachable site, changed payload shape, broken post-processor
  — is logged and swallowed. Metadata hydration can never fail a download.
 * Neither stage advances the byte counter, so both are announced to the
  progress UI (`_report_phase()` → `{"status": "metadata", "stage": ...}`) as
  they start. Without that the bar sits at 100% reporting `0 B/s` for several
  seconds and the download looks stuck. `stage` and `platform` are
  machine-readable; the wording is localised in `LoadingManager`.
 ## Adding a New Skill
 ### 1. Create the skill directory
@@ -129,7 +245,7 @@ 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}}
@@ -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
@@ -0,0 +1,590 @@
 # 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, 2025 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".
 >
 > **Status (2026-09, folder sidebar):** the model-root sidebar gained on-disk folder management
 > (create / rename / delete folders, show empty folders, tree vs list view) plus its `...`
 > view-options menu, adding 35 `sidebar.*` keys. Those were the only `[TODO: Translate]`
 > placeholders left behind by the feature series, and all 35 are now translated in all 9
 > locales, so the "no remaining placeholders" claim above holds again. Terminology in §2,
 > "Folder sidebar feature".
 >
 > **Status (2026-09, chip reordering):** model tags and trigger words now share one drag/`⠿`
 > grip reorder affordance, which added the single `common.reorder.dragHandle` key (it lives
 > under `common` because both editors render it). All 9 locales are translated (renderings in
 > §2, "Chip reordering"). Reordering is pointer-only by design: an `Alt + Arrow` shortcut was
 > prototyped and removed because it collided with the browser's Alt + Arrow handling and the
 > modal's arrow-key navigation.
 > **Status (2026-09, standalone no-paths guidance):** the standalone branch of the
 > `other.noPaths` empty state now shows the real `settings.json` path plus an
 > `other.noPaths.openSettingsFolder` button (each locale reuses its
 > `settings.openSettingsFileLocation.label` rendering), and `descriptionStandalone` was
 > reworded in `en.json` — from "none of the configured folders exist on disk" to "no
 > other-model folders were found; add the folder keys you need to the `folder_paths`
 > section" — and re-translated in all 9 locales. The `on disk` phrase now survives only in
 > the ComfyUI variant (`descriptionComfyUI`).
 > **Status (2026-09, settings Organization tab):** the settings modal split its overloaded
 > Library tab, adding the single `settings.nav.organization` key (renderings in §2,
 > "Settings Organization tab"). All 9 locales are translated, so the "no remaining
 > placeholders" claim holds again.
 > **Status (2026-09, filename templates):** the Filename Templates feature (per-model-type
 > download filename templates + bulk "Apply to Library Now" rename, with an empty template
 > restoring recorded original filenames) added 26 keys across `settings.filenameTemplates.*`,
 > `loras.bulkOperations.filenameTemplateProgress.*`, `modals.filenameTemplateConfirm.*` and
 > the `toast.loras.filenameTemplate*` / `toast.settings.filenameTemplates*` toasts. All 9
 > locales are translated (terminology in §2, "Filename Templates 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).
 ### Folder sidebar feature (create / rename / delete folders, empty folders, view options)
 The model-root sidebar manages on-disk folders. "Folder" reuses the noun already fixed in §2
 (the `folder key` row); the rest is new surface:
 | Term | Rendering |
 |---|---|
 | folder | zh-CN 文件夹 · zh-TW 資料夾 · ja フォルダ · ko 폴더 · fr dossier · de Ordner · es carpeta · ru папка · he תיקייה |
 | model root (as in "no model root is configured") | zh-CN 模型根目录 · zh-TW 模型根目錄 · ja モデルルート · ko 모델 루트 · fr racine de modèle · de Modell-Stammverzeichnis · es raíz de modelo · ru корневая папка моделей · he שורש מודלים — note `sidebar.modelRoot` alone is the shorter 根目录 / 根目錄 / ルート / 루트 / Racine / Stammverzeichnis / Raíz / Корень / שורש |
 | tree view / list view | zh-CN 树形视图 / 列表视图 · zh-TW 樹狀檢視 / 清單檢視 · ja ツリー表示 / リスト表示 · ko 트리 보기 / 목록 보기 · fr Vue arborescente / Vue liste · de Baumansicht / Listenansicht · es Vista de árbol / Vista de lista · ru Дерево / Список · he תצוגת עץ / תצוגת רשימה |
 | sidebar | reuse each locale's `sidebar.hideOnThisPage` noun: zh-CN 侧边栏 · zh-TW 側邊欄 · ja サイドバー · ko 사이드바 · fr barre latérale · de Seitenleiste · es barra lateral · ru боковая панель · he סרגל צד |
 Deleting a folder **never cascades over model files** — the backend refuses it and
 `sidebar.deleteFolderModal.notEmptyMessage` states the rule in every locale, so keep that
 clause (and its `—`) when the copy is edited. The `{name}` / `{count}` / `{message}` tokens in
 `sidebar.createFolderResult.*`, `sidebar.deleteFolderResult.*` and `sidebar.renameFolderResult.*`
 are verbatim §1-R2 placeholders; `successWithFiles` is the only key carrying `{count}`.
 ### Settings Organization tab
 The settings modal's fourth nav tab groups everything about how files are arranged on
 disk: download path templates, priority tags, and auto-organize exclusions. The label is
 the **noun for arranging files**, matching each locale's existing
 `settings.sections.autoOrganize` rendering minus the "auto":
 | Locale | `settings.nav.organization` |
 |---|---|
 | fr | Organisation |
 | zh-CN | 整理 |
 | zh-TW | 整理 |
 | ja | 整理 |
 | ko | 정리 |
 | de | Organisation |
 | es | Organización |
 | ru | Организация |
 | he | ארגון |
 zh-CN/zh-TW use 整理 ("tidying/arranging"), not 组织/組織 (an organization as a group).
 ### Filename Templates feature
 Per-model-type templates that name downloaded model files; "Apply to Library Now"
 bulk-renames existing files, and an **empty template restores the recorded original
 filenames** (recorded in each model's metadata at its first rename). "Template" follows
 each locale's existing download-path-template noun (zh-CN 模板 vs zh-TW 範本 — note the
 split); progress strings mirror `loras.bulkOperations.autoOrganizeProgress` verbatim with
 the locale's "moved" verb swapped for its "renamed" verb, and the toasts mirror the
 `autoOrganize*` / `downloadTemplates*` toast shapes.
 | Term | Rendering |
 |---|---|
 | filename template(s) | zh-CN 文件名模板 · zh-TW 檔案名稱範本 · ja ファイル名テンプレート · ko 파일명 템플릿 · fr modèle(s) de nom de fichier · de Dateinamen-Vorlage(n) · es plantilla(s) de nombres de archivo · ru шаблон(ы) имён файлов · he תבנית שם קובץ / תבניות שמות קבצים |
 | Apply to Library Now (button) | zh-CN 立即应用到库 · zh-TW 立即套用至模型庫 · ja ライブラリに今すぐ適用 · ko 지금 라이브러리에 적용 · fr Appliquer à la bibliothèque maintenant · de Jetzt auf Bibliothek anwenden · es Aplicar a la biblioteca ahora · ru Применить к библиотеке сейчас · he החל על הספרייה כעת |
 | Restore original filenames (modal title / button) | zh-CN 恢复原始文件名？/ 恢复原始文件名 · zh-TW 要還原原始檔案名稱嗎？/ 還原原始檔案名稱 · ja 元のファイル名を復元しますか？/ 元のファイル名を復元 · ko 원본 파일명을 복원하시겠습니까? / 원본 파일명 복원 · fr Restaurer les noms de fichier d'origine ? / Restaurer les noms de fichier d'origine · de Ursprüngliche Dateinamen wiederherstellen? / Ursprüngliche Dateinamen wiederherstellen · es ¿Restaurar los nombres de archivo originales? / Restaurar nombres de archivo originales · ru Восстановить исходные имена файлов? / Восстановить исходные имена файлов · he לשחזר שמות קבצים מקוריים? / שחזר שמות קבצים מקוריים |
 | "renamed" (progress/toast counter) | zh-CN 已重命名 · zh-TW 已重新命名 · ja リネーム · ko 이름 변경 · fr renommés · de umbenannt · es renombrados · ru переименовано · he שונו שמותם |
 ### Chip reordering (model tags / trigger words)
 Model tags and trigger-word chips share a single reorder affordance (drag the chip, or its
 `⠿` grip where the chip body is click-to-edit), so the copy sits in `common.reorder.dragHandle`
 instead of a feature namespace. It is used twice per editor: as the grip tooltip and as the
 hint shown in the edit controls row. There is deliberately **no keyboard shortcut** — an
 `Alt + Arrow` binding fought the browser's own Alt + Arrow handling and the modal's arrow-key
 navigation, so reordering is pointer-only and the grip is a decorative, non-focusable
 affordance. Do not reintroduce a shortcut or a "position X of Y" screen-reader string without
 re-adding the corresponding keys.
 `dragHandle` is a fragment, not a sentence: it labels both the grip and the hint, so keep it
 short and imperative and do not append a keyboard hint in any locale.
 | Term | Rendering |
 |---|---|
 | drag to reorder | zh-CN 拖拽以调整顺序 · zh-TW 拖曳以調整順序 · ja ドラッグして並べ替え · ko 드래그하여 순서 변경 · fr Glisser pour réordonner · de Zum Neuordnen ziehen · es Arrastra para reordenar · ru Перетащите, чтобы изменить порядок · he גרור כדי לשנות סדר |
 The grip itself is an icon and is never translated.
 ---
 ## 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)
@@ -0,0 +1,107 @@
 # Plan: Filename Template Follow-ups
 **Issue:** [#1071 — Lora Renaming](https://github.com/willmiao/ComfyUI-Lora-Manager/issues/1071)
 **Status:** Core feature **implemented** (2026-09-19, commit `2bc9860b`,
 preceded by the settings-tab split in `327da046`). Follow-ups 1 and 2 were
 resolved together on 2026-09-19 by redefining the empty template as
 "revert to recorded original filename" (see below). Follow-up 3 remains open.
 ## What shipped in `2bc9860b`
 - Per-model-type `download_filename_templates` setting (empty = keep current
  filename; opt-in). Placeholders: `{model_name}`, `{version_name}`,
  `{base_model}`, `{author}`, `{first_tag}`, `{hash_short}`,
  `{original_name}`.
 - `calculate_filename_for_model()` in `py/utils/utils.py` renders the
  template; templates containing path separators are rejected.
 - Downloads apply the template post-download
  (`DownloadManager._apply_download_filename_template`); rename conflicts
  keep the original name and never fail the download.
 - `ModelLifecycleService.rename_model` records `original_file_name` in the
  `.metadata.json` sidecar (first rename wins via `setdefault`).
 - Bulk apply: `GET|POST /api/lm/{prefix}/apply-filename-template`
  (`FilenameTemplateUseCase`, shares the auto-organize lock, WS progress type
  `filename_template_progress`).
 - Settings UI: "Filename Templates" subsection in the new **Organization**
  settings tab (`templates/components/modals/settings/organization.html`),
  with validation, live preview, and per-type "Apply to Library Now".
 Sandbox E2E verified: rename incl. companion files (previews, sidecars),
 metadata pointer updates, `original_file_name` recording, idempotency,
 conflict handling (failure counted, batch continues), empty-template no-op,
 GET variant.
 ## Follow-ups 1 & 2 — RESOLVED: empty template = revert to recorded original
 Follow-up 1 asked to reword the ambiguous "Valid (keep original filename)"
 empty-template message; Follow-up 2 asked for a bulk revert to the recorded
 `original_file_name`. Both were resolved by a single semantic change: **an
 empty template now means "restore the recorded original filename"** instead of
 "leave the current filename untouched".
 Rationale: for never-renamed models a revert is a no-op (no recorded
 original), for renamed models it restores the pre-rename name, and new
 downloads with an empty template keep the download name as before — so the
 two contexts (download path and bulk apply) share one coherent meaning, and
 no separate revert feature or `{recorded_original}` placeholder is needed.
 Implemented changes:
 - `FilenameTemplateUseCase._process_model`: an empty template now resolves
  the target name from the sidecar's `original_file_name` via the injected
  `metadata_loader` (default `load_local_metadata`); models without a
  recorded original or whose original matches the current name are skipped.
  Cache entries do not project `original_file_name`, so the sidecar is read
  per model.
 - `SettingsManager.js`: removed the empty-template early return and the
  apply-button disable (`updateFilenameTemplateApplyButton` deleted — the
  button is now always enabled). The browser-native `confirm()` was replaced
  with `filenameTemplateConfirmModal`
  (`templates/components/modals/confirm_modals.html`), a **self-managed**
  modal (like `DirectoryPickerModal`, NOT registered with ModalManager):
  ModalManager's "close current modal on open" behavior would kill the
  settings modal underneath. It stacks via `z-index: 10010`
  (`delete-modal.css`), handles ESC in capture phase with
  `stopPropagation`, and shows apply vs revert wording
  (`modals.filenameTemplateConfirm.titleApply` / `titleRevert` /
  `revertButton`; messages reuse `settings.filenameTemplates.confirmApply` /
  `confirmRevert`).
 - `locales/en.json`: reworded `help` / `applyHelp`, replaced
  `validation.keepOriginal` with `validation.restoreOriginal`
  ("Valid (empty template restores original filenames)"), added
  `confirmRevert`, removed the now-unused `emptyTemplateInfo`. Other locales
  re-synced with `[TODO: Translate]` placeholders — retranslation waits for
  the feature owner's request per `docs/i18n-translation-guidelines.md` §7.
 - Tests: revert / no-record-skip / same-name-skip cases in
  `tests/services/test_use_cases.py`; modal confirm-and-revert and
  cancel paths in
  `tests/frontend/managers/settingsManager.filenameTemplates.test.js`.
 Sandbox E2E verified (standalone server, sandboxed settings + library under
 `/tmp`, 2026-09-19): template apply renames and records
 `original_file_name`; empty-template apply reverts to the recorded name;
 revert target occupied by a newer file counts as failure and keeps the
 current name; models without a recorded original are skipped;
 apply → revert → re-apply cycles repeat cleanly.
 Standing caveats (unchanged):
 - The revert target may collide with an existing file — the existing conflict
  handling (count as failure, keep current name) covers this.
 - `original_file_name` only exists for models renamed after `2bc9860b`;
  older renames have no recorded original and are skipped.
 - `original_file_name` is kept (not cleared) after a revert, so
  apply → revert → re-apply stays repeatable.
 ## Follow-up 3 — Cross-page refresh after bulk apply
 **Problem:** the settings-modal "Apply to Library Now" button calls
 `resetAndReload(true)`, which refreshes only the page type currently open.
 Applying the checkpoint template while on the loras page leaves the loras
 view refreshed but does not touch the checkpoints page state (same
 limitation as the existing bulk auto-organize flow in
 `static/js/managers/SettingsManager.js#applyFilenameTemplate`).
 **Fix options:** broadcast a generic "library changed" event that every
 page's state listens to, or accept the limitation (the other page reloads
 its cache on next visit). Low priority.
@@ -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.
@@ -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.
@@ -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.
@@ -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。
@@ -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 ""
@@ -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)
@@ -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
+    """Determine the model type (``\"lora\"``, ``\"checkpoint\"``,
-    ``\"embedding\"``) for *model_path*.
+    ``\"embedding\"``, or ``\"other\"``) for *model_path*.
    Falls back to ``\"lora\"`` when unknown.
    """
@@ -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,
@@ -78,7 +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
@@ -126,7 +126,7 @@ class CheckpointLoaderLM:
                cache = await scanner.get_cached_data()
                base_models = set()
-                for item in cache.raw_data:
+                for item in list(cache.raw_data):
                    if item.get("sub_type") != "checkpoint":
                        continue
                    base_model = item.get("base_model")
@@ -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,)
@@ -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)}"
            )
@@ -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
@@ -93,7 +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
@@ -141,7 +141,7 @@ class UNETLoaderLM:
                cache = await scanner.get_cached_data()
                base_models = set()
-                for item in cache.raw_data:
+                for item in list(cache.raw_data):
                    if item.get("sub_type") != "diffusion_model":
                        continue
                    base_model = item.get("base_model")
@@ -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
@@ -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
+                                    # Lora hashes carries the 12-char AutoV3
-                                    # regular hashes.  Only override an
+                                    # hash (resolvable on CivitAI and the local
-                                    # existing entry if its value is empty
+                                    # autov3 index); the Hashes JSON value is
-                                    # (Lora hashes is the more reliable
+                                    # only the 10-char AutoV2 prefix, so on
-                                    # source when Hashes JSON has blanks).
+                                    # conflict the Lora hashes value wins.
                                    key = f"lora:{lora_name}"
-                                    existing = metadata["hashes"].get(key, "")
+                                    metadata["hashes"][key] = lora_hash
                                    if not existing:
                                        metadata["hashes"][key] = lora_hash
                            # Remove lora hashes from params section
                            params_section = params_section.replace(lora_hashes_match.group(0), '')
@@ -526,6 +525,26 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                weight = prompt_entries[0][1] if len(prompt_entries) == 1 else 1.0
                lora_entry = make_lora_entry(lora_type, lora_name, weight, lora_hash)
                if is_empty_placeholder_hash(lora_hash):
                    # The empty-hash placeholder (SHA256 of an empty byte
                    # string) is not a real hash: never look it up in the
                    # local hash index or on CivitAI. Match by filename;
                    # otherwise keep the item as unresolved (no hash, flagged
                    # hashInvalid so the UI shows the unresolvable-hash state
                    # and offers reconnect instead of download) rather than
                    # dropping it.
                    if recipe_scanner and lora_type == 'lora' and basename_key not in queried_local_basenames:
                        local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
                        if local_lora:
                            local_entry = self.populate_lora_from_local(lora_entry, local_lora)
                            merge_or_append_local(local_entry)
                            continue
                    lora_entry['hash'] = ''
                    lora_entry['hashInvalid'] = True
                    if not resource_lora_count:
                        loras.append(lora_entry)
                    continue
                if lora_hash and recipe_scanner and lora_type == 'lora':
                    local_lora = await recipe_scanner.get_local_lora_by_hash(lora_hash)
                    if local_lora:
@@ -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,
@@ -40,24 +40,34 @@ class ComfyMetadataParser(RecipeMetadataParser):
                checkpoint_node = next(iter(checkpoint_nodes.values()))
                if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
                    checkpoint_name = checkpoint_node['inputs']['ckpt_name']
-                    checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
+                    # Some ComfyUI workflows serialize ckpt_name as a
-                    if checkpoint_match:
+                    # single-element list (e.g. ["model.safetensors"]) or leave
-                        checkpoint_id = checkpoint_match.group(1)
+                    # the value unset (None). Neither is a string, so skip the
-                        checkpoint_version_id = checkpoint_match.group(2)
+                    # CivitAI-URN lookup instead of crashing re.search with a
-                        checkpoint = {
+                    # TypeError that fails the whole image import.
-                            'id': checkpoint_version_id,
+                    if isinstance(checkpoint_name, list):
-                            'modelId': checkpoint_id,
+                        checkpoint_name = (
-                            'name': f"Checkpoint {checkpoint_id}",
+                            checkpoint_name[0] if checkpoint_name else None
-                            'version': '',
+                        )
-                            'type': 'checkpoint'
+                    if isinstance(checkpoint_name, str):
-                        }
+                        checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
-                        if metadata_provider:
+                        if checkpoint_match:
-                            try:
+                            checkpoint_id = checkpoint_match.group(1)
-                                civitai_info_tuple = await metadata_provider.get_model_version_info(checkpoint_version_id)
+                            checkpoint_version_id = checkpoint_match.group(2)
-                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+                            checkpoint = {
-                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                                'id': checkpoint_version_id,
-                            except Exception as e:
+                                'modelId': checkpoint_id,
-                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
+                                '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 = []
@@ -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()
@@ -24,9 +24,11 @@ from ..services.use_cases import (
    AutoOrganizeUseCase,
    BulkMetadataRefreshUseCase,
    DownloadModelUseCase,
    FilenameTemplateUseCase,
 )
 from ..services.websocket_progress_callback import (
    WebSocketBroadcastCallback,
    WebSocketFilenameTemplateProgressCallback,
    WebSocketProgressCallback,
 )
 from ..utils.exif_utils import ExifUtils
@@ -37,6 +39,7 @@ from .handlers.model_handlers import (
    ModelAutoOrganizeHandler,
    ModelCivitaiHandler,
    ModelDownloadHandler,
    ModelFilenameTemplateHandler,
    ModelHandlerSet,
    ModelListingHandler,
    ModelManagementHandler,
@@ -83,6 +86,9 @@ class BaseModelRoutes(ABC):
        self.model_lifecycle_service: ModelLifecycleService | None = None
        self.websocket_progress_callback = WebSocketProgressCallback()
        self.metadata_progress_callback = WebSocketBroadcastCallback()
        self.filename_template_progress_callback = (
            WebSocketFilenameTemplateProgressCallback()
        )
        self._handler_set: ModelHandlerSet | None = None
        self._handler_mapping: Dict[str, Callable[[web.Request], Awaitable[web.Response]]] | None = None
@@ -149,6 +155,7 @@ 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,
@@ -201,6 +208,17 @@ class BaseModelRoutes(ABC):
            ws_manager=self._ws_manager,
            logger=logger,
        )
        filename_template_use_case = FilenameTemplateUseCase(
            scanner=service.scanner,
            lifecycle_service=self._ensure_lifecycle_service(),
            lock_provider=self._ws_manager,
            model_type=service.model_type,
        )
        filename_template = ModelFilenameTemplateHandler(
            use_case=filename_template_use_case,
            progress_callback=self.filename_template_progress_callback,
            logger=logger,
        )
        updates = ModelUpdateHandler(
            service=service,
            update_service=update_service,
@@ -217,6 +235,7 @@ class BaseModelRoutes(ABC):
            civitai=civitai,
            move=move,
            auto_organize=auto_organize,
            filename_template=filename_template,
            updates=updates,
        )
@@ -250,6 +269,10 @@ 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)
@@ -47,15 +47,16 @@ class CheckpointRoutes(BaseModelRoutes):
        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/checkpoints_roots', prefix, self.get_checkpoints_roots)
        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/unet_roots', prefix, self.get_unet_roots)
-        # Name/base_model pool for the Random Checkpoint/Unet Loader nodes
+        # Name/base_model pool for the Checkpoint/Unet Loader nodes' base_model filtering
        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/loader-pool', prefix, self.get_loader_pool)
    async def get_loader_pool(self, request: web.Request) -> web.Response:
        """Return ComfyUI-formatted model names with their base_model.
-        Backing data for the Random Checkpoint/Unet Loader nodes: the front-end
+        Backing data for the Checkpoint/Unet Loader nodes'
-        filters the ckpt_name/unet_name combo options by base_model using this
+        control_after_generate feature: the front-end filters the
-        pool, so control_after_generate randomizes within the narrowed set.
+        ckpt_name/unet_name combo options by base_model using this pool, so
        randomize mode picks within the narrowed set.
        """
        try:
            sub_type = request.query.get("sub_type", "checkpoint")
@@ -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,
            }
        )
@@ -1,508 +0,0 @@
 """Handlers for Hugging Face model listing and download.
 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.
 """
 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
 from ...services.downloader import (
    DownloadProgress,
    get_downloader,
 )
 from ...services.aria2_downloader import Aria2Downloader
 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
 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
    configured root paths for each model type (from ``Config``).
    The ``model_root`` value comes from the frontend's model-root dropdown,
    which is populated from the current page's scanner roots.  By checking
    which scanner's root list it belongs to, we avoid fragile heuristics
    like substring-matching path names.
    """
    norm = os.path.normpath(model_root).replace(os.sep, "/")
    # LoRA roots
    for p in (config.loras_roots or []) + (config.extra_loras_roots or []):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return LoraMetadata, "get_lora_scanner"
    # Checkpoint / UNet roots
    for p in (
        (config.checkpoints_roots or [])
        + (config.extra_checkpoints_roots or [])
        + (config.unet_roots or [])
        + (config.extra_unet_roots or [])
    ):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return CheckpointMetadata, "get_checkpoint_scanner"
    # Embedding roots
    for p in (config.embeddings_roots or []) + (config.extra_embeddings_roots or []):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return EmbeddingMetadata, "get_embedding_scanner"
    # Fallback — should not happen in normal use
    logger.warning(
        "Could not determine model type for root '%s'; defaulting to LoRA",
        model_root,
    )
    return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
 async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
    """Create a proper .metadata.json and add the model to the scanner cache.
    Uses ``MetadataManager.create_default_metadata()`` which computes the
    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
    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)
        metadata = await MetadataManager.create_default_metadata(
            dest_path, model_class=model_class
        )
        if metadata is 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"]
        # 3. Save metadata atomically
        await MetadataManager.save_metadata(dest_path, metadata_dict)
        logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
        # 4. Determine relative folder path for cache
        #    model_root is an absolute path; dest_path is under it
        folder = ""
        if os.path.isabs(model_root) and dest_path.startswith(model_root):
            rel = os.path.relpath(os.path.dirname(dest_path), model_root)
            folder = rel.replace(os.sep, "/") if rel != "." else ""
        # 5. Add to scanner cache (same as CivitAI's _execute_download does)
        scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
        if scanner_getter is not None:
            scanner = await scanner_getter()
            if scanner is not None:
                metadata_dict = metadata.to_dict()
                metadata_dict["hf_url"] = hf_url
                await scanner.add_model_to_cache(metadata_dict, folder)
                logger.info("Added %s to scanner cache (folder=%s)", dest_path, folder)
    except Exception as exc:
        logger.warning("Failed to save HF metadata for %s: %s", dest_path, exc)
 def _find_matching_root(dest_dir: str) -> str | None:
    """Walk up *dest_dir* to find which configured scanner root it belongs to."""
    norm = os.path.normpath(dest_dir).replace(os.sep, "/")
    all_roots = []
    for root_list in (
        config.loras_roots or [],
        config.extra_loras_roots or [],
        config.checkpoints_roots or [],
        config.extra_checkpoints_roots or [],
        config.unet_roots or [],
        config.extra_unet_roots or [],
        config.embeddings_roots or [],
        config.extra_embeddings_roots or [],
    ):
        all_roots.extend([os.path.normpath(p).replace(os.sep, "/") for p in root_list])
    # Find the longest matching prefix
    match: str | None = None
    for root in all_roots:
        if norm.startswith(root):
            if match is None or len(root) > len(match):
                match = root
    return match
 async def _add_to_scanner_cache(dest_path: str, metadata: dict[str, Any]) -> None:
    model_dir = os.path.dirname(dest_path)
    model_root = _find_matching_root(model_dir)
    if not model_root:
        raise ValueError(f"File path {dest_path} is not within any configured scanner root")
    scanner_getter_name = _infer_model_type(model_root)[1]
    scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
    if scanner_getter is None:
        raise RuntimeError(f"Scanner getter '{scanner_getter_name}' not found in ServiceRegistry")
    scanner = await scanner_getter()
    if scanner is None:
        raise RuntimeError(f"Scanner '{scanner_getter_name}' returned None")
    await scanner.update_single_model_cache(dest_path, dest_path, metadata)
 class HfHandler:
    """Handle Hugging Face model browsing and download."""
    async def set_hf_url(self, request: web.Request) -> web.Response:
        try:
            payload: dict[str, Any] = await request.json()
        except json.JSONDecodeError:
            return web.json_response({"success": False, "error": "Invalid JSON"}, status=400)
        file_path = (payload.get("file_path") or "").strip()
        hf_url = (payload.get("hf_url") or "").strip()
        if not file_path or not hf_url:
            return web.json_response(
                {"success": False, "error": "Missing required fields: 'file_path' and 'hf_url'"},
                status=400,
            )
        m = re.match(r"^https?://huggingface\.co/([^/]+/[^/]+)/?$", hf_url)
        if not m:
            return web.json_response(
                {
                    "success": False,
                    "error": "Invalid HuggingFace URL. Expected format: https://huggingface.co/user/repo",
                },
                status=400,
            )
        if not os.path.isfile(file_path):
            return web.json_response(
                {"success": False, "error": f"File not found: {file_path}"},
                status=404,
            )
        model_root = _find_matching_root(os.path.dirname(file_path))
        if not model_root:
            return web.json_response(
                {
                    "success": False,
                    "error": "File is not within any configured model directory. Cannot link to HuggingFace.",
                },
                status=400,
            )
        try:
            existing = await MetadataManager.load_metadata_payload(file_path)
            if existing.get("hf_url") == hf_url:
                return web.json_response({
                    "success": True,
                    "message": "hf_url already set",
                    "hf_url": hf_url,
                })
            existing["hf_url"] = hf_url
            existing["from_civitai"] = False
            await MetadataManager.save_metadata(file_path, existing)
            await _add_to_scanner_cache(file_path, existing)
            logger.info("Set hf_url=%s for %s", hf_url, file_path)
            return web.json_response({
                "success": True,
                "message": f"hf_url set to {hf_url}",
                "hf_url": hf_url,
            })
        except Exception as exc:
            logger.error("Failed to set hf_url for %s: %s", file_path, exc)
            return web.json_response(
                {"success": False, "error": str(exc)},
                status=500,
            )
    async def get_hf_repo_files(self, request: web.Request) -> web.Response:
        """List model-weight files from a HF repo with real file sizes.
        Uses the HF tree API endpoint which returns accurate file sizes
        (including LFS-tracked files), unlike the model info endpoint.
        """
        repo = request.query.get("repo", "").strip()
        if not repo or "/" not in repo:
            return web.json_response(
                {"error": "Missing or invalid 'repo' parameter (expected user/repo)"},
                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()
        except Exception as exc:
            logger.error("Failed to fetch HF repo files: %s", 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.
        POST JSON body::
            {
              "repo": "dx8152/Flux2-Klein-9B-Consistency",
              "filename": "Flux2-Klein-9B-consistency-V2.safetensors",
              "revision": "main",
              "model_root": "loras",
              "relative_path": "",
              "use_default_paths": false,
              "download_id": "optional-batch-id"
            }
        If ``download_id`` is provided, real-time progress (bytes, speed,
        percentage) is broadcast via the WebSocket progress system, matching
        the CivitAI download experience.
        Respects the ``download_backend`` setting (``aria2`` or ``default``).
        """
        try:
            payload: dict[str, Any] = await request.json()
        except json.JSONDecodeError:
            return web.json_response({"error": "Invalid JSON"}, status=400)
        repo = (payload.get("repo") or "").strip()
        filename = (payload.get("filename") or "").strip()
        revision = (payload.get("revision") or "main").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,
        )
        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):
            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
        # Validate filename — must not contain path traversal
        if ".." in filename:
            return web.json_response({"error": "Invalid filename"}, status=400)
        # Validate relative_path — must not be absolute or escape base directory
        if relative_path:
            if os.path.isabs(relative_path):
                return web.json_response({"error": "relative_path must not be absolute"}, status=400)
            if ".." in relative_path.split("/") or "\\" in relative_path:
                return web.json_response({"error": "Invalid relative_path"}, status=400)
        # Use model_root directly as the base directory — same approach as
        # CivitAI's download path (download_manager.py).  No realpath, no
        # allowed-roots validation, no path-traversal check; those are
        # unnecessary when the frontend sends the path from its own dropdown
        # (populated from scanner roots).  Using the "business path" directly
        # keeps dest_path consistent with scanner roots so that later folder
        # derivation (in _save_hf_metadata) works correctly.
        if os.path.isabs(model_root):
            base_dir = os.path.normpath(model_root)
        else:
            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)
        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.
        file_base = os.path.basename(filename)
        os.makedirs(target_dir, exist_ok=True)
        dest_path = os.path.join(target_dir, file_base)
        # Check if already exists (simple skip)
        if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
            logger.info("download_hf_model: 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}"
        )
        # Set up progress callback if download_id is provided
        progress_callback = None
        if download_id:
            async def _progress_callback(
                progress: float | DownloadProgress,
                snapshot: DownloadProgress | None = None,
            ) -> None:
                percent = 0.0
                metrics = snapshot if isinstance(snapshot, DownloadProgress) else None
                if isinstance(progress, DownloadProgress):
                    percent = progress.percent_complete
                    metrics = progress
                elif isinstance(snapshot, DownloadProgress):
                    percent = snapshot.percent_complete
                else:
                    percent = float(progress)
                broadcast: dict[str, Any] = {
                    "status": "progress",
                    "progress": round(percent),
                }
                if metrics:
                    broadcast["bytes_downloaded"] = metrics.bytes_downloaded
                    broadcast["total_bytes"] = metrics.total_bytes
                    broadcast["bytes_per_second"] = metrics.bytes_per_second
                await ws_manager.broadcast_download_progress(download_id, broadcast)
            progress_callback = _progress_callback
        # Respect download backend setting (aria2 vs default)
        download_backend = (
            get_settings_manager().get("download_backend", "default")
        )
        if download_backend == "aria2":
            aria2 = await Aria2Downloader.get_instance()
            aid = download_id or f"hf_{repo}_{filename}"
            try:
                hf_success, hf_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)
                    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,
                    )
            except Exception as exc:
                logger.error("HF download (aria2) failed: %s", exc)
                return web.json_response(
                    {"success": False, "error": str(exc)}, status=500
                )
        # Default: use built-in aiohttp Downloader
        downloader = await get_downloader()
        try:
            success, result = await downloader.download_file(
                url=resolve_url,
                save_path=dest_path,
                use_auth=False,
                allow_resume=True,
                progress_callback=progress_callback,
            )
            if success:
                await _save_hf_metadata(dest_path, repo, 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,
                )
        except Exception as exc:
            logger.error("HF download failed: %s", exc)
            return web.json_response(
                {"success": False, "error": str(exc)}, status=500
            )
@@ -53,11 +53,15 @@ from ...utils.constants import (
    PREVIEW_EXTENSIONS,
    SUPPORTED_MEDIA_EXTENSIONS,
    VALID_LORA_TYPES,
    VALID_OTHER_CIVITAI_TYPES,
    folder_path_schema,
 )
-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.directory_browser import browse_directory
 from ...utils.example_images_paths import (
    find_non_compliant_items_in_example_images_root,
    is_valid_example_images_root,
@@ -419,6 +423,11 @@ def _wsl_to_windows_path(wsl_path: str) -> str | None:
        return None
 def _has_gui_display() -> bool:
    """Check whether a GUI session is reachable for xdg-open."""
    return bool(os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"))
 class PromptServerProtocol(Protocol):
    """Subset of PromptServer used by the handlers."""
@@ -657,9 +666,21 @@ class HealthCheckHandler:
            "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"})
@@ -671,7 +692,7 @@ class HealthCheckHandler:
        page accepts the update and only reloads once all scanners are done.
        """
        pending: list[str] = []
-        for name, getter in self._scanner_getters.items():
+        for name, getter in self._active_scanner_getters().items():
            try:
                scanner = await getter()
            except Exception:
@@ -756,10 +777,19 @@ 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()
@@ -807,7 +837,7 @@ 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)
@@ -839,7 +869,7 @@ 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)
@@ -1071,7 +1101,7 @@ 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
@@ -1156,7 +1186,7 @@ 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
@@ -1536,6 +1566,46 @@ 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
            standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
            response_data["standalone_mode"] = standalone_mode
            if standalone_mode:
                # Standalone reads its model roots exclusively from
                # settings.json, so the Model Paths settings UI needs the
                # current values plus the editable-key schema. In plugin mode
                # the paths come from the ComfyUI host and stay hidden.
                folder_paths = self._settings.get("folder_paths") or {}
                # A fresh install is seeded from settings.json.example, whose
                # folder_paths are documentation placeholders — hide them so
                # the UI starts with empty editors instead of fake paths.
                get_placeholders = getattr(
                    self._settings, "get_template_folder_path_placeholders", None
                )
                placeholders = get_placeholders() if get_placeholders else set()
                if placeholders:
                    folder_paths = {
                        key: [p for p in paths if p not in placeholders]
                        if isinstance(paths, list)
                        else paths
                        for key, paths in folder_paths.items()
                    }
                response_data["folder_paths"] = folder_paths
                response_data["folder_path_schema"] = folder_path_schema()
            settings_file = getattr(self._settings, "settings_file", None)
            if settings_file:
                response_data["settings_file"] = settings_file
@@ -2065,6 +2135,7 @@ 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:
@@ -2089,6 +2160,8 @@ 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):
@@ -2099,6 +2172,13 @@ 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):
@@ -2190,6 +2270,11 @@ 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:
@@ -2228,6 +2313,13 @@ 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(
@@ -2245,7 +2337,7 @@ class ModelLibraryHandler:
                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,
@@ -2267,6 +2359,7 @@ class ModelLibraryHandler:
            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
@@ -2275,6 +2368,13 @@ 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 = []
@@ -2306,9 +2406,18 @@ 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,
@@ -2363,6 +2472,11 @@ 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:
@@ -2398,6 +2512,17 @@ 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,
@@ -2665,12 +2790,40 @@ class ModelLibraryHandler:
            normalized_type, scanner = await self._get_scanner_for_type(model_type)
            if not normalized_type:
                # The lookup cannot be served as a fully interactive list. Two
                # cases share this branch: a CivitAI type with no scanner at all
                # (Wildcards, Workflows, Hypernetwork, Poses, AestheticGradient)
                # and an Other-model type while the opt-in master switch is off.
                # Answer 200 with the CivitAI list marked read-only plus a
                # machine-readable reason, so clients can still show the
                # versions and explain why the actions are missing. Legacy
                # clients keep working: they only read `success`/`versions`.
                reason = (
                    "other_models_disabled"
                    if self._normalize_model_type(model_type) == "other"
                    else "model_type_unsupported"
                )
                return web.json_response(
                    {
-                        "success": False,
+                        "success": True,
-                        "error": f'Model type "{model_type}" is not supported',
+                        "modelId": model_id,
-                    },
+                        "modelName": model_name,
-                    status=400,
+                        "modelType": model_type,
                        "supported": False,
                        "reason": reason,
                        "versions": [
                            {
                                "id": version.get("id"),
                                "name": version.get("name", ""),
                                "thumbnailUrl": version.get("images")[0]["url"]
                                if version.get("images")
                                else None,
                                "inLibrary": False,
                                "hasBeenDownloaded": False,
                            }
                            for version in versions
                        ],
                    }
                )
            if not scanner:
@@ -2712,6 +2865,7 @@ class ModelLibraryHandler:
                    "modelId": model_id,
                    "modelName": model_name,
                    "modelType": model_type,
                    "supported": True,
                    "versions": enriched_versions,
                }
            )
@@ -2786,12 +2940,32 @@ 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()
@@ -2815,12 +2989,17 @@ 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):
@@ -3274,6 +3453,18 @@ class FileSystemHandler:
                    subprocess.Popen(["open", "-R", settings_file])
                else:
                    folder = os.path.dirname(settings_file)
                    if not _has_gui_display():
                        # Headless/SSH session: xdg-open cannot open a file
                        # manager, so hand the path to the browser for copying
                        # instead of reporting a success that never happened.
                        return web.json_response(
                            {
                                "success": True,
                                "message": "Headless session: path available for copying",
                                "path": settings_file,
                                "mode": "clipboard",
                            }
                        )
                    subprocess.Popen(["xdg-open", folder])
            return web.json_response(
@@ -3307,6 +3498,76 @@ class FileSystemHandler:
            logger.error("Failed to open wildcards location: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
    async def browse_directory(self, request: web.Request) -> web.Response:
        """Browse a directory for the settings-UI directory picker."""
        try:
            data = await request.json()
            payload, status = browse_directory(data.get("path", ""))
            return web.json_response(payload, status=status)
        except json.JSONDecodeError:
            return web.json_response(
                {"success": False, "error": "Invalid JSON"}, status=400
            )
        except Exception as exc:  # pragma: no cover - defensive logging
            logger.error("Failed to browse directory: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
    async def validate_path(self, request: web.Request) -> web.Response:
        """Validate a filesystem path for the settings UI.
        A well-formed request always returns HTTP 200; invalid paths are
        reported via ``error_code`` in the payload. HTTP 400 is reserved for
        malformed requests (missing path, invalid JSON).
        """
        try:
            data = await request.json()
            raw_path = data.get("path")
            expect = data.get("expect", "directory")
            if not raw_path or not isinstance(raw_path, str):
                return web.json_response(
                    {"success": False, "error": "Missing path parameter"}, status=400
                )
            # Business path convention: abspath only, never realpath.
            path = os.path.abspath(os.path.expanduser(raw_path))
            exists = os.path.exists(path)
            is_directory = os.path.isdir(path) if exists else False
            readable = bool(exists and os.access(path, os.R_OK))
            writable = bool(exists and os.access(path, os.W_OK))
            error_code = None
            if not exists:
                error_code = "path_not_found"
            elif expect == "directory" and not is_directory:
                error_code = "not_a_directory"
            elif expect == "file" and not os.path.isfile(path):
                error_code = "not_a_file"
            elif not readable:
                error_code = "not_readable"
            elif not writable:
                error_code = "not_writable"
            return web.json_response(
                {
                    "success": True,
                    "path": path,
                    "exists": exists,
                    "is_directory": is_directory,
                    "readable": readable,
                    "writable": writable,
                    "error_code": error_code,
                }
            )
        except json.JSONDecodeError:
            return web.json_response(
                {"success": False, "error": "Invalid JSON"}, status=400
            )
        except Exception as exc:  # pragma: no cover - defensive logging
            logger.error("Failed to validate path: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
 class CustomWordsHandler:
    """Handler for autocomplete via TagFTSIndex."""
@@ -3882,8 +4143,9 @@ 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
@@ -3902,8 +4164,9 @@ 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,
@@ -3949,19 +4212,27 @@ class MiscHandlerSet:
            "open_settings_location": self.filesystem.open_settings_location,
            "open_backup_location": self.filesystem.open_backup_location,
            "open_wildcards_location": self.filesystem.open_wildcards_location,
            "browse_directory": self.filesystem.browse_directory,
            "validate_path": self.filesystem.validate_path,
            "search_custom_words": self.custom_words.search_custom_words,
            "search_wildcards": self.wildcards.search_wildcards,
            "get_supporters": self.supporters.get_supporters,
            "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,
+            # External model sources (Hugging Face / ModelScope)
-            "download_hf_model": self.hf_handler.download_hf_model,
+            "list_model_source_files": self.model_source_handler.list_model_source_files,
-            "set_hf_url": self.hf_handler.set_hf_url,
+            "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,
@@ -3975,6 +4246,7 @@ 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,
    )
@@ -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,
@@ -33,10 +37,14 @@ from ...services.use_cases import (
    DownloadModelEarlyAccessError,
    DownloadModelUseCase,
    DownloadModelValidationError,
    FilenameTemplateUseCase,
    MetadataRefreshProgressReporter,
 )
 from ...services.websocket_manager import WebSocketManager
-from ...services.websocket_progress_callback import WebSocketProgressCallback
+from ...services.websocket_progress_callback import (
    WebSocketFilenameTemplateProgressCallback,
    WebSocketProgressCallback,
 )
 from ...services.download_queue_service import DownloadQueueService
 from ...services.errors import RateLimitError, ResourceNotFoundError
 from ...utils.civitai_utils import resolve_license_payload
@@ -86,6 +94,7 @@ 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 +102,7 @@ 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 +216,16 @@ 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
            )
@@ -634,6 +654,16 @@ 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(
@@ -649,20 +679,33 @@ 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,
+                **relink_kwargs
                metadata=local_metadata,
                model_id=int(model_id),
                model_version_id=int(model_version_id) if model_version_id else None,
            )
            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}" + (
+            if source == "civarchive":
-                f" version {model_version_id}" if model_version_id else ""
+                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,
@@ -670,6 +713,8 @@ 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(
@@ -1570,12 +1615,50 @@ 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)
@@ -1609,6 +1692,50 @@ 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."""
@@ -1787,6 +1914,11 @@ class ModelDownloadHandler:
                response_payload["status"] = status
                if "message" in progress_data:
                    response_payload["message"] = progress_data["message"]
                # Post-transfer stage (indexing / source metadata); polling
                # consumers need it to tell "working" from "stuck".
                for field in ("stage", "platform"):
                    if field in progress_data:
                        response_payload[field] = progress_data[field]
            elif status is None and "message" in progress_data:
                response_payload["message"] = progress_data["message"]
@@ -1904,8 +2036,18 @@ 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)
+            cleared_ids = await service.clear_queue(status_filter=status_filter)
-            return web.json_response({"success": True, "cleared": cleared})
+            # 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
@@ -1988,9 +2130,11 @@ 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"},
+                    {"success": False, "error": "History item not found or not retryable"}
                    status=404,
                )
            return web.json_response({"success": True, "item": item})
        except Exception as exc:
@@ -2041,8 +2185,12 @@ 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:
@@ -2084,9 +2232,10 @@ 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"},
+                    {"success": False, "error": "Download not found in queue"}
                    status=404,
                )
            return web.json_response({"success": True})
        except Exception as exc:
@@ -2339,6 +2488,90 @@ class ModelMoveHandler:
        self._move_service = move_service
        self._logger = logger
    async def create_folder(self, request: web.Request) -> web.Response:
        try:
            data = await request.json()
        except Exception:
            return web.json_response(
                {"success": False, "error": "Invalid JSON body"}, status=400
            )
        try:
            folder_path = data.get("folder_path")
            if not folder_path:
                return web.json_response(
                    {"success": False, "error": "Folder path is required"}, status=400
                )
            result = await self._move_service.create_folder(folder_path)
            status = 200 if result.get("success") else 400
            return web.json_response(result, status=status)
        except Exception as exc:
            self._logger.error("Error creating folder: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
    async def delete_folder(self, request: web.Request) -> web.Response:
        try:
            data = await request.json()
        except Exception:
            return web.json_response(
                {"success": False, "error": "Invalid JSON body"}, status=400
            )
        try:
            folder_path = data.get("folder_path")
            if not folder_path:
                return web.json_response(
                    {"success": False, "error": "Folder path is required"}, status=400
                )
            dry_run = bool(data.get("dry_run"))
            result = await self._move_service.delete_folder(
                folder_path, dry_run=dry_run
            )
            if result.get("success"):
                if not dry_run:
                    _broadcast_models_changed()
                return web.json_response(result, status=200)
            # "not_empty" / "busy" are conflicts between the tree the client
            # rendered and the on-disk truth; everything else is a bad request.
            code = result.get("code")
            status = 409 if code in ("not_empty", "busy") else 400
            return web.json_response(result, status=status)
        except Exception as exc:
            self._logger.error("Error deleting folder: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
    async def rename_folder(self, request: web.Request) -> web.Response:
        try:
            data = await request.json()
        except Exception:
            return web.json_response(
                {"success": False, "error": "Invalid JSON body"}, status=400
            )
        try:
            folder_path = data.get("folder_path")
            new_name = data.get("new_name")
            if not folder_path:
                return web.json_response(
                    {"success": False, "error": "Folder path is required"}, status=400
                )
            if not new_name:
                return web.json_response(
                    {"success": False, "error": "New folder name is required"}, status=400
                )
            result = await self._move_service.rename_folder(folder_path, new_name)
            if result.get("success"):
                if result.get("renamed"):
                    _broadcast_models_changed()
                return web.json_response(result, status=200)
            # A name collision or a staged delete inside the subtree is a
            # conflict with the state the client rendered, not a bad request.
            code = result.get("code")
            status = 409 if code in ("target_exists", "busy") else 400
            return web.json_response(result, status=status)
        except Exception as exc:
            self._logger.error("Error renaming folder: %s", exc, exc_info=True)
            return web.json_response({"success": False, "error": str(exc)}, status=500)
    async def move_model(self, request: web.Request) -> web.Response:
        try:
            data = await request.json()
@@ -2463,6 +2696,71 @@ class ModelAutoOrganizeHandler:
            return web.json_response({"success": False, "error": str(exc)}, status=500)
 class ModelFilenameTemplateHandler:
    """Apply the configured filename template to existing library models."""
    def __init__(
        self,
        *,
        use_case: FilenameTemplateUseCase,
        progress_callback: WebSocketFilenameTemplateProgressCallback,
        logger: logging.Logger,
    ) -> None:
        self._use_case = use_case
        self._progress_callback = progress_callback
        self._logger = logger
    async def apply_filename_template(self, request: web.Request) -> web.Response:
        try:
            file_paths = None
            if request.method == "POST":
                try:
                    data = await request.json()
                    file_paths = data.get("file_paths")
                except Exception:  # pragma: no cover - permissive path
                    pass
            else:
                # GET variant (browser extension is GET-only): comma-separated
                # file_paths query parameter.
                raw_file_paths = request.query.get("file_paths")
                if raw_file_paths:
                    file_paths = [
                        path.strip()
                        for path in raw_file_paths.split(",")
                        if path.strip()
                    ]
            result = await self._use_case.execute(
                file_paths=file_paths,
                progress_callback=self._progress_callback,
            )
            _broadcast_models_changed()
            return web.json_response(result.to_dict())
        except AutoOrganizeInProgressError:
            return web.json_response(
                {
                    "success": False,
                    "error": "Another library operation is already running. Please wait for it to complete.",
                },
                status=409,
            )
        except Exception as exc:
            self._logger.error(
                "Error in apply_filename_template: %s", exc, exc_info=True
            )
            try:
                await self._progress_callback.on_progress(
                    {
                        "type": "filename_template_progress",
                        "status": "error",
                        "error": str(exc),
                    }
                )
            except Exception:  # pragma: no cover - defensive reporting
                pass
            return web.json_response({"success": False, "error": str(exc)}, status=500)
 class ModelUpdateHandler:
    """Handle update tracking requests."""
@@ -2637,10 +2935,20 @@ 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,
            ):
@@ -2653,6 +2961,26 @@ 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"))
@@ -3200,6 +3528,7 @@ class ModelHandlerSet:
    civitai: ModelCivitaiHandler
    move: ModelMoveHandler
    auto_organize: ModelAutoOrganizeHandler
    filename_template: ModelFilenameTemplateHandler
    updates: ModelUpdateHandler
    def to_route_mapping(
@@ -3259,14 +3588,20 @@ class ModelHandlerSet:
            "get_civitai_model_by_hash": self.civitai.get_civitai_model_by_hash,
            "move_model": self.move.move_model,
            "move_models_bulk": self.move.move_models_bulk,
            "create_folder": self.move.create_folder,
            "delete_folder": self.move.delete_folder,
            "rename_folder": self.move.rename_folder,
            "auto_organize_models": self.auto_organize.auto_organize_models,
            "get_auto_organize_progress": self.auto_organize.get_auto_organize_progress,
            "apply_filename_template": self.filename_template.apply_filename_template,
            "get_model_notes": self.query.get_model_notes,
            "get_model_preview_url": self.query.get_model_preview_url,
            "get_model_civitai_url": self.query.get_model_civitai_url,
            "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,
@@ -0,0 +1,639 @@
 """Handlers for external model sources: linking, file listing and downloads.
 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
 import json
 import logging
 import os
 from typing import Any
 from aiohttp import web
 from ...config import config
 from ...services.downloader import (
    DownloadProgress,
    get_downloader,
 )
 from ...services.aria2_downloader import Aria2Downloader
 from ...services.model_sources import (
    ModelSourceError,
    SourceRef,
    detect_source,
    get_download_source,
    hydrate_from_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.metadata_manager import MetadataManager
 from ...utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 logger = logging.getLogger(__name__)
 _DEFAULT_MODEL_CLASS = LoraMetadata
 _DEFAULT_SCANNER_GETTER = "get_lora_scanner"
 def _infer_model_type(model_root: str) -> tuple[Any, str]:
    """Determine model class and scanner by matching ``model_root`` against the
    configured root paths for each model type (from ``Config``).
    The ``model_root`` value comes from the frontend's model-root dropdown,
    which is populated from the current page's scanner roots.  By checking
    which scanner's root list it belongs to, we avoid fragile heuristics
    like substring-matching path names.
    """
    norm = os.path.normpath(model_root).replace(os.sep, "/")
    # LoRA roots
    for p in (config.loras_roots or []) + (config.extra_loras_roots or []):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return LoraMetadata, "get_lora_scanner"
    # Checkpoint / UNet roots
    for p in (
        (config.checkpoints_roots or [])
        + (config.extra_checkpoints_roots or [])
        + (config.unet_roots or [])
        + (config.extra_unet_roots or [])
    ):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return CheckpointMetadata, "get_checkpoint_scanner"
    # Embedding roots
    for p in (config.embeddings_roots or []) + (config.extra_embeddings_roots or []):
        if os.path.normpath(p).replace(os.sep, "/") == norm:
            return EmbeddingMetadata, "get_embedding_scanner"
    # Fallback — should not happen in normal use
    logger.warning(
        "Could not determine model type for root '%s'; defaulting to LoRA",
        model_root,
    )
    return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
 async def _report_phase(
    download_id: str | None, stage: str, platform: str = ""
 ) -> None:
    """Tell the progress UI which post-transfer stage is running.
    A download's byte counter stops the moment the last byte lands, but the
    backend still has to index the file and read the model site's API.  Without
    this the bar sits at 100% reporting "0 B/s" and the download looks stuck for
    several seconds.  *stage* is machine-readable — the UI localises it — and
    *platform* lets it name the site the metadata comes from.
    """
    if not download_id:
        return
    try:
        await ws_manager.broadcast_download_progress(
            download_id,
            {
                "status": "metadata",
                "stage": stage,
                "platform": platform,
                "progress": 100,
            },
        )
    except Exception as exc:  # pragma: no cover - progress must never be fatal
        logger.debug("Failed to report the '%s' phase: %s", stage, exc)
 async def _save_source_metadata(
    dest_path: str, ref: SourceRef, model_root: str, *, download_id: str | None = None
 ) -> None:
    """Create a proper .metadata.json and add the model to the scanner cache.
    The metadata is created through the owning scanner rather than
    ``MetadataManager.create_default_metadata()``, because that is the only
    factory that knows when hashing must be deferred: ``CheckpointScanner`` and
    ``OtherScanner`` deliberately record ``hash_status="pending"`` with an empty
    ``sha256`` for their multi-GB files, and the generic helper would read a
    10 GB checkpoint end to end *inside the download request*.  Scanners for the
    small types delegate straight back to it, so nothing changes for them.
    The external-source fields are then overlaid and the model is registered in
    the in-memory scanner cache so it appears immediately without a full
    filesystem walk.
    Finally the site's own published metadata is applied (see
    :func:`~py.services.model_sources.hydration.hydrate_from_source`), so a
    ModelScope or Hugging Face download lands with the same populated model
    card a CivitAI download produces instead of a bare filename and hash.
    Both post-transfer stages are reported through *download_id* when the UI is
    watching one, because neither advances the byte counter.
    """
    try:
        model_class, scanner_getter_name = _infer_model_type(model_root)
        scanner = None
        scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
        if scanner_getter is not None:
            scanner = await scanner_getter()
        # 1. Create proper metadata (reads safetensors headers; hashes only for
        #    the model types whose scanner does not defer it)
        await _report_phase(download_id, "indexing", ref.platform)
        create_metadata = getattr(scanner, "_create_default_metadata", None)
        if create_metadata is not None:
            metadata = await create_metadata(dest_path)
        else:
            metadata = await MetadataManager.create_default_metadata(
                dest_path, model_class=model_class
            )
        if metadata is None:
            logger.warning("create_default_metadata returned None for %s", dest_path)
            return
        # 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)
        logger.info(
            "Saved %s metadata (source=%s, hash_status=%s) for %s",
            ref.platform, ref.url, getattr(metadata, "hash_status", "?"), dest_path,
        )
        # 4. Determine relative folder path for cache
        #    model_root is an absolute path; dest_path is under it
        folder = ""
        if os.path.isabs(model_root) and dest_path.startswith(model_root):
            rel = os.path.relpath(os.path.dirname(dest_path), model_root)
            folder = rel.replace(os.sep, "/") if rel != "." else ""
        # 5. Add to scanner cache (same as CivitAI's _execute_download does)
        if scanner is not None:
            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)
        # 6. Top up from the site's public API. Runs last so the scanner-cache
        #    refresh it performs lands on the entry created above. It never
        #    raises and never fails the download.
        await _report_phase(download_id, "source", ref.platform)
        await hydrate_from_source(dest_path, ref=ref)
    except Exception as exc:
        logger.warning("Failed to save source metadata for %s: %s", dest_path, exc)
 def _find_matching_root(dest_dir: str) -> str | None:
    """Walk up *dest_dir* to find which configured scanner root it belongs to."""
    norm = os.path.normpath(dest_dir).replace(os.sep, "/")
    all_roots = []
    for root_list in (
        config.loras_roots or [],
        config.extra_loras_roots or [],
        config.checkpoints_roots or [],
        config.extra_checkpoints_roots or [],
        config.unet_roots or [],
        config.extra_unet_roots or [],
        config.embeddings_roots or [],
        config.extra_embeddings_roots or [],
    ):
        all_roots.extend([os.path.normpath(p).replace(os.sep, "/") for p in root_list])
    # Find the longest matching prefix
    match: str | None = None
    for root in all_roots:
        if norm.startswith(root):
            if match is None or len(root) > len(match):
                match = root
    return match
 async def _add_to_scanner_cache(dest_path: str, metadata: dict[str, Any]) -> None:
    model_dir = os.path.dirname(dest_path)
    model_root = _find_matching_root(model_dir)
    if not model_root:
        raise ValueError(f"File path {dest_path} is not within any configured scanner root")
    scanner_getter_name = _infer_model_type(model_root)[1]
    scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
    if scanner_getter is None:
        raise RuntimeError(f"Scanner getter '{scanner_getter_name}' not found in ServiceRegistry")
    scanner = await scanner_getter()
    if scanner is None:
        raise RuntimeError(f"Scanner '{scanner_getter_name}' returned None")
    await scanner.update_single_model_cache(dest_path, dest_path, metadata)
 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()
        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 source_url:
            return web.json_response(
                {
                    "success": False,
                    "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,
            )
        if not os.path.isfile(file_path):
            return web.json_response(
                {"success": False, "error": f"File not found: {file_path}"},
                status=404,
            )
        model_root = _find_matching_root(os.path.dirname(file_path))
        if not model_root:
            return web.json_response(
                {
                    "success": False,
                    "error": "File is not within any configured model directory. Cannot link to a model source.",
                },
                status=400,
            )
        try:
            existing = await MetadataManager.load_metadata_payload(file_path)
            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": "source_url already set",
                    "source_url": ref.url,
                    "source_platform": ref.platform,
                    "hf_url": ref.url if ref.platform == "huggingface" else "",
                })
            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(
                "Linked %s to %s source (%s)", file_path, ref.platform, ref.url
            )
            return web.json_response({
                "success": True,
                "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 link %s to a model source: %s", file_path, exc)
            return web.json_response(
                {"success": False, "error": str(exc)},
                status=500,
            )
    async def list_model_source_files(self, request: web.Request) -> web.Response:
        """List the downloadable weight files of an external repository.
        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.
        """
        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 owner/name)"},
                status=400,
            )
        try:
            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 list %s files in %s: %s", platform, repo, exc)
            return web.json_response({"error": str(exc)}, status=502)
        return web.json_response(files)
    async def download_model_source(self, request: web.Request) -> web.Response:
        """Download a single file from an external repository.
        POST JSON body::
            {
              "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.
        Respects the ``download_backend`` setting (``aria2`` or ``default``).
        """
        try:
            payload: dict[str, Any] = await request.json()
        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 "").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_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
            )
        # `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:
            return web.json_response({"error": "Invalid filename"}, status=400)
        # Validate relative_path — must not be absolute or escape base directory
        if relative_path:
            if os.path.isabs(relative_path):
                return web.json_response({"error": "relative_path must not be absolute"}, status=400)
            if ".." in relative_path.split("/") or "\\" in relative_path:
                return web.json_response({"error": "Invalid relative_path"}, status=400)
        # Use model_root directly as the base directory — same approach as
        # CivitAI's download path (download_manager.py).  No realpath, no
        # allowed-roots validation, no path-traversal check; those are
        # unnecessary when the frontend sends the path from its own dropdown
        # (populated from scanner roots).  Using the "business path" directly
        # keeps dest_path consistent with scanner roots so that later folder
        # derivation (in _save_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, source.default_subdir, owner, repo_name)
        elif relative_path:
            target_dir = os.path.join(base_dir, relative_path)
        else:
            target_dir = base_dir
        # 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)
        dest_path = os.path.join(target_dir, file_base)
        # 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)
        )
        # Check if already exists (simple skip)
        if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
            logger.info("download_model_source: file already exists, skipping — %s", dest_path)
            # The sidecar may predate the source metadata being fetched, or may
            # have been deleted, so top it up instead of skipping past it.
            # Hydration no-ops when there is no sidecar to update.
            await _report_phase(download_id, "source", source.platform)
            await hydrate_from_source(dest_path, ref=ref)
            return web.json_response({
                "success": True,
                "message": f"File already exists: {dest_path}",
                "path": dest_path,
            })
        # Set up progress callback if download_id is provided
        progress_callback = None
        if download_id:
            async def _progress_callback(
                progress: float | DownloadProgress,
                snapshot: DownloadProgress | None = None,
            ) -> None:
                percent = 0.0
                metrics = snapshot if isinstance(snapshot, DownloadProgress) else None
                if isinstance(progress, DownloadProgress):
                    percent = progress.percent_complete
                    metrics = progress
                elif isinstance(snapshot, DownloadProgress):
                    percent = snapshot.percent_complete
                else:
                    percent = float(progress)
                broadcast: dict[str, Any] = {
                    "status": "progress",
                    "progress": round(percent),
                }
                if metrics:
                    broadcast["bytes_downloaded"] = metrics.bytes_downloaded
                    broadcast["total_bytes"] = metrics.total_bytes
                    broadcast["bytes_per_second"] = metrics.bytes_per_second
                await ws_manager.broadcast_download_progress(download_id, broadcast)
            progress_callback = _progress_callback
        # Respect download backend setting (aria2 vs default)
        download_backend = (
            get_settings_manager().get("download_backend", "default")
        )
        if download_backend == "aria2":
            aria2 = await Aria2Downloader.get_instance()
            aid = download_id or f"{source.platform}_{repo}_{filename}"
            try:
                ok, result = await aria2.download_file(
                    url=resolve_url,
                    save_path=dest_path,
                    download_id=aid,
                    progress_callback=progress_callback,
                )
                if ok:
                    await _save_source_metadata(
                        dest_path, ref, model_root, download_id=download_id
                    )
                    return web.json_response({
                        "success": True,
                        "message": f"Downloaded to {dest_path}",
                        "path": dest_path,
                    })
                return web.json_response(
                    {"success": False, "error": result or "aria2 download failed"},
                    status=500,
                )
            except Exception as exc:
                logger.error("%s download (aria2) failed: %s", platform, exc)
                return web.json_response(
                    {"success": False, "error": str(exc)}, status=500
                )
        # Default: use built-in aiohttp Downloader
        downloader = await get_downloader()
        try:
            success, result = await downloader.download_file(
                url=resolve_url,
                save_path=dest_path,
                use_auth=False,
                allow_resume=True,
                progress_callback=progress_callback,
            )
            if success:
                await _save_source_metadata(
                    dest_path, ref, model_root, download_id=download_id
                )
                return web.json_response({
                    "success": True,
                    "message": f"Downloaded to {result}",
                    "path": result,
                })
            return web.json_response(
                {"success": False, "error": result or "Download failed"},
                status=500,
            )
        except Exception as exc:
            logger.error("%s download failed: %s", platform, exc)
            return web.json_response(
                {"success": False, "error": str(exc)}, status=500
            )
@@ -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
@@ -37,6 +37,8 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
    RouteDefinition("GET", "/api/lm/wildcards/search", "search_wildcards"),
    RouteDefinition("POST", "/api/lm/wildcards/open-location", "open_wildcards_location"),
    RouteDefinition("POST", "/api/lm/open-file-location", "open_file_location"),
    RouteDefinition("POST", "/api/lm/browse-directory", "browse_directory"),
    RouteDefinition("POST", "/api/lm/validate-path", "validate_path"),
    RouteDefinition("POST", "/api/lm/update-usage-stats", "update_usage_stats"),
    RouteDefinition("GET", "/api/lm/get-usage-stats", "get_usage_stats"),
    RouteDefinition("POST", "/api/lm/update-lora-code", "update_lora_code"),
@@ -99,16 +101,31 @@ 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"
@@ -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,
        )
@@ -40,11 +40,20 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
    RouteDefinition("POST", "/api/lm/{prefix}/verify-duplicates", "verify_duplicates"),
    RouteDefinition("POST", "/api/lm/{prefix}/move_model", "move_model"),
    RouteDefinition("POST", "/api/lm/{prefix}/move_models_bulk", "move_models_bulk"),
    RouteDefinition("POST", "/api/lm/{prefix}/create-folder", "create_folder"),
    RouteDefinition("POST", "/api/lm/{prefix}/delete-folder", "delete_folder"),
    RouteDefinition("POST", "/api/lm/{prefix}/rename-folder", "rename_folder"),
    RouteDefinition("GET", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
    RouteDefinition("POST", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
    RouteDefinition(
        "GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"
    ),
    RouteDefinition(
        "GET", "/api/lm/{prefix}/apply-filename-template", "apply_filename_template"
    ),
    RouteDefinition(
        "POST", "/api/lm/{prefix}/apply-filename-template", "apply_filename_template"
    ),
    RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
    RouteDefinition("GET", "/api/lm/{prefix}/search-tags", "search_tags"),
    RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
@@ -68,6 +77,8 @@ 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"
    ),
@@ -0,0 +1,144 @@
 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"
        context = {
            "other_disabled": False,
            "other_no_paths": not bool(config.other_roots),
            "standalone_mode": standalone_mode,
        }
        if standalone_mode:
            # The empty state points at the Model Paths settings section and
            # shows the settings.json path as a fallback reference.
            context["settings_file"] = getattr(self._settings, "settings_file", "") or ""
        return context
    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)
@@ -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 +110,11 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
    RouteDefinition(
        "POST", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
    ),
    # The companion browser extension only ever issues GET requests, so the
    # payload-based re-import variant must also be reachable via GET.
    RouteDefinition(
        "GET", "/api/lm/recipe/{recipe_id}/reimport", "reimport_recipe"
    ),
    RouteDefinition(
        "POST", "/api/lm/recipe/{recipe_id}/send-workflow", "send_recipe_workflow"
    ),
@@ -21,7 +21,20 @@ NETWORK_EXCEPTIONS = (ClientError, OSError, asyncio.TimeoutError)
 # otherwise delete them because they are untracked and, in released tags,
 # not listed in ``.gitignore``. ``-e`` excludes a path from cleaning
 # regardless of whether it is ignored.
-_PRESERVE_DIRS = ('settings.json', 'civitai', 'wildcards', 'backups', 'stats', 'logs', 'cache', 'model_cache')
+# ``cache`` covers the resolved cache tree (cache/model, cache/recipe,
 # cache/fts, ...); the legacy ``recipe_cache`` / ``model_cache`` directories
 # are listed too because a portable install can predate the cache/ move.
 _PRESERVE_DIRS = (
    'settings.json',
    'civitai',
    'wildcards',
    'backups',
    'stats',
    'logs',
    'cache',
    'model_cache',
    'recipe_cache',
 )
 def _clean_excludes() -> List[str]:
@@ -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
@@ -19,16 +19,21 @@ 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 (
    ModelCardContext,
    ModelSourceCache,
    get_source,
    resolve_source_ref,
    source_label,
 )
 from ..model_sources.hydration import load_model_card, resolve_site_base_model
 from ..websocket_manager import ws_manager
 from .post_processor import PostProcessor
 from .skill_registry import SkillRegistry
@@ -255,6 +260,11 @@ class AgentService:
        llm = await self._ensure_llm()
        llm_configured = llm.is_configured() if skill.llm_required else True
        # A collection repository holds many model files under one source id;
        # this memo keeps the README and the repository metadata from being
        # re-fetched once per file.  It lives for this run only.
        source_cache = ModelSourceCache()
        for model_path in model_paths:
            model_filename = os.path.basename(model_path)
            logger.info(
@@ -267,24 +277,50 @@ 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
+                # Fast-fail: enrich_hf_metadata needs an external model source
-                if skill_name == "enrich_hf_metadata" and not metadata.get("hf_url", ""):
+                # that exposes an accessible model card.
-                    logger.info(
+                if skill_name == "enrich_hf_metadata":
-                        "[%s] SKIP %s — no hf_url in metadata",
+                    skip_reason = self._enrichment_skip_reason(metadata)
-                        skill_name, model_filename,
+                    if skip_reason:
-                    )
+                        logger.info(
-                    skipped_count += 1
+                            "[%s] SKIP %s — %s",
-                    skip_model = True
+                            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}
+                    # The site's own data is deterministic and must land whether
-                    if skill.llm_required and llm_configured:
+                    # or not an LLM is available: a user without a key still gets
-                        prompt_vars = await self._build_prompt_context(
+                    # the author summary, the example images and the tags.
-                            skill_name, model_path, metadata, registry, llm,
+                    source_vars, source_context = await self._load_source_card(
                        model_path, metadata, cache=source_cache,
                    )
                    resolved_base_model = ""
                    if skill_name == "enrich_hf_metadata" and not (
                        metadata.get("base_model") or ""
                    ).strip():
                        resolved_base_model = await self._resolve_site_base_model(
                            source_context,
                        )
                    llm_response: Optional[Dict[str, Any]] = None
-                    if skill.llm_required and llm_configured:
+                    if skill.llm_required and not llm_configured:
                        # Without a provider the deterministic model-source data
                        # still lands; the LLM-only fields simply stay untouched.
                        logger.info(
                            "[%s] No LLM configured for %s — applying %s data only",
                            skill_name, model_filename,
                            "model-source"
                            if not source_context.is_empty()
                            else "README",
                        )
                    elif skill.llm_required:
                        prompt_vars = await self._build_prompt_context(
                            skill_name, model_path, metadata, registry, llm,
                            source_vars=source_vars,
                            source_context=source_context,
                        )
                        prompt_template = registry.load_prompt(skill_name)
                        rendered = _render_prompt(prompt_template, prompt_vars)
                        llm_response = await llm.chat_completion_json(
@@ -307,7 +343,9 @@ class AgentService:
                        model_path=model_path,
                        llm_output=llm_response or {},
                        metadata=metadata,
-                        readme_content=prompt_vars.get("readme_content_full", ""),
+                        readme_content=source_vars.get("readme_content_full", ""),
                        source_context=source_context,
                        resolved_base_model=resolved_base_model,
                    )
                    if model_result.get("success", True):
@@ -358,6 +396,28 @@ 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.
@@ -368,6 +428,82 @@ class AgentService:
        """
        return "\n".join(f"- {m}" for m in models)
    async def _load_source_card(
        self,
        model_path: str,
        metadata: Dict[str, Any],
        *,
        cache: Optional[ModelSourceCache] = None,
    ) -> tuple[Dict[str, Any], ModelCardContext]:
        """Fetch the model card and site-published extras for one model.
        Runs for every source-backed enrichment regardless of LLM
        availability, because everything it returns is deterministic data that
        should be applied even without a configured provider.
        *cache* is the per-run memo created by :meth:`execute_skill`.  The
        README is repository-wide, so it is fetched once per source id; only
        successful reads are memoised, leaving a transient failure to be
        retried for the next file.
        """
        variables: Dict[str, Any] = {
            "asset_base_url": "",
            "source_description": "",
            "source_base_model": "",
            "source_official_tags": "",
            "source_example_images": "",
            "source_trigger_words": "",
            "readme_content": "(README not available)",
            "readme_content_full": "",
        }
        ref = resolve_source_ref(metadata)
        source = get_source(ref.platform) if ref is not None else None
        if ref is None or source is None or not source.supports_enrichment:
            return variables, ModelCardContext()
        raw_basename = os.path.splitext(os.path.basename(model_path))[0]
        variables["asset_base_url"] = source.asset_base_url(ref.source_id)
        readme = await load_model_card(source, ref.source_id, cache)
        # Sites such as ModelScope keep part of the model card outside the
        # README (author summary, curated tags, per-file example images).  The
        # recorded hash identifies the file even after the user renames it.
        card_context = await source.fetch_model_card_context(
            ref.source_id,
            os.path.basename(model_path),
            sha256=(metadata.get("sha256") or "").strip(),
            cache=cache,
        )
        variables["source_description"] = card_context.description
        variables["source_base_model"] = card_context.base_model
        variables["source_official_tags"] = "\n".join(
            f"- {tag}" for tag in card_context.official_tags
        )
        variables["source_example_images"] = "\n".join(
            f"- {url}" for url in card_context.example_images
        )
        variables["source_trigger_words"] = ", ".join(card_context.trigger_words)
        # Trim README to the section relevant to this model file
        # (collection repos often have multiple models in one README).
        if readme and raw_basename:
            trimmed = extract_relevant_section(readme, raw_basename)
            cleaned = clean_readme_for_llm(trimmed) if trimmed else ""
        else:
            cleaned = clean_readme_for_llm(readme) if readme else ""
        variables["readme_content"] = cleaned if cleaned else "(README not available)"
        variables["readme_content_full"] = readme or ""
        return variables, card_context
    async def _resolve_site_base_model(self, source_context: ModelCardContext) -> str:
        """Resolve the site's base-model hints to a canonical name, or ``""``."""
        return await resolve_site_base_model(source_context)
    async def _build_prompt_context(
        self,
        skill_name: str,
@@ -375,19 +511,45 @@ class AgentService:
        metadata: Dict[str, Any],
        registry: SkillRegistry,
        llm: Any,
        *,
        source_vars: Optional[Dict[str, Any]] = None,
        source_context: Optional[ModelCardContext] = None,
    ) -> Dict[str, Any]:
        """Gather variables for the skill's prompt template.
-        Reads metadata, fetches the HF README (if applicable), lists available
+        Reads metadata, fetches the model card (unless a pre-fetched
        *source_vars* / *source_context* pair is supplied), lists available
        base models, loads user priority tags, and returns a dict that maps to
        ``{{variable}}`` placeholders in ``prompt.md``.
        """
        from ...metadata_ops import identify_model_type, list_base_models
        from ..settings_manager import SettingsManager
        if source_vars is None or source_context is None:
            source_vars, source_context = await self._load_source_card(
                model_path, metadata,
            )
        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": "",
            # Site-provided card extras (see ModelSource.fetch_model_card_context)
            "source_description": "",
            "source_base_model": "",
            "source_official_tags": "",
            "source_example_images": "",
            "source_trigger_words": "",
            # Carrier for the structured context handed to the post-processor;
            # never rendered into the prompt.
            "source_context": ModelCardContext(),
            # Legacy Hugging Face aliases (kept so older prompt templates and
            # third-party skills keep rendering)
            "hf_url": "",
            "repo": "",
            "readme_content": "",
@@ -407,26 +569,33 @@ 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", "")
+        ref = resolve_source_ref(metadata)
-        context["hf_url"] = hf_url
+        if ref is not None:
-        repo = self._extract_repo_from_url(hf_url) if hf_url else ""
+            context["source_url"] = ref.url
-        context["repo"] = repo or ""
+            context["source_id"] = ref.source_id
-        if repo:
+            context["source_platform"] = ref.platform
-            readme = await self._fetch_readme(repo)
+            context["source_label"] = source_label(ref.platform, ref.platform)
-            # Trim README to the section relevant to this model file
+            if ref.platform == "huggingface":
-            # (collection repos often have multiple models in one README).
+                context["hf_url"] = ref.url
-            if readme and raw_basename:
+            context["repo"] = ref.source_id
-                trimmed = extract_relevant_section(readme, raw_basename)
+
-                cleaned = clean_readme_for_llm(trimmed) if trimmed else ""
+        source = get_source(ref.platform) if ref is not None else None
-            else:
+        if ref is not None and source is not None and source.supports_enrichment:
-                cleaned = clean_readme_for_llm(readme) if readme else ""
+            # Values fetched once by _load_source_card and shared with the
-            context["readme_content"] = cleaned if cleaned else "(README not available)"
+            # post-processor, so the network is not hit twice per model.
-            context["readme_content_full"] = readme or ""
+            context["asset_base_url"] = source_vars["asset_base_url"]
            context["source_context"] = source_context
            context["source_description"] = source_vars["source_description"]
            context["source_base_model"] = source_vars["source_base_model"]
            context["source_official_tags"] = source_vars["source_official_tags"]
            context["source_example_images"] = source_vars["source_example_images"]
            context["source_trigger_words"] = source_vars["source_trigger_words"]
            context["readme_content"] = source_vars["readme_content"]
            context["readme_content_full"] = source_vars["readme_content_full"]
        try:
            raw_models = await list_base_models()
@@ -459,20 +628,14 @@ class AgentService:
    @staticmethod
    async def _fetch_readme(repo: str) -> str:
-        """Fetch README.md from HuggingFace (tries ``main``, then ``master``)."""
+        """Fetch a Hugging Face README (tries ``main``, then ``master``).
-        async with aiohttp.ClientSession(
+
-            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
+        Kept for backward compatibility; new code should go through the
-            timeout=aiohttp.ClientTimeout(total=30),
+        model-source registry so every supported site works.
-        ) as session:
+        """
-            for branch in ("main", "master"):
+        from ..model_sources import HuggingFaceSource
-                url = f"https://huggingface.co/{repo}/raw/{branch}/README.md"
+
-                try:
+        return await HuggingFaceSource().fetch_model_card(repo)
                    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 ""
    async def _emit_progress(
        self,
@@ -0,0 +1,94 @@
 """Map a site-reported base model onto this system's canonical vocabulary.
 Model sites name base models in their own terms: ModelScope publishes
 ``krea/Krea-2-Turbo`` and ``KREA_2_TURBO`` where this system expects the
 canonical ``Krea 2``.  Turning one into the other is normally the LLM's job;
 this module resolves the cases that can be decided safely so the canonical
 field is still populated when the LLM returns nothing usable for it.
 The resolver is deliberately strict, because a wrong base model written with
 apparent authority is worse than no value at all:
 * it only ever returns a name that is already present in *known_names*;
 * matching is on the normalised form (lowercased, non-alphanumerics removed),
  so separators and casing are ignored but nothing is inferred;
 * a bounded set of published variant suffixes may be stripped, and only when
  the remainder still matches a known name exactly.
 Anything it cannot decide returns ``""``, and the caller falls back to the LLM.
 """
 from __future__ import annotations
 import re
 from typing import Iterable, Sequence
 #: Variant suffixes sites append to a base-model *family* name.  Stripping one
 #: is only attempted when the remainder matches a known name exactly, so an
 #: unrecognised suffix can never produce a bogus match.
 _VARIANT_SUFFIXES: tuple[str, ...] = (
    "turbo",
    "schnell",
    "lightning",
    "dev",
    "beta",
    "alpha",
 )
 _NON_ALNUM = re.compile(r"[^a-z0-9]+")
 def _normalize(value: str) -> str:
    """Return the comparison form of *value*.
    Lowercases and drops every non-alphanumeric character, so ``KREA_2``,
    ``Krea 2``, ``krea-2`` and ``krea.2`` all collapse to ``krea2``.
    """
    return _NON_ALNUM.sub("", (value or "").lower())
 def resolve_base_model(
    hints: Iterable[str], known_names: Sequence[str]
 ) -> str:
    """Return the canonical base model that *hints* refers to, or ``""``.
    Args:
        hints: Site-reported names, best first (e.g. an architecture enum
            before a link-style repository id).
        known_names: The canonical vocabulary; only these are ever returned.
    Returns:
        One of *known_names*, or ``""`` when nothing matches exactly.
    """
    normalized: dict[str, str] = {}
    for name in known_names:
        key = _normalize(name)
        if key and key not in normalized:
            normalized[key] = name
    if not normalized:
        return ""
    ordered = [hint for hint in hints if hint]
    # 1. Exact normalised match — the unambiguous case.
    for hint in ordered:
        candidate = _normalize(hint)
        if candidate in normalized:
            return normalized[candidate]
    # 2. Drop one published variant suffix and retry exactly.
    for hint in ordered:
        candidate = _normalize(hint)
        for suffix in _VARIANT_SUFFIXES:
            if not candidate.endswith(suffix) or candidate == suffix:
                continue
            stem = candidate[: -len(suffix)]
            if stem in normalized:
                return normalized[stem]
    return ""
 __all__ = ["resolve_base_model"]
@@ -10,12 +10,16 @@ refresh cache).  All actual I/O is delegated to :mod:`~py.metadata_ops`.
 from __future__ import annotations
 import html
 import json
 import logging
 import os
 import re
 from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
 if TYPE_CHECKING:  # pragma: no cover - typing only
    from ..model_sources import ModelCardContext
 logger = logging.getLogger(__name__)
@@ -42,6 +46,9 @@ class PostProcessor:
        llm_output: Dict[str, Any],
        metadata: Dict[str, Any],
        readme_content: str = "",
        source_context: Optional["ModelCardContext"] = None,
        resolved_base_model: str = "",
        metadata_source: str = "agent:enrich_hf_metadata",
    ) -> Dict[str, Any]:
        """Route *llm_output* to the correct skill post-processor.
@@ -49,12 +56,26 @@ class PostProcessor:
        that is converted to HTML and stored as ``modelDescription`` for
        the description tab.
        *source_context* carries the extras the model site publishes outside
        the README (author description, per-file example images, trigger
        words).  It is ``None`` for callers that have none.
        *resolved_base_model* is the canonical base-model name the site's own
        hints resolve to, used when the LLM did not supply one (which is the
        normal case when the LLM was skipped).
        *metadata_source* records who produced the metadata.  The AI skill
        keeps its historical value; the deterministic download-time hydration
        passes its own so the two remain distinguishable.  ``llm_enriched_at``
        is only stamped when *llm_output* actually carries a provider answer.
        Returns a dict with keys ``success`` (bool), ``updated_fields`` (list),
        ``preview_downloaded`` (bool), and ``errors`` (list).
        """
        if skill_name == "enrich_hf_metadata":
            return await self._process_enrich_hf_metadata(
-                model_path, llm_output, metadata, readme_content,
+                model_path, llm_output, metadata, readme_content, source_context,
                resolved_base_model, metadata_source,
            )
        return {
            "success": False,
@@ -72,12 +93,16 @@ class PostProcessor:
        llm_output: Dict[str, Any],
        metadata: Dict[str, Any],
        readme_content: str = "",
        source_context: Optional["ModelCardContext"] = None,
        resolved_base_model: str = "",
        metadata_source: str = "agent:enrich_hf_metadata",
    ) -> Dict[str, Any]:
        from ...metadata_ops import (
            apply_metadata_updates,
            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,
@@ -85,24 +110,49 @@ 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 -----------------
+        # -- Determine whether this is an externally-sourced model ---------
-        is_hf_model = not metadata.get("from_civitai", True)
+        # 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] = {}
-        # base_model
+        # base_model — the LLM's mapping wins; when it returned nothing usable,
        # fall back to the canonical name the site's own hints resolve to.
        new_base = (llm_output.get("base_model") or "").strip()
        if not new_base:
            new_base = (resolved_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
        # model_name — the site's own display name, so a source download never
        # shows up under its local filename.  Written only while the name is
        # still the untouched file stem: once a user renames a model that
        # choice is theirs to keep.
        site_name = ((source_context.model_name if source_context else "") or "").strip()
        if is_source_model and site_name:
            current_name = (metadata.get("model_name") or "").strip()
            file_stem = (metadata.get("file_name") or "").strip()
            if not current_name or current_name == file_stem:
                updates["model_name"] = site_name
        # trigger words → civitai.trainedWords
        new_triggers = llm_output.get("trigger_words", [])
        trigger_words_empty = True
@@ -110,45 +160,71 @@ class PostProcessor:
            cleaned = [t.strip() for t in new_triggers if t.strip()]
            cleaned = [t for t in cleaned if t.lower() not in ("none", "null", "n/a")]
            trigger_words_empty = not cleaned
-            current_civitai = metadata.get("civitai") or {}
+            current_triggers = (metadata.get("civitai") or {}).get("trainedWords") or []
-            current_triggers = current_civitai.get("trainedWords") or []
+            if self._should_overwrite_list(current_triggers, is_source_model):
-            if self._should_overwrite_list(current_triggers, is_hf_model):
+                self._merge_civitai(updates, metadata, trainedWords=cleaned)
                trig_civitai = dict(current_civitai)
                if "civitai" in updates and isinstance(updates["civitai"], dict):
                    trig_civitai.update(updates["civitai"])
                trig_civitai["trainedWords"] = cleaned
                updates["civitai"] = trig_civitai
-        # modelDescription — from raw README content (converted to HTML)
+        # modelDescription — the author's own summary (when the site keeps one
-        if readme_content and is_hf_model:
+        # outside the README, e.g. ModelScope's ``Description``) followed by the
-            converted = convert_readme_to_html(readme_content)
+        # README converted to HTML.
-            if converted:
+        site_description = (
-                updates["modelDescription"] = converted
+            (source_context.description if source_context else "") or ""
        ).strip()
        if is_source_model and (site_description or readme_content):
            parts: List[str] = []
            if site_description:
                parts.append(f"<p>{html.escape(site_description)}</p>")
            if readme_content:
                converted = convert_readme_to_html(readme_content)
                if converted:
                    parts.append(converted)
            if parts:
                updates["modelDescription"] = "\n".join(parts)
-        # short_description → civitai.description (for "About this version")
+        # short_description → civitai.description (for "About this version").
        # Falls back to the site's author summary, which for ModelScope AIGC
        # models is frequently the only human-written text available.
        short_desc = (llm_output.get("short_description") or "").strip()
-        if short_desc and is_hf_model:
+        if not short_desc:
-            current_civitai = metadata.get("civitai") or {}
+            short_desc = site_description
-            desc_civitai = dict(current_civitai)
+        if short_desc and is_source_model:
-            if "civitai" in updates and isinstance(updates["civitai"], dict):
+            self._merge_civitai(updates, metadata, description=short_desc)
-                desc_civitai.update(updates["civitai"])
+
-            desc_civitai["description"] = short_desc
+        # The version label completes the card the way a CivitAI download does:
-            updates["civitai"] = desc_civitai
+        # the UI renders `civitai.name` as the version chip.  It is per file,
        # so a collection repository shows that checkpoint's own label.
        site_version = (
            (source_context.version_name if source_context else "") or ""
        ).strip()
        if is_source_model and site_version:
            self._merge_civitai(updates, metadata, name=site_version)
        # gallery images → civitai.images (site example images, YAML frontmatter
        # widget entries, and Sample Gallery markdown tables in the README body)
        rec_width = llm_output.get("recommended_width") or 0
        rec_height = llm_output.get("recommended_height") or 0
        # Example images the site publishes for *this* file.  They are matched
        # by filename, so they are the most precise preview source available
        # and the only one for repositories whose README carries no images.
        site_images: List[Dict[str, Any]] = []
        if is_source_model and source_context is not None:
            site_images = [
                _example_image(url, rec_width, rec_height)
                for url in source_context.example_images
                if url
            ]
        # 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:
+        if (readme_content or site_images) and is_source_model:
-            hf_url = metadata.get("hf_url", "") or ""
+            repo = source_id
-            repo = extract_repo_from_hf_url(hf_url)
+            readme_images: List[Dict[str, Any]] = []
-            if repo:
+            if readme_content and repo:
                rec_w = llm_output.get("recommended_width") or 0
                rec_h = llm_output.get("recommended_height") or 0
                # 1. Widget images (YAML frontmatter)
                gallery = extract_gallery_images(
                    readme_content, repo,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                    base_url=asset_base_url,
                )
                # 2. Sample Gallery table images (markdown body), deduplicated
@@ -156,7 +232,8 @@ class PostProcessor:
                table_images = extract_gallery_table_images(
                    readme_content, repo,
                    existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                    base_url=asset_base_url,
                )
                existing_urls.update(img["url"] for img in table_images if img.get("url"))
@@ -164,7 +241,8 @@ class PostProcessor:
                simple_images = extract_simple_markdown_images(
                    readme_content, repo,
                    existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                    base_url=asset_base_url,
                )
                existing_urls.update(img["url"] for img in simple_images if img.get("url"))
@@ -172,54 +250,71 @@ class PostProcessor:
                html_images = extract_html_img_tags(
                    readme_content, repo,
                    existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                    base_url=asset_base_url,
                )
-                all_images = gallery + table_images + simple_images + html_images
+                readme_images = gallery + table_images + simple_images + html_images
                if all_images:
                    gallery_images = all_images
                    current_civitai = metadata.get("civitai") or {}
                    gallery_civitai = dict(current_civitai)
                    if "civitai" in updates and isinstance(updates["civitai"], dict):
                        gallery_civitai.update(updates["civitai"])
                    gallery_civitai["images"] = all_images
                    updates["civitai"] = gallery_civitai
-        # tags
+            # Site images come first so the preview fallback below prefers an
            # image that is known to belong to this exact file.
            all_images = _dedupe_images(site_images + readme_images)
            if all_images:
                gallery_images = all_images
                self._merge_civitai(updates, metadata, images=all_images)
        # tags — the site's curated tags are authoritative content vocabulary, so
        # they are kept alongside whatever the LLM proposed (the LLM is skipped
        # entirely when the site data is complete, which is why this cannot rely
        # on ``llm_output`` alone).
        new_tags = llm_output.get("tags", [])
-        if isinstance(new_tags, list) and new_tags:
+        candidate_tags: List[str] = []
        if is_source_model and source_context is not None:
            candidate_tags.extend(source_context.official_tags)
        if isinstance(new_tags, list):
            candidate_tags.extend(
                tag for tag in new_tags if tag not in candidate_tags
            )
        if candidate_tags:
            existing_tags = metadata.get("tags") or []
-            merged = self._merge_tags(existing_tags, new_tags)
+            merged = self._merge_tags(existing_tags, candidate_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)
+        # metadata_source is recorded for provenance; llm_enriched_at only means
-        updates["metadata_source"] = "agent:enrich_hf_metadata"
+        # something when a provider actually answered, so the deterministic
-        updates["llm_enriched_at"] = datetime.now(timezone.utc).isoformat()
+        # download-time hydration does not claim an enrichment that never ran.
        updates["metadata_source"] = metadata_source
        if llm_output:
            updates["llm_enriched_at"] = datetime.now(timezone.utc).isoformat()
-        # Store LLM confidence in metadata so it's accessible for evaluation
+        # LLM confidence, stored for the enrichment evaluation harness.  The key
        # must NOT start with an underscore: `BaseModelMetadata.from_dict()`
        # deliberately drops underscore-prefixed keys so they never round-trip,
        # which silently erased this field on the next metadata write.
        raw_confidence = (llm_output.get("confidence") or "").strip()
        if raw_confidence:
-            updates["_llm_confidence"] = raw_confidence
+            updates["llm_confidence"] = raw_confidence
-        # Fallback: extract instance_prompt from YAML frontmatter when the LLM
+        # Fallback: use the trigger words the site records for this exact file,
-        # returned empty trigger words but the README has instance_prompt.
+        # then the README's YAML `instance_prompt`, when the LLM returned none.
        if trigger_words_empty:
-            instance_prompt = _extract_yaml_instance_prompt(readme_content)
+            site_triggers = (
-            if instance_prompt:
+                list(source_context.trigger_words) if source_context else []
-                current_civitai = metadata.get("civitai") or {}
+            )
-                trig_civitai = dict(current_civitai)
+            if not site_triggers:
-                if "civitai" in updates and isinstance(updates["civitai"], dict):
+                instance_prompt = _extract_yaml_instance_prompt(readme_content)
-                    trig_civitai.update(updates["civitai"])
+                if instance_prompt:
-                trig_civitai["trainedWords"] = [instance_prompt]
+                    site_triggers = [instance_prompt]
-                updates["civitai"] = trig_civitai
+            if site_triggers:
                self._merge_civitai(updates, metadata, trainedWords=site_triggers)
        preview_remote_url = (llm_output.get("preview_url") or "").strip()
        # Fallback: if the LLM couldn't find a preview image in the cleaned
        # 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,
@@ -245,8 +340,12 @@ class PostProcessor:
        if new_notes:
            updates["notes"] = new_notes
-        # usage_tips — JSON string (e.g. {"strength_min":0.85,"strength_max":1.4})
+        # usage_tips — JSON string (e.g. {"strength_min":0.85,"strength_max":1.4}).
        # When the LLM returned nothing, recover an explicitly stated strength
        # range from the author summary so the value is not lost.
        raw_tips = (llm_output.get("usage_tips") or "").strip()
        if not raw_tips or raw_tips == "{}":
            raw_tips = _extract_usage_tips(site_description)
        if raw_tips and raw_tips != "{}":
            try:
                json.loads(raw_tips)
@@ -276,16 +375,35 @@ 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 _merge_civitai(
        updates: Dict[str, Any], metadata: Dict[str, Any], **fields: Any
    ) -> None:
        """Layer *fields* onto the ``civitai`` block being assembled.
        Description, version label, trigger words and gallery images all live
        in the same dict and are contributed by separate branches, so each one
        starts from what is already on disk and then applies whatever an
        earlier branch queued in *updates*.
        """
        merged = dict(metadata.get("civitai") or {})
        queued = updates.get("civitai")
        if isinstance(queued, dict):
            merged.update(queued)
        merged.update(fields)
        updates["civitai"] = merged
    @staticmethod
    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]:
@@ -309,6 +427,129 @@ class PostProcessor:
 # ------------------------------------------------------------------
 #: Separator between a label and its value.  Published model cards routinely
 #: wrap the numbers in markdown emphasis or quotes (``strength: **0.85 - 1.4**``,
 #: ``CLIP 强度「0.5」``), so those are absorbed rather than treated as a break.
 _EMPHASIS = "[\"'\u201c\u201d\u300c\u300d*_`\\s]*"
 #: An explicitly stated strength/weight range, e.g. ``权重0.5-1.2``,
 #: ``强度 0.8 ~ 1.2``, ``strength: **0.85 - 1.4**``.
 _RANGE_DASH = "(?:-|\u2010|\u2011|\u2012|\u2013|\u2014|\uff0d|~|\uff5e|\u81f3|\u5230|to)"
 _STRENGTH_RANGE_RE = re.compile(
    "(?:\u6743\u91cd|\u5f3a\u5ea6|strength|weight)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
    + r"(\d+(?:\.\d+)?)" + _EMPHASIS + _RANGE_DASH + _EMPHASIS
    + r"(\d+(?:\.\d+)?)",
    re.IGNORECASE,
 )
 #: A single strength/weight value, e.g. ``strength: 0.6``, ``权重 0.8``.
 _STRENGTH_VALUE_RE = re.compile(
    "(?:\u6743\u91cd|\u5f3a\u5ea6|strength|weight)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
    + r"(\d+(?:\.\d+)?)",
    re.IGNORECASE,
 )
 #: ``clip strength: 0.5`` / ``CLIP 强度 0.5``.
 _CLIP_STRENGTH_RE = re.compile(
    "clip" + _EMPHASIS + "(?:\u5f3a\u5ea6|strength)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
    + r"(\d+(?:\.\d+)?)",
    re.IGNORECASE,
 )
 #: ``clip skip: 2`` / ``CLIP 跳过 2``.
 _CLIP_SKIP_RE = re.compile(
    "clip" + _EMPHASIS + "(?:skip|\u8df3\u8fc7)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
    + r"(\d+)",
    re.IGNORECASE,
 )
 def _extract_usage_tips(text: str) -> str:
    """Extract stated strength/CLIP recommendations from prose.
    This is the deterministic counterpart to the LLM's ``usage_tips`` output,
    used when the LLM was skipped.  It only recognises explicitly written
    values — it never infers a range — and returns ``""`` when it finds none.
    Returns:
        A JSON string matching the skill's ``usage_tips`` schema, or ``""``.
    """
    if not text:
        return ""
    tips: Dict[str, Any] = {}
    # CLIP strength is resolved first and then blanked out, so the generic
    # strength patterns cannot mistake `CLIP 强度 0.5` for the LoRA strength.
    text_for_strength = text
    clip_strength = _CLIP_STRENGTH_RE.search(text_for_strength)
    if clip_strength:
        tips["clip_strength"] = float(clip_strength.group(1))
        text_for_strength = (
            text_for_strength[: clip_strength.start()]
            + " "
            + text_for_strength[clip_strength.end() :]
        )
    range_match = _STRENGTH_RANGE_RE.search(text_for_strength)
    if range_match:
        low = float(range_match.group(1))
        high = float(range_match.group(2))
        if low > high:
            low, high = high, low
        tips["strength_min"] = low
        tips["strength_max"] = high
        tips["strength_range"] = f"{low:g}-{high:g}"
    else:
        value_match = _STRENGTH_VALUE_RE.search(text_for_strength)
        if value_match:
            tips["strength"] = float(value_match.group(1))
    clip_skip = _CLIP_SKIP_RE.search(text)
    if clip_skip:
        tips["clip_skip"] = int(clip_skip.group(1))
    if not tips:
        return ""
    return json.dumps(tips, ensure_ascii=False)
 def _example_image(url: str, width: int, height: int) -> Dict[str, Any]:
    """Build a ``civitai.images`` entry for a site-provided example image.
    The site publishes no prompt alongside these images, so the entry carries
    empty prompt metadata and the LLM's recommended dimensions when it found
    any (falling back to the same 512px placeholder the README extractors use).
    """
    return {
        "url": url,
        "type": "image",
        "nsfwLevel": 0,
        "width": width or 512,
        "height": height or 512,
        "meta": {"prompt": "", "negativePrompt": ""},
        "hasMeta": False,
        "hasPositivePrompt": False,
    }
 def _dedupe_images(images: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Drop later entries that repeat an earlier image URL, keeping order."""
    seen: set[str] = set()
    unique: List[Dict[str, Any]] = []
    for image in images:
        url = image.get("url") or ""
        if not url or url in seen:
            continue
        seen.add(url)
        unique.append(image)
    return unique
 def _extract_yaml_instance_prompt(readme_content: str) -> str:
    """Extract ``instance_prompt`` from the YAML frontmatter of a HF README.
@@ -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
+  Parse the model card (README) from HuggingFace, ModelScope, or any other
-  words, base model, tags, and preview image URL.
+  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)
@@ -22,6 +25,34 @@ You are an expert assistant for AI image generation models. Your task is to extr
 {{current_metadata}}
 ```
 ## Site-Provided Metadata (any field may be empty)
 The model site publishes the following **alongside** the README. It is
 first-hand information recorded by the site itself, so it outranks anything
 you would otherwise guess:
 - **Author description**: {{source_description}}
 - **Base model reported by the site**: {{source_base_model}}
 - **Trigger words recorded for this file**: {{source_trigger_words}}
 - **Site-curated tags**:
 {{source_official_tags}}
 - **Example image URLs for this file**:
 {{source_example_images}}
 Use it as follows:
 - A weight or strength range stated in the **author description** belongs in
  ``usage_tips`` (and in ``notes``); do not leave ``usage_tips`` empty when the
  description states one.
 - When the author description exists, base ``short_description`` on it rather
  than on the README, which on some sites is auto-generated boilerplate.
 - Treat the **site-curated tags** as strong signals for ``tags``: they are
  already a curated content vocabulary, so prefer them over invented words.
 - Treat the **base model reported by the site** as a strong hint for
  ``base_model``, but still map it to the EXACT canonical name from the
  available base-model list.
 - Use the **example image URLs** when the README contains no usable image.
 ## User Priority Tags Reference
 The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`):
@@ -39,7 +70,7 @@ name listed — do not invent aliases or modify variant suffixes.
 {{base_models}}
-## HuggingFace README Content
+## Model Card Content
 ```
 {{readme_content}}
@@ -52,10 +83,11 @@ Extract the following information from the README content above:
 ### base_model
 The base model this model was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
-Check the YAML frontmatter for ``base_model:`` first. If the frontmatter has no ``base_model:``, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
+Check the **base model reported by the site** (above) and the YAML frontmatter ``base_model:`` first. If neither yields a match, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
 ### trigger_words
 The trigger words or activation prompts needed to use this LoRA. Look for:
 - The **trigger words recorded for this file** in the site-provided metadata (most authoritative)
 - `instance_prompt:` in the YAML frontmatter
 - Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
 - In collection repos: the trigger section **specific to this model file** (look near matching download links or anchor IDs)
@@ -63,12 +95,13 @@ The trigger words or activation prompts needed to use this LoRA. Look for:
 Return as an array of strings. If none found, return an empty array `[]`. **Never** return `["None"]` or any placeholder value — a truly empty list means no trigger words exist.
 ### short_description
-A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole.  Return empty string if the README is too minimal.
+A concise 1-2 sentence summary of what this model does. For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole.  Prefer the **author description** from the site-provided metadata when it is present; otherwise extract from the "Model description" section or the first paragraph.  Return empty string if the available content is too minimal.
 ### tags
 3-8 relevant tags for categorizing this model. **Quality over quantity.**
 Sources to consider:
 - The **site-curated tags** from the site-provided metadata (these are already filtered content tags — prefer them)
 - The YAML frontmatter `tags:` list (filter out technical ones — see below)
 - The subject, style, character, or concept the model represents
 - The model filename itself may give clues (e.g. "pokemon", "anime", "pixelart")
@@ -79,7 +112,9 @@ Sources to consider:
 2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags.
-3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`).
+3. **All lowercase, and keep each tag's own wording.** Prefer the spelling already used by the site, the frontmatter, or the author — including hyphenated and multi-word tags such as `"sci-fi"`, `"semi-realistic"`, `"character-enhancement"` or `"art style"`. Do **not** strip separators or invent a single-word variant of a tag you are already including (e.g. do not emit both `"character-enhancement"` and `"character"`). When a tag is written in another script (e.g. Chinese), likewise keep it verbatim instead of translating it.
 4. **Never invent a tag** that neither the site-provided metadata, the YAML frontmatter, nor the README text supports.
 Return empty array if no meaningful content tags remain after filtering.
@@ -92,13 +127,13 @@ 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 the README has no suitable image, fall back to the site-provided **example image URLs** for this file. If nothing is available, 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.
+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}}`.  Include the **author description** from the site-provided metadata when it is present.  Return empty string if there is no useful usage info.
 ### usage_tips
-A JSON string with structured usage recommendations. Extract from the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5"). Possible fields (include only those you can determine):
+A JSON string with structured usage recommendations. Extract from the **author description** (site-provided metadata) and the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5", "权重0.5-1.2"). Possible fields (include only those you can determine):
 ```json
 {
@@ -121,7 +156,7 @@ 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}}`**
@@ -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")
@@ -368,12 +392,18 @@ def _extract_frontmatter(text: str) -> str:
 def convert_readme_to_html(markdown_text: str | None) -> str:
-    """Convert HF README markdown to sanitised HTML."""
+    """Convert HF README markdown to sanitised HTML.
    Site-generated placeholder notices are dropped here too, so a repository
    whose author wrote nothing does not store the download instructions as its
    model description; the result is an empty string in that case.
    """
    if not markdown_text:
        return ""
    text = markdown_text
    text = _strip_frontmatter(text)
    text = _strip_generated_card_boilerplate(text)
    text = _strip_gallery(text)
    text = _strip_badge_images(text)
    text = _strip_html_comments(text)
@@ -420,6 +450,59 @@ _MASSIVE_LIST_LINE_MIN_LEN = 150
 #: Minimum consecutive enumeration lines to trigger massive-list stripping.
 _MASSIVE_LIST_THRESHOLD = 8
 #: Substrings identifying text a *site* generated to fill a model card whose
 #: author wrote nothing, as opposed to the author's own content.  ModelScope
 #: renders such a card as a placeholder notice, a block of SDK/git download
 #: instructions, and a closing invitation to improve the card.
 #:
 #: Matched as substrings rather than whole headings because the notices are
 #: prose, and because non-Latin scripts are not space-delimited — the notice
 #: continues with a full-width period, so the ``title == kw`` style matching
 #: used for :data:`_BOILERPLATE_HEADERS` would never fire.
 _GENERATED_CARD_MARKERS: tuple[str, ...] = (
    "当前模型的贡献者未提供更加详细的模型介绍",
    "您可以通过如下",
    "如果您是本模型的贡献者",
 )
 def _strip_generated_card_boilerplate(text: str) -> str:
    """Remove the notices a site generates to fill an empty model card.
    A repository whose uploader wrote no README still gets a card: ModelScope
    answers with "the contributor provided no further description", the SDK
    and git download commands, and an invitation to complete the card.  None
    of it describes the model, yet it was landing in both the LLM prompt and
    the stored description.
    A notice that is a heading takes its whole section with it, so the
    download block goes too; a stand-alone notice line is dropped on its own.
    Content the author added later — under a heading of equal or higher
    level — is kept, so an improved card is not thrown away.
    """
    lines = text.split("\n")
    out: list[str] = []
    skip_until_level: int | None = None
    for line in lines:
        level = _heading_level(line)
        if any(marker in line for marker in _GENERATED_CARD_MARKERS):
            if level > 0:
                skip_until_level = level
            continue
        if skip_until_level is not None:
            if level > 0 and level <= skip_until_level:
                skip_until_level = None
            else:
                continue
        out.append(line)
    return "\n".join(out)
 def clean_readme_for_llm(markdown_text: str | None, max_length: int = 6000) -> str:
    """Clean a HF README for injection into an LLM metadata-extraction prompt.
@@ -429,6 +512,8 @@ def clean_readme_for_llm(markdown_text: str | None, max_length: int = 6000) -> s
    * ``widget:`` YAML block (example prompts + output URLs)
    * ``<Gallery />`` tags and wrappers
    * Site-generated placeholder notices for a card the author never wrote
      (see :func:`_strip_generated_card_boilerplate`)
    * Fenced code blocks (Python / bash / bibtex / yaml)
    * Standalone ``![...](...)`` image lines and ``<img>`` tags
    * Training-parameter tables
@@ -454,6 +539,7 @@ def clean_readme_for_llm(markdown_text: str | None, max_length: int = 6000) -> s
    # Order matters — broader strips first, then finer ones.
    text = _strip_gallery(text)
    text = _strip_widget_section(text)
    text = _strip_generated_card_boilerplate(text)
    text = _strip_fenced_code_blocks(text)
    text = _strip_standalone_images(text)
    text = _strip_training_tables(text)
@@ -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
+        resumes from the on-disk ``.aria2`` control file.  The same
-        by ``MAX_TRANSFER_RECOVERY_ATTEMPTS``.
+        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
+        ``None`` immediately when the transfer is not tracked or its GID is
-        transfer is not a transient condition, so retrying is pointless).
+        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,
@@ -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
+        key = BaseModelService._extract_source_group_key(item)
-        if not hf_url or not isinstance(hf_url, str):
+        return key if key and key.startswith("hf:") else None
-            return None
+
-        m = re.match(
+    @staticmethod
-            r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url.strip()
+    def _extract_source_group_key(item: Dict[str, Any]) -> Optional[str]:
-        )
+        """Return the external-source group key for *item*, or None.
-        if not m:
+
-            return None
+        Hugging Face keeps the historical ``hf:{owner}/{repo}`` shape; other
-        return f"hf:{m.group(1)}"
+        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
@@ -1295,6 +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,
@@ -1404,9 +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
@@ -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)
@@ -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(
+        sub_type = self.resolve_sub_type_for_path(entry.get("file_path"))
            self._find_root_for_file(entry.get("file_path"))
        )
        if sub_type:
            entry["sub_type"] = sub_type
        return entry
@@ -67,6 +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", ""),
        }
@@ -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:
+            # Treat a missing payload as an error even when the error string is
-                if "not found" in error.lower():
+            # 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)
@@ -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",
@@ -3,7 +3,7 @@
 from __future__ import annotations
 import logging
-from typing import Any, Awaitable, Callable, Dict, Optional
+from typing import Any, Awaitable, Callable, Dict, Iterable, Optional
 from .downloader import DownloadProgress
@@ -186,6 +186,14 @@ class DownloadCoordinator:
        download_manager = await self._download_manager_factory()
        return await download_manager.get_active_downloads()
    async def discard_cleared_downloads(self, download_ids: Iterable[str]) -> int:
        """Tear down in-memory/aria2 tracking for queue-cleared downloads."""
        if not download_ids:
            return 0
        download_manager = await self._download_manager_factory()
        return await download_manager.discard_cleared_downloads(download_ids)
    def _parse_optional_int(self, value: Any, field: str) -> Optional[int]:
        """Parse an optional integer from user input."""
@@ -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
@@ -12,30 +13,39 @@ 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
 from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
-from ..utils.utils import sanitize_folder_name
+from ..utils.utils import calculate_filename_for_model, 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
 from .model_lifecycle_service import ModelLifecycleService, load_local_metadata
 # Download to temporary file first
 import tempfile
@@ -53,6 +63,12 @@ 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 +98,11 @@ 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:
@@ -214,12 +235,21 @@ class DownloadManager:
            return False
    async def _get_scanner_for_model_type(self, model_type: str):
-        """Return the scanner responsible for the given model type."""
+        """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()
-        return await self._get_lora_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(
@@ -704,6 +734,47 @@ class DownloadManager:
                await asyncio.sleep(delay)
        return False
    @staticmethod
    def _reconcile_failed_aria2_partial(save_path: str) -> None:
        """Reconcile on-disk partial state after a failed aria2 transfer.
        The payload and its ``.aria2`` control file form a resumable pair and
        are preserved together so a retry (with a refreshed URL when needed)
        can resume via aria2's ``continue=true``.  A control file without its
        payload cannot resume anything, so the orphan is reported and removed.
        """
        control_path = f"{save_path}.aria2"
        payload_exists = os.path.exists(save_path)
        control_exists = os.path.exists(control_path)
        if payload_exists and not control_exists:
            # If the .aria2 control file is missing, aria2 considers the
            # download complete.  A transient RPC failure may have made us
            # think the download failed even though the file is fully on disk.
            # Keep the file so a retry can find it already complete.
            logger.warning(
                "aria2 download reported failure but .aria2 file is absent "
                "for %s — the file is likely complete.  Preserving it for retry.",
                save_path,
            )
        elif payload_exists and control_exists:
            logger.info(
                "Preserving aria2 partial download for resume: %s", save_path
            )
        elif control_exists:
            logger.warning(
                "Orphaned aria2 control file without payload: %s — removing it",
                control_path,
            )
            try:
                os.remove(control_path)
            except OSError as exc:
                logger.warning(
                    "Failed to remove orphaned aria2 control file %s: %s",
                    control_path,
                    exc,
                )
    async def _cleanup_cancelled_download_files(
        self,
        download_id: str,
@@ -875,6 +946,42 @@ 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,
        *,
@@ -887,6 +994,8 @@ 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]:
@@ -1100,6 +1209,11 @@ class DownloadManager:
                save_path = self._resolve_save_path_from_persisted_record(record)
                if save_path is None:
                    # No resolvable target path (e.g. a queued download whose
                    # paths were never resolved before shutdown): the record
                    # can never be restored, so drop it instead of letting it
                    # accumulate in the state store forever.
                    await self._aria2_state_store.remove(download_id)
                    continue
                if (
@@ -1208,6 +1322,24 @@ 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
@@ -1324,6 +1456,7 @@ class DownloadManager:
                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):
@@ -1348,6 +1481,13 @@ 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":
@@ -1386,6 +1526,20 @@ 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,
@@ -1507,27 +1661,13 @@ class DownloadManager:
                }
            # Check if this checkpoint should be treated as a diffusion model
-            # Priority: (1) any file has type "UNet" or "Diffusion Model",
+            # (shared with the download routing endpoint so the UI location
-            #            (2) baseModel is in DIFFUSION_MODEL_BASE_MODELS
+            # step and the actual download agree on the target roots).
-            is_diffusion_model = False
+            is_diffusion_model = is_diffusion_model_download(
-            if model_type == "checkpoint":
+                model_type,
-                # Check file types first (more direct signal from CivitAI)
+                file_types=(f.get("type", "") for f in version_info.get("files", [])),
-                version_files = version_info.get("files", [])
+                base_model=base_model_value,
-                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
                # 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
@@ -1586,6 +1726,13 @@ 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:
@@ -1625,6 +1772,60 @@ 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)
@@ -1781,6 +1982,24 @@ 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:
@@ -1803,6 +2022,11 @@ 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,
@@ -2015,6 +2239,8 @@ 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)
@@ -2127,6 +2353,28 @@ class DownloadManager:
        return formatted_path
    @contextlib.asynccontextmanager
    async def _exclusive_target_slot(self, target_key: str):
        async with self._path_slot_guard:
            slot = self._path_slots.get(target_key)
            if slot is None:
                slot = _PathSlot()
                self._path_slots[target_key] = slot
            slot.refs += 1
        try:
            async with slot.lock:
                yield
        finally:
            async with self._path_slot_guard:
                slot.refs -= 1
                if slot.refs <= 0:
                    _ = self._path_slots.pop(target_key, None)
    def _target_slot_key(self, save_dir: str, metadata) -> str:
        return os.path.abspath(
            os.path.join(save_dir, os.path.basename(metadata.file_path))
        )
    async def _execute_download(
        self,
        download_urls: List[str],
@@ -2138,6 +2386,33 @@ class DownloadManager:
        model_type: str = "lora",
        download_id: str | None = None,
        transfer_backend: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Execute the download serialized against other downloads targeting the same path."""
        target_key = self._target_slot_key(save_dir, metadata)
        async with self._exclusive_target_slot(target_key):
            return await self._execute_download_pipeline(
                download_urls=download_urls,
                save_dir=save_dir,
                metadata=metadata,
                version_info=version_info,
                relative_path=relative_path,
                progress_callback=progress_callback,
                model_type=model_type,
                download_id=download_id,
                transfer_backend=transfer_backend,
            )
    async def _execute_download_pipeline(
        self,
        download_urls: List[str],
        save_dir: str,
        metadata,
        version_info: Dict[str, Any],
        relative_path: str,
        progress_callback=None,
        model_type: str = "lora",
        download_id: str | None = None,
        transfer_backend: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Execute the actual download process including preview images and model files"""
        metadata_entries: List[Any] = []
@@ -2356,20 +2631,8 @@ class DownloadManager:
                    break
                last_error = result
-                # For aria2: if the .aria2 control file is missing, aria2 considers
+                if transfer_backend == "aria2":
-                # the download complete.  A transient RPC failure may have made us
+                    self._reconcile_failed_aria2_partial(save_path)
                # 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,
                    )
                elif os.path.exists(save_path):
                    try:
                        os.remove(save_path)
@@ -2474,6 +2737,9 @@ 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)
@@ -2481,6 +2747,7 @@ class DownloadManager:
                else None
            )
            downloaded_metadata: List[Dict[str, Any]] = []
            for index, entry in enumerate(metadata_entries):
                file_path_for_adjust = getattr(
                    entry, "file_path", actual_file_paths[index]
@@ -2523,6 +2790,15 @@ class DownloadManager:
                if scanner is not None:
                    await scanner.add_model_to_cache(metadata_dict, relative_path)
                downloaded_metadata.append(metadata_dict)
            await self._apply_download_filename_template(
                scanner=scanner,
                model_type=model_type,
                downloaded_metadata=downloaded_metadata,
                download_id=download_id,
            )
            if transfer_backend == "aria2" and download_id:
                await self._aria2_state_store.remove(download_id)
@@ -2562,8 +2838,85 @@ class DownloadManager:
            return {"success": False, "error": str(e)}
    async def _apply_download_filename_template(
        self,
        *,
        scanner,
        model_type: str,
        downloaded_metadata: List[Dict[str, Any]],
        download_id: Optional[str],
    ) -> None:
        """Rename freshly downloaded models according to the filename template.
        Best-effort post-download step: any failure (including name conflicts)
        is logged and skipped so a successful download is never turned into a
        failure by a rename problem.
        """
        try:
            if scanner is None or not downloaded_metadata:
                return
            template = get_settings_manager().get_download_filename_template(
                model_type
            )
            if not template:
                return
            lifecycle_service = ModelLifecycleService(
                scanner=scanner,
                metadata_manager=MetadataManager,
                metadata_loader=load_local_metadata,
                recipe_scanner_factory=ServiceRegistry.get_recipe_scanner,
            )
            for metadata_dict in downloaded_metadata:
                file_path = metadata_dict.get("file_path")
                if not isinstance(file_path, str) or not file_path:
                    continue
                new_stem = calculate_filename_for_model(metadata_dict, model_type)
                if not new_stem:
                    continue
                current_stem = os.path.splitext(os.path.basename(file_path))[0]
                if new_stem == current_stem or os.path.normcase(
                    new_stem
                ) == os.path.normcase(current_stem):
                    continue
                try:
                    result = await lifecycle_service.rename_model(
                        file_path=file_path, new_file_name=new_stem
                    )
                except ValueError as exc:
                    logger.warning(
                        "Keeping original filename for %s: %s", file_path, exc
                    )
                    continue
                new_file_path = result.get("new_file_path")
                if download_id and isinstance(new_file_path, str):
                    info = self._active_downloads.get(download_id)
                    if info is None:
                        continue
                    if info.get("file_path") == file_path:
                        info["file_path"] = new_file_path
                    extracted = info.get("extracted_paths")
                    if isinstance(extracted, list):
                        info["extracted_paths"] = [
                            new_file_path if path == file_path else path
                            for path in extracted
                        ]
        except Exception as exc:  # Rename phase must never fail the download
            logger.warning(
                "Filename template rename failed for %s download: %s",
                model_type,
                exc,
                exc_info=True,
            )
    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",
@@ -2897,6 +3250,64 @@ 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."""
@@ -6,7 +6,7 @@ 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
@@ -390,23 +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,
@@ -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
@@ -236,24 +236,33 @@ class DownloadedVersionHistoryService:
            return
        async with self._lock:
-            conn = self._get_conn()
+            # The connection is created with check_same_thread=False and all
-            conn.executemany(
+            # access is serialized by self._lock, so the executemany upsert +
-                """
+            # commit can run in the default executor without blocking the
-                INSERT INTO downloaded_model_versions (
+            # event loop on large hydration payloads.
-                    model_type, version_id, model_id, first_seen_at, last_seen_at,
+            loop = asyncio.get_running_loop()
-                    source, last_file_path, last_library_name, is_deleted_override
+            await loop.run_in_executor(None, self._mark_downloaded_bulk_sync, payload)
-                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
+
-                ON CONFLICT(model_type, version_id) DO UPDATE SET
+    def _mark_downloaded_bulk_sync(self, payload: Sequence[tuple[object, ...]]) -> None:
-                    model_id = COALESCE(excluded.model_id, downloaded_model_versions.model_id),
+        """Synchronous executemany upsert + commit; runs in a worker thread."""
-                    last_seen_at = excluded.last_seen_at,
+        conn = self._get_conn()
-                    source = excluded.source,
+        conn.executemany(
-                    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),
+            INSERT INTO downloaded_model_versions (
-                    is_deleted_override = 0
+                model_type, version_id, model_id, first_seen_at, last_seen_at,
-                """,
+                source, last_file_path, last_library_name, is_deleted_override
-                payload,
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
-            )
+            ON CONFLICT(model_type, version_id) DO UPDATE SET
-            conn.commit()
+                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)
@@ -32,6 +32,7 @@ from .connectivity_guard import (
    ConnectivityGuard,
 )
 from .errors import RateLimitError
 from .rate_limit_coordinator import RateLimitCoordinator
 logger = logging.getLogger(__name__)
@@ -595,6 +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}"
@@ -972,6 +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
@@ -1041,6 +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}"
@@ -1074,74 +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:
+        coordinator = await RateLimitCoordinator.get_instance()
-            session = await self.session
+        gate_enabled = coordinator.enabled
-            # Debug log for proxy mode at request time
+        # Safety bound on the wait-and-resend loop; each 429 normally exits
-            if self.proxy_url:
+        # via the wait cap in wait_for_slot, this covers pathological 429s
-                logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
+        # with tiny Retry-After values.
-            else:
+        max_resend_attempts = 5
-                logger.debug(
+        attempt = 0
                    "[make_request] Using system-level proxy (trust_env) if configured."
                )
-            # Prepare headers
+        while True:
-            headers = self._get_auth_headers(use_auth)
+            if gate_enabled:
-            if custom_headers:
+                try:
-                headers.update(custom_headers)
+                    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
+            try:
-            if "proxy" not in kwargs:
+                session = await self.session
-                kwargs["proxy"] = self.proxy_url
+                # Debug log for proxy mode at request time
-
+                if self.proxy_url:
-            async with session.request(
+                    logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
                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,
                    )
                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:
+                # Prepare headers
-            if guard.is_network_unreachable_error(e):
+                headers = self._get_auth_headers(use_auth)
-                guard.register_network_failure(e, destination)
+                if custom_headers:
-                if guard.should_block_request(destination):
+                    headers.update(custom_headers)
-                    return False, OFFLINE_COOLDOWN_ERROR
+
-                logger.debug("Network unavailable for %s %s: %s", method, url, e)
+                # 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"""
@@ -67,6 +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", ""),
        }
@@ -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 _catalog_lock:
-        async with aiohttp.ClientSession(timeout=_CATALOG_TIMEOUT) as session:
+        # Re-check under the lock: another caller may have fetched (or
-            async with session.get(_MODEL_CATALOG_URL) as resp:
+        # failed) while we were waiting.
-                if resp.status != 200:
+        if _catalog_cache is not None:
-                    logger.warning("Model catalog returned HTTP %s", resp.status)
+            return _catalog_cache
-                    return _catalog_cache or {}
+        if (
-                data = await resp.json()
+            _catalog_last_failure is not None
-    except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as exc:
+            and time.monotonic() - _catalog_last_failure < _CATALOG_FAILURE_COOLDOWN
-        logger.warning("Failed to fetch model catalog: %s", exc)
+        ):
-        return _catalog_cache or {}
+            logger.debug(
                "Skipping model catalog fetch: last attempt failed %.0fs ago",
                time.monotonic() - _catalog_last_failure,
            )
            return {}
-    if not isinstance(data, dict):
+        try:
-        logger.warning("Model catalog is not a dict, got %s", type(data).__name__)
+            async with aiohttp.ClientSession(timeout=_CATALOG_TIMEOUT) as session:
-        return _catalog_cache or {}
+                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]] = {}
+        if not isinstance(data, dict):
-    output_limits: Dict[str, Dict[str, int]] = {}
+            logger.warning("Model catalog is not a dict, got %s", type(data).__name__)
-    for provider_id, provider_info in data.items():
+            _catalog_last_failure = time.monotonic()
-        if not isinstance(provider_info, dict):
+            return {}
-            continue
+
-        models_dict = provider_info.get("models")
+        result: Dict[str, List[str]] = {}
-        if not isinstance(models_dict, dict):
+        output_limits: Dict[str, Dict[str, int]] = {}
-            continue
+        for provider_id, provider_info in data.items():
-        model_ids: List[str] = []
+            if not isinstance(provider_info, dict):
        provider_limits: Dict[str, int] = {}
        for mid, model_info in models_dict.items():
            if not isinstance(mid, str):
                continue
-            model_ids.append(mid)
+            models_dict = provider_info.get("models")
-            if isinstance(model_info, dict):
+            if not isinstance(models_dict, dict):
-                limit = model_info.get("limit")
+                continue
-                if isinstance(limit, dict):
+            model_ids: List[str] = []
-                    output = limit.get("output")
+            provider_limits: Dict[str, int] = {}
-                    if isinstance(output, (int, float)) and output > 0:
+            for mid, model_info in models_dict.items():
-                        provider_limits[mid] = int(output)
+                if not isinstance(mid, str):
-        if model_ids:
+                    continue
-            result[provider_id] = model_ids
+                model_ids.append(mid)
-        if provider_limits:
+                if isinstance(model_info, dict):
-            output_limits[provider_id] = provider_limits
+                    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
+        _catalog_cache = result
-    _model_output_limits = output_limits
+        _model_output_limits = output_limits
-    logger.debug(
+        logger.debug(
-        "Loaded model catalog: %d providers, %d total models "
+            "Loaded model catalog: %d providers, %d total models "
-        "(%d providers have output limits)",
+            "(%d providers have output limits)",
-        len(result),
+            len(result),
-        sum(len(m) for m in result.values()),
+            sum(len(m) for m in result.values()),
-        len(output_limits),
+            len(output_limits),
-    )
+        )
-    return result
+        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:
+        # Structured-output format.  ``json_schema`` is preferred because LM
-        # LM Studio and some other OpenAI-compatible servers reject
+        # Studio and other local OpenAI-compatible servers reject
-        # json_object but accept json_schema.  {"type": "object"} is
+        # ``json_object`` but accept ``json_schema``; ``{"type": "object"}``
-        # functionally equivalent — it accepts any JSON object without
+        # accepts any JSON object without constraining specific fields, so the
-        # constraining specific fields.
+        # two modes are functionally equivalent here.  Providers known to
-        response_format = {
+        # 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:
+        if self._get_config()["provider"] in _JSON_OBJECT_ONLY_PROVIDERS:
-            result = await self.chat_completion(
+            format_chain: List[Optional[Dict[str, Any]]] = [
-                messages=messages,
+                json_object_format,
-                model=model,
+                None,
-                temperature=temperature,
+            ]
-                response_format=response_format,
+        else:
-                max_tokens=effective_max,
+            format_chain = [schema_format, json_object_format, None]
-            )
+
-        except LLMResponseError as e:
+        result: Optional[Dict[str, Any]] = None
-            # Only fall back when the provider rejects the response_format
+        for index, fmt in enumerate(format_chain):
-            # type value (e.g. "'response_format.type' must be...").  Avoid
+            try:
-            # catching unrelated 400 errors whose body happens to mention
+                result = await self.chat_completion(
-            # "response_format" (e.g. "model does not support
+                    messages=messages,
-            # response_format restrictions on this endpoint").
+                    model=model,
-            if "'response_format.type'" not in str(e).lower():
+                    temperature=temperature,
-                raise
+                    response_format=fmt,
-            logger.info(
+                    max_tokens=effective_max,
-                "Provider rejected response_format, retrying without it. "
+                )
-                "Falling back to prompt-only JSON mode. Error: %s",
+                break
-                e,
+            except LLMResponseError as e:
-            )
+                message = str(e).lower()
-            result = await self.chat_completion(
+                if index + 1 >= len(format_chain):
-                messages=messages,
+                    raise
-                model=model,
+                # Only downgrade when the failure is about ``response_format``.
-                temperature=temperature,
+                # Everything else (auth, unknown model, rate limits) must
-                response_format=None,
+                # surface unchanged.  Matching on the bare parameter name also
-                max_tokens=effective_max,
+                # 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:
@@ -79,6 +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", ""),
        }
@@ -712,12 +714,18 @@ class LoraService(BaseModelService):
                ),
            )
-        # Return minimal data needed for cycling
+        # Return minimal data needed for cycling. usage_tips is only included
-        return [
+        # when non-empty so widget consumers (recommended strength range cues)
-            {
+        # can build their lookup without inflating the payload.
        result = []
        for lora in available_loras:
            entry = {
                "file_name": f"{lora['folder']}/{lora['file_name']}" if lora.get("folder") else lora["file_name"],
                "model_name": lora.get("model_name", lora["file_name"]),
                "folder": lora.get("folder", ""),
            }
-            for lora in available_loras
+            usage_tips = lora.get("usage_tips")
-        ]
+            if usage_tips:
                entry["usage_tips"] = usage_tips
            result.append(entry)
        return result
@@ -14,10 +14,33 @@ 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__)
 def _merge_ordered_unique(existing: Iterable[str], new: Iterable[str]) -> list[str]:
    """Concatenate two word lists, dropping duplicates without reordering.
    Trigger word order is meaningful: the sequence stored in
    ``civitai.trainedWords`` is the order used when building prompts, and users
    can reorder it in the UI. A plain ``set`` union used to shuffle that order on
    every metadata refresh, so existing words are kept first (in their saved
    order) and newly discovered ones are appended.
    """
    merged: list[str] = []
    seen: set[str] = set()
    for word in list(existing) + list(new):
        if word in seen:
            continue
        seen.add(word)
        merged.append(word)
    return merged
 class MetadataProviderProtocol(Protocol):
    """Subset of metadata provider interface consumed by the sync service."""
@@ -114,9 +137,10 @@ class MetadataSyncService:
                )
            if "trainedWords" in existing_civitai:
-                existing_trained = existing_civitai.get("trainedWords", [])
+                existing_trained = existing_civitai.get("trainedWords", []) or []
-                new_trained = civitai_metadata.get("trainedWords", [])
+                new_trained = civitai_metadata.get("trainedWords", []) or []
-                merged_trained = list(set(existing_trained + new_trained))
+                # Order preserving merge: the saved order drives prompt order.
                merged_trained = _merge_ordered_unique(existing_trained, new_trained)
                merged_civitai["trainedWords"] = merged_trained
            local_metadata["civitai"] = merged_civitai
@@ -222,9 +246,10 @@ 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 +270,23 @@ 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 +451,37 @@ 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 "")
            )
@@ -33,6 +33,11 @@ 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
@@ -2,13 +2,15 @@ import asyncio
 import fnmatch
 import os
 import logging
 import shutil
 from typing import Any, Dict, List, Optional, Sequence, Set
 from abc import ABC, abstractmethod
 from ..utils.utils import calculate_relative_path_for_model, remove_empty_dirs
-from ..utils.constants import AUTO_ORGANIZE_BATCH_SIZE
+from ..utils.constants import AUTO_ORGANIZE_BATCH_SIZE, MODEL_FILE_EXTENSIONS
 from ..services.settings_manager import get_settings_manager
 from ..services.model_lifecycle_service import _require_path_in_library_roots
 from ..services.pending_delete_service import PENDING_DELETE_DIR_NAME
 logger = logging.getLogger(__name__)
@@ -41,10 +43,22 @@ class AutoOrganizeResult:
    def to_dict(self) -> Dict[str, Any]:
        """Convert result to dictionary"""
        if self.operation_type == 'filename_template':
            message = (
                f'Filename template applied: {self.success_count} renamed, '
                f'{self.skipped_count} skipped, {self.failure_count} failed '
                f'out of {self.total} total'
            )
        else:
            message = (
                f'Auto-organize {self.operation_type} completed: '
                f'{self.success_count} moved, {self.skipped_count} skipped, '
                f'{self.failure_count} failed out of {self.total} total'
            )
        result: Dict[str, Any] = {
            'success': self.status != 'error',
            'status': self.status,
-            'message': f'Auto-organize {self.operation_type} completed: {self.success_count} moved, {self.skipped_count} skipped, {self.failure_count} failed out of {self.total} total',
+            'message': message,
            'summary': {
                'total': self.total,
                'success': self.success_count,
@@ -473,17 +487,368 @@ class ModelFileService:
 class ModelMoveService:
    """Service for handling individual model moves"""
-    
+
    def __init__(self, scanner, model_type: str):
        """Initialize the service
-        
+
        Args:
            scanner: Model scanner instance
            model_type: Type of model (e.g., 'lora', 'checkpoint')
        """
        self.scanner = scanner
        self.model_type = model_type
-    
+
    async def create_folder(self, folder_path: str) -> Dict[str, Any]:
        """Create a directory inside the model library roots.
        Args:
            folder_path: Absolute path of the directory to create (business
                path — symlinks are not resolved)
        Returns:
            Dictionary with success flag, the created path and the
            library-relative folder name used by folder trees.
        """
        try:
            if not folder_path or not str(folder_path).strip():
                return {"success": False, "error": "Folder path is required"}
            _require_path_in_library_roots(folder_path, self.scanner, label="Folder path")
            absolute_path = os.path.abspath(folder_path)
            already_exists = os.path.isdir(absolute_path)
            os.makedirs(absolute_path, exist_ok=True)
            relative_folder = self._calculate_relative_folder(absolute_path)
            if relative_folder:
                await self.scanner.add_known_folder(relative_folder)
            return {
                "success": True,
                "folder_path": absolute_path.replace(os.sep, "/"),
                "folder": relative_folder,
                "created": not already_exists,
            }
        except ValueError as exc:
            return {"success": False, "error": str(exc)}
        except Exception as exc:
            logger.error(f"Error creating folder: {exc}", exc_info=True)
            return {"success": False, "error": str(exc)}
    def _calculate_relative_folder(self, absolute_path: str) -> str:
        """Return the library-relative folder for an absolute directory path."""
        normalized = os.path.abspath(absolute_path)
        for root in self.scanner.get_model_roots():
            abs_root = os.path.abspath(root)
            try:
                rel = os.path.relpath(normalized, abs_root)
            except ValueError:
                continue
            if rel == ".":
                return ""
            if not rel.startswith(".."):
                return rel.replace(os.sep, "/")
        return ""
    async def delete_folder(self, folder_path: str, dry_run: bool = False) -> Dict[str, Any]:
        """Delete a model-free directory inside the model library roots.
        Only directories whose subtree holds no model weight files can be
        removed: a folder-level cascade would bypass the per-model lifecycle
        bookkeeping (metadata sidecars, previews, cache entries, pending-delete
        staging and recipe references), so it is deliberately refused. Leftover
        non-model files (stray previews, sidecars, ``.bak`` files) are reported
        in the manifest before they are removed.
        Args:
            folder_path: Absolute path of the directory to remove (business
                path — symlinks are not resolved)
            dry_run: When true, only report what would be removed
        Returns:
            Dictionary with the success flag plus a removal manifest
            (``model_count``/``file_count``/``dir_count``/``symlink_count``/
            ``total_bytes``/``restorable``) on success.
        """
        try:
            if not folder_path or not str(folder_path).strip():
                return {"success": False, "error": "Folder path is required"}
            _require_path_in_library_roots(folder_path, self.scanner, label="Folder path")
            absolute_path = os.path.abspath(folder_path)
            if os.path.islink(absolute_path):
                # shutil.rmtree refuses symlinked roots, and silently deleting
                # the link (leaving the real directory behind) is a separate
                # decision we do not make here.
                return {
                    "success": False,
                    "error": "Symlinked folders cannot be deleted",
                }
            if not os.path.isdir(absolute_path):
                return {"success": False, "error": "Folder no longer exists"}
            if self._is_model_root(absolute_path):
                return {
                    "success": False,
                    "error": "The library root itself cannot be deleted",
                }
            manifest = self._collect_folder_manifest(absolute_path)
            if manifest["pending_delete_job"]:
                return {
                    "success": False,
                    "code": "busy",
                    "error": (
                        "A staged delete is still pending inside this folder; "
                        "wait for the undo window to expire"
                    ),
                    "manifest": manifest,
                }
            if manifest["model_count"] > 0:
                return {
                    "success": False,
                    "code": "not_empty",
                    "error": (
                        f"Folder still contains {manifest['model_count']} model "
                        "file(s); delete or move them first"
                    ),
                    "manifest": manifest,
                }
            relative_folder = self._calculate_relative_folder(absolute_path)
            if dry_run:
                return {
                    "success": True,
                    "dry_run": True,
                    "folder_path": absolute_path.replace(os.sep, "/"),
                    "folder": relative_folder,
                    **manifest,
                }
            shutil.rmtree(absolute_path)
            await self._forget_folder(relative_folder)
            return {
                "success": True,
                "dry_run": False,
                "folder_path": absolute_path.replace(os.sep, "/"),
                "folder": relative_folder,
                **manifest,
            }
        except ValueError as exc:
            return {"success": False, "error": str(exc)}
        except Exception as exc:
            logger.error(f"Error deleting folder: {exc}", exc_info=True)
            return {"success": False, "error": str(exc)}
    def _is_model_root(self, absolute_path: str) -> bool:
        """Return True when the path *is* one of the configured library roots."""
        normalized = os.path.normpath(absolute_path)
        for root in self.scanner.get_model_roots():
            if os.path.normpath(os.path.abspath(root)) == normalized:
                return True
        return False
    @staticmethod
    def _is_model_file(file_name: str) -> bool:
        """Return True when the file name carries a model weight extension."""
        return os.path.splitext(file_name)[1].lower() in MODEL_FILE_EXTENSIONS
    def _collect_folder_manifest(self, absolute_path: str) -> Dict[str, Any]:
        """Describe everything a recursive delete of *absolute_path* removes.
        Walking is intentional: the scanner cache can be stale, and a model file
        that appeared on disk since the last scan must still block the delete.
        Symbolic links are never followed (``os.walk`` default) and are counted
        separately — ``shutil.rmtree`` unlinks them without touching their
        targets.
        """
        model_count = 0
        file_count = 0
        dir_count = 0
        symlink_count = 0
        total_bytes = 0
        pending_delete_job = False
        for dirpath, dirnames, filenames in os.walk(absolute_path):
            if PENDING_DELETE_DIR_NAME in dirnames:
                pending_delete_job = True
            for name in dirnames:
                if os.path.islink(os.path.join(dirpath, name)):
                    symlink_count += 1
                else:
                    dir_count += 1
            for name in filenames:
                full_path = os.path.join(dirpath, name)
                if os.path.islink(full_path):
                    symlink_count += 1
                    continue
                if self._is_model_file(name):
                    model_count += 1
                else:
                    file_count += 1
                try:
                    total_bytes += os.path.getsize(full_path)
                except OSError:  # pragma: no cover - defensive
                    pass
        return {
            "model_count": model_count,
            "file_count": file_count,
            "dir_count": dir_count,
            "symlink_count": symlink_count,
            "total_bytes": total_bytes,
            "pending_delete_job": pending_delete_job,
            # A truly empty directory is the only case an "undo" can restore by
            # simply recreating it; a folder holding stray files is gone for good.
            "restorable": (
                model_count == 0
                and file_count == 0
                and dir_count == 0
                and symlink_count == 0
            ),
        }
    async def _forget_folder(self, relative_folder: str) -> None:
        """Drop a removed directory from the scanner's folder/cache records."""
        if not relative_folder:
            return
        remove_known_folder = getattr(self.scanner, "remove_known_folder", None)
        if callable(remove_known_folder):
            await remove_known_folder(relative_folder)
    async def rename_folder(self, folder_path: str, new_name: str) -> Dict[str, Any]:
        """Rename a directory inside the model library roots.
        Unlike :meth:`delete_folder` this works on folders that hold models.
        A rename keeps every file, so no per-model lifecycle step is bypassed:
        the directory is renamed on disk and the affected folder, cache, hash
        index and metadata-sidecar records are re-keyed onto the new prefix by
        the scanner.
        Args:
            folder_path: Absolute path of the directory to rename (business
                path — symlinks are not resolved)
            new_name: New leaf name; a single path segment, not a path
        Returns:
            Dictionary with the success flag, the previous/next library-relative
            folder names and whether the directory actually moved.
        """
        try:
            if not folder_path or not str(folder_path).strip():
                return {"success": False, "error": "Folder path is required"}
            new_name = str(new_name or "").strip()
            if not new_name:
                return {"success": False, "error": "New folder name is required"}
            if new_name in (".", "..") or any(
                char in new_name for char in '/\\:*?"<>|'
            ):
                return {"success": False, "error": "Invalid characters in folder name"}
            _require_path_in_library_roots(folder_path, self.scanner, label="Folder path")
            absolute_path = os.path.abspath(folder_path)
            if os.path.islink(absolute_path):
                return {
                    "success": False,
                    "error": "Symlinked folders cannot be renamed",
                }
            if not os.path.isdir(absolute_path):
                return {"success": False, "error": "Folder no longer exists"}
            if self._is_model_root(absolute_path):
                return {
                    "success": False,
                    "error": "The library root itself cannot be renamed",
                }
            previous_relative = self._calculate_relative_folder(absolute_path)
            target = os.path.join(os.path.dirname(absolute_path), new_name)
            if os.path.normpath(target) == os.path.normpath(absolute_path):
                return {
                    "success": True,
                    "renamed": False,
                    "folder": previous_relative,
                    "previous_folder": previous_relative,
                    "folder_path": absolute_path.replace(os.sep, "/"),
                }
            if os.path.exists(target):
                return {
                    "success": False,
                    "code": "target_exists",
                    "error": f"A folder named \"{new_name}\" already exists here",
                }
            # A staging manifest records absolute original/staged paths, so
            # moving a folder that holds one would break its undo and purge.
            if self._has_pending_delete_job(absolute_path):
                return {
                    "success": False,
                    "code": "busy",
                    "error": (
                        "A staged delete is still pending inside this folder; "
                        "wait for the undo window to expire"
                    ),
                }
            os.rename(absolute_path, target)
            new_relative = self._calculate_relative_folder(target)
            await self._rename_folder_records(
                previous_relative, new_relative, absolute_path, target
            )
            return {
                "success": True,
                "renamed": True,
                "folder": new_relative,
                "previous_folder": previous_relative,
                "folder_path": target.replace(os.sep, "/"),
            }
        except ValueError as exc:
            return {"success": False, "error": str(exc)}
        except Exception as exc:
            logger.error(f"Error renaming folder: {exc}", exc_info=True)
            return {"success": False, "error": str(exc)}
    @staticmethod
    def _has_pending_delete_job(absolute_path: str) -> bool:
        """Return True when a staged-delete batch lives inside the subtree."""
        for _dirpath, dirnames, _filenames in os.walk(absolute_path):
            if PENDING_DELETE_DIR_NAME in dirnames:
                return True
        return False
    async def _rename_folder_records(
        self,
        previous_relative: str,
        new_relative: str,
        previous_path: str,
        new_path: str,
    ) -> None:
        """Hand the rename to the scanner so folder/cache records follow it."""
        if not previous_relative or not new_relative:
            return
        rename_known_folder = getattr(self.scanner, "rename_known_folder", None)
        if callable(rename_known_folder):
            await rename_known_folder(
                previous_relative,
                new_relative,
                previous_path=previous_path,
                new_path=new_path,
            )
    async def move_model(self, file_path: str, target_path: str, use_default_paths: bool = False) -> Dict[str, Any]:
        """Move a single model file
@@ -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:
@@ -2,6 +2,7 @@
 from __future__ import annotations
 import json
 import logging
 import os
 from typing import Any, Awaitable, Callable, Dict, Iterable, List, Mapping, Optional, TYPE_CHECKING, cast
@@ -17,6 +18,26 @@ if TYPE_CHECKING:
    from ..services.model_update_service import ModelUpdateService
 async def load_local_metadata(metadata_path: str) -> Dict[str, Any]:
    """Load a metadata sidecar JSON, returning an empty dict when missing.
    Thin equivalent of ``MetadataSyncService.load_local_metadata`` for callers
    (download manager, use cases) that do not hold a sync-service instance.
    """
    if not os.path.exists(metadata_path):
        return {}
    try:
        with open(metadata_path, "r", encoding="utf-8") as handle:
            payload = json.load(handle)
    except Exception as exc:
        logger.warning("Failed to load metadata from %s: %s", metadata_path, exc)
        return {}
    return payload if isinstance(payload, dict) else {}
 async def delete_model_artifacts(
    target_dir: str, file_name: str, main_extension: str | None = None
 ) -> List[str]:
@@ -404,6 +425,9 @@ class ModelLifecycleService:
        if metadata and new_metadata_path:
            metadata["file_name"] = new_file_name
            metadata["file_path"] = new_file_path
            # Preserve the pre-rename stem so the original download filename
            # stays recoverable after template-driven renames.
            metadata.setdefault("original_file_name", old_file_name)
            if metadata.get("preview_url"):
                old_preview = str(metadata["preview_url"])
@@ -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"""
@@ -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)
@@ -0,0 +1,86 @@
 """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,
    ModelCardContext,
    ModelSource,
    ModelSourceCache,
    ModelSourceError,
    SourceRef,
    USER_AGENT,
    clean_source_url,
    fetch_json,
    fetch_text,
    filter_weight_files,
    is_valid_source_id,
 )
 from .huggingface import HuggingFaceSource
 from .hydration import (
    hydrate_from_source,
    load_model_card,
    resolve_site_base_model,
 )
 from .modelscope import ModelScopeIntlSource, 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",
    "ModelCardContext",
    "ModelSource",
    "ModelSourceCache",
    "ModelSourceError",
    "HuggingFaceSource",
    "ModelScopeIntlSource",
    "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",
    "hydrate_from_source",
    "is_valid_source_id",
    "list_sources",
    "load_model_card",
    "normalize_metadata_source",
    "resolve_site_base_model",
    "resolve_source_ref",
    "source_group_key",
    "source_label",
 ]
@@ -0,0 +1,446 @@
 """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 fetch the extras that live *outside* the README
  (:meth:`ModelSource.fetch_model_card_context`)
 * 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, field
 from typing import Any, Dict, 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",
    "modelscope-ai": "msai",
    "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."""
@dataclass
 class ModelCardContext:
    """Site-specific extras that accompany a model's README model card.
    A model card is not always just ``README.md``.  ModelScope, for example,
    keeps the author's summary, the site-curated tags, and the per-file
    example images in its model-detail API rather than in the repository.
    Sources with no such extras return an empty context (the default), so
    every field here must be treated as optional by callers.
    """
    description: str = ""
    """Author-written summary shown on the model page, outside the README."""
    model_name: str = ""
    """Site-published display name for the repository.
    Sites publish this next to the repository id (ModelScope's ``Name``).
    It is what a CivitAI download would store as the model's name, so the
    card never has to fall back to the local filename.
    """
    model_name_localized: str = ""
    """Site-published localized name (ModelScope's ``ChineseName``)."""
    version_name: str = ""
    """Site-published label for the requested file's version.
    Resolved per file, like :attr:`example_images`: a repository publishes
    one label per checkpoint (ModelScope's ``modelVersion.showName``).
    """
    license: str = ""
    """License the site records for the repository."""
    model_type: str = ""
    """Site-reported model type, e.g. ModelScope's ``AigcType`` (``LoRA``)."""
    base_model: str = ""
    """Base model as reported by the site (possibly a site-local id)."""
    base_model_aliases: list[str] = field(default_factory=list)
    """Other names the site uses for the same base model.
    Sites often publish both a link-style id (``krea/Krea-2-Turbo``) and an
    internal architecture enum (``KREA_2``).  The enum usually normalises
    cleanly onto this system's canonical vocabulary, so it is the better
    resolution hint for :mod:`py.services.agent.base_model_resolver`.
    """
    official_tags: list[str] = field(default_factory=list)
    """Content tags curated by the site itself."""
    example_images: list[str] = field(default_factory=list)
    """Absolute URLs of example images for the requested model file."""
    trigger_words: list[str] = field(default_factory=list)
    """Trigger words the site records for the requested model file."""
    def is_empty(self) -> bool:
        """Return ``True`` when the site contributed nothing extra."""
        return not any(
            (
                self.description,
                self.model_name,
                self.model_name_localized,
                self.version_name,
                self.license,
                self.model_type,
                self.base_model,
                self.base_model_aliases,
                self.official_tags,
                self.example_images,
                self.trigger_words,
            )
        )
 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
 class ModelSourceCache:
    """Per-run memo shared between the agent pipeline and a model source.
    A collection repository publishes many model files under a single source
    id, so enriching each file re-fetches the same README and the same
    repository metadata.  One cache is created per enrichment run and thrown
    away afterwards: nothing is retained across runs (a model card can change
    at any time), and download URLs are never routed through it.
    """
    def __init__(self) -> None:
        #: Provider-agnostic: ``"<platform>:<source_id>"`` → raw README text.
        self.readmes: Dict[str, str] = {}
        #: Provider-owned scratch space.  Keys must be namespaced by the
        #: provider (``(platform, kind, source_id)``) so two providers can
        #: never collide.  Only successful results should be stored, so a
        #: transient failure is still retried for the next file.
        self.provider: Dict[Any, Any] = {}
 #: 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 ""
    async def fetch_model_card_context(
        self,
        source_id: str,
        filename: str = "",
        *,
        sha256: str = "",
        cache: Optional["ModelSourceCache"] = None,
    ) -> ModelCardContext:
        """Return the card extras the site keeps outside the README.
        *filename* is the model file's basename (no directory) and *sha256*
        its content hash; between them they select the right entry when a
        repository holds several models.  A site that records per-file hashes
        should prefer *sha256*, because it is the only identifier that
        survives the user renaming the weights.
        *cache* is an optional per-run memo (see :class:`ModelSourceCache`)
        that lets a provider avoid re-fetching repository-wide data for every
        file in a collection repository.
        Sites whose model card is fully described by :meth:`fetch_model_card`
        need no override and inherit this empty context.
        Implementations must never raise: enrichment treats a missing
        context as "the site had nothing extra to say".
        """
        return ModelCardContext()
    # ------------------------------------------------------------------
    # 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",
    "ModelCardContext",
    "ModelSource",
    "ModelSourceCache",
    "ModelSourceError",
    "SourceRef",
    "USER_AGENT",
    "clean_source_url",
    "fetch_json",
    "fetch_text",
    "filter_weight_files",
    "is_valid_source_id",
 ]
@@ -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"]
@@ -0,0 +1,235 @@
 """Deterministic metadata hydration for freshly downloaded source models.
 A CivitAI download writes a fully-populated metadata sidecar as part of the
 download itself: the name, the description, the tags, the trigger words and
 the example images all arrive with the file.  A download from an external
 model source (ModelScope, Hugging Face) has the same information behind a
 public API, but historically landed as a bare filename plus a source URL that
 the user had to enrich by hand ("Enrich Metadata with AI").
 This module closes that gap without involving an LLM.  It fetches the linked
 site's model card, hands it to the same :class:`~py.services.agent.post_processor.PostProcessor`
 the AI skill uses, and writes the result.  Everything it applies is data the
 site published, so it is safe to run automatically on every download and to
 treat as a fallback for the gaps the LLM would otherwise fill.
 Nothing here may break a download: every failure is logged and normalised to
 "the site had nothing to contribute".
 """
 from __future__ import annotations
 import logging
 import os
 import time
 from typing import TYPE_CHECKING, Optional
 from .base import ModelCardContext, ModelSourceCache
 from .registry import get_source, resolve_source_ref
 if TYPE_CHECKING:  # pragma: no cover - typing only
    from .base import ModelSource, SourceRef
 logger = logging.getLogger(__name__)
 #: How long a fetched repository payload stays usable.  A download batch walks
 #: a repository's files one HTTP request at a time, and the README plus the
 #: detail payload describe the *repository*, not the file, so re-fetching them
 #: per file would be pure waste.  They expire so an edited model card is still
 #: picked up by the next batch.
 SHARED_CACHE_TTL = 300.0
 #: Upper bound on memoised repositories; a long-running server must not grow
 #: without limit.
 SHARED_CACHE_MAX_ENTRIES = 32
 #: ``"<platform>:<source_id>"`` → ``(expiry, memo)``.
 _shared_caches: dict[str, tuple[float, ModelSourceCache]] = {}
 def shared_source_cache(platform: str, source_id: str) -> ModelSourceCache:
    """Return a short-lived per-repository memo for download-time hydration."""
    now = time.monotonic()
    key = f"{platform}:{source_id}"
    entry = _shared_caches.get(key)
    if entry is not None and entry[0] > now:
        return entry[1]
    for expired in [k for k, (expiry, _) in _shared_caches.items() if expiry <= now]:
        _shared_caches.pop(expired, None)
    if len(_shared_caches) >= SHARED_CACHE_MAX_ENTRIES:
        oldest = min(_shared_caches, key=lambda k: _shared_caches[k][0])
        _shared_caches.pop(oldest, None)
    cache = ModelSourceCache()
    _shared_caches[key] = (now + SHARED_CACHE_TTL, cache)
    return cache
 def reset_shared_caches() -> None:
    """Drop every memoised repository — used by tests."""
    _shared_caches.clear()
 async def load_model_card(
    source: "ModelSource",
    source_id: str,
    cache: Optional[ModelSourceCache] = None,
 ) -> str:
    """Return *source_id*'s README, reusing *cache* when one is supplied.
    Only successful reads are memoised, leaving a transient failure to be
    retried for the next file of the same repository.
    """
    key = f"{source.platform}:{source_id}"
    if cache is not None:
        cached = cache.readmes.get(key)
        if cached is not None:
            return cached
    readme = await source.fetch_model_card(source_id)
    if cache is not None and readme:
        cache.readmes[key] = readme
    return readme or ""
 async def resolve_site_base_model(context: ModelCardContext) -> str:
    """Resolve the site's base-model hints to a canonical name, or ``""``.
    Sites name base models in their own vocabulary (ModelScope publishes both
    ``krea/Krea-2-Turbo`` and the ``KREA_2_TURBO`` enum).  The resolver is
    strict and only ever returns a name the canonical vocabulary already
    contains, so an uncertain hint yields ``""`` rather than a plausible-looking
    wrong value.
    """
    hints = [*context.base_model_aliases, context.base_model]
    if not any(hints):
        return ""
    # Imported lazily: pulling in the agent package at module scope would make
    # the model-source package import itself while it is still initialising.
    try:
        from ...metadata_ops import list_base_models
        from ..agent.base_model_resolver import resolve_base_model
        known_names = await list_base_models()
    except Exception as exc:
        logger.warning("Could not resolve a site base model: %s", exc)
        return ""
    return resolve_base_model(hints, known_names)
 async def hydrate_from_source(
    file_path: str,
    *,
    ref: "SourceRef",
    cache: Optional[ModelSourceCache] = None,
 ) -> list[str]:
    """Apply the linked site's published metadata to a downloaded model.
    This is the deterministic counterpart of the ``enrich_hf_metadata`` skill:
    it produces the same populated model card a CivitAI download produces,
    without an LLM and without user action.
    Args:
        file_path: The just-downloaded model file, whose sidecar already
            carries the SHA256 used to match the right file in a collection
            repository.
        ref: The source the file came from.
        cache: Optional per-call memo; defaults to a short-lived shared one so
            a batch over one repository fetches its card only once.
    Returns:
        The names of the metadata fields that changed.  Never raises — a site
        that is down, or an API that changed shape, must not fail a download.
    """
    try:
        source = get_source(ref.platform)
        if source is None or not source.supports_enrichment:
            return []
        from ...metadata_ops import read_metadata
        metadata = await read_metadata(file_path)
        if not metadata:
            logger.debug("No metadata to hydrate for %s", file_path)
            return []
        # Only a model that is actually linked to this repository may be
        # updated.  The download path writes those fields just before calling
        # us; a file that merely shares a name with the requested one must not
        # be given another model's card.
        linked = resolve_source_ref(metadata)
        if linked is None or (linked.platform, linked.source_id) != (
            ref.platform,
            ref.source_id,
        ):
            logger.debug(
                "Not hydrating %s: linked to %s, not %s",
                file_path, linked.url if linked else "no model source", ref.url,
            )
            return []
        memo = cache if cache is not None else shared_source_cache(
            ref.platform, ref.source_id
        )
        readme = await load_model_card(source, ref.source_id, memo)
        context = await source.fetch_model_card_context(
            ref.source_id,
            os.path.basename(file_path),
            sha256=(metadata.get("sha256") or "").strip(),
            cache=memo,
        )
        if context.is_empty() and not readme:
            logger.debug(
                "No published metadata for %s on %s", ref.source_id, ref.platform
            )
            return []
        resolved_base_model = await resolve_site_base_model(context)
        from ..agent.post_processor import PostProcessor
        result = await PostProcessor().process(
            skill_name="enrich_hf_metadata",
            model_path=file_path,
            llm_output={},
            metadata=metadata,
            readme_content=readme,
            source_context=context,
            resolved_base_model=resolved_base_model,
            metadata_source=f"source:{ref.platform}",
        )
        if not result.get("success", True):
            logger.debug(
                "Hydration reported failure for %s: %s",
                file_path, result.get("errors"),
            )
            return []
        updated = list(result.get("updated_fields") or [])
        logger.info(
            "Hydrated %s from %s (%s): %s",
            file_path, source.label or ref.platform, ref.source_id,
            ", ".join(updated) or "nothing to change",
        )
        return updated
    except Exception as exc:  # pragma: no cover - defensive by design
        logger.warning("Source hydration failed for %s: %s", file_path, exc)
        return []
 __all__ = [
    "SHARED_CACHE_MAX_ENTRIES",
    "SHARED_CACHE_TTL",
    "hydrate_from_source",
    "load_model_card",
    "reset_shared_caches",
    "resolve_site_base_model",
    "shared_source_cache",
 ]
@@ -0,0 +1,644 @@
 """ModelScope (魔搭社区) model sources.
 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:``.  Four 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}`` — the model-detail payload behind the
  model page.  It carries the repository's display name (``Name`` /
  ``ChineseName``), the author's summary (``Description``), the license, the
  AIGC type, the site tags (``OfficialTags``, falling back to ``Tags``), and,
  per published version, the model filenames
  (``MuseInfo.versions[].stats.fileList``) together with that version's label
  (``modelVersion.showName``), example images (``coverImages``) and trigger
  words.  See :meth:`ModelScopeSource.fetch_model_card_context`.
 * ``/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.
 The README and the detail payload both describe the whole repository rather
 than one file, so a per-run ``ModelSourceCache`` keeps them from being read
 again for every checkpoint of a collection repository.
 Two deployments are served by this module.  ``modelscope.cn`` (with
 ``modelscope.com`` as a redirect alias) and ``modelscope.ai`` are *separate
 catalogues*, not mirrors, so they are registered as distinct sources:
 :class:`ModelScopeSource` and :class:`ModelScopeIntlSource`.  Every URL either
 class builds is derived from its ``base_url``.
 """
 from __future__ import annotations
 import json
 import logging
 import os
 import re
 from typing import TYPE_CHECKING, Any, Iterable, Optional
 from .base import (
    ModelCardContext,
    ModelSource,
    ModelSourceError,
    fetch_json,
    fetch_text,
    filter_weight_files,
 )
 if TYPE_CHECKING:  # pragma: no cover - typing only
    from .base import ModelSourceCache
 logger = logging.getLogger(__name__)
 #: ModelScope runs two independent catalogues.  ``modelscope.com`` is a
 #: redirect alias of the mainland site, but ``modelscope.ai`` is the
 #: *international* deployment with its own repository catalogue — a repository
 #: published on one is routinely absent from the other (``referall13/EM1``
 #: exists only on ``.ai``, ``jj3550945163/Krea-2-LORA`` only on ``.cn``).  The
 #: host therefore decides which site, API and CDN a model belongs to, and the
 #: two deployments are registered as separate sources rather than folded into
 #: one id.
 _MAINLAND_HOSTS = r"modelscope\.(?:cn|com)"
 _INTERNATIONAL_HOSTS = r"modelscope\.ai"
 #: 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)?"
 def _url_patterns(hosts: str) -> tuple[re.Pattern[str], re.Pattern[str]]:
    """Build the lenient and strict model-URL patterns for *hosts*."""
    body = rf"https?://(?:www\.)?(?:{hosts})/models/(?P<id>[^/?#\s]+/[^/?#\s]+)"
    return re.compile(body), re.compile(rf"{body}/?{_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's mainland site (``modelscope.cn``).
    ``modelscope.com`` is accepted as an alias of it.  The international
    deployment is :class:`ModelScopeIntlSource`; everything below is written in
    terms of ``base_url`` so both share one implementation.
    """
    platform = "modelscope"
    label = "ModelScope"
    supports_enrichment = True
    supports_download = True
    default_revision = "master"
    default_subdir = "modelscope"
    #: Origin every outgoing URL is built from.
    base_url = "https://modelscope.cn"
    url_pattern, strict_url_pattern = _url_patterns(_MAINLAND_HOSTS)
    def canonical_url(self, source_id: str) -> str:
        return f"{self.base_url}/models/{source_id}"
    def asset_base_url(self, source_id: str, revision: str = "") -> str:
        return (
            f"{self.base_url}/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"{self.base_url}/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(
                f"{self.base_url}/api/v1/models/"
                f"{source_id}/repo?Revision={revision}&FilePath=README.md"
            )
            if text:
                return text
        return ""
    async def fetch_model_card_context(
        self,
        source_id: str,
        filename: str = "",
        *,
        sha256: str = "",
        cache: Optional["ModelSourceCache"] = None,
    ) -> ModelCardContext:
        """Read the model-detail API that backs the ModelScope model page.
        ModelScope splits a model card in two: ``README.md`` holds the
        long-form content, while the author's summary, the site-curated tags,
        and the per-file example images live only here.  AIGC repositories
        frequently ship an auto-generated README ("the contributor provided
        no further description") and put everything useful in ``Description``,
        so enrichment that reads only the README comes back nearly empty.
        The wanted file is identified by its sha256 when the caller knows it
        and by *filename* otherwise; see :func:`_matching_versions`.  The
        images and trigger words returned belong to that exact
        ``.safetensors`` — essential for collection repositories, where every
        checkpoint has its own sample image.
        The detail payload describes the whole repository and is therefore
        shared across every file in it, so it is read through *cache* when the
        caller supplies one; only the per-file selection is redone.
        """
        data = await self._fetch_detail(source_id, cache=cache)
        if data is None:
            return ModelCardContext()
        return _build_card_context(data, filename, sha256)
    async def _fetch_detail(
        self,
        source_id: str,
        *,
        cache: Optional["ModelSourceCache"] = None,
    ) -> Optional[dict[str, Any]]:
        """Fetch (or reuse) the model-detail payload for *source_id*."""
        cache_key = (self.platform, "detail", source_id)
        if cache is not None and cache_key in cache.provider:
            return cache.provider[cache_key]
        status, payload = await fetch_json(
            f"{self.base_url}/api/v1/models/{source_id}"
        )
        if status != 200 or not isinstance(payload, dict):
            logger.debug(
                "ModelScope detail API returned HTTP %s for %s", status, source_id
            )
            return None
        data = payload.get("Data")
        if not isinstance(data, dict):
            return None
        if cache is not None:
            cache.provider[cache_key] = data
        return data
    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(
            f"{self.base_url}/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"{self.base_url}/models/{source_id}/resolve/"
            f"{self.resolve_revision(revision)}/{filename}"
        )
    def page_url_for_file(self, source_id: str, filename: str) -> str:
        return (
            f"{self.base_url}/models/{source_id}/file/view/"
            f"{self.default_revision}/{filename}"
        )
 class ModelScopeIntlSource(ModelScopeSource):
    """ModelScope's international site (``modelscope.ai``).
    A separate catalogue rather than a mirror, so it is registered under its
    own platform id: the two deployments must not share a version group, a
    "use default paths" directory, or a stored ``source_url``.  The detail API,
    the file listing, the resolve URLs and the CDN redirect all behave exactly
    like the mainland site, which is why every URL here is derived from
    :attr:`base_url` instead of being duplicated.
    """
    platform = "modelscope-ai"
    label = "ModelScope (International)"
    default_subdir = "modelscope-ai"
    base_url = "https://www.modelscope.ai"
    url_pattern, strict_url_pattern = _url_patterns(_INTERNATIONAL_HOSTS)
 __all__ = ["ModelScopeIntlSource", "ModelScopeSource"]
 # ---------------------------------------------------------------------------
 # Model-detail API parsing helpers
 # ---------------------------------------------------------------------------
 #: Trigger-word values that mean "the author left this blank".
 _EMPTY_TRIGGER_VALUES = frozenset({"none", "null", "n/a"})
 #: Repository tags that only restate what the model *is* (its library, task or
 #: framework) rather than what it depicts.  ModelScope mixes both into the
 #: plain ``Tags`` list, and a card tagged "lora" or "text-to-image" is noise.
 _GENERIC_TAGS = frozenset(
    {
        "any-to-any",
        "checkpoint",
        "controlnet",
        "diffusers",
        "embedding",
        "image-text-to-text",
        "image-to-image",
        "image-to-video",
        "lora",
        "lycoris",
        "onnx",
        "pytorch",
        "safetensors",
        "tensorflow",
        "text-to-image",
        "text-to-speech",
        "text-to-video",
        "textual-inversion",
        "vae",
    }
 )
 def _clean_text(value: Any) -> str:
    """Return a stripped string for *value*, or ``""`` for anything else."""
    return value.strip() if isinstance(value, str) else ""
 def _first_string(value: Any) -> str:
    """Return the first non-empty string in a list, or ``""``."""
    if isinstance(value, list):
        for item in value:
            text = _clean_text(item)
            if text:
                return text
    return ""
 def _build_card_context(
    data: dict[str, Any], filename: str, sha256: str = ""
 ) -> ModelCardContext:
    """Turn a model-detail payload into a :class:`ModelCardContext`.
    Separated from the HTTP fetch so the repository-wide payload can be cached
    across the files of a collection repository while the per-file selection
    is still redone for each one.
    """
    context = ModelCardContext(
        description=_clean_text(data.get("Description")),
        model_name=_clean_text(data.get("Name")),
        model_name_localized=_clean_text(data.get("ChineseName")),
        license=_clean_text(data.get("License")),
        model_type=_clean_text(data.get("AigcType")),
        base_model=_first_string(data.get("BaseModel")),
        base_model_aliases=_base_model_aliases(data),
        official_tags=_official_tags(data),
    )
    versions = _matching_versions(
        data.get("MuseInfo"),
        filename,
        digests=_file_digests(data),
        sha256=sha256,
    )
    if versions:
        context.version_name = _version_label(versions)
        context.example_images = _cover_image_urls(versions)
        context.trigger_words = _version_trigger_words(versions)
    return context
 def _base_model_aliases(data: dict[str, Any]) -> list[str]:
    """Return the site's own names for the base model.
    ModelScope publishes a link-style id (``krea/Krea-2-Turbo``) plus its
    internal architecture enums (``VisionFoundation: KREA_2``,
    ``SubVisionFoundation: KREA_2_TURBO``).  The enums are the better
    resolution hint because they normalise onto this system's canonical
    vocabulary, so they come first; the owner prefix is also stripped from
    the link-style ids.
    """
    aliases: list[str] = []
    for key in ("VisionFoundation", "SubVisionFoundation"):
        value = _clean_text(data.get(key))
        if value and value not in aliases:
            aliases.append(value)
    base_models = data.get("BaseModel")
    if isinstance(base_models, list):
        for item in base_models:
            text = _clean_text(item)
            leaf = text.rsplit("/", 1)[-1] if text else ""
            if leaf and leaf not in aliases:
                aliases.append(leaf)
    return aliases
 def _official_tags(data: dict[str, Any]) -> list[str]:
    """Return the content tags the site publishes for the repository.
    ``OfficialTags`` is ModelScope's curated content vocabulary and is
    preferred whenever it is populated.  Plenty of AIGC repositories leave it
    empty and carry only the plain ``Tags`` list, which mixes content tags with
    framework and task categories; those categories are dropped so a card is
    not handed "lora" and "text-to-image" as if they described the model.
    """
    curated = _dedupe(_tag_values(data.get("OfficialTags")))
    if curated:
        return curated
    generic = set(_GENERIC_TAGS)
    for value in (
        data.get("AigcType"),
        data.get("Libraries"),
        data.get("Frameworks"),
    ):
        for item in value if isinstance(value, list) else [value]:
            text = _clean_text(item).lower()
            if text:
                generic.add(text)
    return _dedupe(
        tag for tag in _tag_values(data.get("Tags")) if tag.lower() not in generic
    )
 def _tag_values(value: Any) -> list[str]:
    """Return the tag strings from either shape ModelScope publishes.
    ``OfficialTags`` is a list of ``{"Tag": ..., "ChineseName": ...}`` dicts
    carrying an English value; the plain ``Tags`` list is already strings.
    """
    if not isinstance(value, list):
        return []
    tags: list[str] = []
    for entry in value:
        tag = _clean_text(entry.get("Tag") if isinstance(entry, dict) else entry)
        if tag:
            tags.append(tag)
    return tags
 def _dedupe(values: Iterable[str]) -> list[str]:
    """Drop empties and repeats, keeping the first spelling seen."""
    unique: list[str] = []
    for value in values:
        if value and value not in unique:
            unique.append(value)
    return unique
 def _version_files(version: dict[str, Any]) -> list[str]:
    """Return the model filenames covered by one ``MuseInfo.versions`` entry.
    The listing normally sits in ``stats.fileList``; some payloads only
    carry the same field as a JSON-encoded string under
    ``modelVersion.stats``, so both shapes are accepted.
    """
    stats = version.get("stats")
    files = stats.get("fileList") if isinstance(stats, dict) else None
    if not isinstance(files, list):
        model_version = version.get("modelVersion")
        raw = model_version.get("stats") if isinstance(model_version, dict) else None
        if isinstance(raw, str) and raw.strip():
            try:
                decoded = json.loads(raw)
            except (json.JSONDecodeError, TypeError):
                decoded = None
            if isinstance(decoded, dict):
                files = decoded.get("fileList")
    if not isinstance(files, list):
        return []
    return [item for item in files if isinstance(item, str) and item]
 def _version_show_name(version: dict[str, Any]) -> str:
    """Return the human-facing version label (e.g. ``c1-st1000``)."""
    model_version = version.get("modelVersion")
    if not isinstance(model_version, dict):
        return ""
    return _clean_text(model_version.get("showName")).lower()
 def _version_label(versions: list[dict[str, Any]]) -> str:
    """Return the first published version label, preserving its spelling.
    Unlike :func:`_version_show_name` this is for display, so the label is
    not lowercased.
    """
    for version in versions:
        model_version = version.get("modelVersion")
        if not isinstance(model_version, dict):
            continue
        label = _clean_text(model_version.get("showName"))
        if label:
            return label
    return ""
 def _file_digests(data: dict[str, Any]) -> dict[str, str]:
    """Return ``basename -> sha256`` for every published weight file.
    ``ModelInfos`` groups the repository's files by kind (``safetensor``,
    …) and records a real sha256 for each, which is what makes it possible to
    recognise a file the user has renamed.
    """
    digests: dict[str, str] = {}
    model_infos = data.get("ModelInfos")
    if not isinstance(model_infos, dict):
        return digests
    for info in model_infos.values():
        files = info.get("files") if isinstance(info, dict) else None
        if not isinstance(files, list):
            continue
        for entry in files:
            if not isinstance(entry, dict):
                continue
            name = _clean_text(entry.get("name"))
            digest = _clean_text(entry.get("sha256"))
            if name and digest:
                digests.setdefault(os.path.basename(name).lower(), digest.lower())
    return digests
 def _matching_versions(
    muse_info: Any,
    filename: str,
    *,
    digests: dict[str, str] | None = None,
    sha256: str = "",
 ) -> list[dict[str, Any]]:
    """Return the ``versions`` entries that publish the wanted model file.
    Strategies, in order:
    1. **sha256** — the file's content hash, looked up through
       :func:`_file_digests`.  This is the only strategy that survives the
       user renaming the weights, which is common once a model is filed away.
    2. **Exact basename** against each version's ``stats.fileList``.
    3. **``showName`` inside the file stem**, which absorbs the naming drift
       ModelScope sometimes applies to uploaded weights.
    A known-but-unmatched hash falls through to the filename strategies
    rather than giving up, in case the local file was re-encoded.  All matches
    are returned so a file re-published across several versions contributes
    all of its example images.  With no *filename* and no *sha256*, only an
    unambiguous single-version repository is used, because a per-file image
    must never be attributed to the wrong file.
    """
    if not isinstance(muse_info, dict):
        return []
    versions = muse_info.get("versions")
    if not isinstance(versions, list):
        return []
    entries = [entry for entry in versions if isinstance(entry, dict)]
    if not entries:
        return []
    target_hash = (sha256 or "").strip().lower()
    if target_hash:
        known = digests or {}
        by_hash: list[dict[str, Any]] = []
        for version in entries:
            for path in _version_files(version):
                if known.get(os.path.basename(path).lower()) == target_hash:
                    by_hash.append(version)
                    break
        if by_hash:
            return by_hash
    if not filename:
        return entries if len(entries) == 1 else []
    target = os.path.basename(filename).strip().lower()
    if not target:
        return []
    stem = os.path.splitext(target)[0]
    exact: list[dict[str, Any]] = []
    fuzzy: list[dict[str, Any]] = []
    for version in entries:
        files = {os.path.basename(path).lower() for path in _version_files(version)}
        if target in files:
            exact.append(version)
            continue
        show_name = _version_show_name(version)
        if show_name and show_name in stem:
            fuzzy.append(version)
    return exact or fuzzy
 def _cover_image_urls(versions: list[dict[str, Any]]) -> list[str]:
    """Collect the example-image URLs published by the given versions."""
    urls: list[str] = []
    for version in versions:
        covers = version.get("coverImages")
        if not isinstance(covers, list):
            continue
        for cover in covers:
            if not isinstance(cover, dict):
                continue
            url = _clean_text(cover.get("url"))
            if url and url not in urls:
                urls.append(url)
    return urls
 def _version_trigger_words(versions: list[dict[str, Any]]) -> list[str]:
    """Return the first non-empty trigger-word list across *versions*."""
    for version in versions:
        model_version = version.get("modelVersion")
        raw = (
            model_version.get("triggerWords")
            if isinstance(model_version, dict)
            else None
        )
        words = _parse_trigger_words(raw)
        if words:
            return words
    return []
 def _parse_trigger_words(raw: Any) -> list[str]:
    """Decode ModelScope's JSON-encoded trigger-word string list."""
    if isinstance(raw, list):
        candidates = raw
    elif isinstance(raw, str) and raw.strip():
        try:
            decoded = json.loads(raw)
        except (json.JSONDecodeError, TypeError):
            return []
        if not isinstance(decoded, list):
            return []
        candidates = decoded
    else:
        return []
    words: list[str] = []
    for item in candidates:
        word = _clean_text(item)
        if not word or word.lower() in _EMPTY_TRIGGER_VALUES:
            continue
        if word not in words:
            words.append(word)
    return words
@@ -0,0 +1,228 @@
 """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 ModelScopeIntlSource, ModelScopeSource
 from .tensorart import TensorArtSource
 logger = logging.getLogger(__name__)
 #: Order matters only for disambiguation; the URL patterns are disjoint.
 #: ``modelscope.ai`` is a separate catalogue from ``modelscope.cn`` rather than
 #: an alias, which is why it gets its own entry (see ``modelscope.py``).
 _SOURCES: tuple[ModelSource, ...] = (
    HuggingFaceSource(),
    ModelScopeSource(),
    ModelScopeIntlSource(),
    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",
 ]
@@ -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"]
@@ -13,7 +13,7 @@ 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
@@ -250,6 +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."""
@@ -786,6 +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]] = {}
@@ -838,6 +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")
@@ -872,12 +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(
@@ -1053,6 +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
@@ -1177,6 +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(
@@ -1383,27 +1438,17 @@ class ModelUpdateService:
        await self._enrich_version_entries(metadata_provider, aggregated)
        return aggregated
-    async def _collect_local_versions(
+    @staticmethod
-        self,
+    def _iter_local_civitai_items(
-        scanner,
+        cache,
        *,
-        target_model_ids: Optional[Sequence[int]] = None,
+        target_set: Optional[set[int]] = None,
-        folder_path: Optional[str] = None,
+        normalized_folder: Optional[str] = None,
-    ) -> Dict[int, List[int]]:
+    ) -> Iterator[tuple[int, int, Any]]:
-        cache = await scanner.get_cached_data()
+        """Yield ``(modelId, versionId, base_model)`` for each scannable item."""
-        mapping: Dict[int, set[int]] = {}
+
        if not cache or not getattr(cache, "raw_data", None):
-            return {}
+            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("/")
        for item in cache.raw_data:
            # Apply folder filter first (cheapest check)
@@ -1423,10 +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],
@@ -1506,6 +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
@@ -1552,6 +1663,7 @@ class ModelUpdateService:
        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:
@@ -1566,7 +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,
@@ -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()
@@ -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
@@ -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
+        Winner is ``batch_ids[0]``. Merging is MANIFEST-ONLY: staged files
-        MOVED (os.rename) into the winner's batch dir and their ``staged``
+        are NEVER moved, so the merge is a pure metadata operation with zero
-        paths rewritten in the merged manifest BEFORE any loser dir is
+        data IO and is inherently cross-volume safe (no EXDEV, no rollback).
-        removed. ``expires_at`` is re-anchored to ``now + TTL`` at merge time
+        Every loser's entries are appended to the winner's manifest with
-        and a FRESH purge timer is armed for the winner.
+        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
+        Returns the winner id, or ``None`` when the winner batch cannot be
-        original batch dirs/manifests are left intact; ``None`` is returned so
+        resolved (callers then fall back to the ``batch_ids`` array
-        callers fall back to the ``batch_ids`` array contract. Cross-volume
+        contract).
        merges hit EXDEV here - expected and fine (the fallback is the normal
        path for those bulks).
        """
        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.
+            # Build the merged manifest in memory: loser entries are appended
-            moved: List[Tuple[Dict[str, Any], str, str]] = []
+            # with their staged paths UNCHANGED - no file moves, no IO, no
-            processed_losers: List[Tuple[str, str]] = []  # (loser_id, loser_dir)
+            # EXDEV. Loser dirs remain as physical storage until the merged
-
+            # batch is undone or purged.
-            try:
+            merged_sources: List[str] = []
-                for loser_id in batch_ids[1:]:
+            seen_loser_dirs: Set[str] = set()
-                    loser_dir = await self._find_batch_dir(loser_id)
+            for loser_id in batch_ids[1:]:
-                    if not loser_dir or os.path.normpath(loser_dir) == os.path.normpath(
+                loser_dir = await self._find_batch_dir(loser_id)
-                        winner_dir
+                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)
+                    staged_path = entry.get("staged")
-                    if loser_manifest is None:
+                    if not staged_path or not os.path.exists(staged_path):
                        # Corrupted loser: leave it for the sweep to quarantine.
                        continue
-                    for entry in loser_manifest.get("entries") or []:
+                    winner_manifest["entries"].append(entry)
-                        if entry.get("restored"):
+                merged_sources.append(loser_abs)
                            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
-            # 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)
+            # Stamp each loser manifest so its own purge timer / a later sweep
-            # and drop them from the registry. Skipped losers (missing /
+            # / a direct undo call no-op: the winner owns those files from
-            # corrupted / same-dir) stay registered so the sweep still
+            # here on. Best-effort coordination; a failed stamp only risks the
-            # quarantines them, exactly as before the registry existed.
+            # loser being swept at its own (earlier) expiry after a restart.
-            for loser_id, loser_dir in processed_losers:
+            for loser_dir in merged_sources:
-                self._remove_manifest(loser_dir)
+                try:
-                self._remove_empty_dir(loser_dir)
+                    self._mark_merged(loser_dir, winner_id)
-                await self._forget_batch(loser_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.
+                    # a model root itself is one): collect its batches.
-                    await self._register_batch_candidates(dirpath)
+                    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(
+                        staging_parents.append(os.path.join(dirpath, name))
                            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(
+    def _mark_merged(self, loser_dir: str, winner_id: str) -> None:
-        self, moved: Sequence[Tuple[Dict[str, Any], str, str]]
+        """Stamp ``merged_into`` on a loser manifest (best-effort).
-    ) -> None:
+
-        """Move already-merged files back to their original loser batch dirs."""
+        The stamp makes the loser's own purge timer, post-restart sweeps and
-        for _entry, original_staged, _loser_dir in reversed(list(moved)):
+        direct undo calls no-op, so the winner's merged batch stays the only
-            current = _entry.get("staged")
+        owner of the loser's staged files until it is undone or purged.
-            if not current or not original_staged:
+        """
        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):
+            self._remove_manifest(src)
-                continue
+            self._remove_empty_dir(src)
-            try:
+            removed.append(os.path.basename(src))
-                os.rename(current, original_staged)
+        return removed
            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,
                )
    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):
--- a/Show More
+++ b/Show More