Installing a second LoRA Manager instance (standalone or a second
ComfyUI install) that shares the settings directory puts two processes
on the same cache databases. Three things made that unsafe.
- The updater preserved cache/ and model_cache/ but not a legacy
recipe_cache/ directory, so a portable install predating the cache/
move lost its recipe database on a git-based update. Add it to
_PRESERVE_DIRS and to .gitignore.
- Cache connections used the sqlite3 default 5s timeout, which a
scanning instance can exceed, turning a concurrent write into
"database is locked". Route every shared cache connection through
connect_cache_db(), which raises the timeout to 30s and sets
busy_timeout + synchronous=NORMAL to match the existing WAL mode.
App-private databases (download queue, update history) are unchanged.
- A full-table cache replace is a read-modify-write that SQLite cannot
make atomic across processes, so two instances could interleave and
one snapshot could overwrite the other. Guard the recipe and model
save_cache paths with a cross-process advisory lock (flock on POSIX,
msvcrt on Windows). Locking is best-effort: if it is unavailable the
call proceeds and the SQLite busy timeout is the fallback.
The lock file is a hidden sibling of the database and is deliberately
never unlinked, so a second process cannot lock a fresh inode.
A model file could only ever be linked to huggingface.co: `set_hf_url`
validated the URL with a huggingface-only regex, the agent fetched the card
from a hardcoded HF URL, and the readme processor built every relative image
path off `https://huggingface.co/{repo}/resolve/main`. ModelScope publishes the
same model-card convention (README.md + YAML frontmatter, often carrying
`base_model:` and `trigger_words:`) behind a public, key-less API, so the
enrichment pipeline could already serve it - it was the plumbing that was
HF-shaped, not the idea.
Make the external source a first-class, provider-driven concept:
- New `py/services/model_sources/` registry. A `ModelSource` owns URL
recognition (lenient for stored values, strict for user input), the
canonical page URL, model-card fetching, the asset base URL and the
capability flags. `HuggingFaceSource` is the previous logic relocated;
`ModelScopeSource` reads `/models/{o}/{n}/resolve/{master|main}/README.md`
and falls back to `/api/v1/models/{o}/{n}/repo`. `TensorArtSource` is
link-only on purpose: tensor.art answers plain HTTP clients with a
Cloudflare challenge and its internal API (ap-east-1.tensorart.cloud /
cn.tensorart.net) rejects every /v1/model/* route with "invalid
authorization header", so it declares supports_enrichment=False rather than
failing silently later.
- Metadata gains `source_platform` + `source_url`; `hf_url` stays as a
read/write alias, written only for Hugging Face, so existing sidecars,
cached rows and third-party consumers keep working. Normalisation runs at
the scanner, the persistent cache (both directions, plus two new columns
behind an ALTER migration) and the linking handler - which is what stops a
user who switches sources from leaving a stale `hf_url` on a ModelScope
model.
- The agent pipeline keys off the provider instead of `hf_url`: the fast-fail
gate now explains *why* a model is skipped (no source / unknown source /
source without a reachable card), the prompt context exposes
source_url/source_id/source_label/asset_base_url while still filling the
legacy hf_url/repo aliases, and the four README image extractors take a
base_url (defaulting to HF) so relative paths resolve against the right
site. Version grouping generalises to hf: / ms: / ta: keys.
- `POST /api/lm/set-hf-url` keeps its path and its legacy payload keys but
accepts `source_url`, validates against every provider and returns the
platform. `GET /api/lm/model-sources` lets the UI render the supported-site
list from the server.
- Frontend: a `modelSourceHelpers` mirror of the registry drives the link
dialog, the card/modal globe (branded "View on ModelScope/TensorArt"), the
version-group key and the enrichment gate; the versions tab no longer sends
ms:/ta: keys to the CivitAI API.
TensorArt stays in the list because provenance is worth keeping even when the
card is unreadable - the dialog says so plainly ("Sites that don't expose one
(currently TensorArt) can only be linked") and the context menu disables
enrichment with a matching tooltip, instead of the user getting
"Unsupported URL".
Verified against the real ModelScope API: jj3550945163/Krea-2-LORA returns a
1882-byte card whose frontmatter carries base_model/tags/trigger_words, and
relative images resolve to .../resolve/master/....
Tests: backend 2815 passed; frontend 1130 JS + 91 Vue passed; pytest
tests/i18n and a Jinja compile pass over templates/. The nine locales carry
[TODO: Translate] for the new strings, completed in the next commit.
The include_empty folder tree (download/move modals) walked every model
root synchronously on the event loop via get_all_folders(). On network
(NAS) roots this froze the whole server for the duration of the walk —
blocking WebSocket progress, aria2 RPC and the download queue — and the
5s TTL re-triggered the walk on nearly every modal interaction.
The scanners already visit every directory during cache scans, so record
the full directory list (including empty folders) there instead:
- _gather_model_data/_reconcile_cache collect directories during the
existing walks; reconcile refreshes and persists the list even when no
model files changed.
- ModelCache gains an all_folders field (None = never recorded).
- PersistentModelCache stores the list in a new folders table, with a
cache_meta flag distinguishing 'recorded empty' from legacy snapshots.
- get_all_folders() is now a pure in-memory read. A legacy snapshot
triggers a one-shot backfill walk in a worker thread (never on the
event loop) that records and persists the list.
- Moves add the destination folder (and parents) incrementally instead
of invalidating a TTL cache.
- Three-state autov3 field (not-checked / checked-unavailable / 12-hex value)
in .metadata.json sidecars, in-memory ModelHashIndex, and SQLite
(models.autov3 column + autov3_index table) with column-presence migration
- Background self-terminating backfill for legacy rows: per-model-type
concurrency guard, executor-offloaded I/O, Civitai-first resolution
(SHA256-matched version file) falling back to the embedded safetensors
header hash
- Civitai-first propagation on metadata refresh, scan, and download paths;
reject the empty-string SHA256 placeholder and strip OneTrainer 0x prefix
- List API hash filters and hash index lookups accept 12-char AutoV3
- Cap safetensors header reads at 64 MiB to prevent crafted-file allocation
- Prevent stale AutoV3 mappings on file replacement while preserving them on
same-file re-registration (lazy-hash completion)
- Add PersistentModelCache.update_single_model() for lightweight targeted
SQL update (single row + incremental tag/hash deltas, no full table scan)
- Add ModelScanner.sync_cache_from_metadata() with compare-first logic:
skips entirely when cache is already in sync; when stale, updates the
entry in-place (O(1) instead of O(n) remove+append), incrementally
adjusts tag counts/hash index/version index, and resorts only when
sort-relevant fields changed
- Wire sync_cache_from_metadata() into BaseModelService.get_model_metadata()
via fire-and-forget asyncio.create_task — disk I/O is already paid for
- Include identity re-validation guard against concurrent cache replacement
- Add 16 tests covering _cache_entries_differ, sync_cache_from_metadata
(no-change, in-place, fallback, conditional resort), and
update_single_model (insert, tag delta, hash delta)
Cache corruption (NULL model_name/file_name from legacy DB rows or partial
writes) caused format_response to raise KeyError/AttributeError, failing the
entire /loras/list request and showing no models in the UI.
Fix across three layers:
- format_response (lora/checkpoint/embedding): replace direct dict[] access
with .get() fallbacks; return None for entries missing file_path
- handlers: filter None entries from list/excluded/fetch/duplicate/conflict
endpoints instead of letting them crash or appear as null in responses
- model_scanner: always use validate_batch repaired copies (previously
discarded when no invalid entries, leaving None values in raw_data)
- persistent_model_cache: add or-empty-string guards on read and write for
nullable TEXT columns (model_name, file_name, folder, base_model, etc.)
- Centralize cache path resolution in new py/utils/cache_paths.py module
- Migrate legacy cache files to organized structure: {settings_dir}/cache/{model|recipe|fts|symlink}/
- Automatically clean up legacy files after successful migration with integrity verification
- Update Config symlink cache to use new path and migrate from old location
- Simplify service classes (PersistentModelCache, PersistentRecipeCache, RecipeFTSIndex, TagFTSIndex) to use centralized migration logic
- Add comprehensive test coverage for cache paths and automatic cleanup
- Add model_types parameter to ModelListingHandler to support filtering by model type
- Implement get_model_types endpoint in ModelQueryHandler to retrieve available model types
- Register new /api/lm/{prefix}/model-types route for model type queries
- Extend BaseModelService to handle model type filtering in queries
- Support both model_type and civitai_model_type query parameters for backward compatibility
This enables users to filter models by specific types, improving model discovery and organization capabilities.
- Update DEFAULT_LICENSE_FLAGS from 57 to 127 to enable all commercial modes by default
- Replace CommercialUseLevel enum with bitwise commercial permission handling
- Simplify commercial value normalization and validation using allowed values set
- Adjust bit shifting in license flag construction to accommodate new commercial bits structure
- Remove CommercialUseLevel from exports and update tests accordingly
- Improve handling of empty commercial use values with proper type checking
The changes streamline commercial permission processing and align with CivitAI's default license configuration while maintaining backward compatibility.
Add license resolution utilities and integrate license information into model metadata processing. The changes include:
- Add `resolve_license_payload` function to extract license data from Civitai model responses
- Integrate license information into model metadata in CivitaiClient and MetadataSyncService
- Add license flags support in model scanning and caching
- Implement CommercialUseLevel enum for standardized license classification
- Update model scanner to handle unknown fields when extracting metadata values
This ensures proper license attribution and compliance when working with Civitai models.
- Add metadata_source field to track origin of model metadata
- Define MODEL_COLUMNS constants for consistent column management
- Refactor SQL queries to use dynamic column selection
- Improve Civitai data detection to include creator_username and trained_words
- Update database operations to handle new metadata field and tag management
Will Miao