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.
Example videos added through the "Add examples" flow were stored with a
hardcoded 720x1280 entry. The dimension probe next to it only ran for
images (PIL cannot open .mp4/.webm files), so every video entry stayed
portrait regardless of the source. The showcase viewer then sizes its
container straight from that value (--media-aspect in showcase.css), so
landscape clips were letterboxed inside a 9:16 box. CivitAI-sourced
examples were unaffected because their dimensions come from the API.
PIL cannot read video containers, so add a dependency-free reader that
parses the container headers instead: moov/trak/tkhd for ISO base media
(with the sample description as a fallback), Segment/Tracks/Pixel* for
WebM/Matroska, and RIFF/WebP for animated examples saved with a video
extension. The sniffed signature decides which reader runs, so a .mp4
that is really WebM still reports the right size; the extension is only
a fallback. Both readers seek past mdat rather than reading it, so a
large file costs the same as a small one.
Imported entries now record the file's real size and keep the previous
placeholder only when the file cannot be parsed.
Existing libraries keep their wrong entries, so backfill them once via
the existing naming migration: bump CURRENT_NAMING_VERSION to 3 and
repair each model's empty-url entries from the files on disk, then sync
the scanner cache. Only entries with no remote url are touched -- those
have no other source, which makes the rewrite lossless -- and entries
already carrying the right size are left byte-identical, so the pass is
idempotent and a no-op for libraries that never imported a video.
A LoRA named `lora-sd1.5-backlight_slider_v10.safetensors` showed up in the
manager as `lora-sd1`, hid itself from searches for the rest of its name, and
collapsed into the same lora syntax tag as every sibling sharing the prefix.
The name was cut twice. `_process_model_file()` imports a third-party
`.civitai.info` sidecar by handing `from_civitai_info()` the local stem with
the extension already stripped, and the builder then stripped a second
"extension" from it -- `os.path.splitext` reads everything after the last dot
as one, so the version dot in `1.5` ended the name. The download path never
hit this because API filenames keep their extension and only need one strip.
Pass the real basename from the migration site, and make the builder strip
only a recognized model extension (`strip_model_extension`), so both input
shapes resolve to the same stem. The `model_name` fallback that reused the
same expression is fixed with it: on a sidecar without `model.name` the
display name was truncated too.
Libraries already corrupted do not heal on their own: the incremental Refresh
skips paths already in the cache (only a full rebuild reloads metadata) and
startup hydrates rows from SQLite as-is, so the wrong name survives restarts.
Reconcile now compares each cached row against the stem of its file path --
one string compare per file and no extra syscall, so a clean library pays
nothing -- and repairs mismatching rows through `load_metadata()` (which
normalizes the sidecar) and the existing in-place `_sync_cache_from_metadata_impl()`
path, which writes a targeted single-row SQL delta instead of a full save.
Repairs are one-shot, and a missing or corrupt sidecar keeps its row so a full
rebuild can recreate it without losing tags or civitai data.
Tests: the builder keeps dotted stems for all four model classes and still
strips real extensions; the migration writes the full local name to the
sidecar; and reconcile repairs memory, sidecar and SQLite row, runs exactly
once, and never reads metadata on a clean library.
DEFAULT_ENABLED_OTHER_SUB_TYPES managed vae, upscaler, text_encoder and
clip_vision while controlnet was the sole opt-in type. That split was not
defensible on demand breadth: ControlNet is the broader category by install
base, and clip_vision is the narrower one (IPAdapter/SVD image conditioning,
usually one to three files) whose CivitAI type is retired upstream.
Keep the default set to the dependency-style assets every pipeline needs and
where "which one am I actually using" is the real problem - VAE, upscalers
and text encoders - and treat clip_vision and controlnet symmetrically as
opt-in. The feature is still unreleased, so the change needs no migration.
- Sync all five surfaces holding a default: DEFAULT_ENABLED_OTHER_SUB_TYPES,
DEFAULT_SETTINGS, both DEFAULT_SETTINGS_BASE/createDefaultSettings lists,
updateOtherModelsControls()'s fallback and the Jinja fallback.
- The selection is persisted per user, so only the untouched default moves;
existing default_other_roots entries for a disabled sub_type are preserved.
- Fix the Jinja fallback using `or`, which treated an all-unchecked empty
allow-list as "unset" and re-checked every box on render; `is none` keeps
the empty list empty.
- Document the revised defaults and rationale in the plan.
Tests assert the new default trio, the normalize fallback, that both opt-in
types stay out of the default scan, and the auto-set iteration test now
enables clip_vision explicitly since it exercises the loop, not the default.
get_download_path_template() fell back to "{base_model}/{first_tag}" for any
unconfigured model type, so other-model downloads were silently nested under an
arbitrary CivitAI tag even though the settings UI exposes no template row for
"other" and priority_tags has no "other" entry (making {first_tag} resolve to
tags[0]).
Add DEFAULT_DOWNLOAD_PATH_TEMPLATES with other -> "" so unconfigured and
unknown types resolve to a flat layout under the already sub_type-scoped
default_other_roots; explicit settings.json values still win. Mirror the flat
default in the frontend DEFAULT_PATH_TEMPLATES and stop the download/move
default-path previews from rendering "/undefined" or a dangling slash.
Other Models management is now opt-in: enable_other_models (default false)
plus the enabled_other_sub_types allow-list replace the unreleased additive
enabled_other_folders key.
- config._get_enabled_other_folder_keys() is the single scan gate; a new
refresh_other_roots() rebuilds roots and preview roots on toggle.
- ModelScanner gains a _should_keep_cached_entry() hydration hook and
on_library_changed(reconcile=...) so switching a sub_type off drops its
entries (and hash/autov3 rows) at load time and switching it on rescans.
- OtherScanner filters location-derived entries accordingly.
- Other routes reject every other type while off (or a disabled sub_type) and
expose an "other_disabled" page flag; download routing returns a disabled
marker instead of guessing; the download manager refuses other-type
downloads and default-path routing for switched-off sub_types.
- Doctor / init-status / refresh-all skip the other scanner while off; the
scanner stays registered so staged pending-deletes still merge.
- Tests updated with explicit opt-in fixtures plus new gating coverage.
The field dates back to a development-stage bug in the enrich-metadata
(agent) pipeline, which briefly wrote trigger words at the top level of
model metadata instead of the established civitai.trainedWords location.
The write path was fixed before the feature merged to main (PR #1013)
and never shipped in any release, so no writer has existed since.
Remove the leftover pieces:
- BaseModelMetadata.trainedWords field (py/utils/models.py); sidecars
from that dev window now pass the key through _unknown_fields instead
- HF download handler's strip-empty-trainedWords special case, reverting
to saving the metadata object directly (py/routes/handlers/hf_handlers.py)
- trainedWords in the LLM enrichment context (agent_service.py)
- matching fallbacks/fixtures in the enrich_hf_validation harness and
post-processor test
Trigger words continue to live in civitai.trainedWords for all model
sources, which is what the UI, agent post-processor, and metadata sync
all read and write.
The SHA256 of an empty byte string (written by repackaging tools into
safetensors metadata, or produced by hashing an empty/unreadable file)
was previously resolved against CivitAI's by-hash API, which can contain
polluted entries for it (e.g. a broken SD 1.5 LoRA whose AutoV3 equals
the placeholder) and falsely attributed the wrong model to a recipe.
Guard all lookup paths for the 10/12/64-char AutoV2/AutoV3/full-SHA256
spellings: CivitaiClient.get_model_by_hash/_fetch_version_by_hash return
not-found without a request, and ModelHashIndex ignores the placeholder
in has_hash/get_path/add_autov3.
The Automatic1111 metadata parser keeps the LoRA item itself when its
hash is the placeholder: it matches by filename locally, or retains the
entry with an empty hash flagged hashInvalid (unresolvable-hash state in
the UI, with reconnect as the remedy) instead of dropping it or resolving
it to a polluted CivitAI entry.
Recipes imported by drag & drop / file-picker record no source_path and
were rejected by re-import. Fall back to the recipe's own saved image,
which still carries the original embedded generation metadata.
Re-import now re-parses that original metadata instead of the appended
recipe JSON block, so parser upgrades produce fresh results. The
already-optimized preview image is kept verbatim: only its WebP EXIF
chunk is rewritten in place to replace the recipe metadata block, and
the recipe JSON is rewritten with the new analysis plus carried-over
user edits.
Enhance the deleted-LoRA reconnect flow in the recipe modal:
- Suggest local reconnect candidates when the panel opens, ranked by
identity (same hash / same CivitAI version) then filename/name
similarity, with a hard filter on confident base-model mismatches;
the input gets a Combobox backed by the same endpoint as you type.
- Snapshot the pre-reconnect entry and offer a permanent restore:
reconnected entries show an undo icon at the right end of the info
row, with the original filename in the tooltip.
- Relax the manual reconnect base-model guard to a three-tier check:
exact/unknown labels pass silently, same-architecture families
(e.g. Pony <-> Illustrious) pass with a warning toast, and only
cross-architecture mismatches stay hard-rejected.
Add LORA_MANAGER_SETTINGS_DIR env var and standalone --settings-path to pin
the settings location (settings.json, cache/, wildcards/, backups/, logs/,
stats/) to an arbitrary directory. The override takes precedence over
portable mode and the platform user config dir, and skips legacy migration,
so sandboxed dev/E2E runs no longer need to write settings.json in the repo
root or collide with the real instance.
standalone.py pre-scans argv for --settings-path at import time because the
settings location is resolved before main() parses arguments. SettingsManager
portable-switch migration is a no-op while the directory is pinned.
Update the lora-manager-e2e skill (prefer --settings-path sandboxing;
start_server.py passes it through) and the lora-manager-runtime-context
skill (document precedence; inspect script honors the override).
Track recipe modal opens in a separate stats file (never touching recipe
JSON/EXIF), expose a fire-and-forget POST endpoint, and add an 'opened'
sort that hides never-opened recipes as a true recently-opened view.
Includes i18n for all locales and backend/frontend tests.
The sampler field now accepts either a manual string or a SAMPLER
connection. When wired, the sampler name is extracted from the
KSAMPLER object's sampler_function __name__ (sample_euler -> euler),
with special-casing for dpm_fast/dpm_adaptive local closures and
uni_pc/uni_pc_bh2 function names.
- sampler input declared as "STRING,SAMPLER" with widgetType STRING,
mirroring the existing model field union pattern
- shared collect_overwrite_params() handles the non-str branch so the
node and the metadata extractor conversion logic stay in sync;
unrecognized sampler functions are logged and skipped
- note: ddim is constructed by ComfyUI as euler with random inpaint,
so the ddim name is unrecoverable and extracts as euler
Downloads failed with "No suitable file found in metadata" for models whose
only file uses newer CivitAI file types (e.g. 'Enhancement LoRA' for
Anima/AIR image-editing LoRAs) because the primary-file allowlist only
covered legacy types.
- unify the weights-type allowlist as MODEL_WEIGHT_FILE_TYPES
(py/utils/constants.py) and apply it across download, recipe and
metadata-refresh lookups
- mirror CivitAI's getPrimaryFile() semantics: prefer weights-type primary,
fall back to weights files, then trust CivitAI's primary flag (excluding
non-downloadable artifacts like Config/Archive/Workflow)
- mirror the allowlist in the frontend via shared isModelWeightFile() helper
- add regression tests for the Enhancement LoRA primary-file download,
primary-flag fallback and weights-over-non-weights-primary preference
Refresh after manual .metadata.json deletion rebuilds the payload without
file_name/size/modified, which are required by BaseModelMetadata.from_dict.
The recreated sidecar then fails to parse and the scanner skips the model.
- load_metadata_payload fills missing file facts from os.stat
- hydrate_model_data restores every missing key from the cache snapshot
only when the sidecar is missing entirely (disk stays authoritative
otherwise), preferring the cached import timestamp for modified
- save_metadata fills file facts on write so no write path can produce
an unparseable sidecar
- Read AutoV3 directly from the downloaded file's own file_info hashes
(no SHA256 cross-matching against version_info.files, so the value is
captured even when the API omits SHA256)
- Extract normalize_autov3() validation helper shared with the
sha256-matching autov3_from_civitai_files path
- Fall back to the embedded safetensors header hash at download
completion; mark '' (checked-unavailable) so the startup backfill
query (autov3 IS NULL) never revisits the row
- Clear archive-level AutoV3 for zip-extracted models so per-file
header resolution applies to every extracted model
- 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)
Split the single-model and bulk context menu actions into 'Download
Missing Example Images' (regular endpoint, skips already-processed
models) and 'Re-process Example Images' (force endpoint, retries
failed models).
- start_download accepts model_hashes so a selected subset can be
processed with the progress-aware skip logic; explicitly targeted
models bypass the failed/processed model-level guards so per-image
gaps are filled
- pre-download existence check in the processor skips network requests
for image files already on disk across all download paths
- force download retries previously failed models and clears their
failed status on success
- add i18n keys for the new menu items across all locales
The model field now accepts either a manual string or a MODEL connection.
When wired, the model name is extracted from the patcher's
cached_patcher_init (registered by core loaders load_checkpoint_guess_config
and load_diffusion_model, preserved through LoRA clones) and converted to a
ComfyUI-style relative name via config model roots.
- model input declared as "STRING,MODEL" with widgetType STRING, so the
text widget and the dual-type connection slot coexist; non-STRING/MODEL
links are rejected by frontend and backend type validation
- UNETLoaderLM GGUF branch now registers a custom cached_patcher_init reload
factory so GGUF models participate in name extraction and ModelPatcher
deepclone/dynamic machinery
- shared collect_overwrite_params() helper keeps the node and the metadata
extractor conversion logic in sync; extraction failures are logged instead
of silently dropping the overwrite
Move reverse-migration logic from get_model_folder() (hot path, called on
every metadata/example-images request) to ExampleImagesMigration, where it
runs once at startup. On network storage this was causing 22-38s delays
per LoRA card click.
Additionally optimize prune_stale_example_images() to read the directory
listing once instead of per image entry (O(N*M) → O(M)). Also reorder
consolidation checks so regex filters run before filesystem stat calls.
When Civitai returns 429 (Too Many Requests) during example image
downloads, the previous behavior treated all failures identically and
permanently removed the corresponding images from model metadata —
making them impossible to retry.
This commit adds:
- 429 detection + Retry-After header parsing in download_to_memory
- Exponential backoff retry (up to 3 attempts) in
download_model_images_with_tracking
- Separate tracking of rate-limited vs permanently failed URLs
- rate_limited_models progress tracking persisted to disk
- Rate-limited models are NOT added to failed_models/processed_models
so they are automatically retried on subsequent download runs
- Force mode clears failed_models when rate-limited images exist
Introduce an agent skill framework for LLM-driven metadata enrichment:
- AgentCLI (py/agent_cli/): in-process wrappers around internal services
using standard relative imports, eliminating the need for sys.path hacks
- LLMService: centralized BYOK (bring-your-own-key) LLM client supporting
OpenAI, Ollama, and custom OpenAI-compatible endpoints
- PostProcessor: deterministic engine that applies LLM output via AgentCLI
(replaces old handler.py + _BASE_MODEL_ALIASES approach)
- SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md)
- AgentService: orchestrates skill execution with WebSocket progress
- Frontend AgentManager: WebSocket listeners, skill execution, config UI
- Context menu entries (single + bulk) for "Enrich Metadata (Agent)"
- Settings UI for AI Provider configuration (BYOK)
- Full i18n support across 9 locales
Bug fixes found during review:
- aiohttp.web.json_response: status_code= -> status=
- settings_modal cancelEditApiKey: wrong argument position
- AgentManager.isLlmConfigured: allow Ollama without API key
- PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
- Prefer file type (UNet/Diffusion Model) over baseModel name when
deciding whether a checkpoint routes to the unet folder
- Add UNet to backend primary file type whitelist
- Add Krea 2 to DIFFUSION_MODEL_BASE_MODELS
- Include UNet/Diffusion Model files in frontend file selection UI
- Use actual file type from CivitAI in download params instead of
hardcoded 'Model'
_load_stats() was missing the embeddings section, so on every restart
the embeddings usage tracking hash would start from an empty dict.
This caused all previously saved embedding usage data to appear reset.
Added the missing load path for the 'embeddings' key, parallel to the
existing checkpoints and loras loading logic.
- Change _get_stats_file_path() to use get_settings_dir()/stats/ instead of
first loras root directory
- Add _migrate_from_old_location() to copy existing stats from loras root
to new location on first access, then clean up old file
- Add 'stats' to update protection skip lists (clean, extract, tracking)
to prevent data loss during ZIP/git upgrades in portable mode
- Add usage_stats entry to backup targets and restore resolver so stats
are included in automatic snapshots
Will Miao