Adds per-image integrity tracking so corrupt files are detected, excluded
from random/showcase/ML/suggestion paths, and recoverable by dropping a
fresh copy in /import — closing the gap that surfaced as the WD14
'6 bytes not processed' OSError.
Schema (migration l26042501)
- image_record.integrity_status: unknown | ok | truncated | unreadable | missing
- image_record.integrity_checked_at: timestamptz
- partial index on status <> 'ok' for cheap report/filter queries
Verifier
- app/services/integrity.py: verify_path() dispatches by extension
- PIL two-stage (verify + load with LOAD_TRUNCATED_IMAGES disabled)
- ffprobe for video, zipfile.testzip for archives
- Truncation-vs-unreadable distinction via PIL message hints
Pipeline
- verify_media_integrity Celery task: per-image, idempotent
- verify_unverified_images sweep: only_unknown by default, skips
paths in active import tasks
- Hooked into the end of import_media_file (new + archive paths) and
the supersede branch
- supersede_image() resets status to 'unknown' so the post-supersede
verify writes a fresh truth
- Supersede-on-replace: a fresh /import/<artist>/<filename> matching
a flagged-corrupt record routes through _supersede_existing,
preserving tags/series/embeddings
Exclusions
- /, /api/random-images, tag_and_embed, ml.backfill enqueue, and
get_suggestions all filter integrity_status IN ('ok', 'unknown') so
flagged rows don't poison the gallery, ML, or suggestion math.
'unknown' is treated as healthy so post-migration data stays visible
until the sweep runs.
UI / report
- Settings -> Maintenance: 'Verify unknown' + 'Force re-verify all'
- GET /api/integrity/failed (paginated list of flagged rows)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Archive identifier string (artist:archive_name) stays as the tag's
opaque name — the archive kind lives in the kind column.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Videos now route through a 10-frame sampling branch (configurable via
VIDEO_ML_FRAMES env) instead of the previous unsupported_format early
return. WD14 predictions are aggregated by max-confidence per (name,
category) across frames so sparse signals aren't diluted; SigLIP
embeddings are mean-pooled for a representative shot. Also generates a
fallback thumbnail when the record is missing one, and removes the
video_filter from backfill so videos get enqueued.
Celery soft/hard limits bumped to 240s/360s to accommodate 10x inference.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
bvandeusen
Bryan Van Deusen