Both the auto and manual wishlist download paths called
`remove_tracks_already_in_library` before submitting the batch — a
serial DB lookup per track per artist (~1s/track on a 24-track
wishlist). The batches set `force_download_all=True` which is
explicitly documented as "skip the expensive library check" — the
pre-flight cleanup was contradicting that flag.
Removed the cleanup call from both flows. Kept `remove_wishlist_duplicates`
(fast SQL DELETE) and the standalone `/api/wishlist/cleanup` endpoint
that exposes the library scan as explicit user-triggered maintenance.
Safety check on the trade-off:
- post-processing at `core/imports/pipeline.py:576-624` already handles
re-downloads defensively: existing file with metadata → skip overwrite
+ delete source duplicate, no library corruption.
- Master worker's analysis loop normally removes wishlist entries for
found tracks via `_check_and_remove_track_from_wishlist_by_metadata`,
so stale wishlist entries should be rare in practice.
- Worst case for the rare orphan: one redundant download attempt that
the post-processing layer no-ops on. Bandwidth waste, not data damage.
Tests updated:
- `..._does_not_run_library_cleanup` (renamed from `_skips_enhance_tracks_during_cleanup`)
asserts no DB track-existence checks happen and no wishlist removals
fire — both `enhance` and "owned" tracks reach the master worker.
- `..._marks_batch_complete_when_wishlist_genuinely_empty` (renamed from
`..._after_cleanup`) covers the path where the wishlist starts empty.
Full suite: 1232 passing. Ruff clean.
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