Three related placeholders were shipping as working features:

- audio_stems.separate_with_provider() and audio_clean.restore_with_provider()
  were one-line stubs that unconditionally raised 501, so /v1/audio/stems and
  /v1/audio/cleanup only ever worked in ffmpeg fallback_mode despite demucs and
  deepfilternet being declared dependencies and probed by detect_audio_backends.
- run_full_music_dub() ran only the STT step and returned hardcoded paths
  (vocals.wav, final_mix.wav) plus 'status': 'placeholder' for the other four,
  while the endpoint reported 'status': 'available'.

Now:

- separate_with_provider drives demucs (demucs.api, CLI fallback), keeps the
  vocals stem and sums drums+bass+other into an instrumental bed for
  vocals-instrumental; 4-stem passes the four through.
- restore_with_provider runs DeepFilterNet (VoiceFixer fallback) and then applies
  the hum/click/loudness DSP with ffmpeg, so those options are no longer lost.
- audio-music-dub is a real six-stage chain: separate -> transcribe the ISOLATED
  vocal -> adapt lyrics (LLM prompted for singable output with matching syllable
  count, argostranslate fallback) -> re-sing with F5-TTS cloned from the original
  vocal -> optional Seed-VC f0-conditioned singing pass -> ffmpeg remix + limiter.
  New request fields: text_model, voice_name/ref_text, sing_convert,
  diffusion_steps, pitch_shift, speed, seed, fallback_mode.

Missing optional deps now degrade to a reported 'skipped'/'failed' step with a
reason and a top-level status of 'complete' vs 'degraded' plus warnings[], rather
than silently producing placeholders. 501s name the package to install.

Verified offline with a fake-model harness: the chain produces a real RIFF wav
mix end to end, feeds the vocal stem (not the mix) to STT, and correctly reports
degraded when seed-vc is unavailable.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R6iugoNeEgt9PkajKrFyq4

Tasks / queue management:
- Central in-memory task registry with cooperative cancel, pause/resume,
  and step progress across image/video/audio/text generation + LoRA training
- Tasks admin page (live 2s poll): cancel, interrupt, pause/resume, restart,
  remove; done jobs auto-drop from the list; bounded persisted job history
- Disable interrupted-training recovery via --no-resume-jobs + settings toggle

Quantization / acceleration:
- TurboQuant embedding vector quantization (data-free, inner-product
  preserving): built-in NumPy backend + optional turboquant-py library,
  selectable per embedding model; /v1/embeddings `quantization` param
- llama.cpp KV-cache quantization (cache_type_k/v) for GGUF text models,
  configurable in the Models UI

Hardware telemetry:
- Thermal cooldown state surfaced on the Tasks page (banner + per-task badge)
- Live CPU/GPU/RAM/VRAM usage + temperature panel via /admin/api/system-stats

Docs: API documentation gaps/accuracy pass + Swagger overhaul; DISTRIBUTION.md
implementation spec. Plus I2V LoRA training channel-mismatch fix.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>