Three upstream gaps surfaced once /generate was actually exercised:
1. infer-api.py builds an 18-arg positional tuple but the pipeline
expects 24 — first missing arg is `format`, so audio_duration
shifts into format's slot and the pipeline calls len() on an
int. Ship a patched copy of infer-api.py and COPY over upstream's
in the Dockerfile. Also handle empty lora_name_or_path -> "none"
(empty string trips HF Hub's repo-id validator).
2. torchcodec + ffmpeg are required by the WAV save path but neither
is in upstream requirements.txt. Without them every /generate
runs to completion and then 500s at write-time.
3. ACE-Step caches checkpoints at /root/.cache/ace-step/checkpoints
(HARDCODED, not honored by HF_HOME). Mount our persistent dir
there so the ~7 GB model survives container recreates.
Bench on A6000 (cached model, lo-fi hip hop, 60-step euler/apg):
10s @ 27 steps -> 9.4s (0.94x)
30s @ 60 steps -> 11.2s (0.37x, ~2.7x realtime)
60s @ 60 steps -> 14.8s (0.24x, ~4x realtime)
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