vh
d2ed7671d7
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)
75 lines
3.3 KiB
YAML
75 lines
3.3 KiB
YAML
# ACE-Step 1.5 — open-source music generation foundation model
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# (April 2026). Hybrid diffusion + LLM architecture, Apache 2.0.
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# ~50-80 s for a 4-minute song on A6000; under 4 GB VRAM at idle,
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# ~10-12 GB during inference. Beats YuE / DiffRhythm on the
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# speed/coherence trade.
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#
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# We launch upstream's REST API (`infer-api.py`) instead of the
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# default `gui.py` (Gradio). The REST surface is what we'll point
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# clients + automation at; Gradio is dev-time eye candy.
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#
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# Image is built locally from upstream's repo via docker buildx git
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# context, same pattern as fish-s2.
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#
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# All tunables live in .env — edit that, not this file.
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services:
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ace-step:
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image: local/ace-step:${ACE_STEP_TAG}
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build:
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# Build from local Dockerfile (not upstream's git context) — we
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# ship a patched Dockerfile that fixes upstream's torch/cu126
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# resolution bug. Playbook uploads Dockerfile alongside this
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# compose.yaml.
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context: .
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dockerfile: Dockerfile
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args:
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ACE_STEP_REF: ${ACE_STEP_SHA}
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container_name: ace-step
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restart: unless-stopped
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runtime: nvidia
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ports:
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# Container default for infer-api.py is 8000 (hardcoded
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# uvicorn.run(host=0.0.0.0, port=8000) — no flags). Map host
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# ACE_STEP_PORT to it.
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- "${ACE_STEP_BIND:-0.0.0.0}:${ACE_STEP_PORT}:8000"
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environment:
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- NVIDIA_VISIBLE_DEVICES=${ACE_STEP_GPU_DEVICES:-1}
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# ACE_OUTPUT_DIR is read by acestep at generation time — keep
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# in sync with the bind mount below.
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- ACE_OUTPUT_DIR=/app/outputs
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# HF_HOME points the HuggingFace cache at the bind mount so the
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# ~5-10 GB checkpoint download survives container recreates.
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- HF_HOME=/app/hf_cache
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volumes:
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# ACE-Step has a HARDCODED checkpoint cache at
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# /root/.cache/ace-step/checkpoints — not honored by HF_HOME.
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# Mount our persistent dir there so model re-pull doesn't
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# happen on every container recreate.
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- ${ACE_STEP_CHECKPOINTS_DIR}:/root/.cache/ace-step/checkpoints
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- ${ACE_STEP_OUTPUTS_DIR}:/app/outputs
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- ${ACE_STEP_LOGS_DIR}:/app/logs
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- ${ACE_STEP_CACHE_DIR}:/app/hf_cache
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# Override upstream's default `python3 acestep/gui.py` with the
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# REST API entry point. infer-api.py self-binds 0.0.0.0:8000 and
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# exposes POST /generate + GET /health.
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command: ["python3", "infer-api.py"]
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healthcheck:
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# /health is the cheapest signal infer-api.py exposes — returns
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# 200 as soon as the FastAPI app is up. The pipeline lazy-loads
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# on first /generate, so /health says "process alive" not
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# "model warm". Good enough for a liveness signal; first
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# /generate has the ~30-60 s warmup baked in.
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test: ["CMD-SHELL", "python3 -c \"import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=5).status==200 else 1)\""]
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interval: 30s
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timeout: 10s
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retries: 3
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# First boot pulls ACE-Step checkpoint (~5-10 GB) into HF cache.
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start_period: 600s
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labels:
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- homepage.group=AI Systems
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- homepage.name=ACE-Step
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- homepage.icon=mdi-music-note-eighth
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- homepage.description=Open-source music generation — 4-min song in ~60s, lyrics + style prompts (irv-ml1)
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- homepage.href=http://10.100.79.3:${ACE_STEP_PORT}
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