vllm-omni/recipes/StabilityAI/Stable-Audio-Open.md

Stable-Audio-Open Text-To-Audio Generation on 1x GPU

Text-to-audio recipe for Stable Audio Open with offline inference and OpenAI-compatible online serving on 1x RTX 4090 24GB.

Summary

• Vendor: Stability AI
• Model: stabilityai/stable-audio-open-1.0
• Task: Text-to-audio generation
• Mode: Offline inference and online serving
• Maintainer: Community

When to use this recipe

Use this recipe when you want to run Stable Audio Open on a single RTX 4090 24GB GPU for music or sound-effect generation. The recipe covers a 10-second offline validation sample with TeaCache and online serving through the /v1/audio/generate endpoint.

References

• Model: https://huggingface.co/stabilityai/stable-audio-open-1.0
• Offline example: examples/offline_inference/text_to_audio
• Audio generation API: docs/serving/audio_generate_api.md
• Related issue: #2645

Hardware Support

GPU

1x RTX 4090 24GB

Environment

• OS: Ubuntu 22.04.5
• Python: 3.12
• GPU: NVIDIA GeForce RTX 4090, 24564 MiB VRAM
• Driver / runtime: NVIDIA driver 595.80, CUDA-capable runtime matching the repository build
• vLLM version: 0.22.0
• vLLM-Omni version: source checkout
• PyTorch: 2.11.0+cu130
• Model path used in the commands below: /path/to/stable-audio-open-1.0

Stable Audio Open is a gated Hugging Face model. Accept the model license on the Hugging Face model card before downloading the checkpoint.

hf auth login

hf download stabilityai/stable-audio-open-1.0 \
  --local-dir /path/to/stable-audio-open-1.0

Commands

Run a 10-second offline validation sample from the repository root:

python examples/offline_inference/text_to_audio/text_to_audio.py \
  --model /path/to/stable-audio-open-1.0 \
  --prompt "A gentle piano melody with soft room ambience" \
  --negative-prompt "Low quality, distorted, noisy" \
  --seed 42 \
  --guidance-scale 7.0 \
  --audio-length 10.0 \
  --num-inference-steps 50 \
  --cache-backend tea_cache \
  --output examples/offline_inference/text_to_audio/stable_audio_10s.wav

Start the online serving endpoint:

vllm serve /path/to/stable-audio-open-1.0 \
  --host 0.0.0.0 \
  --port 8091 \
  --gpu-memory-utilization 0.9 \
  --trust-remote-code \
  --enforce-eager \
  --omni

Generate a 10-second WAV file from the repository root in another terminal:

curl http://localhost:8091/health

curl -X POST http://localhost:8091/v1/audio/generate \
  -H "Content-Type: application/json" \
  -d '{
    "input": "A gentle piano melody with soft room ambience",
    "audio_length": 10.0,
    "num_inference_steps": 50,
    "guidance_scale": 7.0,
    "negative_prompt": "Low quality, distorted, noisy",
    "seed": 42,
    "response_format": "wav"
  }' \
  --output piano_10s.wav

Verification

Check that:

• The offline command writes a valid WAV file.
• The server responds on http://localhost:8091/health.
• The online request writes a valid WAV file.
• The generated audio sample rate is 44.1 kHz.
• The generated duration is approximately 10 seconds.
• Peak sampled GPU memory is within the 24GB RTX 4090 budget. In the validated run, offline and online generation each peaked at about 12.6 GiB.

Validate the offline outputs:

ls -lh examples/offline_inference/text_to_audio/stable_audio_10s.wav

python - <<'PY'
import soundfile as sf

path = "examples/offline_inference/text_to_audio/stable_audio_10s.wav"
audio, sample_rate = sf.read(path)
print("sample_rate:", sample_rate)
print("shape:", audio.shape)
print("duration:", len(audio) / sample_rate)
PY

Validate the online output:

ls -lh piano_10s.wav

python - <<'PY'
import soundfile as sf

path = "piano_10s.wav"
audio, sample_rate = sf.read(path)
print("sample_rate:", sample_rate)
print("shape:", audio.shape)
print("duration:", len(audio) / sample_rate)
PY

Notes

• stable-audio-open-1.0 can generate up to about 47 seconds of 44.1 kHz stereo audio. This recipe validates 10-second WAV outputs.
• --cache-backend tea_cache is supported and was used for the 10-second offline validation command.
• The model is gated on Hugging Face and requires license acceptance before download.
• If online serving fails while importing torchaudio, make sure the torchaudio wheel matches the installed PyTorch and CUDA build. The validated environment used torch==2.11.0+cu130 and torchaudio==2.11.0+cu130.
• The NIXL is not available, GLOO_SOCKET_IFNAME, and torchsde boundary warnings observed during validation did not prevent successful generation.
• The RTX 4090 entry was validated on one 24 GB GPU. The MI300X entry below covers one 192 GB GPU.
• Long generations, higher inference-step counts, and non-WAV response formats were not benchmarked in this recipe.

1x AMD MI300X 192GB

Environment

• OS: Linux 6.8.0-134-generic, x86_64
• Container: official ROCm image built from docker/Dockerfile.rocm
• Python: 3.12.13
• PyTorch: 2.11.0+gitd0c8b1f
• Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
• GPU: one AMD Instinct MI300X, gfx942:sramecc+:xnack-, 191.69 GiB visible HBM
• vLLM version: 0.27.0+rocm723
• vLLM Omni version or commit: 73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0
• Installed vLLM Omni package metadata: 0.27.0rc2.dev44+g55abdade9.rocm

Commands

python3 examples/offline_inference/text_to_audio/text_to_audio.py \
    --model stabilityai/stable-audio-open-1.0 \
    --prompt "A gentle piano melody with soft room ambience" \
    --negative-prompt "Low quality, distorted, noisy" \
    --seed 42 \
    --guidance-scale 7.0 \
    --audio-length 10.0 \
    --num-inference-steps 50 \
    --cache-backend tea_cache \
    --enable-diffusion-pipeline-profiler \
    --output stable_audio_10s.wav

Verification

The command completed and wrote a valid 44.1 kHz stereo WAV with 10.00 seconds of audio.

Notes

• TeaCache ran with rel_l1_thresh=0.2.
• Model loading used 2.7891 GiB and took 3.706 seconds.
• Generation took 4.750 seconds, which gives a real time factor of 0.475 for the 10.00 second output.
• The internal profiler recorded 15.65 GB reserved and 9.68 GB allocated for the request.
• The highest one second whole device memory sample was 19.61 GiB.
• The output RMS was 0.0887, and its peak absolute amplitude was 0.5761.
• The full process took 384 seconds, including startup and compilation.