Stable Audio 3

A state-of-the-art open platform for fast, high-quality generated audio and music.

Technical Report · 🤗 Models · 🤗 Extra Models · Discord · Demo · Blog Post

Stable Audio 3 is the next generation of Stable Audio: a focused, streamlined platform for inference and fine-tuning, built on lessons from stable-audio-tools. If you're doing foundational research or working with previous Stable Audio models, that repo is still the place to go.

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Models

Model	Model ID	Autoencoder	Hardware	Params	Max length	Use case
Stable Audio 3 Small-Music	small-music	SAME-Small	CPU	433M	120s	Lightweight music-only inference, no GPU required
Stable Audio 3 Small-SFX	small-sfx	SAME-Small	CPU	433M	120s	Lightweight sound effects-only inference, no GPU required
Stable Audio 3 Medium	medium	SAME-Large	GPU (CUDA)	1.4B	380s	High Quality, Fast Inference
Stable Audio 3 Large	—	SAME-Large	API only	2.7B	380s	Highest quality, API only. Not supported by this repo, see the API docs

Base (un-post-trained) checkpoints, the SAME autoencoders, and optimized variants are available in the Extra Models collection.

Performance

Model	Duration	H200	H200 + TensorRT	Mac CPU*	Mac CoreML	Peak VRAM†
small	5s	0.41s	0.017s	0.70s	0.23s	1.69 GB
small	30s	0.46s	0.022s	1.72s	0.63s	1.89 GB
small	120s	0.45s	0.044s	5.92s	3.09s	2.40 GB
medium	5s	0.60s	0.02s	–	–	5.07 GB
medium	30s	0.65s	0.05s	–	–	5.49 GB
medium	120s	0.78s	0.13s	–	–	6.49 GB
medium	380s	1.31s	0.43s	–	–	6.52 GB

* CPU-only via CoreML (Diffusion Transformer) + TFLite (SAME-S decoder) † Peak allocated VRAM on H200, unchunked decode. Chunked decoding reduces this — e.g. medium at 120s drops from 6.49 GB to ~5.14 GB.

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Features

• ⚡ Fast, state-of-the-art generation - Generate minutes of audio in milliseconds
• 🎛️ Three inference modes — text-to-audio, audio-to-audio editing, and inpainting/continuation
• ↔️ Variable-length generation — handles generation of a variety of sequences without wasting inference time and VRAM on unused latents
• 🎯 Personalization through LoRA fine-tuning — adapt any model to a target style; stackable, adjustable at runtime
• 💻 Broad hardware support — CPU (Small), CUDA/TensorRT (Medium), Apple Silicon via CoreML, Others coming soon
• 🎵 SAME autoencoder — new Semantic-Acoustic Music Encoder; stereo, 44.1 kHz, 256-dimensional latents optimized for both generative tractability and high-quality reconstruction

Installation

Stable Audio 3 uses uv for fast, lightweight installs. Install only what you need.

# Base install (Python API only)
uv sync

# With Gradio UI
uv sync --extra ui

# With LoRA training support
uv sync --extra lora

# Everything
uv sync --extra ui --extra lora

CUDA Version

By default, uv sync installs PyTorch built against CUDA 12.6. If you need a different CUDA version, install torch and torchaudio manually first (pinning the same version as pyproject.toml), then sync without reinstalling them, for example:

uv pip install torch==2.7.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu118
uv sync --no-install-package torch --no-install-package torchaudio

Replace cu118 with your target version. For torch 2.7.1, available CUDA variants are cu118, cu126, and cu128. Not all versions are published for every CUDA channel — check the PyTorch install page to confirm your target is available.

Flash Attention

Stable Audio 3 Medium requires Flash Attention 2.

Install from a pre-built wheel (fast, no compilation). The easiest source is the flash-attention-prebuild-wheels community repo — browse the releases for a wheel matching your CUDA, PyTorch, and Python versions, then install it directly:

uv pip install https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.6.3+cu126torch2.7-cp310-cp310-linux_x86_64.whl

The filename encodes the requirements — cu126 is CUDA 12.6, torch2.7 is PyTorch 2.7, cp310 is Python 3.10. Pick the URL that matches your environment.

If no pre-built wheel matches your setup, build from source. Install ninja first to speed up the C++ compile, then set the environment variables for your machine:

uv pip install ninja
.venv/bin/python -m ensurepip
FLASH_ATTENTION_SKIP_CUDA_BUILD=FALSE \
FLASH_ATTENTION_FORCE_BUILD=TRUE \
TORCH_CUDA_ARCH_LIST="9.0" \
MAX_JOBS=8 \
.venv/bin/pip3 install flash-attn --no-build-isolation --no-binary flash-attn \
    --force-reinstall --no-cache-dir --no-deps

• TORCH_CUDA_ARCH_LIST — set to your GPU's compute capability: 8.0 (A100), 8.6 (A10/RTX 3090), 8.9 (L4/RTX 4090), 9.0 (H100/H200)
• MAX_JOBS — number of parallel compile jobs; 4–8 is typical, reduce if you run out of RAM during compilation

Note: flash-attn is not declared in pyproject.toml, so a plain uv sync will remove it. Use uv sync --inexact to install/update dependencies without removing packages that aren't in the lockfile:

uv sync --inexact

Quick Start

Launch the Gradio UI:

uv run python run_gradio.py --model medium

This starts a local web interface with a shareable link. To load a LoRA checkpoint:

uv run python run_gradio.py --model medium --lora-ckpt-path path/to/lora.ckpt

Usage

Stable Audio 3 supports several inference modes. For full details, see Inference Methods.

Text-to-Audio — Generate audio from a text prompt:

from stable_audio_3 import StableAudioModel

model = StableAudioModel.from_pretrained("medium")
audio = model.generate(
    prompt="House music that encapsulates the feeling of being at a festival in the sunny weather with all your friends 124 BPM",
    duration=250,
)

Audio-to-Audio — Edit an existing recording using a prompt to steer style and mood:

import torchaudio
from stable_audio_3 import StableAudioModel

model = StableAudioModel.from_pretrained("medium")
init_audio = torchaudio.load("/path/to/audio.wav")
audio = model.generate(
    init_audio=init_audio,
    init_noise_level=0.9,
    prompt="bossa nova bassline",
    duration=30,
)

Inpainting / Continuation — Regenerate a specific region of an audio file while keeping the rest intact:

import torchaudio
from stable_audio_3 import StableAudioModel

model = StableAudioModel.from_pretrained("medium")

inpaint_audio = torchaudio.load("/path/to/audio.wav")
audio = model.generate(
    inpaint_audio=inpaint_audio,
    inpaint_mask_start_seconds=4.0,
    inpaint_mask_end_seconds=8.0,
    prompt="punchy kick drum fill",
    duration=30,
)

To regenerate multiple non-contiguous regions in one pass, pass lists to both mask parameters:

audio = model.generate(
    inpaint_audio=inpaint_audio,
    inpaint_mask_start_seconds=[4.0, 16.0],
    inpaint_mask_end_seconds=[8.0, 20.0],
    prompt="punchy kick drum fill",
    duration=30,
)

To extend an audio clip (continuation), set inpaint_mask_start_seconds to the length of the source file and choose a longer duration. See Inference Methods for the full controls reference.

Encoding / Decoding — Use the autoencoder directly to encode audio to latents or decode latents back to audio:

import torchaudio
from stable_audio_3 import AutoencoderModel

ae = AutoencoderModel.from_pretrained("same-l")
waveform, sr = torchaudio.load("audio.wav")
latents = ae.encode(waveform, sr)
audio_out = ae.decode(latents)

See Autoencoder Workflows for encoding batches, chunked processing, and pre-encoding datasets for LoRA training.

CLI

A stable-audio cli is included for running generation without writing any Python.

Text-to-audio:

stable-audio --model small-music -p "lo-fi hip hop beat, 90 BPM" --duration 30 -o beat.wav

Audio-to-audio — restyle an existing recording:

stable-audio -p "bossa nova bassline" --init-audio input.wav --init-noise-level 0.8 -o out.wav

Inpainting — regenerate a region while keeping the rest:

stable-audio -p "punchy kick drum fill" --inpaint-audio input.wav --inpaint-start 4 --inpaint-end 8 -o out.wav

Continuation — extend a clip beyond its original length:

stable-audio -p "dreamy synth outro" --inpaint-audio input.wav --inpaint-start 10 --inpaint-end 30 --duration 30 -o out.wav

With a LoRA:

stable-audio -p "orchestral strings" --lora-ckpt-path my_lora.safetensors --lora-strength 0.8 -o out.wav

Run stable-audio --help for the full list of flags.

Hardware Support

Stable Audio 3 scales from a laptop to a GPU server.

Hardware Support Scripts COMING SOON

Docs

Guide	Description
Inference Methods	Overview of inference modes (text-to-audio, inpainting, etc.)
LoRA Training	Fine-tune with LoRA: setup, training loop, and checkpointing
Autoencoder Workflows	Encode and decode audio with the VAE directly
Prompting Guide	Prompt and control signal reference
Model Overview	Architecture and design overview

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Community

• Harmonai Discord: Check out our Harmonai Discord server run by the research team. Besides good discussions, we host weekly office hours talking all things AI audio and music and want to hear what you come up with!

• Underfit: A LoRA training poweruser dream from Dadabots. If LoRA training in this repo is not enough, check out some experimental tools there like agentic LoRA orchestrations and monitoring.

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Troubleshooting

Output audio is a static glitch sound (affects Stable Audio 3 Medium-only)

Likely an issue with flash-attention. Verify it is importable:

uv run python -c "import flash_attn; from flash_attn import flash_attn_func; print('Version:', flash_attn.__version__, '| flash_attn_func:', flash_attn_func)"

If this errors, flash-attn is not installed correctly — see the Flash Attention install instructions above.

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License

Please refer to the Stability AI Community License

Testing

Install dev dependencies:

uv sync --group dev

Run the test suite:

uv run pytest

Save generated audio outputs to test_audio_outputs/ for manual inspection:

uv run pytest --save-audio

Citation

For Stable Audio 3, please cite

@misc{evans2026stableaudio3,
  title={Stable Audio 3},
  author={Zach Evans and Julian D. Parker and Matthew Rice and CJ Carr and Zack Zukowski and Josiah Taylor and Jordi Pons},
  year={2026},
  eprint={2605.17991},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2605.17991}
}

For SAME, please cite

@misc{parker2026SAME,
  title={SAME: A Semantically-Aligned Music Autoencoder},
  author={Julian D. Parker and Zach Evans and CJ Carr and Zack Zukowski and Josiah Taylor and Matthew Rice and Jordi Pons},
  year={2026},
  eprint={2605.18613},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2605.18613}
}