Stable Audio 3

Stable Audio 3

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

[Technical Report](https://arxiv.org/abs/2605.17991) · [🤗 Models](https://huggingface.co/collections/stabilityai/stable-audio-3) · [🤗 Extra Models](https://huggingface.co/collections/stabilityai/stable-audio-3-extra) · [Discord](https://discord.gg/cKpvjey8b) · [Demo](https://huggingface.co/spaces/stabilityai/stable-audio-3) · [Blog Post](https://stability.ai/news-updates/meet-stable-audio-3-the-model-family-built-for-artistic-experimentation-with-open-weight-models)


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](https://github.com/Stability-AI/stable-audio-tools). If you're doing foundational research or working with previous Stable Audio models, that repo is still the place to go.

---

Models

Base (un-post-trained) checkpoints, the SAME autoencoders, and optimized variants are available in the [Extra Models collection](https://huggingface.co/collections/stabilityai/stable-audio-3-extra).

Performance

\* 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.

---

## 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](https://github.com/astral-sh/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](https://pytorch.org/get-started/locally/) to confirm your target is available.

Flash Attention

Stable Audio 3 Medium requires [Flash Attention 2](https://github.com/Dao-AILab/flash-attention).

Install from a pre-built wheel (fast, no compilation). The easiest source is the [flash-attention-prebuild-wheels](https://github.com/mjun0812/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](docs/workflows/inference.md).

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](docs/workflows/inference.md) 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](docs/workflows/autoencoder.md) 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

---

Community

- [Harmonai Discord](https://discord.gg/cKpvjey8b): 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](https://github.com/dada-bots/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.

---

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](#flash-attention) above.

---

License

Please refer to the [Stability AI Community License](https://stability.ai/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}
}