stabilityai/stable-audio-3-optimized

Stable Audio 3 Optimized

Note: This repository contains experimental checkpoints optimised for acceleration on specific hardware. For standard checkpoints, please use Stable Audio 3 Medium instead.

Please note: For commercial use, please refer to https://stability.ai/license

Model Description

Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline.

Usage

This model can be used with:

1. the stable-audio-3 inference and fine-tuning library
2. the stable-audio-tools research library

Using with stable-audio-3

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=180
)

Using with stable-audio-tools

import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond_inpaint

device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
  model_half = True

# Download model
model, model_config = get_pretrained_model("stabilityai/stable-audio-3-medium")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]

model = model.to(device)
if model_half:
  model = model.to(torch.float16)
# Set up text and timing conditioning
conditioning = [{
    "prompt": (
        "A dream-like Synthpop instrumental that would accompany "
        "a dream-sequence in a surrealist movie 120 BPM"
    ),
    "seconds_total": 380
}]

# Generate stereo audio
output = generate_diffusion_cond_inpaint(
    model,
    steps=8,
    cfg_scale=1.0,
    conditioning=conditioning,
    sample_size=sample_size,
    sampler_type="pingpong",
    device=device
)

# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")

# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save("output.wav", output, sample_rate)

Optimized CPU runtime (TFLite / LiteRT)

Portable CPU builds live in optimized/tflite. The SAME autoencoder ships as static rung .tflite codecs at tflite/same-{s,l}/{enc,dec}_{fp32,w8a8}.tflite — one weight-shared file per encoder/decoder that dispatches a fixed-size subgraph per length, keeping RAM flat and avoiding a chunk-size knob. They require ai_edge_litert>=2.2.0 and are driven by the RungEncoder / RungDecoder runtime in that directory (w8a8 is the default; fp32 is the bit-exact reference). The earlier dynamic-length codecs are preserved under tflite/same-*/legacy/.

Runtime LoRA on TensorRT (NVIDIA)

dit_fp16_lora.trt is a DiT with a runtime low-rank branch on each adapted linear. All three DiTs have one:

engine	DiT	targets	size
tensorRT/sm_90/sa3-m/dit_fp16_lora.trt	medium, 24 × 1536	229	2.96 GB
tensorRT/sm_90/sa3-sm-music/dit_fp16_lora.trt	small-music, 20 × 1024	193	0.95 GB
tensorRT/sm_90/sa3-sm-sfx/dit_fp16_lora.trt	small-sfx, 20 × 1024	193	0.95 GB

A TensorRT plan has its weights compiled in, so an adapter cannot be merged into it the way the CPU runtime does; these engines instead take the adapter as network inputs — A, Bt and a per-output-row rescale P are fed in like activations, with the rank as a dynamic dimension — and compute y = W₀·x·(1+P) + Btᵀ·(srow ⊙ (A·x)).

So loading an adapter is a buffer write rather than a rebuild: adapters swap in milliseconds, several stack by concatenating along the rank axis (up to a total of 512), and srow gives each one its own strength. P is what DoRA needs — its per-row magnitude rescale is a change to the base path that no low-rank branch can express.

The branch costs a flat few ms per step, so its relative price depends on which DiT it is attached to. On medium that is +13% at L=4096 and +37% at 1292; on the small DiTs the same absolute cost lands on a base step about 2.7× cheaper, so at stack rank 16 on an H200 it is +34% at L=4096, +60% at 1292 and +83% at 323. It is paid even with an empty stack — in fact rank 1 is the most expensive configuration, since TensorRT picks a worse tactic below rank 8 — so use dit_fp16.trt until an adapter is actually wanted. That matters more on the small DiTs than on medium.

Adapters are not portable between the three DiTs: different depth and width, so a medium adapter will not load against a small engine.

sm_90 only; other architectures build locally in ~5–8 min, since TensorRT bakes the GPU architecture into the plan. The runtime, the adapter row-norm baking step and the layer maps all live in optimized/tensorRT/scripts/lora — this repository holds only the compiled plans.

Model Details

• Model type: Stable Audio 3 is a latent diffusion model based on a transformer architecture.
• Language(s): English
• License: Stability AI Community License.
• Research Paper: https://arxiv.org/abs/2605.17991

We use a publicly available pre-trained T5Gemma model (t5gemma-b-b-ul2) for text conditioning. T5Gemma is redistributed under the Gemma Terms of Use.

Training dataset

Datasets Used

Our dataset consists of 1,278,902 audio recordings, where 806,284 recordings are licensed from AudioSparx and a further 472,618 are from Freesound. The Freesound portion consists of recordings licensed under CC-0, CC-BY, or CCSampling+. To ensure no copyrighted content was present in the Freesound data, music recordings were identified using the PANNs [89] tagger. We flagged audio that activated music-related tags for at least 30s (threshold of 0.15), that was sent to a trusted content detection company to verify the absence of copyrighted material. All identified copyrighted content was removed. After filtering, the Freesound part includes 266,324 CC-0, 194,840 CC-BY, and 11,454 CC-Sampling+ recordings. The same subset of Freesound audio we used to train Stable Audio Open: https://info.stability.ai/attributions.