---
language:
- en
license: other
license_name: stable-audio-community
license_link: LICENSE.md
pipeline_tag: text-to-audio
base_model: stabilityai/stable-audio-3-medium
tags:
- audio-generation
- music
- sound-effects
- diffusion
- gguf
- sa3.cpp
---

# Stable Audio 3 Medium — GGUF (for sa3.cpp)

GGUF conversions of [stabilityai/stable-audio-3-medium](https://huggingface.co/stabilityai/stable-audio-3-medium)
for [**sa3.cpp**](https://github.com/betweentwomidnights/sa3.cpp) — a portable C++/GGML port of
Stable Audio 3, no PyTorch in the loop. Runs on CPU, CUDA, Vulkan, or Metal (Apple Silicon). Every component is validated
against the PyTorch reference at cosine similarity ~1.0.

## Files

This is a multi-file model. Grab the **DiT** + **SAME** at your chosen precision and the
**conditioner**, plus the shared **encoder + tokenizer** from the
[t5gemma-b-b-ul2-GGUF](https://huggingface.co/thepatch/t5gemma-b-b-ul2-GGUF) repo.

| component | file | notes |
|---|---|---|
| DiT (diffusion transformer) | `stable-audio-3-medium-dit-1.5B-v1.0-{F16,F32}.gguf` | pick one precision |
| autoencoder (SAME-L) | `stable-audio-3-medium-same-l-v1.0-{F16,F32}.gguf` | pick one precision |
| conditioner | `stable-audio-3-medium-conditioner-v1.0-F32.gguf` | tiny sidecar (prompt padding + seconds_total) |
| encoder + tokenizer | → [t5gemma-b-b-ul2-GGUF](https://huggingface.co/thepatch/t5gemma-b-b-ul2-GGUF) | **shared** across all SA3 variants |

**F16** is the production path (~3.5s for 12s of audio on an 8GB laptop GPU); **F32** is for CPU
validation. The conditioner + encoder + tokenizer stay F32 (small / quality-critical).

## Usage

For use with [**sa3.cpp**](https://github.com/betweentwomidnights/sa3.cpp):

```bash
# pip install huggingface_hub
python tools/download_models.py --variant medium --encoding f16   # fetches this set + the shared encoder

# --model resolves the 5 gguf files in ./models by name
sa3-generate --model medium --prompt "upbeat funk groove with slap bass" --out song.wav
```

## Performance

Roughly **3s for a 12s clip** at f16 on an 8GB laptop GPU (RTX 5070), and ~6s on an Apple M4 — end to end,
including model load. The sliding-window decoder keeps long generations linear (a 2-minute clip is ~9s on
the 5070). CPU works but is ~10× slower. Full numbers + the f16 / flash-attention levers:
[docs/BENCHMARKS.md](https://github.com/betweentwomidnights/sa3.cpp/blob/main/docs/BENCHMARKS.md).

## License

These are format conversions of [stabilityai/stable-audio-3-medium](https://huggingface.co/stabilityai/stable-audio-3-medium),
whose weights Stability AI releases under the [Stability AI Community License](https://stability.ai/license):
free for organizations under $1M annual revenue, with commercial use, fine-tuning, and derivative works
permitted within that threshold (above it, contact Stability AI for an Enterprise License). Outputs are yours.
That license carries over to these converted weights.

The upstream [stable-audio-3 source code](https://github.com/Stability-AI/stable-audio-3) is released
separately under MIT. Pair these with the shared T5Gemma text encoder, which is Google's under the
[Gemma Terms of Use](https://ai.google.dev/gemma/terms).

## Relationship to the original

**Format conversions** (weights → GGUF) for inference in sa3.cpp — no retraining, no architectural
changes. See [sa3.cpp/docs/DISTRIBUTION.md](https://github.com/betweentwomidnights/sa3.cpp/blob/main/docs/DISTRIBUTION.md)
for the naming convention and how the pieces fit together.