thepatch/stable-audio-3-small-music-GGUF

Stable Audio 3 Small-Music — GGUF (for sa3.cpp)

GGUF conversions of stabilityai/stable-audio-3-small-music for 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). The small-music model is a lighter, faster music generator (SAME-S autoencoder, 0.5B DiT). Validated against the PyTorch reference at cosine similarity ~1.0.

Files

Multi-file model. Grab the DiT + SAME at your chosen precision and the conditioner, plus the shared encoder + tokenizer from t5gemma-b-b-ul2-GGUF.

component	file	notes
DiT (diffusion transformer)	stable-audio-3-small-music-dit-0.5B-v1.0-{F16,F32}.gguf	pick one precision
autoencoder (SAME-S)	stable-audio-3-small-music-same-s-v1.0-{F16,F32}.gguf	pick one precision
conditioner	stable-audio-3-small-music-conditioner-v1.0-F32.gguf	tiny sidecar (prompt padding + seconds_total)
encoder + tokenizer	→ t5gemma-b-b-ul2-GGUF	shared across all SA3 variants

note: SAME-S needs an even --frames count (the packed sequence must divide the chunk size).

Usage

For use with sa3.cpp:

python tools/download_models.py --variant small-music --encoding f16

# --model resolves the gguf set in ./models by name
sa3-generate --model small-music --prompt "lo-fi hip hop beat, warm vinyl, mellow keys" --out song.wav

Performance

Roughly 1.7s for a 12s clip at f16 on an 8GB laptop GPU (RTX 5070) — about 2× faster than the medium model. The sliding-window decoder keeps long generations linear. Full numbers + levers: docs/BENCHMARKS.md.

License

These are format conversions of stabilityai/stable-audio-3-small-music, whose weights Stability AI releases under the Stability AI Community 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 is released separately under MIT. Pair these with the shared T5Gemma text encoder, which is Google's under the Gemma Terms of Use.

Relationship to the original

Format conversions (weights → GGUF) for inference in sa3.cpp — no retraining. See sa3.cpp/docs/DISTRIBUTION.md.