Papers › Long-form music generation with latent diffusion
Long-form music generation with latent diffusion
Zach Evans, Julian D. Parker, CJ Carr, Zack Zukowski, Josiah Taylor, Jordi Pons
Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure from text prompts. We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transformer operating on a highly downsampled continuous latent representation (latent rate of 21.5Hz). It obtains state-of-the-art generations according to metrics on audio quality and prompt alignment, and subjective tests reveal that it produces full-length music with coherent structure.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Generation | AudioCaps | Stable Audio 2.0 | FD_openl3 | 110.62 | #7 of 23 | Archive leaderboard | report |
| Audio Generation | AudioCaps | Stable Audio 2.0 | KL_passt | 2.70 | #7 of 23 | Archive leaderboard | report |
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