DITTO: Diffusion Inference-Time T-Optimization for Music Generation

Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan

DITTO: Diffusion Inference-Time T-Optimization for Music Generation: 21 upvotes on Hugging Face Daily Papers, #7 of 16 papers on 2024-01-23. Day-by-day upvote history.

We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/.

Paper page on Hugging Face · arXiv

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