  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/.
