Multistep Distillation of Diffusion Models via Moment Matching
<|reference_start|>Multistep Distillation of Diffusion Models via Moment Matching: We present a new method for making diffusion models faster to sample. The method distills many-step diffusion models into few-step models by matching conditional expectations of the clean data given noisy data along the sampling trajectory. Our approach extends recently proposed one-step methods to the multi-step case, and provides a new perspective by interpreting these approaches in terms of moment matching. By using up to 8 sampling steps, we obtain distilled models that outperform not only their one-step versions but also their original many-step teacher models, obtaining new state-of-the-art results on the Imagenet dataset. We also show promising results on a large text-to-image model where we achieve fast generation of high resolution images directly in image space, without needing autoencoders or upsamplers.<|reference_end|>
@article{salimans2024multistep,
    title={Multistep Distillation of Diffusion Models via Moment Matching},
    author={Tim Salimans and Thomas Mensink and Jonathan Heek and Emiel Hoogeboom},
    journal={arXiv preprint arXiv:2406.04103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04103},
    primaryClass={cs.LG cs.AI cs.CV cs.NE}
}