  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.
