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...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
06.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2406.04103 |