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- W4286587899 abstract "Diffusion models have emerged as one of the most promising frameworks for deep generative modeling. In this work, we explore the potential of non-uniform diffusion models. We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows. We experimentally find that in the same or less training time, the multi-scale diffusion model achieves better FID score than the standard uniform diffusion model. More importantly, it generates samples $4.4$ times faster in $128times 128$ resolution. The speed-up is expected to be higher in higher resolutions where more scales are used. Moreover, we show that non-uniform diffusion leads to a novel estimator for the conditional score function which achieves on par performance with the state-of-the-art conditional denoising estimator. Our theoretical and experimental findings are accompanied by an open source library MSDiff which can facilitate further research of non-uniform diffusion models." @default.
- W4286587899 created "2022-07-22" @default.
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- W4286587899 date "2022-07-20" @default.
- W4286587899 modified "2023-09-29" @default.
- W4286587899 title "Non-Uniform Diffusion Models" @default.
- W4286587899 doi "https://doi.org/10.48550/arxiv.2207.09786" @default.
- W4286587899 hasPublicationYear "2022" @default.
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