Papers › On Fast Sampling of Diffusion Probabilistic Models

On Fast Sampling of Diffusion Probabilistic Models

31 May 2021ICML Workshop INNF 2021 7arXiv:2106.00132archive 2025-07-28

Zhifeng Kong, Wei Ping

In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the fast sampling methods under this framework across different domains, on different datasets, and with different amount of conditional information provided for generation. We find the performance of a particular method depends on data domains (e.g., image or audio), the trade-off between sampling speed and sample quality, and the amount of conditional information. We further provide insights and recipes on the choice of methods for practitioners.

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