On Fast Sampling of Diffusion Probabilistic Models
This work addresses the sampling speed issue in diffusion models for practitioners, but it is incremental as it builds on existing methods.
The authors tackled the problem of slow sampling in diffusion probabilistic models by proposing FastDPM, a unified framework that generalizes previous methods and leads to new algorithms with improved sample quality, as demonstrated through systematic investigations across various domains and datasets.
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.