On Fast Sampling of Diffusion Probabilistic Models

05/31/2021 ∙ by Zhifeng Kong, et al. ∙ 22

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 13

page 14

page 15

page 16

page 17

page 18

page 19

page 21

Code Repositories

FastDPM_pytorch

Official PyTorch implementation for FastDPM, a fast sampling algorithm for diffusion probabilistic models


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.