Fast Sampling of Diffusion Models with Exponential IntegratorDownload PDF

Published: 29 Nov 2022, Last Modified: 16 Jun 2024SBM 2022 PosterReaders: Everyone
Keywords: Fast sampling, diffusion model
Abstract: Our goal is to develop a fast sampling method for Diffusion models~(DMs) with a small number of steps while retaining high sample quality. To achieve this, we systematically analyze the sampling procedure in DMs and identify key factors that affect the sample quality, among which the method of discretization is most crucial. By carefully examining the learned diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS). It is based on the Exponential Integrator designed for discretizing ordinary differential equations (ODEs) and leverages a semilinear structure of the learned diffusion process to reduce the discretization error. The proposed method can be applied to any DMs and can generate high-fidelity samples in as few as 10 steps. By directly using pre-trained DMs, we achieve superior sampling performance when the number of score function evaluation~(NFE) is limited, e.g., 4.17 FID with 10 NFEs, 2.86 FID with only 20 NFEs on CIFAR10.
Student Paper: Yes
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