Improving Diffusion Model Efficiency Through Patching

TL;DR

This paper introduces a patching transformation for diffusion models that significantly reduces sampling time and memory usage, supported by theoretical analysis and experiments on multiple datasets.

Contribution

The paper proposes a simple patching method to improve diffusion model efficiency, addressing the often overlooked per-iteration computational cost.

Findings

Sampling time reduced significantly

Memory usage decreased notably

Effective across multiple datasets

Abstract

Diffusion models are a powerful class of generative models that iteratively denoise samples to produce data. While many works have focused on the number of iterations in this sampling procedure, few have focused on the cost of each iteration. We find that adding a simple ViT-style patching transformation can considerably reduce a diffusion model's sampling time and memory usage. We justify our approach both through an analysis of the diffusion model objective, and through empirical experiments on LSUN Church, ImageNet 256, and FFHQ 1024. We provide implementations in Tensorflow and Pytorch.