# Improving Diffusion Model Efficiency Through Patching

Source: [Improving Diffusion Model Efficiency Through Patching](<https://www.sota2.com/research/paper/68fa6cf51e209131cea36d86>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:22:36.594Z

Authors: Troy Luhman, Eric Luhman

Published: 2022-07-09T18:21:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.04316>)

[PDF](<https://arxiv.org/pdf/2207.04316.pdf>)