  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.
