In recent years, Denoising Diffusion Probabilistic Models (DDPM) have caught
significant attention. By composing a Markovian process that starts in the data
domain and then gradually adds noise until reaching pure white noise, they
achieve superior performance in learning data distributions. Yet, these models
require a large number of diffusion steps to produce aesthetically pleasing
samples, which is inefficient. In addition, unlike common generative
adversarial networks, the latent space of diffusion models is not
interpretable. In this work, we propose to generalize the denoising diffusion
process into an Upsampling Diffusion Probabilistic Model (UDPM), in which we
reduce the latent variable dimension in addition to the traditional noise level
addition. As a result, we are able to sample images of size $256\times 256$
with only 7 diffusion steps, which is less than two orders of magnitude
compared to standard DDPMs. We formally develop the Markovian diffusion
processes of the UDPM, and demonstrate its generation capabilities on the
popular FFHQ, LSUN horses, ImageNet, and AFHQv2 datasets. Another favorable
property of UDPM is that it is very easy to interpolate its latent space, which
is not the case with standard diffusion models. Our code is available online
https://github.com/shadyabh/UDPM

本文介绍了一种基于 Markovian 过程的 Upsampling Diffusion Probabilistic Model（UDPM），相较于传统的 Denoising Diffusion Probabilistic Models（DDPM），它在降低潜变量维度的同时，仅需 7 个扩散步骤即可生成 $256	imes 256$ 的高清图像。