论文标题
通过修补提高扩散模型效率
Improving Diffusion Model Efficiency Through Patching
论文作者
论文摘要
扩散模型是一类强大的生成模型类别，可以迭代地将样品迭代样品产生数据。尽管许多作品都集中在此抽样过程中的迭代次数上，但很少有人专注于每次迭代的成本。我们发现，添加简单的VIT风格的修补转换可以大大减少扩散模型的采样时间和内存使用情况。我们通过对扩散模型目标的分析以及通过对LSUN教堂，Imagenet 256和FFHQ 1024进行的经验实验来证明我们的方法是合理的。我们在Tensorflow和Pytorch中提供实现。
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.
