DDPM

Implementation really matters when it comes to training Diffusion Models. In this post, we will discuss some tircks, some problems I encountered, coding details and model architectures that avoid awkward failure of noise in noise out. Our goal is to generate satisfying samples from 2 common datasets: MNIST (easier) and CIFAR-10 (harder). References: The Annotated Diffusion Model Diffusers Lil’Log What are Diffusion Models? UNet Architecture UNet is a common architecture for image generation....

In this blog we will try to understand the member of diffusion family, DDPM. Given the common structure shared by variational autoencoders (VAEs) and diffusion models, we will first discuss some important content in VAE, and then introduce the DDPM. The blog will try to be self-contained and mathematically clear, but some prior knowledge of VAEs and generative models is assumed. References: VAE on Wikipedia Lil’Log What are Diffusion Models?...