Diffusion Model Paper Walkthrough: The Art of Noise

Understanding and implementing diffusion model from scratch with PyTorch

Photo by Jr Korpa on Unsplash

Introduction

In my last several articles I talked about generative deep learning algorithms, which mostly are related to text generation tasks. So, I think it would be interesting to switch to generative algorithms for image generation now. We knew that nowadays there have been plenty of deep learning models specialized for generating images out there, such as Autoencoder, Variational Autoencoder (VAE), Generative Adversarial Network (GAN) and Neural Style Transfer (NST). I actually got some of my writings about these topics posted on Medium as well. I provide you the links at the end of this article if you want to read them.

In today’s article, I would like to discuss the so-called diffusion model — one of the most impactful models in the field of deep learning for image generation. The idea of this algorithm was first proposed in the paper titled Deep Unsupervised Learning using Nonequilibrium Thermodynamics written by Sohl-Dickstein et al. back in 2015 [1]. Their framework was then developed further by Ho et al. in 2020 in their paper titled Denoising Diffusion Probabilistic Models [2]. DDPM was later adapted by OpenAI and Google to develop DALLE-2 and Imagen, which we knew…