Disclaimer: This post is my notes on understanding diffusion models from an intuitive perspective. It is not a formal explanation, and I might have made mistakes. Please reach out to me if you find any errors!

Introduction

The original paper was from UC Berkley and went by the name Denoising Diffusion Probabilistic Model.

The original idea might sound counter-intuitive, but it takes a random noise and transforms it into a realistic image step-by-step.

Does this work?