Diffusion Models Explained: Step by Step

In this video, I break down the fundamentals of how diffusion models work, avoiding complex jargon and theories. Learn the basics of generative modeling, the process of organizing data features into a probability distribution, and how diffusion models help create meaningful images from noise. We'll also cover the forward and reverse diffusion processes, and how small, iterative steps of adding and removing noise make image generation possible. Some maths is involved to make learning a bit more concrete. 00:00 Intro 00:45 Understanding Generative Modeling 01:58 Diffusion Process and Training 05:30 Diffusion Models: Forward and Reverse Processes 09:32 Solving the conditional with Bayes 13:49 The conditional in Diffusion requires making an assumption but with on one condition 17:02 Loss function in a diffusion