This is my live (to the most extent) coding video, where I implement from a scratch a diffusion model that generates 32 x 32 RGB images. The tutorial assumes a basic knowledge of deep learning and Python.

Links:
The Jupiter notebook built in this video: https://github.com/dtransposed/code_v...
My website: https://dtransposed.github.io
My Twitter:   / dtransposed  

Sources:
Lil' Log What are Diffusion Models: https://lilianweng.github.io/posts/20...
Understanding Diffusion Models: A Unified Perspective: https://arxiv.org/abs/2208.11970
Denoising Diffusion Probabilistic Models: https://arxiv.org/abs/2006.11239

Timestamps:
0:00 Introduction
0:32 Theoretical background
13:13 Live Coding Forward diffusion
41:29 Live Coding Training loop
1:00:05 Live Coding Overfitting one batch
1:03:36 Live Coding Reverse diffusion
1:13:40 Live Coding Training on CIFAR 10 dataset
1:17:24 Live Coding Result evaluation
1:19:40 (Bonus) Quick explanation of the UNet architecture used in the tutorial