Diffusion Models - Live Coding Tutorial

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