Coding Stable Diffusion from scratch in PyTorch

Full coding of Stable Diffusion from scratch, with full explanation, including explanation of the mathematics. Visual explanation of text-to-image, image-to-image, inpainting

Repository with PDF slides: https://github.com/hkproj/pytorch-stable-diffusion

Prerequisites:
1) Transformer explained: https://www.youtube.com/watch?v=bCz4OMemCcA

Chapters
00:00:00 – Introduction
00:04:30 – What is Stable Diffusion?
00:05:40 – Generative Models
00:12:07 – Forward and Reverse Process
00:17:44 – ELBO and Loss
00:20:30 – Generating New Data
00:22:20 – Classifier-Free Guidance
00:31:00 – CLIP
00:33:20 – Variational Auto Encoder
00:37:26 – Text to Image
00:39:54 – Image to Image
00:41:40 – Inpainting
00:44:30 – Coding the VAE
01:54:50 – Coding CLIP
02:09:10 – Coding the Unet
03:04:40 – Coding the Pipeline
03:53:00 – Coding the Scheduler (DDPM)
04:38:00 – Coding the Inference code