Coding Stable Diffusion from scratch (By Umar)

[Simmed]

 

Prerequisites:
1) Transformer explained:    • Attention is all you need (Transformer) - ...  (see below)

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





Attention is all you need (Transformer) - Model explanation (including math), Inference and Training
00:00 - Intro
01:10 - RNN and their problems
08:04 - Transformer Model
09:02 - Maths background and notations
12:20 - Encoder (overview)
12:31 - Input Embeddings
15:04 - Positional Encoding
20:08 - Single Head Self-Attention
28:30 - Multi-Head Attention
35:39 - Query, Key, Value
37:55 - Layer Normalization
40:13 - Decoder (overview)
42:24 - Masked Multi-Head Attention
44:59 - Training
52:09 - Inference




additional bonus series
CS230 Deep learning (stanford)
Andrew Ng Adjunct Professor