Lesson 24: Deep Learning Foundations to Stable Diffusion

(All lesson resources are available at course.fast.ai.) In this lesson, we wrap up our exploration of the unconditional stable diffusion model. We then implement the unconditional model, train it on fashion MNIST, and discuss the importance of time embedding. We also dive into sine and cosine embeddings, attention mechanisms, self-attention, and multi-headed attention in the context of stable diffusion. We discuss the `rearrange` function, transformers, and their potential use in vision tasks. Lastly, we create a conditional model by adding a label to the input of the UNet model, allowing it to generate images of a specific class.

[@kashifsiddhu]

00:04 Creating a unit based on diffusion
02:31 Introduction to Preact Resnet block
07:07 Explanation of saved res blocks and saved convolution
09:46 Mix-ins are used to create units combining functionalities.
14:55 Stable Diffusion unit has varying channels for filters in resnet blocks
17:08 The model consists of sequential down blocks with down sampling and a middle res block.
21:21 Vector embeddings represent each time step.
23:14 Exponential calculation in deep learning
27:36 Explanation of Time Step Embedding and Sine Waves
29:50 Configuring parameters for deep learning model
33:50 Activation Function Celia in Deep Learning
36:01 Utilizing a new approach for the block implementation in deep learning
40:09 Unit model with time embeddings for deep learning
42:28 Activation functions and batch norms are essential for training deep learning models.
46:31 Introduction to attention for building Transformers
48:41 Attention in diffusion models may not always improve performance
53:04 Flattening out pixels for stable diffusion
55:22 Overview of Pixel Attention Calculations
1:00:27 Introduction to different projections in self-attention
1:02:33 Initializing V NH projection to have a mean of zero for stable diffusion.
1:06:34 Implementing self-attention and normalization in deep learning.
1:09:03 Self-attention module with X Plus addition
1:13:23 Multi-headed attention simplifies information gathering
1:15:46 Softmax tends to focus on one thing very strongly
1:20:01 Understanding how to rearrange dimensions in deep learning models
1:22:20 Understanding Ionops Rearrange for Tensor Manipulation
1:26:42 Using JAX Inops for Efficient Operations
1:28:39 Exploring different attention mechanisms in AI diffusion
1:33:10 Adding attention in deep learning models
1:35:26 Adding attention to a network requires finding the right balance for optimal performance.
1:39:19 Transformer Network is a sequential of Transformers
1:41:30 Transformer network mixes pixels, channels, and sequences for flexibility
1:46:15 Transformers and diffusion require pre-training on large datasets
1:48:28 Introduction to Conditional Models in Deep Learning
1:52:49 Embedding model for conditional sampling
1:55:07 Introduction to latent diffusion in the context of deep learning foundations
Crafted by Merlin AI.

[@PaulScotti]

Best explanation I've come across so far for how attention works!

[@howardjeremyp]

Great to hear!

[@maxim_ml]

Hm, I wonder why Jeremy thinks Key-Query-Value is not a fitting metaphor. It sure made it easy for me to understand how self-attention works when I first heard it

[@franckalbinet]

I had several eureka moments in this video but the greatest jubilation was felt when Jonathan rationalised the use of multi-head attention as a way to mitigate the "masking" effect of the softmax layer at 1:15:33 -:) There are so many gems in this series! Thank you all!

[@MrCurryBadger]

Thanks for this Easter gift!

[@howardjeremyp]

You are so welcome!

[@pranayreddy9041]

This is a gold mine

[@SamiRaza-n9o]

The content of this course is very good but i am unable to find the notebook discussed in this lesson. Where can i find it ?

[@seikatsu_ki]

Great people! thank you guys.

[@felipe5195]

amazing stuff!

[@howardjeremyp]

Glad you think so!

[@satirthapaulshyam7769]

Preact conv 2:30