Lesson 24: Deep Learning Foundations to Stable Diffusion

[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!

[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!

[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

[ybxjfmj]

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 ?