Lesson 24: Deep Learning Foundations to Stable Diffusion

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Жыл бұрын

(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.

Пікірлер: 13

@PaulScotti Жыл бұрын

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

@howardjeremyp Жыл бұрын

Great to hear!

@kashifsiddhu 3 ай бұрын

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.

@franckalbinet Жыл бұрын

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 10 ай бұрын

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