Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Image Generation Using Diffusion

We learned how to generate images using GANs in Chapter 9, Deep Convolutional GANs. In this chapter, we will explore a more recent approach to generating images – by using diffusion. The idea behind diffusion, just like all great ideas, is pretty simple and intuitive. We will first understand how diffusion works. We will then use PyTorch to train a diffusion model from scratch to generate realistic images. We will then further our understanding of diffusion for generating images from text. Finally, we will generate some high-quality, realistic images from text using a pre-trained diffusion model with the help of PyTorch and Hugging Face.

By the end of this chapter, you will understand the core idea behind most of the cutting-edge generative AI models in computer vision, such as Stable Diffusion, DALL-E, Imagen, Midjourney, and so on. You will learn how to train a diffusion model from scratch using PyTorch, and how to use cutting-edge diffusion...