Hands-On Generative AI with Transformers and Diffusion Models

 

Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains.

This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work.

Build and customize models that can generate text and images

Explore trade-offs between using a pretrained model and fine-tuning your own model

Create and utilize models that can generate, edit, and modify images in any style

Customize transformers and diffusion models for multiple creative purposes

Train models that can reflect your own unique style

Overview

Generative AI has revolutionized various domains, from creating high-quality images and videos to generating natural language text and even synthesizing music. This book dives into the core of generative AI, focusing on two prominent and widely-used model architectures:

Transformers: Models such as GPT, BERT, and T5, which are integral to natural language processing (NLP) tasks like text generation, summarization, and translation.

Diffusion Models: A newer paradigm powering image synthesis systems like DALL-E 2, Stable Diffusion, and MidJourney.

The book combines foundational theory with hands-on coding examples, enabling readers to build, fine-tune, and deploy generative AI systems effectively.

Key Features

Comprehensive Introduction to Generative AI:

The book begins with an accessible introduction to generative AI, exploring how these models work conceptually and their real-world applications.

Readers will gain a strong grasp of foundational concepts like sequence modeling, attention mechanisms, and generative pretraining.

Focus on Open-Source Tools:

The book leverages popular open-source libraries like Hugging Face Transformers and Diffusers.

Through detailed coding examples, readers learn to implement generative models using these libraries, reducing the complexity of building models from scratch.

Hands-On Applications:

Practical projects guide readers in generating content such as:

Text: Generating coherent and contextually relevant paragraphs, stories, and answers to questions.

Images: Creating and editing high-quality images using diffusion models.

Audio and Music: Generating or modifying audio content in creative and artistic ways.

The book also introduces techniques for training generative models to align with specific styles or preferences.

Customization and Fine-Tuning:

Readers learn how to fine-tune pre-trained models on custom datasets.

Techniques for adapting generative models to specific use cases, such as generating text in a professional tone or producing artwork in a particular style, are thoroughly explained.

Image and Text Manipulation:

The book explores advanced features like inpainting, which allows users to edit portions of images, and text-to-image synthesis, enabling readers to generate images from textual descriptions.

This hands-on approach teaches how to generate and modify creative content using practical tools.

Intuitive Theoretical Explanations:

While practical in focus, the book doesn’t shy away from explaining theoretical concepts like:

The transformer architecture (e.g., self-attention mechanisms).

How diffusion models progressively denoise random inputs to create images.

The role of latent spaces in generative tasks.

Target Audience:

The book is ideal for data scientists, software engineers, and AI practitioners who wish to explore generative AI.

It caters to professionals with a basic understanding of Python and machine learning who want to advance their skills in generative modeling.

Real-World Relevance:

Practical examples demonstrate how generative AI is applied in industries such as entertainment, healthcare, marketing, and gaming.

Case studies highlight real-world challenges and how to address them with generative AI.

Guided Exercises:

Throughout the book, readers will encounter step-by-step exercises and projects that reinforce the concepts learned.

These exercises are designed to ensure that readers can confidently implement and adapt generative AI models for their unique requirements.

Learning Outcomes

By the end of the book, readers will be able to:

• Understand the principles and mechanics behind transformers and diffusion models.
• Build and fine-tune generative AI models using open-source tools.
• Generate text, images, and other media using practical techniques.
• Customize models for specific tasks and evaluate their performance.

Who Should Read This Book?

AI enthusiasts aiming to break into the world of generative AI.

Professionals seeking to incorporate generative AI into their workflows.

Students and researchers interested in exploring cutting-edge AI technologies

Deploy generative AI systems in real-world applications.

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