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 Copyright Table of Contents Preface Who Should Read This Book Prerequisites What You Will Learn How to Read This Book Software and Hardware Requirements Conventions Used in This Book Using Code Examples How to Contact Us State of the Art: A Moving Target Acknowledgments Jonathan Apolinário Pedro Omar Part I. Leveraging Open Models Chapter 1. An Introduction to Generative Media Generating Images Generating Text Generating Sound Clips Ethical and Societal Implications Where We’ve Been and Where Things Stand How Are Generative AI Models Created? Summary Chapter 2. Transformers A Language Model in Action Tokenizing Text Predicting Probabilities Generating Text Zero-Shot Generalization Few-Shot Generalization A Transformer Block Transformer Model Genealogy Sequence-to-Sequence Tasks Encoder-Only Models The Power of Pretraining Transformers Recap Limitations Beyond Text Project Time: Using LMs to Generate Text Summary Exercises Challenges References Chapter 3. Compressing and Representing Information AutoEncoders Preparing the Data Modeling the Encoder Decoder Training Exploring the Latent Space Visualizing the Latent Space Variational AutoEncoders VAE Encoders and Decoders Sampling from the Encoder Distribution Training the VAE VAEs for Generative Modeling CLIP Contrastive Loss Using CLIP, Step-by-Step Zero-Shot Image Classification with CLIP Zero-Shot Image-Classification Pipeline CLIP Use Cases Alternatives to CLIP Project Time: Semantic Image Search Summary Exercises Challenges References Chapter 4. Diffusion Models The Key Insight: Iterative Refinement Training a Diffusion Model The Data Adding Noise The UNet Training Sampling Evaluation In Depth: Noise Schedules Why Add Noise? Starting Simple The Math Effect of Input Resolution and Scaling In Depth: UNets and Alternatives A Simple UNet Improving the UNet Alternative Architectures In Depth: Diffusion Objectives Project Time: Train Your Diffusion Model Summary Exercises Challenges References Chapter 5. Stable Diffusion and Conditional Generation Adding Control: Conditional Diffusion Models Preparing the Data Creating a Class-Conditioned Model Training the Model Sampling Improving Efficiency: Latent Diffusion Stable Diffusion: Components in Depth The Text Encoder The Variational AutoEncoder The UNet Stable Diffusion XL FLUX, SD3, and Video Classifier-Free Guidance Putting It All Together: Annotated Sampling Loop Open Data, Open Models Challenges and the Sunset of LAION-5B Alternatives Fair and Commercial Use Project Time: Build an Interactive ML Demo with Gradio Summary Exercises Challenge References Part II. Transfer Learning for Generative Models Chapter 6. Fine-Tuning Language Models Classifying Text Identify a Dataset Define Which Model Type to Use Select a Good Base Model Preprocess the Dataset Define Evaluation Metrics Train the Model Still Relevant? Generating Text Picking the Right Generative Model Training a Generative Model Instructions A Quick Introduction to Adapters A Light Introduction to Quantization Putting It All Together A Deeper Dive into Evaluation Project Time: Retrieval-Augmented Generation Summary Exercises Challenge References Chapter 7. Fine-Tuning Stable Diffusion Full Stable Diffusion Fine-Tuning Preparing the Dataset Fine-Tuning the Model Inference DreamBooth Preparing the Dataset Prior Preservation DreamBoothing the Model Inference Training LoRAs Giving Stable Diffusion New Capabilities Inpainting Additional Inputs for Special Conditionings Project Time: Train an SDXL DreamBooth LoRA by Yourself Summary Exercises Challenge References Part III. Going Further Chapter 8. Creative Applications of Text-to-Image Models Image to Image Inpainting Prompt Weighting and Image Editing Prompt Weighting and Merging Editing Diffusion Images with Semantic Guidance Real Image Editing via Inversion Editing with LEDITS++ Real Image Editing via Instruction Fine-Tuning ControlNet Image Prompting and Image Variations Image Variations Image Prompting Project Time: Your Creative Canvas Summary Exercises References Chapter 9. Generating Audio Audio Data Waveforms Spectrograms Speech to Text with Transformer-Based Architectures Encoder-Based Techniques Encoder-Decoder Techniques From Model to Pipeline Evaluation From Text to Speech to Generative Audio Generating Audio with Sequence-to-Sequence Models Going Beyond Speech with Bark AudioLM and MusicLM AudioGen and MusicGen Audio Diffusion and Riffusion Dance Diffusion More on Diffusion Models for Generative Audio Evaluating Audio-Generation Systems What’s Next? Project Time: End-to-End Conversational System Summary Exercises Challenges References Chapter 10. Rapidly Advancing Areas in Generative AI Preference Optimization Long Contexts Mixture of Experts Optimizations and Quantizations Data One Model to Rule Them All Computer Vision 3D Computer Vision Video Generation Multimodality Community Appendix A. Open Source Tools The Hugging Face Stack Data Wrappers Local Inference Deployment Tools Appendix B. LLM Memory Requirements Inference Memory Requirements Training Memory Requirements Further Reading Appendix C. End-to-End Retrieval-Augmented Generation Processing the Data Embedding the Documents Retrieval Generation Production-Level RAG Index About the Authors Colophon