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

About the Author :
Omar Sanseviero was the Chief Llama Officer and Head of Platform and Community at Hugging Face, leading the developer advocacy engineering, on-device, and moonshot teams. Omar has extensive engineering experience working at Google in Google Assistant and TensorFlow Graphics. Omar's work at Hugging Face was at the intersection of open source, product, research, and technical communities. Pedro Cuenca is a Machine Learning Engineer at Hugging Face working on diffusion software, models, and applications. He has 20+ years of software development experience in fields like Internet applications (in Spain, he helped create the first interactive educational portal, the first book store, and the first free ISP) and, more recently, iOS. As a co-founder and CTO of LateNiteSoft, he worked on the technology behind Camera+, a successful iPhone photography app. He created deep-learning models for tasks such as photography enhancement and super-resolution. He was also involved in the development and operations behind dalle-mini. He brings a practical vision of integrating AI research into real-world services and the challenges and optimizations involved. Apolinário Passos is a Machine Learning Art Engineer at Hugging Face working across different teams on multiple machine learning for art and creativity use-cases. Apolinario has 10+ years of professional and artistic experience, alternating between holding art exhibitions, coding, and product management, having been a Head of Product in World Data Lab. Apolinario aims to ensure that the ML ecosystem supports and makes sense for artistic use cases. Jonathan Whitaker is a data scientist and deep learning researcher focused on generative modeling. He has previously worked on several courses related to the topics covered in this book, including the Hugging Face diffusion models class and Fast.AI's 'From Deep Learning Foundations to Stable Diffusion' which he co-created with Jeremy Howard in 2022. He has also applied these techniques in industry during his time as a consultant and now works full-time on AI research and development at Answer.AI.