Using Stable Diffusion with Python
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Using Stable Diffusion with Python

Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

About the Book

Stable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques.

You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction.

By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. Introducing Stable Diffusion
  2. Setting Up the Environment for Stable Diffusion
  3. Generating Images Using Stable Diffusion
  4. Understanding the Theory Behind Diffusion Models
  5. Understanding How Stable Diffusion Works
  6. Using Stable Diffusion Models
  7. Optimizing Performance and VRAM Usage
  8. Using Community-Shared LoRAs
  9. Using Textual Inversion
  10. Overcoming 77-Token Limitations and Enabling Prompt Weighting
  11. Image Restore and Super-Resolution
  12. Scheduled Prompt Parsing
  13. Generating Images with ControlNet
  14. Generating Video Using Stable Diffusion
  15. Generating Image Descriptions using BLIP-2 and LLaVA
  16. Exploring Stable Diffusion XL
  17. Building Optimized Prompts for Stable Diffusion
  18. Applications - Object Editing and Style Transferring
  19. Generation Data Persistence
  20. Creating Interactive User Interfaces
  21. Diffusion Model Transfer Learning
  22. Exploring Beyond Stable Diffusion

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