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Using Stable Diffusion with Python

You're reading from   Using Stable Diffusion with Python Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781835086377
Length 352 pages
Edition 1st Edition
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Author (1):
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Andrew Zhu (Shudong Zhu) Andrew Zhu (Shudong Zhu)
Author Profile Icon Andrew Zhu (Shudong Zhu)
Andrew Zhu (Shudong Zhu)
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Table of Contents (29) Chapters Close

Preface 1. Part 1 – A Whirlwind of Stable Diffusion FREE CHAPTER
2. Chapter 1: Introducing Stable Diffusion 3. Chapter 2: Setting Up the Environment for Stable Diffusion 4. Chapter 3: Generating Images Using Stable Diffusion 5. Chapter 4: Understanding the Theory Behind Diffusion Models 6. Chapter 5: Understanding How Stable Diffusion Works 7. Chapter 6: Using Stable Diffusion Models 8. Part 2 – Improving Diffusers with Custom Features
9. Chapter 7: Optimizing Performance and VRAM Usage 10. Chapter 8: Using Community-Shared LoRAs 11. Chapter 9: Using Textual Inversion 12. Chapter 10: Overcoming 77-Token Limitations and Enabling Prompt Weighting 13. Chapter 11: Image Restore and Super-Resolution 14. Chapter 12: Scheduled Prompt Parsing 15. Part 3 – Advanced Topics
16. Chapter 13: Generating Images with ControlNet 17. Chapter 14: Generating Video Using Stable Diffusion 18. Chapter 15: Generating Image Descriptions Using BLIP-2 and LLaVA 19. Chapter 16: Exploring Stable Diffusion XL 20. Chapter 17: Building Optimized Prompts for Stable Diffusion 21. Part 4 – Building Stable Diffusion into an Application
22. Chapter 18: Applications – Object Editing and Style Transferring 23. Chapter 19: Generation Data Persistence 24. Chapter 20: Creating Interactive User Interfaces 25. Chapter 21: Diffusion Model Transfer Learning 26. Chapter 22: Exploring Beyond Stable Diffusion 27. Index 28. Other Books You May Enjoy

Preface

When Stable Diffusion was released on August 22, 2022, this Diffusion-based image generation model quickly caught the attention of the whole world. Both its model and source code are completely open source and hosted on GitHub. With millions of community participants and users, numerous new and mixed models have been released. Tools such as Stable Diffusion WebUI and InvokeAI have been created.

While the Stable Diffusion WebUI tool can generate fantastic images driven by the diffusion model, its usability is limited. The open source Diffusers package from Hugging Face allows users to have full control over Stable Diffusion using Python. However, it lacks many key features, such as loading custom LoRA models and Textual Inversion, utilizing community-shared models/checkpoints, scheduling and weighted prompts, unlimited prompt tokens, fixing the resolution of the images, and upscaling. This book will assist you in overcoming the limitations of Diffusers and implementing the advanced features to create a fully customized and industrial-level Stable Diffusion application.

By the end of this book, you will be able to not only use Python to generate and edit images but also leverage the solutions provided in the book to build Stable Diffusion applications for your business and users.

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