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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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781835086377

Length 352 pages

Edition 1st Edition

Languages

Python

Concepts

GPT/LLMs

Author (1):

Andrew Zhu (Shudong Zhu)

View More author details

Table of Contents (29) Chapters

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

Why subscribe?

28. Other Books You May Enjoy

Technical requirements

Before you start, make sure you have the safetensors package installed:

pip install safetensors

The safetensors Python package offers a simple and efficient way to access, store, and share tensors securely.

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Authors (1)

Andrew Zhu (Shudong Zhu)

Andrew Zhu (Shudong Zhu)

Andrew Zhu is an experienced Microsoft Applied Data Scientist with over 15 years of experience in the tech field. He is a highly regarded writer known for his ability to explain complex concepts in machine learning and AI in an engaging and informative manner. Andrew frequently contributes articles to Toward Data Science and other prominent tech publishers. He has authored the book "Microsoft Workflow Foundation 4.0 Cookbook," which has received a 4.5-star review. Andrew has a strong command of programming languages such as C/C++, Java, C#, and Javascript, with his current focus primarily on Python. With a passion for AI and Automation, Andrew resides in WA, US, with his family, which includes two boys.

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