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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

Using Stable Diffusion XL

Stable Diffusion XL (SDXL) is a model from Stability AI. Slightly different compared to previous models, SDXL is designed to be a two-stage model. We will need the base model to generate an image and can leverage a second, refiner model to refine an image, as shown in Figure 6.1. The refiner model is optional:

Figure 6.1: SDXL, a two-model pipeline

Figure 6.1: SDXL, a two-model pipeline

Figure 6.1 shows that to generate images of the best quality from the SDXL model, we will need to use the base model to generate a raw image, output as a 128x128 latent, and then use the refiner model to enhance it.

Before trying out the SDXL model, please ensure you have at least 15 GB of VRAM, otherwise, you may see a CUDA out of memory error right before the refiner model outputs the image. You can also use the optimization methods from Chapter 5, to build a custom pipeline to move the model out of VRAM whenever possible.

Here are the steps to load up an SDXL model:

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