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Using Stable Diffusion with Python

Using Stable Diffusion with Python

By : Andrew Zhu (Shudong Zhu)
4.8 (5)
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Using Stable Diffusion with Python

Using Stable Diffusion with Python

4.8 (5)
By: Andrew Zhu (Shudong Zhu)

Overview of this 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.
Table of Contents (29 chapters)
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Free Chapter
1
Part 1 – A Whirlwind of Stable Diffusion
8
Part 2 – Improving Diffusers with Custom Features
15
Part 3 – Advanced Topics
21
Part 4 – Building Stable Diffusion into an Application

What makes a good prompt?

Some say using Stable Diffusion is like being a magician, where tiny tricks and alterations make a huge difference. Writing good prompts for Stable Diffusion is essential for getting the most out of this powerful text-to-image AI model. Let me introduce some best practices that will make your prompts more effective.

In the long run, AI models will understand natural language better and better, but for now, let’s put in a bit of extra effort to make our prompts work better.

In the code files associated with this chapter, you will find that Stable Diffusion v1.5 is much more sensitive to prompts, as different prompts will significantly impact the outcome’s image quality. Meanwhile, Stable Diffusion XL is much improved and is not so sensitive to prompts. In other words, a short prompt description for Stable Diffusion XL will generate relatively stable-quality images.

You can also find the code that generates all images in the code repository...

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