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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

Generation Data Persistence

Imagine a Python program generates images but when you go back to the image hoping to make improvements or simply generate new images based on the original prompt, you can’t find the exact prompt, inference steps, guidance scale, and the other things that actually generate the image!

One of the solutions to solve this problem is saving all the metadata in the generated image file. The Portable Network Graphics (PNG) [1] image format provides a mechanism for us to store a piece of metadata along with the image pixel data. We will explore this solution.

In this chapter, we are going to look at the following:

  • Exploring and understanding the PNG file structure
  • Storing the Stable Diffusion generation metadata in the PNG file
  • Extracting the Stable Diffusion generation metadata from the PNG file

By employing the solution provided by this chapter, you will be able to maintain the generation prompt and parameters in the image file...

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