Zhu (Shudong Zhu) Using Stable Diffusion with Python

Leverage Python to control and automate high-quality AI image generation using Stable Diffusion
1. Auflage 2024
ISBN: 978-1-83508-431-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 352 Seiten

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.

Autoren/Hrsg.

Zhu (Shudong Zhu), Andrew

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - Introducing Stable Diffusion
- Setting Up the Environment for Stable Diffusion
- Generating Images Using Stable Diffusion
- Understanding the Theory Behind Diffusion Models
- Understanding How Stable Diffusion Works
- Using Stable Diffusion Models
- Optimizing Performance and VRAM Usage
- Using Community-Shared LoRAs
- Using Textual Inversion
- Overcoming 77-Token Limitations and Enabling Prompt Weighting
- Image Restore and Super-Resolution
- Scheduled Prompt Parsing
- Generating Images with ControlNet
- Generating Video Using Stable Diffusion
- Generating Image Descriptions using BLIP-2 and LLaVA
- Exploring Stable Diffusion XL
- Building Optimized Prompts for Stable Diffusion
- Applications - Object Editing and Style Transferring
- Generation Data Persistence
- Creating Interactive User Interfaces
- Diffusion Model Transfer Learning
- Exploring Beyond Stable Diffusion

Über Autor(innen)

Zhu (Shudong Zhu) Andrew :

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.