Using Stable Diffusion with Python : Leverage Python to control and automate high-quality AI image generation using Stable Diffusion
Language: English
Published by Packt Publishing, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - This book shows you how to use Python to control Stable Diffusion and generate high-quality images.
Seller Inventory # 9781835086377
- Title
- Using Stable Diffusion with Python : Leverage Python to control and automate high-quality AI image generation using Stable Diffusion
- Author
- Andrew Zhu (Shudong Zhu)
- Publisher
- Packt Publishing
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1835086373
- ISBN 13
- 9781835086377
- Item weight
- 658 grams
- Dimensions
- 235x191x20 mm
Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design in this hands-on guide, with key images printed in color
Key Features
- Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code
- Get instant access to emerging extensions and open-source models
- Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Explore core concepts and applications of Stable Diffusion and set up your environment for success
- Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion
- Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality
- Explore developments in Stable Diffusion such as video generation using AnimateDiff
- Write effective prompts and leverage LLMs to automate the process
- Discover how to train a Stable Diffusion LoRA from scratch
Who this book is for
If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.
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
(N.B. Please use the Read Sample option to see further chapters)
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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