Using Stable Diffusion with Python: Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

Zhu, Andrew, Fisher, Matthew

Using Stable Diffusion with Python: Leverage Python to control and automate high-quality AI image generation using Stable Diffusion
  • 出版商: Packt Publishing
  • 出版日期: 2024-06-03
  • 售價: $1,740
  • 貴賓價: 9.5 折 $1,653
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1835086373
  • ISBN-13: 9781835086377
  • 相關分類: Python、程式語言、人工智慧

    • 海外代購書籍(需單獨結帳)

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商品描述

Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design

Key Features
  1. Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code
  2. Get instant access to emerging extensions and open-source models
  3. Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences
  4. Purchase of the print or Kindle book includes a free PDF eBook
Book Description

Stable Diffusion is a game-changing AI tool for image generation, enabling you to create stunning artwork with code. However, mastering it requires an understanding of the underlying concepts and techniques. This book guides you through unlocking the full potential of Stable Diffusion with Python.

Starting with an introduction to Stable Diffusion, you'll explore the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll learn how to optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. After 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 application. This book also delves 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
  1. Explore core concepts and applications of Stable Diffusion and set up your environment for success
  2. Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion
  3. Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality
  4. Explore developments in Stable Diffusion such as video generation using AnimateDiff
  5. Write effective prompts and leverage LLMs to automate the process
  6. 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.

商品描述(中文翻譯)

這本書介紹了如何利用GenAI工具和技術（如diffusers、LoRA、文字反轉、ControlNet和prompt設計）來掌握AI圖像生成的技巧。以下是本書的主要特點：
1. 通過專家指導和現成的Python代碼，掌握生成令人驚艷的AI藝術品的技巧。
2. 獲得即時訪問新興擴展和開源模型。
3. 利用社區共享模型和LoRA的力量，生成引人入勝的高質量圖像。
4. 購買印刷版或Kindle版書籍，可獲得免費的PDF電子書。

《穩定擴散》是一個改變遊戲規則的AI圖像生成工具，可以通過代碼創建令人驚艷的藝術品。然而，要掌握它，需要理解其中的概念和技術。本書將指導您如何使用Python充分發揮穩定擴散的潛力。

從穩定擴散的介紹開始，您將探索擴散模型背後的理論，設置環境並使用diffusers生成第一張圖像。您將學習如何優化性能，利用自定義模型，並整合社區共享資源，如LoRA、文字反轉和ControlNet，以增強您的創作。在介紹面部恢復、圖像放大和圖像恢復等技術後，您將專注於解鎖prompt限制、定期prompt解析和加權prompt，以創建完全定制和行業級的穩定擴散應用程序。本書還深入探討了在醫學影像、遙感和照片增強等實際應用中的應用。最後，您將獲得有關提取生成數據、確保數據持久性以及利用BLIP等AI模型進行圖像描述提取的見解。

通過閱讀本書，您將能夠使用Python生成和編輯圖像，並利用解決方案為您的業務和用戶構建穩定擴散應用程序。

本書的學習重點包括：
1. 探索穩定擴散的核心概念和應用，並為成功設置您的環境。
2. 進一步優化性能，管理VRAM使用情況，並利用社區驅動的資源，如LoRA和文字反轉。
3. 利用ControlNet、IP-Adapter和其他方法生成具有前所未有的控制和質量的圖像。
4. 探索穩定擴散的最新發展，如使用AnimateDiff生成視頻。
5. 撰寫有效的prompt並利用LLMs自動化過程。
6. 發現如何從頭開始訓練穩定擴散LoRA。

本書適合希望掌握AI圖像生成技術，特別是通過擴散模型的讀者。此外，數據科學家、機器學習工程師、研究人員和Python應用程序開發人員，尋求基於穩定擴散框架創建AI圖像生成應用程序的讀者，也可以從本書提供的見解中受益。

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