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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

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Harshavardhan K Jul 03, 2024

I recently picked up this book, and it's been a game-changer for me. If you're into AI image generation, this book is the new gold.They walk you through setting up everything you need - CUDA, PyTorch, the works - without making it feel like a slog. And when they get into the nitty-gritty of diffusion models, they somehow make it all click. The authors have this knack for explaining tricky concepts in a way that just makes sense. Whether you're just dipping your toes into this stuff or you've been at it for a while, you'll get something out of this book.All in all, if you're even slightly curious about AI image generation, grab a copy. Trust me, you won't regret it!

Amazon Verified review

Chenxi L. Jul 05, 2024

From the outset, the author sets a clear and engaging tone, making complex concepts accessible to a broad audience. The book's structure is well-organized, beginning with an introduction to the fundamentals of stable diffusion processes and their significance in various applications. This foundational knowledge is crucial for readers who may not have a strong background in statistics or stochastic processes.One of the book's standout features is its practical approach. The author does an excellent job of not just explaining theoretical concepts but also demonstrating how to implement them using Python. The step-by-step examples and code snippets are particularly useful, providing readers with hands-on experience and making it easy to follow along. The inclusion of real-world applications further enhances the learning experience, showcasing how these techniques can resolve practical issues and save valuable time.

Petar Dimov Sep 13, 2024

💡 My exploration through Shudong Zhu’s comprehensive guide has been both informative and inspiring.✍ 𝗧𝗵𝗲 𝗯𝗼𝗼𝗸 𝗯𝗲𝗴𝗶𝗻𝘀 𝗯𝘆 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗼𝗳 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻, providing a solid foundation on its evolution, setup, and practical image generation techniques. The initial sections cover everything from the theory behind diffusion models to hands-on image creation.👨‍💻 𝗣𝗮𝗿𝘁 𝟮 𝗱𝗶𝘃𝗲𝘀 𝗶𝗻𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗮𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻. It offers strategies to enhance performance, leverage community-driven resources like LoRAs, and tackle advanced features such as image restoration and prompt management.⚖️ 𝗧𝗵𝗲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝗮𝗿𝘁 𝟯 𝗽𝘂𝘀𝗵 𝘁𝗵𝗲 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗰𝗮𝗻 𝗱𝗼. It covers the integration of ControlNet, video generation, image description with BLIP-2 and LLaVA, and the new capabilities of Stable Diffusion XL. The section also addresses prompt optimization techniques for improved results.🛠️ 𝗜𝗻 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗹 𝘀𝗲𝗰𝘁𝗶𝗼𝗻, 𝗣𝗮𝗿𝘁 𝟰, 𝘁𝗵𝗲 𝗯𝗼𝗼𝗸 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. It discusses object editing, style transferring, data persistence, and building interactive UIs with Gradio, concluding with insights on diffusion model transfer learning and the broader implications of AI advancements.✨ Overall, "𝗨𝘀𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻" is an essential resource for anyone interested in mastering Stable Diffusion. It combines foundational theory with practical applications, making it a valuable guide for both newcomers and experienced practitioners in the field of AI image generation.

R V Jun 06, 2024

If you ever tried to write your own application for Stable Diffusion, you'd notice that the prompt is limited to only 77 tokens, and you will receive no warning. You need to chunk your big prompt and use a package like Compel to make it work. I learned it the hard way, the book would have told me immediately. Adding LoRas and LyCos are not as easy as doing so in A1111 or ComfyUI. I only hoped I had this book way before it was launched. The author is one of the developers of the Diffusers library and he knows what he is doing. He is also a talented writer. It's not a beginner's book and you are buying it because you are hard core programmer. I bought this book twice in the pre-order by mistake and received a copy as present from a great friend.

Steven Fernandes Aug 06, 2024

The book outlines strategies to refine performance, manage VRAM effectively, and utilize community innovations like LoRAs and textual inversion. It introduces powerful tools like ControlNet and IP-Adapter, enhancing control and quality in image generation. The exploration extends to video creation with AnimateDiff and the art of crafting effective prompts, including the use of LLMs for automation. Additionally, it offers a detailed walkthrough on training a Stable Diffusion LoRA from the ground up, making it an invaluable resource for both novice and advanced users in the field of AI-driven art.

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

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