Using Stable Diffusion With Python
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Using Stable Diffusion with Python
Author: Andrew Zhu (Shudong Zhu)
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-06-03
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 DescriptionStable 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.
Implementing MLOps in the Enterprise
Author: Yaron Haviv
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-11-30
With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
Advances in Information and Communication
This book comprises the proceedings of the Future of Information and Communication Conference (FICC) 2025, held on 28–29 April 2025 in Berlin, Germany. The conference brought together leading researchers, industry experts, and academics from across the globe to discuss the latest advancements, challenges, and opportunities in the rapidly evolving field of information and communication technologies. The conference received an impressive 401 submissions, of which 138 high-quality papers were selected after a rigorous peer-review process. These contributions span a diverse range of topics, including artificial intelligence, cybersecurity, data science, networking, human-computer interaction, and more. FICC 2025 provided an engaging platform for collaboration and knowledge exchange, highlighting state-of-the-art research and practical solutions to global challenges. This proceedings book serves as a valuable resource for researchers, practitioners, and innovators seeking insights into the future of information and communication technologies.