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    Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu), Matthew Fisher

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu), Matthew Fisher - okladka książki

    Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu), Matthew Fisher - okladka książki

    Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu), Matthew Fisher - audiobook MP3

    Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu), Matthew Fisher - audiobook CD

    Autorzy:
    Andrew Zhu (Shudong Zhu), Matthew Fisher
    Serie wydawnicze:
    Hands-on
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    352
    Dostępne formaty:
         PDF
         ePub
    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.

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    O autorach książki

    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.
    Matthew Fisher is a Software Engineer at Microsoft and one of the core maintainers of the Helm project. Born and raised on Vancouver Island, he studied computer systems at the British Columbia Institute of Technology. Outside of work, he has an extensive list of hobbies which is forever growing. On any given day, he is a musician, a luthier, a woodworker, a blacksmith, a cook, a photographer, and an artist. When he's not practicing with his guitar or rushing to and from the workshop, you’ll find him out on another adventure with his wife, Brandy. He goes by the name @bacongobbler on GitHub and Twitter

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