Build your own vision transformer and diffusion models for text-to-image generation-from scratch! Build a Text-to-Image Generator (from Scratch) takes you step-by-step through creating your own AI models that can generate images from text. You’ll explore two methods of image generation–vision transformers and diffusion models–and learn vital AI development techniques as you go. This book is designed for those who have a solid command of Python and a basic understanding of Machine Learning, especially neural networks in PyTorch. It aims to guide you through the creation of text-to-image generative models from the ground up. Build a Text-to-Image Generator (from Scratch) guides you through creating AI models that can generate amazing images from simple text prompts. You’ll explore two distinct methods, learning how transformers turn images into sequences of patches, and how diffusion models refine noise into coherent images. Author Mark Liu explains each stage with clear text, diagrams, and examples. You’ll develop models that can classify images, automatically add image captions, reconstruct images, and deliver high-resolution content. By the time you’re done, you’ll have a deep understanding of how image generation AI works–and the satisfaction of building your text-to-image models! You’re also invited to replicate the Python code and Jupyter notebooks available on the book’s GitHub repository and engage with exercises in each chapter. For Machine Learning enthusiasts and data scientists with intermediate Python skills.

Build a Text-to-Image Generator (MEAP v3) PDF

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