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This book begins with my curiosity about how machines could create images from nothing more than words. When I first encountered DALL-E and Stable Diffusion, the results seemed magical: type a prompt, and out came a lifelike image that matched the description perfectly. But behind the magic were mathematics, code, and a long line of ideas in machine learning. I wanted to demystify those ideas, not just for myself, but for anyone who learns best by building things from scratch. Generative AI is advancing at a pace few of us could have predicted, reshaping not only the way we work but also how we create, design, and communicate. Text-to- image models in particular are among the most visible and transformative of these technologies. They embody the leap from unimodal to multimodal AI, systems that reason across different types of data. While the headlines focused on their impressive outputs, I found myself drawn to this question: How do they really work? The only satisfying answer, I decided, was to build one myself. This book is the result of that journey. It’s not a collection of high-level explanations or black box demonstrations. Instead, it’s a hands-on guide to re-creating the fundamental building blocks of text-to-image generation: transformers, vision models, diffusion processes, and latent representations. By reconstructing these systems piece by piece, readers like you gain a deeper understanding of both their power and their limitations. As Richard Feynman once put it, “What I cannot create, I do not understand.” That spirit guides every chapter. Writing this book also came from a desire to bridge two communities: the machine learning researchers pushing the frontier of generative models and the developers, designers, and enthusiasts who are eager to harness these tools but uncertain where to begin. My hope is that by working through code, experiments, and projects, you’ll see that these models aren’t impenetrable black boxes, but accessible systems built from understandable components. The examples in this book are intentionally playful (pandas in top hats, bananas on motorcycles) because creativity should be joyful. But the lessons carry serious value— from rapid prototyping in design and marketing, to aiding education, to enabling new forms of artistic expression. By the end of this book, I hope you not only understand how text-to-image models function but also feel comfortable extending, adapting, and imagining new applications of your own.