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Total Size:
54.6 MB
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6825BEFCA84FCE4DB3E638B925494A97A3372D51
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Added:
Dec. 19, 2025, 1:06 p.m.
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(Last updated: Dec. 19, 2025, 1:08 p.m.)
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| Liu M. Build a Text-to-Image Generator. With transformers and diffusions 2026.pdf | 19.9 MB |
| Code.zip | 34.7 MB |
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| Uploaded by indexFroggy | Size 30.2 MB | Health [ 19 /3 ] | Added 2024-07-07 |
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SOURCE: Liu M. Build a Text-to-Image Generator. With transformers and diffusions 2026
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COVER
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MEDIAINFO
This book 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. Dive into the powerful models behind AI image generators. The best way to learn is to build something from scratch, and in this book you’ll build your very own diffusion model and vision transformer. As you work through each stage of development, you’ll develop an understanding of how these models can be customized, applied, and integrated for impressive multimodal AI. Build a Text-to-Image Generator (from Scratch) teaches you how to: Build and train models to generate high resolution images based on text descriptions Edit an existing image based on text prompts Build and train a model to add captions to images Build and train a vision transformer to classify images Fine-tune LLMs for downstream tasks such as classification, text or image generation Better differentiate real images from deepfakes About the technology AI-generated images appear everywhere from high-end advertising to casual social media feeds. Text-to-image tools like Dall-e, Midjourney, and Flux make it easy to create AI art, but how do they work? In this book, you’ll find out by building your own text-to-image generator! Text-to-image generation stands out as one of the most captivating advances within Generative AI. These models translate natural language prompts into detailed, visually compelling images, often with remarkable creativity and realism. Recent breakthroughs such as OpenAI’s DALL-E 2, Google’s Imagen, and Stability AI’s Stable Diffusion have captured the world’s attention by turning abstract descriptions into vivid pictures, sometimes indistinguishable from photographs or human art. Beyond creating images from scratch, these systems can also edit existing photos through text commands (e.g., cropping, removing objects, or changing backgrounds), making them valuable tools for photographers and designers alike. Companies such as Adobe have integrated text-to-image and text-based editing into their design suites, enabling graphic designers to instantly visualize and refine concepts. About the book Build a Text-to-Image Generator (from Scratch) explores both transformer-based image generation and diffusion models. You’ll work hands-on to build a pair of simple generation models that can classify images, automatically add captions, reconstruct images, and enhance existing graphics. Author Mark Liu guides you every step of the way with clear explanations, informative diagrams, and eye-opening examples you can build on your own laptop. What's inside Build a vision transformer to classify images Edit images using text prompts Fine-tune image models About the reader Requires basic knowledge of Generative AI models and intermediate Python skills. This book is written for developers, researchers, students, and curious practitioners who want to move beyond simply running prebuilt AI models and instead learn how they are designed. You should have a solid command of Python and a working knowledge of machine learning, especially neural networks in PyTorch. A background in Deep learning fundamentals, such as convolutional networks, embeddings, and training loops, will be helpful, though the book introduces each concept in context. If you’re an engineer seeking to deepen your AI skills, a researcher exploring multi-modal learning, or simply an enthusiast who learns best by coding, this book is for you. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. He is also the author of Learn Generative AI with PyTorch
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