Build a Text-to-Image Generator, With transformers and diffusions, Liu M., 2026

Build a Text-to-Image Generator, With transformers and diffusions, Liu M., 2026.

   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 multimodal learning, or simply an enthusiast who learns best by coding, this book is for you.




Practical use cases of text-to-image models.
Text-to-image models have a wide range of practical applications in real-world scenarios, such as content generation, product design, and educational and training tools. While images are their primary output, learning how to build these models from scratch also equips you with skills relevant to related tasks. For example, the ability to align text and image representations can be extended to measuring similarity between text and images or to selecting the most appropriate image for a given description. You’ll also learn to build and train an image-to-text model to add captions to images.

These models can rapidly generate high-quality content, making them ideal for producing art, illustrations, and other creative visuals based on textual input. This capability is particularly useful for artists, designers, and writers who need to quickly prototype visual concepts. In advertising and marketing, businesses can use these models to create targeted advertisements, generate engaging marketing content, or quickly produce visuals tailored to specific customer descriptions or product requirements.

Contents.
Part 1 Understanding attention and transformers.
1 A tale of two models: Transformers and diffusions.
2 Build a transformer.
3 Classify images with a vision transformer.
4 Add captions to images.
Part 2 Introduction to diffusion models.
5 Generate images with diffusion models.
6 Control what images to generate in diffusion models.
7 Generate high-resolution images with diffusion models.
Part 3 Text-to-image generation with diffusion models.
8 CLIP: A model to measure the similarity between image and text.
9 Text-to-image generation with latent diffusion.
10 A deep dive into Stable Diffusion.
Part 4 Text-to-image generation with transformers.
11 VQGAN: Convert images into sequences of integers.
12 A minimal implementation of DALL-E.
Part 5 New developments and challenges.
13 New developments and challenges in text-to-image generation.
Appendix Installing PyTorch and enabling GPU training locally and in Colab.



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