AI images flood feeds, yet the models behind them feel mysterious. Relying on black boxes risks bias, errors, and costly creative dead ends. You deserve hands-on skills to build, audit, and improve these generators yourself. This book starts from a blank notebook, guiding every line of Python code. Learn transformers for vision, then craft diffusion models that sharpen noise into art. Finish with a custom system generating high-resolution images from any text prompt.
- Vision transformer anatomy: Decode image patches and attention flows for transparent decision paths.
- End-to-end diffusion pipeline: Transform random noise into detailed, photorealistic pictures you can trust.
- Captioning and classification builds: Extend models to describe or categorize images for downstream tasks.
- Fine-tuning walkthroughs: Adapt pretrained networks quickly, saving compute while boosting domain accuracy.
- Deepfake detection skills: Differentiate authentic photos from generated fakes to safeguard projects and brands.
- Fully runnable notebooks: Experiment, tweak, and visualize results without configuration hassles.
In Build a Text-to-Image Generator (from Scratch), the author combines clear prose, diagrams, and production-ready Python to deliver practical authority.
Starting with patch tokenization, you implement a vision transformer, then pivot to diffusion. Step-by-step chapters layer theory, code, and visual outputs, ensuring concepts click before you move on. By the final page you can craft, tune, and deploy image generators that suit your data, budget, and ethical standards. You control every hyperparameter and understand every pixel produced.
Ideal for data scientists and Python-savvy enthusiasts eager to master state-of-the-art image generation.
Table of 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 (VIT)
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
INSTALL PYTORCH AND ENABLE GPU TRAINING LOCALLY AND IN COLAB
About the Author :
Mark Liu is a professor and program director known for translating cutting-edge AI into practical curricula. With years mentoring graduate students and professionals, Mark brings clarity, rigor, and enthusiasm to every page. He distills deep generative-model expertise into step-by-step guidance that empowers readers to build powerful visual AI systems.
Review :
- This book stands out for its hands-on, no-fluff approach to text-to-image generation—perfect for practitioners who want to build rather than just theorize. The clear PyTorch implementations, Colab-friendly examples, and practical exercises make even advanced concepts like Diffusion Models feel achievable.
Simeon Leyzerzon, President, Excelsior Software Ltd.
- This book is a great hands-on intro to how text-to-image models like Stable Diffusion actually work under the hood. It explains the roles of transformers, VAEs, and denoising U-Nets in a super approachable way, with lots of code you can run yourself. If you’re curious about generative AI and want to build or tweak your own models, this is a solid place to start.
Ravikumar Sanapala, Product Manager, Reality Labs, Meta
