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Build a Text-to-Image Generator (from Scratch) de Mark Liu

Build a Text-to-Image Generator (from Scratch)

Autor Mark Liu
en Limba Engleză Hardback – 23 ian 2026

Codul sursă și accesul la platforma liveBook (care include un asistent AI pentru suport) constituie fundamentul pe care Mark Liu își construiește acest ghid tehnic publicat de Manning Publications. Observăm o abordare riguroasă, de tip „build from scratch”, care demistifică procesele complexe din spatele sistemelor precum DALL-E sau Stable Diffusion. În cele 360 de pagini, găsim o structură progresivă ce transformă conceptele abstracte de învățare automată în aplicații funcționale. Merită menționat că autorul nu se limitează la simpla generare de conținut vizual. Găsim în această carte metodologii detaliate pentru antrenarea modelelor vision transformer, începând de la fragmentarea imaginilor în secvențe de patch-uri, până la implementarea modelelor de difuzie care rafinează zgomotul digital în reprezentări coerente. Pe linia practică a volumului Generative Deep Learning de David Foster, dar cu focus pe implementarea specifică a generatoarelor text-to-image, această lucrare oferă un control granular asupra arhitecturilor neuronale. Spre deosebire de Creating Images Using AI, care se concentrează pe utilizarea platformelor existente precum Midjourney, volumul de față este destinat celor care doresc să programeze și să antreneze propriile modele în Python. Etapele de dezvoltare acoperă scenarii variate: de la clasificarea imaginilor și reconstrucția acestora la înaltă rezoluție, până la tehnici avansate de editare bazate pe prompt-uri. Un aspect distinctiv este capitolul dedicat identificării imaginilor deepfake, o competență esențială în peisajul actual al inteligenței artificiale. Stilul este unul aplicat, facilitând înțelegerea modului în care modelele multimodale pot fi integrate în fluxuri de lucru complexe.

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Specificații

ISBN-13: 9781633435421
ISBN-10: 1633435423
Pagini: 360
Dimensiuni: 212 x 235 x 23 mm
Greutate: 0.65 kg
Editura: Manning Publications

De ce să citești această carte

Această carte se adresează entuziastului în machine learning și specialistului în date care dorește să treacă de la statutul de utilizator de AI la cel de arhitect. Cititorul câștigă o înțelegere profundă a arhitecturilor de tip transformer și diffusion, învățând să construiască sisteme capabile să interpreteze și să genereze imagini. Este resursa ideală pentru a stăpâni procesul tehnic de transformare a textului în realitate vizuală.

Descriere

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. 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 Build a Text-to-Image Generator (from Scratch) dives into the powerful models behind AI image generators like DALL-E and Stable Diffusion. We believe that the best way to learn is to build something from scratch, so 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. About the book 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! About the reader For machine learning enthusiasts and data scientists with intermediate Python skills. 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. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Descriere scurtă

Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

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!

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.

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.

Table of Contents

Part 1
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
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
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
11 VQGAN: Convert images into sequences of integers
12 A minimal implementation of DALL-E
Part 5
13 New developments and challenges in text-to-image generation
A Installing PyTorch and enabling GPU training locally and in Colab