Image Generation Models (MEAP V05)

From digital special effects to medical image augmentation and analysis, generative AI is revolutionizing the way we create and interpret visual information. Innovations including diffusion models, text-to-image generators, GANs, and more help you create photorealistic graphics, empower creative expression with text-to-image tools, and accurately recognize and describe visual elements for applications like image search. Image Generation Models will teach you the foundations of modern computer vision and equip you with the practical techniques you need to bring your ideas to life.
In Image Generation Models you'll learn about:
Variational autoencoders (VAEs) and generative adversarial networks (GANs)
Diffusion models for high-quality image generation
Evaluating models with metrics such as inception score and Fréchet inception distance
Conditional and guided generation techniques
Bridging language and vision using transformers and models like CLIP
Implementing text-to-image models
Image Generation Models guides you from core concepts of digital image creation to the cutting edge of AI-powered visual computing. You'll unpack tools like DALL-E and Stable Diffusion and learn to build your own by following the detailed code samples and practical tutorials.
about the book
Image Generation Models explores the inner workings of the generative AI models behind modern computer vision. You'll start by developing a simple autoencoder and extending it into a variational autoencoder for image generation. Next, you'll dive deep into GANs and discover how to upgrade their performance with next-generation techniques like Wasserstein GAN. Create your own denoising diffusion probabilistic models that can generate original imagery, and even implement a simplified text-to-image model! Plus, you'll explore hybrid models that benefit from the strengths of multiple approaches, and even video-based generative AI. Throughout, real-world case studies demonstrate how these models can be put into action.

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