Building upon the established understanding of advanced GANs and diffusion models, this chapter concentrates on applying these techniques to sophisticated generation tasks and integrating different approaches. We transition from the core mechanics of individual model families to their practical deployment in complex scenarios.

You will learn methods for scaling generation to high resolutions and constructing models capable of synthesizing images from textual descriptions, often involving conditioning mechanisms. We will examine the use of generated data for augmenting training sets and its potential role in privacy-preserving contexts. Additionally, this chapter introduces the extension of these generative principles to video synthesis and looks into hybrid architectures that combine elements from both GANs and diffusion models. The discussion covers essential considerations for managing computational resources required for training large-scale models. Practical implementation focuses on building a conditional image generation system.