8 Hybrid Architectures and Latent Diffusion Models
This chapter covers
- Overview of the generative learning trilemma and model architecture balances (VAEs, GANs, Diffusion Models)
- How hybrid architectures combine the strengths of different generative models while mitigating their individual limitations
- Latent Diffusion Models as a case study of a successful hybrid architecture
- Implementation of a Latent Diffusion Model from scratch
Throughout this book, we explored three major classes of generative modeling approaches: Variational Autoencoders (VAEs) in Chapter 2, Generative Adversarial Networks (GANs) in Chapter 3, and Diffusion Models in Chapters 4 and 5. These models represent distinct paradigms in generative modeling, each with its own strengths and limitations. In this chapter, we explore an emerging frontier in generative AI: hybrid models that combine the best aspects of these architectures to overcome their individual shortcomings. A key example is the integration of VAEs with Diffusion models, as seen in Latent Diffusion approaches, which we will explore later in this chapter.
By the end of this chapter, you will have a deep understanding of how hybrid models—and Latent Diffusion Models in particular—are shaping the future of generative AI, offering innovative solutions to longstanding challenges while opening new frontiers for research and application.