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.

8.1 Understanding the Path to Hybrid Models

8.1.1 The Generative AI Trilemma

8.1.2 A Comparative Look at Generative Model Architectures

8.2 The Rise of Hybrid Models: Latent Diffusion as a Case Study

8.2.1 Latent Diffusion: Diffusion in the Latent Space

8.2.2 The Best of Both Worlds: Benefits of Latent Diffusion

8.2.3 The Drawbacks of Latent Diffusion

8.2.4 Balancing Trade-Offs in Modern Generative AI

8.3 Implementing a Latent Diffusion Model

8.3.1 Step 1: Importing Necessary Libraries

8.3.2 Step 2: Setting the Device

8.3.3 Step 3: Building the VAE Architecture – Encoder and Decoder Networks

8.3.4 Step 4: Assembling the Complete VAE

8.3.5 Step 5: VAE Training Setup

8.3.6 Step 6: VAE Training Pipeline

8.3.7 Step 7: Building the Diffusion Components: U-Net Architecture with Residual Blocks

8.3.8 Step 8: Implementing the Diffusion Process: The DDPM Class

8.3.9 Step 9: Tying it All Together – The Complete Latent Diffusion Model

8.3.10 Step 10: Training the Latent Diffusion Model

8.3.11 Step 11: Putting It All Together – Training Pipeline Initialization

8.3.12 Step 12: Generating Samples

8.3.13 Model Output Analysis

8.4 Conclusion

8.5 Summary