Summary
In this chapter, we looked at how the Stable Diffusion algorithm was developed and how it is implemented through the Hugging Face pipeline API. In the process, we saw how a diffusion model addresses conceptual problems with autoregressive transformer and GAN models by modeling the distribution of natural pixels. We also saw how this generative diffusion process can be represented as a reversible Markov process, and how we can train the parameters of a diffusion model using a variational bound, similar to a VAE.
Furthermore, we saw how the efficiency of a diffusion model is improved by executing the forward and reverse process in latent space in the Stable Diffusion model. We also illustrated how natural language user prompts are represented as byte encodings and transformed into numerical vectors. Finally, we looked at the role of the VAE in generating compressed image vectors, and how the U-Net of Stable Diffusion uses the embedded user prompt and a vector of random numbers...
