4 Diffusion Models: Forward Diffusion
This chapter covers
- Introduction to Diffusion models and how their work
- Intuition behind Diffusion-based approaches to generative modeling
- Forward Diffusion process and its role in Diffusion models
Diffusion models offer a unique approach to synthesizing images that is both intriguing in theory and effective in practice. This class of models stands in contrast to other prominent generative paradigms, including VAEs and GANs, which we explored in previous chapters. While VAEs and GANs typically generate images directly from a latent representation, Diffusion models adopt a more gradual approach with many intermediate, iterative steps. This methodological shift offers a compelling alternative in the field of AI image synthesis, achieving unprecedented realism, training stability, output diversity, and other desirable characteristics.