Elucidating the Design Space of Diffusion-Based Generative Models

We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks. 2022: Tero Karras, M. Aittala, Timo Aila, S. Laine https://arxiv.org/pdf/2206.00364v2.pdf