# Elucidating the Design Space of Diffusion-Based Generative Models

**Authors:** Tero Karras, Miika Aittala, Timo Aila, Samuli Laine  
**arXiv:** [2206.00364](https://arxiv.org/abs/2206.00364)  
**Date:** 2022-06-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2206.00364v1/

## Key points

- Authors Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine address complexity in diffusion-based generative models
- Proposed solution: Design space to clarify design choices
- Modifications to sampling, training processes, and preconditioning of score networks
- Achieved state-of-the-art FID scores for CIFAR-10 (1.79 in class-conditional setting, 1.97 in unconditional setting)
- Faster sampling with only 35 network evaluations per image
- Modular design changes enhance efficiency and quality when using pre-trained score networks
- Improved existing ImageNet-64 model FID score from 2.07 to near-SOTA score of 1.55

## Summary

In their paper titled "Elucidating the Design Space of Diffusion-Based Generative Models," authors Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine address the complexity surrounding diffusion-based generative models. They argue that the current theory and practice in this field are unnecessarily convoluted and propose a solution by presenting a design space that clearly delineates concrete design choices. Through this approach, they are able to pinpoint various modifications to both the sampling and training processes, as well as the preconditioning of score networks. The authors' proposed improvements have led to significant advancements in the field, with their model achieving a new state-of-the-art Frechet Inception Distance (FID) score of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting. Notably, their model also boasts much faster sampling capabilities, requiring only 35 network evaluations per image compared to previous designs. Furthermore, the modular nature of their design changes is demonstrated through enhancements in efficiency and quality when utilizing pre-trained score networks from prior research. For instance, they were able to improve the FID score of an existing ImageNet-64 model from 2.07 to a near-state-of-the-art (SOTA) score of 1.55. Overall, this work not only sheds light on the intricacies of diffusion-based generative models but also showcases tangible advancements that push the boundaries of performance and efficiency in this domain.