# Multistep Consistency Models

**Authors:** Jonathan Heek, Emiel Hoogeboom, Tim Salimans  
**arXiv:** [2403.06807](https://arxiv.org/abs/2403.06807)  
**Date:** 2024-03-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.06807v1/

## Key points

- Multistep Consistency Models combine consistency models and TRACT to create a unique approach
- They offer a trade-off between sampling speed and quality by allowing interpolation between consistency models and diffusion models
- The key innovation is the ability to vary the number of steps taken during sampling, leading to higher quality samples while maintaining speed advantages
- Achieved notable results such as 1.4 FID on Imagenet 64 in 8 steps and 2.1 FID on Imagenet128 in 8 steps with consistency distillation
- Successfully applied to text-to-image diffusion model, showcasing versatility beyond image generation tasks
- Offers a promising solution for balancing speed and quality in generative modeling tasks

## Summary

In their paper "Multistep Consistency Models," authors Jonathan Heek, Emiel Hoogeboom, and Tim Salimans introduce a novel approach that combines elements of consistency models and TRACT to create Multistep Consistency Models. These models offer a unique trade-off between sampling speed and quality by allowing for interpolation between consistency models and diffusion models. The key innovation of Multistep Consistency Models lies in their ability to vary the number of steps taken during sampling. By increasing the sample budget from a single step to 2-8 steps, the authors demonstrate that it is possible to train models that generate higher quality samples while still benefiting from the speed advantages of consistency models. Notable results include achieving a 1.4 FID on Imagenet 64 in 8 steps and a 2.1 FID on Imagenet128 in 8 steps with consistency distillation. This showcases significant improvements over traditional consistency and diffusion models. Furthermore, the authors successfully apply Multistep Consistency Models to a text-to-image diffusion model, showing its versatility beyond image generation tasks. The generated samples closely match the quality of the original model. Overall, Multistep Consistency Models offer a promising solution for balancing sampling speed and quality in generative modeling tasks. provide an effective way to achieve high-quality samples without sacrificing speed or requiring multiple steps like traditional diffusion models do. With this approach,  can be optimized while maintaining high-quality results through an innovative . Additionally, this method extends beyond image generation tasks as shown by its successful application in a text-to-image diffusion model, highlighting its  in various domains.