# A Comprehensive Review on Noise Control of Diffusion Model

**Authors:** Zhehao Guo, Jiedong Lang, Shuyu Huang, Yunfei Gao, Xintong Ding  
**arXiv:** [2502.04669](https://arxiv.org/abs/2502.04669)  
**Date:** 2025-02-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.04669v1/

## Key points

- Diffusion models are powerful tools for high-quality image production
- Noise schedule is a key element in diffusion models, governing the rate of noise injection
- The noise schedule significantly impacts sampling quality and training quality
- Various noise schedules are examined in a review to understand their features and performance characteristics
- Understanding different noise schedules is essential for optimizing diffusion models in generating high-quality images

## Summary

In the realm of generative frameworks for high-quality image production, diffusion models have recently emerged as powerful tools. These models rely on a key element known as the noise schedule, which plays a crucial role in governing the rate of noise injection throughout the diffusion process. The impact of the noise schedule on both sampling quality and training quality cannot be understated, making it essential to fully understand its design and implications. In their comprehensive review authored by Zhehao Guo, Jiedong Lang, Shuyu Huang, Yunfei Gao, and Xintong Ding, various noise schedules are meticulously examined to shed light on their distinguishing features and performance characteristics. This exploration aims to provide a deeper understanding of how different noise schedules influence the overall effectiveness of diffusion models in generating high-quality images. By delving into the nuances of noise control within diffusion models, this review offers valuable insights for researchers and practitioners seeking to optimize their approach to image generation. With a focus on elucidating the intricacies of noise schedules, this discussion serves as a valuable resource for advancing knowledge in this evolving field.