Rectified Noise: A Generative Model Using Positive-incentive Noise

Rectified Flow (RF) has been widely used as an effective generative model. Although RF is primarily based on probability flow Ordinary Differential Equations (ODE), recent studies have shown that injecting noise through reversetime Stochastic Differential Equations (SDE) for sampling can achieve superior generative performance. Inspired by Positive-incentive Noise (π-noise), we propose an innovative generative algorithm to train π-noise generators, namely Rectified Noise (∆RN), which improves the generative performance by injecting π-noise into the velocity field of pretrained RF models. After introducing the Rectified Noise pipeline, pre-trained RF models can be efficiently transformed into π-noise generators. We validate Rectified Noise by conducting extensive experiments across various model architectures on different datasets. Notably, we find that: (1) RF models using Rectified Noise reduce FID from 10.16 to 9.05 on ImageNet-1k. (2) The models of πnoise generators achieve improved performance with only 0.39% additional training parameters. Code is available here: https://github.com/simulateuser538/Rectified-Noise