Boosting Diffusion Models with Moving Average Sampling in Frequency Domain

Abstract

Diffusion models have recently brought a powerful rev-olution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sample to denoise the next one, possibly resulting in denoising instability. In this paper, we reinterpret the iterative denoising process as model optimization and leverage a moving average mechanism to ensemble all the prior samples. Instead of simply applying moving average to the denoised samples at different timesteps, we first map the denoised samples to data space and then perform moving average to avoid distribution shift across timesteps. In view that diffusion models evolve the recovery from low-frequency components to high-frequency details, we fur-ther decompose the samples into different frequency components and execute moving average separately on each component. We name the complete approach “Moving Aver-age Sampling in Frequency domain (MASF)”. MASF could be seamlessly integrated into mainstream pre-trained dif-fusion models and sampling schedules. Extensive experi-ments on both unconditional and conditional diffusion mod-els demonstrate that our MASF leads to superior performances compared to the baselines, with almost negligible additional complexity cost.