  Diffusion models have recently brought a powerful revolution 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 further decompose the
samples into different frequency components and execute moving average
separately on each component. We name the complete approach "Moving Average
Sampling in Frequency domain (MASF)". MASF could be seamlessly integrated into
mainstream pre-trained diffusion models and sampling schedules. Extensive
experiments on both unconditional and conditional diffusion models demonstrate
that our MASF leads to superior performances compared to the baselines, with
almost negligible additional complexity cost.
