Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models
Xin Yuan Linjie Li Jianfeng Wang Zhengyuan Yang Kevin Lin Zicheng Liu Lijuan Wang
In this paper, we study the denoising diffusion probabilistic model (DDPM) in
wavelet space, instead of pixel space, for visual synthesis. Considering the
wavelet transform represents the image in spatial and frequency domains, we
carefully design a novel architecture SFUNet to effectively capture the
correlation for both domains. Specifically, in the standard denoising U-Net for
pixel data, we supplement the 2D convolutions and spatial-only attention layers
with our spatial frequency-aware convolution and attention modules to jointly
model the complementary information from spatial and frequency domains in
wavelet data. Our new architecture can be used as a drop-in replacement to the
pixel-based network and is compatible with the vanilla DDPM training process.
By explicitly modeling the wavelet signals, we find our model is able to
generate images with higher quality on CIFAR-10, FFHQ, LSUN-Bedroom, and
LSUN-Church datasets, than the pixel-based counterpart.