  Pixel synthesis is a promising research paradigm for image generation, which
can well exploit pixel-wise prior knowledge for generation. However, existing
methods still suffer from excessive memory footprint and computation overhead.
In this paper, we propose a progressive pixel synthesis network towards
efficient image generation, coined as PixelFolder. Specifically, PixelFolder
formulates image generation as a progressive pixel regression problem and
synthesizes images via a multi-stage structure, which can greatly reduce the
overhead caused by large tensor transformations. In addition, we introduce
novel pixel folding operations to further improve model efficiency while
maintaining pixel-wise prior knowledge for end-to-end regression. With these
innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g.,
reducing 89% computation and 53% parameters compared with the latest pixel
synthesis method CIPS. To validate our approach, we conduct extensive
experiments on two benchmark datasets, namely FFHQ and LSUN Church. The
experimental results show that with much less expenditure, PixelFolder obtains
new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77
FID and 2.45 FID on FFHQ and LSUN Church, respectively.Meanwhile, PixelFolder
is also more efficient than the SOTA methods like StyleGAN2, reducing about 72%
computation and 31% parameters, respectively. These results greatly validate
the effectiveness of the proposed PixelFolder.
