  We propose Styleformer, which is a style-based generator for GAN
architecture, but a convolution-free transformer-based generator. In our paper,
we explain how a transformer can generate high-quality images, overcoming the
disadvantage that convolution operations are difficult to capture global
features in an image. Furthermore, we change the demodulation of StyleGAN2 and
modify the existing transformer structure (e.g., residual connection, layer
normalization) to create a strong style-based generator with a convolution-free
structure. We also make Styleformer lighter by applying Linformer, enabling
Styleformer to generate higher resolution images and result in improvements in
terms of speed and memory. We experiment with the low-resolution image dataset
such as CIFAR-10, as well as the high-resolution image dataset like
LSUN-church. Styleformer records FID 2.82 and IS 9.94 on CIFAR-10, a benchmark
dataset, which is comparable performance to the current state-of-the-art and
outperforms all GAN-based generative models, including StyleGAN2-ADA with fewer
parameters on the unconditional setting. We also both achieve new
state-of-the-art with FID 15.17, IS 11.01, and FID 3.66, respectively on STL-10
and CelebA. We release our code at
https://github.com/Jeeseung-Park/Styleformer.
