  One-shot image generation (OSG) with generative adversarial networks that
learn from the internal patches of a given image has attracted world wide
attention. In recent studies, scholars have primarily focused on extracting
features of images from probabilistically distributed inputs with pure
convolutional neural networks (CNNs). However, it is quite difficult for CNNs
with limited receptive domain to extract and maintain the global structural
information. Therefore, in this paper, we propose a novel structure-preserved
method TcGAN with individual vision transformer to overcome the shortcomings of
the existing one-shot image generation methods. Specifically, TcGAN preserves
global structure of an image during training to be compatible with local
details while maintaining the integrity of semantic-aware information by
exploiting the powerful long-range dependencies modeling capability of the
transformer. We also propose a new scaling formula having scale-invariance
during the calculation period, which effectively improves the generated image
quality of the OSG model on image super-resolution tasks. We present the design
of the TcGAN converter framework, comprehensive experimental as well as
ablation studies demonstrating the ability of TcGAN to achieve arbitrary image
generation with the fastest running time. Lastly, TcGAN achieves the most
excellent performance in terms of applying it to other image processing tasks,
e.g., super-resolution as well as image harmonization, the results further
prove its superiority.
