  Recent studies have shown remarkable success in the unsupervised image to
image (I2I) translation. However, due to the imbalance in the data, learning
joint distribution for various domains is still very challenging. Although
existing models can generate realistic target images, it's difficult to
maintain the structure of the source image. In addition, training a generative
model on large data in multiple domains requires a lot of time and computer
resources. To address these limitations, we propose a novel image-to-image
translation method that generates images of the target domain by finetuning a
stylegan2 pretrained model. The stylegan2 model is suitable for unsupervised
I2I translation on unbalanced datasets; it is highly stable, produces realistic
images, and even learns properly from limited data when applied with simple
fine-tuning techniques. Thus, in this paper, we propose new methods to preserve
the structure of the source images and generate realistic images in the target
domain. The code and results are available at
https://github.com/happy-jihye/Cartoon-StyleGan2
