Sketch2Manga: Shaded Manga Screening from Sketch with Diffusion Models
Jian Lin Xueting Liu Chengze Li Minshan Xie Tien-Tsin Wong
While manga is a popular entertainment form, creating manga is tedious,
especially adding screentones to the created sketch, namely manga screening.
Unfortunately, there is no existing method that tailors for automatic manga
screening, probably due to the difficulty of generating high-quality shaded
high-frequency screentones. The classic manga screening approaches generally
require user input to provide screentone exemplars or a reference manga image.
The recent deep learning models enables the automatic generation by learning
from a large-scale dataset. However, the state-of-the-art models still fail to
generate high-quality shaded screentones due to the lack of a tailored model
and high-quality manga training data. In this paper, we propose a novel
sketch-to-manga framework that first generates a color illustration from the
sketch and then generates a screentoned manga based on the intensity guidance.
Our method significantly outperforms existing methods in generating
high-quality manga with shaded high-frequency screentones.