  Font generation is a difficult and time-consuming task, especially in those
languages using ideograms that have complicated structures with a large number
of characters, such as Chinese. To solve this problem, few-shot font generation
and even one-shot font generation have attracted a lot of attention. However,
most existing font generation methods may still suffer from (i) large
cross-font gap challenge; (ii) subtle cross-font variation problem; and (iii)
incorrect generation of complicated characters. In this paper, we propose a
novel one-shot font generation method based on a diffusion model, named
Diff-Font, which can be stably trained on large datasets. The proposed model
aims to generate the entire font library by giving only one sample as the
reference. Specifically, a large stroke-wise dataset is constructed, and a
stroke-wise diffusion model is proposed to preserve the structure and the
completion of each generated character. To our best knowledge, the proposed
Diff-Font is the first work that developed diffusion models to handle the font
generation task. The well-trained Diff-Font is not only robust to font gap and
font variation, but also achieved promising performance on difficult character
generation. Compared to previous font generation methods, our model reaches
state-of-the-art performance both qualitatively and quantitatively.
