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.

翻译：字体生成是一项困难且耗时的任务，尤其在中文等使用表意文字的语言中，其字符结构复杂且数量庞大。为解决此问题，少样本甚至单一样本字体生成技术备受关注。然而，现有多数字体生成方法仍面临以下挑战：（i）跨字体域差异大；（ii）字体间细微变化难以捕捉；（iii）复杂字符生成错误。本文提出一种基于扩散模型的新型单一样本字体生成方法——Diff-Font，该方法可在大规模数据集上稳定训练。该模型仅需提供一个参考样本即可生成整个字体库。具体而言，我们构建了大规模笔画级数据集，并提出笔画级扩散模型以保持每个生成字符的结构完整性与完成度。据我们所知，Diff-Font是首个将扩散模型应用于字体生成任务的工作。训练完成的Diff-Font不仅对字体差异与变化具有鲁棒性，还在困难字符生成上取得了优异性能。与先前的字体生成方法相比，我们的模型在定性与定量评估中均达到了最先进水平。