We introduced "Presidifussion," a novel approach to learning and replicating
the unique style of calligraphy of President Xu, using a pretrained diffusion
model adapted through a two-stage training process. Initially, our model is
pretrained on a diverse dataset containing works from various calligraphers.
This is followed by fine-tuning on a smaller, specialized dataset of President
Xu's calligraphy, comprising just under 200 images. Our method introduces
innovative techniques of font image conditioning and stroke information
conditioning, enabling the model to capture the intricate structural elements
of Chinese characters. The effectiveness of our approach is demonstrated
through a comparison with traditional methods like zi2zi and CalliGAN, with our
model achieving comparable performance using significantly smaller datasets and
reduced computational resources. This work not only presents a breakthrough in
the digital preservation of calligraphic art but also sets a new standard for
data-efficient generative modeling in the domain of cultural heritage
digitization.

通过预训练扩散模型在不同书法家作品的多样数据集上以及在包含将近 200 张徐院长书法作品的较小特殊数据集上进行微调，引入了创新的字体图像和笔画信息条件技术，并展示了与 zi2zi 和 CalliGAN 等传统方法相媲美的性能、更小数据集和较少计算资源的成果，从而为书法艺术的数字保存提供了突破，并在文化遗产数字化领域设立了新的数据有效的生成建模标准。