Calliffusion: Chinese Calligraphy Generation and Style Transfer with
  Diffusion Modeling
Qisheng Liao Gus Xia Zhinuo Wang
In this paper, we propose Calliffusion, a system for generating high-quality
Chinese calligraphy using diffusion models. Our model architecture is based on
DDPM (Denoising Diffusion Probabilistic Models), and it is capable of
generating common characters in five different scripts and mimicking the styles
of famous calligraphers. Experiments demonstrate that our model can generate
calligraphy that is difficult to distinguish from real artworks and that our
controls for characters, scripts, and styles are effective. Moreover, we
demonstrate one-shot transfer learning, using LoRA (Low-Rank Adaptation) to
transfer Chinese calligraphy art styles to unseen characters and even
out-of-domain symbols such as English letters and digits.