# Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling

arXiv: https://arxiv.org/abs/2305.19124 · HTML: https://mlsanity.com/paper/2305.19124/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 30 May 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator](https://mlsanity.com/paper/2005.12500/) (2020)
- [ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach](https://mlsanity.com/paper/2108.03596/) (2021)

## Built on by (most cited first, 9 of 9)

- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [SplitFlux: Learning to Decouple Content and Style from a Single Image](https://mlsanity.com/paper/2511.15258/) (2025)
- [CalliPaint: Chinese Calligraphy Inpainting with Diffusion Model](https://mlsanity.com/paper/2312.01536/) (2023)
- [CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control](https://mlsanity.com/paper/2410.03787/) (2024)
- [MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks](https://mlsanity.com/paper/2312.16251/) (2023)
- [Moyun: A Diffusion-Based Model for Style-Specific Chinese Calligraphy Generation](https://mlsanity.com/paper/2410.07618/) (2024)
- [HanMoVLM: Large Vision-Language Models for Professional Artistic Painting Evaluation](https://mlsanity.com/paper/2603.10814/) (2026)
- [InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization](https://mlsanity.com/paper/2605.05865/) (2026)
- [NURBS Splatting: A Unified Differentiable Rendering Framework for Vector Graphics](https://mlsanity.com/paper/2606.31764/) (2026)

## Abstract (arXiv)

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.