---
tags:
- font-generation
- image-generation
- autoregressive
- multimodal
- few-shot-learning
- computer-vision
library_name: pytorch
---
# GAR-Font: Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
[](https://arxiv.org/abs/2601.01593)
[](https://xtryer-s.github.io/projects_pages/GAR_Font/)
[](https://github.com/xTryer-s/GAR-Font)
---
## 📌 Overview
GAR-Font is a **global-aware autoregressive model for multimodal few-shot font generation**.
It aims to generate high-quality glyphs with limited style references by modeling both global font characteristics and local glyph details.
GAR-Font supports two generation settings:
- **Vision-Only GAR-Font**
Generate glyphs using only visual style references.
- **Vision-Language GAR-Font**
Generate glyphs using both visual references and natural language style descriptions.
This repository provides a pretrained checkpoint of Vision-Only GAR-Font.
---
## 📦 Model Weights
We provide the pretrained checkpoint:
**generator_ckpt_pruned.pt**
The checkpoint contains:
- `vq_model`: pretrained VQ tokenizer for glyph representation
- `model`: pretrained GAR-Font autoregressive generator
The checkpoint can be directly used for inference and further research.
---
## 🚀 Usage
Download:
**generator_ckpt_pruned.pt**
and use it as the `--generator-ckpt` argument in the inference scripts.
For installation instructions, inference examples, training details, and complete implementation, please refer to our official GitHub repository:
🔗 https://github.com/xTryer-s/GAR-Font
---
## 📖 Paper
**Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation**
arXiv:
https://arxiv.org/abs/2601.01593
---
## Citation
If you find GAR-Font useful, please cite our paper:
```bibtex
@misc{cai2026patchesglobalawareautoregressivemodel,
title={Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation},
author={Haonan Cai and Yuxuan Luo and Zhouhui Lian},
year={2026},
eprint={2601.01593},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.01593},
}