README.md

---
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

<p align="center">
  <img src="https://raw.githubusercontent.com/xTryer-s/GAR-Font/main/figures/teaser.jpg" width="80%"/>
</p>

<div align="center">

[![arXiv](https://img.shields.io/badge/arXiv-2601.01593-b31b1b?logo=arxiv)](https://arxiv.org/abs/2601.01593)
[![Homepage](https://img.shields.io/badge/Homepage-GAR--Font-orange)](https://xtryer-s.github.io/projects_pages/GAR_Font/)
[![Code](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/xTryer-s/GAR-Font)

</div>

---

## 📌 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.

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## 🚀 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}, 
}