OmniSVG/OmniSVG

OmniSVG: A Unified Scalable Vector Graphics Generation Model

              

1. Introduction

OmniSVG is the first family of end-to-end multimodal SVG generators that leverage pre-trained Vision-Language Models (VLMs), capable of generating complex and detailed SVGs, from simple icons to intricate anime characters. We also introduce MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks.

2. Models Downloading

Model	Download link	Size	Update date
OmniSVG-3B	🤗 Huggingface	8.49 GB	2025-07-21

3. Dependencies and Installation

The dependencies configured according to the following instructions provide an environment equipped for inference

3.1 Clone the Repository

git clone https://github.com/OmniSVG/OmniSVG.git
cd OmniSVG

3.2 Create Conda Environment

Create and activate a new conda environment with Python 3.10:

conda create -n omnisvg python=3.10
conda activate omnisvg

3.3 Install Dependencies

System Dependencies

Before installing Python packages, you need to install Cairo library which is required by CairoSVG in our dependencies:

macOS:

brew install cairo

Linux (Ubuntu/Debian):

sudo apt update
sudo apt install libcairo2 libcairo2-dev

Note: Installing Cairo system library beforehand helps prevent potential build errors when installing CairoSVG via pip.

Python Dependencies

We have tested our environment with CUDA 12.1. You can install CUDA 12.1 by following the CUDA Toolkit installation guide.

Install PyTorch with CUDA 12.1 support:

pip install torch==2.3.0+cu121 torchvision==0.18.0+cu121 --index-url https://download.pytorch.org/whl/cu121

Install remaining dependencies:

pip install -r requirements.txt

4. Inference Script

	GPU Memory Usage	Time per 256/512/1024/2048/4096 tokens
OmniSVG-3B	17G	4.08/8.68/18.07/37.51/82.70 seconds

Note: The inference time shown here is measured per OmniSVG SVG tokens, while the inference time reported in our paper is measured per XML code tokens for fair comparison with baseline methods.

Quick Start

1. Download Model Weights

   Download the model weights from Hugging Face and place them in the ./pretrained_models/OmniSVG-3B directory.

2. Run Inference

   Execute the following command to run inference on your images:

   python inference.py --input_dir ./examples --output_dir ./output --task_type image-to-svg/text-to-svg --weight_path ./pretrained_models/OmniSVG-3B
   

Interactive Demo

We provide an interactive generation interface using Gradio:

• Local Deployment

  python app.py
  

• Online Demo

  Try our live demo on Hugging Face Spaces

5. License

OmniSVG is licensed under the Apache License 2.0, while MMSVG dataset is under Creative Commons Attribution Non Commercial Share Alike 4.0 License. You can find the license files in the respective github and HuggingFace repositories.

Citation

@article{yang2025omnisvg,
  title={OmniSVG: A Unified Scalable Vector Graphics Generation Model}, 
  author={Yiying Yang and Wei Cheng and Sijin Chen and Xianfang Zeng and Jiaxu Zhang and Liao Wang and Gang Yu and Xinjun Ma and Yu-Gang Jiang},
  journal={arXiv preprint arxiv:2504.06263},
  year={2025}
}

Acknowledgments

We thank the following excellent open-source works:

IconShop: is the first advanced work that leverages LLMs to generate monochrome, icon-level SVGs. We referred to its parametric implementation.

Here is the list of highly related concurrent works:

LLM4SVG: treats SVG coordinates as number strings and predicts decimal part for higher spatial accuracy.

StarVector: equips LLM with an image encoder for Image-to-SVG generation.