OmniSVG is a novel framework for generating Scalable Vector Graphics (SVG) using pre-trained Vision-Language Models (VLMs). It aims to produce complex and detailed SVGs, ranging from icons to intricate illustrations and characters, addressing the need for automated, high-fidelity vector asset creation.

How It Works

OmniSVG leverages a multimodal approach, integrating VLMs to interpret visual and textual prompts for SVG generation. This allows for a unified model capable of handling diverse SVG creation tasks, from simple icons to complex artistic renderings, by treating SVG generation as a sequence-to-sequence problem.

Quick Start & Requirements

• Requirements: Python, PyTorch, Transformers, Gradio. Specific VLM dependencies are implied but not detailed.
• Dataset Release: MMSVG-Icon and MMSVG-Illustration datasets are available on Hugging Face.
• Resources: Code and pretrained models are slated for future release.

Highlighted Details

• First family of end-to-end multimodal SVG generators.
• Capable of generating complex and detailed SVGs, from icons to anime characters.
• Leverages pre-trained Vision-Language Models (VLMs).
• Released MMSVG-Icon and MMSVG-Illustration datasets.

Maintenance & Community

• Project initiated in April 2025.
• Paper and technical report are available.
• References concurrent works like LLM4SVG and StarVector.

Licensing & Compatibility

• License not specified in the README.
• Compatibility for commercial or closed-source use is undetermined.

Limitations & Caveats

The project is in its early stages, with code and pretrained models yet to be released. The full capabilities and specific technical requirements for running the models are not yet detailed.