Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models

TL;DR

Chat2SVG introduces a hybrid AI framework combining language models and diffusion models to generate high-quality, editable SVG vector graphics from text prompts, improving accessibility and visual fidelity.

Contribution

The paper presents a novel hybrid approach that integrates LLMs and diffusion models for text-to-SVG generation, addressing shape regularity and expressiveness limitations of prior methods.

Findings

Outperforms existing methods in visual fidelity and semantic alignment

Enables intuitive editing with natural language instructions

Produces more regular and complex SVG paths

Abstract

Scalable Vector Graphics (SVG) has become the de facto standard for vector graphics in digital design, offering resolution independence and precise control over individual elements. Despite their advantages, creating high-quality SVG content remains challenging, as it demands technical expertise with professional editing software and a considerable time investment to craft complex shapes. Recent text-to-SVG generation methods aim to make vector graphics creation more accessible, but they still encounter limitations in shape regularity, generalization ability, and expressiveness. To address these challenges, we introduce Chat2SVG, a hybrid framework that combines the strengths of Large Language Models (LLMs) and image diffusion models for text-to-SVG generation. Our approach first uses an LLM to generate semantically meaningful SVG templates from basic geometric primitives. Guided by image diffusion models, a dual-stage optimization pipeline refines paths in latent space and adjusts point coordinates to enhance geometric complexity. Extensive experiments show that Chat2SVG outperforms existing methods in visual fidelity, path regularity, and semantic alignment. Additionally, our system enables intuitive editing through natural language instructions, making professional vector graphics creation accessible to all users.