Style Customization of Text-to-Vector Generation with Image Diffusion Priors

To address these challenges, we propose a novel two-stage style customization pipeline for SVG generation, making use of the advantages of both feed-forward T2V models and T2I image priors.In the first stage, we train a T2V diffusion model with a path-level representation to ensure the structural regularity of SVGs while preserving diverse expressive capabilities.In the second stage, we customize the T2V diffusion model to different styles by distilling customized T2I models.By integrating these techniques, our pipeline can generate high-quality and diverse SVGs in custom styles based on text prompts in an efficient feed-forward manner.The effectiveness of our method has been validated through extensive experiments.The project page is https://customsvg.github.io.