基于图像扩散先验的文本到矢量生成风格定制

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

May 15, 2025

作者: Peiying Zhang, Nanxuan Zhao, Jing Liao

cs.AI

摘要

可缩放矢量图形（SVG）因其分辨率独立性和层次分明的结构而深受设计师青睐。尽管现有的文本到矢量（T2V）生成方法能够根据文本提示创建SVG，但它们往往忽视了实际应用中的一个重要需求：风格定制，这对于生成视觉外观一致、美学连贯的矢量图形集合至关重要。扩展现有T2V方法以实现风格定制面临一定挑战。基于优化的T2V模型虽可利用文本到图像（T2I）模型的先验进行定制，但在保持结构规整性方面存在困难。另一方面，前馈式T2V模型虽能确保结构规整，却因SVG训练数据有限，在分离内容与风格时遇到难题。 针对这些挑战，我们提出了一种新颖的两阶段风格定制流程，用于SVG生成，充分利用了前馈式T2V模型和T2I图像先验的优势。在第一阶段，我们训练了一个采用路径级表示的T2V扩散模型，以确保SVG的结构规整性，同时保留多样化的表达能力。在第二阶段，通过蒸馏定制化的T2I模型，我们将T2V扩散模型适配到不同风格。通过整合这些技术，我们的流程能够以前馈方式高效地根据文本提示生成高质量且风格多样的定制SVG。大量实验验证了我们方法的有效性。项目页面请访问https://customsvg.github.io。

English

Scalable Vector Graphics (SVGs) are highly favored by designers due to their resolution independence and well-organized layer structure. Although existing text-to-vector (T2V) generation methods can create SVGs from text prompts, they often overlook an important need in practical applications: style customization, which is vital for producing a collection of vector graphics with consistent visual appearance and coherent aesthetics. Extending existing T2V methods for style customization poses certain challenges. Optimization-based T2V models can utilize the priors of text-to-image (T2I) models for customization, but struggle with maintaining structural regularity. On the other hand, feed-forward T2V models can ensure structural regularity, yet they encounter difficulties in disentangling content and style due to limited SVG training data. 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.