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.
