title: Style Customization of Text-to-Vector Generation with Image Diffusion
    Priors
summary: 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.
paper_authors._id: 6826bc3cf032d8147549ac6b
paper_authors.name: Peiying Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-16T04:17:01.151Z
paper_authors._id: 635eac5ea81c7f7424a23b8c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/635eac5ea81c7f7424a23b8c/Cei8xiGF5jrwscDpMgftt.jpeg
paper_authors.fullname: intchous
paper_authors.type: user
paper_authors.user: intchous
paper_authors._id: 6826bc3cf032d8147549ac6c
paper_authors.name: Nanxuan Zhao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-16T04:17:01.151Z
paper_authors._id: 6412b90e6e51a8e21887ff30
paper_authors.avatarUrl: /avatars/b0c3a8624a686481b9f609be96b1307c.svg
paper_authors.fullname: Z
paper_authors.type: user
paper_authors.user: CHERRY-Z
paper_authors._id: 6826bc3cf032d8147549ac6d
paper_authors.name: Jing Liao
paper_title: Style Customization of Text-to-Vector Generation with Image Diffusion
    Priors
paper_summary: 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.
paper_ai_summary: A two-stage pipeline using a T2V diffusion model with path-level representation and distillation of customized T2I models achieves high-quality and diverse SVG generation with style customization.
paper_ai_keywords: T2V diffusion model path-level representation structural regularity text-to-vector text-to-image style customization T2I models SVG generation