SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916735402115072 |
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| author | Zhang, Zhanjie Zhang, Quanwei Luan, Junsheng Yang, Mengyuan Wang, Yun Zhao, Lei |
| author_facet | Zhang, Zhanjie Zhang, Quanwei Luan, Junsheng Yang, Mengyuan Wang, Yun Zhao, Lei |
| contents | Given an arbitrary content and style image, arbitrary style transfer aims to render a new stylized
image which preserves the content image's structure and possesses the style image's style. Existing
arbitrary style transfer methods are based on either small models or pre-trained large-scale models.
The small model-based methods fail to generate high-quality stylized images, bringing artifacts and
disharmonious patterns. The pre-trained large-scale model-based methods can generate high-quality
stylized images but struggle to preserve the content structure and cost long inference time. To this
end, we propose a new framework, called SPAST, to generate high-quality stylized images with
less inference time. Specifically, we design a novel Local-global Window Size Stylization Module
(LGWSSM)tofuse style features into content features. Besides, we introduce a novel style prior loss,
which can dig out the style priors from a pre-trained large-scale model into the SPAST and motivate
the SPAST to generate high-quality stylized images with short inference time.We conduct abundant
experiments to verify that our proposed method can generate high-quality stylized images and less
inference time compared with the SOTA arbitrary style transfer methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model Zhang, Zhanjie Zhang, Quanwei Luan, Junsheng Yang, Mengyuan Wang, Yun Zhao, Lei Computer Vision and Pattern Recognition Given an arbitrary content and style image, arbitrary style transfer aims to render a new stylized image which preserves the content image's structure and possesses the style image's style. Existing arbitrary style transfer methods are based on either small models or pre-trained large-scale models. The small model-based methods fail to generate high-quality stylized images, bringing artifacts and disharmonious patterns. The pre-trained large-scale model-based methods can generate high-quality stylized images but struggle to preserve the content structure and cost long inference time. To this end, we propose a new framework, called SPAST, to generate high-quality stylized images with less inference time. Specifically, we design a novel Local-global Window Size Stylization Module (LGWSSM)tofuse style features into content features. Besides, we introduce a novel style prior loss, which can dig out the style priors from a pre-trained large-scale model into the SPAST and motivate the SPAST to generate high-quality stylized images with short inference time.We conduct abundant experiments to verify that our proposed method can generate high-quality stylized images and less inference time compared with the SOTA arbitrary style transfer methods. |
| title | SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.08695 |