title: CSGO: Content-Style Composition in Text-to-Image Generation
summary: The diffusion model has shown exceptional capabilities in controlled image
  generation, which has further fueled interest in image style transfer. Existing
  works mainly focus on training free-based methods (e.g., image inversion) due
  to the scarcity of specific data. In this study, we present a data construction
  pipeline for content-style-stylized image triplets that generates and
  automatically cleanses stylized data triplets. Based on this pipeline, we
  construct a dataset IMAGStyle, the first large-scale style transfer dataset
  containing 210k image triplets, available for the community to explore and
  research. Equipped with IMAGStyle, we propose CSGO, a style transfer model
  based on end-to-end training, which explicitly decouples content and style
  features employing independent feature injection. The unified CSGO implements
  image-driven style transfer, text-driven stylized synthesis, and text
  editing-driven stylized synthesis. Extensive experiments demonstrate the
  effectiveness of our approach in enhancing style control capabilities in image
  generation. Additional visualization and access to the source code can be
  located on the project page: https://csgo-gen.github.io/.
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paper_authors.name: Peng Xing
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paper_authors.name: Haofan Wang
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paper_authors.name: Yanpeng Sun
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paper_authors.name: Qixun Wang
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paper_authors.fullname: Qixun Wang
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paper_authors.name: Xu Bai
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paper_authors.name: Hao Ai
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paper_authors.user: AisingioroHao0
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paper_authors.name: Renyuan Huang
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paper_authors.fullname: Jen-Yuan Huang
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paper_authors._id: 66d126452ba3de746a83136e
paper_authors.name: Zechao Li
paper_title: CSGO: Content-Style Composition in Text-to-Image Generation
paper_summary: The diffusion model has shown exceptional capabilities in controlled image
  generation, which has further fueled interest in image style transfer. Existing
  works mainly focus on training free-based methods (e.g., image inversion) due
  to the scarcity of specific data. In this study, we present a data construction
  pipeline for content-style-stylized image triplets that generates and
  automatically cleanses stylized data triplets. Based on this pipeline, we
  construct a dataset IMAGStyle, the first large-scale style transfer dataset
  containing 210k image triplets, available for the community to explore and
  research. Equipped with IMAGStyle, we propose CSGO, a style transfer model
  based on end-to-end training, which explicitly decouples content and style
  features employing independent feature injection. The unified CSGO implements
  image-driven style transfer, text-driven stylized synthesis, and text
  editing-driven stylized synthesis. Extensive experiments demonstrate the
  effectiveness of our approach in enhancing style control capabilities in image
  generation. Additional visualization and access to the source code can be
  located on the project page: https://csgo-gen.github.io/.
paper_ai_summary: A data construction pipeline and dataset called IMAGStyle enable a style transfer model named CSGO to enhance style control in image generation through end-to-end training and independent feature injection.
paper_ai_keywords: diffusion model image style transfer data construction pipeline content-style-stylized image triplets IMAGStyle style transfer model CSGO end-to-end training feature injection image-driven style transfer text-driven stylized synthesis text editing-driven stylized synthesis