title: FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion
    Models
summary: Recent advances in text-to-image generation have enabled the creation of
  high-quality images with diverse applications. However, accurately describing
  desired visual attributes can be challenging, especially for non-experts in art
  and photography. An intuitive solution involves adopting favorable attributes
  from the source images. Current methods attempt to distill identity and style
  from source images. However, "style" is a broad concept that includes texture,
  color, and artistic elements, but does not cover other important attributes
  such as lighting and dynamics. Additionally, a simplified "style" adaptation
  prevents combining multiple attributes from different sources into one
  generated image. In this work, we formulate a more effective approach to
  decompose the aesthetics of a picture into specific visual attributes, allowing
  users to apply characteristics such as lighting, texture, and dynamics from
  different images. To achieve this goal, we constructed the first fine-grained
  visual attributes dataset (FiVA) to the best of our knowledge. This FiVA
  dataset features a well-organized taxonomy for visual attributes and includes
  around 1 M high-quality generated images with visual attribute annotations.
  Leveraging this dataset, we propose a fine-grained visual attribute adaptation
  framework (FiVA-Adapter), which decouples and adapts visual attributes from one
  or more source images into a generated one. This approach enhances
  user-friendly customization, allowing users to selectively apply desired
  attributes to create images that meet their unique preferences and specific
  content requirements.
paper_authors._id: 67592cfa672bea990a20b0d1
paper_authors.name: Tong Wu
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paper_authors.fullname: Tong Wu
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paper_authors.name: Yinghao Xu
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paper_authors.fullname: Yinghao Xu
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paper_authors.user: justimyhxu
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paper_authors.name: Ryan Po
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paper_authors.name: Mengchen Zhang
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paper_authors.fullname: Zhang Mengchen
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paper_authors.user: Dubhe-zmc
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paper_authors.name: Guandao Yang
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paper_authors.fullname: Guandao Yang
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paper_authors.user: guandao
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paper_authors.name: Jiaqi Wang
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paper_authors.avatarUrl: /avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
paper_authors.fullname: Jiaqi Wang
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paper_authors.user: myownskyW7
paper_authors._id: 67592cfa672bea990a20b0d7
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 67592cfa672bea990a20b0d8
paper_authors.name: Dahua Lin
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paper_authors.fullname: Dahua Lin
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paper_authors.user: lindahua
paper_authors._id: 67592cfa672bea990a20b0d9
paper_authors.name: Gordon Wetzstein
paper_authors.status: admin_assigned
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paper_authors.fullname: Gordon Wetzstein
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paper_title: FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion
    Models
paper_summary: Recent advances in text-to-image generation have enabled the creation of
  high-quality images with diverse applications. However, accurately describing
  desired visual attributes can be challenging, especially for non-experts in art
  and photography. An intuitive solution involves adopting favorable attributes
  from the source images. Current methods attempt to distill identity and style
  from source images. However, "style" is a broad concept that includes texture,
  color, and artistic elements, but does not cover other important attributes
  such as lighting and dynamics. Additionally, a simplified "style" adaptation
  prevents combining multiple attributes from different sources into one
  generated image. In this work, we formulate a more effective approach to
  decompose the aesthetics of a picture into specific visual attributes, allowing
  users to apply characteristics such as lighting, texture, and dynamics from
  different images. To achieve this goal, we constructed the first fine-grained
  visual attributes dataset (FiVA) to the best of our knowledge. This FiVA
  dataset features a well-organized taxonomy for visual attributes and includes
  around 1 M high-quality generated images with visual attribute annotations.
  Leveraging this dataset, we propose a fine-grained visual attribute adaptation
  framework (FiVA-Adapter), which decouples and adapts visual attributes from one
  or more source images into a generated one. This approach enhances
  user-friendly customization, allowing users to selectively apply desired
  attributes to create images that meet their unique preferences and specific
  content requirements.
paper_ai_keywords: text-to-image generation fine-grained visual attributes visual attributes lighting texture dynamics fine-grained visual attributes dataset (FiLM) FiVA-Dataset fine-grained visual attribute adaptation framework (FiVA-Adapter) decoupling visual attributes adapts visual attributes user-friendly customization high-quality images