FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion
  Models
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.
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Tong Wu
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2024-12-11T10:35:37.538Z
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Tong Wu
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Yinghao Xu
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2024-12-11T10:35:22.282Z
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Yinghao Xu
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Ryan Po
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Mengchen Zhang
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2024-12-11T09:19:34.086Z
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Zhang Mengchen
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Guandao Yang
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2024-12-11T10:35:11.580Z
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Guandao Yang
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Jiaqi Wang
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2024-12-11T09:19:36.146Z
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Jiaqi Wang
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Ziwei Liu
admin_assigned
2024-12-11T10:34:51.048Z
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Ziwei Liu
user
liuziwei7
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Dahua Lin
admin_assigned
2024-12-11T10:35:03.851Z
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Dahua Lin
user
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Gordon Wetzstein
admin_assigned
2024-12-11T10:34:57.689Z
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Gordon Wetzstein
user
wetzste1
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion
  Models
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.
A fine-grained visual attribute adaptation framework enables users to selectively apply specific attributes from source images into generated images using a new dataset.
fine-grained visual attributes visual attribute adaptation framework FiVA dataset visual attribute annotations user-friendly customization