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.

翻译：近年来，文本到图像生成技术的进步使得能够创建高质量图像，并具有多样化的应用。然而，准确描述所需的视觉属性可能具有挑战性，特别是对于艺术和摄影领域的非专业人士而言。一种直观的解决方案涉及从源图像中采用有利的属性。当前的方法试图从源图像中提取身份和风格。然而，“风格”是一个宽泛的概念，包括纹理、色彩和艺术元素，但并未涵盖其他重要属性，如光照和动态效果。此外，简化的“风格”适配阻碍了将来自不同源的多个属性组合到一张生成的图像中。在这项工作中，我们提出了一种更有效的方法，将图片的美学分解为特定的视觉属性，允许用户应用来自不同图像的特性，如光照、纹理和动态效果。为了实现这一目标，我们构建了据我们所知首个细粒度视觉属性数据集（FiVA）。该FiVA数据集具有组织良好的视觉属性分类体系，并包含约100万张带有视觉属性标注的高质量生成图像。利用该数据集，我们提出了一个细粒度视觉属性适配框架（FiVA-Adapter），该框架将一个或多个源图像中的视觉属性解耦并适配到生成的图像中。这种方法增强了用户友好的定制能力，允许用户有选择地应用所需属性，以创建满足其独特偏好和特定内容需求的图像。