Abstract
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style categories, each enhanced with textual descriptions and instruction prompts. We show that OmniStyle-1M can not only enable efficient and scalable of style transfer models through supervised training but also facilitate precise control over target stylization. Especially, to ensure the quality of the dataset, we introduce OmniFilter, a comprehensive style transfer quality assessment framework, which filters high-quality triplets based on content preservation, style consistency, and aesthetic appeal. Building upon this foundation, we propose OmniStyle, a framework based on the Diffusion Transformer (DiT) architecture designed for high-quality and efficient style transfer. This framework supports both instruction-guided and image-guided style transfer, generating high resolution outputs with exceptional detail. Extensive qualitative and quantitative evaluations demonstrate OmniStyle's superior performance compared to existing approaches, highlighting its efficiency and versatility. OmniStyle-1M and its accompanying methodologies provide a significant contribution to advancing high-quality style transfer, offering a valuable resource for the research community.
Abstract (translated)
在这篇论文中，我们介绍了OmniStyle-1M，这是一个大规模的配对风格迁移数据集，包含超过一百万张内容-样式-渲染后的图像三元组，涵盖了1000种多样化的风格类别，并且每一种都配备了文本描述和指令提示。我们展示了OmniStyle-1M不仅可以通过监督训练实现高效可扩展的风格迁移模型开发，还能促进对目标样式的精准控制。特别是为了确保数据集的质量，我们引入了OmniFilter，一个全面的风格迁移质量评估框架，该框架基于内容保留、风格一致性和美学吸引力过滤出高质量的三元组。 在此基础上，我们提出了OmniStyle框架，该框架基于Diffusion Transformer（DiT）架构设计，旨在实现高质量和高效的风格迁移。此框架支持指令引导和图像引导的风格迁移，能够生成高分辨率且细节出色的输出。 广泛的定性和定量评估表明，与现有方法相比，OmniStyle具有显著优越的表现，突显了其效率和灵活性。OmniStyle-1M及其相关方法为推动高质量风格迁移的发展做出了重大贡献，提供了宝贵的资源给研究社区。
URL
https://arxiv.org/abs/2505.14028
