构建百万级高质量风格迁移数据集，支持精准可控的图像风格化。
OmniStyle: Filtering High Quality Style Transfer Data at Scale
- 基于扩散变压器架构，实现图文与图像双引导风格迁移。
- 筛选100万组三元组，内容保留率高、风格一致且美学评分优。
- 适合需要高精度、高分辨率风格迁移的研究与应用开发。
本文提出OmniStyle-1M，一个包含超过一百万对内容-风格-风格化图像三元组的大规模配对风格迁移数据集，覆盖1000种不同风格类别，每项均配有文本描述和指令提示。该数据集支持监督训练，可高效构建风格迁移模型，并实现对目标风格的精确控制。为保障数据质量，我们设计OmniFilter框架，从内容保留度、风格一致性及审美吸引力三个维度筛选优质三元组。在此基础上，提出OmniStyle框架，基于扩散变压器（DiT）架构，支持指令引导与图像引导两种风格迁移方式，生成高分辨率且细节丰富的输出。大量定性与定量评估表明，OmniStyle在性能上优于现有方法，展现出高效与多功能优势。OmniStyle-1M及其配套方法为高质量风格迁移研究提供了重要资源。
原文摘要 · Abstract (English)
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.
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