OmniStyle: Filtering High Quality Style Transfer Data at Scale
This work addresses the need for scalable, high-quality datasets in style transfer research, offering a valuable resource for the community, though it is incremental in building upon existing methods like Diffusion Transformers.
The paper tackles the problem of generating high-quality style transfer data at scale by introducing OmniStyle-1M, a large-scale paired dataset with over one million image triplets across 1,000 style categories, and shows that it enables efficient training and precise control for style transfer models, with OmniStyle achieving superior performance in evaluations.
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.