OmniStyle: Filtering High Quality Style Transfer Data at Scale

TL;DR

OmniStyle introduces a large-scale, high-quality style transfer dataset and a filtering framework, enabling efficient, controllable, and high-resolution style transfer with superior performance.

Contribution

The paper presents OmniStyle-1M dataset and OmniFilter framework, advancing style transfer with scalable data, quality assessment, and a diffusion transformer-based model.

Findings

Superior qualitative and quantitative results

Effective filtering of high-quality triplets

Supports high-resolution, instruction-guided style transfer

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.