Abstract
OmniStyle2 introduces a novel approach to artistic style transfer by reframing it as a data problem. Our key insight is destylization, reversing style transfer by removing stylistic elements from artworks to recover natural, style-free counterparts. This yields DST-100K, a large-scale dataset that provides authentic supervision signals by aligning real artistic styles with their underlying content. To build DST-100K, we develop (1) DST, a text-guided destylization model that reconstructs stylefree content, and (2) DST-Filter, a multi-stage evaluation model that employs Chain-of-Thought reasoning to automatically discard low-quality pairs while ensuring content fidelity and style accuracy. Leveraging DST-100K, we train OmniStyle2, a simple feed-forward model based on FLUX.1-dev. Despite its simplicity, OmniStyle2 consistently surpasses state-of-the-art methods across both qualitative and quantitative benchmarks. Our results demonstrate that scalable data generation via destylization provides a reliable supervision paradigm, overcoming the fundamental challenge posed by the lack of ground-truth data in artistic style transfer.
Abstract (translated)
OmniStyle2 引入了一种新颖的艺术风格转换方法，将这一过程重新定义为一个数据问题。我们的关键洞察是去风格化（destylization），即通过移除艺术品中的风格元素来逆转风格转移，从而恢复自然且无特定风格的原作。这种方法产生了 DST-100K 数据集，这是一个大规模的数据集，它提供真实的监督信号，将实际的艺术风格与其底层内容相匹配。 为了构建 DST-100K，我们开发了以下两个模型： 1. DST：一个文本引导去风格化（destylization）模型，用于重建无特定风格的内容。 2. DST-Filter：一个多阶段评估模型，采用链式思维推理（Chain-of-Thought reasoning），自动筛选出低质量的样本对，并确保内容的真实性和风格的准确性。 利用 DST-100K 数据集，我们训练了 OmniStyle2，这是一个基于 FLUX.1-dev 的简单前馈模型。尽管其结构相对简单，OmniStyle2 在定性与定量基准测试中始终优于现有的最先进方法。 我们的研究结果表明，通过去风格化生成可扩展的数据提供了可靠的监督范例，从而克服了艺术风格转移中由于缺乏真实数据所面临的根本挑战。
URL
https://arxiv.org/abs/2509.05970
