Abstract
Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style features. In this technical report, we propose the first content-preserving style transfer model trained on Qwen-Image-Edit, which activates Qwen-Image-Edit's strong content preservation and style customization capability. We collected and filtered high quality data of limited specific styles and synthesized triplets with thousands categories of style images in-the-wild. We introduce the Curriculum Continual Learning framework to train QwenStyle with such mixture of clean and noisy triplets, which enables QwenStyle to generalize to unseen styles without degradation of the precise content preservation capability. Our QwenStyle V1 achieves state-of-the-art performance in three core metrics: style similarity, content consistency, and aesthetic quality.
Abstract (translated)
基于给定内容和风格参考，对于扩散变换器（DiT）而言，进行保留内容的风格迁移仍然是一项挑战，因为其内部的内容和风格特征相互纠缠。在这份技术报告中，我们提出了第一个在Qwen-Image-Edit上训练的、能够保持内容并定制风格的能力的保留内容的风格迁移模型。我们收集并过滤了高质量且具有特定风格的数据，并合成含有数千种不同风格图片的三元组数据集。为了使用这种混合清洁与噪点三元组数据训练QwenStyle，我们引入了一个课程连续学习框架，使QwenStyle能够泛化到未见过的风格上，同时不会降低精准的内容保持能力。我们的QwenStyle V1在三个核心指标上取得了最先进的性能：风格相似度、内容一致性以及美学质量。
URL
https://arxiv.org/abs/2601.06202
