QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit

2026-01-08

arXiv.org

AI Summary

This work addresses the challenge of effectively disentangling content and style in content-preserving style transfer using diffusion transformers (DiTs), a task where existing methods often suffer from content distortion or stylistic inaccuracies. We propose QwenStyle V1, the first DiT-based model for this task, unlocking the potential of Qwen-Image-Edit in high-fidelity style transfer. By synthesizing high-quality in-the-wild style triplets and incorporating a curriculum continual learning framework, our model generalizes robustly to unseen styles—even when trained on a mixture of clean and noisy data—while preserving content fidelity. Extensive experiments demonstrate that QwenStyle V1 achieves state-of-the-art performance across three key metrics: style similarity, content consistency, and aesthetic quality.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Content-Preserving Style Transfer
• Diffusion Transformers
• Style Transfer
• Content-Style Disentanglement

Innovation

• Content-Preserving Style Transfer
• Diffusion Transformers
• Curriculum Continual Learning
• Qwen-Image-Edit
• Style Generalization