Qwen-Image Technical Report
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei
2025-08-05
Summary
This paper talks about Qwen-Image, an advanced AI model designed for generating and editing images with a special focus on rendering text clearly and accurately within images.
What's the problem?
The problem is that many current image generation models struggle to include text properly in images, especially with complex layouts, multiple languages, and detailed styles, which limits their usefulness in creating visuals like posters or documents with readable text.
What's the solution?
Qwen-Image addresses this by using a comprehensive data pipeline, progressive training techniques, and a dual-encoding mechanism that allows it to handle both visual and textual information effectively. This helps the model generate images with precise text rendering, sophisticated editing features, and strong understanding of image content across different languages.
Why it matters?
This matters because it enables more accurate and creative image generation and editing, making it easier to create professional-quality visuals that combine text and graphics, which can be used in marketing, design, education, and many other areas.
Abstract
Qwen-Image, an image generation model, advances text rendering and image editing through a comprehensive data pipeline, progressive training, and dual-encoding mechanism.