titleQwen-Image-2.0 Technical Report
authorsBing Zhao, Chenfei Wu, Deqing Li, Hao Meng, Jiahao Li, Jie Zhang, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kuan Cao, Kun Yan, Liang Peng, Lihan Jiang, Niantong Li, Ningyuan Tang, Shengming Yin, Tianhe Wu, Xiao Xu, Xiaoyue Chen, Xihua Wang, Yan Shu, Yanran Zhang, Yi Wang, Yilei Chen, Ying Ba, Yixian Xu, Yujia Wu, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhendong Wang, Zihao Liu, Zikai Zhou, An Yang, Chen Cheng, Chenxu Lv, Dayiheng Liu, Fan Zhou, Hantian Xiong, Hongzhu Shi, Hu Wei, Huihong Zhao, Ivy Liu, Jianwei Zhang, Jiawei Zhang, Kai Chen, Kang He, Levon Xue, Lin Qu, Linhan Tang, Luwen Feng, Minggang Wu, Minmin Sun, Na Ni, Rui Men, Shuai Bai, Sishou Zheng, Tao Lan, Tianqi Zhang, Tingkun Wen, Wei Wang, Weixu Qiao, Weiyi Lu, Wenmeng Zhou, Xiaodong Deng, Xiaoxiao Xu, Xinlei Fang, Xionghui Chen, Yanan Wang, Yang Fan, Yichang Zhang, Yixuan Xu, Yu Wu, Zhiyuan Ma, Zhizhi Cai
date2026-05-11
venuearXiv (preprint, not peer reviewed)
idarXiv:2605.10730
affiliationQwen
upvotes119 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractWe present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robust instruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. This enables strong multimodal understanding while preserving flexible generation and editing capabilities. The model supports instructions of up to 1K tokens for generating text-rich content such as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhances photorealistic generation with richer details, more realistic textures, and coherent lighting, and follows complex prompts more reliably across diverse styles. Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation models.

cite

@misc{zhao2026qwen,
  title={Qwen-Image-2.0 Technical Report},
  author={Bing Zhao and Chenfei Wu and Deqing Li and Hao Meng and Jiahao Li and Jie Zhang and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kuan Cao and Kun Yan and Liang Peng and Lihan Jiang and Niantong Li and Ningyuan Tang and Shengming Yin and Tianhe Wu and Xiao Xu and Xiaoyue Chen and Xihua Wang and Yan Shu and Yanran Zhang and Yi Wang and Yilei Chen and Ying Ba and Yixian Xu and Yujia Wu and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhendong Wang and Zihao Liu and Zikai Zhou and An Yang and Chen Cheng and Chenxu Lv and Dayiheng Liu and Fan Zhou and Hantian Xiong and Hongzhu Shi and Hu Wei and Huihong Zhao and Ivy Liu and Jianwei Zhang and Jiawei Zhang and Kai Chen and Kang He and Levon Xue and Lin Qu and Linhan Tang and Luwen Feng and Minggang Wu and Minmin Sun and Na Ni and Rui Men and Shuai Bai and Sishou Zheng and Tao Lan and Tianqi Zhang and Tingkun Wen and Wei Wang and Weixu Qiao and Weiyi Lu and Wenmeng Zhou and Xiaodong Deng and Xiaoxiao Xu and Xinlei Fang and Xionghui Chen and Yanan Wang and Yang Fan and Yichang Zhang and Yixuan Xu and Yu Wu and Zhiyuan Ma and Zhizhi Cai},
  year={2026},
  eprint={2605.10730},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.10730}
}