Copertina

Qwen-Image-2.0 Technical Report

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhao, Bing, Wu, Chenfei, Li, Deqing, Meng, Hao, Li, Jiahao, Zhang, Jie, Zhou, Jingren, Lin, Junyang, Gao, Kaiyuan, Cao, Kuan, Yan, Kun, Peng, Liang, Jiang, Lihan, Li, Niantong, Tang, Ningyuan, Yin, Shengming, Wu, Tianhe, Xu, Xiao, Chen, Xiaoyue, Wang, Xihua, Shu, Yan, Zhang, Yanran, Wang, Yi, Chen, Yilei, Ba, Ying, Xu, Yixian, Wu, Yujia, Chen, Yuxiang, Tang, Zecheng, Zhang, Zekai, Wang, Zhendong, Liu, Zihao, Zhou, Zikai, Yang, An, Cheng, Chen, Lv, Chenxu, Liu, Dayiheng, Zhou, Fan, Xiong, Hantian, Shi, Hongzhu, Wei, Hu, Zhao, Huihong, Liu, Ivy, Zhang, Jianwei, Zhang, Jiawei, Chen, Kai, He, Kang, Xue, Levon, Qu, Lin, Tang, Linhan, Feng, Luwen, Wu, Minggang, Sun, Minmin, Ni, Na, Men, Rui, Bai, Shuai, Zheng, Sishou, Lan, Tao, Zhang, Tianqi, Wen, Tingkun, Wang, Wei, Qiao, Weixu, Lu, Weiyi, Zhou, Wenmeng, Deng, Xiaodong, Xu, Xiaoxiao, Fang, Xinlei, Chen, Xionghui, Wang, Yanan, Fan, Yang, Zhang, Yichang, Xu, Yixuan, Wu, Yu, Ma, Zhiyuan, Cai, Zhizhi
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
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_version_	1866917480986836992
author	Zhao, Bing Wu, Chenfei Li, Deqing Meng, Hao Li, Jiahao Zhang, Jie Zhou, Jingren Lin, Junyang Gao, Kaiyuan Cao, Kuan Yan, Kun Peng, Liang Jiang, Lihan Li, Niantong Tang, Ningyuan Yin, Shengming Wu, Tianhe Xu, Xiao Chen, Xiaoyue Wang, Xihua Shu, Yan Zhang, Yanran Wang, Yi Chen, Yilei Ba, Ying Xu, Yixian Wu, Yujia Chen, Yuxiang Tang, Zecheng Zhang, Zekai Wang, Zhendong Liu, Zihao Zhou, Zikai Yang, An Cheng, Chen Lv, Chenxu Liu, Dayiheng Zhou, Fan Xiong, Hantian Shi, Hongzhu Wei, Hu Zhao, Huihong Liu, Ivy Zhang, Jianwei Zhang, Jiawei Chen, Kai He, Kang Xue, Levon Qu, Lin Tang, Linhan Feng, Luwen Wu, Minggang Sun, Minmin Ni, Na Men, Rui Bai, Shuai Zheng, Sishou Lan, Tao Zhang, Tianqi Wen, Tingkun Wang, Wei Qiao, Weixu Lu, Weiyi Zhou, Wenmeng Deng, Xiaodong Xu, Xiaoxiao Fang, Xinlei Chen, Xionghui Wang, Yanan Fan, Yang Zhang, Yichang Xu, Yixuan Wu, Yu Ma, Zhiyuan Cai, Zhizhi
author_facet	Zhao, Bing Wu, Chenfei Li, Deqing Meng, Hao Li, Jiahao Zhang, Jie Zhou, Jingren Lin, Junyang Gao, Kaiyuan Cao, Kuan Yan, Kun Peng, Liang Jiang, Lihan Li, Niantong Tang, Ningyuan Yin, Shengming Wu, Tianhe Xu, Xiao Chen, Xiaoyue Wang, Xihua Shu, Yan Zhang, Yanran Wang, Yi Chen, Yilei Ba, Ying Xu, Yixian Wu, Yujia Chen, Yuxiang Tang, Zecheng Zhang, Zekai Wang, Zhendong Liu, Zihao Zhou, Zikai Yang, An Cheng, Chen Lv, Chenxu Liu, Dayiheng Zhou, Fan Xiong, Hantian Shi, Hongzhu Wei, Hu Zhao, Huihong Liu, Ivy Zhang, Jianwei Zhang, Jiawei Chen, Kai He, Kang Xue, Levon Qu, Lin Tang, Linhan Feng, Luwen Wu, Minggang Sun, Minmin Ni, Na Men, Rui Bai, Shuai Zheng, Sishou Lan, Tao Zhang, Tianqi Wen, Tingkun Wang, Wei Qiao, Weixu Lu, Weiyi Zhou, Wenmeng Deng, Xiaodong Xu, Xiaoxiao Fang, Xinlei Chen, Xionghui Wang, Yanan Fan, Yang Zhang, Yichang Xu, Yixuan Wu, Yu Ma, Zhiyuan Cai, Zhizhi
contents	We 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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2605_10730
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Qwen-Image-2.0 Technical Report Zhao, Bing Wu, Chenfei Li, Deqing Meng, Hao Li, Jiahao Zhang, Jie Zhou, Jingren Lin, Junyang Gao, Kaiyuan Cao, Kuan Yan, Kun Peng, Liang Jiang, Lihan Li, Niantong Tang, Ningyuan Yin, Shengming Wu, Tianhe Xu, Xiao Chen, Xiaoyue Wang, Xihua Shu, Yan Zhang, Yanran Wang, Yi Chen, Yilei Ba, Ying Xu, Yixian Wu, Yujia Chen, Yuxiang Tang, Zecheng Zhang, Zekai Wang, Zhendong Liu, Zihao Zhou, Zikai Yang, An Cheng, Chen Lv, Chenxu Liu, Dayiheng Zhou, Fan Xiong, Hantian Shi, Hongzhu Wei, Hu Zhao, Huihong Liu, Ivy Zhang, Jianwei Zhang, Jiawei Chen, Kai He, Kang Xue, Levon Qu, Lin Tang, Linhan Feng, Luwen Wu, Minggang Sun, Minmin Ni, Na Men, Rui Bai, Shuai Zheng, Sishou Lan, Tao Zhang, Tianqi Wen, Tingkun Wang, Wei Qiao, Weixu Lu, Weiyi Zhou, Wenmeng Deng, Xiaodong Xu, Xiaoxiao Fang, Xinlei Chen, Xionghui Wang, Yanan Fan, Yang Zhang, Yichang Xu, Yixuan Wu, Yu Ma, Zhiyuan Cai, Zhizhi Computer Vision and Pattern Recognition We 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.
title	Qwen-Image-2.0 Technical Report
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2605.10730