Qwen-Image Technical Report
We present Qwen-Image, an image generation foundation model in the Qwen
series that achieves significant advances in complex text rendering and precise
image editing. To address the challenges of complex text rendering, we design a
comprehensive data pipeline that includes large-scale data collection,
filtering, annotation, synthesis, and balancing. Moreover, we adopt a
progressive training strategy that starts with non-text-to-text rendering,
evolves from simple to complex textual inputs, and gradually scales up to
paragraph-level descriptions. This curriculum learning approach substantially
enhances the model's native text rendering capabilities. As a result,
Qwen-Image not only performs exceptionally well in alphabetic languages such as
English, but also achieves remarkable progress on more challenging logographic
languages like Chinese. To enhance image editing consistency, we introduce an
improved multi-task training paradigm that incorporates not only traditional
text-to-image (T2I) and text-image-to-image (TI2I) tasks but also
image-to-image (I2I) reconstruction, effectively aligning the latent
representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed
the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and
reconstructive representations, respectively. This dual-encoding mechanism
enables the editing module to strike a balance between preserving semantic
consistency and maintaining visual fidelity. Qwen-Image achieves
state-of-the-art performance, demonstrating its strong capabilities in both
image generation and editing across multiple benchmarks.
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Chenfei Wu
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Jiahao Li
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Jingren Zhou
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Junyang Lin
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Kaiyuan Gao
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Kun Yan
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Sheng-ming Yin
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Shuai Bai
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Xiao Xu
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Yilei Chen
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Yuxiang Chen
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Zecheng Tang
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Zekai Zhang
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Zhengyi Wang
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An Yang
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Bowen Yu
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Chen Cheng
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Dayiheng Liu
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Deqing Li
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Hang Zhang
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Hao Meng
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Hu Wei
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Jingyuan Ni
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Kai Chen
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Kuan Cao
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Liang Peng
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Lin Qu
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Minggang Wu
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Peng Wang
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Shuting Yu
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Tingkun Wen
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Wensen Feng
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Xiaoxiao Xu
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Yi Wang
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Yichang Zhang
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Yongqiang Zhu
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Yujia Wu
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Yuxuan Cai
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Zenan Liu
Qwen-Image Technical Report
We present Qwen-Image, an image generation foundation model in the Qwen
series that achieves significant advances in complex text rendering and precise
image editing. To address the challenges of complex text rendering, we design a
comprehensive data pipeline that includes large-scale data collection,
filtering, annotation, synthesis, and balancing. Moreover, we adopt a
progressive training strategy that starts with non-text-to-text rendering,
evolves from simple to complex textual inputs, and gradually scales up to
paragraph-level descriptions. This curriculum learning approach substantially
enhances the model's native text rendering capabilities. As a result,
Qwen-Image not only performs exceptionally well in alphabetic languages such as
English, but also achieves remarkable progress on more challenging logographic
languages like Chinese. To enhance image editing consistency, we introduce an
improved multi-task training paradigm that incorporates not only traditional
text-to-image (T2I) and text-image-to-image (TI2I) tasks but also
image-to-image (I2I) reconstruction, effectively aligning the latent
representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed
the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and
reconstructive representations, respectively. This dual-encoding mechanism
enables the editing module to strike a balance between preserving semantic
consistency and maintaining visual fidelity. Qwen-Image achieves
state-of-the-art performance, demonstrating its strong capabilities in both
image generation and editing across multiple benchmarks.
Qwen-Image, an image generation model, advances text rendering and image editing through a comprehensive data pipeline, progressive training, and dual-encoding mechanism.
data pipeline progressive training curriculum learning text-to-image text-image-to-image image-to-image latent representations dual-encoding mechanism semantic consistency visual fidelity