@inproceedings{yin-etal-2026-qwen,
title = "{Q}wen-Image-Layered 1.5: Unified Image Generation and Editing with Native Transparency Awareness",
author = "Yin, Shengming and
Zhang, Zekai and
Xu, Yixian and
Gao, Kaiyuan and
Wang, Zhendong and
Jiang, Lihan and
Tang, Ningyuan and
Wu, Tianhe and
Chen, Xiaoyue and
Zhang, Yanran and
Liu, Zihao and
Zhou, Zikai and
Li, Jiahao and
Xu, Xiao and
Yan, Kun and
Chen, Yuxiang and
Chen, Yilei and
Shu, Yan and
Zhang, Jie and
Shum, Heung-Yeung and
Ni, Lionel and
Zhou, Jingren and
Wu, Chenfei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2409/",
pages = "54711--54723",
ISBN = "979-8-89176-459-0",
abstract = "Recent advances in image generation and editing have been driven by powerful pretrained unified models, yet most existing approaches remain fundamentally raster-centric: they represent images as opaque pixels without natively modeling transparency. This limitation restricts their applicability to a broad range of real-world applications. In this work, we present Qwen-Image-Layered 1.5, a pretrained unified image generation and editing model with native transparency awareness. By jointly modeling visual appearance and transparency, our framework supports a wide range of both raster-centric and transparency-aware tasks within a single model. Notably, we observe that the model can perform transparent image editing in a zero-shot manner. To systematically evaluate this new task, we introduce TransEdit-Bench, the first benchmark specifically designed for transparent image editing. Extensive experiments across various tasks demonstrate strong performance and highlight the great potential of transparency-native image modeling as a unified paradigm for future image synthesis and editing."
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<abstract>Recent advances in image generation and editing have been driven by powerful pretrained unified models, yet most existing approaches remain fundamentally raster-centric: they represent images as opaque pixels without natively modeling transparency. This limitation restricts their applicability to a broad range of real-world applications. In this work, we present Qwen-Image-Layered 1.5, a pretrained unified image generation and editing model with native transparency awareness. By jointly modeling visual appearance and transparency, our framework supports a wide range of both raster-centric and transparency-aware tasks within a single model. Notably, we observe that the model can perform transparent image editing in a zero-shot manner. To systematically evaluate this new task, we introduce TransEdit-Bench, the first benchmark specifically designed for transparent image editing. Extensive experiments across various tasks demonstrate strong performance and highlight the great potential of transparency-native image modeling as a unified paradigm for future image synthesis and editing.</abstract>
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%0 Conference Proceedings
%T Qwen-Image-Layered 1.5: Unified Image Generation and Editing with Native Transparency Awareness
%A Yin, Shengming
%A Zhang, Zekai
%A Xu, Yixian
%A Gao, Kaiyuan
%A Wang, Zhendong
%A Jiang, Lihan
%A Tang, Ningyuan
%A Wu, Tianhe
%A Chen, Xiaoyue
%A Zhang, Yanran
%A Liu, Zihao
%A Zhou, Zikai
%A Li, Jiahao
%A Xu, Xiao
%A Yan, Kun
%A Chen, Yuxiang
%A Chen, Yilei
%A Shu, Yan
%A Zhang, Jie
%A Shum, Heung-Yeung
%A Ni, Lionel
%A Zhou, Jingren
%A Wu, Chenfei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yin-etal-2026-qwen
%X Recent advances in image generation and editing have been driven by powerful pretrained unified models, yet most existing approaches remain fundamentally raster-centric: they represent images as opaque pixels without natively modeling transparency. This limitation restricts their applicability to a broad range of real-world applications. In this work, we present Qwen-Image-Layered 1.5, a pretrained unified image generation and editing model with native transparency awareness. By jointly modeling visual appearance and transparency, our framework supports a wide range of both raster-centric and transparency-aware tasks within a single model. Notably, we observe that the model can perform transparent image editing in a zero-shot manner. To systematically evaluate this new task, we introduce TransEdit-Bench, the first benchmark specifically designed for transparent image editing. Extensive experiments across various tasks demonstrate strong performance and highlight the great potential of transparency-native image modeling as a unified paradigm for future image synthesis and editing.
%U https://aclanthology.org/2026.emnlp-main.2409/
%P 54711-54723
Markdown (Informal)
[Qwen-Image-Layered 1.5: Unified Image Generation and Editing with Native Transparency Awareness](https://aclanthology.org/2026.emnlp-main.2409/) (Yin et al., EMNLP 2026)
ACL
- Shengming Yin, Zekai Zhang, Yixian Xu, Kaiyuan Gao, Zhendong Wang, Lihan Jiang, Ningyuan Tang, Tianhe Wu, Xiaoyue Chen, Yanran Zhang, Zihao Liu, Zikai Zhou, Jiahao Li, Xiao Xu, Kun Yan, Yuxiang Chen, Yilei Chen, Yan Shu, Jie Zhang, Heung-Yeung Shum, Lionel Ni, Jingren Zhou, and Chenfei Wu. 2026. Qwen-Image-Layered 1.5: Unified Image Generation and Editing with Native Transparency Awareness. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54711–54723, Budapest, Hungary. Association for Computational Linguistics.