Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911324409167872 |
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| author | Yin, Shengming Zhang, Zekai Tang, Zecheng Gao, Kaiyuan Xu, Xiao Yan, Kun Li, Jiahao Chen, Yilei Chen, Yuxiang Shum, Heung-Yeung Ni, Lionel M. Zhou, Jingren Lin, Junyang Wu, Chenfei |
| author_facet | Yin, Shengming Zhang, Zekai Tang, Zecheng Gao, Kaiyuan Xu, Xiao Yan, Kun Li, Jiahao Chen, Yilei Chen, Yuxiang Shum, Heung-Yeung Ni, Lionel M. Zhou, Jingren Lin, Junyang Wu, Chenfei |
| contents | Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose \textbf{Qwen-Image-Layered}, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling \textbf{inherent editability}, where each RGBA layer can be independently manipulated without affecting other content. To support variable-length decomposition, we introduce three key components: (1) an RGBA-VAE to unify the latent representations of RGB and RGBA images; (2) a VLD-MMDiT (Variable Layers Decomposition MMDiT) architecture capable of decomposing a variable number of image layers; and (3) a Multi-stage Training strategy to adapt a pretrained image generation model into a multilayer image decomposer. Furthermore, to address the scarcity of high-quality multilayer training images, we build a pipeline to extract and annotate multilayer images from Photoshop documents (PSD). Experiments demonstrate that our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing. Our code and models are released on \href{https://github.com/QwenLM/Qwen-Image-Layered}{https://github.com/QwenLM/Qwen-Image-Layered} |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_15603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition Yin, Shengming Zhang, Zekai Tang, Zecheng Gao, Kaiyuan Xu, Xiao Yan, Kun Li, Jiahao Chen, Yilei Chen, Yuxiang Shum, Heung-Yeung Ni, Lionel M. Zhou, Jingren Lin, Junyang Wu, Chenfei Computer Vision and Pattern Recognition Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose \textbf{Qwen-Image-Layered}, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling \textbf{inherent editability}, where each RGBA layer can be independently manipulated without affecting other content. To support variable-length decomposition, we introduce three key components: (1) an RGBA-VAE to unify the latent representations of RGB and RGBA images; (2) a VLD-MMDiT (Variable Layers Decomposition MMDiT) architecture capable of decomposing a variable number of image layers; and (3) a Multi-stage Training strategy to adapt a pretrained image generation model into a multilayer image decomposer. Furthermore, to address the scarcity of high-quality multilayer training images, we build a pipeline to extract and annotate multilayer images from Photoshop documents (PSD). Experiments demonstrate that our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing. Our code and models are released on \href{https://github.com/QwenLM/Qwen-Image-Layered}{https://github.com/QwenLM/Qwen-Image-Layered} |
| title | Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.15603 |