Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
Authors: name: Shengming Yin; name: Zekai Zhang; name: Zecheng Tang; name: Kaiyuan Gao; name: Xiao Xu; name: Kun Yan; name: Jiahao Li; name: Yilei Chen; name: Yuxiang Chen; name: Heung-Yeung Shum; name: Lionel M. Ni; name: Jingren Zhou; name: Junyang Lin; name: Chenfei Wu
Published: 2025-12-17 17:12:42

Abstract: 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 Qwen-Image-Layered, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling 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 https://github.com/QwenLM/Qwen-Image-Layered{https://github.com/QwenLM/Qwen-Image-Layered}