Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
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}
694373cc542d62d58a7bf6b4
Shengming Yin
694373cc542d62d58a7bf6b5
Zekai Zhang
694373cc542d62d58a7bf6b6
Zecheng Tang
claimed_verified
2026-01-21T09:25:45.046Z
64096ef79e9f790c905b846d
https://cdn-avatars.huggingface.co/v1/production/uploads/64096ef79e9f790c905b846d/hVzw656lXdzbCxTtnheud.jpeg
Zecheng Tang
ZetangForward
user
ZetangForward
694373cc542d62d58a7bf6b7
Kaiyuan Gao
694373cc542d62d58a7bf6b8
Xiao Xu
694373cc542d62d58a7bf6b9
Kun Yan
694373cc542d62d58a7bf6ba
Jiahao Li
694373cc542d62d58a7bf6bb
Yilei Chen
694373cc542d62d58a7bf6bc
Yuxiang Chen
694373cc542d62d58a7bf6bd
Heung-Yeung Shum
694373cc542d62d58a7bf6be
Lionel M. Ni
694373cc542d62d58a7bf6bf
Jingren Zhou
694373cc542d62d58a7bf6c0
Junyang Lin
694373cc542d62d58a7bf6c1
Chenfei Wu
Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
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}
A diffusion model for decomposing RGB images into semantically disentangled RGBA layers enables consistent image editing through independent manipulation of individual layers.
diffusion models RGBA-VAE VLD-MMDiT Multi-stage Training image decomposition semantically disentangled layers image editing consistency