Qwen-Image-Layered：通过图层分解实现内在可编辑性
Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
把单张 RGB 图分解为多个语义解耦的 RGBA 图层，使编辑只需改动单个图层即可保持其余内容一致；RGBA-VAE 统一 RGB/RGBA 潜变量表示与可变层数 MMDiT 的设计，可直接迁移到需要分层表示的编辑、合成与重渲染流程。
原文依据 decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling inherent editability
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期视觉生成模型在图像编辑过程中常常难以保持一致性，原因在于栅格图像的各部分相互纠缠，所有视觉内容都被融合在单一画布上。与之相反，专业设计工具采用分层表示，从而能够在保持一致性的同时进行孤立编辑。受此启发，我们提出 Qwen-Image-Layered，这是一个端到端扩散模型，可将单张 RGB 图像分解为多个语义解耦的 RGBA 图层，从而实现内在可编辑性，即每个 RGBA 图层都可被独立操作而不影响其他内容。为支持变长分解，我们引入三个关键组件：(1) RGBA-VAE，用于统一 RGB 图像与 RGBA 图像的潜变量表示；(2) VLD-MMDiT（可变图层分解 MMDiT）架构，能够分解可变数量的图像图层；(3) 多阶段训练策略，用于将预训练的图像生成模型适配为多层图像分解器。此外，为缓解高质量多层训练图像稀缺的问题，我们构建了一条从 Photoshop 文档（PSD）中提取并标注多层图像的流水线。实验表明，我们的方法在分解质量上显著超越现有方法，并为一致性图像编辑建立了新的范式。
英文原文摘要 1342 字符
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.