Qwen-Image-Layered: 通过层分解实现内在可编辑性
Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
AI总结：
Qwen-Image-Layered通过层分解实现图像的内在可编辑性，提出端到端扩散模型及多阶段训练策略，提升图像分解质量与编辑一致性。
摘要：
最近的视觉生成模型在图像编辑中常常因位图的纠缠性质而难以保持一致性，其中所有视觉内容都被融合到单一画布中。相比之下，专业设计工具使用分层表示，允许孤立编辑同时保持一致性。受此启发，我们提出了Qwen-Image-Layered，一种端到端的扩散模型，能够将单个RGB图像分解为多个语义解耦的RGBA层，实现内在可编辑性，即每个RGBA层可以独立操作而不影响其他内容。为了支持可变长度的分解，我们引入了三个关键组件：（1）一个RGBA-VAE，统一RGB和RGBA图像的潜在表示；（2）一个VLD-MMDiT（可变层分解MMDiT）架构，能够分解任意数量的图像层；（3）一个多阶段训练策略，将预训练的图像生成模型适应为多层图像分解器。此外，为了解决高质量多层训练图像稀缺的问题，我们构建了一个从Photoshop文档（PSD）中提取和标注多层图像的流程。实验表明，我们的方法在分解质量上显著优于现有方法，并建立了一致图像编辑的新范式。我们的代码和模型已发布在https://github.com/QwenLM/Qwen-Image-Layered。
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
