ART：匿名区域变换器——面向可变多层透明图像生成

ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation

February 25, 2025

作者: Yifan Pu, Yiming Zhao, Zhicong Tang, Ruihong Yin, Haoxing Ye, Yuhui Yuan, Dong Chen, Jianmin Bao, Sirui Zhang, Yanbin Wang, Lin Liang, Lijuan Wang, Ji Li, Xiu Li, Zhouhui Lian, Gao Huang, Baining Guo

cs.AI

摘要

多层图像生成是一项基础性任务，它使用户能够隔离、选择和编辑特定的图像层，从而彻底革新了与生成模型的交互方式。本文介绍了匿名区域变换器（ART），它能够基于全局文本提示和匿名区域布局直接生成可变的多层透明图像。受图式理论的启发，该理论认为知识是以框架（图式）形式组织的，使人们能够通过将新信息与已有知识联系起来进行解释和学习，这种匿名区域布局让生成模型能够自主决定哪些视觉标记应与哪些文本标记对齐，这与之前图像生成任务中占主导地位的语义布局形成鲜明对比。此外，分层区域裁剪机制仅选择属于每个匿名区域的视觉标记，显著降低了注意力计算成本，并实现了具有众多独立层（如50层以上）图像的高效生成。与全注意力方法相比，我们的方法速度提升了12倍以上，且层间冲突更少。进一步地，我们提出了一种高质量的多层透明图像自动编码器，支持以联合方式直接编码和解码可变多层图像的透明度。通过实现精确控制和可扩展的层生成，ART为交互式内容创作确立了新范式。

English

Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory suggests that knowledge is organized in frameworks (schemas) that enable people to interpret and learn from new information by linking it to prior knowledge.}, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.