ART: Anonymous Region Transformer for Variable Multi-Layer Transparent
  Image Generation
Authors: name: Yifan Pu, name: Yiming Zhao, name: Zhicong Tang, name: Ruihong Yin, name: Haoxing Ye, name: Yuhui Yuan, name: Dong Chen, name: Jianmin Bao, name: Sirui Zhang, name: Yanbin Wang, name: Lin Liang, name: Lijuan Wang, name: Ji Li, name: Xiu Li, name: Zhouhui Lian, name: Gao Huang, name: Baining Guo

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.