title: ART: Anonymous Region Transformer for Variable Multi-Layer Transparent
    Image Generation
summary: 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.
paper_authors._id: 67be81414084d82ee69ad4a2
paper_authors.name: Yifan Pu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:58:24.942Z
paper_authors._id: 647e83257f9ad5e44babe82a
paper_authors.avatarUrl: /avatars/2d9593775c49856fe5dfa5bd23dfcda7.svg
paper_authors.fullname: yifan pu
paper_authors.type: user
paper_authors.user: yifanpu001
paper_authors._id: 67be81414084d82ee69ad4a3
paper_authors.name: Yiming Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T09:17:43.946Z
paper_authors._id: 637a2be47ce76c3b8347aae2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637a2be47ce76c3b8347aae2/rQdt1j35MA2OXJnkGQfHJ.jpeg
paper_authors.fullname: Yiming Zhao
paper_authors.type: user
paper_authors.user: ZYMPKU
paper_authors._id: 67be81414084d82ee69ad4a4
paper_authors.name: Zhicong Tang
paper_authors._id: 67be81414084d82ee69ad4a5
paper_authors.name: Ruihong Yin
paper_authors._id: 67be81414084d82ee69ad4a6
paper_authors.name: Haoxing Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:58:52.821Z
paper_authors._id: 65229f2f6b01183a67e86370
paper_authors.avatarUrl: /avatars/b218207fce28497b30e22c807d44b2d2.svg
paper_authors.fullname: Haoxing Ye
paper_authors.type: user
paper_authors.user: 131131yhx
paper_authors._id: 67be81414084d82ee69ad4a7
paper_authors.name: Yuhui Yuan
paper_authors._id: 67be81414084d82ee69ad4a8
paper_authors.name: Dong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:59:16.526Z
paper_authors._id: 666470a28f5513b0cf11e850
paper_authors.avatarUrl: /avatars/7beea758882677ad32a12ce56d4d084a.svg
paper_authors.fullname: Dong Chen
paper_authors.type: user
paper_authors.user: DongChen06
paper_authors._id: 67be81414084d82ee69ad4a9
paper_authors.name: Jianmin Bao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:59:22.654Z
paper_authors._id: 646b2f4bb1202bc77c0fb396
paper_authors.avatarUrl: /avatars/6b09dec5d5affe817ad6acda60f61740.svg
paper_authors.fullname: Jianmin_bao
paper_authors.type: user
paper_authors.user: JianminBao
paper_authors._id: 67be81414084d82ee69ad4aa
paper_authors.name: Sirui Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:59:30.766Z
paper_authors._id: 64f7f119a92703ef65d9a717
paper_authors.avatarUrl: /avatars/118524faab66cecba6d4da622034b44b.svg
paper_authors.fullname: Sirui Zhang
paper_authors.type: user
paper_authors.user: zsr200901
paper_authors._id: 67be81414084d82ee69ad4ab
paper_authors.name: Yanbin Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:59:38.138Z
paper_authors._id: 67965a5a9f57883759a6efc3
paper_authors.avatarUrl: /avatars/9138a879fbe1f60c2f4720810bfdfda6.svg
paper_authors.fullname: Yanbin Wang
paper_authors.type: user
paper_authors.user: yanbinwang
paper_authors._id: 67be81414084d82ee69ad4ac
paper_authors.name: Lin Liang
paper_authors._id: 67be81414084d82ee69ad4ad
paper_authors.name: Lijuan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T09:00:05.520Z
paper_authors._id: 6672e20d1dbdf7da8310dd92
paper_authors.avatarUrl: /avatars/5d2fb23f92a7f9ff025a5be17a26de4d.svg
paper_authors.fullname: lijuanwang
paper_authors.type: user
paper_authors.user: lijuanwang228
paper_authors._id: 67be81414084d82ee69ad4ae
paper_authors.name: Ji Li
paper_authors._id: 67be81414084d82ee69ad4af
paper_authors.name: Xiu Li
paper_authors._id: 67be81414084d82ee69ad4b0
paper_authors.name: Zhouhui Lian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:59:57.943Z
paper_authors._id: 64c882f7527d7636555bbb2c
paper_authors.avatarUrl: /avatars/578a118a945dd6fa62fd3be9d6e4e986.svg
paper_authors.fullname: Zhouhui Lian
paper_authors.type: user
paper_authors.user: lianzhouhui
paper_authors._id: 67be81414084d82ee69ad4b1
paper_authors.name: Gao Huang
paper_authors._id: 67be81414084d82ee69ad4b2
paper_authors.name: Baining Guo
paper_title: ART: Anonymous Region Transformer for Variable Multi-Layer Transparent
    Image Generation
paper_summary: 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.
paper_ai_keywords: Region Transformer multi-layer transparent images global text prompt anonymous region layout Schema theory schemas visual tokens text tokens semantic layout layer-wise region crop attention computation high-quality multi-layer transparent image autoencoder