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

arXiv: https://arxiv.org/abs/2502.18364 · HTML: https://mlsanity.com/paper/2502.18364/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 88 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 30 of 70)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs](https://mlsanity.com/paper/2401.11708/) (2024)
- [ReCo: Region-Controlled Text-to-Image Generation](https://mlsanity.com/paper/2211.15518/) (2022)
- [InstanceDiffusion: Instance-level Control for Image Generation](https://mlsanity.com/paper/2402.03290/) (2024)
- [Dense Text-to-Image Generation with Attention Modulation](https://mlsanity.com/paper/2308.12964/) (2023)
- [GSVA: Generalized Segmentation via Multimodal Large Language Models](https://mlsanity.com/paper/2312.10103/) (2023)
- [MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance](https://mlsanity.com/paper/2406.07209/) (2024)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [Prodigy: An Expeditiously Adaptive Parameter-Free Learner](https://mlsanity.com/paper/2306.06101/) (2023)
- [DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution](https://mlsanity.com/paper/2411.02359/) (2024)
- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Adaptive Focus for Efficient Video Recognition](https://mlsanity.com/paper/2105.03245/) (2021)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [Dynamic Diffusion Transformer](https://mlsanity.com/paper/2410.03456/) (2024)
- [LayoutDM: Transformer-based Diffusion Model for Layout Generation](https://mlsanity.com/paper/2305.02567/) (2023)

## Built on by (most cited first, 30 of 37)

- [Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning](https://mlsanity.com/paper/2506.01939/) (2025)
- [OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion](https://mlsanity.com/paper/2507.06165/) (2025)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition](https://mlsanity.com/paper/2512.15603/) (2025)
- [BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation](https://mlsanity.com/paper/2503.20672/) (2025)
- [CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation](https://mlsanity.com/paper/2506.10890/) (2025)
- [DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design](https://mlsanity.com/paper/2507.04218/) (2025)
- [PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models](https://mlsanity.com/paper/2505.22523/) (2025)
- [OmniPSD: Layered PSD Generation with Diffusion Transformer](https://mlsanity.com/paper/2512.09247/) (2025)
- [PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment](https://mlsanity.com/paper/2505.11468/) (2025)
- [Rethinking Layered Graphic Design Generation with a Top-Down Approach](https://mlsanity.com/paper/2507.05601/) (2025)
- [UTDesign: A Unified Framework for Stylized Text Editing and Generation in Graphic Design Images](https://mlsanity.com/paper/2512.20479/) (2025)
- [Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting](https://mlsanity.com/paper/2508.01098/) (2025)
- [LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition](https://mlsanity.com/paper/2603.17965/) (2026)
- [Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling](https://mlsanity.com/paper/2604.28185/) (2026)
- [Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials](https://mlsanity.com/paper/2511.00833/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [MiLDEdit: Reasoning-Based Multi-Layer Design Document Editing](https://mlsanity.com/paper/2601.04589/) (2026)
- [See-through: Single-image Layer Decomposition for Anime Characters](https://mlsanity.com/paper/2602.03749/) (2026)
- [TAUE: Training-free Noise Transplant and Cultivation Diffusion Model](https://mlsanity.com/paper/2511.02580/) (2025)
- [Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission](https://mlsanity.com/paper/2601.07512/) (2026)
- [Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition](https://mlsanity.com/paper/2511.17454/) (2025)
- [Workflow-Aware Structured Layer Decomposition for Illustration Production](https://mlsanity.com/paper/2603.14925/) (2026)
- [AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimization](https://mlsanity.com/paper/2603.25118/) (2026)
- [LiWi: Layering in the Wild](https://mlsanity.com/paper/2605.14552/) (2026)
- [Does Synthetic Layered Design Data Benefit Layered Design Decomposition?](https://mlsanity.com/paper/2605.15167/) (2026)
- [ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition](https://mlsanity.com/paper/2607.25565/) (2026)
- [Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection](https://mlsanity.com/paper/2609.07072/) (2026)
- [A Unified and Controllable Framework for Layered Image Generation with Visual Effects](https://mlsanity.com/paper/2601.15507/) (2026)
- [Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Design](https://mlsanity.com/paper/2604.11019/) (2026)

## Abstract (arXiv)

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.