PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models

• URL: http://arxiv.org/abs/2505.22523v1
• Date: Wed, 28 May 2025 16:09:33 GMT
• Title: PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models
• Authors: Junwen Chen, Heyang Jiang, Yanbin Wang, Keming Wu, Ji Li, Chao Zhang, Keiji Yanai, Dong Chen, Yuhui Yuan,
• Abstract summary: We release the first open, ultra-high-fidelity PrismLayers dataset of 200K (20K) multilayer transparent images with accurate alpha mattes.<n>We also deliver a strong, open-source multi-layer generation model, ART+, which matches the aesthetics of modern text-to-image generation models.
• Score: 25.859278092788237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the development of multi-layer generative models lags behind that of conventional text-to-image models due to the absence of a large, high-quality corpus of multi-layer transparent data. In this paper, we address this fundamental challenge by: (i) releasing the first open, ultra-high-fidelity PrismLayers (PrismLayersPro) dataset of 200K (20K) multilayer transparent images with accurate alpha mattes, (ii) introducing a trainingfree synthesis pipeline that generates such data on demand using off-the-shelf diffusion models, and (iii) delivering a strong, open-source multi-layer generation model, ART+, which matches the aesthetics of modern text-to-image generation models. The key technical contributions include: LayerFLUX, which excels at generating high-quality single transparent layers with accurate alpha mattes, and MultiLayerFLUX, which composes multiple LayerFLUX outputs into complete images, guided by human-annotated semantic layout. To ensure higher quality, we apply a rigorous filtering stage to remove artifacts and semantic mismatches, followed by human selection. Fine-tuning the state-of-the-art ART model on our synthetic PrismLayersPro yields ART+, which outperforms the original ART in 60% of head-to-head user study comparisons and even matches the visual quality of images generated by the FLUX.1-[dev] model. We anticipate that our work will establish a solid dataset foundation for the multi-layer transparent image generation task, enabling research and applications that require precise, editable, and visually compelling layered imagery.

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