PrismLayers：高品質多層透明圖像生成模型的開放數據集

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

May 28, 2025

作者: Junwen Chen, Heyang Jiang, Yanbin Wang, Keming Wu, Ji Li, Chao Zhang, Keiji Yanai, Dong Chen, Yuhui Yuan

cs.AI

摘要

從文本提示生成高品質、多層透明圖像，能夠開啟創意控制的新層次，讓使用者能像編輯大型語言模型（LLM）的文本輸出一樣輕鬆地編輯每一層。然而，由於缺乏大規模、高品質的多層透明數據集，多層生成模型的發展落後於傳統的文本到圖像模型。在本論文中，我們通過以下方式應對這一根本挑戰：(i) 發布首個開放的超高保真PrismLayers（PrismLayersPro）數據集，包含20萬（2萬）張帶有精確Alpha遮罩的多層透明圖像；(ii) 引入一種無需訓練的合成管道，利用現成的擴散模型按需生成此類數據；(iii) 提供一個強大的開源多層生成模型ART+，其美學效果與現代文本到圖像生成模型相媲美。關鍵技術貢獻包括：LayerFLUX，擅長生成帶有精確Alpha遮罩的高品質單層透明圖像；以及MultiLayerFLUX，根據人工標註的語義佈局將多個LayerFLUX輸出組合成完整圖像。為確保更高品質，我們應用嚴格的過濾階段去除偽影和語義不匹配，並進行人工篩選。在我們的合成PrismLayersPro數據集上微調最先進的ART模型，得到ART+，其在60%的用戶對比研究中表現優於原始ART，甚至與FLUX.1-[dev]模型生成的圖像視覺品質相當。我們預計，這項工作將為多層透明圖像生成任務奠定堅實的數據集基礎，推動需要精確、可編輯且視覺效果出色的分層圖像的研究與應用。

English

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.