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

High-quality multi-layer transparent image generation enables layer-level editing, with graphic design as its most crucial testbed. Progress on both fronts lags behind text-to-image generation, as web data is flattened, professional source files are scarce, and alpha supervision is costly to annotate. To break this bottleneck, we build an end-to-end data route that turns existing single-layer generators into a coherent supervision source for both layered synthesis and photorealistic object-transparency editing. We release the first large-scale series of open, ultra-high-fidelity multi-layer datasets—PrismLayers, PrismLayersPlus, PrismLayersPro, and PrismLayersReal—comprising 200K, 100K, 20K, and 1K multi-layer transparent images and accurate alpha mattes, respectively, and further construct a photorealistic object-transparency editing corpus, PrismLayersEdit—containing 80K paired edit targets across 10 opacity levels. On top of this, we introduce a systematic training-free agentic pipeline that orchestrates pre-trained diffusion models into coherent layered generation rather than isolated layer prediction. Its core is a two-stage design: LayerFLUX produces clean, high-detail transparent layers with reliable alpha quality, while MultiLayerFLUX composes them into semantically aligned full designs under human-annotated layout guidance. A strict quality-control loop (artifact filtering, semantic consistency checks, and human selection) further pushes visual fidelity to production-level quality, and an advanced Qwen-Image-Edit-based engine extends the pipeline to construct paired supervision for real-image object-transparency editing. Finally, fine-tuning the state-of-the-art ART model on PrismLayersPro yields ART+, which beats the original ART in 60% of head-to-head user comparisons. Our results establish a strong foundation for editable, precise, and aesthetically competitive multi-layer transparent image generation and introduce object transparency editing as a new frontier for future research.