PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
  Generative Models
Authors: name: Junwen Chen, name: Heyang Jiang, name: Yanbin Wang, name: Keming Wu, name: Ji Li, name: Chao Zhang, name: Keiji Yanai, name: Dong Chen, name: Yuhui Yuan

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.