title: PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
    Generative Models
summary: 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.
paper_authors._id: 6837c1cb80fc90ca2d9e8153
paper_authors.name: Junwen Chen
paper_authors._id: 6837c1cb80fc90ca2d9e8154
paper_authors.name: Heyang Jiang
paper_authors._id: 6837c1cb80fc90ca2d9e8155
paper_authors.name: Yanbin Wang
paper_authors._id: 6837c1cb80fc90ca2d9e8156
paper_authors.name: Keming Wu
paper_authors._id: 6837c1cb80fc90ca2d9e8157
paper_authors.name: Ji Li
paper_authors._id: 6837c1cb80fc90ca2d9e8158
paper_authors.name: Chao Zhang
paper_authors._id: 6837c1cb80fc90ca2d9e8159
paper_authors.name: Keiji Yanai
paper_authors._id: 6837c1cb80fc90ca2d9e815a
paper_authors.name: Dong Chen
paper_authors._id: 6837c1cb80fc90ca2d9e815b
paper_authors.name: Yuhui Yuan
paper_title: PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
    Generative Models
paper_summary: 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.
paper_ai_summary: The work introduces a dataset and model for generating high-quality, multi-layer transparent images using diffusion models and a novel synthesis pipeline.
paper_ai_keywords: PrismLayers diffusion models LayerFLUX MultiLayerFLUX alpha mattes semantic layout user study ART model FLUX.1-[dev]