PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
  Generative Models
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.
6837c1cb80fc90ca2d9e8153
Junwen Chen
6837c1cb80fc90ca2d9e8154
Heyang Jiang
6837c1cb80fc90ca2d9e8155
Yanbin Wang
6837c1cb80fc90ca2d9e8156
Keming Wu
claimed_verified
2025-07-30T14:18:33.887Z
66bf00ca5b4e241fe266059d
https://cdn-avatars.huggingface.co/v1/production/uploads/66bf00ca5b4e241fe266059d/UPjEAg5fHAeC44QvsJcwX.jpeg
Keming Wu
user
wukeming11
6837c1cb80fc90ca2d9e8157
Ji Li
6837c1cb80fc90ca2d9e8158
Chao Zhang
6837c1cb80fc90ca2d9e8159
Keiji Yanai
6837c1cb80fc90ca2d9e815a
Dong Chen
6837c1cb80fc90ca2d9e815b
Yuhui Yuan
PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
  Generative Models
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.
The work introduces a dataset and model for generating high-quality, multi-layer transparent images using diffusion models and a novel synthesis pipeline.
PrismLayers diffusion models LayerFLUX MultiLayerFLUX alpha mattes semantic layout user study ART model FLUX.1-[dev]