Instructions to use cyberagent/layerd-birefnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cyberagent/layerd-birefnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="cyberagent/layerd-birefnet", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("cyberagent/layerd-birefnet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
LayerD BiRefNet Matting Module
This repository contains the code and model weights for the matting module in LayerD [ICCV'25], a layer decomposition framework for graphic design images. The model in this repository is intended to be used as a part of the original LayerD github repository. Please visit https://github.com/CyberAgentAILab/LayerD for more information.
The model architecture code is based on the BiRefNet repository. We thank the authors for releasing their high-quality matting model.
Usage
This repository is intended for use with LayerD, so we recommend following the instructions in the LayerD repository.
For reference, the original LayerD uses this model as follows:
from transformers import AutoModelForImageSegmentation
birefnet = AutoModelForImageSegmentation.from_pretrained('cyberagent/layerd-birefnet', trust_remote_code=True)
License
This repository is released under the Apache-2.0 license, the same as the LayerD repository. The original BiRefNet is released under the MIT license.
Citation
@inproceedings{suzuki2025layerd,
title={LayerD: Decomposing Raster Graphic Designs into Layers},
author={Suzuki, Tomoyuki and Liu, Kang-Jun and Inoue, Naoto and Yamaguchi, Kota},
booktitle={ICCV},
year={2025}
}
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