Instructions to use lixiaowen/diffuEraser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lixiaowen/diffuEraser with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lixiaowen/diffuEraser", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
library_name: diffusers
tags:
- video-inpainting
DiffuEraser: A Diffusion Model for Video Inpainting
DiffuEraser is a diffusion model for video inpainting, which outperforms state-of-the-art model Propainter in both content completeness and temporal consistency while maintaining acceptable efficiency.
Model Sources
- Project Page: https://lixiaowen-xw.github.io/DiffuEraser-page
- Repository: https://github.com/lixiaowen-xw/DiffuEraser.git
- Paper: https://arxiv.org/abs/2501.10018
Citation
BibTeX:
@misc{li2025diffueraserdiffusionmodelvideo,
title={DiffuEraser: A Diffusion Model for Video Inpainting},
author={Xiaowen Li and Haolan Xue and Peiran Ren and Liefeng Bo},
year={2025},
eprint={2501.10018},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.10018},
}
Model Card Authors
Xiaowen Li / xwlxw@qq.com / lxw262398@alibaba-inc.com