Instructions to use ameerazam08/BrushNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ameerazam08/BrushNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ameerazam08/BrushNet", 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
title: BrushNet
emoji: ⚡
colorFrom: yellow
colorTo: indigo
sdk: gradio
sdk_version: 3.50.2
python_version: 3.9
app_file: app.py
pinned: false
license: apache-2.0
BrushNet
This repository contains the gradio demo of the paper "BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion"
Keywords: Image Inpainting, Diffusion Models, Image Generation
Xuan Ju12, Xian Liu12, Xintao Wang1*, Yuxuan Bian2, Ying Shan1, Qiang Xu2*
1ARC Lab, Tencent PCG 2The Chinese University of Hong Kong *Corresponding Author
Project Page | Code | Arxiv | Data | Video |
🤝🏼 Cite Us
@misc{ju2024brushnet,
title={BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion},
author={Xuan Ju and Xian Liu and Xintao Wang and Yuxuan Bian and Ying Shan and Qiang Xu},
year={2024},
eprint={2403.06976},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
💖 Acknowledgement
Our code is modified based on diffusers, thanks to all the contributors!