Instructions to use Saint-lsy/Polyp-Gen-sd2-inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Saint-lsy/Polyp-Gen-sd2-inpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Saint-lsy/Polyp-Gen-sd2-inpainting", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
license: openrail++
tags:
- Polyp-Gen
library_name: diffusers
pipeline_tag: image-to-image
language:
- en
base_model:
- sd2-community/stable-diffusion-2-inpainting
Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion
Polyp-Gen is a text-guided full-automatic diffusion-based endoscopic image generation framework for realistic and diverse polyp image generation for endoscopic dataset expansion, as presented in Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion. You can use our model for polyp generation.
Code is available here.
This model card is based on stable-diffusion-2-inpainting model, available here.
Citation
If you find this work helpful, please consider to star🌟 this repo and cite the following paper:
@article{liu2025polyp,
title={Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion},
author={Liu, Shengyuan and Chen, Zhen and Yang, Qiushi and Yu, Weihao and Dong, Di and Hu, Jiancong and Yuan, Yixuan},
journal={arXiv preprint arXiv:2501.16679},
year={2025}
}