Image-to-Image
Diffusers
Safetensors
diffusion
virtual try-on
virtual try-off
image generation
fashion
e-commerce
Instructions to use ixarchakos/tryOffAnyone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ixarchakos/tryOffAnyone 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("ixarchakos/tryOffAnyone", 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: other
license_name: server-side-public-license
license_link: https://www.mongodb.com/legal/licensing/server-side-public-license
tags:
- diffusion
- virtual try-on
- virtual try-off
- image generation
- fashion
- e-commerce
base_model:
- stable-diffusion-v1-5/stable-diffusion-inpainting
pipeline_tag: image-to-image
library_name: diffusers
TryOffAnyone
The models proposed in the paper "TryOffAnyone: Tiled Cloth Generation from a Dressed Person" [paper_arxiv] [github]:
Citation
If you find this repository useful in your research, please consider giving a star ⭐ and a citation:
@misc{xarchakos2024tryoffanyonetiledclothgeneration,
title={TryOffAnyone: Tiled Cloth Generation from a Dressed Person},
author={Ioannis Xarchakos and Theodoros Koukopoulos},
year={2024},
eprint={2412.08573},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.08573},
}
