README.MD

OmniTry: Virtual Try-On Anything without Masks

Yutong Feng · Linlin Zhang · Hengyuan Cao · Yiming Chen · Xiaoduan Feng · Jian Cao · Yuxiong Wu · Bin Wang
Kunbyte AI   |   Zhejiang University

News

• [2025.08.20] 🎉🎉🎉 We release the model weights, inference demo and evaluation benchmark of OmniTry! To experience our advanced version and other related features, please visit our product website k-fashionshop (in Chinese) or visboom (in English).
• [2025.08.29] 🛠️🛠️🛠️ We release the data pre-processing scripts for OmniTry! Now you can process your own dataset following data_preprocess.

Get Started

Noted: Currently, OmniTry requires at least 28GB of VRAM for inference under torch.bfloat16. We will continue work to decrease memory requirements.

Download Checkpoints

1. Create the checkpoint directory: mkdir checkpoints

2. Download the FLUX.1-Fill-dev into checkpoints/FLUX.1-Fill-dev

3. Download the LoRA of OmniTry into checkpoints/omnitry_v1_unified.safetensors. You can also download the omnitry_v1_clothes.safetensors that specifically finetuned on the clothe data only.

Environment Prepartion

Install the environment with conda

conda env create -f environment.yml
conda activate omnitry

or pip:

pip install -r requirements.txt

(Optional) We recommend to install the flash-attention to accelerate the inference process:

pip install flash-attn==2.6.3

Usage

For running the gradio demo:

python gradio_demo.py

To change different versions of checkpoints for OmniTry, replace the lora_path in configs/omnitry_v1_unified.yaml.

OmniTry-Bench

We present a unified evaluation benchmark for OmniTry. Please refer to the OmniTry-Bench.

Acknowledgements

This project is developped on the diffusers and FLUX. We appreciate the contributors for their awesome works.

Citation

If you find this codebase useful for your research, please use the following entry.

@article{feng2025omnitry,
  title={OmniTry: Virtual Try-On Anything without Masks},
  author={Feng, Yutong and Zhang, Linlin and Cao, Hengyuan and Chen, Yiming and Feng, Xiaoduan and Cao, Jian and Wu, Yuxiong and Wang, Bin},
  journal={arXiv preprint arXiv:2508.13632},
  year={2025}
}