StableGarment: Garment-Centric Generation via Stable Diffusion

This repository is the official implementation of StableGarment.

[Arxiv Paper]  [Website Page] 

 

Environments

git clone https://github.com/logn-2024/StableGarment
cd StableGarment

conda create --name StableGarment python=3.11 -y
conda activate StableGarment

pip3 install -r requirements.txt

Demos, Models and Data

To acquire the VITON-HD dataset, refer to VITON-HD. Similarly, for the Dress Code dataset, visit Dress Code. Before testing, ensure to generate masks for the Dress Code dataset using the following command and place them in the respective directories.

python stablegarment/data/generate_mask.py

You can access the pretrained garment encoder for text-to-image task from this huggingface Repository. Our huggingface demo is available here . To run the demo locally, execute the following command:

python app.py

Inference

To conduct the text-to-image task with garment conditions, execute the following command. You can alter the base model to achieve different styles:

python infer_t2i.py

The try-on task necessitates additional inputs, primarily concerning humans. These inputs can be found in the VITON-HD and Dress Code datasets. If you intend to perform virtual try-on on arbitrary images, ensure you obtain densepose and agnostic masks akin to those in the VITON-HD dataset. To utilize the virtual try-on application, run the following code:

python infer_tryon.py

Test

To test StableGarment on the VITON-HD dataset, execute the following command:

python test.py

You can adjust between paired and unpaired settings by modifying the is_pair variable. For testing on the Dress Code dataset, simply substitute the relevant variables and load the target dataset in test.py.

Acknowledgements

Thanks to magic-animate, our code is heavily based on it.

Citation

If you find our work useful for your research, please cite us:

@article{wang2024stablegarment,
  title={StableGarment: Garment-Centric Generation via Stable Diffusion},
  author={Wang, Rui and Guo, Hailong and Liu, Jiaming and Li, Huaxia and Zhao, Haibo and Tang, Xu and Hu, Yao and Tang, Hao and Li, Peipei},
  journal={arXiv preprint arXiv:2403.10783},
  year={2024}
}

License

Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).