[AAAI 2025]IMAGDressing: Interactive Modular Apparel Generation for Virtual Dressing. It enables customizable human image generation with flexible garment,
[AAAI 2025]IMAGDressing: Interactive Modular Apparel Generation for Virtual Dressing. It enables customizable human image generation with flexible garment,
You can download the dataset from Baidu Cloud or Huggingface Dataset. By requesting access, you agree to use the data only for academic and personal purposes and not for commercial use.
To address the need for flexible and controllable customizations in virtual try-on systems, we propose IMAGDressing-v1. Specifically, we introduce a garment UNet that captures semantic features from CLIP and texture features from VAE. Our hybrid attention module includes a frozen self-attention and a trainable cross-attention, integrating these features into a frozen denoising UNet to ensure user-controlled editing. We will release a comprehensive dataset, IGPair, with over 300,000 pairs of clothing and dressed images, and establish a standard data assembly pipeline. Furthermore, IMAGDressing-v1 can be combined with extensions like ControlNet, IP-Adapter, T2I-Adapter, and AnimateDiff to enhance diversity and controllability.
conda create --name IMAGDressing python=3.8.10
conda activate IMAGDressing
pip install -U pip
# Install requirements
pip install -r requirements.txt
You can download our models from HuggingFace or 百度云. You can download the other component models from the original repository, as follows.
# Please download the IGPair data first and modify the path in run.sh
sh run.sh
python inference_IMAGdressing.py --cloth_path [your cloth path]
python inference_IMAGdressing_controlnetpose.py --cloth_path [your cloth path] --pose_path [your posture path]
python inference_IMAGdressing_ipa_controlnetpose.py --cloth_path [your cloth path] --face_path [your face path] --pose_path [your posture path]
Please download the humanparsing and openpose model file from IDM-VTON-Huggingface to the ckpt folder first.
python inference_IMAGdressing_controlnetinpainting.py --cloth_path [your cloth path] --model_path [your model path]
python inference_IMAGdressing_counterfeit-v30.py --cloth_path [your cloth path] --model_path [your model path]
Please use our inference_IMAGdressing.py to generate model images. Then, generate the cloth_mask based on the model image. You can use the Self-Correction-Human-Parsing to generate cloth mask. Finally, use the following code to evaluate the score for image generation without specified pose, face, and text scenarios.
python metric/eval.py
First, use inference_IMAGdressing_ipa_controlnetpose.py to generate model images. Then, generate the cloth mask based on the model image. Finally, use the following code to evaluate the image generation score for specified pose, face, and text scenarios.
python metric/eval_s.py
We also provide a Gradio interface for a better experience, just run by:
pip install modelscope==1.15.0
pip install mmcv-full==1.7.2
pip install mmdet==2.26.0
python app.py --model_weight $MODEL PATH --server_port 7860
You can specify the --server_port arguments to satisfy your needs!
**Or, try it out effortlessly on HuggingFace **
Join us on this exciting journey to transform virtual dressing systems. Star⭐️ our repository to stay updated with the latest advancements, and contribute to making IMAGDressing the leading solution for virtual dressing generation.
We would like to thank the contributors to the IDM-VTON, MagicClothing, IP-Adapter, ControlNet, T2I-Adapter, and AnimateDiff repositories, for their open research and exploration.
The IMAGDressing code is available for both academic and commercial use. However, the models available for manual and automatic download from IMAGDressing are intended solely for non-commercial research purposes. Similarly, our released checkpoints are restricted to research use only. Users are free to create images using this tool, but they must adhere to local laws and use it responsibly. The developers disclaim any liability for potential misuse by users.
If you find IMAGDressing-v1 useful for your research and applications, please cite using this BibTeX:
@article{shen2024IMAGDressing-v1,
title={IMAGDressing-v1: Customizable Virtual Dressing},
author={Shen, Fei and Jiang, Xin and He, Xin and Ye, Hu and Wang, Cong, and Du, Xiaoyu, Li Zechao, and Tang, Jinhui},
booktitle={arXiv preprint arXiv:2407.12705},
year={2024}
}
If you have any questions, please feel free to contact with me at [email protected].
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