# IJCV (2025): TryOn-Adapter This repository is the official implementation of [TryOn-Adapter](https://arxiv.org/abs/2404.00878) TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
> > Jiazheng Xing, Chao Xu, Yijie Qian, Yang Liu, Guang Dai, Baigui Sun, Yong Liu, Jingdong Wang [[arXiv Paper](https://arxiv.org/abs/2404.00878)]  ![teaser](assets/teaser.jpg)  ## TODO List - [x] ~~Release Texture Highlighting Map and Segmentation Map~~ - [x] ~~Release Data Preparation Code~~ - [x] ~~Release Inference Code~~ - [x] ~~Release Model Weights~~ ## Getting Started ### Installation 1. Clone the repository ```shell git clone https://github.com/jiazheng-xing/TryOn-Adapter.git cd TryOn-Adapter ``` 2. Install Python dependencies ```shell conda env create -f environment.yaml conda activate tryon-adapter ``` ### Data Preparation #### VITON-HD 1. The VITON-HD dataset serves as a benchmark. Download [VITON-HD](https://github.com/shadow2496/VITON-HD) dataset. 2. In addition to above content, some other preprocessed conditions are in use in TryOn-Adapter. The preprocessed data could be downloaded, respectively. The detail information and code see [data_preparation/README.md](data_preparation/README.md). |Content|Google|Baidu| |---|---|---| |Segmentation Map|[link](https://drive.google.com/file/d/18KvGWR-3siJ_mt7g4CcEVFi_51E7ZifA/view?usp=sharing)|[link](https://pan.baidu.com/s/1zm3XV34tcrXpYt6uAN4R9Q?pwd=ekyn)| |Highlighting Texture Map|[link](https://drive.google.com/file/d/111KBYA8-d9xl9a2aS9yUaTp0edflb7qT/view?usp=sharing)|[link](https://pan.baidu.com/s/1xWnvF7TeKB_2AzlCEbPsAQ?pwd=jnlz)| 3. Generate Warped Cloth and Warped Mask based on the [GP-VTON](https://github.com/xiezhy6/GP-VTON.git). Once everything is set up, the folders should be organized like this: ``` ├── VITON-HD | ├── test_pairs.txt | ├── train_pairs.txt │ ├── [train | test] | | ├── image │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── cloth │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── cloth-mask │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── image-parse-v3 │ │ │ ├── [000006_00.png | 000008_00.png | ...] │ │ ├── openpose_img │ │ │ ├── [000006_00_rendered.png | 000008_00_rendered.png | ...] │ │ ├── openpose_json │ │ │ ├── [000006_00_keypoints.json | 000008_00_keypoints.json | ...] │ │ ├── train_paired/test_(un)paired │ │ │ ├── mask [000006_00.png | 000008_00.png | ...] │ │ │ ├── seg_preds [000006_00.png | 000008_00.png | ...] │ │ │ ├── warped [000006_00.png | 000008_00.png | ...] ``` #### DressCode 1. The DressCode dataset serves as a benchmark. Download the [DressCode](https://github.com/aimagelab/dress-code) dataset. 2. In addition to above content, some other preprocessed conditions are in use in TryOn-Adapter. The detail information and code see [data_preparation/README.md](data_preparation/README.md). 3. Generate Warped Cloth and Warped Mask based on the [GP-VTON](https://github.com/xiezhy6/GP-VTON.git). Once everything is set up, the folders should be organized like this: ``` ├── DressCode | ├── test_pairs_paired.txt | ├── test_pairs_unpaired.txt | ├── train_pairs.txt | ├── train_pairs.txt │ ├── [test_paird | test_unpaird | train_paird] │ │ ├── [dresses | lower_body | upper_body] │ │ │ │ ├── mask [013563_1.png| 013564_1.png | ...] │ │ │ │ ├── seg_preds [013563_1.png| 013564_1.png | ...] │ │ │ │ ├── warped [013563_1.png| 013564_1.png | ...] │ ├── [dresses | lower_body | upper_body] | | ├── test_pairs_paired.txt | | ├── test_pairs_unpaired.txt | | ├── train_pairs.txt │ │ ├── images │ │ │ ├── [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...] │ │ ├── masks │ │ │ ├── [013563_1.png| 013564_1.png | ...] │ │ ├── keypoints │ │ │ ├── [013563_2.json | 013564_2.json | ...] │ │ ├── label_maps │ │ │ ├── [013563_4.png | 013564_4.png | ...] │ │ ├── skeletons │ │ │ ├── [013563_5.jpg | 013564_5.jpg | ...] │ │ ├── dense │ │ │ ├── [013563_5.png | 013563_5_uv.npz | 013564_5.png | 013564_5_uv.npz | ...] ``` ### Inference Please download the pretrained model from [HuggingFace](https://huggingface.co/Ockham98/TryOn-Adapter). To perform inference on the Dress Code or VITON-HD dataset, use the following command: ```shell python test_viton.py/test_dresscode.py --plms --gpu_id 0 \ --ddim_steps 100 \ --outdir \ --config [configs/viton.yaml | configs/dresscode.yaml] \ --dataroot \ --ckpt \ --ckpt_elbm_path \ --use_T_repaint [True | False] \ --n_samples 1 \ --seed 23 \ --scale 1 \ --H 512 \ --W 512 \ --unpaired ``` ```shell --ddim_steps sampling steps --outdir output direction path --config config path of viton-hd/dresscode --ckpt diffusion model checkpoint path --ckpt_elbm_path elbm module checkpoint dirction path --use_T_repaint whether to use T-Repaint technique --n_samples numbers of samples per inference --unpaired whether to use the unpaired setting ``` or just simply run: ```shell bash test_viton.sh bash test_dresscode.sh ``` ## Acknowledgements Our code is heavily borrowed from [Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example). We also thank [GP-VTON](https://github.com/xiezhy6/GP-VTON.git), our warping garments are generated from it. ## Citation ``` @article{xing2025tryon, title={TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On}, author={Xing, Jiazheng and Xu, Chao and Qian, Yijie and Liu, Yang and Dai, Guang and Sun, Baigui and Liu, Yong and Wang, Jingdong}, journal={International Journal of Computer Vision}, pages={1--22}, year={2025}, publisher={Springer} } ```