Update README.md

README.md

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-license: mit
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+# 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**<br>
+
+>
+> Jiazheng Xing, Chao Xu, Yijie Qian, Yang Liu, Guang Dai, Baigui Sun, Yong Liu, Jingdong Wang
+
+[[arXiv Paper](https://arxiv.org/abs/2404.00878)]&nbsp;
+
+![teaser](assets/teaser.jpg)&nbsp;
+
+## 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 <path> \
+--config [configs/viton.yaml | configs/dresscode.yaml] \
+--dataroot <path> \
+--ckpt <path> \
+--ckpt_elbm_path <path> \
+--use_T_repaint [True | False] \
+--n_samples 1 \
+--seed 23 \
+--scale 1 \
+--H 512 \
+--W 512 \
+--unpaired
+```
+
+```shell
+--ddim_steps <int>         sampling steps
+--outdir <str>             output direction path
+--config <str>             config path of viton-hd/dresscode
+--ckpt <str>               diffusion model checkpoint path
+--ckpt_elbm_path <str>     elbm module checkpoint dirction path
+--use_T_repaint <bool>     whether to use T-Repaint technique
+--n_samples <int>          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}
+}
+```