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README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
+
+
+
+# <center> 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
+
+<p align="center">
+  <a href="https://github.com/Zheng-Chong/CatVTON">
+    <img src='https://img.shields.io/badge/arXiv-Paper(soon)-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
+  </a>
+  <a href="http://120.76.142.206:8888">
+    <img src='https://img.shields.io/badge/Demo-Gradio-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href='https://huggingface.co/zhengchong/CatVTON'>
+    <img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
+  </a>
+  <a href="https://github.com/Zheng-Chong/CatVTON">
+    <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
+  </a>
+    <a href="https://github.com/Zheng-Chong/CatVTON/LICENCE"><img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
+  </a>
+</p>
+
+<div align="center">
+  <img src="resource/img/teaser.jpg" width="100%" height="100%"/>
+</div>
+
+<!-- This repository is the official implementation of ***CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffsuion Models***. -->
+
+**CatVTON** is a simple and efficient virtual try-on diffusion model with ***1) Lightweight Network (899.06M parameters totally)***, ***2) Parameter-Efficient Training (49.57M parameters trainable)*** and ***3) Simplified Inference (< 8G VRAM for 1024X768 resolution)***.
+
+
+## Updates
+- **`2024/7/21`**: Our **Inference Code** and [**🤗Weights**](https://huggingface.co/zhengchong/CatVTON) are released.
+- **`2024/7/11`**: [**Online Demo**](http://120.76.142.206:8888) is released.
+
+
+
+## Inference
+### Data Preparation
+Before inference, you need to download the [VITON-HD](https://github.com/shadow2496/VITON-HD) or [DressCode](https://github.com/aimagelab/dress-code) dataset.
+Once the datasets are downloaded, the folder structures should look like these:
+```
+├── VITON-HD
+|   ├── test_pairs_unpaired.txt
+│   ├── test
+|   |   ├── image
+│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
+│   │   ├── cloth
+│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
+│   │   ├── agnostic-mask
+│   │   │   ├── [000006_00_mask.png | 000008_00.png | ...]
+...
+```
+For DressCode dataset, we provide [our preprocessed agnostic masks](https://drive.google.com/drive/folders/1uT88nYQl0n5qHz6zngb9WxGlX4ArAbVX?usp=share_link), download and place in `agnostic_masks` folders under each category.
+```
+├── DressCode
+|   ├── test_pairs_paired.txt
+|   ├── test_pairs_unpaired.txt
+│   ├── [dresses | lower_body | upper_body]
+|   |   ├── test_pairs_paired.txt
+|   |   ├── test_pairs_unpaired.txt
+│   │   ├── images
+│   │   │   ├── [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...]
+│   │   ├── agnostic_masks
+│   │   │   ├── [013563_0.png| 013564_0.png | ...]
+...
+```
+
+### Inference on VTIONHD/DressCode
+To run the inference on the DressCode or VITON-HD dataset, run the following command, checkpoints will be automaticly download from HuggingFace.
+
+```PowerShell
+CUDA_VISIBLE_DEVICES=0 python inference.py \
+--dataset [dresscode | vitonhd] \
+--data_root_path <path> \
+--output_dir <path> 
+--dataloader_num_workers 8 \
+--batch_size 8 \
+--seed 555 \
+--mixed_precision [no | fp16 | bf16] \
+--allow_tf32 \
+--repaint \
+--eval_pair  
+```
+
+
+## Acknowledgement
+Our code is modified based on [Diffusers](https://github.com/huggingface/diffusers). We use [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master) and [DensePose](https://github.com/facebookresearch/DensePose) to automaticly generate mask in our [Gradio](https://github.com/gradio-app/gradio) App. Thanks to all the contributors!
+<!-- ## Citation
+
+
+```
+``` -->