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README.md
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# 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
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**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)***.
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## Updates
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- **`2024/7/
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- **`2024/7/
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## Inference
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### Data Preparation
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# <center> 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
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<!-- <p align="center"> -->
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<div style="display: flex; justify-content: center; align-items: center;">
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<a href="https://github.com/Zheng-Chong/CatVTON" style="margin: 0 5px;">
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<img src='https://img.shields.io/badge/arXiv-Paper(soon)-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
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</a>
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<a href="http://120.76.142.206:8888" style="margin: 0 5px;">
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<img src='https://img.shields.io/badge/Demo-Gradio-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
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</a>
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<a href='https://huggingface.co/zhengchong/CatVTON' style="margin: 0 5px;">
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<img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
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</a>
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<a href="https://github.com/Zheng-Chong/CatVTON" style="margin: 0 5px;">
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<img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
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</a>
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<a href="https://github.com/Zheng-Chong/CatVTON/LICENCE" style="margin: 0 5px;">
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<img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
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</a>
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<!-- </p> -->
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</div>
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<div align="center">
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<img src="resource/img/teaser.jpg" width="100%" height="100%"/>
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</div>
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<!-- This repository is the official implementation of ***CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffsuion Models***. -->
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**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)***.
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## Updates
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- **`2024/7/22`**: Our [**App Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/app.py) is released, deploy and enjoy CatVTON on your own mechine 🎉!
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- **`2024/7/21`**: Our [**Inference Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/inference.py) and [**Weights** 🤗](https://huggingface.co/zhengchong/CatVTON) are released.
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- **`2024/7/11`**: Our [**Online Demo**](http://120.76.142.206:8888) is released 😁.
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## Deployment (Gradio App)
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To deploy the Gradio App for CatVTON on your own mechine, just run the following command, and checkpoints will be automaticly download from HuggingFace.
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```PowerShell
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CUDA_VISIBLE_DEVICES=0 python app.py \
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--output_dir="resource/demo/output" \
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--mixed_precision="bf16" \
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--allow_tf32
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```
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When using `bf16` precision, generating results with a resolution of `1024x768` only requires about `8G` VRAM.
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## Inference
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### Data Preparation
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