Update README.md

README.md

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 license: cc-by-nc-sa-4.0
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 # 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
 
 <div style="display: flex; justify-content: center; align-items: center;">
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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)***.
 
 
 
 ## Updates
+- **`2024/10/17`**:[**Mask-free version**](https://huggingface.co/zhengchong/CatVTON-MaskFree)🤗 of CatVTON is release and please try it in our [**Online Demo**](http://120.76.142.206:8888). 
+- **`2024/10/13`**: We have built a repo [**Awesome-Try-On-Models**](https://github.com/Zheng-Chong/Awesome-Try-On-Models) that focuses on image, video, and 3D-based try-on models published after 2023, aiming to provide insights into the latest technological trends. If you're interested, feel free to contribute or give it a 🌟 star!
+- **`2024/08/13`**: We localize DensePose & SCHP to avoid certain environment issues.
 - **`2024/08/10`**: Our 🤗 [**HuggingFace Space**](https://huggingface.co/spaces/zhengchong/CatVTON) is available now! Thanks for the grant from [**ZeroGPU**](https://huggingface.co/zero-gpu-explorers)！
 - **`2024/08/09`**: [**Evaluation code**](https://github.com/Zheng-Chong/CatVTON?tab=readme-ov-file#3-calculate-metrics) is provided to calculate metrics 📚.
 - **`2024/07/27`**: We provide code and workflow for deploying CatVTON on [**ComfyUI**](https://github.com/Zheng-Chong/CatVTON?tab=readme-ov-file#comfyui-workflow) 💥.
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 ## Installation
-An [Installation Guide](https://github.com/Zheng-Chong/CatVTON/blob/main/INSTALL.md) is provided to help build the conda environment for CatVTON. When deploying the app, you will need Detectron2 & DensePose, which are not required for inference on datasets. Install the packages according to your needs.
+
+Create a conda environment & Install requirments
+```shell
+conda create -n catvton python==3.9.0
+conda activate catvton
+cd CatVTON-main  # or your path to CatVTON project dir
+pip install -r requirements.txt
+```
 
 ## Deployment 
 ### ComfyUI Workflow
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 > 
 When you run the CatVTON workflow for the first time, the weight files will be automatically downloaded, usually taking dozens of minutes.
 
- 
+<div align="center">
+  <img src="resource/img/comfyui-1.png" width="100%" height="100%"/>
+</div>
+
 <!-- <div align="center">
  <img src="resource/img/comfyui.png" width="100%" height="100%"/>
 </div> -->
@@ -96,7 +107,7 @@ Once the datasets are downloaded, the folder structures should look like these:
 │   │   │   ├── [000006_00_mask.png | 000008_00.png | ...]
 ...
 ```
-For the 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
@@ -110,6 +121,11 @@ For the DressCode dataset, we provide [our preprocessed agnostic masks](https://
 │   │   │   ├── [013563_0.png| 013564_0.png | ...]
 ...
 ```
+For the DressCode dataset, we provide script to preprocessed agnostic masks, run the following command:
+```PowerShell
+CUDA_VISIBLE_DEVICES=0 python preprocess_agnostic_mask.py \
+--data_root_path <your_path_to_DressCode> 
+```
 
 ### 2. Inference on VTIONHD/DressCode
 To run the inference on the DressCode or VITON-HD dataset, run the following command, checkpoints will be automatically downloaded from HuggingFace.