catvton

.gitignore

+__pycache__
+model/__pycache__
+model/DensePose/__pycache__
+model/SCHP/__pycache__
+model/SCHP/*/__pycache__
+resource/demo/output
+resource/demo/example/.DS_Store
+Models
+Datasets
+densepose_
+.vscode
+playground.py
+output
+model/cloth_masker_segformer.py
+temp_output

Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu22.04-dtk24.04.2-py3.10
\ No newline at end of file

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README.md

+# CatVTON
+
+## 论文
+
+`CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models`
+
+* https://arxiv.org/pdf/2407.15886
+
+## 模型结构
+
+该模型基于`stable diffusion`结构，移除了`ReferenceNet`或其他图像（衣物）编码器，同时移除`cross-attention`及文本编码器。
+
+![alt text](readme_imgs/model_arch.png)
+
+## 算法原理
+
+该算法基于`stable diffusion`，去除了多余的网络结构，直接将控制条件作为`Unet`的输入。
+
+![alt text](readme_imgs/alg.png)
+
+## 环境配置
+
+### Docker（方法一）
+    
+    docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu22.04-dtk24.04.2-py3.10
+
+    docker run --shm-size 50g --network=host --name=catvton --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+
+### Dockerfile（方法二）
+
+    docker build -t <IMAGE_NAME>:<TAG> .
+
+    docker run --shm-size 50g --network=host --name=catvton --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+
+### Anaconda (方法三)
+
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+https://developer.hpccube.com/tool/
+
+    DTK驱动：dtk24.04.2
+    python：python3.10
+    torch: 2.1.0
+    torchvision: 0.16.0
+
+Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应
+
+2、其它非特殊库参照requirements.txt安装
+
+    pip install -r requirements.txt
+
+## 数据集
+
+完整数据集可通过下述链接下载，本项目提供测试数据，位于`datasets`中。
+
+|DressCode|VITON-HD|
+|:---:|:---:|
+|[SCNet高速下载通道](http://113.200.138.88:18080/aidatasets/project-dependency/dress-code)|[SCNet高速下载通道](http://113.200.138.88:18080/aidatasets/project-dependency/viton-hd)|
+
+### 数据处理
+
+VITON-HD本身包含已处理好的agnostic-mask，仅对DressCode处理即可。
+
+```bash
+HIP_VISIBLE_DEVICES=0 python preprocess_agnostic_mask.py \
+--data_root_path <your_path_to_DressCode> 
+```
+
+```bash
+├── 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.png | 000008_00.png | ...]
+...
+```
+
+```bash
+├── 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 | ...]
+...
+```
+
+## 训练
+
+无
+
+## 推理
+
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+### 命令行
+
+
+```bash
+HIP_VISIBLE_DEVICES=0 python inference.py \
+--dataset [ dresscode | vitonhd ] \
+--data_root_path <path/to/datasets> \
+--output_dir temp_output \
+--dataloader_num_workers 8 \
+--batch_size 2 \
+--repaint \
+--eval_pair \
+--base_model_path <path/to/stable-diffusion-inpainting> \
+--resume_path <path/to/CatVTON/Models>
+```
+
+注意：请使用fp16或fp32进行推理。
+
+### webui
+
+```bash
+HIP_VISIBLE_DEVICES=0 python app.py \
+--output_dir="resource/demo/output" \
+--mixed_precision="fp16" \
+--base_model_path <path/to/stable-diffusion-inpainting> \
+--resume_path <path/to/CatVTON/Models>
+```
+
+### 指标计算
+
+```bash
+HIP_VISIBLE_DEVICES=0 python eval.py \
+--gt_folder <your_path_to_gt_image_folder> \
+--pred_folder <your_path_to_predicted_image_folder> \
+--paired \
+--batch_size=2 \
+--num_workers=2 
+```
+
+## result
+
+|数据源|输入1|输入2|输出|
+|:---:|:---:|:---:|:---:|
+|VITON-HD|![alt text](readme_imgs/input1_vh.jpg)|<img src="readme_imgs/input2_vh.jpg" style="zoom:50%;">|![alt text](readme_imgs/output_vh.jpg)|
+|DressCode|<img src="readme_imgs/input1_dr.jpg" style="zoom:50%;">|<img src="readme_imgs/input2_dr.jpg" style="zoom:50%;">|![alt text](readme_imgs/output_dr.jpg)|
+
+
+
+### 精度
+
+无
+
+## 应用场景
+
+### 算法类别
+
+`AIGC`
+
+### 热点应用行业
+
+`电商,绘画,广媒`
+
+## 预训练权重
+
+stable-diffusion-inpainting: [huggingface](https://hf-mirror.com/booksforcharlie/stable-diffusion-inpainting/tree/main) | [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/stable-diffusion-inpainting)
+
+CatVTON: [huggingface](https://hf-mirror.com/zhengchong/CatVTON) | [SCNet高速下载通道](http://113.200.138.88:18080/aimodels/findsource-dependency/catvton)
+
+注意：可将下载的权重文件放入`Models`文件夹（需自行创建）。
+
+## 源码仓库及问题反馈
+
+* https://developer.sourcefind.cn/codes/aibear/catvton_pytorch
+
+## 参考资料
+
+* https://github.com/Zheng-Chong/CatVTON
+

README_official.md

+# 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
+
+<div style="display: flex; justify-content: center; align-items: center;">
+  <a href="http://arxiv.org/abs/2407.15886" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/arXiv-2407.15886-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
+  </a>
+  <a href='https://huggingface.co/zhengchong/CatVTON' style="margin: 0 2px;">
+    <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" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
+  </a>
+  <a href="http://120.76.142.206:8888" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href="https://huggingface.co/spaces/zhengchong/CatVTON" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Space-ZeroGPU-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href='https://zheng-chong.github.io/CatVTON/' style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
+  </a>
+  <a href="https://github.com/Zheng-Chong/CatVTON/LICENCE" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
+  </a>
+</div>
+
+
+
+**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)***.
+<div align="center">
+  <img src="resource/img/teaser.jpg" width="85%" height="100%"/>
+</div>
+
+
+
+
+## Updates
+- **`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) 💥.
+- **`2024/07/24`**: Our [**Paper on ArXiv**](http://arxiv.org/abs/2407.15886) is available 🥳!
+- **`2024/07/22`**: Our [**App Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/app.py) is released, deploy and enjoy CatVTON on your mechine 🎉!
+- **`2024/07/21`**: Our [**Inference Code**](https://github.com/Zheng-Chong/CatVTON/blob/main/inference.py) and [**Weights** 🤗](https://huggingface.co/zhengchong/CatVTON) are released.
+- **`2024/07/11`**: Our [**Online Demo**](http://120.76.142.206:8888) is released 😁.
+
+
+
+
+## Installation
+
+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
+We have modified the main code to enable easy deployment of CatVTON on [ComfyUI](https://github.com/comfyanonymous/ComfyUI). Due to the incompatibility of the code structure, we have released this part in the [Releases](https://github.com/Zheng-Chong/CatVTON/releases/tag/ComfyUI), which includes the code placed under `custom_nodes` of ComfyUI and our workflow JSON files.
+
+To deploy CatVTON to your ComfyUI, follow these steps:
+1. Install all the requirements for both CatVTON and ComfyUI, refer to [Installation Guide for CatVTON](https://github.com/Zheng-Chong/CatVTON/blob/main/INSTALL.md) and [Installation Guide for ComfyUI](https://github.com/comfyanonymous/ComfyUI?tab=readme-ov-file#installing).
+2. Download [`ComfyUI-CatVTON.zip`](https://github.com/Zheng-Chong/CatVTON/releases/download/ComfyUI/ComfyUI-CatVTON.zip) and unzip it in the `custom_nodes` folder under your ComfyUI project (clone from [ComfyUI](https://github.com/comfyanonymous/ComfyUI)).
+3. Run the ComfyUI.
+4. Download [`catvton_workflow.json`](https://github.com/Zheng-Chong/CatVTON/releases/download/ComfyUI/catvton_workflow.json) and drag it into you ComfyUI webpage and enjoy 😆!
+
+> Problems under Windows OS, please refer to [issue#8](https://github.com/Zheng-Chong/CatVTON/issues/8).
+> 
+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> -->
+
+### Gradio App
+
+To deploy the Gradio App for CatVTON on your machine, run the following command, and checkpoints will be automatically downloaded from HuggingFace.
+
+```PowerShell
+CUDA_VISIBLE_DEVICES=0 python app.py \
+--output_dir="resource/demo/output" \
+--mixed_precision="bf16" \
+--allow_tf32 
+```
+When using `bf16` precision, generating results with a resolution of `1024x768` only requires about `8G` VRAM.
+
+## Inference
+### 1. 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 | ...]
+...
+```
+
+```
+├── 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 | ...]
+...
+```
+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.
+
+```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  
+```
+### 3. Calculate Metrics
+
+After obtaining the inference results, calculate the metrics using the following command: 
+
+```PowerShell
+CUDA_VISIBLE_DEVICES=0 python eval.py \
+--gt_folder <your_path_to_gt_image_folder> \
+--pred_folder <your_path_to_predicted_image_folder> \
+--paired \
+--batch_size=16 \
+--num_workers=16 
+```
+
+-  `--gt_folder` and `--pred_folder` should be folders that contain **only images**.
+- To evaluate the results in a paired setting, use `--paired`; for an unpaired setting, simply omit it.
+- `--batch_size` and `--num_workers` should be adjusted based on your machine.
+
+
+## Acknowledgement
+Our code is modified based on [Diffusers](https://github.com/huggingface/diffusers). We adopt [Stable Diffusion v1.5 inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) as the base model. We use [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master) and [DensePose](https://github.com/facebookresearch/DensePose) to automatically generate masks in our [Gradio](https://github.com/gradio-app/gradio) App and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflow. Thanks to all the contributors!
+
+## License
+All the materials, including code, checkpoints, and demo, are made available under the [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. You are free to copy, redistribute, remix, transform, and build upon the project for non-commercial purposes, as long as you give appropriate credit and distribute your contributions under the same license.
+
+
+## Citation
+
+```bibtex
+@misc{chong2024catvtonconcatenationneedvirtual,
+ title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models}, 
+ author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang},
+ year={2024},
+ eprint={2407.15886},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV},
+ url={https://arxiv.org/abs/2407.15886}, 
+}
+```
\ No newline at end of file

app.py

+import argparse
+import os
+from datetime import datetime
+
+import gradio as gr
+import numpy as np
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+from model.cloth_masker import AutoMasker, vis_mask
+from model.pipeline import CatVTONPipeline
+from utils import init_weight_dtype, resize_and_crop, resize_and_padding
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--base_model_path",
+        type=str,
+        default="booksforcharlie/stable-diffusion-inpainting",  # Change to a copy repo as runawayml delete original repo
+        help=(
+            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
+        ),
+    )
+    parser.add_argument(
+        "--resume_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path to the checkpoint of trained tryon model."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="resource/demo/output",
+        help="The output directory where the model predictions will be written.",
+    )
+
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=768,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--repaint", 
+        action="store_true", 
+        help="Whether to repaint the result image with the original background."
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        default=True,
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="fp16",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows * cols
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+args = parse_args()
+# repo_path = snapshot_download(repo_id=args.resume_path)
+repo_path = args.resume_path
+# Pipeline
+pipeline = CatVTONPipeline(
+    base_ckpt=args.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(args.mixed_precision),
+    use_tf32=args.allow_tf32,
+    device='cuda'
+)
+# AutoMasker
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    device='cuda', 
+)
+
+def submit_function(
+    person_image,
+    cloth_image,
+    cloth_type,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    show_type
+):
+    person_image, mask = person_image["background"], person_image["layers"][0]
+    mask = Image.open(mask).convert("L")
+    if len(np.unique(np.array(mask))) == 1:
+        mask = None
+    else:
+        mask = np.array(mask)
+        mask[mask > 0] = 255
+        mask = Image.fromarray(mask)
+
+    tmp_folder = args.output_dir
+    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
+    if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
+        os.makedirs(os.path.join(tmp_folder, date_str[:8]))
+
+    generator = None
+    if seed != -1:
+        generator = torch.Generator(device='cuda').manual_seed(seed)
+
+    person_image = Image.open(person_image).convert("RGB")
+    cloth_image = Image.open(cloth_image).convert("RGB")
+    person_image = resize_and_crop(person_image, (args.width, args.height))
+    cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
+    
+    # Process mask
+    if mask is not None:
+        mask = resize_and_crop(mask, (args.width, args.height))
+    else:
+        mask = automasker(
+            person_image,
+            cloth_type
+        )['mask']
+    mask = mask_processor.blur(mask, blur_factor=9)
+
+    # Inference
+    # try:
+    result_image = pipeline(
+        image=person_image,
+        condition_image=cloth_image,
+        mask=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    )[0]
+    # except Exception as e:
+    #     raise gr.Error(
+    #         "An error occurred. Please try again later: {}".format(e)
+    #     )
+    
+    # Post-process
+    masked_person = vis_mask(person_image, mask)
+    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
+    save_result_image.save(result_save_path)
+    if show_type == "result only":
+        return result_image
+    else:
+        width, height = person_image.size
+        if show_type == "input & result":
+            condition_width = width // 2
+            conditions = image_grid([person_image, cloth_image], 2, 1)
+        else:
+            condition_width = width // 3
+            conditions = image_grid([person_image, masked_person , cloth_image], 3, 1)
+        conditions = conditions.resize((condition_width, height), Image.NEAREST)
+        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
+        new_result_image.paste(conditions, (0, 0))
+        new_result_image.paste(result_image, (condition_width + 5, 0))
+    return new_result_image
+
+
+def person_example_fn(image_path):
+    return image_path
+
+HEADER = """
+<h1 style="text-align: center;"> 🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models </h1>
+<div style="display: flex; justify-content: center; align-items: center;">
+  <a href="http://arxiv.org/abs/2407.15886" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/arXiv-2407.15886-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
+  </a>
+  <a href='https://huggingface.co/zhengchong/CatVTON' style="margin: 0 2px;">
+    <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" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
+  </a>
+  <a href="http://120.76.142.206:8888" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href="https://huggingface.co/spaces/zhengchong/CatVTON" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Space-ZeroGPU-orange?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
+  </a>
+  <a href='https://zheng-chong.github.io/CatVTON/' style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='webpage'>
+  </a>
+  <a href="https://github.com/Zheng-Chong/CatVTON/LICENCE" style="margin: 0 2px;">
+    <img src='https://img.shields.io/badge/License-CC BY--NC--SA--4.0-lightgreen?style=flat&logo=Lisence' alt='License'>
+  </a>
+</div>
+<br>
+· This demo and our weights are only for <span>Non-commercial Use</span>. <br>
+· You can try CatVTON in our <a href="https://huggingface.co/spaces/zhengchong/CatVTON">HuggingFace Space</a> or our <a href="http://120.76.142.206:8888">online demo</a> (run on 3090). <br>
+· Thanks to <a href="https://huggingface.co/zero-gpu-explorers">ZeroGPU</a> for providing A100 for our <a href="https://huggingface.co/spaces/zhengchong/CatVTON">HuggingFace Space</a>. <br>
+· SafetyChecker is set to filter NSFW content, but it may block normal results too. Please adjust the <span>`seed`</span> for normal outcomes.<br> 
+"""
+
+def app_gradio():
+    with gr.Blocks(title="CatVTON") as demo:
+        gr.Markdown(HEADER)
+        with gr.Row():
+            with gr.Column(scale=1, min_width=350):
+                with gr.Row():
+                    image_path = gr.Image(
+                        type="filepath",
+                        interactive=True,
+                        visible=False,
+                    )
+                    person_image = gr.ImageEditor(
+                        interactive=True, label="Person Image", type="filepath"
+                    )
+
+                with gr.Row():
+                    with gr.Column(scale=1, min_width=230):
+                        cloth_image = gr.Image(
+                            interactive=True, label="Condition Image", type="filepath"
+                        )
+                    with gr.Column(scale=1, min_width=120):
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>'
+                        )
+                        cloth_type = gr.Radio(
+                            label="Try-On Cloth Type",
+                            choices=["upper", "lower", "overall"],
+                            value="upper",
+                        )
+
+
+                submit = gr.Button("Submit")
+                gr.Markdown(
+                    '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
+                )
+                
+                gr.Markdown(
+                    '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>'
+                )
+                with gr.Accordion("Advanced Options", open=False):
+                    num_inference_steps = gr.Slider(
+                        label="Inference Step", minimum=10, maximum=100, step=5, value=50
+                    )
+                    # Guidence Scale
+                    guidance_scale = gr.Slider(
+                        label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
+                    )
+                    # Random Seed
+                    seed = gr.Slider(
+                        label="Seed", minimum=-1, maximum=10000, step=1, value=42
+                    )
+                    show_type = gr.Radio(
+                        label="Show Type",
+                        choices=["result only", "input & result", "input & mask & result"],
+                        value="input & mask & result",
+                    )
+
+            with gr.Column(scale=2, min_width=500):
+                result_image = gr.Image(interactive=False, label="Result")
+                with gr.Row():
+                    # Photo Examples
+                    root_path = "resource/demo/example"
+                    with gr.Column():
+                        men_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "men", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "men"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ①",
+                        )
+                        women_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "women", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "women"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ②",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>'
+                        )
+                    with gr.Column():
+                        condition_upper_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "upper", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Upper Examples",
+                        )
+                        condition_overall_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "overall", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Overall Examples",
+                        )
+                        condition_person_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "person", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "person"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Reference Person Examples",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
+                        )
+
+            image_path.change(
+                person_example_fn, inputs=image_path, outputs=person_image
+            )
+
+            submit.click(
+                submit_function,
+                [
+                    person_image,
+                    cloth_image,
+                    cloth_type,
+                    num_inference_steps,
+                    guidance_scale,
+                    seed,
+                    show_type,
+                ],
+                result_image,
+            )
+            
+    demo.queue().launch(server_name="0.0.0.0", show_error=True)
+
+
+if __name__ == "__main__":
+    app_gradio()