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__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
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu22.04-dtk24.04.2-py3.10
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# 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 0 → 100644
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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;">
<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 0 → 100644
View file @ 3144257c
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()
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