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).
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).
2024/10/17
:Mask-free versionπ€ of CatVTON is release and please try it in our Online Demo.2024/10/13
: We have built a repo 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 youβre interested, feel free to contribute or give it a π star!2024/08/13
: We localize DensePose & SCHP to avoid certain environment issues.2024/08/10
: Our π€ HuggingFace Space is available now! Thanks for the grant from ZeroGPUοΌ2024/08/09
: Evaluation code is provided to calculate metrics π.2024/07/27
: We provide code and workflow for deploying CatVTON on ComfyUI π₯.2024/07/24
: Our Paper on ArXiv is available π₯³!2024/07/22
: Our App Code is released, deploy and enjoy CatVTON on your mechine π!2024/07/21
: Our Inference Code and Weights π€ are released.2024/07/11
: Our Online Demo is released π.Create a conda environment & Install requirments
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
We have modified the main code to enable easy deployment of CatVTON on ComfyUI. Due to the incompatibility of the code structure, we have released this part in the Releases, which includes the code placed under custom_nodes
of ComfyUI and our workflow JSON files.
To deploy CatVTON to your ComfyUI, follow these steps:
ComfyUI-CatVTON.zip
and unzip it in the custom_nodes
folder under your ComfyUI project (clone from ComfyUI).catvton_workflow.json
and drag it into you ComfyUI webpage and enjoy π!Problems under Windows OS, please refer to issue#8.
When you run the CatVTON workflow for the first time, the weight files will be automatically downloaded, usually taking dozens of minutes.
To deploy the Gradio App for CatVTON on your machine, run the following command, and checkpoints will be automatically downloaded from HuggingFace.
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.
Before inference, you need to download the VITON-HD or DressCode dataset.
Once the datasets are downloaded, the folder structures should look like these:
βββ VITON-HD
| βββ test_pairs_unpaired.txt
β βββ test
| | βββ image
β β β βββ [000006_00.jpg | 000008_00.jpg | ...]
β β βββ cloth
β β β βββ [000006_00.jpg | 000008_00.jpg | ...]
β β βββ agnostic-mask
β β β βββ [000006_00_mask.png | 000008_00.png | ...]
...
βββ DressCode
| βββ test_pairs_paired.txt
| βββ test_pairs_unpaired.txt
β βββ [dresses | lower_body | upper_body]
| | βββ test_pairs_paired.txt
| | βββ test_pairs_unpaired.txt
β β βββ images
β β β βββ [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...]
β β βββ agnostic_masks
β β β βββ [013563_0.png| 013564_0.png | ...]
...
For the DressCode dataset, we provide script to preprocessed agnostic masks, run the following command:
CUDA_VISIBLE_DEVICES=0 python preprocess_agnostic_mask.py \
--data_root_path <your_path_to_DressCode>
To run the inference on the DressCode or VITON-HD dataset, run the following command, checkpoints will be automatically downloaded from HuggingFace.
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
After obtaining the inference results, calculate the metrics using the following command:
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.--paired
; for an unpaired setting, simply omit it.--batch_size
and --num_workers
should be adjusted based on your machine.Our code is modified based on Diffusers. We adopt Stable Diffusion v1.5 inpainting as the base model. We use SCHP and DensePose to automatically generate masks in our Gradio App and ComfyUI workflow. Thanks to all the contributors!
All the materials, including code, checkpoints, and demo, are made available under the Creative Commons 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.
@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},
}