Parser-Free Virtual Try-on via Distilling Appearance Flows, CVPR 2021

Official code for CVPR 2021 paper 'Parser-Free Virtual Try-on via Distilling Appearance Flows'

[Checkpoints]

Our Envirenment

anaconda3

pytorch 1.1.0

torchvision 0.3.0

cuda 9.0

cupy 6.0.0

opencv-python 4.5.1

1 GTX1080 GPU

python 3.6

Installation

conda create -n tryon python=3.6

source activate tryon or conda activate tryon

conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=9.0 -c pytorch

conda install cupy or pip install cupy==6.0.0

pip install opencv-python

git clone https://github.com/geyuying/PF-AFN.git

cd PF-AFN

Test With the Models

1. First, you need to download the checkpoints from google drive and put the models under the folder "checkpoints/PFAFN".
2. The "dataset" folder contains the images for test, and 'demo.txt' records the test pairs.
3. To test with the models, run test.sh and the results will be saved in the folder "results".
4. To reproduce our results from the saved model, your test environment should be the same as our test environment.
5. In oder to test the model with our checkpoints, please make sure that your environment is the same as our test envirenment.