Versatile Unpaired Virtual Try-on via Patch-Routed Spatially-Adaptive GAN++

Official implementation of "Versatile Unpaired Virtual Try-on via Patch-Routed Spatially-Adaptive GAN++".

Requirements

Create a virtual environment:

virtualenv pasta --python=python3.7
source pasta/bin/activate

Install required packages:

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3
pip install psutil scipy matplotlib opencv-python scikit-image==0.18.3 pycocotools
apt install libgl1-mesa-glx

Running Inference

We provide the pre-trained models of PASTA-GAN++ which are trained by using the full UPT dataset (i.e., our newly collected data, data from Deepfashion dataset, data from MPV dataset) with the resolution of 512 separately.

we provide some test data under the directory test_datas, and provide a simple script to test the pre-trained model provided above on the UPT dataset as follow:

CUDA_VISIBLE_DEVICES=0 python3 -W ignore test.py \
    --dataroot test_datas --testtxt test_pairs.txt \
    --network checkpoints/pasta-gan++/network-snapshot-004408.pkl \
    --outdir test_results/upper \
    --batchsize 1 --testpart upper

or you can run the bash script by using the following command:

bash test.sh 1

Note that, in the testing script, the parameter --network refers to the path of the pre-trained model, the parameter --outdir refers to the path of the directory for generated results, the parameter --dataroot refers to the path of the data root, the parameter --testtxt refers to pair list of the garment-person pairs, the parameter --testpart refers to the garment part PASTA-GAN++ conducts the garment transfer. --use-sleeve-mask refers to whether to use the sleeve mask for data preprocess (if sleeve mask is unavailable, just ignore this parameter). As for the configuration for these parameters, please refer to test.sh.