Install dependencies.
conda create -n pvp python=3.9
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
conda install hydra-core hydra-colorlog hydra-optuna-sweeper tqdm pytorch-lightning==1.8.1 torchmetrics kornia==0.6.7 scipy scikit-image
conda install bottler nvidiacub
pip install timm tensorboardX blobfile gpustat torchinfo fairseq==0.10.0 click einops safetensors chumpy face_alignment
pip install git+https://github.com/facebookresearch/pytorch3d.git
conda activate pvp
Download the following and put them in ./assets
| Path | Description |
|---|---|
| FFHQ StyleGAN | StyleGAN2-ada model trained on FFHQ with 1024x1024 output resolution. |
| ArcFace | ArcFace model for identity loss. |
See NHA
python make_masked_images.py --folder [nha processed data]
python extract_expr_cli.py --folder [nha processed data]
python make_keyframes_expr.py --folder [nha processed data] --out_folder [keyframe folder] --n_cluster 200 --n_sample 1 --val_start [validation frame start]
python -m scripts.run_pti --input_data_path [keyframe folder] --input_data_id [name] --experiments_output_dir [PTI output folder]
Set up the configs (train.yaml), and replace paths starting with /home/ken with your own paths.
Change train_size and test_size in train.yaml to your desired sizes.
Default train_size=750 means the first 750 frames will be used as training data. test_size=700 means frame 751 to 1450 will be used as test data.
Run python train.py --config-name=train name="[your experiment name]"