lior1990/hp-vae-gan

Official repository for HP-VAE-GAN

Hierarchical Patch VAE-GAN

Official repository of the paper "Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample"

Project | arXiv | Code

Real Videos

Fake Videos

Environment setting

Use commands in env.sh to setup the correct conda environment

Training Video

For training a single video, use the following command for example:

CUDA_VISIBLE_DEVICES=0 python train_video.py --video-path data/vids/air_balloons.mp4 --vae-levels 3 --checkname myvideotest --visualize

Common training options:

# Networks Hyper Parameters
--nfc                model basic # channels
--latent-dim         Latent dim size
--vae-levels         # VAE levels
--generator          generator mode

# Optimization hyper parameters
--niter              number of iterations to train per scale
--rec-weight         reconstruction loss weight
--train-all          train all levels w.r.t. train-depth

# Dataset
--video-path         video path (required)
--start-frame        start frame number
--max-frames         # frames to save
--sampling-rates     sampling rates

# Misc
--visualize     visualize using tensorboard

Training Image

For training a single video, use the following command for example:

CUDA_VISIBLE_DEVICES=0 python train_image.py --image-path data/imgs/air_balloons.jpg --vae-levels 3 --checkname myimagetest --visualize

Training baselines for video

For training a single video using SinGan re-implementation, use the following command:

CUDA_VISIBLE_DEVICES=0 python train_video_baselines.py --video-path data/vids/air_balloons.mp4 --checkname myimagetest --visualize --generator GeneratorSG --train-depth 1