VideoGPT: Video Generation using VQ-VAE and Transformers

[Paper][Website][Colab][Gradio Demo]

We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled discrete latent representations of a raw video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings. Despite the simplicity in formulation and ease of training, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate high fidelity natural images from UCF-101 and Tumbler GIF Dataset (TGIF). We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models.

Approach

Installation

Change the cudatoolkit version compatible to your machine.

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install git+https://github.com/wilson1yan/VideoGPT.git

Sparse Attention (Optional)

For limited compute scenarios, it may be beneficial to use sparse attention.

$ sudo apt-get install llvm-9-dev
$ DS_BUILD_SPARSE_ATTN=1 pip install deepspeed

After installng deepspeed, you can train a sparse transformer by setting the flag --attn_type sparse in scripts/train_videogpt.py. The default support sparsity configuration is an N-d strided sparsity layout, however, you can write your own arbitrary layouts to use.

Dataset

The default code accepts data as an HDF5 file with the specified format in videogpt/data.py, and a directory format with the follow structure:

video_dataset/
    train/
        class_0/
            video1.mp4
            video2.mp4
            ...
        class_1/
            video1.mp4
            ...
        ...
        class_n/
            ...
    test/
        class_0/
            video1.mp4
            video2.mp4
            ...
        class_1/
            video1.mp4
            ...
        ...
        class_n/
            ...

An example of such a dataset can be constructed from UCF-101 data by running the script

sh scripts/preprocess/create_ucf_dataset.sh datasets/ucf101

You may need to install unrar and unzip for the code to work correctly.

If you do not care about classes, the class folders are not necessary and the dataset file structure can be collapsed into train and test directories of just videos.

Using Pretrained VQ-VAEs

There are four available pre-trained VQ-VAE models. All strides listed with each model are downsampling amounts across THW for the encoders.

• bair_stride4x2x2: trained on 16 frame 64 x 64 videos from the BAIR Robot Pushing dataset
• ucf101_stride4x4x4: trained on 16 frame 128 x 128 videos from UCF-101
• kinetics_stride4x4x4: trained on 16 frame 128 x 128 videos from Kinetics-600
• kinetics_stride2x4x4: trained on 16 frame 128 x 128 videos from Kinetics-600, with 2x larger temporal latent codes (achieves slightly better reconstruction)

from torchvision.io import read_video
from videogpt import load_vqvae
from videogpt.data import preprocess

video_filename = 'path/to/video_file.mp4'
sequence_length = 16
resolution = 128
device = torch.device('cuda')

vqvae = load_vqvae('kinetics_stride2x4x4')
video = read_video(video_filename, pts_unit='sec')[0]
video = preprocess(video, resolution, sequence_length).unsqueeze(0).to(device)

encodings = vqvae.encode(video)
video_recon = vqvae.decode(encodings)

Training VQ-VAE

Use the scripts/train_vqvae.py script to train a VQ-VAE. Execute python scripts/train_vqvae.py -h for information on all available training settings. A subset of more relevant settings are listed below, along with default values.

VQ-VAE Specific Settings

• --embedding_dim: number of dimensions for codebooks embeddings
• --n_codes 2048: number of codes in the codebook
• --n_hiddens 240: number of hidden features in the residual blocks
• --n_res_layers 4: number of residual blocks
• --downsample 4 4 4: T H W downsampling stride of the encoder

Training Settings

• --gpus 2: number of gpus for distributed training
• --sync_batchnorm: uses SyncBatchNorm instead of BatchNorm3d when using > 1 gpu
• --gradient_clip_val 1: gradient clipping threshold for training
• --batch_size 16: batch size per gpu
• --num_workers 8: number of workers for each DataLoader

Dataset Settings

• --data_path <path>: path to an hdf5 file or a folder containing train and test folders with subdirectories of videos
• --resolution 128: spatial resolution to train on
• --sequence_length 16: temporal resolution, or video clip length

Training VideoGPT

You can download a pretrained VQ-VAE, or train your own. Afterwards, use the scripts/train_videogpt.py script to train an VideoGPT model for sampling. Execute python scripts/train_videogpt.py -h for information on all available training settings. A subset of more relevant settings are listed below, along with default values.

VideoGPT Specific Settings

• --vqvae kinetics_stride4x4x4: path to a vqvae checkpoint file, OR a pretrained model name to download. Available pretrained models are: bair_stride4x2x2, ucf101_stride4x4x4, kinetics_stride4x4x4, kinetics_stride2x4x4. BAIR was trained on 64 x 64 videos, and the rest on 128 x 128 videos
• --n_cond_frames 0: number of frames to condition on. 0 represents a non-frame conditioned model
• --class_cond: trains a class conditional model if activated
• --hidden_dim 576: number of transformer hidden features
• --heads 4: number of heads for multihead attention
• --layers 8: number of transformer layers
• --dropout 0.2': dropout probability applied to features after attention and positionwise feedforward layers
• --attn_type full: full or sparse attention. Refer to the Installation section for install sparse attention
• --attn_dropout 0.3: dropout probability applied to the attention weight matrix

Training Settings

• --gpus 2: number of gpus for distributed training
• --sync_batchnorm: uses SyncBatchNorm instead of BatchNorm3d when using > 1 gpu
• --gradient_clip_val 1: gradient clipping threshold for training
• --batch_size 16: batch size per gpu
• --num_workers 8: number of workers for each DataLoader

Dataset Settings

• --data_path <path>: path to an hdf5 file or a folder containing train and test folders with subdirectories of videos
• --resolution 128: spatial resolution to train on
• --sequence_length 16: temporal resolution, or video clip length

Sampling VideoGPT

After training, the VideoGPT model can be sampled using the scripts/sample_videogpt.py. You may need to install ffmpeg: sudo apt-get install ffmpeg

Reproducing Paper Results

Note that this repo is primarily designed for simplicity and extending off of our method. Reproducing the full paper results can be done using code found at a separate repo. However, be aware that the code is not as clean.

Citation

Please consider using the follow citation when using our code:

@misc{yan2021videogpt,
      title={VideoGPT: Video Generation using VQ-VAE and Transformers}, 
      author={Wilson Yan and Yunzhi Zhang and Pieter Abbeel and Aravind Srinivas},
      year={2021},
      eprint={2104.10157},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}