VQVAE Documentation

Introduction

Vector Quantized Variational AutoEncoders (VQ-VAE) is a type of autoencoder that uses a discrete latent representation. It is particularly useful for tasks that require discrete latent variables, such as text-to-speech and video generation.

Usage

Initialization

To initialize a VQVAE model, you can use the VideoGPTVQVAE class. This class is a part of the opensora.models.ae module.

from opensora.models.ae import VideoGPTVQVAE

vqvae = VideoGPTVQVAE()

Training

To train the VQVAE model, you can use the train_videogpt.sh script. This script will train the model using the parameters specified in the script.

bash scripts/videogpt/train_videogpt.sh

Loading Pretrained Models

You can load a pretrained model using the download_and_load_model method. This method will download the checkpoint file and load the model.

vqvae = VideoGPTVQVAE.download_and_load_model("bair_stride4x2x2")

Alternatively, you can load a model from a checkpoint using the load_from_checkpoint method.

vqvae = VQVAEModel.load_from_checkpoint("results/VQVAE/checkpoint-1000")

Encoding and Decoding

You can encode a video using the encode method. This method will return the encodings and embeddings of the video.

encodings, embeddings = vqvae.encode(x_vae, include_embeddings=True)

You can reconstruct a video from its encodings using the decode method.

video_recon = vqvae.decode(encodings)

Testing

You can test the VQVAE model by reconstructing a video. The examples/rec_video.py script provides an example of how to do this.