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# 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.
```python
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
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.
```python
vqvae = VideoGPTVQVAE.download_and_load_model("bair_stride4x2x2")
```
Alternatively, you can load a model from a checkpoint using the `load_from_checkpoint` method.
```python
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.
```python
encodings, embeddings = vqvae.encode(x_vae, include_embeddings=True)
```
You can reconstruct a video from its encodings using the decode method.
```python
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.