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# SVQVAE (Scalable Vector Quantized Variational Autoencoder)

Github: https://github.com/Open-Model-Initiative/SVQVAE


A scalable Vector Quantized Variational Autoencoder (VQVAE) for high-resolution image generation and reconstruction. This model supports tiled processing for handling large images efficiently.

## Model Description

SVQVAE is a scalable variant of the Vector Quantized Variational Autoencoder that can process high-resolution images through tiled encoding and decoding. The model uses a discrete codebook to compress images into a latent representation and can reconstruct them at multiple scales.

### Key Features

- **Scalable Processing**: Handles high-resolution images through tiled processing
- **Multi-scale Output**: Can generate reconstructions at different scales
- **Vector Quantization**: Uses a discrete codebook for efficient compression
- **Attention Mechanisms**: Includes self-attention blocks for better feature learning
- **Flexible Architecture**: Configurable encoder/decoder with customizable channel multipliers

## Citation

If you use this code in your research, please cite Austin J. Bryant and the Open Model Initiative.

## Acknowledgments

This implementation is based on the VQVAE architecture and includes improvements for scalable processing of high-resolution images.

## Repository Links

- **GitHub Repository**: [Open-Model-Initiative/SVQVAE](https://github.com/Open-Model-Initiative/SVQVAE)
- **Model Weights**: Available in this Hugging Face repository
- **Documentation**: See the GitHub repository for detailed documentation and examples


This model is licensed under the OpenMDW License Agreement (See LICENSE)