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+# SVQVAE (Scalable Vector Quantized Variational Autoencoder)
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+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)