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# SVQVAE (Scalable Vector Quantized Variational Autoencoder) |
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Github: https://github.com/Open-Model-Initiative/SVQVAE |
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A scalable Vector Quantized Variational Autoencoder (VQVAE) for high-resolution image generation and reconstruction. This model supports tiled processing for handling large images efficiently. |
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## Model Description |
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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. |
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### Key Features |
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- **Scalable Processing**: Handles high-resolution images through tiled processing |
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- **Multi-scale Output**: Can generate reconstructions at different scales |
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- **Vector Quantization**: Uses a discrete codebook for efficient compression |
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- **Attention Mechanisms**: Includes self-attention blocks for better feature learning |
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- **Flexible Architecture**: Configurable encoder/decoder with customizable channel multipliers |
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## Citation |
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If you use this code in your research, please cite Austin J. Bryant and the Open Model Initiative. |
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## Acknowledgments |
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This implementation is based on the VQVAE architecture and includes improvements for scalable processing of high-resolution images. |
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## Repository Links |
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- **GitHub Repository**: [Open-Model-Initiative/SVQVAE](https://github.com/Open-Model-Initiative/SVQVAE) |
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- **Model Weights**: Available in this Hugging Face repository |
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- **Documentation**: See the GitHub repository for detailed documentation and examples |
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This model is licensed under the OpenMDW License Agreement (See LICENSE) |
