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 # **Scaling Image Tokenizers with Grouped Spherical Quantization**
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-[Paper link](https://arxiv.org/abs/2412.02632) | [HF Checkpoints](https://huggingface.co/collections/HelmholtzAI-FZJ/grouped-spherical-quantization-674d6f9f548e472d0eaf179e)
+[Paper link](https://arxiv.org/abs/2412.02632) | [GITHUB REPO](https://github.com/HelmholtzAI-FZJ/flex_gen) [HF Checkpoints](https://huggingface.co/collections/HelmholtzAI-FZJ/grouped-spherical-quantization-674d6f9f548e472d0eaf179e)
 
 In [GSQ](https://arxiv.org/abs/2412.02632), we show the optimized training hyper-parameters and configs for quantization based image tokenizer. We also show how to scale the latent, vocab size etc. appropriately to achieve better reconstruction performance.