title: Scaling Image Tokenizers with Grouped Spherical Quantization
summary: Vision tokenizers have gained a lot of attraction due to their scalability
  and compactness; previous works depend on old-school GAN-based hyperparameters,
  biased comparisons, and a lack of comprehensive analysis of the scaling
  behaviours. To tackle those issues, we introduce Grouped Spherical Quantization
  (GSQ), featuring spherical codebook initialization and lookup regularization to
  constrain codebook latent to a spherical surface. Our empirical analysis of
  image tokenizer training strategies demonstrates that GSQ-GAN achieves superior
  reconstruction quality over state-of-the-art methods with fewer training
  iterations, providing a solid foundation for scaling studies. Building on this,
  we systematically examine the scaling behaviours of GSQ, specifically in latent
  dimensionality, codebook size, and compression ratios, and their impact on
  model performance. Our findings reveal distinct behaviours at high and low
  spatial compression levels, underscoring challenges in representing
  high-dimensional latent spaces. We show that GSQ can restructure
  high-dimensional latent into compact, low-dimensional spaces, thus enabling
  efficient scaling with improved quality. As a result, GSQ-GAN achieves a 16x
  down-sampling with a reconstruction FID (rFID) of 0.50.
paper_authors._id: 674fd182418fad09e5c02283
paper_authors.name: Jiangtao Wang
paper_authors._id: 674fd182418fad09e5c02284
paper_authors.name: Zhen Qin
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paper_authors.name: Yifan Zhang
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paper_authors.fullname: Yifan Zhang
paper_authors.type: user
paper_authors.user: yifAI
paper_authors._id: 674fd182418fad09e5c02286
paper_authors.name: Vincent Tao Hu
paper_authors._id: 674fd182418fad09e5c02287
paper_authors.name: Björn Ommer
paper_authors._id: 674fd182418fad09e5c02288
paper_authors.name: Rania Briq
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paper_authors.fullname: rania briq
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paper_authors.user: briqnn
paper_authors._id: 674fd182418fad09e5c02289
paper_authors.name: Stefan Kesselheim
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paper_authors.fullname: Stefan Kesselheim
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paper_authors.user: kessel666
paper_title: Scaling Image Tokenizers with Grouped Spherical Quantization
paper_summary: Vision tokenizers have gained a lot of attraction due to their scalability
  and compactness; previous works depend on old-school GAN-based hyperparameters,
  biased comparisons, and a lack of comprehensive analysis of the scaling
  behaviours. To tackle those issues, we introduce Grouped Spherical Quantization
  (GSQ), featuring spherical codebook initialization and lookup regularization to
  constrain codebook latent to a spherical surface. Our empirical analysis of
  image tokenizer training strategies demonstrates that GSQ-GAN achieves superior
  reconstruction quality over state-of-the-art methods with fewer training
  iterations, providing a solid foundation for scaling studies. Building on this,
  we systematically examine the scaling behaviours of GSQ, specifically in latent
  dimensionality, codebook size, and compression ratios, and their impact on
  model performance. Our findings reveal distinct behaviours at high and low
  spatial compression levels, underscoring challenges in representing
  high-dimensional latent spaces. We show that GSQ can restructure
  high-dimensional latent into compact, low-dimensional spaces, thus enabling
  efficient scaling with improved quality. As a result, GSQ-GAN achieves a 16x
  down-sampling with a reconstruction FID (rFID) of 0.50.
paper_ai_keywords: Grouped Spherical Quantization (GSQ) spherical codebook initialization lookup regularization codebook latent spherical surface image tokenizer training strategies GSQ-GAN reconstruction quality state-of-the-art methods empirical analysis training iterations scaling behaviours latent dimensionality codebook size compression ratios model performance high-dimensional latent spaces compact, low-dimensional spaces down-sampling reconstruction FID (rFID)