Scaling Image Tokenizers with Grouped Spherical Quantization (2412.02632v2)

Abstract: 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.

Scaling Image Tokenizers with Grouped Spherical Quantization

The paper, "Scaling Image Tokenizers with Grouped Spherical Quantization," explores optimizing image tokenizers for enhanced scalability and efficiency. Image tokenizers are pivotal in generative models, converting continuous image data into discrete tokens to improve fidelity and computational performance. Traditional methods often rely on outdated GAN-centric hyperparameters and biased benchmarks that may not adequately capture the nuanced scalability behaviors of these models.

Innovative Approach: Grouped Spherical Quantization (GSQ)

GSQ introduces a novel quantization approach using spherical codebook initialization and lookup regularization, which confines the latent vectors to a spherical surface. The paper positions GSQ-GAN, a specific implementation of this technique, as superior in both reconstruction quality and training efficiency compared to existing state-of-the-art methods. Notably, GSQ-GAN achieves high-fidelity reconstruction with reduced training epochs—a reconstruction FID (rFID) of 0.50 with a 16× down-sampling factor.

Key Findings

1. Latent Dimensionality and Codebook Size: The paper systematically examines the scaling behaviors associated with latent dimensionality and codebook size. It finds significant variation in performance across different compression ratios, particularly highlighting challenges at high compression levels.
2. Efficient Latent Space Utilization: GSQ demonstrates superior use of latent space by effectively balancing dimensionality and codebook size. The analysis underpins the inefficiencies in lower spatial compression scenarios, emphasizing the utility of large codebook sizes paired with compact latent vectors.
3. Scalability with Latent Dimensions: A notable contribution is the decoupling of latent dimensionality from codebook size. This allows for independent scaling, enabling the model to maintain fidelity even at larger compressions—a task where other models typically strain.

Experimental Analysis

The experimental section contrasts GSQ-GAN against other quantization methods like FSQ and LFQ, presenting a comprehensive ablation paper. The results reveal GSQ's robust codebook usage across varying configurations, notably maintaining near 100% codebook utilization. Key metrics such as rFID and perceptual loss indicate GSQ’s enhanced reconstruction capabilities.

Implications and Future Directions

The paper sets a benchmark for scalable image tokenizer design, advocating for GSQ in applications requiring high-fidelity image generation with optimal encoding efficiency. The implications extend to a range of AI-driven tasks, notably in generative modeling where efficiency and fidelity are critical.

Speculatively, the scalability introduced through GSQ could drive advancements in large-scale generative tasks, potentially influencing areas such as video synthesis and multimodal representations, where complex and abundant data must be handled effectively.

Conclusion

This investigation into GSQ offers substantial contributions toward efficient image tokenization, balancing compression and reconstruction quality. The refined approach to tokenizer scaling behavior provides a pathway for enhanced model performance with fewer computational resources, setting the stage for future research into even more efficient generative models.