Learnings from Scaling Visual Tokenizers for Reconstruction and Generation (2501.09755v1)

Abstract: Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based generators has been central to recent advances, the tokenizer component itself is rarely scaled, leaving open questions about how auto-encoder design choices influence both its objective of reconstruction and downstream generative performance. Our work aims to conduct an exploration of scaling in auto-encoders to fill in this blank. To facilitate this exploration, we replace the typical convolutional backbone with an enhanced Vision Transformer architecture for Tokenization (ViTok). We train ViTok on large-scale image and video datasets far exceeding ImageNet-1K, removing data constraints on tokenizer scaling. We first study how scaling the auto-encoder bottleneck affects both reconstruction and generation -- and find that while it is highly correlated with reconstruction, its relationship with generation is more complex. We next explored the effect of separately scaling the auto-encoders' encoder and decoder on reconstruction and generation performance. Crucially, we find that scaling the encoder yields minimal gains for either reconstruction or generation, while scaling the decoder boosts reconstruction but the benefits for generation are mixed. Building on our exploration, we design ViTok as a lightweight auto-encoder that achieves competitive performance with state-of-the-art auto-encoders on ImageNet-1K and COCO reconstruction tasks (256p and 512p) while outperforming existing auto-encoders on 16-frame 128p video reconstruction for UCF-101, all with 2-5x fewer FLOPs. When integrated with Diffusion Transformers, ViTok demonstrates competitive performance on image generation for ImageNet-1K and sets new state-of-the-art benchmarks for class-conditional video generation on UCF-101.

• The paper demonstrates that increasing the auto-encoder bottleneck significantly improves reconstruction metrics like rFID, PSNR, and SSIM while yielding diminishing returns for generation.
• The study finds that enlarging the encoder component offers minimal benefits, indicating its limited influence on improving downstream generative tasks.
• A larger decoder boosts reconstruction quality, though its scaling must be balanced to maintain efficiency and effectiveness in generative performance.

Analysis of "Learnings from Scaling Visual Tokenizers for Reconstruction and Generation"

The paper "Learnings from Scaling Visual Tokenizers for Reconstruction and Generation" offers an exploration into scaling auto-encoders to enhance image and video generative models, focusing primarily on the tokenizer component. The researchers aim to address a gap in existing literature, where the scaling of the tokenizer within Transformer-based architectures has been less emphasized compared to their generator counterparts.

Overview

The core innovation presented is the Vision Transformer Tokenizer (ViTok), an approach that replaces traditional convolutional backbones with an improved Vision Transformer (ViT) architecture for tokenization. ViTok allows for more effective processing of large-resolution images and videos. This enhanced scalability is especially relevant given the increasing size and complexity of datasets beyond ImageNet-1K, which are used for training modern state-of-the-art models.

Findings and Results

The paper systematically investigates three primary axes of scaling within auto-encoders: the auto-encoder bottleneck, the encoder, and the decoder components. It provides a detailed analysis of how each scaling choice impacts reconstruction quality and generative performance:

1. Bottleneck Scaling: The paper reveals that the size of the auto-encoder bottleneck plays a pivotal role in reconstruction performance, suggesting a strong correlation between bottleneck size and quantitative metrics such as rFID, PSNR, and SSIM. However, when it comes to generation, the relationship becomes more complex, indicating there are diminishing returns with excessively large bottlenecks.
2. Encoder Scaling: Findings show that enlarging the encoder does not significantly benefit reconstruction or generation. This indicates that the encoder's role is somewhat static and does not heavily influence the downstream tasks when increased in complexity beyond a certain point.
3. Decoder Scaling: A larger decoder improves reconstruction quality but offers mixed results for generative tasks. The outcomes suggest that while decoding contributes to reconstructive fidelity, its scaling should be managed to maintain efficiency and effectiveness in generation scenarios.

ViTok demonstrates competitive performance in both image and video reconstruction tasks on datasets such as ImageNet-1K and COCO. In particular, ViTok significantly reduces computational overhead, achieving equivalent or superior reconstruction quality using 2-5 times fewer FLOPs compared to existing state-of-the-art tokenizers. Furthermore, when combined with Diffusion Transformers, ViTok sets new performance benchmarks in class-conditional video generation on UCF-101.

Theoretical and Practical Implications

The theoretical implications of this work challenge the common assumption that scaling all components of neural architectures results in a linear performance enhancement. The observation that increased encoder capacities yield minimal performance gains emphasizes the importance of focusing on bottleneck and decoder components. Practically, these findings provide valuable guidelines for constructing more efficient generative models, with particular attention to optimizing reconstruction without necessitating cumbersome architectures.

Future Directions

Future research could further explore the interaction between bottleneck scaling and generation quality, potentially unraveling how to better align these two aspects for optimal performance. Additionally, the dynamics of encoder and decoder roles in various generative architectures could be examined more closely to improve understandings of their contributions in different contexts, especially as data scales continue to grow.

In conclusion, the paper offers significant insights into scaling visual tokenizers, emphasizing a nuanced approach to component scaling. It highlights the importance of bottleneck size in reconstruction and suggests careful calibration of decoder capacities to balance between reconstruction fidelity and generative efficiency. These insights lay foundational work for more resource-efficient advances in generative modeling.