Researchers from Meta AI and UT Austin explored scaling in autoencoders and introduced ViTok: a ViT-style autoencoder for performing exploration

Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advances in scale generation models have been substantial, tokenizers, primarily based on convolutional neural networks (CNN), have received comparatively less attention. This raises questions about how scaling tokenizers could improve the accuracy of reconstruction and generative tasks. Challenges include architectural limitations and restricted data sets, which impact scalability and broader applicability. It is also necessary to understand how design choices in autoencoders influence performance metrics such as fidelity, compression, and generation.

Researchers from Meta and UT Austin have addressed these issues by introducing ViTok, an autoencoder based on the Vision Transformer (ViT). Unlike traditional CNN-based tokenizers, ViTok employs a Transformer-based architecture enhanced by the Llama framework. This design supports large-scale tokenization for images and videos, overcoming dataset limitations by training with large and diverse data.

ViTok focuses on three aspects of scaling:

1. Bottleneck escalation: Examine the relationship between latent code size and performance.
2. Encoder scaling: Assessing the impact of increasing encoder complexity.
3. Decoder scaling: Evaluate how larger decoders influence reconstruction and generation.

These efforts aim to optimize visual tokenization for both images and videos by addressing inefficiencies in existing architectures.

Technical details and advantages of ViTok

ViTok uses an asymmetric autoencoder framework with several distinctive features:

1. Embedding of patches and tubes: Inputs are divided into patches (for images) or tubelets (for videos) to capture spatial and spatiotemporal details.
2. Latent bottleneck: The size of the latent space, defined by the number of floating points (E), determines the balance between the quality of compression and reconstruction.
3. Design of encoders and decoders: ViTok employs a lightweight encoder for efficiency and a more computationally intensive decoder for robust reconstruction.

By leveraging Vision Transformers, ViTok improves scalability. Its improved decoder incorporates perceptual and adversarial losses to produce high-quality results. Together, these components allow ViTok to:

• Achieve efficient reconstruction with fewer computational FLOPs.
• Handle image and video data efficiently, taking advantage of redundancy in video streams.
• Balance trade-offs between fidelity (e.g., PSNR, SSIM) and perceptual quality (e.g., FID, IS).

Results and insights

The performance of ViTok was evaluated using benchmarks such as ImageNet-1K, COCO for images, and UCF-101 for videos. Key findings include:

• Bottleneck escalation: Increasing the size of the bottleneck improves reconstruction, but can complicate generative tasks if the latent space is too large.
• Encoder scaling: Larger encoders show minimal benefits for reconstruction and may hinder generative performance due to higher decoding complexity.
• Decoder scaling: Larger decoders improve reconstruction quality, but their benefits for generative tasks vary. A balanced design is often required.

The results highlight ViTok's strengths in terms of efficiency and accuracy:

• State-of-the-art metrics for image reconstruction at 256p and 512p resolutions.
• Improved video reconstruction scores, demonstrating adaptability to spatiotemporal data.
• Competitive generative performance on class-conditional tasks with reduced computational demands.

Conclusion

ViTok offers a scalable Transformer-based alternative to traditional CNN tokenizers, addressing key challenges in bottleneck design, encoder scaling, and decoder optimization. Its strong performance in reconstruction and generation tasks highlights its potential for a wide range of applications. By effectively handling image and video data, ViTok underscores the importance of well-thought-out architectural design to advance visual tokenization.

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