Modern image and video generation methods rely heavily on tokenization, which encodes high-dimensional data into compact latent representations. Although scaling generator models have made significant progress, tokenizers, which are primarily based on convolutional neural networks (CNNs), have received relatively little attention. This raises the question of how tokenizer scaling improves reconstruction accuracy and generation tasks. Challenges include architectural limitations and constrained datasets that impact scalability and broad applicability. You also need to understand how your autoencoder design choices affect performance metrics such as fidelity, compression, and generation.
Meta and UT Austin researchers addressed these issues by introducing ViTok, a Vision Transformer (ViT)-based autoencoder. Unlike traditional CNN-based tokenizers, ViTok employs a Transformer-based architecture powered by the Llama framework. This design supports large-scale tokenization of images and videos and overcomes dataset limitations by training on extensive and diverse data.
ViTok focuses on three aspects of scaling.
Scaling bottlenecks: Investigating potential code size and performance relationships. Encoder scaling: Evaluate the impact of increasing encoder complexity. Decoder scaling: Evaluate how a larger decoder affects reconstruction and generation.
These efforts aim to optimize visual tokenization for both images and videos by addressing inefficiencies in existing architectures.
ViTok technical details and benefits
ViTok uses an asymmetric autoencoder framework with several distinctive features.
Patch and tubelet embedding: The input is split into patches (for images) or tubelets (for videos) to capture spatial and spatiotemporal details. Potential bottlenecks: The size of the potential space, defined by the number of floating points (E), determines the balance between compression and reconstruction quality. Encoder and decoder design: ViTok employs a lightweight encoder for efficiency and a more computationally intensive decoder for robust reconstruction.
By leveraging Vision Transformers, ViTok improves scalability. The enhanced decoder incorporates perceptual and adversarial losses to produce high-quality output. Together, these components enable ViTok to:
Achieve effective reconstruction with fewer computational flops. Exploit redundancy in video sequences to efficiently process image and video data. Balance the trade-off between fidelity (PSNR, SSIM, etc.) and perceptual quality (FID, IS, etc.).
Results and insights
ViTok’s performance was evaluated using benchmarks such as ImageNet-1K, COCO for images, and UCF-101 for videos. Key findings include:
Scaling the bottleneck: Increasing the size of the bottleneck improves the reconstruction, but can complicate the generation task if the latent space is too large. Encoder scaling: A larger encoder can hinder production performance by minimizing the benefit of reconstruction and increasing decoding complexity. Decoder scaling: Larger decoders improve reconstruction quality, but have different benefits for generation tasks. A balanced design is often required.
The results highlight ViTok’s strengths in efficiency and accuracy.
State-of-the-art metrics for image reconstruction at 256p and 512p resolutions. The video reconstruction score was improved and the adaptability to spatiotemporal data was demonstrated. Achieve competitive generative performance in class-conditional tasks while reducing computational complexity.
conclusion
ViTok provides a scalable Transformer-based alternative to traditional CNN tokenizers, addressing key challenges in bottleneck design, encoder scaling, and decoder optimization. Robust performance across reconstruction and generation tasks highlights its potential for a wide range of applications. ViTok emphasizes the importance of thoughtful architectural design in advancing visual tokenization by effectively processing both image and video data.
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