1. We introduce TiTok, a novel 1D image tokenization framework that breaks grid constraints existing in 2D tokenization methods, leading to a much more flexible and compact image latent representation.
2. The proposed TiTok can tokenize a 256 × 256 image into as few as 32 discrete tokens, leading to a signigicant speed-up (hundreds times faster than diffusion models) in generation process, while maintaining state-of-the-art generation quality.
3. We conduct a series of experiments to probe the properties of rarely studied 1D image tokenization, paving the path towards compact latent space for efficient and effective image representation.
Recent advancements in generative models have highlighted the crucial role of image tokenization in the efficient synthesis of high-resolution images. Tokenization, which transforms images into latent representations, reduces computational demands compared to directly processing pixels and enhances the effectiveness and efficiency of the generation process. Prior methods, such as VQGAN, typically utilize 2D latent grids with fixed downsampling factors. However, these 2D tokenizations face challenges in managing the inherent redundancies present in images, where adjacent regions frequently display similarities. To overcome this issue, we introduce Transformer-based 1-Dimensional Tokenizer (TiTok), an innovative approach that tokenizes images into 1D latent sequences. TiTok provides a more compact latent representation, yielding substantially more efficient and effective representations than conventional techniques. For example, a 256 × 256 × 3 image can be reduced to just 32 discrete tokens, a significant reduction from the 256 or 1024 tokens obtained by prior methods. Despite its compact nature, TiTok achieves competitive performance to state-of-the-art approaches. Specifically, using the same generator framework, TiTok attains 1.97 gFID, outperforming MaskGIT baseline significantly by 4.21 at ImageNet 256 × 256 benchmark. The advantages of TiTok become even more significant when it comes to higher resolution. At ImageNet 512 × 512 benchmark, TiTok not only outperforms state-of-the-art diffusion model DiT-XL/2 (gFID 2.74 vs. 3.04), but also reduces the image tokens by 64 × , leading to 410 × faster generation process. Our best-performing variant can significantly surpasses DiT-XL/2 (gFID 2.13 vs. 3.04) while still generating high-quality samples 74 × faster.
@article{yu2024an,
author = {Qihang Yu and Mark Weber and Xueqing Deng and Xiaohui Shen and Daniel Cremers and Liang-Chieh Chen},
title = {An Image is Worth 32 Tokens for Reconstruction and Generation},
journal = {arxiv: 2406.07550},
year = {2024}
}