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 introduceTransformer-based 1-DimensionalTokenizer (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, aimage can be reduced to just32discrete 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 attains1.97gFID, outperforming MaskGIT baseline significantly by 4.21 at ImageNetbenchmark. The advantages of TiTok become even more significant when it comes to higher resolution. At ImageNetbenchmark, TiTok not only outperforms state-of-the-art diffusion model DiT-XL/2 (gFID 2.74vs.3.04) , but also reduces the image tokens by, leading tofastergeneration process. Our best-performing variant can significantly surpasses DiT-XL/2 (gFID2.13vs.3.04)  while still generating high-quality samplesfaster.