TokenFlow: Unified Image Tokenizer for Multimodal Understanding and
  Generation
Authors: name: Liao Qu, name: Huichao Zhang, name: Yiheng Liu, name: Xu Wang, name: Yi Jiang, name: Yiming Gao, name: Hu Ye, name: Daniel K. Du, name: Zehuan Yuan, name: Xinglong Wu

We present TokenFlow, a novel unified image tokenizer that bridges the
long-standing gap between multimodal understanding and generation. Prior
research attempt to employ a single reconstruction-targeted Vector Quantization
(VQ) encoder for unifying these two tasks. We observe that understanding and
generation require fundamentally different granularities of visual information.
This leads to a critical trade-off, particularly compromising performance in
multimodal understanding tasks. TokenFlow addresses this challenge through an
innovative dual-codebook architecture that decouples semantic and pixel-level
feature learning while maintaining their alignment via a shared mapping
mechanism. This design enables direct access to both high-level semantic
representations crucial for understanding tasks and fine-grained visual
features essential for generation through shared indices. Our extensive
experiments demonstrate TokenFlow's superiority across multiple dimensions.
Leveraging TokenFlow, we demonstrate for the first time that discrete visual
input can surpass LLaVA-1.5 13B in understanding performance, achieving a 7.2\%
average improvement. For image reconstruction, we achieve a strong FID score of
0.63 at 384*384 resolution. Moreover, TokenFlow establishes state-of-the-art
performance in autoregressive image generation with a GenEval score of 0.55 at
256*256 resolution, achieving comparable results to SDXL.