FlowTok: Flowing Seamlessly Across Text and Image Tokens
Authors: name: Ju He, name: Qihang Yu, name: Qihao Liu, name: Liang-Chieh Chen
Organization: ByteDance Seed

Bridging different modalities lies at the heart of cross-modality generation.
While conventional approaches treat the text modality as a conditioning signal
that gradually guides the denoising process from Gaussian noise to the target
image modality, we explore a much simpler paradigm-directly evolving between
text and image modalities through flow matching. This requires projecting both
modalities into a shared latent space, which poses a significant challenge due
to their inherently different representations: text is highly semantic and
encoded as 1D tokens, whereas images are spatially redundant and represented as
2D latent embeddings. To address this, we introduce FlowTok, a minimal
framework that seamlessly flows across text and images by encoding images into
a compact 1D token representation. Compared to prior methods, this design
reduces the latent space size by 3.3x at an image resolution of 256,
eliminating the need for complex conditioning mechanisms or noise scheduling.
Moreover, FlowTok naturally extends to image-to-text generation under the same
formulation. With its streamlined architecture centered around compact 1D
tokens, FlowTok is highly memory-efficient, requires significantly fewer
training resources, and achieves much faster sampling speeds-all while
delivering performance comparable to state-of-the-art models. Code will be
available at https://github.com/bytedance/1d-tokenizer.