FlowTok: Flowing Seamlessly Across Text and Image Tokens
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.
67d78ce0b4d0fefa68385d7f
Ju He
claimed_verified
2025-03-17T08:44:17.453Z
661c9059bcd78151e5c06ea1
https://cdn-avatars.huggingface.co/v1/production/uploads/661c9059bcd78151e5c06ea1/27bfNo1LZeZQ77vWuAa10.png
Ju He
user
turkeyju
67d78ce0b4d0fefa68385d80
Qihang Yu
admin_assigned
2025-03-17T08:55:33.453Z
677b60e17279b5c57354108b
https://cdn-avatars.huggingface.co/v1/production/uploads/677b60e17279b5c57354108b/YOwDhVf9DkeRjOCOLErb6.png
QihangYu
user
QihangYu
67d78ce0b4d0fefa68385d81
Qihao Liu
admin_assigned
2025-03-17T08:55:40.463Z
639f1e519f1f2baab2f00d22
https://cdn-avatars.huggingface.co/v1/production/uploads/639f1e519f1f2baab2f00d22/pFjd51WZuVZ3A11rItvmk.jpeg
Qihao Liu
user
QHL067
67d78ce0b4d0fefa68385d82
Liang-Chieh Chen
FlowTok: Flowing Seamlessly Across Text and Image Tokens
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.
FlowTok is a framework enabling cross-modality generation between text and images using compact 1D token representations, improving efficiency and speed without compromising performance.
cross-modality generation flow matching latent space text modality image modality denoising process Gaussian noise 1D tokens 2D latent embeddings FlowTok memory-efficient sampling speeds