title: FlowTok: Flowing Seamlessly Across Text and Image Tokens
summary: 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.
paper_authors._id: 67d78ce0b4d0fefa68385d7f
paper_authors.name: Ju He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:44:17.453Z
paper_authors._id: 661c9059bcd78151e5c06ea1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661c9059bcd78151e5c06ea1/27bfNo1LZeZQ77vWuAa10.png
paper_authors.fullname: Ju He
paper_authors.type: user
paper_authors.user: turkeyju
paper_authors._id: 67d78ce0b4d0fefa68385d80
paper_authors.name: Qihang Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:55:33.453Z
paper_authors._id: 677b60e17279b5c57354108b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/677b60e17279b5c57354108b/YOwDhVf9DkeRjOCOLErb6.png
paper_authors.fullname: QihangYu
paper_authors.type: user
paper_authors.user: QihangYu
paper_authors._id: 67d78ce0b4d0fefa68385d81
paper_authors.name: Qihao Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:55:40.463Z
paper_authors._id: 639f1e519f1f2baab2f00d22
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/639f1e519f1f2baab2f00d22/pFjd51WZuVZ3A11rItvmk.jpeg
paper_authors.fullname: Qihao Liu
paper_authors.type: user
paper_authors.user: QHL067
paper_authors._id: 67d78ce0b4d0fefa68385d82
paper_authors.name: Liang-Chieh Chen
paper_title: FlowTok: Flowing Seamlessly Across Text and Image Tokens
paper_summary: 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.
paper_ai_summary: FlowTok is a framework enabling cross-modality generation between text and images using compact 1D token representations, improving efficiency and speed without compromising performance.
paper_ai_keywords: 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