OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit
  Flows
We present OneFlow, the first non-autoregressive multimodal model that
enables variable-length and concurrent mixed-modal generation. Unlike
autoregressive models that enforce rigid causal ordering between text and image
generation, OneFlow combines an insertion-based Edit Flow for discrete text
tokens with Flow Matching for image latents. OneFlow enables concurrent
text-image synthesis with hierarchical sampling that prioritizes content over
grammar. Through controlled experiments across model sizes from 1B to 8B, we
demonstrate that OneFlow outperforms autoregressive baselines on both
generation and understanding tasks while using up to 50% fewer training FLOPs.
OneFlow surpasses both autoregressive and diffusion-based approaches while
unlocking new capabilities for concurrent generation, iterative refinement, and
natural reasoning-like generation.
68e5625d975ac4c405ef1f40
John Nguyen
claimed_verified
2025-10-08T08:01:17.940Z
648a14e002c8497f58ebff62
/avatars/0d30f7bd843ac94f317d8cfc53256450.svg
John Nguyen
user
ngjhn
68e5625d975ac4c405ef1f41
Marton Havasi
68e5625d975ac4c405ef1f42
Tariq Berrada
68e5625d975ac4c405ef1f43
Luke Zettlemoyer
68e5625d975ac4c405ef1f44
Ricky T. Q. Chen
OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit
  Flows
We present OneFlow, the first non-autoregressive multimodal model that
enables variable-length and concurrent mixed-modal generation. Unlike
autoregressive models that enforce rigid causal ordering between text and image
generation, OneFlow combines an insertion-based Edit Flow for discrete text
tokens with Flow Matching for image latents. OneFlow enables concurrent
text-image synthesis with hierarchical sampling that prioritizes content over
grammar. Through controlled experiments across model sizes from 1B to 8B, we
demonstrate that OneFlow outperforms autoregressive baselines on both
generation and understanding tasks while using up to 50% fewer training FLOPs.
OneFlow surpasses both autoregressive and diffusion-based approaches while
unlocking new capabilities for concurrent generation, iterative refinement, and
natural reasoning-like generation.
OneFlow, a non-autoregressive multimodal model, achieves superior performance in text-image generation and understanding tasks with reduced computational cost compared to autoregressive and diffusion-based models.
non-autoregressive multimodal model variable-length concurrent mixed-modal generation insertion-based Edit Flow discrete text tokens Flow Matching image latents hierarchical sampling content over grammar training FLOPs autoregressive baselines diffusion-based approaches iterative refinement natural reasoning-like generation