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Existing unified vision--language models derived from them recover bidirectional capability through large-scale joint pretraining or substantial retraining of the text pathway, discarding the strong image prior the text-to-image backbone already encodes. We introduce \\emph{FullFlow}, a parameter-efficient recipe that upgrades a pretrained rectified-flow text-to-image model into a bidirectional vision--language generator by training only LoRA adapters and lightweight text heads. FullFlow keeps images in their native continuous flow and adds a discrete insertion process for text. Separate image and text timesteps turn inference into trajectory selection in a two-dimensional generative space, enabling text$\\rightarrow$image, image$\\rightarrow$text, joint sampling, and partial-text prediction with a single back","title":"FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation","url":"https://arxiv.org/abs/2605.20316","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20316v1 Announce Type: cross \nAbstract: Modern text-to-image diffusion models encode rich visual priors, but expose them only through one-way text-conditioned generation. Existing unified vision--language models derived from them recover bidirectional capability through large-scale joint pretraining or substantial retraining of the text pathway, discarding the strong image prior the text-to-image backbone already encodes. 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