FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation
arXiv:2605.20316v1 Announce Type: cross Abstract: 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. We introduce \emph{FullFlow}, a parameter-efficient recipe that upgrades a pretrained rectified-flow text-to-image model into a bidirectional vision--language gener
The continuous evolution of AI models pushes for greater efficiency and versatility, with a current focus on refining large pre-trained models for new capabilities without extensive retraining.
This development represents a significant step towards more flexible and efficient vision-language AI models, enhancing their ability to understand and generate both text and images bidirectionally.
Pre-trained text-to-image models can now be upgraded to bidirectional vision-language models with significantly less computational and data-intensive retraining, expanding their utility.
- · AI researchers and developers
- · Companies utilizing multimodal AI platforms
- · Industries requiring efficient vision-language understanding
- · Models requiring extensive retraining for bidirectional capabilities
- · Less parameter-efficient multimodal AI approaches
More sophisticated and cost-effective multimodal AI applications become feasible.
Accelerated development of AI agents capable of complex interactions across visual and textual domains.
Potential for new human-computer interfaces and content creation tools leveraging improved bidirectional understanding.
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Read at arXiv cs.AI