Vision Bridge Transformer at Scale

We introduce Vision Bridge Transformer (ViBT), alarge-scale instantiation of Brownian Bridge Models designed for con-ditional generation. Unlike traditional diffusion models that transformnoise into data, Bridge Models offer an efficient data-to-data translationparadigm by directly modeling the trajectory between inputs and out-puts. We first demonstrate the advantages of this paradigm through acontrolled apples-to-apples comparison against diffusion models, reveal-ing improved efficiency and performance in this setting. Building on this,we successfully scale the Bridge paradigm up to a 20B-parameter modelto tackle complex image and video translation tasks. To enable stabletraining at this scale, we adopt a Transformer architecture equipped witha variance-stabilized velocity matching objective. Together, these ad-vances unlock the potential of scaling Bridge Models, establishing strongperformance for instruction-based image editing and video stylization.