When Unified Models Meet Visual Experts: Lightweight Collaboration via Generative Handoff

Unified multimodal models (UMMs) offer a single, flexible interface for visual understanding, generation, and editing, but specialized image generators and editors often achieve better visual quality on the tasks they are built for. Getting the two to work together, however, typically requires additional alignment training, which is costly and can compromise the UMM's broader capabilities. We find that, for several UMM–expert pairs, their latent spaces and generative trajectories are already compatible enough for one model to continue sampling from a state produced by the other. Therefore, we introduce Handoff, a training-free collaboration strategy, where the UMM performs the early sampling steps, then passes its intermediate latent state directly to a visual expert, which completes generation from a selected timestep. Handoff retains the UMM's instruction-following and semantic capabilities while improving perceptual quality, without learned connectors or cross-model alignment. We further introduce Partial On-Policy Distillation (P-OPD) for settings where the expert is unavailable at inference time. P-OPD distills the expert's behavior only over the later sampling steps, using a timestep-gated LoRA, so that the resulting UMM can reproduce the benefit without calling the expert. Across image generation and editing tasks and multiple UMM–expert pairs, both methods consistently improve the underlying UMM. Handoff can even outperform both standalone models, including on the challenging LongText-Bench and CVTG-2K benchmarks. Together, these results show that UMMs and specialized visual experts can collaborate through their existing generative representations, with little or no additional alignment.