Variational Test-time Optimization for Diffusion Synchronization

Collaborative generation, which coordinates multiple diffusion trajectories to extend the capabilities of pretrained priors, has emerged as a powerful paradigm for extending the applicability of diffusion models. Among existing approaches, diffusion synchronization provides a scenario-agnostic solution by introducing general guidance mechanisms. However, existing approaches rely heavily on heuristics and still require task-specific tailoring, which limits their generalizability and performance. In this work, we mathematically derive a synchronization framework based on optimal control, providing a principled explanation of diffusion synchronization. During sampling, we optimize control variables to guide multiple trajectories toward coherent solutions while remaining close to the underlying diffusion prior. Our method operates entirely at test time without additional training, thereby enabling broad applicability across diverse scenarios. Experimental results show that our method consistently improves generation quality. For wide image generation, our method reduces color and style inconsistency by 42.7% and 26.7% relative to the strongest baseline. It also achieves the best distributional alignment among the baselines for 3D mesh texturing. These results establish a principled framework for leveraging pretrained generative models for collaborative generation.