  We introduce a general framework for generating diverse visual content,
including ambiguous images, panorama images, mesh textures, and Gaussian splat
textures, by synchronizing multiple diffusion processes. We present exhaustive
investigation into all possible scenarios for synchronizing multiple diffusion
processes through a canonical space and analyze their characteristics across
applications. In doing so, we reveal a previously unexplored case: averaging
the outputs of Tweedie's formula while conducting denoising in multiple
instance spaces. This case also provides the best quality with the widest
applicability to downstream tasks. We name this case SyncTweedies. In our
experiments generating visual content aforementioned, we demonstrate the
superior quality of generation by SyncTweedies compared to other
synchronization methods, optimization-based and iterative-update-based methods.
