  The remarkable capabilities of pretrained image diffusion models have been
utilized not only for generating fixed-size images but also for creating
panoramas. However, naive stitching of multiple images often results in visible
seams. Recent techniques have attempted to address this issue by performing
joint diffusions in multiple windows and averaging latent features in
overlapping regions. However, these approaches, which focus on seamless montage
generation, often yield incoherent outputs by blending different scenes within
a single image. To overcome this limitation, we propose SyncDiffusion, a
plug-and-play module that synchronizes multiple diffusions through gradient
descent from a perceptual similarity loss. Specifically, we compute the
gradient of the perceptual loss using the predicted denoised images at each
denoising step, providing meaningful guidance for achieving coherent montages.
Our experimental results demonstrate that our method produces significantly
more coherent outputs compared to previous methods (66.35% vs. 33.65% in our
user study) while still maintaining fidelity (as assessed by GIQA) and
compatibility with the input prompt (as measured by CLIP score). We further
demonstrate the versatility of our method across three plug-and-play
applications: layout-guided image generation, conditional image generation and
360-degree panorama generation. Our project page is at
https://syncdiffusion.github.io.
