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).

翻译：预训练图像扩散模型不仅用于生成固定尺寸图像，还展现出在制作全景图方面的卓越能力。然而，对多幅图像进行简单拼接往往会产生可见的接缝。近期技术尝试通过在多窗口中执行联合扩散并对重叠区域的潜在特征进行平均化处理来应对这一挑战。但这些专注于生成无缝拼接的方法，常因在单幅图像中融合不同场景而生成不连贯的输出。为突破这一局限，我们提出SyncDiffusion——一种即插即用模块，通过基于感知相似度损失的梯度下降来同步多个扩散过程。具体而言，我们利用每个去噪步骤中预测的去噪图像来计算感知损失的梯度，从而为生成连贯拼接提供有效指导。实验结果表明，与先前方法相比，我们的方法在产生显著更连贯的输出（用户研究中66.35%对比33.65%）的同时，仍能保持图像保真度（通过GIQA评估）及对输入提示的兼容性（通过CLIP分数衡量）。