Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation

We propose LF-MultiDiffusion, a training-free panorama gen-eration method that extends MultiDiffusion to support linear projectionsbetween target and reference image spaces. Our key idea is to reformulatelatent aggregation as a regularized least-squares problem and solve itefficiently with a Krylov-based iterative solver inside the denoising loop.This formulation enables denser and more natural mappings than priortraining-free methods, yielding more stable generation with far fewerperspective views. As a result, LF-MultiDiffusion reduces the number ofimage generator evaluations during denoising and significantly improvesinference efficiency. Experiments show that LF-MultiDiffusion achievesbetter visual quality, text alignment, and panoramic consistency thanthe strongest training-free baseline, while providing a 15.36× speedup.Our project page is available at: https://ahykw.github.io/lfmd.