Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation

Abstract:We propose LF-MultiDiffusion, a training-free panorama generation method that extends MultiDiffusion to support linear projections between target and reference image spaces. Our key idea is to reformulate latent aggregation as a regularized least-squares problem and solve it efficiently with a Krylov-based iterative solver inside the denoising loop. This formulation enables denser and more natural mappings than prior training-free methods, yielding more stable generation with far fewer perspective views. As a result, LF-MultiDiffusion reduces the number of image generator evaluations during denoising and significantly improves inference efficiency. Experiments show that LF-MultiDiffusion achieves better visual quality, text alignment, and panoramic consistency than the strongest training-free baseline, while providing a 15.36$\times$ speedup. Our project page is available at: this https URL.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2609.01997 [cs.CV]

(or arXiv:2609.01997v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.01997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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