  Diffusion models have emerged as effective tools for generating diverse and
high-quality content. However, their capability in high-resolution image
generation, particularly for panoramic images, still faces challenges such as
visible seams and incoherent transitions. In this paper, we propose
TwinDiffusion, an optimized framework designed to address these challenges
through two key innovations: the Crop Fusion for quality enhancement and the
Cross Sampling for efficiency optimization. We introduce a training-free
optimizing stage to refine the similarity of adjacent image areas, as well as
an interleaving sampling strategy to yield dynamic patches during the cropping
process. A comprehensive evaluation is conducted to compare TwinDiffusion with
the prior works, considering factors including coherence, fidelity,
compatibility, and efficiency. The results demonstrate the superior performance
of our approach in generating seamless and coherent panoramas, setting a new
standard in quality and efficiency for panoramic image generation.
