  Immersive scene generation, notably panorama creation, benefits significantly
from the adaptation of large pre-trained text-to-image (T2I) models for
multi-view image generation. Due to the high cost of acquiring multi-view
images, tuning-free generation is preferred. However, existing methods are
either limited to simple correspondences or require extensive fine-tuning to
capture complex ones. We present PanoFree, a novel method for tuning-free
multi-view image generation that supports an extensive array of
correspondences. PanoFree sequentially generates multi-view images using
iterative warping and inpainting, addressing the key issues of inconsistency
and artifacts from error accumulation without the need for fine-tuning. It
improves error accumulation by enhancing cross-view awareness and refines the
warping and inpainting processes via cross-view guidance, risky area estimation
and erasing, and symmetric bidirectional guided generation for loop closure,
alongside guidance-based semantic and density control for scene structure
preservation. In experiments on Planar, 360{\deg}, and Full Spherical
Panoramas, PanoFree demonstrates significant error reduction, improves global
consistency, and boosts image quality without extra fine-tuning. Compared to
existing methods, PanoFree is up to 5x more efficient in time and 3x more
efficient in GPU memory usage, and maintains superior diversity of results (2x
better in our user study). PanoFree offers a viable alternative to costly
fine-tuning or the use of additional pre-trained models. Project website at
https://panofree.github.io/.
