PanoForcing: Enforcing Memory-Scalable Full-Context for Blind Panoramic Super-Resolution

Blind panoramic super-resolution requires realistic details that remain coherent across the full scene and faithful to the input. Such scene-wide coherence depends on full panoramic context, which is costly to preserve: processing isolated regions fragments this context, while even a one-step diffusion baseline requires over 80 GB of GPU memory for full-panorama fine-tuning. We propose PanoForcing, a one-step diffusion framework that preserves full panoramic context during the forward pass under configurable training and inference memory budgets. PanoForcing selectively constructs the training-memory-related autograd graph while maintaining full-context forward computation, and incorporates lightweight observation guidance within a single network pass to constrain generated details. Experimental results demonstrate high-quality full-context panoramic reconstruction across a broad range of controllable memory budgets. PanoForcing achieves the best WS-PSNR, LPIPS, and FID among the compared methods.