ScrollScape: Unlocking 32K Image Generation With Video Diffusion Priors

While diffusion models excel at generating images with con-ventional dimensions, pushing them to synthesize ultra-high-resolutionimagery at extreme aspect ratios (EAR) often triggers catastrophic struc-tural failures, such as object repetition and spatial fragmentation. Thislimitation fundamentally stems from a lack of robust spatial priors, asstatic text-to-image models are primarily trained on image distributionswith conventional dimensions. To overcome this bottleneck, we presentScrollScape, a novel framework that reformulates EAR image synthe-sis into a continuous video generation process through two core inno-vations. By mapping the spatial expansion of a massive canvas to thetemporal evolution of video frames, ScrollScape leverages the inherenttemporal consistency of video models as a powerful global constraint toensure long-range structural integrity. Specifically, Scanning PositionalEncoding (ScanPE) distributes global coordinates across frames to actas a flexible moving camera, while Scrolling Super-Resolution (ScrollSR)leverages video super-resolution priors to circumvent memory bottle-necks, efficiently scaling outputs to an unprecedented 32K resolution.Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape ef-fectively aligns pre-trained video priors with the EAR generation task.Extensive evaluations demonstrate that it significantly outperforms ex-isting image-diffusion baselines by eliminating severe localized artifacts.Consequently, our method overcomes inherent structural bottlenecks toensure exceptional global coherence and visual fidelity across diverse do-mains at extreme scales.