Abstract
While diffusion models excel at generating images with conventional dimensions, pushing them to synthesize ultra-high-resolution imagery at extreme aspect ratios (EAR) often triggers catastrophic structural failures, such as object repetition and spatial this http URL limitation fundamentally stems from a lack of robust spatial priors, as static text-to-image models are primarily trained on image distributions with conventional this http URL overcome this bottleneck, we present ScrollScape, a novel framework that reformulates EAR image synthesis into a continuous video generation process through two core this http URL mapping the spatial expansion of a massive canvas to the temporal evolution of video frames, ScrollScape leverages the inherent temporal consistency of video models as a powerful global constraint to ensure long-range structural this http URL, Scanning Positional Encoding (ScanPE) distributes global coordinates across frames to act as a flexible moving camera, while Scrolling Super-Resolution (ScrollSR) leverages video super-resolution priors to circumvent memory bottlenecks, efficiently scaling outputs to an unprecedented 32K resolution. Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape effectively aligns pre-trained video priors with the EAR generation task. Extensive evaluations demonstrate that it significantly outperforms existing image-diffusion baselines by eliminating severe localized artifacts. Consequently, our method overcomes inherent structural bottlenecks to ensure exceptional global coherence and visual fidelity across diverse domains at extreme scales.
Abstract (translated)
尽管扩散模型在生成常规尺寸图像方面表现出色，但将其推向极端纵横比（EAR）的超高分辨率图像合成时，常会引发灾难性的结构失效，例如物体重复和空间局限。这一根本限制源于缺乏稳健的空间先验，因为静态文本到图像模型主要基于常规尺寸图像分布进行训练。为突破这一瓶颈，我们提出了ScrollScape，一种新颖框架，通过两个核心机制将EAR图像合成重新构建为连续视频生成过程：通过将超大画布的空间扩展映射到视频帧的时间演变，ScrollScape利用视频模型固有的时间一致性作为强大的全局约束，以确保长程结构连贯性。扫描位置编码（ScanPE）将全局坐标分布到各帧中，充当灵活的移动摄像机；而滚动超分辨率（ScrollSR）则利用视频超分辨率先验来规避内存瓶颈，高效将输出扩展至前所未有的32K分辨率。经精心策划的3K多纵横比图像数据集微调后，ScrollScape能有效将预训练的视频先验与EAR生成任务对齐。广泛评估表明，该方法通过消除严重的局部伪影，显著优于现有图像扩散基线。因此，我们的方法克服了固有的结构瓶颈，确保了在极端尺度下跨不同领域的卓越全局连贯性和视觉保真度。
URL
https://arxiv.org/abs/2603.24270
