Abstract
Text-conditioned diffusion models have advanced image and video super-resolution by using prompts as semantic priors, but modern super-resolution pipelines typically rely on latent tiling to scale to high resolutions, where a single global caption causes prompt underspecification. A coarse global prompt often misses localized details (prompt sparsity) and provides locally irrelevant guidance (prompt misguidance) that can be amplified by classifier-free guidance. We propose Tiled Prompts, a unified framework for image and video super-resolution that generates a tile-specific prompt for each latent tile and performs super-resolution under locally text-conditioned posteriors, providing high-information guidance that resolves prompt underspecification with minimal overhead. Experiments on high resolution real-world images and videos show consistent gains in perceptual quality and text alignment, while reducing hallucinations and tile-level artifacts relative to global-prompt baselines.
Abstract (translated)
文本条件扩散模型通过将提示用作语义先验，推动了图像和视频超分辨率的发展。然而，现代的超分辨率管道通常依赖于潜在图块化来扩展到高分辨率场景，在这种情况下，单一的整体描述会导致提示信息不足。一个粗略的整体提示往往忽略了局部细节（提示稀疏性），并且提供不相关的本地指导（提示误导），这可能会被无分类器引导进一步放大。 我们提出了Tiled Prompts，这是一种统一的框架，用于图像和视频超分辨率，它为每个潜在图块生成特定于该图块的提示，并在局部文本条件后验下执行超分辨率处理。这种方法提供了高信息量的指导，以最小的开销解决了提示信息不足的问题。 实验结果表明，在高分辨率的真实世界图像和视频上，Tiled Prompts相比整体提示基线方法，在感知质量、文本对齐方面取得了一致性的提升，并且减少了幻觉现象和图块级别的伪影。
URL
https://arxiv.org/abs/2602.03342
