Tiled Prompts: Overcoming Prompt Misguidance in Image and Video Super-Resolution

Text-conditioned diffusion models have advanced image andvideo super-resolution by using prompts as semantic priors, and modernsuper-resolution pipelines typically rely on latent tiling to scale to highresolutions. In practice, a single global caption is used with the latenttiling, often causing prompt misguidance. Specifically, a coarse globalprompt often misses localized details (errors of omission) and provideslocally irrelevant guidance (errors of commission) which leads to substan-dard results at the tile level. To solve this, we propose Tiled Prompts,a unified framework for image and video super-resolution that generatesa tile-specific prompt for each latent tile and performs super-resolutionunder locally text-conditioned posteriors to resolve prompt misguidancewith minimal overhead. Our experiments on high resolution real-worldimages and videos show that tiled prompts bring consistent gains in per-ceptual quality and fidelity, while reducing hallucinations and tile-levelartifacts that can be found in global-prompt baselines. Project Page:https://bryanswkim.github.io/tiled-prompts/.