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

• URL: http://arxiv.org/abs/2602.03342v1
• Date: Tue, 03 Feb 2026 10:09:27 GMT
• Title: Tiled Prompts: Overcoming Prompt Underspecification in Image and Video Super-Resolution
• Abstract summary: Text-conditioned diffusion models have advanced image and video super-resolution by using prompts as semantic priors.<n>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.
• Score: 47.027290803102666
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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