GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution
Despite recent advances, single-image super-resolution (SR) remains challenging, especially in real-world scenarios with complex degradations. Diffusion-based SR methods, particularly those built on Stable Diffusion, leverage strong generative priors but commonly rely on text conditioning derived from semantic captioning. Such textual descriptions provide only high-level semantics and lack the spatially aligned visual information required for faithful restoration, leading to a representation gap between abstract semantics and spatially aligned visual details. To address this limitation, we propose GramSR, a one-step diffusion-based SR framework that replaces text conditioning with dense v...
| Authors | Fabio D'Oronzio, Federico Putamorsi, Leonardo Zini et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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