GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution

TL;DR

GramSR introduces a diffusion-based super-resolution framework that replaces text conditioning with dense visual features, enabling faithful restoration and superior texture preservation in single-image SR tasks.

Contribution

It proposes a novel one-step diffusion SR method using dense visual features and a three-stage LoRA architecture for improved detail and texture recovery.

Findings

Outperforms existing diffusion-based SR methods on standard benchmarks.

Achieves better structural fidelity and texture realism.

Provides flexible control over different restoration aspects during inference.

Abstract

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 visual features extracted from the low-resolution input using a pre-trained DINOv3 encoder. GramSR adopts a three-stage LoRA architecture, where pixel-level, semantic-level, and texture-level LoRA modules are trained sequentially. The pixel-level module focuses on degradation removal using $\ell_2$ loss, the semantic-level module enhances perceptual details via LPIPS and CSD losses, and the texture-level module enforces feature correlation consistency through a Gram matrix loss computed from DINOv3 features. At inference, independent guidance scales enable flexible control over degradation removal, semantic enhancement, and texture preservation. Extensive experiments on standard SR benchmarks demonstrate that GramSR consistently outperforms existing one-step diffusion-based methods, achieving superior structural fidelity and texture realism. The code for this work is available at: https://github.com/aimagelab/GramSR.