Disentangled Textual Priors for Diffusion-based Image Super-Resolution

TL;DR

This paper introduces DTPSR, a diffusion-based image super-resolution framework that uses disentangled textual priors across spatial and frequency domains to improve semantic controllability, interpretability, and visual quality.

Contribution

The work proposes a novel disentangled textual prior approach for diffusion-based SR, including a large-scale dataset and a multi-branch guidance strategy for enhanced control and performance.

Findings

Achieves high perceptual quality and competitive fidelity in super-resolution tasks.

Demonstrates strong generalization across various degradation scenarios.

Enhances controllability and reduces semantic drift with frequency-aware guidance.

Abstract

Image Super-Resolution (SR) aims to reconstruct high-resolution images from degraded low-resolution inputs. While diffusion-based SR methods offer powerful generative capabilities, their performance heavily depends on how semantic priors are structured and integrated into the generation process. Existing approaches often rely on entangled or coarse-grained priors that mix global layout with local details, or conflate structural and textural cues, thereby limiting semantic controllability and interpretability. In this work, we propose DTPSR, a novel diffusion-based SR framework that introduces disentangled textual priors along two complementary dimensions: spatial hierarchy (global vs. local) and frequency semantics (low- vs. high-frequency). By explicitly separating these priors, DTPSR enables the model to simultaneously capture scene-level structure and object-specific details with frequency-aware semantic guidance. The corresponding embeddings are injected via specialized cross-attention modules, forming a progressive generation pipeline that reflects the semantic granularity of visual content, from global layout to fine-grained textures. To support this paradigm, we construct DisText-SR, a large-scale dataset containing approximately 95,000 image-text pairs with carefully disentangled global, low-frequency, and high-frequency descriptions. To further enhance controllability and consistency, we adopt a multi-branch classifier-free guidance strategy with frequency-aware negative prompts to suppress hallucinations and semantic drift. Extensive experiments on synthetic and real-world benchmarks show that DTPSR achieves high perceptual quality, competitive fidelity, and strong generalization across diverse degradation scenarios.