TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution

TL;DR

TSD-SR introduces a one-step diffusion-based framework with target score distillation and distribution-aware sampling for efficient and high-quality real-world image super-resolution, significantly outperforming previous methods in speed and restoration quality.

Contribution

The paper presents TSD-SR, a novel one-step diffusion model for Real-ISR that improves efficiency and detail recovery through target score distillation and a distribution-aware sampling module.

Findings

Achieves superior restoration results across multiple metrics.

Demonstrates 40x faster inference speed than previous diffusion-based methods.

Outperforms existing approaches in both quality and speed.

Abstract

Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods such as SinSR and OSEDiff have emerged to condense inference steps via distillation, their performance in image restoration or details recovery is not satisfied. To address this, we propose TSD-SR, a novel distillation framework specifically designed for real-world image super-resolution, aiming to construct an efficient and effective one-step model. We first introduce the Target Score Distillation, which leverages the priors of diffusion models and real image references to achieve more realistic image restoration. Secondly, we propose a Distribution-Aware Sampling Module to make detail-oriented gradients more readily accessible, addressing the challenge of recovering fine details. Extensive experiments demonstrate that our TSD-SR has superior restoration results (most of the metrics perform the best) and the fastest inference speed (e.g. 40 times faster than SeeSR) compared to the past Real-ISR approaches based on pre-trained diffusion priors.