Adversarial Score Distillation for Stable One-Step Diffusion in Real-World Image Super-Resolution

In recent years, super-resolution (SR) methods leveragingpre-trained text-to-image diffusion models have shown impressive po-tential. However, their deployment in real-world applications remainsconstrained by the computational overhead of numerous inference steps.Recent one-step approaches, such as OSEDiff and TSD-SR, mitigate thisissue via score distillation techniques. However, their discriminators areoptimized solely with the diffusion loss, resulting in weak and unstablegradients for the generator, making it difficult to preserve fine details andstructural fidelity. To tackle these limitations, we introduce ASDSR, anovel one-step distillation framework tailored for real-world image super-resolution. Our approach enhances Adversarial Score Distillation (ASD)by adopting a WGAN-based formulation with a stabilized penalty termthat utilizes the full discriminator loss to improve the distillation pro-cess. Furthermore, we incorporate CLIP-derived semantic priors to alignreconstructed images with their high-quality counterparts in the fea-ture space, effectively guiding the generator toward perceptual realism.Extensive experiments on multiple real-world benchmarks demonstratethat ASDSR achieves superior performance compared to state-of-the-artmethods, offering a practical and efficient solution for real-world imagerestoration.