When 512×512 is Not Enough: Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
Brian B. Moser, Stanislav Frolov, Tobias Christian Nauen, Federico Rauev, Andreas Dengel · 2025
Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image synthesis and have shown unexpected potential in image Super-Resolution (SR). However, they are usually trained with a resolution limit of 512×512, making scaling beyond this resolution an unresolved but necessary challenge. To address this limitation, we propose a novel approach that enables them to generate 2K, 4K, and even 8K images without any additional training. Our method leverages MultiDiffusion, which distributes the generation across multiple diffusion paths, and local degradation-aware prompt extraction, which guides the T2I model according to its low-resolution input. As a result, we unlock higher resolutions, allowing T2I diffusion to be applied to image SR tasks without limitation on resolution.