Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models

Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models and fully exploiting theirpotential remain significant challenges. To address this issue, we proposeResFlow-Tuner, an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev, which integrates unifiedmulti-modal fusion (UMMF) with test-time scaling (TTS) to achieveunprecedented restoration performance. Our approach fully leveragesthe advantages of the Multi-Modal Diffusion Transformer (MM-DiT) ar-chitecture by encoding multi-modal conditions into a unified sequencethat guides the synthesis of high-quality images. Furthermore, we in-troduce a training-free test-time scaling paradigm tailored for imagerestoration. During inference, this technique dynamically steers the de-noising direction through feedback from a reward model (RM), therebyachieving significant performance gains with controllable computationaloverhead. Extensive experiments demonstrate that our method achievesstate-of-the-art performance across multiple standard benchmarks. Thiswork not only validates the powerful capabilities of the flow match-ing model in low-level vision tasks but, more importantly, proposes anovel and efficient inference-time scaling paradigm suitable for largepre-trained models. The source code is publicly available at https://github.com/Rorschach-1010/ResFlow-Tuner.