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

• URL: http://arxiv.org/abs/2603.22027v1
• Date: Mon, 23 Mar 2026 14:33:43 GMT
• Title: Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models
• Abstract summary: ResFlow-Tuner is an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev.<n>We propose a training-free test-time scaling paradigm tailored for image restoration.
• Score: 26.002982722748726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 their potential remain significant challenges. To address this issue, we propose ResFlow-Tuner, an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev, which integrates unified multi-modal fusion (UMMF) with test-time scaling (TTS) to achieve unprecedented restoration performance. Our approach fully leverages the advantages of the Multi-Modal Diffusion Transformer (MM-DiT) architecture by encoding multi-modal conditions into a unified sequence that guides the synthesis of high-quality images. Furthermore, we introduce a training-free test-time scaling paradigm tailored for image restoration. During inference, this technique dynamically steers the denoising direction through feedback from a reward model (RM), thereby achieving significant performance gains with controllable computational overhead. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple standard benchmarks. This work not only validates the powerful capabilities of the flow matching model in low-level vision tasks but, more importantly, proposes a novel and efficient inference-time scaling paradigm suitable for large pre-trained models.

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