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.
Abstract (translated)
尽管基于扩散的真实世界图像恢复（Real-IR）已取得显著进展，但高效利用超大规模预训练文本到图像（T2I）模型并充分发挥其潜力仍是重大挑战。针对此问题，我们提出了基于最先进流匹配模型FLUX.1-dev的图像恢复框架ResFlow-Tuner，该框架融合了统一多模态融合（UMMF）与测试时缩放（TTS）技术，实现了前所未有的恢复性能。我们的方法通过将多模态条件编码为统一序列来指导高质量图像合成，充分利用了多模态扩散Transformer（MM-DiT）架构的优势。此外，我们引入了专为图像恢复设计的无训练测试时缩放范式：推理过程中，该技术通过奖励模型（RM）的反馈动态引导去噪方向，从而在可控的计算开销下实现显著的性能提升。大量实验表明，我们的方法在多个标准基准上达到了最先进性能。本研究不仅验证了流匹配模型在低层视觉任务中的强大能力，更重要的是提出了一种适用于大型预训练模型的新型高效推理时缩放范式。
URL
https://arxiv.org/abs/2603.22027
