Flow-based text-to-image (T2I) models excel at prompt-driven image generation, but falter on Image Restoration (IR), often "drifting away" from being faithful to the measurement. Prior work mitigate this drift with data-specific flows or task-specific adapters that are computationally heavy and not scalable across tasks. This raises the question "Can't we efficiently manipulate the existing generative capabilities of a flow model?" To this end, we introduce FlowSteer (FS), an operator-aware conditioning scheme that injects measurement priors along the sampling path,coupling a frozed flow's implicit guidance with explicit measurement constraints. Across super-resolution, deblurring, denoising, and colorization, FS improves measurement consistency and identity preservation in a strictly zero-shot setting-no retrained models, no adapters. We show how the nature of flow models and their sensitivities to noise inform the design of such a scheduler. FlowSteer, although simple, achieves a higher fidelity of reconstructed images, while leveraging the rich generative priors of flow models. All data and code will be publicly available \href{https://tharindu-nirmal.github.io/FlowSteer/}{in this link}.
@article{arxiv.2512.08125,
title = {FlowSteer: Conditioning Flow Field for Consistent Image Restoration},
author = {Tharindu Wickremasinghe and Chenyang Qi and Harshana Weligampola and Zhengzhong Tu and Stanley H. Chan},
journal= {arXiv preprint arXiv:2512.08125},
year = {2026}
}
Comments
Accepted by CVPRF 2026. Camera Ready version. Project page is \href{https://tharindu-nirmal.github.io/FlowSteer/}{in this link}