FlowSteer: Conditioning Flow Field for Consistent Image Restoration

• URL: http://arxiv.org/abs/2512.08125v1
• Date: Tue, 09 Dec 2025 00:09:21 GMT
• Title: FlowSteer: Conditioning Flow Field for Consistent Image Restoration
• Authors: Tharindu Wickremasinghe, Chenyang Qi, Harshana Weligampola, Zhengzhong Tu, Stanley H. Chan,
• Abstract summary: Flow-based text-to-image (T2I) models excel at prompt-driven image generation, but falter on Image Restoration (IR)<n>We introduce FlowSteer (FS), an operator-aware conditioning scheme that injects measurement priors along the sampling path.<n>FS improves measurement consistency and identity preservation in a strictly zero-shot setting-no retrained models, no adapters.
• Score: 29.10704687691786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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