{"ID":2830949,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08125","arxiv_id":"2512.08125","title":"FlowSteer: Conditioning Flow Field for Consistent Image Restoration","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. All data and code will be publicly available \\href{https://tharindu-nirmal.github.io/FlowSteer/}{in this link}.","short_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 task...","url_abs":"https://arxiv.org/abs/2512.08125","url_pdf":"https://arxiv.org/pdf/2512.08125v2","authors":"[\"Tharindu Wickremasinghe\",\"Chenyang Qi\",\"Harshana Weligampola\",\"Zhengzhong Tu\",\"Stanley H. Chan\"]","published":"2025-12-09T00:09:21Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}