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.
Abstract (translated)
基于流的文本到图像（T2I）模型在指令驱动的图像生成方面表现出色，但在图像修复（IR）任务上表现不佳，往往偏离了对测量值的忠实度。先前的工作通过使用特定数据的流或特定任务的适配器来减轻这种漂移现象，但这些方法计算成本高且无法跨任务扩展。这引发了这样一个问题：“我们能否有效地操纵现有生成模型的能力？”为此，我们引入了一种名为FlowSteer（FS）的操作感知条件方案，该方案在采样路径上注入测量先验信息，并结合冻结流的隐式指导与明确的测量约束。 在超分辨率、去模糊化、降噪和色彩处理等任务中，FlowSteer在严格零样本设置下提升了图像的一致性和身份保持性——无需重新训练模型或适配器。我们展示了流模型的本质及其对噪声的敏感度是如何影响调度设计的。尽管简单，FlowSteer通过利用流模型丰富的生成先验信息实现了更高的重建图像保真度。 总结来说，这项工作提出了一个简洁而高效的方法，可以增强基于流的模型在图像修复任务中的性能，并且无需额外训练或复杂适配器的支持，在零样本设置下实现显著改进。
URL
https://arxiv.org/abs/2512.08125
