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Reflective flares are common artifacts in photography that degrade image quality, introducing in-focus flares, which appear as bright, regular spot patterns, and out-of-focus flares, which are diffuse and semi-transparent, obscuring the underlying scene. While previous methods have achieved some success in removing in-focus flares, they struggle with the diffuse nature of out-of-focus flares. The lack of an out-of-focus flare dataset has further hindered the development of effective flare removal models. In this work, we construct a large-scale out-of-focus flare dataset generated with Blender. We propose to use a color alignment approach on diffusion models to address the challenges of out-of-focus reflective flare removal. Rather than fully reconstructing flare-affected regions, our method adjusts the color distribution to reduce artifact visibility while preserving image content. Specifically, we apply a differentiable histogram loss on the diffusion model, which is derived from the Earth Mover's Distance (EMD), to effectively align color distributions. The proposed approach, trained exclusively on synthetic data, outperforms existing methods on both synthetic and real-world data, demonstrating improved performance in flare removal.