Precise color control remains a persistent failure mode in text-to-image diffusion systems, particularly in design-oriented workflows where outputs must satisfy explicit, user-specified color targets. We present an inference-time, region-constrained color preservation method that steers a pretrained diffusion model without any additional training. Our approach combines (i) ROI-based inpainting for spatial selectivity, (ii) background-latent re-imposition to prevent color drift outside the ROI, and (iii) latent nudging via gradient guidance using a composite loss defined in CIE Lab and linear RGB. The loss is constructed to control not only the mean ROI color but also the tail of the pixelwise error distribution through CVaR-style and soft-maximum penalties, with a late-start gate and a time-dependent schedule to stabilize guidance across denoising steps. We show that mean-only baselines can satisfy average color constraints while producing perceptually salient local failures, motivating our distribution-aware objective. The resulting method provides a practical, training-free mechanism for targeted color adherence that can be integrated into standard Stable Diffusion inpainting pipelines.

翻译：精确的色彩控制仍然是文本到图像扩散系统中持续存在的失效模式，尤其在面向设计的工作流程中，输出必须满足用户明确指定的色彩目标。我们提出一种推理时、区域约束的色彩保持方法，该方法无需任何额外训练即可引导预训练的扩散模型。我们的方法结合了：（i）基于感兴趣区域的修复以实现空间选择性；（ii）背景潜在重新施加以防止感兴趣区域外的色彩漂移；（iii）通过在CIE Lab和线性RGB空间中定义的复合损失进行梯度引导的潜在微调。该损失函数的构建不仅控制感兴趣区域的平均色彩，还通过CVaR风格和软最大值惩罚控制逐像素误差分布的尾部，并采用延迟启动门控和依赖于时间的调度策略，以在去噪步骤间稳定引导。我们证明仅使用平均值的基线方法虽然能满足平均色彩约束，但会产生感知上显著的局部失效，这促使我们采用分布感知的目标函数。最终的方法提供了一种实用的、无需训练的目标色彩遵循机制，可集成到标准的Stable Diffusion修复流程中。