Generated images of score-based models can suffer from errors in their spatial means, an effect, referred to as a color shift, which grows for larger images. This paper introduces a computationally inexpensive solution to mitigate color shifts in score-based diffusion models. We propose a simple nonlinear bypass connection in the score network, designed to process the spatial mean of the input and to predict the mean of the score function. This network architecture substantially improves the resulting spatial means of the generated images, and we show that the improvement is approximately independent of the size of the generated images. As a result, our solution offers a comparatively inexpensive solution for the color shift problem across image sizes. Lastly, we discuss the origin of color shifts in an idealized setting in order to motivate our approach.

翻译：基于分数的模型生成的图像可能会因空间均值误差而产生色彩偏移，且该效应随图像尺寸增大而加剧。本文提出一种计算成本低廉的解决方案，用于缓解基于分数的扩散模型中的色彩偏移问题。我们在分数网络中引入一种简单的非线性旁路连接，该结构专门用于处理输入的空间均值并预测分数函数的均值。这种网络架构显著改善了生成图像的空间均值，且实验证明改善效果与生成图像的尺寸近似无关。因此，我们的方法能够以相对较低的计算代价解决不同图像尺寸下的色彩偏移问题。最后，我们在理想化条件下探讨色彩偏移的成因，以阐释本方法的理论依据。