Diffusion models are a powerful class of generative models that can produce high-quality images, but they may suffer from data bias. Data bias occurs when the training data does not reflect the true distribution of the data domain, but rather exhibits some skewed or imbalanced patterns. For example, the CelebA dataset contains more female images than male images, which can lead to biased generation results and affect downstream applications. In this paper, we propose a novel method to mitigate data bias in diffusion models by applying manifold guidance. Our key idea is to estimate the manifold of the training data using a learnable information-theoretic approach, and then use it to guide the sampling process of diffusion models. In this way, we can encourage the generated images to be uniformly distributed on the data manifold, without changing the model architecture or requiring labels or retraining. We provide theoretical analysis and empirical evidence to show that our method can improve the quality and unbiasedness of image generation compared to standard diffusion models.

本文提出了一种通过使用流形引导来减轻扩散模型中数据偏置的方法，该方法可以在不改变模型架构或需要标签或重新训练的情况下，改善生成图像的品质和无偏性。

扩散模型中的流形导向采样用于无偏图像生成