Title: Atrous Learning for Diffusion Models
Primary area: generative models
Keywords: generative models, flow matching, locality

Abstract: Locality has been recognized as a crucial mechanism that enables diffusion models to generalize to unseen images. However, this property may also lead to spatially inconsistent generations. For example, a diffusion model trained on natural images might generate hands with six fingers. To mitigate this issue, we propose atrous learning for diffusion models, a simple yet effective masking strategy. Specifically, our method randomly masks pixel positions when computing the regression loss, thereby encouraging the model to exploit broader contextual information for prediction. The proposed approach is lightweight and can be implemented with only a few lines of code. Experiments show that our method consistently outperforms baselines.

TL;DR: We propose a simple masking strategy to improve the contextual representation in diffusion models.