Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. This paper explores Rolling Diffusion: a new approach that uses a sliding window denoising process. It ensures that the diffusion process progressively corrupts through time by assigning more noise to frames that appear later in a sequence, reflecting greater uncertainty about the future as the generation process unfolds. Empirically, we show that when the temporal dynamics are complex, Rolling Diffusion is superior to standard diffusion. In particular, this result is demonstrated in a video prediction task using the Kinetics-600 video dataset and in a chaotic fluid dynamics forecasting experiment.

翻译：扩散模型近来被越来越多地应用于时间序列数据，如视频、流体力学模拟或气候数据。这些方法通常在扩散过程中对后续帧施加等量的噪声。本文提出滚动扩散：一种采用滑动窗口去噪过程的新方法。该方法通过为序列中较晚出现的帧分配更多噪声，确保扩散过程随时间逐步退化，从而在生成过程展开时反映对未来更大的不确定性。实验表明，当时间动态较为复杂时，滚动扩散优于标准扩散模型。这一结果在基于Kinetics-600视频数据集的视频预测任务和混沌流体动力学预测实验中得到了验证。