滾動擴散模型

摘要

擴散模型最近越來越多地應用於時間數據，例如視頻、流體力學模擬或氣候數據。這些方法通常對擴散過程中的噪聲量平等地處理後續幀。本文探討滾動擴散：一種使用滑動窗口降噪過程的新方法。它確保擴散過程通過時間逐漸變壞，通過為後續序列中出現的幀賦予更多噪聲，反映生成過程展開時對未來的更大不確定性。從實證上來看，我們展示了當時間動態複雜時，滾動擴散優於標準擴散。具體而言，這一結果在使用Kinetics-600視頻數據集進行視頻預測任務以及在混沌流體動力學預測實驗中得到證明。

English

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.

滾動擴散模型

Rolling Diffusion Models

摘要

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