title: Rolling Diffusion Models
summary: 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.
paper_authors._id: 65ced598dbabc794a126c77b
paper_authors.name: David Ruhe
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T14:26:47.815Z
paper_authors._id: 64c2b612831aa8a12856a0a7
paper_authors.avatarUrl: /avatars/40aa035450f665571dd0650eca2c4ea6.svg
paper_authors.fullname: David Ruhe
paper_authors.type: user
paper_authors.user: druhe
paper_authors._id: 65ced598dbabc794a126c77c
paper_authors.name: Jonathan Heek
paper_authors._id: 65ced598dbabc794a126c77d
paper_authors.name: Tim Salimans
paper_authors._id: 65ced598dbabc794a126c77e
paper_authors.name: Emiel Hoogeboom
paper_title: Rolling Diffusion Models
paper_summary: 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.
paper_ai_keywords: diffusion models temporal data video fluid mechanics simulations climate data sliding window denoising process diffusion process Rolling Diffusion temporal dynamics video prediction task Kinetics-600 video dataset chaotic fluid dynamics forecasting