用判别器引导图像扩散模型生成时序一致的动态，无需微调即可实现高精度气候模拟。
Generating time-consistent dynamics with discriminator-guided image diffusion models
- 引入时序一致性判别器，指导预训练图像扩散模型生成视频动态。
- 在湍流与降水数据上表现媲美从头训练的视频扩散模型，且偏差更低。
- 可稳定模拟百年尺度气候，适合气候建模等长期预测任务。
真实的时间动态对视频生成、处理与建模应用至关重要，如计算流体动力学、天气预报或长期气候模拟。当前最先进的视频扩散模型（VDMs）虽能生成高度逼真的动态，但训练成本高、需大量算力，限制了广泛应用。本文提出一种时序一致性判别器，使预训练图像扩散模型可生成逼真的时空动态，该判别器仅在采样阶段引导生成过程，无需修改或微调原模型。我们在理想化湍流模拟和真实全球降水数据集上对比了该方法与从头训练的VDM。结果表明，本方法在时间一致性上表现相当，不确定性校准更优且偏差更低，并可在每日时间步下实现稳定的百年尺度气候模拟。
原文摘要 · Abstract (English)
Realistic temporal dynamics are crucial for many video generation, processing and modelling applications, e.g. in computational fluid dynamics, weather prediction, or long-term climate simulations. Video diffusion models (VDMs) are the current state-of-the-art method for generating highly realistic dynamics. However, training VDMs from scratch can be challenging and requires large computational resources, limiting their wider application. Here, we propose a time-consistency discriminator that enables pretrained image diffusion models to generate realistic spatiotemporal dynamics. The discriminator guides the sampling inference process and does not require extensions or finetuning of the image diffusion model. We compare our approach against a VDM trained from scratch on an idealized turbulence simulation and a real-world global precipitation dataset. Our approach performs equally well in terms of temporal consistency, shows improved uncertainty calibration and lower biases compared to the VDM, and achieves stable centennial-scale climate simulations at daily time steps.
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