  Generating temporally coherent high fidelity video is an important milestone
in generative modeling research. We make progress towards this milestone by
proposing a diffusion model for video generation that shows very promising
initial results. Our model is a natural extension of the standard image
diffusion architecture, and it enables jointly training from image and video
data, which we find to reduce the variance of minibatch gradients and speed up
optimization. To generate long and higher resolution videos we introduce a new
conditional sampling technique for spatial and temporal video extension that
performs better than previously proposed methods. We present the first results
on a large text-conditioned video generation task, as well as state-of-the-art
results on established benchmarks for video prediction and unconditional video
generation. Supplementary material is available at
https://video-diffusion.github.io/
