We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high deﬁnition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we scale up the system as a high deﬁnition text-to-video model including design decisions such as the choice of fully-convolutional temporal and spatial super-resolution models at certain resolutions, and the choice of the v-parameterization of diffusion models. In addition, we conﬁrm and transfer ﬁndings from previous work on diffusion-based image generation to the video generation setting. Fi-nally, we apply progressive distillation to our video models with classiﬁer-free guidance for fast, high quality sampling. We ﬁnd Imagen Video not only capable of generating videos of high ﬁdelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos and text animations in various artistic styles and with 3D object understanding. See for

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