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. Finally, 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 https://imagen.research.google/video/ for samples.

基于级联的视频扩散模型，Imagen Video是一种文本有条件的视频生成系统，它使用基础视频生成模型和序列的交错空间和时间视频超分辨率模型生成高清视频。该系统可扩展为高清文本到视频模型，具有高度的可控性和世界知识，包括生成各种艺术风格的多样化视频和文本动画以及3D对象理解。

图像视频：扩散模型生成高清晰度视频