Imagen Video: High Definition Video Generation with Diffusion Models

Jonathan Ho; William Chan; Chitwan Saharia; Jay Whang; Ruiqi Gao; Alexey Gritsenko; Diederik P. Kingma; Ben Poole; Mohammad Norouzi; David J. Fleet; Tim Salimans

arXiv · 2210.02303

Imagen Video: High Definition Video Generation with Diffusion Models

Jonathan Ho·William Chan·Chitwan Saharia·Jay Whang·Ruiqi Gao·Alexey Gritsenko·Diederik P. Kingma·Ben Poole·Mohammad Norouzi·David J. Fleet·Tim Salimans

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Abstract

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.

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Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans. 2022-10-05. Imagen Video: High Definition Video Generation with Diffusion Models. https://arxiv.org/abs/2210.02303

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