Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu, Rui Zhao, Lingmin Ran, Yuchao Gu, Difei Gao, Mike Zheng Shou

Keywords: video generation, diffusion models

TL;DR: We propose a text-to-video method that marries the pixel and latent diffusion models

Abstract: Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as MPL-Video , which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution. Compared to latent VDMs, MPL-Video can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, MPL-Video is much more efficient (GPU memory usage during inference is 15G vs 72G). We also validate our model on standard video generation benchmarks. Our code will be publicly available.

Primary Area: generative models

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Submission Number: 1864