HARIVO: Harnessing Text-to-Image Models for Video Generation

• URL: http://arxiv.org/abs/2410.07763v1
• Date: Thu, 10 Oct 2024 09:47:39 GMT
• Title: HARIVO: Harnessing Text-to-Image Models for Video Generation
• Authors: Mingi Kwon, Seoung Wug Oh, Yang Zhou, Difan Liu, Joon-Young Lee, Haoran Cai, Baqiao Liu, Feng Liu, Youngjung Uh,
• Abstract summary: We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth.
• Score: 45.63338167699105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training temporal layers. We advance this method by proposing a unique architecture, incorporating a mapping network and frame-wise tokens, tailored for video generation while maintaining the diversity and creativity of the original T2I model. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique, ensuring realistic and temporally consistent video generation despite limited public video data. We have successfully integrated video-specific inductive biases into the architecture and loss functions. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth. project page: https://kwonminki.github.io/HARIVO

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