Discover NVIDIA VideoLDM, a latent diffusion model for high-resolution text-to-video synthesis, and see how it generates consistent, realistic video.

Video Latent Diffusion Models (Video LDMs) are a method for creating high-resolution videos without requiring extreme compute power. They build on Latent Diffusion Models (LDMs) by first compressing videos into a smaller, more manageable latent space, then working with a sequence of these compressed video frames.
VideoLDM extends existing image LDMs by adding layers that understand how content changes over time, using temporal attention and 3D convolutions. This helps the model generate videos that look consistent and flow naturally. The process typically starts by generating a few key frames, which are then expanded to fill in the gaps and produce a full, high-quality video.
Video LDMs are used in applications such as creating realistic driving scenes for simulations and generating creative content from text descriptions. The approach has shown strong performance in generating longer, high-resolution videos and supports personalized text-to-video creation.
The project was a collaborative effort by Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis, developed during their internships at NVIDIA. The main goal was to explore high-resolution video synthesis using Latent Diffusion Models, with notable results in creating realistic driving videos and generating videos from text descriptions.
VideoLDM is a research project rather than a polished consumer product. To use it:
For details on setup and advanced options, consult the documentation available on the official website.
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