Damo Text To Video
The damo-text-to-video deep learning model has the potential to revolutionize several industries. In the entertainment industry, it could be used to automate video production by generating videos based on scripts or storyboards. This could save time and resources for filmmakers and animators. It could also be used in game development to automatically generate cutscenes or animated sequences based on dialogue or in-game events. The model could be utilized in the advertising industry to create personalized video ads based on customer preferences, making advertisements more engaging and relevant. In the education sector, it could be used to create interactive educational videos based on lesson plans or textbooks. Additionally, this technology has the potential to be integrated into virtual reality and augmented reality experiences, enabling the creation of immersive and interactive narratives. With its ability to generate high-quality videos based on textual descriptions, the damo-text-to-video model opens up a realm of possibilities for innovative products and practical applications.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Compositional Vsual Generation With Composable Diffusion Models Pytorch
You can use this area to play around with demo applications that incorporate the Damo Text To Video model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Damo Text To Video
Multi-stage text-to-video generation
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|View on Github
|No paper link provided
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run
|Nvidia A100 (40GB) GPU
|Average Completion Time