This AI model for real-time music generation, called riffusion, has several potential use cases for technical users. One possible use case is in live music performances, where the model can take a MIDI file as input and generate coherent and continuous output in real-time. This can enhance the performance by providing dynamic and improvisational elements to the music. Another use case is in interactive music generation systems, where users can input MIDI files and interact with the generated music in real-time. This can be useful for creating unique and personalized compositions. Additionally, the stable diffusion process used in the model ensures smooth transitions between musical segments, making it suitable for applications such as background music generation for videos, games, or virtual reality experiences. Overall, riffusion opens up possibilities for innovative and interactive music experiences and can be integrated into various products or services targeting musicians, performers, and music enthusiasts.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|No other models by this creator|
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Summary of this model and related resources.
Stable diffusion for real-time music generation
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
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||$0.0066|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||12 seconds|