Riffusion is an app for real-time music generation with stable diffusion.

Read about it at https://www.riffusion.com/about and try it at https://www.riffusion.com/.

• Web app: https://github.com/hmartiro/riffusion-app
• Inference server: https://github.com/hmartiro/riffusion-inference
• Model checkpoint: https://huggingface.co/riffusion/riffusion-model-v1

This repository contains the interactive web app that powers the website.

It is built with Next.js, React, Typescript, three.js, Tailwind, and Vercel.

This is a Next.js project bootstrapped with create-next-app.

Install:

npm install

Run the development server:

npm run dev
# or
yarn dev

Open http://localhost:3000 with your browser to see the app.

The app home is at pages/index.js. The page auto-updates as you edit the file. The about page is at pages/about.tsx.

The pages/api directory is mapped to /api/*. Files in this directory are treated as API routes instead of React pages.

To actually generate model outputs, we need a model backend that responds to inference requests via API. If you have a large GPU that can run stable diffusion in under five seconds, clone and run the instructions in the inference server to run the Flask app.

This app also has a configuration to run with Baseten for auto-scaling and load balancing. To use BaseTen, you need an API key.

To configure these backends, add a .env.local file:

# URL to your flask instance
RIFFUSION_FLASK_URL=http://localhost:3013/run_inference/

# Whether to use baseten as the model backend
NEXT_PUBLIC_RIFFUSION_USE_BASETEN=false

# If using BaseTen, the URL and API key
RIFFUSION_BASETEN_URL=https://app.baseten.co/applications/XXX
RIFFUSION_BASETEN_API_KEY=XXX

If you build on this work, please cite it as follows:

@software{Forsgren_Martiros_2022,
  author = {Forsgren, Seth* and Martiros, Hayk*},
  title = {{Riffusion - Stable diffusion for real-time music generation}},
  url = {https://riffusion.com/about},
  year = {2022}
}