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README.md
Riffusion App
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.
Run
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.
Inference Server
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
Citation
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}
}