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Riffusion App Hobby

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Updated: 22 Jul 2024
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Stable diffusion for real-time music generation (web app)

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Nextjs Styled components Tailwind Daisyui Headless ui

Overview:

The Riffusion app is no longer being actively maintained but offers a platform for real-time music generation with stable diffusion. Built with Next.js, React, Typescript, three.js, Tailwind, and deployed on Vercel, it provides an interactive web app for creating music.

Features:

  • Real-time Music Generation: Create music in real-time using the app.
  • Stable Diffusion: Utilizes stable diffusion for generating music outputs.
  • Next.js: Built using Next.js for efficient and fast performance.
  • React and Typescript: Utilizes React and Typescript for a structured and type-safe codebase.
  • Three.js Integration: Incorporates three.js for 3D visualizations within the app.
  • Tailwind CSS: Styling is done with Tailwind CSS for a customizable design.
  • Vercel Deployment: Easily deploy the app on Vercel for a seamless hosting experience.

Installation:

  1. Ensure Node v18 or greater is installed by running node --version.
  2. Install packages by running npm install.
  3. Start the development server with npm run dev.
  4. Access the app at http://localhost:3000 and navigate through the pages.
  5. Edit the files in the pages directory, where index.js serves as the app’s home page and about.tsx as the about page.
  6. The pages/api directory is used for API routes instead of React pages.
  7. For running the Inference Server for model outputs:
    • Clone the inference server repository.
    • Follow the instructions in the repository to set up the Flask app.
    • Create a .env.local file in the root of the Riffusion repository with the inference server’s URL.

Summary:

The Riffusion app, though no longer actively maintained, offers a platform for real-time music generation using stable diffusion. Built with Next.js, React, Typescript, three.js, and Tailwind, the app provides an interactive experience for users. The installation process involves setting up Node, installing packages, and starting the development server. Additionally, an Inference Server is required for model outputs, which can be set up by following the instructions provided.