riffusion-inference/README.md

Riffusion Inference Server

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 Python backend does the model inference and audio processing, including:

• a diffusers pipeline that performs prompt interpolation combined with image conditioning
• a module for (approximately) converting between spectrograms and waveforms
• a flask server to provide model inference via API to the next.js app
• a model template titled baseten.py for deploying as a Truss

Install

Tested with Python 3.9 and diffusers 0.9.0

conda create --name riffusion-inference python=3.9
conda activate riffusion-inference
python -m pip install -r requirements.txt

Run

Start the Flask server:

python -m riffusion.server --port 3013 --host 127.0.0.1

You can specify --checkpoint with your own directory or huggingface ID in diffusers format.

The model endpoint is now available at http://127.0.0.1:3013/run_inference via POST request.

Example input (see InferenceInput for the API):

{
  alpha: 0.75,
  num_inference_steps: 50,
  seed_image_id: "og_beat",

  start: {
    prompt: "church bells on sunday",
    seed: 42,
    denoising: 0.75,
    guidance: 7.0,
  },

  end: {
    prompt: "jazz with piano",
    seed: 123,
    denoising: 0.75,
    guidance: 7.0,
  },
}

Example output (see InferenceOutput for the API):

{
  image: "< base64 encoded JPEG image >",
  audio: "< base64 encoded MP3 clip >",,
}

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}
}