riffusion
Maintainer: riffusion
950
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
riffusion is a library for real-time music and audio generation using the Stable Diffusion text-to-image diffusion model. It was developed by Seth Forsgren and Hayk Martiros as a hobby project. riffusion fine-tunes Stable Diffusion to generate spectrogram images that can be converted into audio clips, allowing for the creation of music based on text prompts. This is in contrast to other similar models like inkpunk-diffusion and multidiffusion which focus on visual art generation.
Model inputs and outputs
riffusion takes in a text prompt, an optional second prompt for interpolation, a seed image ID, and parameters controlling the diffusion process. It outputs a spectrogram image and the corresponding audio clip.
Inputs
- Prompt A: The primary text prompt describing the desired audio
- Prompt B: An optional second prompt to interpolate with the first
- Alpha: The interpolation value between the two prompts, from 0 to 1
- Denoising: How much to transform the input spectrogram, from 0 to 1
- Seed Image ID: The ID of a seed spectrogram image to use
- Num Inference Steps: The number of steps to run the diffusion model
Outputs
- Spectrogram Image: A spectrogram visualization of the generated audio
- Audio Clip: The generated audio clip in MP3 format
Capabilities
riffusion can generate a wide variety of musical styles and genres based on the provided text prompts. For example, it can create "funky synth solos", "jazz with piano", or "church bells on Sunday". The model is able to capture complex musical concepts and translate them into coherent audio clips.
What can I use it for?
The riffusion model is intended for research and creative applications. It could be used to generate audio for educational or creative tools, or as part of artistic projects exploring the intersection of language and music. Additionally, researchers studying generative models and the connection between text and audio may find riffusion useful for their work.
Things to try
One interesting aspect of riffusion is its ability to interpolate between two text prompts. By adjusting the alpha parameter, you can create a smooth transition from one style of music to another, allowing for the generation of unique and unexpected audio clips. Another interesting area to explore is the model's handling of seed images - by providing different starting spectrograms, you can influence the character and direction of the generated music.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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