stable-diffusion-dance

Maintainer: pollinations

Total Score

5

Last updated 6/20/2024
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Model overview

stable-diffusion-dance is an audio reactive version of the Stable Diffusion model, created by pollinations. It builds upon the original Stable Diffusion model, which is a latent text-to-image diffusion model capable of generating photo-realistic images from any text prompt. The stable-diffusion-dance variant adds the ability to react the generated images to input audio, creating an audiovisual experience.

Model inputs and outputs

The stable-diffusion-dance model takes in a text prompt, an optional audio file, and various parameters to control the generation process. The outputs are a series of generated images that are synchronized to the input audio.

Inputs

  • Prompts: Text prompts that describe the desired image content, such as "a moth", "a killer dragonfly", or "Two fishes talking to each other in deep sea".
  • Audio File: An optional audio file that the generated images will be synchronized to.
  • Batch Size: The number of images to generate at once, up to 24.
  • Frame Rate: The frames per second for the generated video.
  • Random Seed: A seed value to ensure reproducibility of the generated images.
  • Prompt Scale: The influence of the text prompt on the generated images.
  • Style Suffix: An optional suffix to add to the prompt, to influence the artistic style.
  • Audio Smoothing: A factor to smooth the audio input.
  • Diffusion Steps: The number of diffusion steps to use, up to 30.
  • Audio Noise Scale: The scale of the audio influence on the image generation.
  • Audio Loudness Type: The type of audio loudness to use, either 'rms' or 'peak'.
  • Frame Interpolation: Whether to interpolate between frames for a smoother video.

Outputs

  • A series of generated images that are synchronized to the input audio.

Capabilities

The stable-diffusion-dance model builds on the impressive capabilities of the original Stable Diffusion model, allowing users to generate dynamic, audiovisual content. By combining the text-to-image generation abilities of Stable Diffusion with audio-reactive features, stable-diffusion-dance can create unique, expressive visuals that respond to the input audio in real-time.

What can I use it for?

The stable-diffusion-dance model can be used to create a variety of audiovisual experiences, from music visualizations and interactive art installations to dynamic background imagery for videos and presentations. The model's ability to generate images that closely match the input audio makes it a powerful tool for artists, musicians, and content creators looking to add an extra level of dynamism and interactivity to their work.

Things to try

One interesting application of the stable-diffusion-dance model could be to use it for live music performances, where the generated visuals would react and evolve in real-time to the music being played. Another idea could be to use the model to create dynamic, procedural backgrounds for video games or virtual environments, where the visuals would continuously change and adapt to the audio cues and gameplay.



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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