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stable-diffusion-videos-mo-di

Maintainer: wcarle

Total Score

2

Last updated 5/16/2024
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Model LinkView on Replicate
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Model overview

The stable-diffusion-videos-mo-di model, developed by wcarle, allows you to generate videos by interpolating the latent space of Stable Diffusion. This model builds upon existing work like Stable Video Diffusion and Lavie, which explore generating videos from text or images using diffusion models. The stable-diffusion-videos-mo-di model specifically uses the Mo-Di Diffusion Model to create smooth video transitions between different text prompts.

Model inputs and outputs

The stable-diffusion-videos-mo-di model takes in a set of text prompts and associated seeds, and generates a video by interpolating the latent space between the prompts. The user can specify the number of interpolation steps, as well as the guidance scale and number of inference steps to control the video generation process.

Inputs

  • Prompts: The text prompts to use as the starting and ending points for the video generation. Separate multiple prompts with '|' to create a transition between them.
  • Seeds: The random seeds to use for each prompt, separated by '|'. Leave blank to randomize the seeds.
  • Num Steps: The number of interpolation steps to use between the prompts. More steps will result in smoother transitions but longer generation times.
  • Guidance Scale: A value between 1 and 20 that controls how closely the generated images adhere to the input prompts.
  • Num Inference Steps: The number of denoising steps to use during image generation, with a higher number leading to higher quality but slower generation.

Outputs

  • Video: The generated video, which transitions between the input prompts using the Mo-Di Diffusion Model.

Capabilities

The stable-diffusion-videos-mo-di model can create visually striking videos by smoothly interpolating between different text prompts. This allows for the generation of videos that morph or transform organically, such as a video that starts with "blueberry spaghetti" and ends with "strawberry spaghetti". The model can also be used to generate videos for a wide range of creative applications, from abstract art to product demonstrations.

What can I use it for?

The stable-diffusion-videos-mo-di model is a powerful tool for artists, designers, and content creators looking to generate unique and compelling video content. You could use it to create dynamic video backgrounds, explainer videos, or even experimental art pieces. The model is available to use in a Colab notebook or through the Replicate platform, making it accessible to a wide range of users.

Things to try

One interesting feature of the stable-diffusion-videos-mo-di model is its ability to incorporate audio into the video generation process. By providing an audio file, the model can use the audio's beat and rhythm to inform the rate of interpolation, allowing the videos to move in sync with the music. This opens up new creative possibilities, such as generating music videos or visualizations that are tightly coupled with a soundtrack.



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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