stable-video-diffusion

Maintainer: christophy

Total Score

15

Last updated 6/13/2024

🐍

PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

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Model overview

stable-video-diffusion is a text-to-video generation model developed by Replicate creator christophy. It builds upon the capabilities of the Stable Diffusion image generation model, allowing users to create short videos from text prompts. This model is similar to other video generation models like consisti2v, AnimateDiff-Lightning, and Champ, which focus on enhancing visual consistency, cross-model distillation, and controllable human animation, respectively.

Model inputs and outputs

stable-video-diffusion takes an input image and various parameters to generate a short video clip. The input image can be any image, and the model will use it as a starting point to generate the video. The other parameters include the video length, frame rate, motion, and noise levels.

Inputs

  • Input Image: The starting image for the video generation.
  • Video Length: The length of the generated video, either 14 or 25 frames.
  • Frames Per Second: The number of frames per second in the output video, between 5 and 30.
  • Sizing Strategy: How the input image should be resized for the output video.
  • Motion Bucket ID: A parameter that controls the overall motion in the generated video.
  • Seed: A random seed value to ensure consistent output.
  • Cond Aug: The amount of noise to add to the input image.

Outputs

  • Output Video: The generated video clip, in GIF format.

Capabilities

stable-video-diffusion can generate short, animated video clips from a single input image and text-based parameters. The model is capable of creating a wide range of video content, from abstract animations to more realistic scenes, depending on the input prompt and settings.

What can I use it for?

With stable-video-diffusion, you can create unique and engaging video content for a variety of applications, such as social media, video essays, presentations, or even as a starting point for more complex video projects. The model's ability to generate videos from a single image and text-based parameters makes it a versatile tool for content creators and artists.

Things to try

One interesting thing to try with stable-video-diffusion is to experiment with the different input parameters, such as the video length, frame rate, and motion bucket ID. By adjusting these settings, you can create a wide range of video styles, from smooth and cinematic to more dynamic and energetic. Additionally, you can try using different input images as the starting point for the video generation to see how the model responds to different visual cues.



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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