Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a 25 frame video from it.
image: The image to be used as a conditioning frame. Must be of size (1024, 576) pixels.
motion_bucket_id: An int This can be used to control the motion of the generated video. Increasing the motion bucket id will increase the motion of the generated video.
Defaults to 127.
noise_aug_strength: A float between [0, 1]. A multiplier, used to determine the amount of noise added to the conditioning image. The higher the values the less the video will resemble the conditioning image. Increasing this value will also increase the motion of the generated video.
Default 0.2.
Run on a cuda device with:
$ docker run --gpus all -p 8080:8080 -t <image-name>
To test the model:
cd svd
pip install -r client_requirements.txt
python client_request.py
The client_request.py demo code uses the ffmpeg Python bindings to change the codec of the output video to h264, so that the resulting video can be previewed in VSCode. This is not essential to the functionality of the container and can be removed.