Easy to use Inference Code #71
Description
If someone has tried inferencing the model using shell scripts provided by the authors you may have found that videos are not saved in output directory.
I have a written a simple script for inferencing the model and it's easy to use.
import argparse
import numpy as np
from PIL import Image
import imageio
from demo.animate import MagicAnimate
def read_video(video_path):
reader = imageio.get_reader(video_path)
return reader
def read_image(image_path, size=512):
image = Image.open(image_path)
return np.array(image.resize((size, size)))
def main(reference_image_path, motion_sequence_path, seed, steps, guidance_scale):
animator = MagicAnimate()
reference_image = read_image(reference_image_path)
motion_sequence = read_video(motion_sequence_path)
animation = animator(reference_image, motion_sequence, seed, steps, guidance_scale)
return animation
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Animate images using MagicAnimate.")
parser.add_argument("reference_image", help="Path to the reference image")
parser.add_argument("motion_sequence", help="Path to the motion sequence video")
parser.add_argument("--seed", type=int, default=1, help="Random seed (default: 1)")
parser.add_argument("--steps", type=int, default=25, help="Sampling steps (default: 25)")
parser.add_argument("--guidance_scale", type=float, default=7.5, help="Guidance scale (default: 7.5)")
args = parser.parse_args()
main(args.reference_image, args.motion_sequence, args.seed, args.steps, args.guidance_scale)
run this script like this
python script_name.py path_to_reference_image.jpg path_to_motion_sequence.mp4 --seed 123 --steps 50 --guidance_scale 5.0