Easy to use Inference code

.gitignore

@@ -9,4 +9,5 @@ pretrained_models
 ./*.png
 ./*.mp4
 demo/tmp
-demo/outputs
+demo/outputs
+.venv

inference.py

@@ -0,0 +1,35 @@
+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 video_path
+
+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)