app.py

import gradio as gr
from PIL import Image
import numpy as np

from diffusers import DiffusionPipeline

# Load the model
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt")

# Define the function for Gradio to use
def generate_video(input_image):
    # Convert NumPy array to PIL Image
    input_image_pil = Image.fromarray((input_image * 255).astype(np.uint8))

    output_video_path = "output_video.mp4"
    pipeline(input_image_pil, output_video_path)
    return output_video_path

# Create the Gradio interface
iface = gr.Interface(fn=generate_video, inputs="image", outputs="file")

# Launch the Gradio app with sharing enabled
iface.launch(share=True)