app.py

import gradio as gr
from diffusers import DiffusionPipeline
import torch
from PIL import Image
import spaces

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

# Define the Gradio interface
interface = gr.Interface(
    fn=lambda img: generate_video(img),
    inputs=gr.Image(type="pil"),
    outputs=gr.Video(),
    title="Stable Video Diffusion",
    description="Upload an image to generate a video",
    theme="soft"
)

@spaces.GPU(duration=250)
def generate_video(image):
  """
  Generates a video from an input image using the pipeline.

  Args:
      image: A PIL Image object representing the input image.

  Returns:
      A list of PIL Images representing the video frames.
  """
  video_frames = pipeline(image=image, num_inference_steps=20).images

  return video_frames

# Launch the Gradio app
interface.launch()