Create app.py

app.py

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+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-1-1")
+
+# 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"
+)
+
+# Define the function to generate the video
+def generate_video(img):
+    # Convert the input image to a tensor
+    img_tensor = torch.tensor(img).unsqueeze(0) / 255.0
+    
+    # Run the pipeline to generate the video
+    output = pipeline(img_tensor)
+    
+    # Extract the video frames from the output
+    video_frames = output["video_frames"]
+    
+    # Convert the video frames to a video
+    video = []
+    for frame in video_frames:
+        video.append(Image.fromarray(frame.detach().cpu().numpy()))
+    
+    # Return the generated video
+    return video
+
+# Launch the Gradio app
+interface.launch()