Update app.py

app.py

@@ -1,36 +1,31 @@
 import torch
 import gradio as gr
 from diffusers import StableVideoDiffusionPipeline
-from PIL import Image
-import numpy as np
-from moviepy import ImageSequenceClip
-import spaces
+from diffusers.utils import load_image, export_to_video
+
+# Check if GPU is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the pipeline
 pipeline = StableVideoDiffusionPipeline.from_pretrained(
     "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
 )
-pipeline.enable_model_cpu_offload()
+pipeline.to(device)
 
-@spaces.GPU
-def generate_video(image, seed):
-    # Preprocess the image
-    image = Image.open(image)
+def generate_video(image_path, seed):
+    # Load and preprocess the image
+    image = load_image(image_path)
     image = image.resize((1024, 576))
 
     # Set the generator seed
-    generator = torch.manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(seed)
 
     # Generate the video frames
     frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]
 
-    # Convert frames to a format suitable for video export
-    frames = [(frame * 255).astype(np.uint8) for frame in frames]
-
     # Export the frames to a video file
-    clip = ImageSequenceClip(frames, fps=7)
     output_video_path = "generated.mp4"
-    clip.write_videofile(output_video_path, codec="libx264")
+    export_to_video(frames, output_video_path, fps=7)
 
     return output_video_path
 
@@ -38,7 +33,7 @@ def generate_video(image, seed):
 iface = gr.Interface(
     fn=generate_video,
     inputs=[
-        gr.Image(type="file", label="Upload Image"),
+        gr.Image(type="filepath", label="Upload Image"),
         gr.Number(label="Seed", value=42)
     ],
     outputs=gr.Video(label="Generated Video"),