Update app.py

app.py

@@ -1,25 +1,15 @@
 import gradio as gr
-from diffusers import StableVideoDiffusionPipeline
+from transformers import pipeline
 import torch
-from PIL import Image
 
 # Load the model and pipeline
 model_id = "stabilityai/stable-video-diffusion-img2vid-xt"
-pipe = StableVideoDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
+pipe = pipeline("text-to-video-generation", model=model_id, torch_dtype=torch.float16, device="cuda")
 
 def generate_video(image, prompt):
-    # Convert the image to a format suitable for the model
-    image = Image.fromarray(image)
-
     # Generate the video from the image and prompt
-    video = pipe(prompt, image, num_inference_steps=50, guidance_scale=7.5).frames
-
-    # Convert the video frames to a format suitable for Gradio
-    video_path = "output_video.mp4"
-    video[0].save(video_path, save_all=True, append_images=video[1:], duration=100, loop=0)
-
-    return video_path
+    video = pipe(prompt, image, num_inference_steps=50, guidance_scale=7.5)
+    return video
 
 # Create the Gradio interface
 iface = gr.Interface(
@@ -35,4 +25,4 @@ iface = gr.Interface(
 
 # Launch the interface
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()