Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers import DiffusionPipeline
 from diffusers.utils import export_to_video
 
 # Explicitly set the device to CPU
@@ -12,16 +12,13 @@ pipe = DiffusionPipeline.from_pretrained(
     torch_dtype=torch.float32  # Use float32 for CPU
 ).to(device)
 
-# Set the scheduler to use CPU
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config).to(device)
-
-# Disable model offloading if running on CPU
-# pipe.enable_model_cpu_offload()  # This line should be removed or commented out
+# Initialize the scheduler from the pipeline's configuration, no need to move it to the CPU
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 
 prompt = "Pop international experimental music"
 
 # Generate the video frames on the CPU
-video_frames = pipe(prompt, num_inference_steps=25, device=device).frames
+video_frames = pipe(prompt, num_inference_steps=25).frames
 
 # Export the frames to a video file
 video_path = export_to_video(video_frames)