Update app.py

app.py

@@ -3,10 +3,25 @@ from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
 import streamlit as st
 
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
+# Title and User Input
+st.title("Text-to-Video with Streamlit")
+prompt = st.text_input("Enter your text prompt:", "Spiderman is surfing")
 
-prompt = "Spiderman is surfing"
-video_frames = pipe(prompt, num_inference_steps=25).frames
-video_path = export_to_video(video_frames)
+# Button to trigger generation
+if st.button("Generate Video"):
+
+    # Ensure you have 'accelerate' version 0.17.0 or higher (see previous explanation)
+    import accelerate
+    if accelerate.__version__ < "0.17.0":
+        st.warning("Please upgrade 'accelerate' to version 0.17.0 or higher for CPU offloading.")
+    else:
+        with st.spinner("Generating video..."):
+            pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
+            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+            pipe.enable_model_cpu_offload()  # Assuming 'accelerate' is updated  
+
+            video_frames = pipe(prompt, num_inference_steps=25).frames
+            video_path = export_to_video(video_frames)
+
+            # Display the video in the Streamlit app
+            st.video(video_path)