Create app.py

app.py

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+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+import gradio as gr
+
+# Load the DiffusionPipeline
+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()
+
+def generate_video(prompt):
+    video_frames = pipe(prompt, num_inference_steps=25).frames
+    video_path = export_to_video(video_frames[0])
+    return video_path
+
+# Create the Gradio Interface
+interface = gr.Interface(
+    fn=generate_video,
+    inputs=gr.Textbox(label="Enter your prompt"),
+    outputs=gr.Video(label="Generated Video"),
+    title="Text-to-Video Generator",
+    description="Enter a prompt to generate a video using diffusion models."
+)
+
+# Launch the Gradio app
+interface.launch()