Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+from diffusers.schedulers import DPMSolverMultistepScheduler
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+from base64 import b64encode
+
+# Load pipeline (outside the function for efficiency)
+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()
+pipe.enable_vae_slicing()
+
+def Generate_video(prompt, video_duration_seconds):
+    num_frames = video_duration_seconds * 10
+    video_frames = pipe(prompt=prompt, negative_prompt="low quality",
+                         num_inference_steps=25, num_frames=num_frames).frames
+    video_path = export_to_video(video_frames)  # Assuming you have this function defined
+    return video_path
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=Generate_video,
+    inputs=[
+        gr.Textbox(lines=5, label="Prompt"),
+        gr.Number(label="Video Duration (seconds)", value=3),
+    ],
+    outputs=gr.Video(label="Generated Video"),
+)
+
+# Launch the app
+iface.launch()