app.py

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()