app.py

import gradio as gr
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video

# load pipeline
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)

# optimize for GPU memory
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
def ttv():
    
    # generate
    prompt = "Spiderman is surfing. Darth Vader is also surfing and following Spiderman"
    video_frames = pipe(prompt, num_inference_steps=25, num_frames=200).frames
    
    # convent to video
    video_path = export_to_video(video_frames)
    return video_path

with gr.Blocks() as app:
    inp = gr.Textbox()
    btn = gr.Button()
    outp = gr.Video()
    btn.click(ttv,None,outp)
app.launch()