app.py

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

device = "cuda" if torch.cuda.is_available() else "cpu"
#model = gr.Interface.load("models/camenduru/text2_video_zero")
# load pipeline
pipe = DiffusionPipeline.from_pretrained("camenduru/text2-video-zero").to(device)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
#if device == "cuda":
    # optimize for GPU memory
#    pipe.enable_model_cpu_offload()
#else:
#    pass
#pipe.enable_vae_slicing()
    
def ttv():
    
    # generate
    prompt = "Spiderman is surfing. Darth Vader is also surfing and following Spiderman"
    video_frames = model(prompt, num_inference_steps=25, num_frames=20)
    
    # convent to video
    #video_path = export_to_video(video_frames)
    return video_frames

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