app.py

import gradio as gr
import os

from huggingface_hub import snapshot_download

model_ids = [
    'runwayml/stable-diffusion-v1-5',
    'lllyasviel/sd-controlnet-depth', 
    'lllyasviel/sd-controlnet-canny', 
    'lllyasviel/sd-controlnet-openpose',
]
for model_id in model_ids:
    model_name = model_id.split('/')[-1]
    snapshot_download(model_id, local_dir=f'checkpoints/{model_name}')

import subprocess

def run_inference(prompt, video_path, condition, video_length):
    print(video_length)
    video_length = int(video_length)
    print(video_length)
    command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{video_path}' --output_path '' --video_length {video_length} --smoother_steps 19 20"
    subprocess.run(command, shell=True)

    # Construct the video path
    video_path_output = f"{prompt}.mp4"
    return "done", video_path_output 

    

    #return f"{output_path}/{prompt}.mp4"

with gr.Blocks() as demo:
    with gr.Column():
        prompt = gr.Textbox(label="prompt")
        video_path = gr.Video(source="upload", type="filepath")
        condition = gr.Textbox(label="Condition", value="depth")
        video_length = gr.Slider(label="video length", minimum=1, maximum=15, step=1, value=2)
        #seed = gr.Number(label="seed", value=42)
        submit_btn = gr.Button("Submit")
        video_res = gr.Video(label="result")
        status = gr.Textbox(label="result")

    submit_btn.click(fn=run_inference, 
                     inputs=[prompt,
                             video_path,
                             condition,
                             video_length
                            ],
                    outputs=[status, video_res])

demo.queue(max_size=12).launch()