app.py

import gradio as gr
from huggingface_hub import hf_hub_download, snapshot_download
import subprocess
import tempfile
import shutil
import os
import spaces

from transformers import T5ForConditionalGeneration, T5Tokenizer
import os

print ("starting the app.")

def download_t5_model(model_id, save_directory):
    # Modelin tokenizer'ını ve modeli indir
    if not os.path.exists(save_directory):
        os.makedirs(save_directory)
    snapshot_download(repo_id="DeepFloyd/t5-v1_1-xxl",local_dir=save_directory, local_dir_use_symlinks=False)

# Model ID ve kaydedilecek dizin
model_id = "DeepFloyd/t5-v1_1-xxl"
save_directory = "pretrained_models/t5_ckpts/t5-v1_1-xxl"

# Modeli indir
download_t5_model(model_id, save_directory)

def download_model(repo_id, model_name):
    model_path = hf_hub_download(repo_id=repo_id, filename=model_name)
    return model_path

import glob

@spaces.GPU
def run_inference(model_name, prompt_text):
    repo_id = "hpcai-tech/Open-Sora"
    
    # Map model names to their respective configuration files
    config_mapping = {
        "OpenSora-v1-16x256x256.pth": "configs/opensora/inference/16x256x256.py",
        "OpenSora-v1-HQ-16x256x256.pth": "configs/opensora/inference/16x512x512.py",
        "OpenSora-v1-HQ-16x512x512.pth": "configs/opensora/inference/64x512x512.py"
    }
    
    config_path = config_mapping[model_name]
    ckpt_path = download_model(repo_id, model_name)

    # Save prompt_text to a temporary text file
    prompt_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w')
    prompt_file.write(prompt_text)
    prompt_file.close()

    with open(config_path, 'r') as file:
        config_content = file.read()
    config_content = config_content.replace('prompt_path = "./assets/texts/t2v_samples.txt"', f'prompt_path = "{prompt_file.name}"')
    
    with tempfile.NamedTemporaryFile('w', delete=False, suffix='.py') as temp_file:
        temp_file.write(config_content)
        temp_config_path = temp_file.name

    cmd = [
        "torchrun", "--standalone", "--nproc_per_node", "1",
        "scripts/inference.py", temp_config_path,
        "--ckpt-path", ckpt_path
    ]
    subprocess.run(cmd)

    save_dir = "./outputs/samples/"  # Örneğin, inference.py tarafından kullanılan kayıt dizini
    list_of_files = glob.glob(f'{save_dir}/*')
    if list_of_files:
        latest_file = max(list_of_files, key=os.path.getctime)
        return latest_file
    else:
        print("No files found in the output directory.")
        return None

    # Clean up the temporary files
    os.remove(temp_file.name)
    os.remove(prompt_file.name)

def main():
    with gr.Blocks() as demo:
        gr.Markdown("# Open-Sora Inference")
        gr.Markdown("Run Open-Sora Inference with custom parameters.")
        
        with gr.Row():
            with gr.Column(scale=1):
                model_dropdown = gr.Dropdown(
                    choices=[
                        "OpenSora-v1-16x256x256.pth",
                        "OpenSora-v1-HQ-16x256x256.pth",
                        "OpenSora-v1-HQ-16x512x512.pth"
                    ], 
                    label="Model Selection"
                )
                prompt_text = gr.Textbox(label="Prompt Text", value="Enter prompt text here", lines=4)
                submit_button = gr.Button("Run Inference")

        output_video = gr.Video(label="Output Video")

        submit_button.click(
            fn=run_inference, 
            inputs=[model_dropdown, prompt_text], 
            outputs=output_video
        )

    demo.launch()

if __name__ == "__main__":
    main()