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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:
with gr.Row():
with gr.Column():
gr.HTML(
"""
<h1 style='text-align: center'>
Open-Sora: Democratizing Efficient Video Production for All
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
</h3>
"""
)
with gr.Row():
with gr.Column():
model_dropdown = gr.Dropdown(
choices=[
"OpenSora-v1-16x256x256.pth",
"OpenSora-v1-HQ-16x256x256.pth",
"OpenSora-v1-HQ-16x512x512.pth"
],
value="OpenSora-v1-16x256x256.pth"
)
prompt_text = gr.Textbox(show_label=False, placeholder="Enter prompt text here", lines=4)
submit_button = gr.Button("Run Inference")
with gr.Column():
output_video = gr.Video()
submit_button.click(
fn=run_inference,
inputs=[model_dropdown, prompt_text],
outputs=output_video
)
demo.launch()
if __name__ == "__main__":
main()
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