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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 | |
import importlib | |
from transformers import T5ForConditionalGeneration, T5Tokenizer | |
import os | |
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 | |
def run_inference(prompt_text): | |
repo_id = "hpcai-tech/Open-Sora" | |
# Map model names to their respective configuration files | |
model_name = "OpenSora-v1-HQ-16x256x256.pth" | |
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(): | |
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=[prompt_text], | |
outputs=output_video | |
) | |
gr.Examples( | |
examples=[ | |
[ | |
"Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field.", | |
], | |
], | |
fn=run_inference, | |
inputs=[prompt_text,], | |
outputs=[output_video], | |
cache_examples=True, | |
) | |
demo.launch(debug=True) | |
if __name__ == "__main__": | |
main() | |