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 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) repo_id = "hpcai-tech/Open-Sora" # Map model names to their respective configuration files model_name = "OpenSora-v1-HQ-16x512x512.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) @spaces.GPU(duration=200) def run_inference(prompt_text): # 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( """

Open-Sora: Democratizing Efficient Video Production for All

""" ) gr.HTML( """

Follow me for more! Twitter | Github | Linkedin

""" ) 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=[ [ "A serene underwater scene featuring a sea turtle swimming through a coral reef. The turtle, with its greenish-brown shell, is the main focus of the video, swimming gracefully towards the right side of the frame. The coral reef, teeming with life, is visible in the background, providing a vibrant and colorful backdrop to the turtle's journey. Several small fish, darting around the turtle, add a sense of movement and dynamism to the scene. The video is shot from a slightly elevated angle, providing a comprehensive view of the turtle's surroundings. The overall style of the video is calm and peaceful, capturing the beauty and tranquility of the underwater world.", ], ], fn=run_inference, inputs=[prompt_text,], outputs=[output_video], cache_examples=True, ) demo.launch(debug=True) if __name__ == "__main__": main()