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| import gradio as gr | |
| from huggingface_hub import hf_hub_download, snapshot_download | |
| import subprocess | |
| import tempfile, time | |
| 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 | |
| st_time_t5 = time.time() | |
| download_t5_model(model_id, save_directory) | |
| print(f"T5 Download Time : {time.time()-st_time_t5} seconds") | |
| 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_model(temp_config_path, ckpt_path): | |
| start_time = time.time() # Record the start time | |
| cmd = [ | |
| "torchrun", "--standalone", "--nproc_per_node", "1", | |
| "scripts/inference.py", temp_config_path, | |
| "--ckpt-path", ckpt_path | |
| ] | |
| subprocess.run(cmd) | |
| end_time = time.time() # Record the end time | |
| execution_time = end_time - start_time # Calculate the execution time | |
| print(f"Model Execution time: {execution_time} seconds") | |
| 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/16x256x256.py", | |
| "OpenSora-v1-HQ-16x512x512.pth": "configs/opensora/inference/16x512x512.py" | |
| } | |
| config_path = config_mapping[model_name] | |
| st_time_sora = time.time() | |
| ckpt_path = download_model(repo_id, model_name) | |
| print(f"Open-Sora Download Time : {time.time()-st_time_sora} seconds") | |
| # 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 | |
| run_model(temp_config_path, ckpt_path) | |
| 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(): | |
| gr.Interface( | |
| fn=run_inference, | |
| inputs=[ | |
| gr.Dropdown(choices=[ | |
| "OpenSora-v1-16x256x256.pth", | |
| "OpenSora-v1-HQ-16x256x256.pth", | |
| "OpenSora-v1-HQ-16x512x512.pth" | |
| ], | |
| value="OpenSora-v1-16x256x256.pth", | |
| label="Model Selection"), | |
| gr.Textbox(label="Prompt Text", value="iron man riding a skateboard in new york city") | |
| ], | |
| outputs=gr.Video(label="Output Video"), | |
| title="Open-Sora Inference", | |
| description="Run Open-Sora Inference with Custom Parameters", | |
| examples=[["OpenSora-v1-16x256x256.pth", "iron man riding a skateboard in new york city"] | |
| # ["OpenSora-v1-16x256x256.pth", "a man is skiing down a snowy mountain. a drone shot from above. an avalanche is chasing him from behind."], | |
| # ["OpenSora-v1-16x256x256.pth", "Extreme close up of a 24 year old woman’s eye blinking, standing in Marrakech during magic hour, cinematic film shot in 70mm, depth of field, vivid colors, cinematic"], | |
| # ["OpenSora-v1-16x256x256.pth", "A gorgeously rendered papercraft world of a coral reef, rife with colorful fish and sea creatures."], | |
| # ["OpenSora-v1-16x256x256.pth", "A close up view of a glass sphere that has a zen garden within it. There is a small dwarf in the sphere who is raking the zen garden and creating patterns in the sand."], | |
| # ["OpenSora-v1-16x256x256.pth", "A petri dish with a bamboo forest growing within it that has tiny red pandas running around."], | |
| # ["OpenSora-v1-16x256x256.pth", "3D animation of a small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest."], | |
| # ["OpenSora-v1-16x256x256.pth", "a ferrari driving in a snowy road."] | |
| ], | |
| article = """ | |
| # Examples | |
| | Model | Description | Video Player Embedding | | |
| |------------------------------|----------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| | |
| | OpenSora-v1-HQ-16x256x256.pth | Iron Man riding a skateboard in New York City |  | | |
| | OpenSora-v1-16x256x256.pth | A man is skiing down a snowy mountain. A drone shot from above. An avalanche is chasing him from behind. |  | | |
| | OpenSora-v1-16x256x256.pth | Extreme close-up of a 24-year-old woman’s eye blinking, standing in Marrakech during magic hour, cinematic film shot in 70mm, depth of field, vivid colors, cinematic |  | | |
| """ | |
| ).launch() | |
| if __name__ == "__main__": | |
| main() |