import os import shutil from huggingface_hub import snapshot_download import gradio as gr from scripts.inference import inference_process import argparse # Download the repository contents into a directory hallo_dir = snapshot_download(repo_id="fudan-generative-ai/hallo") # Define the new directory path for the pretrained models new_dir = 'pretrained_models' # Ensure the new directory exists os.makedirs(new_dir, exist_ok=True) # Move all contents from the downloaded directory to the new directory for filename in os.listdir(hallo_dir): shutil.move(os.path.join(hallo_dir, filename), os.path.join(new_dir, filename)) def run_inference(source_image, driving_audio, progress=gr.Progress(track_tqdm=True)): # Construct the argparse.Namespace object with all necessary attributes args = argparse.Namespace( config='configs/inference/default.yaml', # Adjust this path as necessary source_image=source_image.name, driving_audio=driving_audio.name, output='output.mp4', # You might want to manage output paths dynamically pose_weight=1.0, face_weight=1.0, lip_weight=1.0, face_expand_ratio=1.2, checkpoint=None # Adjust or set this according to your checkpointing strategy ) # Call the imported function inference_process(args) # Return output or path to output return 'output.mp4' # Modify based on your output handling iface = gr.Interface( fn=run_inference, inputs=[gr.inputs.Image(type="file"), gr.inputs.Audio(type="file")], outputs="text" ) iface.launch()