scripts/inference.py

import argparse
import yaml
import torch
import os
import sys
from pathlib import Path
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def get_device(config_device):
    """Auto-detect available device"""
    if config_device == "auto":
        if torch.cuda.is_available():
            device = "cuda"
            logger.info("CUDA available, using GPU")
        else:
            device = "cpu"
            logger.info("CUDA not available, using CPU")
    else:
        device = config_device
        logger.info(f"Using configured device: {device}")
    
    return device

def parse_args():
    parser = argparse.ArgumentParser(description="OmniAvatar-14B Inference")
    parser.add_argument("--config", type=str, required=True, help="Path to config file")
    parser.add_argument("--input_file", type=str, required=True, help="Path to input samples file")
    parser.add_argument("--guidance_scale", type=float, default=5.0, help="Guidance scale")
    parser.add_argument("--audio_scale", type=float, default=3.0, help="Audio guidance scale")
    parser.add_argument("--num_steps", type=int, default=30, help="Number of inference steps")
    parser.add_argument("--sp_size", type=int, default=1, help="Multi-GPU size")
    parser.add_argument("--tea_cache_l1_thresh", type=float, default=None, help="TeaCache threshold")
    return parser.parse_args()

def load_config(config_path):
    with open(config_path, 'r') as f:
        return yaml.safe_load(f)

def process_input_file(input_file):
    """Parse input file with format: prompt@@image_path@@audio_path"""
    samples = []
    with open(input_file, 'r') as f:
        for line in f:
            line = line.strip()
            if line:
                parts = line.split('@@')
                if len(parts) >= 3:
                    prompt = parts[0]
                    image_path = parts[1] if parts[1] else None
                    audio_path = parts[2]
                    samples.append({
                        'prompt': prompt,
                        'image_path': image_path,
                        'audio_path': audio_path
                    })
    return samples

def create_placeholder_video(output_path, duration=5.0, fps=24):
    """Create a simple placeholder video"""
    import numpy as np
    import cv2
    
    logger.info(f"Creating placeholder video: {output_path}")
    
    # Video properties
    width, height = 480, 480
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    
    # Create video writer
    out = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
    
    # Generate frames
    total_frames = int(duration * fps)
    for frame_idx in range(total_frames):
        # Create a simple animated frame
        frame = np.zeros((height, width, 3), dtype=np.uint8)
        
        # Add some animation - moving circle
        center_x = int(width/2 + 100 * np.sin(2 * np.pi * frame_idx / 60))
        center_y = int(height/2 + 50 * np.cos(2 * np.pi * frame_idx / 60))
        
        # Draw circle
        cv2.circle(frame, (center_x, center_y), 30, (0, 255, 0), -1)
        
        # Add text
        text = f"Avatar Placeholder Frame {frame_idx + 1}"
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(frame, text, (10, 30), font, 0.5, (255, 255, 255), 1)
        
        out.write(frame)
    
    out.release()
    logger.info(f"✅ Placeholder video created: {output_path}")

def main():
    args = parse_args()
    
    logger.info("🚀 Starting OmniAvatar-14B Inference")
    logger.info(f"Arguments: {args}")
    
    # Load configuration
    config = load_config(args.config)
    
    # Auto-detect device
    device = get_device(config["hardware"]["device"])
    config["hardware"]["device"] = device
    
    # Process input samples
    samples = process_input_file(args.input_file)
    logger.info(f"Processing {len(samples)} samples")
    
    if not samples:
        logger.error("No valid samples found in input file")
        return
    
    # Create output directory
    output_dir = Path(config['output']['output_dir'])
    output_dir.mkdir(exist_ok=True)
    
    # Process each sample
    for i, sample in enumerate(samples):
        logger.info(f"Processing sample {i+1}/{len(samples)}: {sample['prompt'][:50]}...")
        
        # For now, create a placeholder video
        output_filename = f"avatar_output_{i:03d}.mp4"
        output_path = output_dir / output_filename
        
        try:
            # Create placeholder video (in the future, this would be actual avatar generation)
            create_placeholder_video(output_path, duration=5.0, fps=24)
            
            logger.info(f"✅ Sample {i+1} completed: {output_path}")
            
        except Exception as e:
            logger.error(f"❌ Error processing sample {i+1}: {e}")
    
    logger.info("🎉 Inference completed!")
    logger.info("📝 Note: Currently generating placeholder videos.")
    logger.info("🔜 Future updates will include actual OmniAvatar model inference.")

if __name__ == "__main__":
    main()