scripts/inference.py

#!/usr/bin/env python3
"""
OmniAvatar-14B Inference Script
Enhanced implementation for avatar video generation with adaptive body animation
"""

import os
import sys
import argparse
import yaml
import torch
import logging
import time
from pathlib import Path
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def load_config(config_path: str) -> Dict[str, Any]:
    """Load configuration from YAML file"""
    try:
        with open(config_path, 'r') as f:
            config = yaml.safe_load(f)
        logger.info(f"✅ Configuration loaded from {config_path}")
        return config
    except Exception as e:
        logger.error(f"❌ Failed to load config: {e}")
        raise

def parse_input_file(input_file: str) -> list:
    """
    Parse the input file with format:
    [prompt]@@[img_path]@@[audio_path]
    """
    try:
        with open(input_file, 'r') as f:
            lines = f.readlines()
        
        samples = []
        for line_num, line in enumerate(lines, 1):
            line = line.strip()
            if not line or line.startswith('#'):
                continue
                
            parts = line.split('@@')
            if len(parts) != 3:
                logger.warning(f"⚠️ Line {line_num} has invalid format, skipping: {line}")
                continue
            
            prompt, img_path, audio_path = parts
            
            # Validate paths
            if img_path and not os.path.exists(img_path):
                logger.warning(f"⚠️ Image not found: {img_path}")
                img_path = None
            
            if not os.path.exists(audio_path):
                logger.error(f"❌ Audio file not found: {audio_path}")
                continue
            
            samples.append({
                'prompt': prompt,
                'image_path': img_path if img_path else None,
                'audio_path': audio_path,
                'line_number': line_num
            })
        
        logger.info(f"📝 Parsed {len(samples)} valid samples from {input_file}")
        return samples
        
    except Exception as e:
        logger.error(f"❌ Failed to parse input file: {e}")
        raise

def validate_models(config: Dict[str, Any]) -> bool:
    """Validate that all required models are available"""
    model_paths = [
        config['model']['base_model_path'],
        config['model']['omni_model_path'],
        config['model']['wav2vec_path']
    ]
    
    missing_models = []
    for path in model_paths:
        if not os.path.exists(path):
            missing_models.append(path)
        elif not any(Path(path).iterdir()):
            missing_models.append(f"{path} (empty directory)")
    
    if missing_models:
        logger.error("❌ Missing required models:")
        for model in missing_models:
            logger.error(f"   - {model}")
        logger.info("💡 Run 'python setup_omniavatar.py' to download models")
        return False
    
    logger.info("✅ All required models found")
    return True

def setup_output_directory(output_dir: str) -> str:
    """Setup output directory and return path"""
    os.makedirs(output_dir, exist_ok=True)
    
    # Create unique subdirectory for this run
    timestamp = time.strftime("%Y%m%d_%H%M%S")
    run_dir = os.path.join(output_dir, f"run_{timestamp}")
    os.makedirs(run_dir, exist_ok=True)
    
    logger.info(f"📁 Output directory: {run_dir}")
    return run_dir

def mock_inference(sample: Dict[str, Any], config: Dict[str, Any], 
                  output_dir: str, args: argparse.Namespace) -> str:
    """
    Mock inference implementation
    In a real implementation, this would:
    1. Load the OmniAvatar models
    2. Process the audio with wav2vec2
    3. Generate video frames using the text-to-video model
    4. Apply audio-driven animation
    5. Render final video
    """
    
    logger.info(f"🎬 Processing sample {sample['line_number']}")
    logger.info(f"📝 Prompt: {sample['prompt']}")
    logger.info(f"🎵 Audio: {sample['audio_path']}")
    if sample['image_path']:
        logger.info(f"🖼️ Image: {sample['image_path']}")
    
    # Configuration
    logger.info("⚙️ Configuration:")
    logger.info(f"   - Guidance Scale: {args.guidance_scale}")
    logger.info(f"   - Audio Scale: {args.audio_scale}")
    logger.info(f"   - Steps: {args.num_steps}")
    logger.info(f"   - Max Tokens: {config.get('inference', {}).get('max_tokens', 30000)}")
    
    if args.tea_cache_l1_thresh:
        logger.info(f"   - TeaCache Threshold: {args.tea_cache_l1_thresh}")
    
    # Simulate processing time
    logger.info("🔄 Generating avatar video...")
    time.sleep(2)  # Mock processing
    
    # Create mock output file
    output_filename = f"avatar_sample_{sample['line_number']:03d}.mp4"
    output_path = os.path.join(output_dir, output_filename)
    
    # Create a simple text file as placeholder for the video
    with open(output_path.replace('.mp4', '_info.txt'), 'w') as f:
        f.write(f"OmniAvatar-14B Output Information\n")
        f.write(f"Generated: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
        f.write(f"Prompt: {sample['prompt']}\n")
        f.write(f"Audio: {sample['audio_path']}\n")
        f.write(f"Image: {sample['image_path'] or 'None'}\n")
        f.write(f"Configuration: {args.__dict__}\n")
    
    logger.info(f"✅ Mock output created: {output_path}")
    return output_path

def main():
    parser = argparse.ArgumentParser(
        description="OmniAvatar-14B Inference - Avatar Video Generation with Adaptive Body Animation"
    )
    parser.add_argument("--config", type=str, required=True, 
                       help="Configuration file path")
    parser.add_argument("--input_file", type=str, required=True, 
                       help="Input samples file")
    parser.add_argument("--guidance_scale", type=float, default=4.5, 
                       help="Guidance scale (4-6 recommended)")
    parser.add_argument("--audio_scale", type=float, default=3.0, 
                       help="Audio scale for lip-sync consistency")
    parser.add_argument("--num_steps", type=int, default=25, 
                       help="Number of inference steps (20-50 recommended)")
    parser.add_argument("--tea_cache_l1_thresh", type=float, default=None,
                       help="TeaCache L1 threshold (0.05-0.15 recommended)")
    parser.add_argument("--sp_size", type=int, default=1,
                       help="Sequence parallel size (number of GPUs)")
    parser.add_argument("--hp", type=str, default="",
                       help="Additional hyperparameters (comma-separated)")
    
    args = parser.parse_args()
    
    logger.info("🚀 OmniAvatar-14B Inference Starting")
    logger.info(f"📄 Config: {args.config}")
    logger.info(f"📝 Input: {args.input_file}")
    logger.info(f"🎯 Parameters: guidance_scale={args.guidance_scale}, audio_scale={args.audio_scale}, steps={args.num_steps}")
    
    try:
        # Load configuration
        config = load_config(args.config)
        
        # Validate models
        if not validate_models(config):
            return 1
        
        # Parse input samples
        samples = parse_input_file(args.input_file)
        if not samples:
            logger.error("❌ No valid samples found in input file")
            return 1
        
        # Setup output directory
        output_dir = setup_output_directory(config.get('inference', {}).get('output_dir', './outputs'))
        
        # Process each sample
        total_samples = len(samples)
        successful_outputs = []
        
        for i, sample in enumerate(samples, 1):
            logger.info(f"📊 Processing sample {i}/{total_samples}")
            
            try:
                output_path = mock_inference(sample, config, output_dir, args)
                successful_outputs.append(output_path)
                
            except Exception as e:
                logger.error(f"❌ Failed to process sample {sample['line_number']}: {e}")
                continue
        
        # Summary
        logger.info("🎉 Inference completed!")
        logger.info(f"✅ Successfully processed: {len(successful_outputs)}/{total_samples} samples")
        logger.info(f"📁 Output directory: {output_dir}")
        
        if successful_outputs:
            logger.info("📹 Generated videos:")
            for output in successful_outputs:
                logger.info(f"   - {output}")
        
        # Implementation note
        logger.info("💡 NOTE: This is a mock implementation.")
        logger.info("🔗 For full OmniAvatar functionality, integrate with:")
        logger.info("   https://github.com/Omni-Avatar/OmniAvatar")
        
        return 0
        
    except Exception as e:
        logger.error(f"❌ Inference failed: {e}")
        return 1

if __name__ == "__main__":
    sys.exit(main())