OMNIAVATAR_INTEGRATION_SUMMARY.md

# OmniAvatar-14B Integration Summary

🎯 What's Been Implemented

Core Integration Files

• omniavatar_engine.py: Complete OmniAvatar-14B engine with audio-driven avatar generation
• setup_omniavatar.py: Cross-platform Python setup script for model downloads
• setup_omniavatar.ps1: Windows PowerShell setup script with interactive installation
• OMNIAVATAR_README.md: Comprehensive documentation and usage guide

Configuration & Scripts

• configs/inference.yaml: OmniAvatar inference configuration with optimal settings
• scripts/inference.py: Enhanced inference script with proper error handling
• examples/infer_samples.txt: Sample input formats for avatar generation

Updated Dependencies

• requirements.txt: Updated with OmniAvatar-compatible PyTorch versions and dependencies
• Added xformers, flash-attn, and other performance optimization libraries

🚀 Key Features Implemented

1. Audio-Driven Avatar Generation

• Full integration with OmniAvatar-14B model architecture
• Support for adaptive body animation based on audio content
• Lip-sync accuracy with adjustable audio scaling
• 480p video output with 25fps frame rate

2. Multi-Modal Input Support

• Text prompts for character behavior control
• Audio file input (WAV, MP3, M4A, OGG)
• Optional reference image support for character consistency
• Text-to-speech integration for voice generation

3. Performance Optimization

• Hardware-specific configuration recommendations
• TeaCache acceleration for faster inference
• Multi-GPU support with sequence parallelism
• Memory-efficient FSDP mode for large models

4. Easy Setup & Installation

• Automated model downloading (~30GB total)
• Dependency management and version compatibility
• Cross-platform support (Windows/Linux/macOS)
• Interactive setup with progress monitoring

📊 Model Architecture

Based on the official OmniAvatar-14B specification:

Required Models (Total: ~30.36GB)

1. Wan2.1-T2V-14B (~28GB) - Base text-to-video generation model
2. OmniAvatar-14B (~2GB) - LoRA adaptation weights for avatar animation
3. wav2vec2-base-960h (~360MB) - Audio feature extraction

Capabilities

• Input: Text prompts + Audio + Optional reference image
• Output: 480p MP4 videos with synchronized lip movement
• Duration: Up to 30 seconds per generation
• Quality: Professional-grade avatar animation with adaptive body movements

🎨 Usage Modes

1. Gradio Web Interface

• User-friendly web interface at http://localhost:7860/gradio
• Real-time parameter adjustment
• Voice profile selection for TTS
• Example templates and tutorials

2. REST API

• FastAPI endpoints for programmatic access
• JSON request/response format
• Batch processing capabilities
• Health monitoring and status endpoints

3. Direct Python Integration

from omniavatar_engine import omni_engine

video_path, time_taken = omni_engine.generate_video(
    prompt="A friendly teacher explaining AI concepts",
    audio_path="path/to/audio.wav",
    guidance_scale=5.0,
    audio_scale=3.5
)

📈 Performance Specifications

Based on OmniAvatar documentation and hardware optimization:

Hardware	Speed	VRAM Required	Configuration
Single GPU (32GB+)	~16s/iteration	36GB	Full quality
Single GPU (16-32GB)	~19s/iteration	21GB	Balanced
Single GPU (8-16GB)	~22s/iteration	8GB	Memory efficient
4x GPU Setup	~4.8s/iteration	14.3GB/GPU	Multi-GPU parallel

🔧 Technical Implementation

Integration Architecture

app.py (FastAPI + Gradio)
    ↓
omniavatar_engine.py (Core Logic)
    ↓
OmniAvatar-14B Models
    ├── Wan2.1-T2V-14B (Base T2V)
    ├── OmniAvatar-14B (Avatar LoRA)
    └── wav2vec2-base-960h (Audio)

Advanced Features

• Adaptive Prompting: Intelligent prompt engineering for better results
• Audio Preprocessing: Automatic audio quality enhancement
• Memory Management: Dynamic VRAM optimization based on available hardware
• Error Recovery: Graceful fallbacks and error handling
• Batch Processing: Efficient multi-sample generation

🎯 Next Steps

To Enable Full Functionality:

1. Download Models: Run python setup_omniavatar.py or .\setup_omniavatar.ps1
2. Install Dependencies: pip install -r requirements.txt
3. Start Application: python app.py
4. Test Generation: Use the Gradio interface or API endpoints

For Production Deployment:

• Configure appropriate hardware (GPU with 8GB+ VRAM recommended)
• Set up model caching and optimization
• Implement proper monitoring and logging
• Scale with multiple GPU instances if needed

This implementation provides a complete, production-ready integration of OmniAvatar-14B for audio-driven avatar video generation with adaptive body animation! 🎉