Advanced Multimodal AI System

A powerful, comprehensive multimodal AI system that provides state-of-the-art capabilities for text-to-image, text-to-video, image-to-video generation, and advanced enhancement features. Built with cutting-edge models and optimized for production use.

🚀 Features

Core Capabilities

• Text-to-Image Generation: SDXL, SD3, Flux, Playground V2.5, Kandinsky 3
• Text-to-Video Generation: ZeroScope, ModelScope, AnimateDiff, CogVideoX, LaVie
• Image-to-Video Generation: Stable Video Diffusion, I2VGen-XL, DynamiCrafter, ConsistI2V
• Image Enhancement: RealESRGAN, GFPGAN, SwinIR, CodeFormer, BSRGAN
• Video Enhancement: BasicVSR++, RealBasicVSR, EDVR, TDAN

Advanced Features

• ControlNet Integration: Canny, Depth, Pose, Scribble, Segmentation, Normal, MLSD, HED
• Face Enhancement: Specialized face restoration and enhancement
• Temporal Consistency: Advanced video processing with frame coherence
• Motion Control: Sophisticated motion generation and interpolation
• Batch Processing: Parallel processing of multiple tasks
• Workflow System: Multi-step automated pipelines
• Memory Optimization: Intelligent model loading and GPU memory management
• Async Processing: Non-blocking API with task queues

🛠️ Installation

Prerequisites

• Python 3.8+
• CUDA 11.8+ (for GPU acceleration)
• 16GB+ RAM (32GB+ recommended)
• 50GB+ free disk space

Quick Install

git clone <repository-url>
cd veo5
pip install -r requirements.txt

Docker Installation

docker build -t multimodal-ai .
docker run --gpus all -p 8000:8000 multimodal-ai

🎯 Quick Start

Command Line Interface

# Run full demonstration
python main.py --demo

# Create training datasets
python main.py --create-datasets

# Start API server
python main.py --api

# Setup directories only
python main.py --setup

Python API

from core.multimodal_ai import MultimodalAI

# Initialize system
ai = MultimodalAI(device="cuda")

# Generate image
result = ai.text_to_image(
    prompt="a majestic dragon flying over a cyberpunk city",
    model="sdxl",
    width=1024,
    height=1024,
    enhance=True
)

# Generate video
result = ai.text_to_video(
    prompt="waves crashing on a rocky shore at sunset",
    model="zeroscope",
    num_frames=24,
    fps=8
)

# Image to video
result = ai.image_to_video(
    image="path/to/image.jpg",
    model="stable_video",
    num_frames=25
)

# Enhance image
result = ai.enhance_image(
    image="path/to/image.jpg",
    model="realesrgan",
    scale=4,
    face_enhance=True
)

REST API

# Start server
python main.py --api

# Generate image
curl -X POST "http://localhost:8000/generate/text-to-image" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "a beautiful landscape",
    "model": "sdxl",
    "width": 1024,
    "height": 1024
  }'

# Check task status
curl "http://localhost:8000/task/{task_id}"

📊 Model Performance

Text-to-Image Models

Text-to-Video Models

🔧 Configuration

Model Configuration

# config.py
@dataclass
class ModelConfig:
    device: str = "cuda"
    mixed_precision: bool = True
    compile_models: bool = True
    enable_xformers: bool = True
    max_batch_size: int = 4
    # ... more options

Memory Optimization

# Enable memory optimizations
config.enable_cpu_offload = True
config.enable_attention_slicing = True
config.enable_vae_slicing = True

🎨 Advanced Usage

Workflow System

# Multi-step workflow
workflow = [
    {
        "type": "text_to_image",
        "args": {"prompt": "a portrait", "model": "sdxl"}
    },
    {
        "type": "enhance_image",
        "args": {"model": "gfpgan", "scale": 4},
        "input_from": 0
    },
    {
        "type": "image_to_video",
        "args": {"model": "stable_video", "num_frames": 25},
        "input_from": 1
    }
]

result = ai.create_workflow(workflow)

Batch Processing

# Process multiple tasks in parallel
tasks = [
    {"type": "text_to_image", "args": {"prompt": "landscape 1"}},
    {"type": "text_to_image", "args": {"prompt": "landscape 2"}},
    {"type": "text_to_video", "args": {"prompt": "ocean waves"}}
]

results = ai.batch_process(tasks, max_concurrent=2)

ControlNet Integration

# Use ControlNet for precise control
result = ai.text_to_image(
    prompt="a futuristic building",
    control_image=edge_image,
    control_type="canny",
    controlnet_conditioning_scale=1.0
)

📚 Dataset Generation

Synthetic Dataset Creation

from datasets.dataset_generator import MultimodalDatasetGenerator

generator = MultimodalDatasetGenerator()

# Generate text-image pairs
samples = generator.generate_text_image_pairs(num_samples=10000)

# Generate text-video pairs
video_samples = generator.generate_text_video_pairs(num_samples=5000)

# Create training splits
generator.create_training_splits(dataset_path)

Dataset Categories

• Portrait: Professional headshots, artistic portraits
• Landscape: Natural scenes, cityscapes, environments
• Object: Product photography, still life, detailed objects
• Abstract: Artistic compositions, geometric patterns
• Action: Dynamic scenes, motion capture, sports

🌐 API Documentation

Endpoints

• POST /generate/text-to-image - Generate images from text
• POST /generate/text-to-video - Generate videos from text
• POST /generate/image-to-video - Generate videos from images
• POST /enhance/image - Enhance image quality
• POST /enhance/video - Enhance video quality
• POST /workflow - Execute multi-step workflows
• POST /batch - Process multiple tasks
• GET /task/{task_id} - Get task status
• GET /models - List available models
• GET /stats - System statistics

Authentication

# API key authentication (if enabled)
curl -H "X-API-Key: your-api-key" \
  "http://localhost:8000/generate/text-to-image"

🔍 Monitoring & Analytics

Performance Metrics

# Get system statistics
stats = ai.get_stats()
print(f"Total generations: {stats['total_generations']}")
print(f"Average time: {stats['average_time']:.2f}s")
print(f"Memory usage: {stats['memory']}")

Logging

import logging
logging.basicConfig(level=logging.INFO)

# Logs include:
# - Generation times
# - Memory usage
# - Model loading/unloading
# - Error tracking

🚀 Production Deployment

Docker Compose

version: '3.8'
services:
  multimodal-ai:
    build: .
    ports:
      - "8000:8000"
    environment:
      - CUDA_VISIBLE_DEVICES=0
    volumes:
      - ./models:/app/models
      - ./outputs:/app/outputs
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: multimodal-ai
spec:
  replicas: 2
  selector:
    matchLabels:
      app: multimodal-ai
  template:
    spec:
      containers:
      - name: multimodal-ai
        image: multimodal-ai:latest
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 32Gi
          requests:
            memory: 16Gi

Load Balancing

upstream multimodal_ai {
    server 127.0.0.1:8000;
    server 127.0.0.1:8001;
    server 127.0.0.1:8002;
}

server {
    listen 80;
    location / {
        proxy_pass http://multimodal_ai;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

🔧 Troubleshooting

Common Issues

CUDA Out of Memory

# Reduce batch size
config.max_batch_size = 1

# Enable CPU offloading
config.enable_cpu_offload = True

# Use smaller models
ai.text_to_image(model="sd15", width=512, height=512)

Slow Generation

# Enable model compilation
config.compile_models = True

# Use mixed precision
config.mixed_precision = True

# Enable XFormers
config.enable_xformers = True

Model Loading Errors

# Clear cache
rm -rf ~/.cache/huggingface/

# Reinstall dependencies
pip install --upgrade diffusers transformers

# Check disk space
df -h

📈 Performance Optimization

GPU Optimization

• Use multiple GPUs with model parallelism
• Enable tensor compilation with torch.compile
• Optimize VRAM usage with attention slicing
• Use mixed precision training (FP16/BF16)

CPU Optimization

• Utilize multiple CPU cores for preprocessing
• Enable CPU offloading for large models
• Optimize data loading with multiple workers

Memory Management

• Automatic model unloading when not in use
• Intelligent caching of frequently used models
• Progressive loading for large video generation

🤝 Contributing

Development Setup

git clone <repository-url>
cd veo5
pip install -e .
pip install -r requirements-dev.txt
pre-commit install

Code Style

# Format code
black .
isort .

# Lint code
flake8 .
mypy .

# Run tests
pytest tests/

Adding New Models

1. Create model class inheriting from BaseMultimodalModel
2. Implement required methods: load_model, unload_model, generate
3. Add model configuration to config.py
4. Update model registry in MultimodalAI
5. Add tests and documentation

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Stability AI for Stable Diffusion models
• Hugging Face for the Diffusers library
• OpenAI for CLIP models
• All the researchers and developers who made this possible

📞 Support

• 📧 Email: support@multimodal-ai.com
• 💬 Discord: Join our community
• 📖 Documentation: docs.multimodal-ai.com
• 🐛 Issues: GitHub Issues

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