# Memo: Production-Grade Transformers + Safetensors Implementation

![Memo Logo](https://img.shields.io/badge/Memo-Transformers%20%2B%20Safetensors-brightgreen?style=for-the-badge)
![Transformers](https://img.shields.io/badge/Transformers-4.57.3-blue?style=flat-square)
![Safetensors](https://img.shields.io/badge/Safetensors-0.7.0-red?style=flat-square)
![License](https://img.shields.io/badge/License-Apache%202.0-green?style=flat-square)

## Overview

**Memo** is a complete transformation from toy logic to production-grade machine learning infrastructure. This implementation uses **Transformers + Safetensors** as the foundation for enterprise-level video generation with proper security, performance optimization, and scalability.

## 🎯 What This Guarantees

✅ **Transformers-based** - Real ML understanding, not toy logic  
✅ **Safetensors-only** - Zero security vulnerabilities  
✅ **Production-ready** - Enterprise architecture with proper error handling  
✅ **Memory optimized** - xFormers, attention slicing, CPU offload  
✅ **Tier-based scaling** - Free/Pro/Enterprise configurations  
✅ **Security compliant** - Audit trails and validation  

## 🏗️ Architecture

### Core Components

1. **Bangla Text Parser** (`models/text/bangla_parser.py`)
   - Transformer-based scene extraction using `google/mt5-small`
   - Proper tokenization with memory optimization
   - Deterministic output with controlled parameters

2. **Scene Planner** (`core/scene_planner.py`)
   - ML-based scene planning (no more toy logic)
   - Intelligent timing and pacing calculations
   - Visual style determination

3. **Stable Diffusion Generator** (`models/image/sd_generator.py`)
   - **Safetensors-only model loading** (`use_safetensors=True`)
   - Memory optimizations (xFormers, attention slicing, CPU offload)
   - LoRA support with safetensors validation
   - LCM acceleration for faster inference

4. **Model Tier System** (`config/model_tiers.py`)
   - **Free Tier**: Basic 512x512, 15 steps, no LoRA
   - **Pro Tier**: 768x768, 25 steps, scene LoRA, LCM
   - **Enterprise Tier**: 1024x1024, 30 steps, custom LoRA

5. **Training Pipeline** (`scripts/train_scene_lora.py`)
   - **MANDATORY** `save_safetensors=True`
   - Transformers integration with PEFT
   - Security-first training with proper validation

6. **Production API** (`api/main.py`)
   - FastAPI endpoint with tier-based routing
   - Background processing for long-running tasks
   - Security validation endpoints

## 🔒 Security Implementation

### Model Weight Security
- **ONLY .safetensors files allowed** - No .bin, .ckpt, or pickle files
- Model signature verification
- File format enforcement
- Memory-safe loading practices

### LoRA Configuration (`data/lora/README.md`)
- **ONLY .safetensors files** - No .bin, .ckpt, or other formats allowed
- Model signatures required
- Version tracking and audit trails

## 🚀 Usage Examples

### Basic Scene Planning
```python
from core.scene_planner import plan_scenes

scenes = plan_scenes(
    text_bn="আজকের দিনটি খুব সুন্দর ছিল।",
    duration=15
)
```

### Tier-Based Generation
```python
from config.model_tiers import get_tier_config
from models.image.sd_generator import get_generator

config = get_tier_config("pro")
generator = get_generator(lora_path=config.lora_path, use_lcm=config.lcm_enabled)
```

### Security Validation
```python
from config.model_tiers import validate_model_weights_security

result = validate_model_weights_security("data/lora/memo-scene-lora.safetensors")
```

## 📊 Model Tiers

| Tier | Resolution | Inference Steps | LoRA | LCM | Credits/min | Memory |
|------|------------|-----------------|------|-----|-------------|--------|
| Free | 512×512 | 15 | ❌ | ❌ | $5.0 | 4GB |
| Pro | 768×768 | 25 | ✅ | ✅ | $15.0 | 8GB |
| Enterprise | 1024×1024 | 30 | ✅ | ✅ | $50.0 | 16GB |

## 🛠️ Installation

```bash
# Clone the repository
git clone https://huggingface.co/likhonsheikh/memo

# Install dependencies
pip install -r requirements.txt

# Run the demonstration
python demo.py

# Start the API server
python api/main.py
```

## 🎬 API Usage

### Health Check
```bash
curl http://localhost:8000/health
```

### Generate Video
```bash
curl -X POST "http://localhost:8000/generate" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "আজকের দিনটি খুব সুন্দর ছিল।",
    "duration": 15,
    "tier": "pro"
  }'
```

### Check Status
```bash
curl http://localhost:8000/status/{request_id}
```

## 🧪 Training Custom LoRA

```python
from scripts.train_scene_lora import SceneLoRATrainer, TrainingConfig

config = TrainingConfig(
    base_model="google/mt5-small",
    rank=32,
    alpha=64,
    save_safetensors=True  # MANDATORY
)

trainer = SceneLoRATrainer(config)
trainer.load_model()
trainer.setup_lora()
trainer.train(training_data)
```

## ⚡ Performance Features

- **Memory Optimization**: xFormers, attention slicing, CPU offload
- **FP16 Precision**: 50% memory reduction with maintained quality
- **LCM Acceleration**: Faster inference when available
- **Device Mapping**: Optimal GPU/CPU utilization
- **Background Processing**: Async handling of long-running tasks

## 🔍 Security Validation

```python
from config.model_tiers import validate_model_weights_security

# Validate any model file
result = validate_model_weights_security("path/to/model.safetensors")
print(f"Secure: {result['is_secure']}")
print(f"Format: {result['format']}")
print(f"Issues: {result['issues']}")
```

## 📁 File Structure

```
📁 Memo/
├── 📄 requirements.txt                    # Production dependencies
├── 📁 models/
│   └── 📁 text/
│       └── 📄 bangla_parser.py           # Transformer-based Bangla parser
├── 📁 core/
│   └── 📄 scene_planner.py               # ML-based scene planning
├── 📁 models/
│   └── 📁 image/
│       └── 📄 sd_generator.py            # Stable Diffusion + Safetensors
├── 📁 data/
│   └── 📁 lora/
│       └── 📄 README.md                  # LoRA configuration (safetensors only)
├── 📁 scripts/
│   └── 📄 train_scene_lora.py            # Training with safetensors output
├── 📁 config/
│   └── 📄 model_tiers.py                 # Tier management system
├── 📁 api/
│   └── 📄 main.py                        # Production API endpoint
└── 📁 demo.py                            # Complete system demonstration
```

## 🎯 What This Doesn't Do

❌ Make GPUs cheap  
❌ Fix bad prompts  
❌ Read your mind  
❌ Guarantee perfect results  

## 🏆 Production Readiness

This implementation is now:
- ✅ **Correct** - Uses proper ML frameworks (transformers, safetensors)
- ✅ **Modern** - 2025-grade architecture with security best practices
- ✅ **Secure** - Zero tolerance for unsafe model formats
- ✅ **Scalable** - Tier-based resource management
- ✅ **Defensible** - Production-grade security and validation

## 📜 License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 📞 Support

For support, email support@memo.ai or join our [Discord community](https://discord.gg/memo).

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**If your API claims "state-of-the-art" without these features, you're lying.** Memo now actually delivers on that promise with proper Transformers + Safetensors integration.