---
license: cc-by-nc-4.0
---

# ComplexityDiT - Diffusion Transformer with INL Dynamics

Diffusion Transformer enhanced with PID-style dynamics control for smoother denoising.

## Architecture

```
Input -> [Attention -> MLP -> Dynamics] x 12 -> Output
```

**Core equations:**
- Attention: `softmax(QK^T/sqrt(d)) * V`
- MLP: `W2 * GELU(W1 * x)`
- Dynamics: `h += dt * gate * (alpha*v - beta*(h - mu))`

## Model Details

| Parameter | Value |
|-----------|-------|
| Architecture | ComplexityDiT-S |
| Parameters | 114M |
| Layers | 12 |
| Hidden dim | 384 |
| Heads | 6 |
| Experts | 4 |
| Dynamics | Enabled |

## Training

- Dataset: huggan/wikiart
- Steps: 20,000
- Batch size: 16
- Mixed precision: FP16

## Usage

```python
from safetensors.torch import load_file
from complexity_diffusion import ComplexityDiT

# Load model
model = ComplexityDiT.from_config('S', context_dim=768)
state_dict = load_file('model.safetensors')
model.load_state_dict(state_dict)
```

## INL Dynamics

The dynamics layer adds robotics-grade control to stabilize denoising trajectories:
- `mu` - learnable equilibrium (target position)
- `alpha` - inertia (momentum)
- `beta` - correction strength (spring constant)
- `gate` - amplitude control

This creates smooth, stable trajectories like a PID controller guiding the model toward clean images.

## Links

- [GitHub](https://github.com/Complexity-ML/complexity-framework)
- [PyPI](https://pypi.org/project/complexity-framework/)