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license: cc-by-nc-4.0 |
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--- |
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# ComplexityDiT - Diffusion Transformer with INL Dynamics |
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Diffusion Transformer enhanced with PID-style dynamics control for smoother denoising. |
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## Architecture |
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``` |
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Input -> [Attention -> MLP -> Dynamics] x 12 -> Output |
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``` |
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**Core equations:** |
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- Attention: `softmax(QK^T/sqrt(d)) * V` |
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- MLP: `W2 * GELU(W1 * x)` |
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- Dynamics: `h += dt * gate * (alpha*v - beta*(h - mu))` |
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## Model Details |
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| Parameter | Value | |
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|-----------|-------| |
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| Architecture | ComplexityDiT-S | |
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| Parameters | 114M | |
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| Layers | 12 | |
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| Hidden dim | 384 | |
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| Heads | 6 | |
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| Experts | 4 | |
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| Dynamics | Enabled | |
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## Training |
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- Dataset: huggan/wikiart |
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- Steps: 20,000 |
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- Batch size: 16 |
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- Mixed precision: FP16 |
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## Usage |
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```python |
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from safetensors.torch import load_file |
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from complexity_diffusion import ComplexityDiT |
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# Load model |
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model = ComplexityDiT.from_config('S', context_dim=768) |
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state_dict = load_file('model.safetensors') |
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model.load_state_dict(state_dict) |
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``` |
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## INL Dynamics |
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The dynamics layer adds robotics-grade control to stabilize denoising trajectories: |
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- `mu` - learnable equilibrium (target position) |
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- `alpha` - inertia (momentum) |
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- `beta` - correction strength (spring constant) |
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- `gate` - amplitude control |
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This creates smooth, stable trajectories like a PID controller guiding the model toward clean images. |
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## Links |
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- [GitHub](https://github.com/Complexity-ML/complexity-framework) |
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- [PyPI](https://pypi.org/project/complexity-framework/) |
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