Add ComplexityDiT checkpoint

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README.md

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+---
+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.

complexity-dit.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:53d988bef8dad7ef93d32db358ebf4372d84913f9984791d9db89d7d26a78672
+size 456055600

config.json

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+{
+  "model_type": "complexity-dit",
+  "architecture": "ComplexityDiT-S",
+  "d_model": 384,
+  "num_layers": 12,
+  "num_heads": 6,
+  "num_experts": 4,
+  "context_dim": 768,
+  "patch_size": 2,
+  "in_channels": 4,
+  "image_size": 32,
+  "dynamics_enabled": true,
+  "dynamics_alpha": 0.9,
+  "dynamics_beta": 0.1,
+  "dynamics_dt": 0.1,
+  "parameters": "114M",
+  "training_steps": 20000,
+  "training_dataset": "huggan/wikiart",
+  "framework": "pytorch"
+}