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README.md

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+---
+license: mit
+tags:
+  - pytorch
+  - diffusion
+  - ddpm
+  - image-generation
+  - cifar10
+datasets:
+  - cifar10
+---
+
+# DDPM trained on CIFAR-10
+
+A Denoising Diffusion Probabilistic Model (DDPM) trained on CIFAR-10 using distributed training across 6 V100 GPUs.
+
+## Model Description
+
+- **Architecture -** U-Net with attention (128 base channels)
+- **Training -** 100 epochs on CIFAR-10 (50,000 images)
+- **Hardware -** 3 nodes × 2 V100-16GB GPUs
+- **Framework -** PyTorch DDP
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Batch Size | 64 per GPU (384 effective) |
+| Learning Rate | 2e-4 |
+| Timesteps | 1000 |
+| EMA Decay | 0.9999 |
+
+## Usage
+
+```python
+import torch
+from models import UNet, GaussianDiffusion
+
+model = UNet(in_channels=3, out_channels=3, base_channels=128, 
+             channel_mults=(1,2,2,2), num_res_blocks=2, attention_resolutions=(2,))
+model.load_state_dict(torch.load("model_ema.pt"))
+model.eval()
+
+diffusion = GaussianDiffusion(timesteps=1000)
+samples = diffusion.sample(model, image_size=32, batch_size=16, channels=3)
+```
+
+## Training Code
+
+[GitHub Repository](https://github.com/arvinsingh/Darkbird)
+
+## Citation
+
+```bibtex
+@misc{darkbird2026,
+  author = {Arvin Singh},
+  title = {Darkbird: Distributed Training Examples},
+  year = {2026},
+  url = {https://github.com/arvinsingh/Darkbird}
+}
+```