U-Net, 18.7M parameters: three resolution levels (32→16→8), residual blocks with sinusoidal timestep embeddings, self-attention at 16×16 and 8×8, dropout 0.1
Linear beta schedule, T=1000, trained with the simplified noise-prediction MSE objective
EMA of weights (decay 0.9995) used for sampling; DDIM sampling with 50 steps
Unconditional — no class or text control
Trained 300 epochs (~117k steps, batch 128) in ~4 hours on a single RTX 3090; final loss ≈ 0.029

Files

File	Description
`checkpoint.pt`	Full PyTorch checkpoint: raw weights, EMA weights, optimizer state (resumable)
`unet.onnx`	EMA weights exported to ONNX (fp32), verified to 4e-6 against PyTorch
`train_diffusion.py`	Complete training script
`sample.py`	Generate image grids from the checkpoint
`export_onnx.py`	Reproduce the ONNX export

Usage

bash

pip install torch torchvision
python sample.py --n 64 --seed 42          # sample a grid from checkpoint.pt
python train_diffusion.py --base 128 --epochs 300 --out out_big --sample-every 10 --ema-decay 0.9995   # retrain

Source and local web app: github.com/dannysheesh/cifar10-ddpm

PeterRabbit/cifar10-ddpm

CIFAR-10 DDPM (18.7M params, from scratch)

Model details

Files

Usage

PeterRabbit/
cifar10-ddpm