diffusion-from-scratch — trained checkpoints
Weights for github.com/adimunot21/diffusion-from-scratch, a DDPM/DDIM implementation written from scratch in PyTorch — forward process, noise schedules, U-Net denoiser, samplers and classifier-free guidance, no diffusers dependency.
Three models. Each folder has config.json plus two weight files:
ema.safetensors— exponential moving average of the weights (ema_decay=0.9999). Use these for sampling.model.safetensors— the raw final training weights, kept for completeness.
| Folder | Model | Params | Schedule | Epochs | Final train loss | FID |
|---|---|---|---|---|---|---|
mnist/ |
MNIST, unconditional | 9.53 M | linear, T=1000 | 50 | 0.0210 | 34.1 |
cifar10_uncond/ |
CIFAR-10, unconditional | 46.03 M | cosine, T=1000 | 100 | 0.0547 | 71.2 |
cifar10_cond/ |
CIFAR-10, class-conditional | 46.03 M | cosine, T=1000 | 100 | 0.0557 | 65.3 |
The CIFAR-10 U-Nets use channels [128, 256, 256, 512], 2 residual blocks per level and
4-head self-attention at the two middle resolutions. The conditional model was trained
with classifier-free guidance (uncond_prob=0.1, 10 classes).
Loading
These are plain state_dicts for the UNet class in the source repo — not a
diffusers pipeline. Build the model from the repo, then load:
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
path = hf_hub_download("adimunot/diffusion-from-scratch",
"cifar10_uncond/ema.safetensors")
model.load_state_dict(load_file(path)) # model = UNet(**config) from the repo
model.eval()
config.json carries the exact architecture and training hyperparameters each
checkpoint was produced with, so it can be fed straight into the constructor.
Notes
- FID was computed during the project's evaluation phase; treat the numbers as self-reported and comparable only within this repo.
- These are learning-project models trained on a single GPU, not competitive baselines. CIFAR-10 FID in the 65–71 range reflects the small model and 100-epoch budget.