Diffusion

A minimal, hackable repro of Denoising Diffusion Probabilistic Model with parts of Improved DDPM (cosine beta schedule) and Classifier Free Guidance. Model is roughly same UNet used in the paper with Self Attention. Also support classifier free guidance for conditional generation, cosine and linear beta schedules, EMA etc. Experimented with include CIFAR-10, MNIST and Landscapes datasets. Training script uses Hydra for config and (optional) WandB logging. Still work in progress, but good enough to mess around with generative diffusion models.

Generated samples

CIFAR10 with CFG (32x32)

MNIST with CFG (28x28)

Tiny ImageNet with CFG (64x6x) (10 epochs)

Landscapes (32x32)

CIFAR10 (32x32)

Usage

Install dependencies

pip install -r requirements.txt

CIFAR10 with linear and without CFG

uv run train.py

Landscapes with linear and without CFG

uv run train.py +run=landscapes

MNIST with cosine and CFG

uv run train.py +run=mnist_cfg

• uses lite version of the model since mnist is 24x24x1

CIFAR10 with cosine and CFG

uv run train.py +run=cifar10_cfg

Tiny ImageNet with cosine and CFG

uv run train.py +run=tiny_imagenet_cfg

Sampling

uv run sample.py <model checkpoint.pt> -n <number of samples> <--save>