# Tiny DDPM (and DDIM)

This is a bare bones and simple DDPM ([Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)) implementation on PyTorch. The whole implementation (model + training + sampling) does not exceed 400 lines of code. The training setup and U-Net model loosely resemble the description of the original paper, but it is not a 1 to 1 implementation.

![Predictions on CIFAR-10](./media/cifar-10-predicted.png)

These images were generated after training on CIFAR-10 for 256 epochs on a single RTX 4090.

# Usage

## Installation

It is recommended (but not required) to use [uv](https://github.com/astral-sh/uv) to replicate the Python environment:

```bash
uv sync                 # If using uv
python -m pip install . # Otherwise
```

## Training

```bash
uv run src/simple_ddpm/train.py # If using uv
python src/simple_ddpm/train.py # Otherwise
```
## Sampling

```bash
uv run src/simple_ddpm/sample.py # If using uv
python src/simple_ddpm/sample.py # Otherwise
```

By default it will perform DDPM sampling. If you want to use DDIM, simply
change the function called at the bottom of `src/tiny-ddpm/sample.py` to call
`ddim_sample_images` instead of `ddpm_sample_images`.