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PyTorch reimplementation of Diffusion Models
| Date | Stars |
|---|---|
| 2026-07-31 | 585 |
| 2026-08-06 | 585 |
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# PyTorch pretrained Diffusion Models
A PyTorch reimplementation of [Denoising Diffusion Probabilistic
Models](https://hojonathanho.github.io/diffusion/) with checkpoints converted
from [the author's TensorFlow
implementation](https://github.com/hojonathanho/diffusion).
## Quickstart
Running
```
pip install -e git+https://github.com/pesser/pytorch_diffusion.git#egg=pytorch_diffusion
pytorch_diffusion_demo
```
will start a [Streamlit](https://www.streamlit.io/) demo. It is recommended to
run the demo with a GPU available.
## Usage
Diffusion models with pretrained weights for `cifar10`, `lsun-bedroom`,
`lsun_cat` or `lsun_church` can be loaded as follows:
```
from pytorch_diffusion import Diffusion
diffusion = Diffusion.from_pretrained("lsun_church")
samples = diffusion.denoise(4)
diffusion.save(samples, "lsun_church_sample_{:02}.png")
```
Prefix the name with `ema_` to load the averaged weights that produce better
results. The U-Net model used for denoising is available via `diffusion.model`
and can also be instantiated on its own:
```
from pytorch_diffusion import Model
model = Model(resolution=32,
in_channels=3,
out_ch=3,
ch=128,
ch_mult=(1,2,2,2),
num_res_blocks=2,
attn_resolutions=(16,),
dropout=0.1)
```
This configuration example corresponds to the model used on CIFAR-10.
## Producing samples
If you installed directly from github, you can [find the cloned
repository](https://pip.pypa.io/en/stable/reference/pip_install/#vcs-support)
in `<venv path>/src/pytorch_diffusion` for virtual environments, and
`<cwd>/src/pytorch_diffusion` for global installs. There, you can run
```
python pytorch_diffusion/diffusion.py <name> <bs> <nb>
```
where `<name>` is one of `cifar10`, `lsun-bedroom`, `lsun_cat`, `lsun_church`,
or one of these names prefixed with `ema_`, `<bs>` is the batch size and `<nb>`
the number of batches. This will produce samples from the PyTorch models and
save them to `results/<name>/`.
## Results
Evaluating 50k samples with
[torch-fidelity](https://github.com/toshas/torch-fidelity) gives
| Dataset | EMA | Framework | Model | FID |
|--------------------|-----|------------|------------------|----------|
| CIFAR10 Train | no | PyTorch | `cifar10` | 12.13775 |
| | | TensorFlow | `tf_cifar10` | 12.30003 |
| | yes | PyTorch | `ema_cifar10` | 3.21213 |
| | | TensorFlow | `tf_ema_cifar10` | 3.245872 |
| CIFAR10 Validation | no | PyTorch | `cifar10` | 14.30163 |
| | | TensorFlow | `tf_cifar10` | 14.44705 |
| | yes | PyTorch | `ema_cifar10` | 5.274105 |
| | | TensorFlow | `tf_ema_cifar10` | 5.325035 |
To reproduce, generate 50k samples from the converted PyTorch models provided
in this repo with
```
`python pytorch_diffusion/diffusion.py <Model> 500 100`
```
and with
```
python -c "import convert as m; m.sample_tf(500, 100, which=['cifar10', 'ema_cifar10'])"
```
for the original TensorFlow models.
## Running conversions
The [converted pytorch checkpoints are provided for
download](https://heibox.uni-heidelberg.de/d/01207c3f6b8441779abf/). If you
want to convert them on your own, you can follow the steps described here.
### Setup
This section assumes your working directory is the root of this repository.
Download the pretrained TensorFlow checkpoints. It should follow the original
structure,
```
diffusion_models_release/
diffusion_cifar10_model/
model.ckpt-790000.data-00000-of-00001
model.ckpt-790000.index
model.ckpt-790000.meta
diffusion_lsun_bedroom_model/
...
...
```
Set the environment variable `TFROOT` to the directory where you want to store
the author's repository, e.g.
```
export TFROOT=".."
```
Clone the [diffusion repository](https://gitExcerpt of 6,102 characters
Read on GitHub
Patrick Esser
13
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a3bb3f7bf07e5875, llm:Repository description: 'PyTorch reimplementation of Diffusion Models' (language: Python).
matched fp:a3bb3f7bf07e5875, llm:Repository description: 'PyTorch reimplementation of Diffusion Models' (language: Python).
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