docs/source/using-diffusers/unconditional_image_generation.mdx

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# Unconditional Image Generation

The [`DiffusionPipeline`] is the easiest way to use a pre-trained diffusion system for inference

Start by creating an instance of [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
You can use the [`DiffusionPipeline`] for any [Diffusers' checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads).
In this guide though, you'll use [`DiffusionPipeline`] for unconditional image generation with [DDPM](https://arxiv.org/abs/2006.11239):

```python
>>> from diffusers import DiffusionPipeline

>>> generator = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
```
The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. 
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on GPU.
You can move the generator object to GPU, just like you would in PyTorch.

```python
>>> generator.to("cuda")
```

Now you can use the `generator` on your text prompt:

```python
>>> image = generator().images[0]
```

The output is by default wrapped into a [PIL Image object](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class).

You can save the image by simply calling:

```python
>>> image.save("generated_image.png")
```