> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/diffusers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/diffusers/using-diffusers/tasks/unconditional-image-generation.md).

# Unconditional image generation

## Unconditional image generation

![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)

Unconditional image generation is a relatively straightforward task. The model only generates images - without any additional context like text or an image - resembling the training data it was trained on.

The [DiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/overview#diffusers.DiffusionPipeline) is the easiest way to use a pre-trained diffusion system for inference.

Start by creating an instance of [DiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/overview#diffusers.DiffusionPipeline) and specify which pipeline checkpoint you would like to download. You can use any of the 🧨 Diffusers [checkpoints](https://huggingface.co/models?library=diffusers\&sort=downloads) from the Hub (the checkpoint you’ll use generates images of butterflies).

💡 Want to train your own unconditional image generation model? Take a look at the training [guide](https://huggingface.co/docs/diffusers/using-diffusers/training/unconditional_training) to learn how to generate your own images.

In this guide, you’ll use [DiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/overview#diffusers.DiffusionPipeline) for unconditional image generation with [DDPM](https://arxiv.org/abs/2006.11239):

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```
>>> from diffusers import DiffusionPipeline

>>> generator = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128", use_safetensors=True)
```

The [DiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/overview#diffusers.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 a GPU. You can move the generator object to a GPU, just like you would in PyTorch:

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

Now you can use the `generator` to generate an image:

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

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

You can save the image by calling:

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

Try out the Spaces below, and feel free to play around with the inference steps parameter to see how it affects the image quality!

                                                  Unconditional butterflies

A DDPM scheduler and UNet model trained (from this [checkpoint](https://huggingface.co/anton-l/ddpm-butterflies-128)) on a subset of the [Smithsonian Butterflies](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) dataset for unconditional image generation

<https://huggingface.co/spaces/stevhliu/ddpm-butterflies-128><br>