# Unconditional image generation [[open-in-colab]] 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`] 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 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](training/unconditional_training) to learn how to generate your own images. In this guide, 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("anton-l/ddpm-butterflies-128") ``` 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 a GPU. You can move the generator object to a GPU, just like you would in PyTorch: ```python >>> generator.to("cuda") ``` Now you can use the `generator` to generate an image: ```python >>> 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: ```python >>> 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!