diffusers/docs/source/en/using-diffusers/conditional_image_generation.mdx

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# Conditional image generation

[[open-in-colab]]

Conditional image generation allows you to generate images from a text prompt. The text is converted into embeddings which are used to condition the model to generate an image from noise.

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](https://huggingface.co/models?library=diffusers&sort=downloads) you would like to download.

In this guide, you'll use [`DiffusionPipeline`] for text-to-image generation with [Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256):

```python
>>> from diffusers import DiffusionPipeline

>>> generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-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 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` on your text prompt:

```python
>>> image = generator("An image of a squirrel in Picasso style").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("image_of_squirrel_painting.png")
```

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

<iframe
	src="https://stabilityai-stable-diffusion.hf.space"
	frameborder="0"
	width="850"
	height="500"
></iframe>