diffusers/docs/source/en/quicktour.mdx

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# Quicktour

Get up and running with 🧨 Diffusers quickly!
Whether you're a developer or an everyday user, this quick tour will help you get started and show you how to use [`DiffusionPipeline`] for inference.

Before you begin, make sure you have all the necessary libraries installed:

```bash
pip install --upgrade diffusers accelerate transformers
```

- [`accelerate`](https://huggingface.co/docs/accelerate/index) speeds up model loading for inference and training
- [`transformers`](https://huggingface.co/docs/transformers/index) is required to run the most popular diffusion models, such as [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview)

## DiffusionPipeline

The [`DiffusionPipeline`] is the easiest way to use a pre-trained diffusion system for inference. You can use the [`DiffusionPipeline`] out-of-the-box for many tasks across different modalities. Take a look at the table below for some supported tasks:

| **Task**                     | **Description**                                                                                              | **Pipeline**
|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
| Unconditional Image Generation          | generate an image from gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation`) |
| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
| Text-Guided Image-to-Image Translation     | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |
| Text-Guided Image-Inpainting          | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |
| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2image](./using-diffusers/depth2image) |

For more in-detail information on how diffusion pipelines function for the different tasks, please have a look at the [**Using Diffusers**](./using-diffusers/overview) section.

As an example, 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 text-to-image generation with [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion).

For [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion), please carefully read its [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) before running the model.
This is due to the improved image generation capabilities of the model and the potentially harmful content that could be produced with it.
Please, head over to your stable diffusion model of choice, *e.g.*  [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5), and read the license.

You can load the model as follows:

```python
>>> from diffusers import DiffusionPipeline

>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
```

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

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

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

**Note**: You can also use the pipeline locally by downloading the weights via:

```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
```

and then loading the saved weights into the pipeline. 

```python
>>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
```

Running the pipeline is then identical to the code above as it's the same model architecture.

```python
>>> generator.to("cuda")
>>> image = generator("An image of a squirrel in Picasso style").images[0]
>>> image.save("image_of_squirrel_painting.png")
```

Diffusion systems can be used with multiple different [schedulers](./api/schedulers/overview) each with their
pros and cons. By default, Stable Diffusion runs with [`PNDMScheduler`], but it's very simple to 
use a different scheduler. *E.g.* if you would instead like to use the [`EulerDiscreteScheduler`] scheduler,
you could use it as follows:

```python
>>> from diffusers import EulerDiscreteScheduler

>>> pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

>>> # change scheduler to Euler
>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
```

For more in-detail information on how to change between schedulers, please refer to the [Using Schedulers](./using-diffusers/schedulers) guide.

[Stability AI's](https://stability.ai/) Stable Diffusion model is an impressive image generation model
and can do much more than just generating images from text. We have dedicated a whole documentation page,
just for Stable Diffusion [here](./conceptual/stable_diffusion).

If you want to know how to optimize Stable Diffusion to run on less memory, higher inference speeds, on specific hardware, such as Mac, or with [ONNX Runtime](https://onnxruntime.ai/), please have a look at our 
optimization pages:

- [Optimized PyTorch on GPU](./optimization/fp16)
- [Mac OS with PyTorch](./optimization/mps)
- [ONNX](./optimization/onnx)
- [OpenVINO](./optimization/open_vino)

If you want to fine-tune or train your diffusion model, please have a look at the [**training section**](./training/overview)

Finally, please be considerate when distributing generated images publicly 🤗.