quicktour.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# 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
```

## 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     | generate an image given an original image and 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) |

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 [Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256):

```python
>>> from diffusers import DiffusionPipeline

>>> pipeline = 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 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")
```

More advanced models, like [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) require you to accept a [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), read the license carefully and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Having "click-accepted" the license, you can save your token:

```python
AUTH_TOKEN = "<please-fill-with-your-token>"
```

You can then load [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)
just like we did before only that now you need to pass your `AUTH_TOKEN`:

```python
>>> from diffusers import DiffusionPipeline

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

If you do not pass your authentication token you will see that the diffusion system will not be correctly 
downloaded. Forcing the user to pass an authentication token ensures that it can be verified that the 
user has indeed read and accepted the license, which also means that an internet connection is required.

**Note**: If you do not want to be forced to pass an authentication token, you can also simply download 
the weights locally via:

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

and then load locally saved weights into the pipeline. This way, you do not need to pass an authentication
token. Assuming that `"./stable-diffusion-v1-5"` is the local path to the cloned stable-diffusion-v1-5 repo,
you can also load the pipeline as follows:

```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) 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", use_auth_token=AUTH_TOKEN)

>>> # 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 🤗.