146 lines
7.5 KiB
Plaintext
146 lines
7.5 KiB
Plaintext
<!--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
|
|
|
|
>>> 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 GPU.
|
|
You can move the generator object to 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 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
|
|
|
|
>>> generator = 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
|
|
>>> generator = 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 [`LMSDiscreteScheduler`] scheduler,
|
|
you could use it as follows:
|
|
|
|
```python
|
|
>>> from diffusers import LMSDiscreteScheduler
|
|
|
|
>>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
|
|
>>> generator = StableDiffusionPipeline.from_pretrained(
|
|
... "runwayml/stable-diffusion-v1-5", scheduler=scheduler, use_auth_token=AUTH_TOKEN
|
|
... )
|
|
```
|
|
|
|
[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 🤗.
|