<!--Copyright 2023 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.
-->

# Loading and Adding Custom Pipelines

Diffusers allows you to conveniently load any custom pipeline from the Hugging Face Hub as well as any [official community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community) 
via the [`DiffusionPipeline`] class.

## Loading custom pipelines from the Hub

Custom pipelines can be easily loaded from any model repository on the Hub that defines a diffusion pipeline in a `pipeline.py` file.
Let's load a dummy pipeline from [hf-internal-testing/diffusers-dummy-pipeline](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline).

All you need to do is pass the custom pipeline repo id with the `custom_pipeline` argument alongside the repo from where you wish to load the pipeline modules.

```python
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
```

This will load the custom pipeline as defined in the [model repository](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py).

<Tip warning={true} >

By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading 
is safe 🔒. Make sure to check out the code online before loading & running it automatically.

</Tip>

## Loading official community pipelines

Community pipelines are summarized in the [community examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community). 

Similarly, you need to pass both the *repo id* from where you wish to load the weights as well as the `custom_pipeline` argument. Here the `custom_pipeline` argument should consist simply of the filename of the community pipeline excluding the `.py` suffix, *e.g.* `clip_guided_stable_diffusion`.

Since community pipelines are often more complex, one can mix loading weights from an official *repo id*
and passing pipeline modules directly.

```python
from diffusers import DiffusionPipeline
from transformers import CLIPImageProcessor, CLIPModel

clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"

feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
clip_model = CLIPModel.from_pretrained(clip_model_id)

pipeline = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    custom_pipeline="clip_guided_stable_diffusion",
    clip_model=clip_model,
    feature_extractor=feature_extractor,
)
```

## Adding custom pipelines to the Hub

To add a custom pipeline to the Hub, all you need to do is to define a pipeline class that inherits 
from [`DiffusionPipeline`] in a `pipeline.py` file.
Make sure that the whole pipeline is encapsulated within a single class and that the `pipeline.py` file
has only one such class.

Let's quickly define an example pipeline.


```python
import torch
from diffusers import DiffusionPipeline


class MyPipeline(DiffusionPipeline):
    def __init__(self, unet, scheduler):
        super().__init__()

    self.register_modules(unet=unet, scheduler=scheduler)

    @torch.no_grad()
    def __call__(self, batch_size: int = 1, num_inference_steps: int = 50):
        # Sample gaussian noise to begin loop
        image = torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)
        )

        image = image.to(self.device)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps)

        for t in self.progress_bar(self.scheduler.timesteps):
            # 1. predict noise model_output
            model_output = self.unet(image, t).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            image = self.scheduler.step(model_output, t, image, eta).prev_sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()

        return image
```

Now you can upload this short file under the name `pipeline.py` in your preferred [model repository](https://huggingface.co/docs/hub/models-uploading). For Stable Diffusion pipelines, you may also [join the community organisation for shared pipelines](https://huggingface.co/organizations/sd-diffusers-pipelines-library/share/BUPyDUuHcciGTOKaExlqtfFcyCZsVFdrjr) to upload yours.
Finally, we can load the custom pipeline by passing the model repository name, *e.g.* `sd-diffusers-pipelines-library/my_custom_pipeline` alongside the model repository from where we want to load the `unet` and `scheduler` components.

```python
my_pipeline = DiffusionPipeline.from_pretrained(
    "google/ddpm-cifar10-32", custom_pipeline="patrickvonplaten/my_custom_pipeline"
)
```
