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

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# 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.in_channels, self.unet.sample_size, self.unet.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"
)
```