# Loading and Saving 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). 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. ## 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 CLIPFeatureExtractor, CLIPModel clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" feature_extractor = CLIPFeatureExtractor.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" ) ```