Load community pipelines

Load community pipelines

Community pipelines are any DiffusionPipeline class that are different from the original implementation as specified in their paper (for example, the StableDiffusionControlNetPipeline corresponds to the Text-to-Image Generation with ControlNet Conditioning paper). They provide additional functionality or extend the original implementation of a pipeline.

There are many cool community pipelines like Speech to Image or Composable Stable Diffusion, and you can find all the official community pipelines here.

To load any community pipeline on the Hub, pass the repository id of the community pipeline to the custom_pipeline argument and the model repository where you’d like to load the pipeline weights and components from. For example, the example below loads a dummy pipeline from hf-internal-testing/diffusers-dummy-pipeline and the pipeline weights and components from google/ddpm-cifar10-32:

🔒 By loading a community pipeline from the BOINC AI Hub, you are trusting that the code you are loading is safe. Make sure to inspect the code online before loading and running it automatically!

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from diffusers import DiffusionPipeline

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

Loading an official community pipeline is similar, but you can mix loading weights from an official repository id and pass pipeline components directly. The example below loads the community CLIP Guided Stable Diffusion pipeline, and you can pass the CLIP model components directly to it:

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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,
    use_safetensors=True,
)

For more information about community pipelines, take a look at the Community pipelines guide for how to use them and if you’re interested in adding a community pipeline check out the How to contribute a community pipeline guide!