Pipelines

Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different scheduler or even model components.

All pipelines are built from the base [DiffusionPipeline] class which provides basic functionality for loading, downloading, and saving all the components.

Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [~DiffusionPipeline.__call__] method with a torch.no_grad decorator because pipelines should not be used for training. If you're interested in training, please take a look at the Training guides instead!

DiffusionPipeline

[[autodoc]] DiffusionPipeline - all - call - device - to - components

FlaxDiffusionPipeline

[[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline

PushToHubMixin

[[autodoc]] utils.PushToHubMixin