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| # 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. | |
| <Tip warning={true}> | |
| 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`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../traininig/overview) guides instead! | |
| </Tip> | |
| ## DiffusionPipeline | |
| [[autodoc]] DiffusionPipeline | |
| - all | |
| - __call__ | |
| - device | |
| - to | |
| - components | |
| ## FlaxDiffusionPipeline | |
| [[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline | |