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# Pipelines

The [`DiffusionPipeline`] is the easiest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) and to use it in inference.

<Tip>
	
	One should not use the Diffusion Pipeline class for training or fine-tuning a diffusion model. Individual 
	components of diffusion pipelines are usually trained individually, so we suggest to directly work 
	with [`UNetModel`] and [`UNetConditionModel`].

</Tip>

Any diffusion pipeline that is loaded with [`~DiffusionPipeline.from_pretrained`] will automatically 
detect the pipeline type, *e.g.* [`StableDiffusionPipeline`] and consequently load each component of the 
pipeline and pass them into the `__init__` function of the pipeline, *e.g.* [`~StableDiffusionPipeline.__init__`].

Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].

## DiffusionPipeline
[[autodoc]] DiffusionPipeline
	- all
	- __call__
	- device
	- to
	- components

## ImagePipelineOutput
By default diffusion pipelines return an object of class

[[autodoc]] pipelines.ImagePipelineOutput

## AudioPipelineOutput
By default diffusion pipelines return an object of class

[[autodoc]] pipelines.AudioPipelineOutput