The [DiffusionPipeline] is the easiest way to load any pretrained diffusion pipeline from the Hub and to use it in inference.

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`].

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].

[[autodoc]] DiffusionPipeline - from_pretrained - save_pretrained - to - device - components

By default diffusion pipelines return an object of class

[[autodoc]] pipeline_utils.ImagePipelineOutput