docs/source/en/api/diffusion_pipeline.md

Pipelines

The [DiffusionPipeline] is the quickest way to load any pretrained diffusion pipeline from the Hub for inference.

You shouldn't use the [DiffusionPipeline] class for training or finetuning a diffusion model. Individual components (for example, [UNet2DModel] and [UNet2DConditionModel]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.

The pipeline type (for example [StableDiffusionPipeline]) of any diffusion pipeline loaded with [~DiffusionPipeline.from_pretrained] is automatically detected and pipeline components are loaded and passed to the __init__ function of the pipeline.

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

DiffusionPipeline

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