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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 schedulers 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. Specific pipeline types (for example [StableDiffusionPipeline]) loaded with [~DiffusionPipeline.from_pretrained] are automatically detected and the pipeline components are loaded and passed to the __init__ function of the pipeline.
Warning
You shouldn't use the [
DiffusionPipeline] class for training. Individual components (for example, [UNet2DModel] and [UNet2DConditionModel]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [
~DiffusionPipeline.__call__] method with atorch.no_graddecorator because pipelines should not be used for training. If you're interested in training, please take a look at the Training guides instead!
Use API > Pipelines in the sidebar to browse all pipeline references by modality. For task-oriented workflows, start with the Quickstart or a guide such as text-to-image, image-to-image, inpainting, or video generation.
DiffusionPipeline
autodoc DiffusionPipeline - all - call - device - to - components
autodoc pipelines.StableDiffusionMixin.enable_freeu
autodoc pipelines.StableDiffusionMixin.disable_freeu
PushToHubMixin
autodoc utils.PushToHubMixin
Callbacks
autodoc callbacks.PipelineCallback
autodoc callbacks.SDCFGCutoffCallback
autodoc callbacks.SDXLCFGCutoffCallback
autodoc callbacks.SDXLControlnetCFGCutoffCallback
autodoc callbacks.IPAdapterScaleCutoffCallback
autodoc callbacks.SD3CFGCutoffCallback