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* add: pushtohubmixin to pipelines and schedulers docs overview. * Apply suggestions from code review Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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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.
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 decorator because pipelines should not be used for training. If you're interested in training, please take a look at the Training guides instead!
DiffusionPipeline
autodoc DiffusionPipeline - all - call - device - to - components
FlaxDiffusionPipeline
autodoc pipelines.pipeline_flax_utils.FlaxDiffusionPipeline
PushToHubMixin
autodoc utils.PushToHubMixin