#
BaseOutputs

All models have outputs that are instances of subclasses of BaseOutput. Those are data structures containing all the information returned by the model, but that can also be used as tuples or dictionaries.

Let’s see how this looks in an example:

    from diffusers import DDIMPipeline

    pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
    outputs = pipeline()

The `outputs` object is a ImagePipelineOutput, as we can see in the documentation of that class below, it means it has an image attribute.

You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`:

    outputs.images

or via keyword lookup

    outputs["images"]

When considering our `outputs` object as tuple, it only considers the attributes that don’t have `None` values. Here for instance, we could retrieve images via indexing:

    outputs[:1]

which will return the tuple `(outputs.images)` for instance.

##
BaseOutput

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class diffusers.utils.BaseOutput

< source >

( )

Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular python dictionary.

You can’t unpack a `BaseOutput` directly. Use the to_tuple() method to convert it to a tuple before.

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to_tuple

< source >

( )

Convert self to a tuple containing all the attributes/keys that are not `None`.