#
Outputs

All model outputs are subclasses of BaseOutput, data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.

For example:

    from diffusers import DDIMPipeline

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

The `outputs` object is a ImagePipelineOutput which means it has an image attribute.

You can access each attribute as you normally would or with a keyword lookup, and if that attribute is not returned by the model, you will get `None`:

    outputs.images
    outputs["images"]

When considering the `outputs` object as a tuple, it only considers the attributes that don’t have `None` values. For instance, retrieving an image by indexing into it returns the tuple `(outputs.images)`:

    outputs[:1]

To check a specific pipeline or model output, refer to its corresponding API documentation.

##
BaseOutput

###
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 first.

####
to_tuple

< source >

( )

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

##
ImagePipelineOutput

###
class diffusers.ImagePipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`.

Output class for image pipelines.

##
FlaxImagePipelineOutput

###
class diffusers.pipelines.pipeline_flax_utils.FlaxImagePipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`.

Output class for image pipelines.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

##
AudioPipelineOutput

###
class diffusers.AudioPipelineOutput

< source >

( audios: ndarray )

Parameters

- audios (`np.ndarray`) — List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.

Output class for audio pipelines.

##
ImageTextPipelineOutput

###
class diffusers.ImageTextPipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray, NoneType] text: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`.
- text (`List[str]` or `List[List[str]]`) — List of generated text strings of length `batch_size` or a list of list of strings whose outer list has length `batch_size`.

Output class for joint image-text pipelines.