#
BaseOutputs

All models have outputs that are subclasses of BaseOutput. Those are data structures containing all the information returned by the model, but they 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 before.

####
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)`. PIL images or numpy array present the denoised images of the diffusion pipeline.

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)`. PIL images or numpy array present the denoised images of the diffusion pipeline.

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 samples of shape `(batch_size, num_channels, sample_rate)`. Numpy array present the denoised audio samples of the diffusion pipeline.

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)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- 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`. Text generated by the diffusion pipeline.

Output class for joint image-text pipelines.