diffusers/main/en/api/outputs.md

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[[diffusers.utils.BaseOutput]]

diffusers.utils.BaseOutput[[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[[diffusers.utils.BaseOutput.to_tuple]]

to_tuple()

Source

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

ImagePipelineOutput[[diffusers.ImagePipelineOutput]]

diffusers.ImagePipelineOutput[[diffusers.ImagePipelineOutput]]

diffusers.ImagePipelineOutput(images: list[PIL.Image.Image] | numpy.ndarray)

Source

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.

AudioPipelineOutput[[diffusers.AudioPipelineOutput]]

diffusers.AudioPipelineOutput[[diffusers.AudioPipelineOutput]]

diffusers.AudioPipelineOutput(audios: ndarray)

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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[[diffusers.ImageTextPipelineOutput]]

diffusers.ImageTextPipelineOutput[[diffusers.ImageTextPipelineOutput]]

diffusers.ImageTextPipelineOutput(images: list[PIL.Image.Image] | numpy.ndarray | None, text: list[str] | list[list[str]] | None)

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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.