diffusers/pr_12229/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]]

class diffusers.utils.BaseOutputdiffusers.utils.BaseOutputhttps://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/utils/outputs.py#L40""

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_tuplediffusers.utils.BaseOutput.to_tuplehttps://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/utils/outputs.py#L130[]

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

ImagePipelineOutput[[diffusers.ImagePipelineOutput]]

class diffusers.ImagePipelineOutputdiffusers.ImagePipelineOutputhttps://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/pipelines/pipeline_utils.py#L118[{"name": "images", "val": ": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}]- 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).0

Output class for image pipelines.

AudioPipelineOutput[[diffusers.AudioPipelineOutput]]

class diffusers.AudioPipelineOutputdiffusers.AudioPipelineOutputhttps://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/pipelines/pipeline_utils.py#L132[{"name": "audios", "val": ": ndarray"}]- audios (np.ndarray) -- List of denoised audio samples of a NumPy array of shape (batch_size, num_channels, sample_rate).0

Output class for audio pipelines.

ImageTextPipelineOutput[[diffusers.ImageTextPipelineOutput]]

class diffusers.ImageTextPipelineOutputdiffusers.ImageTextPipelineOutputhttps://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py#L48[{"name": "images", "val": ": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray, NoneType]"}, {"name": "text", "val": ": typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType]"}]- 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.0

Output class for joint image-text pipelines.