All models 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:
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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:
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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):
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outputs[:1]
To check a specific pipeline or model output, refer to its corresponding API documentation.
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.
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
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).
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.
