# Outputs

All model outputs are subclasses of [BaseOutput](/docs/diffusers/main/en/api/outputs#diffusers.utils.BaseOutput), data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.

For example:

```python
from diffusers import DDIMPipeline

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

The `outputs` object is a [ImagePipelineOutput](/docs/diffusers/main/en/api/pipelines/ddim#diffusers.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`:

```python
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)`:

```python
outputs[:1]
```

> [!TIP]
> To check a specific pipeline or model output, refer to its corresponding API documentation.

## BaseOutput[[diffusers.utils.BaseOutput]]

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.

> [!WARNING] > You can't unpack a `BaseOutput` directly. Use the [to_tuple()](/docs/diffusers/main/en/api/outputs#diffusers.utils.BaseOutput.to_tuple) method to convert
it to a tuple > first.

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

## ImagePipelineOutput[[diffusers.ImagePipelineOutput]]

- **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]]

- **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]]

- **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.