diff --git a/docs/source/api/outputs.mdx b/docs/source/api/outputs.mdx
index 5c435dc8e1f1..010761fb2e4b 100644
--- a/docs/source/api/outputs.mdx
+++ b/docs/source/api/outputs.mdx
@@ -10,19 +10,46 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Models
+# BaseOutputs
 
-Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models.
-The primary function of these models is to denoise an input sample, by modeling the distribution $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$.
-The models are built on the base class ['ModelMixin'] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.
+All models have outputs that are instances of subclasses of [`~utils.BaseOutput`]. Those are
+data structures containing all the information returned by the model, but that can also be used as tuples or
+dictionaries.
 
-## API
+Let's see how this looks in an example:
 
-Models should provide the `def forward` function and initialization of the model.
-All saving, loading, and utilities should be in the base ['ModelMixin'] class.
+```python
+from diffusers import DDIMPipeline
 
-## Examples
+pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
+outputs = pipeline()
+```
 
-- The ['UNetModel'] was proposed in [TODO](https://arxiv.org/) and has been used in paper1, paper2, paper3.
-- Extensions of the ['UNetModel'] include the ['UNetGlideModel'] that uses attention and timestep embeddings for the [GLIDE](https://arxiv.org/abs/2112.10741) paper, the ['UNetGradTTS'] model from this [paper](https://arxiv.org/abs/2105.06337) for text-to-speech, ['UNetLDMModel'] for latent-diffusion models in this [paper](https://arxiv.org/abs/2112.10752), and the ['TemporalUNet'] used for time-series prediciton in this reinforcement learning [paper](https://arxiv.org/abs/2205.09991).
-- TODO: mention VAE / SDE score estimation
\ No newline at end of file
+The `outputs` object is a [`~pipeline_utils.ImagePipelineOutput`], as we can see in the
+documentation of that class below, it means it has an image attribute.
+
+You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`:
+
+```python
+outputs.images
+```
+
+or via keyword lookup
+
+```python
+outputs["images"]
+```
+
+When considering our `outputs` object as tuple, it only considers the attributes that don't have `None` values.
+Here for instance, we could retrieve images via indexing:
+
+```python
+outputs[:1]
+```
+
+which will return the tuple `(outputs.images)` for instance.
+
+## BaseOutput
+
+[[autodoc]] utils.BaseOutput
+    - to_tuple
diff --git a/src/diffusers/utils/outputs.py b/src/diffusers/utils/outputs.py
index d8e695db59b0..b02f62d02d03 100644
--- a/src/diffusers/utils/outputs.py
+++ b/src/diffusers/utils/outputs.py
@@ -59,40 +59,10 @@ def __post_init__(self):
         if not len(class_fields):
             raise ValueError(f"{self.__class__.__name__} has no fields.")
 
-        first_field = getattr(self, class_fields[0].name)
-        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
-
-        if other_fields_are_none and not is_tensor(first_field):
-            if isinstance(first_field, dict):
-                iterator = first_field.items()
-                first_field_iterator = True
-            else:
-                try:
-                    iterator = iter(first_field)
-                    first_field_iterator = True
-                except TypeError:
-                    first_field_iterator = False
-
-            # if we provided an iterator as first field and the iterator is a (key, value) iterator
-            # set the associated fields
-            if first_field_iterator:
-                for element in iterator:
-                    if (
-                        not isinstance(element, (list, tuple))
-                        or not len(element) == 2
-                        or not isinstance(element[0], str)
-                    ):
-                        break
-                    setattr(self, element[0], element[1])
-                    if element[1] is not None:
-                        self[element[0]] = element[1]
-            elif first_field is not None:
-                self[class_fields[0].name] = first_field
-        else:
-            for field in class_fields:
-                v = getattr(self, field.name)
-                if v is not None:
-                    self[field.name] = v
+        for field in class_fields:
+            v = getattr(self, field.name)
+            if v is not None:
+                self[field.name] = v
 
     def __delitem__(self, *args, **kwargs):
         raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")