Add a docstring for the AutoencoderKL's encode (huggingface#5239)

* Add docstring for the AutoencoderKL's encode

huggingface#5229

* Support Python 3.8 syntax in AutoencoderKL.decode type hints

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Follow the style guidelines in AutoencoderKL's encode

huggingface#5230

---------

Co-authored-by: stano <>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/models/autoencoder_kl.py

@@ -249,7 +249,21 @@ def set_default_attn_processor(self):
         self.set_attn_processor(processor, _remove_lora=True)
 
     @apply_forward_hook
-    def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
+    def encode(
+        self, x: torch.FloatTensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded images. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
         if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
             return self.tiled_encode(x, return_dict=return_dict)