make style

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -1016,9 +1016,9 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
                 A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
                 are passed along to the UNet blocks.
             down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
-                A tuple of tensors that if specified are added to the residuals of down blocks.
+                A tuple of tensors that if specified are added to the residuals of down unet blocks.
             mid_block_additional_residual: (`torch.Tensor`, *optional*):
-                A tensor that if specified is added to the residual of the middle block.
+                A tensor that if specified is added to the residual of the middle unet block.
             encoder_attention_mask (`torch.Tensor`):
                 A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
                 `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
@@ -1026,6 +1026,11 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                 tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
 
         Returns:
             [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: