diff --git a/src/diffusers/models/unet_2d_blocks.py b/src/diffusers/models/unet_2d_blocks.py
index d02cf0290e8d..e52f6bbae52c 100644
--- a/src/diffusers/models/unet_2d_blocks.py
+++ b/src/diffusers/models/unet_2d_blocks.py
@@ -512,9 +512,9 @@ class UNetMidBlock2D(nn.Module):
         dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
         num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
         resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
-        resnet_time_scale_shift (`str`, *optional*, defaults to `default`): The time scale shift for the resnet blocks.
+        resnet_time_scale_shift (`str`, *optional*, defaults to `default`): The type of normalization to apply to the time embeddings. This can help to improve the performance of the model on tasks with long-range temporal dependencies.
         resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
-        resnet_groups (`int`, *optional*, defaults to 32): The number of groups for the resnet blocks.
+        resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks. This can help to improve performance and reduce memory usage.
         attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
         resnet_pre_norm (`bool`, *optional*, defaults to `True`): Whether to use pre-normalization for the resnet blocks.
         add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.