diff --git a/src/diffusers/models/unets/unet_3d_condition.py b/src/diffusers/models/unets/unet_3d_condition.py
index bca9fc4ab373..1d5bd57cf8e0 100644
--- a/src/diffusers/models/unets/unet_3d_condition.py
+++ b/src/diffusers/models/unets/unet_3d_condition.py
@@ -54,7 +54,7 @@ class UNet3DConditionOutput(BaseOutput):
     The output of [`UNet3DConditionModel`].
 
     Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
             The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
     """
 
@@ -74,9 +74,9 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
             Height and width of input/output sample.
         in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
         out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")`):
             The tuple of downsample blocks to use.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")`):
             The tuple of upsample blocks to use.
         block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
             The tuple of output channels for each block.
@@ -87,8 +87,8 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
         norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
             If `None`, normalization and activation layers is skipped in post-processing.
         norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        cross_attention_dim (`int`, *optional*, defaults to 1024): The dimension of the cross attention features.
+        attention_head_dim (`int`, *optional*, defaults to 64): The dimension of the attention heads.
         num_attention_heads (`int`, *optional*): The number of attention heads.
     """
 
@@ -533,7 +533,7 @@ def forward(
 
         Args:
             sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
+                The noisy input tensor with the following shape `(batch, num_channels, num_frames, height, width`.
             timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
             encoder_hidden_states (`torch.FloatTensor`):
                 The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.