diff --git a/generative/networks/nets/vqvae.py b/generative/networks/nets/vqvae.py
index 361d92ec..07c7aeba 100644
--- a/generative/networks/nets/vqvae.py
+++ b/generative/networks/nets/vqvae.py
@@ -100,26 +100,24 @@ class VQVAE(nn.Module):
         spatial_dims: number of spatial spatial_dims.
         in_channels: number of input channels.
         out_channels: number of output channels.
-        num_levels: number of levels that the network has. Defaults to 3.
+        num_levels: number of levels that the network has.
         downsample_parameters: A Tuple of Tuples for defining the downsampling convolutions. Each Tuple should hold the
-            following information stride (int), kernel_size (int), dilation(int) and padding (int).
-            Defaults to ((2,4,1,1),(2,4,1,1),(2,4,1,1)).
+            following information stride (int), kernel_size (int), dilation (int) and padding (int).
         upsample_parameters: A Tuple of Tuples for defining the upsampling convolutions. Each Tuple should hold the
-            following information stride (int), kernel_size (int), dilation(int), padding (int), output_padding (int).
+            following information stride (int), kernel_size (int), dilation (int), padding (int), output_padding (int).
             If use_subpixel_conv is True, only the stride will be used for the last conv as the scale_factor.
-            Defaults to ((2,4,1,1,0),(2,4,1,1,0),(2,4,1,1,0)).
-        num_res_layers: number of sequential residual layers at each level. Defaults to 3.
-        num_channels: number of channels at the deepest level, besides that is num_channels//2 . Defaults to 192.
-        num_res_channels: number of channels in the residual layers. Defaults to 64.
-        num_embeddings: VectorQuantization number of atomic elements in the codebook. Defaults to 32.
-        embedding_dim: VectorQuantization number of channels of the input and atomic elements. Defaults to 64.
-        commitment_cost: VectorQuantization commitment_cost. Defaults to 0.25.
-        decay: VectorQuantization decay. Defaults to 0.5.
-        epsilon: VectorQuantization epsilon. Defaults to 1e-5 as.
-        adn_ordering: a string representing the ordering of activation, normalization, and dropout. Defaults to "NDA".
-        act: activation type and arguments. Defaults to Relu.
-        dropout: dropout ratio. Defaults to 0.1.
-        ddp_sync: whether to synchronize the codebook across processes. Defaults to True.
+        num_res_layers: number of sequential residual layers at each level.
+        num_channels: number of channels at each level.
+        num_res_channels: number of channels in the residual layers at each level.
+        num_embeddings: VectorQuantization number of atomic elements in the codebook.
+        embedding_dim: VectorQuantization number of channels of the input and atomic elements.
+        commitment_cost: VectorQuantization commitment_cost.
+        decay: VectorQuantization decay.
+        epsilon: VectorQuantization epsilon.
+        adn_ordering: a string representing the ordering of activation, normalization, and dropout, e.g. "NDA".
+        act: activation type and arguments.
+        dropout: dropout ratio.
+        ddp_sync: whether to synchronize the codebook across processes.
     """
 
     # < Python 3.9 TorchScript requirement for ModuleList
@@ -166,7 +164,7 @@ def __init__(
         ), (
             f"downsample_parameters, upsample_parameters, num_channels and num_res_channels must have the same number of"
             f" elements as num_levels. But got {len(downsample_parameters)}, {len(upsample_parameters)}, "
-            f"{len(num_res_channels)} and {len(num_res_channels)} instead of {num_levels}."
+            f"{len(num_channels)} and {len(num_res_channels)} instead of {num_levels}."
         )
 
         self.num_levels = num_levels