diff --git a/numalogic/models/vae/variants/conv.py b/numalogic/models/vae/variants/conv.py
index 6aa99cfd..aebb1d70 100644
--- a/numalogic/models/vae/variants/conv.py
+++ b/numalogic/models/vae/variants/conv.py
@@ -1,4 +1,5 @@
 from collections.abc import Sequence, Callable
+from typing import Final
 
 import torch
 from torch import nn, Tensor, optim
@@ -6,6 +7,12 @@
 import torch.nn.functional as F
 import pytorch_lightning as pl
 
+from numalogic.tools.exceptions import ModelInitializationError
+
+
+_DEFAULT_KERNEL_SIZE: Final[int] = 3
+_DEFAULT_STRIDE: Final[int] = 2
+
 
 class CausalConv1d(nn.Conv1d):
     """Temporal convolutional layer with causal padding."""
@@ -96,8 +103,8 @@ def __init__(
         conv_layer = CausalConvBlock(
             in_channels=n_features,
             out_channels=conv_channels[0],
-            kernel_size=3,
-            stride=2,
+            kernel_size=_DEFAULT_KERNEL_SIZE,
+            stride=_DEFAULT_STRIDE,
             dilation=1,
         )
         layers = self._construct_conv_layers(conv_channels)
@@ -114,18 +121,18 @@ def __init__(
     @staticmethod
     def _construct_conv_layers(conv_channels) -> nn.ModuleList:
         layers = nn.ModuleList()
-        idx = 0
-        while idx < len(conv_channels) - 1:
+        layer_idx = 1
+        while layer_idx < len(conv_channels):
             layers.append(
                 CausalConvBlock(
-                    conv_channels[idx],
-                    conv_channels[idx + 1],
-                    kernel_size=3,
-                    stride=2,
-                    dilation=2,
+                    conv_channels[layer_idx - 1],
+                    conv_channels[layer_idx],
+                    kernel_size=_DEFAULT_KERNEL_SIZE,
+                    stride=_DEFAULT_STRIDE,
+                    dilation=2**layer_idx,
                 )
             )
-            idx += 1
+            layer_idx += 1
         return layers
 
     def forward(self, x: Tensor) -> tuple[Tensor, Tensor]:
@@ -156,10 +163,10 @@ def __init__(self, seq_len: int, n_features: int, num_conv_filters: int, latent_
         self.fc = nn.Linear(latent_dim, num_conv_filters * 6)
         self.unflatten = nn.Unflatten(dim=1, unflattened_size=(num_conv_filters, 6))
         self.conv_tr = nn.ConvTranspose1d(
-            in_channels=16,
+            in_channels=num_conv_filters,
             out_channels=n_features,
-            kernel_size=3,
-            stride=2,
+            kernel_size=_DEFAULT_KERNEL_SIZE,
+            stride=_DEFAULT_STRIDE,
             padding=1,
             output_padding=1,
         )
@@ -249,6 +256,15 @@ def __init__(
             latent_dim=latent_dim,
         )
 
+        # Do a dry run to initialize lazy modules
+        try:
+            self.forward(torch.rand(1, seq_len, n_features))
+        except (ValueError, RuntimeError) as err:
+            raise ModelInitializationError(
+                "Model forward pass failed. "
+                "Please validate input arguments and the expected input shape "
+            ) from err
+
     def forward(self, x: Tensor) -> tuple[MultivariateNormal, Tensor]:
         x = self.configure_shape(x)
         z_mu, z_logvar = self.encoder(x)