diff --git a/magvit2_pytorch/magvit2_pytorch.py b/magvit2_pytorch/magvit2_pytorch.py
index 33e74b7..5df0938 100644
--- a/magvit2_pytorch/magvit2_pytorch.py
+++ b/magvit2_pytorch/magvit2_pytorch.py
@@ -910,6 +910,8 @@ def __init__(
         self.conv_out = CausalConv3d(init_dim, channels, output_conv_kernel_size, pad_mode = pad_mode)
 
         dim = init_dim
+        dim_out = dim
+
         time_downsample_factor = 1
         has_cond = False
 
@@ -919,7 +921,11 @@ def __init__(
             if layer_type == 'residual':
                 encoder_layer = ResidualUnit(dim, residual_conv_kernel_size)
                 decoder_layer = ResidualUnit(dim, residual_conv_kernel_size)
-                dim_out = dim
+
+            elif layer_type == 'consecutive_residual':
+                num_consecutive, = layer_params
+                encoder_layer = Sequential(*[ResidualUnit(dim, residual_conv_kernel_size) for _ in range(num_consecutive)])
+                decoder_layer = Sequential(*[ResidualUnit(dim, residual_conv_kernel_size) for _ in range(num_consecutive)])
 
             elif layer_type == 'cond_residual':
                 assert exists(dim_cond), 'dim_cond must be passed into VideoTokenizer, if tokenizer is to be conditioned'
@@ -928,6 +934,7 @@ def __init__(
 
                 encoder_layer = ResidualUnitMod(dim, residual_conv_kernel_size, dim_cond = int(dim_cond * dim_cond_expansion_factor))
                 decoder_layer = ResidualUnitMod(dim, residual_conv_kernel_size, dim_cond = int(dim_cond * dim_cond_expansion_factor))
+                dim_out = dim
 
             elif layer_type == 'compress_space':
                 dim_out, = layer_params
diff --git a/magvit2_pytorch/version.py b/magvit2_pytorch/version.py
index abebd78..369521b 100644
--- a/magvit2_pytorch/version.py
+++ b/magvit2_pytorch/version.py
@@ -1 +1 @@
-__version__ = '0.0.44'
+__version__ = '0.0.45'