diff --git a/timm/models/gen_efficientnet.py b/timm/models/gen_efficientnet.py
index 9460e9af..418ca5d7 100644
--- a/timm/models/gen_efficientnet.py
+++ b/timm/models/gen_efficientnet.py
@@ -138,6 +138,8 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'),
     'mixnet_l': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth'),
+    'mixnet_xl': _cfg(),
+    'mixnet_xxl': _cfg(),
     'tf_mixnet_s': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
     'tf_mixnet_m': _cfg(
@@ -312,21 +314,59 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
     else:
         assert False, 'Unknown block type (%s)' % block_type
 
-    # return a list of block args expanded by num_repeat and
-    # scaled by depth_multiplier
-    num_repeat = int(math.ceil(num_repeat * depth_multiplier))
-    return [deepcopy(block_args) for _ in range(num_repeat)]
+    return block_args, num_repeat
 
 
-def _decode_arch_def(arch_def, depth_multiplier=1.0):
+def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='ceil'):
+    """ Per-stage depth scaling
+    Scales the block repeats in each stage. This depth scaling impl maintains
+    compatibility with the EfficientNet scaling method, while allowing sensible
+    scaling for other models that may have multiple block arg definitions in each stage.
+    """
+
+    # We scale the total repeat count for each stage, there may be multiple
+    # block arg defs per stage so we need to sum.
+    num_repeat = sum(repeats)
+    if depth_trunc == 'round':
+        # Truncating to int by rounding allows stages with few repeats to remain
+        # proportionally smaller for longer. This is a good choice when stage definitions
+        # include single repeat stages that we'd prefer to keep that way as long as possible
+        num_repeat_scaled = max(1, round(num_repeat * depth_multiplier))
+    else:
+        # The default for EfficientNet truncates repeats to int via 'ceil'.
+        # Any multiplier > 1.0 will result in an increased depth for every stage.
+        num_repeat_scaled = int(math.ceil(num_repeat * depth_multiplier))
+
+    # Proportionally distribute repeat count scaling to each block definition in the stage.
+    # Allocation is done in reverse as it results in the first block being less likely to be scaled.
+    # The first block makes less sense to repeat in most of the arch definitions.
+    repeats_scaled = []
+    for r in repeats[::-1]:
+        rs = max(1, round((r / num_repeat * num_repeat_scaled)))
+        repeats_scaled.append(rs)
+        num_repeat -= r
+        num_repeat_scaled -= rs
+    repeats_scaled = repeats_scaled[::-1]
+
+    # Apply the calculated scaling to each block arg in the stage
+    sa_scaled = []
+    for ba, rep in zip(stack_args, repeats_scaled):
+        sa_scaled.extend([deepcopy(ba) for _ in range(rep)])
+    return sa_scaled
+
+
+def _decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil'):
     arch_args = []
     for stack_idx, block_strings in enumerate(arch_def):
         assert isinstance(block_strings, list)
         stack_args = []
+        repeats = []
         for block_str in block_strings:
             assert isinstance(block_str, str)
-            stack_args.extend(_decode_block_str(block_str, depth_multiplier))
-        arch_args.append(stack_args)
+            ba, rep = _decode_block_str(block_str)
+            stack_args.append(ba)
+            repeats.append(rep)
+        arch_args.append(_scale_stage_depth(stack_args, repeats, depth_multiplier, depth_trunc))
     return arch_args
 
 
@@ -1261,7 +1301,7 @@ def _gen_mixnet_s(channel_multiplier=1.0, num_classes=1000, **kwargs):
     return model
 
 
-def _gen_mixnet_m(channel_multiplier=1.0, num_classes=1000, **kwargs):
+def _gen_mixnet_m(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=1000, **kwargs):
     """Creates a MixNet Medium-Large model.
 
     Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
@@ -1283,7 +1323,7 @@ def _gen_mixnet_m(channel_multiplier=1.0, num_classes=1000, **kwargs):
         # 7x7
     ]
     model = GenEfficientNet(
-        _decode_arch_def(arch_def),
+        _decode_arch_def(arch_def, depth_multiplier=depth_multiplier, depth_trunc='round'),
         num_classes=num_classes,
         stem_size=24,
         num_features=1536,
@@ -1876,6 +1916,33 @@ def mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     return model
 
 
+@register_model
+def mixnet_xl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Extra-Large model.
+    """
+    default_cfg = default_cfgs['mixnet_xl']
+    #kwargs['drop_connect_rate'] = 0.2
+    model = _gen_mixnet_m(
+        channel_multiplier=1.6, depth_multiplier=1.2, num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def mixnet_xxl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """Creates a MixNet Double Extra Large model.
+    """
+    default_cfg = default_cfgs['mixnet_xxl']
+    model = _gen_mixnet_m(
+        channel_multiplier=2.4, depth_multiplier=1.3, num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
 @register_model
 def tf_mixnet_s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Creates a MixNet Small model. Tensorflow compatible variant