diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 79778ab1..17faba53 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -170,6 +170,11 @@ default_cfgs = {
             '/vit_base_patch16_224_1k_miil_84_4.pth',
         mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
     ),
+
+    # experimental
+    'vit_small_patch16_36x1_224': _cfg(url=''),
+    'vit_small_patch16_18x2_224': _cfg(url=''),
+    'vit_base_patch16_18x2_224': _cfg(url=''),
 }
 
 
@@ -201,28 +206,81 @@ class Attention(nn.Module):
         return x
 
 
+class LayerScale(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
 class Block(nn.Module):
 
     def __init__(
-            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
             drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
         super().__init__()
         self.norm1 = norm_layer(dim)
         self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
         # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
     def forward(self, x):
-        x = x + self.drop_path1(self.attn(self.norm1(x)))
-        x = x + self.drop_path2(self.mlp(self.norm2(x)))
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
         return x
 
 
+class ParallelBlock(nn.Module):
+
+    def __init__(
+            self, dim, num_heads, num_parallel=2, mlp_ratio=4., qkv_bias=False, init_values=None,
+            drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.num_parallel = num_parallel
+        self.attns = nn.ModuleList()
+        self.ffns = nn.ModuleList()
+        for _ in range(num_parallel):
+            self.attns.append(nn.Sequential(OrderedDict([
+                ('norm', norm_layer(dim)),
+                ('attn', Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)),
+                ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
+                ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
+            ])))
+            self.ffns.append(nn.Sequential(OrderedDict([
+                ('norm', norm_layer(dim)),
+                ('mlp', Mlp(dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)),
+                ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
+                ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
+            ])))
+
+    def _forward_jit(self, x):
+        x = x + torch.stack([attn(x) for attn in self.attns]).sum(dim=0)
+        x = x + torch.stack([ffn(x) for ffn in self.ffns]).sum(dim=0)
+        return x
+
+    @torch.jit.ignore
+    def _forward(self, x):
+        x = x + sum(attn(x) for attn in self.attns)
+        x = x + sum(ffn(x) for ffn in self.ffns)
+        return x
+
+    def forward(self, x):
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            return self._forward_jit(x)
+        else:
+            return self._forward(x)
+
+
 class VisionTransformer(nn.Module):
     """ Vision Transformer
 
@@ -233,8 +291,8 @@ class VisionTransformer(nn.Module):
     def __init__(
             self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None,
-            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='',
-            embed_layer=PatchEmbed, norm_layer=None, act_layer=None):
+            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', init_values=None,
+            embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
         """
         Args:
             img_size (int, tuple): input image size
@@ -248,10 +306,11 @@ class VisionTransformer(nn.Module):
             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
             qkv_bias (bool): enable bias for qkv if True
             representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            weight_init: (str): weight init scheme
             drop_rate (float): dropout rate
             attn_drop_rate (float): attention dropout rate
             drop_path_rate (float): stochastic depth rate
+            weight_init: (str): weight init scheme
+            init_values: (float): layer-scale init values
             embed_layer (nn.Module): patch embedding layer
             norm_layer: (nn.Module): normalization layer
             act_layer: (nn.Module): MLP activation layer
@@ -277,9 +336,9 @@ class VisionTransformer(nn.Module):
 
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
         self.blocks = nn.Sequential(*[
-            Block(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
-                attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
+            block_fn(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, init_values=init_values,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
             for i in range(depth)])
         use_fc_norm = self.global_pool == 'avg'
         self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
@@ -941,3 +1000,37 @@ def vit_base_patch16_224_miil(pretrained=False, **kwargs):
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
     model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
     return model
+
+
+@register_model
+def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
+    """ ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
+    Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
+    Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5, **kwargs)
+    model = _create_vision_transformer('vit_small_patch16_36x1_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
+    """ ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
+    Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
+    Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
+    model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
+    """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
+    Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
+    return model