diff --git a/tests/test_models.py b/tests/test_models.py
index fe4fcd9a..a11ab7cf 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -17,7 +17,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
 # transformer models don't support many of the spatial / feature based model functionalities
 NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
-    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*']
+    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures
diff --git a/timm/models/__init__.py b/timm/models/__init__.py
index 5a48e325..56c812d4 100644
--- a/timm/models/__init__.py
+++ b/timm/models/__init__.py
@@ -43,6 +43,7 @@ from .vision_transformer_hybrid import *
 from .vovnet import *
 from .xception import *
 from .xception_aligned import *
+from .xcit import *
 from .twins import *
 
 from .factory import create_model, split_model_name, safe_model_name
diff --git a/timm/models/xcit.py b/timm/models/xcit.py
new file mode 100644
index 00000000..b7af3b26
--- /dev/null
+++ b/timm/models/xcit.py
@@ -0,0 +1,810 @@
+""" Cross-Covariance Image Transformer (XCiT) in PyTorch
+
+Same as the official implementation, with some minor adaptations.
+    - https://github.com/facebookresearch/xcit/blob/master/xcit.py
+
+Paper:
+    - https://arxiv.org/abs/2106.09681
+"""
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .helpers import build_model_with_cfg
+from .vision_transformer import _cfg, Mlp
+from .registry import register_model
+from .layers import DropPath, trunc_normal_, to_2tuple
+from .cait import ClassAttn
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': 1.0, 'interpolation': 'bicubic', 'fixed_input_size': True,
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj.0.0', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # Patch size 16
+    'xcit_nano_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_224.pth'),  
+    'xcit_nano_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_224_dist.pth'),
+    'xcit_nano_12_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_tiny_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_224.pth'),
+    'xcit_tiny_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_224_dist.pth'),
+    'xcit_tiny_12_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_tiny_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_224.pth'),
+    'xcit_tiny_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_224_dist.pth'),
+    'xcit_tiny_24_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_small_12_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_224.pth'),
+    'xcit_small_12_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_224_dist.pth'),
+    'xcit_small_12_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_small_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_224.pth'),
+    'xcit_small_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_224_dist.pth'),
+    'xcit_small_24_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_medium_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_224.pth'),
+    'xcit_medium_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_224_dist.pth'),
+    'xcit_medium_24_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p16_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_large_24_p16_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_224.pth'),
+    'xcit_large_24_p16_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_224_dist.pth'),
+    'xcit_large_24_p16_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p16_384_dist.pth', input_size=(3, 384, 384)),
+
+    # Patch size 8
+    'xcit_nano_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_224.pth'),  
+    'xcit_nano_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_224_dist.pth'),
+    'xcit_nano_12_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_nano_12_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_tiny_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_224.pth'),
+    'xcit_tiny_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_224_dist.pth'),
+    'xcit_tiny_12_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_12_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_tiny_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_224.pth'),
+    'xcit_tiny_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_224_dist.pth'),
+    'xcit_tiny_24_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_tiny_24_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_small_12_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_224.pth'),
+    'xcit_small_12_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_224_dist.pth'),
+    'xcit_small_12_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_small_12_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_small_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_224.pth'),
+    'xcit_small_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_224_dist.pth'),
+    'xcit_small_24_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_small_24_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_medium_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_224.pth'),
+    'xcit_medium_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_224_dist.pth'),
+    'xcit_medium_24_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_medium_24_p8_384_dist.pth', input_size=(3, 384, 384)),
+    'xcit_large_24_p8_224': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_224.pth'),
+    'xcit_large_24_p8_224_dist': _cfg(url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_224_dist.pth'),
+    'xcit_large_24_p8_384_dist': _cfg(
+        url='https://dl.fbaipublicfiles.com/xcit/xcit_large_24_p8_384_dist.pth', input_size=(3, 384, 384)),
+}
+
+
+class PositionalEncodingFourier(nn.Module):
+    """
+    Positional encoding relying on a fourier kernel matching the one used in the "Attention is all of Need" paper.
+    Based on the official XCiT code
+        - https://github.com/facebookresearch/xcit/blob/master/xcit.py
+    """
+
+    def __init__(self, hidden_dim=32, dim=768, temperature=10000):
+        super().__init__()
+        self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
+        self.scale = 2 * math.pi
+        self.temperature = temperature
+        self.hidden_dim = hidden_dim
+        self.dim = dim
+        self.eps = 1e-6
+
+    def forward(self, B: int, H: int, W: int):
+        device = self.token_projection.weight.device
+        y_embed = torch.arange(1, H+1, dtype=torch.float32, device=device).unsqueeze(1).repeat(1, 1, W)
+        x_embed = torch.arange(1, W+1, dtype=torch.float32, device=device).repeat(1, H, 1)
+        y_embed = y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
+        x_embed = x_embed / (x_embed[:, :, -1:] + self.eps) * self.scale
+        dim_t = torch.arange(self.hidden_dim, dtype=torch.float32, device=device)
+        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode='floor') / self.hidden_dim)
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack([pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()], dim=4).flatten(3)
+        pos_y = torch.stack([pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()], dim=4).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        pos = self.token_projection(pos)
+        return pos.repeat(B, 1, 1, 1)  # (B, C, H, W)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution + batch norm"""
+    return torch.nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
+        nn.BatchNorm2d(out_planes)
+    )
+
+
+class ConvPatchEmbed(nn.Module):
+    """Image to Patch Embedding using multiple convolutional layers"""
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, act_layer=nn.GELU):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        num_patches = (img_size[1] // patch_size) * (img_size[0] // patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        if patch_size == 16:
+            self.proj = torch.nn.Sequential(
+                conv3x3(in_chans, embed_dim // 8, 2),
+                act_layer(),
+                conv3x3(embed_dim // 8, embed_dim // 4, 2),
+                act_layer(),
+                conv3x3(embed_dim // 4, embed_dim // 2, 2),
+                act_layer(),
+                conv3x3(embed_dim // 2, embed_dim, 2),
+            )
+        elif patch_size == 8:
+            self.proj = torch.nn.Sequential(
+                conv3x3(in_chans, embed_dim // 4, 2),
+                act_layer(),
+                conv3x3(embed_dim // 4, embed_dim // 2, 2),
+                act_layer(),
+                conv3x3(embed_dim // 2, embed_dim, 2),
+            )
+        else:
+            raise('For convolutional projection, patch size has to be in [8, 16]')
+
+    def forward(self, x):
+        x = self.proj(x)
+        Hp, Wp = x.shape[2], x.shape[3]
+        x = x.flatten(2).transpose(1, 2)  # (B, N, C)
+        return x, (Hp, Wp)
+
+
+class LPI(nn.Module):
+    """
+    Local Patch Interaction module that allows explicit communication between tokens in 3x3 windows to augment the
+    implicit communication performed by the block diagonal scatter attention. Implemented using 2 layers of separable
+    3x3 convolutions with GeLU and BatchNorm2d
+    """
+
+    def __init__(self, in_features, out_features=None, act_layer=nn.GELU, kernel_size=3):
+        super().__init__()
+        out_features = out_features or in_features
+
+        padding = kernel_size // 2
+
+        self.conv1 = torch.nn.Conv2d(
+            in_features, in_features, kernel_size=kernel_size, padding=padding, groups=in_features)
+        self.act = act_layer()
+        self.bn = nn.BatchNorm2d(in_features)
+        self.conv2 = torch.nn.Conv2d(
+            in_features, out_features, kernel_size=kernel_size, padding=padding, groups=out_features)
+
+    def forward(self, x, H: int, W: int):
+        B, N, C = x.shape
+        x = x.permute(0, 2, 1).reshape(B, C, H, W)
+        x = self.conv1(x)
+        x = self.act(x)
+        x = self.bn(x)
+        x = self.conv2(x)
+        x = x.reshape(B, C, N).permute(0, 2, 1)
+        return x
+
+
+class ClassAttentionBlock(nn.Module):
+    """Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239"""
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, eta=1., tokens_norm=False):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+
+        self.attn = ClassAttn(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+
+        if eta is not None:  # LayerScale Initialization (no layerscale when None)
+            self.gamma1 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
+            self.gamma2 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
+        else:
+            self.gamma1, self.gamma2 = 1.0, 1.0
+
+        # See https://github.com/rwightman/pytorch-image-models/pull/747#issuecomment-877795721
+        self.tokens_norm = tokens_norm
+
+    def forward(self, x):
+        x_norm1 = self.norm1(x)
+        x_attn = torch.cat([self.attn(x_norm1), x_norm1[:, 1:]], dim=1)
+        x = x + self.drop_path(self.gamma1 * x_attn)
+        if self.tokens_norm:
+            x = self.norm2(x)
+        else:
+            x = torch.cat([self.norm2(x[:, 0:1]), x[:, 1:]], dim=1)
+        x_res = x
+        cls_token = x[:, 0:1]
+        cls_token = self.gamma2 * self.mlp(cls_token)
+        x = torch.cat([cls_token, x[:, 1:]], dim=1)
+        x = x_res + self.drop_path(x)
+        return x
+
+
+class XCA(nn.Module):
+    """ Cross-Covariance Attention (XCA)
+    Operation where the channels are updated using a weighted sum. The weights are obtained from the (softmax
+    normalized) Cross-covariance matrix (Q^T \\cdot K \\in d_h \\times d_h)
+    """
+
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        # Result of next line is (qkv, B, num (H)eads,  (C')hannels per head, N)
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 4, 1)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+        
+        # Paper section 3.2 l2-Normalization and temperature scaling
+        q = torch.nn.functional.normalize(q, dim=-1)
+        k = torch.nn.functional.normalize(k, dim=-1)
+        attn = (q @ k.transpose(-2, -1)) * self.temperature
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        # (B, H, C', N), permute -> (B, N, H, C')
+        x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'temperature'}
+
+
+class XCABlock(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, eta=1.):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = XCA(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm3 = norm_layer(dim)
+        self.local_mp = LPI(in_features=dim, act_layer=act_layer)
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+
+        self.gamma1 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
+        self.gamma3 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
+        self.gamma2 = nn.Parameter(eta * torch.ones(dim), requires_grad=True)
+
+    def forward(self, x, H: int, W: int):
+        x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x)))
+        # NOTE official code has 3 then 2, so keeping it the same to be consistent with loaded weights
+        # See https://github.com/rwightman/pytorch-image-models/pull/747#issuecomment-877795721
+        x = x + self.drop_path(self.gamma3 * self.local_mp(self.norm3(x), H, W))
+        x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class XCiT(nn.Module):
+    """
+    Based on timm and DeiT code bases
+    https://github.com/rwightman/pytorch-image-models/tree/master/timm
+    https://github.com/facebookresearch/deit/
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
+                 act_layer=None, norm_layer=None, cls_attn_layers=2, use_pos_embed=True, eta=1., tokens_norm=False):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int): patch size
+            in_chans (int): number of input channels
+            num_classes (int): number of classes for classification head
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            drop_rate (float): dropout rate after positional embedding, and in XCA/CA projection + MLP
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate (constant across all layers)
+            norm_layer: (nn.Module): normalization layer
+            cls_attn_layers: (int) Depth of Class attention layers
+            use_pos_embed: (bool) whether to use positional encoding
+            eta: (float) layerscale initialization value
+            tokens_norm: (bool) Whether to normalize all tokens or just the cls_token in the CA
+
+        Notes:
+            - Although `layer_norm` is user specifiable, there are hard-coded `BatchNorm2d`s in the local patch
+              interaction (class LPI) and the patch embedding (class ConvPatchEmbed)
+        """
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        assert (img_size[0] % patch_size == 0) and (img_size[0] % patch_size == 0), \
+            '`patch_size` should divide image dimensions evenly'
+
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+
+        self.patch_embed = ConvPatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, act_layer=act_layer)
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.use_pos_embed = use_pos_embed
+        if use_pos_embed:
+            self.pos_embed = PositionalEncodingFourier(dim=embed_dim)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        self.blocks = nn.ModuleList([
+            XCABlock(
+                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=drop_path_rate, act_layer=act_layer, norm_layer=norm_layer, eta=eta)
+            for _ in range(depth)])
+
+        self.cls_attn_blocks = nn.ModuleList([
+            ClassAttentionBlock(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
+                attn_drop=attn_drop_rate, act_layer=act_layer, norm_layer=norm_layer, eta=eta, tokens_norm=tokens_norm)
+            for _ in range(cls_attn_layers)])
+
+        # Classifier head
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        # Init weights
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        B = x.shape[0]
+        # x is (B, N, C). (Hp, Hw) is (height in units of patches, width in units of patches)
+        x, (Hp, Wp) = self.patch_embed(x)
+
+        if self.use_pos_embed:
+            # `pos_embed` (B, C, Hp, Wp), reshape -> (B, C, N), permute -> (B, N, C)
+            pos_encoding = self.pos_embed(B, Hp, Wp).reshape(B, -1, x.shape[1]).permute(0, 2, 1)
+            x = x + pos_encoding
+
+        x = self.pos_drop(x)
+
+        for blk in self.blocks:
+            x = blk(x, Hp, Wp)
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        for blk in self.cls_attn_blocks:
+            x = blk(x)
+
+        x = self.norm(x)[:, 0]
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+
+def checkpoint_filter_fn(state_dict, model):
+    if 'model' in state_dict:
+        state_dict = state_dict['model']
+    # For consistency with timm's transformer models while being compatible with official weights source we rename
+    # pos_embeder to pos_embed. Also account for use_pos_embed == False
+    use_pos_embed = getattr(model, 'pos_embed', None) is not None
+    pos_embed_keys = [k for k in state_dict if k.startswith('pos_embed')]
+    for k in pos_embed_keys:
+        if use_pos_embed:
+            state_dict[k.replace('pos_embeder.', 'pos_embed.')] = state_dict.pop(k)
+        else:
+            del state_dict[k]
+    # timm's implementation of class attention in CaiT is slightly more efficient as it does not compute query vectors
+    # for all tokens, just the class token. To use official weights source we must split qkv into q, k, v
+    if 'cls_attn_blocks.0.attn.qkv.weight' in state_dict and 'cls_attn_blocks.0.attn.q.weight' in model.state_dict():
+        num_ca_blocks = len(model.cls_attn_blocks)
+        for i in range(num_ca_blocks):
+            qkv_weight = state_dict.pop(f'cls_attn_blocks.{i}.attn.qkv.weight')
+            qkv_weight = qkv_weight.reshape(3, -1, qkv_weight.shape[-1])
+            for j, subscript in enumerate('qkv'):
+                state_dict[f'cls_attn_blocks.{i}.attn.{subscript}.weight'] = qkv_weight[j]
+            qkv_bias = state_dict.pop(f'cls_attn_blocks.{i}.attn.qkv.bias', None)
+            if qkv_bias is not None:
+                qkv_bias = qkv_bias.reshape(3, -1)
+                for j, subscript in enumerate('qkv'):
+                    state_dict[f'cls_attn_blocks.{i}.attn.{subscript}.bias'] = qkv_bias[j]
+    return state_dict
+
+
+def _create_xcit(variant, pretrained=False, default_cfg=None, **kwargs):
+    default_cfg = default_cfg or default_cfgs[variant]
+    model = build_model_with_cfg(
+        XCiT, variant, pretrained, default_cfg=default_cfg, pretrained_filter_fn=checkpoint_filter_fn, **kwargs)
+    return model
+
+
+@register_model
+def xcit_nano_12_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
+    model = _create_xcit('xcit_nano_12_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_nano_12_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
+    model = _create_xcit('xcit_nano_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_nano_12_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, img_size=384, **kwargs)
+    model = _create_xcit('xcit_nano_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p16_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p16_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p16_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p16_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p16_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+# Patch size 8x8 models
+@register_model
+def xcit_nano_12_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
+    model = _create_xcit('xcit_nano_12_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_nano_12_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
+    model = _create_xcit('xcit_nano_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_nano_12_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=128, depth=12, num_heads=4, eta=1.0, tokens_norm=False, **kwargs)
+    model = _create_xcit('xcit_nano_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_12_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=4, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_12_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=8, eta=1.0, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_12_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_tiny_24_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=24, num_heads=4, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_tiny_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_small_24_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_small_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_medium_24_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=512, depth=24, num_heads=8, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_medium_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p8_224(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p8_224_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p8_224_dist', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def xcit_large_24_p8_384_dist(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=8, embed_dim=768, depth=24, num_heads=16, eta=1e-5, tokens_norm=True, **kwargs)
+    model = _create_xcit('xcit_large_24_p8_384_dist', pretrained=pretrained, **model_kwargs)
+    return model