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diff --git a/r_basicsr/archs/dfdnet_arch.py b/r_basicsr/archs/dfdnet_arch.py
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+++ b/r_basicsr/archs/dfdnet_arch.py
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+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils.spectral_norm import spectral_norm
+
+from r_basicsr.utils.registry import ARCH_REGISTRY
+from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization
+from .vgg_arch import VGGFeatureExtractor
+
+
+class SFTUpBlock(nn.Module):
+    """Spatial feature transform (SFT) with upsampling block.
+
+    Args:
+        in_channel (int): Number of input channels.
+        out_channel (int): Number of output channels.
+        kernel_size (int): Kernel size in convolutions. Default: 3.
+        padding (int): Padding in convolutions. Default: 1.
+    """
+
+    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1):
+        super(SFTUpBlock, self).__init__()
+        self.conv1 = nn.Sequential(
+            Blur(in_channel),
+            spectral_norm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.04, True),
+            # The official codes use two LeakyReLU here, so 0.04 for equivalent
+        )
+        self.convup = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        # for SFT scale and shift
+        self.scale_block = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)))
+        self.shift_block = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid())
+        # The official codes use sigmoid for shift block, do not know why
+
+    def forward(self, x, updated_feat):
+        out = self.conv1(x)
+        # SFT
+        scale = self.scale_block(updated_feat)
+        shift = self.shift_block(updated_feat)
+        out = out * scale + shift
+        # upsample
+        out = self.convup(out)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class DFDNet(nn.Module):
+    """DFDNet: Deep Face Dictionary Network.
+
+    It only processes faces with 512x512 size.
+
+    Args:
+        num_feat (int): Number of feature channels.
+        dict_path (str): Path to the facial component dictionary.
+    """
+
+    def __init__(self, num_feat, dict_path):
+        super().__init__()
+        self.parts = ['left_eye', 'right_eye', 'nose', 'mouth']
+        # part_sizes: [80, 80, 50, 110]
+        channel_sizes = [128, 256, 512, 512]
+        self.feature_sizes = np.array([256, 128, 64, 32])
+        self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4']
+        self.flag_dict_device = False
+
+        # dict
+        self.dict = torch.load(dict_path)
+
+        # vgg face extractor
+        self.vgg_extractor = VGGFeatureExtractor(
+            layer_name_list=self.vgg_layers,
+            vgg_type='vgg19',
+            use_input_norm=True,
+            range_norm=True,
+            requires_grad=False)
+
+        # attention block for fusing dictionary features and input features
+        self.attn_blocks = nn.ModuleDict()
+        for idx, feat_size in enumerate(self.feature_sizes):
+            for name in self.parts:
+                self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx])
+
+        # multi scale dilation block
+        self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1])
+
+        # upsampling and reconstruction
+        self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8)
+        self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4)
+        self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2)
+        self.upsample3 = SFTUpBlock(num_feat * 2, num_feat)
+        self.upsample4 = nn.Sequential(
+            spectral_norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat),
+            UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh())
+
+    def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size):
+        """swap the features from the dictionary."""
+        # get the original vgg features
+        part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone()
+        # resize original vgg features
+        part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False)
+        # use adaptive instance normalization to adjust color and illuminations
+        dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat)
+        # get similarity scores
+        similarity_score = F.conv2d(part_resize_feat, dict_feat)
+        similarity_score = F.softmax(similarity_score.view(-1), dim=0)
+        # select the most similar features in the dict (after norm)
+        select_idx = torch.argmax(similarity_score)
+        swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4])
+        # attention
+        attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat)
+        attn_feat = attn * swap_feat
+        # update features
+        updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat
+        return updated_feat
+
+    def put_dict_to_device(self, x):
+        if self.flag_dict_device is False:
+            for k, v in self.dict.items():
+                for kk, vv in v.items():
+                    self.dict[k][kk] = vv.to(x)
+            self.flag_dict_device = True
+
+    def forward(self, x, part_locations):
+        """
+        Now only support testing with batch size = 0.
+
+        Args:
+            x (Tensor): Input faces with shape (b, c, 512, 512).
+            part_locations (list[Tensor]): Part locations.
+        """
+        self.put_dict_to_device(x)
+        # extract vggface features
+        vgg_features = self.vgg_extractor(x)
+        # update vggface features using the dictionary for each part
+        updated_vgg_features = []
+        batch = 0  # only supports testing with batch size = 0
+        for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes):
+            dict_features = self.dict[f'{f_size}']
+            vgg_feat = vgg_features[vgg_layer]
+            updated_feat = vgg_feat.clone()
+
+            # swap features from dictionary
+            for part_idx, part_name in enumerate(self.parts):
+                location = (part_locations[part_idx][batch] // (512 / f_size)).int()
+                updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name,
+                                              f_size)
+
+            updated_vgg_features.append(updated_feat)
+
+        vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4'])
+        # use updated vgg features to modulate the upsampled features with
+        # SFT (Spatial Feature Transform) scaling and shifting manner.
+        upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3])
+        upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2])
+        upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1])
+        upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0])
+        out = self.upsample4(upsampled_feat)
+
+        return out