mirror of
https://github.com/facefusion/facefusion-labs.git
synced 2026-05-22 23:59:40 +02:00
henryruhs
122 lines
6.6 KiB
Python
122 lines
6.6 KiB
Python
import torch
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from torch import Tensor, nn
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from face_swapper.src.types import Embedding, TargetAttributes
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class AADGenerator(nn.Module):
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def __init__(self, id_channels : int, num_blocks : int) -> None:
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super(AADGenerator, self).__init__()
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self.upsample = PixelShuffleUpsample(id_channels, 1024 * 4)
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self.res_block_1 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks)
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self.res_block_2 = AADResBlock(1024, 1024, 2048, id_channels, num_blocks)
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self.res_block_3 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks)
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self.res_block_4 = AADResBlock(1024, 512, 512, id_channels, num_blocks)
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self.res_block_5 = AADResBlock(512, 256, 256, id_channels, num_blocks)
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self.res_block_6 = AADResBlock(256, 128, 128, id_channels, num_blocks)
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self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks)
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self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks)
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def forward(self, target_attributes : TargetAttributes, source_embedding : Embedding) -> Tensor:
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feature_map = self.upsample(source_embedding)
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feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_3 = nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_4 = nn.functional.interpolate(self.res_block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_5 = nn.functional.interpolate(self.res_block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_6 = nn.functional.interpolate(self.res_block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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feature_map_7 = nn.functional.interpolate(self.res_block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False)
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output = self.res_block_8(feature_map_7, target_attributes[7], source_embedding)
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return torch.tanh(output)
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class AADLayer(nn.Module):
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def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None:
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super(AADLayer, self).__init__()
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self.input_channels = input_channels
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self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1)
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self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1)
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self.fc_beta = nn.Linear(id_channels, input_channels)
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self.fc_gamma = nn.Linear(id_channels, input_channels)
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self.instance_norm = nn.InstanceNorm2d(input_channels)
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self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1)
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def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor:
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feature_map = self.instance_norm(feature_map)
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gamma_attribute = self.conv_gamma(attribute_embedding)
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beta_attribute = self.conv_beta(attribute_embedding)
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attribute_modulation = gamma_attribute * feature_map + beta_attribute
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id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map)
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id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map)
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id_modulation = id_gamma * feature_map + id_beta
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feature_mask = torch.sigmoid(self.conv_mask(feature_map))
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feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * id_modulation
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return feature_blend
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class AADSequential(nn.Module):
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def __init__(self, *args : nn.Module) -> None:
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super(AADSequential, self).__init__()
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self.layers = nn.ModuleList(args)
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def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor:
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for layer in self.layers:
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if isinstance(layer, AADLayer):
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feature_map = layer(feature_map, attribute_embedding, id_embedding)
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else:
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feature_map = layer(feature_map)
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return feature_map
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class AADResBlock(nn.Module):
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def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, id_channels : int, num_blocks : int) -> None:
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super(AADResBlock, self).__init__()
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self.input_channels = input_channels
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self.output_channels = output_channels
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self.prepare_primary_add_blocks(input_channels, attribute_channels, id_channels, output_channels, num_blocks)
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self.prepare_auxiliary_add_blocks(input_channels, attribute_channels, id_channels, output_channels)
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def prepare_primary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int, num_blocks : int) -> None:
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primary_add_blocks = []
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for index in range(num_blocks):
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intermediate_channels = input_channels if index < (num_blocks - 1) else output_channels
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primary_add_blocks.extend(
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[
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AADLayer(input_channels, attribute_channels, id_channels),
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nn.ReLU(inplace = True),
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nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False)
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]
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)
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self.primary_add_blocks = AADSequential(*primary_add_blocks)
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def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int) -> None:
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if input_channels > output_channels:
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auxiliary_add_blocks = AADSequential(
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AADLayer(input_channels, attribute_channels, id_channels),
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nn.ReLU(inplace = True),
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nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False)
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)
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self.auxiliary_add_blocks = auxiliary_add_blocks
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def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor:
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primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding)
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if self.input_channels > self.output_channels:
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feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, id_embedding)
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output_feature = primary_feature + feature_map
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return output_feature
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class PixelShuffleUpsample(nn.Module):
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def __init__(self, input_channels : int, output_channels : int) -> None:
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super(PixelShuffleUpsample, self).__init__()
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self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 3, padding = 1)
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self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2)
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def forward(self, input_tensor : Tensor) -> Tensor:
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temp_tensor = self.conv(input_tensor.view(input_tensor.shape[0], -1, 1, 1))
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temp_tensor = self.pixel_shuffle(temp_tensor)
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return temp_tensor
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