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ad06aed b7c5eaf ad06aed b7c5eaf ad06aed b7c5eaf ad06aed b7c5eaf ad06aed b7c5eaf ad06aed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | # Copyright (c) 2023, Zexin He
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
class BasicTransformerBlock(nn.Module):
"""
Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
"""
# use attention from torch.nn.MultiHeadAttention
# Block contains a cross-attention layer, a self-attention layer, and a MLP
def __init__(
self,
inner_dim: int,
cond_dim: int,
num_heads: int,
eps: float,
attn_drop: float = 0.,
attn_bias: bool = False,
mlp_ratio: float = 4.,
mlp_drop: float = 0.,
):
super().__init__()
self.norm1 = nn.LayerNorm(inner_dim)
self.cross_attn = nn.MultiheadAttention(
embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
dropout=attn_drop, bias=attn_bias, batch_first=True)
self.norm2 = nn.LayerNorm(inner_dim)
self.self_attn = nn.MultiheadAttention(
embed_dim=inner_dim, num_heads=num_heads,
dropout=attn_drop, bias=attn_bias, batch_first=True)
self.norm3 = nn.LayerNorm(inner_dim)
self.mlp = nn.Sequential(
nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
nn.GELU(),
nn.Dropout(mlp_drop),
nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
nn.Dropout(mlp_drop),
)
def forward(self, x, cond, i, alpha, content_layers):
# x: [N, L, D] or [x1, x2]
# cond: [content_feats] or [content_feats, style_feats]
if len(cond) == 2:
# Style injection mode
x1, x2 = x[0], x[1]
content, style = cond[0], cond[1]
if i <= content_layers:
x1 = x1 + self.cross_attn(self.norm1(x1), content, content)[0]
else:
x1 = x1 + (1-alpha)*self.cross_attn(self.norm1(x1), content, content)[0] + (alpha)*self.cross_attn(self.norm1(x1), style, style)[0]
x2 = x2 + self.cross_attn(self.norm1(x2), style, style)[0]
before_sa1 = self.norm2(x1)
before_sa2 = self.norm2(x2)
x1 = x1 + self.self_attn(before_sa1, before_sa1, before_sa1)[0]
x2 = x2 + self.self_attn(before_sa2, before_sa2, before_sa2)[0]
x1 = x1 + self.mlp(self.norm3(x1))
x2 = x2 + self.mlp(self.norm3(x2))
return [x1, x2]
else:
# No style, only content
x1 = x[0] if isinstance(x, list) else x
content = cond[0]
x1 = x1 + self.cross_attn(self.norm1(x1), content, content)[0]
before_sa1 = self.norm2(x1)
x1 = x1 + self.self_attn(before_sa1, before_sa1, before_sa1)[0]
x1 = x1 + self.mlp(self.norm3(x1))
return [x1]
class TriplaneTransformer(nn.Module):
"""
Transformer with condition that generates a triplane representation.
Reference:
Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486
"""
def __init__(
self,
inner_dim: int,
image_feat_dim: int,
triplane_low_res: int,
triplane_high_res: int,
triplane_dim: int,
num_layers: int,
num_heads: int,
eps: float = 1e-6,
):
super().__init__()
# attributes
self.triplane_low_res = triplane_low_res
self.triplane_high_res = triplane_high_res
self.triplane_dim = triplane_dim
# modules
# initialize pos_embed with 1/sqrt(dim) * N(0, 1)
self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5)
self.layers = nn.ModuleList([
BasicTransformerBlock(
inner_dim=inner_dim, cond_dim=image_feat_dim, num_heads=num_heads, eps=eps)
for _ in range(num_layers)
])
self.norm = nn.LayerNorm(inner_dim, eps=eps)
self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0)
self.num_layers = num_layers
def forward(self, image_feats, alpha, style_layers):
# image_feats: [content_feats] or [content_feats, style_feats]
N = image_feats[0].shape[0]
H = W = self.triplane_low_res
L = 3 * H * W
content_layers = self.num_layers - style_layers
x = self.pos_embed.repeat(N, 1, 1) # [N, L, D]
i = 1
if len(image_feats) == 2:
# Style injection mode
for layer in self.layers:
if i == 1:
x = layer([x, x], image_feats, i, alpha, content_layers)
else:
x = layer(x, image_feats, i, alpha, content_layers)
i += 1
x = self.norm(x[0])
else:
# No style, only content
for layer in self.layers:
if i == 1:
x = layer([x], image_feats, i, alpha, content_layers)
else:
x = layer(x, image_feats, i, alpha, content_layers)
i += 1
x = self.norm(x[0])
# separate each plane and apply deconv
x = x.view(N, 3, H, W, -1)
x = torch.einsum('nihwd->indhw', x) # [3, N, D, H, W]
x = x.contiguous().view(3*N, -1, H, W) # [3*N, D, H, W]
x = self.deconv(x) # [3*N, D', H', W']
x = x.view(3, N, *x.shape[-3:]) # [3, N, D', H', W']
x = torch.einsum('indhw->nidhw', x) # [N, 3, D', H', W']
x = x.contiguous()
return x
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