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2d7087a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | """Sage-T2I: Custom Diffusion Transformer for Text-to-Image generation."""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class TimestepEmbedder(nn.Module):
def __init__(self, hidden_size, freq_embed_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(freq_embed_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.freq_embed_size = freq_embed_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(half, dtype=torch.float32, device=t.device) / half)
args = t[:, None].float() * freqs[None]
return torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
def forward(self, t):
t_freq = self.timestep_embedding(t, self.freq_embed_size)
t_emb = self.mlp(t_freq)
return t_emb
class CaptionEmbedder(nn.Module):
def __init__(self, in_channels, hidden_size, act_layer=nn.SiLU):
super().__init__()
self.linear = nn.Linear(in_channels, hidden_size, bias=True)
self.act = act_layer()
def forward(self, x):
return self.act(self.linear(x))
class SelfAttention(nn.Module):
def __init__(self, hidden_size, num_heads):
super().__init__()
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=True)
self.proj = nn.Linear(hidden_size, hidden_size, bias=True)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class CrossAttention(nn.Module):
def __init__(self, hidden_size, context_dim, num_heads):
super().__init__()
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.q = nn.Linear(hidden_size, hidden_size, bias=True)
self.k = nn.Linear(context_dim, hidden_size, bias=True)
self.v = nn.Linear(context_dim, hidden_size, bias=True)
self.proj = nn.Linear(hidden_size, hidden_size, bias=True)
def forward(self, x, context):
B, N, C = x.shape
_, M, _ = context.shape
q = self.q(x).reshape(B, N, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k(context).reshape(B, M, self.num_heads, self.head_dim).transpose(1, 2)
v = self.v(context).reshape(B, M, self.num_heads, self.head_dim).transpose(1, 2)
attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class FeedForward(nn.Module):
def __init__(self, hidden_size, mlp_ratio=4.0):
super().__init__()
hidden = int(hidden_size * mlp_ratio)
self.fc1 = nn.Linear(hidden_size, hidden, bias=True)
self.fc2 = nn.Linear(hidden, hidden_size, bias=True)
self.act = nn.GELU(approximate="tanh")
def forward(self, x):
return self.fc2(self.act(self.fc1(x)))
class DiTBlock(nn.Module):
def __init__(self, hidden_size, num_heads, context_dim, mlp_ratio=4.0):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.self_attn = SelfAttention(hidden_size, num_heads)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.cross_attn = CrossAttention(hidden_size, context_dim, num_heads)
self.norm3 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.ff = FeedForward(hidden_size, mlp_ratio)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, context):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = \
self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.self_attn(modulate(self.norm1(x), shift_msa, scale_msa))
x = x + self.cross_attn(self.norm2(x), context)
x = x + gate_mlp.unsqueeze(1) * self.ff(modulate(self.norm3(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
def __init__(self, hidden_size, patch_size, in_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * in_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.in_channels = config.in_channels
self.hidden_size = config.hidden_size
self.patch_size = config.patch_size
self.num_heads = config.num_heads
self.x_embedder = nn.Linear(config.patch_size * config.patch_size * config.in_channels, config.hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(config.hidden_size)
self.c_embedder = CaptionEmbedder(config.context_dim, config.hidden_size)
latent_size = config.image_size // 8
num_patches = (latent_size // config.patch_size) ** 2
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, config.hidden_size), requires_grad=True)
self.blocks = nn.ModuleList([
DiTBlock(config.hidden_size, config.num_heads, config.context_dim, config.mlp_ratio)
for _ in range(config.num_layers)
])
self.final_layer = FinalLayer(config.hidden_size, config.patch_size, config.in_channels)
self.initialize_weights()
def initialize_weights(self):
nn.init.normal_(self.x_embedder.weight, std=0.02)
nn.init.normal_(self.pos_embed, std=0.02)
nn.init.normal_(self.c_embedder.linear.weight, std=0.02)
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def forward(self, x, t, context):
B, C, H, W = x.shape
x = x.reshape(B, C, H // self.patch_size, self.patch_size, W // self.patch_size, self.patch_size)
x = x.permute(0, 2, 4, 1, 3, 5).reshape(B, -1, C * self.patch_size * self.patch_size)
x = self.x_embedder(x) + self.pos_embed
t_emb = self.t_embedder(t)
c_emb = self.c_embedder(context).mean(dim=1)
c = t_emb + c_emb
for block in self.blocks:
x = block(x, c, context)
x = self.final_layer(x, c)
x = x.reshape(B, H // self.patch_size, W // self.patch_size, self.patch_size, self.patch_size, self.in_channels)
x = x.permute(0, 5, 1, 3, 2, 4).reshape(B, self.in_channels, H, W)
return x
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