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1bf3c29 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 | import numpy as np
import torch
import torch.nn as nn
from typing import Optional
from .modules import (
ClassEmbedder,
FinalLayer,
FeedForward,
MLP,
RMSNorm,
RotaryAttention,
TimestepConditioner,
apply_adaln,
get_2d_sincos_pos_embed,
get_2d_sincos_pos_embed_from_grid,
precompute_freqs_cis_2d,
)
class PatchTokenEmbedder(nn.Module):
def __init__(
self,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer = None,
bias: bool = True,
):
super().__init__()
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Linear(in_chans, embed_dim, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class AugmentedDiTBlock(nn.Module):
def __init__(self, hidden_size, groups, mlp_ratio=4.0, adaLN_modulation=None):
super().__init__()
self.norm1 = RMSNorm(hidden_size, eps=1e-6)
self.attn = RotaryAttention(hidden_size, num_heads=groups, qkv_bias=False)
self.norm2 = RMSNorm(hidden_size, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.mlp = FeedForward(hidden_size, mlp_hidden_dim)
self.adaLN_modulation = adaLN_modulation if adaLN_modulation is not None else nn.Sequential(
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, pos, mask=None):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
x = x + gate_msa * self.attn(apply_adaln(self.norm1(x), shift_msa, scale_msa), pos, mask=mask)
x = x + gate_mlp * self.mlp(apply_adaln(self.norm2(x), shift_mlp, scale_mlp))
return x
class PixelTokenEmbedder(nn.Module):
def __init__(self, in_channels: int, hidden_size_output: int, use_pixel_abs_pos: bool = True):
super().__init__()
self.in_channels = int(in_channels)
self.hidden_size_output = int(hidden_size_output)
self.use_pixel_abs_pos = bool(use_pixel_abs_pos)
self.proj = nn.Linear(self.in_channels, self.hidden_size_output, bias=True)
self._pos_cache = dict()
def _fetch_pixel_pos_image(self, height: int, width: int, device, dtype):
if height == width:
key = ("image", height, width)
if key in self._pos_cache:
pe = self._pos_cache[key]
return pe.to(device=device, dtype=dtype)
pos_np = get_2d_sincos_pos_embed(self.hidden_size_output, height)
pos = torch.from_numpy(pos_np).to(device=device, dtype=dtype)
self._pos_cache[key] = pos
return pos
else:
key = ("image", height, width)
if key in self._pos_cache:
pe = self._pos_cache[key]
return pe.to(device=device, dtype=dtype)
grid_h = np.arange(height, dtype=np.float32)
grid_w = np.arange(width, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h)
grid = np.stack(grid, axis=0).reshape(2, 1, height, width)
pos_np = get_2d_sincos_pos_embed_from_grid(self.hidden_size_output, grid)
pos = torch.from_numpy(pos_np).to(device=device, dtype=dtype)
self._pos_cache[key] = pos
return pos
def forward(self, inputs: torch.Tensor, img_height: int = None, img_width: int = None, patch_size: int = None):
if inputs.dim() != 4:
raise ValueError("PixelTokenEmbedder expects inputs of shape [B,C,H,W]")
assert img_height is not None and img_width is not None and patch_size is not None
B, C, H, W = inputs.shape
assert H == img_height and W == img_width
assert (H % patch_size == 0) and (W % patch_size == 0)
Hs, Ws = H // patch_size, W // patch_size
P2 = patch_size * patch_size
x = inputs.permute(0, 2, 3, 1).contiguous()
x = self.proj(x)
if self.use_pixel_abs_pos:
pos_full = self._fetch_pixel_pos_image(H, W, inputs.device, inputs.dtype)
pos_full = pos_full.view(H, W, self.hidden_size_output)
x = x + pos_full.unsqueeze(0)
x = x.view(B, Hs, patch_size, Ws, patch_size, self.hidden_size_output)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
x = x.view(B * Hs * Ws, P2, self.hidden_size_output)
return x
class PiTBlock(nn.Module):
def __init__(
self,
pixel_hidden_size: int,
patch_hidden_size: int,
patch_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
attn_hidden_size: Optional[int] = None,
attn_num_heads: Optional[int] = None,
rope_fn=None,
):
super().__init__()
self.pixel_dim = int(pixel_hidden_size)
self.context_dim = int(patch_hidden_size)
self.patch_size = int(patch_size)
self.attn_dim = int(attn_hidden_size) if attn_hidden_size is not None else self.context_dim
self.num_heads = int(attn_num_heads) if attn_num_heads is not None else int(num_heads)
assert (
self.attn_dim % self.num_heads == 0
), "pixel attention hidden size must be divisible by pixel num_heads"
p2 = self.patch_size * self.patch_size
self.compress_to_attn = nn.Linear(p2 * self.pixel_dim, self.attn_dim, bias=True)
self.expand_from_attn = nn.Linear(self.attn_dim, p2 * self.pixel_dim, bias=True)
self.norm1 = RMSNorm(self.pixel_dim, eps=1e-6)
self.attn = RotaryAttention(self.attn_dim, num_heads=self.num_heads, qkv_bias=False)
self.norm2 = RMSNorm(self.pixel_dim, eps=1e-6)
self.mlp = MLP(self.pixel_dim, mlp_ratio=mlp_ratio, drop=0.0)
self.adaLN_modulation = nn.Sequential(nn.Linear(self.context_dim, 6 * self.pixel_dim * p2, bias=True))
self._pos_cache = dict()
self._rope_fn = rope_fn if rope_fn is not None else precompute_freqs_cis_2d
def _fetch_pos(self, height: int, width: int, device):
key = (height, width)
if key in self._pos_cache:
return self._pos_cache[key].to(device)
pos = self._rope_fn(self.attn_dim // self.num_heads, height, width).to(device)
self._pos_cache[key] = pos
return pos
def forward(self, x: torch.Tensor, s_cond: torch.Tensor, image_height: int, image_width: int, patch_size: int, mask=None) -> torch.Tensor:
BL, P2, C = x.shape
if C != self.pixel_dim:
raise ValueError(f"PiTBlock expected pixel_dim={self.pixel_dim}, got {C}")
assert (image_height % patch_size == 0) and (image_width % patch_size == 0)
Hs, Ws = image_height // patch_size, image_width // patch_size
L = Hs * Ws
B = BL // L
cond_params = self.adaLN_modulation(s_cond)
cond_params = cond_params.view(BL, P2, 6 * self.pixel_dim)
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(cond_params, 6, dim=-1)
x_norm = apply_adaln(self.norm1(x), shift_msa, scale_msa)
x_flat = x_norm.view(BL, P2 * self.pixel_dim)
x_comp = self.compress_to_attn(x_flat).view(B, L, self.attn_dim)
pos_comp = self._fetch_pos(Hs, Ws, x.device)
attn_out = self.attn(x_comp, pos_comp, mask)
attn_flat = self.expand_from_attn(attn_out.view(B * L, self.attn_dim))
attn_exp = attn_flat.view(BL, P2, self.pixel_dim)
x = x + gate_msa * attn_exp
mlp_out = self.mlp(apply_adaln(self.norm2(x), shift_mlp, scale_mlp))
x = x + gate_mlp * mlp_out
return x
class PixDiT(nn.Module):
def __init__(
self,
in_channels=4,
num_groups=12,
hidden_size=1152,
pixel_hidden_size=64,
patch_depth=18,
pixel_depth=4,
patch_size=2,
num_classes=1000,
use_pixel_abs_pos=True,
):
super().__init__()
self.in_channels = int(in_channels)
self.out_channels = int(in_channels)
self.hidden_size = int(hidden_size)
self.num_groups = int(num_groups)
self.patch_depth = int(patch_depth)
self.pixel_depth = int(pixel_depth)
self.patch_size = int(patch_size)
self.pixel_hidden_size = int(pixel_hidden_size)
self.num_classes = int(num_classes)
self.use_pixel_abs_pos = bool(use_pixel_abs_pos)
if self.pixel_depth <= 0:
raise ValueError("PixDiT expects pixel_depth > 0 to preserve the dual-level pipeline")
self.pixel_embedder = PixelTokenEmbedder(self.in_channels, self.pixel_hidden_size, use_pixel_abs_pos=self.use_pixel_abs_pos)
self.s_embedder = PatchTokenEmbedder(self.in_channels * self.patch_size ** 2, self.hidden_size, bias=True)
self.t_embedder = TimestepConditioner(self.hidden_size)
self.y_embedder = ClassEmbedder(self.num_classes + 1, self.hidden_size)
self.final_layer = FinalLayer(self.pixel_hidden_size, self.out_channels)
self.patch_blocks = nn.ModuleList(
[AugmentedDiTBlock(self.hidden_size, self.num_groups) for _ in range(self.patch_depth)]
)
self.pixel_blocks = nn.ModuleList(
[
PiTBlock(
self.pixel_hidden_size,
self.hidden_size,
patch_size=self.patch_size,
num_heads=self.num_groups,
mlp_ratio=4.0,
)
for _ in range(self.pixel_depth)
]
)
self.initialize_weights()
self.precompute_pos = dict()
def fetch_pos(self, height, width, device):
if (height, width) in self.precompute_pos:
return self.precompute_pos[(height, width)].to(device)
else:
pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device)
self.precompute_pos[(height, width)] = pos
return pos
def initialize_weights(self):
w = self.s_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.s_embedder.proj.bias, 0)
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.zeros_(self.final_layer.linear.weight)
nn.init.zeros_(self.final_layer.linear.bias)
for block in self.patch_blocks:
nn.init.zeros_(block.adaLN_modulation[0].weight)
nn.init.zeros_(block.adaLN_modulation[0].bias)
for block in self.pixel_blocks:
nn.init.zeros_(block.adaLN_modulation[0].weight)
nn.init.zeros_(block.adaLN_modulation[0].bias)
def forward(self, x, t, y, s=None, mask=None):
B, _, H, W = x.shape
pos = self.fetch_pos(H // self.patch_size, W // self.patch_size, x.device)
x_patches = torch.nn.functional.unfold(x, kernel_size=self.patch_size, stride=self.patch_size).transpose(1, 2)
t_emb = self.t_embedder(t.view(-1)).view(B, -1, self.hidden_size)
y_emb = self.y_embedder(y).view(B, 1, self.hidden_size)
c = nn.functional.silu(t_emb + y_emb)
if s is None:
s = self.s_embedder(x_patches)
for block in self.patch_blocks:
s = block(s, c, pos, mask)
s = nn.functional.silu(t_emb + s)
batch_size, length, _ = s.shape
s_cond = s.view(batch_size * length, self.hidden_size)
x_pixels = self.pixel_embedder(x, img_height=H, img_width=W, patch_size=self.patch_size)
for blk in self.pixel_blocks:
x_pixels = blk(x_pixels, s_cond, H, W, self.patch_size, mask)
x_pixels = self.final_layer(x_pixels)
C_out = self.out_channels
P2 = self.patch_size * self.patch_size
x_pixels = x_pixels.view(B, length, P2, C_out).permute(0, 3, 2, 1).contiguous()
x_pixels = x_pixels.view(B, C_out * P2, length)
x_img = torch.nn.functional.fold(x_pixels, (H, W), kernel_size=self.patch_size, stride=self.patch_size)
return x_img
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