File size: 9,737 Bytes
db6a3b7 |
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 |
from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
from ..modules.transformer import AbsolutePositionEmbedder
from ..modules.norm import LayerNorm32
from ..modules import sparse as sp
from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock
from .sparse_structure_flow import TimestepEmbedder
class SparseResBlock3d(nn.Module):
def __init__(
self,
channels: int,
emb_channels: int,
out_channels: Optional[int] = None,
downsample: bool = False,
upsample: bool = False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.out_channels = out_channels or channels
self.downsample = downsample
self.upsample = upsample
assert not (downsample and upsample), "Cannot downsample and upsample at the same time"
self.norm1 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
self.norm2 = LayerNorm32(self.out_channels, elementwise_affine=False, eps=1e-6)
self.conv1 = sp.SparseConv3d(channels, self.out_channels, 3)
self.conv2 = zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3))
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(emb_channels, 2 * self.out_channels, bias=True),
)
self.skip_connection = sp.SparseLinear(channels, self.out_channels) if channels != self.out_channels else nn.Identity()
self.updown = None
if self.downsample:
self.updown = sp.SparseDownsample(2)
elif self.upsample:
self.updown = sp.SparseUpsample(2)
def _updown(self, x: sp.SparseTensor) -> sp.SparseTensor:
if self.updown is not None:
x = self.updown(x)
return x
def forward(self, x: sp.SparseTensor, emb: torch.Tensor) -> sp.SparseTensor:
emb_out = self.emb_layers(emb).type(x.dtype)
scale, shift = torch.chunk(emb_out, 2, dim=1)
x = self._updown(x)
h = x.replace(self.norm1(x.feats))
h = h.replace(F.silu(h.feats))
h = self.conv1(h)
h = h.replace(self.norm2(h.feats)) * (1 + scale) + shift
h = h.replace(F.silu(h.feats))
h = self.conv2(h)
h = h + self.skip_connection(x)
return h
class SLatFlowModel(nn.Module):
def __init__(
self,
resolution: int,
in_channels: int,
model_channels: int,
cond_channels: int,
out_channels: int,
num_blocks: int,
num_heads: Optional[int] = None,
num_head_channels: Optional[int] = 64,
mlp_ratio: float = 4,
patch_size: int = 2,
num_io_res_blocks: int = 2,
io_block_channels: List[int] = None,
pe_mode: Literal["ape", "rope"] = "ape",
use_fp16: bool = False,
use_checkpoint: bool = False,
use_skip_connection: bool = True,
share_mod: bool = False,
qk_rms_norm: bool = False,
qk_rms_norm_cross: bool = False,
):
super().__init__()
self.resolution = resolution
self.in_channels = in_channels
self.model_channels = model_channels
self.cond_channels = cond_channels
self.out_channels = out_channels
self.num_blocks = num_blocks
self.num_heads = num_heads or model_channels // num_head_channels
self.mlp_ratio = mlp_ratio
self.patch_size = patch_size
self.num_io_res_blocks = num_io_res_blocks
self.io_block_channels = io_block_channels
self.pe_mode = pe_mode
self.use_fp16 = use_fp16
self.use_checkpoint = use_checkpoint
self.use_skip_connection = use_skip_connection
self.share_mod = share_mod
self.qk_rms_norm = qk_rms_norm
self.qk_rms_norm_cross = qk_rms_norm_cross
self.dtype = torch.float16 if use_fp16 else torch.float32
assert int(np.log2(patch_size)) == np.log2(patch_size), "Patch size must be a power of 2"
assert np.log2(patch_size) == len(io_block_channels), "Number of IO ResBlocks must match the number of stages"
self.t_embedder = TimestepEmbedder(model_channels)
if share_mod:
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(model_channels, 6 * model_channels, bias=True)
)
if pe_mode == "ape":
self.pos_embedder = AbsolutePositionEmbedder(model_channels)
self.input_layer = sp.SparseLinear(in_channels, io_block_channels[0])
self.input_blocks = nn.ModuleList([])
for chs, next_chs in zip(io_block_channels, io_block_channels[1:] + [model_channels]):
self.input_blocks.extend([
SparseResBlock3d(
chs,
model_channels,
out_channels=chs,
)
for _ in range(num_io_res_blocks-1)
])
self.input_blocks.append(
SparseResBlock3d(
chs,
model_channels,
out_channels=next_chs,
downsample=True,
)
)
self.blocks = nn.ModuleList([
ModulatedSparseTransformerCrossBlock(
model_channels,
cond_channels,
num_heads=self.num_heads,
mlp_ratio=self.mlp_ratio,
attn_mode='full',
use_checkpoint=self.use_checkpoint,
use_rope=(pe_mode == "rope"),
share_mod=self.share_mod,
qk_rms_norm=self.qk_rms_norm,
qk_rms_norm_cross=self.qk_rms_norm_cross,
)
for _ in range(num_blocks)
])
self.out_blocks = nn.ModuleList([])
for chs, prev_chs in zip(reversed(io_block_channels), [model_channels] + list(reversed(io_block_channels[1:]))):
self.out_blocks.append(
SparseResBlock3d(
prev_chs * 2 if self.use_skip_connection else prev_chs,
model_channels,
out_channels=chs,
upsample=True,
)
)
self.out_blocks.extend([
SparseResBlock3d(
chs * 2 if self.use_skip_connection else chs,
model_channels,
out_channels=chs,
)
for _ in range(num_io_res_blocks-1)
])
self.out_layer = sp.SparseLinear(io_block_channels[0], out_channels)
self.initialize_weights()
if use_fp16:
self.convert_to_fp16()
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def convert_to_fp16(self) -> None:
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.blocks.apply(convert_module_to_f16)
self.out_blocks.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.blocks.apply(convert_module_to_f32)
self.out_blocks.apply(convert_module_to_f32)
def initialize_weights(self) -> None:
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in DiT blocks:
if self.share_mod:
nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
else:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.out_layer.weight, 0)
nn.init.constant_(self.out_layer.bias, 0)
def forward(self, x: sp.SparseTensor, t: torch.Tensor, cond: torch.Tensor) -> sp.SparseTensor:
h = self.input_layer(x).type(self.dtype)
t_emb = self.t_embedder(t)
if self.share_mod:
t_emb = self.adaLN_modulation(t_emb)
t_emb = t_emb.type(self.dtype)
cond = cond.type(self.dtype)
skips = []
# pack with input blocks
for block in self.input_blocks:
h = block(h, t_emb)
skips.append(h.feats)
if self.pe_mode == "ape":
h = h + self.pos_embedder(h.coords[:, 1:]).type(self.dtype)
for block in self.blocks:
h = block(h, t_emb, cond)
# unpack with output blocks
for block, skip in zip(self.out_blocks, reversed(skips)):
if self.use_skip_connection:
h = block(h.replace(torch.cat([h.feats, skip], dim=1)), t_emb)
else:
h = block(h, t_emb)
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
h = self.out_layer(h.type(x.dtype))
return h
|