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Running on Zero
Running on Zero
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d066167 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from refnet.util import checkpoint_wrapper
from refnet.modules.unet import TimestepEmbedSequential
from refnet.modules.layers import Upsample, zero_module, RMSNorm, FeedForward
from refnet.modules.attention import MemoryEfficientAttention, MultiScaleCausalAttention
from einops import rearrange
from functools import partial
def make_zero_conv(in_channels, out_channels=None):
out_channels = out_channels or in_channels
return zero_module(nn.Conv2d(in_channels, out_channels, 1, padding=0))
def activate_zero_conv(in_channels, out_channels=None):
out_channels = out_channels or in_channels
return TimestepEmbedSequential(
nn.SiLU(),
zero_module(nn.Conv2d(in_channels, out_channels, 1, padding=0))
)
def sequential_downsample(in_channels, out_channels, sequential_cls=nn.Sequential):
return sequential_cls(
nn.Conv2d(in_channels, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 16, 3, padding=1),
nn.SiLU(),
nn.Conv2d(16, 32, 3, padding=1, stride=2),
nn.SiLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.SiLU(),
nn.Conv2d(32, 96, 3, padding=1, stride=2),
nn.SiLU(),
nn.Conv2d(96, 96, 3, padding=1),
nn.SiLU(),
nn.Conv2d(96, 256, 3, padding=1, stride=2),
nn.SiLU(),
zero_module(nn.Conv2d(256, out_channels, 3, padding=1))
)
class SimpleEncoder(nn.Module):
def __init__(self, c_channels, model_channels):
super().__init__()
self.model = sequential_downsample(c_channels, model_channels)
def forward(self, x, *args, **kwargs):
return self.model(x)
class MultiEncoder(nn.Module):
def __init__(self, in_ch, model_channels, ch_mults, checkpoint=True, time_embed=False):
super().__init__()
sequential_cls = TimestepEmbedSequential if time_embed else nn.Sequential
output_chs = [model_channels * mult for mult in ch_mults]
self.model = sequential_downsample(in_ch, model_channels, sequential_cls)
self.zero_layer = make_zero_conv(output_chs[0])
self.output_blocks = nn.ModuleList()
self.zero_blocks = nn.ModuleList()
block_num = len(ch_mults)
prev_ch = output_chs[0]
for i in range(block_num):
self.output_blocks.append(sequential_cls(
nn.SiLU(),
nn.Conv2d(prev_ch, output_chs[i], 3, padding=1, stride=2 if i != block_num-1 else 1),
nn.SiLU(),
nn.Conv2d(output_chs[i], output_chs[i], 3, padding=1)
))
self.zero_blocks.append(
TimestepEmbedSequential(make_zero_conv(output_chs[i])) if time_embed
else make_zero_conv(output_chs[i])
)
prev_ch = output_chs[i]
self.checkpoint = checkpoint
def forward(self, x):
x = self.model(x)
hints = [self.zero_layer(x)]
for layer, zero_layer in zip(self.output_blocks, self.zero_blocks):
x = layer(x)
hints.append(zero_layer(x))
return hints
class MultiScaleAttentionEncoder(nn.Module):
def __init__(
self,
in_ch,
model_channels,
ch_mults,
dim_head = 128,
transformer_layers = 2,
checkpoint = True
):
super().__init__()
conv_proj = partial(nn.Conv2d, kernel_size=1, padding=0)
output_chs = [model_channels * mult for mult in ch_mults]
block_num = len(ch_mults)
attn_ch = output_chs[-1]
self.model = sequential_downsample(in_ch, output_chs[0])
self.proj_ins = nn.ModuleList([conv_proj(output_chs[0], attn_ch)])
self.proj_outs = nn.ModuleList([zero_module(conv_proj(attn_ch, output_chs[0]))])
prev_ch = output_chs[0]
self.downsample_layers = nn.ModuleList()
for i in range(block_num):
ch = output_chs[i]
self.downsample_layers.append(nn.Sequential(
nn.SiLU(),
nn.Conv2d(prev_ch, ch, 3, padding=1, stride=2 if i != block_num - 1 else 1),
))
self.proj_ins.append(conv_proj(ch, attn_ch))
self.proj_outs.append(zero_module(conv_proj(attn_ch, ch)))
prev_ch = ch
self.proj_ins.append(conv_proj(attn_ch, attn_ch))
self.attn_layer = MultiScaleCausalAttention(attn_ch, rope=True, qk_norm=True, dim_head=dim_head)
# self.transformer = nn.ModuleList([
# BasicTransformerBlock(
# attn_ch,
# rotary_positional_embedding = True,
# qk_norm = True,
# d_head = dim_head,
# disable_cross_attn = True,
# self_attn_type = "multi-scale",
# ff_mult = 2,
# )
# ] * transformer_layers)
self.checkpoint = checkpoint
@checkpoint_wrapper
def forward(self, x):
proj_in_iter = iter(self.proj_ins)
proj_out_iter = iter(self.proj_outs[::-1])
x = self.model(x)
hints = [rearrange(next(proj_in_iter)(x), "b c h w -> b (h w) c")]
grid_sizes = [(x.shape[2], x.shape[3])]
token_lens = [(x.shape[2] * x.shape[3])]
for layer in self.downsample_layers:
x = layer(x)
h, w = x.shape[2], x.shape[3]
grid_sizes.append((h, w))
token_lens.append(h * w)
hints.append(rearrange(next(proj_in_iter)(x), "b c h w -> b (h w) c"))
hints.append(rearrange(
next(proj_in_iter)(x.mean(dim=[2, 3], keepdim=True)),
"b c h w -> b (h w) c"
))
hints = hints[::-1]
grid_sizes = grid_sizes[::-1]
token_lens = token_lens[::-1]
hints = torch.cat(hints, 1)
hints = self.attn_layer(hints, grid_size=grid_sizes, token_lens=token_lens) + hints
# for layer in self.transformer:
# hints = layer(hints, grid_size=grid_sizes, token_lens=token_lens)
prev_idx = 1
controls = []
for gs, token_len in zip(grid_sizes, token_lens):
control = hints[:, prev_idx: prev_idx + token_len]
control = rearrange(control, "b (h w) c -> b c h w", h=gs[0], w=gs[1])
controls.append(next(proj_out_iter)(control))
prev_idx = prev_idx + token_len
return controls[::-1]
class Downsampler(nn.Module):
def __init__(self, scale_factor):
super().__init__()
self.scale_factor = scale_factor
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale_factor, mode="bicubic")
class SpatialConditionEncoder(nn.Module):
def __init__(
self,
in_dim,
dim,
out_dim,
patch_size,
n_layers = 4,
):
super().__init__()
self.patch_embed = nn.Conv2d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
self.conv = nn.Sequential(nn.SiLU(), nn.Conv2d(dim, dim, kernel_size=3, padding=1))
self.transformer = nn.ModuleList(
nn.ModuleList([
RMSNorm(dim),
MemoryEfficientAttention(dim, rope=True),
RMSNorm(dim),
FeedForward(dim, mult=2)
]) for _ in range(n_layers)
)
self.out = nn.Sequential(
nn.SiLU(),
zero_module(nn.Conv2d(dim, out_dim, kernel_size=1, padding=0))
)
def forward(self, x):
x = self.patch_embed(x)
x = self.conv(x)
b, c, h, w = x.shape
x = rearrange(x, "b c h w -> b (h w) c")
for norm, layer, norm2, ff in self.transformer:
x = layer(norm(x), grid_size=(h, w)) + x
x = ff(norm2(x)) + x
x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
return self.out(x)
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