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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from .utils import (
checkpoint,
conv_nd,
linear,
zero_module,
timestep_embedding,
exists,
)
from .attention import SpatialTransformer
from .diffusion import (
TimestepBlock,
TimestepEmbedSequential,
ResBlock,
Downsample,
AttentionBlock,
)
class LocalTimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""Sequential that handles LocalResBlock, TimestepBlock, SpatialTransformer."""
def forward(self, x, emb, context=None, local_features=None):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
elif isinstance(layer, SpatialTransformer):
x = layer(x, context)
elif isinstance(layer, LocalResBlock):
x = layer(x, emb, local_features)
else:
x = layer(x)
return x
class FDN(nn.Module):
def __init__(self, norm_nc, label_nc):
super().__init__()
ks = 3
pw = ks // 2
self.param_free_norm = nn.GroupNorm(32, norm_nc, affine=False)
self.conv_gamma = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw)
self.conv_beta = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw)
def forward(self, x, local_features):
normalized = self.param_free_norm(x)
assert local_features.size()[2:] == x.size()[2:]
gamma = self.conv_gamma(local_features)
beta = self.conv_beta(local_features)
return normalized * (1 + gamma) + beta
class SelfAttention(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.query_conv = nn.Conv2d(in_dim, in_dim // 8, kernel_size=1)
self.key_conv = nn.Conv2d(in_dim, in_dim // 8, kernel_size=1)
self.value_conv = nn.Conv2d(in_dim, in_dim, kernel_size=1)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
batch, C, width, height = x.size()
query = self.query_conv(x).view(batch, -1, width * height).permute(0, 2, 1)
key = self.key_conv(x).view(batch, -1, width * height)
value = self.value_conv(x).view(batch, -1, width * height)
attention = self.softmax(torch.bmm(query, key))
out = torch.bmm(value, attention.permute(0, 2, 1))
out = out.view(batch, C, width, height)
return out + x
class EnhancedFDN(nn.Module):
def __init__(self, norm_nc, label_nc):
super().__init__()
self.fdn = FDN(norm_nc, label_nc)
self.attention = SelfAttention(norm_nc)
def forward(self, x, local_features):
x = self.attention(x)
return self.fdn(x, local_features)
class LocalResBlock(nn.Module):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
use_checkpoint=False,
inject_channels=None,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_checkpoint = use_checkpoint
self.norm_in = EnhancedFDN(channels, inject_channels)
self.norm_out = EnhancedFDN(self.out_channels, inject_channels)
self.in_layers = nn.Sequential(
nn.Identity(),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(emb_channels, self.out_channels),
)
self.out_layers = nn.Sequential(
nn.Identity(),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb, local_conditions):
return checkpoint(
self._forward, (x, emb, local_conditions), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb, local_conditions):
h = self.norm_in(x, local_conditions)
h = self.in_layers(h)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
h = h + emb_out
h = self.norm_out(h, local_conditions)
h = self.out_layers(h)
return self.skip_connection(x) + h
class FeatureExtractor(nn.Module):
def __init__(self, local_channels, inject_channels, dims=2):
super().__init__()
self.pre_extractor = LocalTimestepEmbedSequential(
conv_nd(dims, local_channels, 32, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 32, 64, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 64, 64, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 64, 128, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 128, 128, 3, padding=1),
nn.SiLU(),
)
self.extractors = nn.ModuleList([
LocalTimestepEmbedSequential(
conv_nd(dims, 128, inject_channels[0], 3, padding=1, stride=2),
nn.SiLU(),
),
LocalTimestepEmbedSequential(
conv_nd(dims, inject_channels[0], inject_channels[1], 3, padding=1, stride=2),
nn.SiLU(),
),
LocalTimestepEmbedSequential(
conv_nd(dims, inject_channels[1], inject_channels[2], 3, padding=1, stride=2),
nn.SiLU(),
),
LocalTimestepEmbedSequential(
conv_nd(dims, inject_channels[2], inject_channels[3], 3, padding=1, stride=2),
nn.SiLU(),
),
])
self.zero_convs = nn.ModuleList([
zero_module(conv_nd(dims, inject_channels[0], inject_channels[0], 3, padding=1)),
zero_module(conv_nd(dims, inject_channels[1], inject_channels[1], 3, padding=1)),
zero_module(conv_nd(dims, inject_channels[2], inject_channels[2], 3, padding=1)),
zero_module(conv_nd(dims, inject_channels[3], inject_channels[3], 3, padding=1)),
])
def forward(self, local_conditions):
local_features = self.pre_extractor(local_conditions, None)
output_features = []
for idx in range(len(self.extractors)):
local_features = self.extractors[idx](local_features, None)
output_features.append(self.zero_convs[idx](local_features))
return output_features
class LocalAdapter(nn.Module):
def __init__(
self,
in_channels,
model_channels,
local_channels,
inject_channels,
inject_layers,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_fp16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False,
transformer_depth=1,
context_dim=None,
n_embed=None,
legacy=True,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
):
super().__init__()
if context_dim is not None:
if hasattr(context_dim, "__iter__") and not isinstance(context_dim, (list, tuple)):
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.dims = dims
self.in_channels = in_channels
self.model_channels = model_channels
self.inject_layers = inject_layers
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
assert len(num_res_blocks) == len(channel_mult)
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
self.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.feature_extractor = FeatureExtractor(local_channels, inject_channels)
self.input_blocks = nn.ModuleList([
LocalTimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
),
])
self.zero_convs = nn.ModuleList([self._make_zero_conv(model_channels)])
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
if (1 + 3 * level + nr) in self.inject_layers:
layers = [
LocalResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
inject_channels=inject_channels[level],
)
]
else:
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
dim_head = num_head_channels
if legacy:
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
disabled_sa = (
disable_self_attentions[level]
if exists(disable_self_attentions)
else False
)
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
block = (
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
)
if not use_spatial_transformer
else SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
disable_self_attn=disabled_sa,
use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint,
)
)
layers.append(block)
self.input_blocks.append(LocalTimestepEmbedSequential(*layers))
self.zero_convs.append(self._make_zero_conv(ch))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
down_block = (
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
self.input_blocks.append(LocalTimestepEmbedSequential(down_block))
ch = out_ch
input_block_chans.append(ch)
self.zero_convs.append(self._make_zero_conv(ch))
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
dim_head = num_head_channels
if legacy:
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
mid_attn = (
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
)
if not use_spatial_transformer
else SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
disable_self_attn=disable_middle_self_attn,
use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint,
)
)
self.middle_block = LocalTimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
mid_attn,
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self.middle_block_out = self._make_zero_conv(ch)
self._feature_size += ch
def _make_zero_conv(self, channels):
return LocalTimestepEmbedSequential(
zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))
)
def forward(self, x, timesteps, context, local_conditions, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
local_features = self.feature_extractor(local_conditions)
outs = []
h = x.type(self.dtype)
for layer_idx, (module, zero_conv) in enumerate(zip(self.input_blocks, self.zero_convs)):
if layer_idx in self.inject_layers:
feat_idx = self.inject_layers.index(layer_idx)
h = module(h, emb, context, local_features[feat_idx])
else:
h = module(h, emb, context)
outs.append(zero_conv(h, emb, context))
h = self.middle_block(h, emb, context)
outs.append(self.middle_block_out(h, emb, context))
return outs
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