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import torch
import torch.nn as nn
from modules import devices
try:
from sgm.modules.diffusionmodules.openaimodel import conv_nd, linear, zero_module, timestep_embedding, \
TimestepEmbedSequential, ResBlock, Downsample, SpatialTransformer, exists
using_sgm = True
except:
from ldm.modules.diffusionmodules.openaimodel import conv_nd, linear, zero_module, timestep_embedding, \
TimestepEmbedSequential, ResBlock, Downsample, SpatialTransformer, exists
using_sgm = False
class PlugableControlModel(nn.Module):
def __init__(self, config, state_dict=None):
super().__init__()
self.config = config
self.control_model = ControlNet(**self.config).cpu()
if state_dict is not None:
self.control_model.load_state_dict(state_dict, strict=False)
self.gpu_component = None
self.is_control_lora = False
def reset(self):
pass
def forward(self, *args, **kwargs):
return self.control_model(*args, **kwargs)
def aggressive_lowvram(self):
self.to('cpu')
def send_me_to_gpu(module, _):
if self.gpu_component == module:
return
if self.gpu_component is not None:
self.gpu_component.to('cpu')
module.to(devices.get_device_for("controlnet"))
self.gpu_component = module
self.control_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
self.control_model.input_hint_block.register_forward_pre_hook(send_me_to_gpu)
self.control_model.label_emb.register_forward_pre_hook(send_me_to_gpu)
for m in self.control_model.input_blocks:
m.register_forward_pre_hook(send_me_to_gpu)
for m in self.control_model.zero_convs:
m.register_forward_pre_hook(send_me_to_gpu)
self.control_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
self.control_model.middle_block_out.register_forward_pre_hook(send_me_to_gpu)
return
def fullvram(self):
self.to(devices.get_device_for("controlnet"))
return
class ControlNet(nn.Module):
def __init__(
self,
in_channels,
model_channels,
hint_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=True,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_spatial_transformer=True,
transformer_depth=1,
context_dim=None,
n_embed=None,
legacy=False,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
adm_in_channels=None,
transformer_depth_middle=None,
device=None,
global_average_pooling=False,
):
super().__init__()
self.global_average_pooling = global_average_pooling
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.dims = dims
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(transformer_depth, int):
transformer_depth = len(channel_mult) * [transformer_depth]
if transformer_depth_middle is None:
transformer_depth_middle = transformer_depth[-1]
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
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.num_classes = num_classes
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, dtype=self.dtype, device=device),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
if self.num_classes is not None:
if isinstance(self.num_classes, int):
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
elif self.num_classes == "continuous":
print("setting up linear c_adm embedding layer")
self.label_emb = nn.Linear(1, time_embed_dim)
elif self.num_classes == "sequential":
assert adm_in_channels is not None
self.label_emb = nn.Sequential(
nn.Sequential(
linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
)
else:
raise ValueError()
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
]
)
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
self.input_hint_block = TimestepEmbedSequential(
conv_nd(dims, hint_channels, 16, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 16, 16, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 16, 32, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 32, 32, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 32, 96, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 96, 96, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 96, 256, 3, padding=1, stride=2),
nn.SiLU(),
zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
)
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]):
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:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
if exists(disable_self_attentions):
disabled_sa = disable_self_attentions[level]
else:
disabled_sa = False
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
layers.append(
SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
)
)
self.input_blocks.append(TimestepEmbedSequential(*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
self.input_blocks.append(
TimestepEmbedSequential(
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
)
)
)
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:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm
),
SpatialTransformer( # always uses a self-attn
ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
),
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 TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
def forward(self, x, hint, timesteps, context, y=None, **kwargs):
original_type = x.dtype
x = x.to(self.dtype)
hint = hint.to(self.dtype)
timesteps = timesteps.to(self.dtype)
context = context.to(self.dtype)
if y is not None:
y = y.to(self.dtype)
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
emb = self.time_embed(t_emb)
guided_hint = self.input_hint_block(hint, emb, context)
outs = []
if self.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.label_emb(y)
h = x
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
if guided_hint is not None:
h = module(h, emb, context)
h += guided_hint
guided_hint = None
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))
outs = [o.to(original_type) for o in outs]
return outs
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