#taken from: https://github.com/lllyasviel/ControlNet
#and modified
import torch
import torch.nn as nn
from ..ldm.modules.diffusionmodules.util import (
timestep_embedding,
)
from ..ldm.modules.attention import SpatialTransformer
from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
from ..ldm.util import exists
from .control_types import UNION_CONTROLNET_TYPES
from collections import OrderedDict
import comfy.ops
from comfy.ldm.modules.attention import optimized_attention
class OptimizedAttention(nn.Module):
def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
super().__init__()
self.heads = nhead
self.c = c
self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
def forward(self, x):
x = self.in_proj(x)
q, k, v = x.split(self.c, dim=2)
out = optimized_attention(q, k, v, self.heads)
return self.out_proj(out)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResBlockUnionControlnet(nn.Module):
def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
super().__init__()
self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
self.mlp = nn.Sequential(
OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
def attention(self, x: torch.Tensor):
return self.attn(x)
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class ControlledUnetModel(UNetModel):
#implemented in the ldm unet
pass
class ControlNet(nn.Module):
def __init__(
self,
image_size,
in_channels,
model_channels,
hint_channels,
num_res_blocks,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
dtype=torch.float32,
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, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
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,
transformer_depth_output=None,
attn_precision=None,
union_controlnet_num_control_type=None,
device=None,
operations=comfy.ops.disable_weight_init,
**kwargs,
):
super().__init__()
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.dims = dims
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
if disable_self_attentions is not None:
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
assert len(disable_self_attentions) == len(channel_mult)
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
transformer_depth = transformer_depth[:]
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 = dtype
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(
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
operations.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":
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(
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
)
else:
raise ValueError()
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
operations.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, operations=operations, dtype=self.dtype, device=device)])
self.input_hint_block = TimestepEmbedSequential(
operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
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,
dtype=self.dtype,
device=device,
operations=operations,
)
]
ch = mult * model_channels
num_transformers = transformer_depth.pop(0)
if num_transformers > 0:
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=num_transformers, context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
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,
dtype=self.dtype,
device=device,
operations=operations
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
)
)
)
ch = out_ch
input_block_chans.append(ch)
self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
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
mid_block = [
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype,
device=device,
operations=operations
)]
if transformer_depth_middle >= 0:
mid_block += [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, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype,
device=device,
operations=operations
)]
self.middle_block = TimestepEmbedSequential(*mid_block)
self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
self._feature_size += ch
if union_controlnet_num_control_type is not None:
self.num_control_type = union_controlnet_num_control_type
num_trans_channel = 320
num_trans_head = 8
num_trans_layer = 1
num_proj_channel = 320
# task_scale_factor = num_trans_channel ** 0.5
self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
#-----------------------------------------------------------------------------------------------------
control_add_embed_dim = 256
class ControlAddEmbedding(nn.Module):
def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
super().__init__()
self.num_control_type = num_control_type
self.in_dim = in_dim
self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
def forward(self, control_type, dtype, device):
c_type = torch.zeros((self.num_control_type,), device=device)
c_type[control_type] = 1.0
c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
else:
self.task_embedding = None
self.control_add_embedding = None
def union_controlnet_merge(self, hint, control_type, emb, context):
# Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
inputs = []
condition_list = []
for idx in range(min(1, len(control_type))):
controlnet_cond = self.input_hint_block(hint[idx], emb, context)
feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
if idx < len(control_type):
feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
inputs.append(feat_seq.unsqueeze(1))
condition_list.append(controlnet_cond)
x = torch.cat(inputs, dim=1)
x = self.transformer_layes(x)
controlnet_cond_fuser = None
for idx in range(len(control_type)):
alpha = self.spatial_ch_projs(x[:, idx])
alpha = alpha.unsqueeze(-1).unsqueeze(-1)
o = condition_list[idx] + alpha
if controlnet_cond_fuser is None:
controlnet_cond_fuser = o
else:
controlnet_cond_fuser += o
return controlnet_cond_fuser
def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
def forward(self, x, hint, timesteps, context, y=None, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
emb = self.time_embed(t_emb)
guided_hint = None
if self.control_add_embedding is not None: #Union Controlnet
control_type = kwargs.get("control_type", [])
if any([c >= self.num_control_type for c in control_type]):
max_type = max(control_type)
max_type_name = {
v: k for k, v in UNION_CONTROLNET_TYPES.items()
}[max_type]
raise ValueError(
f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
f"({self.num_control_type}) supported.\n" +
"Please consider using the ProMax ControlNet Union model.\n" +
"https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
)
emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
if len(control_type) > 0:
if len(hint.shape) < 5:
hint = hint.unsqueeze(dim=0)
guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
if guided_hint is None:
guided_hint = self.input_hint_block(hint, emb, context)
out_output = []
out_middle = []
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)
out_output.append(zero_conv(h, emb, context))
h = self.middle_block(h, emb, context)
out_middle.append(self.middle_block_out(h, emb, context))
return {"middle": out_middle, "output": out_output}