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import torch
import torch.nn as nn
from collections import OrderedDict
from omegaconf import OmegaConf
from copy import deepcopy
from modules import devices, lowvram, shared, scripts
cond_cast_unet = getattr(devices, 'cond_cast_unet', lambda x: x)
class TorchHijackForUnet:
"""
This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
"""
def __getattr__(self, item):
if item == 'cat':
return self.cat
if hasattr(torch, item):
return getattr(torch, item)
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
def cat(self, tensors, *args, **kwargs):
if len(tensors) == 2:
a, b = tensors
if a.shape[-2:] != b.shape[-2:]:
a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
tensors = (a, b)
return torch.cat(tensors, *args, **kwargs)
th = TorchHijackForUnet()
def align(hint, size):
b, c, h1, w1 = hint.shape
h, w = size
if h != h1 or w != w1:
hint = th.nn.functional.interpolate(hint, size=size, mode="nearest")
return hint
class PlugableAdapter(nn.Module):
def __init__(self, control_model) -> None:
super().__init__()
self.control_model = control_model
self.control = None
self.hint_cond = None
def reset(self):
self.control = None
self.hint_cond = None
def forward(self, hint=None, x=None, *args, **kwargs):
if self.control is not None:
return deepcopy(self.control)
self.hint_cond = cond_cast_unet(hint)
hint_in = cond_cast_unet(hint)
if hasattr(self.control_model, 'conv_in') and \
(self.control_model.conv_in.in_channels == 64 or self.control_model.conv_in.in_channels == 256):
hint_in = hint_in[:, 0:1, :, :]
self.control = self.control_model(hint_in)
return deepcopy(self.control)
def aggressive_lowvram(self):
self.to(devices.get_device_for("controlnet"))
return
def fullvram(self):
self.to(devices.get_device_for("controlnet"))
return
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResnetBlock(nn.Module):
def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
super().__init__()
ps = ksize//2
if in_c != out_c or sk==False:
self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
# print('n_in')
self.in_conv = None
self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
if sk==False:
self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
# print('n_sk')
self.skep = None
self.down = down
if self.down == True:
self.down_opt = Downsample(in_c, use_conv=use_conv)
def forward(self, x):
if self.down == True:
x = self.down_opt(x)
if self.in_conv is not None: # edit
h = self.in_conv(x)
# x = self.in_conv(x)
# else:
# x = x
h = self.block1(h)
h = self.act(h)
h = self.block2(h)
if self.skep is not None:
return h + self.skep(x)
else:
return h + x
class ResnetBlock(nn.Module):
def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
super().__init__()
ps = ksize//2
if in_c != out_c or sk==False:
self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
# print('n_in')
self.in_conv = None
self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
if sk==False:
self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
self.skep = None
self.down = down
if self.down == True:
self.down_opt = Downsample(in_c, use_conv=use_conv)
def forward(self, x):
if self.down == True:
x = self.down_opt(x)
if self.in_conv is not None: # edit
x = self.in_conv(x)
h = self.block1(x)
h = self.act(h)
h = self.block2(h)
if self.skep is not None:
return h + self.skep(x)
else:
return h + x
class Adapter(nn.Module):
def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, is_sdxl=True):
super(Adapter, self).__init__()
if is_sdxl:
self.pixel_shuffle = 16
downsample_avoided = [1]
downsample_layers = [2]
else:
self.pixel_shuffle = 8
downsample_avoided = []
downsample_layers = [3, 2, 1]
self.input_channels = cin // (self.pixel_shuffle * self.pixel_shuffle)
self.channels = channels
self.nums_rb = nums_rb
self.body = []
self.unshuffle = nn.PixelUnshuffle(self.pixel_shuffle)
for i in range(len(channels)):
for r in range(nums_rb):
if i in downsample_layers and r == 0:
self.body.append(ResnetBlock(
channels[i - 1],
channels[i],
down=True,
ksize=ksize,
sk=sk,
use_conv=use_conv))
continue
if i in downsample_avoided and r == 0:
self.body.append(ResnetBlock(
channels[i - 1],
channels[i],
down=False,
ksize=ksize,
sk=sk,
use_conv=use_conv))
continue
self.body.append(ResnetBlock(
channels[i],
channels[i],
down=False,
ksize=ksize,
sk=sk,
use_conv=use_conv
))
self.body = nn.ModuleList(self.body)
self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
def forward(self, x):
self.to(x.device)
x = self.unshuffle(x)
hs = []
x = self.conv_in(x)
for i in range(len(self.channels)):
for r in range(self.nums_rb):
idx = i * self.nums_rb + r
x = self.body[idx](x)
hs.append(x)
self.to('cpu')
return hs
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(
OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: torch.Tensor):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
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 StyleAdapter(nn.Module):
def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
super().__init__()
scale = width ** -0.5
self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
self.num_token = num_token
self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
self.ln_post = LayerNorm(width)
self.ln_pre = LayerNorm(width)
self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
def forward(self, x):
# x shape [N, HW+1, C]
style_embedding = self.style_embedding + torch.zeros(
(x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
x = torch.cat([x, style_embedding], dim=1)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer_layes(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_post(x[:, -self.num_token:, :])
x = x @ self.proj
return x
class ResnetBlock_light(nn.Module):
def __init__(self, in_c):
super().__init__()
self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
def forward(self, x):
h = self.block1(x)
h = self.act(h)
h = self.block2(h)
return h + x
class extractor(nn.Module):
def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
super().__init__()
self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
self.body = []
for _ in range(nums_rb):
self.body.append(ResnetBlock_light(inter_c))
self.body = nn.Sequential(*self.body)
self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
self.down = down
if self.down == True:
self.down_opt = Downsample(in_c, use_conv=False)
def forward(self, x):
if self.down == True:
x = self.down_opt(x)
x = self.in_conv(x)
x = self.body(x)
x = self.out_conv(x)
return x
class Adapter_light(nn.Module):
def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
super(Adapter_light, self).__init__()
self.unshuffle = nn.PixelUnshuffle(8)
self.channels = channels
self.nums_rb = nums_rb
self.body = []
for i in range(len(channels)):
if i == 0:
self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
else:
self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
self.body = nn.ModuleList(self.body)
def forward(self, x):
# unshuffle
x = self.unshuffle(x)
# extract features
features = []
for i in range(len(self.channels)):
x = self.body[i](x)
features.append(x)
return features
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