265 lines
8.5 KiB
Python
265 lines
8.5 KiB
Python
#taken from https://github.com/TencentARC/T2I-Adapter
|
|
import torch
|
|
import torch.nn as nn
|
|
from collections import OrderedDict
|
|
|
|
|
|
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
|
|
if not self.use_conv:
|
|
padding = [x.shape[2] % 2, x.shape[3] % 2]
|
|
self.op.padding = padding
|
|
|
|
x = self.op(x)
|
|
return 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):
|
|
super(Adapter, self).__init__()
|
|
self.unshuffle = nn.PixelUnshuffle(8)
|
|
self.channels = channels
|
|
self.nums_rb = nums_rb
|
|
self.body = []
|
|
for i in range(len(channels)):
|
|
for j in range(nums_rb):
|
|
if (i != 0) and (j == 0):
|
|
self.body.append(
|
|
ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
|
|
else:
|
|
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):
|
|
# unshuffle
|
|
x = self.unshuffle(x)
|
|
# extract features
|
|
features = []
|
|
x = self.conv_in(x)
|
|
for i in range(len(self.channels)):
|
|
for j in range(self.nums_rb):
|
|
idx = i * self.nums_rb + j
|
|
x = self.body[idx](x)
|
|
features.append(x)
|
|
|
|
return features
|
|
|
|
|
|
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
|