126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
#taken from https://github.com/TencentARC/T2I-Adapter
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ldm.modules.attention import SpatialTransformer, BasicTransformerBlock
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def conv_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D convolution module.
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"""
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if dims == 1:
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return nn.Conv1d(*args, **kwargs)
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elif dims == 2:
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return nn.Conv2d(*args, **kwargs)
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elif dims == 3:
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return nn.Conv3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def avg_pool_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D average pooling module.
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"""
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if dims == 1:
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return nn.AvgPool1d(*args, **kwargs)
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elif dims == 2:
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return nn.AvgPool2d(*args, **kwargs)
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elif dims == 3:
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return nn.AvgPool3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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stride = 2 if dims != 3 else (1, 2, 2)
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if use_conv:
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self.op = conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
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)
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else:
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assert self.channels == self.out_channels
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self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.op(x)
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class ResnetBlock(nn.Module):
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def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
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super().__init__()
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ps = ksize//2
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if in_c != out_c or sk==False:
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self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
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else:
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# print('n_in')
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self.in_conv = None
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self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
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self.act = nn.ReLU()
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self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
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if sk==False:
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self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
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else:
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self.skep = None
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self.down = down
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if self.down == True:
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self.down_opt = Downsample(in_c, use_conv=use_conv)
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def forward(self, x):
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if self.down == True:
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x = self.down_opt(x)
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if self.in_conv is not None: # edit
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x = self.in_conv(x)
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h = self.block1(x)
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h = self.act(h)
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h = self.block2(h)
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if self.skep is not None:
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return h + self.skep(x)
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else:
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return h + x
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class Adapter(nn.Module):
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def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True):
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super(Adapter, self).__init__()
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self.unshuffle = nn.PixelUnshuffle(8)
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self.channels = channels
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self.nums_rb = nums_rb
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self.body = []
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for i in range(len(channels)):
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for j in range(nums_rb):
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if (i!=0) and (j==0):
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self.body.append(ResnetBlock(channels[i-1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
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else:
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self.body.append(ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
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self.body = nn.ModuleList(self.body)
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self.conv_in = nn.Conv2d(cin,channels[0], 3, 1, 1)
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def forward(self, x):
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# unshuffle
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x = self.unshuffle(x)
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# extract features
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features = []
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x = self.conv_in(x)
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for i in range(len(self.channels)):
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for j in range(self.nums_rb):
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idx = i*self.nums_rb +j
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x = self.body[idx](x)
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features.append(x)
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return features
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