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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| #taken from https://github.com/TencentARC/T2I-Adapter | ||
|
|
||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| from ldm.modules.attention import SpatialTransformer, BasicTransformerBlock | ||
|
|
||
| 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: | ||
| 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 |
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