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264 lines
8.5 KiB
264 lines
8.5 KiB
#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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from collections import OrderedDict |
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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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if not self.use_conv: |
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padding = [x.shape[2] % 2, x.shape[3] % 2] |
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self.op.padding = padding |
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x = self.op(x) |
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return 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( |
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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( |
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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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class LayerNorm(nn.LayerNorm): |
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"""Subclass torch's LayerNorm to handle fp16.""" |
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def forward(self, x: torch.Tensor): |
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orig_type = x.dtype |
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ret = super().forward(x.type(torch.float32)) |
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return ret.type(orig_type) |
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class QuickGELU(nn.Module): |
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def forward(self, x: torch.Tensor): |
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return x * torch.sigmoid(1.702 * x) |
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class ResidualAttentionBlock(nn.Module): |
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): |
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super().__init__() |
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self.attn = nn.MultiheadAttention(d_model, n_head) |
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self.ln_1 = LayerNorm(d_model) |
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self.mlp = nn.Sequential( |
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OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), |
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("c_proj", nn.Linear(d_model * 4, d_model))])) |
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self.ln_2 = LayerNorm(d_model) |
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self.attn_mask = attn_mask |
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def attention(self, x: torch.Tensor): |
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self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None |
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return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] |
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def forward(self, x: torch.Tensor): |
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x = x + self.attention(self.ln_1(x)) |
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x = x + self.mlp(self.ln_2(x)) |
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return x |
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class StyleAdapter(nn.Module): |
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def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): |
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super().__init__() |
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scale = width ** -0.5 |
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self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) |
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self.num_token = num_token |
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self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) |
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self.ln_post = LayerNorm(width) |
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self.ln_pre = LayerNorm(width) |
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self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) |
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def forward(self, x): |
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# x shape [N, HW+1, C] |
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style_embedding = self.style_embedding + torch.zeros( |
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(x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) |
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x = torch.cat([x, style_embedding], dim=1) |
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x = self.ln_pre(x) |
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x = x.permute(1, 0, 2) # NLD -> LND |
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x = self.transformer_layes(x) |
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x = x.permute(1, 0, 2) # LND -> NLD |
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x = self.ln_post(x[:, -self.num_token:, :]) |
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x = x @ self.proj |
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return x |
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class ResnetBlock_light(nn.Module): |
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def __init__(self, in_c): |
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super().__init__() |
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self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) |
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self.act = nn.ReLU() |
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self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) |
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def forward(self, 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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return h + x |
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class extractor(nn.Module): |
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def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): |
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super().__init__() |
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self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) |
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self.body = [] |
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for _ in range(nums_rb): |
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self.body.append(ResnetBlock_light(inter_c)) |
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self.body = nn.Sequential(*self.body) |
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self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) |
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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=False) |
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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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x = self.in_conv(x) |
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x = self.body(x) |
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x = self.out_conv(x) |
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return x |
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class Adapter_light(nn.Module): |
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def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): |
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super(Adapter_light, 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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if i == 0: |
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self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) |
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else: |
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self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) |
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self.body = nn.ModuleList(self.body) |
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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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for i in range(len(self.channels)): |
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x = self.body[i](x) |
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features.append(x) |
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return features
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