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 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 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