Spaces:
Runtime error
Runtime error
| 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 | |