diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py index d059ba91..0221fff8 100644 --- a/comfy/t2i_adapter/adapter.py +++ b/comfy/t2i_adapter/adapter.py @@ -1,9 +1,8 @@ #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 +from collections import OrderedDict + def conv_nd(dims, *args, **kwargs): """ @@ -17,6 +16,7 @@ def conv_nd(dims, *args, **kwargs): 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. @@ -29,6 +29,7 @@ def avg_pool_nd(dims, *args, **kwargs): return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") + class Downsample(nn.Module): """ A downsampling layer with an optional convolution. @@ -38,7 +39,7 @@ class Downsample(nn.Module): downsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): + 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 @@ -61,8 +62,8 @@ class Downsample(nn.Module): 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: + 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') @@ -70,7 +71,7 @@ class ResnetBlock(nn.Module): 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: + if sk == False: self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: self.skep = None @@ -82,7 +83,7 @@ class ResnetBlock(nn.Module): def forward(self, x): if self.down == True: x = self.down_opt(x) - if self.in_conv is not None: # edit + if self.in_conv is not None: # edit x = self.in_conv(x) h = self.block1(x) @@ -103,12 +104,14 @@ class Adapter(nn.Module): 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)) + 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.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) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) def forward(self, x): # unshuffle @@ -118,8 +121,139 @@ class Adapter(nn.Module): 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 + 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