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removed .tos

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Yousef Rafat 2026-04-16 23:27:18 +02:00
parent f6aeec66f7
commit 37f80907bc
1 changed files with 3 additions and 11 deletions
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14
comfy/image_encoders/birefnet.py
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@ -102,7 +102,7 @@ class WindowAttention(nn.Module):
q = q * self.scale q = q * self.scale
attn = (q @ k.transpose(-2, -1)) attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0) attn = attn + relative_position_bias.unsqueeze(0)
@ -422,7 +422,7 @@ class DeformableConv2d(nn.Module):
padding=self.padding, padding=self.padding,
bias=True, device=device, dtype=dtype) bias=True, device=device, dtype=dtype)
self.regular_conv = operations.Conv2d(in_channels, self.regular_conv = torch.nn.Conv2d(in_channels,
out_channels=out_channels, out_channels=out_channels,
kernel_size=kernel_size, kernel_size=kernel_size,
stride=stride, stride=stride,
@ -432,11 +432,6 @@ class DeformableConv2d(nn.Module):
def forward(self, x): def forward(self, x):
offset = self.offset_conv(x) offset = self.offset_conv(x)
modulator = 2. * torch.sigmoid(self.modulator_conv(x)) modulator = 2. * torch.sigmoid(self.modulator_conv(x))
dtype = self.regular_conv.weight.dtype
device = self.regular_conv.weight.device
x = x.to(dtype).to(device)
offset = offset.to(dtype).to(device)
modulator = modulator.to(dtype).to(device)
x = deform_conv2d( x = deform_conv2d(
input=x, input=x,
offset=offset, offset=offset,
@ -517,8 +512,6 @@ class ASPPDeformable(nn.Module):
self.relu = nn.ReLU(inplace=True) self.relu = nn.ReLU(inplace=True)
def forward(self, x): def forward(self, x):
device = self.conv1.weight.device
x = x.to(device)
x1 = self.aspp1(x) x1 = self.aspp1(x)
x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms] x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
x5 = self.global_avg_pool(x) x5 = self.global_avg_pool(x)
@ -637,8 +630,7 @@ class Decoder(nn.Module):
return torch.cat(patches_batch, dim=0) return torch.cat(patches_batch, dim=0)
def forward(self, features): def forward(self, features):
device = next(self.ipt_blk5.parameters()).device x, x1, x2, x3, x4 = features
x, x1, x2, x3, x4 = [t.to(device) for t in features]
patches_batch = self.get_patches_batch(x, x4) if self.split else x patches_batch = self.get_patches_batch(x, x4) if self.split else x
x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1) x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)