From 37f80907bc8254cf827abfd21673ee2045aab9dd Mon Sep 17 00:00:00 2001
From: Yousef Rafat <81116377+yousef-rafat@users.noreply.github.com>
Date: Thu, 16 Apr 2026 23:27:18 +0200
Subject: [PATCH] removed .tos

---
 comfy/image_encoders/birefnet.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/comfy/image_encoders/birefnet.py b/comfy/image_encoders/birefnet.py
index 08abcd1a2..167c8c067 100644
--- a/comfy/image_encoders/birefnet.py
+++ b/comfy/image_encoders/birefnet.py
@@ -102,7 +102,7 @@ class WindowAttention(nn.Module):
         q = q * self.scale
         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
         relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
         attn = attn + relative_position_bias.unsqueeze(0)
@@ -422,7 +422,7 @@ class DeformableConv2d(nn.Module):
                                      padding=self.padding,
                                      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,
                                       kernel_size=kernel_size,
                                       stride=stride,
@@ -432,11 +432,6 @@ class DeformableConv2d(nn.Module):
     def forward(self, x):
         offset = self.offset_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(
             input=x,
             offset=offset,
@@ -517,8 +512,6 @@ class ASPPDeformable(nn.Module):
         self.relu = nn.ReLU(inplace=True)
 
     def forward(self, x):
-        device = self.conv1.weight.device
-        x = x.to(device)
         x1 = self.aspp1(x)
         x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
         x5 = self.global_avg_pool(x)
@@ -637,8 +630,7 @@ class Decoder(nn.Module):
         return torch.cat(patches_batch, dim=0)
 
     def forward(self, features):
-        device = next(self.ipt_blk5.parameters()).device
-        x, x1, x2, x3, x4 = [t.to(device) for t in features]
+        x, x1, x2, x3, x4 = features
 
         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)