diff --git a/model/IFNet_HDv2.py b/model/IFNet_HDv2.py
index c8243a8..67658f9 100644
--- a/model/IFNet_HDv2.py
+++ b/model/IFNet_HDv2.py
@@ -39,15 +39,16 @@ class IFBlock(nn.Module):
         )        
         self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
                     
-    def forward(self, x):
-        if self.scale != 1:
+    def forward(self, x, scale=1.0):
+        scale = self.scale / scale
+        if scale != 1.0:
             x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
                               align_corners=False)
         x = self.conv0(x)
         x = self.convblock(x)
         x = self.conv1(x)
         flow = x
-        if self.scale != 1:
+        if scale != 1.0:
             flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
                                  align_corners=False)
         return flow
@@ -62,27 +63,23 @@ class IFNet(nn.Module):
         self.block3 = IFBlock(10, scale=1, c=48)
 
     def forward(self, x, scale=1.0):
-        if scale != 1.0:
-            x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
-        flow0 = self.block0(x)
+        flow0 = self.block0(x, scale)
         F1 = flow0
         F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
         warped_img0 = warp(x[:, :3], F1_large[:, :2])
         warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
-        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1), scale)
         F2 = (flow0 + flow1)
         F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
         warped_img0 = warp(x[:, :3], F2_large[:, :2])
         warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
-        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1), scale)
         F3 = (flow0 + flow1 + flow2)
         F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
         warped_img0 = warp(x[:, :3], F3_large[:, :2])
         warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
-        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
+        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1), scale)
         F4 = (flow0 + flow1 + flow2 + flow3)
-        if scale != 1.0:
-            F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale
         return F4, [F1, F2, F3, F4]
 
 if __name__ == '__main__':