remove if fir from resent block and upsample, downsample for sde unet

src/diffusers/models/resnet.py

@@ -614,19 +614,11 @@ class ResnetBlockBigGANpp(nn.Module):
         h = self.act(self.GroupNorm_0(x))
 
         if self.up:
-            if self.fir:
             h = upsample_2d(h, self.fir_kernel, factor=2)
             x = upsample_2d(x, self.fir_kernel, factor=2)
-            else:
-                h = naive_upsample_2d(h, factor=2)
-                x = naive_upsample_2d(x, factor=2)
         elif self.down:
-            if self.fir:
             h = downsample_2d(h, self.fir_kernel, factor=2)
             x = downsample_2d(x, self.fir_kernel, factor=2)
-            else:
-                h = naive_downsample_2d(h, factor=2)
-                x = naive_downsample_2d(x, factor=2)
 
         h = self.Conv_0(h)
         # Add bias to each feature map conditioned on the time embedding

src/diffusers/models/unet_sde_score_estimation.py

@@ -417,10 +417,6 @@ class Upsample(nn.Module):
     def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_ch = out_ch if out_ch else in_ch
-        if not fir:
-            if with_conv:
-                self.Conv_0 = conv3x3(in_ch, out_ch)
-        else:
         if with_conv:
             self.Conv2d_0 = Conv2d(
                 in_ch,
@@ -438,11 +434,6 @@ class Upsample(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        if not self.fir:
-            h = F.interpolate(x, (H * 2, W * 2), "nearest")
-            if self.with_conv:
-                h = self.Conv_0(h)
-        else:
         if not self.with_conv:
             h = upsample_2d(x, self.fir_kernel, factor=2)
         else:
@@ -455,10 +446,6 @@ class Downsample(nn.Module):
     def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_ch = out_ch if out_ch else in_ch
-        if not fir:
-            if with_conv:
-                self.Conv_0 = conv3x3(in_ch, out_ch, stride=2, padding=0)
-        else:
         if with_conv:
             self.Conv2d_0 = Conv2d(
                 in_ch,
@@ -476,13 +463,6 @@ class Downsample(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        if not self.fir:
-            if self.with_conv:
-                x = F.pad(x, (0, 1, 0, 1))
-                x = self.Conv_0(x)
-            else:
-                x = F.avg_pool2d(x, 2, stride=2)
-        else:
         if not self.with_conv:
             x = downsample_2d(x, self.fir_kernel, factor=2)
         else: