remove fir option

src/diffusers/models/resnet.py

@@ -579,7 +579,6 @@ class ResnetBlockBigGANpp(nn.Module):
         up=False,
         down=False,
         dropout=0.1,
-        fir=False,
         fir_kernel=(1, 3, 3, 1),
         skip_rescale=True,
         init_scale=0.0,
@@ -590,7 +589,6 @@ class ResnetBlockBigGANpp(nn.Module):
         self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
         self.up = up
         self.down = down
-        self.fir = fir
         self.fir_kernel = fir_kernel
 
         self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)

src/diffusers/models/unet_sde_score_estimation.py

@@ -334,7 +334,7 @@ class Combine(nn.Module):
 
 
 class Upsample(nn.Module):
-    def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
+    def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_ch = out_ch if out_ch else in_ch
         if with_conv:
@@ -347,13 +347,11 @@ class Upsample(nn.Module):
                 use_bias=True,
                 kernel_init=variance_scaling(),
             )
-        self.fir = fir
         self.with_conv = with_conv
         self.fir_kernel = fir_kernel
         self.out_ch = out_ch
 
     def forward(self, x):
-        B, C, H, W = x.shape
         if not self.with_conv:
             h = upsample_2d(x, self.fir_kernel, factor=2)
         else:
@@ -363,7 +361,7 @@ class Upsample(nn.Module):
 
 
 class Downsample(nn.Module):
-    def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
+    def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_ch = out_ch if out_ch else in_ch
         if with_conv:
@@ -376,13 +374,11 @@ class Downsample(nn.Module):
                 use_bias=True,
                 kernel_init=variance_scaling(),
             )
-        self.fir = fir
         self.fir_kernel = fir_kernel
         self.with_conv = with_conv
         self.out_ch = out_ch
 
     def forward(self, x):
-        B, C, H, W = x.shape
         if not self.with_conv:
             x = downsample_2d(x, self.fir_kernel, factor=2)
         else:
@@ -404,7 +400,7 @@ class NCSNpp(ModelMixin, ConfigMixin):
         conv_size=3,
         dropout=0.0,
         embedding_type="fourier",
-        fir=True,
+        fir=True, # TODO (patil-suraj) remove this option from here and pre-trained model configs 
         fir_kernel=(1, 3, 3, 1),
         fourier_scale=16,
         init_scale=0.0,
@@ -428,7 +424,6 @@ class NCSNpp(ModelMixin, ConfigMixin):
             conv_size=conv_size,
             dropout=dropout,
             embedding_type=embedding_type,
-            fir=fir,
             fir_kernel=fir_kernel,
             fourier_scale=fourier_scale,
             init_scale=init_scale,
@@ -483,25 +478,24 @@ class NCSNpp(ModelMixin, ConfigMixin):
             nn.init.zeros_(modules[-1].bias)
 
         AttnBlock = functools.partial(AttentionBlock, overwrite_linear=True, rescale_output_factor=math.sqrt(2.0))
-        Up_sample = functools.partial(Upsample, with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
+        Up_sample = functools.partial(Upsample, with_conv=resamp_with_conv, fir_kernel=fir_kernel)
 
         if progressive == "output_skip":
-            self.pyramid_upsample = Up_sample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
+            self.pyramid_upsample = Up_sample(fir_kernel=fir_kernel, with_conv=False)
         elif progressive == "residual":
-            pyramid_upsample = functools.partial(Up_sample, fir=fir, fir_kernel=fir_kernel, with_conv=True)
+            pyramid_upsample = functools.partial(Up_sample, fir_kernel=fir_kernel, with_conv=True)
 
-        Down_sample = functools.partial(Downsample, with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
+        Down_sample = functools.partial(Downsample, with_conv=resamp_with_conv, fir_kernel=fir_kernel)
 
         if progressive_input == "input_skip":
-            self.pyramid_downsample = Down_sample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
+            self.pyramid_downsample = Down_sample(fir_kernel=fir_kernel, with_conv=False)
         elif progressive_input == "residual":
-            pyramid_downsample = functools.partial(Down_sample, fir=fir, fir_kernel=fir_kernel, with_conv=True)
+            pyramid_downsample = functools.partial(Down_sample, fir_kernel=fir_kernel, with_conv=True)
 
         ResnetBlock = functools.partial(
             ResnetBlockBigGANpp,
             act=act,
             dropout=dropout,
-            fir=fir,
             fir_kernel=fir_kernel,
             init_scale=init_scale,
             skip_rescale=skip_rescale,