diff --git a/codes/models/archs/StructuredSwitchedGenerator.py b/codes/models/archs/StructuredSwitchedGenerator.py
index 89f76e6b..3fd3487d 100644
--- a/codes/models/archs/StructuredSwitchedGenerator.py
+++ b/codes/models/archs/StructuredSwitchedGenerator.py
@@ -9,6 +9,7 @@ import torch.nn.functional as F
 from switched_conv_util import save_attention_to_image_rgb
 from switched_conv import compute_attention_specificity
 import os
+import torchvision
 
 
 # VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
@@ -150,15 +151,18 @@ class SSGr1(nn.Module):
                                                    transform_count=self.transformation_counts, init_temp=init_temperature,
                                                    add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
         self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
-        self.upsample = nn.Sequential(*[UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
-        self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
+        self.upsample = nn.Sequential(*[UpconvBlock(nf, 64, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
+        self.final_hr_conv2 = ConvGnLelu(64, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
         self.switches = [self.sw1, self.sw_grad, self.conjoin_sw]
         self.attentions = None
+        self.lr = None
         self.init_temperature = init_temperature
         self.final_temperature_step = 10000
 
     def forward(self, x, embedding):
         noise_stds = []
+        # The attention_maps debugger outputs <x>. Save that here.
+        self.lr = x.detach().cpu()
 
         x_grad = self.get_g_nopadding(x)
 
@@ -200,9 +204,11 @@ class SSGr1(nn.Module):
                        (self.final_temperature_step - step) / self.final_temperature_step)
             self.set_temperature(temp)
             if step % 200 == 0:
-                output_path = os.path.join(experiments_path, "attention_maps", "a%i")
-                prefix = "attention_map_%i_%%i.png" % (step,)
-                [save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
+                output_path = os.path.join(experiments_path, "attention_maps")
+                prefix = "amap_%i_a%i_%%i.png"
+                [save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
+                torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
+
 
     def get_debug_values(self, step):
         temp = self.switches[0].switch.temperature