diff --git a/codes/models/archs/StructuredSwitchedGenerator.py b/codes/models/archs/StructuredSwitchedGenerator.py
index 5b9857c5..0be355dd 100644
--- a/codes/models/archs/StructuredSwitchedGenerator.py
+++ b/codes/models/archs/StructuredSwitchedGenerator.py
@@ -184,10 +184,15 @@ class SSGr1(nn.Module):
         self.init_temperature = init_temperature
         self.final_temperature_step = 10000
 
-    def forward(self, x, ref, ref_center):
+    def forward(self, x, ref, ref_center, save_attentions=True):
         # The attention_maps debugger outputs <x>. Save that here.
         self.lr = x.detach().cpu()
 
+        # If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
+        # norm should only be getting updates with new data, not recurrent generator sampling.
+        for sw in self.switches:
+            sw.set_update_attention_norm(save_attentions)
+
         x_grad = self.get_g_nopadding(x)
         ref_code = checkpoint(self.reference_embedding, ref, ref_center)
         ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
@@ -206,7 +211,8 @@ class SSGr1(nn.Module):
         x_out = checkpoint(self.upsample, x_out)
         x_out = checkpoint(self.final_hr_conv2, x_out)
 
-        self.attentions = [a1, a3, a4]
+        if save_attentions:
+            self.attentions = [a1, a3, a4]
         self.grad_fea_std = grad_fea_std.detach().cpu()
         self.fea_grad_std = fea_grad_std.detach().cpu()
         return x_grad_out, x_out, x_grad
@@ -265,7 +271,7 @@ class StackedSwitchGenerator(nn.Module):
         self.init_temperature = init_temperature
         self.final_temperature_step = 10000
 
-    def forward(self, x, ref, ref_center):
+    def forward(self, x, ref, ref_center, save_attentions=True):
         # The attention_maps debugger outputs <x>. Save that here.
         self.lr = x.detach().cpu()
 
@@ -280,7 +286,8 @@ class StackedSwitchGenerator(nn.Module):
         x_out = checkpoint(self.upsample, x_out)
         x_out = checkpoint(self.final_hr_conv2, x_out)
 
-        self.attentions = [a1, a3, a3]
+        if save_attentions:
+            self.attentions = [a1, a3, a3]
         return x_out,
 
     def set_temperature(self, temp):
diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py
index 7b6edfc8..644beeb1 100644
--- a/codes/models/archs/SwitchedResidualGenerator_arch.py
+++ b/codes/models/archs/SwitchedResidualGenerator_arch.py
@@ -104,7 +104,10 @@ class ConfigurableSwitchComputer(nn.Module):
         # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
         # depending on its needs.
         self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
+        self.update_norm = True
 
+    def set_update_attention_norm(self, set_val):
+        self.update_norm = set_val
 
     # Regarding inputs: it is acceptable to pass in a tuple/list as an input for (x), but the first element
     # *must* be the actual parameter that gets fed through the network - it is assumed to be the identity.
@@ -148,7 +151,7 @@ class ConfigurableSwitchComputer(nn.Module):
             m = self.multiplexer(*att_in)
 
         # It is assumed that [xformed] and [m] are collapsed into tensors at this point.
-        outputs, attention = self.switch(xformed, m, True)
+        outputs, attention = self.switch(xformed, m, True, self.update_norm)
         outputs = identity + outputs * self.switch_scale * fixed_scale
         outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
         if output_attention_weights:
diff --git a/codes/models/steps/injectors.py b/codes/models/steps/injectors.py
index f54a81f6..6e870f92 100644
--- a/codes/models/steps/injectors.py
+++ b/codes/models/steps/injectors.py
@@ -4,6 +4,7 @@ from data.weight_scheduler import get_scheduler_for_opt
 from utils.util import checkpoint
 import torchvision.utils as utils
 #from models.steps.recursive_gen_injectors import ImageFlowInjector
+from models.steps.losses import extract_params_from_state
 
 # Injectors are a way to sythesize data within a step that can then be used (and reused) by loss functions.
 def create_injector(opt_inject, env):
@@ -43,7 +44,6 @@ class Injector(torch.nn.Module):
     def forward(self, state):
         raise NotImplementedError
 
-
 # Uses a generator to synthesize an image from [in] and injects the results into [out]
 # Note that results are *not* detached.
 class ImageGeneratorInjector(Injector):
@@ -53,7 +53,7 @@ class ImageGeneratorInjector(Injector):
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
         if isinstance(self.input, list):
-            params = [state[i] for i in self.input]
+            params = extract_params_from_state(self.input, state)
             results = gen(*params)
         else:
             results = gen(state[self.input])