diff --git a/codes/models/archs/SRG1_arch.py b/codes/models/archs/SRG1_arch.py
new file mode 100644
index 00000000..089d0594
--- /dev/null
+++ b/codes/models/archs/SRG1_arch.py
@@ -0,0 +1,198 @@
+import torch
+from torch import nn
+from switched_conv import BareConvSwitch, compute_attention_specificity
+import torch.nn.functional as F
+import functools
+from collections import OrderedDict
+from models.archs.arch_util import initialize_weights
+from switched_conv_util import save_attention_to_image
+
+
+class ConvBnLelu(nn.Module):
+    def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True):
+        super(ConvBnLelu, self).__init__()
+        padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
+        assert kernel_size in padding_map.keys()
+        self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size])
+        if bn:
+            self.bn = nn.BatchNorm2d(filters_out)
+        else:
+            self.bn = None
+        if lelu:
+            self.lelu = nn.LeakyReLU(negative_slope=.1)
+        else:
+            self.lelu = None
+
+    def forward(self, x):
+        x = self.conv(x)
+        if self.bn:
+            x = self.bn(x)
+        if self.lelu:
+            return self.lelu(x)
+        else:
+            return x
+
+
+class ResidualBranch(nn.Module):
+    def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth):
+        assert depth >= 2
+        super(ResidualBranch, self).__init__()
+        self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
+        self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=False)] +
+                                     [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=False) for i in range(depth-2)] +
+                                     [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False)])
+        self.scale = nn.Parameter(torch.ones(1))
+        self.bias = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, noise=None):
+        if noise is not None:
+            noise = noise * self.noise_scale
+            x = x + noise
+        for m in self.bnconvs:
+            x = m.forward(x)
+        return x * self.scale + self.bias
+
+
+# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
+# Doubles the input filter count.
+class HalvingProcessingBlock(nn.Module):
+    def __init__(self, filters):
+        super(HalvingProcessingBlock, self).__init__()
+        self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False)
+        self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True)
+
+    def forward(self, x):
+        x = self.bnconv1(x)
+        return self.bnconv2(x)
+
+
+# Creates a nested series of convolutional blocks. Each block processes the input data in-place and adds
+# filter_growth filters. Return is (nn.Sequential, ending_filters)
+def create_sequential_growing_processing_block(filters_init, filter_growth, num_convs):
+    convs = []
+    current_filters = filters_init
+    for i in range(num_convs):
+        convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=True))
+        current_filters += filter_growth
+    return nn.Sequential(*convs), current_filters
+
+
+class SwitchComputer(nn.Module):
+    def __init__(self, channels_in, filters, growth, transform_block, transform_count, reduction_blocks, processing_blocks=0,
+                 init_temp=20, enable_negative_transforms=False, add_scalable_noise_to_transforms=False):
+        super(SwitchComputer, self).__init__()
+        self.enable_negative_transforms = enable_negative_transforms
+
+        self.filter_conv = ConvBnLelu(channels_in, filters)
+        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reduction_blocks)])
+        final_filters = filters * 2 ** reduction_blocks
+        self.processing_blocks, final_filters = create_sequential_growing_processing_block(final_filters, growth, processing_blocks)
+        proc_block_filters = max(final_filters // 2, transform_count)
+        self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters, bn=False)
+        tc = transform_count
+        if self.enable_negative_transforms:
+            tc = transform_count * 2
+        self.final_switch_conv = nn.Conv2d(proc_block_filters, tc, 1, 1, 0)
+
+        self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
+        self.add_noise = add_scalable_noise_to_transforms
+
+        # And the switch itself, including learned scalars
+        self.switch = BareConvSwitch(initial_temperature=init_temp)
+        self.scale = nn.Parameter(torch.ones(1))
+        self.bias = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, output_attention_weights=False):
+        if self.add_noise:
+            rand_feature = torch.randn_like(x)
+            xformed = [t.forward(x, rand_feature) for t in self.transforms]
+        else:
+            xformed = [t.forward(x) for t in self.transforms]
+        if self.enable_negative_transforms:
+            xformed.extend([-t for t in xformed])
+
+        multiplexer = self.filter_conv(x)
+        for block in self.reduction_blocks:
+            multiplexer = block.forward(multiplexer)
+        for block in self.processing_blocks:
+            multiplexer = block.forward(multiplexer)
+        multiplexer = self.proc_switch_conv(multiplexer)
+        multiplexer = self.final_switch_conv.forward(multiplexer)
+        # Interpolate the multiplexer across the entire shape of the image.
+        multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')
+
+        outputs, attention = self.switch(xformed, multiplexer, True)
+        outputs = outputs * self.scale + self.bias
+        if output_attention_weights:
+            return outputs, attention
+        else:
+            return outputs
+
+    def set_temperature(self, temp):
+        self.switch.set_attention_temperature(temp)
+
+
+class ConfigurableSwitchedResidualGenerator(nn.Module):
+    def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
+                 trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
+                 heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
+                 add_scalable_noise_to_transforms=False):
+        super(ConfigurableSwitchedResidualGenerator, self).__init__()
+        switches = []
+        for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
+            switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
+        initialize_weights(switches, 1)
+        # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
+        initialize_weights([s.transforms for s in switches], .2 / len(switches))
+        self.switches = nn.ModuleList(switches)
+        self.transformation_counts = trans_counts
+        self.init_temperature = initial_temp
+        self.final_temperature_step = final_temperature_step
+        self.heightened_temp_min = heightened_temp_min
+        self.heightened_final_step = heightened_final_step
+        self.attentions = None
+        self.upsample_factor = upsample_factor
+
+    def forward(self, x):
+        # This network is entirely a "repair" network and operates on full-resolution images. Upsample first if that
+        # is called for, then repair.
+        if self.upsample_factor > 1:
+            x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
+
+        self.attentions = []
+        for i, sw in enumerate(self.switches):
+            sw_out, att = sw.forward(x, True)
+            x = x + sw_out
+            self.attentions.append(att)
+        return x,
+
+    def set_temperature(self, temp):
+        [sw.set_temperature(temp) for sw in self.switches]
+
+    def update_for_step(self, step, experiments_path='.'):
+        if self.attentions:
+            temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
+            if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1:
+                # Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
+                # without this, the attention specificity "spikes" incredibly fast in the last few iterations.
+                h_steps_total = self.heightened_final_step - self.final_temperature_step
+                h_steps_current = min(step - self.final_temperature_step, h_steps_total)
+                # The "gap" will represent the steps that need to be traveled as a linear function.
+                h_gap = 1 / self.heightened_temp_min
+                temp = h_gap * h_steps_current / h_steps_total
+                # Invert temperature to represent reality on this side of the curve
+                temp = 1 / temp
+            self.set_temperature(temp)
+            if step % 50 == 0:
+                [save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,)) for i in range(len(self.switches))]
+
+    def get_debug_values(self, step):
+        temp = self.switches[0].switch.temperature
+        mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
+        means = [i[0] for i in mean_hists]
+        hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
+        val = {"switch_temperature": temp}
+        for i in range(len(means)):
+            val["switch_%i_specificity" % (i,)] = means[i]
+            val["switch_%i_histogram" % (i,)] = hists[i]
+        return val
diff --git a/codes/models/networks.py b/codes/models/networks.py
index 13264a87..927c871a 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -9,6 +9,7 @@ import models.archs.HighToLowResNet as HighToLowResNet
 import models.archs.NestedSwitchGenerator as ng
 import models.archs.feature_arch as feature_arch
 import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
+import models.archs.SRG1_arch as srg1
 import functools
 
 # Generator
@@ -49,7 +50,7 @@ def define_G(opt, net_key='network_G'):
                                         final_temperature_step=opt_net['temperature_final_step'])
         netG = RRDBNet_arch.PixShuffleRRDB(nf=opt_net['nf'], nb=opt_net['nb'], gc=opt_net['gc'], scale=scale, rrdb_block_f=block_f)
     elif which_model == "ConfigurableSwitchedResidualGenerator":
-        netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
+        netG = srg1.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
                                                                       switch_reductions=opt_net['switch_reductions'],
                                                                       switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
                                                                       trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],