diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py
index 2e4f11d8..5886a711 100644
--- a/codes/trainer/ExtensibleTrainer.py
+++ b/codes/trainer/ExtensibleTrainer.py
@@ -54,6 +54,7 @@ class ExtensibleTrainer(BaseModel):
             self.ema_rate = opt_get(train_opt, ['ema_rate'], .999)
             # It is advantageous for large networks to do this to save an extra copy of the model weights.
             # It does come at the cost of a round trip to CPU memory at every batch.
+            self.do_emas = opt_get(train_opt, ['ema_enabled'], True)
             self.ema_on_cpu = opt_get(train_opt, ['ema_on_cpu'], False)
         self.checkpointing_cache = opt['checkpointing_enabled']
         self.auto_recover = opt_get(opt, ['automatically_recover_nan_by_reverting_n_saves'], None)
@@ -156,7 +157,7 @@ class ExtensibleTrainer(BaseModel):
                     if v == dnet.module:
                         net_dict[k] = dnet
                         self.networks[k] = dnet
-                        if self.is_train:
+                        if self.is_train and self.do_emas:
                             self.emas[k] = copy.deepcopy(v)
                             if self.ema_on_cpu:
                                 self.emas[k] = self.emas[k].cpu()
@@ -357,18 +358,20 @@ class ExtensibleTrainer(BaseModel):
                     else:
                         if k in self.networks.keys():  # This isn't always the case, for example for EMAs.
                             self.load_network(ps[-self.auto_recover], self.networks[k], strict=True)
-                            self.load_network(self.save_history[f'{k}_ema'][-self.auto_recover], self.emas[k], strict=True)
+                            if self.do_emas:
+                                self.load_network(self.save_history[f'{k}_ema'][-self.auto_recover], self.emas[k], strict=True)
 
         # Call into custom step hooks as well as update EMA params.
         for name, net in self.networks.items():
             if hasattr(net, "custom_optimizer_step"):
                 net.custom_optimizer_step(it)
-            ema_params = self.emas[name].parameters()
-            net_params = net.parameters()
-            for ep, np in zip(ema_params, net_params):
-                if self.ema_on_cpu:
-                    np = np.cpu()
-                ep.detach().mul_(self.ema_rate).add_(np, alpha=1 - self.ema_rate)
+            if self.do_emas:
+                ema_params = self.emas[name].parameters()
+                net_params = net.parameters()
+                for ep, np in zip(ema_params, net_params):
+                    if self.ema_on_cpu:
+                        np = np.cpu()
+                    ep.detach().mul_(self.ema_rate).add_(np, alpha=1 - self.ema_rate)
         [e.after_optimize(state) for e in self.experiments]
 
 
@@ -455,7 +458,7 @@ class ExtensibleTrainer(BaseModel):
                     logger.info('Loading model for [%s]' % (load_path,))
                 self.load_network(load_path, net, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
                 load_path_ema = load_path.replace('.pth', '_ema.pth')
-                if self.is_train:
+                if self.is_train and self.do_emas:
                     ema_model = self.emas[name]
                     if os.path.exists(load_path_ema):
                         self.load_network(load_path_ema, ema_model, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
@@ -472,7 +475,8 @@ class ExtensibleTrainer(BaseModel):
             # Don't save non-trainable networks.
             if self.opt['networks'][name]['trainable']:
                 self.save_network(net, name, iter_step)
-                self.save_network(self.emas[name], f'{name}_ema', iter_step)
+                if self.do_emas:
+                    self.save_network(self.emas[name], f'{name}_ema', iter_step)
 
     def force_restore_swapout(self):
         # Legacy method. Do nothing.