diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py
index b08e253d..91fd08bd 100644
--- a/codes/models/steps/tecogan_losses.py
+++ b/codes/models/steps/tecogan_losses.py
@@ -50,6 +50,7 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         self.first_inputs = opt['first_inputs'] if 'first_inputs' in opt.keys() else opt['in']  # Use this to specify inputs that will be used in the first teco iteration, the rest will use 'in'.
         self.do_backwards = opt['do_backwards'] if 'do_backwards' in opt.keys() else True
         self.hq_recurrent = opt['hq_recurrent'] if 'hq_recurrent' in opt.keys() else False  # When True, recurrent_index is not touched for the first iteration, allowing you to specify what is fed in. When False, zeros are fed into the recurrent index.
+        self.hq_batched_output_key = opt['hq_batched_key'] if 'hq_batched_key' in opt.keys() else None
 
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
@@ -138,7 +139,8 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         final_results = {}
         # Include 'hq_batched' here - because why not... Don't really need a separate injector for this.
         b, s, c, h, w = state['hq'].shape
-        final_results['hq_batched'] = state['hq'].clone().permute(1,0,2,3,4).reshape(b*s, c, h, w)
+        if self.hq_batched_output_key is not None:
+            final_results[self.hq_batched_output_key] = state['hq'].clone().permute(1,0,2,3,4).reshape(b*s, c, h, w)
         for k, v in results.items():
             final_results[k] = torch.stack(v, dim=1)
             final_results[k + "_batched"] = torch.cat(v[:s], dim=0)  # Only include the original sequence - this output is generally used to compare against HQ.