diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py
index 304e58e8..9f320506 100644
--- a/codes/models/steps/tecogan_losses.py
+++ b/codes/models/steps/tecogan_losses.py
@@ -67,6 +67,7 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         self.resample = Resample2d()
         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.
 
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
@@ -89,7 +90,10 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         for i in range(f):
             if first_step:
                 input = extract_inputs_index(first_inputs, i)
-                recurrent_input = torch.zeros_like(input[self.recurrent_index])
+                if self.hq_recurrent:
+                    recurrent_input = input[self.recurrent_index]
+                else:
+                    recurrent_input = torch.zeros_like(input[self.recurrent_index])
                 first_step = False
             else:
                 input = extract_inputs_index(inputs, i)