diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py
index 46e5335a..5fe1a8ec 100644
--- a/codes/models/steps/tecogan_losses.py
+++ b/codes/models/steps/tecogan_losses.py
@@ -96,7 +96,7 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
                     recurrent_input = self.resample(reduced_recurrent.float(), flowfield.float())
             input[self.recurrent_index] = recurrent_input
             if self.env['step'] % 50 == 0:
-                self.produce_teco_visual_debugs(input[self.input_lq_index], debug_index)
+                self.produce_teco_visual_debugs(input[self.input_lq_index], input[self.recurrent_index], debug_index)
                 debug_index += 1
             gen_out = gen(*input)
             if isinstance(gen_out, torch.Tensor):
@@ -117,7 +117,7 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
                 input[self.recurrent_index
                 ] = recurrent_input
                 if self.env['step'] % 50 == 0:
-                    self.produce_teco_visual_debugs(input[self.input_lq_index], debug_index)
+                    self.produce_teco_visual_debugs(input[self.input_lq_index], input[self.self.recurrent_index], debug_index)
                     debug_index += 1
                 gen_out = gen(*input)
                 if isinstance(gen_out, torch.Tensor):
@@ -127,13 +127,13 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
 
         return {self.output: results}
 
-    def produce_teco_visual_debugs(self, gen_input, it):
+    def produce_teco_visual_debugs(self, gen_input, gen_recurrent, it):
         if self.env['rank'] > 0:
             return
         base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_geninput", str(self.env['step']))
         os.makedirs(base_path, exist_ok=True)
-        torchvision.utils.save_image(gen_input[:, :3], osp.join(base_path, "%s_img.png" % (it,)))
-        torchvision.utils.save_image(gen_input[:, 3:], osp.join(base_path, "%s_recurrent.png" % (it,)))
+        torchvision.utils.save_image(gen_input, osp.join(base_path, "%s_img.png" % (it,)))
+        torchvision.utils.save_image(gen_recurrent, osp.join(base_path, "%s_recurrent.png" % (it,)))
 
 
 class FlowAdjustment(Injector):