diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py
index c95e214b..c1a57e3a 100644
--- a/codes/models/steps/tecogan_losses.py
+++ b/codes/models/steps/tecogan_losses.py
@@ -160,10 +160,10 @@ class FlowAdjustment(Injector):
     def forward(self, state):
         flow = self.env['generators'][self.flow]
         flow_target = state[self.flow_target]
-        flowed = state[self.flowed]
+        flowed = F.interpolate(state[self.flowed], size=flow_target.shape[2:], mode='bicubic')
         flow_input = torch.stack([flow_target, flowed], dim=2)
-        flowfield = flow(flow_input)
-        return {self.output: self.resample(flowed.float(), flowfield.float())}
+        flowfield = F.interpolate(flow(flow_input), size=state[self.flowed].shape[2:], mode='bicubic')
+        return {self.output: self.resample(state[self.flowed].float(), flowfield.float())}
 
 
 # This is the temporal discriminator loss from TecoGAN.
diff --git a/codes/process_video.py b/codes/process_video.py
index a8b411d4..96ec23bf 100644
--- a/codes/process_video.py
+++ b/codes/process_video.py
@@ -142,6 +142,9 @@ if __name__ == "__main__":
     vid_counter = opt['minivid_start_no'] if 'minivid_start_no' in opt.keys() else 0
     img_index = opt['generator_img_index']
     recurrent_mode = opt['recurrent_mode']
+    if recurrent_mode:
+        assert opt['dataset']['batch_size'] == 1   # Can only do 1 frame at a time in recurrent mode, by definition.
+    scale = opt['scale']
     first_frame = True
     ffmpeg_proc = None
 
@@ -150,7 +153,8 @@ if __name__ == "__main__":
         need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
 
         if recurrent_mode and first_frame:
-            recurrent_entry = data['LQ'].detach().clone()
+            b, c, h, w = data['LQ'].shape
+            recurrent_entry = torch.zeros((b,c,h*scale,w*scale), device=data['LQ'].device)
         first_frame = False
         if recurrent_mode:
             data['recurrent'] = recurrent_entry
@@ -163,7 +167,7 @@ if __name__ == "__main__":
         else:
             visuals = model.fake_H.detach()
         if recurrent_mode:
-            recurrent_entry = torch.nn.functional.interpolate(visuals, scale_factor=1/opt['scale'], mode='bicubic')
+            recurrent_entry = visuals
         visuals = visuals.cpu().float()
         for i in range(visuals.shape[0]):
             sr_img = util.tensor2img(visuals[i])  # uint8