diff --git a/backend/src/RenderVideo.py b/backend/src/RenderVideo.py
index a7fb68a0..0ec699a5 100644
--- a/backend/src/RenderVideo.py
+++ b/backend/src/RenderVideo.py
@@ -102,15 +102,15 @@ def __init__(
         self.getVideoProperties(inputFile)
 
         printAndLog("Using backend: " + self.backend)
-        
-        if interpolateModel:
-            self.setupInterpolate()
-            printAndLog("Using Interpolation Model: " + self.interpolateModel)
-
+        # upscale has to be called first to get the scale of the upscale model
         if upscaleModel:
             self.setupUpscale()
+
             printAndLog("Using Upscaling Model: " + self.upscaleModel)
-        
+        if interpolateModel:
+            self.setupInterpolate()
+
+            printAndLog("Using Interpolation Model: " + self.interpolateModel)
 
         super().__init__(
             inputFile=inputFile,
diff --git a/backend/src/pytorch/InterpolateArchs/GMFSS/GMFSS.py b/backend/src/pytorch/InterpolateArchs/GMFSS/GMFSS.py
index f286ea76..0f539216 100644
--- a/backend/src/pytorch/InterpolateArchs/GMFSS/GMFSS.py
+++ b/backend/src/pytorch/InterpolateArchs/GMFSS/GMFSS.py
@@ -25,7 +25,7 @@ def __init__(
         ensemble: bool = False,
         width: int = 1920,
         height: int = 1080,
-        trt=False,
+        trt=True,
         dtype: torch.dtype = torch.float16,
         device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
     ):
@@ -71,14 +71,14 @@ def __init__(
         if trt:
             from ...TensorRTHandler import TorchTensorRTHandler
             trtHandler = TorchTensorRTHandler(multi_precision_engine=False,trt_optimization_level=5)
-            trtHandler.build_engine(self.ifnet, dtype=dtype, device=device, example_inputs=self.rife_example_input(), trt_engine_path="IFNet.engine")
-            trtHandler.build_engine(self.feat_ext, dtype=dtype, device=device, example_inputs=self.img0_example_input(), trt_engine_path="Feat.engine")
             trtHandler.build_engine(self.flownet, dtype=dtype, device=device, example_inputs=self.flownet_example_input(), trt_engine_path="Flownet.engine")
-            trtHandler.build_engine(self.fusionnet, dtype=dtype, device=device, example_inputs=self.flownet_example_input(), trt_engine_path="Flownet.engine")
+            #trtHandler.build_engine(self.ifnet, dtype=dtype, device=device, example_inputs=self.rife_example_input(), trt_engine_path="IFNet.engine")
+            #trtHandler.build_engine(self.feat_ext, dtype=dtype, device=device, example_inputs=self.img0_example_input(), trt_engine_path="Feat.engine")
+            #trtHandler.build_engine(self.fusionnet, dtype=dtype, device=device, example_inputs=self.flownet_example_input(), trt_engine_path="Flownet.engine")
             self.ifnet = None
             self.feat_ext = None
             #self.fusionnet = None
-            #self.flownet = None
+            self.flownet = None
             import gc
             gc.collect()
             torch.cuda.empty_cache()
@@ -86,7 +86,7 @@ def __init__(
             torch.cuda.reset_max_memory_cached()
             self.ifnet = trtHandler.load_engine("IFNet.engine")
             self.feat_ext = trtHandler.load_engine("Feat.engine")
-            #self.flownet = trtHandler.load_engine("Flownet.engine")
+            self.flownet = trtHandler.load_engine("Flownet.engine")