fix black screens with .clone()

Author: TNTwise

backend/src/pytorch/InterpolateGIMM.py

@@ -175,7 +175,7 @@ def __call__(
 
                     output = output[:, :, : self.height, : self.width]
                     retFrame = Frame(self.backend, self.width, self.height, img1.device, gpu_id=img1.gpu_id, hdr_mode=self.hdr_mode, dtype=img1.dtype)
-                    retFrame.set_frame_tensor(output)
+                    retFrame.set_frame_tensor(output.clone())
                     yield retFrame
 
                 else:

backend/src/pytorch/InterpolateGMFSS.py

@@ -176,7 +176,7 @@ def __call__(
 
                     output = output[:, :, : self.height, : self.width]
                     retFrame = Frame(self.backend, self.width, self.height, img1.device, gpu_id=img1.gpu_id, hdr_mode=self.hdr_mode, dtype=img1.dtype)
-                    retFrame.set_frame_tensor(output)
+                    retFrame.set_frame_tensor(output.clone())
                     yield retFrame
                 else:
                     self.flownet.reset_cache_after_transition()

backend/src/pytorch/InterpolateIFRNET.py

@@ -149,7 +149,7 @@ def __call__(
             )
             for frame in frames: 
                 retFrame = Frame(self.backend, self.width, self.height, img1.device, gpu_id=img1.gpu_id, hdr_mode=self.hdr_mode, dtype=img1.dtype)
-                retFrame.set_frame_tensor(frame[:, :self.height, :self.width]) 
+                retFrame.set_frame_tensor(frame[:, :self.height, :self.width].clone()) 
                 if not transition:
                     yield retFrame
             self.torchUtils.copy_tensor(self.frame0, frame1, self.prepareStream)

backend/src/pytorch/InterpolateRIFE.py

@@ -487,7 +487,7 @@ def __call__(
                             )
                     output = output[:, :, : self.height, : self.width]
                     retFrame = Frame(self.backend, self.width, self.height, img1.device, gpu_id=img1.gpu_id, hdr_mode=self.hdr_mode, dtype=img1.dtype)
-                    retFrame.set_frame_tensor(output)
+                    retFrame.set_frame_tensor(output.clone())
                     yield retFrame
 
                 else:

backend/src/pytorch/UpscaleModelWrapper.py

@@ -105,4 +105,4 @@ def load_model(self, model=None) -> torch.nn.Module:
                 self.inference_helper = TemporalSPANInferenceHelper(model=model, scale=self.__scale)
     def __call__(self, *args, **kwargs):
         assert self.inference_helper is not None, "Inference helper is not initialized."
-        return self.inference_helper(*args, **kwargs)
+        return self.inference_helper(*args, **kwargs).clone()