Hi @ptrblck,
Thanks for the fast reply!
Yes, exactly. So the problem of new tensors being allocated starts when increasing the input size from [1, 16, 110, 110, 110] to e.g. [1, 16, 115, 115, 115].
The model definition is:
    self.kernel_1 = torch.nn.Conv3d(16, 16,
                                    kernel_size=3,
                                    stride=1,
                                    padding=0)
    
    self.kernel_2 = torch.nn.Conv3d(32, 16,
                                   kernel_size=1,
                                   stride=1,
                                   padding=0)

    self.kernel_3 = torch.nn.Conv3d(16, 8,
                                   kernel_size=1,
                                   stride=1,
                                   padding=0)

    self.kernel_4 = torch.nn.Conv3d(8, 4,
                                   kernel_size=1,
                                   stride=1,
                                   padding=0)

    self.kernel_5 = torch.nn.Conv3d(4, 1,
                                   kernel_size=1,
                                   stride=1,
                                   padding=0)


    self.padding = torch.nn.ReplicationPad3d(1)

    self.relu = torch.nn.LeakyReLU()
    self.tanh = torch.nn.Tanh()

Thanks!