diff --git a/doctr/models/preprocessor/pytorch.py b/doctr/models/preprocessor/pytorch.py
index fb2cc005d7..b339a8cd52 100644
--- a/doctr/models/preprocessor/pytorch.py
+++ b/doctr/models/preprocessor/pytorch.py
@@ -60,12 +60,21 @@ def batch_inputs(self, samples: list[torch.Tensor]) -> list[torch.Tensor]:
 
         return batches
 
-    def sample_transforms(self, x: np.ndarray) -> torch.Tensor:
-        if x.ndim != 3:
-            raise AssertionError("expected list of 3D Tensors")
-        if x.dtype not in (np.uint8, np.float32, np.float16):
-            raise TypeError("unsupported data type for numpy.ndarray")
-        tensor = torch.from_numpy(x.copy()).permute(2, 0, 1)
+    def sample_transforms(self, x: np.ndarray | torch.Tensor) -> torch.Tensor:
+        if isinstance(x, np.ndarray):
+            if x.ndim != 3:
+                raise AssertionError("expected list of 3D Tensors")
+            if x.dtype not in (np.uint8, np.float32, np.float16):
+                raise TypeError("unsupported data type for numpy.ndarray")
+            x = torch.from_numpy(x.copy())
+        elif isinstance(x, torch.Tensor):
+            if x.ndim != 3:
+                raise AssertionError("expected 3D Tensor")
+        else:
+            raise TypeError(f"invalid input type: {type(x)}")
+        
+        tensor = x.permute(2, 0, 1)
+
         # Resizing
         tensor = self.resize(tensor)
         # Data type
@@ -76,22 +85,28 @@ def sample_transforms(self, x: np.ndarray) -> torch.Tensor:
 
         return tensor
 
-    def __call__(self, x: np.ndarray | list[np.ndarray]) -> list[torch.Tensor]:
+    def __call__(self, x: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor]) -> list[torch.Tensor]:
         """Prepare document data for model forwarding
 
         Args:
-            x: list of images (np.array) or a single image (np.array) of shape (H, W, C)
+            x: list of images (np.array | torch.Tensor) or a single image (np.array | torch.Tensor) of shape (H, W, C)
 
         Returns:
             list of page batches (*, C, H, W) ready for model inference
         """
         # Input type check
-        if isinstance(x, np.ndarray):
+        if isinstance(x, np.ndarray) or isinstance(x, torch.Tensor):
+            
             if x.ndim != 4:
                 raise AssertionError("expected 4D Tensor")
-            if x.dtype not in (np.uint8, np.float32, np.float16):
+            if x.dtype not in (np.uint8, np.float32, np.float16) and isinstance(x, np.ndarray):
                 raise TypeError("unsupported data type for numpy.ndarray")
-            tensor = torch.from_numpy(x.copy()).permute(0, 3, 1, 2)
+            if x.dtype not in (torch.uint8, torch.float32, torch.float16) and isinstance(x, torch.Tensor):
+                raise TypeError("unsupported data type for torch.Tensor")
+            if isinstance(x, np.ndarray):
+                x = torch.from_numpy(x.copy())
+
+            tensor = x.permute(0, 3, 1, 2)
 
             # Resizing
             if tensor.shape[-2] != self.resize.size[0] or tensor.shape[-1] != self.resize.size[1]:
@@ -105,7 +120,7 @@ def __call__(self, x: np.ndarray | list[np.ndarray]) -> list[torch.Tensor]:
                 tensor = tensor.to(dtype=torch.float32)
             batches = [tensor]
 
-        elif isinstance(x, list) and all(isinstance(sample, np.ndarray) for sample in x):
+        elif isinstance(x, list) and all(isinstance(sample, np.ndarray) or isinstance(sample, torch.Tensor) for sample in x):
             # Sample transform (to tensor, resize)
             samples = list(multithread_exec(self.sample_transforms, x))
             # Batching