diff --git a/src/transformers/models/owlv2/image_processing_owlv2.py b/src/transformers/models/owlv2/image_processing_owlv2.py
index 1e9a5163a1a6fd..d3ef04238a8f80 100644
--- a/src/transformers/models/owlv2/image_processing_owlv2.py
+++ b/src/transformers/models/owlv2/image_processing_owlv2.py
@@ -565,9 +565,9 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
         """
         logits, target_boxes = outputs.logits, outputs.target_pred_boxes
 
-        if len(logits) != len(target_sizes):
+        if target_sizes is not None and len(logits) != len(target_sizes):
             raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
+        if target_sizes is not None and target_sizes.shape[1] != 2:
             raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
 
         probs = torch.max(logits, dim=-1)
@@ -588,9 +588,14 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
                     scores[idx][ious > nms_threshold] = 0.0
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
-        target_boxes = target_boxes * scale_fct[:, None, :]
+        if target_sizes is not None:
+            if isinstance(target_sizes, List):
+                img_h = torch.tensor([i[0] for i in target_sizes])
+                img_w = torch.tensor([i[1] for i in target_sizes])
+            else:
+                img_h, img_w = target_sizes.unbind(1)
+            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
+            target_boxes = target_boxes * scale_fct[:, None, :]
 
         # Compute box display alphas based on prediction scores
         results = []
diff --git a/src/transformers/models/owlvit/image_processing_owlvit.py b/src/transformers/models/owlvit/image_processing_owlvit.py
index 25ea5f2720d527..5bc889ba85d501 100644
--- a/src/transformers/models/owlvit/image_processing_owlvit.py
+++ b/src/transformers/models/owlvit/image_processing_owlvit.py
@@ -556,9 +556,9 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
         """
         logits, target_boxes = outputs.logits, outputs.target_pred_boxes
 
-        if len(logits) != len(target_sizes):
+        if target_sizes is not None and len(logits) != len(target_sizes):
             raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
+        if target_sizes is not None and target_sizes.shape[1] != 2:
             raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
 
         probs = torch.max(logits, dim=-1)
@@ -579,9 +579,14 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
                     scores[idx][ious > nms_threshold] = 0.0
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
-        target_boxes = target_boxes * scale_fct[:, None, :]
+        if target_sizes is not None:
+            if isinstance(target_sizes, List):
+                img_h = torch.tensor([i[0] for i in target_sizes])
+                img_w = torch.tensor([i[1] for i in target_sizes])
+            else:
+                img_h, img_w = target_sizes.unbind(1)
+            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
+            target_boxes = target_boxes * scale_fct[:, None, :]
 
         # Compute box display alphas based on prediction scores
         results = []