diff --git a/src/transformers/models/pixio/modeling_pixio.py b/src/transformers/models/pixio/modeling_pixio.py
index e3f9e2626f27..985e6b51216f 100644
--- a/src/transformers/models/pixio/modeling_pixio.py
+++ b/src/transformers/models/pixio/modeling_pixio.py
@@ -132,10 +132,12 @@ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width:
 
         return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
 
-    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
         batch_size, _, height, width = pixel_values.shape
         target_dtype = self.patch_embeddings.projection.weight.dtype
-        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
+        embeddings = self.patch_embeddings(
+            pixel_values.to(dtype=target_dtype), interpolate_pos_encoding=interpolate_pos_encoding
+        )
 
         cls_tokens = self.cls_token.expand(batch_size, -1, -1)
         embeddings = torch.cat((cls_tokens, embeddings), dim=1)
@@ -407,12 +409,13 @@ def get_input_embeddings(self) -> PixioPatchEmbeddings:
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
+        interpolate_pos_encoding: bool = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutputWithPooling:
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
 
-        embedding_output = self.embeddings(pixel_values)
+        embedding_output = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
 
         encoder_outputs: BaseModelOutput = self.encoder(embedding_output, **kwargs)
         sequence_output = encoder_outputs.last_hidden_state
diff --git a/src/transformers/models/pixio/modular_pixio.py b/src/transformers/models/pixio/modular_pixio.py
index 3446ed222283..6e5e9a65375f 100644
--- a/src/transformers/models/pixio/modular_pixio.py
+++ b/src/transformers/models/pixio/modular_pixio.py
@@ -172,10 +172,12 @@ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width:
 
         return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
 
-    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
         batch_size, _, height, width = pixel_values.shape
         target_dtype = self.patch_embeddings.projection.weight.dtype
-        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
+        embeddings = self.patch_embeddings(
+            pixel_values.to(dtype=target_dtype), interpolate_pos_encoding=interpolate_pos_encoding
+        )
 
         cls_tokens = self.cls_token.expand(batch_size, -1, -1)
         embeddings = torch.cat((cls_tokens, embeddings), dim=1)
@@ -274,12 +276,13 @@ def get_input_embeddings(self) -> PixioPatchEmbeddings:
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
+        interpolate_pos_encoding: bool = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutputWithPooling:
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
 
-        embedding_output = self.embeddings(pixel_values)
+        embedding_output = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
 
         encoder_outputs: BaseModelOutput = self.encoder(embedding_output, **kwargs)
         sequence_output = encoder_outputs.last_hidden_state