fix: Add method to get image features in PaliGemmaForConditionalGeneration (huggingface#38730)

* fix: Add method to retrieve image features in PaliGemmaForConditionalGeneration

* feat: Add get_image_features method to multiple models for image feature extraction

* fix: reformat the files with ruff.

* feat: Add methods for packing and retrieving image and video features across multiple models

modified:
- modeling_chameleon.py
- modeling_llava_next.py
- modular_llava_next_video.py
- modeling_qwen2_vl.py

and generate the:
- modeling_llava_next_video.py
- modeling_llava_onevision.py
- modeling_qwen2_5_vl.py

* feat: Implement get_image_features method in Aria, Mistral3, and VipLlava models with updated parameters

* fix: reformatted the code with fix-style

Author: YushunXiang

src/transformers/models/aria/modeling_aria.py

@@ -1225,6 +1225,18 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        vision_feature_layer: int = -1,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+            vision_feature_layer=vision_feature_layer,
+        )
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):

src/transformers/models/aria/modular_aria.py

@@ -1497,6 +1497,18 @@ def forward(
     """
 )
 class AriaForConditionalGeneration(LlavaForConditionalGeneration):
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        vision_feature_layer: int = -1,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+            vision_feature_layer=vision_feature_layer,
+        )
+
     @can_return_tuple
     @auto_docstring
     def forward(

src/transformers/models/aya_vision/modeling_aya_vision.py

@@ -394,6 +394,20 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        vision_feature_layer: Optional[Union[int, List[int]]] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            **kwargs,
+        )
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):

src/transformers/models/chameleon/modeling_chameleon.py

@@ -1229,6 +1229,12 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_tokens(self, pixel_values):
+        return self.model.get_image_tokens(pixel_values)
+
+    def get_image_features(self, pixel_values):
+        return self.model.get_image_features(pixel_values)
+
     @can_return_tuple
     @auto_docstring
     def forward(

src/transformers/models/gemma3/modeling_gemma3.py

@@ -1019,6 +1019,9 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(self, pixel_values):
+        return self.model.get_image_features(pixel_values)
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):

src/transformers/models/got_ocr2/modeling_got_ocr2.py

@@ -762,6 +762,20 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        vision_feature_layer: Optional[Union[int, List[int]]] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            **kwargs,
+        )
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):

src/transformers/models/idefics2/modeling_idefics2.py

@@ -1186,6 +1186,9 @@ def get_output_embeddings(self):
     def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
 
+    def get_image_features(self, pixel_values: torch.FloatTensor, pixel_attention_mask: torch.LongTensor = None):
+        return self.model.get_image_features(pixel_values=pixel_values, pixel_attention_mask=pixel_attention_mask)
+
     @can_return_tuple
     @auto_docstring
     def forward(

src/transformers/models/idefics3/modeling_idefics3.py

@@ -909,6 +909,9 @@ def get_output_embeddings(self):
     def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
 
+    def get_image_features(self, pixel_values: torch.FloatTensor, pixel_attention_mask: torch.LongTensor = None):
+        return self.model.get_image_features(pixel_values=pixel_values, pixel_attention_mask=pixel_attention_mask)
+
     @can_return_tuple
     @auto_docstring
     def forward(

src/transformers/models/internvl/modeling_internvl.py

@@ -883,6 +883,20 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        vision_feature_layer: Optional[Union[int, List[int]]] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            **kwargs,
+        )
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):

src/transformers/models/llava/modeling_llava.py

@@ -376,6 +376,20 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        vision_feature_layer: Optional[Union[int, List[int]]] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            **kwargs,
+        )
+
     # Make modules available throught conditional class for BC
     @property
     def language_model(self):