[LLaVa-NeXT] Small fixes (#30841)

* First draft

* Update docstring

src/transformers/models/llava_next/image_processing_llava_next.py

@@ -156,8 +156,8 @@ class LlavaNextImageProcessor(BaseImageProcessor):
             number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
             Can be overridden by the `image_std` parameter in the `preprocess` method.
         do_pad (`bool`, *optional*, defaults to `True`):
-                Whether to pad the image. If `True` will pad the images in the batch to the largest image in the batch
-                and create a pixel mask. Padding will be applied to the bottom and right of the image with zeros.
+                Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+                number of patches in the batch. Padding will be applied to the bottom and right with zeros.
         do_convert_rgb (`bool`, *optional*, defaults to `True`):
             Whether to convert the image to RGB.
     """
@@ -203,6 +203,7 @@ def __init__(
         self.do_normalize = do_normalize
         self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+        self.do_pad = do_pad
         self.do_convert_rgb = do_convert_rgb
 
     # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize with CLIP->LLaVa
@@ -572,7 +573,7 @@ def preprocess(
         do_normalize: bool = None,
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
-        do_pad: Optional[bool] = True,
+        do_pad: Optional[bool] = None,
         do_convert_rgb: bool = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
         data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
@@ -609,9 +610,9 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                 Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
                 `True`.
-            do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True` will pad the images in the batch to the largest image in the batch
-                and create a pixel mask. Padding will be applied to the bottom and right of the image with zeros.
+            do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+                Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+                number of patches in the batch. Padding will be applied to the bottom and right with zeros.
             do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                 Whether to convert the image to RGB.
             return_tensors (`str` or `TensorType`, *optional*):
@@ -647,6 +648,7 @@ def preprocess(
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize
         image_mean = image_mean if image_mean is not None else self.image_mean
         image_std = image_std if image_std is not None else self.image_std
+        do_pad = do_pad if do_pad is not None else self.do_pad
         do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
 
         images = make_list_of_images(images)