diff --git a/lmms_eval/models/llama_vision.py b/lmms_eval/models/llama_vision.py
index c67f7965..7f33f715 100644
--- a/lmms_eval/models/llama_vision.py
+++ b/lmms_eval/models/llama_vision.py
@@ -15,7 +15,6 @@
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
-from lmms_eval.models.model_utils.load_video import read_video_pyav_pil
 
 warnings.filterwarnings("ignore")
 
@@ -33,12 +32,10 @@ def __init__(
         device: str = "cuda",
         dtype: Optional[Union[str, torch.dtype]] = "auto",
         batch_size: int = 1,
-        trust_remote_code: Optional[bool] = True,
+        trust_remote_code: Optional[bool] = False,
         attn_implementation: Optional[str] = None,
         device_map: str = "",
         max_frames_num: Optional[int] = 32,
-        fps: Optional[int] = None,
-        max_image_size: Optional[int] = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -55,9 +52,7 @@ def __init__(
         if isinstance(dtype, str) and dtype != "auto":
             dtype = getattr(torch, dtype)
 
-        self.fps = fps
         self.max_frames_num = max_frames_num
-        self.max_image_size = max_image_size
         self._model = MllamaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
         self.model.eval()
         self.processor = AutoProcessor.from_pretrained(pretrained)
@@ -182,11 +177,9 @@ def generate_until(self, requests: List[Instance]) -> List[str]:
 
             for visual in visuals:
                 if isinstance(visual, str):
-                    frames = read_video_pyav_pil(visual, num_frm=self.max_frames_num, fps=self.fps, max_image_size=self.max_image_size)
-                    images.extend(frames)
-                    # frames = self.load_video(visual, self.max_frames_num)
-                    # frames = torch.from_numpy(frames).permute(0, 3, 1, 2)
-                    # images.extend([to_pil_image(frame) for frame in frames])
+                    frames = self.load_video(visual, self.max_frames_num)
+                    frames = torch.from_numpy(frames).permute(0, 3, 1, 2)
+                    images.extend([to_pil_image(frame) for frame in frames])
                 elif isinstance(visual, PIL.Image.Image):
                     images.append(visual)