diff --git a/selfdrive/modeld/modeld.py b/selfdrive/modeld/modeld.py
index 1e3d1782e6..ac88dfbb5f 100755
--- a/selfdrive/modeld/modeld.py
+++ b/selfdrive/modeld/modeld.py
@@ -86,10 +86,20 @@ class ModelState:
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
   def __init__(self, context: CLContext):
-    self.frames = {
-      'input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP),
-      'big_input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP)
-    }
+    with open(VISION_METADATA_PATH, 'rb') as f:
+      vision_metadata = pickle.load(f)
+      self.vision_input_shapes =  vision_metadata['input_shapes']
+      self.vision_input_names = list(self.vision_input_shapes.keys())
+      self.vision_output_slices = vision_metadata['output_slices']
+      vision_output_size = vision_metadata['output_shapes']['outputs'][1]
+
+    with open(POLICY_METADATA_PATH, 'rb') as f:
+      policy_metadata = pickle.load(f)
+      self.policy_input_shapes =  policy_metadata['input_shapes']
+      self.policy_output_slices = policy_metadata['output_slices']
+      policy_output_size = policy_metadata['output_shapes']['outputs'][1]
+
+    self.frames = {name: DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP) for name in self.vision_input_names}
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
 
     self.full_features_buffer = np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32)
@@ -106,18 +116,6 @@ class ModelState:
       'features_buffer': np.zeros((1, ModelConstants.INPUT_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32),
     }
 
-    with open(VISION_METADATA_PATH, 'rb') as f:
-      vision_metadata = pickle.load(f)
-      self.vision_input_shapes =  vision_metadata['input_shapes']
-      self.vision_output_slices = vision_metadata['output_slices']
-      vision_output_size = vision_metadata['output_shapes']['outputs'][1]
-
-    with open(POLICY_METADATA_PATH, 'rb') as f:
-      policy_metadata = pickle.load(f)
-      self.policy_input_shapes =  policy_metadata['input_shapes']
-      self.policy_output_slices = policy_metadata['output_slices']
-      policy_output_size = policy_metadata['output_shapes']['outputs'][1]
-
     # img buffers are managed in openCL transform code
     self.vision_inputs: dict[str, Tensor] = {}
     self.vision_output = np.zeros(vision_output_size, dtype=np.float32)
@@ -135,7 +133,7 @@ class ModelState:
     parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
     return parsed_model_outputs
 
-  def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_wide: np.ndarray,
+  def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
                 inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
     # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
     inputs['desire'][0] = 0
@@ -148,8 +146,7 @@ class ModelState:
 
     self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
     self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
-    imgs_cl = {'input_imgs': self.frames['input_imgs'].prepare(buf, transform.flatten()),
-               'big_input_imgs': self.frames['big_input_imgs'].prepare(wbuf, transform_wide.flatten())}
+    imgs_cl = {name: self.frames[name].prepare(bufs[name], transforms[name].flatten()) for name in self.vision_input_names}
 
     if TICI and not USBGPU:
       # The imgs tensors are backed by opencl memory, only need init once
@@ -328,14 +325,16 @@ def main(demo=False):
     if prepare_only:
       cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")
 
+    bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
+    transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}
     inputs:dict[str, np.ndarray] = {
       'desire': vec_desire,
       'traffic_convention': traffic_convention,
       'lateral_control_params': lateral_control_params,
-      }
+    }
 
     mt1 = time.perf_counter()
-    model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
+    model_output = model.run(bufs, transforms, inputs, prepare_only)
     mt2 = time.perf_counter()
     model_execution_time = mt2 - mt1