diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 9eacd2138978b..ee28f477a26ad 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -2439,16 +2439,13 @@ class GPUModelRunner(
     ]:
         num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
         is_first_rank = get_pp_group().is_first_rank
+        is_encoder_decoder = self.model_config.is_encoder_decoder
 
         # _prepare_inputs may reorder the batch, so we must gather multi
         # modal outputs after that to ensure the correct order
         ec_connector_output = None
 
-        if (
-            self.supports_mm_inputs
-            and is_first_rank
-            and not self.model_config.is_encoder_decoder
-        ):
+        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
             # Run the multimodal encoder if any.
             with self.maybe_get_ec_connector_output(
                 scheduler_output,
@@ -2526,10 +2523,7 @@ class GPUModelRunner(
                 num_input_tokens, intermediate_tensors, True
             )
 
-        if (
-            self.model_config.is_encoder_decoder
-            and scheduler_output.scheduled_encoder_inputs
-        ):
+        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
             # Run the encoder, just like we do with other multimodal inputs.
             # For an encoder-decoder model, our processing here is a bit
             # simpler, because the outputs are just passed to the decoder.