diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py
index 014e27bc869d4..e5d2edbd81eb1 100644
--- a/vllm/model_executor/models/idefics3.py
+++ b/vllm/model_executor/models/idefics3.py
@@ -267,54 +267,56 @@ def input_processor_for_idefics3(ctx: InputContext,
     n_images_in_text = []
 
     text = inputs.get("prompt")
-    if text is not None:
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, "
-                             "or a list of strings")
-
-        fake_image_token = processor.fake_image_token.content
-        image_token = processor.image_token.content
-        global_img_token = processor.global_image_tag
-
-        prompt_strings = []
-        for sample, sample_rows, sample_cols in zip(text, image_rows,
-                                                    image_cols):
-            n_images_in_text.append(sample.count(image_token))
-
-            # Replace the image token with fake tokens around the expanded
-            # image token sequence of length `image_seq_len`
-            image_prompt_strings = []
-            for n_rows, n_cols in zip(sample_rows, sample_cols):
-                image_prompt_string = _get_image_prompt_string(
-                    n_rows,
-                    n_cols,
-                    processor.image_seq_len,
-                    image_token=image_token,
-                    fake_token_around_image=fake_image_token,
-                    global_img_token=global_img_token,
-                )
-                image_prompt_strings.append(image_prompt_string)
-
-            split_sample = sample.split(image_token)
-            if len(split_sample) == 0:
-                raise ValueError(
-                    "The image token should be present in the text.")
+    if text is None:
+        prompt_token_ids = inputs.get("prompt_token_ids", [])
+        assert prompt_token_ids
+        text = tokenizer.decode(prompt_token_ids)
+
+    if isinstance(text, str):
+        text = [text]
+    elif not isinstance(text, list) and not isinstance(text[0], str):
+        raise ValueError("Invalid input text. Please provide a string, "
+                         "or a list of strings")
+
+    fake_image_token = processor.fake_image_token.content
+    image_token = processor.image_token.content
+    global_img_token = processor.global_image_tag
+
+    prompt_strings = []
+    for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
+        n_images_in_text.append(sample.count(image_token))
+
+        # Replace the image token with fake tokens around the expanded
+        # image token sequence of length `image_seq_len`
+        image_prompt_strings = []
+        for n_rows, n_cols in zip(sample_rows, sample_cols):
+            image_prompt_string = _get_image_prompt_string(
+                n_rows,
+                n_cols,
+                processor.image_seq_len,
+                image_token=image_token,
+                fake_token_around_image=fake_image_token,
+                global_img_token=global_img_token,
+            )
+            image_prompt_strings.append(image_prompt_string)
 
-            # Place in the image prompt strings where the image tokens are
-            sample = split_sample[0]
-            for i, image_prompt_string in enumerate(image_prompt_strings):
-                sample += image_prompt_string + split_sample[i + 1]
-            prompt_strings.append(sample)
+        split_sample = sample.split(image_token)
+        if len(split_sample) == 0:
+            raise ValueError("The image token should be present in the text.")
 
-        prompt_token_ids = tokenizer(text=prompt_strings[0]).input_ids
+        # Place in the image prompt strings where the image tokens are
+        sample = split_sample[0]
+        for i, image_prompt_string in enumerate(image_prompt_strings):
+            sample += image_prompt_string + split_sample[i + 1]
+        prompt_strings.append(sample)
 
-        return token_inputs(
-            prompt_token_ids=prompt_token_ids,
-            prompt=prompt_strings[0],
-            multi_modal_data=multi_modal_data,
-        )
+    prompt_token_ids = tokenizer(text=prompt_strings[0]).input_ids
+
+    return token_inputs(
+        prompt_token_ids=prompt_token_ids,
+        prompt=prompt_strings[0],
+        multi_modal_data=multi_modal_data,
+    )
 
 
 def _get_max_num_image_patch(image_processor: Idefics3ImageProcessor) -> int: