diff --git a/src/llmtuner/data/aligner.py b/src/llmtuner/data/aligner.py
index 6fd6f404..dc1de865 100644
--- a/src/llmtuner/data/aligner.py
+++ b/src/llmtuner/data/aligner.py
@@ -1,3 +1,4 @@
+import os
 from functools import partial
 from typing import TYPE_CHECKING, Any, Dict, List, Union
 
@@ -13,8 +14,10 @@ if TYPE_CHECKING:
     from .parser import DatasetAttr
 
 
-def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
-    outputs = {"prompt": [], "response": [], "system": [], "tools": []}
+def convert_alpaca(
+    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+) -> Dict[str, List[Any]]:
+    outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
     for i in range(len(examples[dataset_attr.prompt])):
         prompt = []
         if dataset_attr.history and isinstance(examples[dataset_attr.history][i], list):
@@ -44,11 +47,18 @@ def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr")
         outputs["response"].append(response)
         outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
         outputs["tools"].append("")
-        outputs["images"].append([])
+        outputs["images"].append(
+            [os.path.join(data_args.dataset_dir, path) for path in examples[dataset_attr.images][i]]
+            if dataset_attr.images
+            else []
+        )
+
     return outputs
 
 
-def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
+def convert_sharegpt(
+    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+) -> Dict[str, List[Any]]:
     outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
     tag_mapping = {
         dataset_attr.user_tag: Role.USER.value,
@@ -84,7 +94,11 @@ def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr"
         outputs["response"].append(aligned_messages[-1:])
         outputs["system"].append(system)
         outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
-        outputs["images"].append(examples[dataset_attr.images][i] if dataset_attr.images else [])
+        outputs["images"].append(
+            [os.path.join(data_args.dataset_dir, path) for path in examples[dataset_attr.images][i]]
+            if dataset_attr.images
+            else []
+        )
 
     return outputs
 
@@ -97,12 +111,13 @@ def align_dataset(
         prompt: [{"role": "user", "content": "..."}] * (2T - 1)
         response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
         system: "..."
-        tools: "..."
+        tools: "...",
+        images: [],
     """
     if dataset_attr.formatting == "alpaca":
-        convert_func = partial(convert_alpaca, dataset_attr=dataset_attr)
+        convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
     else:
-        convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr)
+        convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr, data_args=data_args)
 
     column_names = list(next(iter(dataset)).keys())
     features = Features.from_dict(
@@ -115,7 +130,7 @@ def align_dataset(
             ],
             "system": {"dtype": "string", "_type": "Value"},
             "tools": {"dtype": "string", "_type": "Value"},
-            "images": {"feature": {"_type": "Image"}, "_type": "Sequence"},
+            "images": [{"_type": "Image"}],
         }
     )
     kwargs = {}