diff --git a/mmdeploy/codebase/mmedit/deploy/super_resolution_model.py b/mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
index ade5d0bee..454de8b95 100644
--- a/mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
+++ b/mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional, Sequence, Union
+import os.path as osp
+from typing import Dict, List, Optional, Sequence, Union
 
 import mmcv
 import numpy as np
@@ -88,6 +89,8 @@ class End2EndModel(BaseBackendModel):
     def forward_test(self,
                      lq: torch.Tensor,
                      gt: Optional[torch.Tensor] = None,
+                     meta: List[Dict] = None,
+                     save_path=None,
                      *args,
                      **kwargs):
         """Run inference for restorer to generate evaluation result.
@@ -96,6 +99,8 @@ class End2EndModel(BaseBackendModel):
             lq (torch.Tensor): The input low-quality image of the model.
             gt (torch.Tensor): The ground truth of input image. Defaults to
                 `None`.
+            meta (List[Dict]): The meta infomations of MMEditing.
+            save_path (str): Path to save image. Default: None.
             *args: Other arguments.
             **kwargs: Other key-pair arguments.
 
@@ -104,6 +109,17 @@ class End2EndModel(BaseBackendModel):
         """
         outputs = self.forward_dummy(lq)
         result = self.test_post_process(outputs, lq, gt)
+
+        # Align to mmediting BasicRestorer
+        if save_path:
+            outputs = [torch.from_numpy(i) for i in outputs]
+
+            lq_path = meta[0]['lq_path']
+            folder_name = osp.splitext(osp.basename(lq_path))[0]
+            save_path = osp.join(save_path, f'{folder_name}.png')
+
+            mmcv.imwrite(tensor2img(outputs), save_path)
+
         return result
 
     def forward_dummy(self, lq: torch.Tensor, *args, **kwargs):