diff --git a/modelscope/models/cv/video_inpainting/__init__.py b/modelscope/models/cv/video_inpainting/__init__.py
index fd93fe3c..f5489da9 100644
--- a/modelscope/models/cv/video_inpainting/__init__.py
+++ b/modelscope/models/cv/video_inpainting/__init__.py
@@ -1,4 +1,4 @@
-# copyright (c) Alibaba, Inc. and its affiliates.
+# Copyright 2021-2022 The Alibaba Fundamental Vision Team Authors. All rights reserved.
 from typing import TYPE_CHECKING
 
 from modelscope.utils.import_utils import LazyImportModule
diff --git a/modelscope/models/cv/video_inpainting/inpainting.py b/modelscope/models/cv/video_inpainting/inpainting.py
index 9632e01c..e2af2ad0 100644
--- a/modelscope/models/cv/video_inpainting/inpainting.py
+++ b/modelscope/models/cv/video_inpainting/inpainting.py
@@ -1,6 +1,6 @@
 """ VideoInpaintingProcess
-Base modules are adapted from https://github.com/researchmm/STTN,
-originally Apache 2.0 License, Copyright (c) 2018-2022 OpenMMLab,
+The implementation here is modified based on STTN,
+originally Apache 2.0 License and publicly avaialbe at https://github.com/researchmm/STTN
 """
 
 import os
@@ -243,7 +243,8 @@ def inpainting_by_model_balance(model, video_inputPath, mask_path,
                 for m in masks_temp
             ]
             masks_temp = _to_tensors(masks_temp).unsqueeze(0)
-            feats_temp, masks_temp = feats_temp.cuda(), masks_temp.cuda()
+            if torch.cuda.is_available():
+                feats_temp, masks_temp = feats_temp.cuda(), masks_temp.cuda()
             comp_frames = [None] * video_length
             model.eval()
             with torch.no_grad():
diff --git a/modelscope/models/cv/video_inpainting/inpainting_model.py b/modelscope/models/cv/video_inpainting/inpainting_model.py
index a791b0ab..ffecde67 100644
--- a/modelscope/models/cv/video_inpainting/inpainting_model.py
+++ b/modelscope/models/cv/video_inpainting/inpainting_model.py
@@ -1,15 +1,18 @@
-""" VideoInpaintingNetwork
-Base modules are adapted from https://github.com/researchmm/STTN,
-originally Apache 2.0 License, Copyright (c) 2018-2022 OpenMMLab,
+""" VideoInpaintingProcess
+The implementation here is modified based on STTN,
+ originally Apache 2.0 License and publicly avaialbe at https://github.com/researchmm/STTN
 """
 
 import math
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.models as models
 
 from modelscope.metainfo import Models
+from modelscope.models import Model
 from modelscope.models.base import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import ModelFile, Tasks
@@ -84,8 +87,13 @@ class VideoInpainting(TorchModel):
         super().__init__(
             model_dir=model_dir, device_id=device_id, *args, **kwargs)
         self.model = InpaintGenerator()
-        pretrained_params = torch.load('{}/{}'.format(
-            model_dir, ModelFile.TORCH_MODEL_BIN_FILE))
+        if torch.cuda.is_available():
+            device = 'cuda'
+        else:
+            device = 'cpu'
+        pretrained_params = torch.load(
+            '{}/{}'.format(model_dir, ModelFile.TORCH_MODEL_BIN_FILE),
+            map_location=device)
         self.model.load_state_dict(pretrained_params['netG'])
         self.model.eval()
         self.device_id = device_id