diff --git a/modelscope/models/cv/image_portrait_enhancement/eqface/fqa.py b/modelscope/models/cv/image_portrait_enhancement/eqface/fqa.py
index 936bed9a..fe4081a4 100755
--- a/modelscope/models/cv/image_portrait_enhancement/eqface/fqa.py
+++ b/modelscope/models/cv/image_portrait_enhancement/eqface/fqa.py
@@ -45,8 +45,8 @@ class FQA(object):
         model.load_state_dict(model_dict)
 
     def get_face_quality(self, img):
-        img = torch.from_numpy(img).permute(2, 0,
-                                            1).unsqueeze(0).flip(1).cuda()
+        img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).flip(1).to(
+            self.device)
         img = (img - 127.5) / 128.0
 
         # extract features & predict quality
diff --git a/modelscope/pipelines/cv/image_colorization_pipeline.py b/modelscope/pipelines/cv/image_colorization_pipeline.py
index 72b2f8cb..0fea729d 100644
--- a/modelscope/pipelines/cv/image_colorization_pipeline.py
+++ b/modelscope/pipelines/cv/image_colorization_pipeline.py
@@ -36,7 +36,6 @@ class ImageColorizationPipeline(Pipeline):
             self.device = torch.device('cuda')
         else:
             self.device = torch.device('cpu')
-            self.size = 1024
 
         self.orig_img = None
         self.model_type = 'stable'
@@ -91,6 +90,8 @@ class ImageColorizationPipeline(Pipeline):
         img = LoadImage.convert_to_img(input).convert('LA').convert('RGB')
 
         self.wide, self.height = img.size
+        if self.wide * self.height < 100000:
+            self.size = 256
         self.orig_img = img.copy()
         img = img.resize((self.size, self.size), resample=PIL.Image.BILINEAR)
 
diff --git a/modelscope/pipelines/cv/image_portrait_enhancement_pipeline.py b/modelscope/pipelines/cv/image_portrait_enhancement_pipeline.py
index 56dca92f..87e692e8 100644
--- a/modelscope/pipelines/cv/image_portrait_enhancement_pipeline.py
+++ b/modelscope/pipelines/cv/image_portrait_enhancement_pipeline.py
@@ -58,7 +58,8 @@ class ImagePortraitEnhancementPipeline(Pipeline):
 
         gpen_model_path = f'{model}/{ModelFile.TORCH_MODEL_FILE}'
         self.face_enhancer.load_state_dict(
-            torch.load(gpen_model_path), strict=True)
+            torch.load(gpen_model_path, map_location=torch.device('cpu')),
+            strict=True)
 
         logger.info('load face enhancer model done')
 
@@ -82,7 +83,9 @@ class ImagePortraitEnhancementPipeline(Pipeline):
 
         sr_model_path = f'{model}/super_resolution/realesrnet_x{self.scale}.pth'
         self.sr_model.load_state_dict(
-            torch.load(sr_model_path)['params_ema'], strict=True)
+            torch.load(sr_model_path,
+                       map_location=torch.device('cpu'))['params_ema'],
+            strict=True)
 
         logger.info('load sr model done')