diff --git a/clip.py b/clip.py
index e3de597..6e78f36 100644
--- a/clip.py
+++ b/clip.py
@@ -22,6 +22,7 @@ from towhee.operator.base import NNOperator, OperatorFlag
 from towhee.types.arg import arg, to_image_color
 from towhee import register
 from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
+#from towhee.dc2 import accelerate
 
 #@accelerate
 class CLIPModelVision(nn.Module):
@@ -49,10 +50,10 @@ class Clip(NNOperator):
     """
     CLIP multi-modal embedding operator
     """
-    def __init__(self, model_name: str, modality: str, device, checkpoint_path):
+    def __init__(self, model_name: str, modality: str, device: str = 'cpu', checkpoint_path: str = None):
         self.model_name = model_name
         self.modality = modality
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = device
         cfg = self._configs()[model_name]
         try:
             clip_model = CLIPModel.from_pretrained(cfg)
@@ -71,6 +72,7 @@ class Clip(NNOperator):
             self.model = CLIPModelText(clip_model)
         else:
             raise ValueError("modality[{}] not implemented.".format(self.modality))
+        self.model.to(self.device)
         self.tokenizer = CLIPTokenizer.from_pretrained(cfg)
         self.processor =  CLIPProcessor.from_pretrained(cfg)
 
@@ -99,14 +101,14 @@ class Clip(NNOperator):
 
     def _inference_from_text(self, text):
         tokens = self.tokenizer([text], padding=True, return_tensors="pt")
-        text_features = self.model(tokens['input_ids'],tokens['attention_mask'])
+        text_features = self.model(tokens['input_ids'].to(self.device), tokens['attention_mask'].to(self.device))
         return text_features
 
     @arg(1, to_image_color('RGB'))
     def _inference_from_image(self, img):
         img = to_pil(img)
         inputs = self.processor(images=img, return_tensors="pt")
-        image_features = self.model(inputs['pixel_values'])
+        image_features = self.model(inputs['pixel_values'].to(self.device))
         return image_features
 
     def train(self, **kwargs):