diff --git a/blip.py b/blip.py
index 83e3ef9..5eecd61 100644
--- a/blip.py
+++ b/blip.py
@@ -26,6 +26,7 @@ from transformers import logging as t_logging
 from towhee import register
 from towhee.operator.base import NNOperator, OperatorFlag
 from towhee.types.arg import arg, to_image_color
+from towhee.dc2 import accelerate
 
 log = logging.getLogger('run_op')
 warnings.filterwarnings('ignore')
@@ -57,8 +58,8 @@ class BLIPModelVision(nn.Module):
         super().__init__()
         self.backbone = model
     
-    def forward(self, image):
-        image_embeds = self.backbone.vision_model(image)[0]
+    def forward(self, pixel_values):
+        image_embeds = self.backbone.vision_model(pixel_values)[0]
         image_embeds = self.backbone.vision_proj(image_embeds[:,0,:])
         return image_embeds
     
@@ -96,12 +97,13 @@ class Blip(NNOperator):
     """
     def __init__(self, model_name: str, modality: str, device:str = 'cpu', checkpoint_path: str = None):
         super().__init__()
+        self.model_name = model_name
         real_name = self._configs()[model_name]['name']
         self.model = Model(real_name, modality, checkpoint_path, device)
         self.modality = modality
         self.device = device
         self.checkpoint_path = checkpoint_path
-        self.processor = AutoProcessor.from_pretrained('Salesforce/blip-itm-base-coco')
+        self.processor = AutoProcessor.from_pretrained(real_name)
 
     def __call__(self, data):
         if not isinstance(data, list):
@@ -125,9 +127,9 @@ class Blip(NNOperator):
 
     @arg(1, to_image_color('RGB'))
     def _inference_from_image(self, img):
-        inputs = self.processor(images=img, return_tensors='pt')['pixel_values']
+        inputs = self.processor(images=img, return_tensors='pt')
         inputs = inputs.to(self.device)
-        image_feature = self.model(inputs)
+        image_feature = self.model(inputs['pixel_values'])
         return image_feature
 
     def inference_single_data(self, data):
@@ -148,7 +150,7 @@ class Blip(NNOperator):
         
     @property
     def _model(self):
-        return self.model
+        return self.model.model
 
     def train(self, **kwargs):
         raise NotImplementedError
@@ -188,8 +190,9 @@ class Blip(NNOperator):
                 output_file = output_file + '.onnx'
             else:
                 raise AttributeError('Unsupported model_type.')
+
         if self.modality == 'image':
-            sz = self.processor.feature_extractor.crop_size
+            sz = self.processor.image_processor.size
             if isinstance(sz, int):
                 h = sz
                 w = sz
@@ -229,7 +232,7 @@ class Blip(NNOperator):
             else:
                 raise ValueError('modality[{}] not implemented.'.format(self.modality))
 
-            onnx_export(self.model,
+            onnx_export(self._model,
                 (dict(inputs),),
                 f=Path(output_file),
                 input_names= input_names,