diff --git a/modelscope/pipelines/multi_modal/ovis_vl_pipeline.py b/modelscope/pipelines/multi_modal/ovis_vl_pipeline.py
index 426d9c97..eeb935d1 100644
--- a/modelscope/pipelines/multi_modal/ovis_vl_pipeline.py
+++ b/modelscope/pipelines/multi_modal/ovis_vl_pipeline.py
@@ -1,20 +1,21 @@
-import torch
 from typing import Any, Dict, Union
 
-from modelscope.preprocessors import load_image
+import torch
+
 from modelscope import AutoModelForCausalLM
 from modelscope.metainfo import Pipelines, Preprocessors
 from modelscope.models.base import Model
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Frameworks, Fields, Tasks
-from modelscope.pipelines.multi_modal.visual_question_answering_pipeline import VisualQuestionAnsweringPipeline
+from modelscope.pipelines.multi_modal.visual_question_answering_pipeline import \
+    VisualQuestionAnsweringPipeline
+from modelscope.preprocessors import Preprocessor, load_image
+from modelscope.utils.constant import Fields, Frameworks, Tasks
 
 
 @PIPELINES.register_module(
-    Tasks.visual_question_answering, module_name="ovis-vl")
+    Tasks.visual_question_answering, module_name='ovis-vl')
 class VisionChatPipeline(VisualQuestionAnsweringPipeline):
 
     def __init__(self,
@@ -32,39 +33,49 @@ class VisionChatPipeline(VisualQuestionAnsweringPipeline):
 
         # ovis
         self.device = 'cuda' if device == 'gpu' else device
-        self.model = AutoModelForCausalLM.from_pretrained(model,
-                                                     torch_dtype=torch.bfloat16,
-                                                     multimodal_max_length=8192,
-                                                     trust_remote_code=True).to(self.device)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model,
+            torch_dtype=torch.bfloat16,
+            multimodal_max_length=8192,
+            trust_remote_code=True).to(self.device)
         self.text_tokenizer = self.model.get_text_tokenizer()
         self.visual_tokenizer = self.model.get_visual_tokenizer()
 
-
     def preprocess(self, inputs: Dict[str, Any]):
         text = inputs['text']
         image_path_or_url = inputs['image']
         image = load_image(image_path_or_url)
         query = f'<image>\n{text}'
-        _, input_ids, pixel_values = self.model.preprocess_inputs(query, [image])
+        _, input_ids, pixel_values = self.model.preprocess_inputs(
+            query, [image])
         attention_mask = torch.ne(input_ids, self.text_tokenizer.pad_token_id)
         input_ids = input_ids.unsqueeze(0).to(device=self.model.device)
-        attention_mask = attention_mask.unsqueeze(0).to(device=self.model.device)
-        pixel_values = [pixel_values.to(dtype=self.visual_tokenizer.dtype, device=self.visual_tokenizer.device)]
+        attention_mask = attention_mask.unsqueeze(0).to(
+            device=self.model.device)
+        pixel_values = [
+            pixel_values.to(
+                dtype=self.visual_tokenizer.dtype,
+                device=self.visual_tokenizer.device)
+        ]
+
+        return {
+            'input_ids': input_ids,
+            'pixel_values': pixel_values,
+            'attention_mask': attention_mask
+        }
 
-        return {'input_ids':input_ids, 'pixel_values': pixel_values, 'attention_mask': attention_mask}
-      
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         input_ids = inputs['input_ids']
         pixel_values = inputs['pixel_values']
         attention_mask = inputs['attention_mask']
 
-        max_new_tokens = forward_params.get("max_new_tokens", 1024)
-        do_sample = forward_params.get("do_sample", False)
-        top_p = forward_params.get("top_p", None)
-        top_k = forward_params.get("top_k", None)
-        temperature = forward_params.get("temperature", None)
-        repetition_penalty = forward_params.get("repetition_penalty", None)
+        max_new_tokens = forward_params.get('max_new_tokens', 1024)
+        do_sample = forward_params.get('do_sample', False)
+        top_p = forward_params.get('top_p', None)
+        top_k = forward_params.get('top_k', None)
+        temperature = forward_params.get('temperature', None)
+        repetition_penalty = forward_params.get('repetition_penalty', None)
         with torch.inference_mode():
             gen_kwargs = dict(
                 max_new_tokens=max_new_tokens,
@@ -75,12 +86,16 @@ class VisionChatPipeline(VisualQuestionAnsweringPipeline):
                 repetition_penalty=repetition_penalty,
                 eos_token_id=self.model.generation_config.eos_token_id,
                 pad_token_id=self.text_tokenizer.pad_token_id,
-                use_cache=True
-            )
-            output_ids = self.model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
+                use_cache=True)
+            output_ids = self.model.generate(
+                input_ids,
+                pixel_values=pixel_values,
+                attention_mask=attention_mask,
+                **gen_kwargs)[0]
         return {'output_ids': output_ids}
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         output_ids = inputs['output_ids']
-        output = self.text_tokenizer.decode(output_ids, skip_special_tokens=True)
+        output = self.text_tokenizer.decode(
+            output_ids, skip_special_tokens=True)
         return {OutputKeys.TEXT: output}