diff --git a/data_juicer/utils/model_utils.py b/data_juicer/utils/model_utils.py
index be74d8699..b0ce54532 100644
--- a/data_juicer/utils/model_utils.py
+++ b/data_juicer/utils/model_utils.py
@@ -330,7 +330,8 @@ def __init__(self, config: Blip2Config) -> None:
 
 
 def prepare_simple_aesthetics_model(pretrained_model_name_or_path,
-                                    return_model=True):
+                                    return_model=True,
+                                    trust_remote_code=False):
     """
     Prepare and load a simple aesthetics model.
 
@@ -344,21 +345,25 @@ def prepare_simple_aesthetics_model(pretrained_model_name_or_path,
                                       AestheticsPredictorV2ReLU)
     from transformers import CLIPProcessor
 
-    processor = CLIPProcessor.from_pretrained(pretrained_model_name_or_path)
+    processor = CLIPProcessor.from_pretrained(
+        pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
     if not return_model:
         return processor
     else:
         if 'v1' in pretrained_model_name_or_path:
             model = AestheticsPredictorV1.from_pretrained(
-                pretrained_model_name_or_path)
+                pretrained_model_name_or_path,
+                trust_remote_code=trust_remote_code)
         elif ('v2' in pretrained_model_name_or_path
               and 'linear' in pretrained_model_name_or_path):
             model = AestheticsPredictorV2Linear.from_pretrained(
-                pretrained_model_name_or_path)
+                pretrained_model_name_or_path,
+                trust_remote_code=trust_remote_code)
         elif ('v2' in pretrained_model_name_or_path
               and 'relu' in pretrained_model_name_or_path):
             model = AestheticsPredictorV2ReLU.from_pretrained(
-                pretrained_model_name_or_path)
+                pretrained_model_name_or_path,
+                trust_remote_code=trust_remote_code)
         else:
             raise ValueError(
                 'Not support {}'.format(pretrained_model_name_or_path))
@@ -439,7 +444,8 @@ def decompress_model(compressed_model_path):
 def prepare_diffusion_model(pretrained_model_name_or_path,
                             diffusion_type,
                             torch_dtype='fp32',
-                            revision='main'):
+                            revision='main',
+                            trust_remote_code=False):
     """
         Prepare and load an Diffusion model from HuggingFace.
 
@@ -493,7 +499,8 @@ def prepare_diffusion_model(pretrained_model_name_or_path,
 
     model = pipeline.from_pretrained(pretrained_model_name_or_path,
                                      revision=revision,
-                                     torch_dtype=torch_dtype)
+                                     torch_dtype=torch_dtype,
+                                     trust_remote_code=trust_remote_code)
 
     return model