diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/README.md
index ac03c3bc9e2785b54a616b0c322b52d2d6e520f8..6a2642d019b16f89ca225bedaf9c3ec85121c4a2 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py
index fb1141586971658b73cea9610e1c6d3a2722a944..7a54b230e51549bf0adc1be8f974ad6f2a48a0b7 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py
@@ -28,13 +28,12 @@ class Animegan_V1_Hayao_60(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/README.md
index 1aa4c0867eb08f13289979d907834e54fc96175f..68e53edef0e82d1f8d6e73a835f37d8cb6cac1b3 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py
index 0c6eacb9d0b93fab32820c31fbe0680b27e25be0..1a1191b8f36f62980fbe06d2af32635bd9eabe68 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Hayao_64(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/README.md
index be08aeeef5039b1198c03ea603b2491c2a57c47a..22c0c41497bcfeee8066edc8af47b46f7af4e64f 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py
index a5228e95c3fabec9ee3cb417a191e2d2280a2d99..09771b275f15def9e1fc420eaea5a731c4699ee5 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Hayao_99(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md
index 7bd3f2e5e09e41c5752e83efd86f406de7581167..50205f868b12c2abaadad3f21d9cea6eaa0542d4 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py
index 50f07186982bce0b7854db0e2697a1230b2925e7..e4b917aa336f4eb8a8819bcadc95688f475d428f 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_54(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/README.md
index 9cd3941a2456b923241dbf55170ef0df14316745..9bd48dc867ec51bc269fb2f5463995764d791db2 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py
index 9b986062fbb7a3c110a9d4f2e064def08adb6b3d..259b1fc8aedfa79489a4c744bf1e744828b062ee 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_74(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/README.md
index eb1ef3a8c1a0a34e623eb6f7eff9b0e81e5c96ee..10af52a3a71f2dd26168b659dab0cb05f3818323 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py
index 73a79baf839897864a850d8c029dc4aff6223488..5a6a9f6c7c58fb1e969093f1f7d1eed2947018d5 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_97(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/README.md
index ee3501eb14d738149cce25b55eee7f4c0a1fe6bd..c501725664d7f690a8774d2cf9114c1c925546f1 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py
index 10f56e468fc101d547b6fec89b06fdad89780a4b..f41c6f704710de9cd41a759b649c6a4ee8f4cc8d 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_98(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/README.md
index 024ee3e5b81533a66e358a0f751b8eac6227863e..e4f5910d4bd41251abad75c32c33f53f0512aeda 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py
index 2ff2c9a17601cca061749fe1d16d83cbf1125e0b..be0be188e7b423c0743a38dab2a0ad9d78124ce5 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Shinkai_33(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/README.md b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/README.md
index 3be8772050b01d68c21b0133e824dd9fb88b585e..87f58ca0494babba99a564b92cdca89a45b2f986 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/README.md
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/README.md
@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；
diff --git a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py
index c0158b5c69bdc30856de39d1d03449e59a13f2d0..21bf5fc4962e49116f0dc0f85d93433d24231de8 100644
--- a/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py
+++ b/modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py
@@ -28,13 +28,12 @@ class Animegan_V2_Shinkai_53(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)