diff --git a/.gitignore b/.gitignore
index 58489ffb..ce6b78e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,7 @@ build
 *.pth
 *.pt
 *.onnx
+*.mnn
 test/temp/*
 !test/temp/.gitkeep
 
diff --git a/README.md b/README.md
index 51538466..7249448b 100644
--- a/README.md
+++ b/README.md
@@ -116,8 +116,9 @@ python scripts/download_model.py
 **方式二：直接下载**
 
 存到项目的`hivision/creator/weights`目录下：
-- `modnet_photographic_portrait_matting.onnx` (24.7MB): [MODNet](https://github.com/ZHKKKe/MODNet)官方权重，[下载](https://drive.google.com/drive/folders/1umYmlCulvIFNaqPjwod1SayFmSRHziyR)
-- `hivision_modnet.onnx` (24.7MB)：对纯色换底适配性更好的抠图模型，[下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/tag/pretrained-model)
+- `modnet_photographic_portrait_matting.onnx` (24.7MB): [MODNet](https://github.com/ZHKKKe/MODNet)官方权重，[下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/modnet_photographic_portrait_matting.onnx)
+- `hivision_modnet.onnx` (24.7MB): 对纯色换底适配性更好的抠图模型，[下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/hivision_modnet.onnx)
+- `mnn_hivision_modnet.mnn` (24.7MB): mnn转换后的抠图模型 by [zjkhahah](https://github.com/zjkhahah)，[下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/mnn_hivision_modnet.mnn)
 
 
 ## 4. 人脸检测模型配置
diff --git a/app.py b/app.py
index 7ed02172..b25746cd 100644
--- a/app.py
+++ b/app.py
@@ -152,6 +152,8 @@ def idphoto_inference(
     creator = IDCreator()
     if matting_model_option == "modnet_photographic_portrait_matting":
         creator.matting_handler = extract_human_modnet_photographic_portrait_matting
+    elif matting_model_option == "mnn_hivision_modnet":
+        creator.matting_handler = extract_human_mnn_modnet
     else:
         creator.matting_handler = extract_human
 
@@ -315,7 +317,7 @@ def idphoto_inference(
     matting_model_list = [
         os.path.splitext(file)[0]
         for file in os.listdir(os.path.join(root_dir, "hivision/creator/weights"))
-        if file.endswith(".onnx")
+        if file.endswith(".onnx") or file.endswith(".mnn")
     ]
     DEFAULT_MATTING_MODEL = "modnet_photographic_portrait_matting"
     if DEFAULT_MATTING_MODEL in matting_model_list:
diff --git a/demo/images/test5.jpg b/demo/images/test5.jpg
deleted file mode 100644
index 4c295450..00000000
Binary files a/demo/images/test5.jpg and /dev/null differ
diff --git a/hivision/creator/human_matting.py b/hivision/creator/human_matting.py
index fdad0ba1..0031e431 100644
--- a/hivision/creator/human_matting.py
+++ b/hivision/creator/human_matting.py
@@ -29,7 +29,7 @@
         os.path.dirname(__file__),
         "weights",
         "mnn_hivision_modnet.mnn",
-    )
+    ),
 }
 
 
@@ -50,18 +50,22 @@ def get_mnn_modnet_matting(input_image, checkpoint_path, ref_size=512):
         import MNN.expr as expr
         import MNN.nn as nn
     except ImportError as e:
-        raise ImportError("MNN模块未安装或导入错误。请确保已安装MNN库，使用命令 'pip install mnn' 安装。") from e
+        raise ImportError(
+            "The MNN module is not installed or there was an import error. Please ensure that the MNN library is installed by using the command 'pip install mnn'."
+        ) from e
     config = {}
-    config['precision'] = 'low'  # 当硬件支持（armv8.2）时使用fp16推理
-    config['backend'] = 0  # CPU
-    config['numThread'] = 4  # 线程数
+    config["precision"] = "low"  # 当硬件支持（armv8.2）时使用fp16推理
+    config["backend"] = 0  # CPU
+    config["numThread"] = 4  # 线程数
     im, width, length = read_modnet_image(input_image, ref_size=512)
     rt = nn.create_runtime_manager((config,))
-    net = nn.load_module_from_file(checkpoint_path, ['input1'], ['output1'], runtime_manager=rt)
+    net = nn.load_module_from_file(
+        checkpoint_path, ["input1"], ["output1"], runtime_manager=rt
+    )
     input_var = expr.convert(im, expr.NCHW)
     output_var = net.forward(input_var)
     matte = expr.convert(output_var, expr.NCHW)
-    matte = matte.read()#var转换为np
+    matte = matte.read()  # var转换为np
     matte = (matte * 255).astype("uint8")
     matte = np.squeeze(matte)
     mask = cv2.resize(matte, (width, length), interpolation=cv2.INTER_AREA)
@@ -71,6 +75,7 @@ def get_mnn_modnet_matting(input_image, checkpoint_path, ref_size=512):
 
     return output_image
 
+
 def extract_human_modnet_photographic_portrait_matting(ctx: Context):
     """
     人像抠图
@@ -84,8 +89,11 @@ def extract_human_modnet_photographic_portrait_matting(ctx: Context):
     ctx.processing_image = hollow_out_fix(matting_image)
     ctx.matting_image = ctx.processing_image.copy()
 
+
 def extract_human_mnn_modnet(ctx: Context):
-    matting_image = get_mnn_modnet_matting(ctx.processing_image, WEIGHTS["mnn_hivision_modnet"])
+    matting_image = get_mnn_modnet_matting(
+        ctx.processing_image, WEIGHTS["mnn_hivision_modnet"]
+    )
     ctx.processing_image = hollow_out_fix(matting_image)
     ctx.matting_image = ctx.processing_image.copy()