diff --git a/AIvector.py b/AIvector.py
new file mode 100644
index 0000000..cc3498d
--- /dev/null
+++ b/AIvector.py
@@ -0,0 +1,216 @@
+import vtracer
+import tempfile
+from wand.image import Image as WandImage
+from PIL import Image as PILImage
+import torch
+import numpy as np
+import os
+
+class AIvector:
+    """
+    A example node
+
+    Class methods
+    -------------
+    INPUT_TYPES (dict): 
+        Tell the main program input parameters of nodes.
+    IS_CHANGED:
+        optional method to control when the node is re executed.
+
+    Attributes
+    ----------
+    RETURN_TYPES (`tuple`): 
+        The type of each element in the output tulple.
+    RETURN_NAMES (`tuple`):
+        Optional: The name of each output in the output tulple.
+    FUNCTION (`str`):
+        The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
+    OUTPUT_NODE ([`bool`]):
+        If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
+        The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
+        Assumed to be False if not present.
+    CATEGORY (`str`):
+        The category the node should appear in the UI.
+    execute(s) -> tuple || None:
+        The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
+        For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
+    """
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        """
+            Return a dictionary which contains config for all input fields.
+            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
+            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
+            The type can be a list for selection.
+
+            Returns: `dict`:
+                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
+                - Value input_fields (`dict`): Contains input fields config:
+                    * Key field_name (`string`): Name of a entry-point method's argument
+                    * Value field_config (`tuple`):
+                        + First value is a string indicate the type of field or a list for selection.
+                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
+        """
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "colormode": (["color", "binary"], {
+                    "default": "color"
+                }),
+                "hierarchical": (["stacked", "cutout"], {
+                    "default": "stacked"
+                }),
+                "mode": (["spline", "polygon", "none"], {
+                    "default": "spline"
+                }),
+                "filter_speckle": ("INT", {
+                    "default": 4,
+                    "min": 1,
+                    "max": 10,
+                    "step": 1
+                }),
+                "color_precision": ("INT", {
+                    "default": 6,
+                    "min": 1,
+                    "max": 10,
+                    "step": 1
+                }),
+                "layer_difference": ("INT", {
+                    "default": 16,
+                    "min": 1,
+                    "max": 20,
+                    "step": 1
+                }),
+                "corner_threshold": ("INT", {
+                    "default": 60,
+                    "min": 1,
+                    "max": 100,
+                    "step": 1
+                }),
+                "length_threshold": ("FLOAT", {
+                    "default": 4.0,
+                    "min": 3.5,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+                "max_iterations": ("INT", {
+                    "default": 10,
+                    "min": 1,
+                    "max": 20,
+                    "step": 1
+                }),
+                "splice_threshold": ("INT", {
+                    "default": 45,
+                    "min": 1,
+                    "max": 100,
+                    "step": 1
+                }),
+                "path_precision": ("INT", {
+                    "default": 8,
+                    "min": 1,
+                    "max": 10,
+                    "step": 1
+                }),
+                "output_directory": ("STRING", {
+                    "default": ""
+                })
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("vector_image",)
+
+    FUNCTION = "process_image"
+
+    #OUTPUT_NODE = False
+
+    CATEGORY = "Vectorization"
+
+
+
+    def process_image(self, image, output_directory, colormode, hierarchical, mode, filter_speckle, color_precision,
+                      layer_difference, corner_threshold, length_threshold, max_iterations, splice_threshold, path_precision):
+        try:
+            print(f"输入图像形状：{image.shape}")
+
+            # 确保输入 Tensor 是四维，具有 (B, C, H, W) 结构
+            if len(image.shape) == 4:
+                # 将图像从 (1, H, W, C) 转换为 (H, W, C)
+                image = image.squeeze(0)
+
+            print(f"处理后的图像形状：{image.shape}")
+
+            # 将 Tensor 转换为 PIL 图像
+            image_array = image.numpy()
+            image_array = (image_array * 255).astype(np.uint8)
+            pil_image = PILImage.fromarray(image_array)
+
+            print(f"转换后的 PIL 图像：{pil_image}")
+
+            # 将输入图像保存到临时文件
+            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_img_file:
+                pil_image.save(temp_img_file, format="PNG")
+                temp_img_path = temp_img_file.name
+
+            print(f"临时 PNG 文件路径：{temp_img_path}")
+            base_filename = os.path.splitext(os.path.basename(temp_img_path))[0]
+
+            # 检查并创建输出目录
+            if not output_directory:
+                output_directory = tempfile.gettempdir()
+            elif not os.path.exists(output_directory):
+                os.makedirs(output_directory)
+
+            # 生成 SVG 文件路径
+            svg_filename = os.path.join(output_directory, f"{base_filename}.svg")
+
+            print(f"SVG 文件路径：{svg_filename}")
+
+            # 使用 vtracer 将图像转换为 SVG
+            vtracer.convert_image_to_svg_py(temp_img_path,
+                                            svg_filename,
+                                            colormode=colormode,
+                                            hierarchical=hierarchical,
+                                            mode=mode,
+                                            filter_speckle=filter_speckle,
+                                            color_precision=color_precision,
+                                            layer_difference=layer_difference,
+                                            corner_threshold=corner_threshold,
+                                            length_threshold=length_threshold,
+                                            max_iterations=max_iterations,
+                                            splice_threshold=splice_threshold,
+                                            path_precision=path_precision)
+
+            print("SVG 文件已成功生成")
+
+            # 使用 wand 将 SVG 转换为 PNG
+            with WandImage(filename=svg_filename) as wand_image:
+                wand_image.format = 'png'
+                output_png_filename = os.path.join(output_directory, f"{base_filename}.png")
+                wand_image.save(filename=output_png_filename)
+
+            # 读取转换后的 PNG 图像
+            output_image = PILImage.open(output_png_filename).convert('RGB')
+            print(f"转换后的图像信息: 模式={output_image.mode}, 尺寸={output_image.size}")
+
+            # 将 PIL 图像转换为 torch tensor
+            tensor_image = torch.from_numpy(np.array(output_image)).permute(2, 0, 1).float() / 255
+            print(f"tensor 图像形状: {tensor_image.shape}, 最大值: {tensor_image.max()}, 最小值: {tensor_image.min()}")
+            
+
+            return (tensor_image,)
+
+        except Exception as e:
+            print(f"错误：{str(e)}")
+            return (None,)
+
+NODE_CLASS_MAPPINGS = {
+    "AIvector": AIvector
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AIvector": "AIvector"
+}
\ No newline at end of file
diff --git a/example/AIvector.json b/example/AIvector.json
new file mode 100644
index 0000000..77e6c1e
--- /dev/null
+++ b/example/AIvector.json
@@ -0,0 +1,150 @@
+{
+  "last_node_id": 84,
+  "last_link_id": 162,
+  "nodes": [
+    {
+      "id": 82,
+      "type": "LoadImage",
+      "pos": [
+        284.0366204412037,
+        814.6605073257773
+      ],
+      "size": [
+        315,
+        314.00001525878906
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            161
+          ],
+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "18035-633794744-jdsh, black_background, animal, a colorful animal, colorful painting, texture  _lora_jdsh-000008_0.9_.png",
+        "image"
+      ]
+    },
+    {
+      "id": 83,
+      "type": "AIvector",
+      "pos": [
+        715,
+        808
+      ],
+      "size": {
+        "0": 315,
+        "1": 322
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 161,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "vector_image",
+          "type": "IMAGE",
+          "links": [
+            162
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AIvector"
+      },
+      "widgets_values": [
+        "color",
+        "stacked",
+        "spline",
+        4,
+        6,
+        16,
+        60,
+        4,
+        10,
+        45,
+        8,
+        "D:\\AARG\\AARG"
+      ]
+    },
+    {
+      "id": 84,
+      "type": "PreviewImage",
+      "pos": [
+        1147,
+        803
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 162,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    }
+  ],
+  "links": [
+    [
+      161,
+      82,
+      0,
+      83,
+      0,
+      "IMAGE"
+    ],
+    [
+      162,
+      83,
+      0,
+      84,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.9229599817709203,
+      "offset": [
+        -262.16629897186385,
+        -591.0167384245041
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..666c661
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+vtracer==0.3.2
+Wand==0.6.10
+Pillow==9.4.0
+torch==2.0.0
+numpy==1.24.2
\ No newline at end of file