将自定义节点放置到custom目录下

ext/load_image_pro.py

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+from io import BytesIO
+
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+
+# 定义节点类
+class LoadNetImg:
+    # 定义节点输入类型
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_url": ("STRING", {
+                    "default": "https://example.com/sample.jpg",
+                    "multiline": False
+                }),
+            }
+        }
+
+    # 定义节点输出类型
+    RETURN_TYPES = ("IMAGE",)  # 返回图像数据
+    RETURN_NAMES = ("image",)  # 命名返回值
+    FUNCTION = "load_image_task"  # 函数标识符，方便注册多个节点功能
+    OUTPUT_NODE = False  # 不允许该节点直接作为最终输出节点
+    CATEGORY = "image"  # 节点所属类别（在 ComfyUI 界面中分类）
+
+    def load_image_task(self, image_url):
+        try:
+            if not image_url or not image_url.strip():
+                raise ValueError("需要提供图片URL")
+            
+            # 下载网络图片
+            response = requests.get(image_url)
+            response.raise_for_status()  # 请求失败时抛出异常
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+
+            # 按照官方格式转换图像数据
+            # Convert to numpy array and normalize to 0-1
+            image_array = np.array(image).astype(np.float32) / 255.0
+            # Convert to torch tensor and add batch dimension
+            image_tensor = torch.from_numpy(image_array)[None,]
+
+            return (image_tensor,)  # 返回torch张量
+        except Exception as e:
+            print(f"Error loading image: {e}")
+            # 返回一个空的黑色图片作为错误处理
+            empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
+            return (empty_image,)
+
+
+
+
+# 映射节点类和名称
+NODE_CLASS_MAPPINGS = {
+    "LoadNetImg": LoadNetImg,  # 将类映射到节点名称
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadNetImg": "load_net_image",  # 节点显示名称
+}