PERF 合并读取文本文件, 读取图片同类型节点

nodes/image_nodes.py

@@ -2,23 +2,29 @@ import os
 import uuid
 from io import BytesIO
 
+import loguru
 import numpy as np
 import requests
 import torch
 from PIL import Image
+import folder_paths
 
 
 # 定义节点类
-class LoadNetImg:
+class LoadImgOptional:
     # 定义节点输入类型
     @classmethod
     def INPUT_TYPES(cls):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        files = folder_paths.filter_files_content_types(files, ["image"])
         return {
             "required": {
                 "image_url": ("STRING", {
                     "default": "https://example.com/sample.jpg",
                     "multiline": False
                 }),
+                "image": (sorted(files), {"image_upload": True})
             }
         }
 
@@ -29,28 +35,27 @@ class LoadNetImg:
     OUTPUT_NODE = False  # 不允许该节点直接作为最终输出节点
     CATEGORY = "不忘科技-自定义节点🚩/图片"  # 节点所属类别（在 ComfyUI 界面中分类）
 
-    def load_image_task(self, image_url):
+    def load_image_task(self, image_url, image):
         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")
+            if not image_url or len(image_url.strip()) == 0 or image_url == "https://example.com/sample.jpg":
+                loguru.logger.info("读取本地文件")
+                image_path = folder_paths.get_annotated_filepath(image)
+                with open(image_path, "rb") as image_file:
+                    image = image_file.read()
+            else:
+                loguru.logger.info("读取线上文件")
+                response = requests.get(image_url)
+                response.raise_for_status()
+                image = response.content
+            image = Image.open(BytesIO(image)).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,]
+            image_tensor = torch.from_numpy(image_array).unsqueeze(0)
 
-            return (image_tensor,)  # 返回torch张量
+            return (image_tensor,)
         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,)
+            raise Exception(f"Error loading image: {e}")
 
 
 class SaveImagePath: