Update

__init__.py

@@ -1,19 +1,20 @@
-from .meyo_node_Computational import StandardSize, CompareInt, FloatToInteger, GenerateNumbers, GetRandomIntegerInRange
-from .meyo_node_String import AddPrefixSuffix, ExtractSubstring, ExtractSubstringByIndices, SplitStringByDelimiter, ProcessString, ExtractBeforeAfter, ReplaceNthOccurrence, BatchReplaceStrings, ReplaceMultiple, RandomLineFromText, CheckSubstringPresence, AddPrefixSuffixToLines, ExtractAndCombineLines, FilterLinesBySubstrings, FilterLinesByWordCount, SplitAndExtractText, CountOccurrences, ExtractLinesByIndex, ExtractSpecificLines, RemoveContentBetweenChars, ShuffleTextLines, ConditionalTextOutput, TextConditionCheck, TextConcatenation, ExtractSpecificData, FindFirstLineContent
-from .meyo_node_File import FileListAndSuffix, ReadExcelData, WriteExcelData, FindExcelData, ReadExcelRowOrColumnDiff
+from .meyo_node_Computational import CompareInt, FloatToInteger, GenerateNumbers, GetRandomIntegerInRange
+from .meyo_node_String import SingleTextInput, MultiParamInputNode, AddPrefixSuffix, ExtractSubstring, ExtractSubstringByIndices, SplitStringByDelimiter, ProcessString, ExtractBeforeAfter, ReplaceNthOccurrence, BatchReplaceStrings, ReplaceMultiple, RandomLineFromText, CheckSubstringPresence, AddPrefixSuffixToLines, ExtractAndCombineLines, FilterLinesBySubstrings, FilterLinesByWordCount, SplitAndExtractText, CountOccurrences, ExtractLinesByIndex, ExtractSpecificLines, RemoveContentBetweenChars, ShuffleTextLines, ConditionalTextOutput, TextConditionCheck, TextConcatenation, ExtractSpecificData, FindFirstLineContent
+from .meyo_node_File import LoadAndAdjustImage, ImageAdjuster, SaveImagEX, FileListAndSuffix, ReadExcelData, WriteExcelData, FindExcelData, ReadExcelRowOrColumnDiff
 from .meyo_node_Functional import GetCurrentTime, SelectionParameter
 
 
 NODE_CLASS_MAPPINGS = {
 
     #运算型节点：meyo_node_Computational
-    "StandardSize": StandardSize,
     "CompareInt": CompareInt,
     "FloatToInteger": FloatToInteger,
     "GenerateNumbers": GenerateNumbers,
     "GetRandomIntegerInRange": GetRandomIntegerInRange,
 
     #字符串处理：meyo_node_String
+    "SingleTextInput": SingleTextInput,  
+    "MultiParamInputNode": MultiParamInputNode,
     "AddPrefixSuffix": AddPrefixSuffix,
     "ExtractSubstring": ExtractSubstring,
     "ExtractSubstringByIndices": ExtractSubstringByIndices,
@@ -42,6 +43,9 @@ NODE_CLASS_MAPPINGS = {
     "FindFirstLineContent": FindFirstLineContent,
 
     #文件处理：meyo_node_File
+    "LoadAndAdjustImage": LoadAndAdjustImage,
+    "ImageAdjuster": ImageAdjuster,
+    "SaveImagEX": SaveImagEX,
     "FileListAndSuffix": FileListAndSuffix,
     "ReadExcelData": ReadExcelData,
     "WriteExcelData": WriteExcelData,
@@ -58,13 +62,14 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
 
    #运算型节点：meyo_node_Computational
-   "StandardSize": "重置尺寸(meeeyo.com)",
    "CompareInt": "比较数值(meeeyo.com)",
    "FloatToInteger": "规范数值(meeeyo.com)",
    "GenerateNumbers": "生成范围数组(meeeyo.com)",
    "GetRandomIntegerInRange": "范围内随机数(meeeyo.com)",
 
    #字符串处理：meyo_node_String
+   "SingleTextInput": "文本输入(meeeyo.com)",  
+   "MultiParamInputNode": "多参数输入(meeeyo.com)",
    "AddPrefixSuffix": "添加前后缀(meeeyo.com)",
    "ExtractSubstring": "提取标签之间(meeeyo.com)",
    "ExtractSubstringByIndices": "按数字范围提取(meeeyo.com)",
@@ -93,6 +98,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "FindFirstLineContent": "指定字符行参数(meeeyo.com)",
 
    #文件处理：meyo_node_File
+   "LoadAndAdjustImage": "加载重置图像(meeeyo.com)",
+   "ImageAdjuster": "重置图像(meeeyo.com)",
+   "SaveImagEX": "保存图像(meeeyo.com)",
    "FileListAndSuffix": "从路径加载(meeeyo.com)",
    "ReadExcelData": "读取表格数据(meeeyo.com)",
    "WriteExcelData": "写入表格数据(meeeyo.com)",
@@ -100,7 +108,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "ReadExcelRowOrColumnDiff": "读取表格数量差(meeeyo.com)",
    
     #功能型节点：meyo_node_Functional
-   "GetCurrentTime": "当前时间",
+   "GetCurrentTime": "当前时间(戳)",
    "SelectionParameter": "选择参数",
   
 }

meyo_node_Computational.py

@@ -2,52 +2,6 @@ import math
 import random
 
 
-#======宽度高度调整
-class StandardSize:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "width": ("INT",),
-                "height": ("INT",),
-                "size_option": (["小尺寸", "中尺寸", "大尺寸"], {"default": "中尺寸"}),  # 下拉选择框
-            },
-            "optional": {},
-        }
-
-    RETURN_TYPES = ("INT", "INT")
-    FUNCTION = "resize_image"
-    CATEGORY = "Meeeyo/Image"
-
-    def resize_image(self, width, height, size_option):
-        # 根据选择的选项调整尺寸像尺寸
-        if size_option == "小尺寸":
-            if width / height >= 1.23:
-                return (768, 512)
-            elif 0.82 < width / height < 1.23:
-                return (768, 768)
-            else:
-                return (512, 768)
-        elif size_option == "中尺寸":
-            if width / height >= 1.23:
-                return (1216, 832)
-            elif 0.82 < width / height < 1.23:
-                return (1216, 1216)
-            else:
-                return (832, 1216)
-        elif size_option == "大尺寸":
-            if width / height >= 1.23:
-                return (1600, 1120)
-            elif 0.82 < width / height < 1.23:
-                return (1600, 1600)
-            else:
-                return (1120, 1600)
-
-
-
 #======比较数值
 class CompareInt:
     def __init__(self):

meyo_node_File.py

@@ -1,6 +1,251 @@
+import os
 import csv
 import openpyxl
-import os
+import pathlib
+import folder_paths
+import torch
+import node_helpers
+import numpy as np
+from PIL import Image, ImageOps, ImageSequence
+
+
+#======加载重置图像
+class LoadAndAdjustImage:
+    _color_channels = ["alpha", "red", "green", "blue"]
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f.name for f in pathlib.Path(input_dir).iterdir() if f.is_file()]
+        return {
+            "required": {
+                "image": (sorted(files), {"image_upload": True}),
+                "max_dimension": ("INT", {"default": 1024, "min": 0, "max": 4096, "step": 8}),
+                "size_option": (["Custom", "Small", "Medium", "Large"], {"default": "Custom"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT")
+    RETURN_NAMES = ("image", "mask", "width", "height")
+    FUNCTION = "load_image"
+    CATEGORY = "Meeeyo/File"
+
+    def load_image(self, image, max_dimension, size_option):
+        image_path = folder_paths.get_annotated_filepath(image)
+        img = node_helpers.pillow(Image.open, image_path)
+
+        W, H = img.size
+        output_images = []
+        output_masks = []
+
+        for frame in ImageSequence.Iterator(img):
+            frame = node_helpers.pillow(ImageOps.exif_transpose, frame)
+
+            if frame.mode == 'P':
+                frame = frame.convert("RGBA")
+            elif 'A' in frame.getbands():
+                frame = frame.convert("RGBA")
+
+            if size_option != "Custom":
+                aspect_ratio = W / H
+
+                if size_option == "Small":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 768, 512
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 768, 768
+                    else:
+                        target_width, target_height = 512, 768
+                elif size_option == "Medium":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 1216, 832
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 1216, 1216
+                    else:
+                        target_width, target_height = 832, 1216
+                elif size_option == "Large":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 1600, 1120
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 1600, 1600
+                    else:
+                        target_width, target_height = 1120, 1600
+
+                image = frame.convert("RGB")
+                image = ImageOps.fit(image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
+
+                if 'A' in frame.getbands():
+                    mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
+                    mask = torch.from_numpy(mask)
+                    mask_image = Image.fromarray((mask.numpy() * 255).astype(np.uint8))
+                    mask_image = ImageOps.fit(mask_image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
+                    mask = np.array(mask_image).astype(np.float32) / 255.0
+                    mask = torch.from_numpy(mask)
+                else:
+                    mask = torch.ones((target_height, target_width), dtype=torch.float32, device="cpu")
+            else:
+                ratio = min(max_dimension / W, max_dimension / H)
+                adjusted_width = round(W * ratio)
+                adjusted_height = round(H * ratio)
+
+                image = frame.convert("RGB")
+                image = image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
+
+                if 'A' in frame.getbands():
+                    mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
+                    mask = torch.from_numpy(mask)
+                    mask_image = Image.fromarray((mask.numpy() * 255).astype(np.uint8))
+                    mask_image = mask_image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
+                    mask = np.array(mask_image).astype(np.float32) / 255.0
+                    mask = torch.from_numpy(mask)
+                else:
+                    mask = torch.ones((adjusted_height, adjusted_width), dtype=torch.float32, device="cpu")
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+        if size_option != "Custom":
+            return (output_image, output_mask, target_width, target_height)
+        else:
+            return (output_image, output_mask, adjusted_width, adjusted_height)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return f"Invalid image file: {image}"
+        return True
+
+
+#======重置图像尺寸
+class ImageAdjuster:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "max_dimension": ("INT", {"default": 1024, "min": 0, "max": 4096, "step": 8}),
+                "size_option": (["Custom", "Small", "Medium", "Large"], {"default": "Custom"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT")
+    RETURN_NAMES = ("image", "width", "height")
+    FUNCTION = "process_image"
+    CATEGORY = "Meeeyo/File"
+
+    def process_image(self, image, max_dimension=1024, size_option="Custom"):
+        input_image = Image.fromarray((image.squeeze(0).numpy() * 255).astype(np.uint8))
+        W, H = input_image.size
+
+        processed_images = []
+
+        for frame in [input_image]:
+            frame = ImageOps.exif_transpose(frame)
+
+            if frame.mode == 'P':
+                frame = frame.convert("RGBA")
+            elif 'A' in frame.getbands():
+                frame = frame.convert("RGBA")
+
+            if size_option != "Custom":
+                aspect_ratio = W / H
+
+                if size_option == "Small":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 768, 512
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 768, 768
+                    else:
+                        target_width, target_height = 512, 768
+                elif size_option == "Medium":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 1216, 832
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 1216, 1216
+                    else:
+                        target_width, target_height = 832, 1216
+                elif size_option == "Large":
+                    if aspect_ratio >= 1.23:
+                        target_width, target_height = 1600, 1120
+                    elif 0.82 < aspect_ratio < 1.23:
+                        target_width, target_height = 1600, 1600
+                    else:
+                        target_width, target_height = 1120, 1600
+                processed_image = frame.convert("RGB")
+                processed_image = ImageOps.fit(processed_image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
+            else:
+                ratio = min(max_dimension / W, max_dimension / H)
+                adjusted_width = round(W * ratio)
+                adjusted_height = round(H * ratio)
+
+                processed_image = frame.convert("RGB")
+                processed_image = processed_image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
+
+            processed_image = np.array(processed_image).astype(np.float32) / 255.0
+            processed_image = torch.from_numpy(processed_image)[None,]
+            processed_images.append(processed_image)
+
+        output_image = torch.cat(processed_images, dim=0) if len(processed_images) > 1 else processed_images[0]
+        if size_option != "Custom":
+            return (output_image, target_width, target_height)
+        else:
+            return (output_image, adjusted_width, adjusted_height)
+
+#======保存图像
+class SaveImagEX:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "save_path": ("STRING", {"default": "./output"}),
+                "image_name": ("STRING", {"default": "ComfyUI"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "save_image"
+    OUTPUT_NODE = True
+    CATEGORY = "Meeeyo/File"
+
+    def save_image(self, image, save_path, image_name):
+        if not isinstance(image, torch.Tensor):
+            raise ValueError("Invalid image tensor format")
+        if save_path == "./output":
+            save_path = self.output_dir
+        elif not os.path.isabs(save_path):
+            save_path = os.path.join(self.output_dir, save_path)
+        os.makedirs(save_path, exist_ok=True)
+        base_name = os.path.splitext(image_name)[0]
+        batch_size = image.shape[0]
+        channel_to_mode = {1: "L", 3: "RGB", 4: "RGBA"}
+
+        for i in range(batch_size):
+            filename = f"{base_name}.png" if batch_size == 1 else f"{base_name}_{i:05d}.png"
+            full_path = os.path.join(save_path, filename)
+            single_image = image[i].cpu().numpy()
+            single_image = (single_image * 255).astype('uint8')
+            channels = single_image.shape[2]
+            if channels not in channel_to_mode:
+                raise ValueError(f"Unsupported channel number: {channels}")
+            mode = channel_to_mode[channels]
+            if channels == 1:
+                single_image = single_image[:, :, 0]
+            pil_image = Image.fromarray(single_image, mode)
+            pil_image.save(full_path, format="PNG", compress_level=0)
+        return (image,)
 
 
 #======文件路径和后缀统计

meyo_node_Functional.py

@@ -1,5 +1,5 @@
-from datetime import datetime
-import ntplib
+import datetime
+import time
 
 #======获取当前时间
 class GetCurrentTime:
@@ -17,7 +17,7 @@ class GetCurrentTime:
             },
         }
 
-    RETURN_TYPES = ("STRING",)
+    RETURN_TYPES = ("STRING", "INT")  # 新增 INTEGER 类型的返回值
     FUNCTION = "get_current_time"
     CATEGORY = "Meeeyo/Functional"
     
@@ -26,13 +26,15 @@ class GetCurrentTime:
 
     def get_current_time(self, prefix="", any=None):
         # Get the current system time
-        current_time = datetime.now()
+        current_time = datetime.datetime.now()
         # Format the time as a string
         formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S")
         # Include the prefix in the output
         result = f"{prefix}{formatted_time}"
-        # Return the formatted time with prefix as a string
-        return (result,)
+        # Get the current timestamp as an integer with milliseconds precision
+        timestamp = int(round(time.time() * 1000))  # 获取当前时间戳的整数部分并精确到毫秒
+        # Return both the formatted time with prefix and the timestamp
+        return (result, timestamp)  # 返回两个值：字符串和整数
         
 
 #======选择性别和模板

meyo_node_String.py

@@ -7,6 +7,47 @@ import math
 import re
 
 
+#======多参数输入
+class SingleTextInput:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"default": "", "multiline": True}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "process_input"
+    CATEGORY = "Meeeyo/Text"
+    OUTPUT_NODE = False
+
+    def process_input(self, text):
+        return (text,)
+    
+
+#======多参数输入
+class MultiParamInputNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text1": ("STRING", {"default": "", "multiline": True}),  # 第一个多行文本输入框
+                "text2": ("STRING", {"default": "", "multiline": True}),  # 第二个多行文本输入框
+                "int1": ("INT", {"default": 0, "min": -1000000, "max": 1000000}),  # 第一个整数输入框
+                "int2": ("INT", {"default": 0, "min": -1000000, "max": 1000000}),  # 第二个整数输入框
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "INT", "INT")  # 返回两个字符串和两个整数
+    FUNCTION = "process_inputs"
+    CATEGORY = "Meeeyo/String"
+    OUTPUT_NODE = False
+
+    def process_inputs(self, text1, text2, int1, int2):
+        return (text1, text2, int1, int2)
+
+
 #======添加前后缀
 class AddPrefixSuffix:
     def __init__(self):