增加节点解码说明

cipher_nodes.py

@@ -1,7 +1,7 @@
 import torch
 
 # ==========================================
-# 🔑 全局密钥：必须保持一致
+# 🔑 固定密钥
 # ==========================================
 SECRET_KEY = 999
 
@@ -12,9 +12,28 @@ class ImageCipherEncode:
 
     @classmethod
     def INPUT_TYPES(s):
+        # 这里定义显示在节点上的文字内容
+        info_text = (
+            "📌【前端解码指南 / Frontend Guide】\n"
+            "本节点专为API工作流设计，前端需按以下算法还原：\n"
+            "------------------------------------------------\n"
+            "1. 固定密钥 (Key): 999\n"
+            "2. 算法原理: RGB通道独立行位移 (Row Rolling)\n"
+            "3. 核心公式 (y=行号, w=图像宽度):\n"
+            "   R_shift = (y * 999 * 1) % w\n"
+            "   G_shift = (y * 999 * 2) % w\n"
+            "   B_shift = (y * 999 * 3) % w\n"
+            "4. 操作: 将对应通道的每行像素向左(反向)回滚 shift 距离\n"
+            "------------------------------------------------"
+        )
+
         return {
             "required": {
                 "images": ("IMAGE",),
+            },
+            # 添加这个可选输入，专门用于显示文字
+            "optional": {
+                "decryption_guide": ("STRING", {"default": info_text, "multiline": True}),
             }
         }
 
@@ -23,25 +42,20 @@ class ImageCipherEncode:
     FUNCTION = "encode"
     CATEGORY = "CipherTools"
 
-    def encode(self, images):
+    # 注意：函数签名里要加上 decryption_guide，尽管我们在代码里不使用它
+    def encode(self, images, decryption_guide=""):
         batch, h, w, c = images.shape
         out_list = []
         base_shift = SECRET_KEY
 
         for i in range(batch):
             img = images[i]
+            r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
 
-            # 分离通道
-            r = img[:, :, 0]
-            g = img[:, :, 1]
-            b = img[:, :, 2]
-
-            # 准备容器
             r_enc = torch.zeros_like(r)
             g_enc = torch.zeros_like(g)
             b_enc = torch.zeros_like(b)
 
-            # 加密循环：正向位移 (shifts=正数)
             for y in range(h):
                 shift_r = (y * base_shift * 1) % w
                 shift_g = (y * base_shift * 2) % w
@@ -56,17 +70,14 @@ class ImageCipherEncode:
         return (torch.stack(out_list, dim=0),)
 
 
+# 解码节点保持不变，这里省略以节省篇幅，请保留你原来的 ImageCipherDecode 类
 class ImageCipherDecode:
     def __init__(self):
         pass
 
     @classmethod
     def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-            }
-        }
+        return {"required": {"images": ("IMAGE",), }}
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("restored_image",)
@@ -80,26 +91,20 @@ class ImageCipherDecode:
 
         for i in range(batch):
             img = images[i]
-
-            r_enc = img[:, :, 0]
-            g_enc = img[:, :, 1]
-            b_enc = img[:, :, 2]
+            r_enc, g_enc, b_enc = img[:, :, 0], img[:, :, 1], img[:, :, 2]
 
             r_dec = torch.zeros_like(r_enc)
             g_dec = torch.zeros_like(g_enc)
             b_dec = torch.zeros_like(b_enc)
 
-            # 解密循环：反向位移 (shifts=负数)
             for y in range(h):
                 shift_r = (y * base_shift * 1) % w
                 shift_g = (y * base_shift * 2) % w
                 shift_b = (y * base_shift * 3) % w
 
-                # 注意这里的负号 -shift
                 r_dec[y, :] = torch.roll(r_enc[y, :], shifts=-shift_r, dims=0)
                 g_dec[y, :] = torch.roll(g_enc[y, :], shifts=-shift_g, dims=0)
                 b_dec[y, :] = torch.roll(b_enc[y, :], shifts=-shift_b, dims=0)
 
             out_list.append(torch.stack((r_dec, g_dec, b_dec), dim=-1))
-
         return (torch.stack(out_list, dim=0),)