Add Sepia Effect

Author: EllangoK

README.md

@@ -34,11 +34,11 @@ Both images have the workflow attached, and are included with the repo. Feel fre
  - $\color{#00A7B5}\textbf{PixelSort:}$ Rearranges the pixels in the input image based on their values, and input mask. Creates a cool glitch like effect.
  - Pixelize: Applies a pixelization effect, simulating the reducing of resolution
  - $\color{#00A7B5}\textbf{Quantize:}$ Set and dither the amount of colors in an image from 0-256, reducing color information
+ - Sepia: Applies a mellow tone mapping, yielding an archival or vintage appearance
  - Sharpen: Enhances the details in an image by applying a sharpening filter
  - $\color{#00A7B5}\textbf{Solarize:}$ Inverts image colors based on a threshold for a striking, high-contrast effect
  - Vignette: Applies a vignette effect, putting the corners of the image in shadow
- 
- 
+
 $\color{#00A7B5}\textbf{Bolded Color Nodes}$ are my personal favorites, and highly recommended to expirement with
 
 </details>

combine_files.py

@@ -1,9 +1,5 @@
 from collections import OrderedDict
 from pathlib import Path
-import sys
-import os
-import glob
-import ast
 import argparse
 
 ignore_dirs = ["old"]
@@ -12,7 +8,7 @@ def get_python_files(path, recursive=False, args=None):
     search_pattern = "**/*.py" if recursive else "*.py"
 
     def should_include(file):
-        if file.is_file() and not file.name.startswith("combine") and not args.output in str(file):
+        if file.is_file() and not file.name.startswith("combine") and not args.output in str(file) and not file.name.startswith("__init__"):
             for ignore_dir in ignore_dirs:
                 if ignore_dir in str(file.parent):
                     return False

post_processing/sepia.py

@@ -0,0 +1,41 @@
+import torch
+
+class Sepia:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "strength": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.1,
+                    "max": 1.0,
+                    "step": 0.1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "sepia"
+
+    CATEGORY = "postprocessing"
+
+    def sepia(self, image: torch.Tensor, strength: float):
+        if strength == 0:
+            return (image,)
+
+        sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
+        sepia_filter = torch.tensor([1.0, 0.8, 0.6]).view(1, 1, 1, 3).to(image.device)
+
+        grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
+        sepia = grayscale * sepia_filter
+
+        result = sepia * strength + image * (1 - strength)
+        return (result,)
+
+NODE_CLASS_MAPPINGS = {
+    "Sepia": Sepia
+}