adds solarize

Author: EllangoK

README.md

@@ -18,6 +18,7 @@ A collection of post processing nodes for [ComfyUI](https://github.com/comfyanon
  - 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
  - Sharpen: Enhances the details in an image by applying a sharpening filter
+ - Solarize: Inverts image colors based on a threshold for a striking, high-contrast effect
 
 ## Example workflow
 

post_processing/solarize.py

@@ -0,0 +1,33 @@
+import torch
+
+class Solarize:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "threshold": ("FLOAT", {
+                    "default": 0.5,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.01
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "solarize_image"
+
+    CATEGORY = "postprocessing"
+
+    def solarize_image(self, image: torch.Tensor, threshold: float):
+        solarized_image = torch.where(image > threshold, 1 - image, image)
+        solarized_image = torch.clamp(solarized_image, 0, 1)
+        return (solarized_image,)
+
+NODE_CLASS_MAPPINGS = {
+    "Solarize": Solarize,
+}

post_processing_nodes.py

@@ -46,6 +46,54 @@ def subtract(self, img1, img2):
     def difference(self, img1, img2):
         return torch.abs(img1 - img2)
 
+class Blend:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+                "image2": ("IMAGE",),
+                "blend_factor": ("FLOAT", {
+                    "default": 0.5,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.01
+                }),
+                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "blend_images"
+
+    CATEGORY = "postprocessing"
+
+    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+        blended_image = self.blend_mode(image1, image2, blend_mode)
+        blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
+        blended_image = torch.clamp(blended_image, 0, 1)
+        return (blended_image,)
+
+    def blend_mode(self, img1, img2, mode):
+        if mode == "normal":
+            return img2
+        elif mode == "multiply":
+            return img1 * img2
+        elif mode == "screen":
+            return 1 - (1 - img1) * (1 - img2)
+        elif mode == "overlay":
+            return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
+        elif mode == "soft_light":
+            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+        else:
+            raise ValueError(f"Unsupported blend mode: {mode}")
+
+    def g(self, x):
+        return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
+
 class Blur:
     def __init__(self):
         pass
@@ -787,6 +835,34 @@ def sharpen(self, image: torch.Tensor, blur_radius: int, alpha: float):
 
         return (result,)
 
+class Solarize:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "threshold": ("FLOAT", {
+                    "default": 0.5,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.01
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "solarize_image"
+
+    CATEGORY = "postprocessing"
+
+    def solarize_image(self, image: torch.Tensor, threshold: float):
+        solarized_image = torch.where(image > threshold, 1 - image, image)
+        solarized_image = torch.clamp(solarized_image, 0, 1)
+        return (solarized_image,)
+
 def sort_span(span, sort_by, reverse_sorting):
     if sort_by == 'H':
         key = lambda x: x[1][0]
@@ -880,6 +956,7 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
 
 NODE_CLASS_MAPPINGS = {
     "ArithmeticBlend": ArithmeticBlend,
+    "Blend": Blend,
     "Blur": Blur,
     "CannyEdgeDetection": CannyEdgeDetection,
     "ColorCorrect": ColorCorrect,
@@ -892,4 +969,5 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
     "PixelSort": PixelSort,
     "Pixelize": Pixelize,
     "Sharpen": Sharpen,
+    "Solarize": Solarize,
 }