adds blend node, combines two images

comfy_extras/nodes_post_processing.py

@@ -354,6 +354,65 @@ class ColorCorrect:
 
         return (result, )
 
+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):
+        batch_size, height, width, _ = image1.shape
+        result = torch.zeros_like(image1)
+
+        for b in range(batch_size):
+            img1 = image1[b].numpy()
+            img2 = image2[b].numpy()
+
+            blended_image = self.blend_mode(img1, img2, blend_mode)
+            blended_image = img1 * (1 - blend_factor) + blended_image * blend_factor
+            blended_image = np.clip(blended_image, 0, 1)
+
+            tensor = torch.from_numpy(blended_image).unsqueeze(0)
+            result[b] = tensor
+
+        return (result,)
+
+    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 np.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
+        elif mode == "soft_light":
+            return np.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 np.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, np.sqrt(x))
+
 
 NODE_CLASS_MAPPINGS = {
     "Dither": Dither,
@@ -362,4 +421,5 @@ NODE_CLASS_MAPPINGS = {
     "Sharpen": Sharpen,
     "CannyEdgeDetection": CannyEdgeDetection,
     "ColorCorrect": ColorCorrect,
+    "Blend": Blend,
 }