blend supports any size, dither -> quantize

comfy_extras/nodes_post_processing.py

+import numpy as np
 import torch
 import torch.nn.functional as F
+from PIL import Image
 
 
 class Blend:
@@ -28,6 +30,9 @@ class Blend:
     CATEGORY = "postprocessing"
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+        if image1.shape != image2.shape:
+            image2 = self.crop_and_resize(image2, image1.shape)
+
         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)
@@ -50,6 +55,29 @@ class Blend:
     def g(self, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
+    def crop_and_resize(self, img: torch.Tensor, target_shape: tuple):
+        batch_size, img_h, img_w, img_c = img.shape
+        _, target_h, target_w, _ = target_shape
+        img_aspect_ratio = img_w / img_h
+        target_aspect_ratio = target_w / target_h
+
+        # Crop center of the image to the target aspect ratio
+        if img_aspect_ratio > target_aspect_ratio:
+            new_width = int(img_h * target_aspect_ratio)
+            left = (img_w - new_width) // 2
+            img = img[:, :, left:left + new_width, :]
+        else:
+            new_height = int(img_w / target_aspect_ratio)
+            top = (img_h - new_height) // 2
+            img = img[:, top:top + new_height, :, :]
+
+        # Resize to target size
+        img = img.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
+        img = F.interpolate(img, size=(target_h, target_w), mode='bilinear', align_corners=False)
+        img = img.permute(0, 2, 3, 1)
+
+        return img
+
 class Blur:
     def __init__(self):
         pass
@@ -100,7 +128,7 @@ class Blur:
 
         return (blurred,)
 
-class Dither:
+class Quantize:
     def __init__(self):
         pass
 
@@ -109,51 +137,37 @@ class Dither:
         return {
             "required": {
                 "image": ("IMAGE",),
-                "bits": ("INT", {
-                    "default": 4,
+                "colors": ("INT", {
+                    "default": 256,
                     "min": 1,
-                    "max": 8,
+                    "max": 256,
                     "step": 1
                 }),
+                "dither": (["none", "floyd-steinberg"],),
             },
         }
 
     RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "dither"
+    FUNCTION = "quantize"
 
     CATEGORY = "postprocessing"
 
-    def dither(self, image: torch.Tensor, bits: int):
+    def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDSTEINBERG"):
         batch_size, height, width, _ = image.shape
         result = torch.zeros_like(image)
 
+        dither_option = Image.Dither.FLOYDSTEINBERG if dither == "floyd-steinberg" else Image.Dither.NONE
+
         for b in range(batch_size):
             tensor_image = image[b]
-            img = (tensor_image * 255)
-            height, width, _ = img.shape
-
-            scale = 255 / (2**bits - 1)
-
-            for y in range(height):
-                for x in range(width):
-                    old_pixel = img[y, x].clone()
-                    new_pixel = torch.round(old_pixel / scale) * scale
-                    img[y, x] = new_pixel
-
-                    quant_error = old_pixel - new_pixel
+            img = (tensor_image * 255).to(torch.uint8).numpy()
+            pil_image = Image.fromarray(img, mode='RGB')
 
-                    if x + 1 < width:
-                        img[y, x + 1] += quant_error * 7 / 16
-                    if y + 1 < height:
-                        if x - 1 >= 0:
-                            img[y + 1, x - 1] += quant_error * 3 / 16
-                        img[y + 1, x] += quant_error * 5 / 16
-                        if x + 1 < width:
-                            img[y + 1, x + 1] += quant_error * 1 / 16
+            palette = pil_image.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
+            quantized_image = pil_image.quantize(colors=colors, palette=palette, dither=dither_option)
 
-            dithered = img / 255
-            tensor = dithered.unsqueeze(0)
-            result[b] = tensor
+            quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
+            result[b] = quantized_array
 
         return (result,)
 
@@ -210,6 +224,6 @@ class Sharpen:
 NODE_CLASS_MAPPINGS = {
     "Blend": Blend,
     "Blur": Blur,
-    "Dither": Dither,
+    "Quantize": Quantize,
     "Sharpen": Sharpen,
 }