replaces dither and kmeans quantize with quantize

much faster dithering and uses PIL
2023-04-03 15:49:06 -04:00
parent 08a83e3d51
commit 99956683a0
6 changed files with 133 additions and 120 deletions
@@ -0,0 +1,50 @@
+import torch
+from PIL import Image
+import numpy as np
+
+class Quantize:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "colors": ("INT", {
+                    "default": 256,
+                    "min": 1,
+                    "max": 256,
+                    "step": 1
+                }),
+                "dither": (["none", "floyd-steinberg"],),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "quantize"
+
+    CATEGORY = "postprocessing"
+
+    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).to(torch.uint8).numpy()
+            pil_image = Image.fromarray(img, mode='RGB')
+
+            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)
+
+            quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
+            result[b] = quantized_array
+
+        return (result,)
+
+NODE_CLASS_MAPPINGS = {
+    "Quantize": Quantize,
+}