custom mosaic node

.gitignore

@@ -5,7 +5,4 @@ input/
 !input/example.png
 models/
 temp/
-custom_nodes/
-!custom_nodes/example_node.py.example
-!custom_nodes/mosaic_node.py
 extra_model_paths.yaml

custom_nodes/mosaic_node.py

@@ -0,0 +1,45 @@
+import torch
+
+class Mosaic:
+    interpolation_methods = ['nearest', 'bilinear', 'bicubic', 'area', 'nearest-exact']
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "pixel_size": ("INT", {
+                    "default": 4, 
+                    "min": 1,
+                    "max": 512,
+                    "step": 1
+                }),
+                "interpolation_method": (s.interpolation_methods, {"default": s.interpolation_methods[0]}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "mosaic"
+
+    CATEGORY = "image"
+
+    def mosaic(self, image, interpolation_method, pixel_size):
+        samples = image.movedim(-1,1)
+
+        starting_width = samples.shape[3]
+        starting_height = samples.shape[2]
+
+        #downsample dimensions
+        dowsample_width = int(starting_width / pixel_size) | 1
+        dowsample_height = int(starting_height / pixel_size) | 1
+
+        downsampled_image = torch.nn.functional.interpolate(samples, size=(dowsample_height, dowsample_width), mode=interpolation_method)
+        output_image = torch.nn.functional.interpolate(downsampled_image, size=(starting_height, starting_width), mode='nearest')
+
+        output = output_image.movedim(1,-1)
+
+        return (output,)
+
+NODE_CLASS_MAPPINGS = {
+    "Mosaic": Mosaic
+}