From 469dd9c16ad88765ffe4e7bfa57dd80faafbaddf Mon Sep 17 00:00:00 2001
From: nomadoor <124905471+nomadoor@users.noreply.github.com>
Date: Wed, 14 Jan 2026 09:48:10 +0900
Subject: [PATCH] Adds crop to multiple mode to ResizeImageMaskNode. (#11838)

* Add crop-to-multiple resize mode

* Make scale-to-multiple shape handling explicit
---
 comfy_extras/nodes_post_processing.py | 44 +++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 01afa13a1..0433bbda2 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -254,6 +254,7 @@ class ResizeType(str, Enum):
     SCALE_HEIGHT = "scale height"
     SCALE_TOTAL_PIXELS = "scale total pixels"
     MATCH_SIZE = "match size"
+    SCALE_TO_MULTIPLE = "scale to multiple"
 
 def is_image(input: torch.Tensor) -> bool:
     # images have 4 dimensions: [batch, height, width, channels]
@@ -363,6 +364,43 @@ def scale_match_size(input: torch.Tensor, match: torch.Tensor, scale_method: str
     input = finalize_image_mask_input(input, is_type_image)
     return input
 
+def scale_to_multiple_cover(input: torch.Tensor, multiple: int, scale_method: str) -> torch.Tensor:
+    if multiple <= 1:
+        return input
+    is_type_image = is_image(input)
+    if is_type_image:
+        _, height, width, _ = input.shape
+    else:
+        _, height, width = input.shape
+    target_w = (width // multiple) * multiple
+    target_h = (height // multiple) * multiple
+    if target_w == 0 or target_h == 0:
+        return input
+    if target_w == width and target_h == height:
+        return input
+    s_w = target_w / width
+    s_h = target_h / height
+    if s_w >= s_h:
+        scaled_w = target_w
+        scaled_h = int(math.ceil(height * s_w))
+        if scaled_h < target_h:
+            scaled_h = target_h
+    else:
+        scaled_h = target_h
+        scaled_w = int(math.ceil(width * s_h))
+        if scaled_w < target_w:
+            scaled_w = target_w
+    input = init_image_mask_input(input, is_type_image)
+    input = comfy.utils.common_upscale(input, scaled_w, scaled_h, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    x0 = (scaled_w - target_w) // 2
+    y0 = (scaled_h - target_h) // 2
+    x1 = x0 + target_w
+    y1 = y0 + target_h
+    if is_type_image:
+        return input[:, y0:y1, x0:x1, :]
+    return input[:, y0:y1, x0:x1]
+
 class ResizeImageMaskNode(io.ComfyNode):
 
     scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@@ -378,6 +416,7 @@ class ResizeImageMaskNode(io.ComfyNode):
         longer_size: int
         shorter_size: int
         megapixels: float
+        multiple: int
 
     @classmethod
     def define_schema(cls):
@@ -417,6 +456,9 @@ class ResizeImageMaskNode(io.ComfyNode):
                         io.MultiType.Input("match", [io.Image, io.Mask]),
                         crop_combo,
                         ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_TO_MULTIPLE, [
+                        io.Int.Input("multiple", default=8, min=1, max=MAX_RESOLUTION, step=1),
+                        ]),
                 ]),
                 io.Combo.Input("scale_method", options=cls.scale_methods, default="area"),
             ],
@@ -442,6 +484,8 @@ class ResizeImageMaskNode(io.ComfyNode):
             return io.NodeOutput(scale_total_pixels(input, resize_type["megapixels"], scale_method))
         elif selected_type == ResizeType.MATCH_SIZE:
             return io.NodeOutput(scale_match_size(input, resize_type["match"], scale_method, resize_type["crop"]))
+        elif selected_type == ResizeType.SCALE_TO_MULTIPLE:
+            return io.NodeOutput(scale_to_multiple_cover(input, resize_type["multiple"], scale_method))
         raise ValueError(f"Unsupported resize type: {selected_type}")
 
 def batch_images(images: list[torch.Tensor]) -> torch.Tensor | None: