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Adds crop to multiple mode to ResizeImageMaskNode. (#11838)

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* Add crop-to-multiple resize mode

* Make scale-to-multiple shape handling explicit
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nomadoor 2026-01-14 09:48:10 +09:00 committed by GitHub
parent eff2b9d412
commit 469dd9c16a
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1 changed files with 44 additions and 0 deletions
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comfy_extras/nodes_post_processing.py
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@ -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: