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Add crop-to-multiple resize mode

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nomadoor 2026-01-13 12:46:37 +09:00
parent c2b65e2fce
commit e5810f7af8
1 changed files with 30 additions and 0 deletions
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30
comfy_extras/nodes_post_processing.py
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@ -254,6 +254,7 @@ class ResizeType(str, Enum):
SCALE_HEIGHT = "scale height" SCALE_HEIGHT = "scale height"
SCALE_TOTAL_PIXELS = "scale total pixels" SCALE_TOTAL_PIXELS = "scale total pixels"
MATCH_SIZE = "match size" MATCH_SIZE = "match size"
CROP_TO_MULTIPLE = "crop to multiple"
def is_image(input: torch.Tensor) -> bool: def is_image(input: torch.Tensor) -> bool:
# images have 4 dimensions: [batch, height, width, channels] # images have 4 dimensions: [batch, height, width, channels]
@ -363,6 +364,29 @@ def scale_match_size(input: torch.Tensor, match: torch.Tensor, scale_method: str
input = finalize_image_mask_input(input, is_type_image) input = finalize_image_mask_input(input, is_type_image)
return input return input
def crop_to_multiple(input: torch.Tensor, multiple: int, crop: str="center") -> torch.Tensor:
if multiple <= 1:
return input
width = input.shape[2]
height = input.shape[1]
new_w = (width // multiple) * multiple
new_h = (height // multiple) * multiple
if new_w == 0 or new_h == 0:
return input
if new_w == width and new_h == height:
return input
if crop == "center":
x0 = (width - new_w) // 2
y0 = (height - new_h) // 2
else:
x0 = 0
y0 = 0
x1 = x0 + new_w
y1 = y0 + new_h
if is_image(input):
return input[:, y0:y1, x0:x1, :]
return input[:, y0:y1, x0:x1]
class ResizeImageMaskNode(io.ComfyNode): class ResizeImageMaskNode(io.ComfyNode):
scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@ -378,6 +402,7 @@ class ResizeImageMaskNode(io.ComfyNode):
longer_size: int longer_size: int
shorter_size: int shorter_size: int
megapixels: float megapixels: float
multiple: int
@classmethod @classmethod
def define_schema(cls): def define_schema(cls):
@ -417,6 +442,9 @@ class ResizeImageMaskNode(io.ComfyNode):
io.MultiType.Input("match", [io.Image, io.Mask]), io.MultiType.Input("match", [io.Image, io.Mask]),
crop_combo, crop_combo,
]), ]),
io.DynamicCombo.Option(ResizeType.CROP_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"), io.Combo.Input("scale_method", options=cls.scale_methods, default="area"),
], ],
@ -442,6 +470,8 @@ class ResizeImageMaskNode(io.ComfyNode):
return io.NodeOutput(scale_total_pixels(input, resize_type["megapixels"], scale_method)) return io.NodeOutput(scale_total_pixels(input, resize_type["megapixels"], scale_method))
elif selected_type == ResizeType.MATCH_SIZE: elif selected_type == ResizeType.MATCH_SIZE:
return io.NodeOutput(scale_match_size(input, resize_type["match"], scale_method, resize_type["crop"])) return io.NodeOutput(scale_match_size(input, resize_type["match"], scale_method, resize_type["crop"]))
elif selected_type == ResizeType.CROP_TO_MULTIPLE:
return io.NodeOutput(crop_to_multiple(input, resize_type["multiple"]))
raise ValueError(f"Unsupported resize type: {selected_type}") raise ValueError(f"Unsupported resize type: {selected_type}")
def batch_images(images: list[torch.Tensor]) -> torch.Tensor | None: def batch_images(images: list[torch.Tensor]) -> torch.Tensor | None: