Image/Mask Autosize combines a longer-dimension target, a shorter-dimension target, configurable constraint priority, and divisible output dimensions in one operation. It also provides anchored cropping, reversible diffusion padding, and reports the exact horizontal and vertical resize scales.

workflow

Sizing behavior

The node calculates two aspect-preserving resize scales:

The scale that makes the longer dimension equal max_size.
The scale that makes the shorter dimension equal min_size.

constraint_priority determines which scale is used:

min_size: Uses the larger scale. This is equivalent to applying max_size first and then enlarging as needed to enforce min_size. The shorter dimension wins when the constraints are incompatible.
max_size: Uses the smaller scale. This is equivalent to applying min_size first and then shrinking as needed to enforce max_size. The longer dimension wins when the constraints are incompatible.

Both dimensions are then rounded to the nearest multiple of divisible_by. This final rounding can move a dimension slightly beyond its nominal constraint.

When crop_mode is an anchor such as center or top_left, the input is resized to cover the calculated dimensions and then cropped from that position. The aspect ratio is preserved.

When crop_mode is none, the input is resized directly to the calculated dimensions. Divisibility rounding can therefore produce slightly different horizontal and vertical scales.

When crop_mode is pad, the input is proportionally resized to fit inside the calculated dimensions. Images use replicated edge padding and masks use zero padding. The returned transform can be applied to aligned masks with Apply Autosize Transform, then reversed after diffusion with Restore Autosized Image/Mask.

Inputs

image: The IMAGE or MASK to resize.
max_size: Target for the longer dimension.
min_size: Target for the shorter dimension.
constraint_priority: Selects whether min_size or max_size wins and whether the larger or smaller candidate resize scale is used.
divisible_by: Rounds both output dimensions to the nearest multiple of this value.
interpolation_mode: nearest-exact, bilinear, area, bicubic, or lanczos.
crop_mode: none, pad, center, an edge, or a corner.

Outputs

resized: The resized value, with the same ComfyUI type as the input.
width: Final output width.
height: Final output height.
scale_x: Actual intermediate resize width divided by the original width.
scale_y: Actual intermediate resize height divided by the original height.
transform: Recorded geometry for aligning related inputs and removing pad regions after diffusion.

For anchored cropping, scale_x and scale_y describe the resize performed before the crop. A crop also introduces an offset, so apply the same settings to related images and masks when their pixels must remain aligned.

Image quality and device behavior

Image/Mask Autosize uses ComfyUI's shared resize implementation. Tensor-native interpolation modes preserve the input device and dtype. ComfyUI's Lanczos implementation uses Pillow internally but returns the result to the original device and dtype.

Relationship to Resize Image/Mask

ComfyUI's native Resize Image/Mask is the better choice for exact dimensions, fixed multipliers, megapixel targets, or matching another input. Image/Mask Autosize is focused on automatic diffusion sizing where longer-edge targeting, shorter-edge constraints, explicit constraint priority, divisible dimensions, anchored cropping, and scale metadata should happen together.

Requirements

A recent ComfyUI build with V3 MatchType support is required. The extension has no third-party dependencies beyond ComfyUI's existing requirements.

Installation

Clone the repository into ComfyUI/custom_nodes and restart ComfyUI:

git clone https://github.com/SparknightLLC/ComfyUI-ImageAutosize.git

The node appears under image.

See CHANGELOG.md for migration notes.

This node was adapted from the [img2img_autosize] shortcode in Unprompted.