ComfyUI Constrain Resolution

A ComfyUI node that resizes any image to fit your model's resolution rules: a minimum size, a maximum size, and dimensions guaranteed divisible by 2, 8, 16, 32, or 64. It keeps the aspect ratio by cropping a few pixels when needed, so the output is always exactly what strict image-to-image and image-to-video models expect.

In the example above, an 1800×1013 image becomes 1280×720 — within the 704–1280 limits, both dimensions divisible by 16, aspect ratio intact.

Why

Many models fail or degrade when dimensions are wrong:

• Image-to-video models often require exact sizes with dimensions divisible by 8 or 16
• Diffusion models (Flux, SD, etc.) need dimensions divisible by at least 2, and some run faster at 32/64 alignment
• VRAM limits mean you need a hard cap on resolution

This node applies all three rules in one step and guarantees divisibility on every output — resize, round, and crop all land on multiples of your chosen number.

Inputs

Input	Default	Purpose
image	—	Image (or batch) to resize
min_res	704	Neither dimension will end up below this
max_res	1280	Neither dimension will end up above this
multiple_of	2	Output width and height are always divisible by this (1 disables)
resize_method	lanczos	Interpolation: lanczos (sharpest), bicubic, bilinear, nearest-exact (pixel art/masks), area (big downscales)
constraint_mode	Prioritize Min Resolution	What wins when an extreme aspect ratio can't satisfy both limits (see below)
crop_as_required	True	Crop minimally to hit exact dimensions instead of distorting the aspect ratio
crop_position	center	Which part to keep when cropping: center / top / bottom / left / right

Constraint modes — with a very wide or very tall image, min and max can conflict:

• Prioritize Min Resolution: neither dimension goes below min_res, even if the long side must exceed max_res. Best default.
• Prioritize Max Resolution (Strict): output always fits in a max_res × max_res box (hard VRAM cap), even if the short side lands below min_res.

Outputs

Output	Description
resized_image	The constrained image — your main output
original_image	Untouched input passthrough
width, height	Final dimensions (handy for downstream nodes)
final_aspect_ratio, original_aspect_ratio	For monitoring how much the ratio changed

Quick recipes

• Image-to-video prep: min_res=768, max_res=1024, multiple_of=16 — exact, divisible dimensions every time
• Hard VRAM cap: max_res=1024, constraint_mode=Prioritize Max Resolution (Strict)
• Keep every pixel (no cropping): crop_as_required=False, multiple_of=1
• Headshots: crop_position=top keeps faces when cropping portraits

Installation

ComfyUI Manager: search for "Constrain Resolution" and install.

Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/EnragedAntelope/ComfyUI-ConstrainResolution.git

Restart ComfyUI. No dependencies beyond ComfyUI itself (Python 3.10+).

Notes

• Resizing uses ComfyUI's own resizer (comfy.utils.common_upscale), so results match core resize nodes; bicubic/lanczos output is clamped to the valid range to avoid overshoot artifacts.
• Divisibility, min/max, and crop dimensions are enforced together — the reported width/height always match the actual output tensor.
• Built on the ComfyUI v3 node API (comfy_api.latest).

Version history

• v2.3.1: Rewrote README with current screenshots; enforce min_res even when rounding to a large multiple_of would dip below it
• v2.3.0: Added resize_method selection (lanczos default); fixed a rare off-by-one in cropped output size; removed unused numpy dependency; Python 3.10 compatibility; proper logging
• v2.2: Fixed strict max-resolution mode exceeding max_res after rounding; added test suite
• v2.1.0: Image resizing, smart cropping, crop position control, tooltips, input validation
• v2.0.0: Migrated to ComfyUI v3 specification
• v1.1: Initial release

License

See LICENSE. Issues and PRs welcome on GitHub.