EnragedAntelope c385f389ee v2.4.1: queue-time validation, absolute pixel ceiling, CI hardening
- Reject invalid configs in validate_inputs instead of mid-execution:
  Strict-mode multiple_of above max_res, out-of-range min_res/max_res/
  multiple_of, and unknown constraint modes
- Share the resolution and multiple_of bounds between define_schema() and
  validate_inputs() so the widget and the queue-time check cannot drift
- Cap the min-res pixel budget at an absolute 64 MP, floored at max_res
  squared; the multiplier alone permitted ~12 GB outputs at max_res=8192
- Warn on large upscales in Strict mode as well as min-res mode
- Pin GitHub Actions by commit SHA on node24-native majors; drop issues:write
  from the publish workflow and set contents:read on both
- Pin Comfy-Org/publish-node-action to the commit @v1 resolves to; the 1.0.1
  tag calls `comfy tracking disable`, removed from comfy-cli
- Add requirements-ci.txt with pip caching; add ruff config and a CI lint step
- Add schema-contract, tooltip, budget, crop-guard and validation tests
- Replace typing.Tuple with tuple
- Bump version to 2.4.1
2026-08-24 14:11:51 -04:00
2025-01-28 11:30:18 -05:00

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.

Example workflow: 1800×1013 input resized to 1280×720

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

Constrain Resolution node

Input Default Purpose
image — Image (or batch) to resize
min_res 704 Neither dimension will end up below this
max_res 1280 Bounds the longest side. In the default mode the long side may exceed it on extreme aspect ratios — see constraint modes below
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

Every image is rescaled so its longest side lands on max_res — images already inside the range are resized too, not passed through. This is what makes the output size predictable for batching.

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. At the default settings, beyond roughly a 52:1 aspect ratio the required output would be large enough to exhaust memory, so the node reports an error instead of trying — switch to Strict mode for those. The limit scales with max_res up to an absolute ceiling of 64 MP, and never refuses an output that fits inside your own max_res × max_res box.
  • 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).
  • Impossible settings fail with a clear error rather than silently passing the image through — e.g. multiple_of larger than max_res in Strict mode, where no valid output size exists.

Version history

  • v2.4.1: Invalid settings are now rejected when the workflow is queued instead of mid-execution — multiple_of above max_res in Strict mode, plus out-of-range min_res/max_res/multiple_of and unknown constraint modes coming from hand-written API workflows, which the widget bounds alone no longer covered; the memory guard gained an absolute 64 MP ceiling, so it stays meaningful at large max_res instead of permitting multi-gigabyte outputs; large-upscale warnings now fire in Strict mode too, so tiny sources blown up 10x+ are no longer silent; CI hardening (GitHub Actions pinned by commit SHA, pip dependency caching, torch bounded to known-good majors); new tests keep the node schema in sync with execute() and enforce tooltips on every input
  • v2.4.0: Refuse extreme-aspect-ratio upscales that would exhaust memory instead of attempting them (a 1×690 input at default settings previously asked for 704×485760, roughly 4 GB per batch item); fixed settings that silently passed the image through untouched (multiple_of above max_res in Strict mode, and thin strips at multiple_of=1); restored ComfyUI's built-in range and combo-option validation, which the previous validate_inputs signature disabled for every input; ~1.85× faster when cropping by dropping a resize whose result was discarded; crop decisions now use exact aspect ratios instead of 4-decimal rounded ones
  • v2.3.2: Added CI test workflow (pytest on Python 3.10–3.12); warn on very large upscales in "Prioritize Min Resolution" mode; pass malformed zero-dimension inputs through unchanged instead of erroring
  • 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.

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