"Do one thing and do it well." — Peter H. Salus / Doug McIlroy, core Unix principle

"Simple is better than complex." — Zen of Python

Best Resolution nodes for ComfyUI

A small modular pack — QoL nodes for semi-automatic calculation of the best (most optimal) sampling resolution:

• ... model-agnostic — i.e., compatible with ANY model (from now or the future),
• ... accounting for upscale,
• ... and for (the essential) pixel-step.

[!NOTE] Nodes from this pack do nothing with actual images on their own — they only calculate the optimal values for the resolution.

Read any further only if you feel you need to.

The purpose

In case you didn't know, you can't just choose any arbitrary resolution for sampling. You need to ensure:

• both width and height are divisible by a latent scale factor, pre-defined by a model family (8 for both SD1.5 and SDXL) - and they must remain multiples of it both in initial generation AND after further upscales;
• the overall resolution (aka megapixels, total pixel count) is as close as possible to the model's "view window" (the resolution it was trained on) - so if you increase one side of the image, you need to proportionally decrease the other.

... and choosing the right resolution is your responsibility. 🤷🏻‍♂️

This pack lets you forget about crunching numbers and handles the calculation of optimal width/height for you, while still leaving control of the image size/proportions/orientation to you. It provides 3 main nodes:

Simple

Just rounds width/height to the given step, ensuring the image CAN be converted to latent - both in initial generation and after some upscales.

From Aspect-Ratio

As the name suggests, you control the resolution indirectly - by specifying the desired aspect ratio, one of the sides, and orientation (landscape/portrait).
The node detects the actual width/height to match those as close as posible.

From Area

To my taste, THE way to select the optimal resolution for diffusion models.

All you need to know is a size (one side) of square images the model was trained on (512 for SD1.5, 1024 for SDXL). The node handles the rest to meet all the restrictions:

• match the total resolution (i.e., image area, number of pixels) as close to the training one as possible,
• ... while still respecting aspect ratio, image orientation and step size.

Tooltips

Each parameter is self-documented in the shortest possible, yet exhaustive detail - just hover mouse over it. If you're new to Comfy and Stable Diffusion, this might be especially helpful.

A note on step value to choose

This parameter has the highest priority. A desired resolution is slightly tweaked to always adhere to it strictly. The nodes do their best to match all other criteria, but no matter what, in the end both width and height must remain a multiple of this value. This means two things:

1. If you don't plan processing the image with diffusion model but otherwise like these nodes for image-size selection, you can still use them - just set step to 1 (effectively disabling any rounding).
2. The right step value can single-handedly save you from unnecessary cropping or out-painting later.

In my workflows, I usually generate images like this:

• initial draft generation, exactly in the resolution the model was trained on - only composition and overall silhouettes matter here.
• "HD fix": immediate first 1.5x upscale in latent space, followed by sampling with high denoise (0.5+) - this becomes the actual base image to improve upon.
  • some tweaking/inpainting is done in this resolution.
• at least one more upscale, still sampling the whole image at once if GPU has VRAM for that (with a lower denoise, though).
• subsequent upscales with USDU.

So, this imposes a few restrictions on the initial gen if we want to avoid cropping/outpainting later:

• The initial image must be a multiple of 8
• After 1.5x upscale it must also be a multiple of 8 - so a +0.5 of size must be divisible by 8, or the initial step must be 8*2.
• After the first 1.5x upscale (with the original step being 8*2, or 16) our image became divisible by 8*2*1.5, or 8*3, so the second upscale could be +1/3 aka 1.333x. And if we do so, we actually reach the total size of 2x of the original gen.
• However, if we do so, at the very next step our divisibility would be 8*3*1.333, or 8*4.
  • This is good since the third upscale could be either 1.5x or 1.25x... but it can't be another +1/3.
• So, to workaround it, it's safer to include another multiple of 3 into the original step (so, together it becomes 8*2*3).
  • With it, we can do the previous 1.5x > 1.333x, followed by either 1.5x/1.25x or 1.333x.
  • If instead we did 1.5x > 2x - then we end up with a triple of the original size AND we have another multiple of 3 built-in, so the image has become divisible by 9. This is not at all necessary for "regular"-sized HD images, but it might become very handy for later ultra-upscales (6k+) with USDU.

Summarizing all of the above, our lowest common denominator to be safe during first few upscales is 8*2*3.

Thus, 48 by default.

Additionally, if you prefer to always upscale by 1.5, you might look into 8*2*2 = 32. Or, to allow a few more of 1.5x upscales (or a 1.25x one), or to get the resolution closer to the ones we used to (1080p, 1440p, etc), you might want to increase it by a power of 2, getting 64, 96, 128, 192, 256, etc.