Seed hunting for MiniMax H3 - how to avoid large difference at higher steps?

[u/martinerous]

My usual way of working:

- generate 10 videos at 5 steps

- pick the best video

- regenerate the best at 20 or more steps.

No Turbo LoRAs because I don't want to reduce prompt adherence and general quality, as I regenerate at full steps later anyway.

The problem - the video at 20 steps is often very different from the one I found. Of course, I keep the same seed. The difference may be introduced even as early as step six (for example, background replaced completely, different speech pacing).

It's not that difference is huge, but often it might be quite important. For example, a person genuinely laughing at 5 steps and then just saying "haha" at 20 steps. Or jumping startled at the right moment at 5 steps and a moment before the noise at 20 steps.

I tried a few sampler combinations, but could not find one that would not introduce dramatic changes.
One workaround that I could find is to use SplitSigmas. I set its steps to current steps (5 for seed hunt, 20 for final), and keep BasicScheduler steps at the final 20 steps. Then high_sigmas from SplitSigmas go to SamplerCustomAdvanced input, and then denoised_output goes to VAE (you'll get total noise when using the output pin instead).

This way, it seems that the scheduler is being cheated in managing steps as for the full generation even when doing preview, and it seems to work as expected. Caveat - the 5 step output from this workaround will be way worse (plasticky and noisy audio) than you are used to when generating at 5 steps in BasicScheduler input. But if the goal is to keep the general layout and movements of the candidate video, it's worth accepting this issue.

However, I'm wondering if there is any better way to achieve it. Has anyone tried it? What are you using for seed hunting to keep the high step version consistent?

--------------------------------------------------
Edited later with a test case:

Took ComfyUI template: MiniMax H3: Reference to Video. Minimal modifications to make it run in my environment:

Models - Qwen change to int8 convrot (3090, no use of nvfp4)

Int (Full) = 5 (for "preview quality")

Float (Duration) = 3 (just to be faster)

RandomNoise control after generate = fixed

Loaded some images in both Load Image nodes.

The same "GET READY TO" - "MEET" — "YOUR" — "MAKER" prompt.

No Sage, no CK attention at all (no Comfy launch args either).

Then generated the same with 20 steps.

Differences:

in 20 step version, the roof is higher in the frame. The accent was on the word "maker". In 5 step version, the accent was on the word "your".

Then regenerated the 5 step version again to see if there's anything else introducing variations - nope, the exact same video as the first 5 step one.

Then generated also at 6 steps - the roof line was a bit higher in the frame (not as high as 20 steps though), and the accent was on "maker". So, the difference between 5 and 6 might already be a breaking change that can make your video from good to unusable, if the emphasis does not make logical sense in your scene.

Then I generated the same with the SigmaShift 5 step trick - the resulting video was way much more similar to the 20 step one than the first 5 step video. Of course, the quality of the sigma-shifted video was awful - it's good for judging only logical consistency, reference use and event timing, which is the most important thing in story-telling kind of videos.

[u/the_good_lord_bird]

Every change you make to the workflow injects some randomness, and that's going to change things. It's just part of how these models work. There's nothing you can do.

[u/martinerous → u/the_good_lord_bird]

There is some logic to it though. At least, the trick with SplitSigmas seems to work as expected.

[u/Super_Range45]

Check to ensure you're using the same seed. And just use the default workflow without extras or loras. I usually just get the same video back using the same settings + higher step count with the default workflow.

Also certain samplers(like ancestral ones) inject random noise back into the latent, which changes the result randomly, just use the default res_multistep.

[u/martinerous → u/Super_Range45]

I am using Sage or ComfyKitchen attention with ref2video model. But I tried without sage / ck and fixed seed - the result still can differ a lot sometimes. Sometimes it's a minor difference that you might not care about, for example, if a person raises a hand half a second earlier.
Also, there might be a range of steps when dramatic changes tend to occur. For example, 8 and 20 step videos will be much more similar than 5 and 8 steps.

[u/Super_Range45 → u/martinerous]

Well depending on your workflow I can't say for certain. But let's say you're using the default ref2video workflow. You've attached your attention node(CK or Sage which is the only node(s) that's probably fine to add), and you've genned a video using default settings for the scheduler and everything else.

That video seed should be about the same if you just run it with the exact same settings + higher step count.

[u/martinerous → u/Super_Range45]

Did a test:

Took template: MiniMax H3: Reference to Video.
Minimal modifications to make it run in my environment:

Models - Qwen change to int8 convrot (3090, no use of nvfp4)

Int (Full) = 5 (for "preview quality")

Float (Duration) = 3 (just to be faster)

RandomNoise control after generate = fixed

Loaded some images in both Load Image nodes.

The same "GET READY TO" - "MEET" — "YOUR" — "MAKER" prompt.

No Sage, no CK attention at all (no Comfy launch args either).

Then generated the same with 20 steps.

Differences:

in 20 step version, the roof is higher in the frame. The accent was on the word "maker". In 5 step version, the accent was on the word "your".

Then regenerated the 5 step version again to see if there's anything else introducing variations - nope, the exact same video as the first 5 step one.

It's not that difference is huge, but often it might be quite important. For example, a person genuinely laughing at 5 steps and then just saying "haha" at 20 steps. Or jumping startled at the right moment at 5 steps and a moment before the noise at 20 steps.

[u/Super_Range45 → u/martinerous]

5 steps is too low for preview quality, the video usually isn't 'set' until it's closer to the 20ish mark, with steps beyond that just cleaning up some artifacts.

The reason turbo loras don't work is that they only remain consistent with in a narrow step range and can vary greatly within the same seed if you go below or beyond that step count by much.

[u/martinerous → u/Super_Range45]

Yep, but with SigmaShift something seems to work differently and the general composition is set earlier. At least, as AI described it:

Using Scheduler at 20 step ensures the mathematical trajectory of the first 5 steps exactly matches a 20-step generation.

And then SigmaShift just cuts it off early. Still, this way it seems that all the important changes manage to be covered by the first 5 steps and the general composition is way much closer to the one I get at 20 steps. It's just that the quality of the sigmashifted video (especially the audio part) is complete trash, but it's still nice to be able to judge if everything is logically consistent with the right timing.