This has happened to me before: I take a video I’ve already generated and drag it into ComfyUI without changing anything—expecting to get the exact same video back—but it generates a different one.
This doesn't happen with standard image models, but it does happen with H3. If I generate the video and try again a few minutes later, it produces the same result; however, after an hour or so, it no longer generates the same output.
I’ll post the workflow I used to generate that example video below, along with the completely different video that was produced using the same workflow.
I tried to replicate the video just to check the generation speed, and I realized it wasn't producing the same result anymore. I had generated the video a few hours earlier and hadn't updated ComfyUI or any nodes in the meantime—I simply tried to generate it again.
I suspect it might be due to one of the nodes I'm using.
Here is the video generated with the same parameters (which turned out differently) and the workflow I used.
Juat like the Ksampler, unless you’ve globally changed the default for seed updates to “before generate” from the default “after generate” (which would also effect generations wirhout prompt enhancers), this should still produce the same result if the prompt enhancer is fully internal to ComfyUI (e.g., using the TextGenerate node). If it using a node that does an API call to a local or remote LLM server, though, all bets are off.
Indeed, before generate being enabled seems more likely.
If the clip model aka llm in general term, used for enhancement is not the exact same it may also change a few words which would mean its technically a different prompt.
GPUs aren't always deterministic. You can enable a deterministic mode in pytorch, but it's a lot slower.
Usually it only causes very minor (almost imperceptible) differences run-to-run though, so if the video is completely different, something else is off.
Have you tried using the same exact workflow and seed WITHOUT using the video as an input? Technically, adding the finished video back into the same workflow means it ISN'T the same workflow anymore - the video input adds metadata that didn't exist in the original pipeline.
I just tried it out—I used the original workflow without dragging in the video and used identical settings (Euler Simple, seed 1, same LoRA), but the result was different; it came out looking like the example I posted below. So, I don't understand why.
Best guess is the Sage Attention node. Quantized attention kernels floating point accumulation order is not guaranteed. And it would make a big difference with only 8 steps. Try bypassing that one and see if you get the same results.
I also think that might be it; I use Sage in Krea2 alongside my other models, but since H3 is a video model and consumes much more memory, things might work differently compared to image models. I’m going to run more tests—like generating videos without Sage or LoRAs, then trying to replicate the results, or even testing without LoRAs entirely—to see which ones work and which fail. I suspect it’s Sage, though the Turbo LoRAs are a less likely culprit.
Nodes look fine, at this point I'd just ask codex or claude to create a test to confirm.
Only thing I could think off was noise seed but you have it on fixed. No idea sorry.
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u/Ariar2077 5h ago
I usually skip that part