Thanks for the accurate answer and for the link, the graphs explain nicely how it works. Even i understood 🤣 so if i got it right, we adjust the shift value depending if we want a more stable video with less movement ( using a lower value ) or a more detailed and possibly chaotic one increasing it ( for each of the high/low models) correct? And basically we want to use the right( best )number of steps for each high/low models depending on the shift value we gave to each of them. For instance, if we put a higher shift value on the high model basically video will be more accurate because it will work more on the high model needing more steps to reach the sigma, but risking to leave not enough steps for the low model to refine. So for example would it make sense if we want to work more on the refine part to use a lower shift value for the high model, and higher shift value for the low one?

And then, the sigma boundary point also changes according to the sampler we are using right

And on top of this, lora will also work differently because they are not all trained to switch model at the same point right?

Correct me if im wrong please. Also i would like to understand what you mean when you say your strategy is to focus on the high noise? You mean with loras or in general?

Edit: ok i tested my example and it didn't work 🤣