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Improving results by using multiple models of the same concept (Turning it to 11!).

Improving results by using multiple models of the same concept (Turning it to 11!).

Hi All!

Today I would like to share with you something I noticed a few weeks ago and I've tested it extensively just to make sure this is not a fluke or just a random occurrence.

As you know, I was always saying that when it comes to the best quality (understood as the best likeness that can be represented by the subject) the rank is as follows:

1. Dreambooth, has the best quality but also has the biggest size (which is a problem for many people)

2. LyCORIS, second best - the quality is still great but a tad bit worse than what Dreambooth can do. However, the size is very small so it is actually a great compromise (and you can use it with other base models)

3. LoRA, third place - still can have a good quality, but does not keep as many details about character as LyCORIS/Dreambooth can, therefore the looks are more stylized (less photoreal) but also this makes them more flexible

4. Textual Inversion, as the last spot - since those do not add new information, just guide the trained model on how to reach what we want - this can work quite well sometimes but fail miserably in other cases. The size is the smallest, however.

But now I would like to introduce you to another technique (didn't want to use the word "new" since several months ago I've seen that using lora/lycoris [even from different people, using different tokens] on dreambooths yields interesting results):

0. Mixing LoRA with LyCORIS. Well, according to me and some friends that tested this concept - the feedback is unanimous - the quality is even BETTER than other techniques!

Before delving into the details, here is an imgur gallery (for a quick glimpses of the results, some of those images are available in the attached samples so you can try to reproduce on your own) ->

So, what is this all about? It is quite simple, actually.

When you want to reproduce a likeness of some person - we will just use both LoRA and LyCORIS models trained on that person and we will play with weights.

For example, if you use only a LyCORIS, you most likely set the weight of that model to something between 0.8 and 1.0 (but usually probably 1.0).

So what we will do instead is we will load LyCORIS and a LoRA of the same person and as a baseline, we will pick 0.7 for both models (which sums up to 1.4 which is on the higher end of what we set for a single model).

You should already notice that with those two models together - the likeness is better and the frequency of good output increases.

With LyCORIS alone I would get a "perfect/really good" output every 4 or 5 tries but a "good enough" every second time pretty much.

With this method, I feel like I'm hitting the "perfect" every 3rd one even but the other two in between are better than "good enough".

There is a zip file attached to this article with some of the test samples, as well as the grids.

Pretty much the 0.7 for both models is a good baseline, but each case (person) is specific so you should actually try out different combinations.

MOREOVER, if you have more than 2 models of the same person - you can include them, and the potential to get the perfect resemblance rises!

With three models together I usually go with 0.5 for all (perhaps one at 0.45) so the summed weight is in the ballpark of 1.45-1.5

With four I'm getting the best results at around 0.4 for each (so, 1.6 total sum weight).

Ok, so what to mix and how?

Well, for me it started with mixing various LyCORIS of the same person but trained on different datasets and other settings.

I've noticed that mixing various LyCORIS grants me better results than increasing the weight of a single LyCORIS.

Including more models of the same person (or even a concept!) with lower weights (but summed to a higher total) seems to bring up the "true" essence of a given person.

This is how I understand what happens, it may be wrong but nonetheless, the results seem to confirm my assumptions. Seems like adding models on top of each other amplifies the shared part of those models.

When you see a model of someone - you can usually figure out who it is because some unique characteristics are visible. With those models loaded on top of each other, it seems like that shared part gets amplified.

If you have a single model and you start at 1.0 and then you try to bring the weight up, the whole model is amplified, and sometimes you get some nice results but at some point, it will collapse.

So, with multiple models of the same concept, the weights are below 1.0 for each, but the shared concept is trained in all of those so that part gets amplified.

However, it went into 11 when I started including LoRA models in the mix as well.

In my understanding this actually makes sense. LyCORIS focuses on details while LoRA focused on the overall concept. This is why LyCORIS stuff like wrinkles, moles, and other stuff gets sometimes enhanced too much, but in LoRAS we get an overall likeness but without the intricate details.

And then when we mix them both together we can get incredible results. We get the general feel of the person from LoRA and get the finer details from the LyCORIS. This works exceptionally well for older people.

Also, the beautiful thing is that you can change the weights and guide the outputs, if you feel like the specific details are too high, you decrease the weight of the LyCORIS and increase the weight of LoRA.

Full disclosure, those generations also using the following resources:

  • "Add Detailer" LoRA: <lora:add_detail:0.7>

  • Some of the common negative TIs: illustration, BadDream, (UnrealisticDream:1.2), realisticvision-negative-embedding

  • My Serenity model as a base.

  • My Perfect Eyes LyCORIS <lora:locon_perfecteyes_v1_from_v1_64_32:0.2> (with 0.2 in base and 0.5 in ADetailer)

As well as the following techniques:

  • hi.res.fix with 8x_NMKD-Faces_160000_G.pth upscaler (scale 2x, denoise 0.35)

  • ADetailer with 0.3 denoise

Those techniques have nothing to do with the concept described in this tutorial (you can run plain 512x base resolution and still benefit) - but they are a staple when we want to boost the quality of our generations. Of course as usual - use common sense, sometimes the ADetailer is not needed (close-up shots) and the values presented by me are not set in stone and you are encouraged to try what works best for you!

I'm including some example generations in the zip file and some grids.

I am still uploading LyCORIS around twice per week, but I will start uploading more and more LoRAs (trained, not extracted) so that you can also enjoy this discovery! :)

As usual, you can always support me on my buymeacoffee page if you like what I am doing (and request some specific subjects/concepts) ->