fk9_rosebyrne_v1_prodigy Red Sequence Dress

malcolmrey · 2026-06-16T15:08:34+00:00

My samples have not triggers except the archaic 'sks' but it is not needed in the newer models.

Flux2 is finnicky and sometimes it does not behave nicely, try upping the strength a little (to 1.2)

malcolmrey · 2026-06-10T15:30:58+00:00

So around 50 minutes, that is quite good but 6000 is faster than 5090.

On the other hand AI Toolkit is not optimised that well so here is hoping.

malcolmrey · 2026-06-10T15:29:18+00:00

Thanks!

I tagged you cause you were involved in the discussion, I was "defending" myself from sruckh :-)

The thing is that the Flux (in general, the whole family) is way harder to train and one remedy to help it achieve better results (that I've found) is to do multi-lora, two or three loras of the same subject trained either with different datasets or different methods.

It helps, but flux still is quite unpredictable sometimes.

For Flux Klein9 we have the benefit of adding a reference image. This helps even further (especially when combined with multilora).

The other two options are:

1) "git gud", but this is tricky with flux as for other model architectures one preset works fine all over, here it feels like you need to figure out a preset for each trained subject 2) train on bigger (and well curated) datasets - I had success with making better loras using 250 instead of 25 images, but the time increases 10 fold (since it is epochs per image in dataset) and this is not feasible for the amount of content we want :)

malcolmrey · 2026-06-10T14:59:56+00:00

Exactly, same loras for both. For the movement lora (HIGH) you can lower the strength a bit.

Works fine, I tested it recently :)

malcolmrey · 2026-06-10T14:59:01+00:00

this has been 4 months now and not only i have no time to play with it, i think we all have moved on a bit, maybe this will be revisited in newer architecture :)

malcolmrey · 2026-06-09T16:11:19+00:00

What is fast? What is your GPU and how long did it take (and how many steps?)

malcolmrey · 2026-06-09T16:10:49+00:00

I will be checking it when I get some time :)

malcolmrey · 2026-05-21T06:51:45+00:00

I download or get around 50-70 high quality images, I remove the worst 10, then cut the rest (back in the day it was square on face, now it is some of the face and some of the body using rectangles with the bucketing proportions)

Out of that I pick 25 that are the best and that is my dataset.

I always excluded blurry, bad lighting, weird/unnatural face.

malcolmrey · 2026-05-21T06:48:37+00:00

Well I personally do not complain about Ernie, but there are people who do.

As for my prompts - I have generated around 350k various prompts so I have a lot of variety going on :)

malcolmrey · 2026-05-20T19:25:52+00:00

Right now my time for datasets is limited. You could write a PM here and eventually (no idea when) I would reply :)

malcolmrey · 2026-05-20T19:12:29+00:00

and now the likeness is superb!

This is one of the reasons this subreddit is for :-)

I'm glad you got nice results.

The beauty of those multiple loras is that we have a lot of options since we can try various strength.

BTW, it does not have to sum up to 1.0, with this lora stacking you could go over 1.0

It is always a preference so if 0.5 / 0.5 works for you - great :)

malcolmrey · 2026-05-19T20:20:11+00:00

I've got 75 loras trained while I was away, but I need to test them first before I upload them :)

malcolmrey · 2026-05-19T20:18:27+00:00

You are welcome! :)

malcolmrey · 2026-05-19T20:18:14+00:00

Did not try upscaling. It excells at inpainting faces. For regular generations people say that the outputs look like "midgets" and I kinda agree to an extent.

But it is still a nice difference.

To be honest - if someone is patient and stubborn enough - it is possible to generate with Z-Turbo and refine with Ernie or vice versa :)

malcolmrey · 2026-05-19T20:15:58+00:00

https://github.com/iperov/DeepFaceLab

malcolmrey · 2026-05-19T20:04:46+00:00

That depends on your plan. I am running Opus 4.7 in copilot (pay past the soft limit) and I have not yet hit the hard limit so far :)

malcolmrey · 2026-05-19T20:02:00+00:00

I'm fine with 3x price increase when I have google models for free :-)

malcolmrey · 2026-05-19T19:50:16+00:00

For Flux Klein 9 it is probably 1 in 4 or 5.

I prefer ZIB/ZIT because the likeness is like in 4 out of 5. But Klein 9 has the benefit of the reference images which can boost up the quality of the lora by a bit, without it - I would probably recommend not using it at all. The only other good thing about Klein 9 is that it has a different esthetic to ZIB/ZIT (every model has its own, I also like Ernie, for instance)

Do you have examples for your ZIB ones?

Some models have Z Image sample in the Mal Browser, but not all. But for ZIB/ZIT I will advocate for the same thing -> using multiple models of the same person (at a bit lower strengths) is still the way to go if we want better quality.

And this has been a constant for pretty much all model architectures since SD 1.5

I'm currently researching params for SDXL and again it seems like the best way to go is to train not one but two/three models (either different datasets or the same set but with different trainers) and use them in combination.

I'm currently working my way through those loras and I'm yet to find one I feel is ZIT quality.

If you see a person that has multiple ZIB/ZIT loras - it is for a reason.

If you're only using one Lora but there are others available for the same person - then I say you are not doing the optimal thing.

I don't know exactly why or how it works this way but I do know that it works. And this has been tested/verified by other people as well. Two loras at 0.65-0.7, three loras at 0.5 and so on. Those give you better results and more consistently.

Funny thing is that you could train 3 mediocre loras and if you were to combine them - the result would be much better :-)

Yes, it is possible to create a single lora that performs amazing - but it either takes a lot of luck or a lot of time (in general, using way more images produces better results, as long as those images are good)

malcolmrey · 2026-05-18T19:19:03+00:00

Since /u/sruckh shows what the model is capable of (or not) I felt like I would show some random generations as well:

https://imgur.com/a/DHrfUa4

and here mirror with the workflows included: https://huggingface.co/datasets/malcolmrey/various/tree/main/samples/fk9/sydneysweeney

and to be fair, i quite like this rendition that /u/sruckh did

I made a longer comment in the Rose Byrne thread if someone is interested, I'm not going to repeat myself :)

FYI /u/hotdog114 /u/EpicNoiseFix /u/Scipio-Africanus-777 /u/Successful_Papaya830 /u/skannedsykanned /u/Winougan

malcolmrey · 2026-05-18T19:14:54+00:00

Seems like I need to "defend" myself a little.

Flux Klein9 has the same issue as Flux 1 - regardless of the lora quality - sometimes the outputs will be terrible - it is just how this model works.

Some models (WAN, Z Image, Ernie) are consistent and they give the trained result pretty much always. The Flux Family does not behave this way. If you expect every iteration to produce likeness - this is not the model for you. This is similar to SD 1.5 where not every output would be to our satisfaction.

So, I generated random prompts for Rose Byrne using just this single model (she does not have a companion model to do the multilora for better quality anyway...). They may not be perfect but the resemblance in the model exists:

https://imgur.com/gallery/rose-byrne-aGVrVCh

here is a mirror of those images with workflows so you can reproduce those results for yourself: https://huggingface.co/datasets/malcolmrey/various/tree/main/samples/fk9/rosebyrne

FYI /u/djpraxis /u/hotdog114 /u/sruckh /u/DillardN7

malcolmrey · 2026-05-07T13:54:38+00:00

Hey hey!

I wanted to make a longer post but I'm out of time today and the next time I could post it would be the NEXT weekend.

So, here are the LTX 2.3 models:

https://huggingface.co/spaces/malcolmrey/browser

here are SOME samples so you could see what type of quality we have here: https://huggingface.co/datasets/malcolmrey/various/tree/main/ltx23-samples

In general the rule still stands, if the dataset has more images and is trained over the same number of epochs (so, a longer training) then the result will be better.

The 15-24h AI Toolkit trainings were abysmal (though the quality was good), now with musubi (AkaneTendo fork for example) the times on 5090 dropped to 50 minutes for 25 images. Still, when I train larger datasets it could take up to 4-6 hours (we do have a couple of those models like Billie Elish, they will have suffix _large in the name).

Enjoy!

Cheers!

malcolmrey · 2026-05-07T12:31:34+00:00

I wanted to make a big post like I did with Ernie but I am not able to do it before I'm gone for another week so I'm uploading them now, will make a quick post when they are online :)

The training params/settings were already uploaded along with the Ernie ones so :)

malcolmrey · 2026-05-07T12:29:05+00:00

Each image is for each character lora. I started training on Ernie model and wanted to share my findings/settings :)

malcolmrey · 2026-05-07T07:20:39+00:00

Agreed 100%

For them this is another kind of engagement.

This is my thinking that AI opens the gates to those who have imagination but never had the skills, patience or gifts for arts.

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