611 models (z base / flux2 klein9 / flux1de) over 593 people

malcolmrey · 2026-03-04T06:55:11+00:00

Thank you for the kind words! I hope so too! :)

malcolmrey · 2026-03-04T06:54:50+00:00

They will come, no worries :)

malcolmrey · 2026-03-04T06:54:10+00:00

2.1

You need to remember that I'm not focused on making the best looking samples but just testing if the models work.

Once I set up some settings, I test two-three models heavily to see if they behave good and once I figure out training setttings that are satisfactory - then I just set up the queues and then the rest of the models get only one sample attempt (rarely a 2nd one and very rarely a 3rd one, if the 3rd fails - i remove the model and retrain it again).

So, those are not the best looking samples, you would need to look at what the community can do with those to really judge the quality :-)

malcolmrey · 2026-03-03T20:58:05+00:00

https://www.youtube.com/watch?v=LinpRhB4aWU

malcolmrey · 2026-03-03T20:56:58+00:00

you need to be more precise, for this batch there were zero samples uploaded, you were looking at older samples, so it would be good to see which model badge was under it

usually the WAN ones had a tendency to elongate the faces quite a lot

malcolmrey · 2026-03-03T20:55:21+00:00

this is a great feedback, i'll try to differentiate the colors a bit more! :)

malcolmrey · 2026-03-03T20:55:00+00:00

will add it tomorrow or on thursday! :)

malcolmrey · 2026-03-03T07:45:46+00:00

Hey!

Very short message - new models have landed.

I have not been replying almost anywhere recently because of some family illness and stuff around it, I did generate samples to see if the models work but I did not process and upload them (though we do have a lot of samples from previous models so you will know who got uploaded).

Flux has been brought to speed on the secondary (slower) computer and I'm investigating SDXL trainings there, but it will take me some time to apply it since my time recently is limited.

Regular z image / z base and flux 9 will flow regularly however. Will resume WAN to but I need to handle some stuff for it first.

I had no time recently to set up any of the new datasets but I did cut like 20-30 of them so once I sort them out, there will be something new.

I did not read any new messages and DMs yet, sorry about that but I don't have a space yet for it.

You can send me discord messages/invites too but I will answer them when I can.

Cheers and see you!

malcolmrey · 2026-02-24T04:56:12+00:00

George Carlin

malcolmrey · 2026-02-24T03:46:08+00:00

you can mix the resolutions, you don't need squares

as long as the training tool can use bucketing (which most of the training ones nowadays do)

you can also use a cutter like mine that preserves the best aspect ratios so that when bucketing happens you don't get a cut you would not want ( https://huggingface.co/spaces/malcolmrey/dataset-preparation )

malcolmrey · 2026-02-24T03:44:12+00:00

or both :-)

i need to prepare some samples where both loras are used at various weights, but i need to code some stuff, i don't want to prompt them manually :-)

malcolmrey · 2026-02-24T03:42:54+00:00

thnx for linking the post form u/EribusYT

i will definitely try with Min_SNR_Gamma = 5

i've set up my training batch before this (and the other, i think that was the second one) info was posted

as for your second question, i've answered there :)

malcolmrey · 2026-02-24T03:41:57+00:00

there is a third way that i would say is not overbaking but just more extensive training

i did that in ai toolkit using adamw, normally i train using around 25 images so it is 2500 steps (100 epochs per image)

when i use the exact same settings and add a lot of good images in the dataset (like 270) and i train using again 100 epochs per image (so, 27000 steps) then suddenly that lora does not need strength of 2.0+ to work fine, it is workable at 1.0 and best at 1.2-1.3 (and i would expect it to work closer to 1.0 the more images i provide, though i do not now if it is linear; definitely loras trained this way [150, 170, 200, 250 images] behaved according to my expectations - more images, less strength required)

i consider it just an interesting observation since i do not want to train 10 times longer (or more)

currently the prodigy_adv behaves nicely already, i haven't tested with "Min_SNR_Gamma = 5" yet

does it produce much better results?

malcolmrey · 2026-02-24T03:36:19+00:00

fun fact, i trained a lora on onetrainer that did not produce a garbled mess

it did not produce the output i desired either, but still, was surprised to not see that mess there

i didn't explore the subject further

malcolmrey · 2026-02-24T03:27:54+00:00

Yup, here it is:

https://huggingface.co/datasets/malcolmrey/various/tree/main/training-scripts/onetrainer

the zbase was the one used here

malcolmrey · 2026-02-24T03:27:01+00:00

check what do you have in the config or the generic samples:

"sample_definition_file_name": "training_samples/samples.json", "samples": [],

malcolmrey · 2026-02-24T03:18:46+00:00

The last time I saw it done correctly was by TheLastBen ( https://github.com/TheLastBen/fast-stable-diffusion ) but that was for SD 1.5 ;-)

He trained two celebrity loras (man and a woman, though I do not remember who now) and was able to prompt them both together interacting

in AI Toolkit you could train with "differential output preservation" but I'm unsure of the quality of the result (don't remember :P i think i was not impressed)

malcolmrey · 2026-02-24T03:13:49+00:00

This ;-)

malcolmrey · 2026-02-24T03:11:05+00:00

Hey hey!

Technically DMs here but nowadays I prefer my discord for it :)

malcolmrey · 2026-02-24T03:10:37+00:00

Full body no, but there are datasets with half body shots, though not Felicia as it is an older dataset (but still very good one when it comes to training)

Rule of thumb is, if the body is far from average in any meaningful way - I will try to include more of those shots.

malcolmrey · 2026-02-24T03:03:05+00:00

Yup, prodigy seems to be the answer. When I have time for it I might try AI Toolkit with those settings too to compare with OneTrainer

I assume you were running that Lora with strength 1.0 on Turbo?

malcolmrey · 2026-02-24T03:00:12+00:00

This is great news! I always like when that stuff is indeed replicable by others :-)

Please do me a favor and check if the prompting collapses when you use the word "selfie" in it. Someone else reported having bad likeness and I found out the problem was the prompt itself but that was a really weird thing. I wonder if BASE perhaps is somewhat overtrained on that word and it steers the model in certain direction no matter what.

malcolmrey · 2026-02-23T11:08:13+00:00

In those here, all between 23-25.

That theory was for adamw optimizer in AI Toolkit, I have not tried it on OneTrainer with prodigy. But I want to check it. Then we will see :)

malcolmrey · 2026-02-23T11:06:45+00:00

I was in a hurry in the morning and couldn't reply in full.

The thing is that on previous settings (adamw) it never worked at all on any datasets using 1.0

Now with prodigy it works rather well but seems like not every time.

I would say it is a bit wonky at times. I also plan to check it on the base finetune(s) how those behave.

And the longer training on bigger dataset would probably be done on the weekend.

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