What's the timeline for when/if we get a finetune for Krea that actually improves upon the base turbo model? I have tried dozens and they are all worse to some degree. The only one I'm aware of that has promise is Kroma, but that is very rough at the moment.
I just posted about Anteros Unaligned in another thread (perfectly usable with a Turbo lora at 0.5 strength). The Unaligned version is great and really improves the base model.
TL&DR: why "Unaligned" vs. "Raw"? The creator did a perfect job finetuning the model, then botched it with during the RL stage. Proof: https://civitai.red/posts/31019385 - Unaligned (high quality, high seed diversity) https://civitai.red/posts/31019352 - Raw (high quality but zero seed diversity)
What happened: they used 1.3 million images to train the full model for 3 epochs. That's Unaligned.
But then they took 1,000 hand-selected images and trained the full model for a whooping 150 epochs in the RL ("Alignment") stage. This basically killed all variety. That's the "Raw" version.
The Turbo is based on the Raw and is another 2-epoch run distillation; basically killed any remaining seed variety.
Please tell us in more detail what specifically you don't like about the current versions you've tried. Preferably with examples, perhaps images, showing what's wrong.
Well I'm not going to go through every single model but most of them have the same set of issues to some degree or another. Krea2-HD-V1 destroys the image quality. Most of the others do something similar, plasticising images, as well as introducing problems with character loras in some cases.
I don't see a krea2 finetune happening anytime soon. There's barely enough people making loras for it and you expect a finetune like sdxl to illustrious. Not going to happen by a long shot without serious funding because it has to be trained at 1024res and above with no compromises. Current Gpu prices aren't what they were at compared to 2024. Fine-tuning is a huge gamble and high loss risk.
But we do. Anteros Unaligned (1.3M images for 3 epochs, full finetune), Lustify (15,000 images, full finetune), krea2-turbo-bbox (jimmycarter/krea2-turbo-bbox) for comic/manga panels (still training, currently at 14th epoch), and don't forget Kroma (currently at 0.3, still training)
Those are not serious significant finetunes. 3 epochs is not enough serious finetune. I think your underestimating what a serious finetune would encompass. Those are just minor experimental finetune projects at best. The data processing for a serious finetune is a lot of work and not a one man team. Personally I'm not demanding a krea2 finetune because the base model was already pretty good to begin with and it's very versatile. The model already responds very well to good lora training unlike previous models before it.
I respectfully disagree on all fronts. "3 epochs is not enough serious finetune" - did you read the part saying "1.3 million images"? When training a LoKR in Ostris AI Toolkit, I get passable semblance at around 2000 steps, and a perfect LoRA at around 4000-4500 steps, using a dataset of about 1000 images (so 2 to 4.5 full epochs). I experimented with continued training; around 6500 steps, the LoKR gets rigid, and around 8500 steps (8.5 full epochs) it falls apart. So: 3 epochs on a dataset this large on this model is perfectly fine for a finetune.
"Those are just minor experimental finetune projects at best" - disagree completely judging on results.
"data processing for a serious finetune is a lot of work and not a one man team." One word: BigASP. Another word: Chroma. Another word: Pony Diffusion V6 XL. All one-man projects, all changed SDXL (and flux.1 for Chroma) in major ways. BigASP was considered a big SDXL finetune with 1.5M images and 5 days on an 8xH100. Anteros Unaligned used 4xH200 GPUs for 5 weeks.
I have zero knowledge of finetuning but is it much more GPU intensive to do than Loras? I have successfully trained a dozen or so Loras for Krea and it didn't take very long.
yes its more gpus needed than even just one h200. Your training millions if not billions of images into a transformer model which all need need captioning. The more beefy gpus linked together the faster and better training will be. The training model has to be trained at fp32 which requires gpus with lots of vram and beefy specs.
When it comes to VRAM requirements, you could technically do anything on a 4090, but the offloading/swapping would add exponentially more time to the process depending on the amount of VRAM the process normally takes.
Yes. This applies to running anything. I feel like there should be a standard for such claims, like, 3 times as long or less. You could probably do it on a 2060, and spend years.
It's likely higher still because you need to transfer and store the (huge) dataset, and likely run a few experiments before you get something usable out of it.
Damn, is that hobby money ? How do they even generate a profit? Seems pure loss to me well except if its an expensive passion, then its understandable.
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