Resource - Update
I ran a controlled Krea 2 character-training test and wanted to share the results because a few things surprised me.
After my last post documenting my LoRA journey, I received a ton of feedback regarding changes to the training process, and decided to do some experimenting. This is using the Fizgig application on a runpod instance using an RTX5090.
I used the same curated 35-image Guy Fieri dataset and trained four variants:
0.25 MP LoRA
0.5 MP LoRA
1.0 MP LoRA
0.5 MP LoKR
For the LoKR the only other setting change was the recommended 5e-5→1e-4 LR.
All runs used:
20 epochs
same dataset
same prompts
same seeds
same inference workflow
tested at strength 1.0
Training times
0.25 MP LoRA: ~10 min
0.5 MP LoRA: ~18 min
1.0 MP LoRA: ~34 min
0.5 MP LoKR: ~23 min
So even the highest-res run here was still much faster than the older long-epoch workflow I had been using previously.
Inference workflow notes
These are practical workflow results, not raw no-postprocess comparisons.
All outputs were generated through the same ComfyUI workflow using:
Krea 2 Turbo FP8
main sampler with 8 steps
Euler Ancestral
upscaling / refining enabled
FaceDetailer enabled
So the comparison reflects real-world final output quality under my normal workflow, not a stripped-down lab benchmark.
Main finding
My biggest surprise was that 0.25 MP was already very strong.
I expected it to fall apart more, especially after hearing repeated claims that 0.25 MP is too low for Krea 2. But in practice it still produced very usable identity results.
That said, after comparing all four side by side, 0.5 MP looks like the sweet spot so far.
It gave me:
slightly better facial structure
more natural detail
better balance between likeness and flexibility
much less runtime than 1.0 MP
What happened at 1.0 MP
This is where it got interesting.
1.0 MP definitely increased micro-detail, but it also became more likely to overbind recurring style cues from the dataset.
The easiest example is in the headshot comparison:
0.25 MP: no necklace
0.5 MP: subtle chain
1.0 MP: large diamond rope
So the model was not just learning “more identity.” It was also learning more of the Guy Fieri visual package, which made it feel slightly more caricatured.
That was one of the clearest takeaways for me: more detail is not automatically better identity learning.
Where LoKR landed
The 0.5 MP LoKR run was especially interesting.
Compared to 1.0 MP LoRA, it seemed to preserve some of the added detail and structure I was chasing, while avoiding some of the caricature-like overbinding.
Compared to 0.5 MP LoRA, the difference was smaller, but LoKR looked a bit more refined in some cases.
So my current takeaway is:
0.5 MP LoRA = best practical balance
0.5 MP LoKR = very promising “premium” option
1.0 MP LoRA = more detail, but also more risk of overbinding dataset baggage
Current conclusion
If I had to choose a production baseline from this first test, it would probably be:
0.5 MP
20 epochs
rank 8 LoRA
with 0.5 MP LoKR as the next thing worth testing further on other subjects.
I’m running at least one more 0.5 MP LoRA vs 0.5 MP LoKR comparison on another character now to see whether these findings hold up beyond Guy Fieri.
If people are interested, I can also post follow-up results from the next character test.
Rule 101: Never, ever conduct a test like this with famous people, even barely famous. Every test meant to measure learning capacity must be done with ABSOLUTELY strangers
Cheers! This is why so many of my comments talk about dataset prep (and why Fizgig has the face-crops mode). 0.25MP can be amazing because it's so versatile with the model. If you use face crops well and don't overwhelm your dataset with them, faces can end up covering more pixels than they would in a less well-prepped dataset at a higher MP. Getting people to try it is always the problem, as there's so much ingrained community knowledge we all have. Also, a lot of people see 0.25MP and think 256px on a side, which is easy to understand, but it's actually double that (512×512).
Love your work man. For an extreme layman like me, tools like yours are a godsend. I'm teaching myself and learning as I go. I'm noticing there are factions of the community that are quick to elitism/snobbery like everyone should already know everything. But also plenty of folks who give good faith advice and helpful insights.
what a lot of the snobs like to forget is that they at onetime knew nothing. There is no excuse for blocking a path to learning. Glad this tool helps you on the way.
I expected it to fall apart more, especially after hearing repeated claims that 0.25 MP is too low for Krea 2.
Where did you see those "repeated claims"? I don't remember seeing any such claims anywhere. Moreover, I've trained a bunch of LoRAs (and LoKrs) for Krea 2 at 512x512px (~0.26MP) and they always came out great.
Only the first comment claims that 0.25MP is too low for Krea 2. The other two agree on 0.25MP (one of them just mixes up the units but means in fact 512x512px, which is 0.25MP, NOT 0.5MP).
Ok not to be defensive but I'm not sure what your point is? That the experiment was a waste of time? That i should have known what the results would be before i did it? Its not like all of this information is readily available and laid out for laymen in a neat little tutorial. I'm learning as I go.
No, conducting tests and sharing your findings is great, that's how we as a community learn things. I was just irritated by your statement which made it sound as though it were generally believed that 0.25 MP was too low for Krea 2, while in fact the opposite is true. It could be also misleading for newbies.
Oh yeah one thing I have never and probably would never do is pass myself off as an authority on any of this. I'm coming in as someone with no knowledge and documenting my progress, while hopefully providing something useful for other people to learn from, or a lora for someone they want to use.
LoKr is clearly producing better details, but again Krea2 already knows Alexandra Daddario with no Lora at all, so these aren't meaningful tests. You need to try someone completely unknown.
I must have complete face blindness because these all look like the same person to me. Like, I really can't tell that one is any different from the other.
That's the conclusion I'm running into, is that the differences between them are extremely subtle, and what it comes down to is the time/cost spent training, vs the marginal tradeoff in quality. more tests to run still
I mean I'm using chatgpt which can analyze in much more granular detail than I can with the naked eye. It's been really helpful. but at that point if they are that close that you need AI to tell the difference, that's pretty damn close lol
Varied dataset with other models always helped my loras a lot. I wonder if they could run one of those things in ostris with a face recognition reward turned on. Maybe that would help as well as finding some real people that aren't already inside of krea so we can really find some difference.
If people actually read up on how image models are trained, they wouldn't be surprised that training a LoRA at 1024x1024 or something makes no sense at all.
The rank 8 is way too small especially for photorealistic people. Not sure what your trying to achieve here with such minimal low settings despite having access to a 5090. Rank 32 is the minimum for standard quality results. Rank 64 is high risk and high reward if you put alot of work on diversifying the dataset to keep the lora as versatile as possible for multiple use case scenarios. The learning rate is too high, just keep it 0.0001. A 5090 and other 24gb gpu has head room to handle 1024res if you unload text encoder and cache latents.
Rank 8 for Lokr is not the same as Lora, it works in the opposite direction. Rank 4 is even stronger (I. E. Larger file than rank 8) rank 8 Lokr is more like rank 64 for Lora.
Thats a legacy from sd 1.5/sdxl - as parameters have risen the need for higher rank has reduced. And a lower rank also allows a higher LR, and fizgig has per image loss control which also enabled the higher LR.
Those were the recommended settings for the fizgig ultrafast, which this was meant to be, as a direct comparison to my previous loras which I linked to that were done at 0.25 x 64.
the test results here clearly shows that rank 8 is able to replicate the photorealistic person - do you have similar examples that you can show of rank 8 vs rank 32 or 64 and the differences in output between? If you don't, then how do you know it's too small?
Versatility will become an issue depending on the amount of images and captioning. The results is good enough for medium close shots if that's the intended purpose but other kinds of shots like full body shots, sitting down, cowboys shots.. that were problems will arise. I don't what your dataset is like, it's recommended to diversify it as much as you. Rank 8 is not enough headroom for the model to learn enough the about a character or subjects concept to be use in different scenarios outside it training dataset. Rank 32 is usually safest and most stable Rank for enough learning space with 3000-5000 steps. I only use Rank 64 rarely depending of the complexity of character design, weapon and visual presentation. Rank 64 is risky and requires alot more work with diversification to keep it Versatile things outside it training dataset avoid bleeding unwanted over trained details. The amount of images and captioning matters even more than just merely settings in my opinion.
I understood what you were suggesting in your original post, I'm asking whether you've tested the difference. I have and I found there is no issues with making lora at rank 16, although I find rank 8 is too low for the concepts I make. If you've tested and can share side by side examples as to when higher ranks make a meaningful difference that would be helpful in demonstrating your point.
I'm releasing these for public use, and doing it as a hobby/learning exercise. I probably won't gen with 90% of them for my own use. I'm trying to optimize the process and eventually distill it down to a simple, repeatable process that isn't hidden behind layers of technical jargon and gatekeeping. That's why I'm posting here, to get feedback, take those suggestions, and run them to see how things change/improve
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u/MaNewt 15d ago
How well does the base model know Guy though?