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. https://preview.redd.it/p8v7ws2hv4sh1.png?width=1069&format=png&auto=webp&s=b4ab665cf6c6bfdf61783858e6a112d82142074d 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. submitted by /u/Cold-Worldliness5392 [link] [comments]

Source: r/StableDiffusion | 2026-09-27

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