KREA 2

Showcase images :

Turbo model : 10 steps / CFG 1 & Euler simple
944x1408
x2 Hires (10 steps, 0.35 with 4x_NMKD_Siax)
Lora strength 1.3

IDEOGRAM 4

Json caption style with :

art_style : Naoki Urasawa style
medium : manga
aesthetics : black_and_white

Example caption

KLEIN 9B

v1:

Showcase images :

Distilled model (same prompt images posted with the base model below)
768x1152
Euler / normal / 4 steps / CFG 1
Hires steps : 4 / NMKD_SIAX @0.35 / x2
Lora strength of 0.8-1

ZIT

v1:

I'm relatively new to Z-Image turbo model, so still experimenting, but I had good results with these settings :

1056*1584 // 10 Steps // CFG 1
DDIM & Beta
Lora strength of 0.9

I've noticed that using previous prompts (like the ones from Qwen v2) doesn't work that well, so I've used a vision model (Qwen3-VL-8B) with this prompt to get updated caption which was giving way better results.

Qwen

v2:

Same as for my ghibli lora, I've retrained with everything I've learned so far, and this version is way better : closer to the target style and more flexible.

v1:

Trained with Ostris Toolkit: Dataset is 44 images (mainly from "Monster", "20th Century Boys" and "Pluto" by Naoki Urasawa) with no trigger word (but sometimes it helps to add "black and white manga style image of").
All the image have been generated in 4 steps in addition to the lightning lora by lightx2v
The prompt adherence of Qwen is really impressive, but I feel like the likeness of the style is not as good as with Flux (even tho is still good), but I'm still experimenting so it's just an early opinion.

Flux

Trained on images from the manga "Monster" and "20th Century boys" by Naoki Urasawa.

Start the prompt with :

A naoUrasawa style image of

Using this upscaler seems to improve the style towards a true manga scan most of the time (and get rid of colors). I've used it with hires fix and denoising strength of 0.5.

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