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Stabilizer Rouwei

32

165

22

5

Updated: Jul 26, 2025

style

Verified:

SafeTensor

Type

LoRA

Stats

84

18

8

Reviews

Published

Jul 25, 2025

Base Model

Illustrious

Usage Tips

Strength: 0.8

Hash

AutoV2
70D988AAC5

Stabilizer for RouWei

I see tails, everywhere.

Note: Cover images are directly from a1111, at default 1MP resolution. No upscale, no plugins, no imprinting fixes.


What is this

RouWei is a large scale finetune from Illustrious v0.1 with a dataset of ~13M pictures (~4M with natural text captions).

  • Solved main problems with tags bleeding and biases, common for Illustrious, NoobAi and other checkpoints

  • Dataset cut-off - end of April 2025.

  • vpred + epslion.

  • Built-in color/brightness/contrast control: low brightness, high brightness, low saturation...

  • too many features...

Stabilizer is a tiny finetune LoRA with ~7k hand-picked images. See this main page for info. Quick recap:

  • It can reduce overfitted noise because it was trained on clean, natural, high res real world images with strong logical and structural relationships.

  • It adds natural lighting, texture, details.

  • It doesn't have a default style. The dataset is very diverse. So no style shifting.

  • Dataset has full natural language captions. (<75 tokens)

  • It only patches U-Net. Does not patch text encoder.


How to use

Version prefix:

  • epv080b: Trained on RouWei epsilon v0.8.0 base.

  • vpv080: Trained on RouWei v-pred v0.8.0.

Note:

  • epsilon version is much easier to use. It's just normal good old SDXL that has already been supported by all software. I personally prefer epsilon, usually has better details and textures.

  • v-pred version requires specific parameters and settings, see RouWei main page for more info.

  • RouWei is based on illustrious v0.1, so it supports most illustrious LoRAs.


Update logs

(07/25/2025) v080epb v0.3

  • This is all most a full training on the "v0.8 epsilon base".

(07/24/2025) v080vp v0.1

  • Initial release. Let's see what we got.

  • The training strategy is very conservative. So it won't bring big changes to images. Most of the time it needs strength > 0.8.

  • Feedback is welcomed.