---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
pipeline_tag: text-to-image
base_model:
- RedRayz/illumina-xl-1.1
tags:
- stable-diffusion
- stable-diffusion-xl
---

# Abydos-XL-1.1

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/630e2d981ef92d4e37a1694e/gmHyCWfexAf9FwBb8CzKb.jpeg)

Modified Illustrious-XL-v0.1 with Blue Archive style

This is the next version of Abydos-XL-1.0, Slightly improved background(scenery), stability and detail rendering.

You can find example images on [Civitai model page](https://civitai.com/models/832248)

## Prompt Guidelines
Almost same as the base model

## Recommended Prompt
None(Works good without `masterpiece, best quality`)

## Recommended Negative Prompt
`worst quality, low quality, bad quality, lowres, jpeg artifacts, unfinished, abstract, oldest, photoshop \(medium\)`

To improve the quality of background, add `simple background, transparent background` to Negative Prompt.

## Recommended Settings
Steps: 14-28

Sampler: DPM++ 2M(dpmpp_2m)

Scheduler: Simple

Guidance Scale: 4-9

### Hires.fix
Upscaler: 4x-UltraSharp or Latent

Denoising strength: 0.5(0.6 for latent)

## Training information
Finetuned Illumina-XL-1.1 by repeating the training and merging a DoRA 6 times with sd-scripts.

- Network module: lycoris_kohya(algo=lora, dora_wd=True)
- Resolution: 1024(Bucketing enabled, min 512, max 2048)
- Optimizer: Lion
- Train U-Net only: Yes
- LR Scheduler: cosine with restart(warmup ratio=0.1, repeat=4-6)
- Learning Rate: various(min=1e-05, max=6e-05)
- Noise Offset: 0.04
- Immiscible Noise: 2048
- Batch size: 1
- Gradient Accumulation steps: 1
- Dim/Alpha: 16/4
- Conv Dim/Alpha: 1/0.25

## Dataset information
Dataset size: 289

## Training scripts:
[sd-scripts](https://github.com/kohya-ss/sd-scripts)

## Notice
This model is licensed under [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)

If you make modify this model, you must share both your changes and the original license.

You are prohibited from monetizing any close-sourced fine-tuned / merged model, which disallows the public from accessing the model's source code / weights and its usages.