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README.md
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---
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license: other
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license_name: faipl-1.0-sd
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license_link: https://freedevproject.org/faipl-1.0-sd/
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---
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license: other
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license_name: faipl-1.0-sd
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license_link: https://freedevproject.org/faipl-1.0-sd/
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pipeline_tag: text-to-image
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base_model:
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- RedRayz/illumina-xl-1.1
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tags:
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- stable-diffusion
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- stable-diffusion-xl
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---
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# Abydos-XL-1.1
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Modified Illustrious-XL-v0.1 with Blue Archive style
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This is the next version of Abydos-XL-1.0, Improved background(scenery), stability and detail rendering.
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You can find example images on [Civitai model page](https://civitai.com/models/832248)
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## Prompt Guidelines
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Almost same as the base model
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## Recommended Prompt
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None(Works good without `masterpiece, best quality`)
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## Recommended Negative Prompt
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`worst quality, low quality, bad quality, lowres, jpeg artifacts, unfinished`
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To improve the quality of background, add `simple background, abstract` to Negative Prompt.
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## Recommended Settings
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Steps: 14-28
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Sampler: DPM++ 2M(dpmpp_2m)
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Scheduler: Simple
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Guidance Scale: 4-9
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### Hires.fix
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Upscaler: 4x-UltraSharp or Latent
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Denoising strength: 0.5(0.6 for latent)
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## Training information
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Finetuned Illumina-XL-1.0 by repeating the training and merging a DoRA 6 times with sd-scripts.
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- Network module: lycoris_kohya(algo=lora, dora_wd=True)
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- Resolution: 1024(Bucketing enabled, min 512, max 2048)
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- Optimizer: Lion
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- Train U-Net only: Yes
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- LR Scheduler: cosine with restart(warmup ratio=0.1, repeat=4-6)
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- Learning Rate: various(min=1e-05, max=6e-05)
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- Noise Offset: 0.04
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- Immiscible Noise: 2048
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- Batch size: 1
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- Gradient Accumulation steps: 1
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- Dim/Alpha: 16/4
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- Conv Dim/Alpha: 1/0.25
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## Dataset information
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Dataset size: 289
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## Training scripts:
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[sd-scripts](https://github.com/kohya-ss/sd-scripts)
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## Notice
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This model is licensed under [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)
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If you make modify this model, you must share both your changes and the original license.
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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.
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