---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
base_model:
- Laxhar/sdxl_noob
language:
- en
tags:
- stable-diffusion
- sdxl
---
# Hikari Noob v-pred 0.5

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/630e2d981ef92d4e37a1694e/b9tyKyu2MwbQTQpuqAg2c.jpeg)

Civitai model page: https://civitai.com/models/938672

Fine-tuned NoobAI-XL(v-prediction) and merged SPO LoRA

NoobAI-XL(v-prediction)をファインチューンし、SPOをマージしました。

日本語での導入手順はページ下部にあります。

## Features/特徴
- Improved stability and quality.
- Works with samplers other than Euler.
- Good results with only 10 steps (12 steps or more recommended)
- Fixed a problem in which the quality of output was significantly degraded when the number of tokens exceeded 76.
- The base style is not strong and can be restyled by prompts or LoRAs.
- 安定性と品質を改善
- わずか10ステップでよい結果を得られます(ただし12ステップ以上を推奨)
- Zero Terminal SNRの代わりにNoise Offsetを使用することでEuler以外のサンプラーでも利用できるようにしました。
- トークン数が76を超えると出力の品質が著しく低下する問題を修正しました。
- 素の画風は強くないので、プロンプトやLoRAによる画風変更ができます。

## Requirements / 動作要件
- AUTOMATIC1111 WebUI on `dev` branch / devブランチ上のAUTOMATIC1111 WebUI
- Latest version of ComfyUI / 最新版のComfyUI
- ReForge on `dev_upstream_experimental` branch / `dev_upstream_experimental`ブランチ上のreForge

### Instruction for AUTOMATIC1111
1. Download the model
2. Switch branch to `dev`
3. Load the model

### Instruction for reForge
1. Download the model
2. Switch branch to `dev_upstream_experimental`
3. Find “Advanced Model Sampling for Forge” at the bottom of the page
4. Enable “Enable Advanced Model Sampling”
5. Select `v_prediction` in Discrete Sampling Type

### Example Workflow for ComfyUI / ComfyUIサンプルワークフロー
Download it from [here](https://files.catbox.moe/83e2wl.json)

## Prompt Guidelines / プロンプト記法
Almost same as the base model/ベースモデルとおおむね同じ

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

## Recommended Prompt / 推奨プロンプト
Positive: None/無し(Works good without `masterpiece, best quality` / `masterpiece, best quality`無しでおｋ)

Negative: `worst quality, low quality, bad quality, lowres, jpeg artifacts, unfinished, photoshop \(medium\), abstract` or empty(または無し)


## Recommended Settings / 推奨設定
Steps: 10-24

Sampler: DPM++ 2M(dpmpp_2m)

Scheduler: Simple

Guidance Scale: 3.5-7

### Hires.fix

Hires upscaler: 4x-UltraSharp or Latent(nearest-exact)

Denoising strength: 0.4-0.5(0.65-0.7 for latent)


## Merge recipe(Weighted sum)
I made 6 Illustrious-based models and merged them.

- Stage 0: finetunes v-pred test model with AI-generated images
- Stage 1: finetunes stage 0 model with 300 scenery images from Gelbooru

- Stage 2: Finetune and merge(see below)

*A-F,sd15: finetuned stage1(ReLoRA)

- A * 0.6 + B * 0.4 = tmp1
- tmp1 * 0.6 + C * 0.4 = tmp2
- tmp2 * 0.7 + F * 0.3 = tmp3
- tmp3 * 0.7 + E * 0.3 = tmp4
- tmp4 * 0.5 + D * 0.5 = tmp5
- tmp5 * 0.65 + sd15 * 0.35 = tmp6
- tmp6 + SPO LoRA = Result

## 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.

### AUTOMATIC1111の導入手順
1. モデルをダウンロードする。
2. devブランチに切り替える(ブランチの切り替えかたは各自調べてください)。
3. モデルを読み込む。

### ReForgeの導入手順
1. `dev_upstream_experimental`ブランチに切り替える
2. モデルをダウンロードする。
3. WebUIのページ下部から“Advanced Model Sampling for Forge”を見つける
4. “Enable Advanced Model Sampling”を有効にする
5. Discrete Sampling Typeを`v_prediction`にする