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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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+ ---
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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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+ language:
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+ - en
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+ tags:
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+ - stable-diffusion
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+ - sdxl
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+ - anime
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+ ---
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+
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+ # DO NOT RE-UPLOAD THIS MODEL
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+ Please make a pull request to add a converted format version.
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+
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+ # Hikari Noob v-pred 1.0.0
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+
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+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/630e2d981ef92d4e37a1694e/FVRSo-AYq1ueetq32bczO.jpeg)
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+
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+ Civitai model page: https://civitai.com/models/938672
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+
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+ Fine-tuned NoobAI-XL(ν-prediction) and merged SPO LoRA
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+
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+ NoobAI-XL(ν-prediction)をファインチューンし、SPOをマージしました。
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+
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+ ## Features/特徴
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+ - Improved stability and quality.
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+ - Fixed a problem in which the quality of output was significantly degraded when the number of tokens exceeded 76.
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+ - The base style is not strong and can be restyled by prompts or LoRAs.
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+ - This model does not include any base model other than NoobAI (v-prediction), so it has the equivalent knowledge.
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+ You can generate characters that have appeared by August 2024.
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+ - 安定性と品質を改善
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+ - 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。
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+ - 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。
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+ - このモデルはNoobAI(v-prediction版)以外のベースモデルを一切含まず、それと同等の知識があります。2024年8月までに登場したキャラクターを生成できます。
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+
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+ ## About v0.6.5
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+ - Enabled Zero Terminal SNR
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+
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+ ## Requirements / 動作要件
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+ - AUTOMATIC1111 WebUI on `dev` branch / devブランチ上のAUTOMATIC1111 WebUI
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+ - **Latest version** of ComfyUI / **最新版**のComfyUI
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+ - **Latest version** of Forge or reForge / **最新版**のForgeまたはreForge
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+
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+ ### Instruction for AUTOMATIC1111 / AUTOMATIC1111の導入手順
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+ 1. Switch branch to `dev` (Run this command in the root directory of the webui: `git checkout -b dev origin/dev` or use Github Desktop)
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+ 2. Use the model as usual!
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+
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+ (日本語)
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+ 1. `dev`ブランチに切り替えます(次のコマンドをwebui直下で実行します: `git checkout -b dev origin/dev` またはGithub Desktopを使う)
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+ 2. 通常通りモデルを使用します。
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+
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+ ## Prompt Guidelines / プロンプト記法
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+ Almost same as the base model/ベースモデルとおおむね同じ
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+
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+ To improve the quality of background, add `simple background, transparent background` to Negative Prompt.
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+
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+ ## Recommended Prompt / 推奨プロンプト
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+ Positive: None/無し(Works good without `masterpiece, best quality` / `masterpiece, best quality`無しでおk)
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+
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+ Negative: `worst quality, low quality, bad quality, lowres, jpeg artifacts, unfinished, photoshop \(medium\), abstract`, Leaving negative empty will break the output! Use `_` instead.
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+
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+
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+ ## Recommended Settings / 推奨設定
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+ Steps: 12-24
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+
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+ Scheduler: Simple or SGM Uniform
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+
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+ Guidance Scale: 2-5(best value is 4)
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+
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+ ### Recommended Samplers
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+ - DPM++ 2M
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+ - DPM++ 3M SDE
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+ - Euler/Euler a
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+ other samplers will not work properly.
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+
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+ ### Hires.fix
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+
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+ Hires upscaler: 4x-UltraSharp or Latent(nearest-exact)
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+
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+ Denoising strength: 0.4-0.5(0.65-0.7 for latent)
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+
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+
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+ ## Merge recipe(Weighted sum)
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+ - Stage 1: Finetune Noob v-pred 0.6 and merge(see below)
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+ *A-K: noobai(v-pred)-based custom checkpoint
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+ - A * 0.6 + B * 0.4 = tmp1
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+ - tmp1 * 0.6 + C * 0.4 = tmp2
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+ - tmp2 * 0.7 + F * 0.3 = tmp3
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+ - tmp3 * 0.7 + E * 0.3 = tmp4
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+ - tmp4 * 0.6 + D * 0.4 = tmp5
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+ - tmp5 * 0.7 + G * 0.3 = tmp6
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+ - Make H,I,J,K from tmp6
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+ - tmp6 * 0.75 + H * 0.25 = tmp7
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+ - tmp7 * 0.7 + I * 0.3 = tmp8
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+ - tmp8 * 0.7 + J * 0.3 = tmp9
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+ - tmp9 * 0.9 + K * 0.1 = tmp10
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+ - tmp10 + SPO LoRA * 1 + sdxl-flat * -0.25 + sdxl-boldline * -1 = tmp11
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+ - Adjust tmp11(0.2,0.2,0.2,0.05,0,0,0,0) = Result
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+
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+ ## Training scripts:
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+ [sd-scripts](https://github.com/kohya-ss/sd-scripts)
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+
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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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+
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+ If you make modify this model, you must share both your changes and the original license.
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+
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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.