Instructions to use RedRayz/hikari_noob_1.0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RedRayz/hikari_noob_1.0.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RedRayz/hikari_noob_1.0.1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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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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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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base_model:
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- Laxhar/noobai-XL-Vpred-1.0
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---
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# Hikari Noob v-pred 1.0.1
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Civitai model page: https://civitai.com/models/938672
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Fine-tuned NoobAI-XL(ν-prediction) and merged SPO LoRA
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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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## 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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## About 1.0.1
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- Better stability?
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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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### 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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(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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## 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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To improve the quality of background, add `simple background, transparent background` to Negative Prompt.
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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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Negative: `worst quality, low quality, bad quality, lowres, photoshop \(medium\), abstract` or empty
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## Recommended Settings / 推奨設定
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Steps: 12-24
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Scheduler: Simple or SGM Uniform
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Guidance Scale: 2-5(best value is 4)
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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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### Hires.fix
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Hires upscaler: 4x-UltraSharp or Latent(nearest-exact)
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Denoising strength: 0.4-0.5(0.65-0.7 for latent)
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## Merge recipe(Weighted sum)
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- Stage 1: Finetune Noob v-pred 1.0 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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## 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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