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hikari_noob_v-pred_0.5

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metadata
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

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.
  • 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。

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

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(0.6 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

Notice

This model is licensed under Fair AI Public License 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