illumina-noob-vpd

Illumina-NoobVpd

Civitai model page: https://civitai.com/models/916947

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

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。

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. Copy configs/sd_xl_v.yaml to models/Stable-Diffusion/
4. Rename it to the same as the model name

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, oldest, old, photoshop \(medium\), abstract

Tips: Leaving Negative Prompt empty will increase the diversity of styles(less 'masterpiece').

ヒント: ネガティブプロンプトを空にしておくと画風の多様性が高くなります(マスピ感を軽減)

Recommended Settings / 推奨設定

Steps: 14-28

Sampler: DPM++ 2M(dpmpp_2m)

Scheduler: Simple

Guidance Scale: 4-9

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. 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)。
2. モデルをダウンロードする。
3. configs/sd_xl_v.yamlをmodels/Stable-Diffusion/にコピーする
4. コピペしたファイルをモデル名と同名にする

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