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any系をACertaintyベースで再現したモデルです。
▼使用モデル

  • ACertainty.ckpt
  • bp_1024_e10.ckpt
  • instagram-latest-plus-clip-v6e1_50000.safetensors
  • Bra6-2(beta).safetensors
  • dreamshaper_631BakedVae.safetensors

ほとんどテストしてないので使いにくかったらマージしたりしてください。
蒸留画像は使用していません。下記のレポジトリからデータセットのキャプションのみダウンロードできます。
DataSet: https://huggingface.co/datasets/paimonimpact/ONN

ONN_anyV3.fp16.safetensors

▼Use Models

  • ACertainty.ckpt
  • bp_1024_e10.ckpt

ACertaintyにany_A ~ FのデータセットでDB。

Model: A Model: B Weight Base alpha Merge Name
A C - 0.3 AC
B E - 0.5 BE
D F - 0.5 DF
BE DF 0.0,0.0,0.1,0.3,0.5,0.7,1.0,0.7,0.5,0.3,0.1,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.5,0.3,0.1,0.0,0.0,0.0,0.0 0.3 BEDF
BEDF AC 1.0,0.9,0.7,0.5,0.3,0.1,0.3,0.3,0.3,0.3,0.3,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.9,1.0 0.3 BEDFAC
BEDFAC bp_1024_e10 - 0.15 ONN_anyV3

ONN_AOM2.fp16.safetensors

▼Use Models

  • ONN_anyV3.fp16.safetensors
  • Bra6-2(beta).safetensors
  • instagram-latest-plus-clip-v6e1_50000.safetensors
  • dreamshaper_631BakedVae.safetensors
Model: A Model: B Weight Base alpha Merge Name
instagram-latest-plus-clip-v6e1_50000 Bra6-2(beta) 0,0.0,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.5,0.3,0.1,0.0,0.0,0.1,0.3,0.5,0.7,0.9,0.7,0.5,0.3,0.1,0.1,0.0,0.0 0(cosineB) insta_bra
Model: A Model: B Model: C Interpolation Method Merge Name
insta_bra dreamshaper_631BakedVae v1-5-pruned Add Difference @ 0.7 onn_real
Model: A Model: B Weight Base alpha Merge Name
ONN_anyV3 onn_real 0,1.0,0.9,0.7,0.5,0.3,0.1,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.3,0.5,0.7,0.9,1.0 0 ONN_AOM2

ONN_anyV4.fp16.safetensors

▼Use Models

  • ACertainty.ckpt
  • ONN_AOM2.fp16.safetensors
  1. ACertaintyをanyV4.zipのデータセットでFT = FT_ACertainty
  2. ONN_AOM2と単純マージ
    Model: A Model: B Base alpha Merge Name
    FT_ACertainty ONN_AOM2 0.5 ONN_anyV4

ONN_pastel.fp16.safetensors

▼Use Models

  • bp_1024_e10.ckpt
  • ONN_AOM2.fp16.safetensors
  • onn_real.fp16.safetensors
  • dreamshaper_631BakedVae.safetensors
  1. pastel_A ~ EのデータセットでLoRaを5つ作成(学習モデルはACertainty) = onnpastelLoRaA~E

  2. ONN_AOM2とpastelLoRAをマージ

    Model Lora Weight Merge Name
    ONN_AOM2 onnpastelLoRaA 0.8 onnpastel_baseA
  3. onnpastel_baseAとbp_1024_e10.ckptをマージ

    Model: A Model: B Base alpha Merge Name
    onnpastel_baseA bp_1024_e1 0.5 onnpastel_baseB
  4. ACertaintyをany_C.zipのデータセットでFT = onnpastel_baseC

  5. onnpastel_baseBとonnpastel_baseCをマージ

    Model: A Model: B Base alpha Merge Name
    onnpastel_baseB onnpastel_baseC 0.5 onnpastel_baseBC
  6. onnpastel_baseBCとonn_realをマージ

    Model: A Model: B Weight Base alpha Merge Name
    onnpastel_baseBC onn_real 1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 0 onnpastel
  7. 各種LoRAをマージ

    Model Lora Weight Merge Name
    onnpastel onnpastelLoRaB 0.2 onnpastel-1
    onnpastel-1 onnpastelLoRaC 0.3 onnpastel-2
    onnpastel-2 onnpastelLoRaE 0.6 onnpastel-3
  8. 調整用モデルの作成、マージ

    Model: A Model: B Weight Base alpha Merge Name
    onnpastel_baseC dreamshaper_631BakedVae 0.7,0.5,0.3,0.1,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7 0 onnpastel_baseC-1
Model Lora Weight Merge Name
onnpastel_baseC-1 onnpastelLoRaA 0.8 onnpastel_baseC-2
Model: A Model: B Weight Base alpha Merge Name
onnpastel-3 onnpastel_baseC-2 0,0.7,0.5,0.3,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7 0 onnpastel-4
  1. LoRAをマージ
    Model Lora Weight Merge Name
    onnpastel-4 onnpastelLoRaD 0.7 ONN_pastel
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