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

ACertaintyをanyV4.zipのデータセットでFT = FT_ACertainty
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

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
ONN_AOM2とpastelLoRAをマージ | Model | Lora | Weight | Merge Name | | --- | --- | --- | --- | | ONN_AOM2 | onnpastelLoRaA | 0.8 | onnpastel_baseA |
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 |
ACertaintyをany_C.zipのデータセットでFT = onnpastel_baseC
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 |
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 |
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 |
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

LoRAをマージ | Model | Lora | Weight | Merge Name | | --- | --- | --- | --- | | onnpastel-4 | onnpastelLoRaD | 0.7 | ONN_pastel |