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

9. LoRAをマージ

   Model	Lora	Weight	Merge Name
   onnpastel-4	onnpastelLoRaD	0.7	ONN_pastel