Lora of satanichia_kurumizawa_mcdowell_gabrieldropout
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix_V11.
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 9000, you need to download 9000/satanichia_kurumizawa_mcdowell_gabrieldropout.pt as the embedding and 9000/satanichia_kurumizawa_mcdowell_gabrieldropout.safetensors for loading Lora. By using both files together, you can generate images for the desired characters.
The best step we recommend is 9000, with the score of 0.967. The trigger words are:
satanichia_kurumizawa_mcdowell_gabrieldropoutred_hair, hair_ornament, hair_rings, bat_hair_ornament, purple_eyes, v-shaped_eyebrows
For the following groups, it is not recommended to use this model and we express regret:
- Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
- Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
- Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
- Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
- Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps |
Score |
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pattern_1 |
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pattern_10 |
pattern_11 |
pattern_12 |
pattern_13 |
pattern_14 |
pattern_15 |
pattern_16 |
bikini |
bondage |
free |
maid |
miko |
nude |
nude2 |
suit |
yukata |
| 9000 |
0.967 |
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| 8400 |
0.963 |
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| 7800 |
0.923 |
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| 7200 |
0.939 |
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| 6600 |
0.891 |
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| 6000 |
0.962 |
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| 5400 |
0.956 |
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| 4800 |
0.955 |
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| 4200 |
0.938 |
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| 3600 |
0.953 |
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| 3000 |
0.946 |
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| 2400 |
0.936 |
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| 1800 |
0.917 |
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| 1200 |
0.935 |
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| 600 |
0.863 |
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