For v.4.0: This time I trained my model with many more images (1887 to be exact). It is a pruned model. It doesn't require vae but you can use it if you want. Clip skip was trained with 1, but it gives successful results with 2 as well. (I recommend using negative embeddings to avoid burning so you can get great results)
For v.3.0: Let me describe what I did. I don't speak English, I hope I can explain. All my previous versions were merged models. In this model, I trained three different models with three different folders containing 188 photos, each image having 150 steps (epoch). The photos in the three folders were also different from each other. I couldn't decide which model was the most successful. All three gave very good results. So I finally combined these three models and published them as a single model. So it went through merging first, then training, then merging again. But the most time-consuming and tiring part was the training part. Since I did not have the opportunity to choose both options, I chose this option to emphasize that it has undergone training.
-*- Let me give you a tip... A model tends to whatever style it was last trained with. You can test it as follows: generate 10-15 times with default settings (20 steps, 7 scale and 512x512 resolution (for 1.5 models)) without writing any negative or positive prompts. (If you want to make the images look clearer, you can write negative prompts provided that you do not use negative embedding). Whichever type of results you got the most in this test, it means that the trend of the model is in that direction -*-
For v1.0: Like my previous anime model, this one consists of several models mixed with each other in certain proportions. I give the list of the mixture below, but I will not specify the mixing ratios. I would like to thank those who created these models.
Ideal for creating ultra-realistic images. Some words can force the model to run nsfw. When the resolution is increased with Hires fix, it creates magnificent visuals. You may have problems with faces in smaller size images. In such cases, you can use adetailer applications such as almostiler.
No need to use vae.
I recommend increasing the resolution by using hires fix instead of using restore faces.
It will continue to be developed.
Description
This is my first training attempt... I trained my model with more than 500 photos. The photographs I used were mostly portraits of beautiful women that I chose myself. I've seen it do quite well on models with red hair and freckles. I would appreciate if you share your experiences so that I can improve it further.
-place the yaml file next to the model file-
I am sharing the photo package I used for training with you. Those who want can create their own experience by downloading it: link
FAQ
Comments (5)
This model is misplaced: it's a MERGED model, not a TRAINED model!
Well, let me describe what I did. I don't speak English, I hope I can explain. All my previous versions were merged models. In this model, I trained three different models with three different folders containing 188 photos, each image having 150 steps (epoch). The photos in the three folders were also different from each other. I couldn't decide which model was the most successful. All three gave very good results. So I finally combined these three models and published them as a single model. So it went through merging first, then training, then merging again. But the most time-consuming and tiring part was the training part. Since I did not have the opportunity to choose both options, I chose this option to emphasize that it has undergone training.
@53rt5355iz You should definitely put that info in the description. From the current description this sounds like a mix that was mislabeled.
And the award for best hair-splitter on CivitAI goes to... @ProseccoSpritz
Merging cant still be involved and considered 'trained' if a original new trained dataset is included in the merge
Details
Files
beautyfoolReality_v30.yaml
Mirrors
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patsantre_.yaml
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ownRealPictures_v10.yaml
letsbereal_v20.yaml
anotherWorldRetro_v10.yaml
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bestiariuszSOwiaSki_v10.yaml
dimensiontwist_v10.yaml
oneFORALL25DWIllustrious_v10.yaml
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coyotewild2_v10Coyotewild4.yaml
puppetStyle1_v10.yaml
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agehaModel_v09.yaml
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everydayartEDA_edav5BakedvaeKlF8.yaml
janaDefi_v20.yaml
coyotewild4_v10.yaml
rivenTheSequelToMYST_v10.yaml
instacurvesCurvy_instacurves10.yaml
nijinewstyleRelease_nijinewstyleRelease.yaml
Melissa Beinoist_0.yaml
bladeRunnerRetro_v10.yaml
seelrealFurry_v2.yaml
letsbereal_v20.yaml
oneFORALLAnimeW_v10.yaml
bigeneias_v10.yaml
janaDefi_v25.yaml
rMix_v30Pruned.yaml
spaceNebula_v10.yaml
artStyleXXX_v10.yaml
janaDefi_v24.yaml
aimanhwa_v10.yaml
coyotewild2_v10Coyotezero.yaml
mytale_v20.yaml
oneFORALL25DWIllustrious_v20.yaml
nuPogodiWolf_v10.yaml
agehaModel_v08.yaml
astreapixieRadiance_v16.yaml
mommyGenerator_v10.yaml
mytale_v3.yaml
lineart_v10.yaml
beautyfoolAnireal_v10.yaml
astreapixieRadiance_v16.yaml
styleOfWorkTitle_v10.yaml
beverlyCrusherBadass_v10.yaml
instacurvesV2Curvy_curvyurpm190423V1.yaml
beautyfoolReality_v30.yaml
desolate_V1.yaml
rMix_v30.yaml
macroFoxesAndWolves_v13.yaml
techwareModel_v10.yaml
fortuneARTERIAL_rev1.yaml
nightPrincess_revision1.yaml
balavoineGenerator_v10.yaml
dimensiontwist_v10.yaml
anotherWorldRetro_v20.yaml
letsbereal_v10.yaml
AdvancedCanonically_configFile.yaml
aFantasyColorVAEBAKE_aFantasyColorVAEBAKE.yaml
nintendoMiiStyle_v11.yaml
dimensiontwist_v10.yaml
nightPrincess_revision2.yaml
nuPogodiHare_v10.yaml
olympusFE190_olympusFE190V10.yaml
Available On (1 platform)
Same model published on other platforms. May have additional downloads or version variants.









