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    Focused mainly on realistic portraits and flexibility, with higher native resolution support. (V8: 896x1152, up to 1024x1280, 1280x1280)

    Example images are somewhat cherry-picked, no hi-res upscale used, no face-fix.

    CLIP Skip 2 might help with realism in some cases while sacrificing fantasy aspects, experiment with it.

    V9/V8/V7 comparison (896x1152):

    V8/V7 comparisons:

    1024x1280

    768x1024

    V1 Recipe (might not be exactly how I did it):

    Exp1-Tier1: (using add difference for all)

    • GreyModel v12.0.0 + Dreamlike Photo v2 (25%)

    • + Woop Photo (25%)

    • + Protogen Infinity (25%)

    • + ArtEros (15%)

    • + HSPU (15%)

    • + URPM (15%)

    • + F222 (15%)

    • + AlmostDirty (15%)

    • + BondageV10 (5%)

    • + TWAM (5%)

    • + SXD1.0 (5%)

    • + 3DKX1.1 (1%)

    • + Hassan1.5 (1%)

    • + OpenJourneyV2 (1%)

    • + StrangeDedication2 (1%)

    Exp1-Tier2-Part1: (still add diff)

    • GreyModel12.0.0 + AnalogPhoto (25%)

    • + Dreamlike Photoreal v1 (25%)

    • + Amphibia Diffusion (15%)

    • + Artstation (15%)

    • + CALARTS (15%)

    • + ChromaV5 (15%)

    • + Corporate Memphis (15%)

    • + NitroDiffusion (10%)

    • + Dreamlike Diffusion (10%)

    • + FracTime (10%)

    • + InkPunk (10%)

    • + Midjourney-v4-diff (10%)

    • + RPGv2 (10%)

    • + SeekArt MEGA (10%)

    • + Colorjizz768 (5%)

    • + Double Exposure (5%)

    • + MegaPals (5%)

    Exp1-Tier2-Part2: (add diff)

    • GreyModel12.0.0 + MosaicArt (5%)

    • + RealisticCartoon (5%)

    • + Strahd (5%)

    • + Technicolor (5%)

    • + DMT Entity (1%)

    • + Elysium (1%)

    • + BalloonArt (1%)

    • + DnDClasses (1%)

    • + DarkVictorian (1%)

    • + ComicDiffusion V2 (1%)

    • + DiscoDiffusion (1%)

    • + Dvmjv4 (1%)

    • + Halloween (1%)

    • + Lichtspiel (1%)

    • + Lnkdn (1%)

    • + Midjrny-v4 (1%)

    • + Mtg-style (1%)

    • + NekoMix (1%)

    • + Nekrofaerie (1%)

    • + SDHeroCalm (1%)

    • + SpiderVerse (1%)

    • + Ssmoky (1%)

    • + SynthwaveV2 (1%)

    • + TDST (1%)

    • + VanGogh (1%)

    • + Vectorartz (1%)

    • + Voxelish (1%)

    • + Wa-vy (1%)

    • + Woolitize768 (1%)

    Exp1-Tier3 (add diff):

    • GreyModel12.0.0 + Babetopia (20%)

    • + Instababe (20%)

    • + PovSkinDream (15%)

    • + PowerfulMix20 (15%)

    • + GTMult v1? (15%)

    • + LatexRaw (10%)

    • + ClassicNegative (10%)

    • + Furrystaber (5%)

    Exp1-Tier4 (add diff):

    • GreyModel12.0.0 + RealismMix (20%)

    • + Hassan1.4 (15%)

    • + AttractiveMix (10%)

    • + VoluptuousMix (10%)

    • + SamDoesBimboes (1%)

    The Tiers separately are UNUSABLE because of all the AddDiffs, but merging them together brings them back to usable territory.

    Probably merged these with Weighted sum as

    ( (Tier1 + Tier2Part1)*0.5 + (Tier2-Part2 + Tier3)*0.5 ) + Tier4 * ?

    Then merged it with a custom DB model (trained on a face, but it also learned a bit of analog photo style) to tone it down a bit

    = Exp1Final

    Exp2 (using weighted sum):

    • ArtEros + RealVision1.2 (50%)

    • Grey12.0.2 + ProjectPhoto BETA (50%)

    • SexyRealism + TheGirlNextDoor (50%)

    • Deliberate1.1 + AnythingFurry (25%)

    • Visigen + Futagen (25%)

    • ArtRealV + GreyProjectPB (50%)

    • DelibAnyFur + VisiFuta (50%)

    • DelibAnyVisiFuta + SexyRGirlND (35%)

    • ArtRealGreyPro + DelibAnyVisiFutaSexyRGirlND (35%)

    • + Emotional Puppeteer (1%)

    = Exp2Final

    Exp1Final + Exp2Final weighted sum 50%?

    = Exp3

    Moving it back a bit towards photorealism:

    Exp3+ (not sure about the weights from here on, but used weighted sum)

    • (Analog + SimpMaker3k1 + DoubleExp + Attractive + Licthspiel)

    • + (SexyRealism + TheGirlNextDoor)

    • + (RealVision1.2 + ProjectPhotoBeta)

    • + (Dreamlike PhotoV2 + Deliberate1.1)

    = WyvernMix V1

    Description

    applied training to a custom merge of XL models, trying to improve portraits.

    The mix contains ChimeraMiXL as well, also the LUT training, so inherits those keywords.