Going old school with this one. Helg was the main artist associated with the now-defunct CartoonValley website, a true relic of the Web2.0 era. His art featured mostly Disney character parodies. The style changed substantially over the years from 2004 to 2018. This LoRA only covers the latter half where shiny, oiled up bodies, huge tits and tanned skin dominated.<pre><code>Update V3
- massive dataset and tagging overhaul project to have generations without watermarks
- less than 1% of the 2069 images in the dataset contain any text now

Update V2
- there are now a whopping 34 tagged characters, including practically all of the Winx cast
- trained on Illustrious 1.0 instead of the old 0.1</code></pre>Trigger: HelgStyleTagged Characters: HelgJasmine, HelgAnastasia, HelgAriel, HelgAurora, HelgBelle, HelgChel, HelgCinderella, HelgEilonwy, HelgElisaMaza, HelgEsmeralda, HelgHolliWould, HelgJane, HelgJessicaRabbit, HelgKida, HelgMulan, HelgNani, HelgPocahontas, HelgSnowWhite, HelgTiana, HelgTuya, HelgVanessa, HelgWinxAisha, HelgWinxBloom, HelgWinxDiaspro, HelgWinxFlora, HelgWinxMusa, HelgWinxRoxy, HelgWinxStella, HelgWinxTecna, HelgTrixDarcy, HelgTrixIcy, HelgTrixStormy,What else? Tan and Tanlines will give you the characteristic darker skin tone. Shiny skin is obvious. Some of the weaker tags may need help with hair color and hair length. Might wanna remove keywords like "masterpiece" etc. Give them large or huge breasts. It's the law. Why not try puffy nipples.Somewhat counterintuitively, cartoon trained checkpoints give worse results for style adherence. I found the anime-centric <a target="_blank" rel="ugc" href="https://civarchive.com/models/827184/wai-nsfw-illustrious-sdxl">WAI-NSFW</a> or <a target="_blank" rel="ugc" href="https://civarchive.com/models/1078146/steinillustrious?modelVersionId=2348168">SteinIllustrious</a> with Euler a to work well for this. For the rest, see the sample images.

Helg Artist Style (Illustrious)

Trained on base Illustrious v1.0Dataset: 2069 images (nice)Watermark is no longer burned in (&lt;1% of images with any text in dataset)

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