[Dune 1984] Stillsuit (Pony)
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Model description
lyncsuit_ponyv1, matte black bodysuit, bodysuit
Another piece of retro goodiness! The stillsuit from 1984s Dune by David Lynch! For this one I used stills from the movie as well as from Westwood's games live-action cinematics - as they used the same props as in the movie, sometimes even using short clips from it.
It works very well even at 1.0, but perhaps there's a minor style influence there, so if you don't want that, lower to 0.8. It tends to make these stillsuits with gloves or half-gloves - not much of a problem here, as many people complained that the stillsuit from the movie does not cover palms. Still, if you don't want that, then put "gloves" in the negative prompt.
It makes blue eyes by default, see no problem here actually. But it also tends to recreate Paul and Chani(along with her hair bun) from the movie by default, so if you leave your prompt just as 1boy/1girl it will do just that. If you don't want that, just put more details in the prompts - skin tone, hair cut, hair color, eye color, etc. It can make characters look very different without any trouble, as shown in the preview. For females I would also put "hair bun" in the negative prompt.
It does work rather well in recreating the nose tube and intricate details, but there may be some variations - it's AI we're talking about. If you use "nose tube" tag it may mess up the face in full body shots, so either lower it to 0.8 or use ADetailer with a face model and appropriate tags, such as "nose tube, detailed eyes, detailed face" or any you fancy.
In the movie, the stillsuit has both a male and a female version. This model recognizes and recreates this almost perfectly, as shown in the previews.
I also tested it with my Crysknife Lora and they do work together, but there's a strong influence of "realistic" style when you do. No surprise here - 1/4 of their datasets is basically the same and even more come from the same source, so they basically reinforce that style. Putting "((realistic))" in the negative tags can help with mitigating it.