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H.R. Giger Arh+

H.R. Giger Arh+

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#abstract
#body horror
#giger
#biomechanical
#แอร์บรัช
#Style
#แฟนตาซ
#ศิลปะประณีต
#organic
#ชม. ยักษ์

H.R. Giger Arh+

[EDIT: Retrained and overhauled version 10.0 is up and should now work with most models and LoRAs out there.]

This LoRA model might work or it might not. I did this mainly for my own enjoyment as I wanted a model that specifically does high quality replication of the organic patterns in Giger's airbrush works. I went through my library and scanned in the highest quality images I had, some of them were digital photographs of the real works, so the source material was really high quality for the most part. When it works, it creates reaaally smooth and wide dynamic range renders.

But, as this was my very first time training anything, I probably messed something up as when the LoRA training rendered out test prompts, the images were 100% what I was aiming for - but when I tested the LoRA out on Automatic1111's WebUI, nothing seemed to work or give the wide contrast results - it's probably something in my settings or the implementation of the LoRA network models is not quite there yet. At some point, like magic, it just started working more and more.

Anyhoo, on WebUI, the best results come from somewhat lower CFG range, like 3-5 and with a base model that gives interesting stuff to paint on. I'll dive deeper into the training stuff and was thinking of making a SDv2 model with 768 sample images, but this seems to work ok so far. Test it out and show me what you create with it.

H.R. Giger (1940-2014)

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T
Duyuru
2024-04-02
Model yayınlama
2023-07-14
Model bilgilerini güncelle
Model detayları
Tür
LORA
Yayınlanma tarihi
2023-07-14
Temel Model
SD 1.5
Tetikleyici Kelime
HRGigerArhP style
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Sürüm tanıtımı

Turns out that training with specific version of XFormers, you get a LoRA that doesn't activate or activates at random, which is why previous versions were a bit iffy. Now with a new card (3060, 12GB) and a polished set of sample images, I managed to train a LoRA that passes all (at least on my end) tests. Trained with 65 images at 1024px so it might produce weird stuff with resolutions below 768.

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