ModelsLab/Astigmatism (Formerly 'Semantic Shift') - Astigmatic Correction 0.1

This is my first attempt at using my method of diversifying overtrained areas in the core SDXL model to try to coax out more "intelligence" in the model.This obviously is challenging to do with LoRas, and a checkpoint fine-tune would be a LOT more effective, but mostly I'm just testing out the idea and seeing what can happen with this.This first LoRa is just 45 images, and the correction targets only 10 specific overtrained areas, with each prompt getting a few images to diversify its output:Pillars of CreationThe Birth of VenusFlaming JuneIrises in Monet's GardenA Sunday Afternoon on the Island of La Grande JatteGirl with a Pearl EarringThe Mona LisaAbbey RoadLiberty Leading the PeopleThe ScreamNote that this one wasn't semantically perfect, or rather, the correction wasn't as strict as I'd like to apply in the future if I were to actually see what I can do with this method in terms of improving prompt understanding. It's my thought that these overtrained images directly affect the semantic understanding of the model, which can be partially corrected for.In this case, the images included tried to keep elements of the semantic meaning of the titles above, but in some cases this may not have been as strict or obvious as I'd like in order to guide the model the way I want to.