Tangentially related: for reasons I don't yet really understand, the LORAs that I build for Stable Diffusion XL only work well if I give a pretty generic negati

Tangentially related: for reasons I don't yet really understand, the LORAs that I build for Stable Diffusion XL only work well if I give a pretty generic negative prompt. These are fine-tuned on 6 photos of my face, and if I use them with positive prompts, the generated characters don't look much like me. But if I add generic negative terms like "low quality", suddenly the depiction of my face is almost exactly right. I've trained several models and this has been true across a range of learning rates and number of training epochs. To me, this feels like it will somehow ultimately be connected to whatever is driving minimaxir's observations in this post.