Hello everyone. I am Dr. Furkan Gözükara. PhD Computer Engineer. SECourses is a dedicated YouTube channel for the following topics : Tech, AI, News, Science, Robotics, Singularity, ComfyUI, SwarmUI, ML, Artificial Intelligence, Humanoid Robots, Wan 2.2, FLUX, Krea, Qwen Image, VLMs, Stable Diffusion
Yes, I am doing that now on most smaller resolution datasets for concepts, however I would be very wary of any noticeable artificial enhancements it may be doing to your dataset as that "artificialness" may be enhanced in your SD outputs when you include the LoRA in your inference. I find that keeping the prompt text influence fairly low ~4 or less seems to keep the upscale/detail recovery adequate without introducing artificialness into the AI upscaled image. Your mileage may vary...
Not sure who has responded but I train LoRAs (presently using Kohya, will attempt to transition to OneTrainer soon) that require a high amount of quality and accuracy due to celebrity reproduction. What I am finding is that approaching model sized datasets (~300 images) is the key to increased accuracy across the board of your concept. This is the easiest and most straight forward method, but not always available if you can't acquire a dataset with enough good quality representations of your concept(s). So alternatives: 1) Train multiple concepts - This from reports is a mixed bag. I've done it for a TV celebrity who plays an iconic character with OK success. If I did it again, I would increase the size of my dataset for each concept to ~250 or more images. Further to this, you will also need to watch out when training lots of closeups to get it to learn a concept as it will lose reference with your MAIN subject (depending on what your concept). So say your concept is a male celebrity but you want the abs more accurate, doing all closeups of the abs may cause it to lose reference to the main subject you want. After all it is giving you what its learned (just abs closeups) from the trigger word you associated it with. 2) Train with color association or some other novel machine learning method - See Civitai article here: (sorry it appears he's removed the article and model). There is a reddit post still here about it: https://www.reddit.com/r/StableDiffusion/comments/1aolvxz/instructive_training_for_complex_concepts/?share_id=TyIZDSSNoYSqsqZVR0SzW 3) Train them as separate LoRAs, this would operate like 1) but remove the pitfalls people claim happens with multi-concept training in a single LoRA. 4) Learn how to train a model for ADetailer specific for your subject (tbh, I have no idea where to start with this one, but it may be what you and I both need to get body part accuracy)
I'm having great success with the text guidance set to 4 or lower to remove artificial influence from the base text prompt descriptions (or negative prompt).
@Neo Thanks for all your answers. Good question if SD looses track of the concept or not when labeling the data correctly. I dont know if the trainer will understand a caption file like "abs of ohwx man" or not. For us humans it's clear, but not sure if the modell will understand to output those abs whenever I prompt a "naked body of ohwx man", that the abs will be part of it... Probably needed to be checked
BTW guys, question of questions, everybody asks himself: Any way to get better at avoiding being detected as AI image when using AI image detectors like HIV?
Yeah if the base model has no idea what "abs" is, you will need to train it from scratch which means variation is king. You will need to do cropped shots, full shots, but always make sure the concept is present. Not sure how long it would take for it to understand abs if its not there in the base model you train off. I'd first run some inference checks or caption checks on the sourced model if you can for "abs" or "abdominals" etc.
"photo of owhx man, abs, fit, abdominal muscles" and so on... That should suffice... If the model know abs it can generate ohwx man with abs.... I think!
Yes but I hope the base model knows the basic concepts of body parts. I mean I'm not talking about special terms like "Musculus biceps brachii" More like Legs, Knees, Abs, Arms, Chest, Breast, Back, Lower back, Shoulders... That's it. Very "generic" terms
Thing is: My model has always exaggerated the breasts of my girlfriend or my own biceps and arm sizes... I wanna kind of correct that with some more detailled shots. Let's see if it'll work out
@Dr. Furkan Gözükara I'm watching your OneTrainer presets tutorial vid, and in the video it shows tier2_SDXL, but in this post (https://www.patreon.com/posts/96028218) it only has downloads for tier1. Just want to make sure I'm downloading the latest preset configs.
Get more from SECourses: Tutorials, Guides, Resources, Training, MidJourney, Voice Clone, TTS, ChatGPT, GPT, LLM, Scripts by Furkan Gözükara on Patreon
lol. the military uses the same tiers (tier 1 is best), but it's always been confusing to me, because what happens when you improve tier 1? move to tier 0? lol