Hi Furkan, I've been experimenting with Flux dreambooth and LoRA training for several months now, using a high quality dataset of 3D renders en photographs of a specific car (which is not trained in the base model).
After extensive testing, I am actually getting better results with Flux LoRAs then dreambooth finetune.
The finetuned model does not follow the car's body well, the LoRA is replicating the car's shape a lot better.
For the LoRA, I found that AdamW with network dimension 128 and alpha 64 worked best for me.
For the dreambooth, I've used your configs and left them untouched.
I am surprised that this is the case as you would expect the oposite. Even the extracted LoRAs from the trained checkpoint performed worse than the LoRA.
I know this is very little information to go on, I can send you more grid tests if you want, but I am wondering if you have an idea where to look.
The only thing I can think of is that the LoRA uses AdamW and dreambooth uses Adafactor.
I could not use AdamW for dreambooth, not even on a 48GB GPU.
My goal and quality metrics are product (car) accuracy. So perhaps my configs should differ from your configs that are more leaned towards people?
PS. I've left all the common settings the same like image repeats, dataset/captions, noise offset, snr. And I have tested multiple epochs to find the best sample size
