# Overview

I have reduced the dimensionality from 256 to ?? (a typical level) compared to V1. Which is more appropriate may depend on the backend used.
I trained a lightweight model on a similar dataset, and while the overall quality is nearly the same, I can't discern any differences in the details.

train_network.py --pretrained_model_name_or_path="JosephusCheung/ACertainty" --prior_loss_weight=1.0 --enable_bucket --min_bucket_reso=384 --max_bucket_reso=1280 --train_batch_size=8 --learning_rate=1e-6 --text_encoder_lr 5e-8 --xformers --save_model_as=safetensors --clip_skip=2 --seed=42 --color_aug --flip_aug --network_module=networks.lora --resolution=768,512 --network_dim ?? --save_every_n_epochs 1 --max_train_epochs 2