--- license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ datasets: - pls2000/aiart_channel_nai3_geachu base_model: - OnomaAIResearch/Illustrious-xl-early-release-v0 tags: - lora --- # Lora Training (`arcain_2411.safetensors`) Lora trained on Illustrious-xl v0.1, but this lora can applied with other ILXL-based models such as NoobAI-XL. - Tool: kohya-ss/sd-scripts - GPUs: 4x RTX3060 - Dataset: pls2000/aiart_channel_nai3_geachu + additional data until 24/11/14 - blue archive data - Time taken: 50.5 hours (walltime) #### lora_arcain.sh ``` NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 accelerate launch --num_cpu_threads_per_process 4 sdxl_train_network.py \ --network_train_unet_only \ --network_module="networks.lora" --network_dim 128 --network_alpha 128 \ --pretrained_model_name_or_path="/ai/data/sd/models/Stable-diffusion/SDXL/Illustrious-XL-v0.1.safetensors" \ --dataset_config="arcain.lora.toml" \ --output_dir="results/lora" --output_name="arcain-`date +%y%m`" \ --save_model_as="safetensors" \ --train_batch_size 2 --gradient_accumulation_steps 64 \ --learning_rate=1e-5 --optimizer_type="Lion8bit" \ --lr_scheduler="constant_with_warmup" --lr_warmup_steps 100 --optimizer_args "weight_decay=0.01" "betas=0.9,0.95" --min_snr_gamma 5 \ --sdpa \ --no_half_vae \ --cache_latents --cache_latents_to_disk \ --gradient_checkpointing \ --full_bf16 --mixed_precision="bf16" --save_precision="fp16" \ --ddp_timeout=10000000 \ --max_train_epochs 8 --save_every_n_epochs 1 \ --log_with wandb --log_tracker_name kohya-ss --wandb_run_name "arcain_`date +%y%m%d-%H%M`" --logging_dir wandb \ ``` #### arcain.lora.toml ``` [general] shuffle_caption = true caption_tag_dropout_rate = 0.2 keep_tokens_separator = "|||" caption_extension = ".txt" [[datasets]] enable_bucket = true min_bucket_reso = 512 max_bucket_reso = 4096 resolution = 1024 [[datasets.subsets]] image_dir = "/mnt/wd8tb/train/to_train" num_repeats = 1 ```