Update README.md

README.md

@@ -10,7 +10,7 @@ datasets:
 pipeline_tag: text-to-image
 ---
 
-## Training
+## Training (`arcaillous-xl.safetensors`)
 
 Configuration refered from KBlueLeaf/Kohaku-XL-Zeta, using 2x3090 and sd-scripts.
 
@@ -32,4 +32,30 @@ NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 accelerate launch --num_cpu_threads_per_pro
         --full_bf16 --mixed_precision="bf16" --save_precision="bf16" \
         --ddp_timeout=10000000 \
         --max_train_epochs 4 --save_every_n_epochs 1 --save_every_n_steps 50 \
+```
+
+## Lora Training (`lora_arcain.safetensors`)
+
+Results are simillar with arcaillous-xl with Illustrious-xl, but this lora can applied with other ILXL-based models such as NoobAI-XL.
+
+Configuration is simillar to `arcaillous-xl`.
+
+```
+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 256 --network_alpha 128 \
+        --pretrained_model_name_or_path="/ai/data/sd/models/Stable-diffusion/Illustrious-XL-v0.1.safetensors" \
+        --dataset_config="arcain.lora.toml" \
+        --output_dir="results/lora" --output_name="lora_arcain" \
+        --save_model_as="safetensors" \
+        --gradient_accumulation_steps 32 \
+        --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="bf16" \
+        --ddp_timeout=10000000 \
+        --max_train_epochs 4 --save_every_n_epochs 1 --save_every_n_steps 50 \
 ```