[network_arguments]

unet_lr = 8e-5

text_encoder_lr = 5e-5

network_dim = 32

network_alpha = 16

network_module = "networks.lora"

network_train_unet_only = false

[optimizer_arguments]

learning_rate = 8e-5

lr_scheduler = "cosine_with_restarts"

lr_scheduler_num_cycles = 3

lr_warmup_steps = 17

optimizer_type = "LoraEasyCustomOptimizer.came.CAME"

optimizer_args = [ "weight_decay=0.04",]

loss_type = "l2"

max_grad_norm = 1.0

[training_arguments]

lowram = true

pretrained_model_name_or_path = "/content/downloaded_model.safetensors"

vae = "/content/sdxl_vae.safetensors"

max_train_epochs = 8

train_batch_size = 4

seed = 42

max_token_length = 225

xformers = false

sdpa = true

no_half_vae = true

gradient_checkpointing = true

gradient_accumulation_steps = 1

max_data_loader_n_workers = 1

persistent_data_loader_workers = true

mixed_precision = "fp16"

full_fp16 = true

full_bf16 = false

cache_latents = true

cache_latents_to_disk = true

cache_text_encoder_outputs = false

min_timestep = 0

max_timestep = 1000

prior_loss_weight = 1.0

multires_noise_iterations = 6

multires_noise_discount = 0.3

v_parameterization = true

scale_v_pred_loss_like_noise_pred = true

zero_terminal_snr = true

[saving_arguments]

save_precision = "fp16"

save_model_as = "safetensors"

save_every_n_epochs = 1

save_last_n_epochs = 8

output_name = "anyillu"

output_dir = "/content/drive/MyDrive/Loras/anyillu/output"

log_prefix = "anyillu"

logging_dir = "/content/drive/MyDrive/Loras/_logs"