[model_arguments]
v2 = false
v_parameterization = false
pretrained_model_name_or_path = "/content/pretrained_model/model.ckpt"

[additional_network_arguments]
no_metadata = false
unet_lr = 1.0
text_encoder_lr = 1.0
network_module = "networks.lora"
network_dim = 64
network_alpha = 1
network_train_unet_only = false
network_train_text_encoder_only = false

[optimizer_arguments]
optimizer_type = "Prodigy"
learning_rate = 1.0
max_grad_norm = 1.0
optimizer_args = [ "decouple=True", "weight_decay=0.01", "d_coef=2", "use_bias_correction=True", "safeguard_warmup=True", "betas=0.9,0.99",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100

[dataset_arguments]
cache_latents = true
debug_dataset = false
vae_batch_size = 4

[training_arguments]
output_dir = "/content/LoRA/output"
output_name = "otogi"
save_precision = "fp16"
save_every_n_epochs = 10
train_batch_size = 5
max_token_length = 225
mem_eff_attn = false
xformers = true
max_train_epochs = 20
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
seed = 31337
gradient_checkpointing = false
gradient_accumulation_steps = 1
mixed_precision = "fp16"
clip_skip = 2
logging_dir = "/content/LoRA/logs"
log_prefix = "otogi"
lowram = true
multires_noise_discount = 0.3

[sample_prompt_arguments]
sample_every_n_epochs = 5
sample_sampler = "ddim"

[dreambooth_arguments]
prior_loss_weight = 1.0

[saving_arguments]
save_model_as = "safetensors"