[model_arguments]
v2 = false
v_parameterization = false
pretrained_model_name_or_path = "/content/drive/MyDrive/models/models/Stable-diffusion/AIGEN_1_4.ckpt"

[additional_network_arguments]
no_metadata = false
unet_lr = 0.0001
text_encoder_lr = 5e-5
network_module = "networks.lora"
network_dim = 32
network_alpha = 16
network_train_unet_only = false
network_train_text_encoder_only = false
network_weights = "/content/drive/MyDrive/LoRA/gen-lora-pro-doctors/output/gen_lora_doctors_400.safetensors"

[optimizer_arguments]
optimizer_type = "AdamW8bit"
learning_rate = 0.0001
max_grad_norm = 1.0
lr_scheduler = "constant"
lr_warmup_steps = 0

[dataset_arguments]
cache_latents = true
debug_dataset = false
vae_batch_size = 2

[training_arguments]
output_dir = "/content/drive/MyDrive/LoRA/gen-lora-pro-doctors/output"
output_name = "gen_lora_doctors"
save_precision = "fp16"
save_every_n_epochs = 100
train_batch_size = 2
max_token_length = 75
mem_eff_attn = false
xformers = true
max_train_epochs = 100
max_data_loader_n_workers = 1
persistent_data_loader_workers = true
gradient_checkpointing = false
gradient_accumulation_steps = 1
mixed_precision = "fp16"
clip_skip = 2
logging_dir = "/content/drive/MyDrive/LoRA/gen-lora-pro-doctorsh/logs"
log_prefix = "gen-lora-pro-doctors"
lowram = true

[sample_prompt_arguments]
sample_every_n_epochs = 1
sample_sampler = "k_dpm_2"

[dreambooth_arguments]
prior_loss_weight = 1.0

[saving_arguments]
save_model_as = "safetensors"