[network_arguments]
unet_lr = 0.0001
text_encoder_lr = 0
network_dim = 64
network_alpha = 32
network_module = "networks.lora"
network_args = [ "conv_dim=16", "conv_alpha=8",]
network_train_unet_only = true
[optimizer_arguments]
learning_rate = 0.0001
lr_scheduler = "cosine_with_restarts"
lr_scheduler_num_cycles = 3
lr_warmup_steps = 70
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 = 1
seed = 42
max_token_length = 225
xformers = false
sdpa = true
min_snr_gamma = 5
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 = "AnyNoobAI"
output_dir = "/content/drive/MyDrive/Loras/AnyNoobAI/output"
log_prefix = "AnyNoobAI"
logging_dir = "/content/drive/MyDrive/Loras/_logs"