[[subsets]]
caption_dropout_every_n_epochs = 1
caption_dropout_rate = 0.08
caption_extension = ".txt"
image_dir = "E:/Everything artificial intelligence/loradataset/2_ohwx sweetonedollar"
keep_tokens = 1
name = "a"
num_repeats = 2
shuffle_caption = true

[general_args.args]
max_data_loader_n_workers = 1
persistent_data_loader_workers = true
seed = 24
max_token_length = 225
prior_loss_weight = 1.0
xformers = true
cache_latents = true
cache_latents_to_disk = true
clip_skip = 2
max_train_epochs = 18
vae = "E:/Everything artificial intelligence/stable-diffusion-webui/models/VAE/klF8Anime2VAE_klF8Anime2VAE.safetensors"
pretrained_model_name_or_path = "E:/Everything artificial intelligence/stable-diffusion-webui/models/Stable-diffusion/animefull-final-pruned-fp16.safetensors"
mixed_precision = "fp16"

[general_args.dataset_args]
resolution = 768
batch_size = 2

[network_args.args]
network_dropout = 0.3
network_dim = 8
network_alpha = 4.0
min_timestep = 0
max_timestep = 1000

[optimizer_args.args]
lr_scheduler = "cosine"
learning_rate = 0.0001
warmup_ratio = 0.1
unet_lr = 0.0003
text_encoder_lr = 5e-5
scale_weight_norms = 5.0
max_grad_norm = 1.0
min_snr_gamma = 8
optimizer_type = "pytorch_optimizer.optimizer.came.CAME"
lr_scheduler_type = "LoraEasyCustomOptimizer.CustomOptimizers.Rex"

[saving_args.args]
save_precision = "fp16"
save_model_as = "safetensors"
save_toml = true
output_dir = "E:/Everything artificial intelligence/stable-diffusion-webui/models/Lora/sweetonedollar/sd15-test5"
save_toml_location = "E:/Everything artificial intelligence/stable-diffusion-webui/models/Lora/sweetonedollar/sd15-test5"
output_name = "sweetonedollartest5"
save_every_n_epochs = 2

[noise_args.args]
noise_offset = 0.03

[logging_args.args]
log_with = "tensorboard"
logging_dir = "E:/Everything artificial intelligence/derrianscript-devbranch/LoRA_Easy_Training_Scripts/logs/sweetonedollartest5"

[bucket_args.dataset_args]
enable_bucket = true
bucket_no_upscale = true
min_bucket_reso = 512
max_bucket_reso = 2048
bucket_reso_steps = 64

[network_args.args.network_args]
conv_dim = 12
conv_alpha = 6.0
module_dropout = 0.25

[optimizer_args.args.optimizer_args]
weight_decay = "0.1"
betas = "0.9,0.99"

[optimizer_args.args.lr_scheduler_args]
min_lr = 1e-6