akjx_config/config_file.toml

[sdxl_arguments]
cache_text_encoder_outputs = false
no_half_vae = true
min_timestep = 0
max_timestep = 1000
shuffle_caption = true
lowram = true

[model_arguments]
pretrained_model_name_or_path = "dominic1021/ponyxl"
vae = "/content/vae/sdxl_vae.safetensors"

[dataset_arguments]
debug_dataset = false
in_json = "/content/LoRA/meta_lat.json"
train_data_dir = "/content/images"
dataset_repeats = 8
keep_tokens = 0
resolution = "1024,1024"
color_aug = false
token_warmup_min = 1
token_warmup_step = 0

[training_arguments]
output_dir = "/content/LoRA/output/akjx"
output_name = "akjx"
save_precision = "fp16"
save_every_n_epochs = 5
train_batch_size = 1
max_token_length = 225
mem_eff_attn = false
sdpa = false
xformers = true
max_train_epochs = 50
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"
cache_latents = true
cache_latents_to_disk = true

[logging_arguments]
log_with = "tensorboard"
logging_dir = "/content/LoRA/logs"
log_prefix = "akjx"

[sample_prompt_arguments]
sample_every_n_epochs = 5
sample_sampler = "euler_a"

[saving_arguments]
save_model_as = "safetensors"

[optimizer_arguments]
optimizer_type = "Prodigy"
learning_rate = 1
text_encoder_lr = 1
network_train_unet_only = false
max_grad_norm = 0
optimizer_args = [ "decouple=True", "weight_decay=0.01", "d_coef=0.8", "use_bias_correction=True", "safeguard_warmup=True", "betas=0.9,0.99",]
lr_scheduler = "cosine"
lr_warmup_steps = 64

[additional_network_arguments]
no_metadata = false
network_module = "networks.lora"
network_dim = 16
network_alpha = 16
network_args = []

[advanced_training_config]
save_state = false
save_last_n_epochs_state = false
multires_noise_iterations = 6
multires_noise_discount = 0.4
caption_dropout_rate = 0
caption_tag_dropout_rate = 0
caption_dropout_every_n_epochs = 0
min_snr_gamma = 1