sdxl_lora_architecture_siheyuan_config/config_file.toml

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

[model_arguments]
pretrained_model_name_or_path = "/content/pretrained_model/sd_xl_base_1.0.safetensors"
vae = "/content/vae/sdxl_vae.safetensors"

[dataset_arguments]
debug_dataset = false
in_json = "/content/LoRA/meta_lat.json"
train_data_dir = "/content/LoRA/train_data"
dataset_repeats = 20
keep_tokens = 0
resolution = "1024,1024"
color_aug = false
token_warmup_min = 1
token_warmup_step = 0

[training_arguments]
output_dir = "/content/drive/MyDrive/kohya-trainer/output"
output_name = "sdxl_lora_architecture_siheyuan"
save_precision = "fp16"
save_every_n_epochs = 1
train_batch_size = 4
max_token_length = 225
mem_eff_attn = false
sdpa = true
xformers = false
max_train_epochs = 10
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"

[logging_arguments]
log_with = "wandb"
log_tracker_name = "sdxl_lora_architecture_siheyuan"
logging_dir = "/content/LoRA/logs"

[sample_prompt_arguments]
sample_every_n_epochs = 1
sample_sampler = "euler_a"

[saving_arguments]
save_model_as = "safetensors"

[optimizer_arguments]
optimizer_type = "AdaFactor"
learning_rate = 1e-5
max_grad_norm = 0
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100

[additional_network_arguments]
no_metadata = false
network_module = "networks.lora"
network_dim = 32
network_alpha = 16
network_args = [ "conv_dim=32", "conv_alpha=16",]
network_train_unet_only = true

[advanced_training_config]
noise_offset = 0.1
adaptive_noise_scale = 0.01
min_snr_gamma = 5