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[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 = "Linaqruf/animagine-xl"
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 = 2
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/hanumanSDXL"
output_name = "hanumanSDXL"
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 = "tensorboard"
logging_dir = "/content/LoRA/logs"
log_prefix = "hanumanSDXL"
[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 = 0.0001
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 = []
network_train_unet_only = true
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