config_file.toml

[model_arguments]
pretrained_model_name_or_path = "C:/stable-diffusion-trainer/fluffusion_r1_e9_640x.ckpt"
vae = "C:/stable-diffusion-trainer/stablediffusion.vae.pt"
# Parametrization is for V2.768
v2 = false
v_parameterization = false

[additional_network_arguments]
network_train_unet_only = false          # Train everything
network_train_text_encoder_only = false  # both UNet and TE
# Text encoder = Unet / 2
unet_lr = 1e-4
text_encoder_lr = 5e-5
# Module = Standart LORA
network_module = "networks.lora"
network_dim = 32
network_alpha = 1  # Alpha must be = or less than dim.
# IDK whats that, probably wants to incopropate metadata in lora.
no_metadata = false

[optimizer_arguments]
learning_rate = 1e-4  # Should be equal to unet_lr!!!
lr_scheduler = "constant"
lr_warmup_steps = 0
optimizer_type = "AdamW8bit"
max_grad_norm = 1.0

[dataset_arguments]
cache_latents = true
debug_dataset = false
vae_batch_size = 4

[training_arguments]
max_train_epochs = 20
clip_skip = 1
save_every_n_epochs = 1
#noise_offset = 0
output_dir = "C:/stable-diffusion-trainer/Loras"
output_name = "sks"
logging_dir = "C:/stable-diffusion-trainer/Logs"
log_prefix = "sks"
gradient_checkpointing = false
gradient_accumulation_steps = 1
save_precision = "fp16"
train_batch_size = 6
max_token_length = 225
mem_eff_attn = false
xformers = true
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
mixed_precision = "fp16"
lowram = true

[sample_prompt_arguments]
sample_every_n_epochs = 999999
sample_sampler = "ddim"

[dreambooth_arguments]
prior_loss_weight = 1.0

[saving_arguments]
save_model_as = "safetensors"