Someone mentioned that there's not much mecha spaceships.

Decided to make one.

[additional_network_arguments]

unet_lr = 0.0005

text_encoder_lr = 0

network_dim = 128

network_alpha = 16

network_module = "networks.lora_flux"

network_train_unet_only = true

the large file is generally due to the above as far as i can tell.

[optimizer_arguments]

learning_rate = 0.0005

lr_scheduler = "cosine_with_restarts"

lr_scheduler_num_cycles = 3

lr_warmup_steps = 0

optimizer_type = "Adafactor"

optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]

[training_arguments]

max_train_steps = 0

max_train_epochs = 31

save_every_n_epochs = 1

sample_every_n_epochs = 1

sample_prompts = xxxxxxxxxx

sample_sampler = "euler_a"

train_batch_size = 2

noise_offset = 0.1

clip_skip = 2

weighted_captions = false

max_token_length = 225

lowram = false

max_data_loader_n_workers = 8

persistent_data_loader_workers = true

save_precision = "bf16"

mixed_precision = "bf16"

output_dir = xxxxxxxxxx

logging_dir = xxxxxxxxxx

output_name = "sdf-1_proto_2025_02_01"

save_state = false

xformers = false

sdpa = true

no_half_vae = false

gradient_checkpointing = true

gradient_accumulation_steps = 1

highvram = true

fp8_base = true

timestep_sampling = "sigmoid"

model_prediction_type = "raw"

guidance_scale = 1.0

loss_type = "l2"

clip_l = xxxxxxxxxx

ae = xxxxxxxxxx

t5xxl = xxxxxxxxxx

[advanced_training_config]

multires_noise_iterations = 6

multires_noise_discount = 0.3

min_snr_gamma = 5.0

v2 = false

[saving_arguments]

save_model_as = "safetensors"

[dreambooth_arguments]

prior_loss_weight = 1.0

[dataset_arguments]

cache_latents = true

cache_text_encoder_outputs = true

cache_text_encoder_ouutputs_to_disk = true