# Ideogram 4 LoRA and full fine-tuning defaults
training_mode = "LoRA Training"
mixed_precision = "bf16"
num_cpu_threads_per_process = 1
num_processes = 1
num_machines = 1
multi_gpu = false
gpu_ids = "0"
main_process_port = 0
dynamo_backend = "no"

dataset_config_mode = "Generate from Folder Structure"
dataset_config = ""
parent_folder_path = ""
dataset_resolution_width = 1024
dataset_resolution_height = 1024
dataset_caption_extension = ".txt"
create_missing_captions = true
caption_strategy = "folder_name"
dataset_batch_size = 1
dataset_enable_bucket = true
dataset_bucket_no_upscale = false
dataset_cache_directory = "cache_dir"

dit = ""
vae = ""
text_encoder = ""
unconditional_dit = ""
use_unconditional_dit_for_lora_sampling = false
dit_dtype = "bfloat16"
vae_dtype = "bfloat16"
disable_numpy_memmap = false
blocks_to_swap = 33
use_pinned_memory_for_block_swap = false
block_swap_h2d_only = true
block_swap_ring_size = 1

compile = false
compile_backend = "inductor"
compile_mode = "default"
compile_dynamic = "auto"
compile_fullgraph = false
compile_cache_size_limit = 0

timestep_sampling = "ideogram4_shift"
weighting_scheme = "none"
discrete_flow_shift = 1.0
sigmoid_scale = 1.0
min_timestep = 0
max_timestep = 1000
sampler_preset = "V4_DEFAULT_20"
initial_sigma = 1.004
validate_caption_structure = false
warn_on_caption_issues = false
log_loss_stats = false

sdpa = true
flash_attn = false
sage_attn = false
xformers = false
split_attn = false
use_legacy_sdpa = false
max_train_steps = 80000
max_train_epochs = 200
max_data_loader_n_workers = 1
persistent_data_loader_workers = true
seed = 42
gradient_checkpointing = true
gradient_checkpointing_cpu_offload = false
gradient_accumulation_steps = 1
full_bf16 = false
full_fp16 = false
fused_backward_pass = false
block_swap_optimizer_patch_params = false

sample_every_n_steps = 0
sample_every_n_epochs = 0
sample_at_first = false
sample_prompts = ""
sample_output_dir = ""
disable_prompt_enhancement = false
sample_width = 1024
sample_height = 1024
sample_steps = 20
sample_seed = 42
sample_negative_prompt = ""
sample_cfg_scale = 7.0

cache_latents = true
caching_latent_device = "cuda"
caching_latent_batch_size = 1
caching_latent_num_workers = 1
caching_latent_skip_existing = true
caching_latent_keep_cache = true
cache_text_encoder_outputs = true
caching_teo_device = "cuda"
caching_teo_batch_size = 1
caching_teo_num_workers = 1
caching_teo_skip_existing = true
caching_teo_keep_cache = true
caching_teo_text_cache_dtype = "bf16"

optimizer_type = "AdaFactor"
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False", "weight_decay=0.01",]
learning_rate = 6e-5
max_grad_norm = 0.0
lr_scheduler = "constant"
lr_warmup_steps = 0

network_module = "networks.lora_ideogram4"
network_dim = 128
network_alpha = 128
network_dropout = 0.0
network_args = []
no_metadata = false

output_dir = ""
output_name = "my-ideogram4-lora"
resume = ""
save_precision = "bf16"
save_every_n_epochs = 1
save_every_n_steps = 0
save_state = false
save_state_on_train_end = false
mem_eff_save = false

additional_parameters = ""
debug_mode = "None"