SECourses_Musubi_Trainer/zimage_defaults.toml

# Z-Image Training - Recommended Defaults (Base + Turbo)
# Based on musubi-tuner/docs/zimage.md training example.

# Z-Image variant selector (GUI-only; training uses your selected DiT checkpoint)
zimage_variant = "base"  # "base" | "turbo"

# Training mode (GUI only)
training_mode = "LoRA Training"  # "LoRA Training" | "DreamBooth Fine-Tuning"

# Optional: Turbo Training Adapter (LoRA). If set, GUI passes it as base_weights (--base_weights) during training.
training_adapter_path = ""
training_adapter_multiplier = 1.0

# Required model paths (set these)
dit = ""          # REQUIRED: path to Z-Image DiT checkpoint (Base or Turbo/De-Turbo)
vae = ""          # REQUIRED: path to Z-Image VAE checkpoint
text_encoder = "" # REQUIRED: path to Qwen3 text encoder (split file 00001-of-...)

# Dataset config
dataset_config_mode = "Generate from Folder Structure"  # "Generate from Folder Structure" | "Use TOML File"
dataset_config = ""        # Used when dataset_config_mode="Use TOML File"
parent_folder_path = ""    # Used when dataset_config_mode="Generate from Folder Structure"
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"
generated_toml_path = ""

# Precision / memory
mixed_precision = "bf16"
num_cpu_threads_per_process = 1
fp8_base = false
fp8_scaled = false
fp8_llm = false
disable_numpy_memmap = false
blocks_to_swap = 0
use_pinned_memory_for_block_swap = false
block_swap_h2d_only = false
block_swap_ring_size = 2

# Training core
sdpa = true
flash_attn = false
sage_attn = false
xformers = false
split_attn = false
use_legacy_sdpa = false
max_train_epochs = 16
max_train_steps = 1600
max_data_loader_n_workers = 2
persistent_data_loader_workers = true
seed = 42
gradient_checkpointing = true
gradient_checkpointing_cpu_offload = false
gradient_accumulation_steps = 1

# Torch compile (optional)
compile = false
compile_backend = "inductor"
compile_mode = "default"
compile_dynamic = "auto"         # "auto" | "true" | "false"
compile_fullgraph = false
compile_cache_size_limit = 0     # 0 = use PyTorch default

# Flow matching / timestep sampling (example defaults; adjust if needed)
timestep_sampling = "shift"
weighting_scheme = "none"
discrete_flow_shift = 2.0
sigmoid_scale = 1.0
min_timestep = 0
max_timestep = 1000

# Optimizer
optimizer_type = "adamw8bit"
optimizer_args = ""
learning_rate = 1e-4
max_grad_norm = 1.0
fused_backward_pass = false  # DreamBooth only: reduces VRAM during backward pass with AdaFactor
lr_scheduler = "constant"
lr_warmup_steps = 0
lr_decay_steps = 0
lr_scheduler_num_cycles = 1
lr_scheduler_power = 1.0
lr_scheduler_timescale = 0
lr_scheduler_min_lr_ratio = 0.0
lr_scheduler_type = ""
lr_scheduler_args = ""

# Network (LoRA)
network_module = "networks.lora_zimage"
network_dim = 32
network_alpha = 32.0
network_dropout = 0.0
network_args = ""
network_weights = ""
training_comment = ""
dim_from_weights = false
scale_weight_norms = 0.0
base_weights = ""
base_weights_multiplier = 1.0
no_metadata = false

# Caching (recommended)
caching_latent_device = "cuda"
caching_latent_batch_size = 1
caching_latent_num_workers = 2
caching_latent_skip_existing = true
caching_latent_keep_cache = true

caching_teo_device = "cuda"
caching_teo_batch_size = 8
caching_teo_num_workers = 2
caching_teo_skip_existing = true
caching_teo_keep_cache = true

# Samples (optional)
sample_every_n_steps = 0
sample_every_n_epochs = 0
sample_at_first = false
sample_prompts = ""
disable_prompt_enhancement = false
sample_output_dir = ""
sample_width = 1024
sample_height = 1024
sample_steps = 25
sample_seed = 42
sample_negative_prompt = ""
sample_cfg_scale = 4.0

# Save / resume
output_dir = ""
output_name = "my-zimage-lora"
resume = ""
save_precision = "bf16"
save_every_n_epochs = 1
save_every_n_steps = 0
save_last_n_epochs = 0
save_last_n_steps = 0
save_last_n_epochs_state = 0
save_last_n_steps_state = 0
save_state = false
save_state_on_train_end = false
mem_eff_save = false

# Advanced
additional_parameters = ""
debug_mode = "None"