kohya_ss/config example.toml

# Copy this file and name it config.toml
# Edit the values to suit your needs

# Default folders location
[model]
models_dir = "./models"                    # Pretrained model name or path
output_name = "new model"                  # Trained model output name
train_data_dir = "./data"                  # Image folder (containing training images subfolders) / Image folder (containing training images)
dataset_config = "./test.toml"             # Dataset config file (Optional. Select the toml configuration file to use for the dataset)
training_comment = "Some training comment" # Training comment
save_model_as = "safetensors"              # Save model as (ckpt, safetensors, diffusers, diffusers_safetensors)
save_precision = "bf16"                    # Save model precision (fp16, bf16, float)

[folders]
output_dir = "./outputs"    # Output directory for trained model
reg_data_dir = "./data/reg" # Regularisation directory
logging_dir = "./logs"      # Logging directory

[configuration]
config_dir = "./presets" # Load/Save Config file

[advanced]
adaptive_noise_scale = 0                  # Adaptive noise scale
additional_parameters = ""                # Additional parameters
bucket_no_upscale = true                  # Don't upscale bucket resolution
bucket_reso_steps = 64                    # Bucket resolution steps
caption_dropout_every_n_epochs = 0        # Caption dropout every n epochs
caption_dropout_rate = 0                  # Caption dropout rate
color_aug = false                         # Color augmentation
clip_skip = 1                             # Clip skip
debiased_estimation_loss = false          # Debiased estimation loss
flip_aug = false                          # Flip augmentation
fp8_base = false                          # FP8 base training (experimental)
full_bf16 = false                         # Full bf16 training (experimental)
full_fp16 = false                         # Full fp16 training (experimental)
gradient_accumulation_steps = 1           # Gradient accumulation steps
gradient_checkpointing = false            # Gradient checkpointing
ip_noise_gamma = 0                        # IP noise gamma
ip_noise_gamma_random_strength = false    # IP noise gamma random strength (true, false)
keep_tokens = 0                           # Keep tokens
log_tracker_config_dir = "./logs"         # Log tracker configs directory
log_tracker_name = ""                     # Log tracker name
masked_loss = false                       # Masked loss
max_data_loader_n_workers = "0"           # Max data loader n workers (string)
max_timestep = 1000                       # Max timestep
max_token_length = "150"                  # Max token length ("75", "150", "225")
mem_eff_attn = false                      # Memory efficient attention
min_snr_gamma = 0                         # Min SNR gamma
min_timestep = 0                          # Min timestep
multires_noise_iterations = 0             # Multires noise iterations
multires_noise_discount = 0               # Multires noise discount
no_token_padding = false                  # Disable token padding
noise_offset = 0                          # Noise offset
noise_offset_random_strength = false      # Noise offset random strength (true, false)
noise_offset_type = "Original"            # Noise offset type ("Original", "Multires")
persistent_data_loader_workers = false    # Persistent data loader workers
prior_loss_weight = 1.0                   # Prior loss weight
random_crop = false                       # Random crop
save_every_n_steps = 0                    # Save every n steps
save_last_n_steps = 0                     # Save last n steps
save_last_n_steps_state = 0               # Save last n steps state
save_state = false                        # Save state
save_state_on_train_end = false           # Save state on train end
scale_v_pred_loss_like_noise_pred = false # Scale v pred loss like noise pred
shuffle_caption = false                   # Shuffle captions
state_dir = "./outputs"                   # Resume from saved training state
use_wandb = false                         # Use wandb
vae_batch_size = 0                        # VAE batch size
vae_dir = "./models/vae"                  # VAEs folder path
v_pred_like_loss = 0                      # V pred like loss weight
wandb_api_key = ""                        # Wandb api key
wandb_run_name = ""                       # Wandb run name
weighted_captions = false                 # Weighted captions
xformers = "xformers"                     # CrossAttention (none, sdp, xformers)

# This next section can be used to set default values for the Dataset Preparation section
# The "Destination training direcroty" field will be equal to "train_data_dir" as specified above
[dataset_preparation]
instance_prompt = "instance"
class_prompt = "class"
images_folder = "/some/folder/where/images/are"
reg_images_folder = "/some/folder/where/reg/images/are"