config example.toml

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

[settings]
use_shell = false # Use shell furing process run of sd-scripts oython code. Most secure is false but some systems may require it to be true to properly run sd-scripts.

# 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

[accelerate_launch]
dynamo_backend = "no"             # Dynamo backend
dynamo_mode = "default"           # Dynamo mode
dynamo_use_dynamic = false        # Dynamo use dynamic
dynamo_use_fullgraph = false      # Dynamo use fullgraph
extra_accelerate_launch_args = "" # Extra accelerate launch args
gpu_ids = ""                      # GPU IDs
main_process_port = 0             # Main process port
mixed_precision = "fp16"          # Mixed precision (fp16, bf16, fp8)
multi_gpu = false                 # Multi GPU
num_cpu_threads_per_process = 2   # Number of CPU threads per process
num_machines = 1                  # Number of machines
num_processes = 1                 # Number of processes

[basic]
cache_latents = true           # Cache latents
cache_latents_to_disk = false  # Cache latents to disk
caption_extension = ".txt"     # Caption extension
enable_bucket = true           # Enable bucket
epoch = 1                      # Epoch
learning_rate = 0.0001         # Learning rate
learning_rate_te = 0.0001      # Learning rate text encoder
learning_rate_te1 = 0.0001     # Learning rate text encoder 1
learning_rate_te2 = 0.0001     # Learning rate text encoder 2
lr_scheduler = "cosine"        # LR Scheduler
lr_scheduler_args = ""         # LR Scheduler args
lr_warmup = 0                  # LR Warmup (% of total steps)
lr_scheduler_num_cycles = 1    # LR Scheduler num cycles
lr_scheduler_power = 1.0       # LR Scheduler power
max_bucket_reso = 2048         # Max bucket resolution
max_grad_norm = 1.0            # Max grad norm
max_resolution = "512,512"     # Max resolution
max_train_steps = 0            # Max train steps
max_train_epochs = 0           # Max train epochs
min_bucket_reso = 256          # Min bucket resolution
optimizer = "AdamW8bit"        # Optimizer (AdamW, AdamW8bit, Adafactor, DAdaptation, DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptAdamPreprint, DAdaptLion, DAdaptSGD, Lion, Lion8bit, PagedAdam
optimizer_args = ""            # Optimizer args
save_every_n_epochs = 1        # Save every n epochs
save_every_n_steps = 1         # Save every n steps
seed = 1234                    # Seed
stop_text_encoder_training = 0 # Stop text encoder training (% of total steps)
train_batch_size = 1           # Train batch size

[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
huber_c = 0.1                             # The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type
huber_schedule = "snr"                    # The type of loss to use and whether it's scheduled based on the timestep
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
loss_type = "l2"                          # Loss type (l2, huber, smooth_l1)
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
log_with = ""                             # Logger to use ["wandb", "tensorboard", "all", ""]
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]
class_prompt = "class"                                  # Class prompt
images_folder = "/some/folder/where/images/are"         # Training images directory
instance_prompt = "instance"                            # Instance prompt
reg_images_folder = "/some/folder/where/reg/images/are" # Regularisation images directory
reg_images_repeat = 1                                   # Regularisation images repeat
util_regularization_images_repeat_input = 1             # Regularisation images repeat input
util_training_images_repeat_input = 40                  # Training images repeat input

[huggingface]
async_upload = false              # Async upload
huggingface_path_in_repo = ""     # Huggingface path in repo
huggingface_repo_id = ""          # Huggingface repo id
huggingface_repo_type = ""        # Huggingface repo type
huggingface_repo_visibility = ""  # Huggingface repo visibility
huggingface_token = ""            # Huggingface token
resume_from_huggingface = ""      # Resume from huggingface (ex: {repo_id}/{path_in_repo}:{revision}:{repo_type})
save_state_to_huggingface = false # Save state to huggingface

[samples]
sample_every_n_steps = 0   # Sample every n steps
sample_every_n_epochs = 0  # Sample every n epochs
sample_prompts = ""        # Sample prompts
sample_sampler = "euler_a" # Sampler to use for image sampling

[sdxl]
sdxl_cache_text_encoder_outputs = false # Cache text encoder outputs
sdxl_no_half_vae = true                 # No half VAE

[wd14_caption]
always_first_tags = ""                        # comma-separated list of tags to always put at the beginning, e.g. 1girl,1boy
append_tags = false                           # Append TAGs
batch_size = 8                                # Batch size
caption_extension = ".txt"                    # Extension for caption file (e.g., .caption, .txt)
caption_separator = ", "                      # Caption Separator
character_tag_expand = false                  # Expand tag tail parenthesis to another tag for character tags. `chara_name_(series)` becomes `chara_name, series`
character_threshold = 0.35                    # Character threshold
debug = false                                 # Debug mode
force_download = false                        # Force model re-download when switching to onnx
frequency_tags = false                        # Frequency tags
general_threshold = 0.35                      # General threshold
max_data_loader_n_workers = 2                 # Max dataloader workers
onnx = true                                   # ONNX
recursive = false                             # Recursive
remove_underscore = false                     # Remove underscore
repo_id = "SmilingWolf/wd-convnext-tagger-v3" # Repo id for wd14 tagger on Hugging Face
tag_replacement = ""                          # Tag replacement in the format of `source1,target1;source2,target2; ...`. Escape `,` and `;` with `\`. e.g. `tag1,tag2;tag3,tag4`
thresh = 0.36                                 # Threshold
train_data_dir = ""                           # Image folder to caption (containing the images to caption)
undesired_tags = ""                           # comma-separated list of tags to remove, e.g. 1girl,1boy
use_rating_tags = false                       # Use rating tags
use_rating_tags_as_last_tag = false           # Use rating tags as last tagging tags

[metadata]
metadata_title = ""       # Title for model metadata (default is output_name)
metadata_author = ""      # Author name for model metadata
metadata_description = "" # Description for model metadata
metadata_license = ""     # License for model metadata
metadata_tags = ""        # Tags for model metadata