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# 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
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