# 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