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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 | |