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from util import Map
embedding = Map({
"id_task": 0,
"embedding_name": "",
"learn_rate": -1,
"batch_size": 1,
"steps": 500,
"data_root": "",
"log_directory": "train/log",
"template_filename": "subject_filewords.txt",
"gradient_step": 20,
"training_width": 512,
"training_height": 512,
"shuffle_tags": False,
"tag_drop_out": 0,
"clip_grad_mode": "disabled",
"clip_grad_value": "0.1",
"latent_sampling_method": "deterministic",
"create_image_every": 0,
"save_embedding_every": 0,
"save_image_with_stored_embedding": False,
"preview_from_txt2img": False,
"preview_prompt": "",
"preview_negative_prompt": "blurry, duplicate, ugly, deformed, low res, watermark, text",
"preview_steps": 20,
"preview_sampler_index": 0,
"preview_cfg_scale": 6,
"preview_seed": -1,
"preview_width": 512,
"preview_height": 512,
"varsize": False,
"use_weight": False,
})
lora = Map({
"bucket_no_upscale": False,
"bucket_reso_steps": 64,
"cache_latents": True,
"caption_dropout_every_n_epochs": None,
"caption_dropout_rate": 0.0,
"caption_extension": ".txt",
"caption_extention": ".txt",
"caption_tag_dropout_rate": 0.0,
"clip_skip": None,
"color_aug": False,
"dataset_repeats": 1,
"debug_dataset": False,
"enable_bucket": False,
"face_crop_aug_range": None,
"flip_aug": False,
"full_fp16": False,
"gradient_accumulation_steps": 1,
"gradient_checkpointing": False,
"in_json": "",
"keep_tokens": None,
"learning_rate": 5e-05,
"log_prefix": None,
"logging_dir": None,
"lr_scheduler_num_cycles": 1,
"lr_scheduler_power": 1,
"lr_scheduler": "cosine",
"lr_warmup_steps": 0,
"max_bucket_reso": 1024,
"max_data_loader_n_workers": 8,
"max_grad_norm": 0.0,
"max_token_length": None,
"max_train_epochs": None,
"max_train_steps": 2500,
"mem_eff_attn": False,
"min_bucket_reso": 256,
"mixed_precision": "fp16",
"network_alpha": 1.0,
"network_args": None,
"network_dim": 16,
"network_module": "networks.lora",
"network_train_text_encoder_only": False,
"network_train_unet_only": False,
"network_weights": None,
"no_metadata": False,
"output_dir": "",
"output_name": "",
"persistent_data_loader_workers": False,
"pretrained_model_name_or_path": "",
"prior_loss_weight": 1.0,
"random_crop": False,
"reg_data_dir": None,
"resolution": "512,512",
"resume": None,
"save_every_n_epochs": None,
"save_last_n_epochs_state": None,
"save_last_n_epochs": None,
"save_model_as": "ckpt",
"save_n_epoch_ratio": None,
"save_precision": "fp16",
"save_state": False,
"seed": 42,
"shuffle_caption": False,
"text_encoder_lr": 5e-05,
"train_batch_size": 1,
"train_data_dir": "",
"training_comment": "",
"unet_lr": 1e-04,
"use_8bit_adam": False,
"v_parameterization": False,
"v2": False,
"vae": None,
"xformers": False,
})
process = Map({
# general settings, do not modify
'format': '.jpg', # image format
'target_size': 512, # target resolution
'segmentation_model': 0, # segmentation model 0/general 1/landscape
'segmentation_background': (192, 192, 192), # segmentation background color
'blur_score': 1.8, # max score for face blur detection
'blur_samplesize': 60, # sample size to use for blur detection
'similarity_score': 0.8, # maximum similarity score before image is discarded
'similarity_size': 64, # base similarity detection on reduced images
'range_score': 0.15, # min score for face color dynamicrange detection
# face processing settings
'face_score': 0.7, # min face detection score
'face_pad': 0.1, # pad face image percentage
'face_model': 1, # which face model to use 0/close-up 1/standard
# body processing settings
'body_score': 0.9, # min body detection score
'body_visibility': 0.5, # min visibility score for each detected body part
'body_parts': 15, # min number of detected body parts with sufficient visibility
'body_pad': 0.2, # pad body image percentage
'body_model': 2, # body model to use 0/low 1/medium 2/high
# similarity detection settings
# interrogate settings
'interrogate': False, # interrogate images
'interrogate_model': ['clip', 'deepdanbooru'], # interrogate models
'tag_limit': 5, # number of tags to extract
# validations
# tbd
'face_segmentation': False, # segmentation enabled
'body_segmentation': False, # segmentation enabled
})
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