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[[subsets]] |
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num_repeats = 5 |
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keep_tokens = 0 |
|
caption_extension = ".txt" |
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shuffle_caption = true |
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flip_aug = false |
|
color_aug = false |
|
random_crop = true |
|
is_reg = false |
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image_dir = "F:/Pictures/Grabber/ready_for_training/5_eckert&eich" |
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caption_dropout_rate = 0.04 |
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caption_dropout_every_n_epochs = 0 |
|
caption_tag_dropout_rate = 0.0 |
|
|
|
[sample_args] |
|
|
|
[logging_args] |
|
|
|
[general_args.args] |
|
pretrained_model_name_or_path = "F:/Desktop/stable diffusion/LoRA/AnimeFullFinal.safetensors" |
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mixed_precision = "bf16" |
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seed = 23 |
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clip_skip = 2 |
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max_data_loader_n_workers = 1 |
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persistent_data_loader_workers = true |
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max_token_length = 225 |
|
prior_loss_weight = 1.0 |
|
max_train_epochs = 20 |
|
sdpa = true |
|
|
|
[general_args.dataset_args] |
|
resolution = 896 |
|
batch_size = 8 |
|
|
|
[network_args.args] |
|
network_dim = 4 |
|
network_alpha = 1.0 |
|
min_timestep = 0 |
|
max_timestep = 1000 |
|
|
|
[optimizer_args.args] |
|
optimizer_type = "AdamW8bit" |
|
lr_scheduler = "cosine" |
|
learning_rate = 0.0001 |
|
max_grad_norm = 1.0 |
|
lr_scheduler_type = "LoraEasyCustomOptimizer.CustomOptimizers.CosineAnnealingWarmupRestarts" |
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lr_scheduler_num_cycles = 4 |
|
unet_lr = 0.0005 |
|
warmup_ratio = 0.1 |
|
min_snr_gamma = 8 |
|
scale_weight_norms = 5.0 |
|
|
|
[saving_args.args] |
|
output_dir = "F:/stable_diffusion_models_and_outputs/models/Lora/v1/Styles/eckert&eich" |
|
save_precision = "fp16" |
|
save_model_as = "safetensors" |
|
output_name = "eckert&eich" |
|
tag_occurrence = true |
|
save_toml = true |
|
save_every_n_epochs = 2 |
|
|
|
[bucket_args.dataset_args] |
|
enable_bucket = true |
|
min_bucket_reso = 256 |
|
max_bucket_reso = 1024 |
|
bucket_reso_steps = 64 |
|
|
|
[noise_args.args] |
|
multires_noise_iterations = 6 |
|
multires_noise_discount = 0.3 |
|
|
|
[network_args.args.network_args] |
|
conv_dim = 16 |
|
conv_alpha = 1.0 |
|
dropout = 0.5 |
|
module_dropout = 0.25 |
|
|
|
[optimizer_args.args.lr_scheduler_args] |
|
min_lr = 1e-6 |
|
gamma = 0.85 |
|
|
|
[optimizer_args.args.optimizer_args] |
|
weight_decay = "0.1" |
|
betas = "0.9,0.99" |
|
|