testing / kedama_config /config_file.toml
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feat: upload kedama lora model
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[model_arguments]
v2 = false
v_parameterization = false
pretrained_model_name_or_path = "/content/pretrained_model/Animefull-final-pruned.ckpt"
[additional_network_arguments]
no_metadata = false
unet_lr = 1.0
text_encoder_lr = 1.0
network_module = "lycoris.kohya"
network_dim = 32
network_alpha = 16
network_args = [ "conv_dim=32", "conv_alpha=16", "algo=lora",]
network_train_unet_only = false
network_train_text_encoder_only = false
[optimizer_arguments]
optimizer_type = "Prodigy"
learning_rate = 1.0
max_grad_norm = 1.0
optimizer_args = [ "decouple=True", "weight_decay=0.01", "d_coef=2", "use_bias_correction=True", "safeguard_warmup=True", "betas=0.9,0.99",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100
[dataset_arguments]
cache_latents = true
debug_dataset = false
vae_batch_size = 4
[training_arguments]
output_dir = "/content/LoRA/output"
output_name = "kedama"
save_precision = "fp16"
save_every_n_epochs = 10
train_batch_size = 4
max_token_length = 225
mem_eff_attn = false
xformers = true
max_train_epochs = 20
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
seed = 31337
gradient_checkpointing = false
gradient_accumulation_steps = 1
mixed_precision = "fp16"
clip_skip = 2
logging_dir = "/content/LoRA/logs"
log_prefix = "kedama"
lowram = true
[sample_prompt_arguments]
sample_every_n_epochs = 5
sample_sampler = "ddim"
[dreambooth_arguments]
prior_loss_weight = 1.0
[saving_arguments]
save_model_as = "safetensors"