<pre><code>[network_arguments]
unet_lr = 0.0003
text_encoder_lr = 6e-5
network_dim = 32
network_alpha = 16
network_module = "networks.lora"
network_args = [ "conv_dim=32", "conv_alpha=16",]
network_train_unet_only = false

[optimizer_arguments]
learning_rate = 0.0003
lr_scheduler = "cosine_with_restarts"
lr_scheduler_num_cycles = 3
lr_warmup_steps = 200
optimizer_type = "AdamW8bit"
optimizer_args = [ "weight_decay=0.1", "betas=[0.9,0.99]",]
loss_type = "l2"
max_grad_norm = 1.0

[training_arguments]
lowram = true
pretrained_model_name_or_path = "/content/Illustrious-XL-v0.1.safetensors"
vae = "/content/sdxl_vae.safetensors"
max_train_steps = 4000
train_batch_size = 4
seed = 42
max_token_length = 225
xformers = false
sdpa = true
min_snr_gamma = 8.0
no_half_vae = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
max_data_loader_n_workers = 1
persistent_data_loader_workers = true
mixed_precision = "fp16"
full_fp16 = true
full_bf16 = false
cache_latents = true
cache_latents_to_disk = true
cache_text_encoder_outputs = false
min_timestep = 0
max_timestep = 1000
prior_loss_weight = 1.0
multires_noise_iterations = 6
multires_noise_discount = 0.3

[saving_arguments]
save_precision = "fp16"
save_model_as = "safetensors"
save_every_n_epochs = 1
save_last_n_epochs = 10
output_name = "mystyle200"
output_dir = "/content/drive/MyDrive/Loras/mystyle200/output"
log_prefix = "mystyle200"
logging_dir = "/content/drive/MyDrive/Loras/_logs"












steps: 0% 0/4000 [00:00&lt;?, ?it/s]unet dtype: torch.float16, device: cuda:0
text_encoder [0] dtype: torch.float16, device: cuda:0
text_encoder [1] dtype: torch.float16, device: cuda:0

epoch 1/20
epoch is incremented. current_epoch: 0, epoch: 1
steps: 5% 203/4000 [33:45&lt;10:31:23, 9.98s/it, avr_loss=0.094] 
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-01.safetensors

epoch 2/20
epoch is incremented. current_epoch: 1, epoch: 2
steps: 10% 406/4000 [1:07:48&lt;10:00:16, 10.02s/it, avr_loss=0.0938]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-02.safetensors

epoch 3/20
epoch is incremented. current_epoch: 2, epoch: 3
steps: 15% 609/4000 [1:41:53&lt;9:27:19, 10.04s/it, avr_loss=0.102]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-03.safetensors

epoch 4/20
epoch is incremented. current_epoch: 3, epoch: 4
steps: 20% 812/4000 [2:15:59&lt;8:53:56, 10.05s/it, avr_loss=0.104]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-04.safetensors

epoch 5/20
epoch is incremented. current_epoch: 4, epoch: 5
steps: 25% 1015/4000 [2:50:02&lt;8:20:03, 10.05s/it, avr_loss=0.0899]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-05.safetensors

epoch 6/20
epoch is incremented. current_epoch: 5, epoch: 6
steps: 30% 1218/4000 [3:24:04&lt;7:46:07, 10.05s/it, avr_loss=0.0886]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-06.safetensors

epoch 7/20
epoch is incremented. current_epoch: 6, epoch: 7
steps: 36% 1421/4000 [3:58:08&lt;7:12:12, 10.06s/it, avr_loss=0.0972]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-07.safetensors

epoch 8/20
epoch is incremented. current_epoch: 7, epoch: 8
steps: 41% 1624/4000 [4:32:11&lt;6:38:13, 10.06s/it, avr_loss=0.0981]
saving checkpoint: /content/drive/MyDrive/Loras/mystyle200/output/mystyle200-08.safetensors

epoch 9/20
epoch is incremented. current_epoch: 8, epoch: 9
steps: 44% 1772/4000 [4:57:06&lt;6:13:33, 10.06s/it, avr_loss=0.0949]</code></pre> <hr />

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