Create LionBase.toml

Lion/LionBase.toml

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+[Path]
+pretrained_model_name_or_path = "/notebooks/box/Trim.safetensors"
+train_data_dir = "/notebooks/LoRA_Image/"
+
+__root_dir = "/notebooks/LoRA/sd-scripts/"
+logging_dir = "calc:f'{__root_dir}logs'"
+output_dir = "calc:f'{__root_dir}outputs'"
+sample_prompts = "/notebooks/LoRA_Setting/A.txt"
+
+[Prompt]
+sample_every_n_epochs = 1
+sample_sampler = "k_euler_a"
+
+[Else]
+shuffle_caption = true
+caption_extension = ".txt"
+keep_tokens = 1
+
+color_aug = true
+
+__upper_px = 512
+__resolution_x = "calc:512 + __upper_px"
+__resolution_y = "calc:512 + __upper_px"
+
+resolution = "calc:f'{__resolution_x},{__resolution_y}'"
+enable_bucket = true
+min_bucket_reso = "calc:320 + __upper_px"
+bucket_no_upscale = true
+caption_dropout_every_n_epochs = 9
+caption_tag_dropout_rate = 0.2
+save_every_n_epochs = 2
+train_batch_size = 1
+xformers = true
+max_train_epochs = 14
+persistent_data_loader_workers = true
+seed = 987
+mixed_precision = "fp16"
+save_precision = "ref:mixed_precision"
+clip_skip = 2
+
+optimizer_type = "Lion"
+__div_rate = 8
+learning_rate = "calc:1.0e-4 / __div_rate"
+text_encoder_lr= "calc:5.0e-5 / __div_rate"
+unet_lr = "ref:learning_rate"
+
+lr_scheduler = "cosine_with_restarts"
+lr_warmup_steps = 500
+lr_scheduler_num_cycles = 4
+
+network_module = "networks.lora"
+network_dim = 128
+network_alpha = "calc:float(network_dim) / 2.0"
+
+__upper_mixed_precision = "calc:mixed_precision.upper()"
+output_name = "calc:f'Akashi_{optimizer_type}Div{__div_rate}1024Px'"