55 lines
1.6 KiB
TOML
55 lines
1.6 KiB
TOML
# example LoRA training config for kohya_ss (SDXL)
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# copy this, rename it, and edit the paths/settings for your lora
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# then train with: .\train_lora.ps1 training/my_lora.toml
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[model_arguments]
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pretrained_model_name_or_path = "E:/animepics/models/checkpoints/noobai-xl.safetensors"
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# set to true for vpred models (NoobAI-XL uses vpred)
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v_parameterization = true
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zero_terminal_snr = true
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[saving_arguments]
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save_every_n_epochs = 1
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save_model_as = "safetensors"
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output_dir = "E:/animepics/models/loras"
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output_name = "my_lora_v1"
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[dataset_arguments]
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# dataset dir structure: training_data/<lora_name>/img/<repeats>_<trigger>/
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train_data_dir = "E:/animepics/training_data/my_lora/img"
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resolution = "1024,1024"
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enable_bucket = true
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min_bucket_reso = 512
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max_bucket_reso = 2048
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bucket_reso_steps = 64
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caption_extension = ".txt"
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shuffle_caption = true
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keep_tokens = 1
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[training_arguments]
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output_dir = "E:/animepics/models/loras"
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logging_dir = "E:/animepics/kohya_ss/logs"
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max_train_epochs = 10
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train_batch_size = 1
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gradient_accumulation_steps = 1
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gradient_checkpointing = true
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mixed_precision = "bf16"
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save_precision = "bf16"
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seed = 42
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max_token_length = 225
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xformers = true
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# learning rates — good defaults for NoobAI-XL
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learning_rate = 0.0001
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unet_lr = 0.0001
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text_encoder_lr = 0.00005
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lr_scheduler = "cosine_with_restarts"
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lr_warmup_steps = 100
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optimizer_type = "AdamW8bit"
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[network_arguments]
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network_module = "networks.lora"
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network_dim = 32 # rank — higher = more capacity, 16-64 is typical
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network_alpha = 16 # usually half of dim
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# optional: train only specific layers
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# network_args = ["conv_dim=16", "conv_alpha=8"]
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