| [sdxl_arguments] | |
| cache_text_encoder_outputs = true | |
| no_half_vae = true | |
| min_timestep = 0 | |
| max_timestep = 1000 | |
| shuffle_caption = false | |
| [model_arguments] | |
| pretrained_model_name_or_path = "/workspace/pretrained_model/animagine-xl.safetensors" | |
| vae = "/workspace/vae/sdxl_vae.safetensors" | |
| [dataset_arguments] | |
| debug_dataset = false | |
| in_json = "/workspace/LoRA/meta_lat.json" | |
| train_data_dir = "/workspace/LoRA/train_data" | |
| dataset_repeats = 1 | |
| keep_tokens = 0 | |
| resolution = "1024,1024" | |
| color_aug = false | |
| token_warmup_min = 1 | |
| token_warmup_step = 0 | |
| [training_arguments] | |
| output_dir = "/workspace/LoRA/outputs" | |
| output_name = "jingliu_sdxl_lora" | |
| save_precision = "fp16" | |
| save_every_n_epochs = 4 | |
| train_batch_size = 1 | |
| max_token_length = 225 | |
| mem_eff_attn = false | |
| sdpa = true | |
| xformers = false | |
| max_train_epochs = 20 | |
| max_data_loader_n_workers = 8 | |
| persistent_data_loader_workers = true | |
| gradient_checkpointing = true | |
| gradient_accumulation_steps = 1 | |
| mixed_precision = "fp16" | |
| [logging_arguments] | |
| log_with = "wandb" | |
| log_tracker_name = "sdxl_lora" | |
| logging_dir = "/workspace/LoRA/logs" | |
| [sample_prompt_arguments] | |
| sample_every_n_epochs = 4 | |
| sample_sampler = "euler_a" | |
| [saving_arguments] | |
| save_model_as = "safetensors" | |
| [optimizer_arguments] | |
| optimizer_type = "AdaFactor" | |
| learning_rate = 4e-7 | |
| max_grad_norm = 1.0 | |
| optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",] | |
| lr_scheduler = "constant_with_warmup" | |
| lr_warmup_steps = 100 | |
| [additional_network_arguments] | |
| no_metadata = false | |
| network_weights: network_weight, | |
| network_module = "networks.lora" | |
| network_dim = 64 | |
| network_alpha = 32 | |
| network_args = [] | |
| network_train_unet_only = true | |
| [advanced_training_config] | |