feat: upload sdxl_lora_architecture_siheyuan lora model

sdxl_lora_architecture_siheyuan_config/config_file.toml

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+[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 = "/content/pretrained_model/sd_xl_base_1.0.safetensors"
+vae = "/content/vae/sdxl_vae.safetensors"
+
+[dataset_arguments]
+debug_dataset = false
+in_json = "/content/LoRA/meta_lat.json"
+train_data_dir = "/content/LoRA/train_data"
+dataset_repeats = 20
+keep_tokens = 0
+resolution = "1024,1024"
+color_aug = false
+token_warmup_min = 1
+token_warmup_step = 0
+
+[training_arguments]
+output_dir = "/content/drive/MyDrive/kohya-trainer/output"
+output_name = "sdxl_lora_architecture_siheyuan"
+save_precision = "fp16"
+save_every_n_epochs = 1
+train_batch_size = 4
+max_token_length = 225
+mem_eff_attn = false
+sdpa = true
+xformers = false
+max_train_epochs = 10
+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_architecture_siheyuan"
+logging_dir = "/content/LoRA/logs"
+
+[sample_prompt_arguments]
+sample_every_n_epochs = 1
+sample_sampler = "euler_a"
+
+[saving_arguments]
+save_model_as = "safetensors"
+
+[optimizer_arguments]
+optimizer_type = "AdaFactor"
+learning_rate = 1e-5
+max_grad_norm = 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_module = "networks.lora"
+network_dim = 32
+network_alpha = 16
+network_args = [ "conv_dim=32", "conv_alpha=16",]
+network_train_unet_only = true
+
+[advanced_training_config]
+noise_offset = 0.1
+adaptive_noise_scale = 0.01
+min_snr_gamma = 5

sdxl_lora_architecture_siheyuan_config/sample_prompt.toml

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+[prompt]
+width = 1024
+height = 1024
+scale = 7
+sample_steps = 28
+[[prompt.subset]]
+prompt = "siheyuan, chinese architecture"
+