feat: upload matcha_sdxl_new lora model
Browse files
matcha_sdxl_new_config/config_file.toml
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[sdxl_arguments]
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cache_text_encoder_outputs = false
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no_half_vae = true
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min_timestep = 0
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max_timestep = 1000
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shuffle_caption = true
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[model_arguments]
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pretrained_model_name_or_path = "/content/pretrained_model/animagine-xl.safetensors"
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vae = "/content/vae/sdxl_vae.safetensors"
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[dataset_arguments]
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debug_dataset = false
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in_json = "/content/LoRA/meta_lat.json"
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train_data_dir = "/content/LoRA/train_data"
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dataset_repeats = 1
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keep_tokens = 0
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resolution = "1024,1024"
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color_aug = false
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token_warmup_min = 1
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token_warmup_step = 0
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[training_arguments]
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output_dir = "/content/drive/MyDrive/kohya-trainer/output"
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output_name = "matcha_sdxl_neo"
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save_precision = "fp16"
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save_every_n_epochs = 10
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train_batch_size = 24
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max_token_length = 225
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mem_eff_attn = false
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sdpa = true
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xformers = false
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max_train_epochs = 50
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max_data_loader_n_workers = 8
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persistent_data_loader_workers = true
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gradient_checkpointing = true
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gradient_accumulation_steps = 1
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mixed_precision = "fp16"
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[logging_arguments]
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log_with = "wandb"
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log_tracker_name = "matcha_sdxl_new"
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logging_dir = "/content/LoRA/logs"
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[sample_prompt_arguments]
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sample_every_n_epochs = 10
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sample_sampler = "euler_a"
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[saving_arguments]
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save_model_as = "safetensors"
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[optimizer_arguments]
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optimizer_type = "AdaFactor"
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learning_rate = 0.0001
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max_grad_norm = 0
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optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
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lr_scheduler = "constant"
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lr_warmup_steps = 0
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[additional_network_arguments]
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no_metadata = false
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network_module = "networks.lora"
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network_dim = 32
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network_alpha = 16
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network_args = [ "conv_dim=32", "conv_alpha=16",]
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network_train_unet_only = true
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[advanced_training_config]
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matcha_sdxl_new_config/sample_prompt.toml
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[prompt]
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negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, "
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width = 1024
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height = 1024
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scale = 12
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sample_steps = 50
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[[prompt.subset]]
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prompt = "masterpiece, best quality, face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck"
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