Stable-Diffusion-Vertex/hpo/kohya-lora/train_kohya.py [122:147]:
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if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--method", type=str, default="kohya_lora", help="a tag")
    parser.add_argument("--num_cpu_threads", type=int, default=8, help="num of cpu threads per process")
    parser.add_argument("--model_name", type=str, default="runwayml/stable-diffusion-v1-5", help="bucket_name/model_folder")
    parser.add_argument("--input_storage", type=str,default="/root/dog_image_resize", help="/gcs/bucket_name/input_image_folder")
    parser.add_argument("--metadata_storage", type=str, default=None, help="metadata json path, for native training")
    parser.add_argument("--output_storage", type=str, default="/root/dog_output", help="/gcs/bucket_name/output_folder")
    parser.add_argument("--display_name", type=str, default="sks_dog", help="prompt")
    parser.add_argument("--resolution", type=str, default="512,512", help="resolution group")
    parser.add_argument("--max_train_epochs", type=int, default=10, help="max train epochs")
    parser.add_argument("--lr", type=float, default=1e-4, help="learning rate")
    parser.add_argument("--unet_lr", type=float, default=1e-4, help="unet learning rate")
    parser.add_argument("--text_encoder_lr", type=float, default=1e-5, help="text encoder learning rate")
    parser.add_argument("--lr_scheduler", type=str, default="cosine_with_restarts", help="")
    parser.add_argument("--network_dim", type=int, default=32, help="network dim 4~128")
    parser.add_argument("--network_alpha", type=int, default=32, help="often=network dim")
    parser.add_argument("--batch_size", type=int, default=1, help="batch size")
    parser.add_argument("--save_every_n_epochs", type=int, default=2, help="save every n epochs")
    parser.add_argument("--network_weights", type=str, default="", help="lora model path,/gcs/bucket_name/lora_model")
    parser.add_argument("--reg_dir", type=str, default="", help="regularization data path")
    parser.add_argument("--use_8bit_adam", type=bool, default=True, help="use 8bit adam optimizer")
    parser.add_argument("--use_lion", type=bool, default=False, help="lion optimizer")
    parser.add_argument("--noise_offset", type=int, default=0, help="0.1 if use")
    parser.add_argument("--save_nfs", type=bool, default=False, help="if save the model to file store")
    parser.add_argument("--save_nfs_only", type=bool, default=False, help="only copy file from gcs to filestore, no training")
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Stable-Diffusion-Vertex/pipeline/train_kohya.py [120:145]:
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if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--method", type=str, default="kohya_lora", help="a tag")
    parser.add_argument("--num_cpu_threads", type=int, default=8, help="num of cpu threads per process")
    parser.add_argument("--model_name", type=str, default="runwayml/stable-diffusion-v1-5", help="bucket_name/model_folder")
    parser.add_argument("--input_storage", type=str,default="/root/dog_image_resize", help="/gcs/bucket_name/input_image_folder")
    parser.add_argument("--metadata_storage", type=str, default=None, help="metadata json path, for native training")
    parser.add_argument("--output_storage", type=str, default="/root/dog_output", help="/gcs/bucket_name/output_folder")
    parser.add_argument("--display_name", type=str, default="sks_dog", help="prompt")
    parser.add_argument("--resolution", type=str, default="512,512", help="resolution group")
    parser.add_argument("--max_train_epochs", type=int, default=10, help="max train epochs")
    parser.add_argument("--lr", type=float, default=1e-4, help="learning rate")
    parser.add_argument("--unet_lr", type=float, default=1e-4, help="unet learning rate")
    parser.add_argument("--text_encoder_lr", type=float, default=1e-5, help="text encoder learning rate")
    parser.add_argument("--lr_scheduler", type=str, default="cosine_with_restarts", help="")
    parser.add_argument("--network_dim", type=int, default=32, help="network dim 4~128")
    parser.add_argument("--network_alpha", type=int, default=32, help="often=network dim")
    parser.add_argument("--batch_size", type=int, default=1, help="batch size")
    parser.add_argument("--save_every_n_epochs", type=int, default=2, help="save every n epochs")
    parser.add_argument("--network_weights", type=str, default="", help="lora model path,/gcs/bucket_name/lora_model")
    parser.add_argument("--reg_dir", type=str, default="", help="regularization data path")
    parser.add_argument("--use_8bit_adam", type=bool, default=True, help="use 8bit adam optimizer")
    parser.add_argument("--use_lion", type=bool, default=False, help="lion optimizer")
    parser.add_argument("--noise_offset", type=int, default=0, help="0.1 if use")
    parser.add_argument("--save_nfs", type=bool, default=False, help="if save the model to file store")
    parser.add_argument("--save_nfs_only", type=bool, default=False, help="only copy file from gcs to filestore, no training")
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