Stable-Diffusion-Vertex/Diffusers/train_wo_nfs.py [222:241]:
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if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--method", type=str, default="diffuser_dreambooth_lora", help="dreambooth/dreambooth_lora/peft_lora")
    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="abc", help="/gcs/bucket_name/input_image_folder")
    parser.add_argument("--output_storage", type=str, default="abc",help="/gcs/bucket_name/output_folder")
    parser.add_argument("--prompt", type=str, default="a photo of sks dog",help="instance prompt")
    parser.add_argument("--class_prompt", type=str, required=False,default="a photo of dog",help="instance prompt")
    parser.add_argument("--max_train_steps", type=int, default=400,help="training steps")
    parser.add_argument("--text_encoder", type=bool, default=True,help="train text encoder")
    parser.add_argument("--set_grads_to_none", type=bool, default=False,help="set grads to none if CUDA memory if very low <16GB")
    parser.add_argument("--num_class_images", type=int, default=50, help="generate image number")
    
    parser.add_argument("--resolution", type=int, default=512, help="resize input image resolution to")
    parser.add_argument("--batch_size", type=int, default=1, help="training batch size")
    parser.add_argument("--use_8bit", type=bool, default=True, help="use 8bit adam optimizer")
    parser.add_argument("--lr", type=float, default=1e-4, help="lora=1e-4,others=1e-5")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="gradient accumulation steps")
    parser.add_argument("--num_validation_images", type=int, default=4, help="num_validation_images, text_to_image_lora")
    parser.add_argument("--validation_prompt", type=str, default="a photo of sks dog in the forest", help="validation prompt")
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Stable-Diffusion-Vertex/hpo/diffusers/train_diffusers.py [239:258]:
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if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--method", type=str, default="diffuser_dreambooth_lora", help="dreambooth/dreambooth_lora/peft_lora")
    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="abc", help="/gcs/bucket_name/input_image_folder")
    parser.add_argument("--output_storage", type=str, default="abc",help="/gcs/bucket_name/output_folder")
    parser.add_argument("--prompt", type=str, default="a photo of sks dog",help="instance prompt")
    parser.add_argument("--class_prompt", type=str, required=False,default="a photo of dog",help="instance prompt")
    parser.add_argument("--max_train_steps", type=int, default=400,help="training steps")
    parser.add_argument("--text_encoder", type=bool, default=True,help="train text encoder")
    parser.add_argument("--set_grads_to_none", type=bool, default=False,help="set grads to none if CUDA memory if very low <16GB")
    parser.add_argument("--num_class_images", type=int, default=50, help="generate image number")
    
    parser.add_argument("--resolution", type=int, default=512, help="resize input image resolution to")
    parser.add_argument("--batch_size", type=int, default=1, help="training batch size")
    parser.add_argument("--use_8bit", type=bool, default=True, help="use 8bit adam optimizer")
    parser.add_argument("--lr", type=float, default=1e-4, help="lora=1e-4,others=1e-5")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="gradient accumulation steps")
    parser.add_argument("--num_validation_images", type=int, default=4, help="num_validation_images, text_to_image_lora")
    parser.add_argument("--validation_prompt", type=str, default="a photo of sks dog in the forest", help="validation prompt")
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