Function parse_args

train_MultiviewDiffusion_diffusion.py:48–79

(input_args=None)

    import wandb

def parse_args(input_args=None):
    parser = argparse.ArgumentParser(description="Argparser for ImageDream (diffusers) training script.")

    parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
    parser.add_argument("--guidance_scale", type=float, default=5.0)
    parser.add_argument("--conditioning_dropout_prob", type=float, default=0.1,
        help="Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800"
    )
    parser.add_argument("--checkpointing_steps", type=int, default=100)
    parser.add_argument("--checkpoints_total_limit", type=int, default=10, help=("Max number of checkpoints to store."))
    parser.add_argument("--resume_from_checkpoint", type=str, default=None)
    parser.add_argument("--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.")
    parser.add_argument("--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes.")
    parser.add_argument("--dataloader_num_workers", type=int, default=1)
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--max_grad_norm", default=0.5, type=float, help="Max gradient norm.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument("--logging_dir", type=str, default="logs")
    parser.add_argument("--report_to", type=str, default="wandb")
    parser.add_argument("--set_grads_to_none", default=True)
    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")

    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        args = parser.parse_args()

    return args

def _encode_text_prompt(
        tokenizer,