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import argparse
def get_arguments() -> argparse.Namespace:
parser = argparse.ArgumentParser()
# Inputs
parser.add_argument(
"-p", "--prompt", type=str, help="The prompt for the desired editing", required=True
)
parser.add_argument(
"-i", "--init_image", type=str, help="The path to the source image input", required=True
)
parser.add_argument(
"-i2", "--init_image_2", type=str, help="The path to the source image input", required=True
)
parser.add_argument("--mask", type=str, help="The path to the mask to edit with", default=None)
# Diffusion
parser.add_argument(
"--skip_timesteps",
type=int,
help="How many steps to skip during the diffusion.",
default=25,
)
parser.add_argument(
"--local_clip_guided_diffusion",
help="Indicator for using local CLIP guided diffusion (for baseline comparison)",
action="store_true",
dest="local_clip_guided_diffusion",
)
parser.add_argument(
"--ddim",
help="Indicator for using DDIM instead of DDPM",
action="store_true",
)
# For more details read guided-diffusion/guided_diffusion/respace.py
parser.add_argument(
"--timestep_respacing",
type=str,
help="How to respace the intervals of the diffusion process (number between 1 and 1000).",
default="100",
)
parser.add_argument(
"--model_output_size",
type=int,
help="The resolution of the outputs of the diffusion model",
default=256,
choices=[256, 512],
)
# Augmentations
parser.add_argument("--aug_num", type=int, help="The number of augmentation", default=8)
# Loss
parser.add_argument(
"--clip_guidance_lambda",
type=float,
help="Controls how much the image should look like the prompt",
default=1000,
)
parser.add_argument(
"--range_lambda",
type=float,
help="Controls how far out of range RGB values are allowed to be",
default=50,
)
parser.add_argument(
"--lpips_sim_lambda",
type=float,
help="The LPIPS similarity to the input image",
default=1000,
)
parser.add_argument(
"--l2_sim_lambda", type=float, help="The L2 similarity to the input image", default=10000,
)
parser.add_argument(
"--background_preservation_loss",
help="Indicator for using the background preservation loss",
action="store_true",
)
# Mask
parser.add_argument(
"--invert_mask",
help="Indicator for mask inversion",
action="store_true",
dest="invert_mask",
)
parser.add_argument(
"--no_enforce_background",
help="Indicator disabling the last background enforcement",
action="store_false",
dest="enforce_background",
)
# Misc
parser.add_argument("--seed", type=int, help="The random seed", default=404)
parser.add_argument("--gpu_id", type=int, help="The GPU ID", default=0)
parser.add_argument("--output_path", type=str, default="output")
parser.add_argument(
"-o",
"--output_file",
type=str,
help="The filename to save, must be png",
default="output.png",
)
parser.add_argument("--iterations_num", type=int, help="The number of iterations", default=8)
parser.add_argument(
"--batch_size",
type=int,
help="The number number if images to sample each diffusion process",
default=4,
)
parser.add_argument(
"--vid",
help="Indicator for saving the video of the diffusion process",
action="store_true",
dest="save_video",
)
parser.add_argument(
"--export_assets",
help="Indicator for saving raw assets of the prediction",
action="store_true",
dest="export_assets",
)
parser.add_argument(
"--image_guide",
help="Indicator image or text",
action="store_true",
dest="image_guide",
)
parser.add_argument(
"--coarse_to_fine",
help="Indicator mask from big to small",
action="store_true",
dest="coarse_to_fine",
)
parser.add_argument(
"--classifier_scale",
type=float,
help="Classifer scale for class guided",
default=10.,
)
parser.add_argument(
"--y",
type=int,
help="Target class for classifier guidence",
default=0,
)
parser.add_argument(
"--class_cond",
help="classifer conditioned for diffusion model or not",
action="store_true",
dest="class_cond",
)
parser.add_argument(
"--background_complex",
type=float,
help="BG complex guidance scale",
default=0.,
)
parser.add_argument(
"--final_save_root",
type=str,
help="Final save root",
default="validation-generated/generated-with-25-steps-bg/final/",
)
parser.add_argument(
"--hard",
help="hard or smooth",
action="store_true",
dest="hard",
)
parser.add_argument(
"--random_position",
help="apply random position",
action="store_true",
dest="random_position",
)
parser.add_argument(
"--rotate_obj",
help="apply random rotate to objects",
action="store_true",
dest="rotate_obj",
)
parser.add_argument(
"--angle",
type=int,
help="angle",
default=0,
)
args = parser.parse_args()
print(args)
return args
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