scripts/image_sample.py [15:47]:
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    NUM_CLASSES,
    model_and_diffusion_defaults,
    create_model_and_diffusion,
    add_dict_to_argparser,
    args_to_dict,
)
from cm.random_util import get_generator
from cm.karras_diffusion import karras_sample


def main():
    args = create_argparser().parse_args()

    dist_util.setup_dist()
    logger.configure()

    if "consistency" in args.training_mode:
        distillation = True
    else:
        distillation = False

    logger.log("creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(
        **args_to_dict(args, model_and_diffusion_defaults().keys()),
        distillation=distillation,
    )
    model.load_state_dict(
        dist_util.load_state_dict(args.model_path, map_location="cpu")
    )
    model.to(dist_util.dev())
    if args.use_fp16:
        model.convert_to_fp16()
    model.eval()
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scripts/ternary_search.py [17:50]:
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    NUM_CLASSES,
    model_and_diffusion_defaults,
    create_model_and_diffusion,
    add_dict_to_argparser,
    args_to_dict,
)
from cm.random_util import get_generator
from cm.karras_diffusion import stochastic_iterative_sampler
from evaluations.th_evaluator import FIDAndIS


def main():
    args = create_argparser().parse_args()

    dist_util.setup_dist()
    logger.configure()

    if "consistency" in args.training_mode:
        distillation = True
    else:
        distillation = False

    logger.log("creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(
        **args_to_dict(args, model_and_diffusion_defaults().keys()),
        distillation=distillation,
    )
    model.load_state_dict(
        dist_util.load_state_dict(args.model_path, map_location="cpu")
    )
    model.to(dist_util.dev())
    if args.use_fp16:
        model.convert_to_fp16()
    model.eval()
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