run basic torch calculation at startup in parallel to reduce the performance...

import sys

import contextlib

from functools import lru_cache

import torch

from modules import errors

    message += " Use --disable-nan-check commandline argument to disable this check."

    raise NansException(message)

@lru_cache

def first_time_calculation():

    """

    just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and

    spends about 2.7 seconds doing that, at least wih NVidia.

    """

    x = torch.zeros((1, 1)).to(device, dtype)

    linear = torch.nn.Linear(1, 1).to(device, dtype)

    linear(x)

    x = torch.zeros((1, 1, 3, 3)).to(device, dtype)

    conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)

    conv2d(x)

logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())

from modules import paths, timer, import_hook, errors  # noqa: F401

from modules import paths, timer, import_hook, errors, devices  # noqa: F401

startup_timer = timer.Timer()

    # (when reloading, this does nothing)

    Thread(target=lambda: shared.sd_model).start()

    Thread(target=devices.first_time_calculation).start()

    shared.reload_hypernetworks()

    startup_timer.record("reload hypernetworks")