remove parsing command line from devices.py

def get_optimal_device():

    if torch.cuda.is_available():

        # CUDA device selection support:

        if "shared" not in sys.modules:

            commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.

            sys.argv += shlex.split(commandline_args)

            device_id = extract_device_id(sys.argv, '--device-id')

        else:

        from modules import shared

        device_id = shared.cmd_opts.device_id

        if device_id is not None:

errors.run(enable_tf32, "Enabling TF32")

device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()

device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None

dtype = torch.float16

dtype_vae = torch.float16

import torch

from modules.devices import get_optimal_device

from modules import devices

module_in_gpu = None

cpu = torch.device("cpu")

device = gpu = get_optimal_device()

def send_everything_to_cpu():

        if module_in_gpu is not None:

            module_in_gpu.to(cpu)

        module.to(gpu)

        module.to(devices.device)

        module_in_gpu = module

    # see below for register_forward_pre_hook;

    # send the model to GPU. Then put modules back. the modules will be in CPU.

    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model

    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None

    sd_model.to(device)

    sd_model.to(devices.device)

    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored

    # register hooks for those the first two models

        # so that only one of them is in GPU at a time

        stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed

        diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None

        sd_model.model.to(device)

        sd_model.model.to(devices.device)

        diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored

        # install hooks for bits of third model