use existing function for loading VAE weights from file

import os

import collections

from collections import namedtuple

from modules import shared, devices, script_callbacks

from modules import shared, devices, script_callbacks, sd_models

from modules.paths import models_path

import glob

from copy import deepcopy

            assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"

            print(f"Loading VAE weights from: {vae_file}")

            store_base_vae(model)

            _, extension = os.path.splitext(vae_file)

            if extension.lower() == ".safetensors":

                vae_ckpt = safetensors.torch.load_file(vae_file, device=shared.weight_load_location)

            else:

                vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

            if "state_dict" in vae_ckpt:

                vae_ckpt = vae_ckpt["state_dict"]

            vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)

            vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}

            _load_vae_dict(model, vae_dict_1)

    model.first_stage_model.load_state_dict(vae_dict_1)

    model.first_stage_model.to(devices.dtype_vae)

def clear_loaded_vae():

    global loaded_vae_file

    loaded_vae_file = None

def reload_vae_weights(sd_model=None, vae_file="auto"):

    from modules import lowvram, devices, sd_hijack