use modelloader for #4956

import contextlib

import os

import sys

import traceback

from torchvision.transforms.functional import InterpolationMode

import modules.shared as shared

from modules import devices, paths, lowvram

from modules import devices, paths, lowvram, modelloader

blip_image_eval_size = 384

blip_local_dir = os.path.join('models', 'Interrogator')

blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')

blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'

clip_model_name = 'ViT-L/14'

Category = namedtuple("Category", ["name", "topn", "items"])

    def load_blip_model(self):

        import models.blip

        if not os.path.isfile(blip_local_file):

            if not os.path.isdir(blip_local_dir):

                os.mkdir(blip_local_dir)

        files = modelloader.load_models(

            model_path=os.path.join(paths.models_path, "BLIP"),

            model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',

            ext_filter=[".pth"],

            download_name='model_base_caption_capfilt_large.pth',

        )

            print("Downloading BLIP...")

            from requests import get as reqget

            open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)

            print("BLIP downloaded to", blip_local_file + '.')

        blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))

        blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))

        blip_model.eval()

        return blip_model