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use modelloader for #4956

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AUTOMATIC 2022-12-03 18:45:51 +03:00
parent 2a649154ec
commit 4b0dc206ed
1 changed files with 8 additions and 14 deletions
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22
modules/interrogate.py
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@ -1,4 +1,3 @@
import contextlib
import os
import sys
import traceback
@ -11,12 +10,9 @@ from torchvision import transforms
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"])
@ -49,16 +45,14 @@ class InterrogateModels:
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