use modelloader for #4956

1 files changed, 8 insertions, 14 deletions

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 3a09b366..0068b81c 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -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