1 files changed, 10 insertions, 6 deletions
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..0068b81c 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -1,4 +1,3 @@
-import contextlib
 import os
 import sys
 import traceback
@@ -11,10 +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_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"])
@@ -47,7 +45,14 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        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',
+        )
+
+        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
@@ -148,8 +153,7 @@ class InterrogateModels:
 
             clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
-            precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
-            with torch.no_grad(), precision_scope("cuda"):
+            with torch.no_grad(), devices.autocast():
                 image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)