Merge branch 'AUTOMATIC1111:master' into master

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)