use our blip

extras/interrogate.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import torch
 import ldm_patched.modules.model_management as model_management
 
@@ -8,19 +7,11 @@ from torchvision.transforms.functional import InterpolationMode
 from modules.model_loader import load_file_from_url
 from modules.config import path_clip_vision
 from ldm_patched.modules.model_patcher import ModelPatcher
+from extras.BLIP.models.blip import blip_decoder
 
 
 blip_image_eval_size = 384
 blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP')
-sys.path.append(blip_repo_root)
-
-
-class FakeFairscale:
-    def checkpoint_wrapper(self):
-        pass
-
-
-sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale
 
 
 class Interrogator:
@@ -34,16 +25,14 @@ class Interrogator:
     @torch.inference_mode()
     def interrogate(self, img_rgb):
         if self.blip_model is None:
-            import models.blip
-
             filename = load_file_from_url(
                 url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth',
                 model_dir=path_clip_vision,
                 file_name='model_base_caption_capfilt_large.pth',
             )
 
-            model = models.blip.blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base',
-                                             med_config=os.path.join(blip_repo_root, "configs", "med_config.json"))
+            model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base',
+                                 med_config=os.path.join(blip_repo_root, "configs", "med_config.json"))
             model.eval()
 
             self.load_device = model_management.text_encoder_device()