use our blip

extras/BLIP/models/blip.py

@@ -8,8 +8,8 @@
 import warnings
 warnings.filterwarnings("ignore")
 
-from models.vit import VisionTransformer, interpolate_pos_embed
+from extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed
-from models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
 from transformers import BertTokenizer
 
 import torch

extras/BLIP/models/blip_itm.py

@@ -1,11 +1,11 @@
-from models.med import BertConfig, BertModel
+from extras.BLIP.models.med import BertConfig, BertModel
 from transformers import BertTokenizer
 
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_ITM(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_nlvr.py

@@ -1,7 +1,7 @@
-from models.med import BertConfig
+from extras.BLIP.models.med import BertConfig
-from models.nlvr_encoder import BertModel
+from extras.BLIP.models.nlvr_encoder import BertModel
-from models.vit import interpolate_pos_embed
+from extras.BLIP.models.vit import interpolate_pos_embed
-from models.blip import create_vit, init_tokenizer, is_url
+from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url
 
 from timm.models.hub import download_cached_file
 
@@ -10,6 +10,8 @@ from torch import nn
 import torch.nn.functional as F
 from transformers import BertTokenizer
 import numpy as np
+import os
+
 
 class BLIP_NLVR(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_pretrain.py

@@ -5,7 +5,7 @@
  * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
  * By Junnan Li
 '''
-from models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
 from transformers import BertTokenizer
 import transformers
 transformers.logging.set_verbosity_error()
@@ -14,7 +14,7 @@ import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_Pretrain(nn.Module):
     def __init__(self,                 
@@ -270,7 +270,7 @@ from typing import List
 def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
     uninitialized_encoder_weights: List[str] = []
     if decoder.__class__ != encoder.__class__:
-        logger.info(
+        print(
             f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
         )
 

extras/BLIP/models/blip_retrieval.py

@@ -1,11 +1,11 @@
-from models.med import BertConfig, BertModel
+from extras.BLIP.models.med import BertConfig, BertModel
 from transformers import BertTokenizer
 
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_Retrieval(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_vqa.py

@@ -1,5 +1,5 @@
-from models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 import torch
 from torch import nn

extras/BLIP/models/vit.py

@@ -18,7 +18,10 @@ from timm.models.registry import register_model
 from timm.models.layers import trunc_normal_, DropPath
 from timm.models.helpers import named_apply, adapt_input_conv
 
-from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
+
+def checkpoint_wrapper(x):
+    return x
+
 
 class Mlp(nn.Module):
     """ MLP as used in Vision Transformer, MLP-Mixer and related networks

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',
+            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"))
+                                 med_config=os.path.join(blip_repo_root, "configs", "med_config.json"))
             model.eval()
 
             self.load_device = model_management.text_encoder_device()

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.832'
+version = '2.1.834'