model paths fixed, codeformer needs attention

ldm/modules/encoders/modules.py

@@ -1,5 +1,5 @@
 import math
-
+import os.path
 import torch
 import torch.nn as nn
 from functools import partial
@@ -8,6 +8,7 @@ from einops import rearrange, repeat
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
 from ldm.invoke.devices import choose_torch_device
+from ldm.invoke.globals import Globals
 
 from ldm.modules.x_transformer import (
     Encoder,
@@ -98,21 +99,19 @@ class BERTTokenizer(AbstractEncoder):
     """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
 
     def __init__(
-        self, device=choose_torch_device(), vq_interface=True, max_length=77
+            self, device=choose_torch_device(), vq_interface=True, max_length=77
     ):
         super().__init__()
         from transformers import (
             BertTokenizerFast,
-        )  # TODO: add to reuquirements
+        )
 
-        # Modified to allow to run on non-internet connected compute nodes.
-        # Model needs to be loaded into cache from an internet-connected machine
-        # by running:
-        #   from transformers import BertTokenizerFast
-        #   BertTokenizerFast.from_pretrained("bert-base-uncased")
+        cache = os.path.join(Globals.root,'models/bert-base-uncased')
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
-                'bert-base-uncased', local_files_only=True
+                'bert-base-uncased',
+                cache_dir=cache,
+                local_files_only=True
             )
         except OSError:
             raise SystemExit(
@@ -150,14 +149,14 @@ class BERTEmbedder(AbstractEncoder):
     """Uses the BERT tokenizr model and add some transformer encoder layers"""
 
     def __init__(
-        self,
-        n_embed,
-        n_layer,
-        vocab_size=30522,
-        max_seq_len=77,
-        device=choose_torch_device(),
-        use_tokenizer=True,
-        embedding_dropout=0.0,
+            self,
+            n_embed,
+            n_layer,
+            vocab_size=30522,
+            max_seq_len=77,
+            device=choose_torch_device(),
+            use_tokenizer=True,
+            embedding_dropout=0.0,
     ):
         super().__init__()
         self.use_tknz_fn = use_tokenizer
@@ -245,10 +244,14 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     ):
         super().__init__()
         self.tokenizer = CLIPTokenizer.from_pretrained(
-            version, local_files_only=True
+            version,
+            cache_dir=os.path.join(Globals.root,'models',version),
+            local_files_only=True
         )
         self.transformer = CLIPTextModel.from_pretrained(
-            version, local_files_only=True
+            version,
+            cache_dir=os.path.join(Globals.root,'models',version),
+            local_files_only=True
         )
         self.device = device
         self.max_length = max_length