turn the HuggingFaceConceptsLib into a singleton to prevent redundant downloads

invokeai/backend/invoke_ai_web_server.py

@@ -25,7 +25,7 @@ from invokeai.backend.modules.parameters import parameters_to_command
 import invokeai.frontend.dist as frontend
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.concepts_lib import get_hf_concepts_lib
 from ldm.invoke.conditioning import (
     get_tokens_for_prompt_object,
     get_prompt_structure,
@@ -538,7 +538,7 @@ class InvokeAIWebServer:
             try:
                 local_triggers = self.generate.model.textual_inversion_manager.get_all_trigger_strings()
                 locals = [{'name': x} for x in sorted(local_triggers, key=str.casefold)]
-                concepts = HuggingFaceConceptsLibrary().list_concepts(minimum_likes=5)
+                concepts = get_hf_concepts_lib().list_concepts(minimum_likes=5)
                 concepts = [{'name': f'<{x}>'} for x in sorted(concepts, key=str.casefold) if f'<{x}>' not in local_triggers]
                 socketio.emit("foundTextualInversionTriggers", {'local_triggers': locals, 'huggingface_concepts': concepts})
             except Exception as e:

ldm/invoke/concepts_lib.py

@@ -12,6 +12,14 @@ from urllib import request, error as ul_error
 from huggingface_hub import HfFolder, hf_hub_url, ModelSearchArguments, ModelFilter, HfApi
 from ldm.invoke.globals import Globals
 
+singleton = None
+
+def get_hf_concepts_lib():
+    global singleton
+    if singleton is None:
+        singleton = HuggingFaceConceptsLibrary()
+    return singleton
+
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         '''

ldm/invoke/readline.py

@@ -13,7 +13,7 @@ import re
 import atexit
 from typing import List
 from ldm.invoke.args import Args
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.concepts_lib import get_hf_concepts_lib
 from ldm.invoke.globals import Globals
 from ldm.modules.lora_manager import LoraManager
 
@@ -287,7 +287,7 @@ class Completer(object):
     def _concept_completions(self, text, state):
         if self.concepts is None:
             # cache Concepts() instance so we can check for updates in concepts_list during runtime.
-            self.concepts = HuggingFaceConceptsLibrary()
+            self.concepts = get_hf_concepts_lib()
             self.embedding_terms.update(set(self.concepts.list_concepts()))
         else:
             self.embedding_terms.update(set(self.concepts.list_concepts()))

ldm/modules/embedding_manager.py

@@ -6,7 +6,7 @@ from torch import nn
 
 import sys
 
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.concepts_lib import get_hf_concepts_lib
 from ldm.data.personalized import per_img_token_list
 from transformers import CLIPTokenizer
 from functools import partial
@@ -39,7 +39,7 @@ class EmbeddingManager(nn.Module):
         super().__init__()
 
         self.embedder = embedder
-        self.concepts_library=HuggingFaceConceptsLibrary()
+        self.concepts_library=get_hf_concepts_lib()
 
         self.string_to_token_dict = {}
         self.string_to_param_dict = nn.ParameterDict()

ldm/modules/textual_inversion_manager.py

@@ -9,7 +9,7 @@ from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from compel.embeddings_provider import BaseTextualInversionManager
-from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.concepts_lib import get_hf_concepts_lib
 
 
 @dataclass
@@ -34,7 +34,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         self.tokenizer = tokenizer
         self.text_encoder = text_encoder
         self.full_precision = full_precision
-        self.hf_concepts_library = HuggingFaceConceptsLibrary()
+        self.hf_concepts_library = get_hf_concepts_lib()
         self.trigger_to_sourcefile = dict()
         default_textual_inversions: list[TextualInversion] = []
         self.textual_inversions = default_textual_inversions