diff --git a/ldm/generate.py b/ldm/generate.py
index 80ebfef227..1afb642721 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -445,7 +445,11 @@ class Generate:
             self._set_sampler()
 
         # apply the concepts library to the prompt
-        prompt = self.huggingface_concepts_library.replace_concepts_with_triggers(prompt, lambda concepts: self.load_huggingface_concepts(concepts))
+        prompt = self.huggingface_concepts_library.replace_concepts_with_triggers(
+            prompt,
+            lambda concepts: self.load_huggingface_concepts(concepts),
+            self.model.textual_inversion_manager.get_all_trigger_strings()
+        )
 
         # bit of a hack to change the cached sampler's karras threshold to
         # whatever the user asked for
diff --git a/ldm/invoke/concepts_lib.py b/ldm/invoke/concepts_lib.py
index 246dea362a..c774f29674 100644
--- a/ldm/invoke/concepts_lib.py
+++ b/ldm/invoke/concepts_lib.py
@@ -59,7 +59,7 @@ class HuggingFaceConceptsLibrary(object):
         be downloaded.
         '''
         if not concept_name in self.list_concepts():
-            print(f'This concept is not known to the Hugging Face library. Generation will continue without the concept.')
+            print(f'This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept.')
             return None
         return self.get_concept_file(concept_name.lower(),'learned_embeds.bin')
 
@@ -115,13 +115,19 @@ class HuggingFaceConceptsLibrary(object):
             return self.trigger_to_concept(match.group(1)) or f'<{match.group(1)}>'
         return self.match_trigger.sub(do_replace, prompt)
 
-    def replace_concepts_with_triggers(self, prompt:str, load_concepts_callback: Callable[[list], any])->str:
+    def replace_concepts_with_triggers(self,
+                                       prompt:str,
+                                       load_concepts_callback: Callable[[list], any],
+                                       excluded_tokens:list[str])->str:
         '''
         Given a prompt string that contains `<concept_name>` tags, replace
         these tags with the appropriate trigger.
 
         If any `<concept_name>` tags are found, `load_concepts_callback()` is called with a list
         of `concepts_name` strings.
+
+        `excluded_tokens` are any tokens that should not be replaced, typically because they
+        are trigger tokens from a locally-loaded embedding.
         '''
         concepts = self.match_concept.findall(prompt)
         if not concepts:
@@ -129,6 +135,8 @@ class HuggingFaceConceptsLibrary(object):
         load_concepts_callback(concepts)
 
         def do_replace(match)->str:
+            if excluded_tokens and f'<{match.group(1)}>' in excluded_tokens:
+                return f'<{match.group(1)}>'
             return self.concept_to_trigger(match.group(1)) or f'<{match.group(1)}>'
         return self.match_concept.sub(do_replace, prompt)
 
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index f7ced79a52..cf28cf8c7a 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -38,11 +38,15 @@ class TextualInversionManager():
             if concept_name in self.hf_concepts_library.concepts_loaded:
                 continue
             trigger = self.hf_concepts_library.concept_to_trigger(concept_name)
-            if self.has_textual_inversion_for_trigger_string(trigger):
+            if self.has_textual_inversion_for_trigger_string(trigger) \
+               or self.has_textual_inversion_for_trigger_string(concept_name) \
+               or self.has_textual_inversion_for_trigger_string(f'<{concept_name}>'):  # in case a token with literal angle brackets encountered
+                print(f'>> Loaded local embedding for trigger {concept_name}')
                 continue
             bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
             if not bin_file:
                 continue
+            print(f'>> Loaded remote embedding for trigger {concept_name}')
             self.load_textual_inversion(bin_file)
             self.hf_concepts_library.concepts_loaded[concept_name]=True
 
@@ -50,6 +54,8 @@ class TextualInversionManager():
         return [ti.trigger_string for ti in self.textual_inversions]
 
     def load_textual_inversion(self, ckpt_path, defer_injecting_tokens: bool=False):
+        if str(ckpt_path).endswith('.DS_Store'):
+            return
         try:
             scan_result = scan_file_path(ckpt_path)
             if scan_result.infected_files == 1: