diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index 65459b5c5f..7c095de7b7 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -50,7 +50,7 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
         parsed_prompt = pp.parse_conjunction(prompt_string_cleaned).prompts[0]
 
     parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
-    print("parsed prompt to", parsed_prompt)
+    print(f">> Parsed prompt to {parsed_prompt}")
 
     conditioning = None
     cac_args:CrossAttentionControl.Arguments = None
@@ -59,7 +59,7 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
         blend: Blend = parsed_prompt
         embeddings_to_blend = None
         for flattened_prompt in blend.prompts:
-            this_embedding, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
+            this_embedding, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt, log_tokens=log_tokens)
             embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
                 (embeddings_to_blend, this_embedding))
         conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
@@ -103,14 +103,14 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
                     edit_options.append(None)
                     original_token_count += count
                     edited_token_count += count
-            original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, original_prompt)
+            original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, original_prompt, log_tokens=log_tokens)
             # naïvely building a single edited_embeddings like this disregards the effects of changing the absolute location of
             # subsequent tokens when there is >1 edit and earlier edits change the total token count.
             # eg "a cat.swap(smiling dog, s_start=0.5) eating a hotdog.swap(pizza)" - when the 'pizza' edit is active but the
             # 'cat' edit is not, the 'pizza' feature vector will nevertheless be affected by the introduction of the extra
             # token 'smiling' in the inactive 'cat' edit.
             # todo: build multiple edited_embeddings, one for each edit, and pass just the edited fragments through to the CrossAttentionControl functions
-            edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, edited_prompt)
+            edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, edited_prompt, log_tokens=log_tokens)
 
             conditioning = original_embeddings
             edited_conditioning = edited_embeddings
@@ -121,10 +121,10 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
                 edit_options = edit_options
             )
         else:
-            conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
+            conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt, log_tokens=log_tokens)
 
 
-    unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, parsed_negative_prompt)
+    unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, parsed_negative_prompt, log_tokens=log_tokens)
     return (
         unconditioning, conditioning, InvokeAIDiffuserComponent.ExtraConditioningInfo(
             cross_attention_control_args=cac_args
@@ -138,12 +138,27 @@ def build_token_edit_opcodes(original_tokens, edited_tokens):
 
     return SequenceMatcher(None, original_tokens, edited_tokens).get_opcodes()
 
-def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt):
+def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt, log_tokens: bool=False):
     if type(flattened_prompt) is not FlattenedPrompt:
         raise Exception(f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead")
     fragments = [x.text for x in flattened_prompt.children]
     weights = [x.weight for x in flattened_prompt.children]
     embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True, fragment_weights=[weights])
+    if not flattened_prompt.is_empty and log_tokens:
+        start_token = model.cond_stage_model.tokenizer.bos_token_id
+        end_token = model.cond_stage_model.tokenizer.eos_token_id
+        tokens_list = tokens[0].tolist()
+        if tokens_list[0] == start_token:
+            tokens_list[0] = '<start>'
+        try:
+            first_end_token_index = tokens_list.index(end_token)
+            tokens_list[first_end_token_index] = '<end>'
+            tokens_list = tokens_list[:first_end_token_index+1]
+        except ValueError:
+            pass
+
+        print(f">> Prompt fragments {fragments}, tokenized to \n{tokens_list}")
+
     return embeddings, tokens
 
 def get_tokens_length(model, fragments: list[Fragment]):
diff --git a/ldm/invoke/prompt_parser.py b/ldm/invoke/prompt_parser.py
index 3a96d664f0..6709f48066 100644
--- a/ldm/invoke/prompt_parser.py
+++ b/ldm/invoke/prompt_parser.py
@@ -51,6 +51,11 @@ class FlattenedPrompt():
             raise PromptParser.ParsingException(
                 f"FlattenedPrompt cannot contain {fragment}, only Fragments or (str, float) tuples are allowed")
 
+    @property
+    def is_empty(self):
+        return len(self.children) == 0 or \
+               (len(self.children) == 1 and len(self.children[0].text) == 0)
+
     def __repr__(self):
         return f"FlattenedPrompt:{self.children}"
     def __eq__(self, other):