diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index 9b67d5040d..e3190f6ed6 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -35,9 +35,10 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
 
     pp = PromptParser()
 
-    def build_conditioning_list(prompt_string:str):
+    def build_conditioning_list(prompt_string:str, verbose:bool = False):
         parsed_conjunction: Conjunction = pp.parse(prompt_string)
-        print(f"parsed '{prompt_string}' to {parsed_conjunction}")
+        if verbose:
+            print(f"parsed '{prompt_string}' to {parsed_conjunction}")
         assert (type(parsed_conjunction) is Conjunction)
 
         conditioning_list = []
@@ -46,7 +47,7 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
                 raise f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead"
             fragments = [x[0] for x in flattened_prompt.children]
             attention_weights = [x[1] for x in flattened_prompt.children]
-            print(fragments, attention_weights)
+            #print(fragments, attention_weights)
             return model.get_learned_conditioning([fragments], attention_weights=[attention_weights])
 
         for part,weight in zip(parsed_conjunction.prompts, parsed_conjunction.weights):
@@ -65,14 +66,14 @@ def get_uc_and_c(prompt_string_uncleaned, model, log_tokens=False, skip_normaliz
 
         return conditioning_list
 
-    positive_conditioning_list = build_conditioning_list(prompt_string_cleaned)
-    negative_conditioning_list = build_conditioning_list(unconditioned_words)
+    positive_conditioning_list = build_conditioning_list(prompt_string_cleaned, verbose=True)
+    negative_conditioning_list = build_conditioning_list(unconditioned_words, verbose=(len(unconditioned_words)>0) )
 
     if len(negative_conditioning_list) == 0:
         negative_conditioning = model.get_learned_conditioning([['']], attention_weights=[[1]])
     else:
         if len(negative_conditioning_list)>1:
-            print("cannot do conjunctions on unconditioning for now")
+            print("cannot do conjunctions on unconditioning for now, everything except the first prompt will be ignored")
         negative_conditioning = negative_conditioning_list[0][0]
 
     #positive_conditioning_list.append((get_blend_prompts_and_weights(prompt), this_weight))
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index 857a8a8e3e..fcd0363e80 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -547,13 +547,13 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
             lerped_embeddings = self.apply_embedding_weights(embeddings, per_embedding_weights, normalize=True).squeeze(0)
 
-            print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
+            #print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
 
             # append to batch
             batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat((batch_z, lerped_embeddings.unsqueeze(0)), dim=1)
 
         # should have shape (B, 77, 768)
-        print(f"assembled all tokens into tensor of shape {batch_z.shape}")
+        #print(f"assembled all tokens into tensor of shape {batch_z.shape}")
 
         return batch_z
 
@@ -589,18 +589,19 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
         )['input_ids']
         all_tokens = []
         per_token_weights = []
-        print("all fragments:", fragments, weights)
+        #print("all fragments:", fragments, weights)
         for index, fragment in enumerate(item_encodings):
             weight = weights[index]
-            print("processing fragment", fragment, weight)
+            #print("processing fragment", fragment, weight)
             fragment_tokens = item_encodings[index]
-            print("fragment", fragment, "processed to", fragment_tokens)
+            #print("fragment", fragment, "processed to", fragment_tokens)
             # trim bos and eos markers before appending
             all_tokens.extend(fragment_tokens[1:-1])
             per_token_weights.extend([weight] * (len(fragment_tokens) - 2))
 
-        if len(all_tokens) > self.max_length - 2:
-            print("prompt is too long and has been truncated")
+        if (len(all_tokens) + 2) > self.max_length:
+            excess_token_count = (len(all_tokens) + 2) - self.max_length
+            print(f"prompt is {excess_token_count} token(s) too long and has been truncated")
             all_tokens = all_tokens[:self.max_length - 2]
 
         # pad out to a 77-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
@@ -613,7 +614,7 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
         all_tokens_tensor = torch.tensor(all_tokens, dtype=torch.long).to(self.device)
         per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32).to(self.device)
-        print(f"assembled all_tokens_tensor with shape {all_tokens_tensor.shape}")
+        #print(f"assembled all_tokens_tensor with shape {all_tokens_tensor.shape}")
         return all_tokens_tensor, per_token_weights_tensor
 
     def build_weighted_embedding_tensor(self, tokens: torch.Tensor, per_token_weights: torch.Tensor, weight_delta_from_empty=True, **kwargs) -> torch.Tensor:
@@ -641,7 +642,7 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
             weighted_z = empty_z + (z_delta_from_empty * batch_weights_expanded)
 
             weighted_z_delta_from_empty = (weighted_z-empty_z)
-            print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() )
+            #print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() )
 
             #print("using empty-delta method, first 5 rows:")
             #print(weighted_z[:5])