diff --git a/ldm/dream/conditioning.py b/ldm/dream/conditioning.py
index e2e3b0116d..ed2d4ef431 100644
--- a/ldm/dream/conditioning.py
+++ b/ldm/dream/conditioning.py
@@ -38,14 +38,14 @@ def get_uc_and_c(prompt, model, log_tokens=False, skip_normalize=False):
         c = torch.zeros_like(uc)
         # normalize each "sub prompt" and add it
         for subprompt, weight in weighted_subprompts:
-            log_tokenization(subprompt, model, log_tokens)
+            log_tokenization(subprompt, model, log_tokens, weight)
             c = torch.add(
                 c,
                 model.get_learned_conditioning([subprompt]),
                 alpha=weight,
             )
     else:   # just standard 1 prompt
-        log_tokenization(prompt, model, log_tokens)
+        log_tokenization(prompt, model, log_tokens, 1)
         c = model.get_learned_conditioning([prompt])
         uc = model.get_learned_conditioning([unconditioned_words])
     return (uc, c)
@@ -86,7 +86,7 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
 # shows how the prompt is tokenized
 # usually tokens have '</w>' to indicate end-of-word,
 # but for readability it has been replaced with ' '
-def log_tokenization(text, model, log=False):
+def log_tokenization(text, model, log=False, weight=1):
     if not log:
         return
     tokens    = model.cond_stage_model.tokenizer._tokenize(text)
@@ -103,8 +103,8 @@ def log_tokenization(text, model, log=False):
             usedTokens += 1
         else:  # over max token length
             discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        print(f"\n>> Tokens ({usedTokens}):\n{tokenized}\x1b[0m")
-        if discarded != "":
-            print(
-                f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
-            )
+    print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
+    if discarded != "":
+        print(
+            f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
+        )