Refactor validation into shared helper function and improve type handling

Co-authored-by: jags111 <5968619+jags111@users.noreply.github.com>

efficiency_nodes.py

@@ -53,6 +53,7 @@ from .py import city96_latent_upscaler
 from .py import ttl_nn_latent_upscaler
 from .py import bnk_tiled_samplers
 from .py import bnk_adv_encode
+from .py.bnk_adv_encode import normalize_prompt_text
 sys.path.remove(my_dir)
 
 from comfy import samplers
@@ -73,10 +74,8 @@ def encode_prompts(positive_prompt, negative_prompt, token_normalization, weight
                    return_type="both"):
 
     # Ensure prompts are valid strings to prevent tokenization errors
-    if positive_prompt is None or (isinstance(positive_prompt, str) and not positive_prompt.strip()):
+    positive_prompt = normalize_prompt_text(positive_prompt)
-        positive_prompt = " "
+    negative_prompt = normalize_prompt_text(negative_prompt)
-    if negative_prompt is None or (isinstance(negative_prompt, str) and not negative_prompt.strip()):
-        negative_prompt = " "
 
     positive_encoded = negative_encoded = refiner_positive_encoded = refiner_negative_encoded = None
 

py/bnk_adv_encode.py

@@ -6,6 +6,21 @@ from math import gcd
 from comfy import model_management
 from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
 
+def normalize_prompt_text(text):
+    """
+    Normalize prompt text to prevent tokenization errors.
+    Converts None, empty strings, or whitespace-only strings to a single space.
+    Ensures the input is a string type.
+    """
+    if text is None:
+        return " "
+    if not isinstance(text, str):
+        # Convert non-string types to string
+        text = str(text)
+    if not text.strip():
+        return " "
+    return text
+
 def _grouper(n, iterable):
     it = iter(iterable)
     while True:
@@ -238,8 +253,7 @@ def prepareXL(embs_l, embs_g, pooled, clip_balance):
 
 def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
     # Ensure text is a valid string to prevent tokenization errors
-    if text is None or (isinstance(text, str) and not text.strip()):
+    text = normalize_prompt_text(text)
-        text = " "
     tokenized = clip.tokenize(text, return_word_ids=True)
     if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
         embs_l = None
@@ -269,10 +283,8 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
                                            w_max=w_max)
 def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
     # Ensure texts are valid strings to prevent tokenization errors
-    if text1 is None or (isinstance(text1, str) and not text1.strip()):
+    text1 = normalize_prompt_text(text1)
-        text1 = " "
+    text2 = normalize_prompt_text(text2)
-    if text2 is None or (isinstance(text2, str) and not text2.strip()):
-        text2 = " "
     tokenized1 = clip.tokenize(text1, return_word_ids=True)
     tokenized2 = clip.tokenize(text2, return_word_ids=True)