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Refactor validation into shared helper function and improve type handling

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Co-authored-by: jags111 <5968619+jags111@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
2026-02-03 23:03:17 +00:00
parent 94c9d05e2e
commit 3fa0e8c927
2 changed files with 21 additions and 10 deletions
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7
efficiency_nodes.py
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@@ -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 = " "
if negative_prompt is None or (isinstance(negative_prompt, str) and not negative_prompt.strip()):
negative_prompt = " "
positive_prompt = normalize_prompt_text(positive_prompt)
negative_prompt = normalize_prompt_text(negative_prompt)
positive_encoded = negative_encoded = refiner_positive_encoded = refiner_negative_encoded = None

24
py/bnk_adv_encode.py
View File

@@ -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 = " "
text = normalize_prompt_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 = " "
if text2 is None or (isinstance(text2, str) and not text2.strip()):
text2 = " "
text1 = normalize_prompt_text(text1)
text2 = normalize_prompt_text(text2)
tokenized1 = clip.tokenize(text1, return_word_ids=True)
tokenized2 = clip.tokenize(text2, return_word_ids=True)