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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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fix(wildcards): resolve weighted N::value syntax inside wildcard YAML lists (#1039)
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@@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)
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_WILDCARD_PATTERN = re.compile(r"__([\w\s.\-+/*\\]+?)__")
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_OPTION_PATTERN = re.compile(r"{([^{}]*?)}")
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_TRIGGER_WORD_PATTERN = re.compile(r"^trigger_words\d+$")
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_WEIGHTED_OPTION_PATTERN = re.compile(r"^\s*([0-9.]+)::")
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_WEIGHTED_OPTION_PATTERN = re.compile(r"^\s*-?\d+(\.\d+)?::")
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_NUMERIC_PATTERN = re.compile(r"^-?\d+(\.\d+)?$")
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@@ -390,7 +390,7 @@ class WildcardService:
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) -> str | None:
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keyword = _normalize_wildcard_key(raw_key)
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if keyword in wildcard_dict:
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return rng.choice(wildcard_dict[keyword])
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return self._pick_weighted_or_plain(wildcard_dict[keyword], rng)
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if "*" in keyword:
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regex_pattern = keyword.replace("*", ".*").replace("+", r"\+")
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@@ -400,7 +400,7 @@ class WildcardService:
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if compiled.match(key):
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aggregated.extend(values)
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if aggregated:
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return rng.choice(aggregated)
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return self._pick_weighted_or_plain(aggregated, rng)
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if "/" not in keyword:
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fallback_keyword = _normalize_wildcard_key(f"*/{keyword}")
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@@ -409,6 +409,39 @@ class WildcardService:
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return None
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def _pick_weighted_or_plain(
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self, values: list[str], rng: random.Random
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) -> str:
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"""Pick a value from the list, respecting N::weight prefix if present.
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When any value in the list uses the ``N::value`` weighted syntax with a
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weight different from 1, the pick uses weighted random selection. When
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no such weighting is present, a plain ``rng.choice`` is used (preserving
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backward compatibility for unweighted wildcard files).
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In either case the ``N::`` prefix is always stripped from the returned
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value, matching the behaviour of ``{...}`` option groups.
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"""
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# Fast path: skip weighting logic entirely when no :: syntax exists
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if not any("::" in v for v in values):
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return rng.choice(values)
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weighted_options: list[tuple[float, str]] = []
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for value in values:
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weight = 1.0
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parts = value.split("::", 1)
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if len(parts) == 2 and _is_numeric_string(parts[0].strip()):
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weight = float(parts[0].strip())
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weighted_options.append((weight, value))
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any_weighted = any(w != 1.0 for w, _ in weighted_options)
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if any_weighted:
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picked = self._weighted_choice(weighted_options, rng)
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else:
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picked = rng.choice(values)
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return self._strip_weight_prefix(picked)
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def is_trigger_words_input(name: str) -> bool:
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return bool(_TRIGGER_WORD_PATTERN.match(name))
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