Upload Bam_GPT_Parser.py

Bam_GPT_Parser.py

@@ -1,600 +1,600 @@
-from __future__ import annotations
-
-import re
-from typing import Dict, List, Tuple
-
-
-# =============================================================================
-# Outputs (order matters)
-# =============================================================================
-_OUTPUT_DEFS: List[Tuple[str, str]] = [
-    # Core
-    ("gender_str", "STRING"),
-    ("gender_int", "INT"),
-    ("age_str", "STRING"),
-    ("age_int", "INT"),
-    ("identity_str", "STRING"),
-    ("eyecolor_str", "STRING"),
-    ("hairstyle_str", "STRING"),
-
-    # Equipment
-    ("topwear_str", "STRING"),
-    ("bellywear_str", "STRING"),
-    ("breastwear_str", "STRING"),
-
-    ("handwear_left_str", "STRING"),
-    ("handwear_right_str", "STRING"),
-    ("wristwear_left_str", "STRING"),
-    ("wristwear_right_str", "STRING"),
-    ("forearm_left_str", "STRING"),
-    ("forearm_right_str", "STRING"),
-    ("elbow_left_str", "STRING"),
-    ("elbow_right_str", "STRING"),
-    ("upperarm_left_str", "STRING"),
-    ("upperarm_right_str", "STRING"),
-    ("shoulder_left_str", "STRING"),
-    ("shoulder_right_str", "STRING"),
-
-    ("shank_left_str", "STRING"),
-    ("shank_right_str", "STRING"),
-
-    ("knee_left_str", "STRING"),
-    ("knee_right_str", "STRING"),
-
-    ("foot_left_str", "STRING"),
-    ("foot_right_str", "STRING"),
-
-    ("necklace_str", "STRING"),
-    ("earring_left_str", "STRING"),
-    ("earring_right_str", "STRING"),
-
-    ("kneewear_str", "STRING"),
-    ("headwear_str", "STRING"),
-    ("facemask_str", "STRING"),
-    ("sunglasses_str", "STRING"),
-    ("glasses_str", "STRING"),
-
-    ("crotch_str", "STRING"),
-    ("belt_str", "STRING"),
-    ("skirt_str", "STRING"),
-    ("one_piece_str", "STRING"),
-
-    # Tags
-    ("aesthetic_tag1", "STRING"),
-    ("aesthetic_tag2", "STRING"),
-    ("aesthetic_tag3", "STRING"),
-    ("aesthetic_tag4", "STRING"),
-    ("aesthetic_tag5", "STRING"),
-
-    ("skin_tag1", "STRING"),
-    ("skin_tag2", "STRING"),
-    ("skin_tag3", "STRING"),
-    ("skin_tag4", "STRING"),
-    ("skin_tag5", "STRING"),
-
-    ("expression_tag1", "STRING"),
-    ("expression_tag2", "STRING"),
-    ("expression_tag3", "STRING"),
-    ("expression_tag4", "STRING"),
-    ("expression_tag5", "STRING"),
-
-    # Unique extra headwear slot
-    ("headwear_str_2", "STRING"),
-
-    # Flattened equipment values (equip.* values only, in order, unique)
-    ("all_equip", "STRING"),
-
-    # Converted ancient BAM string
-    ("bam_ancient", "STRING"),
-]
-
-RETURN_NAMES_TUPLE = tuple(n for n, _t in _OUTPUT_DEFS)
-RETURN_TYPES_TUPLE = tuple(_t for _n, _t in _OUTPUT_DEFS)
-
-
-# =============================================================================
-# Constants
-# =============================================================================
-_NEGATIVE_PROMPT_1 = (
-    "monochrome, sketch, colorless, (asymmetrical face:1.5), "
-    "(asymmetrical tail-arched eyebrows:1.0), (terribly drawn eyes:1.2), "
-    "(heterochromia:1.5), watermark, text, visible background objects, visible floor, "
-    "(floor-effects:1.5), (background-effects:1.5), non-character, character-shadow, floor-shadow"
-)
-
-
-# =============================================================================
-# Helpers
-# =============================================================================
-def _strip_quotes(v: str) -> str:
-    v = (v or "").strip()
-    if len(v) >= 2 and ((v[0] == v[-1] == '"') or (v[0] == v[-1] == "'")):
-        return v[1:-1].strip()
-    return v
-
-
-def _norm_key(k: str) -> str:
-    k = (k or "").strip().lower()
-    k = k.replace(" ", "_").replace("-", "_")
-    k = re.sub(r"_+", "_", k)
-    return k
-
-
-def _safe_int(s: str, default: int = 0) -> int:
-    try:
-        return int((s or "").strip())
-    except Exception:
-        return default
-
-
-def _norm_spaces(s: str) -> str:
-    s = (s or "").replace("\r", " ").replace("\n", " ")
-    s = re.sub(r"\s+", " ", s).strip()
-    return s
-
-
-def _extract_gpt_bam_block(text: str) -> str:
-    """
-    Extract first GPT_BAM block payload (between markers).
-    If markers are missing, returns the whole text (still attempts key=value parsing).
-    """
-    text = text or ""
-    m = re.search(r"GPT_BAM_START###(.*?)###GPT_BAM_END", text, flags=re.S | re.I)
-    return m.group(1) if m else text
-
-
-# =============================================================================
-# Ancient conversion cleaners (match your expected example)
-# - Underscores -> spaces for non-equip textual fields
-# - Equipment keeps underscores and internal comma formatting as provided
-# =============================================================================
-def _clean_identity_like(s: str) -> str:
-    s = (s or "").strip().replace("_", " ")
-    return _norm_spaces(s)
-
-
-def _clean_eyes(s: str) -> str:
-    s = (s or "").strip()
-    s = s.replace("_eyes", "")
-    s = s.replace("_", " ")
-    return _norm_spaces(s)
-
-
-def _clean_hair(s: str) -> str:
-    s = (s or "").strip()
-    parts = [p.strip() for p in s.split(",") if p.strip()]
-    cleaned: List[str] = []
-    for p in parts:
-        # remove common suffixes
-        for suf in ("_hairstyle", "_hairsyle", "_hair"):
-            if p.endswith(suf):
-                p = p[: -len(suf)]
-        # also remove occurrences inside
-        p = p.replace("_hairstyle", "").replace("_hairsyle", "").replace("_hair", "")
-        p = p.replace("_", " ")
-        p = _norm_spaces(p)
-        if p:
-            cleaned.append(p)
-    return ", ".join(cleaned)
-
-
-def _clean_tag(s: str, kind: str) -> str:
-    s = (s or "").strip()
-    if kind == "aesthetic":
-        s = s.replace("_aesthetic", "").replace("aesthetic_", "").replace("aesthetic", "")
-    elif kind == "skin":
-        s = s.replace("_skin", "").replace("skin_", "").replace("skin", "")
-    elif kind == "expression":
-        s = s.replace("_expression", "").replace("expression_", "").replace("expression", "")
-    s = s.replace("_", " ")
-    return _norm_spaces(s)
-
-
-def _zero_if_empty(s: str) -> str:
-    s = _norm_spaces(s)
-    return s if s else "0"
-
-
-# =============================================================================
-# Equipment mapping
-# =============================================================================
-_KEY_CANONICAL: Dict[str, str] = {
-    "topwear": "topwear",
-    "belly": "bellywear",
-    "bellywear": "bellywear",
-    "breast": "breastwear",
-    "breastwear": "breastwear",
-
-    "hand": "handwear",
-    "handwear": "handwear",
-    "wrist": "wristwear",
-    "wristwear": "wristwear",
-
-    "forearm": "forearm",
-    "elbow": "elbow",
-    "upperarm": "upperarm",
-    "upper_arm": "upperarm",
-    "shoulder": "shoulder",
-
-    "shank": "shank",
-    "knee": "knee",
-
-    "foot": "foot",
-    "footwear": "foot",
-    "shoe": "foot",
-    "shoes": "foot",
-
-    "necklace": "necklace",
-
-    "earring": "earring",
-    "earrings": "earring",
-
-    "kneewear": "kneewear",
-    "headwear": "headwear",
-    "headwear2": "headwear2",
-
-    "facemask": "facemask",
-    "face_mask": "facemask",
-    "mask": "facemask",
-
-    "sunglasses": "sunglasses",
-    "glasses": "glasses",
-
-    "crotch": "crotch",
-    "belt": "belt",
-    "skirt": "skirt",
-
-    "onepiece": "one_piece",
-    "one_piece": "one_piece",
-    "one_piecewear": "one_piece",
-}
-
-_SIDE_FIELDS: Dict[str, Tuple[str, str]] = {
-    "handwear": ("handwear_left_str", "handwear_right_str"),
-    "wristwear": ("wristwear_left_str", "wristwear_right_str"),
-    "forearm": ("forearm_left_str", "forearm_right_str"),
-    "elbow": ("elbow_left_str", "elbow_right_str"),
-    "upperarm": ("upperarm_left_str", "upperarm_right_str"),
-    "shoulder": ("shoulder_left_str", "shoulder_right_str"),
-    "shank": ("shank_left_str", "shank_right_str"),
-    "knee": ("knee_left_str", "knee_right_str"),
-    "foot": ("foot_left_str", "foot_right_str"),
-    "earring": ("earring_left_str", "earring_right_str"),
-}
-
-_SINGLE_FIELDS: Dict[str, str] = {
-    "topwear": "topwear_str",
-    "bellywear": "bellywear_str",
-    "breastwear": "breastwear_str",
-    "necklace": "necklace_str",
-    "kneewear": "kneewear_str",
-    "headwear": "headwear_str",
-    "facemask": "facemask_str",
-    "sunglasses": "sunglasses_str",
-    "glasses": "glasses_str",
-    "crotch": "crotch_str",
-    "belt": "belt_str",
-    "skirt": "skirt_str",
-    "one_piece": "one_piece_str",
-    "headwear2": "headwear_str_2",
-}
-
-_ALL_EQUIP_OUTPUTS = set(_SINGLE_FIELDS.values())
-for lf, rf in _SIDE_FIELDS.values():
-    _ALL_EQUIP_OUTPUTS.add(lf)
-    _ALL_EQUIP_OUTPUTS.add(rf)
-
-
-def _assign_equip(
-    out: Dict[str, object],
-    equip_values_in_order: List[str],
-    raw_key: str,
-    val: str,
-) -> None:
-    """
-    Assign equipment into structured outputs.
-
-    Precedence rule:
-    - sided keys (.left/.right or _left/_right) overwrite that side
-    - unsided keys fill only empty sides (so sided values win even if unsided appears later)
-
-    Also collects ALL equip values (even unknown keys) into equip_values_in_order.
-    """
-    val = (val or "").strip()
-    k = _norm_key(raw_key)
-
-    # detect side
-    side = None
-    base = k
-
-    if base.endswith(".left"):
-        side = "left"
-        base = base[:-5]
-    elif base.endswith(".right"):
-        side = "right"
-        base = base[:-6]
-
-    if base.endswith("_left"):
-        side = "left"
-        base = base[:-5]
-    elif base.endswith("_right"):
-        side = "right"
-        base = base[:-6]
-
-    base = base.strip("._")
-    base_for_lookup = base.replace(".", "_")
-    canonical = _KEY_CANONICAL.get(base_for_lookup, base_for_lookup)
-
-    # collect equip values (even unknown keys) for all_equip
-    if val:
-        equip_values_in_order.append(val)
-
-    if canonical in _SIDE_FIELDS:
-        left_name, right_name = _SIDE_FIELDS[canonical]
-        if side == "left":
-            out[left_name] = val
-        elif side == "right":
-            out[right_name] = val
-        else:
-            # unsided: fill only empties
-            if not out.get(left_name, ""):
-                out[left_name] = val
-            if not out.get(right_name, ""):
-                out[right_name] = val
-
-    elif canonical in _SINGLE_FIELDS:
-        out[_SINGLE_FIELDS[canonical]] = val
-
-    else:
-        # unknown equip key -> ignored for structured outputs
-        pass
-
-
-# =============================================================================
-# GPT_BAM parsing + ancient conversion
-# =============================================================================
-def _parse_gpt_bam(text: str) -> Dict[str, object]:
-    payload = _extract_gpt_bam_block(text)
-    segments = [s.strip() for s in payload.split("###") if s.strip()]
-
-    # defaults
-    out: Dict[str, object] = {name: (0 if t == "INT" else "") for name, t in _OUTPUT_DEFS}
-    for k in _ALL_EQUIP_OUTPUTS:
-        out[k] = ""
-
-    equip_values_in_order: List[str] = []
-
-    g_int = None
-
-    for seg in segments:
-        if "=" in seg:
-            k, v = seg.split("=", 1)
-        elif ":" in seg:
-            k, v = seg.split(":", 1)
-        else:
-            continue
-
-        k = _norm_key(k)
-        v = _strip_quotes(v)
-
-        # core
-        if k in ("gender", "sex", "gender_int", "gender_num"):
-            vv = v.strip().lower()
-            if vv in ("1", "boy", "male", "m"):
-                g_int = 1
-            elif vv in ("2", "girl", "female", "f"):
-                g_int = 2
-
-        elif k in ("age", "age_str"):
-            out["age_str"] = v.strip()
-            out["age_int"] = _safe_int(out["age_str"], 0)
-
-        elif k in ("identity", "identity_str", "job", "role"):
-            out["identity_str"] = v.strip()
-
-        elif k in ("eyecolor", "eye_color", "eye", "eyecolor_str"):
-            out["eyecolor_str"] = v.strip()
-
-        elif k in ("hairstyle", "hair", "hairstyle_str"):
-            out["hairstyle_str"] = v.strip()
-
-        # equipment
-        elif k.startswith("equip.") or k.startswith("equipment."):
-            raw_equip_key = k.split(".", 1)[1]  # remove equip.
-            _assign_equip(out, equip_values_in_order, raw_equip_key, v)
-
-        # tag slots
-        elif k.startswith("aesthetic.") or k.startswith("aesthetic_tag"):
-            num = None
-            if k.startswith("aesthetic."):
-                suf = k.split(".", 1)[1]
-                if suf.isdigit():
-                    num = int(suf)
-            else:
-                m = re.search(r"aesthetic_tag(\d+)", k)
-                if m:
-                    num = int(m.group(1))
-            if num and 1 <= num <= 5:
-                out[f"aesthetic_tag{num}"] = v.strip()
-
-        elif k.startswith("skin.") or k.startswith("skin_tag"):
-            num = None
-            if k.startswith("skin."):
-                suf = k.split(".", 1)[1]
-                if suf.isdigit():
-                    num = int(suf)
-            else:
-                m = re.search(r"skin_tag(\d+)", k)
-                if m:
-                    num = int(m.group(1))
-            if num and 1 <= num <= 5:
-                out[f"skin_tag{num}"] = v.strip()
-
-        elif k.startswith("expression.") or k.startswith("expression_tag"):
-            num = None
-            if k.startswith("expression."):
-                suf = k.split(".", 1)[1]
-                if suf.isdigit():
-                    num = int(suf)
-            else:
-                m = re.search(r"expression_tag(\d+)", k)
-                if m:
-                    num = int(m.group(1))
-            if num and 1 <= num <= 5:
-                out[f"expression_tag{num}"] = v.strip()
-
-        # headwear2 aliases
-        elif k in ("headwear2", "headwear_tag2", "headwear_str_2", "equip_headwear2"):
-            out["headwear_str_2"] = v.strip()
-
-        else:
-            # explicitly ignore name (and anything else not recognized)
-            # e.g. name=mirela_vance should not affect anything
-            pass
-
-    # finalize gender
-    if g_int is None:
-        g_int = 2  # default: if not "1" => girl (matches your ancient rule)
-    out["gender_int"] = int(g_int)
-    out["gender_str"] = "boy" if g_int == 1 else "girl"
-
-    # all_equip = unique equip values, in order (includes unknown equip keys)
-    seen = set()
-    equip_unique: List[str] = []
-    for v in equip_values_in_order:
-        v = (v or "").strip()
-        if v and v not in seen:
-            equip_unique.append(v)
-            seen.add(v)
-    out["all_equip"] = ", ".join(equip_unique)
-
-    # bam_ancient conversion
-    out["bam_ancient"] = _convert_to_ancient(out, equip_unique)
-
-    return out
-
-
-def _convert_to_ancient(parsed: Dict[str, object], equip_unique: List[str]) -> str:
-    gender_int = int(parsed.get("gender_int", 2) or 2)
-
-    age_str = str(parsed.get("age_str", "") or "").strip()
-    if not age_str:
-        age_str = str(parsed.get("age_int", 0) or 0)
-
-    identity = _clean_identity_like(str(parsed.get("identity_str", "") or ""))
-    eyes = _clean_eyes(str(parsed.get("eyecolor_str", "") or ""))
-    hair = _clean_hair(str(parsed.get("hairstyle_str", "") or ""))
-
-    # Equipment defaults / additions
-    equip_list: List[str] = list(equip_unique)
-
-    def add_unique(val: str) -> None:
-        val = (val or "").strip()
-        if not val:
-            return
-        if val not in equip_list:
-            equip_list.append(val)
-
-    # (No footwear parsed) => add ",bare foot"
-    foot_l = str(parsed.get("foot_left_str", "") or "").strip()
-    foot_r = str(parsed.get("foot_right_str", "") or "").strip()
-    if not foot_l and not foot_r:
-        add_unique("bare foot")
-
-    # (No handwear parsed) => add ",bare hands"
-    hand_l = str(parsed.get("handwear_left_str", "") or "").strip()
-    hand_r = str(parsed.get("handwear_right_str", "") or "").strip()
-    if not hand_l and not hand_r:
-        add_unique("bare hands")
-
-    # (No topwear AND no breastwear AND no one_piece) => add ",naked breasts"
-    top = str(parsed.get("topwear_str", "") or "").strip()
-    breast = str(parsed.get("breastwear_str", "") or "").strip()
-    one_piece = str(parsed.get("one_piece_str", "") or "").strip()
-    if not top and not breast and not one_piece:
-        add_unique("naked breasts")
-
-    # (No topwear AND no one_piece AND no crotch AND no skirt) => exposed crotch (gendered)
-    crotch = str(parsed.get("crotch_str", "") or "").strip()
-    skirt = str(parsed.get("skirt_str", "") or "").strip()
-    if not top and not one_piece and not crotch and not skirt:
-        if gender_int == 1:
-            add_unique("naked crotch exposed penis")
-        else:
-            add_unique("naked crotch exposed vagina")
-
-    equip_str = ", ".join([e for e in equip_list if (e or "").strip()])
-
-    # Tags
-    aest = [_clean_tag(str(parsed.get(f"aesthetic_tag{i}", "") or ""), "aesthetic") for i in range(1, 6)]
-    skin = [_clean_tag(str(parsed.get(f"skin_tag{i}", "") or ""), "skin") for i in range(1, 6)]
-    expr = [_clean_tag(str(parsed.get(f"expression_tag{i}", "") or ""), "expression") for i in range(1, 6)]
-
-    hw_extra = _clean_identity_like(str(parsed.get("headwear_str_2", "") or ""))
-
-    # Fill missing with 0 according to your template
-    fields = [
-        "START",
-        str(gender_int),
-        _zero_if_empty(age_str),
-        _zero_if_empty(identity),
-        _zero_if_empty(eyes),
-        _zero_if_empty(hair),
-        _zero_if_empty(equip_str),
-        *(_zero_if_empty(a) for a in aest),
-        *(_zero_if_empty(s) for s in skin),
-        *(_zero_if_empty(e) for e in expr),
-        _zero_if_empty(hw_extra),
-        "0",                 # POSITIVE_PROMPT_0
-        "0",                 # POSITIVE_PROMPT_1
-        "0",                 # NEGATIVE_PROMPT_0
-        _NEGATIVE_PROMPT_1,  # NEGATIVE_PROMPT_1 (constant)
-        "0",                 # NEGATIVE_PROMPT_2
-        "END",
-    ]
-
-    # Build exactly: START###...###END###
-    out = "###".join(fields[:-1]) + "###" + fields[-1] + "###"
-    out = _norm_spaces(out)  # remove linebreaks, collapse double spaces
-    return out
-
-
-# =============================================================================
-# ComfyUI Node
-# =============================================================================
-class GPTBAMParser:
-    """
-    Parses GPT_BAM v1 (key=value fields separated by ###) and also outputs bam_ancient.
-    """
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "gpt_bam_string": ("STRING", {"multiline": True, "default": ""}),
-            }
-        }
-
-    RETURN_TYPES = RETURN_TYPES_TUPLE
-    RETURN_NAMES = RETURN_NAMES_TUPLE
-    FUNCTION = "parse"
-    CATEGORY = "BAM"
-
-    def parse(self, gpt_bam_string: str):
-        parsed = _parse_gpt_bam(gpt_bam_string)
-
-        # ensure all outputs exist
-        for name, t in _OUTPUT_DEFS:
-            if name not in parsed:
-                parsed[name] = 0 if t == "INT" else ""
-
-        return tuple(parsed[name] for name in RETURN_NAMES_TUPLE)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GPTBAMParser": GPTBAMParser,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GPTBAMParser": "GPT_BAM Parser",
+from __future__ import annotations
+
+import re
+from typing import Dict, List, Tuple
+
+
+# =============================================================================
+# Outputs (order matters)
+# =============================================================================
+_OUTPUT_DEFS: List[Tuple[str, str]] = [
+    # Core
+    ("gender_str", "STRING"),
+    ("gender_int", "INT"),
+    ("age_str", "STRING"),
+    ("age_int", "INT"),
+    ("identity_str", "STRING"),
+    ("eyecolor_str", "STRING"),
+    ("hairstyle_str", "STRING"),
+
+    # Equipment
+    ("topwear_str", "STRING"),
+    ("bellywear_str", "STRING"),
+    ("breastwear_str", "STRING"),
+
+    ("handwear_left_str", "STRING"),
+    ("handwear_right_str", "STRING"),
+    ("wristwear_left_str", "STRING"),
+    ("wristwear_right_str", "STRING"),
+    ("forearm_left_str", "STRING"),
+    ("forearm_right_str", "STRING"),
+    ("elbow_left_str", "STRING"),
+    ("elbow_right_str", "STRING"),
+    ("upperarm_left_str", "STRING"),
+    ("upperarm_right_str", "STRING"),
+    ("shoulder_left_str", "STRING"),
+    ("shoulder_right_str", "STRING"),
+
+    ("shank_left_str", "STRING"),
+    ("shank_right_str", "STRING"),
+
+    ("knee_left_str", "STRING"),
+    ("knee_right_str", "STRING"),
+
+    ("foot_left_str", "STRING"),
+    ("foot_right_str", "STRING"),
+
+    ("necklace_str", "STRING"),
+    ("earring_left_str", "STRING"),
+    ("earring_right_str", "STRING"),
+
+    ("kneewear_str", "STRING"),
+    ("headwear_str", "STRING"),
+    ("facemask_str", "STRING"),
+    ("sunglasses_str", "STRING"),
+    ("glasses_str", "STRING"),
+
+    ("crotch_str", "STRING"),
+    ("belt_str", "STRING"),
+    ("skirt_str", "STRING"),
+    ("one_piece_str", "STRING"),
+
+    # Tags
+    ("aesthetic_tag1", "STRING"),
+    ("aesthetic_tag2", "STRING"),
+    ("aesthetic_tag3", "STRING"),
+    ("aesthetic_tag4", "STRING"),
+    ("aesthetic_tag5", "STRING"),
+
+    ("skin_tag1", "STRING"),
+    ("skin_tag2", "STRING"),
+    ("skin_tag3", "STRING"),
+    ("skin_tag4", "STRING"),
+    ("skin_tag5", "STRING"),
+
+    ("expression_tag1", "STRING"),
+    ("expression_tag2", "STRING"),
+    ("expression_tag3", "STRING"),
+    ("expression_tag4", "STRING"),
+    ("expression_tag5", "STRING"),
+
+    # Unique extra headwear slot
+    ("headwear_str_2", "STRING"),
+
+    # Flattened equipment values (equip.* values only, in order, unique)
+    ("all_equip", "STRING"),
+
+    # Converted ancient BAM string
+    ("bam_ancient", "STRING"),
+]
+
+RETURN_NAMES_TUPLE = tuple(n for n, _t in _OUTPUT_DEFS)
+RETURN_TYPES_TUPLE = tuple(_t for _n, _t in _OUTPUT_DEFS)
+
+
+# =============================================================================
+# Constants
+# =============================================================================
+_NEGATIVE_PROMPT_1 = (
+    "monochrome, sketch, colorless, (asymmetrical face:1.5), "
+    "(asymmetrical tail-arched eyebrows:1.0), (terribly drawn eyes:1.2), "
+    "(heterochromia:1.5), watermark, text, visible background objects, visible floor, "
+    "(floor-effects:1.5), (background-effects:1.5), non-character, character-shadow, floor-shadow"
+)
+
+
+# =============================================================================
+# Helpers
+# =============================================================================
+def _strip_quotes(v: str) -> str:
+    v = (v or "").strip()
+    if len(v) >= 2 and ((v[0] == v[-1] == '"') or (v[0] == v[-1] == "'")):
+        return v[1:-1].strip()
+    return v
+
+
+def _norm_key(k: str) -> str:
+    k = (k or "").strip().lower()
+    k = k.replace(" ", "_").replace("-", "_")
+    k = re.sub(r"_+", "_", k)
+    return k
+
+
+def _safe_int(s: str, default: int = 0) -> int:
+    try:
+        return int((s or "").strip())
+    except Exception:
+        return default
+
+
+def _norm_spaces(s: str) -> str:
+    s = (s or "").replace("\r", " ").replace("\n", " ")
+    s = re.sub(r"\s+", " ", s).strip()
+    return s
+
+
+def _extract_gpt_bam_block(text: str) -> str:
+    """
+    Extract first GPT_BAM block payload (between markers).
+    If markers are missing, returns the whole text (still attempts key=value parsing).
+    """
+    text = text or ""
+    m = re.search(r"GPT_BAM_START###(.*?)###GPT_BAM_END", text, flags=re.S | re.I)
+    return m.group(1) if m else text
+
+
+# =============================================================================
+# Ancient conversion cleaners (match your expected example)
+# - Underscores -> spaces for non-equip textual fields
+# - Equipment keeps underscores and internal comma formatting as provided
+# =============================================================================
+def _clean_identity_like(s: str) -> str:
+    s = (s or "").strip().replace("_", " ")
+    return _norm_spaces(s)
+
+
+def _clean_eyes(s: str) -> str:
+    s = (s or "").strip()
+    s = s.replace("_eyes", "")
+    s = s.replace("_", " ")
+    return _norm_spaces(s)
+
+
+def _clean_hair(s: str) -> str:
+    s = (s or "").strip()
+    parts = [p.strip() for p in s.split(",") if p.strip()]
+    cleaned: List[str] = []
+    for p in parts:
+        # remove common suffixes
+        for suf in ("_hairstyle", "_hairsyle", "_hair"):
+            if p.endswith(suf):
+                p = p[: -len(suf)]
+        # also remove occurrences inside
+        p = p.replace("_hairstyle", "").replace("_hairsyle", "").replace("_hair", "")
+        p = p.replace("_", " ")
+        p = _norm_spaces(p)
+        if p:
+            cleaned.append(p)
+    return ", ".join(cleaned)
+
+
+def _clean_tag(s: str, kind: str) -> str:
+    s = (s or "").strip()
+    if kind == "aesthetic":
+        s = s.replace("_aesthetic", "").replace("aesthetic_", "").replace("aesthetic", "")
+    elif kind == "skin":
+        s = s.replace("_skin", "").replace("skin_", "").replace("skin", "")
+    elif kind == "expression":
+        s = s.replace("_expression", "").replace("expression_", "").replace("expression", "")
+    s = s.replace("_", " ")
+    return _norm_spaces(s)
+
+
+def _zero_if_empty(s: str) -> str:
+    s = _norm_spaces(s)
+    return s if s else "0"
+
+
+# =============================================================================
+# Equipment mapping
+# =============================================================================
+_KEY_CANONICAL: Dict[str, str] = {
+    "topwear": "topwear",
+    "belly": "bellywear",
+    "bellywear": "bellywear",
+    "breast": "breastwear",
+    "breastwear": "breastwear",
+
+    "hand": "handwear",
+    "handwear": "handwear",
+    "wrist": "wristwear",
+    "wristwear": "wristwear",
+
+    "forearm": "forearm",
+    "elbow": "elbow",
+    "upperarm": "upperarm",
+    "upper_arm": "upperarm",
+    "shoulder": "shoulder",
+
+    "shank": "shank",
+    "knee": "knee",
+
+    "foot": "foot",
+    "footwear": "foot",
+    "shoe": "foot",
+    "shoes": "foot",
+
+    "necklace": "necklace",
+
+    "earring": "earring",
+    "earrings": "earring",
+
+    "kneewear": "kneewear",
+    "headwear": "headwear",
+    "headwear2": "headwear2",
+
+    "facemask": "facemask",
+    "face_mask": "facemask",
+    "mask": "facemask",
+
+    "sunglasses": "sunglasses",
+    "glasses": "glasses",
+
+    "crotch": "crotch",
+    "belt": "belt",
+    "skirt": "skirt",
+
+    "onepiece": "one_piece",
+    "one_piece": "one_piece",
+    "one_piecewear": "one_piece",
+}
+
+_SIDE_FIELDS: Dict[str, Tuple[str, str]] = {
+    "handwear": ("handwear_left_str", "handwear_right_str"),
+    "wristwear": ("wristwear_left_str", "wristwear_right_str"),
+    "forearm": ("forearm_left_str", "forearm_right_str"),
+    "elbow": ("elbow_left_str", "elbow_right_str"),
+    "upperarm": ("upperarm_left_str", "upperarm_right_str"),
+    "shoulder": ("shoulder_left_str", "shoulder_right_str"),
+    "shank": ("shank_left_str", "shank_right_str"),
+    "knee": ("knee_left_str", "knee_right_str"),
+    "foot": ("foot_left_str", "foot_right_str"),
+    "earring": ("earring_left_str", "earring_right_str"),
+}
+
+_SINGLE_FIELDS: Dict[str, str] = {
+    "topwear": "topwear_str",
+    "bellywear": "bellywear_str",
+    "breastwear": "breastwear_str",
+    "necklace": "necklace_str",
+    "kneewear": "kneewear_str",
+    "headwear": "headwear_str",
+    "facemask": "facemask_str",
+    "sunglasses": "sunglasses_str",
+    "glasses": "glasses_str",
+    "crotch": "crotch_str",
+    "belt": "belt_str",
+    "skirt": "skirt_str",
+    "one_piece": "one_piece_str",
+    "headwear2": "headwear_str_2",
+}
+
+_ALL_EQUIP_OUTPUTS = set(_SINGLE_FIELDS.values())
+for lf, rf in _SIDE_FIELDS.values():
+    _ALL_EQUIP_OUTPUTS.add(lf)
+    _ALL_EQUIP_OUTPUTS.add(rf)
+
+
+def _assign_equip(
+    out: Dict[str, object],
+    equip_values_in_order: List[str],
+    raw_key: str,
+    val: str,
+) -> None:
+    """
+    Assign equipment into structured outputs.
+
+    Precedence rule:
+    - sided keys (.left/.right or _left/_right) overwrite that side
+    - unsided keys fill only empty sides (so sided values win even if unsided appears later)
+
+    Also collects ALL equip values (even unknown keys) into equip_values_in_order.
+    """
+    val = (val or "").strip()
+    k = _norm_key(raw_key)
+
+    # detect side
+    side = None
+    base = k
+
+    if base.endswith(".left"):
+        side = "left"
+        base = base[:-5]
+    elif base.endswith(".right"):
+        side = "right"
+        base = base[:-6]
+
+    if base.endswith("_left"):
+        side = "left"
+        base = base[:-5]
+    elif base.endswith("_right"):
+        side = "right"
+        base = base[:-6]
+
+    base = base.strip("._")
+    base_for_lookup = base.replace(".", "_")
+    canonical = _KEY_CANONICAL.get(base_for_lookup, base_for_lookup)
+
+    # collect equip values (even unknown keys) for all_equip
+    if val:
+        equip_values_in_order.append(val)
+
+    if canonical in _SIDE_FIELDS:
+        left_name, right_name = _SIDE_FIELDS[canonical]
+        if side == "left":
+            out[left_name] = val
+        elif side == "right":
+            out[right_name] = val
+        else:
+            # unsided: fill only empties
+            if not out.get(left_name, ""):
+                out[left_name] = val
+            if not out.get(right_name, ""):
+                out[right_name] = val
+
+    elif canonical in _SINGLE_FIELDS:
+        out[_SINGLE_FIELDS[canonical]] = val
+
+    else:
+        # unknown equip key -> ignored for structured outputs
+        pass
+
+
+# =============================================================================
+# GPT_BAM parsing + ancient conversion
+# =============================================================================
+def _parse_gpt_bam(text: str) -> Dict[str, object]:
+    payload = _extract_gpt_bam_block(text)
+    segments = [s.strip() for s in payload.split("###") if s.strip()]
+
+    # defaults
+    out: Dict[str, object] = {name: (0 if t == "INT" else "") for name, t in _OUTPUT_DEFS}
+    for k in _ALL_EQUIP_OUTPUTS:
+        out[k] = ""
+
+    equip_values_in_order: List[str] = []
+
+    g_int = None
+
+    for seg in segments:
+        if "=" in seg:
+            k, v = seg.split("=", 1)
+        elif ":" in seg:
+            k, v = seg.split(":", 1)
+        else:
+            continue
+
+        k = _norm_key(k)
+        v = _strip_quotes(v)
+
+        # core
+        if k in ("gender", "sex", "gender_int", "gender_num"):
+            vv = v.strip().lower()
+            if vv in ("1", "boy", "male", "m"):
+                g_int = 1
+            elif vv in ("2", "girl", "female", "f"):
+                g_int = 2
+
+        elif k in ("age", "age_str"):
+            out["age_str"] = v.strip()
+            out["age_int"] = _safe_int(out["age_str"], 0)
+
+        elif k in ("identity", "identity_str", "job", "role"):
+            out["identity_str"] = v.strip()
+
+        elif k in ("eyecolor", "eye_color", "eye", "eyecolor_str"):
+            out["eyecolor_str"] = v.strip()
+
+        elif k in ("hairstyle", "hair", "hairstyle_str"):
+            out["hairstyle_str"] = v.strip()
+
+        # equipment
+        elif k.startswith("equip.") or k.startswith("equipment."):
+            raw_equip_key = k.split(".", 1)[1]  # remove equip.
+            _assign_equip(out, equip_values_in_order, raw_equip_key, v)
+
+        # tag slots
+        elif k.startswith("aesthetic.") or k.startswith("aesthetic_tag"):
+            num = None
+            if k.startswith("aesthetic."):
+                suf = k.split(".", 1)[1]
+                if suf.isdigit():
+                    num = int(suf)
+            else:
+                m = re.search(r"aesthetic_tag(\d+)", k)
+                if m:
+                    num = int(m.group(1))
+            if num and 1 <= num <= 5:
+                out[f"aesthetic_tag{num}"] = v.strip()
+
+        elif k.startswith("skin.") or k.startswith("skin_tag"):
+            num = None
+            if k.startswith("skin."):
+                suf = k.split(".", 1)[1]
+                if suf.isdigit():
+                    num = int(suf)
+            else:
+                m = re.search(r"skin_tag(\d+)", k)
+                if m:
+                    num = int(m.group(1))
+            if num and 1 <= num <= 5:
+                out[f"skin_tag{num}"] = v.strip()
+
+        elif k.startswith("expression.") or k.startswith("expression_tag"):
+            num = None
+            if k.startswith("expression."):
+                suf = k.split(".", 1)[1]
+                if suf.isdigit():
+                    num = int(suf)
+            else:
+                m = re.search(r"expression_tag(\d+)", k)
+                if m:
+                    num = int(m.group(1))
+            if num and 1 <= num <= 5:
+                out[f"expression_tag{num}"] = v.strip()
+
+        # headwear2 aliases
+        elif k in ("headwear2", "headwear_tag2", "headwear_str_2", "equip_headwear2"):
+            out["headwear_str_2"] = v.strip()
+
+        else:
+            # explicitly ignore name (and anything else not recognized)
+            # e.g. name=mirela_vance should not affect anything
+            pass
+
+    # finalize gender
+    if g_int is None:
+        g_int = 2  # default: if not "1" => girl (matches your ancient rule)
+    out["gender_int"] = int(g_int)
+    out["gender_str"] = "boy" if g_int == 1 else "girl"
+
+    # all_equip = unique equip values, in order (includes unknown equip keys)
+    seen = set()
+    equip_unique: List[str] = []
+    for v in equip_values_in_order:
+        v = (v or "").strip()
+        if v and v not in seen:
+            equip_unique.append(v)
+            seen.add(v)
+    out["all_equip"] = ", ".join(equip_unique)
+
+    # bam_ancient conversion
+    out["bam_ancient"] = _convert_to_ancient(out, equip_unique)
+
+    return out
+
+
+def _convert_to_ancient(parsed: Dict[str, object], equip_unique: List[str]) -> str:
+    gender_int = int(parsed.get("gender_int", 2) or 2)
+
+    age_str = str(parsed.get("age_str", "") or "").strip()
+    if not age_str:
+        age_str = str(parsed.get("age_int", 0) or 0)
+
+    identity = _clean_identity_like(str(parsed.get("identity_str", "") or ""))
+    eyes = _clean_eyes(str(parsed.get("eyecolor_str", "") or ""))
+    hair = _clean_hair(str(parsed.get("hairstyle_str", "") or ""))
+
+    # Equipment defaults / additions
+    equip_list: List[str] = list(equip_unique)
+
+    def add_unique(val: str) -> None:
+        val = (val or "").strip()
+        if not val:
+            return
+        if val not in equip_list:
+            equip_list.append(val)
+
+    # (No footwear parsed) => add ",bare foot"
+    foot_l = str(parsed.get("foot_left_str", "") or "").strip()
+    foot_r = str(parsed.get("foot_right_str", "") or "").strip()
+    if not foot_l and not foot_r:
+        add_unique("bare foot")
+
+    # (No handwear parsed) => add ",bare hands"
+    hand_l = str(parsed.get("handwear_left_str", "") or "").strip()
+    hand_r = str(parsed.get("handwear_right_str", "") or "").strip()
+    if not hand_l and not hand_r:
+        add_unique("bare hands")
+
+    # (No topwear AND no breastwear AND no one_piece) => add ",naked breasts"
+    top = str(parsed.get("topwear_str", "") or "").strip()
+    breast = str(parsed.get("breastwear_str", "") or "").strip()
+    one_piece = str(parsed.get("one_piece_str", "") or "").strip()
+    if not top and not breast and not one_piece:
+        add_unique("naked breasts")
+
+    # (No topwear AND no one_piece AND no crotch AND no skirt) => exposed crotch (gendered)
+    crotch = str(parsed.get("crotch_str", "") or "").strip()
+    skirt = str(parsed.get("skirt_str", "") or "").strip()
+    if not top and not one_piece and not crotch and not skirt:
+        if gender_int == 1:
+            add_unique("naked crotch exposed penis")
+        else:
+            add_unique("naked crotch exposed vagina")
+
+    equip_str = ", ".join([e for e in equip_list if (e or "").strip()])
+
+    # Tags
+    aest = [_clean_tag(str(parsed.get(f"aesthetic_tag{i}", "") or ""), "aesthetic") for i in range(1, 6)]
+    skin = [_clean_tag(str(parsed.get(f"skin_tag{i}", "") or ""), "skin") for i in range(1, 6)]
+    expr = [_clean_tag(str(parsed.get(f"expression_tag{i}", "") or ""), "expression") for i in range(1, 6)]
+
+    hw_extra = _clean_identity_like(str(parsed.get("headwear_str_2", "") or ""))
+
+    # Fill missing with 0 according to your template
+    fields = [
+        "START",
+        str(gender_int),
+        _zero_if_empty(age_str),
+        _zero_if_empty(identity),
+        _zero_if_empty(eyes),
+        _zero_if_empty(hair),
+        _zero_if_empty(equip_str),
+        *(_zero_if_empty(a) for a in aest),
+        *(_zero_if_empty(s) for s in skin),
+        *(_zero_if_empty(e) for e in expr),
+        _zero_if_empty(hw_extra),
+        "0",                 # POSITIVE_PROMPT_0
+        "0",                 # POSITIVE_PROMPT_1
+        "0",                 # NEGATIVE_PROMPT_0
+        _NEGATIVE_PROMPT_1,  # NEGATIVE_PROMPT_1 (constant)
+        "0",                 # NEGATIVE_PROMPT_2
+        "END",
+    ]
+
+    # Build exactly: START###...###END###
+    out = "###".join(fields[:-1]) + "###" + fields[-1] + "###"
+    out = _norm_spaces(out)  # remove linebreaks, collapse double spaces
+    return out
+
+
+# =============================================================================
+# ComfyUI Node
+# =============================================================================
+class BAMParser_Ancestral:
+    """
+    Parses GPT_BAM v1 (key=value fields separated by ###) and also outputs bam_ancient.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "gpt_bam_string": ("STRING", {"multiline": True, "default": ""}),
+            }
+        }
+
+    RETURN_TYPES = RETURN_TYPES_TUPLE
+    RETURN_NAMES = RETURN_NAMES_TUPLE
+    FUNCTION = "parse"
+    CATEGORY = "BAM"
+
+    def parse(self, gpt_bam_string: str):
+        parsed = _parse_gpt_bam(gpt_bam_string)
+
+        # ensure all outputs exist
+        for name, t in _OUTPUT_DEFS:
+            if name not in parsed:
+                parsed[name] = 0 if t == "INT" else ""
+
+        return tuple(parsed[name] for name in RETURN_NAMES_TUPLE)
+
+
+NODE_CLASS_MAPPINGS = {
+    "BAMParser_Ancestral": BAMParser_Ancestral,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BAMParser_Ancestral": "BAMParser_Ancestral",
 }