618 lines
23 KiB
Python
618 lines
23 KiB
Python
from __future__ import annotations
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import json
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import re
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from typing import Any
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OLD_TRIGGER = "sxcpinup_coloredpencil"
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DEFAULT_TRIGGER = "sxcppnl7"
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STYLE_TAILS = [
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", coloured pencil comic illustration, crisp linework, hatching, soft pastel palette, warm sensual lighting, textured parchment paper",
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", coloured pencil comic illustration, crisp linework, hatching, soft pastel palette, warm sensual lighting, textured paper",
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]
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PROMPT_FIELD_LABELS = (
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"Ages",
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"Body types",
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"Cast",
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"Scene",
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"Setting",
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"Pose",
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"Sexual pose",
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"Facial expression",
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"Facial expressions",
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"Clothing",
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"Erotic outfit",
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"Prop/detail",
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"Composition",
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"Role graph",
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"Use",
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"Avoid",
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)
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ITEM_LABELS = (
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"Sexual pose",
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"Erotic outfit",
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"Clothing",
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)
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def _clean_text(value: Any) -> str:
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text = "" if value is None else str(value)
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text = text.replace("\n", " ")
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text = re.sub(r"\s+", " ", text).strip()
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text = re.sub(r"\s+([,.;:])", r"\1", text)
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return text
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def _cap_first(text: str) -> str:
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text = _clean_text(text).strip(" ,")
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return text[:1].upper() + text[1:] if text else ""
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def _article(noun_phrase: str) -> str:
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word = noun_phrase.lstrip().lower()
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if word.startswith("hour") or word[:1] in "aeiou":
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return "an"
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return "a"
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def _sentence(text: str) -> str:
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text = _clean_text(text).strip(" ,;")
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if not text:
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return ""
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if text[-1] not in ".!?":
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text += "."
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return _cap_first(text)
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def _join_sentences(parts: list[str]) -> str:
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return " ".join(part for part in (_sentence(part) for part in parts) if part)
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def _human_join(parts: list[str]) -> str:
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parts = [part for part in (_clean_text(part) for part in parts) if part]
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if len(parts) <= 1:
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return "".join(parts)
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if len(parts) == 2:
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return f"{parts[0]} and {parts[1]}"
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return f"{', '.join(parts[:-1])}, and {parts[-1]}"
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def _strip_style_tail(text: str) -> str:
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text = _clean_text(text)
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for tail in STYLE_TAILS:
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if text.endswith(tail):
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return text[: -len(tail)].strip(" ,")
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return text
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def _remove_trigger(text: str, trigger: str) -> str:
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text = _clean_text(text).strip(" ,")
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for candidate in (trigger, OLD_TRIGGER, DEFAULT_TRIGGER):
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candidate = candidate.strip()
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if not candidate:
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continue
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if text.lower().startswith(candidate.lower() + ","):
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return text[len(candidate) + 1 :].strip(" ,")
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if text.lower().startswith(candidate.lower() + "."):
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return text[len(candidate) + 1 :].strip(" ,")
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if text.lower() == candidate.lower():
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return ""
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return text
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def _with_trigger(text: str, trigger: str, include_trigger: bool) -> str:
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text = _join_sentences([text]) if "." not in text else _clean_text(text)
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trigger = _clean_text(trigger or DEFAULT_TRIGGER)
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if not include_trigger or not trigger:
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return text
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if text.lower().startswith(trigger.lower() + "."):
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return text
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return f"{trigger}. {text}"
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def _maybe_json(text: str) -> dict[str, Any] | None:
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text = _clean_text(text)
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if not text or not text.startswith("{"):
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return None
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try:
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value = json.loads(text)
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except json.JSONDecodeError:
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return None
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return value if isinstance(value, dict) else None
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def _row_from_inputs(source_text: str, metadata_json: str, input_hint: str) -> tuple[dict[str, Any] | None, str]:
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candidates: list[tuple[str, str]] = []
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if input_hint in ("auto", "metadata_json"):
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candidates.append((metadata_json, "metadata_json"))
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candidates.append((source_text, "source_json"))
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for text, method in candidates:
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row = _maybe_json(text)
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if row is not None:
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return row, method
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return None, "text"
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def _prompt_field(text: str, label: str) -> str:
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text = _clean_text(text)
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if not text:
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return ""
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labels = "|".join(re.escape(name) for name in PROMPT_FIELD_LABELS)
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pattern = rf"{re.escape(label)}:\s*(.*?)(?=\. (?:{labels}):|\. Use\b|\. Avoid\b|$)"
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match = re.search(pattern, text)
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if not match:
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return ""
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return _clean_text(match.group(1)).rstrip(".")
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def _row_value(row: dict[str, Any], key: str, labels: tuple[str, ...] = ()) -> str:
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value = _clean_text(row.get(key, ""))
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if value:
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return value
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prompt = _clean_text(row.get("prompt", ""))
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for label in labels:
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value = _prompt_field(prompt, label)
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if value:
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return value
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return ""
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def _field_from_any_prompt(text: str, labels: tuple[str, ...]) -> str:
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for label in labels:
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value = _prompt_field(text, label)
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if value:
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return value
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return ""
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def _normalize_composition(text: str) -> str:
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return re.sub(r"^vertical\s+", "", _clean_text(text), flags=re.IGNORECASE)
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def _clean_clothing(text: str) -> str:
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text = _clean_text(text)
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text = re.sub(r",?\s*fashion editorial styling$", "", text, flags=re.IGNORECASE)
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text = re.sub(r",?\s*resort styling$", "", text, flags=re.IGNORECASE)
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return text.strip(" ,")
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def _body_phrase(body: Any, figure_note: Any = "") -> str:
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body = _clean_text(body)
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figure_note = _clean_text(figure_note)
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if not body:
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return figure_note
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if not figure_note:
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return f"{body} figure"
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if "figure" in figure_note.lower():
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return f"{body} build and {figure_note}"
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return f"{body} figure with {figure_note}"
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def _single_caption_front(row: dict[str, Any]) -> dict[str, str]:
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caption = _clean_text(row.get("caption"))
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if not caption:
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return {}
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caption = _remove_trigger(_strip_style_tail(caption), _clean_text(row.get("trigger")) or DEFAULT_TRIGGER)
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caption = _remove_trigger(caption, OLD_TRIGGER)
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subject = _clean_text(row.get("primary_subject"))
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age = _clean_text(row.get("age_band") or row.get("age"))
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body_phrase = _clean_text(row.get("body_phrase"))
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if not body_phrase:
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body = _clean_text(row.get("body_type") or row.get("body"))
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figure = _clean_text(row.get("figure"))
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body_phrase = _body_phrase(body, figure)
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front = f"{subject}, {age}, {body_phrase}, "
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if subject in ("woman", "man") and age and body_phrase and caption.startswith(front):
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try:
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skin, hair, eyes, _rest = caption[len(front) :].split(", ", 3)
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except ValueError:
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return {}
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else:
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pieces = [piece.strip() for piece in caption.split(", ", 6)]
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if len(pieces) < 7:
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return {}
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subject, age, body_phrase, skin, hair, eyes, _rest = pieces
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if subject not in ("woman", "man"):
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return {}
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return {
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"caption_subject": subject,
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"caption_age": age,
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"caption_body_phrase": body_phrase,
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"caption_skin": skin,
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"caption_hair": hair,
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"caption_eyes": eyes,
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}
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def _pose_clause(pose: str) -> str:
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pose = _clean_text(pose)
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if not pose:
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return ""
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first = pose.split(None, 1)[0].lower()
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if first.endswith("ing") or first in ("seated", "reclined", "posed"):
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return pose
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return f"posing in {pose}"
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def _age_subject(age: str, subject: str) -> str:
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age = _clean_text(age)
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subject = _clean_text(subject) or "person"
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if not age:
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return f"An adult {subject}"
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clean_age = re.sub(r"\s+adults?$", "", age).strip()
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if "year-old" in clean_age:
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return f"A {clean_age} adult {subject}"
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if re.search(r"\d", clean_age):
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poss = "her" if subject == "woman" else "his"
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return f"An adult {subject} in {poss} {clean_age}"
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return f"An adult {clean_age} {subject}"
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def _clean_age_phrase(age: str) -> str:
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age = _clean_text(age)
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age = re.sub(r"\s+adults?$", "", age).strip()
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return age.replace("-year-old", " years old")
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def _subject_phrase_from_counts(row: dict[str, Any]) -> str:
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subject = _clean_text(row.get("subject_phrase"))
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if subject:
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return subject
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try:
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women = int(row.get("women_count") or 0)
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men = int(row.get("men_count") or 0)
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except (TypeError, ValueError):
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return _clean_text(row.get("primary_subject")) or "adult scene"
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parts = []
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if women:
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parts.append(f"{women} adult {'woman' if women == 1 else 'women'}")
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if men:
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parts.append(f"{men} adult {'man' if men == 1 else 'men'}")
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if not parts:
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return _clean_text(row.get("primary_subject")) or "adult scene"
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return " and ".join(parts)
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def _verb_for_row(row: dict[str, Any]) -> str:
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try:
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return "is" if int(row.get("person_count") or 0) == 1 else "are"
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except (TypeError, ValueError):
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return "are"
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def _detail_allows(level: str, dense_only: bool = False) -> bool:
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level = (level or "balanced").strip().lower()
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if dense_only:
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return level == "dense"
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return level != "concise"
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def _single_from_row(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str] | None:
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subject = _clean_text(row.get("primary_subject") or row.get("subject") or "")
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if subject not in ("woman", "man"):
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return None
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caption_front = _single_caption_front(row)
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age = _clean_text(row.get("age") or row.get("age_band") or caption_front.get("caption_age") or "")
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body_phrase = _row_value(row, "body_phrase") or caption_front.get("caption_body_phrase", "")
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if not body_phrase:
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body = _clean_text(row.get("body_type") or row.get("body") or "")
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figure = _clean_text(row.get("figure"))
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body_phrase = _body_phrase(body, figure)
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skin = _row_value(row, "skin") or caption_front.get("caption_skin", "")
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hair = _row_value(row, "hair") or caption_front.get("caption_hair", "")
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eyes = _row_value(row, "eyes") or caption_front.get("caption_eyes", "")
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item = _row_value(row, "item", ITEM_LABELS)
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if item:
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item = _clean_clothing(item)
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if not item:
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item = _clean_clothing(_row_value(row, "clothing", ("Clothing", "Erotic outfit")))
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scene = _row_value(row, "scene_text", ("Scene", "Setting"))
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pose = _row_value(row, "pose", ("Pose",))
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expression = _row_value(row, "expression", ("Facial expression", "Facial expressions"))
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composition = _normalize_composition(_row_value(row, "composition", ("Composition",)))
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prop = _row_value(row, "prop", ("Prop/detail",))
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style = _row_value(row, "style") if keep_style else ""
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parts = []
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opener = _age_subject(age, subject)
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appearance_details = [piece for piece in (skin, hair, eyes) if piece]
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if body_phrase:
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parts.append(f"{opener} has {_article(body_phrase)} {body_phrase}")
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elif appearance_details:
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parts.append(f"{opener} has {_human_join(appearance_details)}")
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else:
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parts.append(opener)
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if body_phrase and appearance_details:
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parts.append(f"{pronoun(subject)} has {_human_join(appearance_details)}")
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if item:
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verb = "wears" if subject == "woman" else "is dressed in"
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parts.append(f"{pronoun(subject)} {verb} {item}")
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if prop:
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parts.append(f"{pronoun(subject)} is {prop}")
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if pose:
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parts.append(f"{pronoun(subject)} is {_pose_clause(pose)}")
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if expression:
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parts.append(f"{possessive_pronoun(subject)} expression is {expression}")
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if scene:
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parts.append(f"The setting is {scene}")
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if _detail_allows(detail_level) and composition:
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parts.append(f"The composition is {composition}")
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if keep_style and style:
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parts.append(f"The visual style is {style}")
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return _join_sentences(parts), "metadata(single)"
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def pronoun(subject: str) -> str:
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return "She" if subject == "woman" else "He"
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def possessive_pronoun(subject: str) -> str:
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return "Her" if subject == "woman" else "His"
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def _couple_from_row(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str] | None:
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subject = _clean_text(row.get("subject_phrase") or row.get("primary_subject"))
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primary = _clean_text(row.get("primary_subject"))
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if "couple" not in primary and subject not in ("two women", "two men", "a woman and a man"):
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if not primary.startswith("two ") and " and " not in subject:
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return None
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if subject == "woman and man":
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subject = "a woman and a man"
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ages = _row_value(row, "age", ("Ages",)) or _clean_text(row.get("age_band"))
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body = _row_value(row, "body", ("Body types",)) or _clean_text(row.get("body_type"))
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pose = _row_value(row, "pose", ("Pose",))
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pose = pose.replace(", affectionate and flirtatious but non-explicit", "")
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clothing = _clean_clothing(_row_value(row, "item", ITEM_LABELS) or _row_value(row, "clothing", ("Clothing",)))
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scene = _row_value(row, "scene_text", ("Scene", "Setting"))
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expression = _row_value(row, "expression", ("Facial expressions", "Facial expression"))
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composition = _normalize_composition(_row_value(row, "composition", ("Composition",)))
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style = _row_value(row, "style") if keep_style else ""
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parts = [f"{_cap_first(subject)} are adults"]
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if ages:
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parts.append(f"The age detail is {_clean_age_phrase(ages)}")
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if body:
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parts.append(f"Their body types are {body}")
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if clothing:
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parts.append(f"They wear {clothing}")
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if pose:
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parts.append(f"The pose is {pose}")
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if scene:
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parts.append(f"The setting is {scene}")
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if expression:
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parts.append(f"Their expressions are {expression}")
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if _detail_allows(detail_level) and composition:
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parts.append(f"The composition is {composition}")
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if keep_style and style:
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parts.append(f"The visual style is {style}")
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return _join_sentences(parts), "metadata(couple)"
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def _configured_cast_from_row(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str] | None:
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if _clean_text(row.get("subject_type")) != "configured_cast" and not _clean_text(row.get("women_count")):
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if "hardcore sexual poses" not in _clean_text(row.get("main_category")).lower():
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return None
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subject = _subject_phrase_from_counts(row)
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verb = _verb_for_row(row)
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cast = _row_value(row, "cast_summary", ("Cast",))
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role_graph = _row_value(row, "role_graph", ("Role graph",))
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item = _row_value(row, "item", ITEM_LABELS)
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scene = _row_value(row, "scene_text", ("Setting", "Scene"))
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expression = _row_value(row, "expression", ("Facial expressions", "Facial expression"))
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composition = _normalize_composition(_row_value(row, "composition", ("Composition",)))
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scene_kind = _row_value(row, "scene_kind") or "explicit adult sex scene"
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style = _row_value(row, "style") if keep_style else ""
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parts = [f"{_cap_first(subject)} {verb} shown as a consensual {scene_kind}, with all participants 21+"]
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if cast:
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parts.append(f"The cast is {cast}")
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if role_graph:
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parts.append(role_graph)
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if item:
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parts.append(f"The sexual pose is {item}")
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scene_bits = []
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if scene:
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scene_bits.append(f"set in {scene}")
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if expression:
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scene_bits.append(f"with {expression}")
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if composition:
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scene_bits.append(f"framed as {composition}")
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if scene_bits and _detail_allows(detail_level):
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parts.append(", ".join(scene_bits))
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if keep_style and style:
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parts.append(f"The visual style is {style}")
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return _join_sentences(parts), "metadata(configured_cast)"
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def _group_or_layout_from_row(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str] | None:
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primary = _clean_text(row.get("primary_subject"))
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if "group" not in primary and primary != "layout scene":
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return None
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subject = _row_value(row, "subject_phrase") or primary
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age = _row_value(row, "age", ("Ages",)) or _clean_text(row.get("age_band"))
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item = _clean_clothing(_row_value(row, "item", ITEM_LABELS) or _row_value(row, "clothing", ("Clothing",)))
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scene = _row_value(row, "scene_text", ("Scene", "Setting"))
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expression = _row_value(row, "expression", ("Facial expressions", "Facial expression"))
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composition = _normalize_composition(_row_value(row, "composition", ("Composition",)))
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style = _row_value(row, "style") if keep_style else ""
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if primary == "layout scene":
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parts = [f"{_cap_first(subject)} is arranged as an adults-only designed illustration layout"]
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if expression:
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parts.append(f"The featured expression is {expression}")
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else:
|
|
parts = [f"{_cap_first(subject)} includes adults"]
|
|
if age:
|
|
parts[0] += f" ages {age}"
|
|
if item:
|
|
parts.append(f"They wear {item}")
|
|
if expression:
|
|
parts.append(f"They show {expression}")
|
|
if scene:
|
|
parts.append(f"The setting is {scene}")
|
|
if _detail_allows(detail_level) and composition:
|
|
parts.append(f"The composition is {composition}")
|
|
if keep_style and style:
|
|
parts.append(f"The visual style is {style}")
|
|
return _join_sentences(parts), "metadata(group_layout)"
|
|
|
|
|
|
def _insta_of_pair_from_row(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str] | None:
|
|
if _clean_text(row.get("mode")).lower() != "insta/of":
|
|
return None
|
|
soft_row = row.get("softcore_row")
|
|
hard_row = row.get("hardcore_row")
|
|
if not isinstance(soft_row, dict) or not isinstance(hard_row, dict):
|
|
return None
|
|
|
|
hard_row_for_text = dict(hard_row)
|
|
options = row.get("options")
|
|
if isinstance(options, dict) and options.get("continuity") == "same_creator_same_room":
|
|
if soft_row.get("scene_text"):
|
|
hard_row_for_text["scene_text"] = soft_row["scene_text"]
|
|
if soft_row.get("composition"):
|
|
hard_row_for_text["composition"] = soft_row["composition"]
|
|
|
|
soft_text, _soft_method = _metadata_to_prose(soft_row, detail_level, keep_style)
|
|
hard_text, _hard_method = _metadata_to_prose(hard_row_for_text, detail_level, keep_style)
|
|
descriptor = _clean_text(row.get("shared_descriptor"))
|
|
cast_descriptors = row.get("shared_cast_descriptors")
|
|
if isinstance(cast_descriptors, list):
|
|
cast_descriptor_text = _human_join([_clean_text(item) for item in cast_descriptors if _clean_text(item)])
|
|
else:
|
|
cast_descriptor_text = _clean_text(cast_descriptors)
|
|
|
|
parts = []
|
|
if cast_descriptor_text:
|
|
parts.append(f"The shared cast descriptors are {cast_descriptor_text}")
|
|
elif descriptor:
|
|
parts.append(f"The shared creator descriptor is {descriptor}")
|
|
if soft_text:
|
|
parts.append(f"Softcore version: {soft_text}")
|
|
if hard_text:
|
|
parts.append(f"Hardcore version: {hard_text}")
|
|
if not parts:
|
|
return None
|
|
return _join_sentences(parts), "metadata(insta_of_pair)"
|
|
|
|
|
|
def _metadata_to_prose(row: dict[str, Any], detail_level: str, keep_style: bool) -> tuple[str, str]:
|
|
for builder in (
|
|
_insta_of_pair_from_row,
|
|
_configured_cast_from_row,
|
|
_single_from_row,
|
|
_couple_from_row,
|
|
_group_or_layout_from_row,
|
|
):
|
|
result = builder(row, detail_level, keep_style)
|
|
if result:
|
|
return result
|
|
return _text_to_prose(_clean_text(row.get("caption") or row.get("prompt")), detail_level, keep_style)
|
|
|
|
|
|
def _prompt_to_prose(text: str, detail_level: str, keep_style: bool) -> tuple[str, str] | None:
|
|
if ":" not in text:
|
|
return None
|
|
cast = _field_from_any_prompt(text, ("Cast",))
|
|
item = _field_from_any_prompt(text, ITEM_LABELS)
|
|
scene = _field_from_any_prompt(text, ("Setting", "Scene"))
|
|
pose = _field_from_any_prompt(text, ("Pose",))
|
|
role_graph = _field_from_any_prompt(text, ("Role graph",))
|
|
expression = _field_from_any_prompt(text, ("Facial expressions", "Facial expression"))
|
|
composition = _normalize_composition(_field_from_any_prompt(text, ("Composition",)))
|
|
if not any((cast, item, scene, pose, role_graph, expression, composition)):
|
|
return None
|
|
|
|
subject = _clean_text(text.split(":", 1)[0])
|
|
parts = []
|
|
if subject:
|
|
parts.append(f"{_cap_first(subject)}")
|
|
if cast:
|
|
parts.append(f"The cast is {cast}")
|
|
if role_graph:
|
|
parts.append(role_graph)
|
|
if item:
|
|
item_label = "sexual pose" if _field_from_any_prompt(text, ("Sexual pose",)) else "key detail"
|
|
parts.append(f"The {item_label} is {item}")
|
|
elif pose:
|
|
parts.append(f"The pose is {pose}")
|
|
scene_bits = []
|
|
if scene:
|
|
scene_bits.append(f"set in {scene}")
|
|
if expression:
|
|
scene_bits.append(f"with {expression}")
|
|
if composition:
|
|
scene_bits.append(f"framed as {composition}")
|
|
if scene_bits and _detail_allows(detail_level):
|
|
parts.append(", ".join(scene_bits))
|
|
if keep_style:
|
|
style = _clean_text(text.split(":", 1)[1].split(".", 1)[0])
|
|
if style:
|
|
parts.append(f"The visual style is {style}")
|
|
return _join_sentences(parts), "prompt(labels)"
|
|
|
|
|
|
def _parts_to_sentence(parts: list[str], detail_level: str) -> str:
|
|
parts = [part for part in (_clean_text(part).strip(" ,.") for part in parts) if part]
|
|
if not parts:
|
|
return ""
|
|
if len(parts) == 1:
|
|
return _sentence(parts[0])
|
|
subject = parts[0]
|
|
trailing_style = ""
|
|
if parts[-1].lower().endswith("illustration"):
|
|
trailing_style = parts.pop()
|
|
composition = parts[-1] if len(parts) >= 2 else ""
|
|
scene = parts[-2] if len(parts) >= 3 else ""
|
|
details = parts[1:-2] if len(parts) >= 3 else parts[1:]
|
|
sentences = [f"{_cap_first(subject)} includes {', '.join(details)}" if details else _cap_first(subject)]
|
|
if _detail_allows(detail_level) and scene:
|
|
sentences.append(f"The setting is {scene}")
|
|
if _detail_allows(detail_level) and composition:
|
|
sentences.append(f"The composition is {composition}")
|
|
if trailing_style and _detail_allows(detail_level, dense_only=True):
|
|
sentences.append(f"The visual style is {trailing_style}")
|
|
return _join_sentences(sentences)
|
|
|
|
|
|
def _text_to_prose(text: str, detail_level: str, keep_style: bool) -> tuple[str, str]:
|
|
text = _clean_text(text)
|
|
prompt_result = _prompt_to_prose(text, detail_level, keep_style)
|
|
if prompt_result:
|
|
return prompt_result
|
|
text = _remove_trigger(_strip_style_tail(text), DEFAULT_TRIGGER)
|
|
text = _remove_trigger(text, OLD_TRIGGER)
|
|
parts = [part.strip() for part in text.split(",")]
|
|
prose = _parts_to_sentence(parts, detail_level)
|
|
return prose or _sentence(text), "text(fallback)"
|
|
|
|
|
|
def naturalize_caption(
|
|
source_text: str,
|
|
metadata_json: str = "",
|
|
input_hint: str = "auto",
|
|
trigger: str = DEFAULT_TRIGGER,
|
|
include_trigger: bool = True,
|
|
detail_level: str = "balanced",
|
|
style_policy: str = "drop_style_tail",
|
|
) -> tuple[str, str]:
|
|
"""Rewrite tag-style prompt/caption text into compact natural language."""
|
|
input_hint = input_hint if input_hint in ("auto", "metadata_json", "caption_or_prompt") else "auto"
|
|
detail_level = detail_level if detail_level in ("concise", "balanced", "dense") else "balanced"
|
|
keep_style = style_policy == "keep_style_terms"
|
|
row, row_method = _row_from_inputs(source_text, metadata_json, input_hint)
|
|
if row is not None:
|
|
prose, method = _metadata_to_prose(row, detail_level, keep_style)
|
|
return _with_trigger(prose, trigger, include_trigger), f"{row_method}:{method}"
|
|
prose, method = _text_to_prose(source_text, detail_level, keep_style)
|
|
return _with_trigger(prose, trigger, include_trigger), method
|