472 lines
19 KiB
Python
472 lines
19 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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TRIGGER_CANDIDATES = (
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"sxcpinup_coloredpencil",
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"sxcppnl7",
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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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def _clean(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 _sentence(text: str) -> str:
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text = _clean(text).strip(" ,;")
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if not text:
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return ""
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text = text[:1].upper() + text[1:]
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if text[-1] not in ".!?":
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text += "."
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return text
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def _paragraph(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 _with_indefinite_article(text: str) -> str:
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text = _clean(text)
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if not text or text.lower().startswith(("a ", "an ")):
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return text
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article = "an" if text[:1].lower() in "aeiou" else "a"
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return f"{article} {text}"
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def _maybe_json(text: str) -> dict[str, Any] | None:
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text = _clean(text)
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if 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 _strip_trigger(text: str, preserve_trigger: bool) -> str:
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text = _clean(text)
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if preserve_trigger:
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return text
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for trigger in TRIGGER_CANDIDATES:
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if text.lower().startswith(trigger.lower() + ","):
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return text[len(trigger) + 1 :].strip(" ,")
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if text.lower().startswith(trigger.lower() + "."):
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return text[len(trigger) + 1 :].strip(" ,")
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return text
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def _split_avoid(text: str) -> tuple[str, str]:
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match = re.search(r"\bAvoid:\s*(.*)$", text)
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if not match:
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return text, ""
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return text[: match.start()].strip(" ."), match.group(1).strip(" .")
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def _prompt_field(text: str, label: str) -> str:
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text = _clean(text)
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if not text:
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return ""
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labels = "|".join(re.escape(name) for name in PROMPT_FIELD_LABELS)
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pattern = rf"{re.escape(label)}:\s*(.*?)(?=\. (?:{labels}):|\. Use\b|\. Avoid\b|$)"
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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(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(row.get(key, ""))
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if value:
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return value
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prompt = _clean(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 _body_phrase(body: Any, figure_note: Any = "") -> str:
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body = _clean(body)
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figure_note = _clean(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 = _strip_trigger(_clean(row.get("caption")), False)
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if not caption:
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return {}
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subject = _clean(row.get("primary_subject"))
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age = _clean(row.get("age_band") or row.get("age"))
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body = _clean(row.get("body_phrase"))
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if not body:
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body_type = _clean(row.get("body_type") or row.get("body"))
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figure = _clean(row.get("figure"))
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body = _body_phrase(body_type, figure)
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front = f"{subject}, {age}, {body}, "
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if subject in ("woman", "man") and age and body 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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return {"body_phrase": body, "skin": skin, "hair": hair, "eyes": eyes}
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return {}
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def _combine_negative(*parts: str) -> str:
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cleaned = [_clean(part).strip(" ,.") for part in parts if _clean(part).strip(" ,.")]
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return ", ".join(cleaned)
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def _clean_age(age: Any) -> str:
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return _clean(age)
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def _age_detail_phrase(age: Any) -> str:
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text = _clean(age)
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text = re.sub(r"\s+adults?$", "", text).strip()
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return text.replace("-year-old", " years old")
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def _age_subject(row: dict[str, Any], fallback_subject: str = "adult person") -> str:
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subject = _clean(row.get("subject_phrase") or row.get("primary_subject") or row.get("subject") or fallback_subject)
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age = _clean_age(row.get("age_band") or row.get("age"))
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if row.get("subject_type") == "configured_cast":
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return _clean(row.get("subject_phrase") or subject)
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if subject in ("woman", "man"):
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if age:
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return f"{age} {subject}" if "adult" in age.lower() else f"{age} adult {subject}"
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return f"adult {subject}"
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if age and "adult" not in subject.lower():
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return f"{age} {subject}"
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return subject or fallback_subject
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def _appearance_phrase(row: dict[str, Any]) -> str:
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front = _single_caption_front(row)
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parts = [
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_row_value(row, "body_phrase") or front.get("body_phrase"),
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_row_value(row, "skin") or front.get("skin"),
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_row_value(row, "hair") or front.get("hair"),
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_row_value(row, "eyes") or front.get("eyes"),
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]
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return ", ".join(_clean(part) for part in parts if _clean(part))
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def _camera_phrase(row: dict[str, Any]) -> str:
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directive = _clean(row.get("camera_directive"))
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if directive:
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return directive
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config = row.get("camera_config")
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if isinstance(config, dict):
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mode = _clean(config.get("camera_mode")).replace("_", " ")
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shot = _clean(config.get("shot_size")).replace("_", " ")
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angle = _clean(config.get("angle")).replace("_", " ")
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pieces = [piece for piece in (mode, shot, angle) if piece and piece != "auto" and piece != "standard"]
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if pieces:
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return "Camera framing uses " + ", ".join(pieces)
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return ""
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def _style_phrase(row: dict[str, Any], style_mode: str) -> str:
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if style_mode == "minimal":
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return ""
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if style_mode == "photographic":
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return "realistic creator-shot photography with natural lighting, tactile skin and fabric detail, and clean social-media composition"
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style = _clean(row.get("style"))
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suffix = _clean(row.get("positive_suffix")) or _prompt_field(_clean(row.get("prompt")), "Use")
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if style and suffix:
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return f"{style}; {suffix}"
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return style or suffix
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def _couple_clothing_phrase(item: str) -> str:
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item = _clean(item)
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lower = item.lower()
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partner_text = re.sub(r"\bPartner ([AB]) wears\b", r"Partner \1 wearing", item)
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partner_text = re.sub(r"\bPartner ([AB]) has\b", r"Partner \1 with", partner_text)
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if lower.startswith("partner a "):
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return f"The outfits show {partner_text}"
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if lower.startswith(("two ", "paired ", "coordinated ")):
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return f"The outfits are {partner_text}"
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return f"The couple wears {item}"
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def _normal_row_to_krea(row: dict[str, Any], detail_level: str, style_mode: str) -> tuple[str, str]:
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subject_type = _clean(row.get("subject_type"))
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primary = _clean(row.get("primary_subject"))
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item = _row_value(row, "item", ("Sexual pose", "Erotic outfit", "Clothing")) or _clean(row.get("custom_item"))
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item = re.sub(r",?\s*(fashion editorial|resort) styling$", "", item, flags=re.IGNORECASE)
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scene = _row_value(row, "scene_text", ("Setting", "Scene")) or _clean(row.get("scene"))
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pose = _row_value(row, "pose", ("Sexual pose", "Pose"))
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expression = _row_value(row, "expression", ("Facial expressions", "Facial expression"))
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composition = re.sub(r"^vertical\s+", "", _row_value(row, "composition", ("Composition",)), flags=re.IGNORECASE)
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camera = _camera_phrase(row)
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style = _style_phrase(row, style_mode)
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if subject_type == "configured_cast" or _clean(row.get("cast_summary")):
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subject = _clean(row.get("subject_phrase") or primary or "adult sexual scene")
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cast = _clean(row.get("cast_summary"))
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role_graph = _clean(row.get("role_graph"))
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parts = [
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f"A consensual explicit adult scene with {subject}, all participants 21+ and visibly adult",
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f"The cast includes {cast}" if cast else "",
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role_graph,
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f"The sexual action is {item}" if item else "",
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f"The setting is {scene}" if scene else "",
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f"Facial expressions are {expression}" if expression else "",
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f"The image is framed as {composition}" if composition else "",
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camera,
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style if detail_level != "concise" else "",
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]
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return _paragraph(parts), "metadata(configured_cast)"
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if primary in ("woman", "man") or subject_type in ("woman", "man", "single_any"):
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subject = _age_subject(row, "adult woman")
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appearance = _appearance_phrase(row)
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parts = [
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_with_indefinite_article(subject),
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f"with {appearance}" if appearance else "",
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f"wearing {item}" if item else "",
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f"{pose}" if pose else "",
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f"with {expression}" if expression else "",
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f"in {scene}" if scene else "",
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f"framed as {composition}" if composition else "",
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camera,
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style if detail_level != "concise" else "",
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]
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return _paragraph([", ".join(part for part in parts[:6] if part), *parts[6:]]), "metadata(single)"
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if subject_type == "couple" or primary in ("two women", "two men", "a woman and a man"):
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subject = _clean(row.get("subject_phrase") or primary or "adult couple")
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if subject == "woman and man":
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subject = "a woman and a man"
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ages = _age_detail_phrase(_row_value(row, "age", ("Ages",)) or row.get("age_band"))
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body = _row_value(row, "body", ("Body types",)) or _clean(row.get("body_type"))
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parts = [
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f"An adult couple: {subject}, all visibly adult",
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f"Age detail: {ages}" if ages else "",
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f"Body types: {body}" if body else "",
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_couple_clothing_phrase(item) if item else "",
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f"The pose is {pose}" if pose else "",
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f"The setting is {scene}" if scene else "",
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f"Facial expressions are {expression}" if expression else "",
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f"The image is framed as {composition}" if composition else "",
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camera,
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style if detail_level != "concise" else "",
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]
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return _paragraph(parts), "metadata(couple)"
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subject = _age_subject(row, primary or "adult scene")
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parts = [
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f"{subject}",
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f"featuring {item}" if item else "",
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f"in {scene}" if scene else "",
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f"with {expression}" if expression else "",
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f"framed as {composition}" if composition else "",
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camera,
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style if detail_level != "concise" else "",
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]
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return _paragraph(parts), "metadata(generic)"
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def _insta_pair_to_krea(row: dict[str, Any], detail_level: str, style_mode: str) -> tuple[str, str, str, str]:
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descriptor = _clean(row.get("shared_descriptor"))
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cast_descriptors = row.get("shared_cast_descriptors")
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if isinstance(cast_descriptors, list):
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cast_descriptor_text = "; ".join(_clean(item) for item in cast_descriptors if _clean(item))
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else:
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cast_descriptor_text = _clean(cast_descriptors)
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soft = row.get("softcore_row") if isinstance(row.get("softcore_row"), dict) else {}
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hard = row.get("hardcore_row") if isinstance(row.get("hardcore_row"), dict) else {}
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soft_camera = _clean(row.get("softcore_camera_directive")) or _camera_phrase(soft)
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hard_camera = _clean(row.get("hardcore_camera_directive")) or _camera_phrase(hard)
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soft_style = _style_phrase(soft, style_mode)
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hard_style = _style_phrase(hard, style_mode)
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options = row.get("options") if isinstance(row.get("options"), dict) else {}
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soft_level = _clean(options.get("softcore_level")).replace("_", " ")
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hard_level = _clean(options.get("hardcore_level")).replace("_", " ")
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hard_cast = _clean(row.get("hardcore_women_count"))
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hard_men = _clean(row.get("hardcore_men_count"))
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hard_cast_text = _clean(hard.get("cast_summary")) or (
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f"{hard_cast} adult women and {hard_men} adult men" if hard_cast or hard_men else ""
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)
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same_room = options.get("continuity") == "same_creator_same_room"
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hard_scene = soft.get("scene_text") if same_room and soft.get("scene_text") else hard.get("scene_text")
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hard_composition = soft.get("composition") if same_room and soft.get("composition") else hard.get("composition")
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soft_cast_descriptor_text = (
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cast_descriptor_text
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if options.get("softcore_cast") == "same_as_hardcore"
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else f"Woman A / primary creator: {descriptor}"
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)
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same_soft_cast = options.get("softcore_cast") == "same_as_hardcore"
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soft_cast_presence = (
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"The same listed adult cast is present together in this softcore version in a non-explicit teaser pose, with no sex act or genital contact"
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if same_soft_cast
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else "The softcore version focuses on Woman A alone"
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)
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partner_styling = row.get("softcore_partner_styling")
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if isinstance(partner_styling, dict):
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outfits = partner_styling.get("outfits")
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partner_outfit_text = "; ".join(_clean(item) for item in outfits if _clean(item)) if isinstance(outfits, list) else ""
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partner_pose = _clean(partner_styling.get("pose"))
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else:
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partner_outfit_text = ""
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partner_pose = ""
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soft_parts = [
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f"A visibly adult creator, {descriptor}",
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f"Shared cast descriptors: {soft_cast_descriptor_text}" if same_soft_cast and soft_cast_descriptor_text else "",
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f"Softcore primary creator descriptor: {soft_cast_descriptor_text}" if not same_soft_cast and soft_cast_descriptor_text else "",
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soft_cast_presence,
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f"Partner softcore styling: {partner_outfit_text}" if partner_outfit_text else "",
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f"The shared softcore cast pose is {partner_pose}" if partner_pose else "",
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f"shown in a {soft_level or 'softcore'} Insta/OF creator image",
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f"wearing {soft.get('item')}" if soft.get("item") else "",
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f"{soft.get('pose')}" if soft.get("pose") else "",
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f"with {soft.get('expression')}" if soft.get("expression") else "",
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f"in {soft.get('scene_text')}" if soft.get("scene_text") else "",
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f"framed as {soft.get('composition')}" if soft.get("composition") else "",
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soft_camera,
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soft_style if detail_level != "concise" else "",
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]
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hard_parts = [
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f"The same visibly adult creator, {descriptor}, is the visually central woman in a consensual explicit adult {hard_level or 'hardcore'} scene",
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f"{'Shared' if same_soft_cast else 'Hardcore'} cast descriptors: {cast_descriptor_text}" if cast_descriptor_text else "",
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f"all participants are 21+ and visibly adult; the cast includes {hard_cast_text}" if hard_cast_text else "all participants are 21+ and visibly adult",
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_clean(hard.get("role_graph")),
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f"The sexual action is {hard.get('item')}" if hard.get("item") else "",
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f"set in {hard_scene}" if hard_scene else "",
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f"with {hard.get('expression')}" if hard.get("expression") else "",
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f"framed as {hard_composition}" if hard_composition else "",
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hard_camera,
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hard_style if detail_level != "concise" else "",
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]
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return (
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_paragraph(soft_parts),
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_combine_negative(row.get("softcore_negative_prompt")),
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_paragraph(hard_parts),
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_combine_negative(row.get("hardcore_negative_prompt")),
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)
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def _fallback_text_to_krea(
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source_text: str,
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preserve_trigger: bool,
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detail_level: str,
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style_mode: str,
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) -> tuple[str, str, str]:
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positive, negative = _split_avoid(_strip_trigger(source_text, preserve_trigger))
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positive = re.sub(r"\b(?:Scene|Setting):", "The setting is", positive)
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positive = re.sub(r"\b(?:Pose|Sexual pose):", "The pose is", positive)
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positive = re.sub(r"\bFacial expressions?:", "The facial expression is", positive)
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positive = re.sub(r"\bComposition:", "The composition is", positive)
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positive = re.sub(r"\bRole graph:", "The role choreography is", positive)
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positive = re.sub(r"\bUse\b", "Use", positive)
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positive = _clean(positive)
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return _paragraph([positive]), negative, "text(fallback)"
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def format_krea2_prompt(
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source_text: str,
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metadata_json: str = "",
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negative_prompt: str = "",
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input_hint: str = "auto",
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target: str = "auto",
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detail_level: str = "balanced",
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style_mode: str = "preserve",
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preserve_trigger: bool = False,
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extra_positive: str = "",
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extra_negative: str = "",
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) -> dict[str, str]:
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detail_level = detail_level if detail_level in ("concise", "balanced", "dense") else "balanced"
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style_mode = style_mode if style_mode in ("preserve", "photographic", "minimal") else "preserve"
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target = target if target in ("auto", "single", "softcore", "hardcore") else "auto"
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|
row, method = _row_from_inputs(source_text, metadata_json, input_hint)
|
|
extracted_negative = ""
|
|
|
|
if row and row.get("mode") == "Insta/OF":
|
|
soft_prompt, soft_negative, hard_prompt, hard_negative = _insta_pair_to_krea(row, detail_level, style_mode)
|
|
selected = hard_prompt if target == "hardcore" else soft_prompt if target == "softcore" else soft_prompt
|
|
selected_negative = hard_negative if target == "hardcore" else soft_negative
|
|
if extra_positive.strip():
|
|
selected = f"{selected.rstrip()} {extra_positive.strip()}"
|
|
soft_prompt = f"{soft_prompt.rstrip()} {extra_positive.strip()}"
|
|
hard_prompt = f"{hard_prompt.rstrip()} {extra_positive.strip()}"
|
|
negative = _combine_negative(selected_negative, negative_prompt, extra_negative)
|
|
return {
|
|
"krea_prompt": selected,
|
|
"negative_prompt": negative,
|
|
"krea_softcore_prompt": soft_prompt,
|
|
"krea_hardcore_prompt": hard_prompt,
|
|
"softcore_negative_prompt": _combine_negative(soft_negative, extra_negative),
|
|
"hardcore_negative_prompt": _combine_negative(hard_negative, extra_negative),
|
|
"method": f"{method}:krea2(insta_of_pair)",
|
|
}
|
|
|
|
if row:
|
|
prompt, kind = _normal_row_to_krea(row, detail_level, style_mode)
|
|
extracted_negative = _clean(row.get("negative_prompt"))
|
|
method = f"{method}:krea2({kind})"
|
|
else:
|
|
prompt, extracted_negative, method = _fallback_text_to_krea(source_text, preserve_trigger, detail_level, style_mode)
|
|
|
|
if extra_positive.strip():
|
|
prompt = f"{prompt.rstrip()} {extra_positive.strip()}"
|
|
negative = _combine_negative(extracted_negative, negative_prompt, extra_negative)
|
|
return {
|
|
"krea_prompt": prompt,
|
|
"negative_prompt": negative,
|
|
"krea_softcore_prompt": "",
|
|
"krea_hardcore_prompt": "",
|
|
"softcore_negative_prompt": "",
|
|
"hardcore_negative_prompt": "",
|
|
"method": method,
|
|
}
|