700 lines
28 KiB
Python
700 lines
28 KiB
Python
import os
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import random
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import json
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import requests
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from datetime import datetime
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class PromptCreatorNode:
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@classmethod
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def INPUT_TYPES(cls):
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json_dir = os.path.join(os.path.dirname(__file__), "JSON_DATA")
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json_files = [f for f in os.listdir(json_dir) if f.endswith(".json")]
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# camera angles (optional) from camera_angles.json
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camera_angle_list = ["none"]
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camera_angles_path = os.path.join(os.path.dirname(__file__), "camera_angles.json")
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if os.path.exists(camera_angles_path):
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try:
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with open(camera_angles_path, "r", encoding="utf-8") as f:
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camera_data = json.load(f)
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ids = camera_data.get("ids", [])
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if isinstance(ids, list):
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camera_angle_list += [str(x) for x in ids if str(x).strip()]
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except Exception:
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pass
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# carico le chiavi dal file esterno system_prompt.json
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system_prompt_path = os.path.join(os.path.dirname(__file__), "system_prompt.json")
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if os.path.exists(system_prompt_path):
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with open(system_prompt_path, "r", encoding="utf-8") as f:
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system_prompts = json.load(f)
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enhancer_modes = list(system_prompts.keys())
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else:
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enhancer_modes = ["standard"]
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# carico le chiavi dal file esterno identities.json (volto consistente)
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identities_path = os.path.join(os.path.dirname(__file__), "identities.json")
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if os.path.exists(identities_path):
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try:
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with open(identities_path, "r", encoding="utf-8") as f:
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identities = json.load(f)
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identity_profiles = ["none", "external"] + list(identities.keys())
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except Exception:
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identity_profiles = ["none", "external"]
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else:
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identity_profiles = ["none", "external"]
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return {
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"required": {
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"json_name": (sorted(json_files),),
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"camera_angle": (camera_angle_list,),
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"use_enhancer": (["none", "ollama","llamacpp", "openai", "cohere", "gemini"],),
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"enhancer_mode": (enhancer_modes,),
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"system_prompt_lock": (["auto", "external"], {"default": "auto"}),
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"add_symbols": (["no", "yes"],),
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"seed": ("INT", {"default": 0}),
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"gender": (["neutral", "female", "2 female", "3 female", "female vampire", "anime woman", "male", "custom"],),
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"identity_profile": (identity_profiles,),
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"external_identity": ("STRING", {"default": "", "multiline": True, "forceInput": True}),
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"lock_identity": (["no", "yes"], {"default": "yes"}),
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"custom_intro": ("STRING", {"default": "", "multiline": True}),
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"custom_intro_id": (["Random","0","1","2","3","4","5","6"], {"default":"Random"}),
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"horror_intensity": (["auto"] + [str(i) for i in range(11)],),
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"sensuality_level": (["auto", "0", "1", "2", "3"],),
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"pose_mode": (["random", "world_pick", "lock"], {"default": "random"}),
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"pose_index": ("INT", {"default": 0, "min": 0, "max": 200, "step": 1}),
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"show_pose_preview": ("BOOLEAN", {"default": True}),
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"lora_triggers": ("STRING", {"default": ""}),
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"subject_count": (["1", "2", "3"],),
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"lock_last_prompt": (["no", "yes"], {"default": "no"}),
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"multi_object_count": ("INT", {"default": 1, "min": 1, "max": 5}),
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# Ollama settings
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"ollama_host": ("STRING", {"default": "http://10.10.10.2:11434"}),
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"ollama_model": ("STRING", {"default": "qwen3:8b"})
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}
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}
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RETURN_TYPES = ("STRING", "STRING") # esempio
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RETURN_NAMES = ("prompt", "pose_preview")
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FUNCTION = "generate_prompt"
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CATEGORY = "Prompt Creator"
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def _system_prompts(self):
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system_prompt_path = os.path.join(os.path.dirname(__file__), "system_prompt.json")
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if os.path.exists(system_prompt_path):
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with open(system_prompt_path, "r", encoding="utf-8") as f:
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return json.load(f)
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else:
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# fallback se manca il file
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return {
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"standard": (
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"You are a professional prompt enhancer for AI image generation. "
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"Return ONE long, multi-clause sentence (~120–160 words), richly descriptive. "
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"Cover: genre/style; era; foreground/midground/background; materials & textures; color palette (3–5 tones); "
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"lighting (type/direction/intensity); atmosphere; camera angle & lens (mm); composition rule; 3–5 post-process keywords. "
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"No story, no dialogue, no lists, no headings."
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)
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}
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def _identity_profiles(self):
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"""Carica i profili identità dal file identities.json (se presente)."""
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identities_path = os.path.join(os.path.dirname(__file__), "identities.json")
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if os.path.exists(identities_path):
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try:
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with open(identities_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data if isinstance(data, dict) else {}
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except Exception:
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return {}
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return {}
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def _identity_to_text(self, identity_obj):
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"""Converte un profilo identità in una stringa descrittiva coerente (prompt-only)."""
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if not isinstance(identity_obj, dict):
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return ""
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order = ["age", "face_type", "eyes", "nose", "mouth", "hair", "skin", "expression_base"]
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chunks = []
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for k in order:
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v = identity_obj.get(k) or identity_obj.get(k.upper())
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if isinstance(v, str) and v.strip():
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chunks.append(v.strip())
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return ", ".join(chunks)
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def _camera_angles(self):
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path = os.path.join(os.path.dirname(__file__), "camera_angles.json")
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if os.path.exists(path):
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try:
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception:
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pass
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return {"ids": ["front"], "map": {}}
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@staticmethod
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def _is_none(v):
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return (not v) or (str(v).strip().lower() == "none")
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def _resolve_camera_angle(self, ui_angle, world_data, angle_dict):
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"""
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Se UI ha un valore (≠ none) → usa quello e ignora il mondo.
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Se UI è none → eredita da world_data["camera_angle"] o ["CAMERA_ANGLE"].
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Mappa l'id in camera_angles.map se presente, altrimenti lascia il testo.
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"""
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if not self._is_none(ui_angle):
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chosen = ui_angle
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else:
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chosen = world_data.get("camera_angle") or world_data.get("CAMERA_ANGLE") or ""
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if not chosen:
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return ""
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mapped = (angle_dict or {}).get("map", {}).get(chosen)
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return mapped or chosen
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def _sanitize_world_camera(self, world_data, ui_angle):
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if not self._is_none(ui_angle) and world_data:
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wd = dict(world_data)
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wd.pop("camera_angle", None)
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wd.pop("CAMERA_ANGLE", None)
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return wd
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return world_data
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def _build_prompt_from_json(
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self,
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data,
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gender,
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custom_intro,
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custom_intro_id,
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horror_intensity,
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sensuality_level,
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subject_count,
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multi_object_count,
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camera_angle_txt="",
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):
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parts = []
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# 1) Camera angle (se presente) in testa
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if isinstance(camera_angle_txt, str) and camera_angle_txt.strip():
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parts.append(camera_angle_txt.strip())
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# 2) Gender / subject intro
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# Se gender=custom:
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# - se custom_intro (textarea) non è vuoto -> usa quello (override manuale)
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# - altrimenti usa custom_intro_id (0..6 o Random) dal world JSON (CUSTOM_INTRO come dict o list)
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if gender == "custom":
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ci = (custom_intro or "").strip()
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def _get_custom_intro_pool(world_data):
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pool = {}
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raw = None
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if isinstance(world_data, dict):
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raw = world_data.get("CUSTOM_INTRO") or world_data.get("custom_intro")
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# dict -> keys "0".."6"
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if isinstance(raw, dict):
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for k in [str(i) for i in range(7)]:
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v = raw.get(k)
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if isinstance(v, str) and v.strip():
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pool[k] = v.strip()
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# list -> indices 0..6
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elif isinstance(raw, list):
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for i in range(min(7, len(raw))):
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v = raw[i]
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if isinstance(v, str) and v.strip():
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pool[str(i)] = v.strip()
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return pool
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if not ci:
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pool = _get_custom_intro_pool(data)
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if custom_intro_id and custom_intro_id != "Random":
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chosen = pool.get(str(custom_intro_id))
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if chosen:
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ci = chosen
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if not ci and pool:
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ci = random.choice(list(pool.values()))
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if ci:
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parts.append(ci)
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else:
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gender_inserted = False
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if isinstance(data, dict) and gender in data:
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values = data[gender]
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if isinstance(values, list) and values:
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parts.append(random.choice(values))
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gender_inserted = True
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if not gender_inserted:
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gender_defaults = {
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"male": "a mysterious man",
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"female": "a beautiful woman",
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"2 female": "two beautiful womans",
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"3 female": "three unique beautiful womans",
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"female vampire": "a beautiful vampire woman",
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"anime woman": "a beautiful anime woman",
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}
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parts.append(gender_defaults.get(gender, "a striking figure"))
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# Optional color realm
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color_realm_value = None
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if "COLOR_REALM" in data:
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possible_realms = data["COLOR_REALM"]
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if isinstance(possible_realms, list) and possible_realms:
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color_realm_value = random.choice(possible_realms)
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print(f"[PromptCreator] Using COLOR_REALM: {color_realm_value}")
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parts.append(color_realm_value)
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multi_keys = ["OBJECTS", "ACCESSORIES"]
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# Pick values according to realm or flat lists
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if color_realm_value:
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for key in ["OUTFITS", "LIGHTING", "BACKGROUNDS", "OBJECTS", "ACCESSORIES", "ATMOSPHERES"]:
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values_by_realm = data.get(key, {}).get(color_realm_value, [])
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if isinstance(values_by_realm, list) and values_by_realm:
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if key in multi_keys:
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sampled = random.sample(values_by_realm, min(multi_object_count, len(values_by_realm)))
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parts.extend(sampled)
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else:
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parts.append(random.choice(values_by_realm))
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else:
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for key, values in data.items():
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if key.lower() in [
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"male", "female", "neutral",
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"horror_intensity", "sensuality_level",
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"color_realm",
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"system_prompt", "world_name",
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"custom_intro",
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"camera_angles", "camera_angle",
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"poses"
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]:
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continue
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if isinstance(values, list) and values:
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if key in multi_keys:
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sampled = random.sample(values, min(multi_object_count, len(values)))
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parts.extend(sampled)
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else:
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parts.append(random.choice(values))
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# Horror intensity
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if horror_intensity != "auto" and "HORROR_INTENSITY" in data:
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try:
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horror_level = int(horror_intensity)
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intensity_entries = data["HORROR_INTENSITY"]
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if isinstance(intensity_entries, dict):
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matching = intensity_entries.get(str(horror_level))
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if matching:
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parts.append(matching)
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except ValueError:
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pass
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# Sensuality level (modulatore simile a HORROR_INTENSITY)
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if sensuality_level != "auto" and "SENSUALITY_LEVEL" in data:
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try:
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s_level = int(sensuality_level)
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s_entries = data["SENSUALITY_LEVEL"]
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if isinstance(s_entries, dict):
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matching = s_entries.get(str(s_level))
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if matching:
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parts.append(matching)
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except ValueError:
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pass
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if subject_count != "1":
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parts.append(f"{subject_count} subjects present")
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return ", ".join(parts)
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def _read_api_keys(self, base_path):
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keys_path = os.path.join(base_path, "api_keys.txt")
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keys = {}
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if os.path.exists(keys_path):
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with open(keys_path, "r", encoding="utf-8") as f:
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for line in f:
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if "=" in line:
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k, v = line.strip().split("=", 1)
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keys[k.strip()] = v.strip()
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return keys
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def _enhance_with_ollama(self, host, model, system_prompt, user_prompt):
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# Prefer chat to support system role
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r = requests.post(f"{host}/api/chat", json={
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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],
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"stream": False
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}, timeout=120)
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r.raise_for_status()
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return r.json()["message"]["content"].strip()
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def _enhance_with_openai(self, base_path, system_prompt, user_prompt):
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import openai
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keys = self._read_api_keys(base_path)
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openai.api_key = keys.get("openai", "")
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resp = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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)
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return resp.choices[0].message.content.strip()
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import requests
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def _enhance_with_llamacpp(self, host, system_prompt, user_prompt, temperature=0.7, top_p=0.9, n_predict=220):
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"""
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llama.cpp server backend.
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Tries OpenAI-compatible endpoint first (/v1/chat/completions), then falls back to (/completion).
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host example: http://127.0.0.1:11434
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"""
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host = host.rstrip("/")
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# 1) Try OpenAI-compatible chat endpoint
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try:
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r = requests.post(
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f"{host}/v1/chat/completions",
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json={
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"model": "llama", # ignored by llama.cpp in many builds; kept for compatibility
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": float(temperature),
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"top_p": float(top_p),
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"max_tokens": int(n_predict),
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"stream": False,
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},
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timeout=120,
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)
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if r.status_code == 200:
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j = r.json()
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# OpenAI-like: choices[0].message.content
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content = (
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j.get("choices", [{}])[0]
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.get("message", {})
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.get("content", "")
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)
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content = (content or "").strip()
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if content:
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return content
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# if non-200, fall through to fallback
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except Exception:
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pass
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# 2) Fallback to llama.cpp native /completion
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r = requests.post(
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f"{host}/completion",
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json={
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"prompt": f"{system_prompt}\n\n{user_prompt}",
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"temperature": float(temperature),
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"top_p": float(top_p),
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"n_predict": int(n_predict),
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"stream": False,
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},
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timeout=120,
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)
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r.raise_for_status()
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j = r.json()
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# llama.cpp /completion commonly returns: {"content": "..."} or {"completion": "..."} depending on version
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content = (j.get("content") or j.get("completion") or "").strip()
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if not content:
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raise RuntimeError(f"llama.cpp returned no content. Keys: {list(j.keys())}")
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return content
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def _enhance_with_cohere(self, base_path, system_prompt, user_prompt):
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import re
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try:
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import cohere
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except ImportError:
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print("[PromptCreator] Cohere SDK not installed, skipping enhance.")
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return user_prompt
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keys = self._read_api_keys(base_path)
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api_key = keys.get("cohere", "").strip()
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if not api_key:
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print("[PromptCreator] No Cohere API key found, skipping enhance.")
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return user_prompt
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# Usa il client V2 (nuova API)
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try:
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co = cohere.ClientV2(api_key=api_key)
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except Exception as e:
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print(f"[PromptCreator] Cohere ClientV2 not available: {e}")
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return user_prompt
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# Messaggi Chat v2
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system_msg = (
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f"{system_prompt}\n\n"
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"You are a prompt enhancer for image generation. "
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"Expand the user seed into ONE single, long, flowing sentence "
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"with multiple clauses separated by commas and semicolons. "
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"Do NOT use bullet points or numbered lists. "
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"Keep the original mood and style."
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)
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user_msg = (
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f"Seed: {user_prompt}\n\n"
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"Return exactly one long sentence (around 100 to 150 words), no line breaks."
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)
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# === BLOCCO TRY PRINCIPALE ===
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try:
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resp = co.chat(
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model="command-r-08-2024",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_msg},
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],
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max_tokens=700,
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temperature=0.9,
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)
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content = ""
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if hasattr(resp, "message") and getattr(resp.message, "content", None):
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parts = resp.message.content
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texts = [getattr(p, "text", "") for p in parts if getattr(p, "text", None)]
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content = " ".join(texts).strip()
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if not content:
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print("[PromptCreator] Cohere returned empty content.")
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return user_prompt
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content = re.sub(r"\s*\n+\s*", " ", content).strip()
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return content
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# === ECCO IL BLOCCO EXCEPT CHE ORA È ALLINEATO PERFETTAMENTE ===
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except Exception as e:
|
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print(f"[PromptCreator] Error calling Cohere chat: {e}")
|
||
return user_prompt
|
||
|
||
|
||
def _enhance_with_gemini(self, base_path, system_prompt, user_prompt):
|
||
import google.generativeai as genai
|
||
keys = self._read_api_keys(base_path)
|
||
genai.configure(api_key=keys.get("gemini", ""))
|
||
# In Gemini, passiamo tutto come testo
|
||
model = genai.GenerativeModel("models/gemini-2.5-pro")
|
||
txt = (
|
||
f"SYSTEM: {system_prompt}\n"
|
||
f"USER: {user_prompt}\n"
|
||
"Return only the final prompt as a single paragraph."
|
||
)
|
||
r = model.generate_content(txt)
|
||
return r.candidates[0].content.parts[0].text.strip()
|
||
|
||
@staticmethod
|
||
def log_prompt_run(
|
||
json_name,
|
||
enhancer_mode,
|
||
gender,
|
||
custom_intro,
|
||
lora_triggers,
|
||
final_prompt,
|
||
source="generated",
|
||
node_version="1.5.0",
|
||
):
|
||
"""Append one JSONL entry per run to ./logs/prompt_history.jsonl."""
|
||
log_dir = os.path.join(os.path.dirname(__file__), "logs")
|
||
os.makedirs(log_dir, exist_ok=True)
|
||
|
||
log_path = os.path.join(log_dir, "prompt_history.jsonl")
|
||
|
||
entry = {
|
||
"timestamp": datetime.now().isoformat(timespec="seconds"),
|
||
"json_world": json_name,
|
||
"system_prompt": enhancer_mode,
|
||
"gender": gender,
|
||
"custom_intro": custom_intro.strip() if (gender == "custom" and isinstance(custom_intro, str) and custom_intro.strip()) else None,
|
||
"lora_triggers": lora_triggers,
|
||
"final_prompt": final_prompt,
|
||
"source": source,
|
||
"node_version": node_version
|
||
}
|
||
|
||
with open(log_path, "a", encoding="utf-8") as f:
|
||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||
|
||
|
||
|
||
|
||
def generate_prompt(self, json_name, camera_angle, use_enhancer, enhancer_mode, system_prompt_lock, add_symbols, seed, gender, identity_profile, external_identity, lock_identity, custom_intro, custom_intro_id, horror_intensity, sensuality_level, pose_mode, pose_index, show_pose_preview, lora_triggers, subject_count, lock_last_prompt, multi_object_count, ollama_host, ollama_model):
|
||
base_path = os.path.dirname(__file__)
|
||
json_path = os.path.join(base_path, "JSON_DATA", json_name)
|
||
|
||
# history lock
|
||
history_path = os.path.join(base_path, "history", f"last_prompt_{json_name}.txt")
|
||
if lock_last_prompt == "yes" and os.path.exists(history_path):
|
||
with open(history_path, "r", encoding="utf-8") as f:
|
||
prompt = f.read().strip()
|
||
self.log_prompt_run(
|
||
json_name=json_name,
|
||
enhancer_mode=enhancer_mode,
|
||
gender=gender,
|
||
custom_intro=custom_intro,
|
||
lora_triggers=lora_triggers,
|
||
final_prompt=prompt,
|
||
source="history_lock",
|
||
node_version="1.5.0"
|
||
)
|
||
print("[PromptCreator] Prompt locked and loaded from history")
|
||
return (prompt, )
|
||
|
||
try:
|
||
with open(json_path, "r", encoding="utf-8") as f:
|
||
data = json.load(f)
|
||
except Exception as e:
|
||
print(f"[PromptCreator] Errore nel caricamento del JSON: {e}")
|
||
return ("",)
|
||
|
||
# world-level SYSTEM_PROMPT (unified JSON) if present
|
||
world_system_prompt = None
|
||
if isinstance(data, dict):
|
||
wsp = data.get("SYSTEM_PROMPT") or data.get("system_prompt")
|
||
if isinstance(wsp, str) and wsp.strip():
|
||
world_system_prompt = wsp.strip()
|
||
# Camera angle resolution (UI overrides world)
|
||
angle_dict = self._camera_angles()
|
||
camera_angle_txt = self._resolve_camera_angle(camera_angle, data, angle_dict)
|
||
data = self._sanitize_world_camera(data, camera_angle)
|
||
|
||
# Seed opzionale per coerenza
|
||
if seed:
|
||
random.seed(seed)
|
||
|
||
# Identity profile (prompt-only consistency)
|
||
identity_txt = ""
|
||
if lock_identity == "yes":
|
||
if identity_profile == "external":
|
||
identity_txt = (external_identity or "").strip()
|
||
elif identity_profile != "none":
|
||
identities = self._identity_profiles()
|
||
identity_txt = self._identity_to_text(identities.get(identity_profile, {}))
|
||
print("[PromptCreator][DEBUG] identity_profile:", identity_profile)
|
||
print("[PromptCreator][DEBUG] lock_identity:", lock_identity)
|
||
print("[PromptCreator][DEBUG] identity_txt:", repr(identity_txt))
|
||
|
||
|
||
|
||
# Costruisci prompt base
|
||
prompt = self._build_prompt_from_json(
|
||
data, gender, custom_intro, custom_intro_id, horror_intensity, sensuality_level, subject_count, multi_object_count, camera_angle_txt=camera_angle_txt
|
||
)
|
||
# --- POSE CONTROL ---
|
||
poses = data.get("POSES") or data.get("poses") or []
|
||
poses = poses if isinstance(poses, list) else []
|
||
pose_preview = ""
|
||
chosen_pose = ""
|
||
|
||
if show_pose_preview and poses:
|
||
lines = [f"POSES ({len(poses)}):"]
|
||
for i, p in enumerate(poses):
|
||
marker = " ->" if (chosen_pose and str(p).strip() == chosen_pose) else " "
|
||
lines.append(f"{marker} {i}: {p}")
|
||
pose_preview = "\n".join(lines)
|
||
elif show_pose_preview:
|
||
pose_preview = "POSES: (none found in this world JSON)"
|
||
|
||
pose_path = os.path.join(base_path, "history", f"last_pose_{json_name}.txt")
|
||
|
||
def pick_pose_by_index(i):
|
||
if not poses:
|
||
return ""
|
||
i = max(0, min(int(i), len(poses) - 1))
|
||
return str(poses[i]).strip()
|
||
|
||
|
||
|
||
if pose_mode == "world_pick":
|
||
chosen_pose = pick_pose_by_index(pose_index)
|
||
|
||
elif pose_mode == "lock":
|
||
if os.path.exists(pose_path):
|
||
with open(pose_path, "r", encoding="utf-8") as f:
|
||
chosen_pose = f.read().strip()
|
||
if not chosen_pose:
|
||
chosen_pose = pick_pose_by_index(pose_index)
|
||
|
||
else: # random
|
||
if poses:
|
||
chosen_pose = random.choice(poses).strip()
|
||
|
||
if chosen_pose:
|
||
prompt = prompt + ", " + chosen_pose
|
||
os.makedirs(os.path.dirname(pose_path), exist_ok=True)
|
||
with open(pose_path, "w", encoding="utf-8") as f:
|
||
f.write(chosen_pose)
|
||
# --- END POSE CONTROL ---
|
||
|
||
if identity_txt:
|
||
prompt = prompt + ", " + identity_txt
|
||
print("[PromptCreator][DEBUG] prompt_pre_enhancer:", prompt)
|
||
|
||
|
||
# System prompt condiviso per tutti i backend
|
||
system_prompts = self._system_prompts()
|
||
effective_world_system_prompt = None if system_prompt_lock == "external" else world_system_prompt
|
||
system_prompt = effective_world_system_prompt or system_prompts.get(enhancer_mode, system_prompts["standard"])
|
||
user_prompt = f"Enhance this prompt: {prompt}"
|
||
|
||
# Enhancer backends
|
||
if use_enhancer != "none":
|
||
print("[PromptCreator][DEBUG] use_enhancer:", use_enhancer)
|
||
print("[PromptCreator][DEBUG] enhancer_mode:", enhancer_mode)
|
||
print("[PromptCreator][DEBUG] system_prompt:", system_prompt)
|
||
print("[PromptCreator][DEBUG] user_prompt:", user_prompt)
|
||
|
||
try:
|
||
if use_enhancer == "ollama":
|
||
prompt = self._enhance_with_ollama(ollama_host, ollama_model, system_prompt, user_prompt)
|
||
elif use_enhancer == "llamacpp":
|
||
prompt = self._enhance_with_llamacpp(ollama_host, system_prompt, user_prompt)
|
||
elif use_enhancer == "openai":
|
||
prompt = self._enhance_with_openai(base_path, system_prompt, user_prompt)
|
||
elif use_enhancer == "cohere":
|
||
prompt = self._enhance_with_cohere(base_path, system_prompt, user_prompt)
|
||
elif use_enhancer == "gemini":
|
||
prompt = self._enhance_with_gemini(base_path, system_prompt, user_prompt)
|
||
except Exception as e:
|
||
print(f"[PromptCreator] Errore nell'enhancer ({use_enhancer}): {e}")
|
||
|
||
# LoRA triggers
|
||
if lora_triggers.strip():
|
||
lts = [x.strip() for x in lora_triggers.split(",") if x.strip()]
|
||
if lts:
|
||
prompt += ", " + ", ".join(lts)
|
||
print(f"[PromptCreator] LoRA triggers aggiunti: {lts}")
|
||
|
||
if add_symbols == "yes":
|
||
prompt = f"[{prompt}]"
|
||
|
||
os.makedirs(os.path.join(base_path, "history"), exist_ok=True)
|
||
with open(history_path, "w", encoding="utf-8") as f:
|
||
f.write(prompt.strip())
|
||
|
||
print(f"[PromptCreator] Prompt finale: {prompt}")
|
||
self.log_prompt_run(
|
||
json_name=json_name,
|
||
enhancer_mode=enhancer_mode,
|
||
gender=gender,
|
||
custom_intro=custom_intro,
|
||
lora_triggers=lora_triggers,
|
||
final_prompt=prompt,
|
||
source="generated",
|
||
node_version="1.5.0"
|
||
)
|
||
return (prompt, pose_preview)
|