427 lines
16 KiB
Python
427 lines
16 KiB
Python
"""
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PromptManager: Main custom node implementation that extends CLIPTextEncode
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with persistent prompt storage and search capabilities.
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"""
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import datetime
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import hashlib
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import json
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import os
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import time
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import webbrowser
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from typing import Any, Dict, List, Optional, Tuple
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# Import logging system
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try:
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from .utils.logging_config import get_logger
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except ImportError:
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from utils.logging_config import get_logger
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try:
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from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
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except ImportError:
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# Fallback for older ComfyUI versions
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class ComfyNodeABC:
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pass
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class IO:
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STRING = "STRING"
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CLIP = "CLIP"
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CONDITIONING = "CONDITIONING"
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InputTypeDict = dict
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try:
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from .database.operations import PromptDatabase
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from .utils.comfyui_integration import get_comfyui_integration
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from .utils.image_monitor import ImageMonitor
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from .utils.prompt_tracker import PromptExecutionContext, PromptTracker
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except ImportError:
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# For direct imports when not in a package
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from database.operations import PromptDatabase
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from utils.comfyui_integration import get_comfyui_integration
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from utils.image_monitor import ImageMonitor
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from utils.prompt_tracker import PromptExecutionContext, PromptTracker
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class PromptManager(ComfyNodeABC):
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"""
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A ComfyUI custom node that functions like CLIPTextEncode but adds:
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- Persistent storage of all prompts in SQLite database
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- Search and retrieval capabilities
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- Metadata management (categories, tags, ratings, notes)
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- Duplicate detection via SHA256 hashing
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"""
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def __init__(self):
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self.logger = get_logger("prompt_manager.node")
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self.logger.debug("Initializing PromptManager node")
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self.db = PromptDatabase()
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self.prompt_tracker = PromptTracker(self.db)
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self.image_monitor = ImageMonitor(self.db, self.prompt_tracker)
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self.comfyui_integration = get_comfyui_integration()
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# Start image monitoring automatically
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self._start_gallery_system()
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self.logger.debug("PromptManager node initialization completed")
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@classmethod
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def INPUT_TYPES(cls) -> InputTypeDict:
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return {
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"required": {
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"text": (
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IO.STRING,
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{
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"multiline": True,
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"dynamicPrompts": True,
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"tooltip": "The text prompt to be encoded and saved to database.",
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},
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),
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"clip": (
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IO.CLIP,
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{"tooltip": "The CLIP model used for encoding the text."},
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),
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},
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"optional": {
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"category": (
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IO.STRING,
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{
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"default": "",
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"tooltip": "Optional category for organizing prompts (e.g., 'landscapes', 'portraits')",
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},
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),
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"tags": (
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IO.STRING,
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{
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"default": "",
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"tooltip": "Comma-separated tags for the prompt (e.g., 'anime, detailed, sunset')",
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},
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),
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"search_text": (
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IO.STRING,
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{
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"default": "",
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"tooltip": "Search for past prompts containing this text",
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},
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),
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"prepend_text": (
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IO.STRING,
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{
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"tooltip": "Text to prepend to the main prompt (connected STRING nodes will be added before the main text)"
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},
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),
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"append_text": (
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IO.STRING,
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{
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"tooltip": "Text to append to the main prompt (connected STRING nodes will be added after the main text)"
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},
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),
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},
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}
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RETURN_TYPES = (IO.CONDITIONING, IO.STRING)
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OUTPUT_TOOLTIPS = (
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"A conditioning containing the embedded text used to guide the diffusion model.",
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"The final combined text string (with prepend/append applied) that was encoded.",
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)
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FUNCTION = "encode"
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CATEGORY = "🫶 ComfyAssets/🧠 Prompts"
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DESCRIPTION = (
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"Encodes a text prompt using a CLIP model into an embedding that can be used to guide "
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"the diffusion model towards generating specific images. Additionally saves all prompts "
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"to a local SQLite database with optional metadata for search and retrieval."
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)
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def encode(
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self,
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clip,
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text: str,
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category: str = "",
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tags: str = "",
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search_text: str = "",
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prepend_text: str = "",
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append_text: str = "",
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) -> Tuple[Any]:
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"""
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Encode the text prompt and save it to the database.
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Args:
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clip: The CLIP model for encoding
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text: The text prompt to encode
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category: Optional category for organization
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tags: Comma-separated tags
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search_text: Text to search for in past prompts
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prepend_text: Text to prepend to the main prompt
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append_text: Text to append to the main prompt
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Returns:
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Tuple containing the conditioning for the diffusion model and the final text string
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Raises:
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RuntimeError: If clip input is invalid
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"""
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# Combine prepend, main text, and append text
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final_text = ""
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if prepend_text and prepend_text.strip():
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final_text += prepend_text.strip() + " "
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final_text += text
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if append_text and append_text.strip():
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final_text += " " + append_text.strip()
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# Use the combined text for encoding
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encoding_text = final_text
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# For database storage, save the original main text with metadata about prepend/append
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storage_text = text
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# Search functionality is now handled by the JavaScript UI
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# The search parameters are still available for backend processing if needed
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# Validate CLIP model
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if clip is None:
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error_msg = (
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"ERROR: clip input is invalid: None\n\n"
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"If the clip is from a checkpoint loader node your checkpoint does not "
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"contain a valid clip or text encoder model."
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)
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self.logger.error("CLIP validation failed: clip input is None")
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raise RuntimeError(error_msg)
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# Save prompt to database and set execution context for gallery tracking
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prompt_id = None
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if storage_text and storage_text.strip():
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self.logger.debug(f"Processing prompt text: {storage_text[:100]}...")
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# Add prepend/append info to tags if they exist
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extended_tags = self._parse_tags(tags) or []
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if prepend_text and prepend_text.strip():
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extended_tags.append(f"prepend:{prepend_text.strip()[:50]}")
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if append_text and append_text.strip():
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extended_tags.append(f"append:{append_text.strip()[:50]}")
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try:
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prompt_id = self._save_prompt_to_database(
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text=storage_text.strip(), # Always strip whitespace
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category=category.strip() if category else None,
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tags=extended_tags if extended_tags else None,
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)
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# Set current prompt for image tracking
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if prompt_id:
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execution_id = self.prompt_tracker.set_current_prompt(
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prompt_text=encoding_text.strip(), # Use final combined text for tracking
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additional_data={
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"category": category.strip() if category else None,
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"tags": extended_tags,
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"prompt_id": prompt_id,
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"prepend_text": (
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prepend_text.strip() if prepend_text else None
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),
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"append_text": append_text.strip() if append_text else None,
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"final_text": encoding_text.strip(), # Store final combined text
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},
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)
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self.logger.debug(
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f"Set execution context: {execution_id} for prompt ID: {prompt_id}"
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)
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except Exception as e:
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# Log error but don't fail the encoding
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self.logger.warning(f"Failed to save prompt to database: {e}")
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# Already logged above, no need for additional print
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# Perform standard CLIP text encoding using the combined text
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self.logger.debug(
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f"Performing CLIP text encoding on combined text: {encoding_text[:100]}..."
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)
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tokens = clip.tokenize(encoding_text)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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# Register with ComfyUI integration for standard metadata compatibility
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node_id = f"promptmanager_{int(time.time() * 1000)}" # Unique node ID
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self.comfyui_integration.register_prompt(
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node_id,
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encoding_text.strip(),
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{
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"category": category.strip() if category else None,
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"tags": extended_tags,
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"prompt_id": prompt_id,
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"prepend_text": prepend_text.strip() if prepend_text else None,
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"append_text": append_text.strip() if append_text else None,
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},
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)
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self.logger.debug("CLIP encoding completed successfully")
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return (conditioning, encoding_text)
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def _save_prompt_to_database(
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self, text: str, category: Optional[str] = None, tags: Optional[list] = None
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) -> Optional[int]:
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"""
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Save the prompt to the SQLite database.
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Args:
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text: The prompt text
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category: Optional category
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tags: List of tags
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Returns:
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The prompt ID if saved successfully, None otherwise
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"""
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try:
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# Generate hash for duplicate detection
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prompt_hash = self._generate_hash(text)
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self.logger.debug(f"Generated hash for prompt: {prompt_hash[:16]}...")
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# Check if prompt already exists
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existing = self.db.get_prompt_by_hash(prompt_hash)
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if existing:
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self.logger.info(
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f"Found existing prompt with ID {existing['id']}, updating metadata"
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)
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# Update metadata if this is a duplicate with new info
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if any([category, tags]):
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self.db.update_prompt_metadata(
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prompt_id=existing["id"], category=category, tags=tags
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)
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self.logger.debug("Updated metadata for existing prompt")
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return existing["id"]
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# Save new prompt
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self.logger.debug(
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f"Saving new prompt with category: {category}, tags: {tags}"
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)
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prompt_id = self.db.save_prompt(
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text=text, category=category, tags=tags, prompt_hash=prompt_hash
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)
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if prompt_id:
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self.logger.debug(f"Successfully saved new prompt with ID: {prompt_id}")
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else:
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self.logger.warning("Failed to save prompt - no ID returned")
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return prompt_id
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except Exception as e:
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self.logger.error(f"Error saving prompt to database: {e}")
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# Already logged above, no need for additional print
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return None
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def _generate_hash(self, text: str) -> str:
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"""Generate SHA256 hash for the prompt text."""
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# Normalize text for consistent hashing (strip whitespace, normalize case)
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normalized_text = text.strip().lower()
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return hashlib.sha256(normalized_text.encode("utf-8")).hexdigest()
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def _parse_tags(self, tags_string: str) -> Optional[list]:
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"""Parse comma-separated tags string into a list."""
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if not tags_string or not tags_string.strip():
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return None
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tags = [tag.strip() for tag in tags_string.split(",") if tag.strip()]
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return tags if tags else None
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def _search_prompts(self, search_text: str = "") -> List[Dict[str, Any]]:
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"""Search for past prompts by text content."""
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try:
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if not search_text or not search_text.strip():
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return []
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results = self.db.search_prompts(
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text=search_text.strip(),
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category=None,
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tags=None,
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rating_min=None,
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limit=50,
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)
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return results
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except Exception as e:
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self.logger.error(f"Error searching prompts: {e}")
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return []
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def _open_web_interface(self):
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"""Open the web interface in the default browser."""
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try:
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# Look for a web interface directory
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current_dir = os.path.dirname(os.path.abspath(__file__))
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web_dir = os.path.join(current_dir, "web_interface")
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if os.path.exists(web_dir):
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# If web interface exists, try to start it
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index_path = os.path.join(web_dir, "index.html")
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if os.path.exists(index_path):
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webbrowser.open(f"file://{index_path}")
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self.logger.info("Web interface opened in browser")
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else:
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self.logger.warning(
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f"Web interface directory found but no index.html. Please check {web_dir} for setup instructions"
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)
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else:
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self.logger.info(
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"Web interface not yet implemented. This feature will open a web-based prompt management interface when the web_interface directory is created."
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)
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except Exception as e:
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self.logger.error(f"Error opening web interface: {e}")
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def search_prompts_api(self, search_text: str = "") -> List[Dict[str, Any]]:
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"""API method for JavaScript UI to search prompts."""
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return self._search_prompts(search_text=search_text)
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def get_recent_prompts_api(self, limit: int = 20) -> List[Dict[str, Any]]:
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"""API method for JavaScript UI to get recent prompts."""
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try:
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return self.db.get_recent_prompts(limit=limit)
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except Exception as e:
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self.logger.error(f"Error getting recent prompts: {e}")
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return []
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def _start_gallery_system(self):
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"""Initialize and start the gallery monitoring system."""
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try:
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self.logger.debug("Starting gallery system...")
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# Start image monitoring
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self.image_monitor.start_monitoring()
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self.logger.debug("Gallery system started successfully")
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except Exception as e:
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self.logger.error(f"Failed to start gallery system: {e}")
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self.logger.warning("Gallery features will be disabled")
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def get_gallery_status(self) -> Dict[str, Any]:
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"""Get status of the gallery system."""
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return {
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"image_monitor": self.image_monitor.get_status(),
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"prompt_tracker": self.prompt_tracker.get_status(),
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}
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def cleanup_gallery_system(self):
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"""Clean up gallery system resources."""
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try:
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if hasattr(self, "image_monitor"):
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self.image_monitor.stop_monitoring()
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self.logger.debug("Gallery system cleaned up")
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except Exception as e:
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self.logger.error(f"Error cleaning up gallery system: {e}")
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def __del__(self):
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"""Cleanup when object is destroyed."""
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self.cleanup_gallery_system()
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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"""Always process to ensure database saving."""
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return float("NaN") # Always execute
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