slahiriandClaude Opus 4.5
f5827c3a86
- Remove get_agentic_prompt from config_loader.py (unused after _agentic_generate deletion) - Remove get_agentic_prompt from prompt_generator_core.py (duplicate dead function) - Remove duplicate get_component_prompt definition from prompt_generator_core.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
700 lines
23 KiB
Python
700 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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"""
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ComfyUI-AI-Photography-Toolkit - Configuration Loader
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TOML configuration loader for provider settings, prompts, and filters.
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Loads and caches configuration from TOML files for:
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- Provider settings (tiers, stop strings, capabilities)
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- Prompts (system/user prompts by tier and mode)
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- Components (agentic analysis prompts)
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- Filters (example text patterns to remove)
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Uses Python 3.11+ tomllib or falls back to tomli package.
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Author: Siddhartha Lahiri
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Email: siddhartha.lahiri@gmail.com
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License: MIT
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"""
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import os
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import re
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import sys
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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# Try Python 3.11+ built-in, fall back to tomli
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try:
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import tomllib
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except ImportError:
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try:
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import tomli as tomllib
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except ImportError:
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tomllib = None
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# =============================================================================
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# Configuration Cache
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# =============================================================================
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_config_cache: Dict[str, Dict] = {}
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_CONFIG_DIR = Path(__file__).parent / "config"
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_PROMPTS_DIR = _CONFIG_DIR / "prompts"
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def clear_config_cache(filename: str = None):
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"""
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Clear the configuration cache to force reload from disk.
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Args:
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filename: Specific file to clear, or None to clear all.
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Usage:
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clear_config_cache() # Clear all
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clear_config_cache("templates.toml") # Clear specific file
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clear_config_cache("_modular_prompts") # Clear modular prompts cache
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"""
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global _config_cache
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if filename:
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if filename in _config_cache:
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del _config_cache[filename]
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print(f"[SID-Config] Cleared cache for: {filename}")
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else:
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_config_cache.clear()
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print("[SID-Config] Cleared all config cache - files will reload on next access")
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def reload_all_configs():
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"""Clear cache and force reload of all config files."""
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clear_config_cache()
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# Trigger reload of commonly used configs
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check_config_files()
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print("[SID-Config] All configs reloaded from disk")
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def _load_toml(filename: str, force_reload: bool = False) -> Dict[str, Any]:
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"""Load a TOML file from the config directory with caching.
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Args:
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filename: Name of the TOML file to load
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force_reload: If True, bypass cache and reload from disk
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"""
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if not force_reload and filename in _config_cache:
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return _config_cache[filename]
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if tomllib is None:
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print(f"[SID-Config] Warning: tomllib/tomli not available, using defaults")
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return {}
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filepath = _CONFIG_DIR / filename
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if not filepath.exists():
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print(f"[SID-Config] Warning: Config file not found: {filepath}")
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return {}
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try:
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with open(filepath, "rb") as f:
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data = tomllib.load(f)
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_config_cache[filename] = data
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return data
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except Exception as e:
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print(f"[SID-Config] Error loading {filename}: {e}")
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return {}
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def _deep_merge(base: Dict, override: Dict) -> Dict:
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"""Deep merge two dictionaries, with override taking precedence."""
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result = base.copy()
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for key, value in override.items():
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if key in result and isinstance(result[key], dict) and isinstance(value, dict):
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result[key] = _deep_merge(result[key], value)
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else:
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result[key] = value
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return result
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def _load_modular_prompts(force_reload: bool = False) -> Dict[str, Any]:
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"""
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Load prompts from modular structure in config/prompts/ directory.
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Falls back to single prompts.toml if modular structure doesn't exist.
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Args:
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force_reload: If True, bypass cache and reload from disk
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"""
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cache_key = "_modular_prompts"
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if not force_reload and cache_key in _config_cache:
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return _config_cache[cache_key]
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if tomllib is None:
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return {}
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merged_config: Dict[str, Any] = {}
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# Check if modular structure exists
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if _PROMPTS_DIR.exists() and _PROMPTS_DIR.is_dir():
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# Load in order: _base.toml first, then others
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toml_files = []
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# Base config first
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base_file = _PROMPTS_DIR / "_base.toml"
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if base_file.exists():
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toml_files.append(base_file)
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# Main prompt files (portrait, scene, vocabulary, classification)
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for filename in ["portrait.toml", "scene.toml", "vocabulary.toml", "classification.toml"]:
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filepath = _PROMPTS_DIR / filename
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if filepath.exists():
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toml_files.append(filepath)
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# Style-specific files from styles/ subdirectory
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styles_dir = _PROMPTS_DIR / "styles"
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if styles_dir.exists() and styles_dir.is_dir():
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for filepath in sorted(styles_dir.glob("*.toml")):
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toml_files.append(filepath)
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# Load and merge all files
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for filepath in toml_files:
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try:
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with open(filepath, "rb") as f:
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data = tomllib.load(f)
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merged_config = _deep_merge(merged_config, data)
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except Exception as e:
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print(f"[SID-Config] Error loading {filepath.name}: {e}")
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if merged_config:
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_config_cache[cache_key] = merged_config
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return merged_config
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# Fallback to single prompts.toml
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fallback = _load_toml("prompts.toml")
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_config_cache[cache_key] = fallback
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return fallback
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def _get_prompts_config() -> Dict[str, Any]:
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"""
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Get prompts configuration - uses modular structure if available,
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otherwise falls back to prompts.toml.
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"""
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# Try modular structure first
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modular = _load_modular_prompts()
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if modular:
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return modular
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# Fallback to single file
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return _load_toml("prompts.toml")
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def reload_config():
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"""Clear cache and reload all configs (useful for development)."""
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global _config_cache
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_config_cache = {}
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# =============================================================================
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# Provider Configuration
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# =============================================================================
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def get_provider_tier(provider: str) -> str:
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"""Get the tier for a provider (advanced/standard/basic)."""
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config = _load_toml("providers.toml")
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tiers = config.get("tiers", {})
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return tiers.get(provider.lower(), "standard")
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def get_stop_strings(provider: str) -> List[str]:
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"""Get stop strings for a provider."""
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config = _load_toml("providers.toml")
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stop_strings = config.get("stop_strings", {})
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return stop_strings.get(provider.lower(), stop_strings.get("default", []))
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def get_provider_capabilities(tier: str) -> Dict[str, Any]:
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"""Get capabilities for a tier."""
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config = _load_toml("providers.toml")
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capabilities = config.get("capabilities", {})
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return capabilities.get(tier, capabilities.get("standard", {}))
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def get_image_limit(provider: str) -> int:
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"""Get max image size for a provider."""
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config = _load_toml("providers.toml")
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limits = config.get("image_limits", {})
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return limits.get(provider.lower(), limits.get("default", 1024))
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# =============================================================================
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# Prompt Configuration
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# =============================================================================
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def get_system_prompt(tier: str) -> str:
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"""Get the base system prompt for a tier."""
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config = _get_prompts_config()
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system = config.get("system", {})
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tier_config = system.get(tier, system.get("standard", {}))
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return tier_config.get("base", "Describe this image for AI image generation.")
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def get_style_addon(style: str, addon_type: str = "system_addon") -> str:
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"""Get the style addon for system or user prompt."""
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config = _get_prompts_config()
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styles = config.get("styles", {})
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# Normalize style name
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style_key = style.lower().replace(" & ", "_").replace(" ", "_").replace("-", "_")
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style_map = {
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"auto_detect": "auto",
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"portrait": "portrait",
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"fashion_outfit": "fashion",
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"fashion": "fashion",
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"artistic_style": "artistic",
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"artistic": "artistic",
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"nsfw_detailed": "nsfw",
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"nsfw": "nsfw",
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}
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style_key = style_map.get(style_key, "auto")
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style_config = styles.get(style_key, {})
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return style_config.get(addon_type, "")
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def get_user_prompt(tier: str, mode: str) -> str:
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"""Get the user prompt for a tier and mode."""
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config = _get_prompts_config()
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user = config.get("user", {})
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tier_config = user.get(tier, user.get("standard", {}))
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mode_config = tier_config.get(mode.lower(), {})
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return mode_config.get("prompt", "Describe this image.")
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def get_length_constraint(prompt_length: int) -> str:
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"""
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Get the length constraint instruction for a prompt_length setting.
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Args:
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prompt_length: Target word count (0 = no limit, optimal Z-Image range is 80-250)
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Returns:
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Length constraint instruction string (empty for 0/unlimited)
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"""
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if prompt_length <= 0:
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return "" # No constraint
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return f"OUTPUT LENGTH: Write a prompt of approximately {prompt_length} words. Adjust detail level to match this target."
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# =============================================================================
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# Component Configuration
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# =============================================================================
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def get_component_prompt(component: str, tier: str) -> str:
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"""Get the prompt for a specific component and tier."""
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config = _load_toml("components.toml")
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comp_config = config.get(component, {})
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tier_config = comp_config.get(tier, comp_config.get("standard", {}))
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return tier_config.get("prompt", "")
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def get_component_name(component: str) -> str:
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"""Get the display name for a component."""
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config = _load_toml("components.toml")
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comp_config = config.get(component, {})
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return comp_config.get("name", component.replace("_", " ").title())
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def get_mode_config(mode: str) -> Dict[str, Any]:
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"""Get the configuration for an analysis mode."""
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config = _load_toml("components.toml")
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modes = config.get("modes", {})
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return modes.get(mode.lower(), modes.get("standard", {}))
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def get_mode_components(mode: str) -> List[str]:
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"""Get the list of components for an analysis mode."""
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mode_config = get_mode_config(mode)
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return mode_config.get("components", ["framing", "ethnicity", "clothing"])
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def get_all_components() -> List[str]:
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"""Get list of all available component keys."""
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config = _load_toml("components.toml")
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# Filter out the 'modes' section
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return [k for k in config.keys() if k != "modes"]
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# =============================================================================
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# Filter Configuration
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# =============================================================================
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def get_example_patterns() -> List[str]:
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"""Get regex patterns for example text to remove."""
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config = _load_toml("filters.toml")
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example = config.get("example_patterns", {})
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return example.get("patterns", [])
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def get_markdown_patterns() -> List[str]:
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"""Get regex patterns for markdown cleanup."""
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config = _load_toml("filters.toml")
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markdown = config.get("markdown_patterns", {})
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return markdown.get("patterns", [])
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def get_whitespace_patterns() -> Dict[str, str]:
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"""Get whitespace cleanup patterns."""
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config = _load_toml("filters.toml")
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return config.get("whitespace", {})
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def get_provider_cleanup_patterns(provider: str) -> List[str]:
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"""Get provider-specific cleanup patterns."""
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config = _load_toml("filters.toml")
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cleanup = config.get("provider_cleanup", {})
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provider_config = cleanup.get(provider.lower(), cleanup.get("default", {}))
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return provider_config.get("patterns", [])
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# =============================================================================
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# Output Cleaning Utility
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# =============================================================================
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def clean_output(text: str, provider: str = "default") -> str:
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"""
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Clean LLM output using patterns from filters.toml.
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Args:
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text: Raw LLM output
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provider: Provider name for provider-specific cleanup
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Returns:
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Cleaned text
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"""
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if not text:
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return ""
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# Remove example text patterns
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for pattern in get_example_patterns():
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try:
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text = re.sub(pattern, "", text, flags=re.IGNORECASE)
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except re.error:
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pass
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# Remove markdown patterns
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for pattern in get_markdown_patterns():
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try:
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# Handle patterns with capture groups (like bold/italic)
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if "(" in pattern and ")" in pattern:
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text = re.sub(pattern, r"\1", text, flags=re.MULTILINE)
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else:
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text = re.sub(pattern, "", text, flags=re.MULTILINE)
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except re.error:
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pass
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# Provider-specific cleanup
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for pattern in get_provider_cleanup_patterns(provider):
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try:
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text = re.sub(pattern, "", text, flags=re.MULTILINE)
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except re.error:
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pass
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# Whitespace cleanup
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ws = get_whitespace_patterns()
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if ws.get("collapse_newlines"):
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text = re.sub(ws["collapse_newlines"], " ", text)
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if ws.get("collapse_spaces"):
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text = re.sub(ws["collapse_spaces"], " ", text)
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if ws.get("double_commas"):
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text = re.sub(ws["double_commas"], ",", text)
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if ws.get("leading_comma"):
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text = re.sub(ws["leading_comma"], "", text)
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if ws.get("trailing_comma"):
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text = re.sub(ws["trailing_comma"], "", text)
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if ws.get("double_periods"):
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text = re.sub(ws["double_periods"], ".", text)
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return text.strip()
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# =============================================================================
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# Convenience Functions
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# =============================================================================
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def build_system_prompt(provider: str, preset_style: str, user_guidance: str = "", prompt_length: int = 150) -> str:
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"""
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Build complete system prompt based on provider tier and style.
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Args:
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provider: LLM provider name
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preset_style: Selected style preset
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user_guidance: Optional user guidance text
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prompt_length: Target word count (0 = no limit, default 150 for optimal Z-Image)
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Returns:
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Complete system prompt string
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"""
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tier = get_provider_tier(provider)
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base = get_system_prompt(tier)
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style_addon = get_style_addon(preset_style, "system_addon")
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if style_addon:
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base += f"\n\n{style_addon}"
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# Add length constraint
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length_constraint = get_length_constraint(prompt_length)
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if length_constraint:
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base += f"\n\n{length_constraint}"
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if user_guidance and user_guidance.strip():
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base += f"""
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USER MODIFICATION REQUEST (CRITICAL - MUST APPLY):
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"{user_guidance.strip()}"
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This is a MODIFICATION directive. You MUST:
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1. Apply this modification to your description, overriding what's literally visible
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2. Describe the subject AS IF this modification is true
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3. For clothing/nudity changes: describe the MODIFIED state (e.g., if "make topless" and image shows bikini, describe as topless)
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4. Do NOT describe the original state that contradicts the user request
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5. Start your description with the modified element"""
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return base
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def build_user_prompt(provider: str, analysis_mode: str, preset_style: str, prompt_length: int = 150) -> str:
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"""
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Build user prompt based on provider tier and analysis mode.
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Args:
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provider: LLM provider name
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analysis_mode: Analysis mode (Standard/Detailed)
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preset_style: Selected style preset
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prompt_length: Target word count (0 = no limit, default 150 for optimal Z-Image)
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Returns:
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Complete user prompt string
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"""
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tier = get_provider_tier(provider)
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prompt = get_user_prompt(tier, analysis_mode)
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style_addon = get_style_addon(preset_style, "user_addon")
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if style_addon:
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prompt += f"\n\n{style_addon}"
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# Reinforce length constraint in user prompt (already in system, but reinforce)
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length_constraint = get_length_constraint(prompt_length)
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if length_constraint:
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prompt += f"\n\nREMEMBER: {length_constraint}"
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return prompt
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# =============================================================================
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# Scene-Only Prompts (No Human Keywords - Prevents Hallucination)
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# =============================================================================
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def get_scene_only_system_prompt() -> str:
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"""Get the scene-only system prompt (no human/person keywords)."""
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config = _get_prompts_config()
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system = config.get("system", {})
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scene_config = system.get("scene_only", {})
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return scene_config.get("base", "Describe this scene for AI image generation. There are no people in this image.")
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def get_scene_only_user_prompt(mode: str) -> str:
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"""Get the scene-only user prompt for a mode (no human/person keywords)."""
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config = _get_prompts_config()
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user = config.get("user", {})
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scene_config = user.get("scene_only", {})
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mode_config = scene_config.get(mode.lower(), {})
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return mode_config.get("prompt", "Describe this scene/landscape/object.")
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def build_scene_only_system_prompt(prompt_length: int = 150) -> str:
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"""
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Build complete scene-only system prompt (no human/person keywords).
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Used when human detection returns NO to prevent hallucinating people.
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Args:
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prompt_length: Target word count (0 = no limit)
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Returns:
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Complete scene-only system prompt string
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"""
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base = get_scene_only_system_prompt()
|
|
|
|
# Add length constraint
|
|
length_constraint = get_length_constraint(prompt_length)
|
|
if length_constraint:
|
|
base += f"\n\n{length_constraint}"
|
|
|
|
return base
|
|
|
|
|
|
def build_scene_only_user_prompt(analysis_mode: str, prompt_length: int = 150) -> str:
|
|
"""
|
|
Build complete scene-only user prompt (no human/person keywords).
|
|
Used when human detection returns NO to prevent hallucinating people.
|
|
|
|
Args:
|
|
analysis_mode: Analysis mode (Standard/Detailed)
|
|
prompt_length: Target word count (0 = no limit)
|
|
|
|
Returns:
|
|
Complete scene-only user prompt string
|
|
"""
|
|
prompt = get_scene_only_user_prompt(analysis_mode)
|
|
|
|
# Reinforce length constraint
|
|
length_constraint = get_length_constraint(prompt_length)
|
|
if length_constraint:
|
|
prompt += f"\n\nREMEMBER: {length_constraint}"
|
|
|
|
return prompt
|
|
|
|
|
|
# =============================================================================
|
|
# Human With Subject Prompts (Balanced - Human AND Pet/Vehicle/Landscape)
|
|
# =============================================================================
|
|
|
|
def get_human_with_subject_system_prompt() -> str:
|
|
"""Get the human_with_subject system prompt (balanced human + subject)."""
|
|
config = _get_prompts_config()
|
|
system = config.get("system", {})
|
|
hws_config = system.get("human_with_subject", {})
|
|
return hws_config.get("base", "Describe both the human and the other subject in this image with equal detail.")
|
|
|
|
|
|
def get_human_with_subject_user_prompt(mode: str) -> str:
|
|
"""Get the human_with_subject user prompt for a mode."""
|
|
config = _get_prompts_config()
|
|
user = config.get("user", {})
|
|
hws_config = user.get("human_with_subject", {})
|
|
mode_config = hws_config.get(mode.lower(), {})
|
|
return mode_config.get("prompt", "Describe both the human and the other subject equally.")
|
|
|
|
|
|
def build_human_with_subject_system_prompt(prompt_length: int = 150) -> str:
|
|
"""
|
|
Build complete human_with_subject system prompt.
|
|
Used when image has both a human AND another subject (pet, vehicle, landscape).
|
|
|
|
Args:
|
|
prompt_length: Target word count (0 = no limit)
|
|
|
|
Returns:
|
|
Complete human_with_subject system prompt string
|
|
"""
|
|
base = get_human_with_subject_system_prompt()
|
|
|
|
# Add length constraint
|
|
length_constraint = get_length_constraint(prompt_length)
|
|
if length_constraint:
|
|
base += f"\n\n{length_constraint}"
|
|
|
|
return base
|
|
|
|
|
|
def build_human_with_subject_user_prompt(analysis_mode: str, prompt_length: int = 150) -> str:
|
|
"""
|
|
Build complete human_with_subject user prompt.
|
|
Used when image has both a human AND another subject.
|
|
|
|
Args:
|
|
analysis_mode: Analysis mode (Standard/Detailed)
|
|
prompt_length: Target word count (0 = no limit)
|
|
|
|
Returns:
|
|
Complete human_with_subject user prompt string
|
|
"""
|
|
prompt = get_human_with_subject_user_prompt(analysis_mode)
|
|
|
|
# Reinforce length constraint
|
|
length_constraint = get_length_constraint(prompt_length)
|
|
if length_constraint:
|
|
prompt += f"\n\nREMEMBER: {length_constraint}"
|
|
|
|
return prompt
|
|
|
|
|
|
# =============================================================================
|
|
# Initialization Check
|
|
# =============================================================================
|
|
|
|
def check_config_files() -> Dict[str, bool]:
|
|
"""Check which config files exist and are loadable."""
|
|
files = ["providers.toml", "prompts.toml", "components.toml", "filters.toml"]
|
|
status = {}
|
|
for f in files:
|
|
filepath = _CONFIG_DIR / f
|
|
status[f] = filepath.exists()
|
|
if status[f]:
|
|
try:
|
|
_load_toml(f)
|
|
except Exception:
|
|
status[f] = False
|
|
|
|
# Check modular prompts directory
|
|
if _PROMPTS_DIR.exists():
|
|
status["prompts/ (modular)"] = True
|
|
modular_files = list(_PROMPTS_DIR.glob("*.toml"))
|
|
styles_dir = _PROMPTS_DIR / "styles"
|
|
if styles_dir.exists():
|
|
modular_files.extend(styles_dir.glob("*.toml"))
|
|
status["prompts/ file count"] = len(modular_files)
|
|
else:
|
|
status["prompts/ (modular)"] = False
|
|
|
|
return status
|
|
|
|
|
|
def get_loaded_prompt_files() -> List[str]:
|
|
"""Get list of prompt files that were loaded (for debugging)."""
|
|
files = []
|
|
if _PROMPTS_DIR.exists() and _PROMPTS_DIR.is_dir():
|
|
# Base config first
|
|
base_file = _PROMPTS_DIR / "_base.toml"
|
|
if base_file.exists():
|
|
files.append(base_file.name)
|
|
|
|
# Main prompt files
|
|
for filename in ["portrait.toml", "scene.toml", "vocabulary.toml", "classification.toml"]:
|
|
filepath = _PROMPTS_DIR / filename
|
|
if filepath.exists():
|
|
files.append(filename)
|
|
|
|
# Style-specific files
|
|
styles_dir = _PROMPTS_DIR / "styles"
|
|
if styles_dir.exists() and styles_dir.is_dir():
|
|
for filepath in sorted(styles_dir.glob("*.toml")):
|
|
files.append(f"styles/{filepath.name}")
|
|
|
|
if not files:
|
|
files.append("prompts.toml (fallback)")
|
|
|
|
return files
|
|
|
|
|
|
# Print status on import (for debugging)
|
|
if __name__ == "__main__":
|
|
print("Config file status:")
|
|
for file, value in check_config_files().items():
|
|
if isinstance(value, bool):
|
|
status = "OK" if value else "MISSING"
|
|
else:
|
|
status = str(value)
|
|
print(f" {file}: {status}")
|
|
|
|
print("\nLoaded prompt files:")
|
|
for f in get_loaded_prompt_files():
|
|
print(f" - {f}")
|
|
|
|
print("\nProvider tiers:")
|
|
for p in ["anthropic", "openai", "ollama", "lmstudio"]:
|
|
print(f" {p}: {get_provider_tier(p)}")
|
|
|
|
print("\nSample system prompt (basic tier):")
|
|
prompt = get_system_prompt("basic")
|
|
print(prompt[:200] + "..." if len(prompt) > 200 else prompt)
|
|
|
|
print("\nSample scene-only system prompt:")
|
|
scene_prompt = get_scene_only_system_prompt()
|
|
print(scene_prompt[:200] + "..." if len(scene_prompt) > 200 else scene_prompt)
|