refactor: remove dead and test-only code and the write-only prompt registry (#620)
Removes helpers nothing calls, test-only wrappers and their tests, the unused config writer, three unused images, and the prompt registry the nodes wrote to but nothing read, which grew with every run.
This commit is contained in:
Vito Sansevero
@@ -1,220 +0,0 @@
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"""ComfyUI integration utilities for PromptManager.
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This module tracks the prompts PromptManager nodes encode during execution so
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other components (e.g. image/prompt linking) can look them up.
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ComfyUI already writes every node's own inputs into saved image metadata, so
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SaveImage is deliberately NOT patched. An earlier patch wrote the last-run prompt
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into every PromptManager node, saving negative prompts as copies of the
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positive (#75).
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Key features:
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- Thread-safe prompt registration and retrieval
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- Cross-thread prompt context sharing
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- Standard metadata format compatibility
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- Automatic cleanup of old prompt registrations
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The integration works by:
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1. PromptManager nodes register their prompts during execution
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2. Callers retrieve the current prompt text by node id or recency
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Typical usage:
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from utils.comfyui_integration import get_comfyui_integration
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integration = get_comfyui_integration()
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integration.register_prompt(node_id, prompt_text, metadata)
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text = integration.get_current_prompt_text(node_id)
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"""
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import threading
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import time
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from typing import Dict, Any, Optional
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try:
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from .logging_config import get_logger
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except ImportError:
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from utils.logging_config import get_logger
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class ComfyUIMetadataIntegration:
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"""Integrates PromptManager with ComfyUI's standard metadata system.
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This singleton class manages the integration between PromptManager custom nodes
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and ComfyUI's standard metadata system. It ensures that prompts generated by
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PromptManager appear in the standard ComfyUI metadata format for compatibility
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with third-party tools and parsers.
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The integration uses thread-local storage combined with global tracking to
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handle prompt context across different execution threads, which is necessary
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because ComfyUI executions may span multiple threads.
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Key responsibilities:
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- Register prompts from PromptManager nodes during execution
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- Manage prompt lifecycle and cleanup
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"""
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_instance = None
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_lock = threading.Lock()
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def __new__(cls):
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"""Ensure singleton pattern with thread safety.
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Returns:
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The single instance of ComfyUIMetadataIntegration
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"""
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if cls._instance is None:
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with cls._lock:
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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"""Initialize the ComfyUI integration system.
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Sets up prompt tracking data structures. Uses _initialized flag to
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prevent duplicate initialization.
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"""
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if hasattr(self, "_initialized"):
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return
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self.logger = get_logger("prompt_manager.comfyui_integration")
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self._current_prompts = {}
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self._thread_local = threading.local()
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self._initialized = True
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def register_prompt(self, node_id: str, prompt_text: str, metadata: Dict[str, Any]):
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"""
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Register a prompt from PromptManager for inclusion in ComfyUI metadata.
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This method is called by PromptManager nodes during execution to register
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their prompt text for later inclusion in image metadata. The prompt is stored
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in both thread-local and global storage to handle cross-thread access scenarios.
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Args:
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node_id: Unique identifier for this prompt (typically the node ID)
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prompt_text: The actual prompt text that was encoded by PromptManager
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metadata: Additional metadata from PromptManager (category, tags, etc.)
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"""
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thread_id = threading.current_thread().ident
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# Store in thread-local storage
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if not hasattr(self._thread_local, "prompts"):
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self._thread_local.prompts = {}
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self._thread_local.prompts[node_id] = {
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"text": prompt_text,
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"metadata": metadata,
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"timestamp": time.time(),
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"thread_id": thread_id,
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}
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# Also store globally for cross-thread access
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with self._lock:
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self._current_prompts[f"{thread_id}_{node_id}"] = {
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"text": prompt_text,
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"metadata": metadata,
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"timestamp": time.time(),
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"thread_id": thread_id,
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}
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self.logger.debug(
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f"Registered prompt for node {node_id}: {prompt_text[:50]}..."
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)
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def get_current_prompt_text(self, node_id: str = None) -> Optional[str]:
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"""
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Get the current prompt text for metadata inclusion.
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Retrieves the most appropriate prompt text for inclusion in image metadata.
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Uses a fallback strategy: thread-local storage first, then global storage,
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with recency checks to avoid stale prompts.
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Args:
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node_id: Optional specific node ID to retrieve prompt for
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Returns:
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The prompt text string if available, None if no suitable prompt found
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"""
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# First try thread-local storage
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if hasattr(self._thread_local, "prompts"):
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if node_id and node_id in self._thread_local.prompts:
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return self._thread_local.prompts[node_id]["text"]
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elif self._thread_local.prompts:
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# Return the most recent prompt from this thread
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latest = max(
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self._thread_local.prompts.values(), key=lambda x: x["timestamp"]
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)
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return latest["text"]
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# Fallback to global storage
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thread_id = threading.current_thread().ident
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with self._lock:
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# Look for prompts from current thread
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thread_prompts = {
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k: v
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for k, v in self._current_prompts.items()
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if v["thread_id"] == thread_id
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}
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if thread_prompts:
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latest = max(thread_prompts.values(), key=lambda x: x["timestamp"])
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return latest["text"]
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# Last resort: return the most recent prompt from any thread
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if self._current_prompts:
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latest = max(
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self._current_prompts.values(), key=lambda x: x["timestamp"]
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)
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# Only return if it's recent (within last 5 minutes)
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if time.time() - latest["timestamp"] < 300:
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return latest["text"]
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return None
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def cleanup_old_prompts(self, max_age_seconds: int = 600):
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"""
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Clean up old prompt registrations.
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Removes prompt registrations that are older than the specified age to
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prevent memory leaks and ensure that only recent, relevant prompts are
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used for metadata inclusion.
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Args:
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max_age_seconds: Maximum age in seconds before a prompt registration
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is considered stale and removed (default: 600
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seconds/10 minutes)
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"""
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current_time = time.time()
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with self._lock:
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old_keys = [
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key
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for key, prompt_data in self._current_prompts.items()
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if current_time - prompt_data["timestamp"] > max_age_seconds
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]
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for key in old_keys:
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del self._current_prompts[key]
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if old_keys:
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self.logger.debug(f"Cleaned up {len(old_keys)} old prompt registrations")
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# Global instance
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_integration_instance = None
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def get_comfyui_integration() -> ComfyUIMetadataIntegration:
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"""Get the global ComfyUI integration instance.
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Returns:
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The singleton ComfyUIMetadataIntegration instance, creating it if necessary
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"""
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global _integration_instance
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if _integration_instance is None:
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_integration_instance = ComfyUIMetadataIntegration()
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return _integration_instance
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+1
-33
@@ -8,19 +8,15 @@ regardless of minor formatting differences.
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Key features:
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- SHA256-based prompt text hashing with normalization
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- Content hashing for complex prompt metadata structures
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- Duplicate detection utilities
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- Consistent normalization (lowercase, trimmed whitespace)
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Typical usage:
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from utils.hashing import generate_prompt_hash, is_duplicate_prompt
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from utils.hashing import generate_prompt_hash
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hash1 = generate_prompt_hash("Beautiful landscape")
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hash2 = generate_prompt_hash(" beautiful landscape ")
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# hash1 == hash2 (normalization makes them identical)
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if is_duplicate_prompt(text1, text2):
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print("Duplicate prompts detected")
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The hashing is designed for:
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- Database deduplication (preventing duplicate prompt storage)
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- Quick similarity checking
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@@ -124,31 +120,3 @@ def stored_prompt_text_sql(expression: str) -> str:
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character of Y, multi-byte ones included, so it strips what Python does.
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"""
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return f"trim({expression}, char({_STRIP_CODES}))"
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def is_duplicate_prompt(text1: str, text2: str, threshold: float = 0.95) -> bool:
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"""
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Check if two prompts are likely duplicates using hash comparison.
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Compares the normalized hashes of two prompt texts to determine if
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they are functionally identical. This is an exact match after normalization,
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not a similarity measure.
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Args:
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text1: First prompt text to compare
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text2: Second prompt text to compare
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threshold: Similarity threshold (not used - kept for API compatibility)
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Returns:
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True if the prompts have identical normalized hashes (i.e., are duplicates),
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False otherwise
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Note:
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The threshold parameter is not used in the current implementation as
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this performs exact hash matching rather than similarity scoring.
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It's maintained for potential future fuzzy matching capabilities.
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"""
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hash1 = generate_prompt_hash(text1)
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hash2 = generate_prompt_hash(text2)
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return hash1 == hash2
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@@ -295,30 +295,3 @@ def sanitize_input(text: str) -> str:
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result_lines.append(line)
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return "\n".join(result_lines).strip()
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def parse_tags_string(tags_string: str) -> List[str]:
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"""
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Parse a comma-separated tags string into a clean list.
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Converts comma-separated tag strings into a clean, deduplicated list
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of tags. Each tag is sanitized and trimmed.
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Args:
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tags_string: Comma-separated tags string (e.g., "tag1, tag2, tag3")
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Returns:
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List of unique, cleaned tag strings. Empty input returns empty list.
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Limited to maximum 20 tags to prevent abuse.
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"""
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if not tags_string or not isinstance(tags_string, str):
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return []
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# Split by comma and clean each tag
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tags = []
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for tag in tags_string.split(","):
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clean_tag = sanitize_input(tag).strip()
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if clean_tag and clean_tag not in tags: # Avoid duplicates
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tags.append(clean_tag)
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return tags[:20] # Limit to 20 tags
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