Vito Sansevero
40b91fc44f
- Add debug/test panel to the node with helpful usage hints
- Display counts of available embeddings and LoRAs
- Show sample items and status information
- Include clear instructions for triggering autocomplete
- Update display name with 🫶 branding
- Make node an OUTPUT_NODE to display information
312 lines
11 KiB
Python
312 lines
11 KiB
Python
"""KikoEmbeddingAutocomplete node for ComfyUI.
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Provides autocomplete functionality for embeddings and LoRAs in text inputs.
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"""
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import os
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import json
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from typing import Dict, List, Any, Optional
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import folder_paths
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class KikoEmbeddingAutocomplete:
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"""Node that provides embedding autocomplete functionality."""
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DISPLAY_NAME = "🫶 Embedding Autocomplete (Test Panel)"
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CATEGORY = "ComfyAssets"
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# Settings definition for the settings registry
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SETTINGS = {
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"enabled": {
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"type": "boolean",
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"default": True,
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"description": "Enable embedding autocomplete",
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},
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"trigger_chars": {
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"type": "combo",
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"default": 2,
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"options": [1, 2, 3, 4],
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"description": "Number of characters before showing suggestions",
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},
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"max_suggestions": {
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"type": "combo",
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"default": 20,
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"options": [10, 20, 30, 50],
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"description": "Maximum number of suggestions to show",
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},
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"show_embeddings": {
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"type": "boolean",
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"default": True,
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"description": "Show embeddings in suggestions",
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},
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"show_loras": {
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"type": "boolean",
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"default": True,
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"description": "Show LoRAs in suggestions",
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},
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"case_sensitive": {
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"type": "boolean",
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"default": False,
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"description": "Case sensitive matching",
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},
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}
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@classmethod
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def INPUT_TYPES(cls):
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"""Define input types for the node."""
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hints = """🫶 Embedding Autocomplete Test Panel
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Try these triggers in the text field below:
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• Type 'embedding:' to list all embeddings
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• Type '<lora:' to list all LoRAs
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• Start typing any name to filter results
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• Use Tab or Enter to select, Escape to cancel
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• Arrow keys to navigate suggestions
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This autocomplete works in all prompt fields!"""
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return {
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"required": {
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"test_input": ("STRING", {
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"multiline": True,
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"default": hints,
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"placeholder": "Type here to test autocomplete..."
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}),
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},
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"optional": {
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"refresh": ("BOOLEAN", {"default": False, "label_on": "Refresh Lists", "label_off": "Use Cache"}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ("STRING", "INT", "INT")
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RETURN_NAMES = ("test_output", "embeddings_count", "loras_count")
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FUNCTION = "process_autocomplete"
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OUTPUT_NODE = True
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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return True
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def __init__(self):
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self.embeddings_cache = None
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self.loras_cache = None
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def process_autocomplete(self, test_input, refresh=False, unique_id=None):
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"""Process and display autocomplete information.
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This node serves as both a test panel and information display
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for the embedding autocomplete functionality.
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"""
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if refresh or self.embeddings_cache is None:
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self.refresh_cache()
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# Count available resources
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embeddings_count = len(self.embeddings_cache)
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loras_count = len(self.loras_cache)
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# Generate informative output
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output_lines = [
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f"🫶 Embedding Autocomplete Status",
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f"═" * 40,
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f"📦 Embeddings found: {embeddings_count}",
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f"🎨 LoRAs found: {loras_count}",
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f"",
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f"✅ Autocomplete is {'enabled' if self.embeddings_cache or self.loras_cache else 'ready (no resources found)'}",
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f"",
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f"Your test input:",
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f"─" * 40,
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test_input,
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f"─" * 40,
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f"",
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f"💡 Tips:",
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f"• This node enables autocomplete in ALL prompt fields",
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f"• Settings available in ComfyUI Settings menu",
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f"• Look for 🫶 Embedding Autocomplete options"
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]
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if embeddings_count > 0:
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output_lines.append(f"")
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output_lines.append(f"Sample embeddings (first 5):")
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for emb in self.embeddings_cache[:5]:
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output_lines.append(f" • {emb['name']}")
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if loras_count > 0:
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output_lines.append(f"")
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output_lines.append(f"Sample LoRAs (first 5):")
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for lora in self.loras_cache[:5]:
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output_lines.append(f" • {lora['name']}")
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output_text = "\n".join(output_lines)
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return (output_text, embeddings_count, loras_count)
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def refresh_cache(self):
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"""Refresh the cache of embeddings and LoRAs."""
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self.embeddings_cache = self.get_embeddings()
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self.loras_cache = self.get_loras()
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def get_embeddings(self) -> List[Dict[str, Any]]:
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"""Get list of available embeddings."""
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embeddings = []
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# Get embedding files from ComfyUI's folder system
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try:
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embedding_files = folder_paths.get_filename_list("embeddings")
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for file in embedding_files:
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name = os.path.splitext(file)[0]
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embeddings.append(
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{
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"name": name,
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"file": file,
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"type": "embedding",
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"display": f"embedding:{name}",
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"value": f"embedding:{name}",
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}
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)
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except Exception as e:
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print(f"Error loading embeddings: {e}")
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return embeddings
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def get_loras(self) -> List[Dict[str, Any]]:
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"""Get list of available LoRAs."""
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loras = []
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# Get LoRA files from ComfyUI's folder system
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try:
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lora_files = folder_paths.get_filename_list("loras")
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for file in lora_files:
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name = os.path.splitext(file)[0]
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loras.append(
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{
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"name": name,
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"file": file,
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"type": "lora",
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"display": f"<lora:{name}:1.0>",
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"value": f"<lora:{name}:1.0>",
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}
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)
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except Exception as e:
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print(f"Error loading LoRAs: {e}")
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return loras
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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"""Check if the node needs to be re-executed."""
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# Always re-execute if refresh is True
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if kwargs.get("refresh", False):
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return float("NaN")
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# Check if embeddings/loras folders have changed
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try:
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embeddings_path = folder_paths.get_folder_paths("embeddings")[0]
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loras_path = folder_paths.get_folder_paths("loras")[0]
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# Return combined modification time
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return os.path.getmtime(embeddings_path) + os.path.getmtime(loras_path)
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except:
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return 0
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class KikoEmbeddingAutocompleteAPI:
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"""API endpoints for embedding autocomplete."""
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@staticmethod
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def get_suggestions(
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prefix: str,
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max_results: int = 20,
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include_embeddings: bool = True,
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include_loras: bool = True,
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case_sensitive: bool = False,
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) -> List[Dict[str, Any]]:
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"""Get autocomplete suggestions for a given prefix.
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Args:
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prefix: The text prefix to match
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max_results: Maximum number of results to return
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include_embeddings: Include embeddings in results
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include_loras: Include LoRAs in results
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case_sensitive: Use case-sensitive matching
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Returns:
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List of suggestion dictionaries
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"""
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suggestions = []
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# Normalize prefix for matching
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match_prefix = prefix if case_sensitive else prefix.lower()
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# Get embeddings
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if include_embeddings:
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try:
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embedding_files = folder_paths.get_filename_list("embeddings")
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for file in embedding_files:
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name = os.path.splitext(file)[0]
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match_name = name if case_sensitive else name.lower()
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# Check for match
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if match_name.startswith(match_prefix):
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suggestions.append(
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{
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"name": name,
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"type": "embedding",
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"display": f"embedding:{name}",
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"value": f"embedding:{name}",
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"priority": 1 if match_name == match_prefix else 0,
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}
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)
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elif match_prefix in match_name:
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suggestions.append(
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{
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"name": name,
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"type": "embedding",
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"display": f"embedding:{name}",
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"value": f"embedding:{name}",
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"priority": -1,
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}
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)
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except Exception as e:
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print(f"Error loading embeddings: {e}")
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# Get LoRAs
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if include_loras:
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try:
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lora_files = folder_paths.get_filename_list("loras")
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for file in lora_files:
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name = os.path.splitext(file)[0]
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match_name = name if case_sensitive else name.lower()
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# Check for match
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if match_name.startswith(match_prefix):
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suggestions.append(
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{
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"name": name,
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"type": "lora",
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"display": f"<lora:{name}:1.0>",
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"value": f"<lora:{name}:1.0>",
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"priority": 1 if match_name == match_prefix else 0,
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}
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)
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elif match_prefix in match_name:
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suggestions.append(
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{
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"name": name,
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"type": "lora",
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"display": f"<lora:{name}:1.0>",
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"value": f"<lora:{name}:1.0>",
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"priority": -1,
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}
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)
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except Exception as e:
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print(f"Error loading LoRAs: {e}")
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# Sort by priority and name
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suggestions.sort(key=lambda x: (-x["priority"], x["name"]))
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# Limit results
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return suggestions[:max_results]
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