808 lines
32 KiB
Python
808 lines
32 KiB
Python
"""
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Ultimate Config Builder - Complete HTML UI Version
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ALL data stored in single widget (lora_config)
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Python reads everything from that widget
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"""
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import os
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import json
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import folder_paths
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from typing import List, Dict, Any
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import server
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from aiohttp import web
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import hashlib
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import requests
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# =============================================================================
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# INLINE LORA_UTILS FUNCTIONS (for compatibility)
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# =============================================================================
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def load_json_from_file(file_path):
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"""Load JSON data from file"""
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try:
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with open(file_path, 'r') as json_file:
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data = json.load(json_file)
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return data
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except FileNotFoundError:
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return None
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except json.JSONDecodeError:
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print(f"[ConfigBuilder] Error decoding JSON in file: {file_path}")
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return None
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def save_dict_to_json(data_dict, file_path):
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"""Save dictionary to JSON file"""
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try:
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# Ensure directory exists
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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with open(file_path, 'w') as json_file:
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json.dump(data_dict, json_file, indent=4)
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print(f"[ConfigBuilder] Data saved to {file_path}")
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except Exception as e:
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print(f"[ConfigBuilder] Error saving JSON to file: {e}")
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def calculate_sha256(file_path):
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"""Calculate SHA256 hash of a file"""
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sha256_hash = hashlib.sha256()
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(4096), b""):
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sha256_hash.update(chunk)
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return sha256_hash.hexdigest()
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def get_model_version_info(hash_value):
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"""Fetch model version info from Civitai API using hash"""
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api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}"
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try:
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response = requests.get(api_url, timeout=10)
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if response.status_code == 200:
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return response.json()
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else:
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return None
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except Exception as e:
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print(f"[ConfigBuilder] Error fetching from CivitAI: {e}")
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return None
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def load_and_save_tags(lora_name, force_fetch=False, auto_fetch=True):
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"""
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Load trigger tags for a LoRA, fetching from Civitai API if necessary.
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Caches results to loras_tags.json.
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Args:
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lora_name: Name of the LoRA file (may include path with / or \\)
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force_fetch: Force fetch from API even if cached
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auto_fetch: If False, don't hash uncached LoRAs (just save empty entry)
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Returns:
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List of trigger words/tags
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"""
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output_dir = folder_paths.get_output_directory()
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json_tags_path = os.path.join(output_dir, "benchmarks", "loras_tags.json")
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lora_tags = load_json_from_file(json_tags_path)
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# Normalize the lora_name to use forward slashes for cache lookup
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normalized_name = lora_name.replace("\\", "/")
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# Try to find in cache
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output_tags = None
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found_in_cache = False
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if lora_tags is not None:
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# Try original name first
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if lora_name in lora_tags:
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output_tags = lora_tags[lora_name]
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found_in_cache = True
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# If not found, try normalized name
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elif normalized_name != lora_name and normalized_name in lora_tags:
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output_tags = lora_tags[normalized_name]
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found_in_cache = True
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# If still not found, try backslash version
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else:
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backslash_name = normalized_name.replace("/", "\\")
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if backslash_name in lora_tags:
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output_tags = lora_tags[backslash_name]
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found_in_cache = True
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# If found in cache, return immediately
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if found_in_cache:
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return output_tags if output_tags is not None else []
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# NOT IN CACHE - Need to fetch from API
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lora_path = folder_paths.get_full_path("loras", lora_name)
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# Check if lora_path is valid before attempting to hash
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if lora_path is None:
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print(f"[ConfigBuilder] ⚠️ LoRA file not found: {lora_name}")
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return []
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# Only hash if force_fetch is True OR (not in cache AND auto_fetch is True)
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if force_fetch or auto_fetch:
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print(f"[ConfigBuilder] 🔄 Fetching tags for {lora_name}")
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try:
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LORAsha256 = calculate_sha256(lora_path)
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model_info = get_model_version_info(LORAsha256)
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if model_info is not None and "trainedWords" in model_info:
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print(f"[ConfigBuilder] ✅ Tags found for {lora_name}")
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if lora_tags is None:
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lora_tags = {}
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lora_tags[normalized_name] = model_info["trainedWords"]
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save_dict_to_json(lora_tags, json_tags_path)
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return model_info["trainedWords"]
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else:
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print(f"[ConfigBuilder] No tags found for {lora_name}")
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if lora_tags is None:
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lora_tags = {}
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lora_tags[normalized_name] = []
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save_dict_to_json(lora_tags, json_tags_path)
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return []
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except Exception as e:
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print(f"[ConfigBuilder] Error processing {lora_name}: {e}")
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return []
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else:
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# Not in cache and auto_fetch=False
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if lora_tags is None:
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lora_tags = {}
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lora_tags[normalized_name] = []
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save_dict_to_json(lora_tags, json_tags_path)
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return []
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class UltimateConfigBuilder:
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"""
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Config builder with complete HTML UI.
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All data is stored in the lora_config widget as a single JSON object.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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sessions = cls.get_available_sessions()
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return {
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"required": {
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# Session Management (hidden, controlled by HTML)
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"session_name": ("STRING", {
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"default": "my_test_session",
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"multiline": False
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}),
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"load_session": (sessions, {
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"default": sessions[0] if sessions else "None"
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}),
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# Sampler Settings (hidden, controlled by HTML)
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"samplers": ("STRING", {
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"default": "euler, dpmpp_2m",
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"multiline": False
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}),
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"schedulers": ("STRING", {
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"default": "normal, karras",
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"multiline": False
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}),
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"steps": ("STRING", {
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"default": "20, 30",
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"multiline": False
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}),
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"cfg": ("STRING", {
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"default": "7.0",
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"multiline": False
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}),
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# LoRA Configuration (ACTUAL DATA STORAGE - contains EVERYTHING)
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"lora_config": ("STRING", {
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"default": cls.get_default_config(),
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"multiline": True
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}),
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# Options (hidden, controlled by HTML)
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"include_none": ("BOOLEAN", {
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"default": False
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}),
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},
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"optional": {
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"model": ("STRING", {
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"default": "",
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"multiline": False
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}),
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}
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}
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RETURN_TYPES = ("STRING", "STRING")
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RETURN_NAMES = ("configs_json", "session_name")
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FUNCTION = "generate_config"
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CATEGORY = "sampling/testing"
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OUTPUT_NODE = True
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@staticmethod
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def get_default_config():
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"""Return default complete configuration"""
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config = {
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"session_name": "my_test_session",
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"include_none": False,
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"global_positive_groups": [],
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"global_negative": "",
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"config_arrays": [
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{
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"name": "Config 1",
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"samplers": "euler, dpmpp_2m",
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"schedulers": "normal, karras",
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"steps": "20, 30",
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"cfg": "7.0",
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"model": "",
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"loras": ["None"],
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"lora_omit_triggers": [],
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"lora_triggerwords_append_settings": {},
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"combine": True,
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"positive_prompt_groups": [],
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"negative_prompt": "",
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"use_custom_prompts": False
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}
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]
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}
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return json.dumps(config, indent=2, ensure_ascii=False)
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@staticmethod
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def get_available_sessions() -> List[str]:
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"""Scan benchmarks folder for available sessions, sorted by newest first"""
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sessions = ["None"]
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try:
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output_dir = folder_paths.get_output_directory()
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benchmarks_dir = os.path.join(output_dir, "benchmarks")
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if os.path.exists(benchmarks_dir):
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session_items = []
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for item in os.listdir(benchmarks_dir):
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item_path = os.path.join(benchmarks_dir, item)
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manifest_path = os.path.join(item_path, "manifest.json")
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if os.path.isdir(item_path) and os.path.exists(manifest_path):
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# Get modification time for sorting
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mtime = os.path.getmtime(item_path)
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session_items.append((item, mtime))
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# Sort by modification time (newest first)
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session_items.sort(key=lambda x: x[1], reverse=True)
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sessions.extend([item[0] for item in session_items])
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except Exception as e:
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print(f"[ConfigBuilder] Warning: Could not scan sessions: {e}")
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return sessions
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@staticmethod
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def expand_lora_folders(lora_list: List[str]) -> List[str]:
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"""
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Expand folder references to individual LoRA files.
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Args:
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lora_list: List of LoRA strings (may include folders)
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Returns:
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List with folders expanded to individual files
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"""
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expanded = []
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available_loras = folder_paths.get_filename_list("loras")
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for lora_str in lora_list:
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if not lora_str or lora_str == "None":
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continue
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# Parse out the name (before any : strength modifiers)
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lora_name = lora_str.split(":")[0]
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# Check if it's a folder reference
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is_folder = lora_name.endswith("/") or lora_name.endswith("/*")
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if is_folder:
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# Remove trailing / or /*
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folder_name = lora_name.rstrip("/*").rstrip("/")
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# Normalize to forward slashes for comparison
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folder_prefix = folder_name.replace("\\", "/") + "/"
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# Find all LoRAs in this folder
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for lora_file in available_loras:
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normalized_file = lora_file.replace("\\", "/")
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if normalized_file.startswith(folder_prefix):
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expanded.append(lora_file)
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print(f"[ConfigBuilder] Expanded folder '{lora_name}' to {len([l for l in available_loras if l.replace('\\', '/').startswith(folder_prefix)])} LoRAs")
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else:
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# Regular LoRA file
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expanded.append(lora_str)
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return expanded
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@staticmethod
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def lookup_lora_triggers(lora_list: List[str]) -> Dict[str, List[str]]:
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"""
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Lookup trigger words for a list of LoRAs.
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Args:
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lora_list: List of LoRA strings (may include strengths and folders)
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Returns:
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Dict mapping lora name to list of trigger words
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"""
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# First, expand any folder references
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expanded_list = UltimateConfigBuilder.expand_lora_folders(lora_list)
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trigger_map = {}
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for lora_str in expanded_list:
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if not lora_str or lora_str == "None":
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continue
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# Handle combined LoRAs (e.g., "lora1 + lora2")
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if " + " in lora_str:
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parts = lora_str.split(" + ")
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for part in parts:
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part = part.strip()
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if part and part != "None":
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lora_name = part.split(":")[0]
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if lora_name not in trigger_map:
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try:
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triggers = load_and_save_tags(
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lora_name,
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force_fetch=False,
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auto_fetch=True
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)
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trigger_map[lora_name] = triggers if triggers else []
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except Exception as e:
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print(f"[ConfigBuilder] Error fetching triggers for {lora_name}: {e}")
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trigger_map[lora_name] = []
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else:
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# Single LoRA
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lora_name = lora_str.split(":")[0]
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if lora_name and lora_name != "None" and lora_name not in trigger_map:
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try:
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triggers = load_and_save_tags(
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lora_name,
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force_fetch=False,
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auto_fetch=True
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)
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trigger_map[lora_name] = triggers if triggers else []
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except Exception as e:
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print(f"[ConfigBuilder] Error fetching triggers for {lora_name}: {e}")
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trigger_map[lora_name] = []
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return trigger_map
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def parse_int_list(self, value: str) -> List[int]:
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"""Parse comma-separated integers"""
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items = self.parse_comma_list(value)
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result = []
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for item in items:
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try:
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# Cast to float first to handle strings like "20.0", then to int
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result.append(int(float(item)))
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except ValueError:
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print(f"[ConfigBuilder] Warning: Could not parse integer '{item}'")
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return result
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def parse_comma_list(self, value: str) -> List[str]:
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"""Parse comma-separated string"""
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if not value or value.strip() == "":
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return []
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return [item.strip() for item in value.split(",") if item.strip()]
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def parse_number_list(self, value: str) -> List[float]:
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"""Parse comma-separated numbers"""
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items = self.parse_comma_list(value)
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result = []
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for item in items:
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try:
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result.append(float(item))
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except ValueError:
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print(f"[ConfigBuilder] Warning: Could not parse '{item}'")
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return result
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def process_lora_array(self, config_array: Dict, include_none: bool) -> List[str]:
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"""
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Process a SINGLE config array and return its lora strings.
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Args:
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config_array: Single config array dict from config_arrays
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include_none: Whether to include "None" in results
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Returns:
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List of lora strings for this config array
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"""
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array_name = config_array.get("name", "Unnamed Config")
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# FIX: Force combine to True.
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# The UI defaults new configs to 'combine: false' but the Preview
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# treats them as combined. We enforce True here to match the Preview/Stack behavior.
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combine = True
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loras = config_array.get("loras", [])
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# Loras are already strings
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lora_strings = [str(lora) for lora in loras if lora and lora != "None"]
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# Add combined version if requested
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if combine and len(lora_strings) > 1:
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stackable = [s for s in lora_strings if not s.endswith("/")]
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if len(stackable) > 1:
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# When combine is true, ONLY return the combined version
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combined = " + ".join(stackable)
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lora_strings = [combined]
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print(f"[ConfigBuilder] {array_name}: Combined {len(stackable)} LoRAs into stack")
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# Remove duplicates while preserving order
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seen = set()
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unique_strings = []
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for item in lora_strings:
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if item not in seen:
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seen.add(item)
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unique_strings.append(item)
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print(f"[ConfigBuilder] {array_name}: Processed {len(unique_strings)} LoRA configs")
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return unique_strings
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def generate_config(
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self,
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session_name,
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load_session,
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samplers,
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schedulers,
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steps,
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cfg,
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lora_config,
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include_none,
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model=""
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):
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"""
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Generate configuration.
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NOTE: All widget parameters are IGNORED!
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The actual data comes from the lora_config widget which contains everything.
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"""
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print(f"\n{'='*80}")
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print(f"[ConfigBuilder] 🎯 Generating Configuration")
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print(f"{'='*80}")
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# Parse the COMPLETE state from lora_config widget
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try:
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state = json.loads(lora_config)
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except json.JSONDecodeError as e:
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print(f"[ConfigBuilder] ⚠️ Error parsing lora_config: {e}")
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print(f"[ConfigBuilder] Using default config")
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state = json.loads(self.get_default_config())
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# Extract values from state
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actual_session_name = state.get("session_name", session_name)
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actual_include_none = state.get("include_none", include_none)
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config_arrays = state.get("config_arrays", [])
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# Global prompts (used when per-config prompts are not defined)
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global_positive_groups = state.get("global_positive_groups", [])
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global_negative = state.get("global_negative", "")
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if not config_arrays:
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config_arrays = [{
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"name": "Config 1",
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"samplers": "euler",
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"schedulers": "normal",
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"steps": "20",
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"cfg": "7.0",
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"model": "",
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"loras": ["None"],
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"lora_omit_triggers": [],
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"lora_triggerwords_append_settings": {},
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"combine": True,
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"positive_prompt_groups": [],
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"negative_prompt": "",
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"use_custom_prompts": False
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}]
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configs_output = []
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total_lora_configs = 0
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for config_array in config_arrays:
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# Parse values from this config array
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sampler_list = self.parse_comma_list(config_array.get("samplers", "euler"))
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scheduler_list = self.parse_comma_list(config_array.get("schedulers", "normal"))
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steps_list = self.parse_int_list(config_array.get("steps", "20"))
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cfg_list = self.parse_number_list(config_array.get("cfg", "7.0"))
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models_raw = config_array.get("models", ["None"])
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omit_triggers = config_array.get("lora_omit_triggers", [])
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lora_triggerwords_append_settings = config_array.get("lora_triggerwords_append_settings", {})
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# Process models - handle both object format {path, type} and legacy string format
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model_strings = []
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model_type = "checkpoint" # default
|
|
for m in models_raw:
|
|
if isinstance(m, dict):
|
|
path = m.get("path", "")
|
|
if path and path != "None":
|
|
model_strings.append(str(path))
|
|
model_type = m.get("type", "checkpoint")
|
|
elif isinstance(m, str) and m and m != "None":
|
|
model_strings.append(str(m))
|
|
|
|
# Process loras for this config
|
|
lora_strings = self.process_lora_array(config_array, actual_include_none)
|
|
total_lora_configs += len(lora_strings)
|
|
|
|
# Create ONE config for this array
|
|
config = {
|
|
"sampler": sampler_list if len(sampler_list) > 1 else sampler_list[0] if sampler_list else "euler",
|
|
"scheduler": scheduler_list if len(scheduler_list) > 1 else scheduler_list[0] if scheduler_list else "normal",
|
|
"steps": steps_list if len(steps_list) > 1 else steps_list[0] if steps_list else 20,
|
|
"cfg": cfg_list if len(cfg_list) > 1 else cfg_list[0] if cfg_list else 7.0,
|
|
"lora": lora_strings if len(lora_strings) > 1 else lora_strings[0] if lora_strings else "None",
|
|
"model": model_strings if len(model_strings) > 1 else model_strings[0] if model_strings else "None"
|
|
}
|
|
|
|
# Add seed_behavior if set to randomize
|
|
seed_behavior = config_array.get("seed_behavior", "fixed")
|
|
if seed_behavior == "randomize":
|
|
config["seed_behavior"] = "randomize"
|
|
|
|
# Process VAEs
|
|
vaes_raw = config_array.get("vaes", ["None"])
|
|
vae_strings = [str(v) for v in vaes_raw if v and v != "None"]
|
|
if vae_strings:
|
|
config["vae"] = vae_strings if len(vae_strings) > 1 else vae_strings[0]
|
|
|
|
# Add model_type and related fields for non-checkpoint models
|
|
if model_type != "checkpoint":
|
|
config["model_type"] = model_type
|
|
text_encoders = config_array.get("text_encoders", [])
|
|
if text_encoders:
|
|
config["text_encoders"] = [te for te in text_encoders if te and te != "None"]
|
|
clip_type = config_array.get("clip_type", "")
|
|
if clip_type:
|
|
config["clip_type"] = clip_type
|
|
if model_type == "gguf":
|
|
gguf_options = config_array.get("gguf_options", {})
|
|
if gguf_options:
|
|
config["gguf_options"] = gguf_options
|
|
|
|
# Add omit triggers if present
|
|
if omit_triggers:
|
|
config["lora_omit_triggers"] = omit_triggers
|
|
|
|
# Add trigger append settings if present
|
|
if lora_triggerwords_append_settings and any(v != "none" for v in lora_triggerwords_append_settings.values()):
|
|
config["lora_triggerwords_append_settings"] = lora_triggerwords_append_settings
|
|
|
|
# ==== PROMPT HANDLING ====
|
|
# Priority: per-config > global > node inputs (omitted = use node inputs)
|
|
use_custom = config_array.get("use_custom_prompts", False)
|
|
per_config_positive_groups = config_array.get("positive_prompt_groups", [])
|
|
per_config_negative = config_array.get("negative_prompt", "")
|
|
|
|
if use_custom and per_config_positive_groups:
|
|
# Per-config prompts override everything
|
|
# Store as nested array format for parse_prompt_input_nested() compatibility
|
|
config["positive"] = per_config_positive_groups
|
|
if per_config_negative:
|
|
config["negative"] = per_config_negative
|
|
elif global_positive_groups:
|
|
# Global prompts override node inputs
|
|
config["positive"] = global_positive_groups
|
|
if global_negative:
|
|
config["negative"] = global_negative
|
|
# If neither, omit "positive"/"negative" keys - node inputs will be used as fallback
|
|
|
|
configs_output.append(config)
|
|
|
|
json_output = json.dumps(configs_output, indent=2, ensure_ascii=False)
|
|
|
|
# Calculate totals
|
|
total_combinations = 0
|
|
for config in configs_output:
|
|
lora_count = len(config["lora"]) if isinstance(config["lora"], list) else 1
|
|
sampler_count = len(config["sampler"]) if isinstance(config["sampler"], list) else 1
|
|
scheduler_count = len(config["scheduler"]) if isinstance(config["scheduler"], list) else 1
|
|
steps_count = len(config["steps"]) if isinstance(config["steps"], list) else 1
|
|
cfg_count = len(config["cfg"]) if isinstance(config["cfg"], list) else 1
|
|
|
|
total_combinations += (sampler_count * scheduler_count * steps_count * cfg_count * lora_count)
|
|
|
|
print(f"[ConfigBuilder] 📊 Configuration Summary:")
|
|
print(f" Session: {actual_session_name}")
|
|
print(f" Config Arrays: {len(config_arrays)}")
|
|
print(f" Total LoRA Configs: {total_lora_configs}")
|
|
print(f" Total Combinations: {total_combinations}")
|
|
print(f"{'='*80}\n")
|
|
|
|
# Return just the essentials
|
|
return (json_output, actual_session_name)
|
|
|
|
|
|
# API endpoint for trigger word lookup
|
|
@server.PromptServer.instance.routes.post("/configbuilder/lookup_triggers")
|
|
async def lookup_triggers_endpoint(request):
|
|
"""API endpoint to lookup trigger words for LoRAs"""
|
|
try:
|
|
data = await request.json()
|
|
lora_list = data.get("loras", [])
|
|
|
|
print(f"[ConfigBuilder] 🔍 Lookup request for {len(lora_list)} LoRAs")
|
|
|
|
trigger_map = UltimateConfigBuilder.lookup_lora_triggers(lora_list)
|
|
|
|
print(f"[ConfigBuilder] ✅ Found triggers for {len(trigger_map)} LoRAs")
|
|
|
|
return web.json_response({
|
|
"triggers": trigger_map
|
|
})
|
|
except Exception as e:
|
|
print(f"[ConfigBuilder] ❌ Error in lookup_triggers endpoint: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({
|
|
"error": str(e)
|
|
}, status=500)
|
|
|
|
|
|
# API endpoint for detailed LoRA metadata lookup
|
|
@server.PromptServer.instance.routes.post("/configbuilder/lookup_lora_metadata")
|
|
async def lookup_lora_metadata_endpoint(request):
|
|
"""API endpoint to lookup full metadata for a specific LoRA from CivitAI"""
|
|
try:
|
|
data = await request.json()
|
|
lora_name = data.get("lora_name", "")
|
|
|
|
if not lora_name:
|
|
return web.json_response({
|
|
"error": "No LoRA name provided"
|
|
}, status=400)
|
|
|
|
print(f"[ConfigBuilder] 🔍 Full metadata lookup request for: {lora_name}")
|
|
|
|
# Get the full path to the LoRA file
|
|
lora_path = folder_paths.get_full_path("loras", lora_name)
|
|
|
|
if lora_path is None:
|
|
return web.json_response({
|
|
"error": f"LoRA file not found: {lora_name}"
|
|
}, status=404)
|
|
|
|
# Calculate the hash
|
|
lora_hash = calculate_sha256(lora_path)
|
|
short_hash = lora_hash[:10] # First 10 characters for short hash
|
|
|
|
print(f"[ConfigBuilder] 📊 Hash calculated: {short_hash}")
|
|
|
|
# Fetch metadata from CivitAI
|
|
model_info = get_model_version_info(lora_hash)
|
|
|
|
if model_info is None:
|
|
return web.json_response({
|
|
"error": "No metadata found on CivitAI",
|
|
"hash": lora_hash,
|
|
"short_hash": short_hash
|
|
}, status=404)
|
|
|
|
# Extract relevant information
|
|
metadata = {
|
|
"name": model_info.get("name", "Unknown"),
|
|
"model_name": model_info.get("model", {}).get("name", "Unknown") if isinstance(model_info.get("model"), dict) else "Unknown",
|
|
"trained_words": model_info.get("trainedWords", []),
|
|
"base_model": model_info.get("baseModel", "Unknown"),
|
|
"description": model_info.get("description", ""),
|
|
"tags": model_info.get("model", {}).get("tags", []) if isinstance(model_info.get("model"), dict) else [],
|
|
"images": [],
|
|
"url": f"https://civitai.com/models/{model_info.get('modelId', '')}" if model_info.get("modelId") else "",
|
|
"hash": lora_hash,
|
|
"short_hash": short_hash,
|
|
"file_path": lora_path,
|
|
"stats": model_info.get("stats", {}),
|
|
"creator": model_info.get("creator", {}).get("username", "Unknown") if isinstance(model_info.get("creator"), dict) else "Unknown"
|
|
}
|
|
|
|
# Extract images
|
|
if "images" in model_info and isinstance(model_info["images"], list):
|
|
for img in model_info["images"][:5]: # Limit to first 5 images
|
|
if isinstance(img, dict) and "url" in img:
|
|
metadata["images"].append({
|
|
"url": img["url"],
|
|
"nsfw": img.get("nsfw", "None"),
|
|
"width": img.get("width", 0),
|
|
"height": img.get("height", 0)
|
|
})
|
|
|
|
# Save metadata to file
|
|
output_dir = folder_paths.get_output_directory()
|
|
model_data_dir = os.path.join(output_dir, "benchmarks", "model-data", lora_name.replace("/", "_").replace("\\", "_").replace(".safetensors", ""))
|
|
os.makedirs(model_data_dir, exist_ok=True)
|
|
|
|
metadata_file = os.path.join(model_data_dir, "metadata.json")
|
|
save_dict_to_json(metadata, metadata_file)
|
|
|
|
print(f"[ConfigBuilder] ✅ Metadata saved to: {metadata_file}")
|
|
|
|
return web.json_response({
|
|
"metadata": metadata,
|
|
"saved_to": metadata_file
|
|
})
|
|
|
|
except Exception as e:
|
|
print(f"[ConfigBuilder] ❌ Error in lookup_lora_metadata endpoint: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({
|
|
"error": str(e)
|
|
}, status=500)
|
|
|
|
|
|
# API endpoint to get all model lists for unified model selector
|
|
@server.PromptServer.instance.routes.get("/configbuilder/model_lists")
|
|
async def get_model_lists_endpoint(request):
|
|
"""Return all model lists for the config builder's unified model selector."""
|
|
try:
|
|
checkpoints = folder_paths.get_filename_list("checkpoints")
|
|
diffusion_models = folder_paths.get_filename_list("diffusion_models")
|
|
text_encoders = folder_paths.get_filename_list("text_encoders")
|
|
|
|
# GGUF lists - may not exist if ComfyUI-GGUF is not installed
|
|
try:
|
|
unet_gguf = folder_paths.get_filename_list("unet_gguf")
|
|
except (KeyError, Exception):
|
|
unet_gguf = []
|
|
try:
|
|
clip_gguf = folder_paths.get_filename_list("clip_gguf")
|
|
except (KeyError, Exception):
|
|
clip_gguf = []
|
|
|
|
# CLIPType options (matching ComfyUI's CLIPLoader and DualCLIPLoader)
|
|
clip_types = [
|
|
"stable_diffusion", "stable_cascade", "sd3", "stable_audio",
|
|
"mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan",
|
|
"hidream", "chroma", "ace", "flux"
|
|
]
|
|
dual_clip_types = [
|
|
"sdxl", "sd3", "flux", "hunyuan_video", "hidream",
|
|
"hunyuan_image", "hunyuan_video_15"
|
|
]
|
|
|
|
# VAE list
|
|
vae_list = folder_paths.get_filename_list("vae")
|
|
|
|
return web.json_response({
|
|
"checkpoints": checkpoints,
|
|
"diffusion_models": diffusion_models,
|
|
"unet_gguf": unet_gguf,
|
|
"text_encoders": text_encoders,
|
|
"clip_gguf": clip_gguf,
|
|
"clip_types": clip_types,
|
|
"dual_clip_types": dual_clip_types,
|
|
"vae": vae_list
|
|
})
|
|
except Exception as e:
|
|
print(f"[ConfigBuilder] Error in model_lists endpoint: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
|
|
# API endpoint to refresh model/lora lists (triggered by "Update Node Definitions")
|
|
@server.PromptServer.instance.routes.post("/configbuilder/refresh_models")
|
|
async def refresh_models_endpoint(request):
|
|
"""
|
|
API endpoint to signal frontend to clear its caches.
|
|
Called when ComfyUI updates node definitions.
|
|
"""
|
|
try:
|
|
print(f"[ConfigBuilder] 🔄 Refresh signal received - clearing frontend caches")
|
|
|
|
return web.json_response({
|
|
"status": "ok",
|
|
"message": "Frontend caches should be cleared"
|
|
})
|
|
except Exception as e:
|
|
print(f"[ConfigBuilder] ❌ Error in refresh_models endpoint: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({
|
|
"error": str(e)
|
|
}, status=500) |