mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-07-06 09:21:16 -03:00
51c0135250
- Rename py/agent_cli/ -> py/metadata_ops/ (module was never agent-related) - Rename tests/agent_cli/ -> tests/metadata_ops/ - Remove 9 low-value/debug INFO log points across agent_handlers.py, agent_service.py, llm_service.py, and metadata_ops/__init__.py - Keep LLM raw response at DEBUG level for diagnostics - Consolidate per-model progress + LLM result into single concise log line with basename instead of full path - Update package/class/method docstrings to clarify this is a pipeline infrastructure, not a true agent loop
134 lines
5.0 KiB
Python
134 lines
5.0 KiB
Python
"""Configuration for the HF metadata enrichment validation suite.
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Loads user settings, defines paths, and pulls constants from the main
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codebase (``py.utils.constants``).
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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from typing import Any, Dict, List
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Default paths
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# ---------------------------------------------------------------------------
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_DEFAULT_MODELS_FILE = os.path.join(
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os.path.dirname(__file__), "test_data", "hf_lora_models_with_safetensors.txt"
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)
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_DEFAULT_SETTINGS_PATH = os.path.expanduser(
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"~/.config/ComfyUI-LoRA-Manager/settings.json"
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)
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_DEFAULT_OUTPUT_DIR = "/tmp/hf_enrich_validation"
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# ---------------------------------------------------------------------------
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# Constants from the main codebase (copied at import time)
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# ---------------------------------------------------------------------------
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# Priority tags used in the LLM prompt for tag selection guidance.
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CIVITAI_MODEL_TAGS: List[str] = [
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"character", "concept", "clothing", "realistic", "anime", "toon",
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"furry", "style", "poses", "background", "tool", "vehicle",
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"buildings", "objects", "assets", "animal", "action",
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]
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# ---------------------------------------------------------------------------
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# Base model resolution — dynamically fetched from production code
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# ---------------------------------------------------------------------------
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# Module-level cache — populated by init_supported_base_models().
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# Falls back to a comprehensive hardcoded list when the live fetch fails.
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SUPPORTED_BASE_MODELS: List[str] = []
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# Fallback base models when the production list_base_models() is unavailable.
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_FALLBACK_BASE_MODELS: List[str] = [
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"SD 1.4", "SD 1.5", "SD 1.5 LCM", "SD 1.5 Hyper",
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"SD 2.0", "SD 2.1",
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"SD 3", "SD 3.5", "SD 3.5 Medium", "SD 3.5 Large", "SD 3.5 Large Turbo",
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"SDXL 1.0", "SDXL Lightning", "SDXL Hyper",
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"Flux.1 D", "Flux.1 S", "Flux.1 Krea", "Flux.1 Kontext",
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"Flux.2 D", "Flux.2 Klein 9B", "Flux.2 Klein 9B-base",
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"Flux.2 Klein 4B", "Flux.2 Klein 4B-base",
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"AuraFlow", "Chroma", "PixArt a", "PixArt E",
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"Hunyuan 1", "Lumina", "Kolors",
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"NoobAI", "Illustrious", "Pony", "Pony V7",
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"HiDream", "Qwen", "ZImageTurbo", "ZImageBase",
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"SVD", "LTXV", "LTXV2", "LTXV 2.3",
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"CogVideoX", "Mochi",
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"Wan Video", "Wan Video 1.3B t2v", "Wan Video 14B t2v",
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"Wan Video 14B i2v 480p", "Wan Video 14B i2v 720p",
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"Wan Video 2.2 TI2V-5B", "Wan Video 2.2 T2V-A14B",
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"Wan Video 2.2 I2V-A14B",
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"Wan Video 2.5 T2V", "Wan Video 2.5 I2V",
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"Hunyuan Video", "Anima", "Ernie", "Ernie Turbo",
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"Nucleus", "Krea 2",
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]
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async def init_supported_base_models() -> None:
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"""Populate ``SUPPORTED_BASE_MODELS`` from the production codebase.
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Calls ``py.metadata_ops.list_base_models()`` which merges a hardcoded
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fallback with models fetched from the CivitAI API. When the call
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fails (e.g. offline, API error), falls back to ``_FALLBACK_BASE_MODELS``.
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Must be called from within an async event loop (i.e. during
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``run_validation.main()``, not at module level).
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"""
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try:
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from py.metadata_ops import list_base_models
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models = await list_base_models()
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if models:
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SUPPORTED_BASE_MODELS[:] = models
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logger.info("Loaded %d base models from production code", len(models))
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return
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logger.warning("list_base_models returned empty list, using fallback")
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except Exception as exc:
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logger.warning("Failed to load base models from production: %s", exc)
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SUPPORTED_BASE_MODELS[:] = _FALLBACK_BASE_MODELS
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logger.info("Using fallback base model list (%d entries)", len(SUPPORTED_BASE_MODELS))
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# Placeholder values the LLM sometimes emits that should count as "empty".
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PLACEHOLDER_VALUES = frozenset({
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"none", "null", "n/a", "unknown", "not available",
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"not specified", "no trigger words", "no trigger word",
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})
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# ---------------------------------------------------------------------------
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# User settings loader
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# ---------------------------------------------------------------------------
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def load_settings(settings_path: str) -> Dict[str, Any]:
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"""Load LoRA Manager settings from *settings_path*.
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Returns a flat dict with the LLM configuration fields that the
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enrichment pipeline depends on.
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"""
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path = os.path.expanduser(settings_path)
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if not os.path.exists(path):
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raise FileNotFoundError(
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f"Settings file not found: {path}\n"
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"Please provide a valid --settings path."
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)
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with open(path, "r", encoding="utf-8") as fh:
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raw: Dict[str, Any] = json.load(fh)
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# Extract LLM-relevant config
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return {
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"llm_provider": raw.get("llm_provider", "ollama"),
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"llm_model": raw.get("llm_model", "qwen3.5:9b"),
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"llm_api_base": raw.get("llm_api_base", "http://localhost:11434/v1"),
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"llm_api_key": raw.get("llm_api_key", ""),
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"settings_path": path,
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}
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