1455 lines
46 KiB
Python
1455 lines
46 KiB
Python
"""
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Path Utilities Module
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Provides helper functions for path normalization, symlink resolution,
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and directory matching to be shared across all modules.
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"""
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import hashlib
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import json
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import os
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import re
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from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple
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from .type_utils import normalize_alphanumeric_key
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_log = None
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MODEL_RESOLVER_METADATA_SUFFIX = ".modelresolver.json"
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MODEL_RESOLVER_METADATA_SCHEMA = "comfyui-model-resolver"
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MODEL_RESOLVER_METADATA_SCHEMA_VERSION = 1
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_EXTERNAL_METADATA_PREFERRED_FIELDS = {
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"notes",
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"favorite",
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"exclude",
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"skip_metadata_refresh",
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"db_checked",
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"description",
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"model_description",
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"modelDescription",
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}
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_MERGED_METADATA_LIST_FIELDS = {
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"tags",
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"trained_words",
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"trainedWords",
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}
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def _get_log():
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"""Return a lazily-created module logger (avoids circular imports)."""
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global _log
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if _log is None:
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from .log_system import create_module_logger
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_log = create_module_logger(__name__)
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return _log
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def get_path_identity(path: str) -> str:
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"""Return a stable path identity across symlinks/junctions."""
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if not path:
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return ""
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try:
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return os.path.normcase(os.path.realpath(os.path.abspath(path)))
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except (OSError, ValueError):
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return os.path.normcase(os.path.abspath(path))
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def get_model_path_identity(path_value: Any) -> str:
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"""Return a stable filesystem identity for a model path value."""
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path = str(path_value or "").strip()
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return get_path_identity(path)
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def normalize_absolute_path(path_value: Any, path_module=os.path) -> str:
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"""Return an absolute, normalized and symlink-resolved path."""
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return path_module.realpath(
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path_module.abspath(path_module.normpath(str(path_value)))
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)
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def normalize_metadata_file_path(path_value: Any) -> str:
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"""Normalize metadata paths to portable forward-slash separators."""
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return str(path_value or "").replace(os.sep, "/")
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def get_path_abs(path_value: Any) -> str:
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"""Return absolute path safely, handling OSError/ValueError."""
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try:
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return os.path.abspath(str(path_value or ""))
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except (OSError, ValueError):
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return str(path_value or "")
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def get_path_key(path_value: Any) -> str:
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"""Return a normalized absolute path key for comparison."""
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text = str(path_value or "").strip()
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if not text:
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return ""
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try:
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return os.path.normcase(os.path.abspath(os.path.normpath(text)))
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except (OSError, ValueError):
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return os.path.normcase(os.path.normpath(text))
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def is_path_within(path_value: Any, root_value: Any) -> bool:
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"""Check if a path is located within a root directory."""
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if not path_value or not root_value:
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return False
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try:
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path_key = get_path_identity(str(path_value))
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root_key = get_path_identity(str(root_value))
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return os.path.commonpath([path_key, root_key]) == root_key
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except Exception:
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return False
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def _get_folder_paths_module(folder_paths_module: Optional[Any] = None) -> Optional[Any]:
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if folder_paths_module is not None:
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return folder_paths_module
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try:
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import folder_paths
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return folder_paths
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except ImportError:
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return None
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def normalize_string_values(value: Any) -> List[str]:
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"""Normalize an external string collection without iterating scalar text."""
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if isinstance(value, str):
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return [value] if value else []
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if not isinstance(value, (list, tuple, set, frozenset)):
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return []
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return [item for item in value if isinstance(item, str) and item]
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def normalize_folder_path_values(value: Any) -> List[str]:
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"""Normalize ComfyUI folder-path values across supported registry shapes."""
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if isinstance(value, (list, tuple)) and value:
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first_item = value[0]
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if isinstance(first_item, (list, tuple, set, frozenset)):
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value = first_item
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return normalize_string_values(value)
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def get_configured_model_roots(
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folder_paths_module: Optional[Any] = None,
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*,
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skip_keys: Optional[Iterable[str]] = None,
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) -> List[str]:
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"""Return all configured ComfyUI model roots, including external model folders."""
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fp = _get_folder_paths_module(folder_paths_module)
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if fp is None:
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return []
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skipped = {str(key).strip().lower() for key in (skip_keys or {"custom_nodes", "configs"})}
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names = []
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folder_names_and_paths = getattr(fp, "folder_names_and_paths", None)
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if isinstance(folder_names_and_paths, dict):
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names = list(folder_names_and_paths.keys())
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else:
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try:
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get_folder_names = getattr(fp, "get_folder_names", None)
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if callable(get_folder_names):
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names = list(get_folder_names() or [])
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except Exception:
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names = []
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roots: List[str] = []
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seen = set()
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for raw_name in names:
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name = str(raw_name or "").strip()
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if not name or name.lower() in skipped:
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continue
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paths = []
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try:
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get_folder_paths = getattr(fp, "get_folder_paths", None)
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if callable(get_folder_paths):
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paths = normalize_folder_path_values(get_folder_paths(name))
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except Exception:
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paths = []
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if not paths and isinstance(folder_names_and_paths, dict):
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paths = normalize_folder_path_values(folder_names_and_paths.get(name))
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for path in paths:
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if not path:
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continue
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absolute_path = os.path.abspath(os.path.normpath(str(path)))
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identity = get_path_identity(absolute_path)
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if identity in seen:
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continue
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seen.add(identity)
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roots.append(absolute_path)
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return roots
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def is_path_in_configured_model_roots(
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path_value: Any,
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folder_paths_module: Optional[Any] = None,
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) -> bool:
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"""Return True when a path is inside a configured ComfyUI model root."""
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if not path_value:
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return False
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return any(
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is_path_within(path_value, root)
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for root in get_configured_model_roots(folder_paths_module)
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)
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def prefer_local_base_directory(
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candidate: str,
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current: str,
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preferred_directory: str = "",
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comfy_root: str = "",
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) -> bool:
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"""Determine if candidate directory should be preferred over current directory."""
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if not current:
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return True
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if not candidate:
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return False
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candidate_key = get_path_key(candidate)
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current_key = get_path_key(current)
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preferred_key = get_path_key(preferred_directory)
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if preferred_key:
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if candidate_key == preferred_key and current_key != preferred_key:
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return True
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if current_key == preferred_key and candidate_key != preferred_key:
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return False
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if comfy_root:
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candidate_is_external = not is_path_within(candidate, comfy_root)
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current_is_external = not is_path_within(current, comfy_root)
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if candidate_is_external != current_is_external:
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return candidate_is_external
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candidate_is_canonical = candidate_key == get_path_identity(candidate)
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current_is_canonical = current_key == get_path_identity(current)
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if candidate_is_canonical != current_is_canonical:
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return candidate_is_canonical
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return False
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def dedupe_local_base_directories(
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paths: Optional[list | tuple] = None,
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preferred_directory: str = "",
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comfy_root: str = "",
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) -> list:
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"""Deduplicate a list of directory paths, resolving symlinks/junctions."""
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if paths is None:
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path_values = ()
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elif isinstance(paths, (list, tuple)):
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path_values = paths
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else:
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raise TypeError("paths must be a list or tuple")
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by_identity = {}
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ordered_identities = []
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for path in path_values:
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if not path or not os.path.isdir(path):
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continue
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path_abs = get_path_abs(path)
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path_identity = get_path_identity(path_abs)
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if not path_identity:
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continue
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current = by_identity.get(path_identity)
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if not current:
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by_identity[path_identity] = path_abs
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ordered_identities.append(path_identity)
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elif prefer_local_base_directory(
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path_abs,
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current,
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preferred_directory,
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comfy_root,
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):
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by_identity[path_identity] = path_abs
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return [by_identity[key] for key in ordered_identities]
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def write_json_atomic(
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file_path: str,
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data: Any,
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indent: Optional[int] = None,
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separators: Optional[Tuple[str, str]] = None,
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) -> None:
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"""Safely write data to a JSON file using a temporary file and atomic replace."""
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import json
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import tempfile
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dir_name = os.path.dirname(file_path)
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if dir_name:
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os.makedirs(dir_name, exist_ok=True)
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fd, tmp_path = tempfile.mkstemp(
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prefix=f".{os.path.basename(file_path)}.",
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suffix=".tmp",
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dir=dir_name or None,
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text=True,
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)
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try:
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with os.fdopen(fd, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=indent, separators=separators, ensure_ascii=False)
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if indent is not None:
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f.write("\n")
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os.replace(tmp_path, file_path)
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except Exception:
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try:
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os.remove(tmp_path)
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except OSError:
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pass
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raise
|
|
|
|
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|
def get_filename_from_path(path: Any) -> str:
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"""Return the filename/basename of a path, handling both forward and backward slashes."""
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if not path:
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return ""
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return str(path).replace("\\", "/").split("/")[-1]
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|
|
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# Path to metadata directory
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METADATA_DIR = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "metadata"
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)
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|
|
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class HashCalculationCancelled(Exception):
|
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"""Exception raised when file SHA256 calculation is cancelled by the caller."""
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pass
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|
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SAFETENSORS_HEADER_MAX_BYTES = 100 * 1024 * 1024
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_NON_SHA256_HASH_KEY_PARTS = (
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"blake3",
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"crc32",
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"md5",
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"sha1",
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|
"ss_new_sd_model_hash",
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"ss_sd_model_hash",
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|
"sshs_model_hash",
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|
"sshs_legacy_hash",
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"autov3",
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"autov1",
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"autov2",
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)
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_BASE_MODEL_ALIAS_CACHE: Optional[List[Tuple[str, str]]] = None
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SAFETENSORS_METADATA_TAG_LIMIT = 40
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SAFETENSORS_METADATA_KEY_LIMIT = 80
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SAFETENSORS_METADATA_TEXT_MAX_CHARS = 20000
|
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SAFETENSORS_METADATA_THUMBNAIL_MAX_CHARS = 512 * 1024
|
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|
|
|
|
|
|
def read_safetensors_header(
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|
file_path: str,
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|
max_header_size: int = SAFETENSORS_HEADER_MAX_BYTES,
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|
) -> Optional[Dict[str, Any]]:
|
|
"""Read and parse a safetensors JSON header without touching tensor payloads."""
|
|
if not file_path or not str(file_path).lower().endswith(".safetensors"):
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|
return None
|
|
|
|
try:
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file_size = os.path.getsize(file_path)
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|
except (OSError, ValueError):
|
|
return None
|
|
|
|
if file_size < 8:
|
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return None
|
|
|
|
try:
|
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with open(file_path, "rb") as f:
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|
header_size_bytes = f.read(8)
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|
if len(header_size_bytes) != 8:
|
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return None
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|
|
|
header_size = int.from_bytes(
|
|
header_size_bytes,
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byteorder="little",
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|
signed=False,
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|
)
|
|
if (
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header_size <= 0
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or header_size > max_header_size
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or header_size > file_size - 8
|
|
):
|
|
return None
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|
|
|
header_bytes = f.read(header_size)
|
|
if len(header_bytes) != header_size:
|
|
return None
|
|
|
|
header_json = json.loads(header_bytes.decode("utf-8"))
|
|
if isinstance(header_json, dict):
|
|
return header_json
|
|
except Exception as e:
|
|
_get_log().debug(f"Could not read safetensors header for {file_path}: {e}")
|
|
|
|
return None
|
|
|
|
|
|
def extract_safetensors_header_sha256(
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file_path: str,
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max_header_size: int = SAFETENSORS_HEADER_MAX_BYTES,
|
|
) -> Optional[str]:
|
|
"""Return an embedded SHA256 from a safetensors metadata header when present."""
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from .type_utils import normalize_sha256
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|
|
|
header_json = read_safetensors_header(file_path, max_header_size=max_header_size)
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|
if not isinstance(header_json, dict):
|
|
return None
|
|
|
|
metadata = header_json.get("__metadata__")
|
|
if not isinstance(metadata, dict):
|
|
return None
|
|
|
|
for key in (
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"modelspec.hash.sha256",
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|
"modelspec.hash_sha256",
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|
"sha256",
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"SHA256",
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|
):
|
|
sha256 = normalize_sha256(metadata.get(key))
|
|
if sha256:
|
|
return sha256
|
|
|
|
for key, value in metadata.items():
|
|
key_lower = str(key).lower()
|
|
if not (
|
|
"sha256" in key_lower
|
|
or "hash" in key_lower
|
|
or "civitai" in key_lower
|
|
):
|
|
continue
|
|
if any(part in key_lower for part in _NON_SHA256_HASH_KEY_PARTS):
|
|
continue
|
|
sha256 = normalize_sha256(value)
|
|
if sha256:
|
|
return sha256
|
|
|
|
return None
|
|
|
|
|
|
def _load_base_model_aliases() -> List[Tuple[str, str]]:
|
|
"""Return normalized base-model aliases ordered from most specific first."""
|
|
global _BASE_MODEL_ALIAS_CACHE
|
|
if _BASE_MODEL_ALIAS_CACHE is not None:
|
|
return _BASE_MODEL_ALIAS_CACHE
|
|
|
|
aliases: List[Tuple[str, str]] = []
|
|
try:
|
|
from .sources.popular import load_raw_base_models
|
|
base_models = load_raw_base_models()
|
|
for entry in base_models:
|
|
if not isinstance(entry, dict):
|
|
continue
|
|
name = str(entry.get("name") or "").strip()
|
|
if not name:
|
|
continue
|
|
raw_aliases = entry.get("aliases") or []
|
|
if isinstance(raw_aliases, str):
|
|
raw_aliases = [raw_aliases]
|
|
if not isinstance(raw_aliases, (list, tuple, set)):
|
|
raw_aliases = []
|
|
for raw_alias in [name, *raw_aliases]:
|
|
alias = normalize_alphanumeric_key(raw_alias)
|
|
if alias:
|
|
aliases.append((alias, name))
|
|
except Exception as e:
|
|
_get_log().debug(f"Could not load base model aliases: {e}")
|
|
|
|
aliases.sort(key=lambda item: len(item[0]), reverse=True)
|
|
_BASE_MODEL_ALIAS_CACHE = aliases
|
|
return aliases
|
|
|
|
|
|
def clear_base_model_alias_cache() -> None:
|
|
"""Clear the base model alias cache."""
|
|
global _BASE_MODEL_ALIAS_CACHE
|
|
_BASE_MODEL_ALIAS_CACHE = None
|
|
|
|
|
|
def _canonical_base_model_from_text(value: Any) -> str:
|
|
normalized = normalize_alphanumeric_key(value)
|
|
if not normalized:
|
|
return ""
|
|
|
|
for alias, name in _load_base_model_aliases():
|
|
if normalized == alias or (len(alias) >= 4 and alias in normalized):
|
|
return name
|
|
return ""
|
|
|
|
|
|
|
|
def infer_safetensors_base_model(
|
|
file_path: str,
|
|
max_tensor_keys: int = 500,
|
|
) -> str:
|
|
"""Infer a likely base model from safetensors metadata and tensor names."""
|
|
header_json = read_safetensors_header(file_path)
|
|
if not isinstance(header_json, dict):
|
|
return ""
|
|
|
|
metadata = header_json.get("__metadata__")
|
|
if not isinstance(metadata, dict):
|
|
metadata = {}
|
|
|
|
architecture = str(
|
|
metadata.get("modelspec.architecture")
|
|
or metadata.get("architecture")
|
|
or metadata.get("ss_base_model_version")
|
|
or ""
|
|
).lower()
|
|
|
|
if architecture:
|
|
if "stable-diffusion-xl" in architecture or "sdxl" in architecture:
|
|
return "SDXL 1.0"
|
|
if (
|
|
"stable-diffusion-v1" in architecture
|
|
or "stable-diffusion-1" in architecture
|
|
or "runwayml/stable-diffusion-v1-5" in architecture
|
|
or "sd1.5" in architecture
|
|
or "sd 1.5" in architecture
|
|
):
|
|
return "SD 1.5"
|
|
if "stable-diffusion-v2" in architecture or "sd2" in architecture:
|
|
return "SD 2.1"
|
|
if (
|
|
"stable-diffusion-3" in architecture
|
|
or "stable-diffusion-v3" in architecture
|
|
or "sd3" in architecture
|
|
):
|
|
return "SD 3"
|
|
if "flux" in architecture:
|
|
if "krea" in architecture:
|
|
return "Flux.1 Krea"
|
|
if "schnell" in architecture:
|
|
return "Flux.1 S"
|
|
if "kontext" in architecture:
|
|
return "Flux.1 Kontext"
|
|
return "Flux.1 D"
|
|
alias_match = _canonical_base_model_from_text(architecture)
|
|
if alias_match:
|
|
return alias_match
|
|
|
|
tensor_keys = [
|
|
str(key)
|
|
for key in header_json
|
|
if key != "__metadata__"
|
|
][:max_tensor_keys]
|
|
keys_text = " ".join(tensor_keys)
|
|
|
|
if "double_blocks.0.img_attn" in keys_text or "img_in.weight" in keys_text:
|
|
return "Flux.1 D"
|
|
if "joint_blocks.0.x_block" in keys_text:
|
|
return "SD 3"
|
|
if (
|
|
"conditioner.embedders.1.model" in keys_text
|
|
or "label_emb.0.0.weight" in keys_text
|
|
):
|
|
return "SDXL 1.0"
|
|
if (
|
|
"cond_stage_model.transformer.text_model" in keys_text
|
|
or "model.diffusion_model.input_blocks.0.0.weight" in keys_text
|
|
):
|
|
return "SD 1.5"
|
|
|
|
return ""
|
|
|
|
|
|
def _parse_safetensors_metadata_value(value: Any) -> Any:
|
|
if not isinstance(value, str):
|
|
return value
|
|
|
|
text = value.strip()
|
|
if not text:
|
|
return ""
|
|
if text[:1] in {"{", "["}:
|
|
try:
|
|
return json.loads(text)
|
|
except Exception:
|
|
return text
|
|
return text
|
|
|
|
|
|
def _metadata_get(metadata: Dict[str, Any], *keys: str) -> Any:
|
|
if not isinstance(metadata, dict):
|
|
return None
|
|
|
|
for key in keys:
|
|
if key in metadata:
|
|
return metadata.get(key)
|
|
|
|
lowered = {str(key).lower(): key for key in metadata}
|
|
for key in keys:
|
|
original_key = lowered.get(str(key).lower())
|
|
if original_key is not None:
|
|
return metadata.get(original_key)
|
|
return None
|
|
|
|
|
|
def _metadata_text(
|
|
metadata: Dict[str, Any],
|
|
*keys: str,
|
|
max_chars: int = SAFETENSORS_METADATA_TEXT_MAX_CHARS,
|
|
) -> str:
|
|
value = _parse_safetensors_metadata_value(_metadata_get(metadata, *keys))
|
|
if value is None or isinstance(value, (dict, list, tuple, set)):
|
|
return ""
|
|
|
|
text = str(value).strip()
|
|
if not text or text.lower() in {"none", "null", "undefined"}:
|
|
return ""
|
|
if max_chars and len(text) > max_chars:
|
|
return text[:max_chars].rstrip()
|
|
return text
|
|
|
|
|
|
def _dedupe_strings(values: Iterable[Any], limit: int = 0) -> List[str]:
|
|
result: List[str] = []
|
|
seen = set()
|
|
for value in values:
|
|
text = str(value or "").strip()
|
|
if not text:
|
|
continue
|
|
key = text.lower()
|
|
if key in seen:
|
|
continue
|
|
seen.add(key)
|
|
result.append(text)
|
|
if limit and len(result) >= limit:
|
|
break
|
|
return result
|
|
|
|
|
|
def _metadata_string_list(value: Any, limit: int = SAFETENSORS_METADATA_TAG_LIMIT) -> List[str]:
|
|
parsed = _parse_safetensors_metadata_value(value)
|
|
items: List[Any] = []
|
|
|
|
def collect(item: Any) -> None:
|
|
item = _parse_safetensors_metadata_value(item)
|
|
if item is None:
|
|
return
|
|
if isinstance(item, dict):
|
|
for key in (
|
|
"tags",
|
|
"model_tags",
|
|
"modelTags",
|
|
"trained_words",
|
|
"trainedWords",
|
|
"trigger",
|
|
"trigger_phrase",
|
|
"values",
|
|
"words",
|
|
):
|
|
if key in item:
|
|
collect(item.get(key))
|
|
return
|
|
for value_item in item.values():
|
|
collect(value_item)
|
|
return
|
|
if isinstance(item, (list, tuple, set)):
|
|
for value_item in item:
|
|
collect(value_item)
|
|
return
|
|
if isinstance(item, str):
|
|
parts = re.split(r"[\n,|;]+", item)
|
|
items.extend(part.strip() for part in parts if part.strip())
|
|
return
|
|
items.append(item)
|
|
|
|
collect(parsed)
|
|
return _dedupe_strings(items, limit=limit)
|
|
|
|
|
|
def _collect_tag_frequency_tags(value: Any, limit: int = 20) -> List[str]:
|
|
parsed = _parse_safetensors_metadata_value(value)
|
|
counts: Dict[str, float] = {}
|
|
|
|
def add_tag(tag: Any, count: Any) -> None:
|
|
text = str(tag or "").strip()
|
|
if (
|
|
not text
|
|
or len(text) > 80
|
|
or "/" in text
|
|
or "\\" in text
|
|
or text.lower().endswith((".safetensors", ".ckpt", ".pt"))
|
|
):
|
|
return
|
|
try:
|
|
amount = float(count)
|
|
except (TypeError, ValueError):
|
|
amount = 1.0
|
|
counts[text] = counts.get(text, 0.0) + max(amount, 0.0)
|
|
|
|
def walk(item: Any) -> None:
|
|
item = _parse_safetensors_metadata_value(item)
|
|
if isinstance(item, dict):
|
|
numeric_values = True
|
|
for value_item in item.values():
|
|
try:
|
|
float(value_item)
|
|
except (TypeError, ValueError):
|
|
numeric_values = False
|
|
break
|
|
if numeric_values:
|
|
for key, value_item in item.items():
|
|
add_tag(key, value_item)
|
|
return
|
|
for value_item in item.values():
|
|
walk(value_item)
|
|
elif isinstance(item, (list, tuple, set)):
|
|
for value_item in item:
|
|
walk(value_item)
|
|
|
|
walk(parsed)
|
|
ordered = sorted(counts.items(), key=lambda item: (-item[1], item[0].lower()))
|
|
return [tag for tag, _count in ordered[:limit]]
|
|
|
|
|
|
def _limited_metadata_value(value: Any, *, max_chars: int = 1000, max_items: int = 8) -> Any:
|
|
parsed = _parse_safetensors_metadata_value(value)
|
|
if isinstance(parsed, str):
|
|
text = parsed.strip()
|
|
return text[:max_chars].rstrip() if len(text) > max_chars else text
|
|
if isinstance(parsed, (bool, int, float)) or parsed is None:
|
|
return parsed
|
|
if isinstance(parsed, dict):
|
|
limited: Dict[str, Any] = {}
|
|
for index, (key, item_value) in enumerate(parsed.items()):
|
|
if index >= max_items:
|
|
limited["_truncated"] = True
|
|
break
|
|
limited[str(key)] = _limited_metadata_value(
|
|
item_value,
|
|
max_chars=max_chars,
|
|
max_items=max_items,
|
|
)
|
|
return limited
|
|
if isinstance(parsed, (list, tuple, set)):
|
|
values = list(parsed)
|
|
limited_list = [
|
|
_limited_metadata_value(item, max_chars=max_chars, max_items=max_items)
|
|
for item in values[:max_items]
|
|
]
|
|
if len(values) > max_items:
|
|
limited_list.append({"_truncated": True})
|
|
return limited_list
|
|
return str(parsed)
|
|
|
|
|
|
def _metadata_preview_url(metadata: Dict[str, Any]) -> str:
|
|
value = _metadata_get(
|
|
metadata,
|
|
"modelspec.thumbnail",
|
|
"thumbnail",
|
|
"thumbnail_url",
|
|
"thumbnailUrl",
|
|
"preview_url",
|
|
"previewUrl",
|
|
"ssmd_cover_images",
|
|
)
|
|
parsed = _parse_safetensors_metadata_value(value)
|
|
|
|
def extract(item: Any) -> str:
|
|
item = _parse_safetensors_metadata_value(item)
|
|
if isinstance(item, str):
|
|
text = item.strip()
|
|
if not text or len(text) > SAFETENSORS_METADATA_THUMBNAIL_MAX_CHARS:
|
|
return ""
|
|
if text.startswith(("http://", "https://", "data:image/")):
|
|
return text
|
|
return ""
|
|
if isinstance(item, dict):
|
|
for key in ("url", "src", "image", "thumbnail", "preview", "data"):
|
|
preview = extract(item.get(key))
|
|
if preview:
|
|
return preview
|
|
if isinstance(item, (list, tuple)):
|
|
for value_item in item:
|
|
preview = extract(value_item)
|
|
if preview:
|
|
return preview
|
|
return ""
|
|
|
|
return extract(parsed)
|
|
|
|
|
|
def _metadata_number_or_text(metadata: Dict[str, Any], *keys: str) -> Any:
|
|
value = _metadata_text(metadata, *keys, max_chars=100)
|
|
if not value:
|
|
return None
|
|
try:
|
|
number = int(value)
|
|
if str(number) == value:
|
|
return number
|
|
except (TypeError, ValueError):
|
|
pass
|
|
return value
|
|
|
|
|
|
def extract_safetensors_header_metadata(file_path: str) -> Dict[str, Any]:
|
|
"""Extract displayable local model metadata from a safetensors header."""
|
|
header_json = read_safetensors_header(file_path)
|
|
if not isinstance(header_json, dict):
|
|
return {}
|
|
|
|
metadata = header_json.get("__metadata__")
|
|
if not isinstance(metadata, dict):
|
|
metadata = {}
|
|
|
|
result: Dict[str, Any] = {}
|
|
metadata_keys = sorted(str(key) for key in metadata)
|
|
if metadata_keys:
|
|
result["header_metadata_keys"] = metadata_keys[:SAFETENSORS_METADATA_KEY_LIMIT]
|
|
|
|
title = _metadata_text(
|
|
metadata,
|
|
"modelspec.title",
|
|
"ssmd_display_name",
|
|
"ss_output_name",
|
|
"title",
|
|
"name",
|
|
"model_name",
|
|
max_chars=300,
|
|
)
|
|
if title:
|
|
result["model_name"] = title
|
|
|
|
description = _metadata_text(
|
|
metadata,
|
|
"modelspec.description",
|
|
"ssmd_description",
|
|
"description",
|
|
"model_description",
|
|
"ss_training_comment",
|
|
)
|
|
if description:
|
|
result["description"] = description
|
|
result["model_description"] = description
|
|
|
|
author = _metadata_text(
|
|
metadata,
|
|
"modelspec.author",
|
|
"ssmd_author",
|
|
"author",
|
|
"creator",
|
|
max_chars=200,
|
|
)
|
|
if author:
|
|
result["author"] = author
|
|
result["creator"] = {"username": author, "name": author}
|
|
|
|
license_value = _metadata_text(metadata, "modelspec.license", "license", max_chars=200)
|
|
if license_value:
|
|
result["license"] = license_value
|
|
|
|
usage_hint = _metadata_text(
|
|
metadata,
|
|
"modelspec.usage_hint",
|
|
"usage_hint",
|
|
"usage_tips",
|
|
max_chars=2000,
|
|
)
|
|
if usage_hint:
|
|
result["usage_hint"] = usage_hint
|
|
result["usage_tips"] = usage_hint
|
|
|
|
explicit_tags: List[str] = []
|
|
for key in ("modelspec.tags", "ssmd_tags", "tags", "model_tags"):
|
|
explicit_tags.extend(_metadata_string_list(_metadata_get(metadata, key)))
|
|
explicit_tags.extend(_collect_tag_frequency_tags(_metadata_get(metadata, "ss_tag_frequency")))
|
|
tags = _dedupe_strings(explicit_tags, limit=SAFETENSORS_METADATA_TAG_LIMIT)
|
|
if tags:
|
|
result["tags"] = tags
|
|
|
|
trained_words: List[str] = []
|
|
for key in (
|
|
"modelspec.trigger_phrase",
|
|
"modelspec.trigger_phrases",
|
|
"trigger_phrase",
|
|
"trigger_phrases",
|
|
"trigger",
|
|
"trained_words",
|
|
"trainedWords",
|
|
"ssmd_trained_words",
|
|
):
|
|
trained_words.extend(_metadata_string_list(_metadata_get(metadata, key), limit=20))
|
|
trained_words = _dedupe_strings(trained_words, limit=20)
|
|
if trained_words:
|
|
result["trained_words"] = trained_words
|
|
|
|
preview_url = _metadata_preview_url(metadata)
|
|
if preview_url:
|
|
result["preview_url"] = preview_url
|
|
result["images"] = [
|
|
{
|
|
"url": preview_url,
|
|
"metadata": {"source": "safetensors_header"},
|
|
}
|
|
]
|
|
|
|
clip_skip = _metadata_number_or_text(metadata, "ss_clip_skip", "clip_skip", "clipSkip")
|
|
if clip_skip not in (None, ""):
|
|
result["clip_skip"] = clip_skip
|
|
|
|
sha256 = extract_safetensors_header_sha256(file_path)
|
|
if sha256:
|
|
result["sha256"] = sha256
|
|
result["hash"] = sha256
|
|
result["hashes"] = {"SHA256": sha256}
|
|
result["sha256_source"] = "safetensors_header"
|
|
result["hash_status"] = "completed"
|
|
|
|
base_model = infer_safetensors_base_model(file_path)
|
|
if base_model:
|
|
result["base_model"] = base_model
|
|
result["base_model_source"] = "safetensors_header"
|
|
result["base_model_inferred"] = True
|
|
|
|
raw_base = _metadata_text(
|
|
metadata,
|
|
"ss_base_model_version",
|
|
"modelspec.architecture",
|
|
"architecture",
|
|
max_chars=300,
|
|
)
|
|
if raw_base:
|
|
result["base_model_raw"] = raw_base
|
|
|
|
if _metadata_get(metadata, "ss_network_module"):
|
|
result["model_type"] = "LORA"
|
|
|
|
summary_fields = {
|
|
"architecture": ("modelspec.architecture", "architecture"),
|
|
"base_model_version": ("ss_base_model_version",),
|
|
"resolution": ("modelspec.resolution", "ss_resolution"),
|
|
"prediction_type": ("modelspec.prediction_type", "prediction_type"),
|
|
"date": ("modelspec.date", "date"),
|
|
"implementation": ("modelspec.implementation",),
|
|
"network_module": ("ss_network_module",),
|
|
"network_dim": ("ss_network_dim",),
|
|
"network_alpha": ("ss_network_alpha",),
|
|
"model_hash": ("sshs_model_hash",),
|
|
"legacy_model_hash": ("sshs_legacy_hash",),
|
|
"training_model": ("ss_sd_model_name", "ss_new_sd_model_name"),
|
|
"vae": ("ss_vae_name",),
|
|
"encoder_layer": ("modelspec.encoder_layer",),
|
|
"merged_from": ("modelspec.merged_from",),
|
|
"merge_recipe": ("sd_merge_recipe",),
|
|
"merge_models": ("sd_merge_models",),
|
|
}
|
|
metadata_summary: Dict[str, Any] = {}
|
|
for output_key, source_keys in summary_fields.items():
|
|
value = _metadata_get(metadata, *source_keys)
|
|
if value in (None, ""):
|
|
continue
|
|
limited_value = _limited_metadata_value(value)
|
|
if limited_value not in (None, "", [], {}):
|
|
metadata_summary[output_key] = limited_value
|
|
|
|
if _metadata_get(metadata, "workflow") not in (None, ""):
|
|
metadata_summary["has_embedded_workflow"] = True
|
|
if _metadata_get(metadata, "prompt") not in (None, ""):
|
|
metadata_summary["has_embedded_prompt"] = True
|
|
|
|
if metadata_summary:
|
|
result["metadata_summary"] = metadata_summary
|
|
|
|
meaningful_keys = (
|
|
"model_name",
|
|
"description",
|
|
"author",
|
|
"license",
|
|
"usage_hint",
|
|
"tags",
|
|
"trained_words",
|
|
"preview_url",
|
|
"sha256",
|
|
"base_model",
|
|
"metadata_summary",
|
|
)
|
|
if any(result.get(key) for key in meaningful_keys):
|
|
result["from_safetensors_header"] = True
|
|
result["metadata_source"] = "safetensors_header"
|
|
result["details_source"] = "safetensors_header"
|
|
result["local_metadata_available"] = True
|
|
return result
|
|
|
|
return {}
|
|
|
|
|
|
def calculate_file_sha256(
|
|
file_path: str,
|
|
chunk_size: int = 131072,
|
|
on_progress: Optional[Callable[[int, int], None]] = None,
|
|
is_cancelled: Optional[Callable[[], bool]] = None,
|
|
*,
|
|
use_safetensors_header: bool = True,
|
|
on_hash_source: Optional[Callable[[str], None]] = None,
|
|
) -> Optional[str]:
|
|
"""Return a SHA256 for a local file, preferring safetensors header metadata."""
|
|
if not file_path or not os.path.exists(file_path):
|
|
return None
|
|
|
|
if use_safetensors_header:
|
|
embedded_sha256 = extract_safetensors_header_sha256(file_path)
|
|
if embedded_sha256:
|
|
if on_hash_source:
|
|
on_hash_source("safetensors_header")
|
|
return embedded_sha256
|
|
|
|
sha256_hash = hashlib.sha256()
|
|
try:
|
|
total_bytes = os.path.getsize(file_path) if on_progress else 0
|
|
bytes_read = 0
|
|
with open(file_path, "rb") as f:
|
|
for byte_block in iter(lambda: f.read(chunk_size), b""):
|
|
if is_cancelled and is_cancelled():
|
|
raise HashCalculationCancelled("Hash calculation cancelled")
|
|
if byte_block:
|
|
sha256_hash.update(byte_block)
|
|
bytes_read += len(byte_block)
|
|
if on_progress:
|
|
on_progress(bytes_read, total_bytes)
|
|
if on_hash_source:
|
|
on_hash_source("file")
|
|
return sha256_hash.hexdigest()
|
|
except HashCalculationCancelled:
|
|
raise
|
|
except Exception as e:
|
|
_get_log().error(f"Error computing hash for {file_path}: {e}")
|
|
return None
|
|
|
|
|
|
def save_catalog_with_backup(
|
|
data_path: str, data: Any, meta_path: str, meta: Any, indent: int = 2
|
|
) -> None:
|
|
"""Backup an existing catalog file to .bak, then atomically write the new data and its metadata."""
|
|
import shutil
|
|
# Ensure directory exists
|
|
dir_name = os.path.dirname(data_path)
|
|
if dir_name:
|
|
os.makedirs(dir_name, exist_ok=True)
|
|
|
|
# Backup existing data file
|
|
if os.path.exists(data_path):
|
|
shutil.copy2(data_path, f"{data_path}.bak")
|
|
|
|
# Write data file atomically
|
|
write_json_atomic(data_path, data, indent=indent)
|
|
|
|
# Write metadata file atomically
|
|
write_json_atomic(meta_path, meta, indent=indent)
|
|
|
|
|
|
def read_json_safe(file_path: str, default: Any = None) -> Any:
|
|
"""Safely read and parse a JSON file, returning a default value on error."""
|
|
if not file_path or not os.path.exists(file_path):
|
|
return default
|
|
try:
|
|
with open(file_path, encoding="utf-8") as f:
|
|
return json.load(f)
|
|
except Exception as e:
|
|
_get_log().warning(f"Error reading JSON from {os.path.basename(file_path)}: {e}")
|
|
return default
|
|
|
|
|
|
def get_comfy_root_path(folder_paths_module: Optional[Any] = None) -> str:
|
|
"""Return the absolute path to the ComfyUI root directory."""
|
|
fp = folder_paths_module
|
|
if fp is None:
|
|
try:
|
|
import folder_paths
|
|
fp = folder_paths
|
|
except ImportError:
|
|
return ""
|
|
try:
|
|
module_file = getattr(fp, "__file__", "")
|
|
if module_file:
|
|
return os.path.dirname(os.path.abspath(module_file))
|
|
except Exception:
|
|
pass
|
|
return ""
|
|
|
|
|
|
def get_directory_entry_set(
|
|
directory: str,
|
|
cache: Optional[Dict[str, Set[str]]] = None,
|
|
) -> Set[str]:
|
|
"""
|
|
Return the set of case-normalized filenames in a directory.
|
|
|
|
If ``cache`` is provided, it will be checked and updated with the result
|
|
keyed by normalized directory path.
|
|
"""
|
|
if not directory:
|
|
return set()
|
|
normalized_dir = os.path.normcase(os.path.abspath(directory))
|
|
if cache is not None and normalized_dir in cache:
|
|
return cache[normalized_dir]
|
|
try:
|
|
entries = {os.path.normcase(name) for name in os.listdir(normalized_dir)}
|
|
except OSError:
|
|
entries = set()
|
|
if cache is not None:
|
|
cache[normalized_dir] = entries
|
|
return entries
|
|
|
|
|
|
def find_external_metadata_sidecar_path(
|
|
model_path: str,
|
|
dir_entries: Optional[Set[str]] = None,
|
|
) -> str:
|
|
"""
|
|
Find metadata owned by another tool (for example .metadata.json,
|
|
.civitai.info, or .json). These files are read-only to Model Resolver.
|
|
"""
|
|
if not model_path:
|
|
return ""
|
|
|
|
directory = os.path.dirname(model_path)
|
|
filename = get_filename_from_path(model_path)
|
|
normalized_model_path = os.path.normcase(os.path.abspath(model_path))
|
|
|
|
# Base name without extension
|
|
base_name = filename.rsplit(".", 1)[0] if "." in filename else filename
|
|
has_stem_collision = has_model_sidecar_name_collision(
|
|
model_path,
|
|
dir_entries=dir_entries,
|
|
)
|
|
|
|
# Name patterns based on civitai.py and resolver.py.
|
|
exact_names = [
|
|
filename + ".metadata.json",
|
|
filename + ".civitai.info",
|
|
filename + ".json",
|
|
]
|
|
legacy_names = [
|
|
base_name + ".metadata.json",
|
|
base_name + ".civitai.info",
|
|
base_name + ".json",
|
|
filename.replace("_", " ").split()[0] + ".metadata.json" if "_" in base_name else None,
|
|
]
|
|
possible_names = _ordered_sidecar_names(
|
|
model_path,
|
|
exact_names,
|
|
legacy_names,
|
|
has_stem_collision=has_stem_collision,
|
|
dir_entries=dir_entries,
|
|
)
|
|
|
|
for name in possible_names:
|
|
if name:
|
|
if dir_entries is not None and os.path.normcase(name) not in dir_entries:
|
|
continue
|
|
path = os.path.join(directory, name)
|
|
if os.path.normcase(os.path.abspath(path)) == normalized_model_path:
|
|
continue
|
|
if dir_entries is None and not os.path.exists(path):
|
|
continue
|
|
if (
|
|
has_stem_collision
|
|
and name in legacy_names
|
|
and not _metadata_sidecar_matches_model(path, model_path)
|
|
):
|
|
continue
|
|
return path
|
|
|
|
return ""
|
|
|
|
|
|
def get_model_resolver_sidecar_path(model_path: str) -> str:
|
|
"""Return the sidecar path exclusively owned by Model Resolver."""
|
|
directory = os.path.dirname(model_path)
|
|
filename = get_filename_from_path(model_path)
|
|
return os.path.join(directory, f"{filename}{MODEL_RESOLVER_METADATA_SUFFIX}")
|
|
|
|
|
|
def find_model_resolver_sidecar_path(
|
|
model_path: str,
|
|
dir_entries: Optional[Set[str]] = None,
|
|
) -> str:
|
|
"""Return the existing Model Resolver sidecar path, if present."""
|
|
if not model_path:
|
|
return ""
|
|
filename = get_filename_from_path(model_path)
|
|
expected_name = f"{filename}{MODEL_RESOLVER_METADATA_SUFFIX}"
|
|
if dir_entries is not None and os.path.normcase(expected_name) not in dir_entries:
|
|
return ""
|
|
metadata_path = get_model_resolver_sidecar_path(model_path)
|
|
normalized_model_path = os.path.normcase(os.path.abspath(model_path))
|
|
if (
|
|
os.path.normcase(os.path.abspath(metadata_path)) != normalized_model_path
|
|
and os.path.isfile(metadata_path)
|
|
):
|
|
return metadata_path
|
|
return ""
|
|
|
|
|
|
def find_metadata_sidecar_path(
|
|
model_path: str,
|
|
dir_entries: Optional[Set[str]] = None,
|
|
) -> str:
|
|
"""
|
|
Return the preferred readable sidecar path.
|
|
|
|
Model Resolver metadata takes precedence. External metadata remains
|
|
available as a read-only fallback.
|
|
"""
|
|
return (
|
|
find_model_resolver_sidecar_path(model_path, dir_entries=dir_entries)
|
|
or find_external_metadata_sidecar_path(model_path, dir_entries=dir_entries)
|
|
)
|
|
|
|
|
|
def _metadata_list_values(value: Any) -> List[Any]:
|
|
if isinstance(value, (list, tuple, set)):
|
|
return list(value)
|
|
if value in (None, ""):
|
|
return []
|
|
return [value]
|
|
|
|
|
|
def _merge_metadata_lists(external_value: Any, resolver_value: Any) -> List[Any]:
|
|
result: List[Any] = []
|
|
seen = set()
|
|
for item in _metadata_list_values(external_value) + _metadata_list_values(resolver_value):
|
|
try:
|
|
key = json.dumps(item, sort_keys=True, ensure_ascii=False)
|
|
except (TypeError, ValueError):
|
|
key = str(item)
|
|
if key in seen:
|
|
continue
|
|
seen.add(key)
|
|
result.append(item)
|
|
return result
|
|
|
|
|
|
def read_merged_model_metadata(model_path: str, default: Any = None) -> Any:
|
|
"""
|
|
Read external and Model Resolver sidecars without modifying either file.
|
|
|
|
Resolver-owned technical/provider fields take precedence. User-editable
|
|
external fields remain authoritative, while common list fields are merged.
|
|
"""
|
|
external_path = find_external_metadata_sidecar_path(model_path)
|
|
resolver_path = find_model_resolver_sidecar_path(model_path)
|
|
external = read_json_safe(external_path, {}) if external_path else {}
|
|
resolver = read_json_safe(resolver_path, {}) if resolver_path else {}
|
|
external = external if isinstance(external, dict) else {}
|
|
resolver = resolver if isinstance(resolver, dict) else {}
|
|
|
|
if not external and not resolver:
|
|
return default
|
|
if not external:
|
|
return dict(resolver)
|
|
if not resolver:
|
|
return dict(external)
|
|
|
|
merged = {**external, **resolver}
|
|
for field in _EXTERNAL_METADATA_PREFERRED_FIELDS:
|
|
if field in external:
|
|
merged[field] = external[field]
|
|
for field in _MERGED_METADATA_LIST_FIELDS:
|
|
if field in external or field in resolver:
|
|
merged[field] = _merge_metadata_lists(
|
|
external.get(field),
|
|
resolver.get(field),
|
|
)
|
|
return merged
|
|
|
|
|
|
def split_path_segments(path_value: Any, filter_dots: bool = True) -> list[str]:
|
|
"""Split path into standard segments, replacing backslashes and filtering empty segments."""
|
|
text = str(path_value or "").replace("\\", "/").strip()
|
|
if not text:
|
|
return []
|
|
|
|
parts = []
|
|
for raw_part in text.split("/"):
|
|
part = raw_part.strip()
|
|
if not part:
|
|
continue
|
|
if filter_dots and part in {".", ".."}:
|
|
continue
|
|
parts.append(part)
|
|
|
|
return parts
|
|
|
|
|
|
def has_model_sidecar_name_collision(
|
|
model_path: str,
|
|
dir_entries: Optional[Set[str]] = None,
|
|
) -> bool:
|
|
"""Return whether another model file shares this file's stem."""
|
|
if not model_path:
|
|
return False
|
|
|
|
directory = os.path.dirname(model_path)
|
|
filename = get_filename_from_path(model_path)
|
|
base_name, extension = os.path.splitext(filename)
|
|
if not base_name or not extension:
|
|
return False
|
|
|
|
from .type_utils import MODEL_EXTENSIONS
|
|
|
|
normalized_model_path = os.path.normcase(os.path.abspath(model_path))
|
|
normalized_filename = os.path.normcase(filename)
|
|
for candidate_extension in MODEL_EXTENSIONS:
|
|
candidate_filename = f"{base_name}{candidate_extension}"
|
|
if os.path.normcase(candidate_filename) == normalized_filename:
|
|
continue
|
|
if dir_entries is not None:
|
|
if os.path.normcase(candidate_filename) in dir_entries:
|
|
return True
|
|
else:
|
|
candidate_path = os.path.join(directory, candidate_filename)
|
|
if os.path.normcase(os.path.abspath(candidate_path)) == normalized_model_path:
|
|
continue
|
|
if os.path.isfile(candidate_path):
|
|
return True
|
|
return False
|
|
|
|
|
|
def _metadata_sidecar_matches_model(metadata_path: str, model_path: str) -> bool:
|
|
"""Return whether an ambiguous legacy sidecar names the selected model file."""
|
|
metadata = read_json_safe(metadata_path, {})
|
|
if not isinstance(metadata, dict):
|
|
return False
|
|
|
|
candidate_values = [
|
|
metadata.get("filename"),
|
|
metadata.get("file_path"),
|
|
metadata.get("model_path"),
|
|
metadata.get("path"),
|
|
]
|
|
for key in ("file", "file_info", "selected_file", "selectedFile"):
|
|
nested = metadata.get(key)
|
|
if isinstance(nested, dict):
|
|
candidate_values.extend(
|
|
[
|
|
nested.get("name"),
|
|
nested.get("filename"),
|
|
nested.get("file_name"),
|
|
nested.get("fileName"),
|
|
nested.get("path"),
|
|
]
|
|
)
|
|
|
|
model_filename = os.path.normcase(get_filename_from_path(model_path))
|
|
return any(
|
|
os.path.normcase(get_filename_from_path(value)) == model_filename
|
|
for value in candidate_values
|
|
if value
|
|
)
|
|
|
|
|
|
def _metadata_sidecar_paths(model_path: str) -> List[str]:
|
|
directory = os.path.dirname(model_path)
|
|
filename = get_filename_from_path(model_path)
|
|
base_name = os.path.splitext(filename)[0]
|
|
candidate_names = _ordered_sidecar_names(
|
|
model_path,
|
|
[f"{filename}.metadata.json"],
|
|
[f"{base_name}.metadata.json"],
|
|
)
|
|
candidates = [os.path.join(directory, name) for name in candidate_names]
|
|
|
|
result: List[str] = []
|
|
seen = set()
|
|
for candidate in candidates:
|
|
try:
|
|
key = os.path.normcase(os.path.abspath(candidate))
|
|
except (OSError, ValueError):
|
|
key = os.path.normcase(candidate)
|
|
if key in seen:
|
|
continue
|
|
seen.add(key)
|
|
result.append(candidate)
|
|
return result
|
|
|
|
|
|
def _ordered_sidecar_names(
|
|
model_path: str,
|
|
exact_names: List[Optional[str]],
|
|
legacy_names: List[Optional[str]],
|
|
*,
|
|
has_stem_collision: Optional[bool] = None,
|
|
dir_entries: Optional[Set[str]] = None,
|
|
) -> List[str]:
|
|
"""Return sidecar names in the established exact/legacy preference order."""
|
|
directory = os.path.dirname(model_path)
|
|
exact_names = [name for name in exact_names if name]
|
|
legacy_names = [name for name in legacy_names if name]
|
|
if has_stem_collision is None:
|
|
has_stem_collision = has_model_sidecar_name_collision(
|
|
model_path,
|
|
dir_entries=dir_entries,
|
|
)
|
|
|
|
exact_exists = False
|
|
if exact_names:
|
|
if dir_entries is not None:
|
|
exact_exists = os.path.normcase(exact_names[0]) in dir_entries
|
|
else:
|
|
exact_path = os.path.join(directory, exact_names[0])
|
|
exact_exists = bool(exact_path) and os.path.isfile(exact_path)
|
|
|
|
prefer_exact_names = has_stem_collision or exact_exists
|
|
if prefer_exact_names:
|
|
return exact_names + legacy_names
|
|
|
|
candidates: List[str] = []
|
|
for index in range(max(len(exact_names), len(legacy_names))):
|
|
if index < len(legacy_names):
|
|
candidates.append(legacy_names[index])
|
|
if index < len(exact_names):
|
|
candidates.append(exact_names[index])
|
|
return candidates
|
|
|
|
|
|
def get_metadata_sidecar_path(model_path: str) -> str:
|
|
"""Return the legacy external .metadata.json path for read compatibility."""
|
|
return _metadata_sidecar_paths(model_path)[0]
|
|
|
|
|
|
def _get_safe_sidecar_path(
|
|
file_path: str,
|
|
sidecar_factory: Callable[[str], str],
|
|
) -> str:
|
|
"""Return a validated sidecar path produced by ``sidecar_factory``."""
|
|
raw_model_path = str(file_path or "").strip()
|
|
if not raw_model_path:
|
|
raise ValueError("A model path is required")
|
|
model_path = os.path.realpath(os.path.abspath(raw_model_path))
|
|
model_dir = os.path.realpath(os.path.dirname(model_path))
|
|
metadata_path = os.path.realpath(sidecar_factory(model_path))
|
|
if (
|
|
not model_dir
|
|
or os.path.dirname(metadata_path) != model_dir
|
|
or metadata_path == model_path
|
|
or not is_path_within(metadata_path, model_dir)
|
|
):
|
|
raise ValueError("Metadata path is outside the model directory")
|
|
return metadata_path
|
|
|
|
|
|
def get_safe_metadata_sidecar_path(file_path: str) -> str:
|
|
"""Return a validated legacy external sidecar path for compatibility."""
|
|
return _get_safe_sidecar_path(file_path, get_metadata_sidecar_path)
|
|
|
|
|
|
def get_safe_model_resolver_sidecar_path(file_path: str) -> str:
|
|
"""Return a validated sidecar path owned exclusively by Model Resolver."""
|
|
return _get_safe_sidecar_path(file_path, get_model_resolver_sidecar_path)
|
|
|
|
|