mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-20 18:51:26 -03:00
fix(cache): make shared cache state survive a second instance
Installing a second LoRA Manager instance (standalone or a second ComfyUI install) that shares the settings directory puts two processes on the same cache databases. Three things made that unsafe. - The updater preserved cache/ and model_cache/ but not a legacy recipe_cache/ directory, so a portable install predating the cache/ move lost its recipe database on a git-based update. Add it to _PRESERVE_DIRS and to .gitignore. - Cache connections used the sqlite3 default 5s timeout, which a scanning instance can exceed, turning a concurrent write into "database is locked". Route every shared cache connection through connect_cache_db(), which raises the timeout to 30s and sets busy_timeout + synchronous=NORMAL to match the existing WAL mode. App-private databases (download queue, update history) are unchanged. - A full-table cache replace is a read-modify-write that SQLite cannot make atomic across processes, so two instances could interleave and one snapshot could overwrite the other. Guard the recipe and model save_cache paths with a cross-process advisory lock (flock on POSIX, msvcrt on Windows). Locking is best-effort: if it is unavailable the call proceeds and the SQLite busy timeout is the fallback. The lock file is a hidden sibling of the database and is deliberately never unlinked, so a second process cannot lock a fresh inode.
This commit is contained in:
Will Miao
@@ -6,7 +6,9 @@ import threading
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple
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from ..utils.cache_db import connect_cache_db
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from ..utils.cache_paths import CacheType, resolve_cache_path_with_migration
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from ..utils.file_lock import exclusive_lock
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from .model_sources import normalize_metadata_source
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logger = logging.getLogger(__name__)
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@@ -257,267 +259,271 @@ class PersistentModelCache:
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return
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try:
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with self._db_lock:
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conn = self._connect()
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try:
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conn.execute("PRAGMA foreign_keys = ON")
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conn.execute("BEGIN")
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# Cross-process serialization: another LoRA Manager instance may
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# share this settings directory, and the read-merge-write below
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# spans several statements.
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with exclusive_lock(self._db_path):
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conn = self._connect()
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try:
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conn.execute("PRAGMA foreign_keys = ON")
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conn.execute("BEGIN")
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model_rows = [self._prepare_model_row(model_type, item) for item in raw_data]
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model_map: Dict[str, Tuple[Any, ...]] = {
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row[1]: row for row in model_rows if row[1] # row[1] is file_path
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}
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model_rows = [self._prepare_model_row(model_type, item) for item in raw_data]
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model_map: Dict[str, Tuple[Any, ...]] = {
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row[1]: row for row in model_rows if row[1] # row[1] is file_path
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}
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existing_models = conn.execute(
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"SELECT "
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+ ", ".join(self._MODEL_COLUMNS[1:])
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+ " FROM models WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_model_map: Dict[str, sqlite3.Row] = {
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row["file_path"]: row for row in existing_models
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}
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to_remove_models = [
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(model_type, path)
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for path in existing_model_map.keys()
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if path not in model_map
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]
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if to_remove_models:
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conn.executemany(
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"DELETE FROM models WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM model_tags WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM hash_index WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM autov3_index WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM excluded_models WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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insert_rows: List[Tuple[Any, ...]] = []
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update_rows: List[Tuple[Any, ...]] = []
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for file_path, row in model_map.items():
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existing = existing_model_map.get(file_path)
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if existing is None:
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insert_rows.append(row)
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continue
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existing_values = tuple(
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existing[column] for column in self._MODEL_COLUMNS[1:]
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)
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current_values = row[1:]
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if existing_values != current_values:
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update_rows.append(row[2:] + (model_type, file_path))
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if insert_rows:
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conn.executemany(self._insert_model_sql(), insert_rows)
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if update_rows:
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set_clause = ", ".join(
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f"{column} = ?"
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for column in self._MODEL_UPDATE_COLUMNS
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)
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update_sql = (
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f"UPDATE models SET {set_clause} WHERE model_type = ? AND file_path = ?"
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)
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conn.executemany(update_sql, update_rows)
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existing_tags_rows = conn.execute(
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"SELECT file_path, tag FROM model_tags WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_tags: Dict[str, set[str]] = {}
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for row in existing_tags_rows:
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existing_tags.setdefault(row["file_path"], set()).add(row["tag"])
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new_tags: Dict[str, set[str]] = {}
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for item in raw_data:
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file_path = item.get("file_path")
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if not file_path:
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continue
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tags = set(item.get("tags") or [])
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if tags:
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new_tags[file_path] = tags
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tag_inserts: List[Tuple[str, str, str]] = []
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tag_deletes: List[Tuple[str, str, str]] = []
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all_tag_paths = set(existing_tags.keys()) | set(new_tags.keys())
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for path in all_tag_paths:
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existing_set = existing_tags.get(path, set())
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new_set = new_tags.get(path, set())
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to_add = new_set - existing_set
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to_remove = existing_set - new_set
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for tag in to_add:
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tag_inserts.append((model_type, path, tag))
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for tag in to_remove:
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tag_deletes.append((model_type, path, tag))
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if tag_deletes:
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conn.executemany(
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"DELETE FROM model_tags WHERE model_type = ? AND file_path = ? AND tag = ?",
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tag_deletes,
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)
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if tag_inserts:
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conn.executemany(
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"INSERT INTO model_tags (model_type, file_path, tag) VALUES (?, ?, ?)",
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tag_inserts,
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)
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existing_hash_rows = conn.execute(
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"SELECT sha256, file_path FROM hash_index WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_hash_map: Dict[str, set[str]] = {}
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for row in existing_hash_rows:
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sha_value = (row["sha256"] or "").lower()
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if not sha_value:
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continue
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existing_hash_map.setdefault(sha_value, set()).add(row["file_path"])
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new_hash_map: Dict[str, set[str]] = {}
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for sha_value, paths in hash_index.items():
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normalized_sha = (sha_value or "").lower()
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if not normalized_sha:
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continue
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bucket = new_hash_map.setdefault(normalized_sha, set())
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for path in paths:
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if path:
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bucket.add(path)
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hash_inserts: List[Tuple[str, str, str]] = []
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hash_deletes: List[Tuple[str, str, str]] = []
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all_shas = set(existing_hash_map.keys()) | set(new_hash_map.keys())
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for sha_value in all_shas:
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existing_paths = existing_hash_map.get(sha_value, set())
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new_paths = new_hash_map.get(sha_value, set())
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for path in existing_paths - new_paths:
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hash_deletes.append((model_type, sha_value, path))
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for path in new_paths - existing_paths:
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hash_inserts.append((model_type, sha_value, path))
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if hash_deletes:
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conn.executemany(
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"DELETE FROM hash_index WHERE model_type = ? AND sha256 = ? AND file_path = ?",
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hash_deletes,
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)
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if hash_inserts:
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conn.executemany(
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"INSERT OR IGNORE INTO hash_index (model_type, sha256, file_path) VALUES (?, ?, ?)",
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hash_inserts,
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)
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if autov3_hash_index is not None:
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existing_autov3_rows = conn.execute(
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"SELECT autov3, file_path FROM autov3_index WHERE model_type = ?",
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existing_models = conn.execute(
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"SELECT "
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+ ", ".join(self._MODEL_COLUMNS[1:])
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+ " FROM models WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_autov3_map: Dict[str, set[str]] = {}
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for row in existing_autov3_rows:
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autov3_value = (row["autov3"] or "").lower()
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if not autov3_value:
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continue
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existing_autov3_map.setdefault(autov3_value, set()).add(row["file_path"])
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existing_model_map: Dict[str, sqlite3.Row] = {
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row["file_path"]: row for row in existing_models
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}
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new_autov3_map: Dict[str, set[str]] = {}
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for autov3_value, paths in autov3_hash_index.items():
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normalized_autov3 = (autov3_value or "").lower()
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if not normalized_autov3:
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to_remove_models = [
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(model_type, path)
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for path in existing_model_map.keys()
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if path not in model_map
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]
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if to_remove_models:
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conn.executemany(
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"DELETE FROM models WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM model_tags WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM hash_index WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM autov3_index WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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conn.executemany(
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"DELETE FROM excluded_models WHERE model_type = ? AND file_path = ?",
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to_remove_models,
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)
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insert_rows: List[Tuple[Any, ...]] = []
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update_rows: List[Tuple[Any, ...]] = []
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for file_path, row in model_map.items():
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existing = existing_model_map.get(file_path)
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if existing is None:
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insert_rows.append(row)
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continue
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bucket = new_autov3_map.setdefault(normalized_autov3, set())
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existing_values = tuple(
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existing[column] for column in self._MODEL_COLUMNS[1:]
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)
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current_values = row[1:]
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if existing_values != current_values:
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update_rows.append(row[2:] + (model_type, file_path))
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if insert_rows:
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conn.executemany(self._insert_model_sql(), insert_rows)
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if update_rows:
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set_clause = ", ".join(
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f"{column} = ?"
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for column in self._MODEL_UPDATE_COLUMNS
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)
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update_sql = (
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f"UPDATE models SET {set_clause} WHERE model_type = ? AND file_path = ?"
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)
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conn.executemany(update_sql, update_rows)
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existing_tags_rows = conn.execute(
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"SELECT file_path, tag FROM model_tags WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_tags: Dict[str, set[str]] = {}
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for row in existing_tags_rows:
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existing_tags.setdefault(row["file_path"], set()).add(row["tag"])
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new_tags: Dict[str, set[str]] = {}
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for item in raw_data:
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file_path = item.get("file_path")
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if not file_path:
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continue
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tags = set(item.get("tags") or [])
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if tags:
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new_tags[file_path] = tags
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tag_inserts: List[Tuple[str, str, str]] = []
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tag_deletes: List[Tuple[str, str, str]] = []
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all_tag_paths = set(existing_tags.keys()) | set(new_tags.keys())
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for path in all_tag_paths:
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existing_set = existing_tags.get(path, set())
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new_set = new_tags.get(path, set())
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to_add = new_set - existing_set
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to_remove = existing_set - new_set
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for tag in to_add:
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tag_inserts.append((model_type, path, tag))
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for tag in to_remove:
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tag_deletes.append((model_type, path, tag))
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if tag_deletes:
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conn.executemany(
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"DELETE FROM model_tags WHERE model_type = ? AND file_path = ? AND tag = ?",
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tag_deletes,
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)
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if tag_inserts:
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conn.executemany(
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"INSERT INTO model_tags (model_type, file_path, tag) VALUES (?, ?, ?)",
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tag_inserts,
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)
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existing_hash_rows = conn.execute(
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"SELECT sha256, file_path FROM hash_index WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_hash_map: Dict[str, set[str]] = {}
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for row in existing_hash_rows:
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sha_value = (row["sha256"] or "").lower()
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if not sha_value:
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continue
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existing_hash_map.setdefault(sha_value, set()).add(row["file_path"])
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new_hash_map: Dict[str, set[str]] = {}
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for sha_value, paths in hash_index.items():
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normalized_sha = (sha_value or "").lower()
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if not normalized_sha:
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continue
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bucket = new_hash_map.setdefault(normalized_sha, set())
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for path in paths:
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if path:
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bucket.add(path)
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autov3_inserts: List[Tuple[str, str, str]] = []
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autov3_deletes: List[Tuple[str, str, str]] = []
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hash_inserts: List[Tuple[str, str, str]] = []
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hash_deletes: List[Tuple[str, str, str]] = []
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all_autov3 = set(existing_autov3_map.keys()) | set(new_autov3_map.keys())
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for autov3_value in all_autov3:
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existing_paths = existing_autov3_map.get(autov3_value, set())
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new_paths = new_autov3_map.get(autov3_value, set())
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all_shas = set(existing_hash_map.keys()) | set(new_hash_map.keys())
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for sha_value in all_shas:
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existing_paths = existing_hash_map.get(sha_value, set())
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new_paths = new_hash_map.get(sha_value, set())
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for path in existing_paths - new_paths:
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autov3_deletes.append((model_type, autov3_value, path))
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hash_deletes.append((model_type, sha_value, path))
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for path in new_paths - existing_paths:
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autov3_inserts.append((model_type, autov3_value, path))
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hash_inserts.append((model_type, sha_value, path))
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if autov3_deletes:
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if hash_deletes:
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conn.executemany(
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"DELETE FROM autov3_index WHERE model_type = ? AND autov3 = ? AND file_path = ?",
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autov3_deletes,
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"DELETE FROM hash_index WHERE model_type = ? AND sha256 = ? AND file_path = ?",
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hash_deletes,
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)
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if autov3_inserts:
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if hash_inserts:
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conn.executemany(
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"INSERT OR IGNORE INTO autov3_index (model_type, autov3, file_path) VALUES (?, ?, ?)",
|
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autov3_inserts,
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"INSERT OR IGNORE INTO hash_index (model_type, sha256, file_path) VALUES (?, ?, ?)",
|
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hash_inserts,
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)
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existing_excluded_rows = conn.execute(
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"SELECT file_path FROM excluded_models WHERE model_type = ?",
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(model_type,),
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).fetchall()
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existing_excluded = {row["file_path"] for row in existing_excluded_rows}
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new_excluded = {path for path in excluded_models if path}
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if autov3_hash_index is not None:
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existing_autov3_rows = conn.execute(
|
||||
"SELECT autov3, file_path FROM autov3_index WHERE model_type = ?",
|
||||
(model_type,),
|
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).fetchall()
|
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existing_autov3_map: Dict[str, set[str]] = {}
|
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for row in existing_autov3_rows:
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autov3_value = (row["autov3"] or "").lower()
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if not autov3_value:
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continue
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existing_autov3_map.setdefault(autov3_value, set()).add(row["file_path"])
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excluded_deletes = [
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(model_type, path)
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for path in existing_excluded - new_excluded
|
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]
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excluded_inserts = [
|
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(model_type, path)
|
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for path in new_excluded - existing_excluded
|
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]
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new_autov3_map: Dict[str, set[str]] = {}
|
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for autov3_value, paths in autov3_hash_index.items():
|
||||
normalized_autov3 = (autov3_value or "").lower()
|
||||
if not normalized_autov3:
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continue
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bucket = new_autov3_map.setdefault(normalized_autov3, set())
|
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for path in paths:
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if path:
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bucket.add(path)
|
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|
||||
if excluded_deletes:
|
||||
conn.executemany(
|
||||
"DELETE FROM excluded_models WHERE model_type = ? AND file_path = ?",
|
||||
excluded_deletes,
|
||||
)
|
||||
if excluded_inserts:
|
||||
conn.executemany(
|
||||
"INSERT OR IGNORE INTO excluded_models (model_type, file_path) VALUES (?, ?)",
|
||||
excluded_inserts,
|
||||
)
|
||||
autov3_inserts: List[Tuple[str, str, str]] = []
|
||||
autov3_deletes: List[Tuple[str, str, str]] = []
|
||||
|
||||
if all_folders is not None:
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||||
conn.execute(
|
||||
"DELETE FROM folders WHERE model_type = ?",
|
||||
all_autov3 = set(existing_autov3_map.keys()) | set(new_autov3_map.keys())
|
||||
for autov3_value in all_autov3:
|
||||
existing_paths = existing_autov3_map.get(autov3_value, set())
|
||||
new_paths = new_autov3_map.get(autov3_value, set())
|
||||
|
||||
for path in existing_paths - new_paths:
|
||||
autov3_deletes.append((model_type, autov3_value, path))
|
||||
for path in new_paths - existing_paths:
|
||||
autov3_inserts.append((model_type, autov3_value, path))
|
||||
|
||||
if autov3_deletes:
|
||||
conn.executemany(
|
||||
"DELETE FROM autov3_index WHERE model_type = ? AND autov3 = ? AND file_path = ?",
|
||||
autov3_deletes,
|
||||
)
|
||||
if autov3_inserts:
|
||||
conn.executemany(
|
||||
"INSERT OR IGNORE INTO autov3_index (model_type, autov3, file_path) VALUES (?, ?, ?)",
|
||||
autov3_inserts,
|
||||
)
|
||||
|
||||
existing_excluded_rows = conn.execute(
|
||||
"SELECT file_path FROM excluded_models WHERE model_type = ?",
|
||||
(model_type,),
|
||||
)
|
||||
folder_inserts = [
|
||||
(model_type, path) for path in all_folders if path
|
||||
]
|
||||
if folder_inserts:
|
||||
conn.executemany(
|
||||
"INSERT OR IGNORE INTO folders (model_type, path) VALUES (?, ?)",
|
||||
folder_inserts,
|
||||
)
|
||||
# Mark the snapshot as having folder data even when the
|
||||
# library has no subfolders, so an empty list is not
|
||||
# mistaken for "never recorded" on load.
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO cache_meta (key, value) VALUES (?, ?)",
|
||||
(f"folders_recorded:{model_type}", "1"),
|
||||
)
|
||||
).fetchall()
|
||||
existing_excluded = {row["file_path"] for row in existing_excluded_rows}
|
||||
new_excluded = {path for path in excluded_models if path}
|
||||
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
excluded_deletes = [
|
||||
(model_type, path)
|
||||
for path in existing_excluded - new_excluded
|
||||
]
|
||||
excluded_inserts = [
|
||||
(model_type, path)
|
||||
for path in new_excluded - existing_excluded
|
||||
]
|
||||
|
||||
if excluded_deletes:
|
||||
conn.executemany(
|
||||
"DELETE FROM excluded_models WHERE model_type = ? AND file_path = ?",
|
||||
excluded_deletes,
|
||||
)
|
||||
if excluded_inserts:
|
||||
conn.executemany(
|
||||
"INSERT OR IGNORE INTO excluded_models (model_type, file_path) VALUES (?, ?)",
|
||||
excluded_inserts,
|
||||
)
|
||||
|
||||
if all_folders is not None:
|
||||
conn.execute(
|
||||
"DELETE FROM folders WHERE model_type = ?",
|
||||
(model_type,),
|
||||
)
|
||||
folder_inserts = [
|
||||
(model_type, path) for path in all_folders if path
|
||||
]
|
||||
if folder_inserts:
|
||||
conn.executemany(
|
||||
"INSERT OR IGNORE INTO folders (model_type, path) VALUES (?, ?)",
|
||||
folder_inserts,
|
||||
)
|
||||
# Mark the snapshot as having folder data even when the
|
||||
# library has no subfolders, so an empty list is not
|
||||
# mistaken for "never recorded" on load.
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO cache_meta (key, value) VALUES (?, ?)",
|
||||
(f"folders_recorded:{model_type}", "1"),
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to persist cache for %s: %s", model_type, exc)
|
||||
|
||||
@@ -650,16 +656,14 @@ class PersistentModelCache:
|
||||
conn.execute(f"ALTER TABLE models ADD COLUMN {column} {definition}")
|
||||
|
||||
def _connect(self, readonly: bool = False) -> sqlite3.Connection:
|
||||
uri = False
|
||||
path = self._db_path
|
||||
if readonly:
|
||||
if not os.path.exists(path):
|
||||
raise FileNotFoundError(path)
|
||||
path = f"file:{path}?mode=ro"
|
||||
uri = True
|
||||
conn = sqlite3.connect(path, check_same_thread=False, uri=uri, detect_types=sqlite3.PARSE_DECLTYPES)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
if readonly and not os.path.exists(self._db_path):
|
||||
raise FileNotFoundError(self._db_path)
|
||||
return connect_cache_db(
|
||||
self._db_path,
|
||||
readonly=readonly,
|
||||
detect_types=sqlite3.PARSE_DECLTYPES,
|
||||
row_factory=sqlite3.Row,
|
||||
)
|
||||
|
||||
def _prepare_model_row(self, model_type: str, item: Dict[str, Any]) -> Tuple[Any, ...]:
|
||||
# Keep `source_*` and the legacy `hf_url` alias consistent no matter
|
||||
|
||||
Reference in New Issue
Block a user