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1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Will Miao
|
68c5f79a67 |
@@ -1,80 +0,0 @@
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---
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name: lora-manager-runtime-context
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description: Inspect ComfyUI LoRA Manager runtime configuration and local diagnostic state. Use when debugging LoRA Manager issues that require locating or reading settings.json, active library paths, model metadata JSON sidecars, recipe metadata JSON files, example image folders, SQLite caches, symlink maps, download history, aria2 state, or other cache files under the LoRA Manager user config directory.
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---
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# LoRA Manager Runtime Context
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## Core Rules
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- Treat runtime state as local user data. Prefer read-only inspection unless the user explicitly asks for mutation.
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- Never print secret-like settings values. Redact keys containing `key`, `token`, `secret`, `password`, `auth`, or `credential`, including `civitai_api_key`.
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- Resolve paths from the runtime configuration before guessing. Settings-directory precedence (highest first):
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1. **Explicit override** — env `LORA_MANAGER_SETTINGS_DIR` or standalone `--settings-path` (also accepted by the inspect script as `--settings-path DIR`). Pins EVERYTHING (`settings.json`, `cache/`, `wildcards/`, `backups/`, `logs/`, `stats/`) under the given directory; bypasses portable mode and the user config dir. Common when inspecting a sandboxed/E2E instance.
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2. **Portable** — repository `<repo-root>/settings.json` with `"use_portable_settings": true` (or `LORA_MANAGER_PORTABLE=1`): settings dir = `<repo-root>`.
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3. **Default** — `~/.config/ComfyUI-LoRA-Manager` on this machine (`platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`).
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- Use the active library when selecting per-library caches and paths. Read `active_library` from settings; fall back to `default` if missing.
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- Normalize and expand `~` before comparing paths. Symlinks are common in this repo.
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## Quick Start
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Use the bundled helper for a safe first pass:
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```bash
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py summary
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py caches
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```
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The script redacts sensitive settings, opens SQLite databases read-only, and reports inaccessible or locked databases as warnings.
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For focused checks:
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```bash
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py recipes
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py model --path /path/to/model.safetensors
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py sqlite --db /path/to/cache.sqlite --limit 3
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```
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To inspect a sandboxed/E2E instance that pins its settings directory:
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```bash
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# --settings-path DIR (or LORA_MANAGER_SETTINGS_DIR) works with every subcommand:
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python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py \
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--settings-path /tmp/opencode/<plan>-e2e/settings summary
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```
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## Runtime Path Rules
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- Settings directory: resolve via `py/utils/settings_paths.py` — `get_settings_dir()` honors the `LORA_MANAGER_SETTINGS_DIR` / programmatic override first, then portable mode, then `platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`. The inspect script mirrors this precedence in `resolve_settings_path()`.
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- Settings file: `<settings_dir>/settings.json`.
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- Cache root: `<settings_dir>/cache`.
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- Canonical cache files:
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- Model cache: `cache/model/<active_library>.sqlite`.
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- Recipe cache: `cache/recipe/<active_library>.sqlite`.
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- Model update cache: `cache/model_update/<active_library>.sqlite`.
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- Recipe FTS: `cache/fts/recipe_fts.sqlite`.
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- Tag FTS: `cache/fts/tag_fts.sqlite`.
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- Symlink map: `cache/symlink/symlink_map.json`.
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- Download history: `cache/download_history/downloaded_versions.sqlite`.
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- aria2 state: `cache/aria2/downloads.json`.
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- Legacy cache locations may exist; prefer canonical paths unless diagnosing migrations.
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## Data Location Rules
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- Model roots come from `settings.folder_paths` and the active library payload under `settings.libraries[active_library]`.
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- Model metadata JSON sidecars live next to the model file as `<model basename>.metadata.json`.
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- Recipes root is `settings.recipes_path` when it is a non-empty string. If empty, use the first configured LoRA root plus `/recipes`.
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- Recipe JSON files are named `*.recipe.json` under the recipes root and may be nested in folders.
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- Example image root is `settings.example_images_path`.
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- If multiple libraries are configured, example images are stored under `<example_images_path>/<sanitized_library>/<sha256>/`; otherwise they are under `<example_images_path>/<sha256>/`.
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## Useful Cache Tables
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- Model cache: `models`, `model_tags`, `hash_index`, `excluded_models`.
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- Recipe cache: `recipes`, `cache_metadata`.
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- Model update cache: `model_update_status`, `model_update_versions`.
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- Tag FTS cache: `tags`, `fts_metadata`, plus FTS internal tables.
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- Recipe FTS cache: `recipe_rowid`, `fts_metadata`, plus FTS internal tables.
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- Download history: `downloaded_model_versions`.
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Prefer querying only counts, schema, and a few sample rows unless the user asks for full output.
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@@ -1,4 +0,0 @@
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interface:
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display_name: "LoRA Manager Runtime Context"
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short_description: "Inspect LoRA Manager runtime state"
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default_prompt: "Use $lora-manager-runtime-context to inspect LoRA Manager settings, metadata paths, and caches for debugging."
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@@ -1,398 +0,0 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import json
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import os
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import re
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import shutil
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import sqlite3
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import sys
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import tempfile
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from pathlib import Path
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from typing import Any
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SECRET_PATTERN = re.compile(r"(key|token|secret|password|auth|credential)", re.IGNORECASE)
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APP_NAME = "ComfyUI-LoRA-Manager"
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SETTINGS_DIR_ENV = "LORA_MANAGER_SETTINGS_DIR"
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CACHE_SQLITE = {
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"model": ("model", "{library}.sqlite"),
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"recipe": ("recipe", "{library}.sqlite"),
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"model_update": ("model_update", "{library}.sqlite"),
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"recipe_fts": ("fts", "recipe_fts.sqlite"),
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"tag_fts": ("fts", "tag_fts.sqlite"),
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"download_history": ("download_history", "downloaded_versions.sqlite"),
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}
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CACHE_JSON = {
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"symlink": ("symlink", "symlink_map.json"),
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"aria2": ("aria2", "downloads.json"),
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}
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def main() -> int:
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parser = argparse.ArgumentParser(description="Inspect LoRA Manager runtime state read-only.")
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parser.add_argument(
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"--settings-path",
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type=str,
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default=None,
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metavar="DIR",
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help="Explicit settings directory (same as LORA_MANAGER_SETTINGS_DIR / "
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"standalone --settings-path). Overrides portable mode and the default "
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"user config dir.",
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)
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subparsers = parser.add_subparsers(dest="command", required=True)
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subparsers.add_parser("summary", help="Print redacted settings and resolved paths.")
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subparsers.add_parser("caches", help="Print cache paths and SQLite table summaries.")
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subparsers.add_parser("recipes", help="Print resolved recipes root and recipe JSON count.")
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model_parser = subparsers.add_parser("model", help="Inspect a model metadata sidecar path.")
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model_parser.add_argument("--path", required=True, help="Path to a model file or metadata JSON file.")
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sqlite_parser = subparsers.add_parser("sqlite", help="Inspect a SQLite database read-only.")
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sqlite_parser.add_argument("--db", required=True, help="Path to the SQLite database.")
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sqlite_parser.add_argument("--limit", type=int, default=3, help="Rows to sample from each user table.")
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args = parser.parse_args()
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if args.settings_path:
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os.environ[SETTINGS_DIR_ENV] = args.settings_path
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context = build_context()
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if args.command == "summary":
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print_json(summary_payload(context))
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elif args.command == "caches":
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print_json(caches_payload(context))
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elif args.command == "recipes":
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print_json(recipes_payload(context))
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elif args.command == "model":
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print_json(model_payload(args.path))
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elif args.command == "sqlite":
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print_json(sqlite_payload(Path(args.db).expanduser(), args.limit))
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return 0
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def build_context() -> dict[str, Any]:
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settings_path = resolve_settings_path()
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settings = load_json(settings_path)
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settings_dir = settings_path.parent
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active_library = settings.get("active_library") or "default"
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safe_library = sanitize_library_name(str(active_library))
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cache_root = settings_dir / "cache"
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return {
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"settings_path": str(settings_path),
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"settings_dir": str(settings_dir),
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"settings": settings,
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"active_library": active_library,
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"safe_library": safe_library,
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"cache_root": str(cache_root),
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"cache_paths": resolve_cache_paths(cache_root, safe_library),
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}
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def resolve_settings_path() -> Path:
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# Explicit override: LORA_MANAGER_SETTINGS_DIR env or --settings-path.
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explicit = os.environ.get(SETTINGS_DIR_ENV)
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if explicit:
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return Path(explicit).expanduser() / "settings.json"
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repo_root = find_repo_root()
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portable = repo_root / "settings.json"
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if portable.exists():
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payload = load_json(portable)
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if isinstance(payload, dict) and payload.get("use_portable_settings") is True:
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return portable
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config_home = os.environ.get("XDG_CONFIG_HOME")
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if config_home:
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return Path(config_home).expanduser() / APP_NAME / "settings.json"
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return Path.home() / ".config" / APP_NAME / "settings.json"
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def find_repo_root() -> Path:
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current = Path(__file__).resolve()
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for parent in current.parents:
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if (parent / "py").is_dir() and (parent / "standalone.py").exists():
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return parent
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return Path.cwd()
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def load_json(path: Path) -> dict[str, Any]:
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try:
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with path.open("r", encoding="utf-8") as handle:
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payload = json.load(handle)
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except FileNotFoundError:
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return {}
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except json.JSONDecodeError as exc:
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return {"_error": f"invalid JSON: {exc}"}
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except OSError as exc:
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return {"_error": f"unreadable: {exc}"}
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return payload if isinstance(payload, dict) else {"_error": "JSON root is not an object"}
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def resolve_cache_paths(cache_root: Path, library: str) -> dict[str, str]:
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paths: dict[str, str] = {}
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for name, (subdir, filename) in CACHE_SQLITE.items():
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paths[name] = str(cache_root / subdir / filename.format(library=library))
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for name, (subdir, filename) in CACHE_JSON.items():
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paths[name] = str(cache_root / subdir / filename)
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return paths
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def summary_payload(context: dict[str, Any]) -> dict[str, Any]:
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settings = context["settings"]
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return {
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"settings_path": context["settings_path"],
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"settings_dir": context["settings_dir"],
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"active_library": context["active_library"],
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"settings": redact(settings),
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"model_roots": model_roots(settings, context["active_library"]),
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"recipes_root": str(resolve_recipes_root(settings, context["active_library"]) or ""),
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"example_images": example_images_payload(settings, context["active_library"]),
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"cache_root": context["cache_root"],
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"cache_paths": context["cache_paths"],
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}
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def caches_payload(context: dict[str, Any]) -> dict[str, Any]:
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caches: dict[str, Any] = {}
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for name, path_string in context["cache_paths"].items():
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path = Path(path_string)
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item: dict[str, Any] = {
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"path": str(path),
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"exists": path.exists(),
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"size": path.stat().st_size if path.exists() else None,
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}
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if path.suffix == ".sqlite":
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item["sqlite"] = sqlite_payload(path, limit=0)
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elif path.suffix == ".json":
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item["json"] = json_file_summary(path)
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caches[name] = item
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return {"active_library": context["active_library"], "caches": caches}
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def recipes_payload(context: dict[str, Any]) -> dict[str, Any]:
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root = resolve_recipes_root(context["settings"], context["active_library"])
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files: list[str] = []
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if root and root.exists():
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files = [str(path) for path in sorted(root.rglob("*.recipe.json"))[:20]]
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return {
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"recipes_root": str(root or ""),
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"exists": bool(root and root.exists()),
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"recipe_json_count": count_recipe_files(root),
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"sample_recipe_json": files,
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"recipe_cache": context["cache_paths"].get("recipe"),
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}
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def model_payload(raw_path: str) -> dict[str, Any]:
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path = Path(raw_path).expanduser()
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metadata_path = path if path.name.endswith(".metadata.json") else path.with_suffix(".metadata.json")
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payload = {
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"input_path": str(path),
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"metadata_path": str(metadata_path),
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"model_exists": path.exists(),
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"metadata_exists": metadata_path.exists(),
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}
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if metadata_path.exists():
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data = load_json(metadata_path)
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payload["metadata_summary"] = redact(summarize_value(data))
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return payload
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def sqlite_payload(path: Path, limit: int = 3, allow_copy: bool = True) -> dict[str, Any]:
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result: dict[str, Any] = {"path": str(path), "exists": path.exists(), "tables": {}}
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if not path.exists():
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return result
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try:
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conn = connect_sqlite_readonly(path)
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except sqlite3.Error as exc:
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result["error"] = str(exc)
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return result
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try:
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table_rows = conn.execute(
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"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name"
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).fetchall()
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for table_row in table_rows:
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table = table_row["name"]
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columns = [
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row["name"]
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for row in conn.execute(f"PRAGMA table_info({quote_identifier(table)})").fetchall()
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]
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table_info: dict[str, Any] = {"columns": columns}
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try:
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table_info["count"] = conn.execute(
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f"SELECT COUNT(*) FROM {quote_identifier(table)}"
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).fetchone()[0]
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except sqlite3.Error as exc:
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table_info["count_error"] = str(exc)
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if limit > 0 and columns and not is_internal_sqlite_table(table):
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try:
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rows = conn.execute(
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f"SELECT * FROM {quote_identifier(table)} LIMIT ?", (limit,)
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).fetchall()
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table_info["sample"] = [redact(dict(row)) for row in rows]
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except sqlite3.Error as exc:
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table_info["sample_error"] = str(exc)
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result["tables"][table] = table_info
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except sqlite3.Error as exc:
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fallback = sqlite_copy_payload(path, limit, str(exc)) if allow_copy else None
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if fallback is not None:
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result.update(fallback)
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else:
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result["error"] = str(exc)
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finally:
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conn.close()
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return result
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def connect_sqlite_readonly(path: Path) -> sqlite3.Connection:
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errors: list[str] = []
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for query in ("mode=ro", "mode=ro&immutable=1"):
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try:
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conn = sqlite3.connect(f"file:{path}?{query}", uri=True)
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conn.row_factory = sqlite3.Row
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return conn
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except sqlite3.Error as exc:
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errors.append(f"{query}: {exc}")
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raise sqlite3.OperationalError("; ".join(errors))
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def sqlite_copy_payload(path: Path, limit: int, original_error: str) -> dict[str, Any] | None:
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try:
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with tempfile.TemporaryDirectory(prefix="lm-cache-inspect-") as temp_dir:
|
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copy_path = Path(temp_dir) / path.name
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shutil.copy2(path, copy_path)
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payload = sqlite_payload(copy_path, limit, allow_copy=False)
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payload["path"] = str(path)
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payload["inspected_copy"] = True
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payload["original_error"] = original_error
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return payload
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except Exception:
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return None
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|
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|
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def json_file_summary(path: Path) -> dict[str, Any]:
|
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if not path.exists():
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return {"exists": False}
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data = load_json(path)
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return {"exists": True, "summary": redact(summarize_value(data))}
|
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|
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def model_roots(settings: dict[str, Any], active_library: str) -> dict[str, list[str]]:
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roots: dict[str, list[str]] = {}
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sources = [settings]
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library = settings.get("libraries", {}).get(active_library)
|
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if isinstance(library, dict):
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sources.insert(0, library)
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for source in sources:
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folder_paths = source.get("folder_paths")
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if isinstance(folder_paths, dict):
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for key, value in folder_paths.items():
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roots.setdefault(key, []).extend(normalize_path_list(value))
|
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for default_key, folder_key in (
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("default_lora_root", "loras"),
|
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("default_checkpoint_root", "checkpoints"),
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||||
("default_embedding_root", "embeddings"),
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||||
("default_unet_root", "unet"),
|
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):
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value = settings.get(default_key)
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||||
if isinstance(value, str) and value:
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roots.setdefault(folder_key, []).append(expand_path(value))
|
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return {key: dedupe(values) for key, values in roots.items()}
|
||||
|
||||
|
||||
def resolve_recipes_root(settings: dict[str, Any], active_library: str) -> Path | None:
|
||||
recipes_path = settings.get("recipes_path")
|
||||
library = settings.get("libraries", {}).get(active_library)
|
||||
if isinstance(library, dict) and isinstance(library.get("recipes_path"), str):
|
||||
recipes_path = library["recipes_path"] or recipes_path
|
||||
if isinstance(recipes_path, str) and recipes_path.strip():
|
||||
return Path(expand_path(recipes_path.strip()))
|
||||
lora_roots = model_roots(settings, active_library).get("loras") or []
|
||||
return Path(lora_roots[0]) / "recipes" if lora_roots else None
|
||||
|
||||
|
||||
def example_images_payload(settings: dict[str, Any], active_library: str) -> dict[str, Any]:
|
||||
root = settings.get("example_images_path") or ""
|
||||
libraries = settings.get("libraries")
|
||||
library_count = len(libraries) if isinstance(libraries, dict) else 0
|
||||
scoped = library_count > 1
|
||||
root_path = Path(expand_path(root)) if isinstance(root, str) and root else None
|
||||
library_root = root_path / sanitize_library_name(active_library) if root_path and scoped else root_path
|
||||
return {
|
||||
"root": str(root_path or ""),
|
||||
"uses_library_scoped_folders": scoped,
|
||||
"library_root": str(library_root or ""),
|
||||
}
|
||||
|
||||
|
||||
def count_recipe_files(root: Path | None) -> int:
|
||||
if not root or not root.exists():
|
||||
return 0
|
||||
return sum(1 for _ in root.rglob("*.recipe.json"))
|
||||
|
||||
|
||||
def normalize_path_list(value: Any) -> list[str]:
|
||||
if isinstance(value, str):
|
||||
return [expand_path(value)] if value else []
|
||||
if isinstance(value, list):
|
||||
return [expand_path(item) for item in value if isinstance(item, str) and item]
|
||||
return []
|
||||
|
||||
|
||||
def expand_path(value: str) -> str:
|
||||
return str(Path(value).expanduser().resolve(strict=False))
|
||||
|
||||
|
||||
def sanitize_library_name(name: str) -> str:
|
||||
safe = re.sub(r"[^A-Za-z0-9_.-]", "_", name or "default")
|
||||
return safe or "default"
|
||||
|
||||
|
||||
def dedupe(values: list[str]) -> list[str]:
|
||||
seen: set[str] = set()
|
||||
result: list[str] = []
|
||||
for value in values:
|
||||
if value not in seen:
|
||||
result.append(value)
|
||||
seen.add(value)
|
||||
return result
|
||||
|
||||
|
||||
def redact(value: Any, key: str = "") -> Any:
|
||||
if key and SECRET_PATTERN.search(key):
|
||||
return "<redacted>"
|
||||
if isinstance(value, dict):
|
||||
return {str(k): redact(v, str(k)) for k, v in value.items()}
|
||||
if isinstance(value, list):
|
||||
return [redact(item) for item in value]
|
||||
return value
|
||||
|
||||
|
||||
def summarize_value(value: Any) -> Any:
|
||||
if isinstance(value, dict):
|
||||
return {key: summarize_value(item) for key, item in value.items()}
|
||||
if isinstance(value, list):
|
||||
return {
|
||||
"type": "array",
|
||||
"length": len(value),
|
||||
"first": summarize_value(value[0]) if value else None,
|
||||
}
|
||||
return value
|
||||
|
||||
|
||||
def quote_identifier(identifier: str) -> str:
|
||||
return '"' + identifier.replace('"', '""') + '"'
|
||||
|
||||
|
||||
def is_internal_sqlite_table(table: str) -> bool:
|
||||
return table.startswith("sqlite_") or table.endswith(("_data", "_idx", "_docsize", "_config", "_content"))
|
||||
|
||||
|
||||
def print_json(payload: Any) -> None:
|
||||
json.dump(payload, sys.stdout, indent=2, ensure_ascii=False)
|
||||
sys.stdout.write("\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
+2
-2
@@ -1,5 +1,5 @@
|
||||
# These are supported funding model platforms
|
||||
|
||||
ko_fi: pixelpawsai
|
||||
patreon: PixelPawsAI
|
||||
custom: ['paypal.me/pixelpawsai', 'https://afdian.com/a/pixelpawsai']
|
||||
ko_fi: pixelpawsai
|
||||
custom: ['paypal.me/pixelpawsai']
|
||||
|
||||
@@ -13,5 +13,8 @@ A clear and concise description of what the problem is. Ex. I'm always frustrate
|
||||
**Describe the solution you'd like**
|
||||
A clear and concise description of what you want to happen.
|
||||
|
||||
**Describe alternatives you've considered**
|
||||
A clear and concise description of any alternative solutions or features you've considered.
|
||||
|
||||
**Additional context**
|
||||
Add any other context or screenshots about the feature request here.
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
Always use English for comments.
|
||||
@@ -1,93 +0,0 @@
|
||||
name: Backend Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
- master
|
||||
paths:
|
||||
- 'py/**'
|
||||
- 'standalone.py'
|
||||
- 'tests/**'
|
||||
- 'requirements.txt'
|
||||
- 'requirements-dev.txt'
|
||||
- 'pyproject.toml'
|
||||
- 'pytest.ini'
|
||||
- '.github/workflows/backend-tests.yml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'py/**'
|
||||
- 'standalone.py'
|
||||
- 'tests/**'
|
||||
- 'requirements.txt'
|
||||
- 'requirements-dev.txt'
|
||||
- 'pyproject.toml'
|
||||
- 'pytest.ini'
|
||||
- '.github/workflows/backend-tests.yml'
|
||||
|
||||
jobs:
|
||||
pytest:
|
||||
name: Run pytest with coverage
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python 3.11
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
cache: 'pip'
|
||||
cache-dependency-path: |
|
||||
requirements.txt
|
||||
requirements-dev.txt
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install -r requirements-dev.txt
|
||||
|
||||
- name: Verify symlink support
|
||||
run: |
|
||||
python - <<'PY'
|
||||
import os
|
||||
import pathlib
|
||||
import tempfile
|
||||
|
||||
root = pathlib.Path(tempfile.mkdtemp(prefix="lm-symlink-check-"))
|
||||
target = root / "target"
|
||||
target.mkdir()
|
||||
link = root / "link"
|
||||
try:
|
||||
link.symlink_to(target, target_is_directory=True)
|
||||
except OSError as exc:
|
||||
raise SystemExit(f"Failed to create directory symlink in CI: {exc}")
|
||||
|
||||
is_link = os.path.islink(link)
|
||||
is_dir = os.path.isdir(link)
|
||||
realpath = os.path.realpath(link)
|
||||
print(f"islink={is_link} isdir={is_dir} realpath={realpath}")
|
||||
if not (is_link and is_dir and realpath == str(target)):
|
||||
raise SystemExit("Directory symlink is not functioning correctly in CI; aborting.")
|
||||
PY
|
||||
|
||||
- name: Run pytest with coverage
|
||||
env:
|
||||
COVERAGE_FILE: coverage/backend/.coverage
|
||||
run: |
|
||||
mkdir -p coverage/backend
|
||||
python -m pytest \
|
||||
--cov=py \
|
||||
--cov=standalone \
|
||||
--cov-report=term-missing \
|
||||
--cov-report=xml:coverage/backend/coverage.xml \
|
||||
--cov-report=html:coverage/backend/html \
|
||||
--cov-report=json:coverage/backend/coverage.json
|
||||
|
||||
- name: Upload coverage artifact
|
||||
if: always()
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: backend-coverage
|
||||
path: coverage/backend
|
||||
if-no-files-found: warn
|
||||
@@ -1,52 +0,0 @@
|
||||
name: Frontend Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
- master
|
||||
paths:
|
||||
- 'package.json'
|
||||
- 'package-lock.json'
|
||||
- 'vitest.config.js'
|
||||
- 'tests/frontend/**'
|
||||
- 'static/js/**'
|
||||
- 'scripts/run_frontend_coverage.js'
|
||||
- '.github/workflows/frontend-tests.yml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'package.json'
|
||||
- 'package-lock.json'
|
||||
- 'vitest.config.js'
|
||||
- 'tests/frontend/**'
|
||||
- 'static/js/**'
|
||||
- 'scripts/run_frontend_coverage.js'
|
||||
- '.github/workflows/frontend-tests.yml'
|
||||
|
||||
jobs:
|
||||
vitest:
|
||||
name: Run Vitest with coverage
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Use Node.js 20
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: 20
|
||||
cache: 'npm'
|
||||
|
||||
- name: Install dependencies
|
||||
run: npm ci
|
||||
|
||||
- name: Run frontend tests with coverage
|
||||
run: npm run test:coverage
|
||||
|
||||
- name: Upload coverage artifact
|
||||
if: always()
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: frontend-coverage
|
||||
path: coverage/frontend
|
||||
if-no-files-found: warn
|
||||
@@ -1,31 +0,0 @@
|
||||
name: Update Supporters in README
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- 'data/supporters.json'
|
||||
branches:
|
||||
- main
|
||||
workflow_dispatch: # Allow manual trigger
|
||||
|
||||
jobs:
|
||||
update-readme:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Update README
|
||||
run: python scripts/update_supporters.py
|
||||
|
||||
- name: Commit and push changes
|
||||
uses: stefanzweifel/git-auto-commit-action@v5
|
||||
with:
|
||||
commit_message: "docs: auto-update supporters list in README"
|
||||
file_pattern: "README.md"
|
||||
-37
@@ -1,44 +1,7 @@
|
||||
__pycache__/
|
||||
.pytest_cache/
|
||||
settings.json
|
||||
path_mappings.yaml
|
||||
output/*
|
||||
py/run_test.py
|
||||
.vscode/
|
||||
cache/
|
||||
civitai/
|
||||
stats/
|
||||
wildcards/
|
||||
backups/
|
||||
logs/
|
||||
node_modules/
|
||||
coverage/
|
||||
.coverage
|
||||
model_cache/
|
||||
recipe_cache/
|
||||
|
||||
# agent / dev tooling
|
||||
.opencode/
|
||||
.claude/
|
||||
.sisyphus/
|
||||
.codex
|
||||
.omo
|
||||
reasonix.toml
|
||||
.reasonix/
|
||||
.codegraph/
|
||||
.playwright-mcp/
|
||||
|
||||
# Vue widgets development cache (but keep build output)
|
||||
vue-widgets/node_modules/
|
||||
vue-widgets/.vite/
|
||||
vue-widgets/dist/
|
||||
|
||||
# Hypothesis test cache
|
||||
.hypothesis/
|
||||
|
||||
# Working/research notes (not committed)
|
||||
.docs/
|
||||
|
||||
# HF enrichment validation baseline snapshots (contain potentially
|
||||
# NSFW README content fetched from community model repos)
|
||||
tests/enrich_hf_validation/baselines/
|
||||
|
||||
@@ -1,202 +0,0 @@
|
||||
---
|
||||
slug: undo-delete-staging
|
||||
status: drafting
|
||||
intent: clear
|
||||
review_required: false
|
||||
pending-action: write .omo/plans/undo-delete-staging.md
|
||||
approach: "Option B: delayed physical deletion with Undo. Backend: same-volume rename to per-root staging dir (.lm-pending-delete/) [updated 2026-08: model staging moved to a SIBLING dir inside each deleted model's own folder — see 'Symlink fix (2026-08)' under Decisions] + manifest JSON (batch_id, expires_at, staged->original map) + purge (30s TTL timer + startup sweep + opportunistic) + undo-delete endpoint + settings toggle 'skip undo'. Small files (recipes: JSON+preview) copy to global staging under settings dir instead of rename. Frontend: extend toast system with action button + 30s countdown; delete flows (single model / recipe / bulk / duplicates) consume batch_id from delete response and show Undo toast; expired undo -> 'undo expired' toast. Plus confirm-modal friction (C-friction, NO type-to-confirm): delete button delay-activation 1.5s + modal shows file size 'will free X GB' + Cancel gets initial focus. i18n keys + sync_translation_keys.py."
|
||||
---
|
||||
|
||||
# Draft: undo-delete-staging
|
||||
|
||||
## Components (topology ledger)
|
||||
<!-- Lock the SHAPE before depth. One row per top-level component that can succeed or fail independently. -->
|
||||
<!-- id | outcome (one line) | status: active|deferred | evidence path -->
|
||||
- backend staging module (stage/purge/undo + manifest + per-volume dir resolution) | new module, active | pending exploration: model_lifecycle_service.py delete_model / delete_model_artifacts
|
||||
- delete endpoints return batch_id (model/recipe/bulk/duplicates) | active | pending exploration: handlers + response shapes
|
||||
- undo-delete HTTP endpoint + route registration | active | pending exploration: route registrar pattern
|
||||
- purge scheduling (30s timer + startup sweep + opportunistic) | active | pending exploration: app on_startup hooks
|
||||
- settings toggle "skip undo window" | active | pending exploration: settings service read pattern
|
||||
- frontend toast extension (action button + countdown) | active | pending exploration: showToast impl
|
||||
- frontend delete flows consume batch_id + Undo toast | active | pending exploration: call sites
|
||||
- confirm-modal friction (delay-activate + size display + cancel focus) | active | pending exploration: modal focus behavior
|
||||
- i18n keys + sync_translation_keys.py | active | known
|
||||
|
||||
## Open assumptions (announced defaults)
|
||||
<!-- Record any default you adopt instead of asking, so the user can veto it at the gate. -->
|
||||
<!-- assumption | adopted default | rationale | reversible? -->
|
||||
- Undo window TTL = 30s | 30s balances space-freeing intent vs accident recovery | yes (constant)
|
||||
- Staging dir name: `.lm-pending-delete/` under each model root; recipes: `{settings_dir}/.lm-pending-delete/` | hidden, same-volume [updated 2026-08: same-volume is now guaranteed by sibling staging inside the model's own folder, not by the root location], consistent | yes
|
||||
- Staging failure falls back to existing hard delete | user intent is delete; staging is best-effort; hard delete likely fails identically under same conditions | yes
|
||||
- Purge on startup uses expires_at (not purge-all) so a <30s restart with live tab can still undo | robust, matches client-side timer | yes
|
||||
- Settings toggle label: "Delete permanently immediately (skip undo window)" | power users freeing space | yes
|
||||
- C-friction: delete button enabled after 1.5s + modal shows freed size; NO type-to-confirm (user vetoed) | user explicitly rejected type-to-confirm | n/a
|
||||
- Bulk/duplicates delete: one batch id for whole action, one undo restores all | simplest consistent semantics | yes
|
||||
|
||||
## Findings (cited - path:lines)
|
||||
|
||||
### Backend
|
||||
- `delete_model_artifacts` (py/services/model_lifecycle_service.py:19-48) = physical delete via os.remove; patterns: main file + `{name}.metadata.json` + PREVIEW_EXTENSIONS (py/utils/constants.py:22-37). ALSO called by ModelScanner.bulk_delete_models (py/services/model_scanner.py:2221) - single swap point covers bulk models.
|
||||
- `ModelLifecycleService.delete_model` (model_lifecycle_service.py:101-154): fetches `cached_entry` (111-116) - SNAPSHOT available for cache restore; after delete: cache.raw_data removal + resort + bump_cache_version (136-143), `_hash_index.remove_by_path` (145-146), `_sync_update_for_model` (148; update-service only, no recipe JSON rewrites - recipe refs are hash-based, re-resolve on restore), `_persist_current_cache` (150-152), returns `{"success": True, "deleted_files": [...]}` (154).
|
||||
- Handler `delete_model` (py/routes/handlers/model_handlers.py:478-492): POST /api/lm/{prefix}/delete; response passthrough; `_broadcast_models_changed()` (57-74) after success; 400 `{"success":false,"error"}`; 500 plain text.
|
||||
- Recipe delete: handler (recipe_handlers.py:1422-1438) DELETE /api/lm/recipe/{recipe_id} -> persistence_service.delete_recipe (py/services/recipes/persistence_service.py:193-209): os.remove(recipe_json_path) + os.remove(image_path) (204-206), recipe_scanner.remove_recipe (208), returns `{"success": true, "message": ...}`. PersistenceResult dataclass (20-25).
|
||||
- Bulk models: POST /api/lm/{prefix}/bulk-delete (model_route_registrar.py:39) -> handler (model_handlers.py:974-994) -> lifecycle_service.bulk_delete_models (model_lifecycle_service.py:308-318) -> scanner.bulk_delete_models (model_scanner.py:2181-2269) which calls delete_model_artifacts per file (2221) + `_batch_update_cache_for_deleted_models` (2271-2335); response `{"success","status","total_deleted","total_attempted","cache_updated","results"}` (2254-2269).
|
||||
- Bulk recipes: POST /api/lm/recipes/bulk-delete (recipe_route_registrar.py:50) -> handler (recipe_handlers.py:1554-1573) -> persistence_service.bulk_delete (persistence_service.py:439-482): per-id os.remove x2 (464-466), recipe_scanner.bulk_remove (472); response `{"success","deleted","failed","total_deleted","total_failed"}` (474-482).
|
||||
- Duplicates: NO dedicated delete endpoints (find-only: GET /api/lm/{prefix}/find-duplicates model_route_registrar.py:59, GET /api/lm/recipes/find-duplicates recipe_route_registrar.py:49). Duplicate deletion reuses bulk-delete endpoints.
|
||||
- Startup hooks: lora_manager.py:183-187 `app.on_startup.append(lambda app: cls._initialize_services())` (ComfyUI mode, app = PromptServer.instance.app at :78); standalone.py:370-374 same (StandaloneLoraManager.add_routes). Background tasks: `asyncio.create_task(name=...)` (lora_manager.py:224-239; recipe_handlers.py:793). Singleton+asyncio.Lock pattern: model_scanner.py:40-63.
|
||||
- Settings: DEFAULT_SETTINGS (py/services/settings_manager.py:57-119), `get(key, default)` (1390-1392), get_settings_manager() (2215-2228), reset_settings_manager() (2231). Typed-bool getter example: get_skip_previously_downloaded_model_versions (1253-1262). Handlers: base_model_routes.py:70, base_recipe_routes.py:54.
|
||||
- Model roots: ModelScanner.get_model_roots base NotImplementedError (model_scanner.py:1073-1075); impls lora_scanner.py:31-45, checkpoint_scanner.py:428-441, embedding_scanner.py:24-36. `_find_root_for_file(file_path)` (model_scanner.py:1108-1124) returns containing root - for per-root staging dir computation [updated 2026-08: staging no longer uses the containing root; batches are siblings inside the model's own folder]. Business-path rule (AGENTS.md): use os.path.abspath, never realpath, for staging/undo routing.
|
||||
- Cache restore methods: ModelCache has raw_data + resort (conftest mocks: tests/conftest.py:144-154); ModelHashIndex.add_entry(sha256, file_path, autov3) (py/services/model_hash_index.py:16); RecipeScanner.add_recipe(recipe_data) (recipe_scanner.py:2136) -> recipe_cache.add_recipe (recipe_cache.py:64). No single-file incremental model rescan - use snapshot restore instead of rescan.
|
||||
- Route registrar: model_route_registrar.py:177 add_route(method, path, handler), :180 add_prefixed_route - undo endpoint can be a non-prefixed route via add_route.
|
||||
- Tests: tests/services/test_model_lifecycle_service.py (inline tmp_path files, per-test stub scanners ScannerForDelete/VersionAwareScanner etc); conftest MockScanner/MockCache/MockHashIndex (tests/conftest.py:134-212); integration fixtures tests/integration/conftest.py; lifecycle hook tests tests/routes/test_lora_manager_lifecycle.py:177-178, tests/standalone/test_standalone_server.py:83-84.
|
||||
|
||||
### Frontend
|
||||
- 5 delete call sites:
|
||||
a) Single model: static/js/utils/modalUtils.js confirmDelete (27-42) -> getModelApiClient().deleteModel(path); ignores return.
|
||||
b) Recipe single: static/js/components/RecipeCard.js confirmDeleteRecipe (405-449) - RAW fetch DELETE /api/lm/recipe/{id}, checks only response.ok, showToast toast.recipes.deletedSuccessfully, state.virtualScroller.removeItemByFilePath.
|
||||
c) Bulk: static/js/managers/BulkManager.js confirmBulkDelete (633-672) -> getActiveApiClient() (134-142) -> bulkDeleteModels(filePaths); reads result.cancelled/success/deleted_count/error.
|
||||
d) Recipe duplicates: static/js/components/DuplicatesManager.js confirmDeleteDuplicates (457-494) - RAW fetch POST /api/lm/recipes/bulk-delete, reads data.success/data.total_deleted, exitDuplicateMode().
|
||||
e) Model duplicates: static/js/components/ModelDuplicatesManager.js confirmDeleteDuplicates (710-776) - RAW fetch POST /api/lm/{type}/bulk-delete, reads data.total_deleted, then resetAndReload(true) + find-duplicates re-check.
|
||||
Bonus: static/js/components/shared/ModelVersionsTab.js:1136-1144 client.deleteModel (ignores return).
|
||||
- API clients: BaseModelApiClient.deleteModel (static/js/api/baseModelApi.js:184-216) returns true/false, shows its own toasts, does removeItemByFilePath inside; bulkDeleteModels (1591-1642) returns {success, deleted_count, failed_count, errors} or {success:false, cancelled:true}; RecipeSidebarApiClient.bulkDeleteModels (recipeApi.js:623-664) returns {success, deleted_count: total_deleted, ...}. Endpoint map apiConfig.js:56,64.
|
||||
- Toast: showToast(key, params={}, type='info', fallback=null) (static/js/utils/uiHelpers.js:136-193) - textContent only, NO action/button support; durations 2000/5000ms; CSS static/css/components/toast.css (.toast flex gap:12px - button can be added). Closest action pattern: bannerService.registerBanner actions array + onRegister (static/js/managers/BannerService.js; used uiHelpers.js:18-57).
|
||||
- i18n: locales/en.json delete keys (1303-1314 bulkDelete, 1945-1948 recipes, 1987-1991 models, 2124-2130 duplicates, 2166-2170 toast.api); t()/interpolate (static/js/i18n/index.js:193-248); translate wrapper (utils/i18nHelpers.js:13-23); sync script scripts/sync_translation_keys.py (en reference, [TODO: Translate] placeholders).
|
||||
- Refresh after undo: recipes -> window.recipeManager.loadRecipes(true) (recipes.js:359; used by FilterManager.js:752 etc) or refreshRecipes (recipeApi.js:308); models -> resetAndReload(true) from modelApiFactory (used by ModelDuplicatesManager.js:740).
|
||||
- Size for modal: card.dataset.file_size (ModelCard.js:467), formatFileSize (ModelModal.js:615).
|
||||
- Tests: tests/frontend/utils/uiHelpers.dom.test.js (toast), api/recipeApi.bulk.test.js, components/duplicatesManager.test.js, components/modelDuplicatesManager.test.js, pages/*Page.test.js, i18n tests tests/i18n/test_i18n.py.
|
||||
|
||||
## Decisions (with rationale)
|
||||
|
||||
1. Same-volume rename staging for model files (atomic, no copy cost for multi-GB files); cross-volume rename forbidden. [CORRECTED 2026-08: "same-volume because under the containing root" was only true for plain directories — nested symlinked subdirs could cross volumes. Superseded by sibling staging: `.lm-pending-delete/<batch_id>/` inside the deleted model's own folder makes stage/undo same-device by construction; see "Symlink fix (2026-08)" below.]
|
||||
2. Copy-to-global-staging for recipes (small files; avoids recipe JSON vs preview image cross-volume problem).
|
||||
3. Manifest JSON files are the only state - no DB changes. Manifest includes model cached_entry snapshot for exact cache restore (no rescan needed).
|
||||
4. Undo endpoint returns restored paths; expired batch -> 404-style error -> frontend 'undo expired' toast.
|
||||
5. Skip-undo setting honored server-side (no batch_id in response -> no undo toast client-side).
|
||||
6. Staging failure falls back to existing hard delete (best-effort undo, never blocks delete).
|
||||
7. Undo window TTL = 30s constant (PENDING_DELETE_TTL_SECONDS); startup sweep uses expires_at (survives restart; browser-tab timer survives).
|
||||
8. Purge triple-trigger: per-batch asyncio timer task + on_startup sweep + opportunistic purge at each stage/undo.
|
||||
9. Frontend: new showActionToast (keep showToast signature untouched; extract shared createToastElement/appendToast internals); undo click -> shared handleUndoDelete(batchId, refreshFn); full list refresh after undo (recipes: window.recipeManager.loadRecipes(true); models: resetAndReload(true)).
|
||||
10. C-friction wave (NO type-to-confirm - user vetoed): delete buttons delay-activate 1.5s after modal open, initial focus on Cancel, model delete modal gains "permanently deleted from disk" warning + file size display (card.dataset.file_size + formatFileSize).
|
||||
11. Model cache restore on undo: append snapshot to cache.raw_data (dedupe by file_path) + resort + bump_cache_version + _persist_current_cache + _hash_index.add_entry + _broadcast_models_changed. Recipe restore: copy back files + recipe_scanner.add_recipe(recipe_data loaded from restored JSON).
|
||||
|
||||
### Symlink fix (2026-08)
|
||||
|
||||
Post-execution addendum (plan `.omo/plans/undo-delete-symlink-fix.md`, commits 5fd4946b / 0c00ee22):
|
||||
|
||||
12. Model staging moved from `<model_root>/.lm-pending-delete/<batch_id>/` to `<model_dir>/.lm-pending-delete/<batch_id>/` (sibling of the model artifacts, inside the deleted model's own folder). Stage/undo renames are same-device BY CONSTRUCTION — EXDEV is impossible even when the business path traverses nested symlinks to other volumes (the decision-1 "containing root" guarantee covered only plain directories). EXDEV remains possible only for cross-volume merges, which keep the batch_ids-array fallback. Accepted edge: deleting the model's whole FOLDER during the 30s window destroys that batch (undo returns 404). Batch discovery uses an in-memory registry (`_known_batch_dirs`) with a startup reconciliation scan (`purge_expired(scan_roots=True)`) covering restarts and crash leftovers. Recipe batches unchanged (copy-based settings-dir staging with the `_restore_file` EXDEV fallback).
|
||||
|
||||
## Scope IN
|
||||
|
||||
- Model single delete (model_handlers delete_model / model_lifecycle_service)
|
||||
- Recipe delete (recipe_handlers delete_recipe / persistence_service)
|
||||
- Bulk delete (models scanner + recipes persistence) + duplicates (reuse bulk endpoints)
|
||||
- Undo endpoint POST /api/lm/undo-delete (models + recipes, one batch space)
|
||||
- Purge: timer + startup sweep + opportunistic
|
||||
- Settings toggle delete_undo_enabled + settings page checkbox
|
||||
- Frontend: showActionToast + all 5 delete flows + shared undo handler
|
||||
- C-friction modal changes (delay-activate + cancel focus + warning copy + size display)
|
||||
- i18n keys + sync_translation_keys.py
|
||||
- Backend + frontend tests
|
||||
|
||||
## Scope OUT (Must NOT have)
|
||||
|
||||
- NO type-to-confirm / hold-to-confirm friction (user vetoed)
|
||||
- NO OS trash integration (send2trash) in this iteration
|
||||
- NO persistent recycle-bin UI (no trash browsing page)
|
||||
- NO changes to exclude/unexclude flow
|
||||
- NO DB migrations
|
||||
- NO new dependencies (no send2trash)
|
||||
- NO changes to download flows
|
||||
- NO recipe-JSON rewriting on model undo (hash-based refs re-resolve themselves)
|
||||
|
||||
## Open questions
|
||||
|
||||
None - all implementation details resolved by exploration. Design decisions settled in conversation (B+C, no type-to-confirm).
|
||||
|
||||
## Approval gate
|
||||
status: approved
|
||||
<!-- Approach approved -> rerun scaffold without --draft-only, run Metis gap analysis, APPEND todo batches, fill TL;DR last, run structural self-check, then Phase 4 handoff. -->
|
||||
|
||||
## Review round state (ulw-plan-review-round-state-contract)
|
||||
```json
|
||||
{
|
||||
"transition": "replace",
|
||||
"phase": "review_round_initialized",
|
||||
"applies_when": ["retry_after_plan_change"],
|
||||
"atomic": true,
|
||||
"review_required": true,
|
||||
"plan_path": ".omo/plans/undo-delete-staging.md",
|
||||
"plan_sha256": "8cf7c9be38a76d8ef1fb832aba043d6d7e82b60465b1bf28c6eafa7045117adc",
|
||||
"review_round_id": "rr-undo-del-20260811-006",
|
||||
"round_status": "active",
|
||||
"pending-action": "review .omo/plans/undo-delete-staging.md",
|
||||
"review": {
|
||||
"momus": { "status": "pending", "workspace_root": "/mnt/data/reinstall-backup-2026-04-12/data/workspace/ComfyUI/custom_nodes/ComfyUI-Lora-Manager", "runtime_home": null, "target": ".omo/plans/undo-delete-staging.md", "round_id": "rr-undo-del-20260811-006", "plan_sha256": "8cf7c9be38a76d8ef1fb832aba043d6d7e82b60465b1bf28c6eafa7045117adc", "launch_id": null, "session": null, "result": null },
|
||||
"independent": { "status": "pending", "workspace_root": "/mnt/data/reinstall-backup-2026-04-12/data/workspace/ComfyUI/custom_nodes/ComfyUI-Lora-Manager", "runtime_home": null, "target": ".omo/plans/undo-delete-staging.md", "round_id": "rr-undo-del-20260811-006", "plan_sha256": "8cf7c9be38a76d8ef1fb832aba043d6d7e82b60465b1bf28c6eafa7045117adc", "launch_id": null, "session": null, "result": null }
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Review results + fix/retry ledger
|
||||
|
||||
### Round 1 (rr-undo-del-20260811-001, plan sha256 6c52bf99...)
|
||||
- momus: APPROVE (non-blocking notes: todo1+7 duplicate DEFAULT_SETTINGS key -> fixed todo 7 to verify-only; "batch_ids" plural in todos 8/9 acceptance -> fixed; purge OSError note -> folded into todo 1 purge semantics)
|
||||
- independent (oracle): CHANGES_REQUESTED
|
||||
- BLOCKING S1: scanner walks would index .lm-pending-delete staged files as ghost entries -> fixed: todo 1 now mandates scanner walk exclusion at model_scanner.py:706/:867/:1404/_process_model_file + acceptance (o) scanner-visibility test
|
||||
- BLOCKING S2: manifest lacks model_type, undo could restore into wrong cache/hash index -> fixed: manifest now carries model_type + todo 5 resolves per-type scanner via registrar pattern + acceptance (b) checkpoint-batch test
|
||||
- S3 merged-batch expires_at re-anchor -> fixed: merge_batches re-anchors now+TTL in todo 1 + todo 3/4 assertions
|
||||
- S4 manifest-less dir policy -> fixed: quarantine to <batch_id>.orphaned, never delete (todo 1 + acceptance g)
|
||||
- S5 partial-undo retry semantics -> fixed: per-entry restored flag write-through + retry test (acceptance e)
|
||||
- S6 purge locked-file failure semantics -> fixed: skip file, keep batch, never rmtree past errors (todo 1 + acceptance i)
|
||||
- T8 undo-after-restart test -> fixed: todo 5 acceptance (f)
|
||||
- T9 recipe undo -> re-delete test -> fixed: todo 5 acceptance (h)
|
||||
- T7 rescan-stale-entry test -> fixed: todo 5 acceptance (g)
|
||||
- Route registration pinned to shared routes class per mode (NOT per-model-type registrar which registers 3x) -> fixed: todo 5 now creates py/routes/pending_delete_routes.py registered once in lora_manager.py:170-172 + standalone.py:356-358 + duplicate-route test (e)
|
||||
- Version-index staleness on single-delete undo -> fixed: todo 5 follows bulk cache-update pattern incl. rebuild_version_index (model_scanner.py:2324)
|
||||
- Cancelled-bulk batch_id frontend handling -> fixed: todo 9 shows action toast on cancelled+staged-subset
|
||||
- Single-instance assumption -> added to Scope OUT
|
||||
- Occupied-refusal loss UX -> accepted-intent documented in success criteria + modal copy
|
||||
|
||||
### Round 2 (rr-undo-del-20260811-002, plan sha256 f3d52235...)
|
||||
- momus: APPROVE (all 12 round-1 fixes verified present; zero dead references; non-blocking nits only)
|
||||
- independent (oracle): CHANGES_REQUESTED
|
||||
- BLOCK-1: merge_batches file-movement semantics unspecified (silent data-loss vector) -> fixed: todo 1 now specifies move-into-winner-dir + entry re-point + loser-dirs-removed-only-when-empty + abort-on-move-failure (all batches intact) + merge inside service lock + acceptance (k) file-survival assertions + acceptance (l) merge-failure abort test
|
||||
- BLOCK-2: same-file parallel edits within waves (todo 5 vs 6 on lora_manager.py; todo 8 vs 9 on baseModelApi.js) -> fixed: waves/matrix now serialize 5->6 and 8->9 with explicit reasons; matrix updated
|
||||
- Recommended: checkpoint_scanner.py:331 exclusion -> fixed (todo 1 + acceptance p); S5 pre-check skips restored:true entries -> fixed (todo 1); _tags_count restore on undo -> fixed (todo 5 + acceptance j); undo-blind flows documented (ModelVersionsTab + misc_handlers:2456) -> fixed (todo 8 note + Scope OUT); merge-failure no-merge fallback contract (batch_ids array) -> fixed (todos 3/4/9)
|
||||
|
||||
### Round 3 (rr-undo-del-20260811-003, plan sha256 8f2dfd46...)
|
||||
- momus: APPROVE (all round-2 fixes verified present + spot-checked refs; no new contradictions)
|
||||
- independent (oracle): CHANGES_REQUESTED
|
||||
- BLOCKING A: merged batches never timer-purged after re-anchor (winner's original timer no-ops at old expiry; no fresh timer for re-anchored expiry; idle server -> merged batch lingers, violating "30s purge" success criterion; affects EVERY bulk delete) -> fixed: todo 1 merge_batches now ARMS A FRESH PURGE TIMER for the winner with re-anchored expiry + acceptance (q) fresh-timer test + purge_expired must enumerate ALL scanner types' roots (explicit in todo 1)
|
||||
- BLOCKING B: dependency matrix contradicted same-file policy for todos 8/9<->11 (5 shared files) and 12<->11 -> fixed: todo 11 now "Blocked by: 8, 9 (same files...)"; todo 12 blocked by 11 (sync after 11); wave text updated (11, then 12 AFTER 11); "Can parallelize with" columns corrected
|
||||
- BLOCKING C: frontend batch_ids sequential-undo fallback has NO test + merge->undo loser-restore + merge->purge assertions missing -> fixed: todo 9 acceptance now tests the batch_ids fallback path; todo 1 acceptance now has (k2)/(k3)
|
||||
- Notes folded: sub-second toast-tail expiry race accepted; EXDEV fallback = NORMAL path for cross-volume bulks [annotated 2026-08: after the sibling-staging fix, EXDEV can only arise during cross-volume MERGES, never during single stage/undo renames]
|
||||
|
||||
### Round 4 (rr-undo-del-20260811-004, plan sha256 179e7ff7...)
|
||||
- momus: APPROVE (round-3 fixes verified; one non-blocking nit: todo 11 inline "Blocked by: —" stale -> fixed to "8, 9")
|
||||
- independent (oracle): CHANGES_REQUESTED
|
||||
- BLOCKING GAP-1 (NEW, introduced by round-3 fix): todo 8 handleUndoDelete always-refresh/always-toast contract contradicted todo 9's sequential loop "exactly ONE final refresh" -> fixed: handleUndoDelete(batchId, refreshFn, {showToast, refresh}) suppression options; todo 9 loop uses suppressed calls + one final refresh/toast; acceptance extended (loop failure mid-way -> stop + error toast + no final refresh; 404 body discrimination expired vs occupied)
|
||||
- BLOCKING GAP-2: no cross-type purge enumeration test -> fixed: todo 1 acceptance (r) purges expired batches across lora root + checkpoint root + recipe staging dir in one call
|
||||
- Non-blocking folded: GAP-3 404-copy discrimination -> fixed in todo 8 (d); GAP-4 merge partial-failure rollback direction (move back + restore manifests, extended (l) asserts sequential constituent undo still restores everything) -> fixed in todo 1; GAP-5 post-restart timer-loss residual gap documented -> fixed in todo 6; GAP-6 usage_stats.py:424 walk added to exclusion mandate + todo 5 acceptance (k) embeddings undo test
|
||||
|
||||
### Round 5 (rr-undo-del-20260811-005, plan sha256 dfaa39ea...)
|
||||
- momus: APPROVE (all round-4 fixes verified; no new contradictions)
|
||||
- independent (oracle): CHANGES_REQUESTED
|
||||
- BLOCK-1: lock-ordering deadlock ambiguity (asyncio.Lock not re-entrant: opportunistic purge_expired called while stage/undo hold the lock would deadlock on first use) -> fixed: todo 1 now has explicit LOCK HIERARCHY (lock acquired ONLY by stage/merge/undo/purge_batch; purge_expired is lock-free and must be called BEFORE lock acquisition); todo 6 (c) updated with the same rule + acceptance (u) lock-no-deadlock test
|
||||
- BLOCK-2: purge edge semantics unspecified -> fixed: purge_batch treats missing staged files (partially-restored batches) as already-purged (FileNotFoundError silent no-op); sweep skips `.orphaned`-suffixed dirs (quarantine is terminal); acceptance (s) partially-restored purge + (t) quarantine-terminal tests
|
||||
- Non-blocking folded: todo 2/3 test-file collision -> todo 3's bulk tests moved to tests/services/test_model_scanner.py; todo 9 (d) DuplicatesManager refreshFn stated explicitly (recipes loadRecipes / models resetAndReload); modal-copy + bulk-count trade-offs acknowledged in success criteria; acceptance (r) extended with embeddings root
|
||||
|
||||
### Round 6 (rr-undo-del-20260811-006, plan sha256 8cf7c9be...)
|
||||
- momus: APPROVE (all round-5 fixes verified; no new contradictions; references verified)
|
||||
- independent (oracle): APPROVE — no blocking issues; all round-5 items fixed with working, tested solutions; no new race/data-loss/consistency defects
|
||||
- Deferred optional improvements (non-blocking, recorded for executor awareness; plan file left untouched to preserve the approved digest):
|
||||
1. Tag-count asymmetry: single delete_model never decrements _tags_count (lifecycle 101-154), bulk does (scanner 2297-2303); undo re-increment is exact for bulk, over-counts for single until rescan (cosmetic, self-healing). Optional fix riding in todo 2: decrement tags in the single-delete path to mirror bulk.
|
||||
2. Todo 5 factual nit: ModelCache.resort() already rebuilds the version index — explicit rebuild in undo is belt-and-braces, no action needed.
|
||||
3. Todo 8 premise nit: ModelVersionsTab call ignores deleteModel's return entirely — nothing breaks, no adaptation needed.
|
||||
4. Todo 3's pytest command includes test_model_lifecycle_service.py which todo 2 edits in the same wave — run that file's tests after todo 2 lands.
|
||||
5. merge_batches with a missing/quarantined constituent id: any sane fallback (abort -> batch_ids, or skip missing) acceptable — files stay staged either way.
|
||||
|
||||
## Review lifecycle
|
||||
- rounds: 6 (rr-undo-del-20260811-001..006); final round both lanes APPROVE
|
||||
- final live-plan validation: sha256 = 8cf7c9be38a76d8ef1fb832aba043d6d7e82b60465b1bf28c6eafa7045117adc — MATCHES approved round-6 digest
|
||||
- status: APPROVED — ready for execution handoff ($start-work undo-delete-staging)
|
||||
@@ -1,181 +0,0 @@
|
||||
# Embeddings Usage Tracking — Hybrid Approach (Plan C)
|
||||
|
||||
> **Status**: Reference document for future implementation
|
||||
> **Current implementation**: Plan A (prompt text parsing only, see `usage_stats.py:_process_embeddings`)
|
||||
> **Next step**: Add Plan B as a supplement when edge-case coverage is needed
|
||||
|
||||
## Problem
|
||||
|
||||
Embeddings in ComfyUI are not loaded through dedicated ComfyUI nodes like LoRAs or
|
||||
Checkpoints. They are resolved during CLIP tokenization when the prompt text contains
|
||||
`embedding:<name>` syntax (see `comfy/sd1_clip.py:SDTokenizer.tokenize_with_weights`).
|
||||
|
||||
This means the existing metadata_collector hook (which intercepts node execution via
|
||||
`_map_node_over_list`) cannot capture embeddings the same way it captures LoRAs and
|
||||
checkpoints — there is no "EmbeddingLoader" node to intercept.
|
||||
|
||||
## Solution Architecture
|
||||
|
||||
The hybrid approach combines **two complementary mechanisms** to capture embedding
|
||||
usage from all possible paths.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────┐
|
||||
│ Plan A (已实现) │
|
||||
│ │
|
||||
│ MetadataRegistry.prompt_metadata["prompts"] │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ _process_embeddings() │
|
||||
│ │ │
|
||||
│ ├─ Iterate all prompt node texts │
|
||||
│ ├─ regex extract "embedding:<name>" │
|
||||
│ ├─ resolve name → sha256 via EmbeddingScanner │
|
||||
│ └─ UsageStats.stats["embeddings"][sha256]++ │
|
||||
│ │
|
||||
│ Coverage: ~95% — all CLIPTextEncode/Flux/etc nodes │
|
||||
│ │
|
||||
│ Gap: Custom nodes that load embeddings programmatically │
|
||||
│ without putting embedding:name in prompt text │
|
||||
└─────────────────────────────────────────────────────────┘
|
||||
|
||||
+
|
||||
↓ (future: enable Plan B when needed)
|
||||
|
||||
┌─────────────────────────────────────────────────────────┐
|
||||
│ Plan B (未来 — monkey-patch) │
|
||||
│ │
|
||||
│ comfy/sd1_clip.py:load_embed() │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ Monkey-patch intercepts EVERY embedding file load │
|
||||
│ │ │
|
||||
│ ├─ Records embedding_name + success/failure │
|
||||
│ ├─ Associates with current prompt_id (via registry)│
|
||||
│ └─ Feeds into UsageStats same as Plan A │
|
||||
│ │
|
||||
│ Coverage: 100% — catches ALL embedding loads │
|
||||
│ │
|
||||
│ Cost: Requires patching into ComfyUI internals │
|
||||
│ (sd1_clip.py, sdxl_clip.py, some text_encoders) │
|
||||
└─────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Plan B Detail — Monkey-patch `load_embed`
|
||||
|
||||
### Target Function
|
||||
|
||||
**`comfy.sd1_clip.load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None)`**
|
||||
at line 415 of `sd1_clip.py`.
|
||||
|
||||
This is the **single choke point** for all embedding file loads in ComfyUI. Every
|
||||
CLIP variant (SD1, SDXL, SD3, Flux) calls this same function.
|
||||
|
||||
### Implementation Sketch
|
||||
|
||||
```python
|
||||
# In metadata_collector/metadata_hook.py (or a new module)
|
||||
import comfy.sd1_clip as sd1_clip
|
||||
|
||||
_original_load_embed = sd1_clip.load_embed
|
||||
|
||||
def _patched_load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
|
||||
result = _original_load_embed(
|
||||
embedding_name, embedding_directory, embedding_size, embed_key
|
||||
)
|
||||
if result is not None:
|
||||
_record_embedding_usage(embedding_name)
|
||||
return result
|
||||
|
||||
sd1_clip.load_embed = _patched_load_embed
|
||||
```
|
||||
|
||||
### Prompt ID Association
|
||||
|
||||
The challenge is associating the `load_embed` call with the current `prompt_id`.
|
||||
Options:
|
||||
|
||||
1. **Thread-local / contextvar**: Store current `prompt_id` in a `contextvars.ContextVar`
|
||||
that the metadata_collector sets at the start of each prompt execution.
|
||||
|
||||
2. **MetadataRegistry singleton**: The MetadataRegistry already has `current_prompt_id`.
|
||||
The patch can read it directly since both run in the same thread.
|
||||
|
||||
3. **Lazy aggregation**: Instead of associating with prompt_id at load time, collect
|
||||
all loaded embedding names in a global set during execution, then flush to
|
||||
UsageStats after the prompt completes.
|
||||
|
||||
### Files to Patch
|
||||
|
||||
| File | Function | Coverage |
|
||||
|------|----------|----------|
|
||||
| `comfy/sd1_clip.py:415` | `load_embed()` | Primary — SD1.x, SDXL, SD3, Flux |
|
||||
| `comfy/sdxl_clip.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
|
||||
| `comfy/text_encoders/sd3_clip.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
|
||||
| `comfy/text_encoders/flux.py` | Not needed (calls `sd1_clip.SDTokenizer`) | — |
|
||||
|
||||
The SD1 tokenizer is the base class for all CLIP variants' tokenizers, so patching
|
||||
`load_embed` covers them all.
|
||||
|
||||
### Edge Cases
|
||||
|
||||
| Edge Case | Plan A | Plan B |
|
||||
|-----------|--------|--------|
|
||||
| `embedding:name` in CLIPTextEncode | ✅ | ✅ |
|
||||
| `embedding:name` in CLIPTextEncodeFlux | ✅ | ✅ |
|
||||
| `embedding:name` in PromptLM (LoRA Manager) | ✅ | ✅ |
|
||||
| `embedding:name` in WAS_Text_to_Conditioning | ✅ | ✅ |
|
||||
| Custom node that loads embedding programmatically | ❌ | ✅ |
|
||||
| Embedding loaded multiple times in same prompt | ✅ (dedup via set) | ✅ (dedup via set) |
|
||||
| Embedding file not found | N/A | ✅ (can log) |
|
||||
| Embedding dimension mismatch | N/A | ✅ (can log) |
|
||||
| Text encoder with non-standard tokenizer (LLaMA, T5...) | Partial | ✅ (if it calls load_embed) |
|
||||
|
||||
## Migration Path: Standalone → Hybrid
|
||||
|
||||
### Phase 1 — Plan A (当前状态)
|
||||
- Prompt text parsing only
|
||||
- No monkey-patching required
|
||||
- Covers all standard workflows
|
||||
|
||||
### Phase 2 — Enable Plan B (未来工作)
|
||||
1. Add monkey-patch of `load_embed` in `metadata_collector/metadata_hook.py` (alongside
|
||||
the existing `_map_node_over_list` hook)
|
||||
2. Collect loaded embedding names in a `set()` on the registry
|
||||
3. In `UsageStats._process_embeddings()`, merge the Plan A results (from prompt text)
|
||||
with the Plan B results (from the patch)
|
||||
4. Add `prompt_data` field on MetadataRegistry to store loaded embeddings per prompt
|
||||
|
||||
### Deduplication
|
||||
|
||||
```python
|
||||
# Merge Plan A + Plan B results in _process_embeddings
|
||||
plan_a_names = extract_from_prompt_texts(prompts_data)
|
||||
plan_b_names = registry.get_loaded_embeddings(prompt_id)
|
||||
|
||||
all_names = plan_a_names | plan_b_names
|
||||
```
|
||||
|
||||
## Testing the Hybrid
|
||||
|
||||
| Scenario | What to verify |
|
||||
|----------|---------------|
|
||||
| Standard `embedding:name` in prompt | Plan A captures it |
|
||||
| Embedding loaded by custom node script | Plan B captures it |
|
||||
| Both paths fire for same embedding | No double-counting (dedup) |
|
||||
| Embedding name resolves to hash | EmbeddingScanner.get_hash_by_filename works |
|
||||
| No embedding scanner available | Graceful skip, no crash |
|
||||
| Missing embedding file | Plan B logs warning, Plan A skips gracefully |
|
||||
| Empty prompt | No crash, no entries |
|
||||
| Standalone mode | Both plans disabled gracefully |
|
||||
|
||||
## Key Files Reference
|
||||
|
||||
| File | Role |
|
||||
|------|------|
|
||||
| `py/utils/usage_stats.py` | Core — `_process_embeddings()` for Plan A |
|
||||
| `py/metadata_collector/constants.py` | `EMBEDDINGS` category constant |
|
||||
| `py/metadata_collector/metadata_hook.py` | Future — monkey-patch for Plan B |
|
||||
| `py/services/embedding_scanner.py` | Hash resolution service |
|
||||
| `py/routes/stats_routes.py` | Already handles `usage_data.get('embeddings', {})` |
|
||||
| `comfy/sd1_clip.py` (ComfyUI) | `load_embed()` — Plan B target |
|
||||
File diff suppressed because one or more lines are too long
@@ -1,464 +0,0 @@
|
||||
{
|
||||
"$schema": "http://json-schema.org/draft-07/schema#",
|
||||
"$id": "https://github.com/willmiao/ComfyUI-Lora-Manager/.specs/metadata.schema.json",
|
||||
"title": "ComfyUI LoRa Manager Model Metadata",
|
||||
"description": "Schema for .metadata.json sidecar files used by ComfyUI LoRa Manager",
|
||||
"type": "object",
|
||||
"oneOf": [
|
||||
{
|
||||
"title": "LoRA Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string",
|
||||
"description": "Filename without extension"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string",
|
||||
"description": "Display name of the model"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string",
|
||||
"description": "Full absolute path to the model file"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"description": "File size in bytes at time of import/download"
|
||||
},
|
||||
"modified": {
|
||||
"type": "number",
|
||||
"description": "Unix timestamp when model was imported/added (Date Added)"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$",
|
||||
"description": "SHA256 hash of the model file (lowercase)"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string",
|
||||
"description": "Base model type (SD1.5, SD2.1, SDXL, SD3, Flux, Unknown, etc.)"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string",
|
||||
"description": "Path to preview image file"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"default": 0,
|
||||
"description": "NSFW level using bitmask values: 0 (none), 1 (PG), 2 (PG13), 4 (R), 8 (X), 16 (XXX), 32 (Blocked)"
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": "",
|
||||
"description": "User-defined notes"
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true,
|
||||
"description": "Whether the model originated from Civitai"
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": [],
|
||||
"description": "Model tags"
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": "",
|
||||
"description": "Full model description"
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether the model was deleted from Civitai"
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether the model is marked as favorite"
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether to exclude from cache/scanning"
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether checked against archive database"
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Skip this model during bulk metadata refresh"
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null,
|
||||
"description": "Last provider that supplied metadata"
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0,
|
||||
"description": "Unix timestamp of last metadata check"
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed",
|
||||
"description": "Hash calculation status"
|
||||
},
|
||||
"usage_tips": {
|
||||
"type": "string",
|
||||
"default": "{}",
|
||||
"description": "JSON string containing recommended usage parameters (LoRA only)"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
},
|
||||
{
|
||||
"title": "Checkpoint Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"modified": {
|
||||
"type": "number"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"maximum": 3,
|
||||
"default": 0
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": []
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed"
|
||||
},
|
||||
"sub_type": {
|
||||
"type": "string",
|
||||
"default": "checkpoint",
|
||||
"description": "Model sub-type (checkpoint, diffusion_model, etc.)"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
},
|
||||
{
|
||||
"title": "Embedding Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"modified": {
|
||||
"type": "number"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"maximum": 3,
|
||||
"default": 0
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": []
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed"
|
||||
},
|
||||
"sub_type": {
|
||||
"type": "string",
|
||||
"default": "embedding",
|
||||
"description": "Model sub-type"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
}
|
||||
],
|
||||
"definitions": {
|
||||
"civitaiObject": {
|
||||
"type": "object",
|
||||
"default": {},
|
||||
"description": "Civitai/CivArchive API data and user-defined fields",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "integer",
|
||||
"description": "Version ID from Civitai"
|
||||
},
|
||||
"modelId": {
|
||||
"type": "integer",
|
||||
"description": "Model ID from Civitai"
|
||||
},
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Version name"
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "Version description"
|
||||
},
|
||||
"baseModel": {
|
||||
"type": "string",
|
||||
"description": "Base model type from Civitai"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"description": "Model type (checkpoint, embedding, etc.)"
|
||||
},
|
||||
"trainedWords": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "Trigger words for the model (from API or user-defined)"
|
||||
},
|
||||
"customImages": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
},
|
||||
"description": "Custom example images added by user"
|
||||
},
|
||||
"model": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": {
|
||||
"type": "string"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"files": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"images": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"creator": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"additionalProperties": true
|
||||
},
|
||||
"usageTips": {
|
||||
"type": "object",
|
||||
"description": "Structure for usage_tips JSON string (LoRA models)",
|
||||
"properties": {
|
||||
"strength_min": {
|
||||
"type": "number",
|
||||
"description": "Minimum recommended model strength"
|
||||
},
|
||||
"strength_max": {
|
||||
"type": "number",
|
||||
"description": "Maximum recommended model strength"
|
||||
},
|
||||
"strength_range": {
|
||||
"type": "string",
|
||||
"description": "Human-readable strength range"
|
||||
},
|
||||
"strength": {
|
||||
"type": "number",
|
||||
"description": "Single recommended strength value"
|
||||
},
|
||||
"clip_strength": {
|
||||
"type": "number",
|
||||
"description": "Recommended CLIP/embedding strength"
|
||||
},
|
||||
"clip_skip": {
|
||||
"type": "integer",
|
||||
"description": "Recommended CLIP skip value"
|
||||
}
|
||||
},
|
||||
"additionalProperties": true
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,277 +0,0 @@
|
||||
# AGENTS.md
|
||||
|
||||
This file provides guidance for agentic coding assistants working in this repository.
|
||||
|
||||
## Overview
|
||||
|
||||
ComfyUI LoRA Manager is a comprehensive LoRA management system for ComfyUI that combines a Python backend with browser-based widgets. It provides model organization, downloading from CivitAI/CivArchive, recipe management, and one-click workflow integration.
|
||||
|
||||
## Development Commands
|
||||
|
||||
### Backend Development
|
||||
|
||||
```bash
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
pip install -r requirements-dev.txt
|
||||
|
||||
# Run standalone server (port 8188 by default)
|
||||
python standalone.py --port 8188
|
||||
|
||||
# Run all backend tests
|
||||
pytest
|
||||
|
||||
# Run specific test file
|
||||
pytest tests/test_recipes.py
|
||||
|
||||
# Run specific test function
|
||||
pytest tests/test_recipes.py::test_function_name
|
||||
|
||||
# Run backend tests with coverage
|
||||
COVERAGE_FILE=coverage/backend/.coverage pytest \
|
||||
--cov=py --cov=standalone \
|
||||
--cov-report=term-missing \
|
||||
--cov-report=html:coverage/backend/html \
|
||||
--cov-report=xml:coverage/backend/coverage.xml \
|
||||
--cov-report=json:coverage/backend/coverage.json
|
||||
```
|
||||
|
||||
### Frontend Development (LoRA Manager Web UI)
|
||||
|
||||
```bash
|
||||
# Install dependencies (root and Vue widgets)
|
||||
npm install
|
||||
cd vue-widgets && npm install && cd ..
|
||||
|
||||
npm test # Run all tests (JS + Vue)
|
||||
npm run test:js # Run JS tests only
|
||||
npm run test:vue # Run Vue widget tests only
|
||||
npm run test:watch # Watch mode (JS tests only)
|
||||
npm run test:coverage # Generate coverage report
|
||||
```
|
||||
|
||||
### Vue Widget Development
|
||||
|
||||
```bash
|
||||
cd vue-widgets
|
||||
npm install
|
||||
npm run dev # Build in watch mode
|
||||
npm run build # Build production bundle
|
||||
npm run typecheck # Run TypeScript type checking
|
||||
npm test # Run Vue widget tests
|
||||
npm run test:watch # Watch mode
|
||||
npm run test:coverage # Generate coverage report
|
||||
```
|
||||
|
||||
### Localization
|
||||
|
||||
```bash
|
||||
# Sync translation keys after UI string updates
|
||||
python scripts/sync_translation_keys.py
|
||||
```
|
||||
|
||||
Locale files are in `locales/` (en, zh-CN, zh-TW, ja, ko, fr, de, es, ru, he).
|
||||
|
||||
After adding keys to `en.json` and syncing, **stop**: the `[TODO: Translate]` placeholders in
|
||||
the other locales are the expected end state during feature development. Do NOT translate
|
||||
proactively — translate only when the feature owner explicitly asks (see
|
||||
`docs/i18n-translation-guidelines.md` §7).
|
||||
|
||||
**Before translating anything, read `docs/i18n-translation-guidelines.md`** — it defines the
|
||||
term conventions (e.g. "Recipe" stays untranslated in French, 配方 in Chinese; model-type and
|
||||
brand names are never translated), per-locale preferred renderings, placeholder rules, and
|
||||
the known confusion hot-spots.
|
||||
|
||||
## Code Style
|
||||
|
||||
### Python
|
||||
|
||||
#### Imports & Formatting
|
||||
|
||||
- Use `from __future__ import annotations` for forward references
|
||||
- Group imports: standard library, third-party, local (blank line separated)
|
||||
- Use `TYPE_CHECKING` guard for type-checking-only imports
|
||||
- Absolute imports within `py/`: `from ..services import X`
|
||||
- PEP 8 with 4-space indentation, type hints required
|
||||
|
||||
#### Naming Conventions
|
||||
|
||||
- Files: `snake_case.py`, Classes: `PascalCase`, Functions/vars: `snake_case`
|
||||
- Constants: `UPPER_SNAKE_CASE`, Private: `_protected`, `__mangled`
|
||||
|
||||
#### Error Handling & Async
|
||||
|
||||
- Use `logging.getLogger(__name__)`, define custom exceptions in `py/services/errors.py`
|
||||
- `async def` for I/O, `@pytest.mark.asyncio` for async tests
|
||||
- Singleton with `asyncio.Lock`: see `ModelScanner.get_instance()`
|
||||
- Return `aiohttp.web.json_response` or `web.Response`
|
||||
|
||||
### JavaScript/TypeScript
|
||||
|
||||
#### Imports & Modules
|
||||
|
||||
- ES modules: `import { app } from "../../scripts/app.js"` for ComfyUI
|
||||
- Vue: `import { ref, computed } from 'vue'`, type imports: `import type { Foo }`
|
||||
- Export named functions: `export function foo() {}`
|
||||
|
||||
#### Naming & Formatting
|
||||
|
||||
- camelCase for functions/vars/props, PascalCase for classes
|
||||
- Constants: `UPPER_SNAKE_CASE`, Files: `snake_case.js` or `kebab-case.js`
|
||||
- 2-space indentation preferred (follow existing file conventions)
|
||||
- Vue Single File Components: `<script setup lang="ts">` preferred
|
||||
|
||||
#### Widget Development
|
||||
|
||||
- Prefer vanilla JS for `web/comfyui/` widgets; avoid framework dependencies (except the Vue widgets in `vue-widgets/`)
|
||||
- ComfyUI: `app.registerExtension()`, `node.addDOMWidget(name, type, element, options)`
|
||||
- Event handlers via `addEventListener` or widget callbacks
|
||||
- Shared utilities: `web/comfyui/utils.js`
|
||||
- Dual-mode rendering patterns (canvas vs Vue): see `docs/comfyui-dual-mode-widgets.md`
|
||||
|
||||
#### Vue Composables Pattern
|
||||
|
||||
- Use composition API: `useXxxState(widget)`, return reactive refs and methods
|
||||
- Guard restoration loops with flag: `let isRestoring = false`
|
||||
- Build config from state: `const buildConfig = (): Config => { ... }`
|
||||
|
||||
## Architecture
|
||||
|
||||
### Dual Mode Operation
|
||||
|
||||
The system runs in two modes:
|
||||
- **ComfyUI plugin mode**: Integrates with ComfyUI's PromptServer, uses `folder_paths` for model discovery
|
||||
- **Standalone mode**: `standalone.py` mocks ComfyUI dependencies, reads paths from `settings.json`
|
||||
- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
|
||||
|
||||
### Backend Entry Points
|
||||
|
||||
- `__init__.py` — ComfyUI plugin entry: registers nodes via `NODE_CLASS_MAPPINGS`, sets `WEB_DIRECTORY`, calls `LoraManager.add_routes()`
|
||||
- `standalone.py` — Standalone server: mocks `folder_paths` and node modules, starts aiohttp server
|
||||
- `py/lora_manager.py` — Main `LoraManager` class that registers all HTTP routes
|
||||
|
||||
### Service Layer
|
||||
|
||||
- `ServiceRegistry` singleton for DI, services use `get_instance()` classmethod
|
||||
- `BaseModelService` abstract base → `LoraService`, `CheckpointService`, `EmbeddingService`
|
||||
- `ModelScanner` base → `LoraScanner`, `CheckpointScanner`, `EmbeddingScanner` for file discovery with hash-based deduplication
|
||||
- `PersistentModelCache` (SQLite) for metadata persistence
|
||||
- `MetadataSyncService` — background sync from CivitAI/CivArchive APIs
|
||||
- `SettingsManager` — settings with schema migration support
|
||||
- `WebSocketManager` — real-time progress broadcasting
|
||||
- `ModelServiceFactory` — creates the right service for each model type
|
||||
- Use cases in `py/services/use_cases/` orchestrate complex business logic (auto-organize, bulk refresh, downloads)
|
||||
- Separate scanners (discovery) from services (business logic)
|
||||
- Handlers in `py/routes/handlers/` are pure functions with deps as params
|
||||
|
||||
### Model Types & Routes
|
||||
|
||||
- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*`, `/other/*` patterns
|
||||
- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, `RecipeRouteRegistrar`, etc.
|
||||
- Request handlers in `py/routes/handlers/` implement route logic
|
||||
- All routes use aiohttp, return `web.json_response` or `web.Response`
|
||||
- Endpoints consumed by the companion browser extension (lm-civitai-extension)
|
||||
MUST also accept `GET` with query-string params: the extension is GET-only by
|
||||
convention (see its AGENTS.md), even for state-changing operations such as
|
||||
`GET /api/lm/recipe/{recipe_id}/reimport`
|
||||
|
||||
### Recipe System
|
||||
|
||||
- Base: `py/recipes/base.py`, Enrichment: `RecipeEnrichmentService` in `py/recipes/enrichment.py`
|
||||
- Parsers: `py/recipes/parsers/` for PNG metadata, JSON, and workflow formats
|
||||
|
||||
### Custom Nodes
|
||||
|
||||
- Location: `py/nodes/`, all nodes registered in `__init__.py`
|
||||
- Each node class has a `NAME` class attribute used as key in `NODE_CLASS_MAPPINGS`
|
||||
- Standard ComfyUI node pattern: `INPUT_TYPES()` classmethod, `RETURN_TYPES`, `FUNCTION`
|
||||
|
||||
### Configuration
|
||||
|
||||
- `py/config.py` manages folder paths for models and handles symlink mappings
|
||||
- Auto-saves paths to `settings.json` in ComfyUI mode
|
||||
- `settings.json.example` is intentionally minimal (see Important Notes); all
|
||||
other defaults live in `DEFAULT_SETTINGS` (`py/services/settings_manager.py`)
|
||||
- **`folder_paths` vs `extra_folder_paths` — different purposes, do not conflate:**
|
||||
- `folder_paths` (primary model roots): in ComfyUI plugin mode these come
|
||||
from the ComfyUI host; in standalone mode they are the ONLY source of
|
||||
model library paths and are currently edited by hand in `settings.json`.
|
||||
- `extra_folder_paths` is a **ComfyUI-plugin-mode feature**: paths visible
|
||||
ONLY to LoRA Manager, not to ComfyUI. Its motivation is that a very large
|
||||
model library slows ComfyUI itself down, while LoRA Manager handles large
|
||||
libraries without performance issues — so users keep ComfyUI's library
|
||||
small and add the bulk via `extra_folder_paths`.
|
||||
|
||||
### Frontend UI Architecture
|
||||
|
||||
#### 1. LoRA Manager Web UI
|
||||
- Location: `./static/` (JS/CSS) and `./templates/` (HTML)
|
||||
- Tech: Vanilla JS + CSS, served by the hosting server (ComfyUI app in plugin mode, `standalone.py` in standalone mode)
|
||||
- Tests: `tests/frontend/**/*.test.js` (vitest + jsdom)
|
||||
|
||||
#### 2. ComfyUI Custom Node Widgets
|
||||
- Location: `./web/comfyui/` (Vanilla JS) + `./vue-widgets/` (Vue)
|
||||
- Primary styles: `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
|
||||
- Vue widgets: Vue 3 + TypeScript + PrimeVue + vue-i18n, e.g. `LoraPoolWidget`, `LoraRandomizerWidget`, `LoraCyclerWidget`, `AutocompleteTextWidget`
|
||||
- Vue builds to `./web/comfyui/vue-widgets/`; auto-built on ComfyUI startup via `py/vue_widget_builder.py`, typecheck via `vue-tsc`
|
||||
- Widget registration: `app.registerExtension()` and `getCustomWidgets` hooks; `node.addDOMWidget(...)` embeds HTML in LiteGraph nodes
|
||||
- See `docs/dom_widget_dev_guide.md` for the DOMWidget development guide
|
||||
|
||||
## Testing
|
||||
|
||||
### Backend (pytest)
|
||||
|
||||
- Config in `pytest.ini`: `--import-mode=importlib`, testpaths=`tests`
|
||||
- Fixtures in `tests/conftest.py` mock ComfyUI dependencies; use `tmp_path_factory` for isolation
|
||||
- Markers: `@pytest.mark.asyncio`, `@pytest.mark.no_settings_dir_isolation` (tests needing real settings paths)
|
||||
|
||||
### Frontend (vitest)
|
||||
|
||||
- Vanilla JS tests: `tests/frontend/**/*.test.js` with jsdom; setup in `tests/frontend/setup.js`
|
||||
- Vue widget tests: `vue-widgets/tests/**/*.test.ts` with jsdom + `@vue/test-utils`
|
||||
|
||||
### UI Verification (manual default)
|
||||
|
||||
UI/layout changes are verified by the user by eye — do NOT spin up a sandbox,
|
||||
standalone server, or browser automation to "prove" a visual fix. Ask the user to
|
||||
look instead. The full browser E2E ceremony (server + Chrome DevTools MCP +
|
||||
screenshots) is slow, token-heavy, and fragile; reserve it for genuine
|
||||
server+browser integration bugs, and only when the user explicitly agrees.
|
||||
|
||||
If a cross-layer issue ever needs a live server, the sandboxed helpers live in
|
||||
`scripts/e2e/` (`start_server.py`, `wait_for_server.py`). Non-negotiable rules:
|
||||
|
||||
- Always launch with `--settings-path <sandbox>/settings` and sandboxed
|
||||
`folder_paths` under `/tmp` — the repo folder is the real plugin folder and a
|
||||
`settings.json` there is read by the live instance. Never touch real config or
|
||||
real model libraries.
|
||||
- Never kill a process you did not start; `start_server.py` tracks its own PIDs
|
||||
via pidfile and refuses to touch unrelated processes on the port.
|
||||
- Abort after ~30 minutes or 3 consecutive tool failures; report `BLOCKED` with
|
||||
observed state instead of retrying blindly. Clean up sandbox and server after.
|
||||
|
||||
## Key Integration Points
|
||||
|
||||
- **Settings:** Stored in the user config directory (via `platformdirs`) or portable mode (`"use_portable_settings": true`)
|
||||
- **CivitAI/CivArchive:** API clients for metadata sync and model downloads; CivitAI API key stored in settings
|
||||
- **Symlinks:** Config scans symlinks to map virtual→physical paths; fingerprinting prevents redundant rescans
|
||||
- **WebSocket:** Broadcasts real-time progress for downloads, scans, and metadata sync
|
||||
- **Model scanning flow:** Walk folders → compute hashes → deduplicate → extract safetensors metadata → cache in SQLite → background CivitAI sync → WebSocket broadcast
|
||||
|
||||
## Important Notes
|
||||
|
||||
- ALWAYS use English for comments (per copilot-instructions.md)
|
||||
- **`settings.json.example` must stay minimal**: only `use_portable_settings`,
|
||||
`civitai_api_key`, and the four core `folder_paths` keys (`loras`,
|
||||
`checkpoints`, `unet`, `embeddings`). Do NOT add optional/default keys
|
||||
(model-category folders, `default_*_root`, `auto_organize_exclusions`, etc.)
|
||||
to this file unless the user explicitly asks for it. Defaults belong in
|
||||
`DEFAULT_SETTINGS` in `py/services/settings_manager.py`.
|
||||
- Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
|
||||
- Symlinks require normalized paths.
|
||||
**Business paths vs real paths**: All stored paths and operation routing use the
|
||||
original paths as they appear under configured model roots — symlinks are NOT
|
||||
resolved. `os.path.realpath` is only for scanner dedup and the symlink cache.
|
||||
Any path passed to `os.remove`/`os.rename`/`shutil.move` or validated by a
|
||||
containment check MUST use the business path (i.e. `os.path.abspath`, not
|
||||
`realpath`).
|
||||
+16
-113
@@ -1,124 +1,27 @@
|
||||
try: # pragma: no cover - import fallback for pytest collection
|
||||
from .py.lora_manager import LoraManager
|
||||
from .py.nodes.lora_loader import LoraLoaderLM, LoraTextLoaderLM
|
||||
from .py.nodes.checkpoint_loader import CheckpointLoaderLM
|
||||
from .py.nodes.unet_loader import UNETLoaderLM
|
||||
from .py.nodes.trigger_word_toggle import TriggerWordToggleLM
|
||||
from .py.nodes.prompt import PromptLM
|
||||
from .py.nodes.text import TextLM
|
||||
from .py.nodes.lora_stacker import LoraStackerLM
|
||||
from .py.nodes.lora_stack_combiner import LoraStackCombinerLM
|
||||
from .py.nodes.save_image import SaveImageLM
|
||||
from .py.nodes.debug_metadata import DebugMetadataLM
|
||||
from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
|
||||
from .py.nodes.wanvideo_lora_select_from_text import WanVideoLoraTextSelectLM
|
||||
from .py.nodes.lora_pool import LoraPoolLM
|
||||
from .py.nodes.lora_randomizer import LoraRandomizerLM
|
||||
from .py.nodes.lora_cycler import LoraCyclerLM
|
||||
from .py.nodes.lora_info import LoraInfoLM
|
||||
from .py.nodes.lora_syntax_to_path import LoraSyntaxToPath
|
||||
from .py.nodes.create_hook_lora import CreateHookLoraLM
|
||||
from .py.nodes.metadata_overwrite import MetadataOverwriteLM
|
||||
from .py.metadata_collector import init as init_metadata_collector
|
||||
except (
|
||||
ImportError
|
||||
): # pragma: no cover - allows running under pytest without package install
|
||||
import importlib
|
||||
import pathlib
|
||||
import sys
|
||||
|
||||
package_root = pathlib.Path(__file__).resolve().parent
|
||||
if str(package_root) not in sys.path:
|
||||
sys.path.append(str(package_root))
|
||||
|
||||
PromptLM = importlib.import_module("py.nodes.prompt").PromptLM
|
||||
TextLM = importlib.import_module("py.nodes.text").TextLM
|
||||
LoraManager = importlib.import_module("py.lora_manager").LoraManager
|
||||
LoraLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraLoaderLM
|
||||
LoraTextLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraTextLoaderLM
|
||||
CheckpointLoaderLM = importlib.import_module(
|
||||
"py.nodes.checkpoint_loader"
|
||||
).CheckpointLoaderLM
|
||||
UNETLoaderLM = importlib.import_module("py.nodes.unet_loader").UNETLoaderLM
|
||||
TriggerWordToggleLM = importlib.import_module(
|
||||
"py.nodes.trigger_word_toggle"
|
||||
).TriggerWordToggleLM
|
||||
LoraStackerLM = importlib.import_module("py.nodes.lora_stacker").LoraStackerLM
|
||||
LoraStackCombinerLM = importlib.import_module(
|
||||
"py.nodes.lora_stack_combiner"
|
||||
).LoraStackCombinerLM
|
||||
SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
|
||||
DebugMetadataLM = importlib.import_module("py.nodes.debug_metadata").DebugMetadataLM
|
||||
WanVideoLoraSelectLM = importlib.import_module(
|
||||
"py.nodes.wanvideo_lora_select"
|
||||
).WanVideoLoraSelectLM
|
||||
WanVideoLoraTextSelectLM = importlib.import_module(
|
||||
"py.nodes.wanvideo_lora_select_from_text"
|
||||
).WanVideoLoraTextSelectLM
|
||||
LoraPoolLM = importlib.import_module("py.nodes.lora_pool").LoraPoolLM
|
||||
LoraRandomizerLM = importlib.import_module(
|
||||
"py.nodes.lora_randomizer"
|
||||
).LoraRandomizerLM
|
||||
LoraCyclerLM = importlib.import_module("py.nodes.lora_cycler").LoraCyclerLM
|
||||
LoraInfoLM = importlib.import_module("py.nodes.lora_info").LoraInfoLM
|
||||
LoraSyntaxToPath = importlib.import_module(
|
||||
"py.nodes.lora_syntax_to_path"
|
||||
).LoraSyntaxToPath
|
||||
CreateHookLoraLM = importlib.import_module(
|
||||
"py.nodes.create_hook_lora"
|
||||
).CreateHookLoraLM
|
||||
MetadataOverwriteLM = importlib.import_module(
|
||||
"py.nodes.metadata_overwrite"
|
||||
).MetadataOverwriteLM
|
||||
init_metadata_collector = importlib.import_module("py.metadata_collector").init
|
||||
from .py.lora_manager import LoraManager
|
||||
from .py.nodes.lora_loader import LoraManagerLoader
|
||||
from .py.nodes.trigger_word_toggle import TriggerWordToggle
|
||||
from .py.nodes.lora_stacker import LoraStacker
|
||||
from .py.nodes.save_image import SaveImage
|
||||
from .py.nodes.debug_metadata import DebugMetadata
|
||||
from .py.nodes.wanvideo_lora_select import WanVideoLoraSelect
|
||||
# Import metadata collector to install hooks on startup
|
||||
from .py.metadata_collector import init as init_metadata_collector
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
PromptLM.NAME: PromptLM,
|
||||
TextLM.NAME: TextLM,
|
||||
LoraLoaderLM.NAME: LoraLoaderLM,
|
||||
LoraTextLoaderLM.NAME: LoraTextLoaderLM,
|
||||
CheckpointLoaderLM.NAME: CheckpointLoaderLM,
|
||||
UNETLoaderLM.NAME: UNETLoaderLM,
|
||||
TriggerWordToggleLM.NAME: TriggerWordToggleLM,
|
||||
LoraStackerLM.NAME: LoraStackerLM,
|
||||
LoraStackCombinerLM.NAME: LoraStackCombinerLM,
|
||||
SaveImageLM.NAME: SaveImageLM,
|
||||
DebugMetadataLM.NAME: DebugMetadataLM,
|
||||
WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,
|
||||
WanVideoLoraTextSelectLM.NAME: WanVideoLoraTextSelectLM,
|
||||
LoraPoolLM.NAME: LoraPoolLM,
|
||||
LoraRandomizerLM.NAME: LoraRandomizerLM,
|
||||
LoraCyclerLM.NAME: LoraCyclerLM,
|
||||
LoraInfoLM.NAME: LoraInfoLM,
|
||||
LoraSyntaxToPath.NAME: LoraSyntaxToPath,
|
||||
CreateHookLoraLM.NAME: CreateHookLoraLM,
|
||||
MetadataOverwriteLM.NAME: MetadataOverwriteLM,
|
||||
LoraManagerLoader.NAME: LoraManagerLoader,
|
||||
TriggerWordToggle.NAME: TriggerWordToggle,
|
||||
LoraStacker.NAME: LoraStacker,
|
||||
SaveImage.NAME: SaveImage,
|
||||
DebugMetadata.NAME: DebugMetadata,
|
||||
WanVideoLoraSelect.NAME: WanVideoLoraSelect
|
||||
}
|
||||
|
||||
WEB_DIRECTORY = "./web/comfyui"
|
||||
|
||||
# Check and build Vue widgets if needed (development mode)
|
||||
try:
|
||||
from .py.vue_widget_builder import check_and_build_vue_widgets
|
||||
|
||||
# Auto-build in development, warn only if fails
|
||||
check_and_build_vue_widgets(auto_build=True, warn_only=True)
|
||||
except ImportError:
|
||||
# Fallback for pytest
|
||||
import importlib
|
||||
|
||||
check_and_build_vue_widgets = importlib.import_module(
|
||||
"py.vue_widget_builder"
|
||||
).check_and_build_vue_widgets
|
||||
check_and_build_vue_widgets(auto_build=True, warn_only=True)
|
||||
except Exception as e:
|
||||
import logging
|
||||
|
||||
logging.warning(f"[LoRA Manager] Vue widget build check skipped: {e}")
|
||||
|
||||
# Initialize metadata collector
|
||||
init_metadata_collector()
|
||||
|
||||
# Register routes on import
|
||||
LoraManager.add_routes()
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "WEB_DIRECTORY"]
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']
|
||||
|
||||
@@ -1,972 +0,0 @@
|
||||
{
|
||||
"specialThanks": [
|
||||
"dispenser",
|
||||
"EbonEagle",
|
||||
"DanielMagPizza",
|
||||
"Scott R"
|
||||
],
|
||||
"allSupporters": [
|
||||
"Takkan",
|
||||
"megakirbs",
|
||||
"Brennok",
|
||||
"2018cfh",
|
||||
"Rob Williams",
|
||||
"Charles Blakemore",
|
||||
"Arlecchino Shion",
|
||||
"Insomnia Art Designs",
|
||||
"Mozzel",
|
||||
"Gingko Biloba",
|
||||
"stone9k",
|
||||
"Kiba",
|
||||
"onesecondinosaur",
|
||||
"Skalabananen",
|
||||
"Sterilized",
|
||||
"Polymorphic Indeterminate",
|
||||
"Liam MacDougal",
|
||||
"Christian Byrne",
|
||||
"DM",
|
||||
"Sen314",
|
||||
"Estragon",
|
||||
"Rosenthal",
|
||||
"ClockDaemon",
|
||||
"Francisco Tatis",
|
||||
"Tobi_Swagg",
|
||||
"SG",
|
||||
"jmack",
|
||||
"Andrew Wilson",
|
||||
"Greybush",
|
||||
"Ricky Carter",
|
||||
"JongWon Han",
|
||||
"VantAI",
|
||||
"レプサイ",
|
||||
"Michael Wong",
|
||||
"Illrigger",
|
||||
"Tom Corrigan",
|
||||
"JackieWang",
|
||||
"FreelancerZ",
|
||||
"fnkylove",
|
||||
"Lilleman",
|
||||
"Robert Stacey",
|
||||
"PM",
|
||||
"Marc Whiffen",
|
||||
"Dogwalkerbr",
|
||||
"Birdy",
|
||||
"quarz",
|
||||
"$MetaSamsara",
|
||||
"jean jahren",
|
||||
"Reno Lam",
|
||||
"Aleksander Wujczyk",
|
||||
"AM Kuro",
|
||||
"JSST",
|
||||
"sig",
|
||||
"J\\B/ 8r0wns0n",
|
||||
"Snaggwort",
|
||||
"Anthony+Rizzo",
|
||||
"W+K+White",
|
||||
"Baekdoosixt",
|
||||
"Jonathan Ross",
|
||||
"KD",
|
||||
"Omnidex",
|
||||
"Nolife_M",
|
||||
"Melville Parrish",
|
||||
"daniel dove",
|
||||
"Lustre",
|
||||
"Tyler Trebuchon",
|
||||
"Release Cabrakan",
|
||||
"JW Sin",
|
||||
"Alex",
|
||||
"carozzz",
|
||||
"Marlon Daniels",
|
||||
"James Dooley",
|
||||
"zenbound",
|
||||
"Buzzard",
|
||||
"Adam Shaw",
|
||||
"Mark Corneglio",
|
||||
"RedrockVP",
|
||||
"James Todd",
|
||||
"Wicked Choices by ASLPro3D",
|
||||
"FinalyFree",
|
||||
"Fyf",
|
||||
"Timmy",
|
||||
"Johnny",
|
||||
"Tak",
|
||||
"Lisster",
|
||||
"Big Red",
|
||||
"whudunit",
|
||||
"Luc Job",
|
||||
"corde",
|
||||
"Yushio",
|
||||
"Vik71it",
|
||||
"Bishoujoker",
|
||||
"Echo",
|
||||
"Todd Keck",
|
||||
"Briton Heilbrun",
|
||||
"wildnut",
|
||||
"Edgar Tejeda",
|
||||
"BadassArabianMofo",
|
||||
"MiraiKuriyamaSy",
|
||||
"Pascal Dahle",
|
||||
"Greg",
|
||||
"Akira HentAI",
|
||||
"otaku fra",
|
||||
"lmsupporter",
|
||||
"andrew.tappan",
|
||||
"wackop",
|
||||
"Phil",
|
||||
"Greenmoustache",
|
||||
"Carl G.",
|
||||
"wfpearl",
|
||||
"jeaness",
|
||||
"Dsperado",
|
||||
"Jack B Nimble",
|
||||
"bh",
|
||||
"Jwk0205",
|
||||
"Starkselle",
|
||||
"Olive",
|
||||
"Aaron Bleuer",
|
||||
"LacesOut!",
|
||||
"greebles",
|
||||
"SarcasticHashtag",
|
||||
"Some Guy Named Barry",
|
||||
"M Postkasse",
|
||||
"Jacob Hoehler",
|
||||
"Matt Wenzel",
|
||||
"Weasyl",
|
||||
"Lex Song",
|
||||
"Cory Paza",
|
||||
"Gonzalo Andre Allendes Lopez",
|
||||
"Serge Bekenkamp",
|
||||
"AIJimmy",
|
||||
"Philip Hempel",
|
||||
"dan",
|
||||
"aai",
|
||||
"Ran C",
|
||||
"ViperC",
|
||||
"itismyelement",
|
||||
"Sangheili460",
|
||||
"MagnaInsomnia",
|
||||
"Karl P.",
|
||||
"LarsesFPC",
|
||||
"Weird_With_A_Beard",
|
||||
"N/A",
|
||||
"The Spawn",
|
||||
"graysock",
|
||||
"Pozadine1",
|
||||
"Qarob",
|
||||
"AIGooner",
|
||||
"Luc",
|
||||
"ProtonPrince",
|
||||
"DiffDuck",
|
||||
"fancypants",
|
||||
"John+Edwards",
|
||||
"Joboshy",
|
||||
"Digital",
|
||||
"JaxMax",
|
||||
"Bohemian Corporal",
|
||||
"Dan",
|
||||
"Bro Xie",
|
||||
"seed123_AIart",
|
||||
"batblue",
|
||||
"carey6409",
|
||||
"太郎 ゲーム",
|
||||
"Roslynd",
|
||||
"jinxedx",
|
||||
"AELOX",
|
||||
"Dankin-Pics",
|
||||
"Nicfit23",
|
||||
"Cristian Vazquez",
|
||||
"wamekukyouzin",
|
||||
"drum matthieu",
|
||||
"DogmaR34",
|
||||
"Frank Nitty",
|
||||
"The Magic Noob",
|
||||
"Christopher Michel",
|
||||
"runte3221",
|
||||
"DougPeterson",
|
||||
"LeoZero",
|
||||
"dl0901dm",
|
||||
"Antonio Pontes",
|
||||
"Bruce",
|
||||
"kushiroK9",
|
||||
"Kevin John Duck",
|
||||
"Dustin Chen",
|
||||
"Blackfish95",
|
||||
"Tori",
|
||||
"Mouthlessman",
|
||||
"Paul Kroll",
|
||||
"Fraser Cross",
|
||||
"Bas Imagineer",
|
||||
"John Statham",
|
||||
"Dušan Ryban",
|
||||
"Adam Taylor",
|
||||
"decoy",
|
||||
"elu3199",
|
||||
"Hasturkun",
|
||||
"Jon Sandman",
|
||||
"Ubivis",
|
||||
"zounic",
|
||||
"CloudValley",
|
||||
"thesoftwaredruid",
|
||||
"wundershark",
|
||||
"mr_dinosaur",
|
||||
"Tyrswood",
|
||||
"Ray Wing",
|
||||
"Ranzitho",
|
||||
"Gus",
|
||||
"MJG",
|
||||
"linnfrey",
|
||||
"griffin+dahlberg",
|
||||
"ElitaSSJ4",
|
||||
"Matt+J",
|
||||
"Josef Lanzl",
|
||||
"New folder (1)",
|
||||
"sanborondon",
|
||||
"Error_Rule34_Not_found",
|
||||
"jcay015",
|
||||
"Erik Lopez",
|
||||
"Mateo Curić",
|
||||
"Geolog",
|
||||
"Neco28",
|
||||
"Eris3D",
|
||||
"Resist's Creations - Spicy Edition 🔥",
|
||||
"David Ortega",
|
||||
"Wolffen",
|
||||
"a _",
|
||||
"Jeff",
|
||||
"nwalker94",
|
||||
"James Coleman",
|
||||
"Kevin Christopher",
|
||||
"Chad Idk",
|
||||
"dd",
|
||||
"Sam",
|
||||
"sjon kreutz",
|
||||
"Metryman55",
|
||||
"AlexDuKaNa",
|
||||
"ae",
|
||||
"Tr4shP4nda",
|
||||
"Gamalonia",
|
||||
"capn",
|
||||
"Joseph",
|
||||
"Mirko Katzula",
|
||||
"dan",
|
||||
"Piccio08",
|
||||
"kumakichi",
|
||||
"cppbel",
|
||||
"Moon Knight",
|
||||
"Kland",
|
||||
"Hailshem",
|
||||
"Naomi Hale Danchi",
|
||||
"epicgamer0020690",
|
||||
"Joshua Porrata",
|
||||
"Andrew",
|
||||
"Brian M",
|
||||
"Robert Wegemund",
|
||||
"Littlehuggy",
|
||||
"Brian Buie",
|
||||
"Thought2Form",
|
||||
"RAIDiation",
|
||||
"Sadlip",
|
||||
"Gooohokrbe",
|
||||
"m",
|
||||
"OldBones",
|
||||
"Pierce McBride",
|
||||
"Zach Gonser",
|
||||
"Mikko Hemilä",
|
||||
"Jacob McDaniel",
|
||||
"Jamie Ogletree",
|
||||
"Temikus",
|
||||
"Artokun",
|
||||
"Michael Taylor",
|
||||
"Martial",
|
||||
"Emil Andersson",
|
||||
"Ouro Boros",
|
||||
"Atilla Berke Pekduyar",
|
||||
"Decx _",
|
||||
"Yuji Kaneko",
|
||||
"Rops Alot",
|
||||
"Penfore",
|
||||
"Gordon Cole",
|
||||
"Ace Ventura",
|
||||
"AbstractAss",
|
||||
"David LaVallee",
|
||||
"ken",
|
||||
"Crocket",
|
||||
"keemun",
|
||||
"SuBu",
|
||||
"RedPIXel",
|
||||
"Wind",
|
||||
"Jackthemind",
|
||||
"Nexus",
|
||||
"Ramneek“Guy”Ashok",
|
||||
"squid_actually",
|
||||
"Nat_20",
|
||||
"Edward Weeks",
|
||||
"kyoumei",
|
||||
"RadStorm04",
|
||||
"JohnDoe42054",
|
||||
"BillyHill",
|
||||
"emyth",
|
||||
"KitKatM",
|
||||
"socrasteeze",
|
||||
"MudkipMedkitz",
|
||||
"deanbrian",
|
||||
"Alex Wortman",
|
||||
"Cody",
|
||||
"emadsultan",
|
||||
"InformedViewz",
|
||||
"Bubbafett",
|
||||
"leaf",
|
||||
"Adam Rinehart",
|
||||
"gzmzmvp",
|
||||
"takyamtom",
|
||||
"Aberr",
|
||||
"Gregory Kozhemiak",
|
||||
"aezin",
|
||||
"Eric Whitney",
|
||||
"Joey Callahan",
|
||||
"Ivan Tadic",
|
||||
"Mike Simone",
|
||||
"FloPro4Sho",
|
||||
"John J Linehan",
|
||||
"Elliot E",
|
||||
"Morgandel",
|
||||
"Theerat Jiramate",
|
||||
"X",
|
||||
"SloanSteddyAI",
|
||||
"Steven Owens",
|
||||
"hexxish",
|
||||
"Derek Baker",
|
||||
"NICHOLAS BAXLEY",
|
||||
"Ed Wang",
|
||||
"Saya",
|
||||
"Xeeosat",
|
||||
"yuxz69",
|
||||
"四糸凜音",
|
||||
"esthe",
|
||||
"FrxzenSnxw",
|
||||
"chriphost",
|
||||
"ResidentDeviant",
|
||||
"Ginnie",
|
||||
"Skyfire83",
|
||||
"Pitpe11",
|
||||
"IamAyam",
|
||||
"TheD1rtyD03",
|
||||
"moonpetal",
|
||||
"g9p0o",
|
||||
"Pkrsky",
|
||||
"TheHolySheep",
|
||||
"Monte Won",
|
||||
"SpringBootisTrash",
|
||||
"carsten",
|
||||
"ikok",
|
||||
"quantenmecha",
|
||||
"Jason+Nash",
|
||||
"DarkRoast",
|
||||
"letzte",
|
||||
"Nasty+Hobbit",
|
||||
"Sora+Yori",
|
||||
"Duk3+Rand0m",
|
||||
"Nathen+Choi",
|
||||
"T",
|
||||
"D",
|
||||
"David Schenck",
|
||||
"Wolfe7D1",
|
||||
"Andrew Marshall",
|
||||
"Taylor Funk",
|
||||
"elleshar666",
|
||||
"Gerald Welly",
|
||||
"Tee Gee",
|
||||
"ACTUALLY_the_Real_Willem_Dafoe",
|
||||
"Михал Михалыч",
|
||||
"tarek helmi",
|
||||
"Kauffy",
|
||||
"Max Marklund",
|
||||
"Joshua Gray",
|
||||
"Edward Kennedy",
|
||||
"Nick Kage",
|
||||
"Vane Holzer",
|
||||
"psytrax",
|
||||
"Cyrus Fett",
|
||||
"lh qwe",
|
||||
"conner",
|
||||
"Xenon Xue",
|
||||
"Michael Anthony Scott",
|
||||
"notedfakes",
|
||||
"Princess Bright Eyes",
|
||||
"Michael Scott",
|
||||
"Solixer",
|
||||
"Jimmy Borup",
|
||||
"Wes Sims",
|
||||
"Donor4115",
|
||||
"Filippo Ferrari",
|
||||
"Douglas Gaspar",
|
||||
"George",
|
||||
"dw",
|
||||
"WRL_SPR",
|
||||
"momokai",
|
||||
"몽타주",
|
||||
"kudari",
|
||||
"Whitepinetrader",
|
||||
"OrganicArtifact",
|
||||
"Raku",
|
||||
"CHKeeho80",
|
||||
"nanana",
|
||||
"Alex",
|
||||
"Karru",
|
||||
"ChaChanoKo",
|
||||
"ghoulars",
|
||||
"null",
|
||||
"Beau",
|
||||
"redcarrot",
|
||||
"powerbot99",
|
||||
"Fthehappy",
|
||||
"J",
|
||||
"Jeff+Kesemeyer",
|
||||
"Alan+Cano",
|
||||
"FeralOpticsAI",
|
||||
"Pavlaki",
|
||||
"Doug+Rintoul",
|
||||
"Noor",
|
||||
"Yorunai",
|
||||
"Richard",
|
||||
"奚明 刘",
|
||||
"준희 김",
|
||||
"りん あめ",
|
||||
"Matt",
|
||||
"Tomohiro Baba",
|
||||
"Noora",
|
||||
"Frogmilk",
|
||||
"SPJ",
|
||||
"Kor",
|
||||
"Bryan Rutkowski",
|
||||
"Noah",
|
||||
"TenaciousD",
|
||||
"Dmitry Ryzhov",
|
||||
"DarkSunset",
|
||||
"Edward Ten Eyck",
|
||||
"Steam Steam",
|
||||
"CryptoTraderJK",
|
||||
"Davaitamin",
|
||||
"Pete Pain",
|
||||
"Nathan",
|
||||
"tedcor",
|
||||
"RHopkirk",
|
||||
"jinksta187",
|
||||
"Fotek Design",
|
||||
"Maxim",
|
||||
"Manu Thetug",
|
||||
"Lyavph",
|
||||
"Nihongasuki",
|
||||
"MadSpin",
|
||||
"inbijiburu",
|
||||
"Nick “Loadstone” D",
|
||||
"地獄の禄",
|
||||
"starbugx",
|
||||
"dc7431",
|
||||
"Inversity",
|
||||
"Vir",
|
||||
"Sildoren",
|
||||
"Darv",
|
||||
"Seon+Song",
|
||||
"2turbo",
|
||||
"Dmitry+Viznesenskiy",
|
||||
"tanjin90",
|
||||
"sternenkrieger",
|
||||
"Pascalou",
|
||||
"Patrick+Bryan",
|
||||
"lighthawke",
|
||||
"low9",
|
||||
"Winged",
|
||||
"YassineKhaled",
|
||||
"Y",
|
||||
"MatteKey",
|
||||
"Flob",
|
||||
"ShiroSenpai",
|
||||
"Inkognito",
|
||||
"Gumbyte",
|
||||
"Tan+Huynh",
|
||||
"Bob+Barker",
|
||||
"Dark_Pest",
|
||||
"Eldithor",
|
||||
"Ko-fi+Supporter",
|
||||
"lrdchs2",
|
||||
"Tú Nguyễn Lý Hoàng",
|
||||
"shira1011",
|
||||
"Kalli Core",
|
||||
"Ben D",
|
||||
"Draven T",
|
||||
"marioandluigi",
|
||||
"G",
|
||||
"Ronan Delevacq",
|
||||
"Leslie Andrew Ridings",
|
||||
"Aquatic Coffee",
|
||||
"Dave Abraham",
|
||||
"Joaquin Hierrezuelo",
|
||||
"Locrospiel",
|
||||
"StudOx Tech",
|
||||
"yves.poezevara",
|
||||
"Jarrid Lee",
|
||||
"Poophead27 Blyat",
|
||||
"Joseph Hanson",
|
||||
"John Rednoulf",
|
||||
"Focuschannel",
|
||||
"Boba Smith",
|
||||
"matt",
|
||||
"somethingtosay8",
|
||||
"ivistorm",
|
||||
"Anthony Faxlandez",
|
||||
"Sauv",
|
||||
"Ted Cart",
|
||||
"Sage Himeros",
|
||||
"Zeeble",
|
||||
"Pat Hen",
|
||||
"Draconach",
|
||||
"Tigon",
|
||||
"ItsGeneralButtNaked",
|
||||
"Jordan Shaw",
|
||||
"g unit",
|
||||
"Dkom22",
|
||||
"Marcos Tortosa Carmona",
|
||||
"Distortik",
|
||||
"JC",
|
||||
"Prompt Pirate",
|
||||
"uwutismxd",
|
||||
"Marcus thronico",
|
||||
"zenobeus",
|
||||
"ryoma",
|
||||
"dg",
|
||||
"Stryker",
|
||||
"smart.edge5178",
|
||||
"Menard",
|
||||
"SomeDude",
|
||||
"raf8osz",
|
||||
"Gold_miner_ego",
|
||||
"bakeliteboy",
|
||||
"TequiTequi",
|
||||
"Homero+Banda",
|
||||
"Nick",
|
||||
"Monix",
|
||||
"Trolinka",
|
||||
"PredragR",
|
||||
"Clauzmak",
|
||||
"Nerick",
|
||||
"SundayRage",
|
||||
"matter",
|
||||
"SRCRCOSS",
|
||||
"imer",
|
||||
"Akkas+Haque",
|
||||
"AZ+Party+Oasis",
|
||||
"Alex+Zaw",
|
||||
"Kachac",
|
||||
"Kevin+Isom",
|
||||
"Rune+Osnes",
|
||||
"PoorStudent",
|
||||
"vinter",
|
||||
"Supporter",
|
||||
"Mobius2020",
|
||||
"ExLightSaber",
|
||||
"YaboiRay",
|
||||
"boston666",
|
||||
"cocona",
|
||||
"Obsidian.Studios",
|
||||
"Zomba Mann",
|
||||
"Aquaneo",
|
||||
"blikkies",
|
||||
"JBsuede",
|
||||
"Wolf and Fox Legends",
|
||||
"ゼクス、六",
|
||||
"Neko Desco",
|
||||
"Vinarus",
|
||||
"Josh Snyder",
|
||||
"Shock Shockor",
|
||||
"Goldwaters",
|
||||
"Zude",
|
||||
"Room Light",
|
||||
"Kyler",
|
||||
"Justin Blaylock",
|
||||
"aRtFuL_DodGeR",
|
||||
"Snorklebort",
|
||||
"TheFusion",
|
||||
"MR.Bear",
|
||||
"3zS4QNQ4",
|
||||
"Terminuz",
|
||||
"Matt M.",
|
||||
"Ivan Imes",
|
||||
"J M",
|
||||
"Steven",
|
||||
"Borte",
|
||||
"yyuvuvu",
|
||||
"Billy Gladky",
|
||||
"Nomki",
|
||||
"Probis",
|
||||
"Jack Lawfield",
|
||||
"SkibidiRizzler",
|
||||
"Maxon - Plans",
|
||||
"Kalle Björk",
|
||||
"Karlanx",
|
||||
"operationancut",
|
||||
"Nacho Ferrando",
|
||||
"Youguang",
|
||||
"andrewzpong",
|
||||
"BossGame",
|
||||
"lrdchs",
|
||||
"Tree Tagger",
|
||||
"Janik",
|
||||
"AIVORY3D",
|
||||
"Kevinj",
|
||||
"Mitchell Robson",
|
||||
"POPPIN",
|
||||
"meatyalien",
|
||||
"Tony+V",
|
||||
"draganjankovic1975dj528",
|
||||
"kinz",
|
||||
"YoruHime",
|
||||
"Mark+Staaf",
|
||||
"Michael+Fürmann",
|
||||
"Aether",
|
||||
"JACKY",
|
||||
"Otokomyouri+",
|
||||
"d",
|
||||
"Jasper",
|
||||
"megameganck",
|
||||
"thomasand01",
|
||||
"Shiba+Sama",
|
||||
"Celestial+Kitten",
|
||||
"IshouI;_;",
|
||||
"SAVEagleBasement",
|
||||
"Adam+Spreer",
|
||||
"BillyBoy84",
|
||||
"Buecyb99",
|
||||
"Welkor",
|
||||
"dubious1one",
|
||||
"Brandon Thomas",
|
||||
"Dustin Hendel",
|
||||
"moranqianlong",
|
||||
"Liberation",
|
||||
"Ninja Tom",
|
||||
"75marc",
|
||||
"Elemnt",
|
||||
"Bradley Turner",
|
||||
"swra",
|
||||
"JollRodrigo",
|
||||
"Oliverfish",
|
||||
"uruksayshi",
|
||||
"Patryk Serious",
|
||||
"nk8",
|
||||
"Kyron Mahan",
|
||||
"Mythspire",
|
||||
"Nimhloth",
|
||||
"TBitz33",
|
||||
"Anonym dkjglfleeoeldldldlkf",
|
||||
"Tsani Prodanov",
|
||||
"Ezokewn",
|
||||
"SendingRavens",
|
||||
"Slacks",
|
||||
"Glenn Hoetker",
|
||||
"JackJohnnyJim",
|
||||
"Khánh Đặng",
|
||||
"Michael Hicks",
|
||||
"Homero Banda",
|
||||
"Michael Docherty",
|
||||
"MadGod",
|
||||
"GhostyGhost",
|
||||
"Paul Hartsuyker",
|
||||
"elitassj",
|
||||
"Never_M",
|
||||
"Jacob Winter",
|
||||
"Rudeff VonRod",
|
||||
"Andrew Wilkinson",
|
||||
"David",
|
||||
"floeki75pad",
|
||||
"TheJohnes",
|
||||
"deadwishd",
|
||||
"shinonomeiro",
|
||||
"Snille",
|
||||
"MaartenAlbers",
|
||||
"khanh duy",
|
||||
"xybrightsummer",
|
||||
"jreedatchison",
|
||||
"PhilW",
|
||||
"Cruel",
|
||||
"MRBlack",
|
||||
"Kiyoe",
|
||||
"humptynutz",
|
||||
"michael.isaza",
|
||||
"Kalnei",
|
||||
"Scott",
|
||||
"Muratoraccio",
|
||||
"D",
|
||||
"Daevalus",
|
||||
"Milky+Mai",
|
||||
"Krash",
|
||||
"PP",
|
||||
"thababydjac",
|
||||
"belligerencebk",
|
||||
"tortor",
|
||||
"Peter",
|
||||
"T",
|
||||
"zipzorpp",
|
||||
"Anton",
|
||||
"actual",
|
||||
"eddy",
|
||||
"Neko1967",
|
||||
"sniff",
|
||||
"AIBot",
|
||||
"Darren+Brown",
|
||||
"Nick",
|
||||
"kluu324",
|
||||
"echo",
|
||||
"MackeMan",
|
||||
"conkisdonkis",
|
||||
"badnews",
|
||||
"Lorabitch",
|
||||
"21omen",
|
||||
"NopeNahGoodTy",
|
||||
"Brandon+G",
|
||||
"plonk",
|
||||
"Anvil+Girl",
|
||||
"Kotetsu",
|
||||
"miduzza",
|
||||
"Somebody",
|
||||
"てぃんてぃんひーろー",
|
||||
"you+halo9",
|
||||
"cloudghost",
|
||||
"Yongkwan+Lee",
|
||||
"lucites",
|
||||
"nickname",
|
||||
"eriick",
|
||||
"Lev+Lanevskiy",
|
||||
"Jacky+Ho",
|
||||
"generic404",
|
||||
"abattoirblues",
|
||||
"zounik",
|
||||
"4IXplr0r3r",
|
||||
"hayden",
|
||||
"ahoystan",
|
||||
"Civitaier",
|
||||
"BakunyuuWaifu",
|
||||
"edk",
|
||||
"Joey Leto",
|
||||
"Anagra Nouma",
|
||||
"tafapayo",
|
||||
"ja s",
|
||||
"Doug Mason",
|
||||
"scoreswazey",
|
||||
"Beuhwtf",
|
||||
"Owen Gwosdz",
|
||||
"GJT",
|
||||
"Manuel Reyes",
|
||||
"FinoRulez",
|
||||
"CHEL_C",
|
||||
"Gentle Sartori",
|
||||
"Caleb Larson",
|
||||
"David Murcko",
|
||||
"Justin Defer",
|
||||
"Ben Brogger",
|
||||
"Jack Dole",
|
||||
"dsffsdfsdfsdfsdfsdf",
|
||||
"V Bj",
|
||||
"Rj Joplin",
|
||||
"Kurt",
|
||||
"max blo",
|
||||
"Myrthrac",
|
||||
"Taylor Dominy",
|
||||
"Faith",
|
||||
"Bouya shaka",
|
||||
"Maso",
|
||||
"Kevin Wallace",
|
||||
"ChicRic",
|
||||
"Bastard-Sama",
|
||||
"mercur",
|
||||
"Sunny",
|
||||
"Somebody",
|
||||
"inusanorthcape",
|
||||
"Kane Sturzebecher",
|
||||
"Yavizu3d",
|
||||
"Yves Poezevara",
|
||||
"Teriak47",
|
||||
"Digitalzombie",
|
||||
"Just me",
|
||||
"Evgeniya Smolentseva",
|
||||
"Raf Stahelin",
|
||||
"Вячеслав Маринин",
|
||||
"Cola Matthew",
|
||||
"OniNoKen",
|
||||
"Iain Wisely",
|
||||
"Zertens",
|
||||
"NOHOW",
|
||||
"Apo",
|
||||
"nekotxt",
|
||||
"choowkee",
|
||||
"Clusters",
|
||||
"ibrahim",
|
||||
"Highlandrise",
|
||||
"philcoraz",
|
||||
"mztn",
|
||||
"ImagineerNL",
|
||||
"MrAcrtosSursus",
|
||||
"al300680",
|
||||
"pixl",
|
||||
"Robin",
|
||||
"chahknoir",
|
||||
"nd",
|
||||
"keno94d",
|
||||
"James Melzer",
|
||||
"Bartleby",
|
||||
"Renvertere",
|
||||
"Rahuy",
|
||||
"Hermann003",
|
||||
"D",
|
||||
"Foolish",
|
||||
"RevyHiep",
|
||||
"Captain_Swag",
|
||||
"obkircher",
|
||||
"gwyar",
|
||||
"ResidentDeviant",
|
||||
"D",
|
||||
"edgecase",
|
||||
"Neoxena",
|
||||
"mrmhalo",
|
||||
"Maarten Harms",
|
||||
"Israel",
|
||||
"SelfishMedic",
|
||||
"adderleighn",
|
||||
"EnragedAntelope",
|
||||
"mcmalt",
|
||||
"cesasol",
|
||||
"Null",
|
||||
"fdfac",
|
||||
"Eli",
|
||||
"Somebody",
|
||||
"8/4",
|
||||
"ivan.morgado.siles",
|
||||
"SEI",
|
||||
"Lars+Christian+Nygård",
|
||||
"미킹+말",
|
||||
"Cmissi",
|
||||
"heycloudy",
|
||||
"BlackVortex",
|
||||
"gdfgfdgfds",
|
||||
"Benjamin+Doerr",
|
||||
"D",
|
||||
"Cryphius",
|
||||
"Connor+Hall",
|
||||
"Macho+Grump",
|
||||
"Morcoddd",
|
||||
"fazefour33",
|
||||
"MrSEIGE88",
|
||||
"yarsev",
|
||||
"Somebody",
|
||||
"KB",
|
||||
"shw",
|
||||
"Jim",
|
||||
"JoL",
|
||||
"Srdb",
|
||||
"jcx29",
|
||||
"Drizzly",
|
||||
"Nebuleux",
|
||||
"Join+Chun",
|
||||
"GDS+DEV",
|
||||
"4rt+r3d",
|
||||
"Somebody",
|
||||
"Somebody",
|
||||
"Crescent~San",
|
||||
"AiGirlTS",
|
||||
"datasl4ve",
|
||||
"Somebody",
|
||||
"koopa990",
|
||||
"The+Forgetful+Dev",
|
||||
"Mateusz+Kosela",
|
||||
"Bula",
|
||||
"KUJYAKU",
|
||||
"Coeur+de+cochon",
|
||||
"han b",
|
||||
"Mysil",
|
||||
"Nico",
|
||||
"Maximilian Krischan",
|
||||
"socialcat",
|
||||
"Tim",
|
||||
"proto merp",
|
||||
"_ G3n",
|
||||
"Donovan Jenkins",
|
||||
"Hans Meier",
|
||||
"jboul",
|
||||
"Michael Eid",
|
||||
"Super Sigma Reborne",
|
||||
"Veloce",
|
||||
"Bob barker",
|
||||
"Michael Rivera",
|
||||
"karim ben brik",
|
||||
"Vincent",
|
||||
"Michael Zhu",
|
||||
"Nemisu",
|
||||
"Seraphy",
|
||||
"雨の心 落",
|
||||
"AllTimeNoobie",
|
||||
"jumpd",
|
||||
"John C",
|
||||
"beltaloth",
|
||||
"Rim",
|
||||
"yfx507",
|
||||
"Jairus Knudsen",
|
||||
"Xan Dionysus",
|
||||
"Mario Cano",
|
||||
"Nathan lee",
|
||||
"lylepaul",
|
||||
"Xae Phiel",
|
||||
"DafmanD2",
|
||||
"Middo",
|
||||
"Smokey Jesus",
|
||||
"Gary Chaboya",
|
||||
"forbiddenatelierofficial",
|
||||
"Thomas Sankowski",
|
||||
"ThreadingReality",
|
||||
"DrB",
|
||||
"wknight",
|
||||
"Moneymaker412K",
|
||||
"Jacid",
|
||||
"unkeiknown",
|
||||
"Towelie",
|
||||
"Alex Ross",
|
||||
"Jean-françois SEMA",
|
||||
"Tomi Arola",
|
||||
"fal",
|
||||
"Andrew Ly",
|
||||
"john Greene",
|
||||
"jimyjomson",
|
||||
"JaeHyun Jang",
|
||||
"sbone",
|
||||
"BigBoss",
|
||||
"Chase Kwon",
|
||||
"Bob Ling",
|
||||
"Inyoshu",
|
||||
"nick Meadows",
|
||||
"Chad Barnes",
|
||||
"redlines3",
|
||||
"Adam Gardner",
|
||||
"Cjarasa",
|
||||
"James Ming",
|
||||
"vanditking",
|
||||
"kripitonga",
|
||||
"Rizzi",
|
||||
"nimin",
|
||||
"OMAR LUCIANO",
|
||||
"Somebody",
|
||||
"Somebody",
|
||||
"Somebody",
|
||||
"Somebody",
|
||||
"Somebody",
|
||||
"CoffeeMage",
|
||||
"Ken+Suzuki",
|
||||
"hannibal",
|
||||
"Jo+Example",
|
||||
"BrentBertram",
|
||||
"eumelzocker",
|
||||
"dxjaymz",
|
||||
"L C",
|
||||
"Dude",
|
||||
"Somebody",
|
||||
"CK"
|
||||
],
|
||||
"totalCount": 965
|
||||
}
|
||||
@@ -1,324 +0,0 @@
|
||||
# Agent Skills System
|
||||
|
||||
The LoRA Manager agent skills system enables LLM-powered metadata enrichment and other AI-driven tasks. Users configure their own LLM provider (BYOK), and skills are executed through right-click context menu actions.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────┐
|
||||
│ LoRA Manager Backend │
|
||||
│ │
|
||||
│ ┌──────────────┐ ┌────────────────┐ │
|
||||
│ │ LLMService │───▶│ LLM Provider │ │
|
||||
│ │ (BYOK config, │◀───│ (OpenAI/Ollama │ │
|
||||
│ │ API calls) │ │ /custom) │ │
|
||||
│ └───────┬───────┘ └────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌───────▼───────────────────────┐ │
|
||||
│ │ AgentService │ │
|
||||
│ │ (orchestration: validate │ │
|
||||
│ │ → LLM call → post-process │ │
|
||||
│ │ → WebSocket broadcast) │ │
|
||||
│ └───────┬───────────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌───────▼───────────────────────┐ │
|
||||
│ │ SkillRegistry │ │
|
||||
│ │ ┌─────────────────────────┐ │ │
|
||||
│ │ │ enrich_hf_metadata: │ │ │
|
||||
│ │ │ - skill.yaml │ │ │
|
||||
│ │ │ - prompt.md │ │ │
|
||||
│ │ │ - handler.py │ │ │
|
||||
│ │ └─────────────────────────┘ │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
└──────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Key Design Principle
|
||||
|
||||
**Skills define *what* to do (prompt + post-processing). The AgentService handles *how* (LLM calls, validation, progress).**
|
||||
|
||||
Skills never call the LLM directly. This keeps BYOK configuration centralized and provider-agnostic.
|
||||
|
||||
## BYOK Configuration
|
||||
|
||||
Users configure their LLM provider in **Settings → AI Provider**:
|
||||
|
||||
| Setting | Description | Example |
|
||||
|---|---|---|
|
||||
| `llm_provider` | Provider type | `openai`, `ollama`, or `custom` |
|
||||
| `llm_api_key` | API key (not needed for local Ollama) | `sk-...` |
|
||||
| `llm_api_base` | Custom API base URL (empty = provider default) | `https://api.openai.com/v1` |
|
||||
| `llm_model` | Model name | `gpt-4o-mini` |
|
||||
|
||||
Environment variable overrides: `LLM_API_KEY`, `LLM_MODEL`, `LLM_API_BASE`, `LLM_PROVIDER`.
|
||||
|
||||
### Supported Providers
|
||||
|
||||
- **OpenAI**: Uses `https://api.openai.com/v1` by default
|
||||
- **Ollama** (local): Uses `http://localhost:11434/v1`, no API key required
|
||||
- **Custom**: Any OpenAI-compatible endpoint (vLLM, LM Studio, etc.) — set `llm_api_base` explicitly
|
||||
|
||||
## Available Skills
|
||||
|
||||
### enrich_hf_metadata
|
||||
|
||||
Enriches models linked to an external model site with metadata extracted by an LLM from the site's model card (README).
|
||||
|
||||
**Entry point**: Right-click context menu → "Enrich Metadata with AI"
|
||||
|
||||
**Supported model sources**:
|
||||
|
||||
| Platform | Link | AI enrichment | Direct download |
|
||||
| --- | --- | --- | --- |
|
||||
| Hugging Face | yes | yes | yes |
|
||||
| ModelScope (`modelscope.cn`) | yes | yes | yes |
|
||||
| ModelScope International (`modelscope.ai`) | yes | yes | yes |
|
||||
| TensorArt | yes | no (see below) | no |
|
||||
|
||||
`modelscope.cn` and `modelscope.ai` are **separate catalogues, not mirrors** — a
|
||||
repository published on one is routinely absent from the other — so each is
|
||||
registered as its own source (`ModelScopeSource` / `ModelScopeIntlSource` in
|
||||
`py/services/model_sources/modelscope.py`). The host therefore decides which
|
||||
API and CDN a model resolves against, and the two deployments get separate
|
||||
version groups (`ms:` / `msai:`) and default download directories. Keep the two
|
||||
tables in `modelSourceHelpers.js` and `registry.py` in step when adding a site.
|
||||
|
||||
TensorArt is link-only: `tensor.art` sits behind a Cloudflare managed challenge and its internal API requires session authorization, so the backend cannot read its model pages. Linking still stores the canonical page URL and the "View on TensorArt" link works.
|
||||
|
||||
**What it does**:
|
||||
1. Reads the model's `.metadata.json` to get the source (`source_platform` + `source_url`, or the legacy `hf_url`)
|
||||
2. Fetches the model card through the provider in `py/services/model_sources/` — the README via `fetch_model_card()`, plus any extras the site keeps outside it via `fetch_model_card_context()`
|
||||
3. Sends the README + site-provided extras + local metadata to the LLM for structured extraction
|
||||
4. Writes extracted fields to `.metadata.json`:
|
||||
- `base_model` — only if current value is empty
|
||||
- `trainedWords` — trigger words (LoRA only, if none exist)
|
||||
- `modelDescription` — the site's author description (if any) followed by the README rendered as HTML
|
||||
- `tags` — merged with existing tags, deduplicated
|
||||
- `civitai.images` — example images
|
||||
- `metadata_source` — audit trail: `agent:enrich_hf_metadata`
|
||||
- `llm_enriched_at` — ISO timestamp
|
||||
5. Downloads and optimizes a preview image, using the per-file example image the
|
||||
site publishes when the README has none
|
||||
6. Updates the scanner cache
|
||||
7. Broadcasts WebSocket progress events
|
||||
|
||||
#### Site-provided card extras (`fetch_model_card_context`)
|
||||
|
||||
A model card is not always just `README.md`. ModelScope keeps the author's
|
||||
summary (`Description`), the site-curated tags (`OfficialTags`), and — per
|
||||
published version — the model filenames together with that file's example
|
||||
images (`MuseInfo.versions[].coverImages`) and trigger words in its
|
||||
model-detail API. AIGC repositories there often ship an auto-generated
|
||||
boilerplate README and put everything useful in `Description`, so reading only
|
||||
the README yields almost nothing.
|
||||
|
||||
Providers opt in by overriding `ModelSource.fetch_model_card_context()`, which
|
||||
returns a `ModelCardContext`. The wanted file is identified by its sha256 when
|
||||
the caller knows it (the scanner already records one) and by **basename**
|
||||
otherwise, so each checkpoint in a collection repo gets its own images — and
|
||||
keeps getting them after the user renames the weights, which is the only
|
||||
identifier a rename cannot invalidate. Sites with no such extras inherit an
|
||||
empty context, and the pipeline behaves exactly as before.
|
||||
|
||||
The README and the repository metadata describe the whole repository, not one
|
||||
file, so `execute_skill()` creates a `ModelSourceCache` for the duration of a
|
||||
run and passes it down. Enriching the eight checkpoints of one ModelScope
|
||||
repository costs two HTTP requests instead of sixteen; only the per-file
|
||||
selection is redone for each file. Nothing is cached across runs, and download
|
||||
URLs never go through it.
|
||||
|
||||
#### Deterministic data is applied whether or not an LLM is configured
|
||||
|
||||
`AgentService._load_source_card()` runs for every source-backed enrichment, and
|
||||
the post-processor applies what it returns before the LLM output is merged. A
|
||||
user with **no** provider configured therefore still gets the author summary,
|
||||
the example images, the preview, the site-curated tags, the trigger words and
|
||||
the README rendered as the model description.
|
||||
|
||||
The LLM is always consulted when one is configured — invoking **Enrich Metadata
|
||||
with AI** must call the provider every time, and the site data is never treated
|
||||
as a reason to skip it. The deterministic values act as fallbacks that fill
|
||||
gaps the LLM leaves behind:
|
||||
|
||||
| Field | Deterministic source | LLM role |
|
||||
| --- | --- | --- |
|
||||
| `model_name` | site display name (`Name`), written only while the value is still the file stem | — |
|
||||
| `modelDescription` | author summary + README as HTML | — |
|
||||
| `civitai.name` | the matched version's label (`modelVersion.showName`) | — |
|
||||
| `civitai.images` | site example images, then README images | — |
|
||||
| `preview_url` | first available example image | may propose one from the README |
|
||||
| `tags` | site-curated tags, always merged in | proposes additional content tags |
|
||||
| `civitai.description` | author summary | richer 1-2 sentence summary wins |
|
||||
| `base_model` | site hints resolved against the canonical vocabulary (`py/services/agent/base_model_resolver.py`) | mapping it is the LLM's job; the resolver only fills in when the LLM returns nothing |
|
||||
| `trainedWords` | per-file site trigger words, then YAML `instance_prompt` | primary extraction |
|
||||
| `usage_tips` | regex over an explicitly stated strength range | primary extraction |
|
||||
| `notes` | — | LLM-only |
|
||||
|
||||
Models with no source, an unknown source, or a source without model-card access (TensorArt) are skipped with an explicit reason and counted in the run summary.
|
||||
|
||||
**Model types**: LoRA, Checkpoint, Embedding
|
||||
|
||||
### Download-time hydration
|
||||
|
||||
The same deterministic mapping runs automatically when a model is downloaded
|
||||
from a model source, so a ModelScope or Hugging Face download lands with the
|
||||
populated card a CivitAI download produces instead of a bare filename and
|
||||
hash. Nothing needs to be triggered by hand and no provider is called.
|
||||
|
||||
`py/services/model_sources/hydration.py` owns this path:
|
||||
|
||||
* `_save_source_metadata()` in `py/routes/handlers/model_source_handlers.py`
|
||||
creates the sidecar (hash, source link, scanner-cache entry) and then calls
|
||||
`hydrate_from_source()`. It also runs for a file that was already on disk, so
|
||||
models downloaded before this existed get topped up on the next attempt.
|
||||
* Metadata is created through the **owning scanner**
|
||||
(`scanner._create_default_metadata()`) rather than
|
||||
`MetadataManager.create_default_metadata()`, so the per-type lazy-hash rule
|
||||
applies: `CheckpointScanner` and `OtherScanner` store
|
||||
`hash_status="pending"` with an empty `sha256` for their multi-GB files, and
|
||||
the generic helper would read a 10 GB checkpoint end to end inside the
|
||||
download request. Hydration copes with the empty hash — `_matching_versions()`
|
||||
falls back to the repository basename, which the download just wrote.
|
||||
* Hydration reuses `PostProcessor` with an empty `llm_output`, so the two paths
|
||||
cannot drift apart. It reports `metadata_source = "source:<platform>"` rather
|
||||
than the skill's `agent:enrich_hf_metadata`, and — because no provider ran —
|
||||
it does not stamp `llm_enriched_at`.
|
||||
* `model_name` is only written while it still equals the file stem: once a user
|
||||
renames a model, that choice is kept.
|
||||
* Only a model whose stored `source_platform`/`source_url` match the repository
|
||||
being downloaded is updated; a local file that merely shares a name must not
|
||||
receive another model's card.
|
||||
* The README and repository payload describe the *repository*, so a short-lived
|
||||
process-wide `ModelSourceCache` (`shared_source_cache`, 300 s, 32 entries)
|
||||
keeps a batch over one repository to two HTTP requests.
|
||||
* Every failure — unreachable site, changed payload shape, broken post-processor
|
||||
— is logged and swallowed. Metadata hydration can never fail a download.
|
||||
* Neither stage advances the byte counter, so both are announced to the
|
||||
progress UI (`_report_phase()` → `{"status": "metadata", "stage": ...}`) as
|
||||
they start. Without that the bar sits at 100% reporting `0 B/s` for several
|
||||
seconds and the download looks stuck. `stage` and `platform` are
|
||||
machine-readable; the wording is localised in `LoadingManager`.
|
||||
|
||||
## Adding a New Skill
|
||||
|
||||
### 1. Create the skill directory
|
||||
|
||||
```
|
||||
py/services/agent/skills/<skill_name>/
|
||||
├── skill.yaml # Skill metadata and schemas
|
||||
├── prompt.md # LLM prompt template
|
||||
└── handler.py # Pre-processing and post-processing
|
||||
```
|
||||
|
||||
### 2. Write skill.yaml
|
||||
|
||||
```yaml
|
||||
name: my_skill
|
||||
title: "My Skill"
|
||||
description: "What this skill does"
|
||||
llm_required: true
|
||||
model_type_filter: ["lora"] # or null for all types
|
||||
input_schema:
|
||||
type: object
|
||||
properties:
|
||||
model_paths:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
required:
|
||||
- model_paths
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
# ... JSON schema for LLM output
|
||||
permissions:
|
||||
write_metadata: true
|
||||
write_previews: false
|
||||
network_domains:
|
||||
- "example.com"
|
||||
```
|
||||
|
||||
### 3. Write prompt.md
|
||||
|
||||
Use `{{variable}}` placeholders that will be replaced with data from the `prepare` function:
|
||||
|
||||
```markdown
|
||||
You are an expert assistant...
|
||||
|
||||
Model URL: {{source_url}}
|
||||
README content:
|
||||
{{readme_content}}
|
||||
|
||||
Current metadata:
|
||||
{{current_metadata}}
|
||||
```
|
||||
|
||||
### 4. Write handler.py
|
||||
|
||||
```python
|
||||
async def prepare(model_path: str, input_data: dict) -> dict:
|
||||
"""Gather context for the LLM prompt. Returns variables for template rendering."""
|
||||
return {
|
||||
"model_path": model_path,
|
||||
# ... other variables used in prompt.md
|
||||
}
|
||||
|
||||
async def post_process(context) -> dict:
|
||||
"""Apply the LLM-extracted data to the model."""
|
||||
llm_response = context.llm_response
|
||||
# ... write metadata, download previews, update cache
|
||||
return {
|
||||
"success": True,
|
||||
"updated_fields": ["base_model", "tags"],
|
||||
"errors": [],
|
||||
}
|
||||
```
|
||||
|
||||
**Important**: Use absolute imports (`from py.utils.metadata_manager import MetadataManager`) because skills are loaded via `importlib.util.spec_from_file_location`, which doesn't support relative imports.
|
||||
|
||||
### 5. Test
|
||||
|
||||
The skill is automatically discovered by `SkillRegistry` on startup. Test with:
|
||||
|
||||
```python
|
||||
pytest tests/services/test_agent_service.py
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
||||
| Method | Path | Description |
|
||||
|---|---|---|
|
||||
| GET | `/api/lm/agent/skills` | List available skills |
|
||||
| POST | `/api/lm/agent/execute/{skill_name}` | Execute a skill (body: `{"model_paths": [...]}`) |
|
||||
| POST | `/api/lm/agent/cancel` | Cancel running skill (stub) |
|
||||
|
||||
## WebSocket Events
|
||||
|
||||
| Type | When | Key fields |
|
||||
|---|---|---|
|
||||
| `agent_progress` | Skill started/processing | `skill`, `status`, `total`, `processed`, `success`, `current_path` |
|
||||
| `agent_progress` | Skill completed | `skill`, `status`, `updated_models`, `errors`, `summary` |
|
||||
| `agent_progress` | Skill error | `skill`, `status`, `error` |
|
||||
|
||||
## Security Model
|
||||
|
||||
Skills declare permissions in `skill.yaml`:
|
||||
- `write_metadata` — can write `.metadata.json` files
|
||||
- `write_previews` — can download/replace preview images
|
||||
- `network_domains` — allowed domains for HTTP requests
|
||||
|
||||
These are declarative constraints checked by `AgentService`. They are defense-in-depth, not a sandbox — the Python process can technically do anything, but the contract is clear and auditable.
|
||||
|
||||
## File Locations
|
||||
|
||||
| Component | Path |
|
||||
|---|---|
|
||||
| LLMService | `py/services/llm_service.py` |
|
||||
| AgentService | `py/services/agent/agent_service.py` |
|
||||
| SkillRegistry | `py/services/agent/skill_registry.py` |
|
||||
| SkillDefinition | `py/services/agent/skill_definition.py` |
|
||||
| Skills directory | `py/services/agent/skills/` |
|
||||
| Route handlers | `py/routes/handlers/agent_handlers.py` |
|
||||
| Frontend manager | `static/js/managers/AgentManager.js` |
|
||||
| Settings UI | `templates/components/modals/settings_modal.html` |
|
||||
| Context menu | `templates/components/context_menu.html` |
|
||||
@@ -1,93 +0,0 @@
|
||||
# Example image route architecture
|
||||
|
||||
The example image routing stack mirrors the layered model route stack described in
|
||||
[`docs/architecture/model_routes.md`](model_routes.md). HTTP wiring, controller setup,
|
||||
handler orchestration, and long-running workflows now live in clearly separated modules so
|
||||
we can extend download/import behaviour without touching the entire feature surface.
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
subgraph HTTP
|
||||
A[ExampleImagesRouteRegistrar] -->|binds| B[ExampleImagesRoutes controller]
|
||||
end
|
||||
subgraph Application
|
||||
B --> C[ExampleImagesHandlerSet]
|
||||
C --> D1[Handlers]
|
||||
D1 --> E1[Use cases]
|
||||
E1 --> F1[Download manager / processor / file manager]
|
||||
end
|
||||
subgraph Side Effects
|
||||
F1 --> G1[Filesystem]
|
||||
F1 --> G2[Model metadata]
|
||||
F1 --> G3[WebSocket progress]
|
||||
end
|
||||
```
|
||||
|
||||
## Layer responsibilities
|
||||
|
||||
| Layer | Module(s) | Responsibility |
|
||||
| --- | --- | --- |
|
||||
| Registrar | `py/routes/example_images_route_registrar.py` | Declarative catalogue of every example image endpoint plus helpers that bind them to an `aiohttp` router. Keeps HTTP concerns symmetrical with the model registrar. |
|
||||
| Controller | `py/routes/example_images_routes.py` | Lazily constructs `ExampleImagesHandlerSet`, injects defaults for the download manager, processor, and file manager, and exposes the registrar-ready mapping just like `BaseModelRoutes`. |
|
||||
| Handler set | `py/routes/handlers/example_images_handlers.py` | Groups HTTP adapters by concern (downloads, imports/deletes, filesystem access). Each handler translates domain errors into HTTP responses and defers to a use case or utility service. |
|
||||
| Use cases | `py/services/use_cases/example_images/*.py` | Encapsulate orchestration for downloads and imports. They validate input, translate concurrency/configuration errors, and keep handler logic declarative. |
|
||||
| Supporting services | `py/utils/example_images_download_manager.py`, `py/utils/example_images_processor.py`, `py/utils/example_images_file_manager.py` | Execute long-running work: pull assets from Civitai, persist uploads, clean metadata, expose filesystem actions with guardrails, and broadcast progress snapshots. |
|
||||
|
||||
## Handler responsibilities & invariants
|
||||
|
||||
`ExampleImagesHandlerSet` flattens the handler objects into the `{"handler_name": coroutine}`
|
||||
mapping consumed by the registrar. The table below outlines how each handler collaborates
|
||||
with the use cases and utilities.
|
||||
|
||||
| Handler | Key endpoints | Collaborators | Contracts |
|
||||
| --- | --- | --- | --- |
|
||||
| `ExampleImagesDownloadHandler` | `/api/lm/download-example-images`, `/api/lm/example-images-status`, `/api/lm/pause-example-images`, `/api/lm/resume-example-images`, `/api/lm/force-download-example-images` | `DownloadExampleImagesUseCase`, `DownloadManager` | Delegates payload validation and concurrency checks to the use case; progress/status endpoints expose the same snapshot used for WebSocket broadcasts; pause/resume surface `DownloadNotRunningError` as HTTP 400 instead of 500. |
|
||||
| `ExampleImagesManagementHandler` | `/api/lm/import-example-images`, `/api/lm/delete-example-image` | `ImportExampleImagesUseCase`, `ExampleImagesProcessor` | Multipart uploads are streamed to disk via the use case; validation failures return HTTP 400 with no filesystem side effects; deletion funnels through the processor to prune metadata and cached images consistently. |
|
||||
| `ExampleImagesFileHandler` | `/api/lm/open-example-images-folder`, `/api/lm/example-image-files`, `/api/lm/has-example-images` | `ExampleImagesFileManager` | Centralises filesystem access, enforcing settings-based root paths and returning HTTP 400/404 for missing configuration or folders; responses always include `success`/`has_images` booleans for UI consumption. |
|
||||
|
||||
## Use case boundaries
|
||||
|
||||
| Use case | Entry point | Dependencies | Guarantees |
|
||||
| --- | --- | --- | --- |
|
||||
| `DownloadExampleImagesUseCase` | `execute(payload)` | `DownloadManager.start_download`, download configuration errors | Raises `DownloadExampleImagesInProgressError` when the manager reports an active job, rewraps configuration errors into `DownloadExampleImagesConfigurationError`, and lets `ExampleImagesDownloadError` bubble as 500s so handlers do not duplicate logging. |
|
||||
| `ImportExampleImagesUseCase` | `execute(request)` | `ExampleImagesProcessor.import_images`, temporary file helpers | Supports multipart or JSON payloads, normalises file paths into a single list, cleans up temp files even on failure, and maps validation issues to `ImportExampleImagesValidationError` for HTTP 400 responses. |
|
||||
|
||||
## Maintaining critical invariants
|
||||
|
||||
* **Shared progress snapshots** - The download handler returns the same snapshot built by
|
||||
`DownloadManager`, guaranteeing parity between HTTP polling endpoints and WebSocket
|
||||
progress events.
|
||||
* **Safe filesystem access** - All folder/file actions flow through
|
||||
`ExampleImagesFileManager`, which validates the configured example image root and ensures
|
||||
responses never leak absolute paths outside the allowed directory.
|
||||
* **Metadata hygiene** - Import/delete operations run through `ExampleImagesProcessor`,
|
||||
which updates model metadata via `MetadataManager` and notifies the relevant scanners so
|
||||
cache state stays in sync.
|
||||
|
||||
## Migration notes
|
||||
|
||||
The refactor brings the example image stack in line with the model/recipe stacks:
|
||||
|
||||
1. `ExampleImagesRouteRegistrar` now owns the declarative route list. Downstream projects
|
||||
should rely on `ExampleImagesRoutes.to_route_mapping()` instead of manually wiring
|
||||
handler callables.
|
||||
2. `ExampleImagesRoutes` caches its `ExampleImagesHandlerSet` just like
|
||||
`BaseModelRoutes`. If you previously instantiated handlers directly, inject custom
|
||||
collaborators via the controller constructor (`download_manager`, `processor`,
|
||||
`file_manager`) to keep test seams predictable.
|
||||
3. Tests that mocked `ExampleImagesRoutes.setup_routes` should switch to patching
|
||||
`DownloadExampleImagesUseCase`/`ImportExampleImagesUseCase` at import time. The handlers
|
||||
expect those abstractions to surface validation/concurrency errors, and bypassing them
|
||||
will skip the HTTP-friendly error mapping.
|
||||
|
||||
## Extending the stack
|
||||
|
||||
1. Add the endpoint to `ROUTE_DEFINITIONS` with a unique `handler_name`.
|
||||
2. Expose the coroutine on an existing handler class (or create a new handler and extend
|
||||
`ExampleImagesHandlerSet`).
|
||||
3. Wire additional services or factories inside `_build_handler_set` on
|
||||
`ExampleImagesRoutes`, mirroring how the model stack introduces new use cases.
|
||||
|
||||
`tests/routes/test_example_images_routes.py` exercises registrar binding, download pause
|
||||
flows, and import validations. Use it as a template when introducing new handler
|
||||
collaborators or error mappings.
|
||||
@@ -1,100 +0,0 @@
|
||||
# Base model route architecture
|
||||
|
||||
The model routing stack now splits HTTP wiring, orchestration logic, and
|
||||
business rules into discrete layers. The goal is to make it obvious where a
|
||||
new collaborator should live and which contract it must honour. The diagram
|
||||
below captures the end-to-end flow for a typical request:
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
subgraph HTTP
|
||||
A[ModelRouteRegistrar] -->|binds| B[BaseModelRoutes handler proxy]
|
||||
end
|
||||
subgraph Application
|
||||
B --> C[ModelHandlerSet]
|
||||
C --> D1[Handlers]
|
||||
D1 --> E1[Use cases]
|
||||
E1 --> F1[Services / scanners]
|
||||
end
|
||||
subgraph Side Effects
|
||||
F1 --> G1[Cache & metadata]
|
||||
F1 --> G2[Filesystem]
|
||||
F1 --> G3[WebSocket state]
|
||||
end
|
||||
```
|
||||
|
||||
Every box maps to a concrete module:
|
||||
|
||||
| Layer | Module(s) | Responsibility |
|
||||
| --- | --- | --- |
|
||||
| Registrar | `py/routes/model_route_registrar.py` | Declarative list of routes shared by every model type and helper methods for binding them to an `aiohttp` application. |
|
||||
| Route controller | `py/routes/base_model_routes.py` | Constructs the handler graph, injects shared services, exposes proxies that surface `503 Service not ready` when the model service has not been attached. |
|
||||
| Handler set | `py/routes/handlers/model_handlers.py` | Thin HTTP adapters grouped by concern (page rendering, listings, mutations, queries, downloads, CivitAI integration, move operations, auto-organize). |
|
||||
| Use cases | `py/services/use_cases/*.py` | Encapsulate long-running flows (`DownloadModelUseCase`, `BulkMetadataRefreshUseCase`, `AutoOrganizeUseCase`). They normalise validation errors and concurrency constraints before returning control to the handlers. |
|
||||
| Services | `py/services/*.py` | Existing services and scanners that mutate caches, write metadata, move files, and broadcast WebSocket updates. |
|
||||
|
||||
## Handler responsibilities & contracts
|
||||
|
||||
`ModelHandlerSet` flattens the handler objects into the exact callables used by
|
||||
the registrar. The table below highlights the separation of concerns within
|
||||
the set and the invariants that must hold after each handler returns.
|
||||
|
||||
| Handler | Key endpoints | Collaborators | Contracts |
|
||||
| --- | --- | --- | --- |
|
||||
| `ModelPageView` | `/{prefix}` | `SettingsManager`, `server_i18n`, Jinja environment, `service.scanner` | Template is rendered with `is_initializing` flag when caches are cold; i18n filter is registered exactly once per environment instance. |
|
||||
| `ModelListingHandler` | `/api/lm/{prefix}/list` | `service.get_paginated_data`, `service.format_response` | Listings respect pagination query parameters and cap `page_size` at 100; every item is formatted before response. |
|
||||
| `ModelManagementHandler` | Mutations (delete, exclude, metadata, preview, tags, rename, bulk delete, duplicate verification) | `ModelLifecycleService`, `MetadataSyncService`, `PreviewAssetService`, `TagUpdateService`, scanner cache/index | Cache state mirrors filesystem changes: deletes prune cache & hash index, preview replacements synchronise metadata and cache NSFW levels, metadata saves trigger cache resort when names change. |
|
||||
| `ModelQueryHandler` | Read-only queries (top tags, folders, duplicates, metadata, URLs) | Service query helpers & scanner cache | Outputs always wrapped in `{"success": True}` when no error; duplicate/filename grouping omits empty entries; invalid parameters (e.g. missing `model_root`) return HTTP 400. |
|
||||
| `ModelDownloadHandler` | `/api/lm/download-model`, `/download-model-get`, `/download-progress/{id}`, `/cancel-download-get` | `DownloadModelUseCase`, `DownloadCoordinator`, `WebSocketManager` | Payload validation errors become HTTP 400 without mutating download progress cache; early-access failures surface as HTTP 401; successful downloads cache progress snapshots that back both WebSocket broadcasts and polling endpoints. |
|
||||
| `ModelCivitaiHandler` | CivitAI metadata routes | `MetadataSyncService`, metadata provider factory, `BulkMetadataRefreshUseCase` | `fetch_all_civitai` streams progress via `WebSocketBroadcastCallback`; version lookups validate model type before returning; local availability fields derive from hash lookups without mutating cache state. |
|
||||
| `ModelMoveHandler` | `move_model`, `move_models_bulk` | `ModelMoveService` | Moves execute atomically per request; bulk operations aggregate success/failure per file set. |
|
||||
| `ModelAutoOrganizeHandler` | `/api/lm/{prefix}/auto-organize` (GET/POST), `/auto-organize-progress` | `AutoOrganizeUseCase`, `WebSocketProgressCallback`, `WebSocketManager` | Enforces single-flight execution using the shared lock; progress broadcasts remain available to polling clients until explicitly cleared; conflicts return HTTP 409 with a descriptive error. |
|
||||
|
||||
## Use case boundaries
|
||||
|
||||
Each use case exposes a narrow asynchronous API that hides the underlying
|
||||
services. Their error mapping is essential for predictable HTTP responses.
|
||||
|
||||
| Use case | Entry point | Dependencies | Guarantees |
|
||||
| --- | --- | --- | --- |
|
||||
| `DownloadModelUseCase` | `execute(payload)` | `DownloadCoordinator.schedule_download` | Translates `ValueError` into `DownloadModelValidationError` for HTTP 400, recognises early-access errors (`"401"` in message) and surfaces them as `DownloadModelEarlyAccessError`, forwards success dictionaries untouched. |
|
||||
| `AutoOrganizeUseCase` | `execute(file_paths, progress_callback)` | `ModelFileService.auto_organize_models`, `WebSocketManager` lock | Guarded by `ws_manager` lock + status checks; raises `AutoOrganizeInProgressError` before invoking the file service when another run is already active. |
|
||||
| `BulkMetadataRefreshUseCase` | `execute_with_error_handling(progress_callback)` | `MetadataSyncService`, `SettingsManager`, `WebSocketBroadcastCallback` | Iterates through cached models, applies metadata sync, emits progress snapshots that handlers broadcast unchanged. |
|
||||
|
||||
## Maintaining legacy contracts
|
||||
|
||||
The refactor preserves the invariants called out in the previous architecture
|
||||
notes. The most critical ones are reiterated here to emphasise the
|
||||
collaboration points:
|
||||
|
||||
1. **Cache mutations** – Delete, exclude, rename, and bulk delete operations are
|
||||
channelled through `ModelManagementHandler`. The handler delegates to
|
||||
`ModelLifecycleService` or `MetadataSyncService`, and the scanner cache is
|
||||
mutated in-place before the handler returns. The accompanying tests assert
|
||||
that `scanner._cache.raw_data` and `scanner._hash_index` stay in sync after
|
||||
each mutation.
|
||||
2. **Preview updates** – `PreviewAssetService.replace_preview` writes the new
|
||||
asset, `MetadataSyncService` persists the JSON metadata, and
|
||||
`scanner.update_preview_in_cache` mirrors the change. The handler returns
|
||||
the static URL produced by `config.get_preview_static_url`, keeping browser
|
||||
clients in lockstep with disk state.
|
||||
3. **Download progress** – `DownloadCoordinator.schedule_download` generates the
|
||||
download identifier, registers a WebSocket progress callback, and caches the
|
||||
latest numeric progress via `WebSocketManager`. Both `download_model`
|
||||
responses and `/download-progress/{id}` polling read from the same cache to
|
||||
guarantee consistent progress reporting across transports.
|
||||
|
||||
## Extending the stack
|
||||
|
||||
To add a new shared route:
|
||||
|
||||
1. Declare it in `COMMON_ROUTE_DEFINITIONS` using a unique handler name.
|
||||
2. Implement the corresponding coroutine on one of the handlers inside
|
||||
`ModelHandlerSet` (or introduce a new handler class when the concern does not
|
||||
fit existing ones).
|
||||
3. Inject additional dependencies in `BaseModelRoutes._create_handler_set` by
|
||||
wiring services or use cases through the constructor parameters.
|
||||
|
||||
Model-specific routes should continue to be registered inside the subclass
|
||||
implementation of `setup_specific_routes`, reusing the shared registrar where
|
||||
possible.
|
||||
@@ -1,34 +0,0 @@
|
||||
# Multi-Library Management for Standalone Mode
|
||||
|
||||
## Requirements Summary
|
||||
- **Independent libraries**: In standalone mode, users can maintain multiple libraries, where each library represents a distinct set of model folders (LoRAs, checkpoints, embeddings, etc.). Only one library is active at any given time, but users need a fast way to switch between them.
|
||||
- **Library-specific settings**: The fields that vary per library are `folder_paths`, `default_lora_root`, `default_checkpoint_root`, and `default_embedding_root` inside `settings.json`.
|
||||
- **Persistent caches**: Every library must have its own SQLite persistent model cache so that metadata generated for one library does not leak into another.
|
||||
- **Backward compatibility**: Existing single-library setups should continue to work. When no multi-library configuration is provided, the application should behave exactly as before.
|
||||
|
||||
## Proposed Design
|
||||
1. **Library registry**
|
||||
- Extend the standalone configuration to hold a list of libraries, each identified by a unique name.
|
||||
- Each entry stores the folder path configuration plus any library-scoped metadata (e.g. creation time, display name).
|
||||
- The active library key is stored separately to allow quick switching without rewriting the full config.
|
||||
2. **Settings management**
|
||||
- Update `settings_manager` to load and persist the library registry. When a library is activated, hydrate the in-memory settings object with that library's folder configuration.
|
||||
- Provide helper methods for creating, renaming, and deleting libraries, ensuring validation for duplicate names and path collisions.
|
||||
- Continue writing the active library settings to `settings.json` for compatibility, while storing the registry in a new section such as `libraries`.
|
||||
3. **Persistent model cache**
|
||||
- Derive the SQLite file path from the active library, e.g. `model_cache_<library>.sqlite` or a nested directory structure like `model_cache/<library>/models.sqlite`.
|
||||
- Update `PersistentModelCache` so it resolves the database path dynamically whenever the active library changes. Ensure connections are closed before switching to avoid locking issues.
|
||||
- Migrate existing single cache files by treating them as the default library's cache.
|
||||
4. **Model scanning workflow**
|
||||
- Modify `ModelScanner` and related services to react to library switches by clearing in-memory caches, re-reading folder paths, and rehydrating metadata from the library-specific SQLite cache.
|
||||
- Provide API endpoints in standalone mode to list libraries, activate one, and trigger a rescan.
|
||||
5. **UI/UX considerations**
|
||||
- In the standalone UI, introduce a library selector component that surfaces available libraries and offers quick switching.
|
||||
- Offer feedback when switching libraries (e.g. spinner while rescanning) and guard destructive actions with confirmation prompts.
|
||||
|
||||
## Implementation Notes
|
||||
- **Data migration**: On startup, detect if the old `settings.json` structure is present. If so, create a default library entry using the current folder paths and point the active library to it.
|
||||
- **Thread safety**: Ensure that any long-running scans are cancelled or awaited before switching libraries to prevent race conditions in cache writes.
|
||||
- **Testing**: Add unit tests for the settings manager to cover library CRUD operations and cache path resolution. Include integration tests that simulate switching libraries and verifying that the correct models are loaded.
|
||||
- **Documentation**: Update user guides to explain how to define libraries, switch between them, and where the new cache files are stored.
|
||||
- **Extensibility**: Keep the design open to future per-library settings (e.g. auto-refresh intervals, metadata overrides) by storing library data as objects instead of flat maps.
|
||||
@@ -1,89 +0,0 @@
|
||||
# Recipe route architecture
|
||||
|
||||
The recipe routing stack now mirrors the modular model route design. HTTP
|
||||
bindings, controller wiring, handler orchestration, and business rules live in
|
||||
separate layers so new behaviours can be added without re-threading the entire
|
||||
feature. The diagram below outlines the flow for a typical request:
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
subgraph HTTP
|
||||
A[RecipeRouteRegistrar] -->|binds| B[RecipeRoutes controller]
|
||||
end
|
||||
subgraph Application
|
||||
B --> C[RecipeHandlerSet]
|
||||
C --> D1[Handlers]
|
||||
D1 --> E1[Use cases]
|
||||
E1 --> F1[Services / scanners]
|
||||
end
|
||||
subgraph Side Effects
|
||||
F1 --> G1[Cache & fingerprint index]
|
||||
F1 --> G2[Metadata files]
|
||||
F1 --> G3[Temporary shares]
|
||||
end
|
||||
```
|
||||
|
||||
## Layer responsibilities
|
||||
|
||||
| Layer | Module(s) | Responsibility |
|
||||
| --- | --- | --- |
|
||||
| Registrar | `py/routes/recipe_route_registrar.py` | Declarative list of every recipe endpoint and helper methods that bind them to an `aiohttp` application. |
|
||||
| Controller | `py/routes/base_recipe_routes.py`, `py/routes/recipe_routes.py` | Lazily resolves scanners/clients from the service registry, wires shared templates/i18n, instantiates `RecipeHandlerSet`, and exposes a `{handler_name: coroutine}` mapping for the registrar. |
|
||||
| Handler set | `py/routes/handlers/recipe_handlers.py` | Thin HTTP adapters grouped by concern (page view, listings, queries, mutations, sharing). They normalise responses and translate service exceptions into HTTP status codes. |
|
||||
| Services & scanners | `py/services/recipes/*.py`, `py/services/recipe_scanner.py`, `py/services/service_registry.py` | Concrete business logic: metadata parsing, persistence, sharing, fingerprint/index maintenance, and cache refresh. |
|
||||
|
||||
## Handler responsibilities & invariants
|
||||
|
||||
`RecipeHandlerSet` flattens purpose-built handler objects into the callables the
|
||||
registrar binds. Each handler is responsible for a narrow concern and enforces a
|
||||
set of invariants before returning:
|
||||
|
||||
| Handler | Key endpoints | Collaborators | Contracts |
|
||||
| --- | --- | --- | --- |
|
||||
| `RecipePageView` | `/loras/recipes` | `SettingsManager`, `server_i18n`, Jinja environment, recipe scanner getter | Template rendered with `is_initializing` flag when caches are still warming; i18n filter registered exactly once per environment instance. |
|
||||
| `RecipeListingHandler` | `/api/lm/recipes`, `/api/lm/recipe/{id}` | `recipe_scanner.get_paginated_data`, `recipe_scanner.get_recipe_by_id` | Listings respect pagination and search filters; every item receives a `file_url` fallback even when metadata is incomplete; missing recipes become HTTP 404. |
|
||||
| `RecipeQueryHandler` | Tag/base-model stats, syntax, LoRA lookups | Recipe scanner cache, `format_recipe_file_url` helper | Cache snapshots are reused without forcing refresh; duplicate lookups collapse groups by fingerprint; syntax lookups return helpful errors when LoRAs are absent. |
|
||||
| `RecipeManagementHandler` | Save, update, reconnect, bulk delete, widget ingest | `RecipePersistenceService`, `RecipeAnalysisService`, recipe scanner | Persistence results propagate HTTP status codes; fingerprint/index updates flow through the scanner before returning; validation errors surface as HTTP 400 without touching disk. |
|
||||
| `RecipeAnalysisHandler` | Uploaded/local/remote analysis | `RecipeAnalysisService`, `civitai_client`, recipe scanner | Unsupported content types map to HTTP 400; download errors (`RecipeDownloadError`) are not retried; every response includes a `loras` array for client compatibility. |
|
||||
| `RecipeSharingHandler` | Share + download | `RecipeSharingService`, recipe scanner | Share responses provide a stable download URL and filename; expired shares surface as HTTP 404; downloads stream via `web.FileResponse` with attachment headers. |
|
||||
|
||||
## Use case boundaries
|
||||
|
||||
The dedicated services encapsulate long-running work so handlers stay thin.
|
||||
|
||||
| Use case | Entry point | Dependencies | Guarantees |
|
||||
| --- | --- | --- | --- |
|
||||
| `RecipeAnalysisService` | `analyze_uploaded_image`, `analyze_remote_image`, `analyze_local_image`, `analyze_widget_metadata` | `ExifUtils`, `RecipeParserFactory`, downloader factory, optional metadata collector/processor | Normalises missing/invalid payloads into `RecipeValidationError`; generates consistent fingerprint data to keep duplicate detection stable; temporary files are cleaned up after every analysis path. |
|
||||
| `RecipePersistenceService` | `save_recipe`, `delete_recipe`, `update_recipe`, `reconnect_lora`, `get_reconnect_suggestions`, `bulk_delete`, `save_recipe_from_widget` | `ExifUtils`, recipe scanner, card preview sizing constants | Writes images/JSON metadata atomically; updates scanner caches and hash indices before returning; recalculates fingerprints whenever LoRA assignments change. |
|
||||
| `RecipeSharingService` | `share_recipe`, `prepare_download` | `tempfile`, recipe scanner | Copies originals to TTL-managed temp files; metadata lookups re-use the scanner; expired shares trigger cleanup and `RecipeNotFoundError`. |
|
||||
|
||||
## Maintaining critical invariants
|
||||
|
||||
* **Cache updates** – Mutations (`save`, `delete`, `bulk_delete`, `update`) call
|
||||
back into the recipe scanner to mutate the in-memory cache and fingerprint
|
||||
index before returning a response. Tests assert that these methods are invoked
|
||||
even when stubbing persistence.
|
||||
* **Fingerprint management** – `RecipePersistenceService` recomputes
|
||||
fingerprints whenever LoRA metadata changes and duplicate lookups use those
|
||||
fingerprints to group recipes. Handlers bubble the resulting IDs so clients
|
||||
can merge duplicates without an extra fetch.
|
||||
* **Metadata synchronisation** – Saving or reconnecting a recipe updates the
|
||||
JSON sidecar, refreshes embedded metadata via `ExifUtils`, and instructs the
|
||||
scanner to resort its cache. Sharing relies on this metadata to generate
|
||||
filenames and ensure downloads stay in sync with on-disk state.
|
||||
|
||||
## Extending the stack
|
||||
|
||||
1. Declare the new endpoint in `ROUTE_DEFINITIONS` with a unique handler name.
|
||||
2. Implement the coroutine on an existing handler or introduce a new handler
|
||||
class inside `py/routes/handlers/recipe_handlers.py` when the concern does
|
||||
not fit existing ones.
|
||||
3. Wire additional collaborators inside
|
||||
`BaseRecipeRoutes._create_handler_set` (inject new services or factories) and
|
||||
expose helper getters on the handler owner if the handler needs to share
|
||||
utilities.
|
||||
|
||||
Integration tests in `tests/routes/test_recipe_routes.py` exercise the listing,
|
||||
mutation, analysis-error, and sharing paths end-to-end, ensuring the controller
|
||||
and handler wiring remains valid as new capabilities are added.
|
||||
|
||||
@@ -1,65 +0,0 @@
|
||||
# ComfyUI Dual-Mode Widget Rendering
|
||||
|
||||
ComfyUI custom node widgets render in one of two modes. Patterns that work in one often fail silently in the other. Test both.
|
||||
|
||||
## Mode Detection
|
||||
|
||||
```js
|
||||
typeof LiteGraph !== 'undefined' && LiteGraph.vueNodesMode
|
||||
```
|
||||
|
||||
In Vue SFCs, `window.LiteGraph` is unavailable — pass as a prop from `main.ts`.
|
||||
|
||||
## Canvas Mode Layout
|
||||
|
||||
Uses `computeLayoutSize()` + `distributeSpace()` to allocate widget height within the node. Widgets with `computeLayoutSize` participate in space distribution; those with `computeSize` have fixed height.
|
||||
|
||||
- `getMinHeight()` in `addDOMWidget` options → minimum widget height
|
||||
- `widget.computeLayoutSize()` → `{ minHeight, minWidth, maxHeight? }`
|
||||
- Avoid `getMaxHeight()` unless the widget genuinely needs a fixed cap (prevents user resize)
|
||||
|
||||
## Vue Mode Layout
|
||||
|
||||
Uses CSS Grid (`grid-template-rows`) + `ResizeObserver`. The ResizeObserver watches the widget's DOM and feeds back into grid row sizing. This creates a feedback loop: content grows → row resizes → more space for content → content reflows/grows → row resizes again.
|
||||
|
||||
### Height Containment
|
||||
|
||||
The fix: `contain: layout size` on the widget root. This tells the browser the element's intrinsic size is CSS-determined, not driven by descendant content. The ResizeObserver sees a stable size and the loop is broken.
|
||||
|
||||
```css
|
||||
.widget-root.lm-vue-node {
|
||||
height: 100%;
|
||||
min-height: var(--comfy-widget-min-height, 200px);
|
||||
contain: layout size;
|
||||
}
|
||||
```
|
||||
|
||||
Existing examples: `.lm-loras-container.lm-vue-node` and `.comfy-tags-container.lm-vue-node` in `web/comfyui/lm_styles.css`.
|
||||
|
||||
**Do NOT** fix height issues with `maxHeight`, `getMaxHeight()`, or inline `max-height` — these prevent the user from resizing the node.
|
||||
|
||||
## Scroll Wheel Isolation
|
||||
|
||||
Both modes need to distinguish "user wants to scroll widget content" from "user wants to zoom canvas".
|
||||
|
||||
**Canvas mode:** Add `@wheel` on widget root. Check `event.target.closest(selector)` for scrollable sub-areas. If scrollable → `event.stopPropagation()`. Otherwise → `app.canvas.processMouseWheel(event)`.
|
||||
|
||||
**Vue mode:** Add CSS class `lm-wheel-scrollable` to scrollable elements. The global capture-phase hook in `web/comfyui/utils.js` (`enableListWheelScroll`) detects wheel events on marked elements and manually scrolls them via `element.scrollTop`, consuming the event before canvas zoom sees it.
|
||||
|
||||
## DOM Structure
|
||||
|
||||
`main.ts` creates an outer `<div>` container, then `vueApp.mount(container)`. The Vue app renders its own root element inside.
|
||||
|
||||
- `container.id` / `container.style.*` → outer element
|
||||
- Vue scoped `<style>` → `[data-v-hash]` applies only to Vue root
|
||||
|
||||
Classes needed by scoped Vue CSS must go on the Vue root element. Pass data as props and bind with `:class` rather than manipulating the DOM from `main.ts`.
|
||||
|
||||
## Serialization
|
||||
|
||||
For stateful widgets that need workflow persistence:
|
||||
|
||||
- `serialize: true` in `addDOMWidget` options
|
||||
- `serializeValue()` → state snapshot (called on workflow save)
|
||||
- `onSetValue(v)` → restore state (called on workflow load)
|
||||
- Always handle missing keys in restored value for backward compatibility with old workflows
|
||||
@@ -1,46 +0,0 @@
|
||||
# Custom Priority Tag Format Proposal
|
||||
|
||||
To support user-defined priority tags with flexible aliasing across different model types, the configuration will be stored as editable strings. The format balances readability with enough structure for parsing on both the backend and frontend.
|
||||
|
||||
## Format Overview
|
||||
|
||||
- Each model type is declared on its own line: `model_type: entries`.
|
||||
- Entries are comma-separated and ordered by priority from highest to lowest.
|
||||
- An entry may be a single canonical tag (e.g., `realistic`) or a canonical tag with aliases.
|
||||
- Canonical tags define the final folder name that should be used when matching that entry.
|
||||
- Aliases are enclosed in parentheses and separated by `|` (vertical bar).
|
||||
- All matching is case-insensitive; stored canonical names preserve the user-specified casing for folder creation and UI suggestions.
|
||||
|
||||
### Grammar
|
||||
|
||||
```
|
||||
priority-config := model-config { "\n" model-config }
|
||||
model-config := model-type ":" entry-list
|
||||
model-type := <identifier without spaces>
|
||||
entry-list := entry { "," entry }
|
||||
entry := canonical [ "(" alias { "|" alias } ")" ]
|
||||
canonical := <tag text without parentheses or commas>
|
||||
alias := <tag text without parentheses, commas, or pipes>
|
||||
```
|
||||
|
||||
Examples:
|
||||
|
||||
```
|
||||
lora: celebrity(celeb|celebrity), stylized, character(char)
|
||||
checkpoint: realistic(realism|realistic), anime(anime-style|toon)
|
||||
embedding: face, celeb(celebrity|celeb)
|
||||
```
|
||||
|
||||
## Parsing Notes
|
||||
|
||||
- Whitespace around separators is ignored to make manual editing more forgiving.
|
||||
- Duplicate canonical tags within the same model type collapse to a single entry; the first definition wins.
|
||||
- Aliases map to their canonical tag. When generating folder names, the canonical form is used.
|
||||
- Tags that do not match any alias or canonical entry fall back to the first tag in the model's tag list, preserving current behavior.
|
||||
|
||||
## Usage
|
||||
|
||||
- **Backend:** Convert each model type's string into an ordered list of canonical tags with alias sets. During path generation, iterate by priority order and match tags against both canonical names and their aliases.
|
||||
- **Frontend:** Surface canonical tags as suggestions, optionally displaying aliases in tooltips or secondary text. Input validation should warn about duplicate aliases within the same model type.
|
||||
|
||||
This format allows users to customize priority tag handling per model type while keeping editing simple and avoiding proliferation of folder names through alias normalization.
|
||||
@@ -1,28 +0,0 @@
|
||||
# DOM Widgets Documentation
|
||||
|
||||
Documentation for custom DOM widget development in ComfyUI LoRA Manager.
|
||||
|
||||
## Files
|
||||
|
||||
- **[Value Persistence Best Practices](value-persistence-best-practices.md)** - Essential guide for implementing text input DOM widgets that persist values correctly
|
||||
|
||||
## Key Lessons
|
||||
|
||||
### Common Anti-Patterns
|
||||
|
||||
❌ **Don't**: Create internal state variables
|
||||
❌ **Don't**: Use v-model for text inputs
|
||||
❌ **Don't**: Add serializeValue, onSetValue callbacks
|
||||
❌ **Don't**: Watch props.widget.value
|
||||
|
||||
### Best Practices
|
||||
|
||||
✅ **Do**: Use DOM element as single source of truth
|
||||
✅ **Do**: Store DOM reference on widget.inputEl
|
||||
✅ **Do**: Direct getValue/setValue to DOM
|
||||
✅ **Do**: Clean up reference on unmount
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [DOM Widget Development Guide](../dom_widget_dev_guide.md) - Comprehensive guide for building DOM widgets
|
||||
- [ComfyUI Built-in Example](../../../../code/ComfyUI_frontend/src/renderer/extensions/vueNodes/widgets/composables/useStringWidget.ts) - Reference implementation
|
||||
@@ -1,225 +0,0 @@
|
||||
# DOM Widget Value Persistence - Best Practices
|
||||
|
||||
## Overview
|
||||
|
||||
DOM widgets require different persistence patterns depending on their complexity. This document covers two patterns:
|
||||
|
||||
1. **Simple Text Widgets**: DOM element as source of truth (e.g., textarea, input)
|
||||
2. **Complex Widgets**: Internal value with `widget.callback` (e.g., LoraPoolWidget, RandomizerWidget)
|
||||
|
||||
## Understanding ComfyUI's Built-in Callback Mechanism
|
||||
|
||||
When `widget.value` is set (e.g., during workflow load), ComfyUI's `domWidget.ts` triggers this flow:
|
||||
|
||||
```typescript
|
||||
// From ComfyUI_frontend/src/scripts/domWidget.ts:146-149
|
||||
set value(v: V) {
|
||||
this.options.setValue?.(v) // 1. Update internal state
|
||||
this.callback?.(this.value) // 2. Notify listeners for UI updates
|
||||
}
|
||||
```
|
||||
|
||||
This means:
|
||||
- `setValue()` handles storing the value
|
||||
- `widget.callback()` is automatically called to notify the UI
|
||||
- You don't need custom callback mechanisms like `onSetValue`
|
||||
|
||||
---
|
||||
|
||||
## Pattern 1: Simple Text Input Widgets
|
||||
|
||||
For widgets where the value IS the DOM element's text content (textarea, input fields).
|
||||
|
||||
### When to Use
|
||||
|
||||
- Single text input/textarea widgets
|
||||
- Value is a simple string
|
||||
- No complex state management needed
|
||||
|
||||
### Implementation
|
||||
|
||||
**main.ts:**
|
||||
```typescript
|
||||
const widget = node.addDOMWidget(name, type, container, {
|
||||
getValue() {
|
||||
return widget.inputEl?.value ?? ''
|
||||
},
|
||||
setValue(v: string) {
|
||||
if (widget.inputEl) {
|
||||
widget.inputEl.value = v ?? ''
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
**Vue Component:**
|
||||
```typescript
|
||||
onMounted(() => {
|
||||
if (textareaRef.value) {
|
||||
props.widget.inputEl = textareaRef.value
|
||||
}
|
||||
})
|
||||
|
||||
onUnmounted(() => {
|
||||
if (props.widget.inputEl === textareaRef.value) {
|
||||
props.widget.inputEl = undefined
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Why This Works
|
||||
|
||||
- Single source of truth: the DOM element
|
||||
- `getValue()` reads directly from DOM
|
||||
- `setValue()` writes directly to DOM
|
||||
- No sync issues between multiple state variables
|
||||
|
||||
---
|
||||
|
||||
## Pattern 2: Complex Widgets
|
||||
|
||||
For widgets with structured data (JSON configs, arrays, objects) where the value cannot be stored in a DOM element.
|
||||
|
||||
### When to Use
|
||||
|
||||
- Value is a complex object/array (e.g., `{ loras: [...], settings: {...} }`)
|
||||
- Multiple UI elements contribute to the value
|
||||
- Vue reactive state manages the UI
|
||||
|
||||
### Implementation
|
||||
|
||||
**main.ts:**
|
||||
```typescript
|
||||
let internalValue: MyConfig | undefined
|
||||
|
||||
const widget = node.addDOMWidget(name, type, container, {
|
||||
getValue() {
|
||||
return internalValue
|
||||
},
|
||||
setValue(v: MyConfig) {
|
||||
internalValue = v
|
||||
// NO custom onSetValue needed - widget.callback is called automatically
|
||||
},
|
||||
serialize: true // Ensure value is saved with workflow
|
||||
})
|
||||
```
|
||||
|
||||
**Vue Component:**
|
||||
```typescript
|
||||
const config = ref<MyConfig>(getDefaultConfig())
|
||||
|
||||
onMounted(() => {
|
||||
// Set up callback for UI updates when widget.value changes externally
|
||||
// (e.g., workflow load, undo/redo)
|
||||
props.widget.callback = (newValue: MyConfig) => {
|
||||
if (newValue) {
|
||||
config.value = newValue
|
||||
}
|
||||
}
|
||||
|
||||
// Restore initial value if workflow was already loaded
|
||||
if (props.widget.value) {
|
||||
config.value = props.widget.value
|
||||
}
|
||||
})
|
||||
|
||||
// When UI changes, update widget value
|
||||
function onConfigChange(newConfig: MyConfig) {
|
||||
config.value = newConfig
|
||||
props.widget.value = newConfig // This also triggers callback
|
||||
}
|
||||
```
|
||||
|
||||
### Why This Works
|
||||
|
||||
1. **Clear separation**: `internalValue` stores the data, Vue ref manages the UI
|
||||
2. **Built-in callback**: ComfyUI calls `widget.callback()` automatically after `setValue()`
|
||||
3. **Bidirectional sync**:
|
||||
- External → UI: `setValue()` updates `internalValue`, `callback()` updates Vue ref
|
||||
- UI → External: User interaction updates Vue ref, which updates `widget.value`
|
||||
|
||||
---
|
||||
|
||||
## Common Mistakes
|
||||
|
||||
### ❌ Creating custom callback mechanisms
|
||||
|
||||
```typescript
|
||||
// Wrong - unnecessary complexity
|
||||
setValue(v: MyConfig) {
|
||||
internalValue = v
|
||||
widget.onSetValue?.(v) // Don't add this - use widget.callback instead
|
||||
}
|
||||
```
|
||||
|
||||
Use the built-in `widget.callback` instead.
|
||||
|
||||
### ❌ Using v-model for simple text inputs in DOM widgets
|
||||
|
||||
```html
|
||||
<!-- Wrong - creates sync issues -->
|
||||
<textarea v-model="textValue" />
|
||||
|
||||
<!-- Right for simple text widgets -->
|
||||
<textarea ref="textareaRef" @input="onInput" />
|
||||
```
|
||||
|
||||
### ❌ Watching props.widget.value
|
||||
|
||||
```typescript
|
||||
// Wrong - creates race conditions
|
||||
watch(() => props.widget.value, (newValue) => {
|
||||
config.value = newValue
|
||||
})
|
||||
```
|
||||
|
||||
Use `widget.callback` instead - it's called at the right time in the lifecycle.
|
||||
|
||||
### ❌ Multiple sources of truth
|
||||
|
||||
```typescript
|
||||
// Wrong - who is the source of truth?
|
||||
let internalValue = '' // State 1
|
||||
const textValue = ref('') // State 2
|
||||
const domElement = textarea // State 3
|
||||
props.widget.value // State 4
|
||||
```
|
||||
|
||||
Choose ONE source of truth:
|
||||
- **Simple widgets**: DOM element
|
||||
- **Complex widgets**: `internalValue` (with Vue ref as derived UI state)
|
||||
|
||||
### ❌ Adding serializeValue for simple widgets
|
||||
|
||||
```typescript
|
||||
// Wrong - getValue/setValue handle serialization
|
||||
props.widget.serializeValue = async () => textValue.value
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Decision Guide
|
||||
|
||||
| Widget Type | Source of Truth | Use `widget.callback` | Example |
|
||||
|-------------|-----------------|----------------------|---------|
|
||||
| Simple text input | DOM element (`inputEl`) | Optional | AutocompleteTextWidget |
|
||||
| Complex config | `internalValue` | Yes, for UI sync | LoraPoolWidget |
|
||||
| Vue component widget | Vue ref + `internalValue` | Yes | RandomizerWidget |
|
||||
|
||||
---
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
- [ ] Load workflow - value restores correctly
|
||||
- [ ] Switch workflow - value persists
|
||||
- [ ] Reload page - value persists
|
||||
- [ ] UI interaction - value updates
|
||||
- [ ] Undo/redo - value syncs with UI
|
||||
- [ ] No console errors
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- ComfyUI DOMWidget implementation: `ComfyUI_frontend/src/scripts/domWidget.ts`
|
||||
- Simple text widget example: `ComfyUI_frontend/src/renderer/extensions/vueNodes/widgets/composables/useStringWidget.ts`
|
||||
@@ -1,546 +0,0 @@
|
||||
# DOMWidget Development Guide
|
||||
|
||||
This document provides a comprehensive guide for developing custom DOMWidgets in ComfyUI using Vanilla JavaScript. DOMWidgets allow you to embed standard HTML elements (div, video, canvas, input, etc.) into ComfyUI nodes while benefitting from the frontend's automatic layout and zoom management.
|
||||
|
||||
## 1. Core Concepts
|
||||
|
||||
In ComfyUI, a `DOMWidget` extends the default LiteGraph Canvas rendering logic. It maintains an HTML layer on top of the Canvas, making complex interactions and media displays significantly easier to implement than pure Canvas drawing.
|
||||
|
||||
### Key APIs
|
||||
* **`app.registerExtension`**: The entry point for registering extensions.
|
||||
* **`getCustomWidgets`**: A hook for defining new widget types associated with specific input types.
|
||||
* **`node.addDOMWidget`**: The core method to add HTML elements to a node.
|
||||
|
||||
---
|
||||
|
||||
## 2. Basic Structure
|
||||
|
||||
A standard custom DOMWidget extension typically follows this structure:
|
||||
|
||||
```javascript
|
||||
import { app } from "../../scripts/app.js";
|
||||
|
||||
app.registerExtension({
|
||||
name: "My.Custom.Extension",
|
||||
async getCustomWidgets() {
|
||||
return {
|
||||
// Define a new widget type named "MY_WIDGET_TYPE"
|
||||
MY_WIDGET_TYPE(node, inputName, inputData, app) {
|
||||
// 1. Create the HTML element
|
||||
const container = document.createElement("div");
|
||||
container.innerHTML = "Hello <b>DOMWidget</b>!";
|
||||
|
||||
// 2. Setup styles (Optional but recommended)
|
||||
container.style.color = "white";
|
||||
container.style.backgroundColor = "#222";
|
||||
container.style.padding = "5px";
|
||||
|
||||
// 3. Add the DOMWidget and return the result
|
||||
const widget = node.addDOMWidget(inputName, "MY_WIDGET_TYPE", container, {
|
||||
// Configuration options
|
||||
getValue() {
|
||||
return container.innerText;
|
||||
},
|
||||
setValue(v) {
|
||||
container.innerText = v;
|
||||
}
|
||||
});
|
||||
|
||||
// 4. Return in the standard format
|
||||
return { widget };
|
||||
}
|
||||
};
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ComfyUI Dual Rendering Modes
|
||||
|
||||
ComfyUI frontend supports two rendering modes:
|
||||
|
||||
| Mode | Description | DOM Structure |
|
||||
| :--- | :--- | :--- |
|
||||
| **Canvas Mode** | Traditional rendering where widgets are rendered on top of canvas using absolute positioning | Uses `.dom-widget` class on containers |
|
||||
| **Vue DOM Mode** | New rendering mode where nodes and widgets are rendered as Vue components | Uses `.lg-node-widget` class on containers with dynamic IDs (e.g., `v-1-0`) |
|
||||
|
||||
### Mode Switching
|
||||
|
||||
The frontend switches between modes via `LiteGraph.vueNodesMode` boolean:
|
||||
- `LiteGraph.vueNodesMode = true` → Vue DOM Mode
|
||||
- `LiteGraph.vueNodesMode = false` → Canvas Mode
|
||||
|
||||
**Key Behavior**: Mode switching triggers DOM re-rendering WITHOUT page reload. Widget elements are destroyed and recreated, so any event listeners or references to old DOM elements become invalid.
|
||||
|
||||
### Testing Mode Switches via Chrome DevTools MCP
|
||||
|
||||
```javascript
|
||||
// Trigger render mode change
|
||||
LiteGraph.vueNodesMode = !LiteGraph.vueNodesMode;
|
||||
|
||||
// Force canvas redraw (optional but helps trigger re-render)
|
||||
if (app.canvas) {
|
||||
app.canvas.draw(true, true);
|
||||
}
|
||||
```
|
||||
|
||||
### Development Notes
|
||||
|
||||
When implementing widgets that attach event listeners or maintain external references:
|
||||
1. **Use `node.onRemoved`** to clean up when node is deleted
|
||||
2. **Detect DOM changes** by checking if widget input element is still in document: `document.body.contains(inputElement)`
|
||||
3. **Poll for mode changes** by watching `LiteGraph.vueNodesMode` and re-initializing when it changes
|
||||
4. **Use `loadedGraphNode` hook** for initial setup (guarantees DOM is fully rendered)
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 3. The `addDOMWidget` API
|
||||
|
||||
```javascript
|
||||
node.addDOMWidget(name, type, element, options)
|
||||
```
|
||||
|
||||
### Parameters
|
||||
1. **`name`**: The internal name of the widget (usually matches the input name).
|
||||
2. **`type`**: The type identifier for the widget.
|
||||
3. **`element`**: The actual HTMLElement to embed.
|
||||
4. **`options`**: (Object) Configuration for lifecycle, sizing, and persistence.
|
||||
|
||||
### Common `options` Fields
|
||||
| Field | Type | Description |
|
||||
| :--- | :--- | :--- |
|
||||
| `getValue` | `Function` | Defines how to retrieve the widget's value for serialization. |
|
||||
| `setValue` | `Function` | Defines how to restore the widget's state from workflow data. |
|
||||
| `getMinHeight` | `Function` | Returns the minimum height in pixels. |
|
||||
| `getHeight` | `Function` | Returns the preferred height (supports numbers or percentage strings like `"50%"`). |
|
||||
| `onResize` | `Function` | Callback triggered when the widget is resized. |
|
||||
| `hideOnZoom`| `Boolean` | Whether to hide the DOM element when zoomed out to improve performance (default: `true`). |
|
||||
| `selectOn` | `string[]` | Events on the element that should trigger node selection (default: `['focus', 'click']`). |
|
||||
|
||||
---
|
||||
|
||||
## 4. Size Control
|
||||
|
||||
Custom DOMWidgets must actively inform the parent Node of their size requirements to ensure the Node layout is calculated correctly and connection wires remain aligned.
|
||||
|
||||
### 4.1 Core Mechanism
|
||||
|
||||
Whether in Canvas Mode or Vue Mode, the underlying logic model (`LGraphNode`) calls the widget's `computeLayoutSize` method to determine dimensions. This logic is used to calculate the Node's total size and the position of input/output slots.
|
||||
|
||||
### 4.2 Controlling Height
|
||||
|
||||
It is recommended to use the `options` parameter to define height behavior.
|
||||
|
||||
**Performance Note:** providing `getMinHeight` and `getHeight` via `options` allows the system to skip expensive DOM measurements (`getComputedStyle`) during rendering loop. This significantly improves performance and prevents FPS drops during node resizing.
|
||||
|
||||
**Method 1: Using `options` (Recommended)**
|
||||
|
||||
```javascript
|
||||
const widget = node.addDOMWidget("MyWidget", "custom", element, {
|
||||
// Specify minimum height in pixels
|
||||
getMinHeight: () => 150,
|
||||
|
||||
// Or specify preferred height (pixels or percentage string)
|
||||
// getHeight: () => "50%",
|
||||
});
|
||||
```
|
||||
|
||||
**Method 2: Using CSS Variables**
|
||||
|
||||
You can also set specific CSS variables on the root element:
|
||||
|
||||
```javascript
|
||||
element.style.setProperty("--comfy-widget-min-height", "150px");
|
||||
// or --comfy-widget-height
|
||||
```
|
||||
|
||||
### 4.3 Controlling Width
|
||||
|
||||
By default, a DOMWidget's width automatically stretches to fit the Node's width (which is determined by the Title or other Input Slots).
|
||||
|
||||
If you must **force the Node to be wider** to accommodate your widget, you need to override the widget instance's `computeLayoutSize` method:
|
||||
|
||||
```javascript
|
||||
const widget = node.addDOMWidget("WideWidget", "custom", element);
|
||||
|
||||
// Override the default layout calculation
|
||||
widget.computeLayoutSize = (targetNode) => {
|
||||
return {
|
||||
minHeight: 150, // Must return height
|
||||
minWidth: 300 // Force the Node to be at least 300px wide
|
||||
};
|
||||
};
|
||||
```
|
||||
|
||||
### 4.4 Dynamic Resizing
|
||||
|
||||
If your widget's content changes dynamically (e.g., expanding sections, loading images, or CSS changes), the DOM element will resize, but the Canvas-rendered Node background and Slots will not automatically follow. You must manually trigger a synchronization.
|
||||
|
||||
**The Update Sequence:**
|
||||
Whenever the **actual rendering height** of your DOM element changes, execute the following "three-step combo":
|
||||
|
||||
```javascript
|
||||
// 1. Calculate the new optimal size for the node based on current widget requirements
|
||||
const newSize = node.computeSize();
|
||||
|
||||
// 2. Apply the new size to the node model (updates bounding box and slot positions)
|
||||
node.setSize(newSize);
|
||||
|
||||
// 3. Mark the canvas as dirty to trigger a redraw in the next animation frame
|
||||
node.setDirtyCanvas(true, true);
|
||||
```
|
||||
|
||||
**Common Scenarios:**
|
||||
|
||||
| Scenario | Actual Height Change? | Update Required? |
|
||||
| :--- | :--- | :--- |
|
||||
| **Expand/Collapse content** | **Yes** | ✅ **Yes**. Prevents widget from overflowing node boundaries. |
|
||||
| **Image/Video finished loading** | **Yes** | ✅ **Yes**. Initial height might be 0 until the media loads. |
|
||||
| **Changing `minHeight`** | **Maybe** | ❓ **Only if** the change causes the element's actual height to shift. |
|
||||
| **Changing font size/styles** | **Yes** | ✅ **Yes**. Text reflow often changes the total height. |
|
||||
| **User dragging node corner** | **Yes** | ❌ **No**. LiteGraph handles this internally. |
|
||||
|
||||
---
|
||||
|
||||
## 5. State Persistence (Serialization)
|
||||
|
||||
### 5.1 Default Behavior
|
||||
|
||||
DOMWidgets have **serialization enabled** by default (`serialize` property is `true`).
|
||||
* **Saving**: ComfyUI attempts to read the widget's value to save into the Workflow file.
|
||||
* **Loading**: ComfyUI reads the value from the Workflow file and assigns it to the widget.
|
||||
|
||||
### 5.2 Custom Serialization
|
||||
|
||||
To make persistence work effectively (saving internal DOM state and restoring it), you must implement `getValue` and `setValue` in the `options`:
|
||||
|
||||
* **`getValue`**: Returns the state to be saved (Number, String, or Object).
|
||||
* **`setValue`**: Receives the restored value and updates the DOM element.
|
||||
|
||||
**Example:**
|
||||
|
||||
```javascript
|
||||
const inputEl = document.createElement("input");
|
||||
const widget = node.addDOMWidget("MyInput", "custom", inputEl, {
|
||||
// 1. Called during Save
|
||||
getValue: () => {
|
||||
return inputEl.value;
|
||||
},
|
||||
// 2. Called during Load or Copy/Paste
|
||||
setValue: (value) => {
|
||||
inputEl.value = value || "";
|
||||
}
|
||||
});
|
||||
|
||||
// Optional: Listen for changes to update widget.value immediately
|
||||
inputEl.addEventListener("change", () => {
|
||||
widget.value = inputEl.value; // Triggers callbacks
|
||||
});
|
||||
```
|
||||
|
||||
> **⚠️ Important**: For Vue-based DOM widgets with text inputs, follow the [Value Persistence Best Practices](dom-widgets/value-persistence-best-practices.md) to avoid sync issues. Key takeaway: use DOM element as single source of truth, avoid internal state variables and v-model.
|
||||
|
||||
### 5.3 The Restoration Mechanism (`configure`)
|
||||
|
||||
* **`configure(data)`**: When a Workflow is loaded, `LGraphNode` calls its `configure(data)` method.
|
||||
* **`setValue` Chain**: During `configure`, the Node iterates over the saved `widgets_values` array and assigns each value (`widget.value = savedValue`). For DOMWidgets, this assignment triggers the `setValue` callback defined in your options.
|
||||
|
||||
Therefore, `options.setValue` is the critical hook for restoring widget state.
|
||||
|
||||
### 5.4 Disabling Serialization
|
||||
|
||||
If your widget is purely for display (e.g., a real-time monitor or generated chart) and doesn't need to save state, disable serialization to reduce workflow file size.
|
||||
|
||||
**Note**: You cannot set this via `options`. You must modify the widget instance directly.
|
||||
|
||||
```javascript
|
||||
const widget = node.addDOMWidget("DisplayOnly", "custom", element);
|
||||
widget.serialize = false; // Explicitly disable
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Lifecycle & Events
|
||||
|
||||
### 6.1 `onResize`
|
||||
|
||||
When the Node size changes (e.g., user drags the corner), the widget can receive a notification via `options`:
|
||||
|
||||
```javascript
|
||||
const widget = node.addDOMWidget("ResizingWidget", "custom", element, {
|
||||
onResize: (w) => {
|
||||
// 'w' is the widget instance
|
||||
// Adjust internal DOM layout here if necessary
|
||||
console.log("Widget resized");
|
||||
}
|
||||
});
|
||||
```
|
||||
|
||||
### 6.2 Construction & Mounting
|
||||
|
||||
* **Construction**: Occurs immediately when `addDOMWidget` is called.
|
||||
* **Mounting**:
|
||||
* **Canvas Mode**: Appended to `.dom-widget-container` via `DomWidget.vue`.
|
||||
* **Vue Mode**: Appended inside the Node component via `WidgetDOM.vue`.
|
||||
* **Caution**: When `addDOMWidget` returns, the element may not be in the `document.body` yet. If you need to access layout properties like `getBoundingClientRect`, use `setTimeout` or wait for the first `onResize`.
|
||||
|
||||
### 6.3 Cleanup
|
||||
|
||||
If you create external references (like `setInterval` or global event listeners), ensure you clean them up using `node.onRemoved`:
|
||||
|
||||
```javascript
|
||||
node.onRemoved = function() {
|
||||
clearInterval(myInterval);
|
||||
// Call original onRemoved if it existed
|
||||
};
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Styling & Best Practices
|
||||
|
||||
### 7.1 Styling
|
||||
Since DOMWidgets are placed in absolute positioned containers or managed by Vue, ensure your container handles sizing gracefully:
|
||||
|
||||
```javascript
|
||||
container.style.width = "100%";
|
||||
container.style.boxSizing = "border-box";
|
||||
```
|
||||
|
||||
### 7.2 Path References
|
||||
When importing `app`, adjust the path based on your extension's folder depth. Typically:
|
||||
`import { app } from "../../scripts/app.js";`
|
||||
|
||||
### 7.3 Security
|
||||
If setting `innerHTML` dynamically, ensure the content is sanitized or trusted to prevent XSS attacks.
|
||||
|
||||
### 7.4 UI Constraints for ComfyUI Custom Node Widgets
|
||||
|
||||
When developing DOMWidgets as internal UI widgets for ComfyUI custom nodes, keep the following constraints in mind:
|
||||
|
||||
#### 7.4.1 Minimize Vertical Space
|
||||
|
||||
ComfyUI nodes are often displayed in a compact graph view with many nodes visible simultaneously. Avoid excessive vertical spacing that could clutter the workspace.
|
||||
|
||||
- Keep layouts compact and efficient
|
||||
- Use appropriate padding and margins (4-8px typically)
|
||||
- Stack related controls vertically but avoid unnecessary spacing
|
||||
|
||||
#### 7.4.2 Avoid Dynamic Height Changes
|
||||
|
||||
Dynamic height changes (expand/collapse sections, showing/hiding content) can cause node layout recalculations and affect connection wire positioning.
|
||||
|
||||
- Prefer static layouts over expandable/collapsible sections
|
||||
- Use tooltips or overlays for additional information instead
|
||||
- If dynamic height is unavoidable, manually trigger layout updates (see Section 4.4)
|
||||
|
||||
#### 7.4.3 Keep UI Simple and Intuitive
|
||||
|
||||
As internal widgets for ComfyUI custom nodes, the UI should be accessible to users without technical implementation details.
|
||||
|
||||
- Use clear, user-friendly terminology (avoid "frontend/backend roll" in favor of "fixed/always randomize")
|
||||
- Focus on user intent rather than implementation details
|
||||
- Avoid complex interactions that may confuse users
|
||||
|
||||
#### 7.4.4 Forward Middle Mouse Events to Canvas
|
||||
|
||||
By default, when a DOM widget receives pointer events (e.g., mouse clicks, drags), these events are captured by the widget and not forwarded to the ComfyUI canvas. This prevents users from panning the workflow using the middle mouse button when the cursor is over a DOM widget.
|
||||
|
||||
To enable workflow panning over your widget, you should forward middle mouse events (button 1) to the canvas using the `forwardMiddleMouseToCanvas` utility function:
|
||||
|
||||
```javascript
|
||||
import { forwardMiddleMouseToCanvas } from "./utils.js";
|
||||
|
||||
// In your widget creation function
|
||||
const container = document.createElement("div");
|
||||
container.style.width = "100%";
|
||||
container.style.height = "100%";
|
||||
// ... other styles ...
|
||||
|
||||
// Forward middle mouse events to canvas for panning
|
||||
forwardMiddleMouseToCanvas(container);
|
||||
|
||||
const widget = node.addDOMWidget(name, type, container, { ... });
|
||||
```
|
||||
|
||||
The `forwardMiddleMouseToCanvas` function:
|
||||
- Forwards `pointerdown` events with button 1 (middle mouse button) to `app.canvas.processMouseDown`
|
||||
- Forwards `pointermove` events while middle mouse button is pressed to `app.canvas.processMouseMove`
|
||||
- Forwards `pointerup` events with button 1 to `app.canvas.processMouseUp`
|
||||
|
||||
This allows users to pan the workflow canvas even when their mouse cursor is hovering over your DOM widget.
|
||||
|
||||
---
|
||||
|
||||
## 8. Event Handling in Vue DOM Render Mode
|
||||
|
||||
ComfyUI frontend supports two rendering modes for nodes:
|
||||
- **Legacy Canvas Mode**: Traditional rendering where widgets are rendered on top of the canvas using absolute positioning
|
||||
- **Vue DOM Render Mode**: New rendering mode where nodes and widgets are rendered as Vue components
|
||||
|
||||
In Vue DOM render mode, event handling works differently. The frontend uses `useCanvasInteractions` composable to manage event forwarding to the canvas. This can cause custom event handlers in your widgets (e.g., mouse wheel for sliders, custom drag operations) to be intercepted by the canvas.
|
||||
|
||||
### 8.1 Wheel Event Handling
|
||||
|
||||
By default in Vue DOM render mode, wheel events on widgets may be forwarded to the canvas for workflow zoom, overriding your custom wheel handlers (e.g., adjusting slider values with mouse wheel).
|
||||
|
||||
To fix this, use the `data-capture-wheel="true"` attribute on elements that should capture wheel events:
|
||||
|
||||
```vue
|
||||
<!-- Vue component template -->
|
||||
<div class="my-slider" data-capture-wheel="true" @wheel="onWheel">
|
||||
<!-- Slider content -->
|
||||
</div>
|
||||
|
||||
<script setup lang="ts">
|
||||
const onWheel = (event: WheelEvent) => {
|
||||
event.preventDefault()
|
||||
// Custom wheel handling logic here
|
||||
}
|
||||
</script>
|
||||
```
|
||||
|
||||
**How it works:**
|
||||
- ComfyUI's `useCanvasInteractions.ts` checks `target?.closest('[data-capture-wheel="true"]')` before forwarding wheel events
|
||||
- If an element (or its ancestor) has this attribute, wheel events are not forwarded to canvas
|
||||
- Your custom `@wheel` handler will work as expected
|
||||
|
||||
**Granular control:**
|
||||
- Apply `data-capture-wheel="true"` to specific interactive elements (e.g., sliders, scrollable areas)
|
||||
- Widget container without this attribute will allow workflow zoom when wheel is used elsewhere
|
||||
- This allows users to both: adjust widget values with wheel, and zoom workflow with wheel in widget's non-interactive areas
|
||||
|
||||
**Example from DualRangeSlider.vue:**
|
||||
```vue
|
||||
<template>
|
||||
<div
|
||||
class="dual-range-slider"
|
||||
:class="{ disabled, 'is-dragging': dragging !== null }"
|
||||
data-capture-wheel="true"
|
||||
@wheel="onWheel"
|
||||
>
|
||||
<!-- Slider tracks and handles -->
|
||||
</div>
|
||||
</template>
|
||||
```
|
||||
|
||||
### 8.2 Pointer Event Handling
|
||||
|
||||
In Vue DOM render mode, pointer events (click, drag, etc.) may also be captured by the canvas system. For custom drag operations:
|
||||
|
||||
1. **Use event modifiers to stop propagation:**
|
||||
```vue
|
||||
<div
|
||||
@pointerdown.stop="startDrag"
|
||||
@pointermove.stop="onDrag"
|
||||
@pointerup.stop="stopDrag"
|
||||
>
|
||||
```
|
||||
|
||||
2. **Use pointer capture for reliable drag tracking:**
|
||||
```javascript
|
||||
const startDrag = (event: PointerEvent) => {
|
||||
const target = event.currentTarget as HTMLElement
|
||||
target.setPointerCapture(event.pointerId)
|
||||
// ... drag initialization
|
||||
}
|
||||
|
||||
const stopDrag = (event: PointerEvent) => {
|
||||
const target = event.currentTarget as HTMLElement
|
||||
target.releasePointerCapture(event.pointerId)
|
||||
// ... drag cleanup
|
||||
}
|
||||
```
|
||||
|
||||
3. **Use `touch-action: none` CSS for touch devices:**
|
||||
```css
|
||||
.my-draggable {
|
||||
touch-action: none;
|
||||
}
|
||||
```
|
||||
|
||||
### 8.3 Compatibility Checklist
|
||||
|
||||
Ensure your widget works in both rendering modes:
|
||||
|
||||
| Feature | Canvas Mode | Vue DOM Mode | Solution |
|
||||
|---------|-------------|--------------|----------|
|
||||
| Mouse wheel on sliders | Works by default | Needs `data-capture-wheel` | Add `data-capture-wheel="true"` to slider elements |
|
||||
| Custom drag operations | Works with `stopPropagation()` | Needs `stopPropagation()` | Use `.stop` modifier and pointer capture |
|
||||
| Middle mouse panning | Manual forwarding required | Manual forwarding required | Use `forwardMiddleMouseToCanvas()` |
|
||||
| Workflow zoom on widget edges | Works by default | Works by default | No action needed (works by default) |
|
||||
|
||||
### 8.4 Testing Recommendations
|
||||
|
||||
Test your widget in both rendering modes:
|
||||
1. Toggle between Canvas Mode and Vue DOM Mode in ComfyUI settings
|
||||
2. Verify custom interactions (wheel, drag, etc.) work in both modes
|
||||
3. Verify canvas interactions (zoom, pan) still work when cursor is over non-interactive widget areas
|
||||
4. Test with touch devices if applicable
|
||||
|
||||
---
|
||||
|
||||
## 9. Complete Example: Text Counter
|
||||
|
||||
This example implements a simple widget that displays the character count of another text widget in the same node.
|
||||
|
||||
```javascript
|
||||
import { app } from "../../scripts/app.js";
|
||||
|
||||
app.registerExtension({
|
||||
name: "Comfy.TextCounter",
|
||||
getCustomWidgets() {
|
||||
return {
|
||||
TEXT_COUNTER(node, inputName) {
|
||||
const el = document.createElement("div");
|
||||
Object.assign(el.style, {
|
||||
background: "#222",
|
||||
border: "1px solid #444",
|
||||
padding: "8px",
|
||||
borderRadius: "4px",
|
||||
fontSize: "12px",
|
||||
color: "#eee"
|
||||
});
|
||||
|
||||
const label = document.createElement("span");
|
||||
label.innerText = "Characters: 0";
|
||||
el.appendChild(label);
|
||||
|
||||
const widget = node.addDOMWidget(inputName, "TEXT_COUNTER", el, {
|
||||
getValue() { return ""; }, // Nothing to save
|
||||
setValue(v) { }, // Nothing to restore
|
||||
getMinHeight() { return 40; }
|
||||
});
|
||||
|
||||
// Disable serialization for this display-only widget
|
||||
widget.serialize = false;
|
||||
|
||||
// Custom method to update UI
|
||||
widget.updateCount = (text) => {
|
||||
label.innerText = `Characters: ${text.length}`;
|
||||
};
|
||||
|
||||
return { widget };
|
||||
}
|
||||
};
|
||||
},
|
||||
nodeCreated(node) {
|
||||
// Logic to link widgets after the node is initialized
|
||||
if (node.comfyClass === "MyTextNode") {
|
||||
const counterWidget = node.widgets.find(w => w.type === "TEXT_COUNTER");
|
||||
const textWidget = node.widgets.find(w => w.name === "text");
|
||||
|
||||
if (counterWidget && textWidget) {
|
||||
// Hook into the text widget's callback
|
||||
const oldCallback = textWidget.callback;
|
||||
textWidget.callback = function(v) {
|
||||
if (oldCallback) oldCallback.apply(this, arguments);
|
||||
counterWidget.updateCount(v);
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
```
|
||||
@@ -1,170 +0,0 @@
|
||||
# Recipe Batch Import Feature Requirements
|
||||
|
||||
## Overview
|
||||
Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
|
||||
|
||||
## User Stories
|
||||
|
||||
### US-1: Directory Batch Import
|
||||
As a user with a folder of reference images or workflow screenshots, I want to import all images from a directory at once so that I don't have to import them one by one.
|
||||
|
||||
**Acceptance Criteria:**
|
||||
- User can specify a local directory path containing images
|
||||
- System discovers all supported image files in the directory
|
||||
- Each image is analyzed for metadata and converted to a recipe
|
||||
- Results show which images succeeded, failed, or were skipped
|
||||
|
||||
### US-2: URL Batch Import
|
||||
As a user with a list of image URLs (e.g., from Civitai or other sources), I want to import multiple images by URL in one operation.
|
||||
|
||||
**Acceptance Criteria:**
|
||||
- User can provide multiple image URLs (one per line or as a list)
|
||||
- System downloads and processes each image
|
||||
- URL-specific metadata (like Civitai info) is preserved when available
|
||||
- Failed URLs are reported with clear error messages
|
||||
|
||||
### US-3: Concurrent Processing Control
|
||||
As a user with varying system resources, I want to control how many images are processed simultaneously to balance speed and system load.
|
||||
|
||||
**Acceptance Criteria:**
|
||||
- User can configure the number of concurrent operations (1-10)
|
||||
- System provides sensible defaults based on common hardware configurations
|
||||
- Processing respects the concurrency limit to prevent resource exhaustion
|
||||
|
||||
### US-4: Import Results Summary
|
||||
As a user performing a batch import, I want to see a clear summary of the operation results so I understand what succeeded and what needs attention.
|
||||
|
||||
**Acceptance Criteria:**
|
||||
- Total count of images processed is displayed
|
||||
- Number of successfully imported recipes is shown
|
||||
- Number of failed imports with error details is provided
|
||||
- Number of skipped images (no metadata) is indicated
|
||||
- Results can be exported or saved for reference
|
||||
|
||||
### US-5: Progress Visibility
|
||||
As a user importing a large batch, I want to see the progress of the operation so I know it's working and can estimate completion time.
|
||||
|
||||
**Acceptance Criteria:**
|
||||
- Progress indicator shows current status (e.g., "Processing image 5 of 50")
|
||||
- Real-time updates as each image completes
|
||||
- Ability to view partial results before completion
|
||||
- Clear indication when the operation is finished
|
||||
|
||||
## Functional Requirements
|
||||
|
||||
### FR-1: Image Discovery
|
||||
The system shall discover image files in a specified directory recursively or non-recursively based on user preference.
|
||||
|
||||
**Supported formats:** JPG, JPEG, PNG, WebP, GIF, BMP
|
||||
|
||||
### FR-2: Metadata Extraction
|
||||
For each image, the system shall:
|
||||
- Extract EXIF metadata if present
|
||||
- Parse embedded workflow data (ComfyUI PNG metadata)
|
||||
- Fetch external metadata for known URL patterns (e.g., Civitai)
|
||||
- Generate recipes from extracted information
|
||||
|
||||
### FR-3: Concurrent Processing
|
||||
The system shall support concurrent processing of multiple images with:
|
||||
- Configurable concurrency limit (default: 3)
|
||||
- Resource-aware execution
|
||||
- Graceful handling of individual failures without stopping the batch
|
||||
|
||||
### FR-4: Error Handling
|
||||
The system shall handle various error conditions:
|
||||
- Invalid directory paths
|
||||
- Inaccessible files
|
||||
- Network errors for URL imports
|
||||
- Images without extractable metadata
|
||||
- Malformed or corrupted image files
|
||||
|
||||
### FR-5: Recipe Persistence
|
||||
Successfully analyzed images shall be persisted as recipes with:
|
||||
- Extracted generation parameters
|
||||
- Preview image association
|
||||
- Tags and metadata
|
||||
- Source information (file path or URL)
|
||||
|
||||
## Non-Functional Requirements
|
||||
|
||||
### NFR-1: Performance
|
||||
- Batch operations should complete in reasonable time (< 5 seconds per image on average)
|
||||
- UI should remain responsive during batch operations
|
||||
- Memory usage should scale gracefully with batch size
|
||||
|
||||
### NFR-2: Scalability
|
||||
- Support batches of 1-1000 images
|
||||
- Handle mixed success/failure scenarios gracefully
|
||||
- No hard limits on concurrent operations (configurable)
|
||||
|
||||
### NFR-3: Usability
|
||||
- Clear error messages for common failure cases
|
||||
- Intuitive UI for configuring import options
|
||||
- Accessible from the main Recipes interface
|
||||
|
||||
### NFR-4: Reliability
|
||||
- Failed individual imports should not crash the entire batch
|
||||
- Partial results should be preserved on unexpected termination
|
||||
- All operations should be idempotent (re-importing same image doesn't create duplicates)
|
||||
|
||||
## API Requirements
|
||||
|
||||
### Batch Import Endpoints
|
||||
The system should expose endpoints for:
|
||||
|
||||
1. **Directory Import**
|
||||
- Accept directory path and configuration options
|
||||
- Return operation ID for status tracking
|
||||
- Async or sync operation support
|
||||
|
||||
2. **URL Import**
|
||||
- Accept list of URLs and configuration options
|
||||
- Support URL validation before processing
|
||||
- Return operation ID for status tracking
|
||||
|
||||
3. **Status/Progress**
|
||||
- Query operation status by ID
|
||||
- Get current progress and partial results
|
||||
- Retrieve final results after completion
|
||||
|
||||
## UI/UX Requirements
|
||||
|
||||
### UIR-1: Entry Point
|
||||
Batch import should be accessible from the Recipes page via a clearly labeled button in the toolbar.
|
||||
|
||||
### UIR-2: Import Modal
|
||||
A modal dialog should provide:
|
||||
- Tab or section for Directory import
|
||||
- Tab or section for URL import
|
||||
- Configuration options (concurrency, options)
|
||||
- Start/Stop controls
|
||||
- Results display area
|
||||
|
||||
### UIR-3: Results Display
|
||||
Results should be presented with:
|
||||
- Summary statistics (total, success, failed, skipped)
|
||||
- Expandable details for each category
|
||||
- Export or copy functionality for results
|
||||
- Clear visual distinction between success/failure/skip
|
||||
|
||||
## Future Considerations
|
||||
|
||||
- **Scheduled Imports**: Ability to schedule batch imports for later execution
|
||||
- **Import Templates**: Save import configurations for reuse
|
||||
- **Cloud Storage**: Import from cloud storage services (Google Drive, Dropbox)
|
||||
- **Duplicate Detection**: Advanced duplicate detection based on image hash
|
||||
- **Tag Suggestions**: AI-powered tag suggestions for imported recipes
|
||||
- **Batch Editing**: Apply tags or organization to multiple imported recipes at once
|
||||
|
||||
## Dependencies
|
||||
|
||||
- Recipe analysis service (metadata extraction)
|
||||
- Recipe persistence service (storage)
|
||||
- Image download capability (for URL imports)
|
||||
- Recipe scanner (for refresh after import)
|
||||
- Civitai client (for enhanced URL metadata)
|
||||
|
||||
---
|
||||
|
||||
*Document Version: 1.0*
|
||||
*Status: Requirements Definition*
|
||||
@@ -1,51 +0,0 @@
|
||||
# Frontend DOM Fixture Strategy
|
||||
|
||||
This guide outlines how to reproduce the markup emitted by the Django templates while running Vitest in jsdom. The aim is to make it straightforward to write integration-style unit tests for managers and UI helpers without having to duplicate template fragments inline.
|
||||
|
||||
## Loading Template Markup
|
||||
|
||||
Vitest executes inside Node, so we can read the same HTML templates that ship with the extension:
|
||||
|
||||
1. Use the helper utilities from `tests/frontend/utils/domFixtures.js` to read files under the `templates/` directory.
|
||||
2. Mount the returned markup into `document.body` (or any custom container) before importing the module under test so its query selectors resolve correctly.
|
||||
|
||||
```js
|
||||
import { renderTemplate } from '../utils/domFixtures.js'; // adjust the relative path to your spec
|
||||
|
||||
beforeEach(() => {
|
||||
renderTemplate('loras.html', {
|
||||
dataset: { page: 'loras' }
|
||||
});
|
||||
});
|
||||
```
|
||||
|
||||
The helper ensures the dataset is applied to the container, which mirrors how Django sets `data-page` in production.
|
||||
|
||||
## Working with Partial Components
|
||||
|
||||
Many features are implemented as template partials located under `templates/components/`. When a test only needs a fragment (for example, the progress panel or context menu markup), load the component file directly:
|
||||
|
||||
```js
|
||||
const container = renderTemplate('components/progress_panel.html');
|
||||
|
||||
const progressPanel = container.querySelector('#progress-panel');
|
||||
```
|
||||
|
||||
This pattern avoids hand-written fixture strings and keeps the tests aligned with the actual markup.
|
||||
|
||||
## Resetting Between Tests
|
||||
|
||||
The shared Vitest setup clears `document.body` and storage APIs before each test. If a suite adds additional DOM nodes outside of the body or needs to reset custom attributes mid-test, use `resetDom()` exported from `domFixtures.js`.
|
||||
|
||||
```js
|
||||
import { resetDom } from '../utils/domFixtures.js';
|
||||
|
||||
afterEach(() => {
|
||||
resetDom();
|
||||
});
|
||||
```
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- Provide typed helpers for injecting mock script tags (e.g., replicating ComfyUI globals).
|
||||
- Compose higher-level fixtures that mimic specific pages (loras, checkpoints, recipes) once those managers receive dedicated suites.
|
||||
@@ -1,44 +0,0 @@
|
||||
# LoRA & Checkpoints Filtering/Sorting Test Matrix
|
||||
|
||||
This matrix captures the scenarios that Phase 3 frontend tests should cover for the LoRA and Checkpoint managers. It focuses on how search, filter, sort, and duplicate badge toggles interact so future specs can share fixtures and expectations.
|
||||
|
||||
## Scope
|
||||
|
||||
- **Components**: `PageControls`, `FilterManager`, `SearchManager`, and `ModelDuplicatesManager` wiring invoked through `CheckpointsPageManager` and `LorasPageManager`.
|
||||
- **Templates**: `templates/loras.html` and `templates/checkpoints.html` along with shared filter panel and toolbar partials.
|
||||
- **APIs**: Requests issued through `baseModelApi.fetchModels` (via `resetAndReload`/`refreshModels`) and duplicates badge updates.
|
||||
|
||||
## Shared Setup Considerations
|
||||
|
||||
1. Render full page templates using `renderLorasPage` / `renderCheckpointsPage` helpers before importing modules so DOM queries resolve.
|
||||
2. Stub storage helpers (`getStorageItem`, `setStorageItem`, `getSessionItem`, `setSessionItem`) to observe persistence behavior without mutating real storage.
|
||||
3. Mock `sidebarManager` to capture refresh calls triggered after sort/filter actions.
|
||||
4. Provide fake API implementations exposing `resetAndReload`, `refreshModels`, `fetchFromCivitai`, `toggleBulkMode`, and `clearCustomFilter` so control events remain asynchronous but deterministic.
|
||||
5. Supply a minimal `ModelDuplicatesManager` mock exposing `toggleDuplicateMode`, `checkDuplicatesCount`, and `updateDuplicatesBadgeAfterRefresh` to validate duplicate badge wiring.
|
||||
|
||||
## Scenario Matrix
|
||||
|
||||
| ID | Feature | Scenario | LoRAs Expectations | Checkpoints Expectations | Notes |
|
||||
| --- | --- | --- | --- | --- | --- |
|
||||
| F-01 | Search filter | Typing a query updates `pageState.filters.search`, persists to session, and triggers `resetAndReload` on submit | Validate `SearchManager` writes query and reloads via API stub; confirm LoRA cards pass query downstream | Same as LoRAs | Cover `enter` press and clicking search icon |
|
||||
| F-02 | Tag filter | Selecting a tag chip cycles include ➜ exclude ➜ clear, updates storage, and reloads results | Tag state stored under `filters.tags[tagName] = 'include'|'exclude'`; `FilterManager.applyFilters` persists and triggers `resetAndReload(true)` | Same; ensure base model tag set is scoped to checkpoints dataset | Include removal path |
|
||||
| F-03 | Base model filter | Toggling base model checkboxes updates `filters.baseModel`, persists, and reloads | Ensure only LoRA-supported models show; toggle multi-select | Ensure SDXL/Flux base models appear as expected | Capture UI state restored from storage on next init |
|
||||
| F-04 | Favorites-only | Clicking favorites toggle updates session flag and calls `resetAndReload(true)` | Button gains `.active` class and API called | Same | Verify duplicates badge refresh when active |
|
||||
| F-05 | Sort selection | Changing sort select saves preference (legacy + new format) and reloads | Confirm `PageControls.saveSortPreference` invoked with option and API called | Same with checkpoints-specific defaults | Cover `convertLegacySortFormat` branch |
|
||||
| F-06 | Filter persistence | Re-initializing manager loads stored filters/sort and updates DOM | Filters pre-populate chips/checkboxes; favorites state restored | Same | Requires simulating repeated construction |
|
||||
| F-07 | Combined filters | Applying search + tag + base model yields aggregated query params for fetch | Assert API receives merged filter payload | Same | Validate toast messaging for active filters |
|
||||
| F-08 | Clearing filters | Using "Clear filters" resets state, storage, and reloads list | `FilterManager.clearFilters` empties `filters`, removes active class, shows toast | Same | Ensure favorites-only toggle unaffected |
|
||||
| F-09 | Duplicate badge toggle | Pressing "Find duplicates" toggles duplicate mode and updates badge counts post-refresh | `ModelDuplicatesManager.toggleDuplicateMode` invoked and badge refresh called after API rebuild | Same plus checkpoint-specific duplicate badge dataset | Connects to future duplicate-specific specs |
|
||||
| F-10 | Bulk actions menu | Opening bulk dropdown keeps filters intact and closes on outside click | Validate dropdown class toggling and no unintended reload | Same | Guard against regression when dropdown interacts with filters |
|
||||
|
||||
## Automation Coverage Status
|
||||
|
||||
- ✅ F-01 Search filter, F-02 Tag filter, F-03 Base model filter, F-04 Favorites-only toggle, F-05 Sort selection, and F-09 Duplicate badge toggle are covered by `tests/frontend/components/pageControls.filtering.test.js` for both LoRA and checkpoint pages.
|
||||
- ⏳ F-06 Filter persistence, F-07 Combined filters, F-08 Clearing filters, and F-10 Bulk actions remain to be automated alongside upcoming bulk mode refinements.
|
||||
|
||||
## Coverage Gaps & Follow-Ups
|
||||
|
||||
- Write Vitest suites that exercise the matrix for both managers, sharing fixtures through page helpers to avoid duplication.
|
||||
- Capture API parameter assertions by inspecting `baseModelApi.fetchModels` mocks rather than relying solely on state mutations.
|
||||
- Add regression cases for legacy storage migrations (old filter keys) once fixtures exist for older payloads.
|
||||
- Extend duplicate badge coverage with scenarios where `checkDuplicatesCount` signals zero duplicates versus pending calculations.
|
||||
@@ -1,33 +0,0 @@
|
||||
# Frontend Automation Testing Roadmap
|
||||
|
||||
This roadmap tracks the planned rollout of automated testing for the ComfyUI LoRA Manager frontend. Each phase builds on the infrastructure introduced in this change set and records progress so future contributors can quickly identify the next tasks.
|
||||
|
||||
## Phase Overview
|
||||
|
||||
| Phase | Goal | Primary Focus | Status | Notes |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| Phase 0 | Establish baseline tooling | Add Node test runner, jsdom environment, and seed smoke tests | ✅ Complete | Vitest + jsdom configured, example state tests committed |
|
||||
| Phase 1 | Cover state management logic | Unit test selectors, derived data helpers, and storage utilities under `static/js/state` and `static/js/utils` | ✅ Complete | Storage helpers and state selectors now exercised via deterministic suites |
|
||||
| Phase 2 | Test AppCore orchestration | Simulate page bootstrapping, infinite scroll hooks, and manager registration using JSDOM DOM fixtures | ✅ Complete | AppCore initialization + page feature suites now validate manager wiring, infinite scroll hooks, and onboarding gating |
|
||||
| Phase 3 | Validate page-specific managers | Add focused suites for `loras`, `checkpoints`, `embeddings`, and `recipes` managers covering filtering, sorting, and bulk actions | ✅ Complete | LoRA/checkpoint suites expanded; embeddings + recipes managers now covered with initialization, filtering, and duplicate workflows |
|
||||
| Phase 4 | Interaction-level regression tests | Exercise template fragments, modals, and menus to ensure UI wiring remains intact | ✅ Complete | Vitest DOM suites cover NSFW selector, recipe modal editing, and global context menus |
|
||||
| Phase 5 | Continuous integration & coverage | Integrate frontend tests into CI workflow and track coverage metrics | ✅ Complete | CI workflow runs Vitest and aggregates V8 coverage into `coverage/frontend` via a dedicated script |
|
||||
|
||||
## Next Steps Checklist
|
||||
|
||||
- [x] Expand unit tests for `storageHelpers` covering migrations and namespace behavior.
|
||||
- [x] Document DOM fixture strategy for reproducing template structures in tests.
|
||||
- [x] Prototype AppCore initialization test that verifies manager bootstrapping with stubbed dependencies.
|
||||
- [x] Add AppCore page feature suite exercising context menu creation and infinite scroll registration via DOM fixtures.
|
||||
- [x] Extend AppCore orchestration tests to cover manager wiring, bulk menu setup, and onboarding gating scenarios.
|
||||
- [x] Add interaction regression suites for context menus and recipe modals to complete Phase 4.
|
||||
- [x] Evaluate integrating coverage reporting once test surface grows (> 20 specs).
|
||||
- [x] Create shared fixtures for the loras and checkpoints pages once dedicated manager suites are added.
|
||||
- [x] Draft focused test matrix for loras/checkpoints manager filtering and sorting paths ahead of Phase 3.
|
||||
- [x] Implement LoRAs manager filtering/sorting specs for scenarios F-01–F-05 & F-09; queue remaining edge cases after duplicate/bulk flows stabilize.
|
||||
- [x] Implement checkpoints manager filtering/sorting specs for scenarios F-01–F-05 & F-09; cover remaining paths alongside bulk action work.
|
||||
- [x] Implement checkpoints page manager smoke tests covering initialization and duplicate badge wiring.
|
||||
- [x] Outline focused checkpoints scenarios (filtering, sorting, duplicate badge toggles) to feed into the shared test matrix.
|
||||
- [ ] Add duplicate badge regression coverage for zero/pending states after API refreshes.
|
||||
|
||||
Maintaining this roadmap alongside code changes will make it easier to append new automated test tasks and update their progress.
|
||||
@@ -1,540 +0,0 @@
|
||||
# i18n Translation Guidelines
|
||||
|
||||
This document is the canonical set of conventions for translating LoRA Manager UI strings.
|
||||
It applies to **human translators and AI agents** alike. Read it before editing anything in
|
||||
`locales/`.
|
||||
|
||||
Source of truth: `locales/en.json` (10 locales, 2025 leaf keys; all locales share the exact
|
||||
same key structure).
|
||||
|
||||
Locales: `en`, `zh-CN`, `zh-TW`, `ja`, `ko`, `fr`, `de`, `es`, `ru`, `he` (RTL).
|
||||
|
||||
> **Status (2026-08 sweep):** a full audit was executed and the terminology, placeholder,
|
||||
> stale-text, and untranslated-block fixes described in §2–§6 were applied across all locales
|
||||
> (commits `3c3ac49f` … `fd1227d3`). The tables below are now the **normative target state**,
|
||||
> not a to-do list — future edits should preserve these renderings and only add what is new.
|
||||
>
|
||||
> **Status (2026-09, Other Models):** the `other` model type (VAE / Upscaler / Text Encoder /
|
||||
> CLIP Vision / ControlNet) and the Other Models opt-in toggles added 36 new keys; all of them
|
||||
> are now translated in all 9 locales (terminology in §2 "Other Models feature"). There are no
|
||||
> remaining `[TODO: Translate]` placeholders in any locale.
|
||||
>
|
||||
> **Status (2026-09, revision):** `other.disabled.description`, `banners.otherModels.content` and
|
||||
> `settings.folderSettings.enableOtherModelsHelp` were refreshed in `en.json` to name all five
|
||||
> sub_types (they had listed four, which read as "these are what enabling manages") and
|
||||
> re-translated in all 9 locales in the same pass. `clip_vision` and `controlnet` are now both
|
||||
> opt-in, so the first two describe **capability** and the third the **master switch**, not the
|
||||
> default set — keep all three enumerating the full five (`VAE / upscaler / text encoder /
|
||||
> CLIP vision / ControlNet` in `en`; locale slash-list casing follows each file's existing
|
||||
> `VAE / Upscaler / Text Encoder / …` style, de compounds as `CLIP-Vision- und ControlNet-Ordner`).
|
||||
>
|
||||
> **Status (2026-09, "no folders found" state):** the Other Models page gained an *enabled but
|
||||
> nothing to scan* empty state with 6 new keys (`other.noPaths.*`); translated in all 9 locales
|
||||
> in the same pass. The `folder_paths` JSON snippet shown in that state lives in
|
||||
> `templates/other.html`, **not** in the locale files, so it is never translated — only the
|
||||
> surrounding prose is. Terminology added in §2.
|
||||
>
|
||||
> **Status (2026-09, model sources):** models can now be linked to ModelScope and TensorArt
|
||||
> alongside Hugging Face, which added 15 keys (`modelCard.actions.viewOnSource`,
|
||||
> `loras.contextMenu.linkModelSource`, `modals.linkModelSource.*`,
|
||||
> `modals.model.versions.sourceGroupInfo`, `toast.contextMenu.enrichNeedsSource`,
|
||||
> `toast.contextMenu.enrichUnsupportedSource`) and refreshed the two `enrichHfAgent` labels,
|
||||
> which had hardcoded "HF" for a button that now also enriches ModelScope models. The
|
||||
> `modals.linkModelSource.urlPlaceholder` value stays byte-identical to `en.json` (it is a URL,
|
||||
> the §6 exception). Terminology in §2, "Model source feature".
|
||||
>
|
||||
> **Status (2026-09, folder sidebar):** the model-root sidebar gained on-disk folder management
|
||||
> (create / rename / delete folders, show empty folders, tree vs list view) plus its `...`
|
||||
> view-options menu, adding 35 `sidebar.*` keys. Those were the only `[TODO: Translate]`
|
||||
> placeholders left behind by the feature series, and all 35 are now translated in all 9
|
||||
> locales, so the "no remaining placeholders" claim above holds again. Terminology in §2,
|
||||
> "Folder sidebar feature".
|
||||
>
|
||||
> **Status (2026-09, chip reordering):** model tags and trigger words now share one drag/`⠿`
|
||||
> grip reorder affordance, which added the single `common.reorder.dragHandle` key (it lives
|
||||
> under `common` because both editors render it). All 9 locales are translated (renderings in
|
||||
> §2, "Chip reordering"). Reordering is pointer-only by design: an `Alt + Arrow` shortcut was
|
||||
> prototyped and removed because it collided with the browser's Alt + Arrow handling and the
|
||||
> modal's arrow-key navigation.
|
||||
|
||||
> **Status (2026-09, standalone no-paths guidance):** the standalone branch of the
|
||||
> `other.noPaths` empty state now shows the real `settings.json` path plus an
|
||||
> `other.noPaths.openSettingsFolder` button (each locale reuses its
|
||||
> `settings.openSettingsFileLocation.label` rendering), and `descriptionStandalone` was
|
||||
> reworded in `en.json` — from "none of the configured folders exist on disk" to "no
|
||||
> other-model folders were found; add the folder keys you need to the `folder_paths`
|
||||
> section" — and re-translated in all 9 locales. The `on disk` phrase now survives only in
|
||||
> the ComfyUI variant (`descriptionComfyUI`).
|
||||
|
||||
---
|
||||
|
||||
## 1. Hard rules (do not violate)
|
||||
|
||||
### R1 — Key structure is sacred
|
||||
- Only `locales/en.json` may add/remove/rename keys. All other locales must keep the exact
|
||||
same nested key set. `tests/i18n/test_i18n.py` enforces this.
|
||||
- When a new UI string is added to `en.json`, run
|
||||
`python scripts/sync_translation_keys.py` (adds the missing keys to all locales with
|
||||
`[TODO: Translate]` placeholder copies) — **then stop**. Do NOT translate proactively:
|
||||
placeholders are the expected end state during feature development, and translations are
|
||||
filled in only when the feature owner explicitly asks (workflow details in §7).
|
||||
- Never reorder, re-indent, or reformat a locale file "for tidiness". The sync script
|
||||
preserves formatting; manual reformatting creates noisy diffs.
|
||||
|
||||
### R2 — Placeholders and HTML must be preserved verbatim
|
||||
- `{name}`-style placeholders must appear in the translation exactly as in `en.json`.
|
||||
Do not invent placeholders the source string does not have — the caller may not pass them
|
||||
(example bug: `zh-CN recipes.controls.import.downloadLocationPreview` added `{path}`; the
|
||||
template renders this key with no parameters, so the literal text `{path}` shows in the UI).
|
||||
- `{{...}}` in a locale value is an escaped literal brace — keep it identical.
|
||||
- Keep embedded HTML tags (e.g. `<strong>...</strong>`, `<code>...</code>`) intact.
|
||||
You may move the tag around the sentence if the target language needs different word order.
|
||||
|
||||
### R3 — Never translate or transliterate these
|
||||
- Model types: **LoRA, Checkpoint, Embedding, Diffusion Model**
|
||||
- Products/brands: **LoRA Manager, ComfyUI, CivitAI, CivArchive, HuggingFace, Ko-fi**
|
||||
- Ecosystem names: **LyCORIS, DoRA**, trigger-adjacent jargon **Prompt, Workflow**
|
||||
(these are used as-is in the target-language SD community; see §2 per-language policy)
|
||||
- Theme names: **Nord, Midnight, Monokai, Dracula, Solarized**
|
||||
|
||||
### R4 — The "Recipe" convention (the most important domain term)
|
||||
Product intent: a *Recipe* records a **LoRA combination + generation parameters**
|
||||
(prompt, seed, sampler, …) that reproduces an image style. The metaphor is a **cooking
|
||||
recipe** — "follow it and you get a similar dish". It is **not** a menu, not a dish list,
|
||||
not a prescription.
|
||||
|
||||
Decision per language — translate only into a word whose everyday primary meaning is a
|
||||
cooking recipe; where that word would mislead users, **keep the English "Recipe(s)"**:
|
||||
|
||||
| Locale | Use | Never use |
|
||||
|---|---|---|
|
||||
| fr | **Recipe / Recipes** (keep English) | recette(s) — cooking reading is secondary and it was explicitly judged misleading |
|
||||
| zh-CN / zh-TW | 配方 | 食谱 (reads as "food cookbook") |
|
||||
| ja | レシピ | — (leftover English "Recipe" in `initialization.recipes.title` / `toast.recipes.recipeSaved` → translate) |
|
||||
| ko | 레시피 | — |
|
||||
| de | Rezept / Rezepte | — (cooking meaning dominant; prescription reading acceptable) |
|
||||
| es | receta / recetas | — (cooking meaning dominant) |
|
||||
| ru | рецепт / рецепты | — (leftover English "Recipe" in `initialization.recipes.title` / `toast.recipes.recipeSaved` → translate) |
|
||||
| he | מתכון / מתכונים | — (cooking meaning dominant) |
|
||||
|
||||
Whatever the choice, **one concept = one noun within a locale**. Currently violated in:
|
||||
- `fr` — "Recipe" (~97 keys, incl. nav) mixed with "recette" (~58 keys)
|
||||
- `zh-CN` / `zh-TW` — 配方 (126/122 keys) mixed with 食谱 / 食譜 (14/17 keys, all in the
|
||||
*rematch* flow: `globalContextMenu.rematchRecipes.*`, `toast.recipes.rematch*`)
|
||||
- `de` — "Rezept" (136 keys) mixed with leftover English "Recipe" (5 keys)
|
||||
- `ja` / `ru` — leftover English "Recipe" in `initialization.recipes.title` ("Recipe Manager
|
||||
zu initialisieren" / «Инициализация Recipe Manager») and `toast.recipes.recipeSaved`
|
||||
|
||||
### R5 — One term, one rendering (within each locale)
|
||||
Same source word must not be translated several ways in one file. Known offender areas
|
||||
(see §5 for the full fix list): recipe, Checkpoint, Embedding, prompt, base model, preset,
|
||||
workflow, hash, metadata, tags, bulk. Every locale currently mixes variants of at least one
|
||||
of these — pick the preferred form in the §2 tables and normalize.
|
||||
|
||||
### R6 — Register consistency
|
||||
- `zh-CN` / `zh-TW`: pick 你 or 您 once. Do not mix (zh-CN has 44×你 + 5×您; zh-TW has
|
||||
27×您 + 18×你).
|
||||
- `de`: pick "du" or "Sie" once (currently 143×Sie + ~7×du).
|
||||
- `es`: pick "tú" or "usted" once.
|
||||
|
||||
### R7 — Punctuation per script
|
||||
- Full-width punctuation `：（）` is correct **only in CJK locales** (zh-CN, zh-TW, ja, ko).
|
||||
- Latin/Cyrillic/Hebrew locales must use ASCII `: ()` — full-width colons leaked in there
|
||||
are machine-translation artifacts. Known: `fr toast.recipes.createError/createFailed`,
|
||||
`es toast.recipes.createError/createFailed` (e.g. "…de la receta：" should be "…de la receta:").
|
||||
- `fr` apostrophes must be U+2019 `'` / ASCII `'`, never a straight double quote:
|
||||
`fr header.filter.allowSellingGeneratedContentTooltip` currently reads
|
||||
`vendre d"images` → fix to `d'images`. Do not mix `'` and `'` in one file (fr has 299 vs 15).
|
||||
- Ellipsis: use ASCII `...` (project style). Don't introduce `…`.
|
||||
- Keep the sentence-ending period/omission consistent with the source string where the
|
||||
language allows it.
|
||||
- `he` is RTL: mix of Hebrew and Latin scripts is normal; keep Latin term ordering natural.
|
||||
|
||||
### R8 — No untranslated English leftovers
|
||||
Full sentences left byte-identical to `en.json` are bugs (brand names and URL placeholders
|
||||
are the exception). Every locale has them; see §6 for the per-locale checklist.
|
||||
`[TODO: Translate]` placeholders are the sanctioned intermediate state during feature
|
||||
development (see §7) — do not "fix" them unless the feature owner asked for translations.
|
||||
|
||||
### R9 — Mirror the source even when the source is wrong
|
||||
If `en.json` itself contains an inconsistency (e.g. the `Civitai` vs `CivitAI` casing split,
|
||||
or the `CivitArchive` typo in `modals.relinkCivitai.helpText.format4`), translate/transcribe
|
||||
it as-is in your locale and instead **fix the source** in `en.json` (then propagate by
|
||||
re-syncing and re-translating affected keys). Do not silently diverge in one locale only.
|
||||
|
||||
---
|
||||
|
||||
## 2. Per-language term maps
|
||||
|
||||
Preferred rendering per term. "Fix" means the locale currently contains the wrong variant
|
||||
and must be normalized. `en` = keep the English word as-is.
|
||||
|
||||
### fr
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | Recipe(s) | Replace all "recette(s)" (58 keys, e.g. `recipes.actions.deleteRecipeWithShortcut`, `toast.recipes.rematchComplete`) with "Recipe(s)" |
|
||||
| Checkpoint | Checkpoint | `statistics.modelTypes.checkpoint` = "Point de contrôle" → "Checkpoint" |
|
||||
| trigger words | mot(s)-clé(s) | unify: `modals.model.triggerWords.editWord` uses "mot déclencheur" — pick one |
|
||||
| prompt / negative prompt | Prompt / prompt négatif | — |
|
||||
| base model | modèle(s) de base | — |
|
||||
| preset | préréglage | unify: `modals.model.usageTips.addPresetParameter` "prédéfini", `toast.presets.restored` "par défaut" |
|
||||
| hash | hash | `conflictConfirm.message` "hachage" → "hash" |
|
||||
| tags | tags | `settings.sections.priorityTags` "Étiquettes" → "Tags" |
|
||||
| metadata | métadonnées | `loras.controls.refresh.fullTooltip` keeps English "metadata" |
|
||||
| duplicates | doublon(s) | unify with "dupliqué(e)s" |
|
||||
| bulk | groupé(e) | unify with "par lot / mode lot" variants |
|
||||
|
||||
### de
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | Rezept/Rezepte | leftover English "Recipe" keys → Rezept (e.g. `toast.recipes.recipeSaved`) |
|
||||
| base model | pick Basis-Modell or Basismodell | currently 27× hyphenated vs 15× closed |
|
||||
| metadata | Metadaten | 4 keys use "Modelldaten" (`onboarding.steps.fetch.title/content`) → Metadaten |
|
||||
| bulk | pick Massen- or Sammelmodus | `loras.controls.bulk.action` = "Massen" reads as "crowds" — use "Massenbearbeitung"/"Mehrfachauswahl" |
|
||||
| register | Sie (formal) | 7 keys use "du/dein" (`settings.backup.managementHelp`, `modals.checkUpdates.message/tip`, `doctor.footer`, …) |
|
||||
|
||||
### es
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | receta(s) | — |
|
||||
| Checkpoint | Checkpoint | 5 statistics keys "Punto(s) de control" → "Checkpoints" (`statistics.metrics.checkpoints`, `statistics.insights.unusedCheckpoints.*`, `statistics.modelTypes.checkpoint`) |
|
||||
| trigger words | palabra(s) de activación | 2 keys already use it; ~15 keys "palabra(s) clave" (reads as search keyword) → unify |
|
||||
| base model | modelo base | — |
|
||||
| preset | preajuste | 3 keys keep English "preset", 1 "preestablecido" → preajuste |
|
||||
| workflow | pick flujo de trabajo or workflow | currently 21× "flujo de trabajo" vs 10× "workflow" |
|
||||
| bulk | masivo / por lotes | unify; "Batch Import" → traducción |
|
||||
| tags | etiquetas | — |
|
||||
|
||||
### ru
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | рецепт(ы) | English leftovers: `initialization.recipes.title`, `recipes.batchImport.*`, `toast.recipes.recipeSaved` → translate |
|
||||
| Checkpoint | Checkpoint (recommended) | 3 variants today: "Checkpoint" (17 keys), «Чекпойнт», «Контрольная точка» (statistics, 6 keys) — statistics MUST drop «Контрольная точка» |
|
||||
| Embedding | Embedding | «Эмбеддинг» variant exists in `settings.priorityTags.modelTypes.embedding` — unify |
|
||||
| prompt | промпт | 8 keys use «запрос» (reads as "database/HTTP request") → «промпт» |
|
||||
| base model | базовая модель | — |
|
||||
| preset | пресет | `header.theme.presets` "Предустановки" → пресеты |
|
||||
| workflow | Workflow (recommended) | «рабочий процесс» used in 4 keys — unify |
|
||||
| hash | pick хеш or хэш | both spellings co-occur |
|
||||
| tag(s) | тег(и) | — |
|
||||
| typos | — | `settings.misc.loraSyntaxFormatHelp`: «безпотерьного» → «беспотерьного» |
|
||||
|
||||
### he
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | מתכון / מתכונים | — |
|
||||
| Checkpoint | Checkpoint | 5 statistics keys «נקודת/נקודות ביקורת» (road/security checkpoint) → "Checkpoint(s)" (`statistics.metrics.checkpoints`, `statistics.modelTypes.checkpoint`, `statistics.insights.unusedCheckpoints.*`) |
|
||||
| Embedding | Embedding | `statistics` keys use הטמעות → Embedding |
|
||||
| prompt | pick הנחיה or פרומפט | 9 keys הנחיה vs 3 פרומפט — unify (recommend פרומפט, SD-community loanword) |
|
||||
| preset | קביעה מראש | `header.filter.presetOverwriteConfirm` uses פריסט → unify |
|
||||
| hash | pick one of האש / גיבוב / hash | 3 variants co-occur — unify (recommend hash or גיבוב) |
|
||||
| metadata | pick מטא-דאטה or מטא-נתונים | 38 vs 17 keys — unify |
|
||||
| model | מודל | 13 keys use דגם/דגמים — unify |
|
||||
| bulk | pick one of 5 variants | 5 different renderings ("כמות גדולה", "המוני", "קבוצתי", "אצווה", …) — unify; `loras.controls.bulk.action` "כמות גדולה" reads as "large quantity" |
|
||||
|
||||
### ja
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | レシピ | `initialization.recipes.title` keeps English "Recipe Manager" — translate to レシピマネージャー |
|
||||
| Checkpoint | Checkpoint or チェックポイント (pick one) | 3 variants: Checkpoint (~14), checkpoint lowercase (4), チェックポイント (4, e.g. `settings.priorityTags.modelTypes.checkpoint`) |
|
||||
| Embedding | Embedding | 4 keys lowercase "embedding" mid-sentence |
|
||||
| bulk | 一括 | `modals.checkUpdates.tip` "バルクモード" → 一括モード |
|
||||
| recipe counter | 件 or 個 | `globalContextMenu.rematchRecipes.success` uses 件, `.cancelled` uses 個 — unify |
|
||||
|
||||
### ko
|
||||
|
||||
| Term | Use | Fix |
|
||||
|---|---|---|
|
||||
| recipe | 레시피 | — |
|
||||
| Checkpoint | Checkpoint (recommended) | 4 keys transliterate 체크포인트 (`settings.priorityTags.modelTypes.checkpoint`, `toast.recipes.missingCheckpointPath/missingCheckpointInfo/downloadCheckpointFailed`) |
|
||||
| Embedding | Embedding | 3 keys 임베딩 (`settings.priorityTags.modelTypes.embedding`, `uiHelpers.nodeSelector.embedding`) |
|
||||
| base model | 베이스 모델 | 6 keys «기본 모델» read as "default model" → 베이스 모델 (`settings.downloadSkipBaseModels.*`, `toast.loras.downloadSkippedByBaseModel`) |
|
||||
| workflow | pick 워크플로 or 워크플로우 | 26 vs 6 keys — unify |
|
||||
| bulk | 일괄 | `modals.checkUpdates.tip` "벌크 모드" → 일괄 모드 |
|
||||
| tag logic | — | `header.filter.tagLogicAny` = "모든 태그 일치 (OR)" is **inverted** (should be "하나 이상의 태그 일치") and identical to `tagLogicAll` |
|
||||
| particle | — | `modelCard.sendToWorkflow.checkpointNotImplemented`: "Checkpoint을" → "Checkpoint를" |
|
||||
|
||||
### zh-CN / zh-TW
|
||||
|
||||
| Term | zh-CN | zh-TW |
|
||||
|---|---|---|
|
||||
| recipe | 配方 (fix 食谱 → 配方, 14 keys in rematch flow) | 配方 (fix 食譜 → 配方, 17 keys in rematch flow) |
|
||||
| Checkpoint | Checkpoint (fix 检查点 → Checkpoint, 5 keys: `toast.recipes.missingCheckpointPath/missingCheckpointInfo/downloadCheckpointFailed`, `modelCard.actions.checkpointNameCopied`, `modelCard.sendToWorkflow.checkpointNotImplemented`) | Checkpoint (fix 檢查點 → Checkpoint, 4 keys: `modelCard.actions.copyCheckpointName`, `toast.recipes.missing*`×2, `toast.recipes.downloadCheckpointFailed`) |
|
||||
| base model | 基础模型 (fix 基模型 → 基础模型, 3 keys in `modals.model.versions.filters.*`) | 基礎模型 ✓ consistent |
|
||||
| prompt | 提示词 ✓ | 提示詞 ✓ |
|
||||
| preset | 预设 ✓ | 預設 ✓ |
|
||||
| workflow | 工作流 ✓ | 工作流 ✓ |
|
||||
| trigger words | 触发词 ✓ | 觸發詞 ✓ |
|
||||
| hash | 哈希 (哈希值 variant OK) | 雜湊 ✓ |
|
||||
| register | 你 (fix 5×您 → 你) | 您 (fix 18×你 → 您) |
|
||||
|
||||
### Other Models feature (VAE / Upscaler / Text Encoder / CLIP Vision / ControlNet)
|
||||
|
||||
The `other` model type exposes five sub_types. They are **model-type names**, so they follow
|
||||
R3 and stay in Latin in every locale. The `settings.folderSettings.subType*` values are
|
||||
therefore **intentionally byte-identical to `en.json`** (same precedent as
|
||||
`settings.priorityTags.modelTypes` / `checkpoints.modelTypes.checkpoint`) — a §6 sweep must
|
||||
not "fix" them.
|
||||
|
||||
| Term | Rendering | Note |
|
||||
|---|---|---|
|
||||
| VAE | `VAE` everywhere | acronym, always upper-case |
|
||||
| Upscaler | `Upscaler` everywhere | CivitAI `ModelType` name |
|
||||
| Text Encoder | `Text Encoder` everywhere | de compounds as `Text-Encoder-Stammordner` |
|
||||
| CLIP Vision | `CLIP Vision` everywhere | de compounds as `CLIP-Vision-Stammordner` |
|
||||
| ControlNet | `ControlNet` everywhere | brand casing, capital N |
|
||||
|
||||
In prose these names sit next to localized nouns the same way `Diffusion Model` does
|
||||
(zh `VAE 根目录`, ja `VAEルート`, ko `VAE 루트`, ru `Корневая папка VAE`).
|
||||
|
||||
**"Other Models" is the page/feature name, not a model type — translate it:**
|
||||
|
||||
| Locale | `other.title` | `header.navigation.other` |
|
||||
|---|---|---|
|
||||
| fr | Autres modèles | Autres |
|
||||
| zh-CN | 其他模型 | 其他 |
|
||||
| zh-TW | 其他模型 | 其他 |
|
||||
| ja | その他のモデル | その他 |
|
||||
| ko | 기타 모델 | 기타 |
|
||||
| de | Weitere Modelle | Andere |
|
||||
| es | Otros modelos | Otros |
|
||||
| ru | Другие модели | Другое |
|
||||
| he | מודלים אחרים | אחרים |
|
||||
|
||||
`settings.folderSettings.otherSubTypes` ("Managed Types") must name **model** types, matching
|
||||
each locale's `header.filter.modelTypes` rendering (zh `管理的模型类型`, ja `管理するモデルタイプ`,
|
||||
de `Verwaltete Modelltypen`, …).
|
||||
|
||||
The "no folders found" empty state (`other.noPaths.*`) uses two phrases that must stay
|
||||
consistent whenever that copy is edited. `folder key` means the `folder_paths` key name
|
||||
(`vae`, `upscale_models`, … — Latin per the table above); `on disk` means the folder must
|
||||
physically exist:
|
||||
|
||||
| Phrase | Rendering |
|
||||
|---|---|
|
||||
| folder key | zh-CN 文件夹键 · zh-TW 資料夾鍵 · ja フォルダーキー · ko 폴더 키 · fr clé de dossier · de Ordnerschlüssel · es clave de carpeta · ru ключ папки · he מפתח תיקייה |
|
||||
| on disk | zh-CN 在磁盘上 · zh-TW 在磁碟上 · ja ディスク上 · ko 디스크에 · fr sur le disque · de auf dem Datenträger · es en el disco · ru на диске · he בדיסק |
|
||||
|
||||
`settings.json` and `ComfyUI` stay verbatim in every locale; "reload this page" / "restart
|
||||
LoRA Manager" reuse each locale's existing restart wording (`settings.extraFolderPaths.*`).
|
||||
|
||||
### Model source feature (Hugging Face / ModelScope / TensorArt)
|
||||
|
||||
A model file can be linked to the page of an external model site. **Hugging Face**,
|
||||
**ModelScope** and **TensorArt** are brand names and stay Latin in every locale (R3); the
|
||||
generic nouns around them are translated:
|
||||
|
||||
| Term | Rendering |
|
||||
|---|---|
|
||||
| model source | zh-CN 模型来源 · zh-TW 模型來源 · ja モデルソース · ko 모델 소스 · fr source de modèle · de Modellquelle · es fuente de modelo · ru источник модели · he מקור מודל |
|
||||
| model page | zh-CN 模型页面 · zh-TW 模型頁面 · ja モデルページ · ko 모델 페이지 · fr page du modèle · de Modellseite · es página del modelo · ru страница модели · he עמוד המודל |
|
||||
| model card | zh-CN 模型卡 · zh-TW 模型卡 · ja モデルカード · ko 모델 카드 · fr fiche de modèle · de Modellkarte · es ficha de modelo · ru карточка модели · he כרטיס מודל |
|
||||
| AI enrichment (noun) | reuse the existing pair per locale: zh-CN 增强 · zh-TW 增強 · ja 補完 · ko 보강 · fr enrichissement (par IA) · de Anreicherung (KI-) · es enriquecimiento (con IA) · ru обогащение (с помощью ИИ) · he העשרה (AI) |
|
||||
|
||||
`modelCard.actions.viewOnSource` ("View on {source}") follows each locale's existing
|
||||
`viewOnHuggingFace` pattern — de `Auf … ansehen`, ru `Открыть …`, he `צפייה ב-…`,
|
||||
ja `… で見る`, ko `…에서 보기`, zh `在 … 查看`, fr `Voir sur …`, es `Ver en …`. `{source}` is
|
||||
replaced at runtime with the untranslated platform name, so the brand never appears inside the
|
||||
translated text.
|
||||
|
||||
`modals.linkModelSource.enrichNote` states the rule that only sites exposing a readable model
|
||||
card can be enriched and names TensorArt as the current exception. Keep the parenthetical
|
||||
exception in sync if another link-only source is ever added — the sentence is deliberately
|
||||
phrased as a rule, not as an apology for one site.
|
||||
|
||||
The context-menu and bulk-operation enrichment entry points read **"Enrich Metadata with AI"**
|
||||
in `en`, not "Enrich HF Metadata": they cover ModelScope as well, so no locale may reintroduce
|
||||
an `HF` qualifier in `loras.contextMenu.enrichHfAgent` / `loras.bulkOperations.enrichHfAgent`
|
||||
(the key names keep the historical `Hf`; only the values changed).
|
||||
|
||||
### Folder sidebar feature (create / rename / delete folders, empty folders, view options)
|
||||
|
||||
The model-root sidebar manages on-disk folders. "Folder" reuses the noun already fixed in §2
|
||||
(the `folder key` row); the rest is new surface:
|
||||
|
||||
| Term | Rendering |
|
||||
|---|---|
|
||||
| folder | zh-CN 文件夹 · zh-TW 資料夾 · ja フォルダ · ko 폴더 · fr dossier · de Ordner · es carpeta · ru папка · he תיקייה |
|
||||
| model root (as in "no model root is configured") | zh-CN 模型根目录 · zh-TW 模型根目錄 · ja モデルルート · ko 모델 루트 · fr racine de modèle · de Modell-Stammverzeichnis · es raíz de modelo · ru корневая папка моделей · he שורש מודלים — note `sidebar.modelRoot` alone is the shorter 根目录 / 根目錄 / ルート / 루트 / Racine / Stammverzeichnis / Raíz / Корень / שורש |
|
||||
| tree view / list view | zh-CN 树形视图 / 列表视图 · zh-TW 樹狀檢視 / 清單檢視 · ja ツリー表示 / リスト表示 · ko 트리 보기 / 목록 보기 · fr Vue arborescente / Vue liste · de Baumansicht / Listenansicht · es Vista de árbol / Vista de lista · ru Дерево / Список · he תצוגת עץ / תצוגת רשימה |
|
||||
| sidebar | reuse each locale's `sidebar.hideOnThisPage` noun: zh-CN 侧边栏 · zh-TW 側邊欄 · ja サイドバー · ko 사이드바 · fr barre latérale · de Seitenleiste · es barra lateral · ru боковая панель · he סרגל צד |
|
||||
|
||||
Deleting a folder **never cascades over model files** — the backend refuses it and
|
||||
`sidebar.deleteFolderModal.notEmptyMessage` states the rule in every locale, so keep that
|
||||
clause (and its `—`) when the copy is edited. The `{name}` / `{count}` / `{message}` tokens in
|
||||
`sidebar.createFolderResult.*`, `sidebar.deleteFolderResult.*` and `sidebar.renameFolderResult.*`
|
||||
are verbatim §1-R2 placeholders; `successWithFiles` is the only key carrying `{count}`.
|
||||
|
||||
### Chip reordering (model tags / trigger words)
|
||||
|
||||
Model tags and trigger-word chips share a single reorder affordance (drag the chip, or its
|
||||
`⠿` grip where the chip body is click-to-edit), so the copy sits in `common.reorder.dragHandle`
|
||||
instead of a feature namespace. It is used twice per editor: as the grip tooltip and as the
|
||||
hint shown in the edit controls row. There is deliberately **no keyboard shortcut** — an
|
||||
`Alt + Arrow` binding fought the browser's own Alt + Arrow handling and the modal's arrow-key
|
||||
navigation, so reordering is pointer-only and the grip is a decorative, non-focusable
|
||||
affordance. Do not reintroduce a shortcut or a "position X of Y" screen-reader string without
|
||||
re-adding the corresponding keys.
|
||||
|
||||
`dragHandle` is a fragment, not a sentence: it labels both the grip and the hint, so keep it
|
||||
short and imperative and do not append a keyboard hint in any locale.
|
||||
|
||||
| Term | Rendering |
|
||||
|---|---|
|
||||
| drag to reorder | zh-CN 拖拽以调整顺序 · zh-TW 拖曳以調整順序 · ja ドラッグして並べ替え · ko 드래그하여 순서 변경 · fr Glisser pour réordonner · de Zum Neuordnen ziehen · es Arrastra para reordenar · ru Перетащите, чтобы изменить порядок · he גרור כדי לשנות סדר |
|
||||
|
||||
The grip itself is an icon and is never translated.
|
||||
|
||||
---
|
||||
|
||||
## 3. Cross-cutting confusion hot-spots (must-fix list)
|
||||
|
||||
All items below were **resolved** in the 2026-08 sweep — treat them as a regression
|
||||
watch-list: do not reintroduce these renderings.
|
||||
|
||||
1. **Checkpoint rendered as a literal security/road checkpoint** — fr, es, ru, he, zh-CN,
|
||||
zh-TW all had 4–6 keys in the `statistics.*` domain reading as "control point"; reverted
|
||||
to "Checkpoint".
|
||||
2. **"recipe" variants that break the one-noun rule** — fr "recette" → "Recipe", zh
|
||||
食谱/食譜 → 配方, de/ja/ru leftover English "Recipe" translated.
|
||||
3. **ko `header.filter.tagLogicAny`** — was inverted ("모든 태그 일치 (OR)") and identical
|
||||
to `tagLogicAll`; now "어느 하나의 태그와 일치 (OR)".
|
||||
4. **ja `modals.model.versions.actions.viewLocalTooltip`** — was the stale "近日対応予定"
|
||||
("coming soon"); all 9 locales now describe the actual action.
|
||||
5. **Stale help texts** — `settings.downloadSkipBaseModels.help`,
|
||||
`settings.aiProvider.apiBaseHelp`, `settings.hideEarlyAccessUpdates.help` retranslated
|
||||
in all locales to the current `en.json` wording.
|
||||
6. **en.json source bugs** (fixed in source, then mirrored):
|
||||
- "Civitai" → "CivitAI" brand casing (values only; key names `relinkCivitai` etc. keep
|
||||
their lowercase form and must not be renamed)
|
||||
- `modals.relinkCivitai.helpText.format4` "CivitArchive" typo → "CivArchive"
|
||||
- `zh-CN recipes.controls.import.downloadLocationPreview` invented `{path}` removed
|
||||
|
||||
---
|
||||
|
||||
## 4. Placeholder contract deviations (current)
|
||||
|
||||
`{...}` token sets must match `en.json` per key. All deviations found in the 2026-08 sweep
|
||||
were fixed, with one *intentional* exception:
|
||||
|
||||
**`toast.settings.mappingsUpdated`** — the caller passes a hardcoded English inflection
|
||||
(`plural: count !== 1 ? 's' : ''`). Languages that cannot build a plural by appending that
|
||||
`s` (zh-CN/zh-TW, ja, ko, de, ru, he) **drop `{plural}`** and render a count-friendly form
|
||||
(`({count})` or a measure word); fr and es keep it (`mappage{plural}`, `mapeo{plural}`).
|
||||
|
||||
```python
|
||||
# keep a copy of this rule next to the key if it ever moves:
|
||||
# fr/es: "... ({count} mappage{plural})"
|
||||
# de/ru/he: "... ({count})"
|
||||
# zh-CN: "（{count} 条映射）" / zh-TW: "（{count} 個對應）" / ja: "（{count} マッピング）"
|
||||
```
|
||||
|
||||
Do NOT add `{...}` tokens the source lacks (the caller will not supply them, and the literal
|
||||
text renders in the UI), and do NOT rename source tokens (`{typePlural}` stays `{typePlural}`).
|
||||
|
||||
---
|
||||
|
||||
## 5. One term, one rendering — offender matrix
|
||||
|
||||
Cross-locale summary of §2 inconsistencies. "✓" = already consistent. All ✗ cells were
|
||||
resolved in the 2026-08 sweep; the row shows the single rendering now in force per locale.
|
||||
|
||||
| Term | fr | de | es | ru | he | ja | ko | zh-CN | zh-TW |
|
||||
|---|---|---|---|---|---|---|---|---|---|
|
||||
| recipe | Recipe | Rezept | receta | рецепт | מתכון | レシピ | 레시피 | 配方 | 配方 |
|
||||
| Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint | Checkpoint |
|
||||
| Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding | Embedding |
|
||||
| prompt | Prompt | Prompt | prompt | промпт | פרומפט | プロンプト | 프롬프트 | 提示词 | 提示詞 |
|
||||
| base model | modèle de base | Basismodell | modelo base | базовая модель | מודל בסיס | ベースモデル | 베이스 모델 | 基础模型 | 基礎模型 |
|
||||
| preset | préréglage | Voreinstellung | preajuste | пресет | קביעה מראש | プリセット | 프리셋 | 预设 | 預設 |
|
||||
| workflow | Workflow | Workflow | workflow | Workflow | workflow | ワークフロー | 워크플로 | 工作流 | 工作流 |
|
||||
| hash | hash | Hash | hash | хеш | hash | ハッシュ | 해시 | 哈希 | 雜湊 |
|
||||
| metadata | métadonnées | Metadaten | metadatos | метаданные | מטא-נתונים | メタデータ | 메타데이터 | 元数据 | 中繼資料 |
|
||||
| tags | Tags | Tags | etiquetas | теги | תגיות | タグ | 태그 | 标签 | 標籤 |
|
||||
| duplicates | en double | Duplikate | duplicados | дубликаты | כפילויות | 重複 | 중복 | 重复项 | 重複項 |
|
||||
| bulk | groupé | Massen- | por lotes | пакетный | בכמות גדולה | 一括 | 일괄 | 批量 | 批量 |
|
||||
|
||||
Watch: ja/ko keep the model-type names **Checkpoint/Embedding** and `Diffusion Model` in
|
||||
Latin (consistent with their model-type sections) — do not transliterate them as
|
||||
チェックポイント/체크포인트.
|
||||
|
||||
---
|
||||
|
||||
## 6. Untranslated English leftovers (status)
|
||||
|
||||
Values byte-identical to `en.json` that are actual UI sentences are bugs (brand names and
|
||||
URL placeholders are the exception). As of the 2026-08 sweep, **all previously untranslated
|
||||
blocks are translated** in every locale: `recipes.batchImport.*` + `toast.recipes.batchImport*`
|
||||
(fr/de/es/ru/he/ja/ko), `banners.communitySupport.*`, `modals.model.license.*`,
|
||||
`globalContextMenu.fetchMissingLicenses.*`, the `doctor.*` issue/action/label subset,
|
||||
`toast.settings.libraryLoadFailed` / `libraryActivateFailed`, `toast.api.moveFailed`,
|
||||
`settings.extraFolderPaths.restartRequired`, `toast.recipes.recipeSaved`,
|
||||
`sidebar.dragDrop.moveUnsupported`, `checkpoints.modelTypes.diffusion_model`
|
||||
(ja/ko keep the English loanword), `initialization.recipes.title`.
|
||||
|
||||
The only values that remain intentionally identical to `en.json` are non-translatable:
|
||||
URL/path placeholders (`https://…`, `C:/…`), numeric presets (`5 (1080p), 6 (2K), 8 (4K)`),
|
||||
example token lists (`character, concept, style(toon|toon_style)`), service/provider names
|
||||
(`CivitAI → CivArchive → Archive DB`), model-type names (`settings.priorityTags.modelTypes.*`,
|
||||
`settings.folderSettings.subTypeVae` … `subTypeControlnet` — see §2), and the external playlist
|
||||
title (`help.updateVlogs.playlistTitle`, de: translated to "LoRA Manager-Update-Playlist").
|
||||
|
||||
Rule for `uiHelpers.workflow.noPromptTargets`: the second line (`Mark as → Send Prompt
|
||||
Target`) quotes literal ComfyUI context-menu items — keep those menu labels in English in
|
||||
every locale because that is what the user actually sees in ComfyUI.
|
||||
|
||||
License labels (`modals.model.license.*`): the restriction labels are now translated in all
|
||||
locales (the sibling `creditRequired` has always been translated).
|
||||
|
||||
---
|
||||
|
||||
## 7. Workflow for agents and translators
|
||||
|
||||
### Adding a new UI string
|
||||
1. Add the key to `locales/en.json` only.
|
||||
2. Run `python scripts/sync_translation_keys.py` — it inserts the key into the other 9
|
||||
locales (as a `[TODO: Translate]` placeholder) preserving formatting.
|
||||
3. **During feature development, stop here.** While the UI copy is still in flux, leave the
|
||||
`[TODO: Translate]` placeholders as-is — translating churning strings into 9 locales is
|
||||
wasted work. Placeholders are a normal intermediate state, not a bug.
|
||||
4. Once the wording is final and the feature owner explicitly asks for translations,
|
||||
translate **all** pending `[TODO: Translate]` keys in every locale (not just the latest
|
||||
feature's), applying §1–§3 (placeholders verbatim, Recipe rule, term maps, register).
|
||||
Find pending keys with: `grep -c "TODO: Translate" locales/*.json`
|
||||
5. If the new string contains new terminology, extend §2 tables.
|
||||
|
||||
### Fixing a translation bug
|
||||
1. Locate the key (dotted path) in the relevant locale file.
|
||||
2. Check the corresponding `en.json` value and the actual caller (grep `static/js` or
|
||||
`web/comfyui` for the key) to learn which placeholders are passed.
|
||||
3. Fix trivially; for normalization sweeps (e.g. "recette" → "Recipe"), do it file-wide for
|
||||
the offending keys only — do not touch unrelated lines.
|
||||
4. If the bug is in `en.json` itself (R9), fix the source first, then re-sync and update all
|
||||
locales.
|
||||
|
||||
### Verification
|
||||
```bash
|
||||
pytest tests/i18n/test_i18n.py # key parity + JSON validity + JS key references
|
||||
python scripts/sync_translation_keys.py --dry-run # shows which keys would change; add --verbose for per-key detail
|
||||
npm test # frontend tests incl. i18n helpers
|
||||
```
|
||||
|
||||
`pytest tests/i18n` only checks structure. Quality conventions in this document are not
|
||||
machine-enforced — a human/agent review pass is required.
|
||||
|
||||
### Anti-patterns checklist
|
||||
- [ ] Placeholders `{x}` / `{{x}}` differ from `en.json`
|
||||
- [ ] Same source term translated 2+ ways in the same file (see §5)
|
||||
- [ ] "Checkpoint" became a literal checkpoint; "recipe" became menu/prescription/food-cookbook
|
||||
- [ ] Brand names translated or transliterated (LoRA, CivitAI, ComfyUI, …)
|
||||
- [ ] Latin locale using full-width `：（）`; fr using `"` as apostrophe
|
||||
- [ ] Mixed 你/您, du/Sie, tú/usted
|
||||
- [ ] Full English sentences left behind (see §6)
|
||||
- [ ] Register/typos/mojibake; source string is stale vs `en.json` (compare semantics, not
|
||||
just words)
|
||||
@@ -1,28 +0,0 @@
|
||||
# Library Switching and Preview Routes
|
||||
|
||||
Library switching no longer requires restarting the backend. The preview
|
||||
thumbnails shown in the UI are now served through a dynamic endpoint that
|
||||
resolves files against the folders registered for the active library at request
|
||||
time. This allows the multi-library flow to update model roots without touching
|
||||
the aiohttp router, so previews remain available immediately after a switch.
|
||||
|
||||
## How the dynamic preview endpoint works
|
||||
|
||||
* `config.get_preview_static_url()` now returns `/api/lm/previews?path=<encoded>`
|
||||
for any preview path. The raw filesystem location is URL encoded so that it
|
||||
can be passed through the query string without leaking directory structure in
|
||||
the route itself.【F:py/config.py†L398-L404】
|
||||
* `PreviewRoutes` exposes the `/api/lm/previews` handler which validates the
|
||||
decoded path against the directories registered for the current library. The
|
||||
request is rejected if it falls outside those roots or if the file does not
|
||||
exist.【F:py/routes/preview_routes.py†L5-L21】【F:py/routes/handlers/preview_handlers.py†L9-L48】
|
||||
* `Config` keeps an up-to-date cache of allowed preview roots. Every time a
|
||||
library is applied the cache is rebuilt using the declared LoRA, checkpoint
|
||||
and embedding directories (including symlink targets). The validation logic
|
||||
checks preview requests against this cache.【F:py/config.py†L51-L68】【F:py/config.py†L180-L248】【F:py/config.py†L332-L346】
|
||||
|
||||
Both the ComfyUI runtime (`LoraManager.add_routes`) and the standalone launcher
|
||||
(`StandaloneLoraManager.add_routes`) register the new preview routes instead of
|
||||
mounting a static directory per root. Switching libraries therefore works
|
||||
without restarting the application, and preview URLs generated before or after a
|
||||
switch continue to resolve correctly.【F:py/lora_manager.py†L21-L82】【F:standalone.py†L302-L315】
|
||||
@@ -1,367 +0,0 @@
|
||||
# metadata.json Schema Documentation
|
||||
|
||||
This document defines the complete schema for `.metadata.json` files used by Lora Manager. These sidecar files store model metadata alongside model files (LoRA, Checkpoint, Embedding).
|
||||
|
||||
## Overview
|
||||
|
||||
- **File naming**: `<model_name>.metadata.json` (e.g., `my_lora.safetensors` → `my_lora.metadata.json`)
|
||||
- **Format**: JSON with UTF-8 encoding
|
||||
- **Purpose**: Store model metadata, tags, descriptions, preview images, and Civitai/CivArchive integration data
|
||||
- **Extensibility**: Unknown fields are preserved via `_unknown_fields` mechanism for forward compatibility
|
||||
|
||||
---
|
||||
|
||||
## Base Fields (All Model Types)
|
||||
|
||||
These fields are present in all model metadata files.
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `file_name` | string | ✅ Yes | ✅ Yes | Filename without extension (e.g., `"my_lora"`) |
|
||||
| `model_name` | string | ✅ Yes | ❌ No | Display name of the model. **Default**: `file_name` if no other source |
|
||||
| `file_path` | string | ✅ Yes | ✅ Yes | Full absolute path to the model file (normalized with `/` separators) |
|
||||
| `size` | integer | ✅ Yes | ❌ No | File size in bytes. **Set at**: Initial scan or download completion. Does not change thereafter. |
|
||||
| `modified` | float | ✅ Yes | ❌ No | **Import timestamp** — Unix timestamp when the model was first imported/added to the system. Used for "Date Added" sorting. Does not change after initial creation. |
|
||||
| `sha256` | string | ⚠️ Conditional | ✅ Yes | SHA256 hash of the model file (lowercase). **LoRA**: Required. **Checkpoint**: May be empty when `hash_status="pending"` (lazy hash calculation) |
|
||||
| `base_model` | string | ❌ No | ❌ No | Base model type. **Examples**: `"SD 1.5"`, `"SDXL 1.0"`, `"SDXL Lightning"`, `"Flux.1 D"`, `"Flux.1 S"`, `"Flux.1 Krea"`, `"Illustrious"`, `"Pony"`, `"AuraFlow"`, `"Kolors"`, `"ZImageTurbo"`, `"Wan Video"`, etc. **Default**: `"Unknown"` or `""` |
|
||||
| `preview_url` | string | ❌ No | ✅ Yes | Path to preview image file |
|
||||
| `preview_nsfw_level` | integer | ❌ No | ❌ No | NSFW level using **bitmask values** from Civitai: `1` (PG), `2` (PG13), `4` (R), `8` (X), `16` (XXX), `32` (Blocked). **Default**: `0` (none) |
|
||||
| `notes` | string | ❌ No | ❌ No | User-defined notes |
|
||||
| `from_civitai` | boolean | ❌ No (default: `true`) | ❌ No | Whether the model originated from Civitai |
|
||||
| `civitai` | object | ❌ No | ⚠️ Partial | Civitai/CivArchive API data and user-defined fields |
|
||||
| `tags` | array[string] | ❌ No | ⚠️ Partial | Model tags (merged from API and user input) |
|
||||
| `modelDescription` | string | ❌ No | ⚠️ Partial | Full model description (from API or user) |
|
||||
| `civitai_deleted` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model was deleted from Civitai |
|
||||
| `favorite` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model is marked as favorite |
|
||||
| `exclude` | boolean | ❌ No (default: `false`) | ❌ No | Whether to exclude from cache/scanning. User can set from `false` to `true` (currently no UI to revert) |
|
||||
| `db_checked` | boolean | ❌ No (default: `false`) | ❌ No | Whether checked against archive database |
|
||||
| `skip_metadata_refresh` | boolean | ❌ No (default: `false`) | ❌ No | Skip this model during bulk metadata refresh |
|
||||
| `metadata_source` | string\|null | ❌ No | ✅ Yes | Last provider that supplied metadata (see below) |
|
||||
| `last_checked_at` | float | ❌ No (default: `0`) | ✅ Yes | Unix timestamp of last metadata check |
|
||||
| `hash_status` | string | ❌ No (default: `"completed"`) | ✅ Yes | Hash calculation status: `"pending"`, `"calculating"`, `"completed"`, `"failed"` |
|
||||
| `autov3` | string\|null | ❌ No | ✅ Yes | CivitAI AutoV3 hash (first 12 chars, lowercase hex) sourced from the safetensors embedded metadata (`sshs_model_hash` / `modelspec.hash_sha256`). **Absent** = not yet checked (may be backfilled later); **`null`** = checked but unavailable (header has no recognized hash); **12-char hex string** = value |
|
||||
|
||||
---
|
||||
|
||||
## Model-Specific Fields
|
||||
|
||||
### LoRA Models
|
||||
|
||||
LoRA models do not have a `model_type` field in metadata.json. The type is inferred from context or `civitai.type` (e.g., `"LoRA"`, `"LoCon"`, `"DoRA"`).
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `usage_tips` | string (JSON) | ❌ No (default: `"{}"`) | ❌ No | JSON string containing recommended usage parameters |
|
||||
|
||||
**`usage_tips` JSON structure:**
|
||||
|
||||
```json
|
||||
{
|
||||
"strength_min": 0.3,
|
||||
"strength_max": 0.8,
|
||||
"strength_range": "0.3-0.8",
|
||||
"strength": 0.6,
|
||||
"clip_strength": 0.5,
|
||||
"clip_skip": 2
|
||||
}
|
||||
```
|
||||
|
||||
| Key | Type | Description |
|
||||
|-----|------|-------------|
|
||||
| `strength_min` | number | Minimum recommended model strength |
|
||||
| `strength_max` | number | Maximum recommended model strength |
|
||||
| `strength_range` | string | Human-readable strength range |
|
||||
| `strength` | number | Single recommended strength value |
|
||||
| `clip_strength` | number | Recommended CLIP/embedding strength |
|
||||
| `clip_skip` | integer | Recommended CLIP skip value |
|
||||
|
||||
---
|
||||
|
||||
### Checkpoint Models
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `model_type` | string | ❌ No (default: `"checkpoint"`) | ❌ No | Model type: `"checkpoint"`, `"diffusion_model"` |
|
||||
|
||||
---
|
||||
|
||||
### Embedding Models
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `model_type` | string | ❌ No (default: `"embedding"`) | ❌ No | Model type: `"embedding"` |
|
||||
|
||||
---
|
||||
|
||||
## The `civitai` Field Structure
|
||||
|
||||
The `civitai` object stores the complete Civitai/CivArchive API response. Lora Manager preserves all fields from the API for future compatibility and extracts specific fields for use in the application.
|
||||
|
||||
### Version-Level Fields (Civitai API)
|
||||
|
||||
**Fields Used by Lora Manager:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `id` | integer | Version ID |
|
||||
| `modelId` | integer | Parent model ID |
|
||||
| `name` | string | Version name (e.g., `"v1.0"`, `"v2.0-pruned"`) |
|
||||
| `nsfwLevel` | integer | NSFW level (bitmask: 1=PG, 2=PG13, 4=R, 8=X, 16=XXX, 32=Blocked) |
|
||||
| `baseModel` | string | Base model (e.g., `"SDXL 1.0"`, `"Flux.1 D"`, `"Illustrious"`, `"Pony"`) |
|
||||
| `trainedWords` | array[string] | **Trigger words** for the model |
|
||||
| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
|
||||
| `earlyAccessEndsAt` | string\|null | Early access end date (used for update notifications) |
|
||||
| `description` | string | Version description (HTML) |
|
||||
| `model` | object | Parent model object (see Model-Level Fields below) |
|
||||
| `creator` | object | Creator information (see Creator Fields below) |
|
||||
| `files` | array[object] | File list with hashes, sizes, download URLs (used for metadata extraction) |
|
||||
| `images` | array[object] | Image list with metadata, prompts, NSFW levels (used for preview/examples) |
|
||||
|
||||
**Fields Stored but Not Currently Used:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `createdAt` | string (ISO 8601) | Creation timestamp |
|
||||
| `updatedAt` | string (ISO 8601) | Last update timestamp |
|
||||
| `status` | string | Version status (e.g., `"Published"`, `"Draft"`) |
|
||||
| `publishedAt` | string (ISO 8601) | Publication timestamp |
|
||||
| `baseModelType` | string | Base model type (e.g., `"Standard"`, `"Inpaint"`, `"Refiner"`) |
|
||||
| `earlyAccessConfig` | object | Early access configuration |
|
||||
| `uploadType` | string | Upload type (`"Created"`, `"FineTuned"`, etc.) |
|
||||
| `usageControl` | string | Usage control setting |
|
||||
| `air` | string | Artifact ID (URN format: `urn:air:sdxl:lora:civitai:122359@135867`) |
|
||||
| `stats` | object | Download count, ratings, thumbs up count |
|
||||
| `videos` | array[object] | Video list |
|
||||
| `downloadUrl` | string | Direct download URL |
|
||||
| `trainingStatus` | string\|null | Training status (for on-site training) |
|
||||
| `trainingDetails` | object\|null | Training configuration |
|
||||
|
||||
### Model-Level Fields (`civitai.model.*`)
|
||||
|
||||
**Fields Used by Lora Manager:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `name` | string | Model name |
|
||||
| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
|
||||
| `description` | string | Model description (HTML, used for `modelDescription`) |
|
||||
| `tags` | array[string] | Model tags (used for `tags` field) |
|
||||
| `allowNoCredit` | boolean | License: allow use without credit |
|
||||
| `allowCommercialUse` | array[string] | License: allowed commercial uses. **Values**: `"Image"` (sell generated images), `"Video"` (sell generated videos), `"RentCivit"` (rent on Civitai), `"Rent"` (rent elsewhere) |
|
||||
| `allowDerivatives` | boolean | License: allow derivatives |
|
||||
| `allowDifferentLicense` | boolean | License: allow different license |
|
||||
|
||||
**Fields Stored but Not Currently Used:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `nsfw` | boolean | Model NSFW flag |
|
||||
| `poi` | boolean | Person of Interest flag |
|
||||
|
||||
### Creator Fields (`civitai.creator.*`)
|
||||
|
||||
Both fields are used by Lora Manager:
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `username` | string | Creator username (used for author display and search) |
|
||||
| `image` | string | Creator avatar URL (used for display) |
|
||||
|
||||
### Model Type Field (Top-Level, Outside `civitai`)
|
||||
|
||||
| Field | Type | Values | Description |
|
||||
|-------|------|--------|-------------|
|
||||
| `model_type` | string | `"checkpoint"`, `"diffusion_model"`, `"embedding"` | Stored in metadata.json for Checkpoint and Embedding models. **Note**: LoRA models do not have this field; type is inferred from `civitai.type` or context. |
|
||||
|
||||
### User-Defined Fields (Within `civitai`)
|
||||
|
||||
For models not from Civitai or user-added data:
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `trainedWords` | array[string] | **Trigger words** — manually added by user |
|
||||
| `customImages` | array[object] | Custom example images added by user |
|
||||
|
||||
### customImages Structure
|
||||
|
||||
Each custom image entry has the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"url": "",
|
||||
"id": "short_id",
|
||||
"nsfwLevel": 0,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"type": "image",
|
||||
"meta": {
|
||||
"prompt": "...",
|
||||
"negativePrompt": "...",
|
||||
"steps": 20,
|
||||
"cfgScale": 7,
|
||||
"seed": 123456
|
||||
},
|
||||
"hasMeta": true,
|
||||
"hasPositivePrompt": true
|
||||
}
|
||||
```
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `url` | string | Empty for local custom images |
|
||||
| `id` | string | Short ID or filename |
|
||||
| `nsfwLevel` | integer | NSFW level (bitmask) |
|
||||
| `width` | integer | Image width in pixels |
|
||||
| `height` | integer | Image height in pixels |
|
||||
| `type` | string | `"image"` or `"video"` |
|
||||
| `meta` | object\|null | Generation metadata (prompt, seed, etc.) extracted from image |
|
||||
| `hasMeta` | boolean | Whether metadata is available |
|
||||
| `hasPositivePrompt` | boolean | Whether a positive prompt is available |
|
||||
|
||||
### Minimal Non-Civitai Example
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {
|
||||
"trainedWords": ["my_trigger_word"]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Non-Civitai Example Without Trigger Words
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {}
|
||||
}
|
||||
```
|
||||
|
||||
### Example: User-Added Custom Images
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {
|
||||
"trainedWords": ["custom_style"],
|
||||
"customImages": [
|
||||
{
|
||||
"url": "",
|
||||
"id": "example_1",
|
||||
"nsfwLevel": 0,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"type": "image",
|
||||
"meta": {
|
||||
"prompt": "example prompt",
|
||||
"seed": 12345
|
||||
},
|
||||
"hasMeta": true,
|
||||
"hasPositivePrompt": true
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Metadata Source Values
|
||||
|
||||
The `metadata_source` field indicates which provider last updated the metadata:
|
||||
|
||||
| Value | Source |
|
||||
|-------|--------|
|
||||
| `"civitai_api"` | Civitai API |
|
||||
| `"civarchive"` | CivArchive API |
|
||||
| `"archive_db"` | Metadata Archive Database |
|
||||
| `null` | No external source (user-defined only) |
|
||||
|
||||
---
|
||||
|
||||
## Auto-Update Behavior
|
||||
|
||||
### Fields Updated During Scanning
|
||||
|
||||
These fields are automatically synchronized with the filesystem:
|
||||
|
||||
- `file_name` — Updated if actual filename differs
|
||||
- `file_path` — Normalized and updated if path changes
|
||||
- `preview_url` — Updated if preview file is moved/removed
|
||||
- `sha256` — Updated during hash calculation (when `hash_status="pending"`)
|
||||
- `hash_status` — Updated during hash calculation
|
||||
- `autov3` — Set when metadata is first created (from safetensors header); may be backfilled later for entries where it is absent
|
||||
- `last_checked_at` — Timestamp of scan
|
||||
- `metadata_source` — Set based on metadata provider
|
||||
|
||||
### Fields Set Once (Immutable After Import)
|
||||
|
||||
These fields are set when the model is first imported/scanned and **never change** thereafter:
|
||||
|
||||
- `modified` — Import timestamp (used for "Date Added" sorting)
|
||||
- `size` — File size at time of import/download
|
||||
|
||||
### User-Editable Fields
|
||||
|
||||
These fields can be edited by users at any time through the Lora Manager UI or by manually editing the metadata.json file:
|
||||
|
||||
- `model_name` — Display name
|
||||
- `tags` — Model tags
|
||||
- `modelDescription` — Model description
|
||||
- `notes` — User notes
|
||||
- `favorite` — Favorite flag
|
||||
- `exclude` — Exclude from scanning (user can set `false`→`true`, currently no UI to revert)
|
||||
- `skip_metadata_refresh` — Skip during bulk refresh
|
||||
- `civitai.trainedWords` — Trigger words
|
||||
- `civitai.customImages` — Custom example images
|
||||
- `usage_tips` — Usage recommendations (LoRA only)
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Field Reference by Behavior
|
||||
|
||||
### Required Fields (Must Always Exist)
|
||||
|
||||
- `file_name`
|
||||
- `model_name` (defaults to `file_name` if not provided)
|
||||
- `file_path`
|
||||
- `size`
|
||||
- `modified`
|
||||
- `sha256` (LoRA: always required; Checkpoint: may be empty when `hash_status="pending"`)
|
||||
|
||||
### Optional Fields with Defaults
|
||||
|
||||
| Field | Default |
|
||||
|-------|---------|
|
||||
| `base_model` | `"Unknown"` or `""` |
|
||||
| `preview_nsfw_level` | `0` |
|
||||
| `from_civitai` | `true` |
|
||||
| `civitai` | `{}` |
|
||||
| `tags` | `[]` |
|
||||
| `modelDescription` | `""` |
|
||||
| `notes` | `""` |
|
||||
| `civitai_deleted` | `false` |
|
||||
| `favorite` | `false` |
|
||||
| `exclude` | `false` |
|
||||
| `db_checked` | `false` |
|
||||
| `skip_metadata_refresh` | `false` |
|
||||
| `metadata_source` | `null` |
|
||||
| `last_checked_at` | `0` |
|
||||
| `hash_status` | `"completed"` |
|
||||
| `autov3` | absent (not checked) or `null` (checked, no value) |
|
||||
| `usage_tips` | `"{}"` (LoRA only) |
|
||||
| `model_type` | `"checkpoint"` or `"embedding"` (not present in LoRA models) |
|
||||
|
||||
---
|
||||
|
||||
## Version History
|
||||
|
||||
| Version | Date | Changes |
|
||||
|---------|------|---------|
|
||||
| 1.1 | 2026-08 | Added `autov3` field (CivitAI AutoV3 hash with three-state semantics) |
|
||||
| 1.0 | 2026-03 | Initial schema documentation |
|
||||
|
||||
---
|
||||
|
||||
## See Also
|
||||
|
||||
- [JSON Schema Definition](../.specs/metadata.schema.json) — Formal JSON Schema for validation
|
||||
@@ -1,206 +0,0 @@
|
||||
# Plan: Multi-File Downloads Within a Single CivitAI Model Version
|
||||
|
||||
**Issue:** [#1058 — Cannot download multiple file variants from the same model version](https://github.com/willmiao/ComfyUI-Lora-Manager/issues/1058)
|
||||
**Status:** v2 — revised after adversarial review (backend correctness + frontend/tests)
|
||||
**Scope:** CivitAI/CivArchive downloads of `lora`, `checkpoint`, `embedding` model types. HuggingFace downloads are out of scope (already per-file).
|
||||
|
||||
> v2 changelog: incorporated 18 review findings. Key changes vs v1:
|
||||
> shared file resolver + `resolved_version_id` for the gate (R1); `file_params` normalization at API boundary (R2); D2 hash-matching rule fixed for empty-hash cases (R6/R7); D3 extended to re-point `version_index` on removal (R4); D4 replaced with a child table (R3); `delete_model_version` interaction documented (R5); `ModelVersionsTab` surface added to phase 2 (F6); phase-2 multi-file loop requires a reload-deferred download variant (F7); queue-retry `file_params=NULL` known issue recorded (R9); test-fixture gaps and revised estimates (F10).
|
||||
|
||||
---
|
||||
|
||||
## 1. Problem Statement
|
||||
|
||||
A CivitAI model version can contain multiple downloadable weight files (e.g. fp16/fp32, safetensors/ckpt, different sizes). LoRA Manager already has a working file-selection pipeline (frontend file dialog → `fileParams` → backend file matching), but downloaded state is tracked at the **model-version** level. After any single file of a version is downloaded:
|
||||
|
||||
1. The version is marked **In Library** and the file-selection entry point disappears.
|
||||
2. The backend rejects further download attempts for that version.
|
||||
|
||||
There is no way to download the remaining files of the same version through LoRA Manager.
|
||||
|
||||
## 2. Current State (verified against code; all references confirmed by review)
|
||||
|
||||
### 2.1 Download gating — backend (`py/services/download_manager.py`)
|
||||
|
||||
`_execute_original_download` enforces two version-level gates:
|
||||
|
||||
- **Library gate, early** (lines 1157–1184, before metadata fetch, fires when `model_version_id` given) and **late** (lines 1350–1376, fires only when `model_version_id is None`): `scanner.check_model_version_exists(version_id)` across lora/checkpoint/embedding scanners → hard error `"Model version already exists in ... library"`.
|
||||
- **History gate** (lines 1238–1279): when `skip_previously_downloaded_model_versions` setting is on, `_has_been_downloaded(model_type, version_id)` → silent skip. History DB primary key is `(model_type, version_id)` (`py/services/downloaded_version_history_service.py:61`).
|
||||
|
||||
File selection works: `file_params {id, type, format, size, fp}` is matched against `version_info.files` (lines 1498–1569), **but only under `if file_params and model_version_id:` (line 1499)** — with `model_id`-only requests the selection silently falls back to the primary file (1571–1619). `file_params` currently carries no file `name` or hash.
|
||||
|
||||
### 2.2 Downloaded-state surfacing — backend (`py/routes/handlers/model_handlers.py`)
|
||||
|
||||
`get_civitai_versions` (lines 2148–2188) sets per-version `existsLocally` via `cache.version_index.get(version_id)` (plus a single `localPath` from that entry) and `hasBeenDownloaded` via the history service. No per-file granularity.
|
||||
|
||||
### 2.3 Frontend blockers (`static/js/managers/DownloadManager.js`)
|
||||
|
||||
Three independent gates prevent re-entering the file dialog:
|
||||
|
||||
1. **Line 598:** file-select badge rendered only when `modelFiles.length > 1 && !existsLocally`.
|
||||
2. **Lines 666–681 (`updateNextButtonState`):** Next button disabled with "Already in Library" when `currentVersion.existsLocally`.
|
||||
3. **Lines 784–787 (`proceedToLocation`):** toast + abort when `currentVersion.existsLocally`.
|
||||
|
||||
The badge path (`confirmFileSelection` lines 737–759 → `proceedToLocationContent` → `startDownload` single mode → `executeDownloadWithProgress` → POST `file_params`, `static/js/api/baseModelApi.js:1236–1250`) has **zero** `existsLocally` guards (all 12 occurrences enumerated; none on this path; `import/DownloadManager.js` has none either). The `.exists-locally` CSS class is purely visual (`download-modal.css:496–499`). **Making the badge visible again is sufficient to unlock the flow** for phase 1.
|
||||
|
||||
Post-download refresh is clean: the modal closes and `resetAndReload(true)` performs a full library refetch (`DownloadManager.js:1063`); dialog reopen resets state and refetches versions with no client-side cache. No same-session staleness.
|
||||
|
||||
### 2.4 Local identity of the downloaded file
|
||||
|
||||
`LoraMetadata/CheckpointMetadata/EmbeddingMetadata.from_civitai_info(version_info, file_info, ...)` (`py/utils/models.py:245–369`) persists:
|
||||
|
||||
- `sha256` = `file_info.hashes.SHA256` (lowercased, defaults to `""`) — a stable per-file identity;
|
||||
- `civitai` = the full `version_info` payload (including the `files` list).
|
||||
|
||||
Metadata refresh (`metadata_sync_service.py:104–105`) replaces the `civitai` blob wholesale but never overwrites top-level `sha256`; `verify_duplicate_hashes` (481–526) corrects it to the on-disk hash. Top-level-sha256 matching is refresh-robust.
|
||||
|
||||
**Caveats (review R6/R7):**
|
||||
- SHA256 is not guaranteed: CivArchive's transform only sets `hashes` when source data carries it (`civarchive_client.py:185–189`); `from_civitai_info` defaults to `""`.
|
||||
- Name fallback is unreliable exactly when it matters: local `file_name` is extension-less (`models.py:264`) and `generate_unique_filename` rewrites it with a hash suffix on conflict (`download_manager.py:1125–1136`); checkpoints with `hash_status='pending'` keep empty sha256 until on-demand hashing (`model_scanner.py:1232–1240`).
|
||||
|
||||
### 2.5 Version index collision (pre-existing hazard)
|
||||
|
||||
`ModelCache.version_index` is single-valued (`model_cache.py:133`: `version_index[version_id] = item`). Two files of the same version in the library → second entry overwrites the first; `remove_from_version_index` (lines 151–181) drops the whole version key when the indexed entry is removed, even if a sibling file remains. ~10 read sites depend on this index (48 grep touch points total; readers include `recipe_scanner.py:2682–2726`, `recipe_format.py:37–40`, `misc_handlers.py:2440–2444`, `model_handlers.py`, `model_scanner.check_model_version_exists:2444`).
|
||||
|
||||
Review correction (F3): bulk paths `remove_models` (`model_scanner.py:2376`) and `update_single_model_cache` (`:1689`) call `rebuild_version_index()` right after, so a sibling re-enters the index in those flows — the hazard is narrower than v1 stated, but direct `remove_from_version_index` callers (e.g. `model_scanner.py:1018`) still drop the key, and the user-visible artifact in phase 1 is real: `localPath` in the dialog flips to whichever file was indexed last.
|
||||
|
||||
### 2.6 Entry points that send / don't send `file_params` (fully enumerated by review)
|
||||
|
||||
**Send `file_params` (user-initiated dialog flows only):** `DownloadManager.js:1611–1639` (single mode). API surface accepting arbitrary JSON `file_params`: GET `/api/lm/download-model-get` (`model_handlers.py:1634–1686`), POST `/api/lm/downloads/queue/add` (`model_handlers.py:1799–1832`).
|
||||
|
||||
**Never send `file_params` (keep version-level semantics):** batch download (`DownloadManager.js:1756–1766`; batch also filters out in-library versions at `:1648`), `downloadVersionWithDefaults` (`:1810–1830`), recipe import (`import/DownloadManager.js:269–276`), bulk missing-LoRA (`BulkMissingLoraDownloadManager.js:292–299`), `RecipeModal.js:1728–1736`, `ModelVersionsTab.js:1427`. `web/comfyui/` and `vue-widgets/src` contain **no** download triggers at all (grep-verified). `py/services/use_cases/` has only `download_model_use_case.py` (pass-through).
|
||||
|
||||
### 2.7 Paths that do NOT need changes (verified)
|
||||
|
||||
- **aria2 pause/resume** (`_resume_restored_aria2_download`, line 754+): resumes from persisted `resume_context`; never re-runs existence gates.
|
||||
- **`download_coordinator.py:90`**: pure pass-through of `file_params`.
|
||||
- **Update checker / plugin self-update** (`update_routes.py:496–501`): only closes the history DB handle.
|
||||
- **History delete semantics**: `mark_as_deleted` sets `is_deleted_override=1` and `has_been_downloaded` then returns False (`downloaded_version_history_service.py:276`) — LM-initiated deletes already reset the history skip.
|
||||
|
||||
### 2.8 Related pre-existing issues (record, not necessarily fix)
|
||||
|
||||
- **Queue retry drops file selection** (R9): `download_queue_service.retry_from_history` / `retry_all_failed` re-queue with `file_params=NULL` (`download_queue_service.py:705, 758`) although the queue table has a `file_params` column (`:43`) — a retried non-primary download silently reverts to the primary file. Fix alongside phase 1 (small: persist and reuse the column).
|
||||
- **`delete_model_version`** (`misc_handlers.py:2410–2487`): resolves the file via the single-valued `version_index` (2440–2444), deletes only that one file, and `mark_as_deleted` flags the **entire version** as deleted in history (2479) even when a sibling file remains in the library. See phase 2 item 6.1.5.
|
||||
|
||||
## 3. Goals / Non-Goals
|
||||
|
||||
**Goals**
|
||||
|
||||
- G1: A user can download any not-yet-downloaded file of a version already partially in the library (issue repro steps 6–8).
|
||||
- G2: True duplicates stay blocked: downloading the *same* file of the same version twice is rejected.
|
||||
- G3: Per-file downloaded state visible in the file dialog; multiple files selectable and downloadable in one pass.
|
||||
- G4: No regression for version-level semantics relied on by batch download, recipe missing-LoRA detection, and `skip_previously_downloaded_model_versions`.
|
||||
|
||||
**Non-Goals**
|
||||
|
||||
- No change to recipe `inLibrary` semantics ("any file of the version present" remains sufficient).
|
||||
- No change to the update-checker (version-level comparison).
|
||||
- No primary-key rebuild of the history database.
|
||||
- HuggingFace download flow untouched.
|
||||
|
||||
## 4. Design Decisions
|
||||
|
||||
- **D1 — Explicit file selection bypasses the history gate, version-level gates stay for everyone else.** The history skip exists to dedupe automated flows. A user explicitly picking a file is unambiguous intent; the file-level library gate (G2) still prevents real duplicates. **Guard conditions use normalized truthiness** (see D1a). All confirmed `file_params` senders are user-initiated dialog flows (2.6), and LM-initiated deletes already reset history (2.7), so the bypass only affects "downloaded but not LM-deleted" versions with the setting on — intended.
|
||||
- **D1a — `file_params` normalization at the boundary (R2).** `download-model-get` and `downloads/queue/add` accept arbitrary JSON; `{}` is `not None` but falsy and would bypass gates while downloading the primary file. Normalize `file_params = file_params or None` in the coordinator/handlers, and treat the bypass as active only when a target file id is resolvable.
|
||||
- **D2 — File identity matching rule (R6/R7):** hash-compare **only when both sides are non-empty** (lowercase SHA256 equality); name-compare when either side is empty. Never let `"" == ""` match. Name fallback caveats from 2.4 apply (renamed files, pending checkpoint hashes) — acceptable residual risk, worst case is a blocked re-download the user can retry after hashing completes.
|
||||
- **D3 — Cache indexes: additive multi-index + removal re-pointing (R4).** Add `version_files_index: Dict[int, List[dict]]` maintained alongside `version_index` by the same add/remove/rebuild methods; existing readers of `version_index` untouched. Additionally fix `remove_from_version_index`: when the popped entry has a surviving sibling (per the multi-index), re-point `version_index[version_id]` to the sibling instead of dropping the key; same for the `model_id_index` descriptor. This closes the 2.5 hazard for existing readers (`check_model_version_exists`, `existsLocally`, recipe matching) without restructuring anything.
|
||||
- **D4 — Per-file history via a child table (R3).** v1's additive-column approach is structurally impossible on a `(model_type, version_id)` PK (`ON CONFLICT DO UPDATE` would keep only the last file). Instead add `downloaded_version_files(model_type, version_id, file_id, file_name, downloaded_at, PRIMARY KEY(model_type, version_id, file_id))` — additive, no PK rebuild, honors the Non-Goal. Existing version-level table and queries unchanged. New per-file queries are opt-in. `_initialize_schema` uses `CREATE TABLE IF NOT EXISTS`, so the new table is created for existing DBs without any ALTER.
|
||||
- **D5 — UI flow reuse, with an extracted inner download function for multi-file (F7).** Phase 1 unlocks the existing badge → file dialog → location → download pipeline. Phase 2 upgrades the dialog to multi-select; iterating `executeDownloadWithProgress` as-is would produce N full library reloads, N toasts, and competing failure-summary modals — so phase 2 extracts a reload-deferred, failure-aggregating inner variant and runs one reload + one summary at the end.
|
||||
|
||||
## 5. Implementation — Phase 1 (fix the issue; independently shippable)
|
||||
|
||||
### 5.1 Backend — `py/services/download_manager.py`
|
||||
|
||||
1. **Normalize `file_params`** at the boundary (D1a): `download_coordinator.schedule_download` and the two API handlers (`model_handlers.py:1649–1666`, `1810–1832`) apply `file_params = file_params or None`.
|
||||
2. **Extract a shared file resolver** (R1): pull the matching logic at 1498–1569 into `_resolve_target_file(version_info, file_params) -> Optional[dict]`, used by **both** the new gate and the download-selection path. The selection path's condition (line 1499) switches from `model_version_id` to `resolved_version_id` (already computed at 1230–1236 from `version_info.id`), so gate and download always agree on the target file — including the `model_id`-only case.
|
||||
3. **New helper** `_find_local_file_entry(version_id, target_file) -> Optional[dict]`: iterate the three scanners' cached `raw_data` (NOT `version_index` — single-valued); candidates = entries whose `civitai.id` normalizes to `version_id`; match per D2.
|
||||
4. **Gate restructure in `_execute_original_download`**:
|
||||
- Early scanner gate (1157–1184): add `file_params is None` guard; with normalized `file_params`, defer (file identity not resolvable before metadata fetch).
|
||||
- After `version_info` fetch + `resolved_version_id` (~1229): when `file_params` present, resolve target file via the shared resolver; unresolvable → hard error "No matching file" (fail closed, prevents empty-dict bypass). Resolvable → `_find_local_file_entry`; hit → same hard error shape as today with the file name in the message.
|
||||
- History gate (1238–1279): add `file_params is None` (D1). Base-model skip (1281–1324) unchanged — still applies.
|
||||
- Late gate (1350–1376): add `file_params is None` guard (F2) — the post-fetch file-level check above already covers this case.
|
||||
- Nothing between the early gate and the post-fetch point assumes the version is absent (review task 6: only provider selection + metadata fetch; no DB writes; `_persist_aria2_state` runs only when actually downloading at 1659).
|
||||
5. **Queue retry fix** (2.8, small): persist `file_params` into the queue table on enqueue and reuse it in `retry_from_history` / `retry_all_failed`.
|
||||
6. Logging: `[download]` lines for file-level allow/block, consistent with existing style.
|
||||
|
||||
**Estimated:** ~150–220 LOC + resolver extraction.
|
||||
|
||||
### 5.2 Frontend — `static/js/managers/DownloadManager.js`
|
||||
|
||||
1. Line 598: drop `&& !existsLocally` from the badge condition (badge shows whenever `modelFiles.length > 1`).
|
||||
2. `fileParams` construction (1611–1616): add `name: this.selectedFile.name`.
|
||||
3. Surface the backend "file already in library" hard error as a toast instead of only the batch-summary modal (R10/F12 nit; reuse existing error message field).
|
||||
4. No changes to `updateNextButtonState` / `proceedToLocation` in phase 1; no template or CSS changes.
|
||||
|
||||
**Known phase-1 UX limitations (acknowledged, fixed in phase 2):** with all files downloaded the badge still renders and re-picking a downloaded file fails late (backend error after the location step); `localPath` may point at a sibling file; batch-preview "In Library" badge stays version-level and gives no hint of remaining files.
|
||||
|
||||
**Estimated:** ~10–30 LOC (confirmed realistic by review).
|
||||
|
||||
### 5.3 Phase 1 tests
|
||||
|
||||
Backend — extend `tests/services/test_download_manager_basic.py` (1694 lines; all fixture patterns exist):
|
||||
|
||||
- **Fixture gaps to add (F10):** `DummyScanner.get_cached_data()`/`raw_data` stub (~10 lines); `hashes.SHA256` in the metadata-provider payload's `files`.
|
||||
- Cases: same version + different SHA256 in library + `file_params` → proceeds; same SHA256 → hard error; `file_params=None` + version in library → hard error (unchanged); history-skip on + `file_params` → not skipped; without → skipped (unchanged); empty-dict `file_params` normalized → version-level behavior; `model_id`-only + `file_params` → gate and selection resolve the same file; legacy metadata (empty local sha256) matched by name; target file with empty SHA256 → name fallback, no `""==""` false positive.
|
||||
- Queue retry: `file_params` survives retry.
|
||||
- Assert proceed/abort via the existing `_execute_download` mock pattern.
|
||||
|
||||
Frontend (`tests/frontend/`): badge renders for multi-file version with `existsLocally=true` (pattern from `downloadManager.history.test.js`).
|
||||
|
||||
**Estimated:** ~150–250 LOC (confirmed realistic).
|
||||
|
||||
## 6. Implementation — Phase 2 (per-file status + multi-select + index hardening)
|
||||
|
||||
### 6.1 Backend
|
||||
|
||||
1. **`py/services/model_cache.py`** (D3): add `version_files_index`; maintain in `add_to_version_index` / `remove_from_version_index` / `rebuild_version_index`; removal re-points `version_index[version_id]` (and the `model_id_index` descriptor) to a surviving sibling instead of dropping the key.
|
||||
2. **`py/services/model_scanner.py`**: expose `get_files_for_version(version_id) -> List[dict]`.
|
||||
3. **`py/routes/handlers/model_handlers.py` `get_civitai_versions`**: annotate each version with `downloadedFiles: [{fileId, fileName, filePath}]` via `version_files_index` + D2 matching against `version.files`.
|
||||
4. **`py/services/downloaded_version_history_service.py`** (D4): new child table `downloaded_version_files`; `mark_downloaded` also upserts the child row when `file_id` known; `mark_as_deleted` clears the version's child rows only when no sibling remains in the library; new `get_downloaded_file_ids(model_type, version_id) -> set[int]`. `_record_downloaded_version_history` passes `file_info` through.
|
||||
5. **`delete_model_version`** (`misc_handlers.py:2410–2487`, R5): resolve **all** local files of the version via `version_files_index`; delete all (current endpoint semantics are version-level) or — if kept per-file — only `mark_as_deleted` when no sibling remains. Decide at implementation time; minimum is documenting current behavior.
|
||||
6. **`ModelVersionsTab` backend support**: none needed beyond item 3 (`downloadedFiles`); the tab consumes the same versions payload.
|
||||
|
||||
### 6.2 Frontend
|
||||
|
||||
1. **File dialog multi-select** — change surface (F8): option markup (`DownloadManager.js:712–724`), the single-select click handler (`727–734`), the `input[type="radio"]:checked` selector in `confirmFileSelection` (`738`); template `templates/components/modals/download_modal.html:48–60` (confirm-button label only); CSS `download-modal.css` — checkbox variant of `.file-option-radio input` (595–604) and a **new** `.file-option.disabled` style (does not exist). Files whose id ∈ `downloadedFiles` render disabled with an "In Library" tag.
|
||||
2. **Mixed-type guard (F8):** multi-select is restricted to files sharing the same routing target (`_isDiffusionModel` is computed once from a single `selectedFile` at 798–803; e.g. "Model" + "UNet" files route to different roots). Disallow mixed-type multi-select (simplest, predictable); single-file selection unchanged.
|
||||
3. **Multi-file download loop (D5/F7):** extract from `executeDownloadWithProgress` a reload-deferred, no-toast inner function; iterate per selected file with per-file progress; one `resetAndReload(true)` + one aggregated success/failure summary at the end (reuse `showDownloadBatchSummary`).
|
||||
4. **`updateNextButtonState` / `proceedToLocation`:** for multi-file versions, Next routes into the file dialog; hard block only when *every* weight file is downloaded.
|
||||
5. **`ModelVersionsTab.js` (F6):** the Download action (`:576` hidden when `isInLibrary`) — for multi-file versions with remaining files, show it and route into the download modal's file dialog; keep hidden when all files present.
|
||||
6. **Batch preview (F5):** `batch-preview-local-badge` (`:1320`) gains a "partially downloaded" hint for multi-file versions with remaining files.
|
||||
7. New i18n keys (`modals.download.fileSelection.inLibrary`, `downloadSelected`, partial-download tooltip, etc.) → run `python scripts/sync_translation_keys.py`.
|
||||
|
||||
### 6.3 Phase 2 tests
|
||||
|
||||
- `model_cache` (`tests/services/test_model_cache.py` already covers add/remove at 44–55): multi-valued index; sibling re-point on removal; rebuild.
|
||||
- `get_civitai_versions`: `downloadedFiles` correctness (hash match, name fallback, no match, CivArchive no-hash payload).
|
||||
- History service (`tests/services/test_downloaded_version_history_service.py` uses real SQLite on tmp_path): child-table creation on a legacy DB; per-file record/query; `mark_as_deleted` sibling semantics.
|
||||
- Frontend: dialog checkbox rendering/disabled state and multi-file confirm — **greenfield behavior coverage** (F10: no existing test exercises `showFileSelectionStep`/`confirmFileSelection`; infra exists, patterns must be built).
|
||||
|
||||
## 7. Risks and Mitigations
|
||||
|
||||
| Risk | Impact | Mitigation |
|
||||
|---|---|---|
|
||||
| History-gate bypass (D1) causes unwanted re-downloads in automated flows | Large checkpoint files re-downloaded | Bypass only with normalized, resolvable `file_params` (D1a); all such senders are user-initiated dialog flows (2.6, verified); tests pin batch/recipe/bulk behavior. |
|
||||
| Empty-hash matching edge cases (R6) | Duplicate download of the same file, or false block | D2 rule: hash only when both non-empty; name otherwise; never `""==""`. Residual risk documented (2.4). |
|
||||
| Phase-1 late-failure UX (F12) | User picks a downloaded file, fails only after location step | Toast surfacing (5.2.3); phase 2 disables downloaded files up front. |
|
||||
| Phase-2 index change corrupts existing behavior | Recipe matching, delete flows | Additive index + re-point only; `version_index` read semantics unchanged; `remove_models`/`update_single_model_cache` already rebuild (F3); tests. |
|
||||
| `delete_model_version` marks whole version deleted while sibling remains (R5) | History wrongly suppresses re-download of the surviving sibling's version | Phase 2 item 6.1.5; documented until then. |
|
||||
| History child-table migration failure on user installs | Service init crash | `CREATE TABLE IF NOT EXISTS` in `_initialize_schema`; failure degrades to version-level behavior (per-file queries return empty). |
|
||||
| Batch-preview badge misleading for partial versions (F5) | Minor UX confusion | Acknowledged in phase 1; fixed in phase 2 item 6.2.6. |
|
||||
| UI confusion: version shows "In Library" while files remain downloadable | Support burden | Phase 2: per-file disabled state + partial-download tooltip. |
|
||||
| Hash-identical sibling files (repacked content) | Second file blocked | Acceptable: scanner hash dedup already collapses them. |
|
||||
|
||||
## 8. Rollout
|
||||
|
||||
1. **Commit 1** — `fix(download): allow downloading additional files of an in-library model version (#1058)` → Phase 1 (5.1–5.3).
|
||||
2. **Commit 2** — `feat(download): per-file download status and multi-file selection (#1058)` → Phase 2 (6.1–6.3).
|
||||
|
||||
Phase 1 alone resolves the issue as reported; phase 2 can ship in a later release if review prefers smaller increments.
|
||||
|
||||
## 9. Effort Estimate (revised after review)
|
||||
|
||||
| Phase | Backend | Frontend | Tests | Risk |
|
||||
|---|---|---|---|---|
|
||||
| 1 | ~150–220 LOC (+ queue-retry fix ~30) | ~10–30 LOC | ~150–250 LOC | Low |
|
||||
| 2 | ~250–350 LOC | ~250–350 LOC (multi-file loop refactor + ModelVersionsTab + batch badge) | ~250–350 LOC (dialog tests greenfield) | Medium |
|
||||
@@ -1,337 +0,0 @@
|
||||
# Plan: Global Rate-Limit Abidance for Recipe Ingest & Metadata Fetching
|
||||
|
||||
**Issue:** [#1085 — Large Recipe Ingest Appears to not abide by vendor rate limits, possibly a few other errors?](https://github.com/willmiao/ComfyUI-Lora-Manager/issues/1085)
|
||||
**Status:** v2 — reviewed; decisions recorded in §10. **Phase 1 implemented**
|
||||
(2026-08-27, commit `c2a2048c`): coordinator + downloader gate + Fix C
|
||||
failover semantics + helper double-wait fix + settings. **Phase 2
|
||||
implemented** (2026-08-27): batch-import rate-limit failures map to
|
||||
`SKIPPED` + `rate_limited` WebSocket flag + UI slowdown hint (toast + status
|
||||
text, i18n keys synced); `download_to_memory` / `get_response_headers` /
|
||||
`download_file` register 429 cooldowns. Changes vs v1: Fix C moved to
|
||||
Phase 1, helper double-wait resolved in Phase 1, gate/guard ordering
|
||||
specified.
|
||||
**Scope:** HTTP API traffic to CivitAI (`civitai.red`) and CivArchive (`civarchive.com`) from metadata fetching (bulk refresh, metadata sync, recipe analysis/enrichment, usage-control lookups). Large binary downloads (model files / preview images via `download_file`) are out of scope for *pacing* (they are already single-connection transfers) but their 429 responses should still be *registered*.
|
||||
|
||||
> Context: a first batch of fixes for this issue was already committed as
|
||||
> `ee233548` ("fix(recipes): enforce batch-import concurrency bound and harden
|
||||
> ingest errors (#1085)"): the batch-import concurrency controller now shares a
|
||||
> real semaphore (bounds 1–5 actually apply), the Comfy parser tolerates
|
||||
> list/`None` `ckpt_name`, CivArchive treats empty error payloads as failures,
|
||||
> and offline-cooldown short-circuits log at DEBUG. This plan covers the two
|
||||
> remaining orchestration-level fixes:
|
||||
> **Fix 2** — slow down globally when a vendor rate limit is hit (respect
|
||||
> `Retry-After`, queue instead of hammering); **Fix 3** — stop immediately
|
||||
> failing over to CivArchive when CivitAI is rate-limited.
|
||||
|
||||
---
|
||||
|
||||
## 1. Problem Statement
|
||||
|
||||
During a large recipe ingest (e.g. importing the example-images directory,
|
||||
which can be thousands of images), the manager fires one metadata request per
|
||||
checkpoint + per LoRA per image through the fallback provider chain
|
||||
(`civitai_api → civarchive_api → sqlite`). Consequences observed in #1085:
|
||||
|
||||
1. **CivitAI gets hammered** → 429s. The consumer then *immediately* tries
|
||||
CivArchive for the same lookup, so **CivArchive gets hammered too** before
|
||||
it was ever naturally needed (its only real job is recovering metadata for
|
||||
models deleted from CivitAI).
|
||||
2. Requests are retried per-call after `Retry-After`, but **each concurrent
|
||||
call sleeps independently** → thundering herd: thousands of coroutines wake
|
||||
at the same moment and re-flood the vendor.
|
||||
3. While CivArchive is in the `ConnectivityGuard` cooldown, every batch item
|
||||
short-circuits and is marked `FAILED` — the batch import's success/failure
|
||||
accounting is polluted by a transient vendor state (log spam was fixed in
|
||||
`ee233548`; the item-failure accounting is not).
|
||||
4. `ConnectivityGuard` (`py/services/connectivity_guard.py`) only treats
|
||||
transport-level unreachability as offline; **HTTP 429 is invisible to it**,
|
||||
so nothing ever intentionally paces request rate.
|
||||
|
||||
User expectation from the issue: *"once a vendor rate limit time out is hit,
|
||||
you should trigger a slow down with intentional reduction in request rate"*.
|
||||
|
||||
## 2. Current State (verified against code)
|
||||
|
||||
### 2.1 Where 429s are surfaced
|
||||
|
||||
- `Downloader.make_request` (`py/services/downloader.py:1120-1132`): HTTP 429 →
|
||||
returns `RateLimitError(message, retry_after=…)` parsed from `Retry-After`
|
||||
(missing header defaults to `None`).
|
||||
- `CivitaiClient._make_request` (`py/services/civitai_client.py:97-100`):
|
||||
converts `RateLimitError` to a raise immediately; no waiting. Transient
|
||||
5xx/connection errors are retried 3× with 1s/2s/4s backoff.
|
||||
- `CivArchiveClient._make_request` (`py/services/civarchive_client.py`):
|
||||
raises `RateLimitError` with `provider="civarchive_api"` when not set.
|
||||
- `_RateLimitRetryHelper` (`py/services/model_metadata_provider.py:45-102`):
|
||||
per-call retry loop — sleeps `retry_after` (capped at 1800 s; `≥120 s` ⇒ no
|
||||
retry), then re-raises. Because every concurrent call runs its own helper,
|
||||
they sleep in parallel and re-fire in parallel.
|
||||
- `FallbackMetadataProvider` (`py/services/model_metadata_provider.py:488-508,
|
||||
564-584` etc.): on a final `RateLimitError` from one provider it logs
|
||||
"skipping to next provider" and **continues to the next network provider** —
|
||||
this is the direct cause of the CivArchive flood.
|
||||
- `MetadataSyncService.fetch_and_update_model`
|
||||
(`py/services/metadata_sync_service.py:248-333`): manually iterates
|
||||
`provider_attempts`; on `RateLimitError` it `continue`s to the next provider
|
||||
(same failover problem), then reports `"Rate limited"` when nothing
|
||||
succeeded.
|
||||
- `Downloader.make_request` has a per-destination scope already available:
|
||||
`_guard_destination(url)` returns the hostname (`downloader.py:1194-1199`),
|
||||
used by `ConnectivityGuard`.
|
||||
|
||||
### 2.2 What pacing exists today
|
||||
|
||||
- `ConnectivityGuard`: per-destination cooldown (30 s base, ×2 per extra
|
||||
failure batch, 300 s cap) triggered only by transport errors
|
||||
(`connectivity_guard.py:168-197`).
|
||||
- `AdaptiveConcurrencyController` (batch import, fixed in `ee233548`): shared
|
||||
semaphore enforces 1–5 concurrent items; *duration*-based adjustment only —
|
||||
it never sees HTTP statuses, so it cannot distinguish "slow because rate
|
||||
limited" from "slow because big image".
|
||||
- No token bucket, no minimum inter-request interval, no shared
|
||||
`Retry-After` gate anywhere (`grep` for throttle/token-bucket/rate-limiter:
|
||||
0 hits).
|
||||
|
||||
## 3. Requirements & Constraints
|
||||
|
||||
R1. **Respect `Retry-After`.** After a 429, no further request to that
|
||||
destination may be sent before the vendor's retry window elapses.
|
||||
R2. **No thundering herd.** Concurrent waiters must share one wake-up (gate),
|
||||
not sleep independently.
|
||||
R3. **No double load.** A CivitAI 429 must not trigger a CivArchive request
|
||||
for the same lookup. CivArchive should only be consulted when CivitAI
|
||||
legitimately has no answer (404 / "not found"), or when CivitAI is
|
||||
unreachable long-term.
|
||||
R4. **No spurious item failures.** A rate-limited request must not turn a
|
||||
batch-import item into `FAILED`; it should wait (bounded) and retry, or at
|
||||
worst be `SKIPPED` with a clear "rate limited" reason (re-runnable import).
|
||||
R5. **Never hang forever.** All waiting is bounded by a configurable cap; on
|
||||
expiry the caller receives the `RateLimitError` and can decide.
|
||||
R6. **Keep legitimate failover.** Deleted-model recovery via CivArchive/sqlite
|
||||
must keep working (404 paths unchanged).
|
||||
R7. **Single choke point.** The pacing gate should live where every API call
|
||||
passes (the `Downloader`), so bulk refresh, metadata sync, recipe
|
||||
analysis, and usage-control lookups all benefit without per-feature work.
|
||||
|
||||
## 4. Approach Comparison
|
||||
|
||||
### A. Reactive gate — shared `Retry-After` deadman clock (recommended core)
|
||||
|
||||
A process-wide, per-destination coordinator records the *next-allowed-send*
|
||||
timestamp from each 429 (`now + max(retry_after, backoff)`). Every request
|
||||
through `Downloader.make_request` consults the gate *before sending* and *when
|
||||
a 429 arrives*; waiters block on a shared `asyncio.Event` that fires when the
|
||||
cooldown expires.
|
||||
|
||||
- Pros: single choke point (R7); herd-free (R2); honors server guidance (R1);
|
||||
no guessing at vendor limits; covers all providers automatically; reuses
|
||||
existing per-destination scoping.
|
||||
- Cons: still experiences 429s before slowing down (reactive); long
|
||||
`Retry-After` windows (CivArchive has been observed at ~1500 s) need a sane
|
||||
wait cap + skip/retry UX.
|
||||
|
||||
### B. Preemptive pacing — minimum inter-request interval (recommended companion)
|
||||
|
||||
Per-destination token bucket (simplest form: capacity 1 — at least `N` seconds
|
||||
between consecutive API requests; `N` configurable, default ~0.75 s ≈ 80
|
||||
r/min ceiling).
|
||||
|
||||
- Pros: prevents most 429s before they happen — exactly the "intentional
|
||||
reduction in request rate" the issue asks for; trivial to implement on top
|
||||
of A's coordinator.
|
||||
- Cons: adds latency to bulk operations (thousands of models × `N`); the *exact*
|
||||
vendor limits are unknown (CivitAI anonymous vs keyed vs `civitai.red`
|
||||
mirror differ), so the default must be conservative-but-not-crippling and
|
||||
settings-tunable.
|
||||
|
||||
### C. Fallback semantics change — stop network→network failover on 429 (must-do, low risk)
|
||||
|
||||
`FallbackMetadataProvider` (and `MetadataSyncService.fetch_and_update_model`'s
|
||||
manual loop) must treat a final `RateLimitError` as a **terminal, non-failover
|
||||
result** for network providers. Local-only providers (sqlite archive DB) may
|
||||
stay as a last resort (no vendor cost).
|
||||
|
||||
- Pros: directly removes the CivArchive flood; small, surgical change.
|
||||
- Cons: none significant; requires care to keep 404-failover intact (R6).
|
||||
|
||||
### Rejected / deferred
|
||||
|
||||
- **Per-feature retry queues** (batch import pauses & resumes whole batches):
|
||||
richer UX but much larger change (batch state machine, WebSocket states);
|
||||
unnecessary once A+B make requests wait at the choke point. Defer unless
|
||||
review finds the bounded-wait UX insufficient.
|
||||
- **Full token bucket with burst credit**: overkill; capacity-1 interval is
|
||||
enough given the shared semaphore already caps concurrency at 5.
|
||||
- **Retrying in `connectivity_guard`**: wrong layer — the guard is about
|
||||
transport reachability, not vendor quota.
|
||||
|
||||
## 5. Recommended Architecture
|
||||
|
||||
New singleton **`RateLimitCoordinator`** (`py/services/rate_limit_coordinator.py`,
|
||||
mirroring `ConnectivityGuard`'s singleton + per-destination patterns):
|
||||
|
||||
```
|
||||
state per destination (hostname):
|
||||
next_allowed_send: float (monotonic) # from 429 Retry-After + backoff
|
||||
consecutive_429: int # for backoff growth
|
||||
last_send_at: float # for min-interval pacing
|
||||
waiters: list[Future] | asyncio.Event # shared wake-up per cooldown cycle
|
||||
```
|
||||
|
||||
API:
|
||||
|
||||
- `async wait_for_slot(destination, request_started_within_window: bool)`
|
||||
— called by `Downloader.make_request` *before* sending (blocks until
|
||||
`min(now >= next_allowed_send)` and inter-request interval elapses) and
|
||||
re-armable after a 429.
|
||||
- `register_rate_limit(destination, retry_after: float | None)`
|
||||
— called on 429: `next_allowed_send = max(now + retry_after_or_backoff, current)`;
|
||||
`consecutive_429 += 1`; backoff = `retry_after` honored, else exponential
|
||||
`30 · 2^(n-1)` capped at 1800 s; creates/re-arms the shared wake-up event.
|
||||
- `register_success(destination)` — resets `consecutive_429` (called from the
|
||||
existing 200 path in `make_request`).
|
||||
- `remaining_seconds(destination)`, `in_cooldown(destination)` — for tests and
|
||||
diagnostics.
|
||||
|
||||
Enforcement points:
|
||||
|
||||
1. **`Downloader.make_request`** (`downloader.py:1102-1132`): ordering inside
|
||||
the method is **connectivity-guard fail-fast first** (offline short-circuit
|
||||
costs nothing to check), **then** `await coordinator.wait_for_slot(destination)`
|
||||
before `session.request`. On 429: `coordinator.register_rate_limit(...)`,
|
||||
then *wait for the gate and re-send* (loop, bounded by
|
||||
`rate_limit_max_wait_seconds`, default 300; `retry_after ≥ cap` ⇒ fail
|
||||
immediately). After the loop, return the `RateLimitError` to the caller
|
||||
(unchanged contract) **with `exc.gate_handled = True` set** so downstream
|
||||
retry helpers know the wait already happened. 200 path calls
|
||||
`register_success`.
|
||||
2. **`Downloader.download_to_memory` / `get_response_headers`** (phase 2):
|
||||
register 429s (so API calls queue); waiting only in `make_request`
|
||||
initially.
|
||||
3. **`FallbackMetadataProvider`** (`model_metadata_provider.py`): remove
|
||||
network→network failover on `RateLimitError` — re-raise; only sqlite stays
|
||||
as a local last resort (implementation: per-method `except RateLimitError`
|
||||
handler that marks the chain rate-limited and stops iterating).
|
||||
4. **`MetadataSyncService.fetch_and_update_model`**
|
||||
(`metadata_sync_service.py:248-333`): on `RateLimitError` from the default
|
||||
provider, stop appending further network providers (sqlite may remain);
|
||||
the existing `any_rate_limited` merge already produces `"Rate limited"`.
|
||||
5. **Batch import** (`batch_import_service.py`): no structural change needed —
|
||||
items now wait inside `make_request`; optionally (phase 2) map residual
|
||||
rate-limit failures (after the wait cap) to `SKIPPED` with
|
||||
`"rate limited (retry_after=…s); re-run the import later"` instead of
|
||||
`FAILED`, and surface a `rate_limited` flag in the WebSocket progress
|
||||
broadcast.
|
||||
6. **`_RateLimitRetryHelper` retries** (`model_metadata_provider.py`):
|
||||
**Phase 1** — when the raised `RateLimitError` carries `gate_handled = True`
|
||||
(set by the downloader after honoring the gate), the helper skips its own
|
||||
`retry_after` sleep and re-raises immediately, eliminating the double wait.
|
||||
The wiring stays so a `RateLimitError` still propagates cleanly; full
|
||||
demotion/removal can follow once the gate proves out.
|
||||
|
||||
Settings (`settings.json`, schema extension in `SettingsManager`):
|
||||
|
||||
| key | default | meaning |
|
||||
|---|---|---|
|
||||
| `rate_limit_gate_enabled` | `true` | master switch for the coordinator |
|
||||
| `rate_limit_max_wait_seconds` | `300` | how long `make_request` waits on a 429 gate before returning the error |
|
||||
| `rate_limit_min_interval_seconds` | `0.75` | minimum seconds between API requests per destination (pacing, R6-friendly conservative default) |
|
||||
|
||||
## 6. Changes by File
|
||||
|
||||
| File | Change |
|
||||
|---|---|
|
||||
| `py/services/rate_limit_coordinator.py` (new) | coordinator singleton + per-destination state + tests seam |
|
||||
| `py/services/downloader.py` | gate pre-check + 429 register/wait/retry loop + `register_success`; log the 429 notice at INFO once per cooldown, then DEBUG |
|
||||
| `py/services/model_metadata_provider.py` | `FallbackMetadataProvider`: stop network failover on `RateLimitError`; helper skips its sleep when the error is marked `gate_handled` |
|
||||
| `py/services/metadata_sync_service.py` | `fetch_and_update_model`: same failover semantics; keep sqlite last resort |
|
||||
| `py/services/batch_import_service.py` | (phase 2) rate-limit failures → `SKIPPED` + `rate_limited` progress flag |
|
||||
| `py/services/settings_manager.py` | new settings keys + defaults |
|
||||
| `tests/services/test_rate_limit_coordinator.py` (new) | gate unit tests |
|
||||
| `tests/services/test_civitai_client.py` / `test_civarchive_client.py` | provider-level 429 behavior |
|
||||
| `tests/services/test_metadata_service.py` | failover-chain tests |
|
||||
| `tests/services/test_batch_import_service.py` | SKIPPED-on-rate-limit |
|
||||
|
||||
## 7. Impact, Risks, Open Questions
|
||||
|
||||
- **Behavior change**: with the gate in `make_request`, any request can block
|
||||
up to the wait cap — UI actions that call the API (e.g. a model-details
|
||||
fetch) may take longer during cooldowns. Mitigation: bounded cap + INFO log
|
||||
+ the existing async request handling already tolerates slow responses.
|
||||
**Decided (§10): interactive requests take the same bounded wait** — one
|
||||
behavior, no call-source plumbing; cooldowns are usually short.
|
||||
- **Gate waits occupy batch slots**: with the 1–5 batch semaphore, all slots
|
||||
can park on a gate simultaneously, freezing visible progress for up to one
|
||||
wait cap per wave. Bounded and acceptable; the phase-2 `SKIPPED` mapping +
|
||||
WebSocket `rate_limited` flag (both confirmed in scope, §10) make the stall
|
||||
visible and recoverable.
|
||||
- **Rate limit reality check**: CivitAI anonymous vs keyed limits, and whether
|
||||
`civitai.red` differs, is unverified. Default pacing `0.75 s/req` is a
|
||||
conservative guess (R6). Open question for maintainer: preferred default
|
||||
and whether an API-keyed ceiling should be higher.
|
||||
- **Long CivArchive windows**: `Retry-After ~1500 s` observed in code
|
||||
comments. **Decided (§10): keep the 300 s default cap** — such lookups
|
||||
fail/skip rather than park a request path for 25 minutes; batch import maps
|
||||
them to `SKIPPED` (phase 2) so the user can re-run later.
|
||||
- **Double waiting**: `_RateLimitRetryHelper` + gate could stack waits.
|
||||
**Resolved in Phase 1**: the downloader marks gate-honored errors with
|
||||
`gate_handled = True` and the helper skips its own sleep for those.
|
||||
- **Downloads**: `download_file` 429s return an error to download managers
|
||||
unchanged (already handled); only *registration* is proposed, so future
|
||||
API calls queue behind a large `Retry-After` from a download burst.
|
||||
|
||||
## 8. Test Plan
|
||||
|
||||
1. **Coordinator unit tests** (new file):
|
||||
- 429 with `retry_after` → `wait_for_slot` blocks ~that long, then passes.
|
||||
- N concurrent waiters all wake together (herd test, wall-clock ≈ one
|
||||
window, not N windows).
|
||||
- Consecutive 429s grow backoff; `register_success` resets.
|
||||
- Missing `Retry-After` → default backoff path.
|
||||
- Wait cap: request fails after `rate_limit_max_wait_seconds` with
|
||||
`RateLimitError`.
|
||||
2. **Downloader tests** (mock aiohttp session): 429 then 200 → `make_request`
|
||||
returns success after gate delay; two back-to-back calls to the same
|
||||
destination are spaced ≥ `min_interval`; different destinations are not
|
||||
spaced.
|
||||
3. **Provider tests**: `FallbackMetadataProvider.get_model_version_info` —
|
||||
Civitai raises `RateLimitError` → CivArchive mock **not called**; 404 still
|
||||
falls through to CivArchive; sqlite still tried after network 429.
|
||||
4. **Sync-service test**: `fetch_and_update_model` with a rate-limited default
|
||||
provider → result error contains `"Rate limited"` and sqlite attempt state
|
||||
unchanged.
|
||||
5. **Batch-import test**: analysis provider 429s first, then succeeds →
|
||||
item ends `SUCCESS` (wait path), and post-cap 429 → `SKIPPED` with
|
||||
rate-limit reason (phase 2).
|
||||
6. Full regression: `pytest tests/services tests/routes tests/standalone`
|
||||
(currently 1582 passing).
|
||||
|
||||
## 9. Implementation Phases
|
||||
|
||||
- **Phase 1 (this plan, reviewed):** `RateLimitCoordinator` +
|
||||
`Downloader.make_request` integration (guard fail-fast → gate pre-check
|
||||
pacing → 429 register/wait/retry loop with cap → `gate_handled` marking) +
|
||||
settings + **Fix C failover semantics** (`FallbackMetadataProvider`,
|
||||
`fetch_and_update_model` — moved up from phase 2: smallest diff, kills the
|
||||
CivArchive flood immediately, independent of coordinator correctness) +
|
||||
`_RateLimitRetryHelper` double-wait fix + coordinator/downloader/provider/
|
||||
sync tests.
|
||||
- **Phase 2:** batch-import `SKIPPED`-on-rate-limit + `rate_limited` WebSocket
|
||||
progress flag + slowdown hint (confirmed, §10),
|
||||
`download_to_memory`/HEAD 429 registration, batch tests.
|
||||
- **Phase 3:** full regression + docs + commit referencing `(#1085)`.
|
||||
|
||||
## 10. Review Checklist — Decisions (2026-08-27)
|
||||
|
||||
- [x] Default pacing interval `0.75 s` — **accepted** as conservative default;
|
||||
tunable via `rate_limit_min_interval_seconds`. Revisit if CivitAI
|
||||
publishes keyed/anonymous ceilings.
|
||||
- [x] Wait cap `300 s` — **accepted**; long-window CivArchive lookups fail →
|
||||
batch import marks them `SKIPPED` with a rate-limit reason (phase 2).
|
||||
- [x] Interactive API calls also wait (bounded) — **yes**, same behavior for
|
||||
all callers.
|
||||
- [x] Keep sqlite as last resort behind a network rate limit — **yes**
|
||||
(local-only, no vendor cost).
|
||||
- [x] UI hint — **yes**: WebSocket `rate_limited` flag + "rate limited —
|
||||
slowing down" hint in batch-import progress (phase 2); INFO logging
|
||||
regardless.
|
||||
@@ -1,363 +0,0 @@
|
||||
# Plan: "Other Models" Page — Unified Management for VAE / Upscaler / Text Encoder / etc.
|
||||
|
||||
**Status:** v2 — **Phase 1 implemented** (2026-09-12, commits `27da7b3c` backend + `fa7ce725` frontend; verified live against a running ComfyUI instance: scan/hash/sub_type-derivation/fetch/previews all green). **Phase 2 implemented** (2026-09-12, per §9 design; full pytest + vitest green). **Phase 3 implemented** (§11: opt-in management toggles; default off). **i18n done** (2026-09-13): all 36 new keys translated in the 9 non-English locales — the `[TODO: Translate]` placeholders left by the sync script during development are gone (see `docs/i18n-translation-guidelines.md` §2, "Other Models feature"). **Default set revised (pre-release):** only `vae` / `upscaler` / `text_encoder` are managed by default — `clip_vision` and `controlnet` are both opt-in (§2, §11.1.1).
|
||||
**Scope (Phase 1):** scan + manage (list, search, filter, tags, folders, preview, rename, move, delete/exclude, CivitAI metadata fetch) for a new model type `other`, exposed as a new web page. **Phase 2 (§9):** one-click download from CivitAI for these types.
|
||||
|
||||
## 1. Goal
|
||||
|
||||
Today the manager supports three model types:
|
||||
|
||||
| page | model_type | sub_types |
|
||||
|---|---|---|
|
||||
| `/loras` | `lora` | `lora`, `locon`, `dora` |
|
||||
| `/checkpoints` | `checkpoint` | `checkpoint`, `diffusion_model` |
|
||||
| `/embeddings` | `embedding` | `embedding` |
|
||||
|
||||
Add a fourth page that manages "everything else" — VAE, upscalers, text encoders / CLIP, CLIP vision, optionally ControlNet — with a folder→sub_type mapping table so new ComfyUI folder categories can be added later by configuration, not code.
|
||||
|
||||
## 2. Locked Decisions
|
||||
|
||||
1. **Architecture: one scanner + one service + one page, sub_type derived by location.**
|
||||
Replicates the checkpoint pattern (`CheckpointScanner` aggregates `checkpoints` + `unet` roots and derives `checkpoint` vs `diffusion_model` from the root containing the file, `py/services/checkpoint_scanner.py:384-415`). One `OtherScanner` aggregates all enabled folder roots; `resolve_sub_type_for_path()` maps each root to a sub_type. No per-category scanners.
|
||||
|
||||
2. **Naming: internal `model_type = "other"`, route prefix `/other`, page id `other`.**
|
||||
- `misc` is rejected: `py/routes/misc_routes.py` already owns that name for system/settings routes (`/api/lm/settings`, `/api/lm/doctor/*`).
|
||||
- `components` is rejected: `templates/components/` and `static/js/components/` directories would make `components.html` / `components.js` confusing neighbors.
|
||||
- `other` matches CivitAI's `Other` fallback type semantics. The **display name** is an i18n string (`other.title`, e.g. "Other Models") and can be renamed later without touching code.
|
||||
|
||||
3. **sub_type values:** snake_case, aligned with CivitAI `ModelType` semantics:
|
||||
|
||||
| sub_type | ComfyUI `folder_paths` key(s) | CivitAI ModelType | enabled by default |
|
||||
|---|---|---|---|
|
||||
| `vae` | `vae` | `VAE` | yes |
|
||||
| `upscaler` | `upscale_models` | `Upscaler` | yes |
|
||||
| `text_encoder` | `text_encoders`, `clip` (legacy) | `TextEncoder` (CLIP is retired upstream) | yes |
|
||||
| `clip_vision` | `clip_vision` | `CLIPVision` | no (mapping present, opt-in) |
|
||||
| `controlnet` | `controlnet` | `Controlnet` | no (mapping present, opt-in) |
|
||||
|
||||
New folder categories = one line in the mapping table (see §4.1).
|
||||
|
||||
**Why only three are on by default** (revised in Phase 3, before release):
|
||||
VAE, upscalers and text encoders are dependency-style assets every pipeline
|
||||
needs, and "which one am I actually using" is the recurring problem they
|
||||
solve. `clip_vision` and `controlnet` are workflow-driven instead
|
||||
(IPAdapter/SVD image conditioning; per-workflow ControlNet variants), and
|
||||
ControlNet libraries routinely run to dozens of files, so both are treated
|
||||
symmetrically as opt-in. Enumerating all five as "the default set" was not
|
||||
defensible on demand breadth alone.
|
||||
|
||||
4. **Phase 1 = scan/manage only.** Downloads from CivitAI (`download_manager.py` type mapping, default-root settings keys, download routing) are Phase 2 (§9). CivitAI **metadata fetch** for existing files IS in Phase 1 (hash-based lookup is type-agnostic; only the type-validation hook needs new values).
|
||||
|
||||
5. **Out of scope (default off, revisit later):** usage statistics buckets, recipe matching (`recipe_scanner.py` only merges lora+checkpoint scanners), statistics page, embeddings re-classification (stays its own page — merging would be a breaking change).
|
||||
|
||||
## 3. Why This Works With Minimal Churn
|
||||
|
||||
- `ModelScanner` (`py/services/model_scanner.py:93`) is specialized entirely via constructor params (`model_type`, `model_class`, `file_extensions`) + optional hooks (`adjust_metadata`, `adjust_cached_entry`, `resolve_sub_type_for_path`, `model_scanner.py:1429-1443`).
|
||||
- `BaseModelService` subclasses can be one method (`EmbeddingService` implements only `format_response`, `py/services/embedding_service.py:12`).
|
||||
- Routes: `ModelServiceFactory.register_model_type()` (`py/services/model_service_factory.py:120-136`) + `COMMON_ROUTE_DEFINITIONS` (`py/routes/model_route_registrar.py:23-149`) generate the full `/api/lm/{prefix}/*` surface (~50 endpoints) plus the `GET /{prefix}` page route.
|
||||
- `PersistentModelCache` (`py/services/persistent_model_cache.py:526-606`) is a single `models` table keyed `(model_type, file_path)` with `model_type` as free text — **zero schema change**.
|
||||
- Frontend `apiConfig.js` (`static/js/api/apiConfig.js:51`) generates all endpoints from the model-type string; `ModelCard.js:670-675` renders the sub_type badge from data; the checkpoints page already demonstrates the "one page, multiple sub_types" filter (`header.html:298`).
|
||||
|
||||
## 4. Backend Changes
|
||||
|
||||
### 4.1 New constants — `py/utils/constants.py`
|
||||
|
||||
```python
|
||||
# folder_paths key -> sub_type; single source of truth for extensibility
|
||||
OTHER_MODEL_FOLDER_SUBTYPES = {
|
||||
"vae": "vae",
|
||||
"upscale_models": "upscaler",
|
||||
"text_encoders": "text_encoder",
|
||||
"clip": "text_encoder", # legacy ComfyUI key
|
||||
"clip_vision": "clip_vision",
|
||||
"controlnet": "controlnet",
|
||||
}
|
||||
DEFAULT_OTHER_MODEL_FOLDERS = ("vae", "upscale_models", "text_encoders", "clip", "clip_vision")
|
||||
VALID_OTHER_SUB_TYPES = ["vae", "upscaler", "text_encoder", "clip_vision", "controlnet"]
|
||||
# CivitAI model.type values accepted for this page (fetch-metadata validation)
|
||||
VALID_OTHER_CIVITAI_TYPES = {"vae", "upscaler", "textencoder", "clipvision", "controlnet", "other"}
|
||||
```
|
||||
|
||||
Also extend `CIVITAI_USER_MODEL_TYPES` (`constants.py:90`) if user-model queries should include these types.
|
||||
|
||||
### 4.2 New files (mirror the embedding/checkpoint implementations)
|
||||
|
||||
1. **`py/utils/models.py`** — add `OtherModelMetadata(BaseModelMetadata)`: default `sub_type="vae"` placeholder overridden by scanner hook; `from_civitai_info` mapping CivitAI types → our sub_types (`TextEncoder`→`text_encoder`, `CLIPVision`→`clip_vision`, `Upscaler`→`upscaler`, `VAE`→`vae`, `Controlnet`→`controlnet`, else `other`-ish fallback to folder-derived sub_type).
|
||||
2. **`py/services/other_scanner.py`** — `OtherScanner(ModelScanner)`:
|
||||
- `model_type="other"`, extensions: reuse the checkpoint set (`safetensors/pt/pt2/bin/pth/pkl/sft/gguf`).
|
||||
- `get_model_roots()`: iterate `OTHER_MODEL_FOLDER_SUBTYPES` ∩ enabled keys, pull each from `config` (§4.3); dedupe; build `root → sub_type` map (normalized abspaths; multiple keys may share a sub_type).
|
||||
- Implement all three hooks like `CheckpointScanner` (`checkpoint_scanner.py:384-415`): `resolve_sub_type_for_path` by longest-prefix root match, `adjust_metadata`, `adjust_cached_entry` (sub_type is re-derived on cache load, never persisted).
|
||||
- **Lazy hashing, checkpoint-style**: text encoders (T5-XXL ≈ 10 GB) make eager sha256 painful. Copy the `hash_status="pending"` + singleflight `calculate_hash_for_model` pattern from `CheckpointScanner`.
|
||||
3. **`py/services/other_model_service.py`** — `OtherModelService(BaseModelService)`, `format_response` only (no usage_count, like `EmbeddingService`).
|
||||
4. **`py/routes/other_routes.py`** — `OtherRoutes(BaseModelRoutes)`, `template_name="other.html"`, hooks:
|
||||
- `_validate_civitai_model_type` → `VALID_OTHER_CIVITAI_TYPES`
|
||||
- `_get_expected_model_types`, `_parse_specific_params` (no type-specific download params in Phase 1)
|
||||
- `initialize_services()` on `app.on_startup` pulling `ServiceRegistry.get_other_scanner()`.
|
||||
|
||||
### 4.3 `py/config.py`
|
||||
|
||||
- New `other_roots` property: for each enabled key in `OTHER_MODEL_FOLDER_SUBTYPES`, `folder_paths.get_folder_paths(key)` (plugin mode) — standalone mode needs nothing new: `MockFolderPaths` (`standalone.py:66-105`) already serves arbitrary keys from `settings.json.folder_paths`.
|
||||
- Follow the existing per-type recipe: an `_prepare_other_paths()` (dedupe + symlink registration; also **cross-scanner overlap detection** — warn if an `other` root is already covered by checkpoints/unet/embedding roots, mirroring the checkpoint/unet overlap check).
|
||||
- Wire into: `_apply_library_paths`, `_symlink_roots()`, `_rebuild_preview_roots()` (hard requirement — preview images are served per registered root), `save_folder_paths_to_settings()`.
|
||||
|
||||
### 4.4 Existing-file edits (the "type string scatter" — each is a small branch/entry)
|
||||
|
||||
| file | change |
|
||||
|---|---|
|
||||
| `py/services/model_service_factory.py:120` | register `("other", OtherModelService, OtherRoutes)` in `register_default_model_types()` |
|
||||
| `py/services/service_registry.py` | add `get_other_scanner()` (mirror `:297` `get_embedding_scanner`) |
|
||||
| `py/services/model_scanner.py:67` | `PAGE_TYPE_MAP['other'] = 'other'` (WebSocket progress) |
|
||||
| `py/services/base_model_service.py:896-906` | `get_model_types()` branch → `VALID_OTHER_SUB_TYPES` |
|
||||
| `py/lora_manager.py` | `_initialize_services` scanner task list (`:219-242`), `_cleanup` cancel list (`:463`), `_cleanup_backup_files` roots (`:327-330`) |
|
||||
| `py/routes/handlers/misc_handlers.py` | `scanner_getters` (`:657-661`) + `scanner_factories` (`:757-759`) so Doctor / init-status / refresh-all see the new scanner |
|
||||
| `py/services/pending_delete_service.py` | `_PAGE_TYPE` map (`:57-61`) + scanner getter list (`:983-985`) |
|
||||
| `py/metadata_ops/__init__.py:36-38` | `SCANNER_TYPE_MAP['other']` |
|
||||
| `settings.json.example` | document optional `folder_paths` keys: `vae`, `upscale_models`, `text_encoders`, `clip_vision` |
|
||||
|
||||
**Explicitly NOT touched in Phase 1:** `py/services/download_manager.py`, `py/services/download_routing.py`, `py/services/settings_manager.py` default-root keys, `py/routes/stats_routes.py`, `py/utils/usage_stats.py`, `py/services/recipe_scanner.py`, `py/metadata_collector/`, `py/nodes/`.
|
||||
|
||||
**Zero-change confirmations (verified):** `PersistentModelCache`, `ModelUpdateService`, `DownloadedVersionHistoryService`, `MetadataSyncService` + provider chain (type-agnostic hash lookups), `ModelFileService` / `ModelMoveService` / `ModelLifecycleService` (scanner + model_type injected), `ModelCache` / `ModelHashIndex`, `AutoV3BackfillService`.
|
||||
|
||||
## 5. Frontend Changes
|
||||
|
||||
1. **`static/js/api/apiConfig.js`** — `MODEL_TYPES.OTHER = 'other'`; `MODEL_CONFIG.other` entry (displayName, singularName, `supportsMove`, `supportsBulkOperations`; no letter filter); endpoints come free from `getApiEndpoints()` (`:51`).
|
||||
2. **`static/js/api/otherApi.js`** — thin `OtherApiClient extends BaseModelApiClient` (mirror `embeddingApi.js`); register in `modelApiFactory.js`.
|
||||
3. **`static/js/other.js`** — page entry (mirror `embeddings.js`): `appCore.initialize()` + `createPageControls('other')` + `initializePageFeatures()` + `ModelDuplicatesManager` + `initActiveFiltersSync('other')`.
|
||||
4. **Controls & context menu** — `OtherControls extends PageControls` and `OtherContextMenu` (start from the embedding variants — the smallest); add branches in the two factories (`components/controls/index.js:15`, `components/ContextMenu/index.js:15`). Context-menu template block lives in `templates/other.html` (`{% block additional_components %}`, the checkpoints/embeddings pattern — do NOT touch the shared `context_menu.html`).
|
||||
5. **`templates/other.html`** — copy `embeddings.html`: same content blocks (controls + breadcrumb + duplicates banner + folder sidebar + `#modelGrid`), `data-page="other"`, main script `/loras_static/js/other.js`.
|
||||
6. **`templates/components/header.html`** — nav entry (`:23-43`, active when `request.path.startswith('/other')`); enable the `modelTypes` sub_type filter panel for `other` (`:298-305` pattern from checkpoints); check search-options panel conditions (`:199-224`).
|
||||
7. **`static/js/utils/constants.js`** — `MODEL_SUBTYPE_ABBREVIATIONS` (`:115`): `vae→VAE`, `upscaler→UPS`, `text_encoder→TE`, `clip_vision→CV`, `controlnet→CN`; matching `MODEL_SUBTYPE_DISPLAY_NAMES` (`:99`). (Unknown fallback already uppercases 4 chars, but explicit mappings read better.)
|
||||
8. **`static/js/core.js:110` `getPageType()`** — verify `data-page="other"` flows through `state.pages` generically; add only if the page list is enumerated anywhere.
|
||||
9. No change to `web/comfyui/top_menu_extension.js` (it opens `/loras`; page-to-page nav is the header bar).
|
||||
|
||||
## 6. i18n
|
||||
|
||||
- `locales/en.json`: add `other.title` (e.g. "Other Models") + minimal `other.contextMenu.*` / `other.modelTypes.*` keys; reuse `modelCard.*`, `loras.contextMenu.*`, `common.*` wherever possible (the established pattern — checkpoints/embeddings already reuse lora keys).
|
||||
- Run `python scripts/sync_translation_keys.py`; leave `[TODO: Translate]` placeholders in other locales (per `docs/i18n-translation-guidelines.md` §7 — do not translate proactively).
|
||||
|
||||
## 7. Testing
|
||||
|
||||
Follow existing conventions (`pytest.ini`, `tests/frontend/` vitest):
|
||||
|
||||
1. **Backend (pytest, async where needed):**
|
||||
- `OtherScanner` root aggregation + `resolve_sub_type_for_path` (file under `vae/` root → `vae`; `text_encoders` and legacy `clip` both → `text_encoder`; disabled `controlnet` root not scanned).
|
||||
- Cache round-trip: sub_type re-derived via `adjust_cached_entry` (not persisted).
|
||||
- Lazy hash: `hash_status="pending"` default; `calculate_hash_for_model` singleflight.
|
||||
- `OtherRoutes` registration smoke test: `/api/lm/other/...` endpoints exist; `_validate_civitai_model_type` accepts `vae`/`upscaler`/`textencoder`, rejects `lora`.
|
||||
- Config: `other_roots` in both modes (mock `folder_paths`, and standalone `settings.json.folder_paths`).
|
||||
2. **Frontend (vitest + jsdom, `tests/frontend/`):**
|
||||
- `apiConfig`: `getApiEndpoints('other')` URL shapes; `modelApiFactory` returns the Other client.
|
||||
- `ModelCard` badge rendering for new sub_types.
|
||||
- `createPageControls('other')` / `createPageContextMenu('other')` factories.
|
||||
3. **Manual UI verification by the user** (per AGENTS.md — no sandbox/browser automation): page loads, scans a real library, sub_type filter + badges, context menu actions.
|
||||
|
||||
## 8. Execution Order
|
||||
|
||||
1. `constants.py` + `OtherModelMetadata` + `config.py` roots
|
||||
2. `OtherScanner` (+ registry, factory, `PAGE_TYPE_MAP`) → scanner unit tests green
|
||||
3. `OtherModelService` + `OtherRoutes` + handler/registrar wiring + `lora_manager.py` lifecycle → route tests green
|
||||
4. Doctor/pending-delete/metadata-ops scatter entries
|
||||
5. Template + header nav + frontend API/controls/context-menu/card badges → vitest green
|
||||
6. i18n keys + sync script
|
||||
7. `pytest` + `npm test` full runs; hand to user for manual UI check
|
||||
|
||||
## 9. Phase 2 Detailed Design — CivitAI Downloads for `other`
|
||||
|
||||
Designed 2026-09-12 against the Phase-1 code on this branch; decisions marked **[locked]** follow the same recommendations the feature owner approved for Phase 1.
|
||||
|
||||
### 9.1 Download pipeline touch points
|
||||
|
||||
Flow: `POST /api/lm/download-model` (`py/routes/model_route_registrar.py:104`; GET variant `:105` for the browser extension) → `ModelDownloadHandler.download_model` (`model_handlers.py:1740`) → `DownloadModelUseCase.execute` → `DownloadCoordinator.schedule_download` → `DownloadManager.download_from_civitai` (`download_manager.py:386`) → `_execute_original_download` (`:1415`). Inside, seven scatter points need an `other` branch:
|
||||
|
||||
1. **Type map** (`:1496-1507`): accept `model.type.lower() in VALID_OTHER_CIVITAI_TYPES` → `model_type = "other"` (reuses the Phase-1 set, incl. `"other"` itself).
|
||||
2. **Early version-exists gate** (`:1436-1463`): add `other_scanner.check_model_version_exists`.
|
||||
3. **File-level exists gate** (`:1640-1655` → `_find_local_file_entry` `:320-346` → `_get_scanner_for_model_type` `:230-236`): add explicit `other` branch. **Trap**: the function currently falls through to the lora scanner for unknown types — `"other"` would silently dedupe against loras. Also narrow the fall-through to `"lora"` only / raise on unknown.
|
||||
4. **Version-level fallback gate** (`:1656-1688`): add `elif model_type == "other"`.
|
||||
5. **Default-root selection** (`:1690-1727`): for `other`, first resolve sub_type (§9.2), then read `default_other_roots[sub_type]` (§9.3); if sub_type is undecidable or no default root configured → error guiding the user to pick a folder explicitly.
|
||||
6. **Metadata class selection** (`:1909-1928`) + `_build_metadata_for_resume` (`:969-981`): add `OtherModelMetadata.from_civitai_info` branches.
|
||||
7. **Post-download cache write** (`_execute_download_pipeline` `:2622-2679`): add `other` scanner branch; `adjust_metadata` re-derives sub_type from the on-disk root automatically. `_get_supported_extensions_for_type` (`:2720-2744`): `other` reuses the checkpoint extension set.
|
||||
|
||||
Hooks: `_record_downloaded_version_history` (model_type is free text — zero change); `_sync_downloaded_version` (`:1984` → scanner dispatch `:2130-2135`) add `other`; `py/utils/example_images_download_manager.py` scanner dispatch at `:411-421`, `:591-601`, `:1089+` — add `other` at all three (silent no-scanner otherwise).
|
||||
|
||||
Path templates: `get_download_path_template("other")` is unset, so `other` resolves to a **flat** layout (empty template) — downloads land directly under the resolved sub_type root. This is deliberate: other-model roots are already split per sub_type (`default_other_roots`), and `priority_tags` has no `other` entry, so `{first_tag}` would fall back to an arbitrary CivitAI tag and scatter files into unstable folders. Users who want nesting can still set `download_path_templates["other"]` in `settings.json`. See `DEFAULT_DOWNLOAD_PATH_TEMPLATES` (`py/utils/constants.py`) and `DEFAULT_PATH_TEMPLATES` (`static/js/utils/constants.js`).
|
||||
|
||||
### 9.2 File-level routing (model.type / file.type → sub_type) **[locked]**
|
||||
|
||||
Table-driven, mirroring Phase 1. New in `py/utils/constants.py`:
|
||||
|
||||
```python
|
||||
CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE = {
|
||||
"VAE": "vae", "Upscaler": "upscaler", "Text Encoder": "text_encoder",
|
||||
"Vision Encoder": "clip_vision", "CLIPVision": "clip_vision",
|
||||
"ControlNet": "controlnet",
|
||||
}
|
||||
```
|
||||
|
||||
`download_routing.py` gains `resolve_other_download_sub_type(civitai_model_type, file_types, selected_file_type=None)` with fixed priority:
|
||||
|
||||
1. **Explicit user file pick** (`file_params` from #1058's `_resolve_target_file`) — if the picked file's type maps, it wins even when model.type is `Checkpoint`.
|
||||
2. **model.type** via the existing `CIVITAI_TYPE_TO_OTHER_SUB_TYPE` (`constants.py:120-127`).
|
||||
3. **file.type fallback** — only when model.type maps to nothing (e.g. model.type `Other` or retired `CLIP`). MUST NOT override a mapped model.type: checkpoint models routinely bundle VAE/Text Encoder component files, and unconditional file-type routing would misroute them.
|
||||
4. Still undecidable → `None`; `use_default_paths` errors and the UI offers all other roots for manual selection.
|
||||
|
||||
HTTP: extend `DownloadRoutingHandler.get_download_routing` (`download_routing_handlers.py:23`) with an `other` branch returning `{root_kind: "other", sub_type: ...}`; add `GET /api/lm/other/roots_by_subtype` in `OtherRoutes.setup_specific_routes` (data from `config._prepare_other_paths`'s per-key roots, aggregating `text_encoders` + legacy `clip` under `text_encoder`).
|
||||
|
||||
### 9.3 Settings: single dict key `default_other_roots` **[locked]**
|
||||
|
||||
Rejected: four flat keys (`default_vae_root`…) — each flat key costs ~13 touch points in `settings_manager.py` (defaults `:82-85`, `_check_and_auto_set` `:890-895`, `set()` `:1621-1628`, `_update_active_library_entry` `:738-805`, upsert/create signatures `:1953-2132`, `_build_library_payload` `:552-612`, `_sync_active_library_to_root` `:519-547`, three library constructors, frontend `DEFAULT_SETTINGS_BASE`), repeated per future sub_type.
|
||||
|
||||
Chosen: one mapping key `default_other_roots: {sub_type: path}`, copying the `extra_folder_paths` precedent (generic Mapping handling at `:533-535`, `:573-578`, `:763-767`). `_check_and_auto_set` generalizes to per-sub_type candidates (union over that sub_type's folder keys — `text_encoder` → `text_encoders` + `clip`). `set()` validates keys against `VALID_OTHER_SUB_TYPES`.
|
||||
|
||||
Also fix the Phase-1 omission: add `"other_scanner"` to `_notify_library_change` (`:2150-2156`) and `_notify_model_name_display_change` (`:1795-1800`) — otherwise switching libraries leaves the other page stale.
|
||||
|
||||
### 9.4 Settings UI
|
||||
|
||||
- `templates/components/modals/settings/library.html:34-40`: sub_type selectors after the existing four `setting_select`s (Jinja loop; controlnet selector only when `enabled_other_folders` includes it). Dict-subkey save helper `saveOtherRootSetting(subType, value)` alongside the flat `saveSelectSetting`.
|
||||
- `static/js/managers/SettingsManager.js:1547-1697`: `loadOtherRoots()` mirroring `loadUnetRoots()`, fed by `/api/lm/other/roots_by_subtype`; current values from `state.global.settings.default_other_roots`. `state/index.js:24` `DEFAULT_SETTINGS_BASE` += `default_other_roots: {}`.
|
||||
- Optional: one `other` row in the download-path-template block (`library.html:153-211`).
|
||||
- i18n: `settings.folderSettings.*` keys into `locales/en.json` + sync script; other locales keep `[TODO: Translate]`.
|
||||
- Settings GET (`misc_handlers.py:1528-1536`) already returns all non-sensitive keys — new key reaches the frontend for free.
|
||||
|
||||
### 9.5 Frontend download entry
|
||||
|
||||
- `templates/components/controls.html:83`: drop the `page_id != 'other'` exclusion on the download button (keyboard shortcut D self-enables via `PageControls.js:196-198`).
|
||||
- `OtherControls.js:22-55`: add `showDownloadModal: () => downloadManager.showDownloadModal()` (mirror `EmbeddingsControls.js:43-45`).
|
||||
- `DownloadManager.js` `proceedToLocationContent` (`:955-1017`): add `_resolveOtherSubType()` (mirror `_resolveIsDiffusionModel` `:1026`): selected file type → `/api/lm/download/routing` → `otherApiClient.fetchModelRoots(subType)` (new); default-root preselect reads `default_other_roots[subType]` instead of `` `default_${singularType}_root` `` (`:974`). Undecidable → list all other roots (`/api/lm/other/roots`) for manual pick; an explicit save_dir skips backend default-root logic, so the two paths cannot disagree.
|
||||
- `ModelVersionsTab` download buttons are modelType-generic and already work via `getModelApiClient('other')`; context menu has no CivitAI download entry — no change.
|
||||
- Version-list type validation (`get_civitai_versions` → `_validate_civitai_model_type`) already accepts `VALID_OTHER_CIVITAI_TYPES` from Phase 1.
|
||||
|
||||
### 9.6 CivitAI type mapping decisions **[locked]**
|
||||
|
||||
- Download accepts exactly `VALID_OTHER_CIVITAI_TYPES` (`VAE, Upscaler, TextEncoder, CLIP, CLIPVision, Controlnet, Other`) — reuse the Phase-1 tables; do NOT create new ones.
|
||||
- Extend `CIVITAI_USER_MODEL_TYPES` (`constants.py:133-137`) with the 7 aliases, and point them at the other scanner / `"other"` history bucket in `misc_handlers.py` (`type_scanner_map` `:2793-2797`, `downloaded_version_map` `:2821-2827`) — otherwise creator pages silently filter these models while downloads claim support.
|
||||
- Fix (small Phase-1 bug): `OtherModelMetadata.from_civitai_info` (`py/utils/models.py:343`) reads `version_info.get("type")`, but the type lives at `version["model"]["type"]` — the mapping never fires and always degrades to the placeholder. Read `version_info.get("model", {}).get("type")` instead. (`CheckpointMetadata:290` has the same shape; leave it alone here.)
|
||||
|
||||
### 9.7 Tests
|
||||
|
||||
Existing base: `tests/services/test_download_manager_basic.py` (incl. `test_download_rejects_unsupported_model_type` `:1336`), `test_download_manager_error.py`, `test_download_manager_concurrent.py`, `tests/integration/test_download_flow.py`, `tests/services/test_settings_manager.py`; frontend `tests/frontend/managers/downloadManager.routing.test.js`, `settingsManager.library.test.js`.
|
||||
|
||||
Add: (1) `resolve_other_download_sub_type` unit tests — every priority tier, bundled-component anti-misrouting, undecidable → None, civarchive-shaped payload; (2) download_manager — six model.types accepted → other scanner (mock), unknown still rejected, no lora-scanner fall-through, per-sub_type default roots + unconfigured error, resume metadata, extension set; (3) settings_manager — `default_other_roots` defaults/auto-set (incl. text_encoder dual-key union)/library sync/upsert passthrough/illegal sub_type rejection; (4) routes — `/api/lm/download/routing` other branch, `roots_by_subtype` shape; (5) example-images dispatch accepts `other` (3 sites); (6) vitest — `_resolveOtherSubType` + root select + default preselect, `loadOtherRoots`; (7) user-models existsLocally for VAE.
|
||||
|
||||
### 9.8 Phase 2 file list
|
||||
|
||||
Backend: `py/utils/constants.py`, `py/services/download_routing.py`, `py/routes/handlers/download_routing_handlers.py`, `py/services/download_manager.py`, `py/utils/example_images_download_manager.py`, `py/services/settings_manager.py`, `py/utils/models.py`, `py/routes/other_routes.py`, `py/routes/handlers/misc_handlers.py`, `settings.json.example`.
|
||||
Frontend/templates: `templates/components/controls.html`, `static/js/components/controls/OtherControls.js`, `static/js/managers/DownloadManager.js`, `static/js/api/otherApi.js`, `templates/components/modals/settings/library.html`, `static/js/managers/SettingsManager.js`, `static/js/state/index.js`, `locales/en.json` + sync.
|
||||
|
||||
## 10. Risks / Open Questions
|
||||
|
||||
- **Root overlap**: a user may point `text_encoders` at a directory already scanned as checkpoints/unet. Realpath dedup inside one scanner won't catch cross-scanner overlap → the `_prepare_other_paths` overlap warning (§4.3) is the mitigation; duplicate cards across pages are cosmetic, not corrupting (cache keyed by `(model_type, file_path)`).
|
||||
- **Huge text encoders + lazy hash**: CivitAI fetch for a pending-hash model must trigger on-demand hash like checkpoints do — verify that flow (`calculate_hash_for_model`) is reachable from the `other` routes' fetch-metadata handler.
|
||||
- **Retired CivitAI types**: `CLIP`/`CLIPVision` are retired upstream (grandfathered for existing models); metadata fetch must tolerate both retired and current types — `VALID_OTHER_CIVITAI_TYPES` includes them deliberately.
|
||||
- **Standalone users** must add the new `folder_paths` keys to `settings.json` themselves; document in `settings.json.example` and the feature doc.
|
||||
- **Page display name** is i18n-only; if "Other Models" tests poorly, rename `other.title` without code changes.
|
||||
|
||||
### Phase 2 risks
|
||||
|
||||
- **Bundled component files**: checkpoint models routinely ship VAE/Text Encoder component files — file.type routing must stay a fallback (or explicit user pick), never an override (§9.2 priority is load-bearing; test it).
|
||||
- **`_get_scanner_for_model_type` lora fall-through** (`download_manager.py:236`): without an explicit `other` branch, dedupe checks run against the lora scanner — the most insidious trap in Phase 2.
|
||||
- **text_encoder dual folder keys** (`text_encoders` + legacy `clip`): default-root candidates, `roots_by_subtype`, and auto-set must all merge both keys; miss one and the default-root dropdown comes up empty.
|
||||
- **Undecidable sub_type** (model.type `Other` + unknown file types): must error and ask, never silently default to the vae folder.
|
||||
- **Lazy hash after download**: downloads carry CivitAI SHA256 (no recompute needed) — ensure the post-download cache write doesn't leave `hash_status="pending"`, or the next metadata fetch re-hashes a 10 GB file.
|
||||
- **CivArchive source**: same `_execute_original_download` path, same payload shape — cover it once in tests.
|
||||
|
||||
## 11. Phase 3 — Opt-in Management Toggles (implemented)
|
||||
|
||||
Designed 2026-09-13 against the Phase-1/2 code. Other Models is **opt-in**: after
|
||||
Phase 3 the feature ships disabled, so no other-model folder is scanned and the
|
||||
page shows an "enable" empty state until the user turns it on.
|
||||
|
||||
### 11.1 Settings (global, not per-library)
|
||||
|
||||
| key | type | default | meaning |
|
||||
|---|---|---|---|
|
||||
| `enable_other_models` | bool | `false` | master switch |
|
||||
| `enabled_other_sub_types` | list[str] | `["vae","upscaler","text_encoder"]` | allow-list; `clip_vision` and `controlnet` are opt-in (see §2) |
|
||||
|
||||
`enabled_other_folders` (the unreleased, additive, no-UI backend key) was removed
|
||||
and replaced by the sub_type-level allow-list; there is no migration because the
|
||||
feature never shipped. `text_encoder` expands to `text_encoders` + legacy `clip`
|
||||
via `OTHER_SUB_TYPE_FOLDER_KEYS`.
|
||||
|
||||
The default allow-list lives on five surfaces that must stay in sync:
|
||||
`DEFAULT_ENABLED_OTHER_SUB_TYPES` (`py/utils/constants.py`), `DEFAULT_SETTINGS`
|
||||
(`py/services/settings_manager.py`), the two `DEFAULT_SETTINGS_BASE` /
|
||||
`createDefaultSettings` lists (`static/js/state/index.js`), the
|
||||
`updateOtherModelsControls()` fallback (`static/js/managers/SettingsManager.js`)
|
||||
and the server-rendered Jinja fallback
|
||||
(`templates/components/modals/settings/library.html`).
|
||||
|
||||
### 11.1.1 Legacy key handling in `Config._init_other_paths`
|
||||
|
||||
ComfyUI's `folder_paths` rewrites legacy names before every access (`clip` →
|
||||
`text_encoders`, `unet` → `diffusion_models`) and registers both legacy
|
||||
directories under the canonical key, so `get_folder_paths("clip")` returns
|
||||
exactly the same list as `get_folder_paths("text_encoders")`. Querying both keys
|
||||
made the overlap guard fire twice with `please fix your path configuration` for a
|
||||
configuration the user cannot fix. `Config._collapse_legacy_folder_keys()` now
|
||||
drops a key when the host exposes `map_legacy` and resolves it to another queried
|
||||
key, and `_prepare_other_paths()` downgrades a same-`sub_type` duplicate to
|
||||
`debug` (a cross-`sub_type` collision still warns). In standalone mode
|
||||
`MockFolderPaths` has no `map_legacy` and its keys are independent
|
||||
`settings.json` entries, so every key is still queried there.
|
||||
|
||||
`settings.json.example` intentionally stays minimal (only `use_portable_settings`,
|
||||
`civitai_api_key`, and the four core `folder_paths` keys: `loras`, `checkpoints`,
|
||||
`unet`, `embeddings`). Optional keys — including the other-model folder paths and
|
||||
`enable_other_models` — are NOT documented there; they live in `DEFAULT_SETTINGS`
|
||||
and reach the user's `settings.json` on demand. This supersedes the Phase-1/Phase-2
|
||||
notes that proposed adding the other-model folder keys to the example.
|
||||
|
||||
### 11.2 Behaviour matrix
|
||||
|
||||
| state | scan | nav / `/other` | other downloads | `default_other_roots` | Doctor / refresh-all |
|
||||
|---|---|---|---|---|---|
|
||||
| master off | nothing (`other_roots == []`) | nav entry hidden (`nav-item--hidden`); `/other` still renders the disabled empty state + Enable button; one-time dismissible announcement banner on first visit | rejected | preserved, never auto-set | scanner skipped |
|
||||
| sub_type off | that sub_type's folder keys excluded | page keeps working, type disappears from data | auto-routing refused (manual folder still allowed) | preserved, not preselected | normal |
|
||||
| all on (after enabling) | Phase-1/2 behaviour | normal | normal | normal | normal |
|
||||
|
||||
### 11.3 Backend touch points
|
||||
|
||||
- `py/utils/constants.py` — `DEFAULT_ENABLED_OTHER_SUB_TYPES`, `OTHER_SUB_TYPE_FOLDER_KEYS`, `normalize_other_sub_types`.
|
||||
- `py/config.py` — `_get_enabled_other_folder_keys()` is the single scan gate (master switch + allow-list); new `refresh_other_roots()` rebuilds roots + preview roots on toggle.
|
||||
- `py/services/settings_manager.py` — new defaults, `set()` normalization, `is_other_models_enabled()` / `get_enabled_other_sub_types()` / `is_other_sub_type_enabled()`, and `_apply_other_model_settings_change()` which reapplies config and calls `other_scanner.on_library_changed(reconcile=True)`.
|
||||
- `py/services/model_scanner.py` — `_should_keep_cached_entry()` hydration hook (default keep) plus `on_library_changed(reconcile=...)` / `initialize_in_background(reconcile=...)`; the hook filters `raw_data` and the hash/autov3 index rows.
|
||||
- `py/services/other_scanner.py` — drops persisted entries whose folder is no longer a managed root (sub_type is location-derived, so config is the source of truth).
|
||||
- `py/routes/other_routes.py` — `_validate_civitai_model_type` rejects everything while off / mapped-but-disabled sub_types; `_get_page_context_provider()` injects `other_disabled` into the template.
|
||||
- `py/routes/handlers/model_handlers.py` + `base_model_routes.py` — optional `page_context_provider` hook on `ModelPageView`.
|
||||
- `py/routes/handlers/download_routing_handlers.py` — returns `{sub_type: None, disabled: true, reason}` instead of guessing.
|
||||
- `py/services/download_manager.py` — rejects other-type downloads while off; disabled sub_type refuses default-path routing with a "pick a folder" error.
|
||||
- `py/routes/handlers/misc_handlers.py` — Doctor / init-status / refresh-all skip the other scanner while off (`_active_scanner_factories` / `_active_scanner_getters`).
|
||||
- `py/services/pending_delete_service.py` — deliberately untouched: the scanner stays registered so staged deletes still merge.
|
||||
|
||||
### 11.4 Frontend
|
||||
|
||||
Discoverability: the nav entry is hidden while the feature is off, and three
|
||||
lightweight surfaces replace it — a one-time announcement banner, the download
|
||||
toast, and the settings toggle itself.
|
||||
|
||||
- `templates/components/header.html` + `static/css/components/header.css` — `nav-item--hidden` class (server-rendered when off, client-toggled after enabling) and the `fa-shapes` icon.
|
||||
- `templates/other.html` — `other_disabled` branch in `content` + `main_script`; page-scoped CSS for the empty state.
|
||||
- `static/js/other_disabled.js` — boots `appCore` (shared header) and delegates to the shared enable helper.
|
||||
- `static/js/utils/otherModels.js` — shared `enableOtherModels()` (POST settings + reload) and `openOtherModelsSettings()` (settings modal on the Library section); used by the disabled page, the banner and the download modal.
|
||||
- `static/js/managers/BannerService.js` — `other-models-announcement` banner (only when off and not dismissed; `priority: 0`, dismissal persisted via `dismissed_banners`) with Enable / Open Settings actions; `removeOtherModelsAnnouncement()` drops it without persisting a dismissal.
|
||||
- `templates/components/modals/settings/library.html` + `SettingsManager.updateOtherModelsControls()` / `saveEnabledOtherSubTypes()` / `updateOtherModelsNavVisibility()` — master toggle + five sub_type checkboxes; unchecked/disabled sub_types have their default-root select disabled.
|
||||
- `static/js/managers/DownloadManager.js` — a disabled routing answer surfaces a `showActionToast` with an "Enable Other Models" action (opening settings) and falls back to manual selection.
|
||||
- i18n: `settings.folderSettings.*`, `other.disabled.*` and `banners.otherModels.*` keys in `locales/en.json` + `scripts/sync_translation_keys.py` (other locales keep `[TODO: Translate]`).
|
||||
|
||||
### 11.5 Cache consistency
|
||||
|
||||
- Disabling purges rows from the in-memory view at hydration time (the
|
||||
`_should_keep_cached_entry` hook) and from SQLite on the reconcile triggered by
|
||||
the toggle; the `.metadata.json` sidecars survive, so re-enabling rescans
|
||||
without recomputing hashes (critical for multi-GB text encoders).
|
||||
- Enabling triggers a reconcile so newly managed roots are scanned immediately.
|
||||
- Editing `settings.json` while the server is stopped is still covered by the
|
||||
hydration hook, so disabled types never appear after a restart.
|
||||
|
||||
### 11.6 Tests
|
||||
|
||||
Backend: opt-in fixtures added to the other-related suites; new coverage for
|
||||
"default off scans nothing", per-sub_type gating, routing/download rejection,
|
||||
`_should_keep_cached_entry`, settings normalization and `other_disabled` page
|
||||
context. Frontend: `updateOtherModelsControls` / `saveEnabledOtherSubTypes` and
|
||||
the disabled-page enable flow.
|
||||
@@ -1,71 +0,0 @@
|
||||
# Priority Tags Configuration Guide
|
||||
|
||||
This guide explains how to tailor the tag priority order that powers folder naming and tag suggestions in the LoRA Manager. You only need to edit the comma-separated list of entries shown in the **Priority Tags** field for each model type.
|
||||
|
||||
## 1. Pick the Model Type
|
||||
|
||||
In the **Priority Tags** dialog you will find one tab per model type (LoRA, Checkpoint, Embedding). Select the tab you want to update; changes on one tab do not affect the others.
|
||||
|
||||
## 2. Edit the Entry List
|
||||
|
||||
Inside the textarea you will see a line similar to:
|
||||
|
||||
```
|
||||
character, concept, style(toon|toon_style)
|
||||
```
|
||||
|
||||
This entire line is the **entry list**. Replace it with your own ordered list.
|
||||
|
||||
### Entry Rules
|
||||
|
||||
Each entry is separated by a comma, in order from highest to lowest priority:
|
||||
|
||||
- **Canonical tag only:** `realistic`
|
||||
- **Canonical tag with aliases:** `character(char|chars)`
|
||||
|
||||
Aliases live inside `()` and are separated with `|`. The canonical name is what appears in folder names and UI suggestions when any of the aliases are detected. Matching is case-insensitive.
|
||||
|
||||
## Use `{first_tag}` in Path Templates
|
||||
|
||||
When your path template contains `{first_tag}`, the app picks a folder name based on your priority list and the model’s own tags:
|
||||
|
||||
- It checks the priority list from top to bottom. If a canonical tag or any of its aliases appear in the model tags, that canonical name becomes the folder name.
|
||||
- If no priority tags are found but the model has tags, the very first model tag is used.
|
||||
- If the model has no tags at all, the folder falls back to `no tags`.
|
||||
|
||||
### Example
|
||||
|
||||
With a template like `/{model_type}/{first_tag}` and the priority entry list `character(char|chars), style(anime|toon)`:
|
||||
|
||||
| Model Tags | Folder Name | Why |
|
||||
| --- | --- | --- |
|
||||
| `["chars", "female"]` | `character` | `chars` matches the `character` alias, so the canonical wins. |
|
||||
| `["anime", "portrait"]` | `style` | `anime` hits the `style` entry, so its canonical label is used. |
|
||||
| `["portrait", "bw"]` | `portrait` | No priority match, so the first model tag is used. |
|
||||
| `[]` | `no tags` | Nothing to match, so the fallback is applied. |
|
||||
|
||||
## 3. Save the Settings
|
||||
|
||||
After editing the entry list, press **Enter** to save. Use **Shift+Enter** whenever you need a new line. Clicking outside the field also saves automatically. A success toast confirms the update.
|
||||
|
||||
## Examples
|
||||
|
||||
| Goal | Entry List |
|
||||
| --- | --- |
|
||||
| Prefer people over styles | `character, portraits, style(anime\|toon)` |
|
||||
| Group sci-fi variants | `sci-fi(scifi\|science_fiction), cyberpunk(cyber\|punk)` |
|
||||
| Alias shorthand tags | `realistic(real\|realisim), photorealistic(photo_real)` |
|
||||
|
||||
## Tips
|
||||
|
||||
- Keep canonical names short and meaningful—they become folder names.
|
||||
- Place the most important categories first; the first match wins.
|
||||
- Avoid duplicate canonical names within the same list; only the first instance is used.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- **Unexpected folder name?** Check that the canonical name you want is placed before other matches.
|
||||
- **Alias not working?** Ensure the alias is inside parentheses and separated with `|`, e.g. `character(char|chars)`.
|
||||
- **Validation error?** Look for missing parentheses or stray commas. Each entry must follow the `canonical(alias|alias)` pattern or just `canonical`.
|
||||
|
||||
With these basics you can quickly adapt Priority Tags to match your library’s organization style.
|
||||
@@ -1,58 +0,0 @@
|
||||
# CivitAI image imports can end up with 0 LoRAs
|
||||
|
||||
## Symptom
|
||||
|
||||
Importing a CivitAI image URL can produce a recipe with **zero LoRA
|
||||
entries**, even though the image page lists LoRAs in its resource panel.
|
||||
|
||||
Reported example: `https://civitai.red/images/140818889` was imported as a
|
||||
local recipe with 0 LoRAs, while the page shows 3 LoRAs. Some images (e.g.
|
||||
NSFW / higher browsing level) additionally require a login to view, so their
|
||||
data is not publicly reachable at all.
|
||||
|
||||
## Root cause
|
||||
|
||||
URL imports use only two data sources:
|
||||
|
||||
1. **CivitAI REST image API** — `GET /api/v1/images?imageId=<id>&nsfw=X&withMeta=true` → `meta`
|
||||
2. **Embedded image metadata** — EXIF/XMP read from the downloaded bytes
|
||||
|
||||
For the same image both sources can be empty, and the one source that does
|
||||
contain the data is never queried. Verified for image 140818889:
|
||||
|
||||
| Source | What it returned |
|
||||
|---|---|
|
||||
| REST image API | `meta` holds only a prompt; `modelVersionIds: []`; no `resources`/`hashes`; `baseModel: null` |
|
||||
| Downloaded image | PNG with **no EXIF/XMP** (the CDN URL ends in `.jpeg`, the body is PNG) |
|
||||
| Image page HTML | `__NEXT_DATA__` embeds the trpc `image.getGenerationData` result → full `resources` list: 3 LoRAs, each with `modelId`, `modelVersionId`, `modelName`, `modelType`, `versionName`, `baseModel` |
|
||||
|
||||
Key points:
|
||||
|
||||
- The page's resource panel is fed by an **internal, non-public trpc
|
||||
endpoint**, not by the public REST image API.
|
||||
- That internal endpoint is **login-gated** for some content — the
|
||||
"requires login" symptom.
|
||||
- Even with the version IDs in hand, `/model-versions/{id}` for these
|
||||
(Krea) versions returns **no `sha256`**, so an exact local-file hash match
|
||||
is impossible; only model/version identity is recoverable.
|
||||
|
||||
## Conclusion / status
|
||||
|
||||
0-LoRA imports are a data-source gap: public REST meta and image EXIF are
|
||||
both empty, while the only complete source (page generation data) is
|
||||
internal, sometimes login-gated, and not used by the importer.
|
||||
|
||||
Such imports **cannot be reliably auto-repaired/completed** by the backend
|
||||
alone. The old "Repair Metadata" feature only re-fetched the same incomplete
|
||||
REST meta and could not fix them; it was deprecated and has been removed.
|
||||
|
||||
**Fixed via the companion browser extension.** When the extension is
|
||||
installed with a valid license, it scrapes the image page's internal trpc
|
||||
generation data with the user's session and calls the payload-capable
|
||||
re-import endpoint (`POST /api/lm/recipe/{recipe_id}/reimport` with
|
||||
`image_url`/`name`/`resources`/`gen_params`/`base_model`/`tags` query
|
||||
params), which rebuilds the recipe from the caller-supplied metadata. The
|
||||
web UI delegates re-import of CivitAI-image-sourced recipes to the extension
|
||||
automatically (probe + `lm:reimport*` DOM events); without the extension,
|
||||
re-import silently falls back to the native path, which remains limited by
|
||||
the data-source gap documented above.
|
||||
@@ -1,69 +0,0 @@
|
||||
# Danbooru/E621 Tag Categories Reference
|
||||
|
||||
Reference for category values used in `danbooru_e621_merged.csv` tag files.
|
||||
|
||||
## Category Value Mapping
|
||||
|
||||
### Danbooru Categories
|
||||
|
||||
| Value | Description |
|
||||
|-------|-------------|
|
||||
| 0 | General |
|
||||
| 1 | Artist |
|
||||
| 2 | *(unused)* |
|
||||
| 3 | Copyright |
|
||||
| 4 | Character |
|
||||
| 5 | Meta |
|
||||
|
||||
### e621 Categories
|
||||
|
||||
| Value | Description |
|
||||
|-------|-------------|
|
||||
| 6 | *(unused)* |
|
||||
| 7 | General |
|
||||
| 8 | Artist |
|
||||
| 9 | Contributor |
|
||||
| 10 | Copyright |
|
||||
| 11 | Character |
|
||||
| 12 | Species |
|
||||
| 13 | *(unused)* |
|
||||
| 14 | Meta |
|
||||
| 15 | Lore |
|
||||
|
||||
## Danbooru Category Colors
|
||||
|
||||
| Description | Normal Color | Hover Color |
|
||||
|-------------|--------------|-------------|
|
||||
| General | #009be6 | #4bb4ff |
|
||||
| Artist | #ff8a8b | #ffc3c3 |
|
||||
| Copyright | #c797ff | #ddc9fb |
|
||||
| Character | #35c64a | #93e49a |
|
||||
| Meta | #ead084 | #f7e7c3 |
|
||||
|
||||
## CSV Column Structure
|
||||
|
||||
Each row in the merged CSV file contains 4 columns:
|
||||
|
||||
| Column | Description | Example |
|
||||
|--------|-------------|---------|
|
||||
| 1 | Tag name | `1girl`, `highres`, `solo` |
|
||||
| 2 | Category value (0-15) | `0`, `5`, `7` |
|
||||
| 3 | Post count | `6008644`, `5256195` |
|
||||
| 4 | Aliases (comma-separated, quoted) | `"1girls,sole_female"`, empty string |
|
||||
|
||||
### Sample Data
|
||||
|
||||
```
|
||||
1girl,0,6008644,"1girls,sole_female"
|
||||
highres,5,5256195,"high_res,high_resolution,hires"
|
||||
solo,0,5000954,"alone,female_solo,single,solo_female"
|
||||
long_hair,0,4350743,"/lh,longhair"
|
||||
mammal,12,3437444,"cetancodont,cetancodontamorph,feralmammal"
|
||||
anthro,7,3381927,"adult_anthro,anhtro,antho,anthro_horse"
|
||||
skirt,0,1557883,
|
||||
```
|
||||
|
||||
## Source
|
||||
|
||||
- [PR #312: Add danbooru_e621_merged.csv](https://github.com/DominikDoom/a1111-sd-webui-tagcomplete/pull/312)
|
||||
- [DraconicDragon/dbr-e621-lists-archive](https://github.com/DraconicDragon/dbr-e621-lists-archive)
|
||||
@@ -1,191 +0,0 @@
|
||||
# Model Type 字段重构 - 遗留工作清单
|
||||
|
||||
> **状态**: Phase 1-4 已完成 | **创建日期**: 2026-01-30
|
||||
> **相关文件**: `py/utils/models.py`, `py/services/model_query.py`, `py/services/checkpoint_scanner.py`, etc.
|
||||
|
||||
---
|
||||
|
||||
## 概述
|
||||
|
||||
本次重构旨在解决 `model_type` 字段语义不统一的问题。系统中有两个层面的"类型"概念：
|
||||
|
||||
1. **Scanner Type** (`scanner_type`): 架构层面的大类 - `lora`, `checkpoint`, `embedding`
|
||||
2. **Sub Type** (`sub_type`): 业务层面的细分类型 - `lora`/`locon`/`dora`, `checkpoint`/`diffusion_model`, `embedding`
|
||||
|
||||
重构目标是统一使用 `sub_type` 表示细分类型，保留 `model_type` 作为向后兼容的别名。
|
||||
|
||||
---
|
||||
|
||||
## 已完成工作 ✅
|
||||
|
||||
### Phase 1: 后端字段重命名
|
||||
- [x] `CheckpointMetadata.model_type` → `sub_type`
|
||||
- [x] `EmbeddingMetadata.model_type` → `sub_type`
|
||||
- [x] `model_scanner.py` `_build_cache_entry()` 同时处理 `sub_type` 和 `model_type`
|
||||
|
||||
### Phase 2: 查询逻辑更新
|
||||
- [x] `model_query.py` 新增 `resolve_sub_type()` 和 `normalize_sub_type()`
|
||||
- [x] ~~保持向后兼容的别名 `resolve_civitai_model_type`, `normalize_civitai_model_type`~~ (已在 Phase 5 移除)
|
||||
- [x] `ModelFilterSet.apply()` 更新为使用新的解析函数
|
||||
|
||||
### Phase 3: API 响应更新
|
||||
- [x] `LoraService.format_response()` 返回 `sub_type` ~~+ `model_type`~~ (已移除 `model_type`)
|
||||
- [x] `CheckpointService.format_response()` 返回 `sub_type` ~~+ `model_type`~~ (已移除 `model_type`)
|
||||
- [x] `EmbeddingService.format_response()` 返回 `sub_type` ~~+ `model_type`~~ (已移除 `model_type`)
|
||||
|
||||
### Phase 4: 前端更新
|
||||
- [x] `constants.js` 新增 `MODEL_SUBTYPE_DISPLAY_NAMES`
|
||||
- [x] `MODEL_TYPE_DISPLAY_NAMES` 作为别名保留
|
||||
|
||||
### Phase 5: 清理废弃代码 ✅
|
||||
- [x] 从 `ModelScanner._build_cache_entry()` 中移除 `model_type` 向后兼容代码
|
||||
- [x] 从 `CheckpointScanner` 中移除 `model_type` 兼容处理
|
||||
- [x] 从 `model_query.py` 中移除 `resolve_civitai_model_type` 和 `normalize_civitai_model_type` 别名
|
||||
- [x] 更新前端 `FilterManager.js` 使用 `sub_type` (已在使用 `MODEL_SUBTYPE_DISPLAY_NAMES`)
|
||||
- [x] 更新所有相关测试
|
||||
|
||||
---
|
||||
|
||||
## 遗留工作 ⏳
|
||||
|
||||
### Phase 5: 清理废弃代码 ✅ **已完成**
|
||||
|
||||
所有 Phase 5 的清理工作已完成：
|
||||
|
||||
#### 5.1 移除 `model_type` 字段的向后兼容代码 ✅
|
||||
- 从 `ModelScanner._build_cache_entry()` 中移除了 `model_type` 的设置
|
||||
- 现在只设置 `sub_type` 字段
|
||||
|
||||
#### 5.2 移除 CheckpointScanner 的 model_type 兼容处理 ✅
|
||||
- `adjust_metadata()` 现在只处理 `sub_type`
|
||||
- `adjust_cached_entry()` 现在只设置 `sub_type`
|
||||
|
||||
#### 5.3 移除 model_query 中的向后兼容别名 ✅
|
||||
- 移除了 `resolve_civitai_model_type = resolve_sub_type`
|
||||
- 移除了 `normalize_civitai_model_type = normalize_sub_type`
|
||||
|
||||
#### 5.4 前端清理 ✅
|
||||
- `FilterManager.js` 已经在使用 `MODEL_SUBTYPE_DISPLAY_NAMES` (通过别名 `MODEL_TYPE_DISPLAY_NAMES`)
|
||||
- API list endpoint 现在只返回 `sub_type`，不再返回 `model_type`
|
||||
- `ModelCard.js` 现在设置 `card.dataset.sub_type` (所有模型类型通用)
|
||||
- `CheckpointContextMenu.js` 现在读取 `card.dataset.sub_type`
|
||||
- `MoveManager.js` 现在处理 `cache_entry.sub_type`
|
||||
- `RecipeModal.js` 现在读取 `checkpoint.sub_type`
|
||||
|
||||
---
|
||||
|
||||
## 数据库迁移评估
|
||||
|
||||
### 当前状态
|
||||
- `persistent_model_cache.py` 使用 `civitai_model_type` 列存储 CivitAI 原始类型
|
||||
- 缓存 entry 中的 `sub_type` 在运行期动态计算
|
||||
- 数据库 schema **无需立即修改**
|
||||
|
||||
### 未来可选优化
|
||||
```sql
|
||||
-- 可选：在 models 表中添加 sub_type 列（与 civitai_model_type 保持一致但语义更清晰）
|
||||
ALTER TABLE models ADD COLUMN sub_type TEXT;
|
||||
|
||||
-- 数据迁移
|
||||
UPDATE models SET sub_type = civitai_model_type WHERE sub_type IS NULL;
|
||||
```
|
||||
|
||||
**建议**: 如果决定添加 `sub_type` 列，应与 Phase 5 一起进行。
|
||||
|
||||
---
|
||||
|
||||
## 测试覆盖率
|
||||
|
||||
### 新增/更新测试文件（已全部通过 ✅）
|
||||
|
||||
| 测试文件 | 数量 | 覆盖内容 |
|
||||
|---------|------|---------|
|
||||
| `tests/utils/test_models_sub_type.py` | 7 | Metadata sub_type 字段 |
|
||||
| `tests/services/test_model_query_sub_type.py` | 19 | sub_type 解析和过滤 |
|
||||
| `tests/services/test_checkpoint_scanner_sub_type.py` | 6 | CheckpointScanner sub_type |
|
||||
| `tests/services/test_service_format_response_sub_type.py` | 6 | API 响应 sub_type 包含 |
|
||||
| `tests/services/test_checkpoint_scanner.py` | 1 | Checkpoint 缓存 sub_type |
|
||||
| `tests/services/test_model_scanner.py` | 1 | adjust_cached_entry hook |
|
||||
| `tests/services/test_download_manager.py` | 1 | Checkpoint 下载 sub_type |
|
||||
|
||||
### 需要补充的测试（可选）
|
||||
|
||||
- [ ] 集成测试：验证前端过滤使用 sub_type 字段
|
||||
- [ ] 数据库迁移测试（如果执行可选优化）
|
||||
- [ ] 性能测试：确认 resolve_sub_type 的优先级查找没有显著性能影响
|
||||
|
||||
---
|
||||
|
||||
## 兼容性检查清单
|
||||
|
||||
### 已完成 ✅
|
||||
|
||||
- [x] 前端代码已全部改用 `sub_type` 字段
|
||||
- [x] API list endpoint 已移除 `model_type`，只返回 `sub_type`
|
||||
- [x] 后端 cache entry 已移除 `model_type`，只保留 `sub_type`
|
||||
- [x] 所有测试已更新通过
|
||||
- [x] 文档已更新
|
||||
|
||||
---
|
||||
|
||||
## 相关文件清单
|
||||
|
||||
### 核心文件
|
||||
```
|
||||
py/utils/models.py
|
||||
py/utils/constants.py
|
||||
py/services/model_scanner.py
|
||||
py/services/model_query.py
|
||||
py/services/checkpoint_scanner.py
|
||||
py/services/base_model_service.py
|
||||
py/services/lora_service.py
|
||||
py/services/checkpoint_service.py
|
||||
py/services/embedding_service.py
|
||||
```
|
||||
|
||||
### 前端文件
|
||||
```
|
||||
static/js/utils/constants.js
|
||||
static/js/managers/FilterManager.js
|
||||
static/js/managers/MoveManager.js
|
||||
static/js/components/shared/ModelCard.js
|
||||
static/js/components/ContextMenu/CheckpointContextMenu.js
|
||||
static/js/components/RecipeModal.js
|
||||
```
|
||||
|
||||
### 测试文件
|
||||
```
|
||||
tests/utils/test_models_sub_type.py
|
||||
tests/services/test_model_query_sub_type.py
|
||||
tests/services/test_checkpoint_scanner_sub_type.py
|
||||
tests/services/test_service_format_response_sub_type.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 风险评估
|
||||
|
||||
| 风险项 | 影响 | 缓解措施 |
|
||||
|-------|------|---------|
|
||||
| ~~第三方代码依赖 `model_type`~~ | ~~高~~ | ~~保持别名至少 1 个 major 版本~~ ✅ 已完成移除 |
|
||||
| ~~数据库 schema 变更~~ | ~~中~~ | ~~暂缓 schema 变更，仅运行时计算~~ ✅ 无需变更 |
|
||||
| ~~前端过滤失效~~ | ~~中~~ | ~~全面的集成测试覆盖~~ ✅ 测试通过 |
|
||||
| CivitAI API 变化 | 低 | 保持多源解析策略 |
|
||||
|
||||
---
|
||||
|
||||
## 时间线
|
||||
|
||||
- **v1.x**: Phase 1-4 已完成，保持向后兼容
|
||||
- **v2.0 (当前)**: ✅ Phase 5 已完成 - `model_type` 兼容代码已移除
|
||||
- API list endpoint 只返回 `sub_type`
|
||||
- Cache entry 只保留 `sub_type`
|
||||
- 移除了 `resolve_civitai_model_type` 和 `normalize_civitai_model_type` 别名
|
||||
|
||||
---
|
||||
|
||||
## 备注
|
||||
|
||||
- 重构期间发现 `civitai_model_type` 数据库列命名尚可，但语义上应理解为存储 CivitAI API 返回的原始类型值
|
||||
- Checkpoint 的 `diffusion_model` sub_type 不能通过 CivitAI API 获取，必须通过文件路径（model root）判断
|
||||
- LoRA 的 sub_type（lora/locon/dora）直接来自 CivitAI API 的 `version_info.model.type`
|
||||
@@ -1,92 +0,0 @@
|
||||
# Reconcile 的 Windows 大小写回退分支 - 待验证清单
|
||||
|
||||
> **状态**: 待 Windows 环境验证 | **创建日期**: 2026-09-11
|
||||
> **相关文件**: `py/services/model_scanner.py` (`ModelScanner._reconcile_cache`)
|
||||
> **相关历史**: #871 (`76ee59cd`, 路径重叠去重)、#1108 (按文件夹扫描的需求)
|
||||
|
||||
---
|
||||
|
||||
## 背景
|
||||
|
||||
Refresh 按钮走的是 `_reconcile_cache()`（快速增量对账）。2026-09-11 做了一轮性能优化，把两处"预防性"的
|
||||
realpath 全量遍历改成按需触发（详见下方"已完成"）。优化后，一次零变更 Refresh 在 5 万文件库上从
|
||||
~1400 ms 降到 ~120 ms。
|
||||
|
||||
清理过程中发现**唯一一处遗留的可疑点**：Windows 专属的大小写不敏感回退分支。它无法在 Linux 上验证，
|
||||
因此单独记录，留待 Windows 机器上确认。
|
||||
|
||||
---
|
||||
|
||||
## 待验证分支（现状）
|
||||
|
||||
`py/services/model_scanner.py` 中 `_reconcile_cache()` 的 walk 循环内：
|
||||
|
||||
```python
|
||||
# Try case-insensitive match on Windows
|
||||
if os.name == 'nt':
|
||||
lower_path = file_path.lower()
|
||||
matched = False
|
||||
for cached_path in cached_paths: # 每个未命中文件都全量扫一遍缓存
|
||||
if cached_path.lower() == lower_path:
|
||||
found_paths.add(cached_path)
|
||||
matched = True
|
||||
break
|
||||
if matched:
|
||||
continue
|
||||
```
|
||||
|
||||
它排在精确匹配（`file_path in cached_paths`）和 realpath 别名匹配之后，只有**未命中**的文件才会走到。
|
||||
|
||||
### 为什么可疑
|
||||
|
||||
1. **可能不可达**：Windows 上 `os.path.realpath()` 会返回磁盘上的真实大小写，因此"缓存路径大小写与磁盘
|
||||
不一致"的情形，理论上已经被上一步的 realpath 别名匹配覆盖。若如此，这段就是纯冗余代码。
|
||||
2. **一旦可达就是 O(N×M)**：每个未命中文件都要遍历全部 `cached_paths` 做小写比较。若某种路径写法让
|
||||
整个库都变成"未命中"（例如缓存里的盘符/大小写形式与 walk 结果系统性不一致），一次 Refresh 会退化
|
||||
成 文件数 × 缓存条目数 次字符串比较，比真实 IO 还贵。
|
||||
3. **没有测试覆盖**：`tests/services/test_model_scanner.py` 没有任何针对该分支的用例（它在 Linux 上
|
||||
被 `os.name == 'nt'` 短路，无法覆盖）。
|
||||
|
||||
---
|
||||
|
||||
## 待办
|
||||
|
||||
- [ ] **验证可达性**：在 Windows 上构造"缓存路径与磁盘真实大小写不一致"的场景，确认 realpath 别名匹配
|
||||
是否已经命中，即上面的 `if os.name == 'nt'` 分支是否还有进入的必要。
|
||||
- [ ] **若不可达 / 冗余**：删除该分支，并在删除处留注释说明 realpath 已覆盖大小写归一（附验证记录）。
|
||||
- [ ] **若可达**：保留语义但改成 O(1)——预先构建一次 `lower_path -> cached_path` 映射（与
|
||||
`cached_real_paths` 同样按需、懒构建），把内层全量扫描换成一次字典查询。
|
||||
- [ ] **补一个 Windows-only 的回归测试**（`pytest.mark.skipif(os.name != "nt", ...)`），锁定最终结论。
|
||||
- [ ] 把验证结论回填到本文件，并同步更新状态行。
|
||||
|
||||
---
|
||||
|
||||
## 验证方法（Windows）
|
||||
|
||||
1. **构造不一致的大小写**：让缓存里的 `file_path` 与磁盘实际路径大小写不同（例如改过盘符/目录大小写，
|
||||
或从另一台机器迁移了 `settings.json` 与持久化缓存），然后在 UI 点 Refresh。
|
||||
2. **看后端日志判据**：
|
||||
- 若 realpath 已覆盖 → 日志应显示 `Cache reconciliation completed in X seconds. Added 0, removed 0 models.`，
|
||||
且**没有** `Found N new files to process` / `Processing <path>`。
|
||||
- 若回退分支在起作用 → 同样应该是 `Added 0, removed 0`（因为 `found_paths` 被补上），这是"分支可达"
|
||||
的证据；反之若出现大量 `Processing ...` 并重新 hash，说明连回退分支也没命中，问题更严重
|
||||
（缓存路径被当成了新文件 + 旧条目被删）。
|
||||
3. **跑测试**：`python -m pytest tests/services/test_model_scanner.py -k reconcile`（该文件在 Windows 上会
|
||||
真实执行 `os.name == 'nt'` 分支）。
|
||||
4. **量化**：如果需要，可在 `_reconcile_cache` 里临时插桩统计该分支的进入次数与内层迭代次数，确认是否为 0。
|
||||
|
||||
---
|
||||
|
||||
## 已完成（本轮优化，供对照）
|
||||
|
||||
同一次清理里已经落地并验证的部分（Linux，5 万文件库）：
|
||||
|
||||
- `cached_real_paths` 别名映射改为**首次未命中时**懒构建（原来每次 Refresh 都对全部缓存条目算一次 realpath）。
|
||||
- 每个文件的 `realpath` 移到精确命中检查**之后**（原来对每个文件都算，命中即丢弃）。
|
||||
- `get_model_roots()` 在新增文件处理阶段只快照一次（原来每个新文件重读一次）。
|
||||
- 全量去重 pass 加了 O(1) 前置判断（`cached_size_before != len(cached_paths) or total_added > 0`），
|
||||
零变更且缓存干净时跳过；快照本身含重复路径时仍会自愈。
|
||||
|
||||
结果：零变更 Refresh 5 万文件 **~1400 ms → ~120 ms**；根目录顺序/符号链接别名翻转场景仍是
|
||||
`re-processed=0`（不重新读 metadata、不重新 hash）。测试：`tests/services/test_model_scanner.py`
|
||||
47 项、全量后端 2567 项全部通过。
|
||||
@@ -1,678 +0,0 @@
|
||||
# Backend Testing Improvement Plan
|
||||
|
||||
**Status:** Phase 4 Complete ✅
|
||||
**Created:** 2026-02-11
|
||||
**Updated:** 2026-02-11
|
||||
**Priority:** P0 - Critical
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
This document outlines a comprehensive plan to improve the quality, coverage, and maintainability of the LoRa Manager backend test suite. Recent critical bugs (_handle_download_task_done and get_status methods missing) were not caught by existing tests, highlighting significant gaps in the testing strategy.
|
||||
|
||||
## Current State Assessment
|
||||
|
||||
### Test Statistics
|
||||
- **Total Python Test Files:** 80+
|
||||
- **Total JavaScript Test Files:** 29
|
||||
- **Test Lines of Code:** ~15,000
|
||||
- **Current Pass Rate:** 100% (but missing critical edge cases)
|
||||
|
||||
### Key Findings
|
||||
1. **Coverage Gaps:** Critical modules have no direct tests
|
||||
2. **Mocking Issues:** Over-mocking hides real bugs
|
||||
3. **Integration Deficit:** Missing end-to-end tests
|
||||
4. **Async Inconsistency:** Multiple patterns for async tests
|
||||
5. **Maintenance Burden:** Large, complex test files with duplication
|
||||
|
||||
---
|
||||
|
||||
## Phase 2 Completion Summary (2026-02-11)
|
||||
|
||||
### Completed Items
|
||||
|
||||
1. **Integration Test Framework** ✅
|
||||
- Created `tests/integration/` directory structure
|
||||
- Added `tests/integration/conftest.py` with shared fixtures
|
||||
- Added `tests/integration/__init__.py` for package organization
|
||||
|
||||
2. **Download Flow Integration Tests** ✅
|
||||
- Created `tests/integration/test_download_flow.py` with 7 tests
|
||||
- Tests cover:
|
||||
- Download with mocked network (2 tests)
|
||||
- Progress broadcast verification (1 test)
|
||||
- Error handling (1 test)
|
||||
- Cancellation flow (1 test)
|
||||
- Concurrent download management (1 test)
|
||||
- Route endpoint validation (1 test)
|
||||
|
||||
3. **Recipe Flow Integration Tests** ✅
|
||||
- Created `tests/integration/test_recipe_flow.py` with 9 tests
|
||||
- Tests cover:
|
||||
- Recipe save and retrieve flow (1 test)
|
||||
- Recipe update flow (1 test)
|
||||
- Recipe delete flow (1 test)
|
||||
- Recipe model extraction (1 test)
|
||||
- Generation parameters handling (1 test)
|
||||
- Concurrent recipe reads (1 test)
|
||||
- Concurrent read/write operations (1 test)
|
||||
- Recipe list endpoint (1 test)
|
||||
- Recipe metadata parsing (1 test)
|
||||
|
||||
4. **ModelLifecycleService Coverage** ✅
|
||||
- Added 12 new tests to `tests/services/test_model_lifecycle_service.py`
|
||||
- Tests cover:
|
||||
- `exclude_model` functionality (3 tests)
|
||||
- `bulk_delete_models` functionality (2 tests)
|
||||
- Error path tests (5 tests)
|
||||
- `_extract_model_id_from_payload` utility (3 tests)
|
||||
- Total: 18 tests (up from 6)
|
||||
|
||||
5. **PersistentRecipeCache Concurrent Access** ✅
|
||||
- Added 5 new concurrent access tests to `tests/test_persistent_recipe_cache.py`
|
||||
- Tests cover:
|
||||
- Concurrent reads without corruption (1 test)
|
||||
- Concurrent write and read operations (1 test)
|
||||
- Concurrent updates to same recipe (1 test)
|
||||
- Schema initialization thread safety (1 test)
|
||||
- Concurrent save and remove operations (1 test)
|
||||
- Total: 17 tests (up from 12)
|
||||
|
||||
### Test Results
|
||||
- **Integration Tests:** 16/16 passing
|
||||
- **ModelLifecycleService Tests:** 18/18 passing
|
||||
- **PersistentRecipeCache Tests:** 17/17 passing
|
||||
- **Total New Tests Added:** 28 tests
|
||||
|
||||
---
|
||||
|
||||
## Phase 1 Completion Summary (2026-02-11)
|
||||
|
||||
### Completed Items
|
||||
|
||||
1. **pytest-asyncio Integration** ✅
|
||||
- Added `pytest-asyncio>=0.21.0` to `requirements-dev.txt`
|
||||
- Updated `pytest.ini` with `asyncio_mode = auto` and `asyncio_default_fixture_loop_scope = function`
|
||||
- Removed custom `pytest_pyfunc_call` handler from `tests/conftest.py`
|
||||
- Added `@pytest.mark.asyncio` decorator to 21 async test functions in `tests/services/test_download_manager.py`
|
||||
|
||||
2. **Error Path Tests** ✅
|
||||
- Created `tests/services/test_downloader_error_paths.py` with 19 new tests
|
||||
- Tests cover:
|
||||
- DownloadStreamControl state management (6 tests)
|
||||
- Downloader configuration and initialization (4 tests)
|
||||
- DownloadProgress dataclass (1 test)
|
||||
- Custom exceptions (2 tests)
|
||||
- Authentication headers (3 tests)
|
||||
- Session management (3 tests)
|
||||
|
||||
3. **Test Results**
|
||||
- All 45 tests pass (26 in test_download_manager.py + 19 in test_downloader_error_paths.py)
|
||||
- No regressions introduced
|
||||
|
||||
### Notes
|
||||
- Over-mocking fix in `test_download_manager.py` deferred to Phase 2 as it requires significant refactoring
|
||||
- Error path tests focus on unit-level testing of downloader components rather than complex integration scenarios
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Critical Fixes (P0) - Week 1-2
|
||||
|
||||
### 1.1 Fix Over-Mocking Issues
|
||||
|
||||
**Problem:** Tests mock the methods they purport to test, hiding real bugs.
|
||||
|
||||
**Affected Files:**
|
||||
- `tests/services/test_download_manager.py` - Mocks `_execute_download`
|
||||
- `tests/utils/test_example_images_download_manager_unit.py` - Mocks callbacks
|
||||
- `tests/routes/test_base_model_routes_smoke.py` - Uses fake service stubs
|
||||
|
||||
**Actions:**
|
||||
1. Refactor `test_download_manager.py` to test actual download logic
|
||||
2. Replace method-level mocks with dependency injection
|
||||
3. Add integration tests that verify real behavior
|
||||
|
||||
**Example Fix:**
|
||||
```python
|
||||
# BEFORE (Bad - mocks method under test)
|
||||
async def fake_execute_download(self, **kwargs):
|
||||
return {"success": True}
|
||||
monkeypatch.setattr(DownloadManager, "_execute_download", fake_execute_download)
|
||||
|
||||
# AFTER (Good - tests actual logic with injected dependencies)
|
||||
async def test_download_executes_with_real_logic(
|
||||
tmp_path, mock_downloader, mock_websocket
|
||||
):
|
||||
manager = DownloadManager(
|
||||
downloader=mock_downloader,
|
||||
ws_manager=mock_websocket
|
||||
)
|
||||
result = await manager._execute_download(urls=["http://test.com/file.safetensors"])
|
||||
assert result.success is True
|
||||
assert mock_downloader.download_calls == 1
|
||||
```
|
||||
|
||||
### 1.2 Add Missing Error Path Tests
|
||||
|
||||
**Problem:** Error handling code is not tested, leading to production failures.
|
||||
|
||||
**Required Tests:**
|
||||
|
||||
| Error Type | Module | Priority |
|
||||
|------------|--------|----------|
|
||||
| Network timeout | `downloader.py` | P0 |
|
||||
| Disk full | `download_manager.py` | P0 |
|
||||
| Permission denied | `example_images_download_manager.py` | P0 |
|
||||
| Session refresh failure | `downloader.py` | P1 |
|
||||
| Partial file cleanup | `download_manager.py` | P1 |
|
||||
|
||||
**Implementation:**
|
||||
```python
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_handles_network_timeout():
|
||||
"""Verify download retries on timeout and eventually fails gracefully."""
|
||||
# Arrange
|
||||
downloader = Downloader()
|
||||
mock_session = AsyncMock()
|
||||
mock_session.get.side_effect = asyncio.TimeoutError()
|
||||
|
||||
# Act
|
||||
success, message = await downloader.download_file(
|
||||
url="http://test.com/file.safetensors",
|
||||
target_path=tmp_path / "test.safetensors",
|
||||
session=mock_session
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert success is False
|
||||
assert "timeout" in message.lower()
|
||||
assert mock_session.get.call_count == MAX_RETRIES
|
||||
```
|
||||
|
||||
### 1.3 Standardize Async Test Patterns
|
||||
|
||||
**Problem:** Inconsistent async test patterns across codebase.
|
||||
|
||||
**Current State:**
|
||||
- Some use `@pytest.mark.asyncio`
|
||||
- Some rely on custom `pytest_pyfunc_call` in conftest.py
|
||||
- Some use bare async functions
|
||||
|
||||
**Solution:**
|
||||
1. Add `pytest-asyncio` to requirements-dev.txt
|
||||
2. Update `pytest.ini`:
|
||||
```ini
|
||||
[pytest]
|
||||
asyncio_mode = auto
|
||||
asyncio_default_fixture_loop_scope = function
|
||||
```
|
||||
3. Remove custom `pytest_pyfunc_call` handler from conftest.py
|
||||
4. Bulk update all async tests to use `@pytest.mark.asyncio`
|
||||
|
||||
**Migration Script:**
|
||||
```bash
|
||||
# Find all async test functions missing decorator
|
||||
rg "^async def test_" tests/ --type py -A1 | grep -B1 "@pytest.mark" | grep "async def"
|
||||
|
||||
# Add decorator (manual review required)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Integration & Coverage (P1) - Week 3-4
|
||||
|
||||
### 2.1 Add Critical Module Tests
|
||||
|
||||
**Priority 1: `py/services/model_lifecycle_service.py`**
|
||||
```python
|
||||
# tests/services/test_model_lifecycle_service.py
|
||||
class TestModelLifecycleService:
|
||||
async def test_create_model_registers_in_cache(self):
|
||||
"""Verify new model is registered in both cache and database."""
|
||||
|
||||
async def test_delete_model_cleans_up_files_and_cache(self):
|
||||
"""Verify deletion removes files and updates all indexes."""
|
||||
|
||||
async def test_update_model_metadata_propagates_changes(self):
|
||||
"""Verify metadata updates reach all subscribers."""
|
||||
```
|
||||
|
||||
**Priority 2: `py/services/persistent_recipe_cache.py`**
|
||||
```python
|
||||
# tests/services/test_persistent_recipe_cache.py
|
||||
class TestPersistentRecipeCache:
|
||||
def test_initialization_creates_schema(self):
|
||||
"""Verify SQLite schema is created on first use."""
|
||||
|
||||
async def test_save_recipe_persists_to_sqlite(self):
|
||||
"""Verify recipe data is saved correctly."""
|
||||
|
||||
async def test_concurrent_access_does_not_corrupt_database(self):
|
||||
"""Verify thread safety under concurrent writes."""
|
||||
```
|
||||
|
||||
**Priority 3: Route Handler Tests**
|
||||
- `py/routes/handlers/preview_handlers.py`
|
||||
- `py/routes/handlers/misc_handlers.py`
|
||||
- `py/routes/handlers/model_handlers.py`
|
||||
|
||||
### 2.2 Add End-to-End Integration Tests
|
||||
|
||||
**Download Flow Integration Test:**
|
||||
```python
|
||||
# tests/integration/test_download_flow.py
|
||||
@pytest.mark.integration
|
||||
@pytest.mark.asyncio
|
||||
async def test_complete_download_flow(tmp_path, test_server):
|
||||
"""
|
||||
Integration test covering:
|
||||
1. Route receives download request
|
||||
2. DownloadCoordinator schedules it
|
||||
3. DownloadManager executes actual download
|
||||
4. Downloader makes HTTP request (to test server)
|
||||
5. Progress is broadcast via WebSocket
|
||||
6. File is saved and cache updated
|
||||
"""
|
||||
# Setup test server with known file
|
||||
test_file = tmp_path / "test_model.safetensors"
|
||||
test_file.write_bytes(b"fake model data")
|
||||
|
||||
# Start download
|
||||
async with aiohttp.ClientSession() as session:
|
||||
response = await session.post(
|
||||
"http://localhost:8188/api/lm/download",
|
||||
json={"urls": [f"http://localhost:{test_server.port}/test_model.safetensors"]}
|
||||
)
|
||||
assert response.status == 200
|
||||
|
||||
# Verify file downloaded
|
||||
downloaded = tmp_path / "downloads" / "test_model.safetensors"
|
||||
assert downloaded.exists()
|
||||
assert downloaded.read_bytes() == b"fake model data"
|
||||
|
||||
# Verify WebSocket progress updates
|
||||
assert len(ws_manager.broadcasts) > 0
|
||||
assert any(b["status"] == "completed" for b in ws_manager.broadcasts)
|
||||
```
|
||||
|
||||
**Recipe Flow Integration Test:**
|
||||
```python
|
||||
# tests/integration/test_recipe_flow.py
|
||||
@pytest.mark.integration
|
||||
@pytest.mark.asyncio
|
||||
async def test_recipe_analysis_and_save_flow(tmp_path):
|
||||
"""
|
||||
Integration test covering:
|
||||
1. Import recipe from image
|
||||
2. Parse metadata and extract models
|
||||
3. Save to cache and database
|
||||
4. Retrieve and display
|
||||
"""
|
||||
```
|
||||
|
||||
### 2.3 Strengthen Assertions
|
||||
|
||||
**Replace loose assertions:**
|
||||
```python
|
||||
# BEFORE
|
||||
assert "mismatch" in message.lower()
|
||||
|
||||
# AFTER
|
||||
assert message == "File size mismatch. Expected: 1000 bytes, Got: 500 bytes"
|
||||
assert not target_path.exists()
|
||||
assert not Path(str(target_path) + ".part").exists()
|
||||
assert len(downloader.retry_history) == 3
|
||||
```
|
||||
|
||||
**Add state verification:**
|
||||
```python
|
||||
# BEFORE
|
||||
assert result is True
|
||||
|
||||
# AFTER
|
||||
assert result is True
|
||||
assert model["status"] == "downloaded"
|
||||
assert model["file_path"].exists()
|
||||
assert cache.get_by_hash(model["sha256"]) is not None
|
||||
assert len(ws_manager.payloads) >= 2 # Started + completed
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 4 Completion Summary (2026-02-11)
|
||||
|
||||
### Completed Items
|
||||
|
||||
1. **Property-Based Tests (Hypothesis)** ✅
|
||||
- Created `tests/utils/test_utils_hypothesis.py` with 19 property-based tests
|
||||
- Tests cover:
|
||||
- `sanitize_folder_name` idempotency and invalid character handling (4 tests)
|
||||
- `_sanitize_library_name` idempotency and safe character filtering (2 tests)
|
||||
- `normalize_path` idempotency and forward slash usage (2 tests)
|
||||
- `fuzzy_match` edge cases and threshold behavior (3 tests)
|
||||
- `determine_base_model` return type guarantees (2 tests)
|
||||
- `get_preview_extension` return type validation (2 tests)
|
||||
- `calculate_recipe_fingerprint` determinism and ordering (4 tests)
|
||||
- Fixed Hypothesis plugin compatibility issue by creating a `MockModule` class in `conftest.py` that is hashable (unlike `types.SimpleNamespace`)
|
||||
|
||||
2. **Snapshot Tests (Syrupy)** ✅
|
||||
- Created `tests/routes/test_api_snapshots.py` with 7 snapshot tests
|
||||
- Tests cover:
|
||||
- SettingsHandler response formats (2 tests)
|
||||
- NodeRegistryHandler response formats (2 tests)
|
||||
- Utility function output verification (2 tests)
|
||||
- ModelLibraryHandler empty response format (1 test)
|
||||
- All snapshots generated and tests passing (7/7)
|
||||
|
||||
3. **Performance Benchmarks** ✅
|
||||
- Created `tests/performance/test_cache_performance.py` with 11 benchmark tests
|
||||
- Tests cover:
|
||||
- Hash index lookup performance (100, 1K, 10K models) - 3 tests
|
||||
- Hash index add entry performance (100, 10K existing) - 2 tests
|
||||
- Fuzzy matching performance (short text, long text, many words) - 3 tests
|
||||
- Recipe fingerprint calculation (5, 50, 200 LoRAs) - 3 tests
|
||||
- All benchmarks passing with performance metrics (11/11)
|
||||
|
||||
4. **Package Dependencies** ✅
|
||||
- Added `hypothesis>=6.0` to `requirements-dev.txt`
|
||||
- Added `syrupy>=5.0` to `requirements-dev.txt`
|
||||
- Added `pytest-benchmark>=5.0` to `requirements-dev.txt`
|
||||
|
||||
### Test Results
|
||||
- **Property-Based Tests:** 19/19 passing
|
||||
- **Snapshot Tests:** 7/7 passing
|
||||
- **Performance Benchmarks:** 11/11 passing
|
||||
- **Total New Tests Added:** 37 tests
|
||||
- **Full Test Suite:** 947/947 passing
|
||||
|
||||
---
|
||||
|
||||
## Phase 3 Completion Summary (2026-02-11)
|
||||
|
||||
### Completed Items
|
||||
|
||||
1. **Centralized Test Fixtures** ✅
|
||||
- Added `mock_downloader` fixture to `tests/conftest.py`
|
||||
- Configurable mock with `should_fail` and `return_value` attributes
|
||||
- Records all download calls for verification
|
||||
- Added `mock_websocket_manager` fixture to `tests/conftest.py`
|
||||
- Recording WebSocket manager that captures all broadcast payloads
|
||||
- Includes helper method `get_payloads_by_type()` for filtering
|
||||
- Added `reset_singletons` autouse fixture to `tests/conftest.py`
|
||||
- Resets DownloadManager, ServiceRegistry, ModelScanner, and SettingsManager
|
||||
- Ensures test isolation and prevents singleton pollution
|
||||
|
||||
2. **Split Large Test Files** ✅
|
||||
- Split `tests/services/test_download_manager.py` (1422 lines) into:
|
||||
- `test_download_manager_basic.py` - Core functionality (12 tests)
|
||||
- `test_download_manager_error.py` - Error handling and execution (15 tests)
|
||||
- `test_download_manager_concurrent.py` - Advanced scenarios (6 tests)
|
||||
- Split `tests/utils/test_cache_paths.py` (530 lines) into:
|
||||
- `test_cache_paths_resolution.py` - Path resolution and CacheType tests (11 tests)
|
||||
- `test_cache_paths_validation.py` - Legacy path validation and cleanup (9 tests)
|
||||
- `test_cache_paths_migration.py` - Migration scenarios and auto-cleanup (9 tests)
|
||||
|
||||
3. **Complex Test Refactoring** ✅
|
||||
- Reviewed `test_example_images_download_manager_unit.py`
|
||||
- Existing async event-based patterns are appropriate for testing concurrent behavior
|
||||
- No refactoring needed - tests follow consistent patterns and are maintainable
|
||||
|
||||
### Test Results
|
||||
- **Download Manager Tests:** 33/33 passing across 3 files
|
||||
- **Cache Paths Tests:** 29/29 passing across 3 files
|
||||
- **Total Tests Maintained:** All existing tests preserved and organized
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Architecture & Maintainability (P2) - Week 5-6
|
||||
|
||||
### 3.1 Centralize Test Fixtures
|
||||
|
||||
**Create `tests/conftest.py` improvements:**
|
||||
|
||||
```python
|
||||
# tests/conftest.py additions
|
||||
|
||||
@pytest.fixture
|
||||
def mock_downloader():
|
||||
"""Provide a configurable mock downloader."""
|
||||
class MockDownloader:
|
||||
def __init__(self):
|
||||
self.download_calls = []
|
||||
self.should_fail = False
|
||||
|
||||
async def download_file(self, url, target_path, **kwargs):
|
||||
self.download_calls.append({"url": url, "target_path": target_path})
|
||||
if self.should_fail:
|
||||
return False, "Download failed"
|
||||
return True, str(target_path)
|
||||
|
||||
return MockDownloader()
|
||||
|
||||
@pytest.fixture
|
||||
def mock_websocket_manager():
|
||||
"""Provide a recording WebSocket manager."""
|
||||
class RecordingWebSocketManager:
|
||||
def __init__(self):
|
||||
self.payloads = []
|
||||
|
||||
async def broadcast(self, payload):
|
||||
self.payloads.append(payload)
|
||||
|
||||
return RecordingWebSocketManager()
|
||||
|
||||
@pytest.fixture
|
||||
def mock_scanner():
|
||||
"""Provide a mock model scanner with configurable cache."""
|
||||
# ... existing MockScanner but improved ...
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def reset_singletons():
|
||||
"""Reset all singletons before each test."""
|
||||
# Centralized singleton reset
|
||||
DownloadManager._instance = None
|
||||
ServiceRegistry.clear_services()
|
||||
ModelScanner._instances.clear()
|
||||
yield
|
||||
# Cleanup
|
||||
DownloadManager._instance = None
|
||||
ServiceRegistry.clear_services()
|
||||
ModelScanner._instances.clear()
|
||||
```
|
||||
|
||||
### 3.2 Split Large Test Files
|
||||
|
||||
**Target Files:**
|
||||
- `tests/services/test_download_manager.py` (1000+ lines) → Split into:
|
||||
- `test_download_manager_basic.py` - Core functionality
|
||||
- `test_download_manager_error.py` - Error handling
|
||||
- `test_download_manager_concurrent.py` - Concurrent operations
|
||||
|
||||
- `tests/utils/test_cache_paths.py` (529 lines) → Split into:
|
||||
- `test_cache_paths_resolution.py`
|
||||
- `test_cache_paths_validation.py`
|
||||
- `test_cache_paths_migration.py`
|
||||
|
||||
### 3.3 Refactor Complex Tests
|
||||
|
||||
**Example: Simplify test setup in `test_example_images_download_manager_unit.py`**
|
||||
|
||||
**Current (Complex):**
|
||||
```python
|
||||
async def test_start_download_bootstraps_progress_and_task(
|
||||
monkeypatch: pytest.MonkeyPatch, tmp_path
|
||||
):
|
||||
# 40+ lines of setup
|
||||
started = asyncio.Event()
|
||||
release = asyncio.Event()
|
||||
|
||||
async def fake_download(self, ...):
|
||||
started.set()
|
||||
await release.wait()
|
||||
# ... more logic ...
|
||||
```
|
||||
|
||||
**Improved (Using fixtures):**
|
||||
```python
|
||||
async def test_start_download_bootstraps_progress_and_task(
|
||||
download_manager_with_fake_backend, release_event
|
||||
):
|
||||
# Setup in fixtures, test is clean
|
||||
manager = download_manager_with_fake_backend
|
||||
result = await manager.start_download({"model_types": ["lora"]})
|
||||
assert result["success"] is True
|
||||
assert manager._is_downloading is True
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Advanced Testing (P3) - Week 7-8
|
||||
|
||||
### 4.1 Add Property-Based Tests (Hypothesis)
|
||||
|
||||
**Install:** `pip install hypothesis`
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# tests/utils/test_hash_utils_hypothesis.py
|
||||
from hypothesis import given, strategies as st
|
||||
|
||||
@given(st.text(min_size=1, max_size=100))
|
||||
def test_hash_normalization_idempotent(name):
|
||||
"""Hash normalization should be idempotent."""
|
||||
normalized = normalize_hash(name)
|
||||
assert normalize_hash(normalized) == normalized
|
||||
|
||||
@given(st.lists(st.dictionaries(st.text(), st.text()), min_size=0, max_size=1000))
|
||||
def test_model_cache_handles_any_model_list(models):
|
||||
"""Cache should handle any list of models without crashing."""
|
||||
cache = ModelCache()
|
||||
cache.raw_data = models
|
||||
# Should not raise
|
||||
list(cache.iter_models())
|
||||
```
|
||||
|
||||
### 4.2 Add Snapshot Tests (Syrupy)
|
||||
|
||||
**Install:** `pip install syrupy`
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# tests/routes/test_api_snapshots.py
|
||||
import pytest
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_lora_list_response_format(snapshot, client):
|
||||
"""Verify API response format matches snapshot."""
|
||||
response = await client.get("/api/lm/loras")
|
||||
data = await response.json()
|
||||
assert data == snapshot # Syrupy handles this
|
||||
```
|
||||
|
||||
### 4.3 Add Performance Benchmarks
|
||||
|
||||
**Install:** `pip install pytest-benchmark`
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# tests/performance/test_cache_performance.py
|
||||
import pytest
|
||||
|
||||
def test_cache_lookup_performance(benchmark):
|
||||
"""Benchmark cache lookup with 10,000 models."""
|
||||
cache = create_cache_with_n_models(10000)
|
||||
|
||||
result = benchmark(lambda: cache.get_by_hash("abc123"))
|
||||
# Benchmark automatically collects timing stats
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Checklist
|
||||
|
||||
### Week 1-2: Critical Fixes
|
||||
- [x] Fix over-mocking in `test_download_manager.py` (Skipped - requires major refactoring, see Phase 2)
|
||||
- [x] Add network timeout tests (Added `test_downloader_error_paths.py` with 19 error path tests)
|
||||
- [x] Add disk full error tests (Covered in error path tests)
|
||||
- [x] Add permission denied tests (Covered in error path tests)
|
||||
- [x] Install and configure pytest-asyncio (Added to requirements-dev.txt and pytest.ini)
|
||||
- [x] Remove custom pytest_pyfunc_call handler (Removed from conftest.py)
|
||||
- [x] Add `@pytest.mark.asyncio` to all async tests (Added to 21 async test functions in test_download_manager.py)
|
||||
|
||||
### Week 3-4: Integration & Coverage
|
||||
- [x] Create `test_model_lifecycle_service.py` tests (12 new tests added)
|
||||
- [x] Create `test_persistent_recipe_cache.py` tests (5 new concurrent access tests added)
|
||||
- [x] Create `tests/integration/` directory (created with conftest.py)
|
||||
- [x] Add download flow integration test (7 tests added)
|
||||
- [x] Add recipe flow integration test (9 tests added)
|
||||
- [x] Add route handler tests for preview_handlers.py (already exists in test_preview_routes.py)
|
||||
- [x] Strengthen assertions across integration tests (comprehensive assertions added)
|
||||
|
||||
### Week 5-6: Architecture
|
||||
- [x] Add centralized fixtures to conftest.py
|
||||
- [x] Split `test_download_manager.py` into 3 files
|
||||
- [x] Split `test_cache_paths.py` into 3 files
|
||||
- [x] Refactor complex test setups (reviewed - no changes needed)
|
||||
- [x] Remove duplicate singleton reset fixtures (consolidated in conftest.py)
|
||||
|
||||
### Week 7-8: Advanced Testing
|
||||
- [x] Install hypothesis (Added to requirements-dev.txt)
|
||||
- [x] Add 10 property-based tests (Created 19 tests in test_utils_hypothesis.py)
|
||||
- [x] Install syrupy (Added to requirements-dev.txt)
|
||||
- [x] Add 5 snapshot tests (Created 7 tests in test_api_snapshots.py)
|
||||
- [x] Install pytest-benchmark (Added to requirements-dev.txt)
|
||||
- [x] Add 3 performance benchmarks (Created 11 tests in test_cache_performance.py)
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Quantitative
|
||||
- **Code Coverage:** Increase from ~70% to >90%
|
||||
- **Test Count:** Increase from 400+ to 600+
|
||||
- **Assertion Strength:** Replace 50+ weak assertions
|
||||
- **Integration Test Ratio:** Increase from 5% to 20%
|
||||
|
||||
### Qualitative
|
||||
- **Bug Escape Rate:** Reduce by 80%
|
||||
- **Test Maintenance Time:** Reduce by 50%
|
||||
- **Time to Write New Tests:** Reduce by 30%
|
||||
- **CI Pipeline Speed:** Maintain <5 minutes
|
||||
|
||||
---
|
||||
|
||||
## Risk Mitigation
|
||||
|
||||
| Risk | Mitigation |
|
||||
|------|------------|
|
||||
| Breaking existing tests | Run full test suite after each change |
|
||||
| Increased CI time | Optimize tests, parallelize execution |
|
||||
| Developer resistance | Provide training, pair programming |
|
||||
| Maintenance burden | Document patterns, provide templates |
|
||||
| Coverage gaps | Use coverage.py in CI, fail on <90% |
|
||||
|
||||
---
|
||||
|
||||
## Related Documents
|
||||
|
||||
- `docs/testing/frontend-testing-roadmap.md` - Frontend testing plan
|
||||
- `docs/AGENTS.md` - Development guidelines
|
||||
- `pytest.ini` - Test configuration
|
||||
- `tests/conftest.py` - Shared fixtures
|
||||
|
||||
---
|
||||
|
||||
## Approval
|
||||
|
||||
| Role | Name | Date | Signature |
|
||||
|------|------|------|-----------|
|
||||
| Tech Lead | | | |
|
||||
| QA Lead | | | |
|
||||
| Product Owner | | | |
|
||||
|
||||
---
|
||||
|
||||
**Next Review Date:** 2026-02-25
|
||||
|
||||
**Document Owner:** Backend Team
|
||||
@@ -1,26 +0,0 @@
|
||||
# Backend Test Coverage Notes
|
||||
|
||||
## Pytest Execution
|
||||
- Command: `python -m pytest`
|
||||
- Result: All 283 collected tests passed in the current environment.
|
||||
- Coverage tooling (``pytest-cov``/``coverage``) is unavailable in the offline sandbox, so line-level metrics could not be generated. The earlier attempt to install ``pytest-cov`` failed because the package index cannot be reached from the container.
|
||||
|
||||
## High-Priority Gaps to Address
|
||||
|
||||
### 1. Standalone server bootstrapping
|
||||
* **Source:** [`standalone.py`](../../standalone.py)
|
||||
* **Why it matters:** The standalone entry point wires together the aiohttp application, static asset routes, model-route registration, and configuration validation. None of these behaviours are covered by automated tests, leaving regressions in bootstrapping logic undetected.
|
||||
* **Suggested coverage:** Add integration-style tests that instantiate `StandaloneServer`/`StandaloneLoraManager` with temporary settings and assert that routes (HTTP + websocket) are registered, configuration warnings fire for missing paths, and the mock ComfyUI shims behave as expected.
|
||||
|
||||
### 2. Model service registration factory
|
||||
* **Source:** [`py/services/model_service_factory.py`](../../py/services/model_service_factory.py)
|
||||
* **Why it matters:** The factory coordinates which model services and routes the API exposes, including error handling when unknown model types are requested. No current tests verify registration, memoization of route instances, or the logging path on failures.
|
||||
* **Suggested coverage:** Unit tests that exercise `register_model_type`, `get_route_instance`, error branches in `get_service_class`/`get_route_class`, and `setup_all_routes` when a route setup raises. Use lightweight fakes to confirm the logger is called and state is cleared via `clear_registrations`.
|
||||
|
||||
### 3. Server-side i18n helper
|
||||
* **Source:** [`py/services/server_i18n.py`](../../py/services/server_i18n.py)
|
||||
* **Why it matters:** Template rendering relies on the `ServerI18nManager` to load locale JSON, perform key lookups, and format parameters. The fallback logic (dot-notation lookup, English fallbacks, placeholder substitution) is untested, so malformed locale files or regressions in placeholder handling would slip through.
|
||||
* **Suggested coverage:** Tests that load fixture locale dictionaries, assert `set_locale` fallbacks, verify nested key resolution and placeholder substitution, and ensure missing keys return the original identifier.
|
||||
|
||||
## Next Steps
|
||||
Prioritize creating focused unit tests around these modules, then re-run pytest once coverage tooling is available to confirm the new tests close the identified gaps.
|
||||
@@ -1,196 +0,0 @@
|
||||
# Settings Modal Optimization Progress Tracker
|
||||
|
||||
## Project Overview
|
||||
**Goal**: Optimize Settings Modal UI/UX with left navigation sidebar
|
||||
**Started**: 2026-02-23
|
||||
**Current Phase**: P2 - Search Bar (Completed)
|
||||
|
||||
---
|
||||
|
||||
## Phase 0: Left Navigation Sidebar (P0)
|
||||
|
||||
### Status: Completed ✓
|
||||
|
||||
### Completion Notes
|
||||
- All CSS changes implemented
|
||||
- HTML structure restructured successfully
|
||||
- JavaScript navigation functionality added
|
||||
- Translation keys added and synchronized
|
||||
- Ready for testing and review
|
||||
|
||||
### Tasks
|
||||
|
||||
#### 1. CSS Changes
|
||||
- [x] Add two-column layout styles
|
||||
- [x] `.settings-modal` flex layout
|
||||
- [x] `.settings-nav` sidebar styles
|
||||
- [x] `.settings-content` content area styles
|
||||
- [x] `.settings-nav-item` navigation item styles
|
||||
- [x] `.settings-nav-item.active` active state styles
|
||||
- [x] Adjust modal width to 950px
|
||||
- [x] Add smooth scroll behavior
|
||||
- [x] Add responsive styles for mobile
|
||||
- [x] Ensure dark theme compatibility
|
||||
|
||||
#### 2. HTML Changes
|
||||
- [x] Restructure modal HTML
|
||||
- [x] Wrap content in two-column container
|
||||
- [x] Add navigation sidebar structure
|
||||
- [x] Add navigation items for each section
|
||||
- [x] Add ID anchors to each section
|
||||
- [x] Update section grouping if needed
|
||||
|
||||
#### 3. JavaScript Changes
|
||||
- [x] Add navigation click handlers
|
||||
- [x] Implement smooth scroll to section
|
||||
- [x] Add scroll spy for active nav highlighting
|
||||
- [x] Handle nav item click events
|
||||
- [x] Update SettingsManager initialization
|
||||
|
||||
#### 4. Translation Keys
|
||||
- [x] Add translation keys for navigation groups
|
||||
- [x] `settings.nav.general`
|
||||
- [x] `settings.nav.interface`
|
||||
- [x] `settings.nav.download`
|
||||
- [x] `settings.nav.advanced`
|
||||
|
||||
#### 4. Testing
|
||||
- [x] Verify navigation clicks work
|
||||
- [x] Verify active highlighting works
|
||||
- [x] Verify smooth scrolling works
|
||||
- [ ] Test on mobile viewport (deferred to final QA)
|
||||
- [ ] Test dark/light theme (deferred to final QA)
|
||||
- [x] Verify all existing settings work
|
||||
- [x] Verify save/load functionality
|
||||
|
||||
### Blockers
|
||||
None currently
|
||||
|
||||
### Notes
|
||||
- Started implementation on 2026-02-23
|
||||
- Following existing design system and CSS variables
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Section Collapse/Expand (P1)
|
||||
|
||||
### Status: Completed ✓
|
||||
|
||||
### Completion Notes
|
||||
- All sections now have collapse/expand functionality
|
||||
- Chevron icon rotates smoothly on toggle
|
||||
- State persistence via localStorage working correctly
|
||||
- CSS animations for smooth height transitions
|
||||
- Settings order reorganized to match sidebar navigation
|
||||
|
||||
### Tasks
|
||||
- [x] Add collapse/expand toggle to section headers
|
||||
- [x] Add chevron icon with rotation animation
|
||||
- [x] Implement localStorage for state persistence
|
||||
- [x] Add CSS animations for smooth transitions
|
||||
- [x] Reorder settings sections to match sidebar navigation
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Search Bar (P1)
|
||||
|
||||
### Status: Completed ✓
|
||||
|
||||
### Completion Notes
|
||||
- Search input added to settings modal header with icon and clear button
|
||||
- Real-time filtering with debounced input (150ms delay)
|
||||
- Highlight matching terms with accent color background
|
||||
- Handle empty search results with user-friendly message
|
||||
- Keyboard shortcuts: Escape to clear search
|
||||
- Sections with matches are automatically expanded
|
||||
- All translation keys added and synchronized across languages
|
||||
|
||||
### Tasks
|
||||
- [x] Add search input to header area
|
||||
- [x] Implement real-time filtering
|
||||
- [x] Add highlight for matched terms
|
||||
- [x] Handle empty search results
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Visual Hierarchy (P2)
|
||||
|
||||
### Status: Planned
|
||||
|
||||
### Tasks
|
||||
- [ ] Add accent border to section headers
|
||||
- [ ] Bold setting labels
|
||||
- [ ] Increase section spacing
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Quick Actions (P3)
|
||||
|
||||
### Status: Planned
|
||||
|
||||
### Tasks
|
||||
- [ ] Add reset to defaults button
|
||||
- [ ] Add export config button
|
||||
- [ ] Add import config button
|
||||
- [ ] Implement corresponding functionality
|
||||
|
||||
---
|
||||
|
||||
## Change Log
|
||||
|
||||
### 2026-02-23 (P2)
|
||||
- Completed Phase 2: Search Bar
|
||||
- Added search input to settings modal header with search icon and clear button
|
||||
- Implemented real-time filtering with 150ms debounce for performance
|
||||
- Added visual highlighting for matched search terms using accent color
|
||||
- Implemented empty search results state with user-friendly message
|
||||
- Added keyboard shortcuts (Escape to clear search)
|
||||
- Sections with matching content are automatically expanded during search
|
||||
- Updated SettingsManager.js with search initialization and filtering logic
|
||||
- Added comprehensive CSS styles for search input, highlights, and responsive design
|
||||
- Added translation keys for search feature (placeholder, clear, no results)
|
||||
- Synchronized translations across all language files
|
||||
|
||||
### 2026-02-23 (P1)
|
||||
- Completed Phase 1: Section Collapse/Expand
|
||||
- Added collapse/expand functionality to all settings sections
|
||||
- Implemented chevron icon with smooth rotation animation
|
||||
- Added localStorage persistence for collapse state
|
||||
- Reorganized settings sections to match sidebar navigation order
|
||||
- Updated SettingsManager.js with section collapse initialization
|
||||
- Added CSS styles for smooth transitions and animations
|
||||
|
||||
### 2026-02-23 (P0)
|
||||
- Created project documentation
|
||||
- Started Phase 0 implementation
|
||||
- Analyzed existing code structure
|
||||
- Implemented two-column layout with left navigation sidebar
|
||||
- Added CSS styles for navigation and responsive design
|
||||
- Restructured HTML to support new layout
|
||||
- Added JavaScript navigation functionality with scroll spy
|
||||
- Added translation keys for navigation groups
|
||||
- Synchronized translations across all language files
|
||||
- Tested in browser - navigation working correctly
|
||||
|
||||
---
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
### Functional Testing
|
||||
- [ ] All settings save correctly
|
||||
- [ ] All settings load correctly
|
||||
- [ ] Navigation scrolls to correct section
|
||||
- [ ] Active nav updates on scroll
|
||||
- [ ] Mobile responsive layout
|
||||
|
||||
### Visual Testing
|
||||
- [ ] Design matches existing UI
|
||||
- [ ] Dark theme looks correct
|
||||
- [ ] Light theme looks correct
|
||||
- [ ] Animations are smooth
|
||||
- [ ] No layout shifts or jumps
|
||||
|
||||
### Cross-browser Testing
|
||||
- [ ] Chrome/Chromium
|
||||
- [ ] Firefox
|
||||
- [ ] Safari (if available)
|
||||
@@ -1,331 +0,0 @@
|
||||
# Settings Modal UI/UX Optimization
|
||||
|
||||
## Overview
|
||||
当前Settings Modal采用单列表长页面设计，随着设置项不断增加，已难以高效浏览和定位。本方案采用 **macOS Settings 模式**（左侧导航 + 右侧单Section独占显示），在保持原有设计语言的前提下，重构信息架构，大幅提升用户体验。
|
||||
|
||||
## Goals
|
||||
1. **提升浏览效率**：用户能够快速定位和修改设置
|
||||
2. **保持设计一致性**：延续现有的颜色、间距、动画系统
|
||||
3. **简化交互模型**：移除冗余元素（SETTINGS label、折叠功能）
|
||||
4. **清晰的视觉层次**：Section级导航，右侧独占显示
|
||||
5. **向后兼容**：不影响现有功能逻辑
|
||||
|
||||
## Design Principles
|
||||
- **macOS Settings模式**：点击左侧导航，右侧仅显示该Section内容
|
||||
- **贴近原有设计语言**：使用现有CSS变量和样式模式
|
||||
- **最小化风格改动**：在提升UX的同时保持视觉风格稳定
|
||||
- **简化优于复杂**：移除不必要的折叠/展开交互
|
||||
|
||||
---
|
||||
|
||||
## New Design Architecture
|
||||
|
||||
### Layout Structure
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Settings [×] │
|
||||
├──────────────┬──────────────────────────────────────────────┤
|
||||
│ NAVIGATION │ CONTENT │
|
||||
│ │ │
|
||||
│ General → │ ┌─────────────────────────────────────────┐ │
|
||||
│ Interface │ │ General │ │
|
||||
│ Download │ │ ═══════════════════════════════════════ │ │
|
||||
│ Advanced │ │ │ │
|
||||
│ │ │ ┌─────────────────────────────────────┐ │ │
|
||||
│ │ │ │ Civitai API Key │ │ │
|
||||
│ │ │ │ [ ] [?] │ │ │
|
||||
│ │ │ └─────────────────────────────────────┘ │ │
|
||||
│ │ │ │ │
|
||||
│ │ │ ┌─────────────────────────────────────┐ │ │
|
||||
│ │ │ │ Settings Location │ │ │
|
||||
│ │ │ │ [/path/to/settings] [Browse] │ │ │
|
||||
│ │ │ └─────────────────────────────────────┘ │ │
|
||||
│ │ └─────────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ │ [Cancel] [Save Changes] │
|
||||
└──────────────┴──────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Key Design Decisions
|
||||
|
||||
#### 1. 移除冗余元素
|
||||
- ❌ 删除 sidebar 中的 "SETTINGS" label
|
||||
- ❌ **取消折叠/展开功能**（增加交互成本，无实际收益）
|
||||
- ❌ 不再在左侧导航显示具体设置项（减少认知负荷）
|
||||
|
||||
#### 2. 导航简化
|
||||
- 左侧仅显示 **4个Section**（General / Interface / Download / Advanced）
|
||||
- 当前选中项用 accent 色 background highlight
|
||||
- 无需滚动监听，点击即切换
|
||||
|
||||
#### 3. 右侧单Section独占
|
||||
- 点击左侧导航，右侧仅显示该Section的所有设置项
|
||||
- Section标题作为页面标题（大号字体 + accent色下划线）
|
||||
- 所有设置项平铺展示，无需折叠
|
||||
|
||||
#### 4. 视觉层次
|
||||
```
|
||||
Section Header (20px, bold, accent underline)
|
||||
├── Setting Group (card container, subtle border)
|
||||
│ ├── Setting Label (14px, semibold)
|
||||
│ ├── Setting Description (12px, muted color)
|
||||
│ └── Setting Control (input/select/toggle)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Optimization Phases
|
||||
|
||||
### Phase 0: macOS Settings模式重构 (P0)
|
||||
**Status**: Ready for Development
|
||||
**Priority**: High
|
||||
|
||||
#### Goals
|
||||
- 重构为两栏布局（左侧导航 + 右侧内容）
|
||||
- 实现Section级导航切换
|
||||
- 优化视觉层次和间距
|
||||
- 移除冗余元素
|
||||
|
||||
#### Implementation Details
|
||||
|
||||
##### Layout Specifications
|
||||
| Element | Specification |
|
||||
|---------|--------------|
|
||||
| Modal Width | 800px (比原700px稍宽) |
|
||||
| Modal Height | 600px (固定高度) |
|
||||
| Left Sidebar | 200px 固定宽度 |
|
||||
| Right Content | flex: 1，自动填充 |
|
||||
| Content Padding | --space-3 (24px) |
|
||||
|
||||
##### Navigation Structure
|
||||
```
|
||||
General (通用)
|
||||
├── Language
|
||||
├── Civitai API Key
|
||||
└── Settings Location
|
||||
|
||||
Interface (界面)
|
||||
├── Layout Settings
|
||||
├── Video Settings
|
||||
└── Content Filtering
|
||||
|
||||
Download (下载)
|
||||
├── Folder Settings
|
||||
├── Download Path Templates
|
||||
├── Example Images
|
||||
└── Update Flags
|
||||
|
||||
Advanced (高级)
|
||||
├── Priority Tags
|
||||
├── Auto-organize exclusions
|
||||
├── Metadata refresh skip paths
|
||||
├── Metadata Archive Database
|
||||
├── Proxy Settings
|
||||
└── Misc
|
||||
```
|
||||
|
||||
##### CSS Style Guide
|
||||
|
||||
**Section Header**
|
||||
```css
|
||||
.settings-section-header {
|
||||
font-size: 20px;
|
||||
font-weight: 600;
|
||||
padding-bottom: var(--space-2);
|
||||
border-bottom: 2px solid var(--lora-accent);
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
```
|
||||
|
||||
**Setting Group (Card)**
|
||||
```css
|
||||
.settings-group {
|
||||
background: var(--card-bg);
|
||||
border: 1px solid var(--lora-border);
|
||||
border-radius: var(--border-radius-sm);
|
||||
padding: var(--space-3);
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
```
|
||||
|
||||
**Setting Item**
|
||||
```css
|
||||
.setting-item {
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.setting-item:last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
.setting-label {
|
||||
font-size: 14px;
|
||||
font-weight: 500;
|
||||
margin-bottom: var(--space-1);
|
||||
}
|
||||
|
||||
.setting-description {
|
||||
font-size: 12px;
|
||||
color: var(--text-muted);
|
||||
margin-bottom: var(--space-2);
|
||||
}
|
||||
```
|
||||
|
||||
**Sidebar Navigation**
|
||||
```css
|
||||
.settings-nav-item {
|
||||
padding: var(--space-2) var(--space-3);
|
||||
border-radius: var(--border-radius-xs);
|
||||
cursor: pointer;
|
||||
transition: background 0.2s ease;
|
||||
}
|
||||
|
||||
.settings-nav-item:hover {
|
||||
background: rgba(255, 255, 255, 0.05);
|
||||
}
|
||||
|
||||
.settings-nav-item.active {
|
||||
background: var(--lora-accent);
|
||||
color: white;
|
||||
}
|
||||
```
|
||||
|
||||
#### Files to Modify
|
||||
|
||||
1. **static/css/components/modal/settings-modal.css**
|
||||
- [ ] 新增两栏布局样式
|
||||
- [ ] 新增侧边栏导航样式
|
||||
- [ ] 新增Section标题样式
|
||||
- [ ] 调整设置项卡片样式
|
||||
- [ ] 移除折叠相关的CSS
|
||||
|
||||
2. **templates/components/modals/settings_modal.html**
|
||||
- [ ] 重构为两栏HTML结构
|
||||
- [ ] 添加4个导航项
|
||||
- [ ] 将Section改为独立内容区域
|
||||
- [ ] 移除折叠按钮HTML
|
||||
|
||||
3. **static/js/managers/SettingsManager.js**
|
||||
- [ ] 添加导航点击切换逻辑
|
||||
- [ ] 添加Section显示/隐藏控制
|
||||
- [ ] 移除折叠/展开相关代码
|
||||
- [ ] 默认显示第一个Section
|
||||
|
||||
---
|
||||
|
||||
### Phase 1: 搜索功能 (P1)
|
||||
**Status**: Planned
|
||||
**Priority**: Medium
|
||||
|
||||
#### Goals
|
||||
- 快速定位特定设置项
|
||||
- 支持关键词搜索设置标签和描述
|
||||
|
||||
#### Implementation
|
||||
- 搜索框保持在顶部右侧
|
||||
- 实时过滤：显示匹配的Section和设置项
|
||||
- 高亮匹配的关键词
|
||||
- 无结果时显示友好提示
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: 操作按钮优化 (P2)
|
||||
**Status**: Planned
|
||||
**Priority**: Low
|
||||
|
||||
#### Goals
|
||||
- 增强功能完整性
|
||||
- 提供批量操作能力
|
||||
|
||||
#### Implementation
|
||||
- 底部固定操作栏（position: sticky）
|
||||
- [Cancel] 和 [Save Changes] 按钮
|
||||
- 可选：重置为默认、导出配置、导入配置
|
||||
|
||||
---
|
||||
|
||||
## Migration Notes
|
||||
|
||||
### Removed Features
|
||||
| Feature | Reason |
|
||||
|---------|--------|
|
||||
| Section折叠/展开 | 单Section独占显示后不再需要 |
|
||||
| 滚动监听高亮 | 改为点击切换，无需监听滚动 |
|
||||
| 长页面平滑滚动 | 内容不再超长，无需滚动 |
|
||||
| "SETTINGS" label | 冗余信息，移除以简化UI |
|
||||
|
||||
### Preserved Features
|
||||
- 所有设置项功能和逻辑
|
||||
- 表单验证
|
||||
- 设置项描述和提示
|
||||
- 原有的CSS变量系统
|
||||
|
||||
---
|
||||
|
||||
## Success Criteria
|
||||
|
||||
### Phase 0
|
||||
- [ ] Modal显示为两栏布局
|
||||
- [ ] 左侧显示4个Section导航
|
||||
- [ ] 点击导航切换右侧显示的Section
|
||||
- [ ] 当前选中导航项高亮显示
|
||||
- [ ] Section标题有accent色下划线
|
||||
- [ ] 设置项以卡片形式分组展示
|
||||
- [ ] 移除所有折叠/展开功能
|
||||
- [ ] 移动端响应式正常（单栏堆叠）
|
||||
- [ ] 所有现有设置功能正常工作
|
||||
- [ ] 设计风格与原有UI一致
|
||||
|
||||
### Phase 1
|
||||
- [ ] 搜索框可输入关键词
|
||||
- [ ] 实时过滤显示匹配项
|
||||
- [ ] 高亮匹配的关键词
|
||||
|
||||
### Phase 2
|
||||
- [ ] 底部有固定操作按钮栏
|
||||
- [ ] Cancel和Save Changes按钮工作正常
|
||||
|
||||
---
|
||||
|
||||
## Timeline
|
||||
|
||||
| Phase | Estimated Time | Status |
|
||||
|-------|---------------|--------|
|
||||
| P0 | 3-4 hours | Ready for Development |
|
||||
| P1 | 2-3 hours | Planned |
|
||||
| P2 | 1-2 hours | Planned |
|
||||
|
||||
---
|
||||
|
||||
## Reference
|
||||
|
||||
### Design Inspiration
|
||||
- **macOS System Settings**: 左侧导航 + 右侧单Section独占
|
||||
- **VS Code Settings**: 清晰的视觉层次和搜索体验
|
||||
- **Linear**: 简洁的两栏布局设计
|
||||
|
||||
### CSS Variables Reference
|
||||
```css
|
||||
/* Colors */
|
||||
--lora-accent: #007AFF;
|
||||
--lora-border: rgba(255, 255, 255, 0.1);
|
||||
--card-bg: rgba(255, 255, 255, 0.05);
|
||||
--text-color: #ffffff;
|
||||
--text-muted: rgba(255, 255, 255, 0.6);
|
||||
|
||||
/* Spacing */
|
||||
--space-1: 8px;
|
||||
--space-2: 12px;
|
||||
--space-3: 16px;
|
||||
--space-4: 24px;
|
||||
|
||||
/* Border Radius */
|
||||
--border-radius-xs: 4px;
|
||||
--border-radius-sm: 8px;
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**Last Updated**: 2025-02-24
|
||||
**Author**: AI Assistant
|
||||
**Status**: Ready for Implementation
|
||||
@@ -1,191 +0,0 @@
|
||||
# Settings Modal Optimization Progress
|
||||
|
||||
**Project**: Settings Modal UI/UX Optimization
|
||||
**Status**: Phase 0 - Ready for Development
|
||||
**Last Updated**: 2025-02-24
|
||||
|
||||
---
|
||||
|
||||
## Phase 0: macOS Settings模式重构
|
||||
|
||||
### Overview
|
||||
重构Settings Modal为macOS Settings模式：左侧Section导航 + 右侧单Section独占显示。移除冗余元素，优化视觉层次。
|
||||
|
||||
### Tasks
|
||||
|
||||
#### 1. CSS Updates ✅
|
||||
**File**: `static/css/components/modal/settings-modal.css`
|
||||
|
||||
- [x] **Layout Styles**
|
||||
- [x] Modal固定尺寸 800x600px
|
||||
- [x] 左侧 sidebar 固定宽度 200px
|
||||
- [x] 右侧 content flex: 1 自动填充
|
||||
|
||||
- [x] **Navigation Styles**
|
||||
- [x] `.settings-nav` 容器样式
|
||||
- [x] `.settings-nav-item` 基础样式（更大字体，更醒目的active状态）
|
||||
- [x] `.settings-nav-item.active` 高亮样式（accent背景）
|
||||
- [x] `.settings-nav-item:hover` 悬停效果
|
||||
- [x] 隐藏 "SETTINGS" label
|
||||
- [x] 隐藏 group titles
|
||||
|
||||
- [x] **Content Area Styles**
|
||||
- [x] `.settings-section` 默认隐藏（仅当前显示）
|
||||
- [x] `.settings-section.active` 显示状态
|
||||
- [x] `.settings-section-header` 标题样式（20px + accent下划线）
|
||||
- [x] 添加 fadeIn 动画效果
|
||||
|
||||
- [x] **Cleanup**
|
||||
- [x] 移除折叠相关样式
|
||||
- [x] 移除 `.settings-section-toggle` 按钮样式
|
||||
- [x] 移除展开/折叠动画样式
|
||||
|
||||
**Status**: ✅ Completed
|
||||
|
||||
---
|
||||
|
||||
#### 2. HTML Structure Update ✅
|
||||
**File**: `templates/components/modals/settings_modal.html`
|
||||
|
||||
- [x] **Navigation Items**
|
||||
- [x] General (通用)
|
||||
- [x] Interface (界面)
|
||||
- [x] Download (下载)
|
||||
- [x] Advanced (高级)
|
||||
- [x] 移除 "SETTINGS" label
|
||||
- [x] 移除 group titles
|
||||
|
||||
- [x] **Content Sections**
|
||||
- [x] 重组为4个Section (general/interface/download/advanced)
|
||||
- [x] 每个section添加 `data-section` 属性
|
||||
- [x] 添加Section标题（带accent下划线）
|
||||
- [x] 移除所有折叠按钮（chevron图标）
|
||||
- [x] 平铺显示所有设置项
|
||||
|
||||
**Status**: ✅ Completed
|
||||
|
||||
---
|
||||
|
||||
#### 3. JavaScript Logic Update ✅
|
||||
**File**: `static/js/managers/SettingsManager.js`
|
||||
|
||||
- [x] **Navigation Logic**
|
||||
- [x] `initializeNavigation()` 改为Section切换模式
|
||||
- [x] 点击导航项显示对应Section
|
||||
- [x] 更新导航高亮状态
|
||||
- [x] 默认显示第一个Section
|
||||
|
||||
- [x] **Remove Legacy Code**
|
||||
- [x] 移除 `initializeSectionCollapse()` 方法
|
||||
- [x] 移除滚动监听相关代码
|
||||
- [x] 移除 `localStorage` 折叠状态存储
|
||||
|
||||
- [x] **Search Function**
|
||||
- [x] 更新搜索功能以适配新显示模式
|
||||
- [x] 搜索时自动切换到匹配的Section
|
||||
- [x] 高亮匹配的关键词
|
||||
|
||||
**Status**: ✅ Completed
|
||||
|
||||
---
|
||||
|
||||
### Testing Checklist
|
||||
|
||||
#### Visual Testing
|
||||
- [ ] 两栏布局正确显示
|
||||
- [ ] 左侧导航4个Section正确显示
|
||||
- [ ] 点击导航切换右侧内容
|
||||
- [ ] 当前导航项高亮显示（accent背景）
|
||||
- [ ] Section标题有accent色下划线
|
||||
- [ ] 设置项以卡片形式分组
|
||||
- [ ] 无"SETTINGS" label
|
||||
- [ ] 无折叠/展开按钮
|
||||
|
||||
#### Functional Testing
|
||||
- [ ] 所有设置项可正常编辑
|
||||
- [ ] 设置保存功能正常
|
||||
- [ ] 设置加载功能正常
|
||||
- [ ] 表单验证正常工作
|
||||
- [ ] 帮助提示（tooltip）正常显示
|
||||
|
||||
#### Responsive Testing
|
||||
- [ ] 桌面端（>768px）两栏布局
|
||||
- [ ] 移动端（<768px）单栏堆叠
|
||||
- [ ] 移动端导航可正常切换
|
||||
|
||||
#### Cross-Browser Testing
|
||||
- [ ] Chrome/Edge
|
||||
- [ ] Firefox
|
||||
- [ ] Safari（如适用）
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: 搜索功能
|
||||
|
||||
### Tasks
|
||||
- [ ] 搜索框UI更新
|
||||
- [ ] 搜索逻辑实现
|
||||
- [ ] 实时过滤显示
|
||||
- [ ] 关键词高亮
|
||||
|
||||
**Estimated Time**: 2-3 hours
|
||||
**Status**: 📋 Planned
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: 操作按钮优化
|
||||
|
||||
### Tasks
|
||||
- [ ] 底部操作栏样式
|
||||
- [ ] 固定定位（sticky）
|
||||
- [ ] Cancel/Save按钮功能
|
||||
- [ ] 可选：Reset/Export/Import
|
||||
|
||||
**Estimated Time**: 1-2 hours
|
||||
**Status**: 📋 Planned
|
||||
|
||||
---
|
||||
|
||||
## Progress Summary
|
||||
|
||||
| Phase | Progress | Status |
|
||||
|-------|----------|--------|
|
||||
| Phase 0 | 100% | ✅ Completed |
|
||||
| Phase 1 | 0% | 📋 Planned |
|
||||
| Phase 2 | 0% | 📋 Planned |
|
||||
|
||||
**Overall Progress**: 100% (Phase 0)
|
||||
|
||||
---
|
||||
|
||||
## Development Log
|
||||
|
||||
### 2025-02-24
|
||||
- ✅ 创建优化提案文档（macOS Settings模式）
|
||||
- ✅ 创建进度追踪文档
|
||||
- ✅ Phase 0 开发完成
|
||||
- ✅ CSS重构完成：新增macOS Settings样式，移除折叠相关样式
|
||||
- ✅ HTML重构完成：重组为4个Section，移除所有折叠按钮
|
||||
- ✅ JavaScript重构完成：实现Section切换逻辑，更新搜索功能
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
### Design Decisions
|
||||
- 采用macOS Settings模式而非长页面滚动模式
|
||||
- 左侧仅显示4个Section，不显示具体设置项
|
||||
- 移除折叠/展开功能，简化交互
|
||||
- Section标题使用accent色下划线强调
|
||||
|
||||
### Technical Notes
|
||||
- 优先使用现有CSS变量
|
||||
- 保持向后兼容，不破坏现有设置存储逻辑
|
||||
- 移动端响应式：小屏幕单栏堆叠
|
||||
|
||||
### Blockers
|
||||
None
|
||||
|
||||
---
|
||||
|
||||
**Next Action**: Start Phase 0 - CSS Updates
|
||||
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@@ -1,17 +0,0 @@
|
||||
{
|
||||
"name": "comfyui-lora-manager-frontend",
|
||||
"version": "0.1.0",
|
||||
"private": true,
|
||||
"type": "module",
|
||||
"scripts": {
|
||||
"test": "npm run test:js && npm run test:vue",
|
||||
"test:js": "vitest run",
|
||||
"test:vue": "cd vue-widgets && npx vitest run",
|
||||
"test:watch": "vitest",
|
||||
"test:coverage": "node scripts/run_frontend_coverage.js"
|
||||
},
|
||||
"devDependencies": {
|
||||
"jsdom": "^24.0.0",
|
||||
"vitest": "^1.6.0"
|
||||
}
|
||||
}
|
||||
@@ -1,12 +0,0 @@
|
||||
"""Project namespace package."""
|
||||
|
||||
# pytest's internal compatibility layer still imports ``py.path.local`` from the
|
||||
# historical ``py`` dependency. Because this project reuses the ``py`` package
|
||||
# name, we expose a minimal shim so ``py.path.local`` resolves to ``pathlib.Path``
|
||||
# during test runs without pulling in the external dependency.
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
|
||||
path = SimpleNamespace(local=Path)
|
||||
|
||||
__all__ = ["path"]
|
||||
|
||||
+162
-1608
File diff suppressed because it is too large
Load Diff
+138
-388
@@ -2,128 +2,36 @@ import asyncio
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
from .utils.logging_config import setup_logging
|
||||
|
||||
# Check if we're in standalone mode
|
||||
standalone_mode = (
|
||||
os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
|
||||
or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
|
||||
)
|
||||
|
||||
# Only setup logging prefix if not in standalone mode
|
||||
if not standalone_mode:
|
||||
setup_logging()
|
||||
|
||||
from server import PromptServer # pyright: ignore[reportMissingImports]
|
||||
from pathlib import Path
|
||||
from server import PromptServer # type: ignore
|
||||
|
||||
from .config import config
|
||||
from .services.model_service_factory import (
|
||||
ModelServiceFactory,
|
||||
register_default_model_types,
|
||||
)
|
||||
from .services.model_service_factory import ModelServiceFactory, register_default_model_types
|
||||
from .routes.recipe_routes import RecipeRoutes
|
||||
from .routes.stats_routes import StatsRoutes
|
||||
from .routes.update_routes import UpdateRoutes
|
||||
from .routes.misc_routes import MiscRoutes
|
||||
from .routes.pending_delete_routes import PendingDeleteRoutes
|
||||
from .routes.preview_routes import PreviewRoutes
|
||||
from .routes.example_images_routes import ExampleImagesRoutes
|
||||
from .services.service_registry import ServiceRegistry
|
||||
from .services.settings_manager import get_settings_manager
|
||||
from .services.pending_delete_service import get_pending_delete_service
|
||||
from .services.settings_manager import settings
|
||||
from .utils.example_images_migration import ExampleImagesMigration
|
||||
from .services.websocket_manager import ws_manager
|
||||
from .services.example_images_cleanup_service import ExampleImagesCleanupService
|
||||
from .middleware.csp_middleware import relax_csp_for_remote_media
|
||||
from .middleware.error_middleware import api_json_error
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
HEADER_SIZE_LIMIT = 16384
|
||||
|
||||
|
||||
def _sanitize_size_limit(value):
|
||||
"""Return a non-negative integer size for ``handler_args`` comparisons."""
|
||||
|
||||
try:
|
||||
coerced = int(value)
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
return coerced if coerced >= 0 else 0
|
||||
|
||||
|
||||
class _SettingsProxy:
|
||||
def __init__(self):
|
||||
self._manager = None
|
||||
|
||||
def _resolve(self):
|
||||
if self._manager is None:
|
||||
self._manager = get_settings_manager()
|
||||
return self._manager
|
||||
|
||||
def get(self, *args, **kwargs):
|
||||
return self._resolve().get(*args, **kwargs)
|
||||
|
||||
def __getattr__(self, item):
|
||||
return getattr(self._resolve(), item)
|
||||
|
||||
|
||||
settings = _SettingsProxy()
|
||||
|
||||
# Check if we're in standalone mode
|
||||
STANDALONE_MODE = 'nodes' not in sys.modules
|
||||
|
||||
class LoraManager:
|
||||
"""Main entry point for LoRA Manager plugin"""
|
||||
|
||||
|
||||
@classmethod
|
||||
def add_routes(cls):
|
||||
"""Initialize and register all routes using the new refactored architecture"""
|
||||
app = PromptServer.instance.app
|
||||
|
||||
# Register JSON error middleware for /api/* routes as the outermost
|
||||
# middleware so it catches errors from all other middlewares.
|
||||
if api_json_error not in app.middlewares:
|
||||
app.middlewares.insert(0, api_json_error)
|
||||
|
||||
if relax_csp_for_remote_media not in app.middlewares:
|
||||
# Ensure CSP relaxer executes after ComfyUI's block_external_middleware so it can
|
||||
# see and extend the restrictive header instead of being overwritten by it.
|
||||
block_middleware_index = next(
|
||||
(
|
||||
idx
|
||||
for idx, middleware in enumerate(app.middlewares)
|
||||
if getattr(middleware, "__name__", "")
|
||||
== "block_external_middleware"
|
||||
),
|
||||
None,
|
||||
)
|
||||
|
||||
if block_middleware_index is None:
|
||||
app.middlewares.append(relax_csp_for_remote_media)
|
||||
else:
|
||||
app.middlewares.insert(
|
||||
block_middleware_index, relax_csp_for_remote_media
|
||||
)
|
||||
|
||||
# Increase allowed header sizes so browsers with large localhost cookie
|
||||
# jars (multiple UIs on 127.0.0.1) don't trip aiohttp's 8KB default
|
||||
# limits. Cookies for unrelated apps are still sent to the plugin and
|
||||
# may otherwise raise LineTooLong errors when the request parser reads
|
||||
# them. Preserve any previously configured handler arguments while
|
||||
# ensuring our minimum sizes are applied.
|
||||
handler_args = getattr(app, "_handler_args", {}) or {}
|
||||
updated_handler_args = dict(handler_args)
|
||||
updated_handler_args["max_field_size"] = max(
|
||||
_sanitize_size_limit(handler_args.get("max_field_size", 0)),
|
||||
HEADER_SIZE_LIMIT,
|
||||
)
|
||||
updated_handler_args["max_line_size"] = max(
|
||||
_sanitize_size_limit(handler_args.get("max_line_size", 0)),
|
||||
HEADER_SIZE_LIMIT,
|
||||
)
|
||||
app._handler_args = updated_handler_args
|
||||
|
||||
# Configure aiohttp access logger to be less verbose
|
||||
logging.getLogger("aiohttp.access").setLevel(logging.WARNING)
|
||||
logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
|
||||
|
||||
# Add specific suppression for connection reset errors
|
||||
class ConnectionResetFilter(logging.Filter):
|
||||
@@ -141,54 +49,133 @@ class LoraManager:
|
||||
asyncio_logger = logging.getLogger("asyncio")
|
||||
asyncio_logger.addFilter(ConnectionResetFilter())
|
||||
|
||||
added_targets = set() # Track already added target paths
|
||||
|
||||
# Add static route for example images if the path exists in settings
|
||||
example_images_path = settings.get("example_images_path")
|
||||
example_images_path = settings.get('example_images_path')
|
||||
logger.info(f"Example images path: {example_images_path}")
|
||||
if example_images_path and os.path.exists(example_images_path):
|
||||
app.router.add_static("/example_images_static", example_images_path)
|
||||
logger.info(
|
||||
f"Added static route for example images: /example_images_static -> {example_images_path}"
|
||||
)
|
||||
|
||||
# Add static route for locales JSON files
|
||||
if os.path.exists(config.i18n_path):
|
||||
app.router.add_static("/locales", config.i18n_path)
|
||||
logger.info(
|
||||
f"Added static route for locales: /locales -> {config.i18n_path}"
|
||||
)
|
||||
|
||||
app.router.add_static('/example_images_static', example_images_path)
|
||||
logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
|
||||
|
||||
# Add static routes for each lora root
|
||||
for idx, root in enumerate(config.loras_roots, start=1):
|
||||
preview_path = f'/loras_static/root{idx}/preview'
|
||||
|
||||
real_root = root
|
||||
if root in config._path_mappings.values():
|
||||
for target, link in config._path_mappings.items():
|
||||
if link == root:
|
||||
real_root = target
|
||||
break
|
||||
# Add static route for original path
|
||||
app.router.add_static(preview_path, real_root)
|
||||
logger.info(f"Added static route {preview_path} -> {real_root}")
|
||||
|
||||
# Record route mapping
|
||||
config.add_route_mapping(real_root, preview_path)
|
||||
added_targets.add(real_root)
|
||||
|
||||
# Add static routes for each checkpoint root
|
||||
for idx, root in enumerate(config.base_models_roots, start=1):
|
||||
preview_path = f'/checkpoints_static/root{idx}/preview'
|
||||
|
||||
real_root = root
|
||||
if root in config._path_mappings.values():
|
||||
for target, link in config._path_mappings.items():
|
||||
if link == root:
|
||||
real_root = target
|
||||
break
|
||||
# Add static route for original path
|
||||
app.router.add_static(preview_path, real_root)
|
||||
logger.info(f"Added static route {preview_path} -> {real_root}")
|
||||
|
||||
# Record route mapping
|
||||
config.add_route_mapping(real_root, preview_path)
|
||||
added_targets.add(real_root)
|
||||
|
||||
# Add static routes for each embedding root
|
||||
for idx, root in enumerate(config.embeddings_roots, start=1):
|
||||
preview_path = f'/embeddings_static/root{idx}/preview'
|
||||
|
||||
real_root = root
|
||||
if root in config._path_mappings.values():
|
||||
for target, link in config._path_mappings.items():
|
||||
if link == root:
|
||||
real_root = target
|
||||
break
|
||||
# Add static route for original path
|
||||
app.router.add_static(preview_path, real_root)
|
||||
logger.info(f"Added static route {preview_path} -> {real_root}")
|
||||
|
||||
# Record route mapping
|
||||
config.add_route_mapping(real_root, preview_path)
|
||||
added_targets.add(real_root)
|
||||
|
||||
# Add static routes for symlink target paths
|
||||
link_idx = {
|
||||
'lora': 1,
|
||||
'checkpoint': 1,
|
||||
'embedding': 1
|
||||
}
|
||||
|
||||
for target_path, link_path in config._path_mappings.items():
|
||||
if target_path not in added_targets:
|
||||
# Determine if this is a checkpoint, lora, or embedding link based on path
|
||||
is_checkpoint = any(cp_root in link_path for cp_root in config.base_models_roots)
|
||||
is_checkpoint = is_checkpoint or any(cp_root in target_path for cp_root in config.base_models_roots)
|
||||
is_embedding = any(emb_root in link_path for emb_root in config.embeddings_roots)
|
||||
is_embedding = is_embedding or any(emb_root in target_path for emb_root in config.embeddings_roots)
|
||||
|
||||
if is_checkpoint:
|
||||
route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
|
||||
link_idx["checkpoint"] += 1
|
||||
elif is_embedding:
|
||||
route_path = f'/embeddings_static/link_{link_idx["embedding"]}/preview'
|
||||
link_idx["embedding"] += 1
|
||||
else:
|
||||
route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
|
||||
link_idx["lora"] += 1
|
||||
|
||||
try:
|
||||
app.router.add_static(route_path, Path(target_path).resolve(strict=False))
|
||||
logger.info(f"Added static route for link target {route_path} -> {target_path}")
|
||||
config.add_route_mapping(target_path, route_path)
|
||||
added_targets.add(target_path)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
|
||||
continue
|
||||
|
||||
# Add static route for plugin assets
|
||||
app.router.add_static("/loras_static", config.static_path)
|
||||
|
||||
app.router.add_static('/loras_static', config.static_path)
|
||||
|
||||
# Register default model types with the factory
|
||||
register_default_model_types()
|
||||
|
||||
|
||||
# Setup all model routes using the factory
|
||||
ModelServiceFactory.setup_all_routes(app)
|
||||
|
||||
|
||||
# Setup non-model-specific routes
|
||||
stats_routes = StatsRoutes()
|
||||
stats_routes.setup_routes(app)
|
||||
RecipeRoutes.setup_routes(app)
|
||||
UpdateRoutes.setup_routes(app)
|
||||
UpdateRoutes.setup_routes(app)
|
||||
MiscRoutes.setup_routes(app)
|
||||
PendingDeleteRoutes.setup_routes(app)
|
||||
ExampleImagesRoutes.setup_routes(app, ws_manager=ws_manager)
|
||||
PreviewRoutes.setup_routes(app)
|
||||
|
||||
ExampleImagesRoutes.setup_routes(app)
|
||||
|
||||
# Setup WebSocket routes that are shared across all model types
|
||||
app.router.add_get("/ws/fetch-progress", ws_manager.handle_connection)
|
||||
app.router.add_get(
|
||||
"/ws/download-progress", ws_manager.handle_download_connection
|
||||
)
|
||||
app.router.add_get("/ws/init-progress", ws_manager.handle_init_connection)
|
||||
|
||||
# Schedule service initialization
|
||||
app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
|
||||
app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)
|
||||
app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)
|
||||
|
||||
# Schedule service initialization
|
||||
app.on_startup.append(lambda app: cls._initialize_services())
|
||||
|
||||
|
||||
# Add cleanup
|
||||
app.on_shutdown.append(cls._cleanup)
|
||||
|
||||
|
||||
logger.info(f"LoRA Manager: Set up routes for {len(ModelServiceFactory.get_registered_types())} model types: {', '.join(ModelServiceFactory.get_registered_types())}")
|
||||
|
||||
@classmethod
|
||||
async def _initialize_services(cls):
|
||||
"""Initialize all services using the ServiceRegistry"""
|
||||
@@ -198,279 +185,42 @@ class LoraManager:
|
||||
|
||||
# Register DownloadManager with ServiceRegistry
|
||||
await ServiceRegistry.get_download_manager()
|
||||
|
||||
# Initialize DownloadQueueService for persistent queue/history
|
||||
await ServiceRegistry.get_download_queue_service()
|
||||
|
||||
await ServiceRegistry.get_backup_service()
|
||||
|
||||
from .services.metadata_service import initialize_metadata_providers
|
||||
|
||||
await initialize_metadata_providers()
|
||||
|
||||
|
||||
# Initialize WebSocket manager
|
||||
await ServiceRegistry.get_websocket_manager()
|
||||
|
||||
# Preload LLM model catalog (background task, non-blocking)
|
||||
from .services.llm_service import LLMService
|
||||
await LLMService.get_instance()
|
||||
|
||||
|
||||
# Initialize scanners in background
|
||||
lora_scanner = await ServiceRegistry.get_lora_scanner()
|
||||
checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
|
||||
embedding_scanner = await ServiceRegistry.get_embedding_scanner()
|
||||
other_scanner = await ServiceRegistry.get_other_scanner()
|
||||
|
||||
|
||||
# Initialize recipe scanner if needed
|
||||
recipe_scanner = await ServiceRegistry.get_recipe_scanner()
|
||||
|
||||
|
||||
# Create low-priority initialization tasks
|
||||
init_tasks = [
|
||||
asyncio.create_task(
|
||||
lora_scanner.initialize_in_background(), name="lora_cache_init"
|
||||
),
|
||||
asyncio.create_task(
|
||||
checkpoint_scanner.initialize_in_background(),
|
||||
name="checkpoint_cache_init",
|
||||
),
|
||||
asyncio.create_task(
|
||||
embedding_scanner.initialize_in_background(),
|
||||
name="embedding_cache_init",
|
||||
),
|
||||
asyncio.create_task(
|
||||
other_scanner.initialize_in_background(),
|
||||
name="other_cache_init",
|
||||
),
|
||||
asyncio.create_task(
|
||||
recipe_scanner.initialize_in_background(), name="recipe_cache_init"
|
||||
),
|
||||
]
|
||||
asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init')
|
||||
asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init')
|
||||
asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init')
|
||||
asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
|
||||
|
||||
await ExampleImagesMigration.check_and_run_migrations()
|
||||
|
||||
# Schedule post-initialization tasks to run after scanners complete
|
||||
asyncio.create_task(
|
||||
cls._run_post_initialization_tasks(init_tasks), name="post_init_tasks"
|
||||
)
|
||||
|
||||
# Startup sweep: purge pending-delete batches that expired during a
|
||||
# previous run. Non-blocking (fire-and-forget); purge_expired only
|
||||
# removes already-expired batches, so a staged undo that survived a
|
||||
# restart stays restorable. scan_roots=True runs the reconciliation
|
||||
# pass first so leftover batches (the in-process registry is empty
|
||||
# after a restart) are re-discovered on disk. Covers both plugin
|
||||
# and standalone modes (StandaloneLoraManager reuses this
|
||||
# classmethod).
|
||||
pending_delete_service = await get_pending_delete_service()
|
||||
asyncio.create_task(
|
||||
pending_delete_service.purge_expired(scan_roots=True),
|
||||
name="pending_delete_startup_sweep",
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
"LoRA Manager: All services initialized and background tasks scheduled"
|
||||
)
|
||||
|
||||
|
||||
logger.info("LoRA Manager: All services initialized and background tasks scheduled")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LoRA Manager: Error initializing services: {e}", exc_info=True
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def _run_post_initialization_tasks(cls, init_tasks):
|
||||
"""Run post-initialization tasks after all scanners complete"""
|
||||
try:
|
||||
logger.debug(
|
||||
"LoRA Manager: Waiting for scanner initialization to complete..."
|
||||
)
|
||||
|
||||
# Wait for all scanner initialization tasks to complete
|
||||
await asyncio.gather(*init_tasks, return_exceptions=True)
|
||||
|
||||
logger.debug(
|
||||
"LoRA Manager: Scanner initialization completed, starting post-initialization tasks..."
|
||||
)
|
||||
|
||||
# Run post-initialization tasks
|
||||
post_tasks = [
|
||||
asyncio.create_task(
|
||||
cls._cleanup_backup_files(), name="cleanup_bak_files"
|
||||
),
|
||||
# Add more post-initialization tasks here as needed
|
||||
# asyncio.create_task(cls._another_post_task(), name='another_task'),
|
||||
]
|
||||
|
||||
# Run all post-initialization tasks
|
||||
results = await asyncio.gather(*post_tasks, return_exceptions=True)
|
||||
|
||||
# Log results
|
||||
for i, result in enumerate(results):
|
||||
task_name = post_tasks[i].get_name()
|
||||
if isinstance(result, Exception):
|
||||
logger.error(
|
||||
f"Post-initialization task '{task_name}' failed: {result}"
|
||||
)
|
||||
else:
|
||||
logger.debug(
|
||||
f"Post-initialization task '{task_name}' completed successfully"
|
||||
)
|
||||
|
||||
logger.debug("LoRA Manager: All post-initialization tasks completed")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def _cleanup_backup_files(cls):
|
||||
"""Clean up .bak files in all model roots"""
|
||||
try:
|
||||
logger.debug("Starting cleanup of .bak files in model directories...")
|
||||
|
||||
# Collect all model roots
|
||||
all_roots = set()
|
||||
all_roots.update(config.loras_roots)
|
||||
all_roots.update(config.base_models_roots or [])
|
||||
all_roots.update(config.embeddings_roots or [])
|
||||
all_roots.update(config.other_roots or [])
|
||||
|
||||
total_deleted = 0
|
||||
total_size_freed = 0
|
||||
|
||||
for root_path in all_roots:
|
||||
if not os.path.exists(root_path):
|
||||
continue
|
||||
|
||||
try:
|
||||
(
|
||||
deleted_count,
|
||||
size_freed,
|
||||
) = await cls._cleanup_backup_files_in_directory(root_path)
|
||||
total_deleted += deleted_count
|
||||
total_size_freed += size_freed
|
||||
|
||||
if deleted_count > 0:
|
||||
logger.debug(
|
||||
f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024 * 1024):.2f} MB)"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error cleaning up .bak files in {root_path}: {e}")
|
||||
|
||||
# Yield control periodically
|
||||
await asyncio.sleep(0.01)
|
||||
|
||||
if total_deleted > 0:
|
||||
logger.debug(
|
||||
f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024 * 1024):.2f} MB total"
|
||||
)
|
||||
else:
|
||||
logger.debug("Backup cleanup completed: no .bak files found")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during backup file cleanup: {e}", exc_info=True)
|
||||
|
||||
@classmethod
|
||||
async def _cleanup_backup_files_in_directory(cls, directory_path: str):
|
||||
"""Clean up .bak files in a specific directory recursively
|
||||
|
||||
Args:
|
||||
directory_path: Path to the directory to clean
|
||||
|
||||
Returns:
|
||||
Tuple[int, int]: (number of files deleted, total size freed in bytes)
|
||||
"""
|
||||
deleted_count = 0
|
||||
size_freed = 0
|
||||
visited_paths = set()
|
||||
|
||||
def cleanup_recursive(path):
|
||||
nonlocal deleted_count, size_freed
|
||||
|
||||
try:
|
||||
real_path = os.path.realpath(path)
|
||||
if real_path in visited_paths:
|
||||
return
|
||||
visited_paths.add(real_path)
|
||||
|
||||
with os.scandir(path) as it:
|
||||
for entry in it:
|
||||
try:
|
||||
if entry.is_file(
|
||||
follow_symlinks=True
|
||||
) and entry.name.endswith(".bak"):
|
||||
file_size = entry.stat().st_size
|
||||
os.remove(entry.path)
|
||||
deleted_count += 1
|
||||
size_freed += file_size
|
||||
logger.debug(f"Deleted .bak file: {entry.path}")
|
||||
|
||||
elif entry.is_dir(follow_symlinks=True):
|
||||
cleanup_recursive(entry.path)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Could not delete .bak file {entry.path}: {e}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error scanning directory {path} for .bak files: {e}")
|
||||
|
||||
# Run the recursive cleanup in a thread pool to avoid blocking
|
||||
loop = asyncio.get_event_loop()
|
||||
await loop.run_in_executor(None, cleanup_recursive, directory_path)
|
||||
|
||||
return deleted_count, size_freed
|
||||
|
||||
@classmethod
|
||||
async def _cleanup_example_images_folders(cls):
|
||||
"""Invoke the example images cleanup service for manual execution."""
|
||||
try:
|
||||
service = ExampleImagesCleanupService()
|
||||
result = await service.cleanup_example_image_folders()
|
||||
|
||||
if result.get("success"):
|
||||
logger.debug(
|
||||
"Manual example images cleanup completed: moved=%s",
|
||||
result.get("moved_total"),
|
||||
)
|
||||
elif result.get("partial_success"):
|
||||
logger.warning(
|
||||
"Manual example images cleanup partially succeeded: moved=%s failures=%s",
|
||||
result.get("moved_total"),
|
||||
result.get("move_failures"),
|
||||
)
|
||||
else:
|
||||
logger.debug(
|
||||
"Manual example images cleanup skipped or failed: %s",
|
||||
result.get("error", "no changes"),
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e: # pragma: no cover - defensive guard
|
||||
logger.error(f"Error during example images cleanup: {e}", exc_info=True)
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"error_code": "unexpected_error",
|
||||
}
|
||||
|
||||
logger.error(f"LoRA Manager: Error initializing services: {e}", exc_info=True)
|
||||
|
||||
@classmethod
|
||||
async def _cleanup(cls, app):
|
||||
"""Cleanup resources using ServiceRegistry"""
|
||||
try:
|
||||
logger.info("LoRA Manager: Cleaning up services")
|
||||
|
||||
# Cancel any in-flight scanner initialization tasks so thread-pool
|
||||
# workers (e.g. _initialize_cache_sync) can break out of their loops
|
||||
# when the server shuts down (e.g. Ctrl+C on WSL).
|
||||
for name in ("lora_scanner", "checkpoint_scanner", "embedding_scanner", "other_scanner"):
|
||||
scanner = ServiceRegistry.get_service_sync(name)
|
||||
if scanner is not None and hasattr(scanner, "cancel_task"):
|
||||
scanner.cancel_task()
|
||||
logger.debug("LoRA Manager: Cancelled %s", name)
|
||||
|
||||
|
||||
# Close CivitaiClient gracefully
|
||||
civitai_client = await ServiceRegistry.get_service("civitai_client")
|
||||
if civitai_client:
|
||||
await civitai_client.close()
|
||||
logger.info("Closed CivitaiClient connection")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during cleanup: {e}", exc_info=True)
|
||||
|
||||
@@ -1,13 +1,9 @@
|
||||
import os
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
import importlib
|
||||
import sys
|
||||
|
||||
# Check if running in standalone mode
|
||||
standalone_mode = (
|
||||
os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
|
||||
or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
|
||||
)
|
||||
standalone_mode = 'nodes' not in sys.modules
|
||||
|
||||
if not standalone_mode:
|
||||
from .metadata_hook import MetadataHook
|
||||
@@ -16,21 +12,21 @@ if not standalone_mode:
|
||||
def init():
|
||||
# Install hooks to collect metadata during execution
|
||||
MetadataHook.install()
|
||||
|
||||
|
||||
# Initialize registry
|
||||
registry = MetadataRegistry()
|
||||
|
||||
logger.info("ComfyUI Metadata Collector initialized")
|
||||
|
||||
def get_metadata(prompt_id=None): # pyright: ignore[reportRedeclaration]
|
||||
|
||||
print("ComfyUI Metadata Collector initialized")
|
||||
|
||||
def get_metadata(prompt_id=None):
|
||||
"""Helper function to get metadata from the registry"""
|
||||
registry = MetadataRegistry()
|
||||
return registry.get_metadata(prompt_id)
|
||||
else:
|
||||
# Standalone mode - provide dummy implementations
|
||||
def init():
|
||||
logger.info("ComfyUI Metadata Collector disabled in standalone mode")
|
||||
|
||||
def get_metadata(prompt_id=None): # pyright: ignore[reportRedeclaration]
|
||||
print("ComfyUI Metadata Collector disabled in standalone mode")
|
||||
|
||||
def get_metadata(prompt_id=None):
|
||||
"""Dummy implementation for standalone mode"""
|
||||
return {}
|
||||
|
||||
@@ -1,28 +1,13 @@
|
||||
"""Constants used by the metadata collector"""
|
||||
|
||||
# Sentinel value for clip_skip to distinguish "unconnected / widget default"
|
||||
# from "user wired value 0". Both ComfyUI CLIPSetLastLayer (-24..-1) and
|
||||
# A1111 conventions treat 0 as meaningless for clip skipping, but users may
|
||||
# explicitly wire 0 to the overwrite node to express "no clip skip / default".
|
||||
CLIP_SKIP_SENTINEL = -25
|
||||
|
||||
# Metadata categories
|
||||
MODELS = "models"
|
||||
PROMPTS = "prompts"
|
||||
SAMPLING = "sampling"
|
||||
LORAS = "loras"
|
||||
EMBEDDINGS = "embeddings"
|
||||
SIZE = "size"
|
||||
IMAGES = "images"
|
||||
IS_SAMPLER = "is_sampler" # New constant to mark sampler nodes
|
||||
OVERWRITE = "overwrite" # Manual metadata overwrite from MetadataOverwriteLM node
|
||||
|
||||
# Field names that the MetadataOverwriteLM node and its extractor share
|
||||
METADATA_OVERWRITE_FIELDS = (
|
||||
"prompt", "negative_prompt", "seed", "steps", "cfg_scale",
|
||||
"sampler", "scheduler", "model", "loras", "size",
|
||||
"clip_skip", "additional_data",
|
||||
)
|
||||
|
||||
# Complete list of categories to track
|
||||
METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES, OVERWRITE]
|
||||
METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]
|
||||
|
||||
@@ -1,10 +1,7 @@
|
||||
import sys
|
||||
import inspect
|
||||
import logging
|
||||
from .metadata_registry import MetadataRegistry
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class MetadataHook:
|
||||
"""Install hooks for metadata collection"""
|
||||
|
||||
@@ -16,7 +13,7 @@ class MetadataHook:
|
||||
execution = None
|
||||
try:
|
||||
# Try direct import first
|
||||
import execution # pyright: ignore[reportMissingImports]
|
||||
import execution # type: ignore
|
||||
except ImportError:
|
||||
# Try to locate from system modules
|
||||
for module_name in sys.modules:
|
||||
@@ -26,7 +23,7 @@ class MetadataHook:
|
||||
|
||||
# If we can't find the execution module, we can't install hooks
|
||||
if execution is None:
|
||||
logger.warning("Could not locate ComfyUI execution module, metadata collection disabled")
|
||||
print("Could not locate ComfyUI execution module, metadata collection disabled")
|
||||
return
|
||||
|
||||
# Detect whether we're using the new async version of ComfyUI
|
||||
@@ -40,16 +37,16 @@ class MetadataHook:
|
||||
is_async = inspect.iscoroutinefunction(execution._map_node_over_list)
|
||||
|
||||
if is_async:
|
||||
logger.info("Detected async ComfyUI execution, installing async metadata hooks")
|
||||
print("Detected async ComfyUI execution, installing async metadata hooks")
|
||||
MetadataHook._install_async_hooks(execution, map_node_func_name)
|
||||
else:
|
||||
logger.info("Detected sync ComfyUI execution, installing sync metadata hooks")
|
||||
print("Detected sync ComfyUI execution, installing sync metadata hooks")
|
||||
MetadataHook._install_sync_hooks(execution)
|
||||
|
||||
logger.info("Metadata collection hooks installed for runtime values")
|
||||
print("Metadata collection hooks installed for runtime values")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error installing metadata hooks: {str(e)}")
|
||||
print(f"Error installing metadata hooks: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def _install_sync_hooks(execution):
|
||||
@@ -83,10 +80,9 @@ class MetadataHook:
|
||||
|
||||
# Record inputs before execution
|
||||
if node_id is not None:
|
||||
return_types = getattr(obj, 'RETURN_TYPES', None)
|
||||
registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
|
||||
registry.record_node_execution(node_id, class_type, input_data_all, None)
|
||||
except Exception as e:
|
||||
logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
|
||||
print(f"Error collecting metadata (pre-execution): {str(e)}")
|
||||
|
||||
# Execute the original function
|
||||
results = original_map_node_over_list(obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
|
||||
@@ -115,10 +111,9 @@ class MetadataHook:
|
||||
|
||||
# Record outputs after execution
|
||||
if node_id is not None:
|
||||
return_types = getattr(obj, 'RETURN_TYPES', None)
|
||||
registry.update_node_execution(node_id, class_type, results, return_types=return_types)
|
||||
registry.update_node_execution(node_id, class_type, results)
|
||||
except Exception as e:
|
||||
logger.error(f"Error collecting metadata (post-execution): {str(e)}")
|
||||
print(f"Error collecting metadata (post-execution): {str(e)}")
|
||||
|
||||
return results
|
||||
|
||||
@@ -137,13 +132,10 @@ class MetadataHook:
|
||||
# Store the dynprompt reference for node lookups
|
||||
if hasattr(prompt, 'original_prompt'):
|
||||
registry.set_current_prompt(prompt)
|
||||
|
||||
# Store extra_data for accessing full workflow node properties
|
||||
registry.set_extra_data(extra_data)
|
||||
|
||||
|
||||
# Execute the original function
|
||||
return original_execute(*args, **kwargs)
|
||||
|
||||
|
||||
# Replace the functions
|
||||
execution._map_node_over_list = map_node_over_list_with_metadata
|
||||
execution.execute = execute_with_prompt_tracking
|
||||
@@ -153,68 +145,72 @@ class MetadataHook:
|
||||
"""Install hooks for asynchronous execution model"""
|
||||
# Store the original _async_map_node_over_list function
|
||||
original_map_node_over_list = getattr(execution, map_node_func_name)
|
||||
|
||||
# Wrapped async function - signature must exactly match _async_map_node_over_list
|
||||
async def async_map_node_over_list_with_metadata(
|
||||
prompt_id, unique_id, obj, input_data_all, func,
|
||||
allow_interrupt=False, execution_block_cb=None,
|
||||
pre_execute_cb=None, v3_data=None
|
||||
):
|
||||
|
||||
# Define the wrapped async function - NOTE: Updated signature with prompt_id and unique_id!
|
||||
async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
|
||||
# Only collect metadata when calling the main function of nodes
|
||||
if func == obj.FUNCTION and hasattr(obj, '__class__'):
|
||||
try:
|
||||
# Get the current prompt_id from the registry
|
||||
registry = MetadataRegistry()
|
||||
# We now have prompt_id directly from the function parameters
|
||||
|
||||
if prompt_id is not None:
|
||||
# Get node class type
|
||||
class_type = obj.__class__.__name__
|
||||
|
||||
# Use the passed unique_id parameter instead of trying to extract it
|
||||
node_id = unique_id
|
||||
|
||||
# Record inputs before execution
|
||||
if node_id is not None:
|
||||
return_types = getattr(obj, 'RETURN_TYPES', None)
|
||||
registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
|
||||
registry.record_node_execution(node_id, class_type, input_data_all, None)
|
||||
except Exception as e:
|
||||
logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
|
||||
|
||||
# Call original function with exact parameters
|
||||
results = await original_map_node_over_list(
|
||||
prompt_id, unique_id, obj, input_data_all, func,
|
||||
allow_interrupt, execution_block_cb, pre_execute_cb, v3_data=v3_data
|
||||
)
|
||||
|
||||
print(f"Error collecting metadata (pre-execution): {str(e)}")
|
||||
|
||||
# Execute the original async function with ALL parameters in the correct order
|
||||
results = await original_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
|
||||
|
||||
# After execution, collect outputs for relevant nodes
|
||||
if func == obj.FUNCTION and hasattr(obj, '__class__'):
|
||||
try:
|
||||
# Get the current prompt_id from the registry
|
||||
registry = MetadataRegistry()
|
||||
|
||||
if prompt_id is not None:
|
||||
# Get node class type
|
||||
class_type = obj.__class__.__name__
|
||||
|
||||
# Use the passed unique_id parameter
|
||||
node_id = unique_id
|
||||
|
||||
# Record outputs after execution
|
||||
if node_id is not None:
|
||||
return_types = getattr(obj, 'RETURN_TYPES', None)
|
||||
registry.update_node_execution(node_id, class_type, results, return_types=return_types)
|
||||
registry.update_node_execution(node_id, class_type, results)
|
||||
except Exception as e:
|
||||
logger.error(f"Error collecting metadata (post-execution): {str(e)}")
|
||||
|
||||
print(f"Error collecting metadata (post-execution): {str(e)}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# Also hook the execute function to track the current prompt_id
|
||||
original_execute = execution.execute
|
||||
|
||||
|
||||
async def async_execute_with_prompt_tracking(*args, **kwargs):
|
||||
if len(args) >= 7: # Check if we have enough arguments
|
||||
server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
|
||||
registry = MetadataRegistry()
|
||||
|
||||
|
||||
# Start collection if this is a new prompt
|
||||
if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
|
||||
registry.start_collection(prompt_id)
|
||||
|
||||
|
||||
# Store the dynprompt reference for node lookups
|
||||
if hasattr(prompt, 'original_prompt'):
|
||||
registry.set_current_prompt(prompt)
|
||||
|
||||
# Store extra_data for accessing full workflow node properties
|
||||
registry.set_extra_data(extra_data)
|
||||
|
||||
|
||||
# Execute the original function
|
||||
return await original_execute(*args, **kwargs)
|
||||
|
||||
|
||||
# Replace the functions with async versions
|
||||
setattr(execution, map_node_func_name, async_map_node_over_list_with_metadata)
|
||||
execution.execute = async_execute_with_prompt_tracking
|
||||
|
||||
@@ -1,68 +1,15 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from .constants import IMAGES
|
||||
|
||||
# Check if running in standalone mode
|
||||
standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
|
||||
standalone_mode = 'nodes' not in sys.modules
|
||||
|
||||
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER, OVERWRITE
|
||||
from .node_extractors import NODE_EXTRACTORS
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Keys that identify metadata hint marks stored in node.properties.lm_marker_role
|
||||
_META_MARK_PREFIX = "meta_"
|
||||
_MARK_PRIMARY_MODEL = "primary_model"
|
||||
_MARK_PRIMARY_SAMPLER = "primary_sampler"
|
||||
_MARK_POSITIVE_PROMPT = "positive_prompt"
|
||||
_MARK_NEGATIVE_PROMPT = "negative_prompt"
|
||||
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
|
||||
|
||||
class MetadataProcessor:
|
||||
"""Process and format collected metadata"""
|
||||
|
||||
@staticmethod
|
||||
def _get_user_marks(metadata):
|
||||
"""Scan workflow nodes (from extra_data.extra_pnginfo.workflow) for user-assigned
|
||||
metadata hint marks stored in node.properties.lm_marker_role.
|
||||
|
||||
Returns a dict mapping mark type keys to node IDs.
|
||||
Example: {'primary_model': '42', 'primary_sampler': '17'}
|
||||
"""
|
||||
marks: dict[str, str] = {}
|
||||
|
||||
# Primary source: extra_data.extra_pnginfo.workflow.nodes (has full properties)
|
||||
extra_data = metadata.get("extra_data")
|
||||
if extra_data and isinstance(extra_data, dict):
|
||||
extra_pnginfo = extra_data.get("extra_pnginfo", {})
|
||||
if isinstance(extra_pnginfo, dict):
|
||||
workflow = extra_pnginfo.get("workflow", {})
|
||||
nodes = workflow.get("nodes", [])
|
||||
for node in nodes:
|
||||
node_id = str(node.get("id", ""))
|
||||
role = node.get("properties", {}).get("lm_marker_role", "")
|
||||
if role.startswith(_META_MARK_PREFIX):
|
||||
mark_type = role[len(_META_MARK_PREFIX):]
|
||||
if mark_type in marks:
|
||||
logger.warning(
|
||||
"Duplicate meta hint '%s': node %s (previous: %s), "
|
||||
"last match wins",
|
||||
mark_type, node_id, marks[mark_type],
|
||||
)
|
||||
marks[mark_type] = node_id
|
||||
|
||||
# Fallback: try prompt.original_prompt (API-only submissions may not have workflow)
|
||||
if not marks:
|
||||
prompt = metadata.get("current_prompt")
|
||||
if prompt and getattr(prompt, "original_prompt", None):
|
||||
for node_id, node_data in prompt.original_prompt.items():
|
||||
role = node_data.get("properties", {}).get("lm_marker_role", "")
|
||||
if role.startswith(_META_MARK_PREFIX):
|
||||
mark_type = role[len(_META_MARK_PREFIX):]
|
||||
marks[mark_type] = node_id
|
||||
|
||||
return marks
|
||||
|
||||
|
||||
@staticmethod
|
||||
def find_primary_sampler(metadata, downstream_id=None):
|
||||
"""
|
||||
@@ -92,39 +39,8 @@ class MetadataProcessor:
|
||||
if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
|
||||
candidate_samplers[node_id] = metadata[SAMPLING][node_id]
|
||||
|
||||
# If we found candidate samplers, apply primary sampler logic to these candidates only
|
||||
|
||||
# PRE-PROCESS: Ensure all candidate samplers have their parameters populated
|
||||
# This is especially important for SamplerCustomAdvanced which needs tracing
|
||||
prompt = metadata.get("current_prompt")
|
||||
for node_id in candidate_samplers:
|
||||
# If a sampler is missing common parameters like steps or denoise,
|
||||
# try to populate them using tracing before ranking
|
||||
sampler_info = candidate_samplers[node_id]
|
||||
params = sampler_info.get("parameters", {})
|
||||
|
||||
if prompt and (params.get("steps") is None or params.get("denoise") is None):
|
||||
# Create a temporary params dict to use the handler
|
||||
temp_params = {
|
||||
"steps": params.get("steps"),
|
||||
"denoise": params.get("denoise"),
|
||||
"sampler": params.get("sampler_name"),
|
||||
"scheduler": params.get("scheduler")
|
||||
}
|
||||
|
||||
# Check if it's SamplerCustomAdvanced
|
||||
if prompt.original_prompt and node_id in prompt.original_prompt:
|
||||
if prompt.original_prompt[node_id].get("class_type") == "SamplerCustomAdvanced":
|
||||
MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, node_id, temp_params)
|
||||
|
||||
# Update the actual parameters with found values
|
||||
params["steps"] = temp_params.get("steps")
|
||||
params["denoise"] = temp_params.get("denoise")
|
||||
if temp_params.get("sampler"):
|
||||
params["sampler_name"] = temp_params.get("sampler")
|
||||
if temp_params.get("scheduler"):
|
||||
params["scheduler"] = temp_params.get("scheduler")
|
||||
|
||||
# If we found candidate samplers, apply primary sampler logic to these candidates only
|
||||
if candidate_samplers:
|
||||
# Collect potential primary samplers based on different criteria
|
||||
custom_advanced_samplers = []
|
||||
advanced_add_noise_samplers = []
|
||||
@@ -133,6 +49,7 @@ class MetadataProcessor:
|
||||
high_denoise_id = None
|
||||
|
||||
# First, check for SamplerCustomAdvanced among candidates
|
||||
prompt = metadata.get("current_prompt")
|
||||
if prompt and prompt.original_prompt:
|
||||
for node_id in candidate_samplers:
|
||||
node_info = prompt.original_prompt.get(node_id, {})
|
||||
@@ -160,16 +77,15 @@ class MetadataProcessor:
|
||||
# Combine all potential primary samplers
|
||||
potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
|
||||
|
||||
# Find the first potential primary sampler (prefer base sampler over refine)
|
||||
# Use forward search to prioritize the first one in execution order
|
||||
for i in range(downstream_index):
|
||||
# Find the most recent potential primary sampler (closest to downstream node)
|
||||
for i in range(downstream_index - 1, -1, -1):
|
||||
node_id = execution_order[i]
|
||||
if node_id in potential_samplers:
|
||||
return node_id, candidate_samplers[node_id]
|
||||
|
||||
# If no potential sampler found from our criteria, return the first sampler
|
||||
# If no potential sampler found from our criteria, return the most recent sampler
|
||||
if candidate_samplers:
|
||||
for i in range(downstream_index):
|
||||
for i in range(downstream_index - 1, -1, -1):
|
||||
node_id = execution_order[i]
|
||||
if node_id in candidate_samplers:
|
||||
return node_id, candidate_samplers[node_id]
|
||||
@@ -214,24 +130,6 @@ class MetadataProcessor:
|
||||
max_denoise = denoise
|
||||
primary_sampler = sampler_info
|
||||
primary_sampler_id = node_id
|
||||
|
||||
# Last resort: any registered sampler. Samplers without a denoise or
|
||||
# add_noise parameter (e.g. multi-stage samplers like KreaTwoStageSampler)
|
||||
# are not caught by the criteria above. Prefer execution order so the
|
||||
# first executed sampler wins, matching the downstream_id branch.
|
||||
if primary_sampler is None:
|
||||
sampler_ids = [
|
||||
node_id
|
||||
for node_id, sampler_info in metadata.get(SAMPLING, {}).items()
|
||||
if sampler_info.get(IS_SAMPLER, False)
|
||||
]
|
||||
if sampler_ids:
|
||||
if downstream_id and "execution_order" in metadata:
|
||||
for node_id in metadata["execution_order"]:
|
||||
if node_id in sampler_ids:
|
||||
return node_id, metadata[SAMPLING][node_id]
|
||||
primary_sampler_id = sampler_ids[0]
|
||||
primary_sampler = metadata[SAMPLING][sampler_ids[0]]
|
||||
|
||||
return primary_sampler_id, primary_sampler
|
||||
|
||||
@@ -278,11 +176,8 @@ class MetadataProcessor:
|
||||
found_node_id = input_value[0] # Connected node_id
|
||||
|
||||
# If we're looking for a specific node class
|
||||
if target_class:
|
||||
if found_node_id not in prompt.original_prompt:
|
||||
return None
|
||||
if prompt.original_prompt[found_node_id].get("class_type") == target_class:
|
||||
return found_node_id
|
||||
if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
|
||||
return found_node_id
|
||||
|
||||
# If we're not looking for a specific class, update the last valid node
|
||||
if not target_class:
|
||||
@@ -290,19 +185,11 @@ class MetadataProcessor:
|
||||
|
||||
# Continue tracing through intermediate nodes
|
||||
current_node_id = found_node_id
|
||||
|
||||
# Check if current source node exists
|
||||
if current_node_id not in prompt.original_prompt:
|
||||
return found_node_id if not target_class else None
|
||||
|
||||
# Determine which input to follow next on the source node
|
||||
source_node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
|
||||
if input_name in source_node_inputs:
|
||||
current_input = input_name
|
||||
elif "conditioning" in source_node_inputs:
|
||||
# For most conditioning nodes, the input we want to follow is named "conditioning"
|
||||
if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
|
||||
current_input = "conditioning"
|
||||
else:
|
||||
# If there's no suitable input to follow, return the current node
|
||||
# If there's no "conditioning" input, return the current node
|
||||
# if we're not looking for a specific target_class
|
||||
return found_node_id if not target_class else None
|
||||
else:
|
||||
@@ -315,89 +202,12 @@ class MetadataProcessor:
|
||||
return last_valid_node if not target_class else None
|
||||
|
||||
@staticmethod
|
||||
def trace_model_path(metadata, prompt, start_node_id):
|
||||
"""
|
||||
Trace the model connection path upstream to find the checkpoint
|
||||
"""
|
||||
if not prompt or not prompt.original_prompt:
|
||||
return None
|
||||
|
||||
current_node_id = start_node_id
|
||||
depth = 0
|
||||
max_depth = 50
|
||||
|
||||
while depth < max_depth:
|
||||
# Check if current node is a registered checkpoint in our metadata
|
||||
# This handles cached nodes correctly because metadata contains info for all nodes in the graph
|
||||
if current_node_id in metadata.get(MODELS, {}):
|
||||
if metadata[MODELS][current_node_id].get("type") == "checkpoint":
|
||||
return current_node_id
|
||||
|
||||
if current_node_id not in prompt.original_prompt:
|
||||
return None
|
||||
|
||||
node = prompt.original_prompt[current_node_id]
|
||||
inputs = node.get("inputs", {})
|
||||
class_type = node.get("class_type", "")
|
||||
|
||||
# Determine which input to follow next
|
||||
next_input_name = "model"
|
||||
|
||||
# Special handling for initial node
|
||||
if depth == 0:
|
||||
if class_type == "SamplerCustomAdvanced":
|
||||
next_input_name = "guider"
|
||||
|
||||
# If the specific input doesn't exist, try generic 'model'
|
||||
if next_input_name not in inputs:
|
||||
if "model" in inputs:
|
||||
next_input_name = "model"
|
||||
elif "basic_pipe" in inputs:
|
||||
# Handle pipe nodes like FromBasicPipe by following the pipeline
|
||||
next_input_name = "basic_pipe"
|
||||
else:
|
||||
# Dead end - no model input to follow
|
||||
return None
|
||||
|
||||
# Get connected node
|
||||
input_val = inputs[next_input_name]
|
||||
if isinstance(input_val, list) and len(input_val) > 0:
|
||||
current_node_id = input_val[0]
|
||||
else:
|
||||
return None
|
||||
|
||||
depth += 1
|
||||
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def find_primary_checkpoint(metadata, downstream_id=None, primary_sampler_id=None):
|
||||
"""
|
||||
Find the primary checkpoint model in the workflow
|
||||
|
||||
Parameters:
|
||||
- metadata: The workflow metadata
|
||||
- downstream_id: Optional ID of a downstream node to help identify the specific primary sampler
|
||||
- primary_sampler_id: Optional ID of the primary sampler if already known
|
||||
"""
|
||||
def find_primary_checkpoint(metadata):
|
||||
"""Find the primary checkpoint model in the workflow"""
|
||||
if not metadata.get(MODELS):
|
||||
return None
|
||||
|
||||
# Method 1: Topology-based tracing (More accurate for complex workflows)
|
||||
# First, find the primary sampler if not provided
|
||||
if not primary_sampler_id:
|
||||
primary_sampler_id, _ = MetadataProcessor.find_primary_sampler(metadata, downstream_id)
|
||||
|
||||
if primary_sampler_id:
|
||||
prompt = metadata.get("current_prompt")
|
||||
if prompt:
|
||||
# Trace back from the sampler to find the checkpoint
|
||||
checkpoint_id = MetadataProcessor.trace_model_path(metadata, prompt, primary_sampler_id)
|
||||
if checkpoint_id and checkpoint_id in metadata.get(MODELS, {}):
|
||||
return metadata[MODELS][checkpoint_id].get("name")
|
||||
|
||||
# Method 2: Fallback to the first available checkpoint (Original behavior)
|
||||
# In most simple workflows, there's only one checkpoint, so we can just take the first one
|
||||
# In most workflows, there's only one checkpoint, so we can just take the first one
|
||||
for node_id, model_info in metadata.get(MODELS, {}).items():
|
||||
if model_info.get("type") == "checkpoint":
|
||||
return model_info.get("name")
|
||||
@@ -423,101 +233,50 @@ class MetadataProcessor:
|
||||
|
||||
# Check if we have stored conditioning objects for this sampler
|
||||
if sampler_id in metadata.get(PROMPTS, {}) and (
|
||||
"pos_conditioning" in metadata[PROMPTS][sampler_id] or
|
||||
"neg_conditioning" in metadata[PROMPTS][sampler_id]
|
||||
):
|
||||
"pos_conditioning" in metadata[PROMPTS][sampler_id] or
|
||||
"neg_conditioning" in metadata[PROMPTS][sampler_id]):
|
||||
|
||||
pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
|
||||
neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
|
||||
|
||||
def extend_unique(target, values):
|
||||
for value in values:
|
||||
if value and value not in target:
|
||||
target.append(value)
|
||||
|
||||
# Helper function to recursively find prompt texts for a conditioning object.
|
||||
# Transform nodes can map one output conditioning to multiple source conditionings.
|
||||
def find_prompt_texts_for_conditioning(
|
||||
conditioning_obj, is_positive=True, visited=None
|
||||
):
|
||||
|
||||
# Helper function to recursively find prompt text for a conditioning object
|
||||
def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
|
||||
if conditioning_obj is None:
|
||||
return []
|
||||
|
||||
if visited is None:
|
||||
visited = set()
|
||||
|
||||
conditioning_id = id(conditioning_obj)
|
||||
if conditioning_id in visited:
|
||||
return []
|
||||
visited.add(conditioning_id)
|
||||
|
||||
prompt_texts = []
|
||||
|
||||
return ""
|
||||
|
||||
# Try to match conditioning objects with those stored by extractors
|
||||
for prompt_node_id, prompt_data in metadata[PROMPTS].items():
|
||||
if not isinstance(prompt_data, dict):
|
||||
continue
|
||||
|
||||
# For CLIP text nodes with a single conditioning output.
|
||||
if id(prompt_data.get("conditioning")) == conditioning_id:
|
||||
text = prompt_data.get("text", "")
|
||||
if text:
|
||||
extend_unique(prompt_texts, [text])
|
||||
|
||||
# Generic provenance for passthrough/transform/combine nodes.
|
||||
for source in prompt_data.get("conditioning_sources", []):
|
||||
if id(source.get("output")) != conditioning_id:
|
||||
continue
|
||||
for input_conditioning in source.get("inputs", []):
|
||||
extend_unique(
|
||||
prompt_texts,
|
||||
find_prompt_texts_for_conditioning(
|
||||
input_conditioning, is_positive, visited
|
||||
),
|
||||
)
|
||||
|
||||
# For nodes with separate pos_conditioning and neg_conditioning outputs
|
||||
# like TSC_EfficientLoader and existing ControlNet-style metadata.
|
||||
if (
|
||||
is_positive
|
||||
and id(prompt_data.get("positive_encoded")) == conditioning_id
|
||||
):
|
||||
if prompt_data.get("positive_text"):
|
||||
extend_unique(prompt_texts, [prompt_data["positive_text"]])
|
||||
else:
|
||||
extend_unique(
|
||||
prompt_texts,
|
||||
find_prompt_texts_for_conditioning(
|
||||
prompt_data.get("orig_pos_cond"),
|
||||
is_positive=True,
|
||||
visited=visited,
|
||||
),
|
||||
)
|
||||
|
||||
if (
|
||||
not is_positive
|
||||
and id(prompt_data.get("negative_encoded")) == conditioning_id
|
||||
):
|
||||
if prompt_data.get("negative_text"):
|
||||
extend_unique(prompt_texts, [prompt_data["negative_text"]])
|
||||
else:
|
||||
extend_unique(
|
||||
prompt_texts,
|
||||
find_prompt_texts_for_conditioning(
|
||||
prompt_data.get("orig_neg_cond"),
|
||||
is_positive=False,
|
||||
visited=visited,
|
||||
),
|
||||
)
|
||||
|
||||
return prompt_texts
|
||||
|
||||
# For nodes with single conditioning output
|
||||
if "conditioning" in prompt_data:
|
||||
if id(prompt_data["conditioning"]) == id(conditioning_obj):
|
||||
return prompt_data.get("text", "")
|
||||
|
||||
# For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
|
||||
if is_positive and "positive_encoded" in prompt_data:
|
||||
if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
|
||||
if "positive_text" in prompt_data:
|
||||
return prompt_data["positive_text"]
|
||||
else:
|
||||
orig_conditioning = prompt_data.get("orig_pos_cond", None)
|
||||
if orig_conditioning is not None:
|
||||
# Recursively find the prompt text for the original conditioning
|
||||
return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
|
||||
|
||||
if not is_positive and "negative_encoded" in prompt_data:
|
||||
if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
|
||||
if "negative_text" in prompt_data:
|
||||
return prompt_data["negative_text"]
|
||||
else:
|
||||
orig_conditioning = prompt_data.get("orig_neg_cond", None)
|
||||
if orig_conditioning is not None:
|
||||
# Recursively find the prompt text for the original conditioning
|
||||
return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
|
||||
|
||||
return ""
|
||||
|
||||
# Find prompt texts using the helper function
|
||||
result["prompt"] = ", ".join(
|
||||
find_prompt_texts_for_conditioning(pos_conditioning, is_positive=True)
|
||||
)
|
||||
result["negative_prompt"] = ", ".join(
|
||||
find_prompt_texts_for_conditioning(neg_conditioning, is_positive=False)
|
||||
)
|
||||
result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
|
||||
result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
|
||||
|
||||
return result
|
||||
|
||||
@@ -536,63 +295,25 @@ class MetadataProcessor:
|
||||
"seed": None,
|
||||
"steps": None,
|
||||
"cfg_scale": None,
|
||||
# "guidance": None, # Add guidance parameter
|
||||
"guidance": None, # Add guidance parameter
|
||||
"sampler": None,
|
||||
"scheduler": None,
|
||||
"checkpoint": None,
|
||||
"loras": "",
|
||||
"size": None,
|
||||
"clip_skip": None,
|
||||
"additional_data": "",
|
||||
"clip_skip": None
|
||||
}
|
||||
|
||||
# Get the prompt object for node relationship tracing
|
||||
prompt = metadata.get("current_prompt")
|
||||
|
||||
# ---- User marks: override heuristic inference with user-assigned hints ----
|
||||
user_marks = MetadataProcessor._get_user_marks(metadata)
|
||||
|
||||
# Find the primary KSampler node (user mark takes priority)
|
||||
primary_sampler_id = None
|
||||
primary_sampler = None
|
||||
if _MARK_PRIMARY_SAMPLER in user_marks:
|
||||
marked_id = user_marks[_MARK_PRIMARY_SAMPLER]
|
||||
sampler_data = metadata.get(SAMPLING, {}).get(marked_id)
|
||||
if sampler_data and sampler_data.get(IS_SAMPLER):
|
||||
primary_sampler_id = marked_id
|
||||
primary_sampler = sampler_data
|
||||
else:
|
||||
logger.warning(
|
||||
"User-marked primary sampler %s has no runtime metadata, "
|
||||
"falling back to heuristic",
|
||||
marked_id,
|
||||
)
|
||||
if primary_sampler is None:
|
||||
primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
|
||||
|
||||
# Resolve checkpoint / model (user mark takes priority)
|
||||
if _MARK_PRIMARY_MODEL in user_marks:
|
||||
marked_id = user_marks[_MARK_PRIMARY_MODEL]
|
||||
if marked_id in metadata.get(MODELS, {}):
|
||||
params["checkpoint"] = metadata[MODELS][marked_id].get("name")
|
||||
else:
|
||||
extra_data = metadata.get("extra_data")
|
||||
extra_pnginfo = extra_data.get("extra_pnginfo", {}) if extra_data and isinstance(extra_data, dict) else {}
|
||||
workflow = extra_pnginfo.get("workflow", {}) if isinstance(extra_pnginfo, dict) else {}
|
||||
node_type = "unknown"
|
||||
for n in workflow.get("nodes", []):
|
||||
if str(n.get("id", "")) == marked_id:
|
||||
node_type = n.get("type", "unknown")
|
||||
break
|
||||
logger.warning(
|
||||
"User-marked primary model %s (type=%s, registered=%s) has no runtime metadata, "
|
||||
"falling back to heuristic",
|
||||
marked_id, node_type, node_type in NODE_EXTRACTORS,
|
||||
)
|
||||
if params["checkpoint"] is None:
|
||||
checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
|
||||
if checkpoint:
|
||||
params["checkpoint"] = checkpoint
|
||||
|
||||
# Find the primary KSampler node
|
||||
primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
|
||||
|
||||
# Directly get checkpoint from metadata instead of tracing
|
||||
checkpoint = MetadataProcessor.find_primary_checkpoint(metadata)
|
||||
if checkpoint:
|
||||
params["checkpoint"] = checkpoint
|
||||
|
||||
# Check if guidance parameter exists in any sampling node
|
||||
for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
|
||||
@@ -618,8 +339,44 @@ class MetadataProcessor:
|
||||
is_custom_advanced = prompt.original_prompt[primary_sampler_id].get("class_type") == "SamplerCustomAdvanced"
|
||||
|
||||
if is_custom_advanced:
|
||||
# For SamplerCustomAdvanced, use the new handler method
|
||||
MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
|
||||
# For SamplerCustomAdvanced, trace specific inputs
|
||||
|
||||
# 1. Trace sigmas input to find BasicScheduler
|
||||
scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", "BasicScheduler", max_depth=5)
|
||||
if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
|
||||
scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
|
||||
params["steps"] = scheduler_params.get("steps")
|
||||
params["scheduler"] = scheduler_params.get("scheduler")
|
||||
|
||||
# 2. Trace sampler input to find KSamplerSelect
|
||||
sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
|
||||
if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
|
||||
sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
|
||||
params["sampler"] = sampler_params.get("sampler_name")
|
||||
|
||||
# 3. Trace guider input for CFGGuider and CLIPTextEncode
|
||||
guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
|
||||
if guider_node_id and guider_node_id in prompt.original_prompt:
|
||||
# Check if the guider node is a CFGGuider
|
||||
if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
|
||||
# Extract cfg value from the CFGGuider
|
||||
if guider_node_id in metadata.get(SAMPLING, {}):
|
||||
cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
|
||||
params["cfg_scale"] = cfg_params.get("cfg")
|
||||
|
||||
# Find CLIPTextEncode for positive prompt
|
||||
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
|
||||
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
|
||||
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
|
||||
|
||||
# Find CLIPTextEncode for negative prompt
|
||||
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
|
||||
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
|
||||
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
|
||||
else:
|
||||
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
|
||||
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
|
||||
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
|
||||
|
||||
else:
|
||||
# For standard samplers, match conditioning objects to prompts
|
||||
@@ -644,25 +401,7 @@ class MetadataProcessor:
|
||||
negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
|
||||
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
|
||||
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
|
||||
|
||||
# For SamplerCustom, handle any additional parameters
|
||||
MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
|
||||
|
||||
# ---- User marks: override prompts with explicitly tagged nodes ----
|
||||
prompts_data = metadata.get(PROMPTS, {})
|
||||
if _MARK_POSITIVE_PROMPT in user_marks:
|
||||
pos_id = user_marks[_MARK_POSITIVE_PROMPT]
|
||||
if pos_id in prompts_data:
|
||||
prompt_text = prompts_data[pos_id].get("text") or prompts_data[pos_id].get("positive_text")
|
||||
if prompt_text:
|
||||
params["prompt"] = prompt_text
|
||||
if _MARK_NEGATIVE_PROMPT in user_marks:
|
||||
neg_id = user_marks[_MARK_NEGATIVE_PROMPT]
|
||||
if neg_id in prompts_data:
|
||||
prompt_text = prompts_data[neg_id].get("text") or prompts_data[neg_id].get("negative_text")
|
||||
if prompt_text:
|
||||
params["negative_prompt"] = prompt_text
|
||||
|
||||
|
||||
# Size extraction is same for all sampler types
|
||||
# Check if the sampler itself has size information (from latent_image)
|
||||
if primary_sampler_id in metadata.get(SIZE, {}):
|
||||
@@ -683,34 +422,9 @@ class MetadataProcessor:
|
||||
|
||||
params["loras"] = " ".join(lora_parts)
|
||||
|
||||
# Extract clip_skip from any SAMPLING node that provides it
|
||||
for sampler_info in metadata.get(SAMPLING, {}).values():
|
||||
clip_skip = sampler_info.get("parameters", {}).get("clip_skip")
|
||||
if clip_skip is not None:
|
||||
params["clip_skip"] = clip_skip
|
||||
break
|
||||
if params["clip_skip"] is None:
|
||||
params["clip_skip"] = "1"
|
||||
|
||||
# ---- Apply manual metadata overwrites ----
|
||||
for overwrite_info in metadata.get(OVERWRITE, {}).values():
|
||||
overwrite_params = overwrite_info.get("parameters", {})
|
||||
for key, value in overwrite_params.items():
|
||||
if key == "clip_skip":
|
||||
# Accept any value from overwrite node (sentinel -25 already
|
||||
# filtered upstream). Needed because falsy check treats 0
|
||||
# as "not set" even though 0 is a valid wired input here.
|
||||
params[key] = value
|
||||
elif value: # truthy check — only overwrite when user provided a real value
|
||||
params[key] = value
|
||||
|
||||
# Bridge: the overwrite node exposes the field as "model" (more accurate),
|
||||
# but the internal pipeline key remains "checkpoint" for backward compatibility
|
||||
# with A1111 metadata format and downstream consumers.
|
||||
if params.get("model"):
|
||||
params["checkpoint"] = params["model"]
|
||||
del params["model"]
|
||||
|
||||
# Set default clip_skip value
|
||||
params["clip_skip"] = "1" # Common default
|
||||
|
||||
return params
|
||||
|
||||
@staticmethod
|
||||
@@ -740,69 +454,3 @@ class MetadataProcessor:
|
||||
"""Convert metadata to JSON string"""
|
||||
params = MetadataProcessor.to_dict(metadata, id)
|
||||
return json.dumps(params, indent=4)
|
||||
|
||||
@staticmethod
|
||||
def handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params):
|
||||
"""
|
||||
Handle parameter extraction for SamplerCustomAdvanced nodes
|
||||
|
||||
Parameters:
|
||||
- metadata: The workflow metadata
|
||||
- prompt: The prompt object containing node connections
|
||||
- primary_sampler_id: ID of the SamplerCustomAdvanced node
|
||||
- params: Parameters dictionary to update
|
||||
"""
|
||||
if not prompt.original_prompt or primary_sampler_id not in prompt.original_prompt:
|
||||
return
|
||||
|
||||
sampler_inputs = prompt.original_prompt[primary_sampler_id].get("inputs", {})
|
||||
|
||||
# 1. Trace sigmas input to find BasicScheduler (only if sigmas input exists)
|
||||
if "sigmas" in sampler_inputs:
|
||||
scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", None, max_depth=5)
|
||||
if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
|
||||
scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
|
||||
params["steps"] = scheduler_params.get("steps")
|
||||
params["scheduler"] = scheduler_params.get("scheduler")
|
||||
params["denoise"] = scheduler_params.get("denoise")
|
||||
|
||||
# 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
|
||||
if "sampler" in sampler_inputs:
|
||||
sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
|
||||
if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
|
||||
sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
|
||||
params["sampler"] = sampler_params.get("sampler_name")
|
||||
|
||||
# 3. Trace guider input for CFGGuider and CLIPTextEncode
|
||||
if "guider" in sampler_inputs:
|
||||
guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
|
||||
if guider_node_id and guider_node_id in prompt.original_prompt:
|
||||
# Check if the guider node is a CFGGuider
|
||||
if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
|
||||
# Extract cfg value from the CFGGuider
|
||||
if guider_node_id in metadata.get(SAMPLING, {}):
|
||||
cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
|
||||
params["cfg_scale"] = cfg_params.get("cfg")
|
||||
|
||||
# Find CLIPTextEncode for positive prompt
|
||||
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
|
||||
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
|
||||
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
|
||||
|
||||
# Find CLIPTextEncode for negative prompt
|
||||
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
|
||||
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
|
||||
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
|
||||
else:
|
||||
# Generic guider nodes often expose separate positive/negative inputs.
|
||||
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", max_depth=10)
|
||||
if not positive_node_id:
|
||||
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
|
||||
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
|
||||
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
|
||||
|
||||
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", max_depth=10)
|
||||
if not negative_node_id:
|
||||
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
|
||||
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
|
||||
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
|
||||
|
||||
@@ -1,64 +1,50 @@
|
||||
import time
|
||||
from typing import Any
|
||||
from nodes import NODE_CLASS_MAPPINGS # pyright: ignore[reportMissingImports, reportAttributeAccessIssue]
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
|
||||
from .constants import METADATA_CATEGORIES, IMAGES, OVERWRITE
|
||||
|
||||
from .constants import METADATA_CATEGORIES, IMAGES
|
||||
|
||||
class MetadataRegistry:
|
||||
"""A singleton registry to store and retrieve workflow metadata"""
|
||||
|
||||
_instance = None
|
||||
|
||||
current_prompt_id: Any = None
|
||||
current_prompt: Any = None
|
||||
metadata: dict[str, Any] = {}
|
||||
prompt_metadata: dict[str, Any] = {}
|
||||
executed_nodes: set[str] = set()
|
||||
node_cache: dict[str, Any] = {}
|
||||
max_prompt_history: int = 3
|
||||
metadata_categories: list[str] = METADATA_CATEGORIES
|
||||
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
cls._instance._reset()
|
||||
return cls._instance
|
||||
|
||||
|
||||
def _reset(self):
|
||||
self.current_prompt_id = None
|
||||
self.current_prompt = None
|
||||
self.metadata = {}
|
||||
self.prompt_metadata = {}
|
||||
self.executed_nodes = set()
|
||||
|
||||
|
||||
# Node-level cache for metadata
|
||||
self.node_cache = {}
|
||||
|
||||
|
||||
# Limit the number of stored prompts
|
||||
self.max_prompt_history = 3
|
||||
|
||||
|
||||
# Categories we want to track and retrieve from cache
|
||||
self.metadata_categories = METADATA_CATEGORIES
|
||||
|
||||
|
||||
def _clean_old_prompts(self):
|
||||
"""Clean up old prompt metadata, keeping only recent ones"""
|
||||
if len(self.prompt_metadata) <= self.max_prompt_history:
|
||||
return
|
||||
|
||||
|
||||
# Sort all prompt_ids by timestamp
|
||||
sorted_prompts = sorted(
|
||||
self.prompt_metadata.keys(),
|
||||
key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0),
|
||||
key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0)
|
||||
)
|
||||
|
||||
|
||||
# Remove oldest records
|
||||
prompts_to_remove = sorted_prompts[
|
||||
: len(sorted_prompts) - self.max_prompt_history
|
||||
]
|
||||
prompts_to_remove = sorted_prompts[:len(sorted_prompts) - self.max_prompt_history]
|
||||
for pid in prompts_to_remove:
|
||||
del self.prompt_metadata[pid]
|
||||
|
||||
|
||||
def start_collection(self, prompt_id):
|
||||
"""Begin metadata collection for a new prompt"""
|
||||
self.current_prompt_id = prompt_id
|
||||
@@ -67,110 +53,90 @@ class MetadataRegistry:
|
||||
category: {} for category in METADATA_CATEGORIES
|
||||
}
|
||||
# Add additional metadata fields
|
||||
self.prompt_metadata[prompt_id].update(
|
||||
{
|
||||
"execution_order": [],
|
||||
"current_prompt": None, # Will store the prompt object
|
||||
"extra_data": None, # Will store the API extra_data for workflow metadata
|
||||
"timestamp": time.time(),
|
||||
}
|
||||
)
|
||||
|
||||
self.prompt_metadata[prompt_id].update({
|
||||
"execution_order": [],
|
||||
"current_prompt": None, # Will store the prompt object
|
||||
"timestamp": time.time()
|
||||
})
|
||||
|
||||
# Clean up old prompt data
|
||||
self._clean_old_prompts()
|
||||
|
||||
|
||||
def set_current_prompt(self, prompt):
|
||||
"""Set the current prompt object reference"""
|
||||
self.current_prompt = prompt
|
||||
if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
|
||||
# Store the prompt in the metadata for later relationship tracing
|
||||
self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
|
||||
|
||||
def set_extra_data(self, extra_data):
|
||||
"""Store the API extra_data (contains extra_pnginfo.workflow with node properties)"""
|
||||
if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
|
||||
self.prompt_metadata[self.current_prompt_id]["extra_data"] = extra_data
|
||||
|
||||
|
||||
def get_metadata(self, prompt_id=None):
|
||||
"""Get collected metadata for a prompt"""
|
||||
key = prompt_id if prompt_id is not None else self.current_prompt_id
|
||||
if key not in self.prompt_metadata:
|
||||
return {}
|
||||
|
||||
|
||||
metadata = self.prompt_metadata[key]
|
||||
|
||||
|
||||
# If we have a current prompt object, check for non-executed nodes
|
||||
prompt_obj = metadata.get("current_prompt")
|
||||
if prompt_obj and hasattr(prompt_obj, "original_prompt"):
|
||||
original_prompt = prompt_obj.original_prompt
|
||||
|
||||
|
||||
# Fill in missing metadata from cache for nodes that weren't executed
|
||||
self._fill_missing_metadata(key, original_prompt)
|
||||
|
||||
|
||||
return self.prompt_metadata.get(key, {})
|
||||
|
||||
|
||||
def _fill_missing_metadata(self, prompt_id, original_prompt):
|
||||
"""Fill missing metadata from cache for non-executed nodes"""
|
||||
if not original_prompt:
|
||||
return
|
||||
|
||||
|
||||
executed_nodes = self.executed_nodes
|
||||
metadata = self.prompt_metadata[prompt_id]
|
||||
|
||||
|
||||
# Iterate through nodes in the original prompt
|
||||
for node_id, node_data in original_prompt.items():
|
||||
# Skip if already executed in this run
|
||||
if node_id in executed_nodes:
|
||||
continue
|
||||
|
||||
|
||||
# Get the node type from the prompt (this is the key in NODE_CLASS_MAPPINGS)
|
||||
prompt_class_type = node_data.get("class_type")
|
||||
if not prompt_class_type:
|
||||
continue
|
||||
|
||||
|
||||
# Convert to actual class name (which is what we use in our cache)
|
||||
class_type = prompt_class_type
|
||||
if prompt_class_type in NODE_CLASS_MAPPINGS:
|
||||
class_obj = NODE_CLASS_MAPPINGS[prompt_class_type]
|
||||
class_type = class_obj.__name__
|
||||
|
||||
|
||||
# Create cache key using the actual class name
|
||||
cache_key = f"{node_id}:{class_type}"
|
||||
|
||||
|
||||
# Check if this node type is relevant for metadata collection
|
||||
if class_type in NODE_EXTRACTORS or cache_key in self.node_cache:
|
||||
if class_type in NODE_EXTRACTORS:
|
||||
# Check if we have cached metadata for this node
|
||||
if cache_key in self.node_cache:
|
||||
cached_data = self.node_cache[cache_key]
|
||||
|
||||
# Detect bypass (mode=4) / mute (mode=2) — these nodes
|
||||
# were intentionally disabled and should not contribute
|
||||
# overwrite values from a previous execution's cache.
|
||||
node_mode = node_data.get("mode", 0)
|
||||
node_is_disabled = node_mode in (2, 4)
|
||||
|
||||
|
||||
# Apply cached metadata to the current metadata
|
||||
for category in self.metadata_categories:
|
||||
if category == OVERWRITE and node_is_disabled:
|
||||
continue
|
||||
if category in cached_data and node_id in cached_data[category]:
|
||||
if node_id not in metadata[category]:
|
||||
metadata[category][node_id] = cached_data[category][
|
||||
node_id
|
||||
]
|
||||
|
||||
def record_node_execution(self, node_id, class_type, inputs, outputs, return_types=None):
|
||||
metadata[category][node_id] = cached_data[category][node_id]
|
||||
|
||||
def record_node_execution(self, node_id, class_type, inputs, outputs):
|
||||
"""Record information about a node's execution"""
|
||||
if not self.current_prompt_id:
|
||||
return
|
||||
|
||||
|
||||
# Add to execution order and mark as executed
|
||||
if node_id not in self.executed_nodes:
|
||||
self.executed_nodes.add(node_id)
|
||||
self.prompt_metadata[self.current_prompt_id]["execution_order"].append(
|
||||
node_id
|
||||
)
|
||||
|
||||
self.prompt_metadata[self.current_prompt_id]["execution_order"].append(node_id)
|
||||
|
||||
# Process inputs to simplify working with them
|
||||
processed_inputs = {}
|
||||
for input_name, input_values in inputs.items():
|
||||
@@ -179,70 +145,61 @@ class MetadataRegistry:
|
||||
processed_inputs[input_name] = input_values[0]
|
||||
else:
|
||||
processed_inputs[input_name] = input_values
|
||||
|
||||
|
||||
# Extract node-specific metadata
|
||||
extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
|
||||
if extractor is GenericNodeExtractor:
|
||||
extractor.extract(node_id, processed_inputs, outputs,
|
||||
self.prompt_metadata[self.current_prompt_id],
|
||||
return_types=return_types)
|
||||
else:
|
||||
extractor.extract(node_id, processed_inputs, outputs,
|
||||
self.prompt_metadata[self.current_prompt_id])
|
||||
|
||||
extractor.extract(
|
||||
node_id,
|
||||
processed_inputs,
|
||||
outputs,
|
||||
self.prompt_metadata[self.current_prompt_id]
|
||||
)
|
||||
|
||||
# Cache this node's metadata
|
||||
self._cache_node_metadata(node_id, class_type)
|
||||
|
||||
def update_node_execution(self, node_id, class_type, outputs, return_types=None):
|
||||
|
||||
def update_node_execution(self, node_id, class_type, outputs):
|
||||
"""Update node metadata with output information"""
|
||||
if not self.current_prompt_id:
|
||||
return
|
||||
|
||||
|
||||
# Process outputs to make them more usable
|
||||
processed_outputs = outputs
|
||||
|
||||
|
||||
# Use the same extractor to update with outputs
|
||||
extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
|
||||
if hasattr(extractor, "update"):
|
||||
if extractor is GenericNodeExtractor:
|
||||
extractor.update(
|
||||
node_id, processed_outputs,
|
||||
self.prompt_metadata[self.current_prompt_id],
|
||||
return_types=return_types,
|
||||
)
|
||||
else:
|
||||
extractor.update(
|
||||
node_id, processed_outputs,
|
||||
self.prompt_metadata[self.current_prompt_id],
|
||||
)
|
||||
|
||||
if hasattr(extractor, 'update'):
|
||||
extractor.update(
|
||||
node_id,
|
||||
processed_outputs,
|
||||
self.prompt_metadata[self.current_prompt_id]
|
||||
)
|
||||
|
||||
# Update the cached metadata for this node
|
||||
self._cache_node_metadata(node_id, class_type)
|
||||
|
||||
|
||||
def _cache_node_metadata(self, node_id, class_type):
|
||||
"""Cache the metadata for a specific node"""
|
||||
if not self.current_prompt_id or not node_id or not class_type:
|
||||
return
|
||||
|
||||
|
||||
# Create a cache key combining node_id and class_type
|
||||
cache_key = f"{node_id}:{class_type}"
|
||||
|
||||
|
||||
# Create a shallow copy of the node's metadata
|
||||
node_metadata = {}
|
||||
current_metadata = self.prompt_metadata[self.current_prompt_id]
|
||||
|
||||
|
||||
for category in self.metadata_categories:
|
||||
if category in current_metadata and node_id in current_metadata[category]:
|
||||
if category not in node_metadata:
|
||||
node_metadata[category] = {}
|
||||
node_metadata[category][node_id] = current_metadata[category][node_id]
|
||||
|
||||
# Save new metadata or clear stale cache entries when metadata is empty
|
||||
|
||||
# Save to cache if we have any metadata for this node
|
||||
if any(node_metadata.values()):
|
||||
self.node_cache[cache_key] = node_metadata
|
||||
else:
|
||||
self.node_cache.pop(cache_key, None)
|
||||
|
||||
|
||||
def clear_unused_cache(self):
|
||||
"""Clean up node_cache entries that are no longer in use"""
|
||||
# Collect all node_ids currently in prompt_metadata
|
||||
@@ -251,18 +208,18 @@ class MetadataRegistry:
|
||||
for category in self.metadata_categories:
|
||||
if category in prompt_data:
|
||||
active_node_ids.update(prompt_data[category].keys())
|
||||
|
||||
|
||||
# Find cache keys that are no longer needed
|
||||
keys_to_remove = []
|
||||
for cache_key in self.node_cache:
|
||||
node_id = cache_key.split(":")[0]
|
||||
node_id = cache_key.split(':')[0]
|
||||
if node_id not in active_node_ids:
|
||||
keys_to_remove.append(cache_key)
|
||||
|
||||
|
||||
# Remove cache entries that are no longer needed
|
||||
for key in keys_to_remove:
|
||||
del self.node_cache[key]
|
||||
|
||||
|
||||
def clear_metadata(self, prompt_id=None):
|
||||
"""Clear metadata for a specific prompt or reset all data"""
|
||||
if prompt_id is not None:
|
||||
@@ -273,25 +230,25 @@ class MetadataRegistry:
|
||||
else:
|
||||
# Reset all data
|
||||
self._reset()
|
||||
|
||||
|
||||
def get_first_decoded_image(self, prompt_id=None):
|
||||
"""Get the first decoded image result"""
|
||||
key = prompt_id if prompt_id is not None else self.current_prompt_id
|
||||
if key not in self.prompt_metadata:
|
||||
return None
|
||||
|
||||
|
||||
metadata = self.prompt_metadata[key]
|
||||
if IMAGES in metadata and "first_decode" in metadata[IMAGES]:
|
||||
image_data = metadata[IMAGES]["first_decode"]["image"]
|
||||
|
||||
|
||||
# If it's an image batch or tuple, handle various formats
|
||||
if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
|
||||
# Return first element of list/tuple
|
||||
return image_data[0]
|
||||
|
||||
|
||||
# If it's a tensor, return as is for processing in the route handler
|
||||
return image_data
|
||||
|
||||
|
||||
# If no image is found in the current metadata, try to find it in the cache
|
||||
# This handles the case where VAEDecode was cached by ComfyUI and not executed
|
||||
prompt_obj = metadata.get("current_prompt")
|
||||
@@ -311,11 +268,8 @@ class MetadataRegistry:
|
||||
if IMAGES in cached_data and node_id in cached_data[IMAGES]:
|
||||
image_data = cached_data[IMAGES][node_id]["image"]
|
||||
# Handle different image formats
|
||||
if (
|
||||
isinstance(image_data, (list, tuple))
|
||||
and len(image_data) > 0
|
||||
):
|
||||
if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
|
||||
return image_data[0]
|
||||
return image_data
|
||||
|
||||
|
||||
return None
|
||||
|
||||
@@ -1,21 +1,6 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
|
||||
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE
|
||||
from .overwrite_utils import collect_overwrite_params
|
||||
|
||||
|
||||
def _store_checkpoint_metadata(metadata, node_id, model_name):
|
||||
"""Store checkpoint model information when available."""
|
||||
if not model_name:
|
||||
return
|
||||
metadata.setdefault(MODELS, {})
|
||||
metadata[MODELS][node_id] = {
|
||||
"name": model_name,
|
||||
"type": "checkpoint",
|
||||
"node_id": node_id
|
||||
}
|
||||
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
|
||||
|
||||
|
||||
class NodeMetadataExtractor:
|
||||
@@ -32,95 +17,11 @@ class NodeMetadataExtractor:
|
||||
pass
|
||||
|
||||
class GenericNodeExtractor(NodeMetadataExtractor):
|
||||
"""Fallback extractor with type-signature-based detection.
|
||||
|
||||
When a node is not in the NODE_EXTRACTORS registry, the hook layer
|
||||
passes ``return_types`` from ``obj.RETURN_TYPES``:
|
||||
|
||||
* ``MODEL`` output: common input fields (ckpt_name, unet_name, etc.)
|
||||
are checked for a model file name and stored as checkpoint metadata.
|
||||
* ``CONDITIONING`` output: common text input fields are checked for
|
||||
prompt text, and conditioning inputs are tracked through transforms.
|
||||
"""
|
||||
|
||||
# Input field names that carry a model path in loader-style nodes.
|
||||
_MODEL_NAME_FIELDS = (
|
||||
"ckpt_name", "unet_name", "model_path", "model_name", "gguf_name",
|
||||
)
|
||||
|
||||
# Extensions used by checkpoint_scanner.py — only record values that look
|
||||
# like real model filenames to avoid capturing unrelated string fields.
|
||||
_MODEL_EXTENSIONS = {
|
||||
".ckpt", ".pt", ".pt2", ".bin", ".pth", ".safetensors", ".pkl", ".sft", ".gguf",
|
||||
}
|
||||
|
||||
# Input field names that may carry prompt text in encoder-style nodes.
|
||||
_TEXT_FIELDS = ("text", "clip_l", "t5xxl", "prompt", "positive", "negative")
|
||||
|
||||
"""Default extractor for nodes without specific handling"""
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata, return_types=None):
|
||||
if return_types is None:
|
||||
return
|
||||
|
||||
# — MODEL loader detection (checkpoint / UNET / GGUF) —
|
||||
if "MODEL" in return_types or any("MODEL" in str(t) for t in return_types):
|
||||
for field in GenericNodeExtractor._MODEL_NAME_FIELDS:
|
||||
val = inputs.get(field)
|
||||
if val and isinstance(val, str) and val.strip():
|
||||
name = val.strip()
|
||||
if not any(name.lower().endswith(ext) for ext in GenericNodeExtractor._MODEL_EXTENSIONS):
|
||||
continue
|
||||
_store_checkpoint_metadata(metadata, node_id, name)
|
||||
return
|
||||
|
||||
# — CONDITIONING encoder / transform detection —
|
||||
if "CONDITIONING" in return_types or any("CONDITIONING" in str(t) for t in return_types):
|
||||
text = None
|
||||
for field in GenericNodeExtractor._TEXT_FIELDS:
|
||||
val = inputs.get(field)
|
||||
if val and isinstance(val, str) and val.strip():
|
||||
text = val.strip()
|
||||
break
|
||||
|
||||
input_conditionings = _collect_conditioning_inputs(inputs)
|
||||
if text or input_conditionings:
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
if text:
|
||||
prompt_metadata["text"] = text
|
||||
if input_conditionings:
|
||||
prompt_metadata["orig_conditionings"] = input_conditionings
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata, return_types=None):
|
||||
if return_types is None:
|
||||
return
|
||||
if "CONDITIONING" not in return_types and not any(
|
||||
"CONDITIONING" in str(t) for t in return_types
|
||||
):
|
||||
return
|
||||
if node_id not in metadata.get(PROMPTS, {}):
|
||||
return
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 1:
|
||||
return
|
||||
|
||||
conditioning_index = _first_conditioning_index(return_types)
|
||||
if conditioning_index is None or len(output_tuple) <= conditioning_index:
|
||||
return
|
||||
|
||||
output_conditioning = output_tuple[conditioning_index]
|
||||
if output_conditioning is None:
|
||||
return
|
||||
|
||||
prompt_metadata = metadata[PROMPTS][node_id]
|
||||
prompt_metadata["conditioning"] = output_conditioning
|
||||
_record_conditioning_source(
|
||||
metadata,
|
||||
node_id,
|
||||
output_conditioning,
|
||||
prompt_metadata.get("orig_conditionings", []),
|
||||
)
|
||||
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
pass
|
||||
|
||||
class CheckpointLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -128,48 +29,12 @@ class CheckpointLoaderExtractor(NodeMetadataExtractor):
|
||||
return
|
||||
|
||||
model_name = inputs.get("ckpt_name")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
|
||||
|
||||
class NunchakuFluxDiTLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "model_path" not in inputs:
|
||||
return
|
||||
|
||||
model_name = inputs.get("model_path")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
|
||||
|
||||
class NunchakuQwenImageDiTLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "model_name" not in inputs:
|
||||
return
|
||||
|
||||
model_name = inputs.get("model_name")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
|
||||
class GGUFLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "gguf_name" not in inputs:
|
||||
return
|
||||
|
||||
model_name = inputs.get("gguf_name")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
|
||||
|
||||
class KJNodesModelLoaderExtractor(NodeMetadataExtractor):
|
||||
"""Extract metadata from KJNodes loaders that expose `model_name`."""
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "model_name" not in inputs:
|
||||
return
|
||||
|
||||
model_name = inputs.get("model_name")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
if model_name:
|
||||
metadata[MODELS][node_id] = {
|
||||
"name": model_name,
|
||||
"type": "checkpoint",
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
@@ -178,7 +43,12 @@ class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
|
||||
return
|
||||
|
||||
model_name = inputs.get("ckpt_name")
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
if model_name:
|
||||
metadata[MODELS][node_id] = {
|
||||
"name": model_name,
|
||||
"type": "checkpoint",
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
# For loader node has lora_stack input, like Efficient Loader from Efficient Nodes
|
||||
active_loras = []
|
||||
@@ -229,118 +99,6 @@ class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
|
||||
metadata[PROMPTS][node_id]["positive_encoded"] = positive_conditioning
|
||||
metadata[PROMPTS][node_id]["negative_encoded"] = negative_conditioning
|
||||
|
||||
|
||||
class EasyComfyLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
if "ckpt_name" in inputs:
|
||||
_store_checkpoint_metadata(metadata, node_id, inputs["ckpt_name"])
|
||||
|
||||
# Only extract from optional_lora_stack — skip the single lora_name to
|
||||
# avoid double-counting LoRAs that come through the LORA_STACK path.
|
||||
active_loras = []
|
||||
optional_lora_stack = inputs.get("optional_lora_stack")
|
||||
if optional_lora_stack is not None and isinstance(optional_lora_stack, (list, tuple)):
|
||||
for item in optional_lora_stack:
|
||||
if isinstance(item, (list, tuple)) and len(item) >= 2:
|
||||
lora_path = item[0]
|
||||
model_strength = item[1]
|
||||
lora_name = os.path.splitext(os.path.basename(lora_path))[0]
|
||||
active_loras.append({
|
||||
"name": lora_name,
|
||||
"strength": model_strength
|
||||
})
|
||||
|
||||
if active_loras:
|
||||
metadata[LORAS][node_id] = {
|
||||
"lora_list": active_loras,
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
positive_text = inputs.get("positive", "")
|
||||
negative_text = inputs.get("negative", "")
|
||||
|
||||
if positive_text or negative_text:
|
||||
if node_id not in metadata[PROMPTS]:
|
||||
metadata[PROMPTS][node_id] = {"node_id": node_id}
|
||||
metadata[PROMPTS][node_id]["positive_text"] = positive_text
|
||||
metadata[PROMPTS][node_id]["negative_text"] = negative_text
|
||||
|
||||
if "clip_skip" in inputs:
|
||||
clip_skip = inputs["clip_skip"]
|
||||
if node_id not in metadata[SAMPLING]:
|
||||
metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
|
||||
metadata[SAMPLING][node_id]["parameters"]["clip_skip"] = clip_skip
|
||||
|
||||
width = inputs.get("empty_latent_width")
|
||||
height = inputs.get("empty_latent_height")
|
||||
if width is not None and height is not None:
|
||||
if SIZE not in metadata:
|
||||
metadata[SIZE] = {}
|
||||
metadata[SIZE][node_id] = {
|
||||
"width": int(width),
|
||||
"height": int(height),
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
# outputs: [(pipe_dict, model, vae), ...]
|
||||
if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
|
||||
return
|
||||
first_output = outputs[0]
|
||||
if not isinstance(first_output, tuple) or len(first_output) < 1:
|
||||
return
|
||||
pipe = first_output[0]
|
||||
if not isinstance(pipe, dict):
|
||||
return
|
||||
|
||||
positive_conditioning = pipe.get("positive")
|
||||
negative_conditioning = pipe.get("negative")
|
||||
|
||||
if positive_conditioning is not None or negative_conditioning is not None:
|
||||
if node_id not in metadata[PROMPTS]:
|
||||
metadata[PROMPTS][node_id] = {"node_id": node_id}
|
||||
if positive_conditioning is not None:
|
||||
metadata[PROMPTS][node_id]["positive_encoded"] = positive_conditioning
|
||||
if negative_conditioning is not None:
|
||||
metadata[PROMPTS][node_id]["negative_encoded"] = negative_conditioning
|
||||
|
||||
|
||||
class EasyPreSamplingExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
sampling_params = {}
|
||||
for key in ("steps", "cfg", "sampler_name", "scheduler", "denoise", "seed"):
|
||||
if key in inputs:
|
||||
sampling_params[key] = inputs[key]
|
||||
|
||||
metadata[SAMPLING][node_id] = {
|
||||
"parameters": sampling_params,
|
||||
"node_id": node_id,
|
||||
IS_SAMPLER: True
|
||||
}
|
||||
|
||||
|
||||
class EasySeedExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "seed" not in inputs:
|
||||
return
|
||||
|
||||
metadata[SAMPLING][node_id] = {
|
||||
"parameters": {"seed": inputs["seed"]},
|
||||
"node_id": node_id,
|
||||
IS_SAMPLER: False
|
||||
}
|
||||
|
||||
|
||||
class CLIPTextEncodeExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -360,281 +118,6 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
|
||||
conditioning = outputs[0][0]
|
||||
metadata[PROMPTS][node_id]["conditioning"] = conditioning
|
||||
|
||||
|
||||
class MyOriginalWaifuTextExtractor(NodeMetadataExtractor):
|
||||
"""Extractor for ComfyUI-MyOriginalWaifu TextProvider nodes."""
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
positive_text = inputs.get("positive", "")
|
||||
negative_text = inputs.get("negative", "")
|
||||
|
||||
if positive_text or negative_text:
|
||||
metadata[PROMPTS][node_id] = {
|
||||
"positive_text": positive_text,
|
||||
"negative_text": negative_text,
|
||||
"node_id": node_id,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 2:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata["positive_text"] = output_tuple[0]
|
||||
prompt_metadata["negative_text"] = output_tuple[1]
|
||||
|
||||
|
||||
class MyOriginalWaifuClipExtractor(NodeMetadataExtractor):
|
||||
"""Extractor for ComfyUI-MyOriginalWaifu ClipProvider nodes."""
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
positive_text = inputs.get("positive", "")
|
||||
negative_text = inputs.get("negative", "")
|
||||
|
||||
if positive_text or negative_text:
|
||||
metadata[PROMPTS][node_id] = {
|
||||
"positive_text": positive_text,
|
||||
"negative_text": negative_text,
|
||||
"node_id": node_id,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 2:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata["positive_encoded"] = output_tuple[0]
|
||||
prompt_metadata["negative_encoded"] = output_tuple[1]
|
||||
|
||||
|
||||
def _ensure_prompt_metadata(metadata, node_id):
|
||||
if node_id not in metadata[PROMPTS]:
|
||||
metadata[PROMPTS][node_id] = {"node_id": node_id}
|
||||
return metadata[PROMPTS][node_id]
|
||||
|
||||
|
||||
def _first_output_tuple(outputs):
|
||||
if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
|
||||
return None
|
||||
first_output = outputs[0]
|
||||
if isinstance(first_output, tuple):
|
||||
return first_output
|
||||
return None
|
||||
|
||||
|
||||
def _first_conditioning_index(return_types):
|
||||
"""Return the index of the first CONDITIONING output slot, or None."""
|
||||
if not return_types:
|
||||
return None
|
||||
for index, return_type in enumerate(return_types):
|
||||
if "CONDITIONING" in str(return_type):
|
||||
return index
|
||||
return None
|
||||
|
||||
|
||||
def _collect_conditioning_inputs(inputs):
|
||||
"""Collect conditioning object inputs (``conditioning*`` keys).
|
||||
|
||||
Primitive values (None, str, int, float, bool) are excluded so scalar
|
||||
fields like ``conditioning_strength`` are not mistaken for conditioning
|
||||
objects during provenance tracking.
|
||||
"""
|
||||
if not inputs:
|
||||
return []
|
||||
return [
|
||||
value
|
||||
for input_name, value in inputs.items()
|
||||
if input_name.startswith("conditioning")
|
||||
and value is not None
|
||||
and not isinstance(value, (str, int, float, bool))
|
||||
]
|
||||
|
||||
|
||||
def _record_conditioning_source(
|
||||
metadata, node_id, output_conditioning, input_conditionings
|
||||
):
|
||||
if output_conditioning is None:
|
||||
return
|
||||
|
||||
sources = [
|
||||
conditioning for conditioning in input_conditionings if conditioning is not None
|
||||
]
|
||||
if not sources:
|
||||
return
|
||||
|
||||
# Identity-preserving selectors return one of their inputs unchanged:
|
||||
# only that input contributed to the output, so record it alone instead
|
||||
# of treating every input as a combination source.
|
||||
for conditioning in sources:
|
||||
if id(conditioning) == id(output_conditioning):
|
||||
sources = [conditioning]
|
||||
break
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata.setdefault("conditioning_sources", []).append(
|
||||
{
|
||||
"output": output_conditioning,
|
||||
"inputs": sources,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _get_variable_name(inputs):
|
||||
for key in ("key", "name", "variable_name", "tag", "text"):
|
||||
value = inputs.get(key)
|
||||
if isinstance(value, str) and value:
|
||||
return value
|
||||
return None
|
||||
|
||||
|
||||
def _get_node_variable_name(metadata, node_id, inputs):
|
||||
variable_name = _get_variable_name(inputs)
|
||||
if variable_name:
|
||||
return variable_name
|
||||
|
||||
prompt = metadata.get("current_prompt")
|
||||
original_prompt = getattr(prompt, "original_prompt", None)
|
||||
if not original_prompt or node_id not in original_prompt:
|
||||
return None
|
||||
|
||||
node_data = original_prompt[node_id]
|
||||
variable_name = _get_variable_name(node_data.get("inputs", {}))
|
||||
if variable_name:
|
||||
return variable_name
|
||||
|
||||
widgets_values = node_data.get("widgets_values", [])
|
||||
if widgets_values and isinstance(widgets_values[0], str):
|
||||
return widgets_values[0]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
class ControlNetApplyAdvancedExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
if inputs.get("positive") is not None:
|
||||
prompt_metadata["orig_pos_cond"] = inputs["positive"]
|
||||
if inputs.get("negative") is not None:
|
||||
prompt_metadata["orig_neg_cond"] = inputs["negative"]
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
positive_input = prompt_metadata.get("orig_pos_cond")
|
||||
negative_input = prompt_metadata.get("orig_neg_cond")
|
||||
|
||||
if len(output_tuple) >= 1:
|
||||
prompt_metadata["positive_encoded"] = output_tuple[0]
|
||||
_record_conditioning_source(
|
||||
metadata, node_id, output_tuple[0], [positive_input]
|
||||
)
|
||||
if len(output_tuple) >= 2:
|
||||
prompt_metadata["negative_encoded"] = output_tuple[1]
|
||||
_record_conditioning_source(
|
||||
metadata, node_id, output_tuple[1], [negative_input]
|
||||
)
|
||||
|
||||
|
||||
class ConditioningCombineExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
input_conditionings = _collect_conditioning_inputs(inputs)
|
||||
|
||||
if input_conditionings:
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata["orig_conditionings"] = input_conditionings
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 1:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
output_conditioning = output_tuple[0]
|
||||
prompt_metadata["conditioning"] = output_conditioning
|
||||
_record_conditioning_source(
|
||||
metadata,
|
||||
node_id,
|
||||
output_conditioning,
|
||||
prompt_metadata.get("orig_conditionings", []),
|
||||
)
|
||||
|
||||
|
||||
class SetNodeExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
variable_name = _get_node_variable_name(metadata, node_id, inputs)
|
||||
conditioning = inputs.get("CONDITIONING")
|
||||
if conditioning is None:
|
||||
conditioning = inputs.get("conditioning")
|
||||
if conditioning is None:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata["conditioning"] = conditioning
|
||||
if variable_name:
|
||||
prompt_metadata["variable_name"] = variable_name
|
||||
metadata[PROMPTS].setdefault("__conditioning_variables__", {})[
|
||||
variable_name
|
||||
] = conditioning
|
||||
|
||||
|
||||
class GetNodeExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
variable_name = _get_node_variable_name(metadata, node_id, inputs or {})
|
||||
if variable_name:
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
prompt_metadata["variable_name"] = variable_name
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 1:
|
||||
return
|
||||
|
||||
prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
|
||||
output_conditioning = output_tuple[0]
|
||||
prompt_metadata["conditioning"] = output_conditioning
|
||||
|
||||
variable_name = prompt_metadata.get("variable_name")
|
||||
if not variable_name:
|
||||
return
|
||||
|
||||
input_conditioning = metadata[PROMPTS].get("__conditioning_variables__", {}).get(
|
||||
variable_name
|
||||
)
|
||||
_record_conditioning_source(
|
||||
metadata, node_id, output_conditioning, [input_conditioning]
|
||||
)
|
||||
|
||||
# Base Sampler Extractor to reduce code redundancy
|
||||
class BaseSamplerExtractor(NodeMetadataExtractor):
|
||||
"""Base extractor for sampler nodes with common functionality"""
|
||||
@@ -861,65 +344,6 @@ class TSCKSamplerAdvancedExtractor(KSamplerAdvancedExtractor, TSCSamplerBaseExtr
|
||||
|
||||
# Update method is inherited from TSCSamplerBaseExtractor
|
||||
|
||||
class KreaTwoStageSamplerExtractor(BaseSamplerExtractor):
|
||||
"""Extractor for Krea Two/Three Stage Samplers (Auryg/Krea-2-Two-Stage-Sampler).
|
||||
|
||||
The node samples in two (or three) stages with per-stage settings
|
||||
(stage1_steps/stage2_steps, stage1_cfg/stage2_cfg, ...). The canonical
|
||||
metadata fields consumed by ``extract_generation_params`` (steps, cfg,
|
||||
sampler_name, scheduler) are derived from the base stage (stage 1; the
|
||||
three-stage variant reuses stage 1 settings for stage 3), while the full
|
||||
per-stage breakdown is preserved in the raw parameters.
|
||||
"""
|
||||
|
||||
# All per-stage parameter keys present on both node variants.
|
||||
_STAGE_PARAM_KEYS = (
|
||||
"stage1_steps", "stage1_cfg", "stage1_sampler_name", "stage1_scheduler",
|
||||
"stage2_steps", "stage2_cfg", "stage2_sampler_name", "stage2_scheduler",
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
BaseSamplerExtractor.extract_sampling_params(
|
||||
node_id,
|
||||
inputs,
|
||||
metadata,
|
||||
("seed", "handoff_percent", "stage3_handoff_percent")
|
||||
+ KreaTwoStageSamplerExtractor._STAGE_PARAM_KEYS,
|
||||
)
|
||||
|
||||
# Derive the canonical fields expected by extract_generation_params.
|
||||
sampling_params = metadata[SAMPLING][node_id]["parameters"]
|
||||
if "stage1_steps" in sampling_params or "stage2_steps" in sampling_params:
|
||||
sampling_params["steps"] = (
|
||||
(sampling_params.get("stage1_steps") or 0)
|
||||
+ (sampling_params.get("stage2_steps") or 0)
|
||||
)
|
||||
if "stage1_cfg" in sampling_params:
|
||||
sampling_params["cfg"] = sampling_params["stage1_cfg"]
|
||||
if "stage1_sampler_name" in sampling_params:
|
||||
sampling_params["sampler_name"] = sampling_params["stage1_sampler_name"]
|
||||
if "stage1_scheduler" in sampling_params:
|
||||
sampling_params["scheduler"] = sampling_params["stage1_scheduler"]
|
||||
|
||||
BaseSamplerExtractor.extract_conditioning(node_id, inputs, metadata)
|
||||
|
||||
# Prefer the final generation resolution; latent dims are the fallback.
|
||||
BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
|
||||
final_width = inputs.get("final_width")
|
||||
final_height = inputs.get("final_height")
|
||||
if final_width and final_height:
|
||||
if SIZE not in metadata:
|
||||
metadata[SIZE] = {}
|
||||
metadata[SIZE][node_id] = {
|
||||
"width": final_width,
|
||||
"height": final_height,
|
||||
"node_id": node_id,
|
||||
}
|
||||
|
||||
class LoraLoaderExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -960,106 +384,6 @@ class ImageSizeExtractor(NodeMetadataExtractor):
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
class KreaDualResolutionSelectorExtractor(NodeMetadataExtractor):
|
||||
"""Extract base resolution from Krea Dual Resolution Selector outputs
|
||||
(Auryg/Krea-2-Two-Stage-Sampler).
|
||||
|
||||
The node computes base/final dimensions at runtime from aspect ratio and
|
||||
megapixel settings, so the values are only available in the update phase
|
||||
(outputs: base_width, base_height, final_width, final_height, seed).
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
# Dimensions are computed at runtime; nothing to do here.
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
output_tuple = _first_output_tuple(outputs)
|
||||
if not output_tuple or len(output_tuple) < 2:
|
||||
return
|
||||
width, height = output_tuple[0], output_tuple[1]
|
||||
if not isinstance(width, int) or not isinstance(height, int):
|
||||
return
|
||||
|
||||
if SIZE not in metadata:
|
||||
metadata[SIZE] = {}
|
||||
metadata[SIZE][node_id] = {
|
||||
"width": width,
|
||||
"height": height,
|
||||
"node_id": node_id,
|
||||
}
|
||||
|
||||
class RgthreePowerLoraLoaderExtractor(NodeMetadataExtractor):
|
||||
"""Extract LoRA metadata from rgthree Power Lora Loader.
|
||||
|
||||
The node passes LoRAs as dynamic kwargs: LORA_1, LORA_2, ... each containing
|
||||
{'on': bool, 'lora': filename, 'strength': float, 'strengthTwo': float}.
|
||||
"""
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
active_loras = []
|
||||
for key, value in inputs.items():
|
||||
if not key.upper().startswith('LORA_'):
|
||||
continue
|
||||
if not isinstance(value, dict):
|
||||
continue
|
||||
if not value.get('on') or not value.get('lora'):
|
||||
continue
|
||||
lora_name = os.path.splitext(os.path.basename(value['lora']))[0]
|
||||
active_loras.append({
|
||||
"name": lora_name,
|
||||
"strength": round(float(value.get('strength', 1.0)), 2)
|
||||
})
|
||||
|
||||
if active_loras:
|
||||
metadata[LORAS][node_id] = {
|
||||
"lora_list": active_loras,
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
|
||||
class TensorRTLoaderExtractor(NodeMetadataExtractor):
|
||||
"""Extract checkpoint metadata from TensorRT Loader.
|
||||
|
||||
extract() parses the engine filename from 'unet_name' as a best-effort
|
||||
fallback (strips profile suffix after '_$' and counter suffix).
|
||||
|
||||
update() checks if the output MODEL has attachments["source_model"]
|
||||
set by the node (NubeBuster fork) and overrides with the real name.
|
||||
Vanilla TRT doesn't set this — the filename parse stands.
|
||||
"""
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs or "unet_name" not in inputs:
|
||||
return
|
||||
unet_name = inputs.get("unet_name")
|
||||
# Strip path and extension, then drop the $_profile suffix
|
||||
model_name = os.path.splitext(os.path.basename(unet_name))[0]
|
||||
if "_$" in model_name:
|
||||
model_name = model_name[:model_name.index("_$")]
|
||||
# Strip counter suffix (e.g. _00001_) left by ComfyUI's save path
|
||||
model_name = re.sub(r'_\d+_?$', '', model_name)
|
||||
_store_checkpoint_metadata(metadata, node_id, model_name)
|
||||
|
||||
@staticmethod
|
||||
def update(node_id, outputs, metadata):
|
||||
if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
|
||||
return
|
||||
first_output = outputs[0]
|
||||
if not isinstance(first_output, tuple) or len(first_output) < 1:
|
||||
return
|
||||
model = first_output[0]
|
||||
# NubeBuster fork sets attachments["source_model"] on the ModelPatcher
|
||||
source_model = getattr(model, 'attachments', {}).get("source_model")
|
||||
if source_model:
|
||||
_store_checkpoint_metadata(metadata, node_id, source_model)
|
||||
|
||||
|
||||
class LoraLoaderManagerExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -1106,55 +430,6 @@ class LoraLoaderManagerExtractor(NodeMetadataExtractor):
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
class LoraTextLoaderManagerExtractor(NodeMetadataExtractor):
|
||||
"""Extract LoRA metadata from LoraTextLoaderLM (LoRA Text Loader).
|
||||
|
||||
The node accepts a `lora_syntax` STRING containing <lora:name:strength> tags
|
||||
(same format as the ComfyUI prompt), plus an optional `lora_stack`.
|
||||
This extractor parses the syntax string using the same regex as the node.
|
||||
"""
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
active_loras = []
|
||||
|
||||
# Process lora_stack if available (optional input)
|
||||
if "lora_stack" in inputs:
|
||||
lora_stack = inputs.get("lora_stack", [])
|
||||
for item in lora_stack:
|
||||
# lora_stack entries are (path, model_strength, clip_strength) tuples
|
||||
if isinstance(item, (list, tuple)) and len(item) >= 2:
|
||||
lora_path = item[0]
|
||||
model_strength = item[1]
|
||||
lora_name = os.path.splitext(os.path.basename(lora_path))[0]
|
||||
active_loras.append({
|
||||
"name": lora_name,
|
||||
"strength": round(float(model_strength), 2)
|
||||
})
|
||||
|
||||
# Process lora_syntax string input
|
||||
if "lora_syntax" in inputs:
|
||||
lora_syntax = inputs.get("lora_syntax", "")
|
||||
if lora_syntax and isinstance(lora_syntax, str):
|
||||
pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
|
||||
matches = re.findall(pattern, lora_syntax, re.IGNORECASE)
|
||||
for match in matches:
|
||||
lora_name = match[0]
|
||||
model_strength = float(match[1])
|
||||
active_loras.append({
|
||||
"name": lora_name,
|
||||
"strength": round(model_strength, 2)
|
||||
})
|
||||
|
||||
if active_loras:
|
||||
metadata[LORAS][node_id] = {
|
||||
"lora_list": active_loras,
|
||||
"node_id": node_id
|
||||
}
|
||||
|
||||
|
||||
class FluxGuidanceExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -1259,6 +534,8 @@ class SamplerCustomAdvancedExtractor(BaseSamplerExtractor):
|
||||
# Extract latent dimensions
|
||||
BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
|
||||
|
||||
import json
|
||||
|
||||
class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
@@ -1359,99 +636,43 @@ class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
|
||||
metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
|
||||
metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
|
||||
|
||||
class MetadataOverwriteExtractor(NodeMetadataExtractor):
|
||||
"""Extract manually specified metadata from MetadataOverwriteLM node.
|
||||
|
||||
Stores truthy input values under the OVERWRITE category so that
|
||||
extract_generation_params can merge them over the inferred params.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def extract(node_id, inputs, outputs, metadata):
|
||||
if not inputs:
|
||||
return
|
||||
|
||||
overwrite_params = collect_overwrite_params(inputs)
|
||||
|
||||
if overwrite_params:
|
||||
metadata.setdefault(OVERWRITE, {})
|
||||
metadata[OVERWRITE][node_id] = {
|
||||
"parameters": overwrite_params,
|
||||
"node_id": node_id,
|
||||
}
|
||||
|
||||
|
||||
# Registry of node-specific extractors
|
||||
# Keys are node class names
|
||||
NODE_EXTRACTORS = {
|
||||
# Sampling
|
||||
"KSampler": SamplerExtractor,
|
||||
"KSamplerAdvanced": KSamplerAdvancedExtractor,
|
||||
"SamplerCustom": KSamplerAdvancedExtractor,
|
||||
"SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,
|
||||
"ClownsharKSampler_Beta": SamplerExtractor,
|
||||
"TSC_KSampler": TSCKSamplerExtractor, # Efficient Nodes
|
||||
"TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor, # Efficient Nodes
|
||||
"KreaTwoStageSampler": KreaTwoStageSamplerExtractor, # Auryg/Krea-2-Two-Stage-Sampler
|
||||
"KreaThreeStageSampler": KreaTwoStageSamplerExtractor, # Auryg/Krea-2-Two-Stage-Sampler
|
||||
"KSamplerBasicPipe": KSamplerBasicPipeExtractor, # comfyui-impact-pack
|
||||
"KSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipeExtractor, # comfyui-impact-pack
|
||||
"KSampler_inspire_pipe": KSamplerBasicPipeExtractor, # comfyui-inspire-pack
|
||||
"KSamplerAdvanced_inspire_pipe": KSamplerAdvancedBasicPipeExtractor, # comfyui-inspire-pack
|
||||
"KSampler_inspire": SamplerExtractor, # comfyui-inspire-pack
|
||||
# Sampling Selectors
|
||||
"KSamplerSelect": KSamplerSelectExtractor, # Add KSamplerSelect
|
||||
"BasicScheduler": BasicSchedulerExtractor, # Add BasicScheduler
|
||||
"AlignYourStepsScheduler": BasicSchedulerExtractor, # Add AlignYourStepsScheduler
|
||||
# ComfyUI-Easy-Use pre-sampling / seed
|
||||
"samplerSettings": EasyPreSamplingExtractor, # easy preSampling
|
||||
"easySeed": EasySeedExtractor, # easy seed
|
||||
# Loaders
|
||||
"CheckpointLoaderSimple": CheckpointLoaderExtractor,
|
||||
"comfyLoader": EasyComfyLoaderExtractor, # ComfyUI-Easy-Use easy comfyLoader
|
||||
"CheckpointLoaderSimpleWithImages": CheckpointLoaderExtractor, # CheckpointLoader|pysssss
|
||||
"comfyLoader": CheckpointLoaderExtractor, # easy comfyLoader
|
||||
"TSC_EfficientLoader": TSCCheckpointLoaderExtractor, # Efficient Nodes
|
||||
"NunchakuFluxDiTLoader": NunchakuFluxDiTLoaderExtractor, # ComfyUI-Nunchaku
|
||||
"NunchakuQwenImageDiTLoader": NunchakuQwenImageDiTLoaderExtractor, # ComfyUI-Nunchaku
|
||||
"LoaderGGUF": GGUFLoaderExtractor, # calcuis gguf
|
||||
"LoaderGGUFAdvanced": GGUFLoaderExtractor, # calcuis gguf
|
||||
"GGUFLoaderKJ": KJNodesModelLoaderExtractor, # KJNodes
|
||||
"DiffusionModelLoaderKJ": KJNodesModelLoaderExtractor, # KJNodes
|
||||
"CheckpointLoaderKJ": CheckpointLoaderExtractor, # KJNodes
|
||||
"CheckpointLoaderLM": CheckpointLoaderExtractor, # LoRA Manager
|
||||
"UNETLoader": UNETLoaderExtractor, # Updated to use dedicated extractor
|
||||
"UnetLoaderGGUF": UNETLoaderExtractor, # Updated to use dedicated extractor
|
||||
"UNETLoaderLM": UNETLoaderExtractor, # LoRA Manager
|
||||
"LoraLoader": LoraLoaderExtractor,
|
||||
"LoraLoaderLM": LoraLoaderManagerExtractor,
|
||||
"LoraTextLoaderLM": LoraTextLoaderManagerExtractor,
|
||||
"RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
|
||||
"TensorRTLoader": TensorRTLoaderExtractor,
|
||||
"LoraManagerLoader": LoraLoaderManagerExtractor,
|
||||
# Conditioning
|
||||
"CLIPTextEncode": CLIPTextEncodeExtractor,
|
||||
"CLIPTextEncodeAttentionBias": CLIPTextEncodeExtractor, # From https://github.com/silveroxides/ComfyUI_PromptAttention
|
||||
"PromptLM": CLIPTextEncodeExtractor,
|
||||
"CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor, # Add CLIPTextEncodeFlux
|
||||
"WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
|
||||
"AdvancedCLIPTextEncode": CLIPTextEncodeExtractor, # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
|
||||
"smZ_CLIPTextEncode": CLIPTextEncodeExtractor, # From https://github.com/shiimizu/ComfyUI_smZNodes
|
||||
"CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor, # Add CR_ApplyControlNetStack
|
||||
"PCTextEncode": CLIPTextEncodeExtractor, # From https://github.com/asagi4/comfyui-prompt-control
|
||||
"TextProvider": MyOriginalWaifuTextExtractor, # ComfyUI-MyOriginalWaifu
|
||||
"ClipProvider": MyOriginalWaifuClipExtractor, # ComfyUI-MyOriginalWaifu
|
||||
"ControlNetApplyAdvanced": ControlNetApplyAdvancedExtractor,
|
||||
"ConditioningCombine": ConditioningCombineExtractor,
|
||||
"SetNode": SetNodeExtractor,
|
||||
"GetNode": GetNodeExtractor,
|
||||
# Latent
|
||||
"EmptyLatentImage": ImageSizeExtractor,
|
||||
"KreaDualResolutionSelector": KreaDualResolutionSelectorExtractor, # Auryg/Krea-2-Two-Stage-Sampler
|
||||
# Flux
|
||||
"FluxGuidance": FluxGuidanceExtractor, # Add FluxGuidance
|
||||
"CFGGuider": CFGGuiderExtractor, # Add CFGGuider
|
||||
# Image
|
||||
"VAEDecode": VAEDecodeExtractor, # Added VAEDecode extractor
|
||||
# Metadata overwrite
|
||||
"MetadataOverwriteLM": MetadataOverwriteExtractor,
|
||||
# Add other nodes as needed
|
||||
}
|
||||
|
||||
@@ -1,51 +0,0 @@
|
||||
"""Shared helpers for Metadata Overwrite node metadata collection.
|
||||
|
||||
Used by both the MetadataOverwriteLM node (execution time) and the
|
||||
MetadataOverwriteExtractor (hook time) so the conversion/filtering logic
|
||||
cannot drift between the two paths.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from ..utils.utils import model_patcher_to_name, sampler_object_to_name
|
||||
from .constants import CLIP_SKIP_SENTINEL, METADATA_OVERWRITE_FIELDS
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def collect_overwrite_params(values: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Convert node input values into non-default overwrite parameters.
|
||||
|
||||
For most fields, a falsy value (empty string, 0) means "not set" and is
|
||||
skipped. clip_skip uses a dedicated sentinel (-25) so that a wired value
|
||||
of 0 is preserved. The ``model`` field accepts either a manual string or
|
||||
a wired MODEL (ModelPatcher) connection; in the latter case the source
|
||||
model name is extracted from the patcher's ``cached_patcher_init`` and
|
||||
stored as a ComfyUI-style relative path. The ``sampler`` field likewise
|
||||
accepts a manual string or a wired SAMPLER (KSAMPLER) connection, from
|
||||
which the sampler name is extracted via the sampler function's name.
|
||||
"""
|
||||
result: Dict[str, Any] = {}
|
||||
for key in METADATA_OVERWRITE_FIELDS:
|
||||
value = values.get(key)
|
||||
if key == "model" and not isinstance(value, str):
|
||||
value = model_patcher_to_name(value)
|
||||
if value is None:
|
||||
logger.warning(
|
||||
"Could not extract model name from wired MODEL input "
|
||||
"(no cached_patcher_init); model metadata overwrite skipped"
|
||||
)
|
||||
elif key == "sampler" and not isinstance(value, str):
|
||||
value = sampler_object_to_name(value)
|
||||
if value is None:
|
||||
logger.warning(
|
||||
"Could not extract sampler name from wired SAMPLER input "
|
||||
"(unrecognized sampler function); sampler metadata overwrite skipped"
|
||||
)
|
||||
if key == "clip_skip":
|
||||
if value != CLIP_SKIP_SENTINEL:
|
||||
result[key] = value
|
||||
elif value:
|
||||
result[key] = value
|
||||
return result
|
||||
@@ -1,234 +0,0 @@
|
||||
"""Metadata operations — thin in-process wrappers around LoRA Manager internal services.
|
||||
|
||||
All functions are simple Python async functions that delegate to the
|
||||
appropriate internal service. They use **relative imports** within the
|
||||
``py`` package, so ``sys.modules`` caching works normally and there is no
|
||||
risk of double import or circular dependencies.
|
||||
|
||||
Usage (in-process, primary)::
|
||||
|
||||
from py.metadata_ops import list_base_models, read_metadata
|
||||
|
||||
models = await list_base_models()
|
||||
meta = await read_metadata("/path/to/model.safetensors")
|
||||
|
||||
Usage (subprocess, debugging / external)::
|
||||
|
||||
python -m py.metadata_ops base-models list
|
||||
python -m py.metadata_ops metadata read /path/to/model.safetensors
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
SCANNER_TYPE_MAP: dict[str, str] = {
|
||||
"get_lora_scanner": "lora",
|
||||
"get_checkpoint_scanner": "checkpoint",
|
||||
"get_embedding_scanner": "embedding",
|
||||
"get_other_scanner": "other",
|
||||
}
|
||||
|
||||
SCANNER_GETTER_NAMES = tuple(SCANNER_TYPE_MAP.keys())
|
||||
|
||||
|
||||
async def _find_model_entry(
|
||||
model_path: str,
|
||||
) -> tuple[Any, object, str | None] | tuple[None, None, None]:
|
||||
"""Iterate all scanners and return the first (scanner, entry, getter_name)
|
||||
that owns *model_path*. Returns ``(None, None, None)`` when no scanner
|
||||
claims it.
|
||||
"""
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
normalized = os.path.normpath(model_path)
|
||||
for getter_name in SCANNER_GETTER_NAMES:
|
||||
getter = getattr(ServiceRegistry, getter_name, None)
|
||||
if getter is None:
|
||||
continue
|
||||
try:
|
||||
scanner = await getter()
|
||||
if scanner is None:
|
||||
continue
|
||||
cache = await scanner.get_cached_data()
|
||||
for entry in cache.raw_data:
|
||||
if os.path.normpath(entry.get("file_path", "")) == normalized:
|
||||
return scanner, entry, getter_name
|
||||
except Exception as exc:
|
||||
logger.debug(
|
||||
"Scanner %s check failed for %s: %s",
|
||||
getter_name, model_path, exc,
|
||||
)
|
||||
return None, None, None
|
||||
|
||||
|
||||
async def _find_scanner_for_model(
|
||||
model_path: str,
|
||||
) -> tuple[Any, object] | tuple[None, None]:
|
||||
"""Find the (scanner, cache_entry) responsible for *model_path*."""
|
||||
scanner, entry, _ = await _find_model_entry(model_path)
|
||||
return scanner, entry
|
||||
|
||||
|
||||
async def identify_model_type(model_path: str) -> str:
|
||||
"""Determine the model type (``\"lora\"``, ``\"checkpoint\"``,
|
||||
``\"embedding\"``, or ``\"other\"``) for *model_path*.
|
||||
|
||||
Falls back to ``\"lora\"`` when unknown.
|
||||
"""
|
||||
_, _, getter_name = await _find_model_entry(model_path)
|
||||
return SCANNER_TYPE_MAP[getter_name] if getter_name else "lora"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def list_base_models(limit: int = 0) -> List[str]:
|
||||
"""Return all valid CivitAI base model names.
|
||||
|
||||
Uses ``CivitaiBaseModelService.get_base_models()`` which merges a
|
||||
hardcoded list (``SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS``) with remote
|
||||
models fetched from the CivitAI API. Never empty — the hardcoded
|
||||
fallback always provides a complete set.
|
||||
|
||||
The result is sorted alphabetically. Pass *limit* = 0 for all models.
|
||||
"""
|
||||
from ..services.civitai_base_model_service import (
|
||||
CivitaiBaseModelService,
|
||||
)
|
||||
|
||||
try:
|
||||
service = await CivitaiBaseModelService.get_instance()
|
||||
response = await service.get_base_models()
|
||||
names: List[str] = response.get("models", [])
|
||||
except Exception as exc:
|
||||
logger.warning("list_base_models failed: %s", exc)
|
||||
names = []
|
||||
if limit > 0:
|
||||
return names[:limit]
|
||||
return names
|
||||
|
||||
|
||||
async def read_metadata(model_path: str) -> Dict[str, Any]:
|
||||
"""Load the full metadata payload for *model_path* from disk.
|
||||
|
||||
Returns an empty dict when the metadata file does not exist or cannot
|
||||
be parsed — never raises.
|
||||
"""
|
||||
from ..utils.metadata_manager import MetadataManager
|
||||
|
||||
try:
|
||||
return await MetadataManager.load_metadata_payload(model_path) or {}
|
||||
except Exception as exc:
|
||||
logger.warning("read_metadata failed for %s: %s", model_path, exc)
|
||||
return {}
|
||||
|
||||
|
||||
async def apply_metadata_updates(
|
||||
model_path: str,
|
||||
updates: Dict[str, Any],
|
||||
) -> List[str]:
|
||||
"""Merge *updates* into the model's on-disk metadata and persist.
|
||||
|
||||
Returns the list of field names that actually changed.
|
||||
"""
|
||||
from ..utils.metadata_manager import MetadataManager
|
||||
|
||||
metadata = await read_metadata(model_path)
|
||||
updated_fields: List[str] = []
|
||||
for key, value in updates.items():
|
||||
old = metadata.get(key)
|
||||
if old != value:
|
||||
metadata[key] = value
|
||||
updated_fields.append(key)
|
||||
if updated_fields:
|
||||
await MetadataManager.save_metadata(model_path, metadata)
|
||||
return updated_fields
|
||||
|
||||
|
||||
async def download_preview(
|
||||
model_path: str,
|
||||
url: str,
|
||||
*,
|
||||
target_width: int = 480,
|
||||
quality: int = 85,
|
||||
) -> str | None:
|
||||
"""Download a preview image from *url*, optimise to .webp, and save it.
|
||||
|
||||
The output file is placed alongside the model file with a ``.webp``
|
||||
extension. Returns the local file path on success, ``None`` on failure.
|
||||
"""
|
||||
from ..services.downloader import get_downloader
|
||||
from ..utils.exif_utils import ExifUtils
|
||||
|
||||
if not url or not url.strip():
|
||||
return None
|
||||
|
||||
base_name = os.path.splitext(os.path.basename(model_path))[0]
|
||||
preview_dir = os.path.dirname(model_path)
|
||||
output_path = os.path.join(preview_dir, base_name + ".webp")
|
||||
|
||||
downloader = await get_downloader()
|
||||
|
||||
# Try in-memory download + optimise first
|
||||
success, content, _headers = await downloader.download_to_memory(
|
||||
url, use_auth=False,
|
||||
)
|
||||
if success and content:
|
||||
try:
|
||||
optimized_data, _ = ExifUtils.optimize_image(
|
||||
image_data=content,
|
||||
target_width=target_width,
|
||||
format="webp",
|
||||
quality=quality,
|
||||
preserve_metadata=False,
|
||||
)
|
||||
with open(output_path, "wb") as f:
|
||||
f.write(optimized_data)
|
||||
return output_path
|
||||
except Exception as exc:
|
||||
logger.warning("Preview optimisation failed, saving raw: %s", exc)
|
||||
# Fall through to raw save
|
||||
|
||||
# Fallback: download directly to file
|
||||
try:
|
||||
ok, _ = await downloader.download_file(url, output_path, use_auth=False)
|
||||
if ok:
|
||||
return output_path
|
||||
except Exception as exc:
|
||||
logger.warning("Preview fallback download failed for %s: %s", model_path, exc)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
async def refresh_cache(model_path: str) -> bool:
|
||||
"""Invalidate and reload the scanner cache entry for *model_path*.
|
||||
|
||||
Returns ``True`` when the model was found and the cache was refreshed.
|
||||
"""
|
||||
scanner, entry = await _find_scanner_for_model(model_path)
|
||||
if scanner is None:
|
||||
logger.warning("refresh_cache: no scanner found for %s", model_path)
|
||||
return False
|
||||
try:
|
||||
metadata = await read_metadata(model_path)
|
||||
if not metadata:
|
||||
logger.warning("refresh_cache: no metadata for %s", model_path)
|
||||
return False
|
||||
await scanner.update_single_model_cache(model_path, model_path, metadata)
|
||||
return True
|
||||
except Exception as exc:
|
||||
logger.warning("refresh_cache failed for %s: %s", model_path, exc)
|
||||
return False
|
||||
@@ -1,113 +0,0 @@
|
||||
"""Subprocess entry point for ``metadata_ops`` (debugging / external use).
|
||||
|
||||
Usage::
|
||||
|
||||
python -m py.metadata_ops base-models list [--limit N]
|
||||
python -m py.metadata_ops metadata read <path>
|
||||
python -m py.metadata_ops metadata update <path> --json '{...}'
|
||||
python -m py.metadata_ops preview download <path> --url <url>
|
||||
python -m py.metadata_ops cache refresh <path>
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import sys
|
||||
from typing import Any, Dict, List
|
||||
|
||||
|
||||
def _build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(prog="lmcli", description="LoRA Manager Agent CLI")
|
||||
sub = parser.add_subparsers(dest="command", required=True)
|
||||
|
||||
# base-models list
|
||||
base_models = sub.add_parser("base-models", aliases=["bm"])
|
||||
base_models_cmds = base_models.add_subparsers(dest="subcommand", required=True)
|
||||
base_models_list = base_models_cmds.add_parser("list")
|
||||
base_models_list.add_argument(
|
||||
"--limit", type=int, default=0, help="Max number of models (0 = all)"
|
||||
)
|
||||
|
||||
# metadata read
|
||||
meta = sub.add_parser("metadata", aliases=["md"])
|
||||
meta_cmds = meta.add_subparsers(dest="subcommand", required=True)
|
||||
meta_read = meta_cmds.add_parser("read")
|
||||
meta_read.add_argument("path", type=str, help="Model file path")
|
||||
|
||||
# metadata update
|
||||
meta_update = meta_cmds.add_parser("update")
|
||||
meta_update.add_argument("path", type=str, help="Model file path")
|
||||
meta_update.add_argument(
|
||||
"--json",
|
||||
type=str,
|
||||
required=True,
|
||||
help='JSON object of fields to update, e.g. \'{"base_model": "SDXL 1.0"}\'',
|
||||
)
|
||||
|
||||
# preview download
|
||||
prev = sub.add_parser("preview", aliases=["pv"])
|
||||
prev_cmds = prev.add_subparsers(dest="subcommand", required=True)
|
||||
prev_dl = prev_cmds.add_parser("download")
|
||||
prev_dl.add_argument("path", type=str, help="Model file path")
|
||||
prev_dl.add_argument("--url", type=str, required=True, help="Preview image URL")
|
||||
|
||||
# cache refresh
|
||||
cache = sub.add_parser("cache")
|
||||
cache_cmds = cache.add_subparsers(dest="subcommand", required=True)
|
||||
cache_refresh = cache_cmds.add_parser("refresh")
|
||||
cache_refresh.add_argument("path", type=str, help="Model file path")
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
async def _run(args: argparse.Namespace) -> Any:
|
||||
from . import ( # lazy import so startup is fast
|
||||
list_base_models,
|
||||
read_metadata,
|
||||
apply_metadata_updates,
|
||||
download_preview,
|
||||
refresh_cache,
|
||||
)
|
||||
|
||||
cmd = args.command
|
||||
sub = args.subcommand
|
||||
|
||||
if cmd in ("base-models", "bm") and sub == "list":
|
||||
return await list_base_models(limit=args.limit)
|
||||
|
||||
if cmd in ("metadata", "md") and sub == "read":
|
||||
return await read_metadata(args.path)
|
||||
|
||||
if cmd in ("metadata", "md") and sub == "update":
|
||||
updates: Dict[str, Any] = json.loads(args.json)
|
||||
return await apply_metadata_updates(args.path, updates)
|
||||
|
||||
if cmd in ("preview", "pv") and sub == "download":
|
||||
return await download_preview(args.path, args.url)
|
||||
|
||||
if cmd == "cache" and sub == "refresh":
|
||||
return await refresh_cache(args.path)
|
||||
|
||||
raise ValueError(f"Unknown command: {cmd} {sub}")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = _build_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
result = asyncio.run(_run(args))
|
||||
# Always print as JSON so callers can parse reliably
|
||||
if isinstance(result, list):
|
||||
for item in result:
|
||||
print(item)
|
||||
elif isinstance(result, dict):
|
||||
json.dump(result, sys.stdout, ensure_ascii=False, indent=2)
|
||||
print()
|
||||
else:
|
||||
print(json.dumps(result))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1 +0,0 @@
|
||||
"""Server middleware modules"""
|
||||
@@ -1,55 +0,0 @@
|
||||
"""Cache control middleware for ComfyUI server"""
|
||||
|
||||
from aiohttp import web
|
||||
from typing import Callable, Awaitable
|
||||
|
||||
# Time in seconds
|
||||
ONE_HOUR: int = 3600
|
||||
ONE_DAY: int = 86400
|
||||
IMG_EXTENSIONS = (
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".png",
|
||||
".ppm",
|
||||
".bmp",
|
||||
".pgm",
|
||||
".tif",
|
||||
".tiff",
|
||||
".webp",
|
||||
".avif",
|
||||
".jxl",
|
||||
".mp4"
|
||||
)
|
||||
|
||||
|
||||
@web.middleware
|
||||
async def cache_control(
|
||||
request: web.Request, handler: Callable[[web.Request], Awaitable[web.Response]]
|
||||
) -> web.Response:
|
||||
"""Cache control middleware that sets appropriate cache headers based on file type and response status"""
|
||||
response: web.Response = await handler(request)
|
||||
|
||||
if (
|
||||
request.path.endswith(".js")
|
||||
or request.path.endswith(".css")
|
||||
or request.path.endswith("index.json")
|
||||
):
|
||||
response.headers.setdefault("Cache-Control", "no-cache")
|
||||
return response
|
||||
|
||||
# Early return for non-image files - no cache headers needed
|
||||
if not request.path.lower().endswith(IMG_EXTENSIONS):
|
||||
return response
|
||||
|
||||
# Handle image files
|
||||
if response.status == 404:
|
||||
response.headers.setdefault("Cache-Control", f"public, max-age={ONE_HOUR}")
|
||||
elif response.status in (200, 201, 202, 203, 204, 205, 206, 301, 308):
|
||||
# Success responses and permanent redirects - cache for 1 day
|
||||
response.headers.setdefault("Cache-Control", f"public, max-age={ONE_DAY}")
|
||||
elif response.status in (302, 303, 307):
|
||||
# Temporary redirects - no cache
|
||||
response.headers.setdefault("Cache-Control", "no-cache")
|
||||
# Note: 304 Not Modified falls through - no cache headers set
|
||||
|
||||
return response
|
||||
@@ -1,73 +0,0 @@
|
||||
"""Middleware helpers for adjusting Content Security Policy headers."""
|
||||
|
||||
from typing import Awaitable, Callable, Dict, List
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
# Use wildcard for CivitAI to support their CDN subdomains (e.g., image-b2.civitai.com)
|
||||
# Security note: This is acceptable because:
|
||||
# 1. CSP img-src only controls image/video loading, not script execution
|
||||
# 2. All *.civitai.com subdomains are controlled by Civitai
|
||||
# 3. Explicit domain list would require constant updates as Civitai adds CDN nodes
|
||||
REMOTE_MEDIA_SOURCES = (
|
||||
"https://*.civitai.com",
|
||||
"https://img.genur.art",
|
||||
)
|
||||
|
||||
|
||||
@web.middleware
|
||||
async def relax_csp_for_remote_media(
|
||||
request: web.Request,
|
||||
handler: Callable[[web.Request], Awaitable[web.StreamResponse]],
|
||||
) -> web.StreamResponse:
|
||||
"""Allow LoRA Manager media previews to load from trusted remote domains.
|
||||
|
||||
When ComfyUI is started with ``--disable-api-nodes`` it injects a restrictive
|
||||
``Content-Security-Policy`` header that blocks remote images and videos. The
|
||||
LoRA Manager UI legitimately needs to fetch previews from Civitai and Genur,
|
||||
so this middleware augments the existing CSP to whitelist those hosts while
|
||||
preserving all other directives.
|
||||
"""
|
||||
|
||||
response: web.StreamResponse = await handler(request)
|
||||
header_value = response.headers.get("Content-Security-Policy")
|
||||
|
||||
if not header_value:
|
||||
return response
|
||||
|
||||
directive_order: List[str] = []
|
||||
directives: Dict[str, List[str]] = {}
|
||||
|
||||
for raw_directive in header_value.split(";"):
|
||||
directive = raw_directive.strip()
|
||||
if not directive:
|
||||
continue
|
||||
|
||||
parts = directive.split()
|
||||
name, values = parts[0], parts[1:]
|
||||
if name not in directive_order:
|
||||
directive_order.append(name)
|
||||
directives[name] = values
|
||||
|
||||
def merge_sources(
|
||||
name: str, sources: List[str], defaults: List[str] | None = None
|
||||
) -> None:
|
||||
existing = directives.get(name, list(defaults or []))
|
||||
|
||||
for source in sources:
|
||||
if source not in existing:
|
||||
existing.append(source)
|
||||
|
||||
directives[name] = existing
|
||||
if name not in directive_order:
|
||||
directive_order.append(name)
|
||||
|
||||
merge_sources("img-src", list(REMOTE_MEDIA_SOURCES))
|
||||
merge_sources("media-src", ["'self'", *REMOTE_MEDIA_SOURCES], defaults=["'self'"])
|
||||
|
||||
updated_header = "; ".join(
|
||||
f"{name} {' '.join(directives[name])}".rstrip() for name in directive_order
|
||||
)
|
||||
|
||||
response.headers["Content-Security-Policy"] = f"{updated_header};"
|
||||
return response
|
||||
@@ -1,86 +0,0 @@
|
||||
"""JSON error middleware for API routes.
|
||||
|
||||
Ensures all responses to /api/* requests return valid JSON that the
|
||||
browser-extension frontend can JSON.parse() without crashing, even when
|
||||
the route does not exist (404) or the handler raises an exception (500).
|
||||
|
||||
Extension consumers call response.json() unconditionally — an HTML error
|
||||
page causes ``SyntaxError: unexpected end of data`` that leaks into the
|
||||
popup UI as a toast notification.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Awaitable, Callable
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@web.middleware
|
||||
async def api_json_error(
|
||||
request: web.Request,
|
||||
handler: Callable[[web.Request], Awaitable[web.Response]],
|
||||
) -> web.Response:
|
||||
"""Return JSON ``{"success": false, "error": "..."}`` for API errors.
|
||||
|
||||
Only intercepts paths starting with ``/api/`` — all other routes
|
||||
(frontend pages, static files, WebSocket upgrades) pass through
|
||||
unchanged.
|
||||
"""
|
||||
if not request.path.startswith("/api/"):
|
||||
return await handler(request)
|
||||
|
||||
try:
|
||||
response = await handler(request)
|
||||
return response
|
||||
except web.HTTPException as exc:
|
||||
# Let redirects (301, 302, 307, 308) propagate — they are not errors.
|
||||
if exc.status < 400:
|
||||
raise
|
||||
|
||||
# Preview 404 is routine (file deleted from disk) — not worth a warning.
|
||||
logger_method = logger.warning
|
||||
if request.path.startswith("/api/lm/previews") and exc.status == 404:
|
||||
logger_method = logger.debug
|
||||
|
||||
# Download-progress 404 is routine too: in-memory tracking is removed
|
||||
# once a download finishes/fails, so the extension's final polls 404.
|
||||
# The extension relies on the 404 status itself (failure detection),
|
||||
# so only the log level is lowered.
|
||||
if (
|
||||
request.path.startswith("/api/lm/download-progress/")
|
||||
and exc.status == 404
|
||||
):
|
||||
logger_method = logger.debug
|
||||
|
||||
logger_method(
|
||||
"API %s %s returned HTTP %d: %s",
|
||||
request.method,
|
||||
request.path,
|
||||
exc.status,
|
||||
exc.reason,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{"success": False, "error": f"{exc.status}: {exc.reason}"},
|
||||
status=exc.status,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.error(
|
||||
"API %s %s raised unhandled exception: %s",
|
||||
request.method,
|
||||
request.path,
|
||||
exc,
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{
|
||||
"success": False,
|
||||
"error": f"500: Internal Server Error ({type(exc).__name__})",
|
||||
},
|
||||
status=500,
|
||||
)
|
||||
@@ -1,197 +0,0 @@
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, List, Tuple
|
||||
import comfy.sd # pyright: ignore[reportMissingImports]
|
||||
import folder_paths # pyright: ignore[reportMissingImports]
|
||||
from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CheckpointLoaderLM:
|
||||
"""Checkpoint Loader with support for extra folder paths
|
||||
|
||||
Loads checkpoints from both standard ComfyUI folders and LoRA Manager's
|
||||
extra folder paths, providing a unified interface for checkpoint loading.
|
||||
The ckpt_name combo supports ComfyUI's control_after_generate, letting
|
||||
users pick a random checkpoint on every run; the base_model input narrows
|
||||
the random pool through a front-end extension that filters the combo
|
||||
options.
|
||||
"""
|
||||
|
||||
NAME = "Checkpoint Loader (LoraManager)"
|
||||
CATEGORY = "Lora Manager/loaders"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
# Get list of checkpoint names from scanner (includes extra folder paths)
|
||||
checkpoint_names = cls._get_checkpoint_names()
|
||||
base_models = cls._get_available_base_models()
|
||||
return {
|
||||
"required": {
|
||||
"ckpt_name": (
|
||||
checkpoint_names,
|
||||
{
|
||||
"tooltip": (
|
||||
"The name of the checkpoint (model) to load. Use "
|
||||
"control_after_generate to pick a random model on "
|
||||
"every run."
|
||||
),
|
||||
"control_after_generate": "fixed",
|
||||
},
|
||||
),
|
||||
"base_model": (
|
||||
base_models,
|
||||
{
|
||||
"default": "Any",
|
||||
"tooltip": (
|
||||
"Restrict the random selection pool to this base "
|
||||
"model. 'Any' uses the full pool."
|
||||
),
|
||||
},
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
RETURN_NAMES = ("MODEL", "CLIP", "VAE")
|
||||
OUTPUT_TOOLTIPS = (
|
||||
"The model used for denoising latents.",
|
||||
"The CLIP model used for encoding text prompts.",
|
||||
"The VAE model used for encoding and decoding images to and from latent space.",
|
||||
)
|
||||
FUNCTION = "load_checkpoint"
|
||||
|
||||
@classmethod
|
||||
def _get_checkpoint_names(cls) -> List[str]:
|
||||
"""Get list of checkpoint names from scanner cache in ComfyUI format (relative path with extension)"""
|
||||
try:
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
import asyncio
|
||||
|
||||
async def _get_names():
|
||||
scanner = await ServiceRegistry.get_checkpoint_scanner()
|
||||
cache = await scanner.get_cached_data()
|
||||
|
||||
# Get all model roots for calculating relative paths
|
||||
model_roots = scanner.get_model_roots()
|
||||
|
||||
# Filter only checkpoint type (not diffusion_model) and format names
|
||||
names = []
|
||||
for item in list(cache.raw_data):
|
||||
if item.get("sub_type") == "checkpoint":
|
||||
file_path = item.get("file_path", "")
|
||||
# Only offer models that still exist on disk so ComfyUI
|
||||
# flags missing checkpoints at queue time via
|
||||
# "value not in list" (the scanner cache can be stale).
|
||||
if file_path and os.path.exists(file_path):
|
||||
# Format using relative path with OS-native separator
|
||||
formatted_name = _format_model_name_for_comfyui(
|
||||
file_path, model_roots
|
||||
)
|
||||
if formatted_name:
|
||||
names.append(formatted_name)
|
||||
|
||||
return sorted(names)
|
||||
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
import concurrent.futures
|
||||
|
||||
def run_in_thread():
|
||||
new_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(new_loop)
|
||||
try:
|
||||
return new_loop.run_until_complete(_get_names())
|
||||
finally:
|
||||
new_loop.close()
|
||||
|
||||
with concurrent.futures.ThreadPoolExecutor() as executor:
|
||||
future = executor.submit(run_in_thread)
|
||||
return future.result()
|
||||
except RuntimeError:
|
||||
return asyncio.run(_get_names())
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting checkpoint names: {e}")
|
||||
return []
|
||||
|
||||
@classmethod
|
||||
def _get_available_base_models(cls) -> List[str]:
|
||||
"""Get distinct base_model values present among indexed checkpoints, for the random-selection filter."""
|
||||
try:
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
async def _get_base_models():
|
||||
scanner = await ServiceRegistry.get_checkpoint_scanner()
|
||||
cache = await scanner.get_cached_data()
|
||||
|
||||
base_models = set()
|
||||
for item in list(cache.raw_data):
|
||||
if item.get("sub_type") != "checkpoint":
|
||||
continue
|
||||
base_model = item.get("base_model")
|
||||
file_path = item.get("file_path", "")
|
||||
if base_model and file_path and os.path.exists(file_path):
|
||||
base_models.add(base_model)
|
||||
|
||||
return sorted(base_models)
|
||||
|
||||
return ["Any"] + cls._run_async(_get_base_models)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting available base models: {e}")
|
||||
return ["Any"]
|
||||
|
||||
@staticmethod
|
||||
def _run_async(coro_fn):
|
||||
"""Run an async fetcher, handling the case where an event loop is already running."""
|
||||
import asyncio
|
||||
|
||||
try:
|
||||
asyncio.get_running_loop()
|
||||
import concurrent.futures
|
||||
|
||||
def run_in_thread():
|
||||
new_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(new_loop)
|
||||
try:
|
||||
return new_loop.run_until_complete(coro_fn())
|
||||
finally:
|
||||
new_loop.close()
|
||||
|
||||
with concurrent.futures.ThreadPoolExecutor() as executor:
|
||||
future = executor.submit(run_in_thread)
|
||||
return future.result()
|
||||
except RuntimeError:
|
||||
return asyncio.run(coro_fn())
|
||||
|
||||
def load_checkpoint(
|
||||
self, ckpt_name: str, base_model: str = "Any"
|
||||
) -> Tuple[Any, Any, Any]:
|
||||
"""Load a checkpoint by name, supporting extra folder paths
|
||||
|
||||
Args:
|
||||
ckpt_name: The name of the checkpoint to load (relative path with extension)
|
||||
base_model: Only used by the front-end to filter the random pool
|
||||
|
||||
Returns:
|
||||
Tuple of (MODEL, CLIP, VAE)
|
||||
"""
|
||||
del base_model
|
||||
# Get absolute path from cache using ComfyUI-style name
|
||||
ckpt_path, metadata = get_checkpoint_info_absolute(ckpt_name)
|
||||
|
||||
if metadata is None:
|
||||
raise FileNotFoundError(
|
||||
f"Checkpoint '{ckpt_name}' not found in LoRA Manager cache. "
|
||||
"Make sure the checkpoint is indexed and try again."
|
||||
)
|
||||
|
||||
# Load regular checkpoint using ComfyUI's API
|
||||
logger.info(f"Loading checkpoint from: {ckpt_path}")
|
||||
out = comfy.sd.load_checkpoint_guess_config(
|
||||
ckpt_path,
|
||||
output_vae=True,
|
||||
output_clip=True,
|
||||
embedding_directory=folder_paths.get_folder_paths("embeddings"),
|
||||
)
|
||||
return out[:3]
|
||||
@@ -1,124 +0,0 @@
|
||||
"""Create Hook LoRA (LoraManager) — multi-LoRA hook node compatible with ComfyUI's built-in hook pipeline.
|
||||
|
||||
Produces ``("HOOKS",)`` output that chains seamlessly with downstream hook consumers
|
||||
(ConditioningSetProperties, SetHookKeyframes, CombineHooks, SetClipHooks, etc.).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
|
||||
from ..utils.utils import get_lora_info_absolute
|
||||
from .utils import (
|
||||
FlexibleOptionalInputType,
|
||||
any_type,
|
||||
apply_lora_syntax_format,
|
||||
get_loras_list,
|
||||
validate_lora_entries,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CreateHookLoraLM:
|
||||
NAME = "Create Hook LoRA (LoraManager)"
|
||||
CATEGORY = "Lora Manager/hooks"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": (
|
||||
"AUTOCOMPLETE_TEXT_LORAS",
|
||||
{
|
||||
"placeholder": "Search LoRAs to add...",
|
||||
"tooltip": (
|
||||
"Search and select LoRAs. Each LoRA gets its own "
|
||||
"model/clip strength. Hooks chain with prev_hooks."
|
||||
),
|
||||
},
|
||||
),
|
||||
"loras": ("LORAS", {}),
|
||||
},
|
||||
"optional": FlexibleOptionalInputType(any_type),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def VALIDATE_INPUTS(cls, loras=None):
|
||||
"""Queue-time validation: reject missing local LoRAs before execution."""
|
||||
return validate_lora_entries({"loras": loras}) or True
|
||||
|
||||
RETURN_TYPES = ("HOOKS", "STRING", "STRING")
|
||||
RETURN_NAMES = ("HOOKS", "trigger_words", "active_loras")
|
||||
FUNCTION = "create_hook"
|
||||
|
||||
def create_hook(self, text: str, loras, **kwargs):
|
||||
"""Create a HookGroup from the selected LoRAs, chained with prev_hooks.
|
||||
|
||||
Each active LoRA from the widget is loaded and wrapped in a WeightHook
|
||||
via :func:`comfy.hooks.create_hook_lora`. All hooks are combined into a
|
||||
single group and returned alongside trigger words and a human-readable
|
||||
summary of the active LoRAs.
|
||||
"""
|
||||
del text # used by the frontend widget only
|
||||
|
||||
# Lazy imports: comfy is not available in CI/test environment at module level
|
||||
import comfy.hooks # pyright: ignore[reportMissingImports] # noqa: C0415
|
||||
import comfy.utils # pyright: ignore[reportMissingImports] # noqa: C0415
|
||||
|
||||
prev_hooks: comfy.hooks.HookGroup | None = kwargs.get("prev_hooks")
|
||||
|
||||
hook_group = prev_hooks.clone() if prev_hooks is not None else comfy.hooks.HookGroup()
|
||||
|
||||
all_trigger_words: list[str] = []
|
||||
active_loras: list[tuple[str, float, float]] = []
|
||||
|
||||
for lora in get_loras_list({"loras": loras}):
|
||||
if not lora.get("active", False):
|
||||
continue
|
||||
|
||||
lora_name = apply_lora_syntax_format(lora["name"])
|
||||
model_strength = float(lora["strength"])
|
||||
clip_strength = float(lora.get("clipStrength", model_strength))
|
||||
|
||||
# Skip useless no-op entries (both strengths are zero)
|
||||
if model_strength == 0.0 and clip_strength == 0.0:
|
||||
continue
|
||||
|
||||
lora_path, trigger_words = get_lora_info_absolute(lora_name)
|
||||
if not lora_path or not os.path.isfile(lora_path):
|
||||
logger.warning("LoRA '%s' not found — skipping", lora_name)
|
||||
continue
|
||||
|
||||
try:
|
||||
lora_weights = comfy.utils.load_torch_file(lora_path, safe_load=True)
|
||||
|
||||
lora_hooks = comfy.hooks.create_hook_lora(
|
||||
lora=lora_weights,
|
||||
strength_model=model_strength,
|
||||
strength_clip=clip_strength,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Failed to load LoRA '%s' — skipping", lora_name)
|
||||
continue
|
||||
hook_group = hook_group.clone_and_combine(lora_hooks)
|
||||
|
||||
active_loras.append((lora_name, model_strength, clip_strength))
|
||||
all_trigger_words.extend(trigger_words)
|
||||
|
||||
# Format trigger words (group mode separator)
|
||||
trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
|
||||
|
||||
# Format active LoRAs summary
|
||||
formatted_loras = []
|
||||
for name, model_s, clip_s in active_loras:
|
||||
if abs(model_s - clip_s) > 0.001:
|
||||
formatted_loras.append(
|
||||
f"<lora:{name}:{model_s}:{clip_s}>"
|
||||
)
|
||||
else:
|
||||
formatted_loras.append(f"<lora:{name}:{model_s}>")
|
||||
active_loras_text = " ".join(formatted_loras)
|
||||
|
||||
return (hook_group, trigger_words_text, active_loras_text)
|
||||
+15
-31
@@ -1,15 +1,15 @@
|
||||
import logging
|
||||
from server import PromptServer # type: ignore
|
||||
from ..metadata_collector.metadata_processor import MetadataProcessor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DebugMetadataLM:
|
||||
class DebugMetadata:
|
||||
NAME = "Debug Metadata (LoraManager)"
|
||||
CATEGORY = "Lora Manager/utils"
|
||||
DESCRIPTION = "Debug node to verify metadata_processor functionality"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
@@ -25,37 +25,21 @@ class DebugMetadataLM:
|
||||
FUNCTION = "process_metadata"
|
||||
|
||||
def process_metadata(self, images, id):
|
||||
"""
|
||||
Process metadata from the execution context and return it for UI display.
|
||||
|
||||
The metadata is returned via the 'ui' key in the return dict, which triggers
|
||||
node.onExecuted on the frontend to update the JsonDisplayWidget.
|
||||
|
||||
Args:
|
||||
images: Input images (required for execution flow)
|
||||
id: Node's unique ID (hidden)
|
||||
|
||||
Returns:
|
||||
Dict with 'result' (empty tuple) and 'ui' (metadata dict for widget display)
|
||||
"""
|
||||
try:
|
||||
# Get the current execution context's metadata
|
||||
from ..metadata_collector import get_metadata
|
||||
|
||||
metadata = get_metadata()
|
||||
|
||||
# Use the MetadataProcessor to convert it to dict
|
||||
metadata_dict = MetadataProcessor.to_dict(metadata, id)
|
||||
|
||||
return {
|
||||
"result": (),
|
||||
# ComfyUI expects ui values to be lists, wrap the dict in a list
|
||||
"ui": {"metadata": [metadata_dict]},
|
||||
}
|
||||
|
||||
|
||||
# Use the MetadataProcessor to convert it to JSON string
|
||||
metadata_json = MetadataProcessor.to_json(metadata, id)
|
||||
|
||||
# Send metadata to frontend for display
|
||||
PromptServer.instance.send_sync("metadata_update", {
|
||||
"id": id,
|
||||
"metadata": metadata_json
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing metadata: {e}")
|
||||
return {
|
||||
"result": (),
|
||||
"ui": {"metadata": [{"error": str(e)}]},
|
||||
}
|
||||
|
||||
return ()
|
||||
|
||||
@@ -1,161 +0,0 @@
|
||||
"""
|
||||
Helper module to safely import ComfyUI-GGUF modules.
|
||||
|
||||
This module provides a robust way to import ComfyUI-GGUF functionality
|
||||
regardless of how ComfyUI loaded it.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import importlib.util
|
||||
import logging
|
||||
from typing import Optional, Tuple, Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _get_gguf_path() -> str:
|
||||
"""Get the path to ComfyUI-GGUF based on this file's location.
|
||||
|
||||
Since ComfyUI-Lora-Manager and ComfyUI-GGUF are both in custom_nodes/,
|
||||
we can derive the GGUF path from our own location.
|
||||
"""
|
||||
# This file is at: custom_nodes/ComfyUI-Lora-Manager/py/nodes/gguf_import_helper.py
|
||||
# ComfyUI-GGUF is at: custom_nodes/ComfyUI-GGUF
|
||||
current_file = os.path.abspath(__file__)
|
||||
# Go up 4 levels: nodes -> py -> ComfyUI-Lora-Manager -> custom_nodes
|
||||
custom_nodes_dir = os.path.dirname(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
|
||||
)
|
||||
return os.path.join(custom_nodes_dir, "ComfyUI-GGUF")
|
||||
|
||||
|
||||
def _find_gguf_module() -> Optional[Any]:
|
||||
"""Find ComfyUI-GGUF module in sys.modules.
|
||||
|
||||
ComfyUI registers modules using the full path with dots replaced by _x_.
|
||||
"""
|
||||
gguf_path = _get_gguf_path()
|
||||
sys_module_name = gguf_path.replace(".", "_x_")
|
||||
|
||||
logger.debug(f"[GGUF Import] Looking for module '{sys_module_name}' in sys.modules")
|
||||
if sys_module_name in sys.modules:
|
||||
logger.info(f"[GGUF Import] Found module: '{sys_module_name}'")
|
||||
return sys.modules[sys_module_name]
|
||||
|
||||
logger.debug(f"[GGUF Import] Module not found: '{sys_module_name}'")
|
||||
return None
|
||||
|
||||
|
||||
def _load_gguf_modules_directly() -> Optional[Any]:
|
||||
"""Load ComfyUI-GGUF modules directly from file paths."""
|
||||
gguf_path = _get_gguf_path()
|
||||
|
||||
logger.info(f"[GGUF Import] Direct Load: Attempting to load from '{gguf_path}'")
|
||||
|
||||
if not os.path.exists(gguf_path):
|
||||
logger.warning(f"[GGUF Import] Path does not exist: {gguf_path}")
|
||||
return None
|
||||
|
||||
try:
|
||||
namespace = "ComfyUI_GGUF_Dynamic"
|
||||
init_path = os.path.join(gguf_path, "__init__.py")
|
||||
|
||||
if not os.path.exists(init_path):
|
||||
logger.warning(f"[GGUF Import] __init__.py not found at '{init_path}'")
|
||||
return None
|
||||
|
||||
logger.debug(f"[GGUF Import] Loading from '{init_path}'")
|
||||
spec = importlib.util.spec_from_file_location(namespace, init_path)
|
||||
if not spec or not spec.loader:
|
||||
logger.error(f"[GGUF Import] Failed to create spec for '{init_path}'")
|
||||
return None
|
||||
|
||||
package = importlib.util.module_from_spec(spec)
|
||||
package.__path__ = [gguf_path]
|
||||
sys.modules[namespace] = package
|
||||
spec.loader.exec_module(package)
|
||||
logger.debug(f"[GGUF Import] Loaded main package '{namespace}'")
|
||||
|
||||
# Load submodules
|
||||
loaded = []
|
||||
for submod_name in ["loader", "ops", "nodes"]:
|
||||
submod_path = os.path.join(gguf_path, f"{submod_name}.py")
|
||||
if os.path.exists(submod_path):
|
||||
submod_spec = importlib.util.spec_from_file_location(
|
||||
f"{namespace}.{submod_name}", submod_path
|
||||
)
|
||||
if submod_spec and submod_spec.loader:
|
||||
submod = importlib.util.module_from_spec(submod_spec)
|
||||
submod.__package__ = namespace
|
||||
sys.modules[f"{namespace}.{submod_name}"] = submod
|
||||
submod_spec.loader.exec_module(submod)
|
||||
setattr(package, submod_name, submod)
|
||||
loaded.append(submod_name)
|
||||
logger.debug(f"[GGUF Import] Loaded submodule '{submod_name}'")
|
||||
|
||||
logger.info(f"[GGUF Import] Direct Load success: {loaded}")
|
||||
return package
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[GGUF Import] Direct Load failed: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
def get_gguf_modules() -> Tuple[Any, Any, Any]:
|
||||
"""Get ComfyUI-GGUF modules (loader, ops, nodes).
|
||||
|
||||
Returns:
|
||||
Tuple of (loader_module, ops_module, nodes_module)
|
||||
|
||||
Raises:
|
||||
RuntimeError: If ComfyUI-GGUF cannot be found or loaded.
|
||||
"""
|
||||
logger.debug("[GGUF Import] Starting module search...")
|
||||
|
||||
# Try to find already loaded module first
|
||||
gguf_module = _find_gguf_module()
|
||||
|
||||
if gguf_module is None:
|
||||
logger.info("[GGUF Import] Not found in sys.modules, trying direct load...")
|
||||
gguf_module = _load_gguf_modules_directly()
|
||||
|
||||
if gguf_module is None:
|
||||
raise RuntimeError(
|
||||
"ComfyUI-GGUF is not installed. "
|
||||
"Please install from https://github.com/city96/ComfyUI-GGUF"
|
||||
)
|
||||
|
||||
# Extract submodules
|
||||
loader = getattr(gguf_module, "loader", None)
|
||||
ops = getattr(gguf_module, "ops", None)
|
||||
nodes = getattr(gguf_module, "nodes", None)
|
||||
|
||||
if loader is None or ops is None or nodes is None:
|
||||
missing = [
|
||||
name
|
||||
for name, mod in [("loader", loader), ("ops", ops), ("nodes", nodes)]
|
||||
if mod is None
|
||||
]
|
||||
raise RuntimeError(f"ComfyUI-GGUF missing submodules: {missing}")
|
||||
|
||||
logger.debug("[GGUF Import] All modules loaded successfully")
|
||||
return loader, ops, nodes
|
||||
|
||||
|
||||
def get_gguf_sd_loader():
|
||||
"""Get the gguf_sd_loader function from ComfyUI-GGUF."""
|
||||
loader, _, _ = get_gguf_modules()
|
||||
return getattr(loader, "gguf_sd_loader")
|
||||
|
||||
|
||||
def get_ggml_ops():
|
||||
"""Get the GGMLOps class from ComfyUI-GGUF."""
|
||||
_, ops, _ = get_gguf_modules()
|
||||
return getattr(ops, "GGMLOps")
|
||||
|
||||
|
||||
def get_gguf_model_patcher():
|
||||
"""Get the GGUFModelPatcher class from ComfyUI-GGUF."""
|
||||
_, _, nodes = get_gguf_modules()
|
||||
return getattr(nodes, "GGUFModelPatcher")
|
||||
@@ -1,201 +0,0 @@
|
||||
"""
|
||||
Lora Cycler Node - Sequentially cycles through LoRAs from a pool.
|
||||
|
||||
This node accepts optional pool_config input to filter available LoRAs, and outputs
|
||||
a LORA_STACK with one LoRA at a time. Returns UI updates with current/next LoRA info
|
||||
and tracks the cycle progress which persists across workflow save/load.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
|
||||
from ..utils.utils import get_lora_info
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LoraCyclerLM:
|
||||
"""Node that sequentially cycles through LoRAs from a pool"""
|
||||
|
||||
NAME = "Lora Cycler (LoraManager)"
|
||||
CATEGORY = "Lora Manager/randomizer"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"cycler_config": ("CYCLER_CONFIG", {}),
|
||||
},
|
||||
"optional": {
|
||||
"pool_config": ("POOL_CONFIG", {}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LORA_STACK",)
|
||||
RETURN_NAMES = ("LORA_STACK",)
|
||||
|
||||
FUNCTION = "cycle"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
async def cycle(self, cycler_config, pool_config=None):
|
||||
"""
|
||||
Cycle through LoRAs based on configuration and pool filters.
|
||||
|
||||
Args:
|
||||
cycler_config: Dict with cycler settings (current_index, model_strength, clip_strength, sort_by)
|
||||
pool_config: Optional config from LoRA Pool node for filtering
|
||||
|
||||
Returns:
|
||||
Dictionary with 'result' (LORA_STACK tuple) and 'ui' (for widget display)
|
||||
"""
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
from ..services.lora_service import LoraService
|
||||
|
||||
# Extract settings from cycler_config
|
||||
current_index = cycler_config.get("current_index", 1) # 1-based
|
||||
model_strength = float(cycler_config.get("model_strength", 1.0))
|
||||
clip_strength = float(cycler_config.get("clip_strength", 1.0))
|
||||
use_same_clip_strength = cycler_config.get("use_same_clip_strength", True)
|
||||
use_preset_strength = cycler_config.get("use_preset_strength", False)
|
||||
preset_strength_scale = float(cycler_config.get("preset_strength_scale", 1.0))
|
||||
sort_by = "filename"
|
||||
|
||||
# Include "no lora" option
|
||||
include_no_lora = cycler_config.get("include_no_lora", False)
|
||||
|
||||
# Dual-index mechanism for batch queue synchronization
|
||||
execution_index = cycler_config.get("execution_index") # Can be None
|
||||
# next_index_from_config = cycler_config.get("next_index") # Not used on backend
|
||||
|
||||
# Get scanner and service
|
||||
scanner = await ServiceRegistry.get_lora_scanner()
|
||||
lora_service = LoraService(scanner)
|
||||
|
||||
# Get filtered and sorted LoRA list
|
||||
lora_list = await lora_service.get_cycler_list(
|
||||
pool_config=pool_config, sort_by=sort_by
|
||||
)
|
||||
|
||||
total_count = len(lora_list)
|
||||
|
||||
# Calculate effective total count (includes no lora option if enabled)
|
||||
effective_total_count = total_count + 1 if include_no_lora else total_count
|
||||
|
||||
if total_count == 0 and not include_no_lora:
|
||||
logger.warning("[LoraCyclerLM] No LoRAs available in pool")
|
||||
return {
|
||||
"result": ([],),
|
||||
"ui": {
|
||||
"current_index": [1],
|
||||
"next_index": [1],
|
||||
"total_count": [0],
|
||||
"current_lora_name": [""],
|
||||
"current_lora_filename": [""],
|
||||
"error": ["No LoRAs available in pool"],
|
||||
},
|
||||
}
|
||||
|
||||
# Determine which index to use for this execution
|
||||
# If execution_index is provided (batch queue case), use it
|
||||
# Otherwise use current_index (first execution or non-batch case)
|
||||
if execution_index is not None:
|
||||
actual_index = execution_index
|
||||
else:
|
||||
actual_index = current_index
|
||||
|
||||
# Clamp index to valid range (1-based, includes no lora if enabled)
|
||||
clamped_index = max(1, min(actual_index, effective_total_count))
|
||||
|
||||
# Check if current index is the "no lora" option (last position when include_no_lora is True)
|
||||
is_no_lora = include_no_lora and clamped_index == effective_total_count
|
||||
|
||||
if is_no_lora:
|
||||
# "No LoRA" option - return empty stack
|
||||
lora_stack = []
|
||||
current_lora_name = "No LoRA"
|
||||
current_lora_filename = "No LoRA"
|
||||
else:
|
||||
# Get LoRA at current index (convert to 0-based for list access)
|
||||
current_lora = lora_list[clamped_index - 1]
|
||||
current_lora_name = current_lora["file_name"]
|
||||
current_lora_filename = current_lora["file_name"]
|
||||
|
||||
# Build LORA_STACK with single LoRA
|
||||
if current_lora["file_name"] == "None":
|
||||
lora_path = None
|
||||
else:
|
||||
lora_path, _ = get_lora_info(current_lora["file_name"])
|
||||
|
||||
if not lora_path:
|
||||
if current_lora["file_name"] != "None":
|
||||
logger.warning(
|
||||
f"[LoraCyclerLM] Could not find path for LoRA: {current_lora['file_name']}"
|
||||
)
|
||||
lora_stack = []
|
||||
else:
|
||||
# Normalize path separators
|
||||
lora_path = lora_path.replace("/", os.sep)
|
||||
|
||||
if use_preset_strength:
|
||||
lora_metadata = await lora_service.get_lora_metadata_by_filename(
|
||||
current_lora["file_name"]
|
||||
)
|
||||
if lora_metadata:
|
||||
recommended_strength = (
|
||||
lora_service.get_recommended_strength_from_lora_data(
|
||||
lora_metadata
|
||||
)
|
||||
)
|
||||
if recommended_strength is not None:
|
||||
model_strength = round(
|
||||
recommended_strength * preset_strength_scale, 2
|
||||
)
|
||||
|
||||
if use_same_clip_strength:
|
||||
clip_strength = model_strength
|
||||
else:
|
||||
recommended_clip_strength = (
|
||||
lora_service.get_recommended_clip_strength_from_lora_data(
|
||||
lora_metadata
|
||||
)
|
||||
)
|
||||
if recommended_clip_strength is not None:
|
||||
clip_strength = round(
|
||||
recommended_clip_strength * preset_strength_scale, 2
|
||||
)
|
||||
elif use_same_clip_strength:
|
||||
clip_strength = model_strength
|
||||
elif use_same_clip_strength:
|
||||
clip_strength = model_strength
|
||||
|
||||
lora_stack = [(lora_path, model_strength, clip_strength)]
|
||||
|
||||
# Calculate next index (wrap to 1 if at end)
|
||||
next_index = clamped_index + 1
|
||||
if next_index > effective_total_count:
|
||||
next_index = 1
|
||||
|
||||
# Get next LoRA for UI display (what will be used next generation)
|
||||
is_next_no_lora = include_no_lora and next_index == effective_total_count
|
||||
if is_next_no_lora:
|
||||
next_display_name = "No LoRA"
|
||||
next_lora_filename = "No LoRA"
|
||||
else:
|
||||
next_lora = lora_list[next_index - 1]
|
||||
next_display_name = next_lora["file_name"]
|
||||
next_lora_filename = next_lora["file_name"]
|
||||
|
||||
return {
|
||||
"result": (lora_stack,),
|
||||
"ui": {
|
||||
"current_index": [clamped_index],
|
||||
"next_index": [next_index],
|
||||
"total_count": [
|
||||
total_count
|
||||
], # Return actual LoRA count, not effective_total_count
|
||||
"current_lora_name": [current_lora_name],
|
||||
"current_lora_filename": [current_lora_filename],
|
||||
"next_lora_name": [next_display_name],
|
||||
"next_lora_filename": [next_lora_filename],
|
||||
},
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
"""Lora Info display node — pure frontend node for showing selected LoRA info.
|
||||
|
||||
This node does NOT participate in workflow execution. Its single optional
|
||||
"lora_source" input exists solely as a wire-connection anchor so that the
|
||||
frontend can traverse the graph and push selection data to connected info nodes.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
class LoraInfoLM:
|
||||
"""Display node that shows filename and notes for the selected LoRA."""
|
||||
|
||||
NAME = "Lora Info (LoraManager)"
|
||||
CATEGORY = "Lora Manager/utils"
|
||||
DESCRIPTION = (
|
||||
"Displays information (filename, notes) about the currently selected "
|
||||
"LoRA. Connect any output from a LoRA Loader or Stacker to the "
|
||||
"lora_source input, then select a LoRA in the source widget — the "
|
||||
"info updates automatically. Does not affect workflow execution."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ()
|
||||
RETURN_NAMES = ()
|
||||
OUTPUT_NODE = False
|
||||
FUNCTION = "noop"
|
||||
|
||||
def noop(self, **kwargs):
|
||||
# This node is display-only — no workflow execution needed.
|
||||
return ()
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
LoraInfoLM.NAME: LoraInfoLM,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
LoraInfoLM.NAME: "Lora Info (LoraManager)",
|
||||
}
|
||||
+110
-201
@@ -1,222 +1,131 @@
|
||||
import importlib
|
||||
import logging
|
||||
|
||||
import comfy.sd # pyright: ignore[reportMissingImports]
|
||||
import comfy.utils # pyright: ignore[reportMissingImports]
|
||||
|
||||
from ..utils.utils import get_lora_info_absolute
|
||||
from .utils import (
|
||||
FlexibleOptionalInputType,
|
||||
any_type,
|
||||
apply_lora_syntax_format,
|
||||
detect_nunchaku_model_kind,
|
||||
extract_lora_name,
|
||||
get_loras_list,
|
||||
nunchaku_load_lora,
|
||||
parse_lora_syntax,
|
||||
validate_lora_entries,
|
||||
)
|
||||
from nodes import LoraLoader
|
||||
from comfy.comfy_types import IO # type: ignore
|
||||
from ..utils.utils import get_lora_info
|
||||
from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _get_nunchaku_load_qwen_loras():
|
||||
try:
|
||||
module = importlib.import_module(".nunchaku_qwen", __package__)
|
||||
except ImportError as exc:
|
||||
raise RuntimeError(
|
||||
"Qwen-Image LoRA loading requires the ComfyUI runtime with its torch dependency available."
|
||||
) from exc
|
||||
return module.nunchaku_load_qwen_loras
|
||||
|
||||
|
||||
def _collect_stack_entries(lora_stack):
|
||||
entries = []
|
||||
if not lora_stack:
|
||||
return entries
|
||||
|
||||
for lora_path, model_strength, clip_strength in lora_stack:
|
||||
lora_name = extract_lora_name(lora_path)
|
||||
absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
|
||||
entries.append({
|
||||
"name": lora_name,
|
||||
"absolute_path": absolute_lora_path,
|
||||
"input_path": lora_path,
|
||||
"model_strength": float(model_strength),
|
||||
"clip_strength": float(clip_strength),
|
||||
"trigger_words": trigger_words,
|
||||
})
|
||||
return entries
|
||||
|
||||
|
||||
def _collect_widget_entries(loras):
|
||||
entries = []
|
||||
for lora in get_loras_list({"loras": loras}):
|
||||
if not lora.get("active", False):
|
||||
continue
|
||||
lora_name = apply_lora_syntax_format(lora["name"])
|
||||
model_strength = float(lora["strength"])
|
||||
clip_strength = float(lora.get("clipStrength", model_strength))
|
||||
lora_path, trigger_words = get_lora_info_absolute(lora_name)
|
||||
entries.append({
|
||||
"name": lora_name,
|
||||
"absolute_path": lora_path,
|
||||
"input_path": lora_path,
|
||||
"model_strength": model_strength,
|
||||
"clip_strength": clip_strength,
|
||||
"trigger_words": trigger_words,
|
||||
})
|
||||
return entries
|
||||
|
||||
|
||||
def _format_loaded_loras(loaded_loras):
|
||||
formatted_loras = []
|
||||
for item in loaded_loras:
|
||||
if item["include_clip_strength"]:
|
||||
formatted_loras.append(
|
||||
f"<lora:{item['name']}:{item['model_strength']}:{item['clip_strength']}>"
|
||||
)
|
||||
else:
|
||||
formatted_loras.append(f"<lora:{item['name']}:{item['model_strength']}>")
|
||||
return " ".join(formatted_loras)
|
||||
|
||||
|
||||
def _apply_entries(model, clip, lora_entries, nunchaku_model_kind):
|
||||
loaded_loras = []
|
||||
all_trigger_words = []
|
||||
|
||||
if nunchaku_model_kind == "qwen_image":
|
||||
nunchaku_load_qwen_loras = _get_nunchaku_load_qwen_loras()
|
||||
qwen_lora_configs = []
|
||||
for entry in lora_entries:
|
||||
qwen_lora_configs.append((entry["absolute_path"], entry["model_strength"]))
|
||||
loaded_loras.append({
|
||||
"name": entry["name"],
|
||||
"model_strength": entry["model_strength"],
|
||||
"clip_strength": entry["model_strength"],
|
||||
"include_clip_strength": False,
|
||||
})
|
||||
all_trigger_words.extend(entry["trigger_words"])
|
||||
if qwen_lora_configs:
|
||||
model = nunchaku_load_qwen_loras(model, qwen_lora_configs)
|
||||
return model, clip, loaded_loras, all_trigger_words
|
||||
|
||||
for entry in lora_entries:
|
||||
if nunchaku_model_kind == "flux":
|
||||
model = nunchaku_load_lora(model, entry["input_path"], entry["model_strength"])
|
||||
else:
|
||||
lora = comfy.utils.load_torch_file(entry["absolute_path"], safe_load=True)
|
||||
model, clip = comfy.sd.load_lora_for_models(
|
||||
model,
|
||||
clip,
|
||||
lora,
|
||||
entry["model_strength"],
|
||||
entry["clip_strength"],
|
||||
)
|
||||
|
||||
include_clip_strength = nunchaku_model_kind is None and abs(entry["model_strength"] - entry["clip_strength"]) > 0.001
|
||||
loaded_loras.append({
|
||||
"name": entry["name"],
|
||||
"model_strength": entry["model_strength"],
|
||||
"clip_strength": entry["clip_strength"],
|
||||
"include_clip_strength": include_clip_strength,
|
||||
})
|
||||
all_trigger_words.extend(entry["trigger_words"])
|
||||
|
||||
return model, clip, loaded_loras, all_trigger_words
|
||||
|
||||
|
||||
class LoraLoaderLM:
|
||||
class LoraManagerLoader:
|
||||
NAME = "Lora Loader (LoraManager)"
|
||||
CATEGORY = "Lora Manager/loaders"
|
||||
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"text": ("AUTOCOMPLETE_TEXT_LORAS", {
|
||||
"placeholder": "Search LoRAs to add...",
|
||||
# "clip": ("CLIP",),
|
||||
"text": (IO.STRING, {
|
||||
"multiline": True,
|
||||
"dynamicPrompts": True,
|
||||
"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
|
||||
"placeholder": "LoRA syntax input: <lora:name:strength>"
|
||||
}),
|
||||
"loras": ("LORAS", {}),
|
||||
},
|
||||
"optional": FlexibleOptionalInputType(any_type),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def VALIDATE_INPUTS(cls, loras=None):
|
||||
"""Queue-time validation: reject missing local LoRAs before execution."""
|
||||
return validate_lora_entries({"loras": loras}) or True
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
|
||||
RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
|
||||
RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
|
||||
FUNCTION = "load_loras"
|
||||
|
||||
def load_loras(self, model, text, loras, **kwargs):
|
||||
"""Loads multiple LoRAs based on the widget input and lora_stack."""
|
||||
del text
|
||||
clip = kwargs.get("clip", None)
|
||||
lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
|
||||
lora_entries.extend(_collect_widget_entries(loras))
|
||||
|
||||
nunchaku_model_kind = detect_nunchaku_model_kind(model)
|
||||
if nunchaku_model_kind == "flux":
|
||||
logger.info("Detected Nunchaku Flux model")
|
||||
elif nunchaku_model_kind == "qwen_image":
|
||||
logger.info("Detected Nunchaku Qwen-Image model")
|
||||
|
||||
model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
|
||||
|
||||
def load_loras(self, model, text, **kwargs):
|
||||
"""Loads multiple LoRAs based on the kwargs input and lora_stack."""
|
||||
loaded_loras = []
|
||||
all_trigger_words = []
|
||||
|
||||
clip = kwargs.get('clip', None)
|
||||
lora_stack = kwargs.get('lora_stack', None)
|
||||
|
||||
# Check if model is a Nunchaku Flux model - simplified approach
|
||||
is_nunchaku_model = False
|
||||
|
||||
try:
|
||||
model_wrapper = model.model.diffusion_model
|
||||
# Check if model is a Nunchaku Flux model using only class name
|
||||
if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
|
||||
is_nunchaku_model = True
|
||||
logger.info("Detected Nunchaku Flux model")
|
||||
except (AttributeError, TypeError):
|
||||
# Not a model with the expected structure
|
||||
pass
|
||||
|
||||
# First process lora_stack if available
|
||||
if lora_stack:
|
||||
for lora_path, model_strength, clip_strength in lora_stack:
|
||||
# Apply the LoRA using the appropriate loader
|
||||
if is_nunchaku_model:
|
||||
# Use our custom function for Flux models
|
||||
model = nunchaku_load_lora(model, lora_path, model_strength)
|
||||
# clip remains unchanged for Nunchaku models
|
||||
else:
|
||||
# Use default loader for standard models
|
||||
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
|
||||
|
||||
# Extract lora name for trigger words lookup
|
||||
lora_name = extract_lora_name(lora_path)
|
||||
_, trigger_words = get_lora_info(lora_name)
|
||||
|
||||
all_trigger_words.extend(trigger_words)
|
||||
# Add clip strength to output if different from model strength (except for Nunchaku models)
|
||||
if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
|
||||
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
|
||||
else:
|
||||
loaded_loras.append(f"{lora_name}: {model_strength}")
|
||||
|
||||
# Then process loras from kwargs with support for both old and new formats
|
||||
loras_list = get_loras_list(kwargs)
|
||||
for lora in loras_list:
|
||||
if not lora.get('active', False):
|
||||
continue
|
||||
|
||||
lora_name = lora['name']
|
||||
model_strength = float(lora['strength'])
|
||||
# Get clip strength - use model strength as default if not specified
|
||||
clip_strength = float(lora.get('clipStrength', model_strength))
|
||||
|
||||
# Get lora path and trigger words
|
||||
lora_path, trigger_words = get_lora_info(lora_name)
|
||||
|
||||
# Apply the LoRA using the appropriate loader
|
||||
if is_nunchaku_model:
|
||||
# For Nunchaku models, use our custom function
|
||||
model = nunchaku_load_lora(model, lora_path, model_strength)
|
||||
# clip remains unchanged
|
||||
else:
|
||||
# Use default loader for standard models
|
||||
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
|
||||
|
||||
# Include clip strength in output if different from model strength and not a Nunchaku model
|
||||
if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
|
||||
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
|
||||
else:
|
||||
loaded_loras.append(f"{lora_name}: {model_strength}")
|
||||
|
||||
# Add trigger words to collection
|
||||
all_trigger_words.extend(trigger_words)
|
||||
|
||||
# use ',, ' to separate trigger words for group mode
|
||||
trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
|
||||
formatted_loras_text = _format_loaded_loras(loaded_loras)
|
||||
return (model, clip, trigger_words_text, formatted_loras_text)
|
||||
|
||||
# Format loaded_loras with support for both formats
|
||||
formatted_loras = []
|
||||
for item in loaded_loras:
|
||||
parts = item.split(":")
|
||||
lora_name = parts[0].strip()
|
||||
strength_parts = parts[1].strip().split(",")
|
||||
|
||||
if len(strength_parts) > 1:
|
||||
# Different model and clip strengths
|
||||
model_str = strength_parts[0].strip()
|
||||
clip_str = strength_parts[1].strip()
|
||||
formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
|
||||
else:
|
||||
# Same strength for both
|
||||
model_str = strength_parts[0].strip()
|
||||
formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
|
||||
|
||||
formatted_loras_text = " ".join(formatted_loras)
|
||||
|
||||
|
||||
class LoraTextLoaderLM:
|
||||
NAME = "LoRA Text Loader (LoraManager)"
|
||||
CATEGORY = "Lora Manager/loaders"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"lora_syntax": ("STRING", {
|
||||
"forceInput": True,
|
||||
"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
|
||||
}),
|
||||
},
|
||||
"optional": {
|
||||
"clip": ("CLIP",),
|
||||
"lora_stack": ("LORA_STACK",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
|
||||
RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
|
||||
FUNCTION = "load_loras_from_text"
|
||||
|
||||
def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
|
||||
"""Load LoRAs based on text syntax input."""
|
||||
lora_entries = _collect_stack_entries(lora_stack)
|
||||
for lora in parse_lora_syntax(lora_syntax):
|
||||
lora_path, trigger_words = get_lora_info_absolute(lora["name"])
|
||||
lora_entries.append({
|
||||
"name": lora["name"],
|
||||
"absolute_path": lora_path,
|
||||
"input_path": lora_path,
|
||||
"model_strength": lora["model_strength"],
|
||||
"clip_strength": lora["clip_strength"],
|
||||
"trigger_words": trigger_words,
|
||||
})
|
||||
|
||||
nunchaku_model_kind = detect_nunchaku_model_kind(model)
|
||||
if nunchaku_model_kind == "flux":
|
||||
logger.info("Detected Nunchaku Flux model")
|
||||
elif nunchaku_model_kind == "qwen_image":
|
||||
logger.info("Detected Nunchaku Qwen-Image model")
|
||||
|
||||
model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
|
||||
trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
|
||||
formatted_loras_text = _format_loaded_loras(loaded_loras)
|
||||
return (model, clip, trigger_words_text, formatted_loras_text)
|
||||
return (model, clip, trigger_words_text, formatted_loras_text)
|
||||
@@ -1,88 +0,0 @@
|
||||
"""
|
||||
LoRA Pool Node - Defines filter configuration for LoRA selection.
|
||||
|
||||
This node provides a visual filter editor that generates a LORA_POOL_CONFIG
|
||||
object for use by downstream nodes (like LoRA Randomizer).
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LoraPoolLM:
|
||||
"""
|
||||
A node that defines LoRA filter criteria through a Vue-based widget.
|
||||
|
||||
Outputs a LORA_POOL_CONFIG that can be consumed by:
|
||||
- Frontend: LoRA Randomizer widget reads connected pool's widget value
|
||||
- Backend: LoRA Randomizer receives config during workflow execution
|
||||
"""
|
||||
|
||||
NAME = "Lora Pool (LoraManager)"
|
||||
CATEGORY = "Lora Manager/randomizer"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"pool_config": ("LORA_POOL_CONFIG", {}),
|
||||
},
|
||||
"hidden": {
|
||||
# Hidden input to pass through unique node ID for frontend
|
||||
"unique_id": "UNIQUE_ID",
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("POOL_CONFIG",)
|
||||
RETURN_NAMES = ("POOL_CONFIG",)
|
||||
|
||||
FUNCTION = "process"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
def process(self, pool_config, unique_id=None):
|
||||
"""
|
||||
Pass through the pool configuration filters.
|
||||
|
||||
The config is generated entirely by the frontend widget.
|
||||
This function validates and returns only the filters field.
|
||||
|
||||
Args:
|
||||
pool_config: Dict containing filter criteria from widget
|
||||
unique_id: Node's unique ID (hidden)
|
||||
|
||||
Returns:
|
||||
Tuple containing the filters dict from pool_config
|
||||
"""
|
||||
# Validate required structure
|
||||
if not isinstance(pool_config, dict):
|
||||
logger.warning("Invalid pool_config type, using empty config")
|
||||
pool_config = self._default_config()
|
||||
|
||||
# Ensure version field exists
|
||||
if "version" not in pool_config:
|
||||
pool_config["version"] = 1
|
||||
|
||||
# Extract filters field
|
||||
filters = pool_config.get("filters", self._default_config()["filters"])
|
||||
|
||||
# Log for debugging
|
||||
logger.debug(f"[LoraPoolLM] Processing filters: {filters}")
|
||||
|
||||
return (filters,)
|
||||
|
||||
@staticmethod
|
||||
def _default_config():
|
||||
"""Return default empty configuration."""
|
||||
return {
|
||||
"version": 1,
|
||||
"filters": {
|
||||
"baseModels": [],
|
||||
"tags": {"include": [], "exclude": []},
|
||||
"folders": {"include": [], "exclude": []},
|
||||
"favoritesOnly": False,
|
||||
"license": {"noCreditRequired": False, "allowSelling": False},
|
||||
"namePatterns": {"include": [], "exclude": [], "useRegex": False},
|
||||
},
|
||||
"preview": {"matchCount": 0, "lastUpdated": 0},
|
||||
}
|
||||
@@ -1,210 +0,0 @@
|
||||
"""
|
||||
Lora Randomizer Node - Randomly selects LoRAs from a pool with configurable settings.
|
||||
|
||||
This node accepts optional pool_config input to filter available LoRAs, and outputs
|
||||
a LORA_STACK with randomly selected LoRAs. Returns UI updates with new random LoRAs
|
||||
and tracks the last used combination for reuse.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from ..utils.utils import get_lora_info
|
||||
from .utils import validate_lora_entries
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LoraRandomizerLM:
|
||||
"""Node that randomly selects LoRAs from a pool"""
|
||||
|
||||
NAME = "Lora Randomizer (LoraManager)"
|
||||
CATEGORY = "Lora Manager/randomizer"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"randomizer_config": ("RANDOMIZER_CONFIG", {}),
|
||||
"loras": ("LORAS", {}),
|
||||
},
|
||||
"optional": {
|
||||
"pool_config": ("POOL_CONFIG", {}),
|
||||
},
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def VALIDATE_INPUTS(cls, loras=None):
|
||||
"""Queue-time validation: reject missing local LoRAs before execution."""
|
||||
return validate_lora_entries({"loras": loras}) or True
|
||||
|
||||
RETURN_TYPES = ("LORA_STACK",)
|
||||
RETURN_NAMES = ("LORA_STACK",)
|
||||
|
||||
FUNCTION = "randomize"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
def _preprocess_loras_input(self, loras):
|
||||
"""
|
||||
Preprocess loras input to handle different widget formats.
|
||||
|
||||
Args:
|
||||
loras: Input from widget, either:
|
||||
- List of LoRA dicts (expected format)
|
||||
- Dict with '__value__' key containing the list
|
||||
|
||||
Returns:
|
||||
List of LoRA dicts
|
||||
"""
|
||||
if isinstance(loras, dict) and "__value__" in loras:
|
||||
return loras["__value__"]
|
||||
return loras
|
||||
|
||||
async def randomize(self, randomizer_config, loras, pool_config=None):
|
||||
"""
|
||||
Randomize LoRAs based on configuration and pool filters.
|
||||
|
||||
Args:
|
||||
randomizer_config: Dict with randomizer settings (count, strength ranges, roll_mode)
|
||||
loras: List of LoRA dicts from LORAS widget (includes locked state)
|
||||
pool_config: Optional config from LoRA Pool node for filtering
|
||||
|
||||
Returns:
|
||||
Dictionary with 'result' (LORA_STACK tuple) and 'ui' (for widget display)
|
||||
"""
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
loras = self._preprocess_loras_input(loras)
|
||||
|
||||
roll_mode = randomizer_config.get("roll_mode", "always")
|
||||
logger.debug(f"[LoraRandomizerLM] roll_mode: {roll_mode}")
|
||||
|
||||
# Dual seed mechanism for batch queue synchronization
|
||||
# execution_seed: seed for generating execution_stack (= previous next_seed)
|
||||
# next_seed: seed for generating ui_loras (= what will be displayed after execution)
|
||||
execution_seed = randomizer_config.get("execution_seed", None)
|
||||
next_seed = randomizer_config.get("next_seed", None)
|
||||
|
||||
if roll_mode == "fixed":
|
||||
ui_loras = loras
|
||||
execution_loras = loras
|
||||
else:
|
||||
scanner = await ServiceRegistry.get_lora_scanner()
|
||||
|
||||
# Generate execution_loras from execution_seed (if available)
|
||||
if execution_seed is not None:
|
||||
# Use execution_seed to regenerate the same loras that were shown to user
|
||||
execution_loras = await self._generate_random_loras_for_ui(
|
||||
scanner, randomizer_config, loras, pool_config, seed=execution_seed
|
||||
)
|
||||
else:
|
||||
# First execution: use loras input (what user sees in the widget)
|
||||
execution_loras = loras
|
||||
|
||||
# Generate ui_loras from next_seed (for display after execution)
|
||||
ui_loras = await self._generate_random_loras_for_ui(
|
||||
scanner, randomizer_config, loras, pool_config, seed=next_seed
|
||||
)
|
||||
|
||||
execution_stack = self._build_execution_stack_from_input(execution_loras)
|
||||
|
||||
return {
|
||||
"result": (execution_stack,),
|
||||
"ui": {"loras": ui_loras, "last_used": execution_loras},
|
||||
}
|
||||
|
||||
def _build_execution_stack_from_input(self, loras):
|
||||
"""
|
||||
Build LORA_STACK tuple from input loras list for execution.
|
||||
|
||||
Args:
|
||||
loras: List of LoRA dicts with name, strength, clipStrength, active
|
||||
|
||||
Returns:
|
||||
List of tuples (lora_path, model_strength, clip_strength)
|
||||
"""
|
||||
lora_stack = []
|
||||
for lora in loras:
|
||||
if not lora.get("active", False):
|
||||
continue
|
||||
|
||||
# Get file path
|
||||
lora_path, trigger_words = get_lora_info(lora["name"])
|
||||
if not lora_path:
|
||||
logger.warning(
|
||||
f"[LoraRandomizerLM] Could not find path for LoRA: {lora['name']}"
|
||||
)
|
||||
continue
|
||||
|
||||
# Normalize path separators
|
||||
lora_path = lora_path.replace("/", os.sep)
|
||||
|
||||
# Extract strengths (convert to float to prevent string subtraction errors)
|
||||
model_strength = float(lora.get("strength", 1.0))
|
||||
clip_strength = float(lora.get("clipStrength", model_strength))
|
||||
|
||||
lora_stack.append((lora_path, model_strength, clip_strength))
|
||||
|
||||
return lora_stack
|
||||
|
||||
async def _generate_random_loras_for_ui(
|
||||
self, scanner, randomizer_config, input_loras, pool_config=None, seed=None
|
||||
):
|
||||
"""
|
||||
Generate new random loras for UI display.
|
||||
|
||||
Args:
|
||||
scanner: LoraScanner instance
|
||||
randomizer_config: Dict with randomizer settings
|
||||
input_loras: Current input loras (for extracting locked loras)
|
||||
pool_config: Optional pool filters
|
||||
seed: Optional seed for deterministic randomization
|
||||
|
||||
Returns:
|
||||
List of LoRA dicts for UI display
|
||||
"""
|
||||
from ..services.lora_service import LoraService
|
||||
|
||||
# Parse randomizer settings (convert numeric values to float to prevent type errors)
|
||||
count_mode = randomizer_config.get("count_mode", "range")
|
||||
count_fixed = int(randomizer_config.get("count_fixed", 5))
|
||||
count_min = int(randomizer_config.get("count_min", 3))
|
||||
count_max = int(randomizer_config.get("count_max", 7))
|
||||
model_strength_min = float(randomizer_config.get("model_strength_min", 0.0))
|
||||
model_strength_max = float(randomizer_config.get("model_strength_max", 1.0))
|
||||
use_same_clip_strength = randomizer_config.get("use_same_clip_strength", True)
|
||||
clip_strength_min = float(randomizer_config.get("clip_strength_min", 0.0))
|
||||
clip_strength_max = float(randomizer_config.get("clip_strength_max", 1.0))
|
||||
use_recommended_strength = randomizer_config.get(
|
||||
"use_recommended_strength", False
|
||||
)
|
||||
recommended_strength_scale_min = float(
|
||||
randomizer_config.get("recommended_strength_scale_min", 0.5)
|
||||
)
|
||||
recommended_strength_scale_max = float(
|
||||
randomizer_config.get("recommended_strength_scale_max", 1.0)
|
||||
)
|
||||
|
||||
# Extract locked LoRAs from input
|
||||
locked_loras = [lora for lora in input_loras if lora.get("locked", False)]
|
||||
|
||||
# Use LoraService to generate random LoRAs
|
||||
lora_service = LoraService(scanner)
|
||||
result_loras = await lora_service.get_random_loras(
|
||||
count=count_fixed,
|
||||
model_strength_min=model_strength_min,
|
||||
model_strength_max=model_strength_max,
|
||||
use_same_clip_strength=use_same_clip_strength,
|
||||
clip_strength_min=clip_strength_min,
|
||||
clip_strength_max=clip_strength_max,
|
||||
locked_loras=locked_loras,
|
||||
pool_config=pool_config,
|
||||
count_mode=count_mode,
|
||||
count_min=count_min,
|
||||
count_max=count_max,
|
||||
use_recommended_strength=use_recommended_strength,
|
||||
recommended_strength_scale_min=recommended_strength_scale_min,
|
||||
recommended_strength_scale_max=recommended_strength_scale_max,
|
||||
seed=seed,
|
||||
)
|
||||
|
||||
return result_loras
|
||||
@@ -1,102 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import inspect
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
_STACK_INPUT_PATTERN = re.compile(r"^lora_stack(?:_([ab])|(\d+))$")
|
||||
|
||||
|
||||
def _is_stack_input(name: str) -> bool:
|
||||
return bool(_STACK_INPUT_PATTERN.match(name))
|
||||
|
||||
|
||||
def _stack_slot_number(name: str) -> int:
|
||||
"""Numeric slot used to order stack inputs; legacy a/b map to 1/2."""
|
||||
match = _STACK_INPUT_PATTERN.match(name)
|
||||
if not match:
|
||||
return -1
|
||||
letter, digits = match.group(1), match.group(2)
|
||||
if digits is not None:
|
||||
return int(digits)
|
||||
return 1 if letter == "a" else 2
|
||||
|
||||
|
||||
class _LoraStackOptionalInputs:
|
||||
"""Lookup that preserves explicit optional inputs and dynamic lora_stack slots."""
|
||||
|
||||
def __init__(self, explicit_inputs: dict[str, tuple[str, dict[str, Any]]]) -> None:
|
||||
self._explicit_inputs = explicit_inputs
|
||||
|
||||
def __contains__(self, item: object) -> bool:
|
||||
if not isinstance(item, str):
|
||||
return False
|
||||
return item in self._explicit_inputs or _is_stack_input(item)
|
||||
|
||||
def __getitem__(self, key: str) -> tuple[str, dict[str, Any]]:
|
||||
if key in self._explicit_inputs:
|
||||
return self._explicit_inputs[key]
|
||||
if _is_stack_input(key):
|
||||
return (
|
||||
"LORA_STACK",
|
||||
{
|
||||
"tooltip": "A LoRA stack to combine. Connect to add more inputs.",
|
||||
},
|
||||
)
|
||||
raise KeyError(key)
|
||||
|
||||
|
||||
class LoraStackCombinerLM:
|
||||
NAME = "Lora Stack Combiner (LoraManager)"
|
||||
CATEGORY = "Lora Manager/stackers"
|
||||
DESCRIPTION = (
|
||||
"Combines multiple LoRA stacks into a single stack. "
|
||||
"Supports dynamic inputs: connect a stack to add more inputs."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
optional_inputs: dict[str, tuple[str, dict[str, Any]]] = {
|
||||
"lora_stack1": (
|
||||
"LORA_STACK",
|
||||
{
|
||||
"tooltip": "A LoRA stack to combine. Connect to add more inputs.",
|
||||
},
|
||||
),
|
||||
"lora_stack2": (
|
||||
"LORA_STACK",
|
||||
{
|
||||
"tooltip": "A LoRA stack to combine. Connect to add more inputs.",
|
||||
},
|
||||
),
|
||||
}
|
||||
|
||||
stack = inspect.stack()
|
||||
if len(stack) > 2 and stack[2].function == "get_input_info":
|
||||
optional_inputs = _LoraStackOptionalInputs(optional_inputs) # pyright: ignore[reportAssignmentType]
|
||||
|
||||
return {
|
||||
"required": {},
|
||||
"optional": optional_inputs,
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LORA_STACK",)
|
||||
RETURN_NAMES = ("LORA_STACK",)
|
||||
FUNCTION = "combine_stacks"
|
||||
|
||||
def combine_stacks(self, lora_stack1=None, lora_stack2=None, **kwargs):
|
||||
stacks = {
|
||||
"lora_stack1": lora_stack1,
|
||||
"lora_stack2": lora_stack2,
|
||||
}
|
||||
for key, value in kwargs.items():
|
||||
if _is_stack_input(key) and value is not None:
|
||||
stacks[key] = value
|
||||
|
||||
combined_stack = []
|
||||
for key in sorted(stacks, key=_stack_slot_number):
|
||||
stack = stacks[key]
|
||||
if stack:
|
||||
combined_stack.extend(stack)
|
||||
|
||||
return (combined_stack,)
|
||||
+13
-16
@@ -1,12 +1,13 @@
|
||||
from comfy.comfy_types import IO # type: ignore
|
||||
import os
|
||||
from ..utils.utils import get_lora_info
|
||||
from .utils import FlexibleOptionalInputType, any_type, apply_lora_syntax_format, extract_lora_name, get_loras_list, validate_lora_entries
|
||||
from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class LoraStackerLM:
|
||||
class LoraStacker:
|
||||
NAME = "Lora Stacker (LoraManager)"
|
||||
CATEGORY = "Lora Manager/stackers"
|
||||
|
||||
@@ -14,26 +15,22 @@ class LoraStackerLM:
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": ("AUTOCOMPLETE_TEXT_LORAS", {
|
||||
"placeholder": "Search LoRAs to add...",
|
||||
"text": (IO.STRING, {
|
||||
"multiline": True,
|
||||
"dynamicPrompts": True,
|
||||
"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
|
||||
"placeholder": "LoRA syntax input: <lora:name:strength>"
|
||||
}),
|
||||
"loras": ("LORAS", {}),
|
||||
},
|
||||
"optional": FlexibleOptionalInputType(any_type),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def VALIDATE_INPUTS(cls, loras=None):
|
||||
"""Queue-time validation: reject missing local LoRAs before execution."""
|
||||
return validate_lora_entries({"loras": loras}) or True
|
||||
|
||||
RETURN_TYPES = ("LORA_STACK", "STRING", "STRING")
|
||||
RETURN_TYPES = ("LORA_STACK", IO.STRING, IO.STRING)
|
||||
RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
|
||||
FUNCTION = "stack_loras"
|
||||
|
||||
def stack_loras(self, text, loras, **kwargs):
|
||||
"""Stacks multiple LoRAs based on the widget input without loading them."""
|
||||
def stack_loras(self, text, **kwargs):
|
||||
"""Stacks multiple LoRAs based on the kwargs input without loading them."""
|
||||
stack = []
|
||||
active_loras = []
|
||||
all_trigger_words = []
|
||||
@@ -48,13 +45,13 @@ class LoraStackerLM:
|
||||
_, trigger_words = get_lora_info(lora_name)
|
||||
all_trigger_words.extend(trigger_words)
|
||||
|
||||
# Process loras from the widget with support for both old and new formats
|
||||
loras_list = get_loras_list({"loras": loras})
|
||||
# Process loras from kwargs with support for both old and new formats
|
||||
loras_list = get_loras_list(kwargs)
|
||||
for lora in loras_list:
|
||||
if not lora.get('active', False):
|
||||
continue
|
||||
|
||||
lora_name = apply_lora_syntax_format(lora['name'])
|
||||
lora_name = lora['name']
|
||||
model_strength = float(lora['strength'])
|
||||
# Get clip strength - use model strength as default if not specified
|
||||
clip_strength = float(lora.get('clipStrength', model_strength))
|
||||
|
||||
@@ -1,62 +0,0 @@
|
||||
"""Node to resolve `<lora:name:strength>` syntax to absolute file system paths.
|
||||
|
||||
Takes the loaded_loras / active_loras STRING output from LoraLoaderLM or
|
||||
LoraStackerLM and resolves each lora name to its absolute path on disk via
|
||||
the scanner cache. Unknown names are returned as-is.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from ..utils.utils import get_lora_info_absolute
|
||||
from .utils import parse_lora_syntax
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LoraSyntaxToPath:
|
||||
NAME = "LoRA Syntax → Path (LoraManager)"
|
||||
CATEGORY = "Lora Manager/utils"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"lora_syntax": (
|
||||
"STRING",
|
||||
{
|
||||
"forceInput": True,
|
||||
"multiline": True,
|
||||
"tooltip": (
|
||||
"<lora:name:strength> formatted text from "
|
||||
"loaded_loras / active_loras output"
|
||||
),
|
||||
},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
RETURN_NAMES = ("paths",)
|
||||
FUNCTION = "resolve"
|
||||
|
||||
def resolve(self, lora_syntax: str) -> tuple[str]:
|
||||
"""Parse <lora:...> syntax and resolve each name to its absolute path."""
|
||||
if not lora_syntax or not lora_syntax.strip():
|
||||
logger.info("Received empty lora_syntax input")
|
||||
return ("",)
|
||||
|
||||
parsed = parse_lora_syntax(lora_syntax)
|
||||
if not parsed:
|
||||
logger.info("No valid <lora:...> entries found in input")
|
||||
return ("",)
|
||||
|
||||
paths: list[str] = []
|
||||
for entry in parsed:
|
||||
try:
|
||||
absolute_path, _ = get_lora_info_absolute(entry["name"])
|
||||
paths.append(absolute_path)
|
||||
except Exception:
|
||||
logger.warning("Failed to resolve lora '%s', skipping", entry["name"])
|
||||
continue
|
||||
|
||||
return ("\n".join(paths),)
|
||||
@@ -1,179 +0,0 @@
|
||||
"""Metadata Overwrite node — allows users to manually specify generation parameters
|
||||
that override the automatically collected/inferred metadata.
|
||||
|
||||
Most inputs have falsy defaults (empty string / 0) which are skipped.
|
||||
clip_skip uses a sentinel default (-25) so that a wired value of 0 is
|
||||
preserved — both ComfyUI and A1111 conventions have no meaningful 0 value,
|
||||
but users may wire 0 to express "no clip skip / default".
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from ..metadata_collector.constants import CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL
|
||||
from ..metadata_collector.overwrite_utils import collect_overwrite_params
|
||||
|
||||
|
||||
class MetadataOverwriteLM:
|
||||
NAME = "Metadata Overwrite (LoraManager)"
|
||||
CATEGORY = "Lora Manager/utils"
|
||||
DESCRIPTION = (
|
||||
"Manually specify generation parameters to override automatically collected "
|
||||
"metadata. Only filled/connected inputs will take effect — empty defaults "
|
||||
"are ignored."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls) -> dict[str, Any]:
|
||||
return {
|
||||
"optional": {
|
||||
"prompt": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"tooltip": "Positive prompt. Only overwrites when non-empty.",
|
||||
},
|
||||
),
|
||||
"negative_prompt": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"tooltip": "Negative prompt. Only overwrites when non-empty.",
|
||||
},
|
||||
),
|
||||
"seed": (
|
||||
"INT",
|
||||
{
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": 0xFFFFFFFFFFFFFFFF,
|
||||
"control_after_generate": False,
|
||||
"tooltip": "Seed value. Only overwrites when > 0.",
|
||||
},
|
||||
),
|
||||
"steps": (
|
||||
"INT",
|
||||
{
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": 10000,
|
||||
"tooltip": "Number of steps. Only overwrites when > 0.",
|
||||
},
|
||||
),
|
||||
"cfg_scale": (
|
||||
"FLOAT",
|
||||
{
|
||||
"default": 0.0,
|
||||
"min": 0.0,
|
||||
"max": 100.0,
|
||||
"tooltip": "CFG scale. Only overwrites when > 0.",
|
||||
},
|
||||
),
|
||||
"sampler": (
|
||||
"STRING,SAMPLER",
|
||||
{
|
||||
"default": "",
|
||||
"widgetType": "STRING",
|
||||
"tooltip": (
|
||||
"Sampler name. Fill in the name manually or "
|
||||
"connect a SAMPLER output (e.g. KSamplerSelect) "
|
||||
"— the sampler name is then extracted "
|
||||
"automatically. Note: ddim is recorded as "
|
||||
"euler (ComfyUI internal representation). "
|
||||
"Only overwrites when non-empty."
|
||||
),
|
||||
},
|
||||
),
|
||||
"scheduler": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"tooltip": "Scheduler name. Only overwrites when non-empty.",
|
||||
},
|
||||
),
|
||||
"model": (
|
||||
"STRING,MODEL",
|
||||
{
|
||||
"default": "",
|
||||
"widgetType": "STRING",
|
||||
"tooltip": (
|
||||
"The checkpoint or diffusion model (UNet) used "
|
||||
"for generation. Fill in the name manually or "
|
||||
"connect a MODEL output — the model name is then "
|
||||
"extracted automatically. Only overwrites when "
|
||||
"non-empty."
|
||||
),
|
||||
},
|
||||
),
|
||||
"loras": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"tooltip": (
|
||||
"LoRA syntax, e.g. <lora:name:strength> "
|
||||
"or <lora:name:model_strength:clip_strength>, "
|
||||
"separated by spaces. Only overwrites when non-empty."
|
||||
),
|
||||
},
|
||||
),
|
||||
"size": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"tooltip": (
|
||||
"Image size in WIDTHxHEIGHT format (e.g. 512x768). "
|
||||
"Only overwrites when non-empty."
|
||||
),
|
||||
},
|
||||
),
|
||||
"clip_skip": (
|
||||
"INT",
|
||||
{
|
||||
"default": _CLIP_SKIP_SENTINEL,
|
||||
"min": -25,
|
||||
"max": 24,
|
||||
"tooltip": (
|
||||
"Clip skip (ComfyUI: -24..-1, A1111: 1+). "
|
||||
"Default -25 means not set — any other value "
|
||||
"overwrites."
|
||||
),
|
||||
},
|
||||
),
|
||||
"additional_data": (
|
||||
"STRING",
|
||||
{
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"tooltip": (
|
||||
"Additional data to embed in the image metadata. "
|
||||
"Inserted between Clip skip and Model hash in the "
|
||||
"A1111-compatible parameters string. "
|
||||
'Example: "Copyright": "Some license info"'
|
||||
),
|
||||
},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("METADATA",)
|
||||
RETURN_NAMES = ("metadata",)
|
||||
FUNCTION = "collect_metadata"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
def collect_metadata(self, **kwargs: Any) -> tuple[dict[str, Any]]:
|
||||
"""Collect non-default input values into a metadata dict.
|
||||
|
||||
For most fields, a falsy value (empty string, 0) means "not set"
|
||||
and is skipped. clip_skip uses a dedicated sentinel (-25) so that
|
||||
a wired value of 0 is preserved and reaches the metadata pipeline.
|
||||
|
||||
The ``model`` field accepts either a manual string or a wired MODEL
|
||||
(ModelPatcher) connection; in the latter case the underlying model
|
||||
name is extracted from the patcher's ``cached_patcher_init`` and
|
||||
stored as a ComfyUI-style relative path. The ``sampler`` field
|
||||
likewise accepts a manual string or a wired SAMPLER (KSAMPLER)
|
||||
connection, from which the sampler name is extracted automatically.
|
||||
"""
|
||||
return (collect_overwrite_params(kwargs),)
|
||||
@@ -1,569 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
"""Qwen-Image LoRA support for Nunchaku models.
|
||||
|
||||
Portions of the LoRA mapping/application logic in this file are adapted from
|
||||
ComfyUI-QwenImageLoraLoader by GitHub user ussoewwin:
|
||||
https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader
|
||||
|
||||
The upstream project is licensed under Apache License 2.0.
|
||||
"""
|
||||
|
||||
import copy
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union, cast
|
||||
|
||||
import comfy.utils # pyright: ignore[reportMissingImports]
|
||||
import folder_paths # pyright: ignore[reportMissingImports]
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from safetensors import safe_open
|
||||
|
||||
from nunchaku.lora.flux.nunchaku_converter import ( # pyright: ignore[reportMissingTypeStubs]
|
||||
pack_lowrank_weight,
|
||||
unpack_lowrank_weight,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KEY_MAPPING = [
|
||||
(re.compile(r"^(layers)[._](\d+)[._]attention[._]to[._]([qkv])$"), r"\1.\2.attention.to_qkv", "qkv", lambda m: m.group(3).upper()),
|
||||
(re.compile(r"^(layers)[._](\d+)[._]feed_forward[._](w1|w3)$"), r"\1.\2.feed_forward.net.0.proj", "glu", lambda m: m.group(3)),
|
||||
(re.compile(r"^(layers)[._](\d+)[._]feed_forward[._]w2$"), r"\1.\2.feed_forward.net.2", "regular", None),
|
||||
(re.compile(r"^(layers)[._](\d+)[._](.*)$"), r"\1.\2.\3", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._](q|k|v)[._]proj$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]add[._](q|k|v)[._]proj$"), r"\1.\2.attn.add_qkv_proj", "add_qkv", lambda m: m.group(3).upper()),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj[._]context$"), r"\1.\2.attn.to_add_out", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj$"), r"\1.\2.attn.to_out.0", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out.0", "regular", None),
|
||||
(re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
|
||||
(re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_fc1", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]2$"), r"\1.\2.mlp_fc2", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_context_fc1", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]2$"), r"\1.\2.mlp_context_fc2", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
|
||||
(re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]out$"), r"\1.\2.proj_out", "single_proj_out", None),
|
||||
(re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]mlp$"), r"\1.\2.mlp_fc1", "regular", None),
|
||||
(re.compile(r"^(single_transformer_blocks)[._](\d+)[._]norm[._]linear$"), r"\1.\2.norm.linear", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1[._]linear$"), r"\1.\2.norm1.linear", "regular", None),
|
||||
(re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1_context[._]linear$"), r"\1.\2.norm1_context.linear", "regular", None),
|
||||
(re.compile(r"^(img_in)$"), r"\1", "regular", None),
|
||||
(re.compile(r"^(txt_in)$"), r"\1", "regular", None),
|
||||
(re.compile(r"^(proj_out)$"), r"\1", "regular", None),
|
||||
(re.compile(r"^(norm_out)[._](linear)$"), r"\1.\2", "regular", None),
|
||||
(re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_1)$"), r"\1.\2.\3", "regular", None),
|
||||
(re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_2)$"), r"\1.\2.\3", "regular", None),
|
||||
]
|
||||
|
||||
_RE_LORA_SUFFIX = re.compile(r"\.(?P<tag>lora(?:[._](?:A|B|down|up)))(?:\.[^.]+)*\.weight$")
|
||||
_RE_ALPHA_SUFFIX = re.compile(r"\.(?:alpha|lora_alpha)(?:\.[^.]+)*$")
|
||||
|
||||
|
||||
def _rename_layer_underscore_layer_name(old_name: str) -> str:
|
||||
rules = [
|
||||
(r"_(\d+)_attn_to_out_(\d+)", r".\1.attn.to_out.\2"),
|
||||
(r"_(\d+)_img_mlp_net_(\d+)_proj", r".\1.img_mlp.net.\2.proj"),
|
||||
(r"_(\d+)_txt_mlp_net_(\d+)_proj", r".\1.txt_mlp.net.\2.proj"),
|
||||
(r"_(\d+)_img_mlp_net_(\d+)", r".\1.img_mlp.net.\2"),
|
||||
(r"_(\d+)_txt_mlp_net_(\d+)", r".\1.txt_mlp.net.\2"),
|
||||
(r"_(\d+)_img_mod_(\d+)", r".\1.img_mod.\2"),
|
||||
(r"_(\d+)_txt_mod_(\d+)", r".\1.txt_mod.\2"),
|
||||
(r"_(\d+)_attn_", r".\1.attn."),
|
||||
]
|
||||
new_name = old_name
|
||||
for pattern, replacement in rules:
|
||||
new_name = re.sub(pattern, replacement, new_name)
|
||||
return new_name
|
||||
|
||||
|
||||
def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
|
||||
if not name:
|
||||
return model
|
||||
module = model
|
||||
for part in name.split("."):
|
||||
if not part:
|
||||
continue
|
||||
if hasattr(module, part):
|
||||
module = getattr(module, part)
|
||||
elif part.isdigit() and isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple)):
|
||||
try:
|
||||
module = module[int(part)]
|
||||
except (IndexError, TypeError):
|
||||
return None
|
||||
else:
|
||||
return None
|
||||
return module
|
||||
|
||||
|
||||
def _resolve_module_name(model: nn.Module, name: str) -> Tuple[str, Optional[nn.Module]]:
|
||||
module = _get_module_by_name(model, name)
|
||||
if module is not None:
|
||||
return name, module
|
||||
|
||||
replacements = [
|
||||
(".attn.to_out.0", ".attn.to_out"),
|
||||
(".attention.to_qkv", ".attention.qkv"),
|
||||
(".attention.to_out.0", ".attention.out"),
|
||||
(".feed_forward.net.0.proj", ".feed_forward.w13"),
|
||||
(".feed_forward.net.2", ".feed_forward.w2"),
|
||||
(".ff.net.0.proj", ".mlp_fc1"),
|
||||
(".ff.net.2", ".mlp_fc2"),
|
||||
(".ff_context.net.0.proj", ".mlp_context_fc1"),
|
||||
(".ff_context.net.2", ".mlp_context_fc2"),
|
||||
]
|
||||
for src, dst in replacements:
|
||||
if src in name:
|
||||
alt = name.replace(src, dst)
|
||||
module = _get_module_by_name(model, alt)
|
||||
if module is not None:
|
||||
return alt, module
|
||||
return name, None
|
||||
|
||||
|
||||
def _classify_and_map_key(key: str) -> Optional[Tuple[str, str, Optional[str], str]]:
|
||||
normalized = key
|
||||
if normalized.startswith("transformer."):
|
||||
normalized = normalized[len("transformer."):]
|
||||
if normalized.startswith("diffusion_model."):
|
||||
normalized = normalized[len("diffusion_model."):]
|
||||
if normalized.startswith("lora_unet_"):
|
||||
normalized = _rename_layer_underscore_layer_name(normalized[len("lora_unet_"):])
|
||||
|
||||
match = _RE_LORA_SUFFIX.search(normalized)
|
||||
if match:
|
||||
tag = match.group("tag")
|
||||
base = normalized[:match.start()]
|
||||
ab = "A" if ("lora_A" in tag or tag.endswith(".A") or "down" in tag) else "B"
|
||||
else:
|
||||
match = _RE_ALPHA_SUFFIX.search(normalized)
|
||||
if not match:
|
||||
return None
|
||||
base = normalized[:match.start()]
|
||||
ab = "alpha"
|
||||
|
||||
for pattern, template, group, comp_fn in KEY_MAPPING:
|
||||
key_match = pattern.match(base)
|
||||
if key_match:
|
||||
return group, key_match.expand(template), comp_fn(key_match) if comp_fn else None, ab
|
||||
return None
|
||||
|
||||
|
||||
def _detect_lora_format(lora_state_dict: Dict[str, torch.Tensor]) -> bool:
|
||||
standard_patterns = (
|
||||
".lora_up.",
|
||||
".lora_down.",
|
||||
".lora_A.",
|
||||
".lora_B.",
|
||||
".lora.up.",
|
||||
".lora.down.",
|
||||
".lora.A.",
|
||||
".lora.B.",
|
||||
)
|
||||
return any(pattern in key for key in lora_state_dict for pattern in standard_patterns)
|
||||
|
||||
|
||||
def _load_lora_state_dict(path_or_dict: Union[str, Path, Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
|
||||
if isinstance(path_or_dict, dict):
|
||||
return path_or_dict
|
||||
path = Path(path_or_dict)
|
||||
if path.suffix == ".safetensors":
|
||||
state_dict: Dict[str, torch.Tensor] = {}
|
||||
with safe_open(path, framework="pt", device="cpu") as handle:
|
||||
for key in handle.keys():
|
||||
state_dict[key] = handle.get_tensor(key)
|
||||
return state_dict
|
||||
return comfy.utils.load_torch_file(str(path), safe_load=True)
|
||||
|
||||
|
||||
def _fuse_glu_lora(glu_weights: Dict[str, torch.Tensor]) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
|
||||
if "w1_A" not in glu_weights or "w3_A" not in glu_weights:
|
||||
return None, None, None
|
||||
a_w1, b_w1 = glu_weights["w1_A"], glu_weights["w1_B"]
|
||||
a_w3, b_w3 = glu_weights["w3_A"], glu_weights["w3_B"]
|
||||
if a_w1.shape[1] != a_w3.shape[1]:
|
||||
return None, None, None
|
||||
a_fused = torch.cat([a_w1, a_w3], dim=0)
|
||||
out1, out3 = b_w1.shape[0], b_w3.shape[0]
|
||||
rank1, rank3 = b_w1.shape[1], b_w3.shape[1]
|
||||
b_fused = torch.zeros(out1 + out3, rank1 + rank3, dtype=b_w1.dtype, device=b_w1.device)
|
||||
b_fused[:out1, :rank1] = b_w1
|
||||
b_fused[out1:, rank1:] = b_w3
|
||||
return a_fused, b_fused, glu_weights.get("w1_alpha")
|
||||
|
||||
|
||||
def _fuse_qkv_lora(qkv_weights: Dict[str, torch.Tensor], model: Optional[nn.Module] = None, base_key: Optional[str] = None) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
|
||||
required_keys = ["Q_A", "Q_B", "K_A", "K_B", "V_A", "V_B"]
|
||||
if not all(key in qkv_weights for key in required_keys):
|
||||
return None, None, None
|
||||
a_q, a_k, a_v = qkv_weights["Q_A"], qkv_weights["K_A"], qkv_weights["V_A"]
|
||||
b_q, b_k, b_v = qkv_weights["Q_B"], qkv_weights["K_B"], qkv_weights["V_B"]
|
||||
if not (a_q.shape == a_k.shape == a_v.shape):
|
||||
return None, None, None
|
||||
if not (b_q.shape[1] == b_k.shape[1] == b_v.shape[1]):
|
||||
return None, None, None
|
||||
|
||||
out_features = None
|
||||
if model is not None and base_key is not None:
|
||||
_, module = _resolve_module_name(model, base_key)
|
||||
out_features = getattr(module, "out_features", None) if module is not None else None
|
||||
|
||||
alpha_fused = None
|
||||
alpha_q = qkv_weights.get("Q_alpha")
|
||||
alpha_k = qkv_weights.get("K_alpha")
|
||||
alpha_v = qkv_weights.get("V_alpha")
|
||||
if alpha_q is not None and alpha_k is not None and alpha_v is not None and alpha_q.item() == alpha_k.item() == alpha_v.item():
|
||||
alpha_fused = alpha_q
|
||||
|
||||
a_fused = torch.cat([a_q, a_k, a_v], dim=0)
|
||||
rank = b_q.shape[1]
|
||||
out_q, out_k, out_v = b_q.shape[0], b_k.shape[0], b_v.shape[0]
|
||||
total_out = out_features if out_features is not None else out_q + out_k + out_v
|
||||
b_fused = torch.zeros(total_out, 3 * rank, dtype=b_q.dtype, device=b_q.device)
|
||||
b_fused[:out_q, :rank] = b_q
|
||||
b_fused[out_q:out_q + out_k, rank:2 * rank] = b_k
|
||||
b_fused[out_q + out_k:out_q + out_k + out_v, 2 * rank:] = b_v
|
||||
return a_fused, b_fused, alpha_fused
|
||||
|
||||
|
||||
def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_key: str, model: nn.Module) -> Tuple[Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]], List[str]]:
|
||||
result: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
|
||||
consumed: List[str] = []
|
||||
match = re.search(r"single_transformer_blocks\.(\d+)", base_key)
|
||||
if not match or base_key not in lora_dict:
|
||||
return result, consumed
|
||||
block_idx = match.group(1)
|
||||
block = _get_module_by_name(model, f"single_transformer_blocks.{block_idx}")
|
||||
if block is None:
|
||||
return result, consumed
|
||||
a_full = lora_dict[base_key].get("A")
|
||||
b_full = lora_dict[base_key].get("B")
|
||||
alpha = lora_dict[base_key].get("alpha")
|
||||
attn_to_out = getattr(getattr(block, "attn", None), "to_out", None)
|
||||
mlp_fc2 = getattr(block, "mlp_fc2", None)
|
||||
if a_full is None or b_full is None or attn_to_out is None or mlp_fc2 is None:
|
||||
return result, consumed
|
||||
attn_in = getattr(attn_to_out, "in_features", None)
|
||||
mlp_in = getattr(mlp_fc2, "in_features", None)
|
||||
if attn_in is None or mlp_in is None or a_full.shape[1] != attn_in + mlp_in:
|
||||
return result, consumed
|
||||
result[f"single_transformer_blocks.{block_idx}.attn.to_out"] = (a_full[:, :attn_in], b_full.clone(), alpha)
|
||||
result[f"single_transformer_blocks.{block_idx}.mlp_fc2"] = (a_full[:, attn_in:], b_full.clone(), alpha)
|
||||
consumed.append(base_key)
|
||||
return result, consumed
|
||||
|
||||
|
||||
def _apply_lora_to_module(module: Any, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: Any) -> None:
|
||||
# These modules are dynamic torch containers; monkey-patched attributes
|
||||
# below are set at runtime, so the module/model types are deliberately Any.
|
||||
if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
|
||||
raise ValueError(f"{module_name}: unsupported module without in/out features")
|
||||
if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
|
||||
raise ValueError(f"{module_name}: LoRA shape mismatch")
|
||||
|
||||
if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
|
||||
if not hasattr(module, "_lora_original_forward"):
|
||||
module._lora_original_forward = module.forward
|
||||
if not hasattr(module, "_nunchaku_lora_bundle"):
|
||||
module._nunchaku_lora_bundle = []
|
||||
module._nunchaku_lora_bundle.append((a_tensor, b_tensor))
|
||||
|
||||
def _awq_lora_forward(x, *args, **kwargs):
|
||||
out = module._lora_original_forward(x, *args, **kwargs)
|
||||
x_flat = x.reshape(-1, module.in_features)
|
||||
for local_a, local_b in module._nunchaku_lora_bundle:
|
||||
local_a = local_a.to(device=out.device, dtype=out.dtype)
|
||||
local_b = local_b.to(device=out.device, dtype=out.dtype)
|
||||
lora_term = (x_flat @ local_a.transpose(0, 1)) @ local_b.transpose(0, 1)
|
||||
try:
|
||||
out = out + lora_term.reshape(out.shape)
|
||||
except Exception:
|
||||
pass
|
||||
return out
|
||||
|
||||
module.forward = _awq_lora_forward
|
||||
if not hasattr(model, "_lora_slots"):
|
||||
model._lora_slots = {}
|
||||
model._lora_slots[module_name] = {"type": "awq_w4a16"}
|
||||
return
|
||||
|
||||
if hasattr(module, "proj_down") and hasattr(module, "proj_up"):
|
||||
proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
|
||||
proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
|
||||
base_rank = proj_down.shape[0] if proj_down.shape[1] == module.in_features else proj_down.shape[1]
|
||||
if proj_down.shape[1] == module.in_features:
|
||||
updated_down = torch.cat([proj_down, a_tensor], dim=0)
|
||||
axis_down = 0
|
||||
else:
|
||||
updated_down = torch.cat([proj_down, a_tensor.T], dim=1)
|
||||
axis_down = 1
|
||||
updated_up = torch.cat([proj_up, b_tensor], dim=1)
|
||||
module.proj_down.data = pack_lowrank_weight(updated_down, down=True)
|
||||
module.proj_up.data = pack_lowrank_weight(updated_up, down=False)
|
||||
module.rank = base_rank + a_tensor.shape[0]
|
||||
if not hasattr(model, "_lora_slots"):
|
||||
model._lora_slots = {}
|
||||
model._lora_slots[module_name] = {
|
||||
"type": "nunchaku",
|
||||
"base_rank": base_rank,
|
||||
"axis_down": axis_down,
|
||||
}
|
||||
return
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
if not hasattr(model, "_lora_slots"):
|
||||
model._lora_slots = {}
|
||||
if module_name not in model._lora_slots:
|
||||
model._lora_slots[module_name] = {
|
||||
"type": "linear",
|
||||
"original_weight": module.weight.detach().cpu().clone(),
|
||||
}
|
||||
module.weight.data.add_((b_tensor @ a_tensor).to(dtype=module.weight.dtype, device=module.weight.device))
|
||||
return
|
||||
|
||||
raise ValueError(f"{module_name}: unsupported module type {type(module)}")
|
||||
|
||||
|
||||
def reset_lora_v2(model: Any) -> None:
|
||||
slots = getattr(model, "_lora_slots", None)
|
||||
if not slots:
|
||||
return
|
||||
for name, info in list(slots.items()):
|
||||
module = _get_module_by_name(model, name)
|
||||
if module is None:
|
||||
continue
|
||||
module = cast(Any, module)
|
||||
module_type = info.get("type", "nunchaku")
|
||||
if module_type == "nunchaku":
|
||||
base_rank = info["base_rank"]
|
||||
proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
|
||||
proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
|
||||
if info.get("axis_down", 0) == 0:
|
||||
proj_down = proj_down[:base_rank, :].clone()
|
||||
else:
|
||||
proj_down = proj_down[:, :base_rank].clone()
|
||||
proj_up = proj_up[:, :base_rank].clone()
|
||||
module.proj_down.data = pack_lowrank_weight(proj_down, down=True)
|
||||
module.proj_up.data = pack_lowrank_weight(proj_up, down=False)
|
||||
module.rank = base_rank
|
||||
elif module_type == "linear" and "original_weight" in info:
|
||||
module.weight.data.copy_(info["original_weight"].to(device=module.weight.device, dtype=module.weight.dtype))
|
||||
elif module_type == "awq_w4a16":
|
||||
if hasattr(module, "_lora_original_forward"):
|
||||
module.forward = module._lora_original_forward
|
||||
for attr in ("_lora_original_forward", "_nunchaku_lora_bundle"):
|
||||
if hasattr(module, attr):
|
||||
delattr(module, attr)
|
||||
model._lora_slots = {}
|
||||
|
||||
|
||||
def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
|
||||
del apply_awq_mod # retained for interface compatibility
|
||||
reset_lora_v2(model)
|
||||
aggregated_weights: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
|
||||
saw_supported_format = False
|
||||
unresolved_targets = 0
|
||||
|
||||
for index, (path_or_dict, strength) in enumerate(lora_configs):
|
||||
if abs(strength) < 1e-5:
|
||||
continue
|
||||
lora_name = str(path_or_dict) if not isinstance(path_or_dict, dict) else f"lora_{index}"
|
||||
lora_state_dict = _load_lora_state_dict(path_or_dict)
|
||||
if not lora_state_dict or not _detect_lora_format(lora_state_dict):
|
||||
logger.warning("Skipping unsupported Qwen LoRA: %s", lora_name)
|
||||
continue
|
||||
saw_supported_format = True
|
||||
|
||||
grouped_weights: Dict[str, Dict[str, torch.Tensor]] = defaultdict(dict)
|
||||
for key, value in lora_state_dict.items():
|
||||
parsed = _classify_and_map_key(key)
|
||||
if parsed is None:
|
||||
continue
|
||||
group, base_key, component, ab = parsed
|
||||
if component and ab:
|
||||
grouped_weights[base_key][f"{component}_{ab}"] = value
|
||||
else:
|
||||
grouped_weights[base_key][ab] = value
|
||||
|
||||
processed_groups: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
|
||||
handled: set[str] = set()
|
||||
for base_key, weights in grouped_weights.items():
|
||||
if base_key in handled:
|
||||
continue
|
||||
a_tensor = b_tensor = alpha = None
|
||||
if "qkv" in base_key or "add_qkv_proj" in base_key:
|
||||
a_tensor, b_tensor, alpha = _fuse_qkv_lora(weights, model=model, base_key=base_key)
|
||||
elif "w1_A" in weights or "w3_A" in weights:
|
||||
a_tensor, b_tensor, alpha = _fuse_glu_lora(weights)
|
||||
elif ".proj_out" in base_key and "single_transformer_blocks" in base_key:
|
||||
split_map, consumed = _handle_proj_out_split(grouped_weights, base_key, model)
|
||||
processed_groups.update(split_map)
|
||||
handled.update(consumed)
|
||||
continue
|
||||
else:
|
||||
a_tensor, b_tensor, alpha = weights.get("A"), weights.get("B"), weights.get("alpha")
|
||||
if a_tensor is not None and b_tensor is not None:
|
||||
processed_groups[base_key] = (a_tensor, b_tensor, alpha)
|
||||
|
||||
for module_name, (a_tensor, b_tensor, alpha) in processed_groups.items():
|
||||
aggregated_weights[module_name].append({
|
||||
"A": a_tensor,
|
||||
"B": b_tensor,
|
||||
"alpha": alpha,
|
||||
"strength": strength,
|
||||
})
|
||||
|
||||
for module_name, weight_list in aggregated_weights.items():
|
||||
resolved_name, module = _resolve_module_name(model, module_name)
|
||||
if module is None:
|
||||
logger.warning("Skipping unresolved Qwen LoRA target: %s", module_name)
|
||||
unresolved_targets += 1
|
||||
continue
|
||||
all_a = []
|
||||
all_b_scaled = []
|
||||
for item in weight_list:
|
||||
a_tensor = item["A"]
|
||||
b_tensor = item["B"]
|
||||
alpha = item["alpha"]
|
||||
strength = float(item["strength"])
|
||||
rank = a_tensor.shape[0]
|
||||
scale = strength * ((alpha / rank) if alpha is not None else 1.0)
|
||||
if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
|
||||
target_dtype = torch.float16
|
||||
target_device = module.qweight.device
|
||||
elif hasattr(module, "proj_down"):
|
||||
target_dtype = module.proj_down.dtype
|
||||
target_device = module.proj_down.device
|
||||
elif hasattr(module, "weight"):
|
||||
target_dtype = module.weight.dtype
|
||||
target_device = module.weight.device
|
||||
else:
|
||||
target_dtype = torch.float16
|
||||
target_device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
all_a.append(a_tensor.to(dtype=target_dtype, device=target_device))
|
||||
all_b_scaled.append((b_tensor * scale).to(dtype=target_dtype, device=target_device))
|
||||
if not all_a:
|
||||
continue
|
||||
_apply_lora_to_module(module, torch.cat(all_a, dim=0), torch.cat(all_b_scaled, dim=1), resolved_name, model)
|
||||
|
||||
slot_count = len(getattr(model, "_lora_slots", {}) or {})
|
||||
logger.info(
|
||||
"Qwen LoRA composition finished: requested=%d supported=%s applied_targets=%d unresolved=%d",
|
||||
len(lora_configs),
|
||||
saw_supported_format,
|
||||
slot_count,
|
||||
unresolved_targets,
|
||||
)
|
||||
return saw_supported_format
|
||||
|
||||
|
||||
class ComfyQwenImageWrapperLM(nn.Module):
|
||||
def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
|
||||
super().__init__()
|
||||
self.model: Any = model
|
||||
self.config = {} if config is None else config
|
||||
self.dtype = next(model.parameters()).dtype
|
||||
self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []
|
||||
self._applied_loras: Optional[List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]]] = None
|
||||
self.apply_awq_mod = apply_awq_mod
|
||||
|
||||
def __getattr__(self, name):
|
||||
try:
|
||||
inner = object.__getattribute__(self, "_modules").get("model")
|
||||
except (AttributeError, KeyError):
|
||||
inner = None
|
||||
if inner is None:
|
||||
raise AttributeError(f"{type(self).__name__!s} has no attribute {name}")
|
||||
if name == "model":
|
||||
return inner
|
||||
return getattr(inner, name)
|
||||
|
||||
def process_img(self, *args, **kwargs):
|
||||
return self.model.process_img(*args, **kwargs)
|
||||
|
||||
def _ensure_composed(self):
|
||||
if self._applied_loras != self.loras or (not self.loras and getattr(self.model, "_lora_slots", None)):
|
||||
is_supported_format = compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
|
||||
self._applied_loras = self.loras.copy()
|
||||
has_slots = bool(getattr(self.model, "_lora_slots", None))
|
||||
if self.loras and is_supported_format and not has_slots:
|
||||
logger.warning("Qwen LoRA compose produced 0 target modules. Resetting and retrying once.")
|
||||
reset_lora_v2(self.model)
|
||||
compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
|
||||
has_slots = bool(getattr(self.model, "_lora_slots", None))
|
||||
logger.info("Qwen LoRA retry result: applied_targets=%d", len(getattr(self.model, "_lora_slots", {}) or {}))
|
||||
|
||||
offload_manager = getattr(self.model, "offload_manager", None)
|
||||
if offload_manager is not None:
|
||||
offload_settings = {
|
||||
"num_blocks_on_gpu": getattr(offload_manager, "num_blocks_on_gpu", 1),
|
||||
"use_pin_memory": getattr(offload_manager, "use_pin_memory", False),
|
||||
}
|
||||
logger.info(
|
||||
"Rebuilding Qwen offload manager after LoRA compose: num_blocks_on_gpu=%s use_pin_memory=%s",
|
||||
offload_settings["num_blocks_on_gpu"],
|
||||
offload_settings["use_pin_memory"],
|
||||
)
|
||||
self.model.set_offload(False)
|
||||
self.model.set_offload(True, **offload_settings)
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
self._ensure_composed()
|
||||
return self.model(*args, **kwargs)
|
||||
|
||||
|
||||
def _get_qwen_wrapper_and_transformer(model):
|
||||
model_wrapper = model.model.diffusion_model
|
||||
if hasattr(model_wrapper, "model") and hasattr(model_wrapper, "loras"):
|
||||
transformer = model_wrapper.model
|
||||
if transformer.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
|
||||
return model_wrapper, transformer
|
||||
if model_wrapper.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
|
||||
wrapped_model = ComfyQwenImageWrapperLM(model_wrapper, getattr(model_wrapper, "config", {}))
|
||||
model.model.diffusion_model = wrapped_model
|
||||
return wrapped_model, wrapped_model.model
|
||||
raise TypeError(f"This LoRA loader only works with Nunchaku Qwen Image models, but got {type(model_wrapper).__name__}.")
|
||||
|
||||
|
||||
def nunchaku_load_qwen_loras(model, lora_configs: List[Tuple[str, float]], apply_awq_mod: bool = True):
|
||||
model_wrapper, transformer = _get_qwen_wrapper_and_transformer(model)
|
||||
model_wrapper.apply_awq_mod = apply_awq_mod
|
||||
|
||||
saved_config = None
|
||||
if hasattr(model, "model") and hasattr(model.model, "model_config"):
|
||||
saved_config = model.model.model_config
|
||||
model.model.model_config = None
|
||||
|
||||
model_wrapper.model = None
|
||||
try:
|
||||
ret_model = copy.deepcopy(model)
|
||||
finally:
|
||||
if saved_config is not None:
|
||||
model.model.model_config = saved_config
|
||||
model_wrapper.model = transformer
|
||||
|
||||
ret_model_wrapper = ret_model.model.diffusion_model
|
||||
if saved_config is not None:
|
||||
ret_model.model.model_config = saved_config
|
||||
ret_model_wrapper.model = transformer
|
||||
ret_model_wrapper.apply_awq_mod = apply_awq_mod
|
||||
ret_model_wrapper.loras = list(getattr(model_wrapper, "loras", []))
|
||||
|
||||
for lora_name, lora_strength in lora_configs:
|
||||
lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
|
||||
if not lora_path or not os.path.isfile(lora_path):
|
||||
logger.warning("Skipping Qwen LoRA '%s' because it could not be found", lora_name)
|
||||
continue
|
||||
ret_model_wrapper.loras.append((lora_path, lora_strength))
|
||||
|
||||
return ret_model
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user