mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-07 14:30:15 -03:00
57983df4bd
- Use safe .get() in RecipeCache._resort_locked instead of itemgetter to prevent KeyError when recipe missing created_date; align sort key with _sort_cache_sync (prefer modified, fallback created_date, fallback 0) - Add base_model to allowed_fields in persistence_service.update_recipe() so the field passes validation - Route bulk base model updates through updateRecipeMetadata() on recipes page instead of generic saveModelMetadata(), matching existing isRecipesPage pattern used in setBulkFavorites and saveBulkTags
736 lines
27 KiB
Python
736 lines
27 KiB
Python
"""Services encapsulating recipe persistence workflows."""
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from __future__ import annotations
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import base64
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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 time
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import uuid
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from dataclasses import dataclass
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from typing import Any, Dict, Iterable, Optional
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from ...config import config
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from ...recipes.constants import GEN_PARAM_KEYS
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from ...utils.utils import calculate_recipe_fingerprint
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from .errors import RecipeNotFoundError, RecipeValidationError
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@dataclass(frozen=True)
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class PersistenceResult:
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"""Return payload from persistence operations."""
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payload: dict[str, Any]
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status: int = 200
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class RecipePersistenceService:
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"""Coordinate recipe persistence tasks across storage and caches."""
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def __init__(
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self,
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*,
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exif_utils,
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card_preview_width: int,
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logger,
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) -> None:
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self._exif_utils = exif_utils
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self._card_preview_width = card_preview_width
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self._logger = logger
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async def save_recipe(
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self,
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*,
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recipe_scanner,
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image_bytes: bytes | None,
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image_base64: str | None,
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name: str | None,
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tags: Iterable[str],
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metadata: Optional[dict[str, Any]],
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extension: str | None = None,
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recipe_id: str | None = None,
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target_dir: str | None = None,
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) -> PersistenceResult:
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"""Persist a user uploaded recipe.
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Args:
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recipe_id: If provided, reuse this ID instead of generating a new
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UUID. Used by re-import to preserve the original recipe identity.
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target_dir: If provided, save recipe files to this directory instead
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of the default recipes_dir. Used by re-import to preserve the
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original folder location.
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"""
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missing_fields = []
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if not name:
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missing_fields.append("name")
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if metadata is None:
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missing_fields.append("metadata")
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if missing_fields:
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raise RecipeValidationError(
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f"Missing required fields: {', '.join(missing_fields)}"
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)
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resolved_image_bytes = self._resolve_image_bytes(image_bytes, image_base64)
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recipes_dir = target_dir or recipe_scanner.recipes_dir
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os.makedirs(recipes_dir, exist_ok=True)
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recipe_id = recipe_id or str(uuid.uuid4())
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# Handle video formats by bypassing optimization and metadata embedding
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is_video = extension in [".mp4", ".webm"]
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if is_video:
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optimized_image = resolved_image_bytes
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# extension is already set
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else:
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optimized_image, extension = self._exif_utils.optimize_image(
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image_data=resolved_image_bytes,
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target_width=self._card_preview_width,
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format="webp",
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quality=85,
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preserve_metadata=True,
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)
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image_filename = f"{recipe_id}{extension}"
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image_path = os.path.join(recipes_dir, image_filename)
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normalized_image_path = os.path.normpath(image_path)
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with open(normalized_image_path, "wb") as file_obj:
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file_obj.write(optimized_image)
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current_time = time.time()
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loras_data = [self._normalise_lora_entry(lora) for lora in (metadata.get("loras") or [])]
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checkpoint_entry = self._sanitize_checkpoint_entry(self._extract_checkpoint_entry(metadata))
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gen_params = self._sanitize_gen_params_for_storage(metadata)
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fingerprint = calculate_recipe_fingerprint(loras_data)
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recipe_data: Dict[str, Any] = {
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"id": recipe_id,
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"file_path": normalized_image_path,
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"title": name,
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"modified": current_time,
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"created_date": current_time,
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"base_model": metadata.get("base_model", ""),
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"loras": loras_data,
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"gen_params": gen_params,
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"fingerprint": fingerprint,
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}
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if checkpoint_entry:
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recipe_data["checkpoint"] = checkpoint_entry
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tags_list = list(tags)
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if tags_list:
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recipe_data["tags"] = tags_list
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if metadata.get("source_path"):
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recipe_data["source_path"] = metadata.get("source_path")
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nsfw_level = metadata.get("preview_nsfw_level")
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if nsfw_level is not None and isinstance(nsfw_level, int):
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recipe_data["preview_nsfw_level"] = nsfw_level
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# Compute recipe folder relative to recipes root, mirroring
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# RecipeScanner._calculate_folder() which is only called during scan/load.
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if recipe_scanner.recipes_dir:
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recipe_file_dir = os.path.dirname(normalized_image_path)
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try:
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relative_folder = os.path.relpath(recipe_file_dir, recipe_scanner.recipes_dir)
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if relative_folder in (".", ""):
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relative_folder = ""
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recipe_data["folder"] = relative_folder.replace(os.path.sep, "/")
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except Exception:
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recipe_data["folder"] = ""
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json_filename = f"{recipe_id}.recipe.json"
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json_path = os.path.join(recipes_dir, json_filename)
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json_path = os.path.normpath(json_path)
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with open(json_path, "w", encoding="utf-8") as file_obj:
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json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
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if not is_video:
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self._exif_utils.append_recipe_metadata(normalized_image_path, recipe_data)
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matching_recipes = await self._find_matching_recipes(recipe_scanner, fingerprint, exclude_id=recipe_id)
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await recipe_scanner.add_recipe(recipe_data)
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return PersistenceResult(
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{
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"success": True,
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"recipe_id": recipe_id,
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"image_path": normalized_image_path,
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"json_path": json_path,
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"matching_recipes": matching_recipes,
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}
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)
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@staticmethod
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def _sanitize_gen_params_for_storage(metadata: dict[str, Any]) -> dict[str, Any]:
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gen_params = metadata.get("gen_params")
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if isinstance(gen_params, dict) and gen_params:
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source = gen_params
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else:
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source = metadata.get("raw_metadata")
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if not isinstance(source, dict):
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return {}
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allowed_keys = set(GEN_PARAM_KEYS)
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sanitized: dict[str, Any] = {}
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for key in allowed_keys:
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if key not in source:
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continue
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value = source.get(key)
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if value in (None, ""):
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continue
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sanitized[key] = value
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sanitized.pop("checkpoint", None)
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return sanitized
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async def delete_recipe(self, *, recipe_scanner, recipe_id: str) -> PersistenceResult:
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"""Delete an existing recipe."""
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recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
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if not recipe_json_path or not os.path.exists(recipe_json_path):
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raise RecipeNotFoundError("Recipe not found")
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with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
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recipe_data = json.load(file_obj)
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image_path = recipe_data.get("file_path")
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os.remove(recipe_json_path)
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if image_path and os.path.exists(image_path):
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os.remove(image_path)
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await recipe_scanner.remove_recipe(recipe_id)
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return PersistenceResult({"success": True, "message": "Recipe deleted successfully"})
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async def update_recipe(self, *, recipe_scanner, recipe_id: str, updates: dict[str, Any]) -> PersistenceResult:
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"""Update persisted metadata for a recipe."""
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allowed_fields = (
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"title",
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"tags",
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"source_path",
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"preview_nsfw_level",
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"favorite",
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"gen_params",
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"base_model",
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)
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if not any(key in updates for key in allowed_fields):
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raise RecipeValidationError(
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"At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite or gen_params or base_model)"
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)
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if "gen_params" in updates and not isinstance(updates["gen_params"], dict):
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raise RecipeValidationError("gen_params must be an object")
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success = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
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if not success:
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raise RecipeNotFoundError("Recipe not found or update failed")
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return PersistenceResult({"success": True, "recipe_id": recipe_id, "updates": updates})
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def _normalize_target_path(self, recipe_scanner, target_path: str) -> tuple[str, str]:
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"""Normalize and validate the target path for recipe moves."""
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if not target_path:
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raise RecipeValidationError("Target path is required")
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recipes_root = recipe_scanner.recipes_dir
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if not recipes_root:
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raise RecipeNotFoundError("Recipes directory not found")
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normalized_target = os.path.normpath(target_path)
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recipes_root = os.path.normpath(recipes_root)
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if not os.path.isabs(normalized_target):
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normalized_target = os.path.normpath(os.path.join(recipes_root, normalized_target))
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try:
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common_root = os.path.commonpath([normalized_target, recipes_root])
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except ValueError as exc:
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raise RecipeValidationError("Invalid target path") from exc
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if common_root != recipes_root:
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raise RecipeValidationError("Target path must be inside the recipes directory")
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return normalized_target, recipes_root
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async def _move_recipe_files(
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self,
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*,
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recipe_scanner,
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recipe_id: str,
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normalized_target: str,
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recipes_root: str,
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) -> dict[str, Any]:
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"""Move the recipe's JSON and preview image into the normalized target."""
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recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
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if not recipe_json_path or not os.path.exists(recipe_json_path):
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raise RecipeNotFoundError("Recipe not found")
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recipe_data = await recipe_scanner.get_recipe_by_id(recipe_id)
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if not recipe_data:
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raise RecipeNotFoundError("Recipe not found")
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current_json_dir = os.path.dirname(recipe_json_path)
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normalized_image_path = os.path.normpath(recipe_data.get("file_path") or "") if recipe_data.get("file_path") else None
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os.makedirs(normalized_target, exist_ok=True)
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if os.path.normpath(current_json_dir) == normalized_target:
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return {
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"success": True,
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"message": "Recipe is already in the target folder",
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"recipe_id": recipe_id,
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"original_file_path": recipe_data.get("file_path"),
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"new_file_path": recipe_data.get("file_path"),
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}
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new_json_path = os.path.normpath(os.path.join(normalized_target, os.path.basename(recipe_json_path)))
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shutil.move(recipe_json_path, new_json_path)
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new_image_path = normalized_image_path
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if normalized_image_path:
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target_image_path = os.path.normpath(os.path.join(normalized_target, os.path.basename(normalized_image_path)))
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if os.path.exists(normalized_image_path) and normalized_image_path != target_image_path:
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shutil.move(normalized_image_path, target_image_path)
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new_image_path = target_image_path
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relative_folder = os.path.relpath(normalized_target, recipes_root)
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if relative_folder in (".", ""):
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relative_folder = ""
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updates = {"file_path": new_image_path or recipe_data.get("file_path"), "folder": relative_folder.replace(os.path.sep, "/")}
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updated = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
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if not updated:
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raise RecipeNotFoundError("Recipe not found after move")
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return {
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"success": True,
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"recipe_id": recipe_id,
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"original_file_path": recipe_data.get("file_path"),
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"new_file_path": updates["file_path"],
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"json_path": new_json_path,
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"folder": updates["folder"],
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}
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async def move_recipe(self, *, recipe_scanner, recipe_id: str, target_path: str) -> PersistenceResult:
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"""Move a recipe's assets into a new folder under the recipes root."""
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normalized_target, recipes_root = self._normalize_target_path(recipe_scanner, target_path)
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result = await self._move_recipe_files(
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recipe_scanner=recipe_scanner,
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recipe_id=recipe_id,
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normalized_target=normalized_target,
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recipes_root=recipes_root,
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)
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return PersistenceResult(result)
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async def move_recipes_bulk(
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self,
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*,
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recipe_scanner,
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recipe_ids: Iterable[str],
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target_path: str,
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) -> PersistenceResult:
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"""Move multiple recipes to a new folder."""
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recipe_ids = list(recipe_ids)
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if not recipe_ids:
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raise RecipeValidationError("No recipe IDs provided")
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normalized_target, recipes_root = self._normalize_target_path(recipe_scanner, target_path)
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results: list[dict[str, Any]] = []
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success_count = 0
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failure_count = 0
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for recipe_id in recipe_ids:
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try:
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move_result = await self._move_recipe_files(
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recipe_scanner=recipe_scanner,
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recipe_id=str(recipe_id),
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normalized_target=normalized_target,
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recipes_root=recipes_root,
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)
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results.append(
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{
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"recipe_id": recipe_id,
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"original_file_path": move_result.get("original_file_path"),
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"new_file_path": move_result.get("new_file_path"),
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"success": True,
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"message": move_result.get("message", ""),
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"folder": move_result.get("folder", ""),
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}
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)
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success_count += 1
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except Exception as exc: # pragma: no cover - per-item error handling
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results.append(
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{
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"recipe_id": recipe_id,
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"original_file_path": None,
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"new_file_path": None,
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"success": False,
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"message": str(exc),
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}
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)
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failure_count += 1
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return PersistenceResult(
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{
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"success": True,
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"message": f"Moved {success_count} of {len(recipe_ids)} recipes",
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"results": results,
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"success_count": success_count,
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"failure_count": failure_count,
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}
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)
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async def reconnect_lora(
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self,
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*,
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recipe_scanner,
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recipe_id: str,
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lora_index: int,
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target_name: str,
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) -> PersistenceResult:
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"""Reconnect a LoRA entry within an existing recipe."""
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recipe_path = await recipe_scanner.get_recipe_json_path(recipe_id)
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if not recipe_path or not os.path.exists(recipe_path):
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raise RecipeNotFoundError("Recipe not found")
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target_lora = await recipe_scanner.get_local_lora(target_name)
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if not target_lora:
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raise RecipeNotFoundError(f"Local LoRA not found with name: {target_name}")
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recipe_data, updated_lora = await recipe_scanner.update_lora_entry(
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recipe_id,
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lora_index,
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target_name=target_name,
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target_lora=target_lora,
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)
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image_path = recipe_data.get("file_path")
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if image_path and os.path.exists(image_path):
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self._exif_utils.append_recipe_metadata(image_path, recipe_data)
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matching_recipes = []
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if "fingerprint" in recipe_data:
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matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(recipe_data["fingerprint"])
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if recipe_id in matching_recipes:
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matching_recipes.remove(recipe_id)
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return PersistenceResult(
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{
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"success": True,
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"recipe_id": recipe_id,
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"updated_lora": updated_lora,
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"matching_recipes": matching_recipes,
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}
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)
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async def bulk_delete(
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self,
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*,
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recipe_scanner,
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recipe_ids: Iterable[str],
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) -> PersistenceResult:
|
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"""Delete multiple recipes in a single request."""
|
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recipe_ids = list(recipe_ids)
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if not recipe_ids:
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raise RecipeValidationError("No recipe IDs provided")
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deleted_recipes: list[str] = []
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failed_recipes: list[dict[str, Any]] = []
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|
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for recipe_id in recipe_ids:
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recipe_json_path = await recipe_scanner.get_recipe_json_path(recipe_id)
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if not recipe_json_path or not os.path.exists(recipe_json_path):
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failed_recipes.append({"id": recipe_id, "reason": "Recipe not found"})
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continue
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try:
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with open(recipe_json_path, "r", encoding="utf-8") as file_obj:
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recipe_data = json.load(file_obj)
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image_path = recipe_data.get("file_path")
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os.remove(recipe_json_path)
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if image_path and os.path.exists(image_path):
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os.remove(image_path)
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deleted_recipes.append(recipe_id)
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except Exception as exc:
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failed_recipes.append({"id": recipe_id, "reason": str(exc)})
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if deleted_recipes:
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await recipe_scanner.bulk_remove(deleted_recipes)
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return PersistenceResult(
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{
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"success": True,
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"deleted": deleted_recipes,
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"failed": failed_recipes,
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"total_deleted": len(deleted_recipes),
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"total_failed": len(failed_recipes),
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}
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)
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|
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async def save_recipe_from_widget(
|
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self,
|
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*,
|
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recipe_scanner,
|
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metadata: dict[str, Any],
|
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image_bytes: bytes,
|
|
) -> PersistenceResult:
|
|
"""Save a recipe constructed from widget metadata."""
|
|
|
|
if not metadata:
|
|
raise RecipeValidationError("No generation metadata found")
|
|
|
|
recipes_dir = recipe_scanner.recipes_dir
|
|
os.makedirs(recipes_dir, exist_ok=True)
|
|
|
|
recipe_id = str(uuid.uuid4())
|
|
optimized_image, extension = self._exif_utils.optimize_image(
|
|
image_data=image_bytes,
|
|
target_width=self._card_preview_width,
|
|
format="webp",
|
|
quality=85,
|
|
preserve_metadata=True,
|
|
)
|
|
image_filename = f"{recipe_id}{extension}"
|
|
image_path = os.path.join(recipes_dir, image_filename)
|
|
with open(image_path, "wb") as file_obj:
|
|
file_obj.write(optimized_image)
|
|
|
|
lora_stack = metadata.get("loras", "")
|
|
lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", lora_stack)
|
|
|
|
loras_data = []
|
|
base_model_counts: Dict[str, int] = {}
|
|
|
|
for name, strength in lora_matches:
|
|
lora_info = await recipe_scanner.get_local_lora(name)
|
|
lora_data = {
|
|
"file_name": name,
|
|
"strength": float(strength),
|
|
"hash": (lora_info.get("sha256") or "").lower() if lora_info else "",
|
|
"modelVersionId": (lora_info.get("civitai") or {}).get("id", 0) if lora_info else 0,
|
|
"modelName": ((lora_info.get("civitai") or {}).get("model") or {}).get("name", name) if lora_info else "",
|
|
"modelVersionName": (lora_info.get("civitai") or {}).get("name", "") if lora_info else "",
|
|
"isDeleted": False,
|
|
"exclude": False,
|
|
}
|
|
loras_data.append(lora_data)
|
|
|
|
if lora_info and "base_model" in lora_info:
|
|
base_model = lora_info["base_model"]
|
|
base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
|
|
|
|
recipe_name = self._derive_recipe_name(lora_matches)
|
|
most_common_base_model = (
|
|
max(base_model_counts.items(), key=lambda item: item[1])[0] if base_model_counts else ""
|
|
)
|
|
checkpoint_entry = await self._build_widget_checkpoint_entry(
|
|
recipe_scanner,
|
|
metadata.get("checkpoint"),
|
|
)
|
|
|
|
recipe_data = {
|
|
"id": recipe_id,
|
|
"file_path": image_path,
|
|
"title": recipe_name,
|
|
"modified": time.time(),
|
|
"created_date": time.time(),
|
|
"base_model": most_common_base_model or (checkpoint_entry or {}).get("baseModel", ""),
|
|
"loras": loras_data,
|
|
"gen_params": {
|
|
key: value
|
|
for key, value in metadata.items()
|
|
if key not in ["checkpoint", "loras"]
|
|
},
|
|
"loras_stack": lora_stack,
|
|
}
|
|
if checkpoint_entry:
|
|
recipe_data["checkpoint"] = checkpoint_entry
|
|
|
|
json_filename = f"{recipe_id}.recipe.json"
|
|
json_path = os.path.join(recipes_dir, json_filename)
|
|
with open(json_path, "w", encoding="utf-8") as file_obj:
|
|
json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
|
|
|
|
self._exif_utils.append_recipe_metadata(image_path, recipe_data)
|
|
await recipe_scanner.add_recipe(recipe_data)
|
|
|
|
return PersistenceResult(
|
|
{
|
|
"success": True,
|
|
"recipe_id": recipe_id,
|
|
"image_path": image_path,
|
|
"json_path": json_path,
|
|
"recipe_name": recipe_name,
|
|
}
|
|
)
|
|
|
|
# Helper methods ---------------------------------------------------
|
|
|
|
async def _build_widget_checkpoint_entry(
|
|
self,
|
|
recipe_scanner,
|
|
checkpoint_raw: Any,
|
|
) -> Optional[dict[str, Any]]:
|
|
"""Build recipe checkpoint metadata from widget generation metadata."""
|
|
|
|
if isinstance(checkpoint_raw, dict):
|
|
return self._sanitize_checkpoint_entry(checkpoint_raw)
|
|
|
|
if not isinstance(checkpoint_raw, str):
|
|
return None
|
|
|
|
checkpoint_name = checkpoint_raw.strip()
|
|
if not checkpoint_name:
|
|
return None
|
|
|
|
file_name = os.path.splitext(os.path.basename(checkpoint_name))[0]
|
|
checkpoint_info = await self._lookup_widget_checkpoint(
|
|
recipe_scanner,
|
|
checkpoint_name,
|
|
)
|
|
if not checkpoint_info:
|
|
return {
|
|
"type": "checkpoint",
|
|
"name": checkpoint_name,
|
|
"file_name": file_name,
|
|
"hash": "",
|
|
}
|
|
|
|
civitai = checkpoint_info.get("civitai") or {}
|
|
civitai_model = civitai.get("model") or {}
|
|
file_path = checkpoint_info.get("file_path") or checkpoint_info.get("path") or ""
|
|
cached_file_name = (
|
|
checkpoint_info.get("file_name")
|
|
or (os.path.splitext(os.path.basename(file_path))[0] if file_path else "")
|
|
or file_name
|
|
)
|
|
|
|
return {
|
|
"type": "checkpoint",
|
|
"modelId": civitai_model.get("id", 0),
|
|
"modelVersionId": civitai.get("id", 0),
|
|
"name": civitai_model.get("name") or checkpoint_info.get("model_name") or checkpoint_name,
|
|
"version": civitai.get("name", ""),
|
|
"hash": (checkpoint_info.get("sha256") or checkpoint_info.get("hash") or "").lower(),
|
|
"file_name": cached_file_name,
|
|
"modelName": civitai_model.get("name", ""),
|
|
"modelVersionName": civitai.get("name", ""),
|
|
"baseModel": checkpoint_info.get("base_model") or civitai.get("baseModel", ""),
|
|
}
|
|
|
|
async def _lookup_widget_checkpoint(
|
|
self,
|
|
recipe_scanner,
|
|
checkpoint_name: str,
|
|
) -> Optional[dict[str, Any]]:
|
|
lookup = getattr(recipe_scanner, "get_local_checkpoint", None)
|
|
if not callable(lookup):
|
|
return None
|
|
|
|
candidates = []
|
|
for candidate in (
|
|
checkpoint_name,
|
|
os.path.basename(checkpoint_name),
|
|
os.path.splitext(os.path.basename(checkpoint_name))[0],
|
|
):
|
|
if candidate and candidate not in candidates:
|
|
candidates.append(candidate)
|
|
|
|
for candidate in candidates:
|
|
try:
|
|
checkpoint_info = await lookup(candidate)
|
|
except Exception as exc:
|
|
self._logger.debug(
|
|
"Failed to lookup checkpoint %s while saving widget recipe: %s",
|
|
candidate,
|
|
exc,
|
|
)
|
|
continue
|
|
if checkpoint_info:
|
|
return checkpoint_info
|
|
|
|
return None
|
|
|
|
def _extract_checkpoint_entry(self, metadata: dict[str, Any]) -> Optional[dict[str, Any]]:
|
|
"""Pull a checkpoint entry from various metadata locations."""
|
|
|
|
checkpoint_entry = metadata.get("checkpoint") or metadata.get("model")
|
|
if not checkpoint_entry:
|
|
gen_params = metadata.get("gen_params") or {}
|
|
checkpoint_entry = gen_params.get("checkpoint")
|
|
|
|
return checkpoint_entry if isinstance(checkpoint_entry, dict) else None
|
|
|
|
def _sanitize_checkpoint_entry(self, checkpoint_entry: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]:
|
|
"""Remove transient/local-only fields from checkpoint metadata."""
|
|
|
|
if not checkpoint_entry:
|
|
return None
|
|
|
|
if not isinstance(checkpoint_entry, dict):
|
|
return checkpoint_entry
|
|
|
|
pruned = dict(checkpoint_entry)
|
|
for key in ("existsLocally", "localPath", "thumbnailUrl", "size", "downloadUrl"):
|
|
pruned.pop(key, None)
|
|
return pruned
|
|
|
|
def _resolve_image_bytes(self, image_bytes: bytes | None, image_base64: str | None) -> bytes:
|
|
if image_bytes is not None:
|
|
return image_bytes
|
|
if image_base64:
|
|
try:
|
|
payload = image_base64.split(",", 1)[1] if "," in image_base64 else image_base64
|
|
return base64.b64decode(payload)
|
|
except Exception as exc: # pragma: no cover - validation guard
|
|
raise RecipeValidationError(f"Invalid base64 image data: {exc}") from exc
|
|
raise RecipeValidationError("No image data provided")
|
|
|
|
def _normalise_lora_entry(self, lora: dict[str, Any]) -> dict[str, Any]:
|
|
return {
|
|
"file_name": lora.get("file_name", "")
|
|
or (
|
|
os.path.splitext(os.path.basename(lora.get("localPath", "")))[0]
|
|
if lora.get("localPath")
|
|
else ""
|
|
),
|
|
"hash": (lora.get("hash") or "").lower(),
|
|
"strength": float(lora.get("weight", 1.0)),
|
|
"modelVersionId": lora.get("id", 0),
|
|
"modelName": lora.get("name", ""),
|
|
"modelVersionName": lora.get("version", ""),
|
|
"isDeleted": lora.get("isDeleted", False),
|
|
"exclude": lora.get("exclude", False),
|
|
}
|
|
|
|
async def _find_matching_recipes(
|
|
self,
|
|
recipe_scanner,
|
|
fingerprint: str | None,
|
|
*,
|
|
exclude_id: Optional[str] = None,
|
|
) -> list[str]:
|
|
if not fingerprint:
|
|
return []
|
|
matches = await recipe_scanner.find_recipes_by_fingerprint(fingerprint)
|
|
if exclude_id and exclude_id in matches:
|
|
matches.remove(exclude_id)
|
|
return matches
|
|
|
|
def _derive_recipe_name(self, lora_matches: list[tuple[str, str]]) -> str:
|
|
recipe_name_parts = [f"{name.strip()}-{float(strength):.2f}" for name, strength in lora_matches[:3]]
|
|
recipe_name = "_".join(recipe_name_parts)
|
|
return recipe_name or "recipe"
|