feat(scripts): add restore_suffixed_filenames script to revert leftover hash suffixes

Build a local_cache from the scanner cache before calling the metadata
parser. When a resource hash is found in the cache, populate the entry
directly from cached civitai metadata instead of calling CivitAI's
/model-versions/by-hash endpoint.

This eliminates redundant API calls and retries for the common case
where the example image only uses the parent model plus a checkpoint.

locales/de.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "Keine Rezept-ID verfügbar",
             "sendToWorkflowFailed": "Fehler beim Senden des Rezepts an den Workflow: {message}",
             "copyFailed": "Fehler beim Kopieren der Rezept-Syntax: {message}",
+            "createError": "Fehler beim Erstellen des Rezepts：{message}",
+            "createFailed": "Fehler beim Erstellen des Rezepts：{error}",
+            "createMissingData": "Erforderliche Daten zum Erstellen des Rezepts fehlen",
+            "created": "Rezept erfolgreich erstellt",
             "noMissingLoras": "Keine fehlenden LoRAs zum Herunterladen",
             "missingLorasInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
             "preparingForDownloadFailed": "Fehler beim Vorbereiten der LoRAs für den Download",

locales/en.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "No recipe ID available",
             "sendToWorkflowFailed": "Failed to send recipe to workflow: {message}",
             "copyFailed": "Error copying recipe syntax: {message}",
+            "createError": "Error creating recipe: {message}",
+            "createFailed": "Failed to create recipe: {error}",
+            "createMissingData": "Missing required data to create recipe",
+            "created": "Recipe created successfully",
             "noMissingLoras": "No missing LoRAs to download",
             "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
             "preparingForDownloadFailed": "Error preparing LoRAs for download",

locales/es.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "No hay ID de receta disponible",
             "sendToWorkflowFailed": "Error al enviar la receta al flujo de trabajo: {message}",
             "copyFailed": "Error copiando sintaxis de receta: {message}",
+            "createError": "Error al crear la receta：{message}",
+            "createFailed": "Error al crear la receta：{error}",
+            "createMissingData": "Faltan datos necesarios para crear la receta",
+            "created": "Receta creada exitosamente",
             "noMissingLoras": "No hay LoRAs faltantes para descargar",
             "missingLorasInfoFailed": "Error al obtener información de LoRAs faltantes",
             "preparingForDownloadFailed": "Error preparando LoRAs para descarga",

locales/fr.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "Aucun ID de recipe disponible",
             "sendToWorkflowFailed": "Échec de l'envoi de la recette vers le workflow : {message}",
             "copyFailed": "Erreur lors de la copie de la syntaxe de la recipe : {message}",
+            "createError": "Erreur lors de la création du Recipe ：{message}",
+            "createFailed": "Échec de la création du Recipe ：{error}",
+            "createMissingData": "Données requises manquantes pour créer le Recipe",
+            "created": "Recipe créé avec succès",
             "noMissingLoras": "Aucun LoRA manquant à télécharger",
             "missingLorasInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
             "preparingForDownloadFailed": "Erreur lors de la préparation des LoRAs pour le téléchargement",

locales/he.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "אין מזהה מתכון זמין",
             "sendToWorkflowFailed": "נכשל שליחת המתכון ל-workflow: {message}",
             "copyFailed": "שגיאה בהעתקת תחביר המתכון: {message}",
+            "createError": "שגיאה ביצירת המתכון：{message}",
+            "createFailed": "יצירת המתכון נכשלה：{error}",
+            "createMissingData": "חסרים נתונים נדרשים ליצירת המתכון",
+            "created": "המתכון נוצר בהצלחה",
             "noMissingLoras": "אין LoRAs חסרים להורדה",
             "missingLorasInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
             "preparingForDownloadFailed": "שגיאה בהכנת LoRAs להורדה",

locales/ja.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "レシピIDが利用できません",
             "sendToWorkflowFailed": "ワークフローへのレシピ送信に失敗しました：{message}",
             "copyFailed": "レシピ構文のコピーエラー：{message}",
+            "createError": "レシピ作成中にエラーが発生しました：{message}",
+            "createFailed": "レシピの作成に失敗しました：{error}",
+            "createMissingData": "レシピ作成に必要なデータが不足しています",
+            "created": "レシピを作成しました",
             "noMissingLoras": "ダウンロードする不足LoRAがありません",
             "missingLorasInfoFailed": "不足LoRAの情報取得に失敗しました",
             "preparingForDownloadFailed": "ダウンロード用LoRAの準備中にエラーが発生しました",

locales/ko.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "사용 가능한 레시피 ID가 없습니다",
             "sendToWorkflowFailed": "워크플로우에 레시피 보내기 실패: {message}",
             "copyFailed": "레시피 문법 복사 오류: {message}",
+            "createError": "레시피 생성 중 오류 발생：{message}",
+            "createFailed": "레시피 생성 실패：{error}",
+            "createMissingData": "레시피 생성에 필요한 데이터가 없습니다",
+            "created": "레시피가 생성되었습니다",
             "noMissingLoras": "다운로드할 누락된 LoRA가 없습니다",
             "missingLorasInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
             "preparingForDownloadFailed": "LoRA 다운로드 준비 오류",

locales/ru.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "ID рецепта недоступен",
             "sendToWorkflowFailed": "Не удалось отправить рецепт в рабочий процесс: {message}",
             "copyFailed": "Ошибка копирования синтаксиса рецепта: {message}",
+            "createError": "Ошибка при создании рецепта：{message}",
+            "createFailed": "Не удалось создать рецепт：{error}",
+            "createMissingData": "Отсутствуют необходимые данные для создания рецепта",
+            "created": "Рецепт успешно создан",
             "noMissingLoras": "Нет отсутствующих LoRAs для загрузки",
             "missingLorasInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
             "preparingForDownloadFailed": "Ошибка подготовки LoRAs для загрузки",

locales/zh-CN.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "无配方 ID",
             "sendToWorkflowFailed": "发送配方到工作流失败：{message}",
             "copyFailed": "复制配方语法出错：{message}",
+            "createError": "创建配方时出错：{message}",
+            "createFailed": "创建配方失败：{error}",
+            "createMissingData": "缺少创建配方所需的数据",
+            "created": "配方创建成功",
             "noMissingLoras": "没有缺失的 LoRA 可下载",
             "missingLorasInfoFailed": "获取缺失 LoRA 信息失败",
             "preparingForDownloadFailed": "准备下载 LoRA 时出错",

locales/zh-TW.json

@@ -1668,6 +1668,10 @@
             "noRecipeId": "無配方 ID",
             "sendToWorkflowFailed": "傳送配方到工作流失敗：{message}",
             "copyFailed": "複製配方語法錯誤：{message}",
+            "createError": "建立配方時發生錯誤：{message}",
+            "createFailed": "建立配方失敗：{error}",
+            "createMissingData": "缺少建立配方所需的資料",
+            "created": "配方建立成功",
             "noMissingLoras": "無缺少的 LoRA 可下載",
             "missingLorasInfoFailed": "取得缺少 LoRA 資訊失敗",
             "preparingForDownloadFailed": "準備下載 LoRA 時發生錯誤",

py/recipes/base.py

@@ -58,9 +58,52 @@ class RecipeMetadataParser(ABC):
             civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
             
             if not civitai_info or error_msg == "Model not found":
-                # Model not found or deleted
-                lora_entry['isDeleted'] = True
-                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                # CivitAI may fail to resolve a hash that is still being
+                # computed (known CivitAI issue). Before marking as deleted,
+                # try to reconcile with a local model that has the same
+                # filename and matching AutoV3 hash.
+                reconciled = False
+                file_name = lora_entry.get("file_name")
+                if file_name and recipe_scanner and hash_value:
+                    lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
+                    if lora_scanner:
+                        try:
+                            # Local import to avoid circular dependency:
+                            # base.py → file_utils → settings_manager → ...
+                            #  → recipe_scanner → enrichment → base.py
+                            from ..utils.file_utils import calculate_autov3  # fmt: skip
+                            cache = await lora_scanner.get_cached_data()
+                            for item in getattr(cache, "raw_data", []):
+                                if item.get("file_name") == file_name:
+                                    local_path = item.get("file_path")
+                                    if local_path and os.path.exists(local_path):
+                                        local_autov3 = calculate_autov3(local_path)
+                                        if local_autov3 and local_autov3 == hash_value:
+                                            lora_entry["existsLocally"] = True
+                                            lora_entry["localPath"] = local_path
+                                            lora_entry["hash"] = item.get("sha256", hash_value)
+                                            if "preview_url" in item:
+                                                lora_entry["thumbnailUrl"] = config.get_preview_static_url(item["preview_url"])
+                                            civ = item.get("civitai") or {}
+                                            if isinstance(civ, dict):
+                                                if civ.get("id") is not None:
+                                                    lora_entry["id"] = civ["id"]
+                                                if civ.get("modelId") is not None:
+                                                    lora_entry["modelId"] = civ["modelId"]
+                                                if civ.get("name"):
+                                                    lora_entry["version"] = civ["name"]
+                                                # model_name is the CivitAI model display
+                                                # name stored directly in the cache column.
+                                                cached_model_name = item.get("model_name")
+                                                if cached_model_name:
+                                                    lora_entry["name"] = cached_model_name
+                                            reconciled = True
+                                            break
+                        except Exception:
+                            pass
+                if not reconciled:
+                    lora_entry['isDeleted'] = True
+                    lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
                 return lora_entry
                 
             # Get model type and validate

py/recipes/parsers/civitai_image.py

@@ -6,6 +6,7 @@ from typing import Dict, Any, Union
 from ..base import RecipeMetadataParser
 from ..constants import GEN_PARAM_KEYS
 from ...services.metadata_service import get_default_metadata_provider
+from ...config import config
 
 logger = logging.getLogger(__name__)
 
@@ -73,7 +74,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         return False
 
     async def parse_metadata(  # type: ignore[override]
-        self, user_comment, recipe_scanner=None, civitai_client=None
+        self, user_comment, recipe_scanner=None, civitai_client=None,
+        local_cache: dict[str, Any] | None = None,
     ) -> Dict[str, Any]:
         """Parse metadata from Civitai image format
 
@@ -81,6 +83,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             user_comment: The metadata from the image (dict)
             recipe_scanner: Optional recipe scanner service
             civitai_client: Optional Civitai API client (deprecated, use metadata_provider instead)
+            local_cache: Optional dict mapping sha256/autov3 hash → scanner cache item.
+                         When provided, matching models skip CivitAI API calls.
 
         Returns:
             Dict containing parsed recipe data
@@ -210,35 +214,45 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         }
 
                         # Try to look up base model from the checkpoint hash
-                        if checkpoint_entry["hash"] and metadata_provider:
-                            try:
-                                civitai_info = (
-                                    await metadata_provider.get_model_by_hash(
-                                        checkpoint_entry["hash"]
+                        cp_hash = checkpoint_entry.get("hash")
+                        if cp_hash and metadata_provider:
+                            local_cached = local_cache.get(cp_hash) if local_cache else None
+                            if local_cached:
+                                self._populate_entry_from_cache(
+                                    checkpoint_entry, local_cached
+                                )
+                                bm = checkpoint_entry.get("baseModel", "")
+                                if bm and not result["base_model"]:
+                                    result["base_model"] = bm
+                            else:
+                                try:
+                                    civitai_info = (
+                                        await metadata_provider.get_model_by_hash(
+                                            cp_hash
+                                        )
+                                    )
+                                    civitai_data, error_msg = (
+                                        (civitai_info, None)
+                                        if not isinstance(civitai_info, tuple)
+                                        else civitai_info
+                                    )
+                                    if civitai_data and error_msg != "Model not found":
+                                        if 'model' in civitai_data and 'name' in civitai_data['model']:
+                                            checkpoint_entry['name'] = civitai_data['model']['name']
+                                        checkpoint_entry['id'] = civitai_data.get('id', 0)
+                                        checkpoint_entry['modelId'] = civitai_data.get('modelId', 0)
+                                        if 'name' in civitai_data:
+                                            checkpoint_entry['version'] = civitai_data['name']
+                                        base_model = civitai_data.get('baseModel', '')
+                                        if base_model:
+                                            checkpoint_entry['baseModel'] = base_model
+                                            if not result['base_model']:
+                                                result['base_model'] = base_model
+                                except Exception as e:
+                                    logger.error(
+                                        f"Error fetching checkpoint info for hash "
+                                        f"{cp_hash}: {e}"
                                     )
-                                )
-                                civitai_data, error_msg = (
-                                    (civitai_info, None)
-                                    if not isinstance(civitai_info, tuple)
-                                    else civitai_info
-                                )
-                                if civitai_data and error_msg != "Model not found":
-                                    if 'model' in civitai_data and 'name' in civitai_data['model']:
-                                        checkpoint_entry['name'] = civitai_data['model']['name']
-                                    checkpoint_entry['id'] = civitai_data.get('id', 0)
-                                    checkpoint_entry['modelId'] = civitai_data.get('modelId', 0)
-                                    if 'name' in civitai_data:
-                                        checkpoint_entry['version'] = civitai_data['name']
-                                    base_model = civitai_data.get('baseModel', '')
-                                    if base_model:
-                                        checkpoint_entry['baseModel'] = base_model
-                                        if not result['base_model']:
-                                            result['base_model'] = base_model
-                            except Exception as e:
-                                logger.error(
-                                    f"Error fetching checkpoint info for hash "
-                                    f"{checkpoint_entry['hash']}: {e}"
-                                )
 
                         if result["model"] is None:
                             result["model"] = checkpoint_entry
@@ -279,34 +293,45 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         }
 
                         # Try to get info from Civitai if hash is available
-                        if lora_entry["hash"] and metadata_provider:
-                            try:
-                                civitai_info = (
-                                    await metadata_provider.get_model_by_hash(lora_hash)
+                        if lora_hash and metadata_provider:
+                            local_cached = local_cache.get(lora_hash) if local_cache else None
+                            if local_cached:
+                                self._populate_entry_from_cache(
+                                    lora_entry, local_cached
                                 )
-
-                                populated_entry = await self.populate_lora_from_civitai(
-                                    lora_entry,
-                                    civitai_info,
-                                    recipe_scanner,
-                                    base_model_counts,
-                                    lora_hash,
-                                )
-
-                                if populated_entry is None:
-                                    continue  # Skip invalid LoRA types
-
-                                lora_entry = populated_entry
-
-                                # If we have a version ID from Civitai, track it for deduplication
-                                if "id" in lora_entry and lora_entry["id"]:
+                                # Track by version ID for deduplication
+                                if lora_entry.get("id"):
                                     added_loras[str(lora_entry["id"])] = len(
                                         result["loras"]
                                     )
-                            except Exception as e:
-                                logger.error(
-                                    f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
-                                )
+                            else:
+                                try:
+                                    civitai_info = (
+                                        await metadata_provider.get_model_by_hash(lora_hash)
+                                    )
+
+                                    populated_entry = await self.populate_lora_from_civitai(
+                                        lora_entry,
+                                        civitai_info,
+                                        recipe_scanner,
+                                        base_model_counts,
+                                        lora_hash,
+                                    )
+
+                                    if populated_entry is None:
+                                        continue  # Skip invalid LoRA types
+
+                                    lora_entry = populated_entry
+
+                                    # If we have a version ID from Civitai, track it for deduplication
+                                    if "id" in lora_entry and lora_entry["id"]:
+                                        added_loras[str(lora_entry["id"])] = len(
+                                            result["loras"]
+                                        )
+                                except Exception as e:
+                                    logger.error(
+                                        f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
+                                    )
 
                         # Track by hash if we have it
                         if lora_hash:
@@ -684,3 +709,41 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
         except Exception as e:
             logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
             return {"error": str(e), "loras": []}
+
+    @staticmethod
+    def _populate_entry_from_cache(
+        entry: dict[str, Any],
+        cache_item: dict[str, Any],
+    ) -> None:
+        """Fill a lora/checkpoint entry from a scanner cache item.
+
+        Avoids CivitAI API calls for models that exist locally.
+        Mirrors the population logic in
+        ``RecipeMetadataParser.populate_lora_from_civitai()`` but operates
+        entirely on cached data.
+        """
+        civ = cache_item.get("civitai") or {}
+        if isinstance(civ, dict):
+            if civ.get("id") is not None:
+                entry["id"] = civ["id"]
+            if civ.get("modelId") is not None:
+                entry["modelId"] = civ["modelId"]
+            if civ.get("name"):
+                entry["version"] = civ["name"]
+            cached_name = cache_item.get("model_name")
+            if cached_name:
+                entry["name"] = cached_name
+        entry["existsLocally"] = True
+        local_path = cache_item.get("file_path")
+        if local_path:
+            entry["localPath"] = local_path
+        sha256 = cache_item.get("sha256")
+        if sha256:
+            entry["hash"] = sha256
+        if "preview_url" in cache_item:
+            entry["thumbnailUrl"] = config.get_preview_static_url(
+                cache_item["preview_url"]
+            )
+        base_model = cache_item.get("base_model", "")
+        if base_model:
+            entry["baseModel"] = base_model

py/routes/handlers/recipe_handlers.py

@@ -16,7 +16,7 @@ from aiohttp import web
 
 from ...config import config
 from ...services.server_i18n import server_i18n as default_server_i18n
-from ...services.settings_manager import SettingsManager
+from ...services.settings_manager import SettingsManager, get_settings_manager
 from ...services.recipes import (
     RecipeAnalysisService,
     RecipeDownloadError,
@@ -26,7 +26,12 @@ from ...services.recipes import (
     RecipeValidationError,
 )
 from ...services.metadata_service import get_default_metadata_provider
-from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
+from ...utils.civitai_utils import (
+    build_civitai_image_page_url,
+    extract_civitai_image_id,
+    extract_civitai_image_id_from_cdn_url,
+    rewrite_preview_url,
+)
 from ...utils.exif_utils import ExifUtils
 from ...recipes.merger import GenParamsMerger
 from ...recipes.enrichment import RecipeEnricher
@@ -96,6 +101,7 @@ class RecipeHandlerSet:
             "browse_directory": self.batch_import.browse_directory,
             "check_image_exists": self.management.check_image_exists,
             "import_from_url": self.management.import_from_url,
+            "create_from_example": self.management.create_from_example,
         }
 
 
@@ -1668,6 +1674,272 @@ class RecipeManagementHandler:
         )
         return web.json_response(result.payload, status=result.status)
 
+    async def create_from_example(self, request: web.Request) -> web.Response:
+        """Create a recipe from a model's example image using cached metadata.
+
+        Uses the image's meta data (already cached in .metadata.json from the
+        CivitAI model-versions API) to create a recipe without additional
+        CivitAI API calls.
+
+        If the image metadata doesn't contain any resources of the parent
+        model's type (LoRA-type or Checkpoint), the parent model is
+        auto-populated as a fallback.
+
+        Request body:
+            image_data (dict): The full image object from model-versions API
+                (includes meta, additionalResources, url, etc.)
+            model_hash (str): SHA256 hash of the parent model
+            model_name (str): Filename of the parent model
+            model_type (str): Page type (``"loras"``, ``"checkpoints"``, etc.)
+            local_image_path (str, optional): Local filesystem path to read
+                the image bytes for the recipe preview
+        """
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            data = await request.json()
+            image_data = data.get("image_data")
+            model_hash = data.get("model_hash")
+            model_name = data.get("model_name")
+            model_type = data.get("model_type", "")
+
+            if not image_data or not model_hash or not model_name:
+                raise RecipeValidationError(
+                    "Missing required fields: image_data, model_hash, model_name"
+                )
+
+            # Merge nested meta into top level so the parser finds everything.
+            # CivitaiApiMetadataParser expects prompt, seed, resources, etc.
+            # at the top level or wrapped under a "meta" key.
+            inner_meta = image_data.get("meta") or {}
+            parsed_input = {**image_data, **inner_meta}
+            parsed_input.pop("meta", None)
+
+            # Build a local cache of {hash → cache_item} so the parser can
+            # skip CivitAI API calls for models that exist on disk.
+            local_cache: Dict[str, Dict[str, Any]] = {}
+            lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
+            if lora_scanner and model_hash:
+                try:
+                    parent_cache_data = await lora_scanner.get_cached_data()
+                    for item in getattr(parent_cache_data, "raw_data", []):
+                        if item.get("sha256", "").lower() == model_hash.lower():
+                            local_cache[model_hash.lower()] = item
+                            # Compute AutoV3 so the parser can also match on
+                            # that hash type (CivitAI metadata resources use
+                            # AutoV3).
+                            file_path = item.get("file_path")
+                            if file_path and os.path.exists(file_path):
+                                try:
+                                    from ...utils.file_utils import (
+                                        calculate_autov3,
+                                    )
+                                    autov3 = calculate_autov3(file_path)
+                                    if autov3:
+                                        local_cache[autov3.lower()] = item
+                                except Exception:
+                                    pass
+                            break
+                except Exception:
+                    pass
+
+            parser = self._analysis_service._recipe_parser_factory.create_parser(
+                parsed_input
+            )
+            if not parser:
+                raise RecipeValidationError("Unable to parse image metadata")
+
+            from ...recipes.parsers.civitai_image import CivitaiApiMetadataParser
+
+            if isinstance(parser, CivitaiApiMetadataParser):
+                parsed = await parser.parse_metadata(
+                    parsed_input,
+                    recipe_scanner=recipe_scanner,
+                    local_cache=local_cache,
+                )
+            else:
+                parsed = await parser.parse_metadata(
+                    parsed_input, recipe_scanner=recipe_scanner
+                )
+
+            loras = list(parsed.get("loras") or [])
+            checkpoint = parsed.get("model")
+            is_lora_type = model_type.startswith("lora")
+            is_ckpt_type = model_type.startswith("checkpoint")
+
+            # Extract parent model metadata from local_cache (used below to
+            # reconcile isDeleted entries and enrich auto-populated ones).
+            parent_civitai_id: int | None = None
+            parent_model_id: int | None = None
+            parent_version_name: str | None = None
+            parent_model_name: str | None = None
+            # Prefer sha256 key; fall back to any cached entry.
+            parent_item = local_cache.get(model_hash.lower()) if model_hash else None
+            if parent_item is None and local_cache:
+                parent_item = next(iter(local_cache.values()))
+            if parent_item:
+                civ = parent_item.get("civitai") or {}
+                if isinstance(civ, dict):
+                    parent_civitai_id = civ.get("id")
+                    parent_model_id = civ.get("modelId")
+                    parent_version_name = civ.get("name")
+                parent_model_name = parent_item.get("model_name")
+
+            # Reconcile isDeleted entries against the parent model.
+            # When the CivitAI hash lookup fails (known issue — hashes not
+            # yet computed), the parser marks the entry isDeleted even though
+            # the model exists locally.
+            if is_lora_type:
+                for lora in loras:
+                    if lora.get("isDeleted") and lora.get("file_name") == model_name:
+                        lora["isDeleted"] = False
+                        lora["existsLocally"] = True
+                        lora["hash"] = model_hash
+                        if parent_civitai_id is not None:
+                            lora["id"] = parent_civitai_id
+                        if parent_model_id is not None:
+                            lora["modelId"] = parent_model_id
+                        if parent_version_name is not None:
+                            lora["version"] = parent_version_name
+                        if parent_model_name is not None:
+                            lora["name"] = parent_model_name
+            elif is_ckpt_type and checkpoint and checkpoint.get("isDeleted"):
+                if checkpoint.get("file_name") == model_name:
+                    checkpoint["isDeleted"] = False
+                    checkpoint["existsLocally"] = True
+                    checkpoint["hash"] = model_hash
+                    if parent_civitai_id is not None:
+                        checkpoint["id"] = parent_civitai_id
+                    if parent_model_id is not None:
+                        checkpoint["modelId"] = parent_model_id
+                    if parent_version_name is not None:
+                        checkpoint["version"] = parent_version_name
+
+            # Auto-populate parent model only when the image metadata didn't
+            # contain any resources of that type.
+            if is_lora_type and not loras:
+                lora_entry = {
+                    "name": model_name,
+                    "type": "lora",
+                    "weight": 1.0,
+                    "hash": model_hash,
+                    "existsLocally": True,
+                    "localPath": None,
+                    "file_name": model_name,
+                    "thumbnailUrl": "/loras_static/images/no-preview.png",
+                    "baseModel": parsed.get("base_model", ""),
+                    "size": 0,
+                    "downloadUrl": "",
+                    "isDeleted": False,
+                }
+                if parent_civitai_id is not None:
+                    lora_entry["id"] = parent_civitai_id
+                if parent_model_id is not None:
+                    lora_entry["modelId"] = parent_model_id
+                if parent_version_name is not None:
+                    lora_entry["version"] = parent_version_name
+                if parent_model_name is not None:
+                    lora_entry["name"] = parent_model_name
+                loras.insert(0, lora_entry)
+            elif is_ckpt_type and not checkpoint:
+                checkpoint = {
+                    "name": model_name,
+                    "type": "checkpoint",
+                    "hash": model_hash,
+                    "file_name": model_name,
+                    "existsLocally": True,
+                    "baseModel": parsed.get("base_model", ""),
+                    "isDeleted": False,
+                }
+                if parent_civitai_id is not None:
+                    checkpoint["id"] = parent_civitai_id
+                if parent_model_id is not None:
+                    checkpoint["modelId"] = parent_model_id
+                if parent_version_name is not None:
+                    checkpoint["version"] = parent_version_name
+                if parent_model_name is not None:
+                    checkpoint["name"] = parent_model_name
+
+            image_url = image_data.get("url") or ""
+            image_id = extract_civitai_image_id_from_cdn_url(image_url)
+            settings_mgr = get_settings_manager()
+            civitai_host = settings_mgr.get("civitai_host") if settings_mgr else None
+            page_url = build_civitai_image_page_url(image_id, host=civitai_host) or image_url
+
+            recipe_metadata: dict[str, Any] = {
+                "base_model": parsed.get("base_model") or "",
+                "loras": loras,
+                "gen_params": parsed.get("gen_params") or {},
+                "source_path": page_url,
+            }
+            nsfw_level = image_data.get("nsfwLevel")
+            if isinstance(nsfw_level, int):
+                recipe_metadata["preview_nsfw_level"] = nsfw_level
+            if checkpoint:
+                recipe_metadata["checkpoint"] = checkpoint
+
+            image_bytes: bytes | None = None
+            extension: str | None = None
+            local_image_path = data.get("local_image_path")
+            if local_image_path and os.path.exists(local_image_path):
+                with open(local_image_path, "rb") as f:
+                    image_bytes = f.read()
+                ext = os.path.splitext(local_image_path)[1].lower()
+                if ext in (".jpg", ".jpeg", ".png", ".webp", ".gif"):
+                    extension = ext
+            elif image_data.get("url"):
+                try:
+                    downloader = await self._downloader_factory()
+                    url = image_data["url"]
+                    tmp = tempfile.NamedTemporaryFile(delete=False)
+                    tmp.close()
+                    success, result = await downloader.download_file(
+                        url, tmp.name, use_auth=False
+                    )
+                    if success:
+                        with open(tmp.name, "rb") as f:
+                            image_bytes = f.read()
+                        url_path = url.split("?")[0].split("#")[0]
+                        ext = os.path.splitext(url_path)[1].lower()
+                        if ext:
+                            extension = ext
+                    if os.path.exists(tmp.name):
+                        os.unlink(tmp.name)
+                except Exception as exc:
+                    self._logger.warning(
+                        "Failed to download image for recipe: %s", exc
+                    )
+
+            prompt = (
+                (parsed.get("gen_params") or {}).get("prompt") or ""
+            )
+            if prompt:
+                name = " ".join(str(prompt).split()[:10])
+            else:
+                name = f"Recipe from {model_name}"
+
+            save_result = await self._persistence_service.save_recipe(
+                recipe_scanner=recipe_scanner,
+                image_bytes=image_bytes,
+                image_base64=None,
+                name=name,
+                tags=[],
+                metadata=recipe_metadata,
+                extension=extension,
+            )
+            return web.json_response(save_result.payload, status=save_result.status)
+
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error(
+                "Error creating recipe from example: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
 
 class RecipeAnalysisHandler:
     """Analyze images to extract recipe metadata."""

py/routes/recipe_route_registrar.py

@@ -75,6 +75,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "GET", "/api/lm/recipes/check-image-exists", "check_image_exists"
     ),
     RouteDefinition("GET", "/api/lm/recipes/import-from-url", "import_from_url"),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/create-from-example", "create_from_example"
+    ),
 )
 
 

py/services/recipes/persistence_service.py

@@ -115,6 +115,10 @@ class RecipePersistenceService:
         if metadata.get("source_path"):
             recipe_data["source_path"] = metadata.get("source_path")
 
+        nsfw_level = metadata.get("preview_nsfw_level")
+        if nsfw_level is not None and isinstance(nsfw_level, int):
+            recipe_data["preview_nsfw_level"] = nsfw_level
+
         json_filename = f"{recipe_id}.recipe.json"
         json_path = os.path.join(recipes_dir, json_filename)
         json_path = os.path.normpath(json_path)

py/utils/civitai_utils.py

@@ -66,6 +66,46 @@ def build_civitai_model_page_url(
     return None
 
 
+_RE_CDN_IMAGE_ID = re.compile(r"/(\d+)\.(?:jpeg|jpg|png|webp|gif)(?:\?|#|$)")
+
+
+def extract_civitai_image_id_from_cdn_url(url: str | None) -> str | None:
+    """Extract the numeric image ID from a Cloudflare CDN image URL.
+
+    CivitAI image CDN URLs follow the pattern::
+
+        https://image.civitai.com/{cf_uuid}/{params}/{image_id}.{ext}
+
+    The image database ID is always the last path segment (minus extension)
+    because ``getEdgeUrl(…, name=id.toString())`` embeds it explicitly
+    in the model-versions REST API response.
+    """
+    if not url:
+        return None
+    match = _RE_CDN_IMAGE_ID.search(url)
+    return match.group(1) if match else None
+
+
+def build_civitai_image_page_url(
+    image_id: str | int | None,
+    *,
+    host: str | None = None,
+) -> str | None:
+    """Build a Civitai image page URL.
+
+    Returns something like ``https://civitai.com/images/12345``.
+    The host is resolved through :func:`normalize_civitai_page_host` and
+    therefore respects the user's ``civitai_host`` setting.
+    """
+    if not image_id:
+        return None
+    normalized_host = normalize_civitai_page_host(host)
+    normalized_id = str(image_id).strip()
+    if not normalized_id:
+        return None
+    return urlunparse(("https", normalized_host, f"/images/{normalized_id}", "", "", ""))
+
+
 def _parse_supported_civitai_page_url(url: str | None):
     if not url:
         return None
@@ -328,8 +368,10 @@ def rewrite_preview_url(
 
 
 __all__ = [
+    "build_civitai_image_page_url",
     "build_license_flags",
     "extract_civitai_image_id",
+    "extract_civitai_image_id_from_cdn_url",
     "extract_civitai_page_host",
     "extract_civitai_model_url_parts",
     "is_supported_civitai_page_host",

py/utils/file_utils.py

@@ -1,7 +1,10 @@
 
 import hashlib
+import json
 import logging
 import os
+import struct
+from typing import Any
 
 from .constants import (
     CARD_PREVIEW_WIDTH,
@@ -31,7 +34,7 @@ def _get_hash_chunk_size_bytes() -> int:
 
 
 async def calculate_sha256(file_path: str) -> str:
-    """Calculate SHA256 hash of a file"""
+    """Calculate SHA256 hash of a file (full file content)."""
     sha256_hash = hashlib.sha256()
     chunk_size = _get_hash_chunk_size_bytes()
     with open(file_path, "rb") as f:
@@ -39,6 +42,79 @@ async def calculate_sha256(file_path: str) -> str:
             sha256_hash.update(byte_block)
     return sha256_hash.hexdigest()
 
+
+def calculate_autov2(file_path: str) -> str:
+    """Calculate CivitAI AutoV2 hash.
+
+    AutoV2 is the first 10 characters of the full file SHA256.
+    Used by CivitAI as a shortened file identifier.
+
+    Reference: https://developer.civitai.com/site/reference/model-versions
+    """
+    full_hash = hashlib.sha256()
+    chunk_size = _get_hash_chunk_size_bytes()
+    with open(file_path, "rb") as f:
+        for byte_block in iter(lambda: f.read(chunk_size), b""):
+            full_hash.update(byte_block)
+    return full_hash.hexdigest()[:10]
+
+
+def read_safetensors_metadata(file_path: str) -> dict[str, Any]:
+    """Read the ``__metadata__`` dict from a safetensors file header.
+
+    Safetensors file format:
+      - 8 bytes: header length (little-endian 64-bit)
+      - N bytes: UTF-8 JSON header
+      - The header JSON contains a ``__metadata__`` key holding arbitrary metadata.
+
+    Returns an empty dict if the file is not a valid safetensors file or has no
+    metadata.
+    """
+    try:
+        with open(file_path, "rb") as f:
+            header_len_bytes = f.read(8)
+            if len(header_len_bytes) < 8:
+                return {}
+            header_len = struct.unpack("<Q", header_len_bytes)[0]
+            header_bytes = f.read(header_len)
+            if len(header_bytes) < header_len:
+                return {}
+            header = json.loads(header_bytes.decode("utf-8"))
+            return header.get("__metadata__", {})
+    except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error):
+        return {}
+
+
+def calculate_autov3(file_path: str) -> str | None:
+    """Calculate CivitAI AutoV3 hash from a safetensors file.
+
+    AutoV3 is extracted from the safetensors file's embedded metadata, not
+    computed from the file bytes directly. The orchestrator reads the
+    ``sshs_model_hash`` (kohya-ss format) or ``modelspec.hash_sha256`` field
+    from the safetensors header and stores the first 12 characters.
+
+    The embedded hash itself is the SHA256 of the file after skipping the
+    8-byte header length + JSON header (a.k.a. the addnet hash / tensor-only
+    hash).
+
+    Reference:
+      - CivitAI DB trigger: ``SUBSTRING(NEW.hash FROM 1 FOR 12)``
+      - https://developer.civitai.com/site/reference/model-versions
+
+    Returns ``None`` when no AutoV3 hash can be determined (e.g. the file is
+    not safetensors, or the metadata doesn't contain a recognised hash field).
+    """
+    metadata = read_safetensors_metadata(file_path)
+    if not metadata:
+        return None
+
+    embedded_hash = metadata.get("sshs_model_hash") or metadata.get("modelspec.hash_sha256")
+    if embedded_hash and isinstance(embedded_hash, str) and len(embedded_hash) >= 12:
+        return embedded_hash[:12]
+
+    return None
+
+
 def find_preview_file(base_name: str, dir_path: str) -> str:
     """Find preview file for given base name in directory.
 

scripts/restore_suffixed_filenames.py

@@ -0,0 +1,404 @@
+#!/usr/bin/env python3
+"""
+Restore original filenames by removing leftover 4-char hash suffixes.
+
+When LoRA Manager's old duplicate filename resolver ran, it appended
+``-{first4ofSHA256}`` to duplicate filenames, e.g.::
+
+    my_lora.safetensors  →  my_lora-a3f7.safetensors
+
+With full-path LoRA syntax now available (``<lora:subfolder/name:1.0>``),
+these suffixes are unnecessary.  This script detects such files and, with
+your confirmation, restores their original names.
+
+The same suffix pattern is also used by the download conflict handler
+(``{name}-{hash}.{ext}``).  To avoid false positives, this script skips
+any file whose original name already exists in the same directory — those
+were likely added by a download conflict, not the old resolver.
+
+Usage::
+
+    # Detect only (dry-run, default)
+    python scripts/restore_suffixed_filenames.py
+
+    # Detect + restore (with confirmation prompt)
+    python scripts/restore_suffixed_filenames.py --apply
+
+After restoring filenames, run **Rebuild Cache** in the LoRA Manager
+Doctor panel to refresh the model cache.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Any
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(message)s",
+)
+logger = logging.getLogger(__name__)
+
+APP_NAME = "ComfyUI-LoRA-Manager"
+MODEL_EXTENSIONS = {".safetensors", ".ckpt", ".pt", ".pth", ".bin"}
+PREVIEW_EXTENSIONS = {
+    ".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp",
+    ".mp4", ".webm", ".mov",
+}
+
+# Matches filenames like "my_lora-a3f7.safetensors"
+# Groups: (base_name, 4-char-hex, extension)
+_SUFFIX_RE = re.compile(r"^(.+)-([0-9a-f]{4})(\.[^.]+)$")
+
+
+# ── helpers (copied from migrate_legacy_metadata.py for consistency) ──────────
+
+
+def resolve_settings_path() -> Path:
+    repo_root = Path(__file__).parent.parent.resolve()
+    portable = repo_root / "settings.json"
+    if portable.exists():
+        payload = _load_json(portable)
+        if isinstance(payload, dict) and payload.get("use_portable_settings") is True:
+            return portable
+
+    config_home = os.environ.get("XDG_CONFIG_HOME")
+    if config_home:
+        return Path(config_home).expanduser() / APP_NAME / "settings.json"
+    return Path.home() / ".config" / APP_NAME / "settings.json"
+
+
+def _load_json(path: Path) -> dict[str, Any]:
+    try:
+        with path.open("r", encoding="utf-8") as f:
+            return json.load(f)
+    except (FileNotFoundError, json.JSONDecodeError, OSError):
+        return {}
+
+
+def _expand_path(value: str) -> str:
+    return str(Path(value).expanduser().resolve(strict=False))
+
+
+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 _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 get_model_roots(settings: dict[str, Any]) -> dict[str, list[str]]:
+    """Extract model folder roots from LoRA Manager settings.
+
+    Returns ``{model_type: [path, ...]}`` where *model_type* is one of
+    ``loras``, ``checkpoints``, ``embeddings``, ``unet``, etc.
+
+    Both primary (``folder_paths``) and extra (``extra_folder_paths``)
+    paths are included.  Extra paths can be configured via the UI at
+    Settings → Model Libraries → Extra Folder Paths.
+    """
+    roots: dict[str, list[str]] = {}
+    active_library = settings.get("active_library") or "default"
+    sources = [settings]
+    library = settings.get("libraries", {}).get(active_library)
+    if isinstance(library, dict):
+        sources.insert(0, library)
+    for source in sources:
+        # Primary folder paths.
+        folder_paths = source.get("folder_paths")
+        if isinstance(folder_paths, dict):
+            for key, value in folder_paths.items():
+                roots.setdefault(key, []).extend(_normalize_path_list(value))
+        # Extra folder paths (Settings → Model Libraries → Extra Folder Paths).
+        extra_folder_paths = source.get("extra_folder_paths")
+        if isinstance(extra_folder_paths, dict):
+            for key, value in extra_folder_paths.items():
+                roots.setdefault(key, []).extend(_normalize_path_list(value))
+    for default_key, folder_key in (
+        ("default_lora_root", "loras"),
+        ("default_checkpoint_root", "checkpoints"),
+        ("default_unet_root", "unet"),
+        ("default_embedding_root", "embeddings"),
+    ):
+        value = settings.get(default_key)
+        if isinstance(value, str) and value:
+            roots.setdefault(folder_key, []).append(_expand_path(value))
+    return {key: _dedupe(values) for key, values in roots.items()}
+
+
+def find_model_files(directory: Path) -> list[Path]:
+    """Recursively find all model files in *directory*."""
+    files: list[Path] = []
+    for ext in MODEL_EXTENSIONS:
+        files.extend(directory.rglob(f"*{ext}"))
+    return files
+
+
+# ── core detection logic ──────────────────────────────────────────────────────
+
+
+def check_file(path: Path) -> tuple[str, str, str] | None:
+    """If *path* matches the suffix pattern, return ``(base_name, hex, ext)``.
+
+    Returns ``None`` when:
+    * The filename does not match the pattern, or
+    * The original name (without the suffix) already exists in the same
+      directory (likely a download-conflict rename, not a doctor rename).
+    """
+    match = _SUFFIX_RE.match(path.name)
+    if not match:
+        return None
+
+    base_name = match.group(1)
+    hex_part = match.group(2)
+    extension = match.group(3)
+    orig_name = base_name + extension
+    orig_path = path.with_name(orig_name)
+
+    # Safety: skip if the original name already exists.
+    if orig_path.exists():
+        return None
+
+    return base_name, hex_part, extension
+
+
+def scan_roots(
+    roots: dict[str, list[str]],
+) -> dict[str, list[tuple[Path, str, str, str]]]:
+    """Scan all model roots and return detected files grouped by model type.
+
+    Returns ``{model_type: [(full_path, base_name, hex, ext), ...]}``.
+    """
+    results: dict[str, list[tuple[Path, str, str, str]]] = {}
+
+    for model_type, root_list in roots.items():
+        type_results: list[tuple[Path, str, str, str]] = []
+        for root in root_list:
+            root_path = Path(root)
+            if not root_path.is_dir():
+                continue
+            for model_file in find_model_files(root_path):
+                match = check_file(model_file)
+                if match:
+                    type_results.append((model_file, *match))
+        if type_results:
+            results[model_type] = type_results
+
+    return results
+
+
+def rename_file(
+    path: Path, base_name: str, extension: str, dry_run: bool
+) -> bool:
+    """Rename *path* to ``{base_name}{extension}``.
+
+    Also renames sidecar files (``.metadata.json``, ``.civitai.info``) and
+    preview images.  Returns ``True`` on success.
+    """
+    new_path = path.with_name(base_name + extension)
+    old_stem = path.with_suffix("")  # /dir/base_name-hex  (no ext)
+    new_stem = new_path.with_suffix("")  # /dir/base_name  (no ext)
+
+    if dry_run:
+        logger.info("  would rename:  %s", path.name)
+        logger.info("             ->  %s", new_path.name)
+        return True
+
+    try:
+        os.rename(path, new_path)
+    except OSError as exc:
+        logger.error("  FAILED to rename %s: %s", path.name, exc)
+        return False
+
+    # Rename sidecar metadata files.
+    for suffix in (".metadata.json", ".civitai.info"):
+        old_sidecar = old_stem.with_name(old_stem.name + suffix)
+        new_sidecar = new_stem.with_name(new_stem.name + suffix)
+        if old_sidecar.exists():
+            try:
+                os.rename(old_sidecar, new_sidecar)
+            except OSError as exc:
+                logger.warning("  could not rename sidecar %s: %s", old_sidecar.name, exc)
+
+    # Rename preview images.
+    for preview_ext in PREVIEW_EXTENSIONS:
+        old_preview = old_stem.with_name(old_stem.name + preview_ext)
+        new_preview = new_stem.with_name(new_stem.name + preview_ext)
+        if old_preview.exists():
+            try:
+                os.rename(old_preview, new_preview)
+            except OSError as exc:
+                logger.warning("  could not rename preview %s: %s", old_preview.name, exc)
+
+    logger.info("  renamed: %s  ->  %s", path.name, new_path.name)
+    return True
+
+
+# ── report helpers ────────────────────────────────────────────────────────────
+
+
+def print_report(results: dict[str, list[tuple[Path, str, str, str]]]) -> int:
+    """Print a human-readable report of detected files.  Returns total count."""
+    if not results:
+        logger.info("No leftover suffixed filenames detected.")
+        return 0
+
+    total = 0
+    for model_type in sorted(results):
+        entries = results[model_type]
+        total += len(entries)
+        label = model_type.capitalize()
+        logger.info("")
+        logger.info("─" * 50)
+        logger.info("  %s  (%d file(s))", label, len(entries))
+        logger.info("─" * 50)
+        for path, base_name, hex_part, ext in sorted(entries):
+            logger.info("  %s  →  %s%s", path.name, base_name, ext)
+
+    logger.info("")
+    logger.info("=" * 50)
+    logger.info("  Total: %d file(s) with leftover suffixes.", total)
+    logger.info("=" * 50)
+    return total
+
+
+def prompt_user(count: int) -> bool:
+    """Ask the user whether to proceed with the rename."""
+    try:
+        answer = input(
+            f"\nRestore {count} file(s) to their original names? [y/N] "
+        ).strip().lower()
+    except (EOFError, KeyboardInterrupt):
+        print()
+        return False
+    return answer in ("y", "yes")
+
+
+# ── main ──────────────────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Detect and restore model filenames that have leftover "
+            "4-character hash suffixes from the old conflict resolver."
+        ),
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=(
+            "Examples:\n"
+            "  python scripts/restore_suffixed_filenames.py\n"
+            "  python scripts/restore_suffixed_filenames.py --apply\n"
+            "  python scripts/restore_suffixed_filenames.py --apply --yes\n"
+        ),
+    )
+    parser.add_argument(
+        "--apply",
+        action="store_true",
+        help="Actually rename files (with confirmation prompt unless --yes is given)",
+    )
+    parser.add_argument(
+        "--yes", "-y",
+        action="store_true",
+        help="Skip confirmation prompt (implies --apply)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Detect only — show what would be renamed without making changes",
+    )
+    parser.add_argument(
+        "-v", "--verbose",
+        action="store_true",
+        help="Enable debug-level logging",
+    )
+    args = parser.parse_args()
+
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+
+    # Resolve settings.
+    settings_path = resolve_settings_path()
+    logger.info("Settings: %s", settings_path)
+    settings = _load_json(settings_path)
+    if not settings:
+        logger.error("Could not load settings.json.  Is LoRA Manager configured?")
+        return 1
+
+    roots = get_model_roots(settings)
+    if not roots:
+        logger.error("No model folders found in settings.")
+        return 1
+
+    # Log which roots are being scanned.
+    for model_type, root_list in roots.items():
+        for root in root_list:
+            logger.info("Scanning %s: %s", model_type, root)
+
+    # Detect.
+    results = scan_roots(roots)
+    total = print_report(results)
+
+    if total == 0:
+        return 0
+
+    # Determine mode.
+    dry_run = not args.apply and not args.yes
+
+    if dry_run:
+        logger.info("\n[Dry-run mode — no files modified]")
+        logger.info("Run with --apply to restore filenames.")
+        return 0
+
+    # Confirm unless --yes.
+    if not args.yes:
+        if not prompt_user(total):
+            logger.info("Aborted.")
+            return 0
+
+    # Rename.
+    logger.info("")
+    success = 0
+    fail = 0
+    for model_type in sorted(results):
+        entries = results[model_type]
+        logger.info("")
+        logger.info("─" * 50)
+        logger.info("  Restoring %s  (%d file(s))", model_type, len(entries))
+        logger.info("─" * 50)
+        for path, base_name, hex_part, ext in sorted(entries):
+            ok = rename_file(path, base_name, ext, dry_run=False)
+            if ok:
+                success += 1
+            else:
+                fail += 1
+
+    logger.info("")
+    logger.info("=" * 50)
+    logger.info("  Done: %d restored, %d failed.", success, fail)
+    logger.info("=" * 50)
+    logger.info("")
+    logger.info("  ⚠  Please run Rebuild Cache in the LoRA Manager")
+    logger.info("     Doctor panel to refresh the model cache.")
+
+    return 0 if fail == 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

static/js/components/shared/ModelModal.js

@@ -522,7 +522,7 @@ export async function showModelModal(model, modelType) {
                     </div>
                 </div>
 
-                <div class="showcase-section" data-model-hash="${modelWithFullData.sha256 || ''}" data-filepath="${escapedFilePathAttr}">
+                <div class="showcase-section" data-model-hash="${modelWithFullData.sha256 || ''}" data-model-name="${escapeAttribute(modelWithFullData.file_name || modelWithFullData.model_name || '')}" data-model-type="${modelType}" data-filepath="${escapedFilePathAttr}">
                     <div class="showcase-tabs">
                         ${tabsContent}
                     </div>

static/js/components/shared/showcase/MediaUtils.js

@@ -135,6 +135,39 @@ export function initLazyLoading(container) {
     lazyElements.forEach(element => observer.observe(element));
 }
 
+/**
+ * Check which Create As Recipe buttons correspond to already-imported
+ * images and disable them.
+ */
+async function checkImportedRecipes(container) {
+    const recipeButtons = container.querySelectorAll('.create-recipe-btn');
+    if (!recipeButtons.length) return;
+
+    const imageIds = [];
+    recipeButtons.forEach(btn => {
+        const id = btn.dataset.imageId;
+        if (id) imageIds.push(id);
+    });
+    if (!imageIds.length) return;
+
+    try {
+        const response = await fetch(`/api/lm/recipes/check-image-exists?image_ids=${imageIds.join(',')}`);
+        const data = await response.json();
+        if (!data.success || !data.results) return;
+        recipeButtons.forEach(btn => {
+            const id = btn.dataset.imageId;
+            if (id && data.results[id]?.in_library) {
+                btn.disabled = true;
+                btn.title = 'Already imported as recipe';
+                btn.classList.add('disabled');
+            }
+        });
+    } catch (err) {
+        console.error('Failed to check imported recipes:', err);
+    }
+}
+
+
 /**
  * Get the actual rendered rectangle of a media element with object-fit: contain
  * @param {HTMLElement} mediaElement - The img or video element
@@ -471,6 +504,70 @@ export function initMediaControlHandlers(container) {
         });
     });
     
+    // Create As Recipe buttons
+    const recipeButtons = container.querySelectorAll('.create-recipe-btn');
+    recipeButtons.forEach(btn => {
+        btn.addEventListener('click', async function(e) {
+            e.stopPropagation();
+            
+            const imageMetaRaw = this.dataset.imageMeta;
+            const imageUrl = this.dataset.imageUrl;
+            const imageNsfw = this.dataset.imageNsfw;
+            const localPath = this.dataset.localPath || '';
+            const showcaseSection = this.closest('.showcase-section');
+            const modelHash = showcaseSection ? showcaseSection.dataset.modelHash : '';
+            const modelName = showcaseSection ? showcaseSection.dataset.modelName : '';
+            const modelType = showcaseSection ? showcaseSection.dataset.modelType : '';
+            
+            if (!imageMetaRaw || !modelHash) {
+                showToast('toast.recipes.createMissingData', {}, 'error');
+                return;
+            }
+            
+            // Show loading state
+            const originalHtml = this.innerHTML;
+            this.innerHTML = '<i class="fas fa-spinner fa-spin"></i>';
+            this.disabled = true;
+            
+            try {
+                const imageMeta = JSON.parse(decodeURIComponent(imageMetaRaw));
+                
+                const response = await fetch('/api/lm/recipes/create-from-example', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({
+                        image_data: {
+                            meta: imageMeta,
+                            url: imageUrl,
+                            nsfwLevel: imageNsfw ? parseInt(imageNsfw, 10) : undefined,
+                        },
+                        model_hash: modelHash,
+                        model_name: modelName || modelHash,
+                        model_type: modelType,
+                        local_image_path: localPath,
+                    }),
+                });
+                
+                const result = await response.json();
+                
+                if (result.success && result.recipe_id) {
+                    showToast('toast.recipes.created', { recipeId: result.recipe_id }, 'success');
+                } else {
+                    showToast('toast.recipes.createFailed', { error: result.error || 'Unknown error' }, 'error');
+                }
+            } catch (error) {
+                console.error('Failed to create recipe:', error);
+                showToast('toast.recipes.createError', { message: error.message }, 'error');
+            } finally {
+                this.innerHTML = originalHtml;
+                this.disabled = false;
+            }
+        });
+    });
+    
+    // Check which images are already imported as recipes → disable button
+    checkImportedRecipes(container);
+    
     // Initialize set preview buttons
     initSetPreviewHandlers(container);
 

static/js/components/shared/showcase/ShowcaseView.js

@@ -183,6 +183,9 @@ function renderMediaItem(img, index, exampleFiles) {
         Math.min(maxHeightPercent, aspectRatio)
     );
     
+    // Extract CivitAI image ID from CDN URL for import status check
+    const cdnImageId = (img.url || '').match(/\/(\d+)\.(?:jpeg|jpg|png|webp|gif)(?:\?|#|$)/)?.[1] || '';
+
     // Check if media should be blurred
     const nsfwLevel = img.nsfwLevel !== undefined ? img.nsfwLevel : 0;
     const matureBlurThreshold = getMatureBlurThreshold(state.settings);
@@ -224,12 +227,25 @@ function renderMediaItem(img, index, exampleFiles) {
     // Determine if this is a custom image (has id property)
     const isCustomImage = Boolean(typeof img.id === 'string' && img.id);
     
+    const hasGenMeta = img.hasMeta || (img.meta && (img.meta.prompt || img.meta.seed || img.meta.resources));
+
     // Create the media control buttons HTML
     const mediaControlsHtml = `
         <div class="media-controls">
             <button class="media-control-btn set-preview-btn" title="Set as preview">
                 <i class="fas fa-image"></i>
             </button>
+            ${hasGenMeta ? `
+            <button class="media-control-btn create-recipe-btn"
+                    title="Create As Recipe"
+                    data-image-meta="${encodeURIComponent(JSON.stringify(img.meta || {}))}"
+                    data-image-url="${img.url || ''}"
+                    data-image-nsfw="${img.nsfwLevel ?? ''}"
+                    data-image-id="${cdnImageId}"
+                    data-local-path="${localFile ? localFile.path : ''}">
+                <i class="fas fa-book-open"></i>
+            </button>
+            ` : ''}
             <button class="media-control-btn set-nsfw-btn" 
                     title="Set content rating"
                     data-media-index="${index}"