chore(release): bump version to v1.0.10

When certifi is available, pass its CA bundle path as --ca-certificate
to the aria2c subprocess so that aria2 downloads use the same
certificate store as Python aiohttp downloads. Graceful fallback when
certifi is not installed.

data/supporters.json

@@ -21,7 +21,9 @@
     "stone9k",
     "Rosenthal",
     "Francisco Tatis",
+    "JongWon Han",
     "runte3221",
+    "FreelancerZ",
     "Fraser Cross",
     "Polymorphic Indeterminate",
     "Marc Whiffen",
@@ -43,11 +45,13 @@
     "ClockDaemon",
     "KD",
     "Omnidex",
+    "Tyler Trebuchon",
     "Release Cabrakan",
     "Tobi_Swagg",
     "SG",
     "James Dooley",
     "zenbound",
+    "Buzzard",
     "jmack",
     "Andrew Wilson",
     "Greybush",
@@ -57,7 +61,7 @@
     "Wolffen",
     "Ricky Carter",
     "James Todd",
-    "JongWon Han",
+    "Steven Pfeiffer",
     "VantAI",
     "Tim",
     "Lisster",
@@ -65,7 +69,6 @@
     "Illrigger",
     "Tom Corrigan",
     "JackieWang",
-    "FreelancerZ",
     "fnkylove",
     "Yushio",
     "Vik71it",
@@ -73,6 +76,7 @@
     "Lilleman",
     "Robert Stacey",
     "PM",
+    "Todd Keck",
     "Edgar Tejeda",
     "Jorge Hussni",
     "Liam MacDougal",
@@ -91,7 +95,6 @@
     "Melville Parrish",
     "daniel dove",
     "Lustre",
-    "Tyler Trebuchon",
     "JW Sin",
     "contrite831",
     "Alex",
@@ -99,20 +102,19 @@
     "carozzz",
     "Marlon Daniels",
     "Starkselle",
-    "Buzzard",
     "Aaron Bleuer",
     "LacesOut!",
     "greebles",
     "Adam Shaw",
     "Anthony Rizzo",
+    "M Postkasse",
     "Gooohokrbe",
     "RedrockVP",
-    "ASLPro3D",
+    "Wicked Choices by ASLPro3D",
     "OldBones",
     "Jacob Hoehler",
     "FinalyFree",
     "Weasyl",
-    "Steven Pfeiffer",
     "Timmy",
     "Johnny",
     "Cory Paza",
@@ -126,7 +128,7 @@
     "corde",
     "Nick Walker",
     "Bishoujoker",
-    "Todd Keck",
+    "aai",
     "Briton Heilbrun",
     "Tori",
     "wildnut",
@@ -153,12 +155,13 @@
     "JaxMax",
     "takyamtom",
     "Jwk0205",
+    "Bro Xie",
     "batblue",
     "carey6409",
     "Olive",
+    "太郎 ゲーム",
     "Some Guy Named Barry",
     "Cosmosis",
-    "M Postkasse",
     "AELOX",
     "Nicfit23",
     "FloPro4Sho",
@@ -172,13 +175,13 @@
     "Serge Bekenkamp",
     "Jimmy Ledbetter",
     "LeoZero",
+    "Antonio Pontes",
     "ApathyJones",
     "Julian V",
     "Steven Owens",
     "nahinahi9",
     "Dustin Chen",
     "dan",
-    "aai",
     "Mouthlessman",
     "otaku fra",
     "ViperC",
@@ -199,15 +202,15 @@
     "Jon Sandman",
     "Ubivis",
     "CloudValley",
+    "linnfrey",
     "IamAyam",
+    "skaterb949",
     "Joboshy",
     "Bohemian Corporal",
     "Dan",
     "confiscated Zyra",
-    "Bro Xie",
     "yer fey",
     "Error_Rule34_Not_found",
-    "太郎 ゲーム",
     "Roslynd",
     "Tee Gee",
     "jinxedx",
@@ -221,7 +224,7 @@
     "Magic Noob",
     "Pronredn",
     "DougPeterson",
-    "Antonio Pontes",
+    "Jeff",
     "Bruce",
     "lh qwe",
     "Kevin John Duck",
@@ -249,19 +252,21 @@
     "地獄の禄",
     "MJG",
     "David LaVallee",
-    "linnfrey",
     "ae",
     "Tr4shP4nda",
     "WRL_SPR",
     "capn",
     "Joseph",
     "Mirko Katzula",
+    "dan",
+    "Piccio08",
+    "kumakichi",
+    "cppbel",
     "奚明 刘",
     "Brian M",
     "Josef Lanzl",
     "Nerezza",
     "sanborondon",
-    "Griffin Dahlberg",
     "준희 김",
     "Taylor Funk",
     "aezin",
@@ -278,10 +283,11 @@
     "Noora",
     "Pierce McBride",
     "Mattssn",
+    "Mikko Hemilä",
     "Jamie Ogletree",
     "a _",
-    "Jeff",
     "James Coleman",
+    "Martial",
     "Emil Andersson",
     "Ouro Boros",
     "Chad Idk",
@@ -302,10 +308,6 @@
     "Nick “Loadstone” D",
     "Gamalonia",
     "momokai",
-    "dan",
-    "Piccio08",
-    "kumakichi",
-    "cppbel",
     "starbugx",
     "Moon Knight",
     "몽타주",
@@ -337,6 +339,7 @@
     "Andrew",
     "Robert Wegemund",
     "Littlehuggy",
+    "Gregory Kozhemiak",
     "Draven T",
     "mrjuan",
     "Brian Buie",
@@ -350,7 +353,6 @@
     "Joshua Gray",
     "Morgandel",
     "Focuschannel",
-    "Mikko Hemilä",
     "Noah",
     "Jacob McDaniel",
     "X",
@@ -359,7 +361,6 @@
     "Artokun",
     "Michael Taylor",
     "Derek Baker",
-    "Martial",
     "Anthony Faxlandez",
     "battu",
     "Michael Anthony Scott",
@@ -367,8 +368,6 @@
     "Decx _",
     "Pat Hen",
     "Jordan Shaw",
-    "Thesharingbrother",
-    "ResidentDeviant",
     "四糸凜音",
     "Nihongasuki",
     "JC",
@@ -412,11 +411,11 @@
     "Wolfe7D1",
     "blikkies",
     "Chris",
-    "Gregory Kozhemiak",
     "elleshar666",
     "Shock Shockor",
     "ACTUALLY_the_Real_Willem_Dafoe",
     "Goldwaters",
+    "Kauffy",
     "Zude",
     "John J Linehan",
     "Kyler",
@@ -426,19 +425,21 @@
     "Justin Blaylock",
     "aRtFuL_DodGeR",
     "Vane Holzer",
+    "psytrax",
     "hexxish",
     "notedfakes",
-    "DarkSunset",
     "Nathan",
     "Billy Gladky",
     "NICHOLAS BAXLEY",
     "Michael Scott",
     "Probis",
     "Ed Wang",
+    "Wes Sims",
     "ItsGeneralButtNaked",
     "SRDB",
     "g unit",
     "Distortik",
+    "Filippo Ferrari",
     "Youguang",
     "Saya",
     "andrewzpong",
@@ -456,6 +457,7 @@
     "emadsultan",
     "Pkrsky",
     "nanana",
+    "FeralOpticsAI",
     "Pavlaki",
     "Doug+Rintoul",
     "Noor",
@@ -483,7 +485,6 @@
     "Time Valentine",
     "Михал Михалыч",
     "Matt",
-    "Kauffy",
     "Frogmilk",
     "SPJ",
     "Kyron Mahan",
@@ -491,11 +492,11 @@
     "Nick Kage",
     "TBitz33",
     "Anonym dkjglfleeoeldldldlkf",
-    "psytrax",
     "Cyrus Fett",
     "Ezokewn",
     "SendingRavens",
     "Xenon Xue",
+    "JackJohnnyJim",
     "Edward Ten Eyck",
     "Michael Docherty",
     "Paul Hartsuyker",
@@ -504,15 +505,14 @@
     "Solixer",
     "Jacob Winter",
     "Ryan Presley Ng",
-    "Wes Sims",
     "jinksta187",
     "Donor4115",
     "Manu Thetug",
+    "Karlanx",
     "Lyavph",
     "David",
     "Meilo",
     "operationancut",
-    "Filippo Ferrari",
     "shinonomeiro",
     "Snille",
     "MaartenAlbers",
@@ -531,6 +531,8 @@
     "Scott",
     "Muratoraccio",
     "D",
+    "YassineKhaled",
+    "Y",
     "MatteKey",
     "Flob",
     "ShiroSenpai",
@@ -552,7 +554,6 @@
     "rsamerica",
     "sfasdfasfdsa",
     "Alan+Cano",
-    "FeralOpticsAI",
     "generic404",
     "abattoirblues",
     "zounik",
@@ -584,7 +585,6 @@
     "Sauv",
     "Steven",
     "CptNeo",
-    "JackJohnnyJim",
     "TenaciousD",
     "Dmitry Ryzhov",
     "Khánh Đặng",
@@ -599,7 +599,6 @@
     "Andrew Wilkinson",
     "Yavizu3d",
     "Maxim",
-    "Karlanx",
     "Yves Poezevara",
     "Teriak47",
     "Just me",
@@ -637,6 +636,7 @@
     "Captain_Swag",
     "obkircher",
     "gwyar",
+    "ResidentDeviant",
     "D",
     "edgecase",
     "Neoxena",
@@ -681,8 +681,6 @@
     "low9",
     "Winged",
     "you+halo9",
-    "YassineKhaled",
-    "YK12",
     "Somebody",
     "Somebody",
     "Crescent~San",
@@ -697,6 +695,7 @@
     "Coeur+de+cochon",
     "Obsidian.Studios",
     "han b",
+    "Zomba Mann",
     "Nico",
     "Maximilian Krischan",
     "Banana Joe",
@@ -714,7 +713,6 @@
     "Ronan Delevacq",
     "karim ben brik",
     "Vinarus",
-    "james",
     "Michael Zhu",
     "Nemisu",
     "Seraphy",
@@ -743,9 +741,11 @@
     "dsffsdfsdfsdfsdfsdf",
     "somethingtosay8",
     "Jean-françois SEMA",
+    "3zS4QNQ4",
     "Terminuz",
     "Kurt",
     "ivistorm",
+    "Ivan Imes",
     "Faburizu",
     "Jack Lawfield",
     "jimyjomson",

locales/de.json

@@ -689,6 +689,7 @@
             "setContentRating": "Inhaltsbewertung für alle festlegen",
             "copyAll": "Alle Syntax kopieren",
             "refreshAll": "Alle Metadaten aktualisieren",
+            "repairMetadata": "Metadaten der Auswahl reparieren",
             "checkUpdates": "Auswahl auf Updates prüfen",
             "moveAll": "Alle in Ordner verschieben",
             "autoOrganize": "Automatisch organisieren",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "Keine Rezepte ausgewählt",
+            "repairBulkComplete": "Reparatur abgeschlossen: {repaired} repariert, {skipped} übersprungen (von {total})",
+            "repairBulkSkipped": "Keine Reparatur für die {total} ausgewählten Rezepte erforderlich",
+            "repairBulkFailed": "Reparatur der ausgewählten Rezepte fehlgeschlagen: {message}",
             "noMissingLorasInSelection": "Keine fehlenden LoRAs in ausgewählten Rezepten gefunden",
             "noLoraRootConfigured": "Kein LoRA-Stammverzeichnis konfiguriert. Bitte legen Sie ein Standard-LoRA-Stammverzeichnis in den Einstellungen fest."
         },

locales/en.json

@@ -689,6 +689,7 @@
             "setContentRating": "Set Content Rating for Selected",
             "copyAll": "Copy Selected Syntax",
             "refreshAll": "Refresh Selected Metadata",
+            "repairMetadata": "Repair Metadata for Selected",
             "checkUpdates": "Check Updates for Selected",
             "moveAll": "Move Selected to Folder",
             "autoOrganize": "Auto-Organize Selected",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "No recipes selected",
+            "repairBulkComplete": "Repair complete: {repaired} repaired, {skipped} skipped (of {total})",
+            "repairBulkSkipped": "No repair needed for any of the {total} selected recipes",
+            "repairBulkFailed": "Failed to repair selected recipes: {message}",
             "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
             "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
         },

locales/es.json

@@ -689,6 +689,7 @@
             "setContentRating": "Establecer clasificación de contenido para todos",
             "copyAll": "Copiar toda la sintaxis",
             "refreshAll": "Actualizar todos los metadatos",
+            "repairMetadata": "Reparar metadatos de la selección",
             "checkUpdates": "Comprobar actualizaciones para la selección",
             "moveAll": "Mover todos a carpeta",
             "autoOrganize": "Auto-organizar seleccionados",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "No se han seleccionado recetas",
+            "repairBulkComplete": "Reparación completa: {repaired} reparadas, {skipped} omitidas (de {total})",
+            "repairBulkSkipped": "No se necesita reparación para ninguna de las {total} recetas seleccionadas",
+            "repairBulkFailed": "Error al reparar las recetas seleccionadas: {message}",
             "noMissingLorasInSelection": "No se encontraron LoRAs faltantes en las recetas seleccionadas",
             "noLoraRootConfigured": "No se ha configurado el directorio raíz de LoRA. Por favor, establezca un directorio raíz de LoRA predeterminado en la configuración."
         },

locales/fr.json

@@ -689,6 +689,7 @@
             "setContentRating": "Définir la classification du contenu pour tous",
             "copyAll": "Copier toute la syntaxe",
             "refreshAll": "Actualiser toutes les métadonnées",
+            "repairMetadata": "Réparer les métadonnées de la sélection",
             "checkUpdates": "Vérifier les mises à jour pour la sélection",
             "moveAll": "Déplacer tout vers un dossier",
             "autoOrganize": "Auto-organiser la sélection",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "Aucune recette sélectionnée",
+            "repairBulkComplete": "Réparation terminée : {repaired} réparée(s), {skipped} ignorée(s) (sur {total})",
+            "repairBulkSkipped": "Aucune réparation nécessaire parmi les {total} recettes sélectionnées",
+            "repairBulkFailed": "Échec de la réparation des recettes sélectionnées : {message}",
             "noMissingLorasInSelection": "Aucun LoRA manquant trouvé dans les recettes sélectionnées",
             "noLoraRootConfigured": "Aucun répertoire racine LoRA configuré. Veuillez définir un répertoire racine LoRA par défaut dans les paramètres."
         },

locales/he.json

@@ -689,6 +689,7 @@
             "setContentRating": "הגדר דירוג תוכן לכל המודלים",
             "copyAll": "העתק את כל התחבירים",
             "refreshAll": "רענן את כל המטא-דאטה",
+            "repairMetadata": "תקן מטא-דאטה עבור הנבחרים",
             "checkUpdates": "בדוק עדכונים לבחירה",
             "moveAll": "העבר הכל לתיקייה",
             "autoOrganize": "ארגן אוטומטית נבחרים",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "לא נבחרו מתכונים",
+            "repairBulkComplete": "התיקון הושלם: {repaired} תוקנו, {skipped} דולגו (מתוך {total})",
+            "repairBulkSkipped": "אין צורך בתיקון עבור {total} המתכונים הנבחרים",
+            "repairBulkFailed": "תיקון המתכונים הנבחרים נכשל: {message}",
             "noMissingLorasInSelection": "לא נמצאו LoRAs חסרים במתכונים שנבחרו",
             "noLoraRootConfigured": "תיקיית השורש של LoRA לא מוגדרת. אנא הגדר תיקיית שורש LoRA ברירת מחדל בהגדרות."
         },

locales/ja.json

@@ -689,6 +689,7 @@
             "setContentRating": "すべてのモデルのコンテンツレーティングを設定",
             "copyAll": "すべての構文をコピー",
             "refreshAll": "すべてのメタデータを更新",
+            "repairMetadata": "選択したレシピのメタデータを修復",
             "checkUpdates": "選択項目の更新を確認",
             "moveAll": "すべてをフォルダに移動",
             "autoOrganize": "自動整理を実行",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "レシピが選択されていません",
+            "repairBulkComplete": "修復完了：{repaired} 件修復、{skipped} 件スキップ（合計 {total} 件）",
+            "repairBulkSkipped": "選択した {total} 件のレシピは修復不要です",
+            "repairBulkFailed": "選択したレシピの修復に失敗しました：{message}",
             "noMissingLorasInSelection": "選択したレシピに不足している LoRA が見つかりませんでした",
             "noLoraRootConfigured": "LoRA ルートディレクトリが設定されていません。設定でデフォルトの LoRA ルートを設定してください。"
         },

locales/ko.json

@@ -689,6 +689,7 @@
             "setContentRating": "모든 모델에 콘텐츠 등급 설정",
             "copyAll": "모든 문법 복사",
             "refreshAll": "모든 메타데이터 새로고침",
+            "repairMetadata": "선택한 레시피 메타데이터 복구",
             "checkUpdates": "선택 항목 업데이트 확인",
             "moveAll": "모두 폴더로 이동",
             "autoOrganize": "자동 정리 선택",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "선택한 레시피가 없습니다",
+            "repairBulkComplete": "복구 완료: {repaired}개 복구, {skipped}개 건너뜀 (총 {total}개)",
+            "repairBulkSkipped": "선택한 {total}개 레시피는 복구가 필요하지 않습니다",
+            "repairBulkFailed": "선택한 레시피 복구 실패: {message}",
             "noMissingLorasInSelection": "선택한 레시피에서 누락된 LoRA를 찾을 수 없습니다",
             "noLoraRootConfigured": "LoRA 루트 디렉토리가 구성되지 않았습니다. 설정에서 기본 LoRA 루트를 설정하세요."
         },

locales/ru.json

@@ -689,6 +689,7 @@
             "setContentRating": "Установить рейтинг контента для всех",
             "copyAll": "Копировать весь синтаксис",
             "refreshAll": "Обновить все метаданные",
+            "repairMetadata": "Восстановить метаданные для выбранных",
             "checkUpdates": "Проверить обновления для выбранных",
             "moveAll": "Переместить все в папку",
             "autoOrganize": "Автоматически организовать выбранные",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
             "batchImportDirectorySelected": "Directory selected: {path}",
             "noRecipesSelected": "Рецепты не выбраны",
+            "repairBulkComplete": "Восстановление завершено: {repaired} восстановлено, {skipped} пропущено (из {total})",
+            "repairBulkSkipped": "Ни один из {total} выбранных рецептов не требует восстановления",
+            "repairBulkFailed": "Не удалось восстановить выбранные рецепты: {message}",
             "noMissingLorasInSelection": "В выбранных рецептах не найдены отсутствующие LoRAs",
             "noLoraRootConfigured": "Корневой каталог LoRA не настроен. Пожалуйста, установите корневой каталог LoRA по умолчанию в настройках."
         },

locales/zh-CN.json

@@ -689,6 +689,7 @@
             "setContentRating": "为所选中设置内容评级",
             "copyAll": "复制所选中语法",
             "refreshAll": "刷新所选中元数据",
+            "repairMetadata": "修复所选中元数据",
             "checkUpdates": "检查所选更新",
             "moveAll": "移动所选中到文件夹",
             "autoOrganize": "自动整理所选模型",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "浏览目录失败：{message}",
             "batchImportDirectorySelected": "已选择目录：{path}",
             "noRecipesSelected": "未选择任何配方",
+            "repairBulkComplete": "修复完成：{repaired} 个已修复，{skipped} 个已跳过（共 {total} 个）",
+            "repairBulkSkipped": "所选 {total} 个配方无需修复",
+            "repairBulkFailed": "修复所选配方失败：{message}",
             "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
             "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
         },

locales/zh-TW.json

@@ -689,6 +689,7 @@
             "setContentRating": "為全部設定內容分級",
             "copyAll": "複製全部語法",
             "refreshAll": "刷新全部 metadata",
+            "repairMetadata": "修復所選中元數據",
             "checkUpdates": "檢查所選更新",
             "moveAll": "全部移動到資料夾",
             "autoOrganize": "自動整理所選模型",
@@ -1693,6 +1694,9 @@
             "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
             "batchImportDirectorySelected": "已選擇目錄：{path}",
             "noRecipesSelected": "未選取任何食譜",
+            "repairBulkComplete": "修復完成：{repaired} 個已修復，{skipped} 個已跳過（共 {total} 個）",
+            "repairBulkSkipped": "所選 {total} 個配方無需修復",
+            "repairBulkFailed": "修復所選配方失敗：{message}",
             "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
             "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
         },

py/recipes/base.py

@@ -7,7 +7,7 @@ import re
 from typing import Dict, List, Any, Optional, Tuple
 from abc import ABC, abstractmethod
 from ..config import config
-from ..utils.constants import VALID_LORA_TYPES
+from ..utils.constants import VALID_LORA_TYPES, VALID_CHECKPOINT_SUB_TYPES
 from ..utils.civitai_utils import rewrite_preview_url
 
 logger = logging.getLogger(__name__)
@@ -173,6 +173,20 @@ class RecipeMetadataParser(ABC):
                 checkpoint['isDeleted'] = True
                 return checkpoint
 
+            # Validate that the model type is actually a checkpoint.
+            # Unlike populate_lora_from_civitai which has this check,
+            # this function was missing type validation — allowing LoRA
+            # version data to be saved as the recipe's checkpoint when the
+            # wrong version ID was passed downstream (fixed in v2.7+).
+            model_type = civitai_data.get('model', {}).get('type', '').lower()
+            if model_type not in VALID_CHECKPOINT_SUB_TYPES:
+                logger.warning(
+                    f"Cannot populate checkpoint: model version {civitai_data.get('id')} "
+                    f"has type '{model_type}', expected one of {VALID_CHECKPOINT_SUB_TYPES}. "
+                    f"Skipping checkpoint enrichment."
+                )
+                return checkpoint
+
             if 'model' in civitai_data and 'name' in civitai_data['model']:
                 checkpoint['name'] = civitai_data['model']['name']
 

py/recipes/parsers/civitai_image.py

@@ -185,8 +185,67 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Process standard resources array
             if "resources" in metadata and isinstance(metadata["resources"], list):
                 for resource in metadata["resources"]:
+                    resource_type = resource.get("type", "lora")
+
+                    # Track resources with type "model" — these are checkpoint models.
+                    # The resources array is the most reliable source for checkpoint
+                    # identification because it has an explicit type field and hash,
+                    # unlike modelVersionIds which is a flat list with no type info.
+                    if resource_type == "model":
+                        checkpoint_entry = {
+                            "id": 0,
+                            "modelId": 0,
+                            "name": resource.get("name", "Unknown Model"),
+                            "version": "",
+                            "type": resource.get("type", "model"),
+                            "existsLocally": False,
+                            "localPath": None,
+                            "file_name": resource.get("name", ""),
+                            "hash": resource.get("hash", "") or "",
+                            "thumbnailUrl": "/loras_static/images/no-preview.png",
+                            "baseModel": "",
+                            "size": 0,
+                            "downloadUrl": "",
+                            "isDeleted": False,
+                        }
+
+                        # 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"]
+                                    )
+                                )
+                                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
+                        continue
+
                     # Modified to process resources without a type field as potential LoRAs
-                    if resource.get("type", "lora") == "lora":
+                    if resource_type == "lora":
                         lora_hash = resource.get("hash", "")
 
                         # Try to get hash from the hashes field if not present in resource

py/routes/handlers/recipe_handlers.py

@@ -87,6 +87,7 @@ class RecipeHandlerSet:
             "repair_recipes": self.management.repair_recipes,
             "cancel_repair": self.management.cancel_repair,
             "repair_recipe": self.management.repair_recipe,
+            "repair_recipes_bulk": self.management.repair_recipes_bulk,
             "get_repair_progress": self.management.get_repair_progress,
             "start_batch_import": self.batch_import.start_batch_import,
             "get_batch_import_progress": self.batch_import.get_batch_import_progress,
@@ -706,6 +707,69 @@ class RecipeManagementHandler:
             self._logger.error("Error cancelling recipe repair: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def repair_recipes_bulk(self, request: web.Request) -> web.Response:
+        """Bulk repair metadata for multiple recipes by their IDs.
+
+        Accepts a JSON body with a "recipe_ids" array and iterates
+        repair_recipe_by_id over each entry, collecting statistics.
+        """
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                return web.json_response(
+                    {"success": False, "error": "Recipe scanner unavailable"},
+                    status=503,
+                )
+
+            data = await request.json()
+            recipe_ids = data.get("recipe_ids", [])
+            if not recipe_ids:
+                return web.json_response(
+                    {"success": False, "error": "recipe_ids are required"},
+                    status=400,
+                )
+
+            total = len(recipe_ids)
+            repaired = 0
+            skipped = 0
+            errors = 0
+            recipes = []
+
+            for recipe_id in recipe_ids:
+                try:
+                    result = await recipe_scanner.repair_recipe_by_id(recipe_id)
+                    if result.get("success"):
+                        repaired += result.get("repaired", 0)
+                        skipped += result.get("skipped", 0)
+                        if result.get("recipe"):
+                            recipes.append(result["recipe"])
+                    else:
+                        errors += 1
+                except RecipeNotFoundError:
+                    skipped += 1
+                except Exception as exc:
+                    self._logger.error(
+                        "Error repairing recipe %s: %s", recipe_id, exc
+                    )
+                    errors += 1
+
+            return web.json_response({
+                "success": True,
+                "total": total,
+                "repaired": repaired,
+                "skipped": skipped,
+                "errors": errors,
+                "recipes": recipes,
+            })
+        except Exception as exc:
+            self._logger.error(
+                "Error performing bulk repair: %s", exc, exc_info=True
+            )
+            return web.json_response(
+                {"success": False, "error": str(exc)}, status=500
+            )
+
     async def repair_recipe(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -1293,11 +1357,18 @@ class RecipeManagementHandler:
                     image_info.get("meta") if civitai_image_id and image_info else None
                 )
                 if civitai_image_id and image_info:
+                    # modelVersionId (singular) — the primary version for this
+                    # image on CivitAI.  May be absent, or may *not* be the
+                    # checkpoint (e.g. when the image was generated with a LoRA
+                    # as the primary subject).  When absent, DO NOT fall back to
+                    # modelVersionIds[0] — that array mixes checkpoints, LoRAs,
+                    # and other model version IDs without ordering guarantees.
+                    # The downstream enrichment flow will find the real
+                    # checkpoint via meta.resources (type:"model" hash) or
+                    # meta.civitaiResources (type:"checkpoint" version ID), so
+                    # leaving model_ver_id as None is safe and avoids the bug
+                    # where a LoRA version ID was treated as the checkpoint.
                     model_ver_id = image_info.get("modelVersionId")
-                    if not model_ver_id:
-                        ids = image_info.get("modelVersionIds")
-                        if isinstance(ids, list) and ids:
-                            model_ver_id = ids[0]
 
                     # Inject root-level modelVersionIds into meta so downstream
                     # parsers (CivitaiApiMetadataParser) can discover ALL resources

py/routes/recipe_route_registrar.py

@@ -58,6 +58,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
     RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
+    RouteDefinition("POST", "/api/lm/recipes/repair-bulk", "repair_recipes_bulk"),
     RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
     RouteDefinition("POST", "/api/lm/recipes/batch-import/start", "start_batch_import"),
     RouteDefinition(

py/services/aria2_downloader.py

@@ -14,12 +14,30 @@ from typing import Any, Dict, Optional, Tuple
 
 import aiohttp
 
-from .downloader import DownloadProgress, get_downloader
+from .downloader import DownloadProgress, get_downloader, is_ssl_cert_verify_error
 from .aria2_transfer_state import Aria2TransferStateStore
 from .settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
 
+def _try_certifi_ca_path() -> str | None:
+    """Return the certifi CA bundle path if available, else None."""
+    try:
+        import certifi  # type: ignore[import-untyped]
+
+        path = certifi.where()
+        if os.path.isfile(path):
+            logger.debug(
+                "aria2 --ca-certificate: using certifi CA bundle at %s", path
+            )
+            return path
+    except ImportError:
+        pass
+
+    logger.debug("aria2 --ca-certificate: certifi not available")
+    return None
+
+
 CIVITAI_DOWNLOAD_URL_PREFIXES = (
     "https://civitai.com/api/download/",
     "https://civitai.red/api/download/",
@@ -391,6 +409,15 @@ class Aria2Downloader:
                     f"Failed to resolve authenticated Civitai redirect: status={response.status} body={body[:300]}"
                 )
         except aiohttp.ClientError as exc:
+            if is_ssl_cert_verify_error(exc):
+                logger.error(
+                    "SSL certificate verification failed during Civitai redirect "
+                    "resolution for %s. This is usually caused by an outdated CA "
+                    "certificate bundle. Recommended fixes:\n"
+                    "  1. pip install --upgrade certifi\n"
+                    "  2. pip install pip-system-certs",
+                    url,
+                )
             raise Aria2Error(
                 f"Failed to resolve authenticated Civitai redirect: {exc}"
             ) from exc
@@ -414,6 +441,11 @@ class Aria2Downloader:
                 f"--rpc-listen-port={self._rpc_port}",
                 f"--rpc-secret={self._rpc_secret}",
                 "--check-certificate=true",
+                # Point aria2 at certifi's CA bundle when available so it uses
+                # the same certificate store as Python downloads.
+                *((
+                    f"--ca-certificate={ca_cert}",
+                ) if (ca_cert := _try_certifi_ca_path()) else ()),
                 "--allow-overwrite=true",
                 "--auto-file-renaming=false",
                 "--file-allocation=none",

py/services/civitai_client.py

@@ -410,6 +410,25 @@ class CivitaiClient:
             return None
 
         target_version = self._select_target_version(model_data, model_id, version_id)
+
+        # If modelVersions is empty (e.g. CivitAI cache lag for newly published
+        # models) but a specific version_id is known, fall back to fetching the
+        # version directly via the individual model-versions endpoint, then
+        # enrich it with the model-level data we already have.
+        if target_version is None and version_id is not None:
+            logger.info(
+                "modelVersions empty for model %s; falling back to direct "
+                "version lookup for %s",
+                model_id,
+                version_id,
+            )
+            version = await self._fetch_version_by_id(version_id)
+            if version:
+                self._enrich_version_with_model_data(version, model_data)
+                self._remove_comfy_metadata(version)
+                return version
+            return None
+
         if target_version is None:
             return None
 

py/services/downloader.py

@@ -13,6 +13,7 @@ This module provides a centralized download service with:
 import os
 import logging
 import asyncio
+import ssl
 import aiohttp
 from collections import deque
 from dataclasses import dataclass
@@ -31,6 +32,20 @@ from .errors import RateLimitError
 logger = logging.getLogger(__name__)
 
 
+def is_ssl_cert_verify_error(exc: BaseException) -> bool:
+    """Check if an exception represents an SSL certificate verification failure.
+
+    Matches ``ssl.SSLCertVerificationError``, ``aiohttp.ClientConnectorCertificateError``
+    (which wraps the former), and falls back to the standard OpenSSL error text.
+    """
+    if isinstance(exc, ssl.SSLCertVerificationError):
+        return True
+    cert_error = getattr(exc, "certificate_error", None)
+    if isinstance(cert_error, ssl.SSLCertVerificationError):
+        return True
+    return "CERTIFICATE_VERIFY_FAILED" in str(exc)
+
+
 @dataclass(frozen=True)
 class DownloadProgress:
     """Snapshot of a download transfer at a moment in time."""
@@ -265,9 +280,22 @@ class Downloader:
             logger.debug(
                 "Proxy mode: system-level proxy (trust_env) will be used if configured in environment."
             )
+        # Build SSL context: prefer certifi's CA bundle for broader
+        # CA coverage across different Python environments (especially
+        # embedded/compatibility Python builds).
+        try:
+            import certifi  # type: ignore[import-untyped]
+
+            ca_path = certifi.where()
+            ssl_context = ssl.create_default_context(cafile=ca_path)
+            logger.debug("SSL: using certifi CA bundle at %s", ca_path)
+        except (ImportError, FileNotFoundError, ValueError, OSError):
+            ssl_context = ssl.create_default_context()
+            logger.debug("SSL: certifi unavailable; using system default CA bundle")
+
         # Optimize TCP connection parameters
         connector = aiohttp.TCPConnector(
-            ssl=True,
+            ssl=ssl_context,
             limit=8,  # Concurrent connections
             ttl_dns_cache=300,  # DNS cache timeout
             force_close=False,  # Keep connections for reuse
@@ -736,6 +764,17 @@ class Downloader:
                 DownloadRestartRequested,
             ) as e:
                 retry_count += 1
+
+                if is_ssl_cert_verify_error(e):
+                    logger.error(
+                        "SSL certificate verification failed when connecting to %s. "
+                        "This is usually caused by an outdated CA certificate bundle "
+                        "in the Python environment. Recommended fixes:\n"
+                        "  1. pip install --upgrade certifi\n"
+                        "  2. pip install pip-system-certs",
+                        url,
+                    )
+
                 logger.warning(
                     f"Network error during download (attempt {retry_count}/{self.max_retries + 1}): {e}"
                 )

py/services/model_hash_index.py

@@ -7,6 +7,7 @@ class ModelHashIndex:
     def __init__(self):
         self._hash_to_path: Dict[str, str] = {}
         self._filename_to_hash: Dict[str, str] = {}
+        self._autov2_to_path: Dict[str, str] = {}
         # New data structures for tracking duplicates
         self._duplicate_hashes: Dict[str, List[str]] = {}  # sha256 -> list of paths
         self._duplicate_filenames: Dict[str, List[str]] = {}  # filename -> list of paths
@@ -63,6 +64,9 @@ class ModelHashIndex:
         # Add new mappings
         self._hash_to_path[sha256] = file_path
         self._filename_to_hash[filename] = sha256
+        # AutoV2 = first 10 chars of SHA256
+        if len(sha256) >= 10:
+            self._autov2_to_path[sha256[:10]] = file_path
     
     def _get_filename_from_path(self, file_path: str) -> str:
         """Extract filename without extension from path"""
@@ -157,7 +161,12 @@ class ModelHashIndex:
                 del self._duplicate_filenames[filename]
                 if filename in self._filename_to_hash:
                     del self._filename_to_hash[filename]
-    
+
+        # Remove from AutoV2 index
+        autov2_keys_to_remove = [k for k, v in self._autov2_to_path.items() if v == file_path]
+        for k in autov2_keys_to_remove:
+            del self._autov2_to_path[k]
+
     def remove_by_hash(self, sha256: str) -> None:
         """Remove entry by hash"""
         sha256 = sha256.lower()
@@ -177,6 +186,10 @@ class ModelHashIndex:
         # Remove hash-to-path mapping
         del self._hash_to_path[sha256]
         
+        autov2_key = sha256[:10]
+        if autov2_key in self._autov2_to_path:
+            del self._autov2_to_path[autov2_key]
+        
         # Update filename-to-hash and duplicate filenames for all paths
         for path_to_remove in paths_to_remove:
             fname = self._get_filename_from_path(path_to_remove)
@@ -195,13 +208,24 @@ class ModelHashIndex:
                     # If only one entry remains, it's no longer a duplicate
                     del self._duplicate_filenames[fname]
     
-    def has_hash(self, sha256: str) -> bool:
-        """Check if hash exists in index"""
-        return sha256.lower() in self._hash_to_path
-    
-    def get_path(self, sha256: str) -> Optional[str]:
-        """Get file path for a hash"""
-        return self._hash_to_path.get(sha256.lower())
+    def has_hash(self, hash_value: str) -> bool:
+        """Check if hash exists in index (SHA256 or AutoV2)"""
+        normalized = hash_value.lower()
+        if normalized in self._hash_to_path:
+            return True
+        if len(normalized) == 10:
+            return normalized in self._autov2_to_path
+        return False
+
+    def get_path(self, hash_value: str) -> Optional[str]:
+        """Get file path for a hash (SHA256 or AutoV2)"""
+        normalized = hash_value.lower()
+        path = self._hash_to_path.get(normalized)
+        if path is not None:
+            return path
+        if len(normalized) == 10:
+            return self._autov2_to_path.get(normalized)
+        return None
     
     def get_hash(self, file_path: str) -> Optional[str]:
         """Get hash for a file path"""
@@ -218,6 +242,7 @@ class ModelHashIndex:
         """Clear all entries"""
         self._hash_to_path.clear()
         self._filename_to_hash.clear()
+        self._autov2_to_path.clear()
         self._duplicate_hashes.clear()
         self._duplicate_filenames.clear()
     

py/services/model_metadata_provider.py

@@ -5,7 +5,7 @@ import logging
 import random
 from typing import Optional, Dict, Tuple, Any, List, Sequence
 from .downloader import get_downloader
-from .errors import RateLimitError
+from .errors import RateLimitError, ResourceNotFoundError
 
 try:
     from bs4 import BeautifulSoup
@@ -482,6 +482,7 @@ class FallbackMetadataProvider(ModelMetadataProvider):
         return None, "Model not found"
 
     async def get_model_versions(self, model_id: str) -> Optional[Dict]:
+        not_found_confirmed = False
         for provider, label in self._iter_providers():
             try:
                 result = await self._call_with_rate_limit(
@@ -492,8 +493,24 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 if result:
                     return result
             except RateLimitError as exc:
+                if not_found_confirmed:
+                    logger.debug(
+                        "Suppressing rate limit from %s for model %s: "
+                        "already confirmed as not found by another provider",
+                        label,
+                        model_id,
+                    )
+                    return None
                 exc.provider = exc.provider or label
                 raise exc
+            except ResourceNotFoundError:
+                not_found_confirmed = True
+                logger.debug(
+                    "Provider %s reports model %s as not found",
+                    label,
+                    model_id,
+                )
+                continue
             except Exception as e:
                 logger.debug("Provider %s failed for get_model_versions: %s", label, e)
                 continue

py/services/recipe_scanner.py

@@ -65,7 +65,7 @@ class RecipeScanner:
             cls._instance._civitai_client = None  # Will be lazily initialized
         return cls._instance
 
-    REPAIR_VERSION = 3
+    REPAIR_VERSION = 4
 
     def __init__(
         self,
@@ -292,6 +292,32 @@ class RecipeScanner:
         if recipe.get("repair_version", 0) >= self.REPAIR_VERSION:
             return False
 
+        # 1.5 Detect and clear corrupted checkpoint (LoRA data saved as checkpoint).
+        #     A checkpoint whose modelVersionId also appears in a LoRA entry is
+        #     definitely wrong — the CivitAI import code used to pick
+        #     modelVersionIds[0] as the checkpoint, which was often a LoRA.
+        #     Clearing it lets the enrichment flow re-resolve the correct
+        #     checkpoint from CivitAI image metadata.
+        cp = recipe.get("checkpoint")
+        lora_mvids = {
+            l.get("modelVersionId")
+            for l in recipe.get("loras", [])
+            if l.get("modelVersionId")
+        }
+        if cp and cp.get("modelVersionId") and cp["modelVersionId"] in lora_mvids:
+            cp_mvid = cp["modelVersionId"]
+            logger.info(
+                "Recipe %s: checkpoint modelVersionId %s matches a LoRA — "
+                "clearing corrupted checkpoint and removing matching LoRA entry",
+                recipe.get("id"),
+                cp_mvid,
+            )
+            recipe["checkpoint"] = None
+            recipe["loras"] = [
+                l for l in recipe.get("loras", [])
+                if l.get("modelVersionId") != cp_mvid
+            ]
+
         # 2. Identification: Is repair needed?
         has_checkpoint = (
             "checkpoint" in recipe

py/utils/example_images_download_manager.py

@@ -397,13 +397,12 @@ class DownloadManager:
 
             models_with_hash = len(all_models_with_hash)
 
-            # Calculate pending count: check which models actually need processing
-            # A model is pending if it has a hash, is not in processed_models,
-            # and its folder doesn't exist or is empty
+            # Calculate pending count: check which models actually need processing.
+            # A model is pending if it has a hash, is not already processed or known-failed,
+            # and its folder doesn't exist or is empty.
             pending_hashes = set()
             for model_hash, model_name in all_models_with_hash:
-                if model_hash not in processed_models:
-                    # Check if model folder exists with files
+                if model_hash not in processed_models and model_hash not in failed_models:
                     model_dir = ExampleImagePathResolver.get_model_folder(
                         model_hash, active_library
                     )

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.0.8"
+version = "1.0.10"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

settings.json.example

@@ -10,13 +10,14 @@
       "C:/path/to/your/checkpoints_folder",
       "C:/path/to/another/checkpoints_folder"
     ],
+    "unet": [
+      "C:/path/to/your/diffusion_models_folder",
+      "C:/path/to/another/diffusion_models_folder"
+    ],
     "embeddings": [
       "C:/path/to/your/embeddings_folder",
       "C:/path/to/another/embeddings_folder"
     ]
   },
-  "example_images_open_mode": "system",
-  "example_images_local_root": "",
-  "example_images_open_uri_template": "",
   "auto_organize_exclusions": []
 }

static/js/api/recipeApi.js

@@ -15,6 +15,7 @@ const RECIPE_ENDPOINTS = {
     move: '/api/lm/recipe/move',
     moveBulk: '/api/lm/recipes/move-bulk',
     bulkDelete: '/api/lm/recipes/bulk-delete',
+    repairBulk: '/api/lm/recipes/repair-bulk',
 };
 
 const RECIPE_SIDEBAR_CONFIG = {
@@ -557,6 +558,38 @@ export class RecipeSidebarApiClient {
         };
     }
 
+    async repairBulkModels(filePaths) {
+        if (!filePaths || filePaths.length === 0) {
+            throw new Error('No file paths provided');
+        }
+
+        const recipeIds = filePaths
+            .map((path) => extractRecipeId(path))
+            .filter((id) => !!id);
+
+        if (recipeIds.length === 0) {
+            throw new Error('No recipe IDs could be derived from file paths');
+        }
+
+        const response = await fetch(this.apiConfig.endpoints.repairBulk, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                recipe_ids: recipeIds,
+            }),
+        });
+
+        const result = await response.json();
+
+        if (!response.ok || !result.success) {
+            throw new Error(result.error || 'Failed to repair recipes');
+        }
+
+        return result;
+    }
+
     async bulkDeleteModels(filePaths) {
         if (!filePaths || filePaths.length === 0) {
             throw new Error('No file paths provided');

static/js/components/ContextMenu/BulkContextMenu.js

@@ -41,6 +41,11 @@ export class BulkContextMenu extends BaseContextMenu {
         const autoOrganizeItem = this.menu.querySelector('[data-action="auto-organize"]');
         const deleteAllItem = this.menu.querySelector('[data-action="delete-all"]');
         const downloadMissingLorasItem = this.menu.querySelector('[data-action="download-missing-loras"]');
+        const repairMetadataItem = this.menu.querySelector('[data-action="repair-metadata"]');
+
+        if (repairMetadataItem) {
+            repairMetadataItem.style.display = config.repairMetadata ? 'flex' : 'none';
+        }
 
         if (sendToWorkflowAppendItem) {
             sendToWorkflowAppendItem.style.display = config.sendToWorkflow ? 'flex' : 'none';
@@ -127,33 +132,38 @@ export class BulkContextMenu extends BaseContextMenu {
         const resumeMetadataRefreshItem = this.menu.querySelector('[data-action="resume-metadata-refresh"]');
 
         if (skipMetadataRefreshItem && resumeMetadataRefreshItem) {
-            const skipCount = this.countSkipStatus(true);
-            const resumeCount = this.countSkipStatus(false);
-            const totalCount = skipCount + resumeCount;
-
-            if (skipCount === totalCount) {
+            if (!config.skipMetadataRefresh) {
                 skipMetadataRefreshItem.style.display = 'none';
-                resumeMetadataRefreshItem.style.display = 'flex';
-                resumeMetadataRefreshItem.querySelector('span').textContent = translate(
-                    'loras.bulkOperations.resumeMetadataRefresh'
-                );
-            } else if (resumeCount === totalCount) {
-                skipMetadataRefreshItem.style.display = 'flex';
                 resumeMetadataRefreshItem.style.display = 'none';
-                skipMetadataRefreshItem.querySelector('span').textContent = translate(
-                    'loras.bulkOperations.skipMetadataRefresh'
-                );
             } else {
-                skipMetadataRefreshItem.style.display = 'flex';
-                resumeMetadataRefreshItem.style.display = 'flex';
-                skipMetadataRefreshItem.querySelector('span').textContent = translate(
-                    'loras.bulkOperations.skipMetadataRefreshCount',
-                    { count: resumeCount }
-                );
-                resumeMetadataRefreshItem.querySelector('span').textContent = translate(
-                    'loras.bulkOperations.resumeMetadataRefreshCount',
-                    { count: skipCount }
-                );
+                const skipCount = this.countSkipStatus(true);
+                const resumeCount = this.countSkipStatus(false);
+                const totalCount = skipCount + resumeCount;
+
+                if (skipCount === totalCount) {
+                    skipMetadataRefreshItem.style.display = 'none';
+                    resumeMetadataRefreshItem.style.display = 'flex';
+                    resumeMetadataRefreshItem.querySelector('span').textContent = translate(
+                        'loras.bulkOperations.resumeMetadataRefresh'
+                    );
+                } else if (resumeCount === totalCount) {
+                    skipMetadataRefreshItem.style.display = 'flex';
+                    resumeMetadataRefreshItem.style.display = 'none';
+                    skipMetadataRefreshItem.querySelector('span').textContent = translate(
+                        'loras.bulkOperations.skipMetadataRefresh'
+                    );
+                } else {
+                    skipMetadataRefreshItem.style.display = 'flex';
+                    resumeMetadataRefreshItem.style.display = 'flex';
+                    skipMetadataRefreshItem.querySelector('span').textContent = translate(
+                        'loras.bulkOperations.skipMetadataRefreshCount',
+                        { count: resumeCount }
+                    );
+                    resumeMetadataRefreshItem.querySelector('span').textContent = translate(
+                        'loras.bulkOperations.resumeMetadataRefreshCount',
+                        { count: skipCount }
+                    );
+                }
             }
         }
 
@@ -251,6 +261,9 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'delete-all':
                 bulkManager.showBulkDeleteModal();
                 break;
+            case 'repair-metadata':
+                bulkManager.repairSelectedRecipes();
+                break;
             case 'set-favorite': {
                 const allFavorited = this.countFavoritedInSelection() === state.selectedModels.size;
                 bulkManager.setBulkFavorites(!allFavorited);

static/js/managers/BulkManager.js

@@ -85,7 +85,8 @@ export class BulkManager {
                 setContentRating: false,
                 skipMetadataRefresh: false,
                 setFavorite: true,
-                unfavorite: true
+                unfavorite: true,
+                repairMetadata: true
             }
         };
 
@@ -656,6 +657,76 @@ export class BulkManager {
         }
     }
 
+    async repairSelectedRecipes() {
+        if (state.selectedModels.size === 0) {
+            showToast('toast.recipes.noRecipesSelected', {}, 'warning');
+            return;
+        }
+
+        if (state.currentPageType !== 'recipes') {
+            showToast('This operation is only available for recipes', {}, 'warning');
+            return;
+        }
+
+        try {
+            const apiClient = this.getActiveApiClient();
+            const filePaths = Array.from(state.selectedModels);
+
+            if (typeof apiClient.repairBulkModels !== 'function') {
+                showToast('Bulk repair is not supported for this model type', {}, 'error');
+                return;
+            }
+
+            state.loadingManager.showSimpleLoading('Repairing recipe metadata...');
+
+            const result = await apiClient.repairBulkModels(filePaths);
+
+            if (result.success) {
+                const total = result.total || filePaths.length;
+                const repaired = result.repaired || 0;
+                const skipped = result.skipped || 0;
+
+                const recipes = result.recipes || [];
+                for (const recipe of recipes) {
+                    if (recipe.file_path) {
+                        state.virtualScroller.updateSingleItem(
+                            recipe.file_path,
+                            recipe
+                        );
+                    }
+                }
+
+                if (repaired > 0) {
+                    showToast(
+                        'toast.recipes.repairBulkComplete',
+                        { repaired, skipped, total },
+                        'success'
+                    );
+                } else {
+                    showToast(
+                        'toast.recipes.repairBulkSkipped',
+                        { total },
+                        'info'
+                    );
+                }
+
+                this.clearSelection();
+            } else {
+                throw new Error(result.error || 'Bulk repair failed');
+            }
+        } catch (error) {
+            console.error('Error during bulk recipe repair:', error);
+            showToast('toast.recipes.repairBulkFailed', { message: error.message }, 'error');
+        } finally {
+            if (state.loadingManager?.hide) {
+                state.loadingManager.hide();
+            }
+            if (typeof state.loadingManager?.restoreProgressBar === 'function') {
+                state.loadingManager.restoreProgressBar();
+            }
+        }
+    }
+
     async refreshAllMetadata() {
         if (state.selectedModels.size === 0) {
             showToast('toast.models.noModelsSelected', {}, 'warning');

static/js/managers/UpdateService.js

@@ -731,9 +731,16 @@ export class UpdateService {
     }
     
     // Simple markdown parser for changelog items
+    // Simple markdown parser for changelog items
+    // Escape HTML entities first so angle brackets in content (e.g. `<lora:x>`)
+    // aren't swallowed by innerHTML's HTML parser as invalid tags
     parseMarkdown(text) {
         if (!text) return '';
         
+        text = text.replace(/&/g, '&amp;');
+        text = text.replace(/</g, '&lt;');
+        text = text.replace(/>/g, '&gt;');
+        
         // Handle bold text (**text**)
         text = text.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>');
         

templates/components/context_menu.html

@@ -80,6 +80,9 @@
         <div class="context-menu-item" data-action="check-updates">
             <i class="fas fa-bell"></i> <span>{{ t('loras.bulkOperations.checkUpdates') }}</span>
         </div>
+        <div class="context-menu-item" data-action="repair-metadata">
+            <i class="fas fa-tools"></i> <span>{{ t('loras.bulkOperations.repairMetadata') }}</span>
+        </div>
         <div class="context-menu-item" data-action="skip-metadata-refresh">
             <i class="fas fa-ban"></i> <span>{{ t('loras.bulkOperations.skipMetadataRefresh') }}</span>
         </div>

tests/routes/test_recipe_routes.py

@@ -467,7 +467,10 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
     class Provider:
         async def get_model_version_info(self, model_version_id):
             provider_calls.append(model_version_id)
-            return {"baseModel": "Flux Provider"}, None
+            return {
+                "baseModel": "Flux Provider",
+                "model": {"type": "Checkpoint", "name": "Flux"},
+            }, None
 
     async def fake_get_default_metadata_provider():
         return Provider()

tests/services/test_civitai_image_parser.py

@@ -298,3 +298,113 @@ async def test_parse_metadata_handles_modelVersionIds(monkeypatch):
     assert lora2["type"] == "lora"
     assert lora2["hash"] == "aabbccdd0022"
     assert lora2["baseModel"] == "SDXL"
+
+
+@pytest.mark.asyncio
+async def test_parse_metadata_extracts_checkpoint_from_resources_model_type(monkeypatch):
+    """resources entries with type:"model" should be captured as the checkpoint,
+    not skipped (which was the old buggy behavior), and not mixed into loras."""
+    captured_hashes = []
+
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_by_hash(self, model_hash):
+                captured_hashes.append(model_hash)
+                if model_hash == "a1b2c3d4e5":
+                    return ({
+                        "id": 999,
+                        "modelId": 888,
+                        "name": "v1.0",
+                        "model": {"name": "Real Checkpoint", "type": "Checkpoint"},
+                        "baseModel": "SDXL 1.0",
+                        "images": [{"url": "https://image.civitai.com/cp/original=true"}],
+                        "files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
+                    }, None)
+                return None, "Model not found"
+
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = CivitaiApiMetadataParser()
+
+    metadata = {
+        "prompt": "test",
+        "resources": [
+            {"hash": "a1b2c3d4e5", "name": "Real Checkpoint", "type": "model"},
+            {"hash": "f6g7h8i9j0", "name": "Some LoRA", "type": "lora", "weight": 0.8},
+        ],
+        "Model hash": "a1b2c3d4e5",
+    }
+
+    result = await parser.parse_metadata(metadata)
+
+    # The type:"model" resource should be in result["model"], not in result["loras"]
+    assert result["model"] is not None, "checkpoint model should be extracted"
+    assert result["model"]["name"] == "Real Checkpoint"
+    assert result["model"]["hash"] == "a1b2c3d4e5"
+    assert result["model"]["type"] == "model"
+
+    # The LoRA resource should be in result["loras"]
+    assert len(result["loras"]) == 1
+    assert result["loras"][0]["name"] == "Some LoRA"
+
+    # The checkpoint hash should have triggered a lookup
+    assert "a1b2c3d4e5" in captured_hashes
+
+
+@pytest.mark.asyncio
+async def test_parse_metadata_resources_model_type_does_not_duplicate_checkpoint_in_loras(monkeypatch):
+    """When a resources entry has type:"model", it should NOT also appear in loras.
+    Regression test for the bug where the checkpoint model appeared in both places."""
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_by_hash(self, model_hash):
+                if model_hash == "cp123hash":
+                    return ({
+                        "id": 100,
+                        "modelId": 200,
+                        "name": "v2",
+                        "model": {"name": "My Checkpoint", "type": "Checkpoint"},
+                        "baseModel": "SDXL",
+                        "files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
+                    }, None)
+                if model_hash == "lora1hash":
+                    return ({
+                        "id": 300,
+                        "modelId": 400,
+                        "name": "v1",
+                        "model": {"name": "Style LoRA", "type": "LORA"},
+                        "baseModel": "SDXL",
+                        "files": [{"type": "Model", "primary": True, "sizeKB": 512, "name": "style.safetensors"}]
+                    }, None)
+                return None, "Model not found"
+
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = CivitaiApiMetadataParser()
+    metadata = {
+        "resources": [
+            {"hash": "cp123hash", "name": "My Checkpoint", "type": "model"},
+            {"hash": "lora1hash", "name": "Style LoRA", "type": "lora", "weight": 0.5},
+        ],
+    }
+
+    result = await parser.parse_metadata(metadata)
+
+    # Checkpoint must NOT appear in loras
+    lora_names = {l["name"] for l in result["loras"]}
+    assert "My Checkpoint" not in lora_names
+    assert "Style LoRA" in lora_names
+
+    # Checkpoint must be in result["model"]
+    assert result["model"] is not None
+    assert result["model"]["name"] == "My Checkpoint"

tests/services/test_recipe_repair.py

@@ -94,7 +94,7 @@ async def test_repair_all_recipes_with_enriched_checkpoint_id(setup_scanner):
         "id": 5678,
         "modelId": 1234,
         "name": "v1.0",
-        "model": {"name": "Full Model Name"},
+        "model": {"name": "Full Model Name", "type": "Checkpoint"},
         "baseModel": "SDXL 1.0",
         "images": [{"url": "https://image.url/thumb.jpg"}],
         "files": [{"type": "Model", "hashes": {"SHA256": "ABCDEF"}, "name": "full_filename.safetensors"}]
@@ -142,7 +142,7 @@ async def test_repair_all_recipes_supports_civitai_red_source_url(setup_scanner)
             "id": 5678,
             "modelId": 1234,
             "name": "v1.0",
-            "model": {"name": "Full Model Name"},
+            "model": {"name": "Full Model Name", "type": "Checkpoint"},
             "baseModel": "SDXL 1.0",
             "images": [{"url": "https://image.url/thumb.jpg"}],
             "files": [
@@ -183,7 +183,7 @@ async def test_repair_all_recipes_with_enriched_checkpoint_hash(setup_scanner):
         "id": 999,
         "modelId": 888,
         "name": "v2.0",
-        "model": {"name": "Hashed Model"},
+        "model": {"name": "Hashed Model", "type": "Checkpoint"},
         "baseModel": "SD 1.5",
         "files": [{"type": "Model", "hashes": {"SHA256": "hash123"}, "name": "hashed.safetensors"}]
     }, None)