fix(recipes): fall back to Civitai API meta when EXIF is empty, enrich checkpoint in analyze_remote_image

- When downloaded Civitai image has no embedded EXIF, parse the
  already-fetched Civitai API meta (resources, hashes) directly
  instead of skipping parser altogether.
- Extract loras and model from parser output to fill metadata gaps
  when the primary import path doesn't provide them.
- Read modelVersionIds[0] as fallback when modelVersionId is None
  (Civitai API returns both but the singular form can be absent).
- Run RecipeEnricher in analyze_remote_image before returning, so
  the LM UI receives complete metadata including checkpoint with
  zero additional API calls (reuses the image_info already fetched).

.gitignore

@@ -15,6 +15,7 @@ model_cache/
 # agent
 .opencode/
 .claude/
+.sisyphus/
 .codex
 
 # Vue widgets development cache (but keep build output)

data/supporters.json

@@ -12,33 +12,39 @@
     "2018cfh",
     "W+K+White",
     "wackop",
-    "Takkan",
+    "Phil",
     "Carl G.",
+    "Arlecchino Shion",
+    "stone9k",
     "$MetaSamsara",
     "itismyelement",
+    "Gingko Biloba",
     "onesecondinosaur",
-    "stone9k",
+    "Takkan",
+    "Charles Blakemore",
+    "Rob Williams",
     "Rosenthal",
     "Francisco Tatis",
+    "Tobi_Swagg",
     "Andrew Wilson",
     "Greybush",
-    "Gooohokrbe",
     "Ricky Carter",
     "JongWon Han",
-    "OldBones",
     "VantAI",
     "runte3221",
+    "Illrigger",
     "FreelancerZ",
     "Edgar Tejeda",
+    "Jorge Hussni",
     "Liam MacDougal",
     "Fraser Cross",
     "Polymorphic Indeterminate",
-    "Birdy",
     "Marc Whiffen",
-    "Jorge Hussni",
-    "Kiba",
+    "Birdy",
     "Skalabananen",
+    "Kiba",
     "Reno Lam",
+    "Mozzel",
     "sig",
     "Christian Byrne",
     "DM",
@@ -46,39 +52,41 @@
     "Estragon",
     "J\\B/ 8r0wns0n",
     "Snaggwort",
-    "Arlecchino Shion",
-    "Charles Blakemore",
-    "Rob Williams",
     "ClockDaemon",
+    "Jonathan Ross",
     "KD",
     "Omnidex",
+    "Nazono_hito",
     "Tyler Trebuchon",
     "Release Cabrakan",
-    "Tobi_Swagg",
+    "contrite831",
     "SG",
     "carozzz",
     "James Dooley",
     "zenbound",
     "Buzzard",
     "jmack",
+    "Adam Shaw",
     "Mark Corneglio",
     "SarcasticHashtag",
-    "Cosmosis",
+    "Anthony Rizzo",
     "iamresist",
+    "Gooohokrbe",
     "RedrockVP",
     "Wolffen",
-    "FloPro4Sho",
     "James Todd",
+    "OldBones",
     "Steven Pfeiffer",
     "Tim",
+    "Timmy",
+    "Johnny",
     "Lisster",
     "Michael Wong",
-    "Illrigger",
+    "whudunit",
     "Tom Corrigan",
+    "dl0901dm",
     "JackieWang",
     "fnkylove",
-    "Julian V",
-    "Steven Owens",
     "Yushio",
     "Vik71it",
     "Echo",
@@ -86,147 +94,137 @@
     "Robert Stacey",
     "PM",
     "Todd Keck",
-    "Mozzel",
-    "Gingko Biloba",
-    "Sterilized",
+    "Briton Heilbrun",
+    "Aleksander Wujczyk",
     "BadassArabianMofo",
+    "Sterilized",
     "Pascal Dahle",
     "quarz",
-    "Greg",
     "Penfore",
+    "Greg",
     "JSST",
-    "esthe",
     "lmsupporter",
-    "IamAyam",
+    "zounic",
     "wfpearl",
     "Baekdoosixt",
-    "Jonathan Ross",
     "Jack B Nimble",
-    "Nazono_hito",
     "Melville Parrish",
     "daniel dove",
     "Lustre",
     "JW Sin",
-    "contrite831",
     "Alex",
     "bh",
-    "confiscated Zyra",
     "Marlon Daniels",
     "Starkselle",
     "Aaron Bleuer",
     "LacesOut!",
     "greebles",
-    "Adam Shaw",
-    "Tee Gee",
-    "Anthony Rizzo",
-    "tarek helmi",
+    "Cosmosis",
     "M Postkasse",
+    "FloPro4Sho",
     "ASLPro3D",
     "Jacob Hoehler",
     "FinalyFree",
     "Weasyl",
-    "Timmy",
-    "Johnny",
+    "Lex Song",
     "Cory Paza",
     "Tak",
     "Gonzalo Andre Allendes Lopez",
     "Zach Gonser",
     "Big Red",
-    "whudunit",
+    "Jimmy Ledbetter",
     "Luc Job",
-    "dl0901dm",
     "Philip Hempel",
     "corde",
     "Nick Walker",
-    "lh qwe",
+    "Julian V",
+    "Steven Owens",
     "Bishoujoker",
-    "conner",
     "aai",
-    "Briton Heilbrun",
     "Tori",
     "wildnut",
-    "Princess Bright Eyes",
-    "AbstractAss",
-    "Felipe dos Santos",
-    "ViperC",
     "jean jahren",
-    "Aleksander Wujczyk",
     "AM Kuro",
-    "Markus",
-    "S Sang",
+    "ViperC",
+    "Ran C",
+    "Sangheili460",
+    "MagnaInsomnia",
     "Karl P.",
     "Akira_HentAI",
-    "MagnaInsomnia",
     "Gordon Cole",
     "yuxz69",
-    "Douglas Gaspar",
-    "AlexDuKaNa",
-    "George",
+    "esthe",
     "andrew.tappan",
-    "dw",
     "N/A",
     "The Spawn",
-    "Phil",
     "graysock",
+    "Pozadine1",
     "Greenmoustache",
-    "zounic",
     "fancypants",
+    "IamAyam",
+    "Eldithor",
+    "Joboshy",
     "Digital",
     "JaxMax",
     "takyamtom",
-    "奚明 刘",
+    "Bohemian Corporal",
+    "Dan",
+    "confiscated Zyra",
     "Jwk0205",
     "Bro Xie",
-    "준희 김",
+    "yer fey",
     "batblue",
     "carey6409",
     "Olive",
     "太郎 ゲーム",
+    "Tee Gee",
     "Some Guy Named Barry",
+    "jinxedx",
+    "tarek helmi",
     "Max Marklund",
-    "Tomohiro Baba",
-    "David Ortega",
     "AELOX",
+    "Dankin",
     "Nicfit23",
-    "Noora",
     "wamekukyouzin",
     "drum matthieu",
     "Dogmaster",
     "Matt Wenzel",
-    "Mattssn",
-    "Lex Song",
-    "John Saveas",
+    "Frank Nitty",
+    "Pronredn",
     "Christopher Michel",
     "Serge Bekenkamp",
-    "Jimmy Ledbetter",
+    "DougPeterson",
     "LeoZero",
     "Antonio Pontes",
     "ApathyJones",
     "nahinahi9",
+    "lh qwe",
+    "Kevin John Duck",
+    "conner",
     "Dustin Chen",
     "dan",
-    "Yaboi",
+    "Blackfish95",
     "Mouthlessman",
-    "Steam Steam",
-    "Damon Cunliffe",
-    "CryptoTraderJK",
-    "Davaitamin",
+    "Princess Bright Eyes",
+    "Paul Kroll",
+    "AbstractAss",
     "otaku fra",
-    "Ran C",
-    "tedcor",
-    "Fotek Design",
+    "Felipe dos Santos",
+    "Bas Imagineer",
+    "Markus",
+    "MiraiKuriyamaSy",
     "Adam Taylor",
+    "Douglas Gaspar",
     "Weird_With_A_Beard",
-    "MadSpin",
-    "Pozadine1",
+    "AlexDuKaNa",
+    "George",
+    "dw",
     "Qarob",
     "AIGooner",
-    "inbijiburu",
     "Luc",
     "ProtonPrince",
     "DiffDuck",
     "elu3199",
-    "Nick “Loadstone” D",
     "Hasturkun",
     "Jon Sandman",
     "Ubivis",
@@ -234,54 +232,45 @@
     "thesoftwaredruid",
     "wundershark",
     "mr_dinosaur",
+    "Tyrswood",
     "linnfrey",
-    "Gamalonia",
-    "Vir",
     "Pkrsky",
-    "Joboshy",
-    "Bohemian Corporal",
-    "Dan",
+    "奚明 刘",
     "Josef Lanzl",
-    "Seth Christensen",
+    "Nerezza",
     "Griffin Dahlberg",
-    "Draven T",
-    "yer fey",
+    "준희 김",
     "Error_Rule34_Not_found",
     "Gerald Welly",
     "Roslynd",
     "Geolog",
-    "jinxedx",
     "Neco28",
-    "Aquatic Coffee",
-    "Dankin",
-    "ethanfel",
+    "Tomohiro Baba",
+    "David Ortega",
+    "Noora",
     "Cristian Vazquez",
-    "Frank Nitty",
+    "Mattssn",
     "Magic Noob",
-    "Focuschannel",
-    "DougPeterson",
     "Jeff",
     "Bruce",
-    "Kevin John Duck",
-    "Anthony Faxlandez",
     "Kevin Christopher",
     "Ouro Boros",
-    "Blackfish95",
+    "Chad Idk",
+    "Yaboi",
     "dd",
-    "Paul Kroll",
-    "MiraiKuriyamaSy",
-    "semicolon drainpipe",
-    "Thesharingbrother",
-    "Bas Imagineer",
-    "Pat Hen",
+    "Steam Steam",
+    "CryptoTraderJK",
+    "Davaitamin",
+    "Dušan Ryban",
+    "tedcor",
+    "Fotek Design",
+    "sjon kreutz",
     "John Statham",
-    "ResidentDeviant",
-    "Nihongasuki",
-    "JC",
-    "Prompt Pirate",
-    "uwutismxd",
+    "MadSpin",
+    "Metryman55",
+    "inbijiburu",
     "decoy",
-    "Tyrswood",
+    "Nick “Loadstone” D",
     "Ray Wing",
     "Ranzitho",
     "Gus",
@@ -290,6 +279,7 @@
     "David LaVallee",
     "ae",
     "Tr4shP4nda",
+    "Gamalonia",
     "WRL_SPR",
     "capn",
     "Joseph",
@@ -302,77 +292,60 @@
     "Moon Knight",
     "몽타주",
     "Kland",
-    "zenobeus",
-    "Jackthemind",
-    "ryoma",
-    "Stryker",
-    "raf8osz",
-    "ElitaSSJ4",
-    "blikkies",
-    "Chris",
+    "Hailshem",
+    "kudari",
+    "Naomi Hale Danchi",
+    "dc7431",
+    "Vir",
     "Brian M",
-    "Nerezza",
     "sanborondon",
+    "Seth Christensen",
+    "Draven T",
     "Taylor Funk",
     "aezin",
     "Thought2Form",
     "jcay015",
     "Kevin Picco",
     "Erik Lopez",
-    "Shock Shockor",
     "Mateo Curić",
-    "Goldwaters",
-    "Zude",
+    "Aquatic Coffee",
     "Eris3D",
     "m",
+    "ethanfel",
     "Pierce McBride",
     "Joshua Gray",
-    "Kyler",
+    "Focuschannel",
     "Mikko Hemilä",
-    "aRtFuL_DodGeR",
     "Jamie Ogletree",
     "a _",
     "James Coleman",
-    "CrimsonDX",
     "Martial",
+    "Anthony Faxlandez",
     "battu",
     "Emil Andersson",
-    "Chad Idk",
-    "DarkSunset",
-    "Billy Gladky",
     "Yuji Kaneko",
-    "Probis",
-    "Dušan Ryban",
-    "ItsGeneralButtNaked",
+    "Pat Hen",
+    "semicolon drainpipe",
     "Jordan Shaw",
     "Rops Alot",
+    "Thesharingbrother",
     "Sam",
-    "sjon kreutz",
-    "Nimess",
-    "SRDB",
     "Ace Ventura",
-    "g unit",
-    "Youguang",
-    "Metryman55",
-    "andrewzpong",
-    "FrxzenSnxw",
-    "BossGame",
-    "lrdchs",
+    "ResidentDeviant",
+    "Nihongasuki",
+    "JC",
+    "Prompt Pirate",
+    "uwutismxd",
     "momokai",
-    "Hailshem",
-    "kudari",
-    "Naomi Hale Danchi",
-    "dc7431",
+    "zenobeus",
     "ken",
-    "Inversity",
-    "AIVORY3D",
     "epicgamer0020690",
     "Joshua Porrata",
     "keemun",
     "SuBu",
     "RedPIXel",
-    "Kevinj",
     "Wind",
+    "Jackthemind",
     "Nexus",
     "Ramneek“Guy”Ashok",
     "squid_actually",
@@ -385,80 +358,81 @@
     "emyth",
     "chriphost",
     "KitKatM",
+    "ryoma",
     "socrasteeze",
-    "ResidentDeviant",
+    "OrganicArtifact",
+    "Stryker",
+    "MudkipMedkitz",
     "gzmzmvp",
-    "Welkor",
-    "John Martin",
+    "raf8osz",
+    "ElitaSSJ4",
     "Richard",
+    "blikkies",
     "Andrew",
+    "Chris",
     "Robert Wegemund",
     "Littlehuggy",
-    "moranqianlong",
     "Gregory Kozhemiak",
     "mrjuan",
     "Brian Buie",
+    "Shock Shockor",
     "Sadlip",
-    "Haru Yotu",
+    "Goldwaters",
     "Eric Whitney",
     "Joey Callahan",
+    "Zude",
     "Ivan Tadic",
     "Mike Simone",
+    "John J Linehan",
+    "Kyler",
+    "Elliot E",
     "Morgandel",
-    "Kyron Mahan",
-    "Matura Arbeit",
+    "Theerat Jiramate",
+    "aRtFuL_DodGeR",
     "Noah",
     "Jacob McDaniel",
     "X",
     "Sloan Steddy",
-    "TBitz33",
-    "Anonym dkjglfleeoeldldldlkf",
     "Temikus",
     "Artokun",
     "Michael Taylor",
-    "SendingRavens",
     "Derek Baker",
+    "CrimsonDX",
     "Michael Anthony Scott",
+    "DarkSunset",
     "Atilla Berke Pekduyar",
-    "Michael Docherty",
     "Nathan",
+    "Billy Gladky",
+    "NICHOLAS BAXLEY",
     "Decx _",
-    "Paul Hartsuyker",
-    "elitassj",
-    "Jacob Winter",
+    "Probis",
+    "Ed Wang",
+    "ItsGeneralButtNaked",
+    "Nimess",
+    "SRDB",
+    "g unit",
     "Distortik",
-    "David",
-    "Meilo",
-    "Pen Bouryoung",
+    "Youguang",
     "四糸凜音",
-    "shinonomeiro",
-    "Snille",
-    "MaartenAlbers",
-    "khanh duy",
-    "xybrightsummer",
-    "jreedatchison",
-    "PhilW",
+    "Saya",
+    "andrewzpong",
+    "FrxzenSnxw",
+    "BossGame",
+    "lrdchs",
     "Tree Tagger",
-    "Janik",
+    "Inversity",
     "Crocket",
-    "Cruel",
-    "MRBlack",
+    "AIVORY3D",
+    "Kevinj",
     "Mitchell Robson",
-    "Kiyoe",
-    "humptynutz",
-    "michael.isaza",
-    "Kalnei",
     "Whitepinetrader",
-    "OrganicArtifact",
-    "Scott",
-    "MudkipMedkitz",
+    "ResidentDeviant",
     "deanbrian",
     "POPPIN",
     "Alex Wortman",
     "Cody",
     "Raku",
     "smart.edge5178",
-    "emadsultan",
     "InformedViewz",
     "CHKeeho80",
     "Bubbafett",
@@ -466,76 +440,152 @@
     "Menard",
     "Skyfire83",
     "Adam Rinehart",
-    "D",
     "Pitpe11",
     "TheD1rtyD03",
     "moonpetal",
     "SomeDude",
     "g9p0o",
-    "nanana",
     "TheHolySheep",
     "Monte Won",
     "SpringBootisTrash",
     "carsten",
     "ikok",
+    "Nathen+Choi",
+    "T",
+    "LarsesFPC",
+    "cocona",
+    "sfasdfasfdsa",
     "Buecyb99",
-    "4IXplr0r3r",
-    "dfklsjfkljslfjd",
-    "hayden",
-    "ahoystan",
-    "Leland Saunders",
+    "Welkor",
+    "David Schenck",
+    "John Martin",
     "Wolfe7D1",
     "Ink Temptation",
-    "Bob Barker",
-    "edk",
+    "moranqianlong",
     "Kalli Core",
-    "Aeternyx",
     "elleshar666",
-    "YOU SINWOO",
-    "ja s",
-    "Doug Mason",
+    "ACTUALLY_the_Real_Willem_Dafoe",
+    "Haru Yotu",
     "Kauffy",
-    "Jeremy Townsend",
     "EpicElric",
-    "Sean voets",
-    "Owen Gwosdz",
-    "John J Linehan",
-    "Elliot E",
-    "Thomas Wanner",
-    "Theerat Jiramate",
+    "Kyron Mahan",
     "Edward Kennedy",
     "Justin Blaylock",
-    "Devil Lude",
+    "Matura Arbeit",
     "Nick Kage",
-    "kevin stoddard",
-    "Jack Dole",
+    "TBitz33",
+    "Anonym dkjglfleeoeldldldlkf",
     "Vane Holzer",
     "psytrax",
+    "Cyrus Fett",
     "Ezokewn",
+    "SendingRavens",
     "hexxish",
-    "CptNeo",
     "notedfakes",
-    "Maso",
-    "Eric Ketchum",
-    "NICHOLAS BAXLEY",
+    "Michael Docherty",
     "Michael Scott",
-    "Kevin Wallace",
-    "Matheus Couto",
-    "Saya",
-    "ChicRic",
-    "mercur",
-    "J C",
-    "Ed Wang",
+    "Paul Hartsuyker",
+    "elitassj",
+    "Jacob Winter",
     "Ryan Presley Ng",
     "Wes Sims",
     "Donor4115",
+    "Lyavph",
+    "David",
+    "Meilo",
+    "Filippo Ferrari",
+    "Pen Bouryoung",
+    "shinonomeiro",
+    "Snille",
+    "MaartenAlbers",
+    "khanh duy",
+    "xybrightsummer",
+    "jreedatchison",
+    "PhilW",
+    "Janik",
+    "Cruel",
+    "MRBlack",
+    "Kiyoe",
+    "humptynutz",
+    "michael.isaza",
+    "Kalnei",
+    "Scott",
+    "Muratoraccio",
+    "Ginnie",
+    "emadsultan",
+    "D",
+    "nanana",
+    "Fthehappy",
+    "rsamerica",
+    "Alan+Cano",
+    "FeralOpticsAI",
+    "Pavlaki",
+    "generic404",
+    "Doug+Rintoul",
+    "Noor",
+    "Yorunai",
+    "quantenmecha",
+    "abattoirblues",
+    "Jason+Nash",
+    "BillyBoy84",
+    "zounik",
+    "DarkRoast",
+    "letzte",
+    "Nasty+Hobbit",
+    "Sora+Yori",
+    "lrdchs2",
+    "Duk3+Rand0m",
+    "4IXplr0r3r",
+    "hayden",
+    "ahoystan",
+    "Leland Saunders",
+    "Bob Barker",
+    "edk",
+    "JBsuede",
+    "Time Valentine",
+    "Aeternyx",
+    "YOU SINWOO",
+    "りん あめ",
+    "ja s",
+    "Михал Михалыч",
+    "Matt",
+    "Doug Mason",
+    "Jeremy Townsend",
+    "Frogmilk",
+    "Sean voets",
+    "Owen Gwosdz",
+    "SPJ",
+    "Thomas Wanner",
+    "Bryan Rutkowski",
+    "Devil Lude",
+    "David Murcko",
+    "kevin stoddard",
+    "Jack Dole",
+    "max blo",
+    "Xenon Xue",
+    "CptNeo",
+    "JackJohnnyJim",
+    "Dmitry Ryzhov",
+    "Maso",
+    "Edward Ten Eyck",
+    "Eric Ketchum",
+    "Kevin Wallace",
+    "Matheus Couto",
+    "ChicRic",
+    "Henrique Faiolli",
+    "mercur",
+    "Solixer",
+    "J C",
+    "jinksta187",
+    "Andrew Wilkinson",
+    "Manu Thetug",
+    "Karlanx",
     "Yves Poezevara",
+    "operationancut",
     "Teriak47",
     "Just me",
     "Raf Stahelin",
     "Вячеслав Маринин",
-    "Lyavph",
-    "Filippo Ferrari",
     "Cola Matthew",
     "OniNoKen",
     "Iain Wisely",
@@ -576,98 +626,121 @@
     "dg",
     "Maarten Harms",
     "Israel",
-    "Muratoraccio",
     "SelfishMedic",
-    "Ginnie",
     "adderleighn",
     "EnragedAntelope",
-    "Alan+Cano",
-    "FeralOpticsAI",
-    "Pavlaki",
-    "generic404",
+    "lighthawke",
+    "Terraformer",
+    "GDS+DEV",
+    "4rt+r3d",
+    "low9",
+    "Winged",
+    "you+halo9",
+    "YassineKhaled",
+    "YK12",
+    "MatteKey",
+    "Flob",
+    "ShiroSenpai",
+    "Somebody",
+    "Inkognito",
+    "Somebody",
+    "Gramer+Gumbyte",
+    "Crescent~San",
+    "Tan+Huynh",
+    "AiGirlTS",
+    "D",
+    "datasl4ve",
+    "Somebody",
+    "Dark_Pest",
+    "Aza",
+    "Jacky+Ho",
+    "koopa990",
+    "Karru",
+    "ChaChanoKo",
+    "null",
+    "bo",
+    "The+Forgetful+Dev",
+    "redcarrot",
+    "powerbot99",
     "Mateusz+Kosela",
-    "Doug+Rintoul",
-    "Noor",
-    "Yorunai",
     "Bula",
-    "quantenmecha",
-    "abattoirblues",
-    "Jason+Nash",
-    "BillyBoy84",
-    "DarkRoast",
-    "zounik",
-    "letzte",
-    "Nasty+Hobbit",
-    "SgtFluffles",
-    "lrdchs2",
-    "Duk3+Rand0m",
     "KUJYAKU",
-    "NathenChoi",
-    "Thomas+Reck",
-    "Larses",
-    "cocona",
     "Coeur+de+cochon",
-    "David Schenck",
     "han b",
     "Nico",
     "Banana Joe",
     "_ G3n",
     "Donovan Jenkins",
-    "JBsuede",
+    "Tú Nguyễn Lý Hoàng",
     "Michael Eid",
     "beersandbacon",
     "Maximilian Pyko",
     "Invis",
-    "Justin Houston",
-    "Time Valentine",
+    "Bob barker",
+    "Ben D",
+    "Garrett Wood",
+    "Ronan Delevacq",
     "james",
+    "Christian Schäfer",
     "OrochiNights",
     "Michael Zhu",
-    "ACTUALLY_the_Real_Willem_Dafoe",
     "gonzalo",
     "Seraphy",
-    "Михал Михалыч",
     "雨の心 落",
-    "Matt",
     "AllTimeNoobie",
     "jumpd",
     "John C",
     "Rim",
+    "Dave Abraham",
+    "Joaquin Hierrezuelo",
     "Dismem",
-    "Frogmilk",
-    "SPJ",
+    "Locrospiel",
+    "Jairus Knudsen",
+    "Jarrid Lee",
     "Xan Dionysus",
     "Nathan lee",
+    "Kor",
+    "Joseph Hanson",
     "Mewtora",
     "Middo",
     "Forbidden Atelier",
-    "Bryan Rutkowski",
+    "John Rednoulf",
+    "Spire",
     "Adictedtohumping",
+    "Boba Smith",
     "Towelie",
-    "Cyrus Fett",
+    "MR.Bear",
+    "dsffsdfsdfsdfsdfsdf",
     "Jean-françois SEMA",
     "Kurt",
-    "max blo",
-    "Xenon Xue",
-    "JackJohnnyJim",
-    "Edward Ten Eyck",
+    "ivistorm",
+    "Sauv",
+    "Steven",
+    "TenaciousD",
+    "Khánh Đặng",
     "Chase Kwon",
+    "Ted Cart",
     "Inyoshu",
     "Goober719",
     "Chad Barnes",
+    "Person Y",
+    "David Spearing",
     "James Ming",
     "vanditking",
     "kripitonga",
     "Rizzi",
     "nimin",
     "OMAR LUCIANO",
+    "Ken+Suzuki",
     "hannibal",
     "Jo+Example",
     "BrentBertram",
+    "Tigon",
     "eumelzocker",
     "dxjaymz",
     "L C",
-    "Dude"
+    "Dude",
+    "CK"
   ],
-  "totalCount": 666
+  "totalCount": 739
 }

locales/de.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "Modelliste aktualisieren",
-                "quick": "Änderungen synchronisieren",
-                "quickTooltip": "Nach neuen oder fehlenden Modelldateien suchen, damit die Liste aktuell bleibt.",
                 "full": "Cache neu aufbauen",
                 "fullTooltip": "Alle Modelldetails aus Metadatendateien neu laden – nutzen, wenn die Bibliothek veraltet wirkt oder nach manuellen Änderungen."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "Automatisch organisieren",
             "skipMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle überspringen",
             "resumeMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle fortsetzen",
+            "setFavorite": "Als Favorit setzen",
+            "setFavoriteCount": "Als Favorit setzen ({favorited}/{total})",
+            "unfavorite": "Aus Favoriten entfernen",
             "deleteAll": "Ausgewählte löschen",
             "downloadMissingLoras": "Fehlende LoRAs herunterladen",
+            "downloadExamples": "Beispielbilder herunterladen",
             "clear": "Auswahl löschen",
             "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
             "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
+            "sendToWorkflow": "An Workflow senden",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Metadaten",
+                "attributes": "Attribute",
+                "organize": "Organisieren",
+                "download": "Download"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Automatische Organisation wird initialisiert...",
                 "starting": "Automatische Organisation für {type} wird gestartet...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "Rezeptliste aktualisieren",
-                "quick": "Änderungen synchronisieren",
-                "quickTooltip": "Änderungen synchronisieren - schnelle Aktualisierung ohne Cache-Neubau",
                 "full": "Cache neu aufbauen",
                 "fullTooltip": "Cache neu aufbauen - vollständiger Rescan aller Rezeptdateien"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "Inhaltsbewertung auf {level} für {count} Modell(e) gesetzt",
             "bulkContentRatingPartial": "Inhaltsbewertung auf {level} für {success} Modell(e) gesetzt, {failed} fehlgeschlagen",
             "bulkContentRatingFailed": "Inhaltsbewertung für ausgewählte Modelle konnte nicht aktualisiert werden",
+            "bulkFavoriteUpdating": "Füge {count} Modell(e) zu Favoriten hinzu...",
+            "bulkUnfavoriteUpdating": "Entferne {count} Modell(e) aus Favoriten...",
+            "bulkFavoritePartialAdded": "{success} Modell(e) zu Favoriten hinzugefügt, {failed} fehlgeschlagen",
+            "bulkFavoritePartialRemoved": "{success} Modell(e) aus Favoriten entfernt, {failed} fehlgeschlagen",
+            "bulkFavoriteFailed": "Fehler beim Aktualisieren des Favoritenstatus",
             "bulkUpdatesChecking": "Ausgewählte {type}-Modelle werden auf Updates geprüft...",
             "bulkUpdatesSuccess": "Updates für {count} ausgewählte {type}-Modelle verfügbar",
             "bulkUpdatesNone": "Keine Updates für ausgewählte {type}-Modelle gefunden",

locales/en.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "Refresh model list",
-                "quick": "Sync Changes",
-                "quickTooltip": "Scan for new or missing model files so the list stays current.",
                 "full": "Rebuild Cache",
                 "fullTooltip": "Reload all model details from metadata files—use if the library looks out of date or after manual edits."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "Auto-Organize Selected",
             "skipMetadataRefresh": "Skip Metadata Refresh for Selected",
             "resumeMetadataRefresh": "Resume Metadata Refresh for Selected",
+            "setFavorite": "Set as Favorite",
+            "setFavoriteCount": "Set as Favorite ({favorited}/{total})",
+            "unfavorite": "Remove from Favorites",
             "deleteAll": "Delete Selected",
             "downloadMissingLoras": "Download Missing LoRAs",
+            "downloadExamples": "Download Example Images",
             "clear": "Clear Selection",
             "skipMetadataRefreshCount": "Skip ({count} models)",
             "resumeMetadataRefreshCount": "Resume ({count} models)",
+            "sendToWorkflow": "Send to Workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Metadata",
+                "attributes": "Attributes",
+                "organize": "Organize",
+                "download": "Download"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Initializing auto-organize...",
                 "starting": "Starting auto-organize for {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "Refresh recipe list",
-                "quick": "Sync Changes",
-                "quickTooltip": "Sync changes - quick refresh without rebuilding cache",
                 "full": "Rebuild Cache",
                 "fullTooltip": "Rebuild cache - full rescan of all recipe files"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "Set content rating to {level} for {count} model(s)",
             "bulkContentRatingPartial": "Set content rating to {level} for {success} model(s), {failed} failed",
             "bulkContentRatingFailed": "Failed to update content rating for selected models",
+            "bulkFavoriteUpdating": "Adding {count} model(s) to favorites...",
+            "bulkUnfavoriteUpdating": "Removing {count} model(s) from favorites...",
+            "bulkFavoritePartialAdded": "Added {success} model(s) to favorites, {failed} failed",
+            "bulkFavoritePartialRemoved": "Removed {success} model(s) from favorites, {failed} failed",
+            "bulkFavoriteFailed": "Failed to update favorite status for selected models",
             "bulkUpdatesChecking": "Checking selected {type}(s) for updates...",
             "bulkUpdatesSuccess": "Updates available for {count} selected {type}(s)",
             "bulkUpdatesNone": "No updates found for selected {type}(s)",

locales/es.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "Actualizar lista de modelos",
-                "quick": "Sincronizar cambios",
-                "quickTooltip": "Busca archivos de modelo nuevos o faltantes para mantener la lista al día.",
                 "full": "Reconstruir caché",
                 "fullTooltip": "Vuelve a cargar todos los detalles desde los archivos de metadatos; úsalo si la biblioteca parece desactualizada o tras ediciones manuales."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "Auto-organizar seleccionados",
             "skipMetadataRefresh": "Omitir actualización de metadatos para seleccionados",
             "resumeMetadataRefresh": "Reanudar actualización de metadatos para seleccionados",
+            "setFavorite": "Marcar como favorito",
+            "setFavoriteCount": "Marcar como favorito ({favorited}/{total})",
+            "unfavorite": "Quitar de favoritos",
             "deleteAll": "Eliminar seleccionados",
             "downloadMissingLoras": "Descargar LoRAs faltantes",
+            "downloadExamples": "Descargar imágenes de ejemplo",
             "clear": "Limpiar selección",
             "skipMetadataRefreshCount": "Omitir（{count} modelos）",
             "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
+            "sendToWorkflow": "Enviar al workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Metadatos",
+                "attributes": "Atributos",
+                "organize": "Organizar",
+                "download": "Descargar"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Inicializando auto-organización...",
                 "starting": "Iniciando auto-organización para {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "Actualizar lista de recetas",
-                "quick": "Sincronizar cambios",
-                "quickTooltip": "Sincronizar cambios - actualización rápida sin reconstruir caché",
                 "full": "Reconstruir caché",
                 "fullTooltip": "Reconstruir caché - reescaneo completo de todos los archivos de recetas"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "Clasificación de contenido establecida en {level} para {count} modelo(s)",
             "bulkContentRatingPartial": "Clasificación de contenido establecida en {level} para {success} modelo(s), {failed} fallaron",
             "bulkContentRatingFailed": "No se pudo actualizar la clasificación de contenido para los modelos seleccionados",
+            "bulkFavoriteUpdating": "Añadiendo {count} modelo(s) a favoritos...",
+            "bulkUnfavoriteUpdating": "Eliminando {count} modelo(s) de favoritos...",
+            "bulkFavoritePartialAdded": "{success} modelo(s) añadido(s) a favoritos, {failed} fallido(s)",
+            "bulkFavoritePartialRemoved": "{success} modelo(s) eliminado(s) de favoritos, {failed} fallido(s)",
+            "bulkFavoriteFailed": "Error al actualizar el estado de favorito",
             "bulkUpdatesChecking": "Comprobando actualizaciones para {type} seleccionados...",
             "bulkUpdatesSuccess": "Actualizaciones disponibles para {count} {type} seleccionados",
             "bulkUpdatesNone": "No se encontraron actualizaciones para los {type} seleccionados",

locales/fr.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "Actualiser la liste des modèles",
-                "quick": "Synchroniser les changements",
-                "quickTooltip": "Analyse les nouveaux fichiers de modèle ou les fichiers manquants pour garder la liste à jour.",
                 "full": "Reconstruire le cache",
                 "fullTooltip": "Recharge tous les détails des modèles depuis les fichiers metadata — à utiliser si la bibliothèque paraît obsolète ou après des modifications manuelles."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "Auto-organiser la sélection",
             "skipMetadataRefresh": "Ignorer l'actualisation des métadonnées pour la sélection",
             "resumeMetadataRefresh": "Reprendre l'actualisation des métadonnées pour la sélection",
+            "setFavorite": "Définir comme favori",
+            "setFavoriteCount": "Définir comme favori ({favorited}/{total})",
+            "unfavorite": "Retirer des favoris",
             "deleteAll": "Supprimer la sélection",
             "downloadMissingLoras": "Télécharger les LoRAs manquants",
+            "downloadExamples": "Télécharger les images d'exemple",
             "clear": "Effacer la sélection",
             "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
             "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
+            "sendToWorkflow": "Envoyer au workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Métadonnées",
+                "attributes": "Attributs",
+                "organize": "Organiser",
+                "download": "Télécharger"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Initialisation de l'auto-organisation...",
                 "starting": "Démarrage de l'auto-organisation pour {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "Actualiser la liste des recipes",
-                "quick": "Synchroniser les changements",
-                "quickTooltip": "Synchroniser les changements - actualisation rapide sans reconstruire le cache",
                 "full": "Reconstruire le cache",
                 "fullTooltip": "Reconstruire le cache - rescan complet de tous les fichiers de recipes"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "Classification du contenu définie sur {level} pour {count} modèle(s)",
             "bulkContentRatingPartial": "Classification du contenu définie sur {level} pour {success} modèle(s), {failed} échec(s)",
             "bulkContentRatingFailed": "Impossible de mettre à jour la classification du contenu pour les modèles sélectionnés",
+            "bulkFavoriteUpdating": "Ajout de {count} modèle(s) aux favoris...",
+            "bulkUnfavoriteUpdating": "Suppression de {count} modèle(s) des favoris...",
+            "bulkFavoritePartialAdded": "{success} modèle(s) ajouté(s) aux favoris, {failed} échec(s)",
+            "bulkFavoritePartialRemoved": "{success} modèle(s) retiré(s) des favoris, {failed} échec(s)",
+            "bulkFavoriteFailed": "Échec de la mise à jour du statut de favori",
             "bulkUpdatesChecking": "Vérification des mises à jour pour les {type} sélectionnés...",
             "bulkUpdatesSuccess": "Mises à jour disponibles pour {count} {type} sélectionnés",
             "bulkUpdatesNone": "Aucune mise à jour trouvée pour les {type} sélectionnés",

locales/he.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "רענן רשימת מודלים",
-                "quick": "סנכרון שינויים",
-                "quickTooltip": "סריקה לאיתור קבצי מודל חדשים או חסרים כדי לשמור את הרשימה מעודכנת.",
                 "full": "בניית מטמון מחדש",
                 "fullTooltip": "טוען מחדש את כל פרטי המודלים מקבצי המטא-דאטה – לשימוש אם הספרייה נראית לא מעודכנת או לאחר עריכות ידניות."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "ארגן אוטומטית נבחרים",
             "skipMetadataRefresh": "דילוג על רענון מטא-נתונים לנבחרים",
             "resumeMetadataRefresh": "המשך רענון מטא-נתונים לנבחרים",
+            "setFavorite": "הגדר כמועדף",
+            "setFavoriteCount": "הגדר כמועדף ({favorited}/{total})",
+            "unfavorite": "הסר ממועדפים",
             "deleteAll": "מחק נבחרים",
             "downloadMissingLoras": "הורדת LoRAs חסרים",
+            "downloadExamples": "הורד תמונות דוגמה",
             "clear": "נקה בחירה",
             "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
             "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
+            "sendToWorkflow": "שלח ל-Workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "מטא-נתונים",
+                "attributes": "מאפיינים",
+                "organize": "ארגן",
+                "download": "הורדה"
+            },
             "autoOrganizeProgress": {
                 "initializing": "מאתחל ארגון אוטומטי...",
                 "starting": "מתחיל ארגון אוטומטי עבור {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "רענן רשימת מתכונים",
-                "quick": "סנכרן שינויים",
-                "quickTooltip": "סנכרן שינויים - רענון מהיר ללא בניית מטמון מחדש",
                 "full": "בנה מטמון מחדש",
                 "fullTooltip": "בנה מטמון מחדש - סריקה מחדש מלאה של כל קבצי המתכונים"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "דירוג התוכן הוגדר ל-{level} עבור {count} מודלים",
             "bulkContentRatingPartial": "דירוג התוכן הוגדר ל-{level} עבור {success} מודלים, {failed} נכשלו",
             "bulkContentRatingFailed": "עדכון דירוג התוכן עבור המודלים שנבחרו נכשל",
+            "bulkFavoriteUpdating": "מוסיף {count} דגמים למועדפים...",
+            "bulkUnfavoriteUpdating": "מסיר {count} דגמים ממועדפים...",
+            "bulkFavoritePartialAdded": "{success} דגמים נוספו למועדפים, {failed} נכשלו",
+            "bulkFavoritePartialRemoved": "{success} דגמים הוסרו ממועדפים, {failed} נכשלו",
+            "bulkFavoriteFailed": "עדכון סטטוס מועדפים נכשל",
             "bulkUpdatesChecking": "בודק עדכונים עבור {type} שנבחרו...",
             "bulkUpdatesSuccess": "יש עדכונים עבור {count} {type} שנבחרו",
             "bulkUpdatesNone": "לא נמצאו עדכונים עבור {type} שנבחרו",

locales/ja.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "モデルリストを更新",
-                "quick": "変更を同期",
-                "quickTooltip": "新しいモデルファイルや欠けているファイルをスキャンして一覧を最新に保ちます。",
                 "full": "キャッシュを再構築",
                 "fullTooltip": "メタデータファイルから全モデル情報を再読み込みします。リストが古いと感じるときや手動編集後に使用してください。"
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "自動整理を実行",
             "skipMetadataRefresh": "選択したモデルのメタデータ更新をスキップ",
             "resumeMetadataRefresh": "選択したモデルのメタデータ更新を再開",
+            "setFavorite": "お気に入りに設定",
+            "setFavoriteCount": "お気に入りに設定 ({favorited}/{total})",
+            "unfavorite": "お気に入りから削除",
             "deleteAll": "選択したものを削除",
             "downloadMissingLoras": "不足している LoRA をダウンロード",
+            "downloadExamples": "例画像をダウンロード",
             "clear": "選択をクリア",
             "skipMetadataRefreshCount": "スキップ（{count}モデル）",
             "resumeMetadataRefreshCount": "再開（{count}モデル）",
+            "sendToWorkflow": "ワークフローに送信",
+            "sections": {
+                "workflow": "ワークフロー",
+                "metadata": "メタデータ",
+                "attributes": "属性",
+                "organize": "整理",
+                "download": "ダウンロード"
+            },
             "autoOrganizeProgress": {
                 "initializing": "自動整理を初期化中...",
                 "starting": "{type}の自動整理を開始中...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "レシピリストを更新",
-                "quick": "変更を同期",
-                "quickTooltip": "変更を同期 - キャッシュを再構築せずにクイック更新",
                 "full": "キャッシュを再構築",
                 "fullTooltip": "キャッシュを再構築 - すべてのレシピファイルを完全に再スキャン"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "{count} 件のモデルのコンテンツレーティングを {level} に設定しました",
             "bulkContentRatingPartial": "{success} 件のモデルのコンテンツレーティングを {level} に設定、{failed} 件は失敗しました",
             "bulkContentRatingFailed": "選択したモデルのコンテンツレーティングを更新できませんでした",
+            "bulkFavoriteUpdating": "{count} 個のモデルをお気に入りに追加中...",
+            "bulkUnfavoriteUpdating": "{count} 個のモデルをお気に入りから削除中...",
+            "bulkFavoritePartialAdded": "{success} 個のモデルをお気に入りに追加、{failed} 個失敗",
+            "bulkFavoritePartialRemoved": "{success} 個のモデルをお気に入りから削除、{failed} 個失敗",
+            "bulkFavoriteFailed": "お気に入り状態の更新に失敗しました",
             "bulkUpdatesChecking": "選択された{type}の更新を確認しています...",
             "bulkUpdatesSuccess": "{count} 件の選択された{type}に利用可能な更新があります",
             "bulkUpdatesNone": "選択された{type}には更新が見つかりませんでした",

locales/ko.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "모델 목록 새로고침",
-                "quick": "변경 사항 동기화",
-                "quickTooltip": "새로운 모델 파일이나 누락된 파일을 찾아 목록을 최신 상태로 유지합니다.",
                 "full": "캐시 재구성",
                 "fullTooltip": "메타데이터 파일에서 모든 모델 정보를 다시 불러옵니다. 라이브러리가 오래되어 보이거나 수동 수정 후에 사용하세요."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "자동 정리 선택",
             "skipMetadataRefresh": "선택한 모델의 메타데이터 새로고침 건너뛰기",
             "resumeMetadataRefresh": "선택한 모델의 메타데이터 새로고침 재개",
+            "setFavorite": "즐겨찾기로 설정",
+            "setFavoriteCount": "즐겨찾기로 설정 ({favorited}/{total})",
+            "unfavorite": "즐겨찾기 해제",
             "deleteAll": "선택된 항목 삭제",
             "downloadMissingLoras": "누락된 LoRA 다운로드",
+            "downloadExamples": "예시 이미지 다운로드",
             "clear": "선택 지우기",
             "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
             "resumeMetadataRefreshCount": "재개（{count}개 모델）",
+            "sendToWorkflow": "워크플로우로 보내기",
+            "sections": {
+                "workflow": "워크플로우",
+                "metadata": "메타데이터",
+                "attributes": "속성",
+                "organize": "정리",
+                "download": "다운로드"
+            },
             "autoOrganizeProgress": {
                 "initializing": "자동 정리 초기화 중...",
                 "starting": "{type}에 대한 자동 정리 시작...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "레시피 목록 새로고침",
-                "quick": "변경 사항 동기화",
-                "quickTooltip": "변경 사항 동기화 - 캐시를 재구성하지 않고 빠른 새로고침",
                 "full": "캐시 재구성",
                 "fullTooltip": "캐시 재구성 - 모든 레시피 파일을 완전히 다시 스캔"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "{count}개 모델의 콘텐츠 등급을 {level}(으)로 설정했습니다",
             "bulkContentRatingPartial": "{success}개 모델의 콘텐츠 등급을 {level}(으)로 설정했고, {failed}개는 실패했습니다",
             "bulkContentRatingFailed": "선택한 모델의 콘텐츠 등급을 업데이트하지 못했습니다",
+            "bulkFavoriteUpdating": "{count}개 모델을 즐겨찾기에 추가 중...",
+            "bulkUnfavoriteUpdating": "{count}개 모델을 즐겨찾기에서 제거 중...",
+            "bulkFavoritePartialAdded": "{success}개 모델을 즐겨찾기에 추가, {failed}개 실패",
+            "bulkFavoritePartialRemoved": "{success}개 모델을 즐겨찾기에서 제거, {failed}개 실패",
+            "bulkFavoriteFailed": "즐겨찾기 상태 업데이트 실패",
             "bulkUpdatesChecking": "선택한 {type}의 업데이트를 확인하는 중...",
             "bulkUpdatesSuccess": "선택한 {count}개의 {type}에 사용할 수 있는 업데이트가 있습니다",
             "bulkUpdatesNone": "선택한 {type}에 대한 업데이트가 없습니다",

locales/ru.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "Обновить список моделей",
-                "quick": "Синхронизировать изменения",
-                "quickTooltip": "Находит новые или отсутствующие файлы моделей, чтобы список оставался актуальным.",
                 "full": "Перестроить кэш",
                 "fullTooltip": "Перечитывает все данные моделей из файлов метаданных — используйте, если библиотека выглядит устаревшей или после ручных правок."
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "Автоматически организовать выбранные",
             "skipMetadataRefresh": "Пропустить обновление метаданных для выбранных",
             "resumeMetadataRefresh": "Возобновить обновление метаданных для выбранных",
+            "setFavorite": "Добавить в избранное",
+            "setFavoriteCount": "Добавить в избранное ({favorited}/{total})",
+            "unfavorite": "Удалить из избранного",
             "deleteAll": "Удалить выбранные",
             "downloadMissingLoras": "Скачать отсутствующие LoRAs",
+            "downloadExamples": "Загрузить примеры изображений",
             "clear": "Очистить выбор",
             "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
             "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
+            "sendToWorkflow": "Отправить в Workflow",
+            "sections": {
+                "workflow": "Workflow",
+                "metadata": "Метаданные",
+                "attributes": "Атрибуты",
+                "organize": "Организовать",
+                "download": "Скачать"
+            },
             "autoOrganizeProgress": {
                 "initializing": "Инициализация автоматической организации...",
                 "starting": "Запуск автоматической организации для {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "Обновить список рецептов",
-                "quick": "Синхронизировать изменения",
-                "quickTooltip": "Синхронизировать изменения - быстрое обновление без перестроения кэша",
                 "full": "Перестроить кэш",
                 "fullTooltip": "Перестроить кэш - полное повторное сканирование всех файлов рецептов"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "Рейтинг контента установлен на {level} для {count} модель(ей)",
             "bulkContentRatingPartial": "Рейтинг контента {level} установлен для {success} модель(ей), {failed} не удалось",
             "bulkContentRatingFailed": "Не удалось обновить рейтинг контента для выбранных моделей",
+            "bulkFavoriteUpdating": "Добавление {count} моделей в избранное...",
+            "bulkUnfavoriteUpdating": "Удаление {count} моделей из избранного...",
+            "bulkFavoritePartialAdded": "{success} моделей добавлено в избранное, {failed} не удалось",
+            "bulkFavoritePartialRemoved": "{success} моделей удалено из избранного, {failed} не удалось",
+            "bulkFavoriteFailed": "Не удалось обновить статус избранного",
             "bulkUpdatesChecking": "Проверка обновлений для выбранных {type}...",
             "bulkUpdatesSuccess": "Доступны обновления для {count} выбранных {type}",
             "bulkUpdatesNone": "Обновления для выбранных {type} не найдены",

locales/zh-CN.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "刷新模型列表",
-                "quick": "同步变更",
-                "quickTooltip": "扫描新的或缺失的模型文件，保持列表最新。",
                 "full": "重建缓存",
                 "fullTooltip": "从元数据文件重新加载所有模型信息；用于列表过时或手动编辑后。"
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "自动整理所选模型",
             "skipMetadataRefresh": "跳过所选模型的元数据刷新",
             "resumeMetadataRefresh": "恢复所选模型的元数据刷新",
+            "setFavorite": "设为收藏",
+            "setFavoriteCount": "设为收藏 ({favorited}/{total})",
+            "unfavorite": "取消收藏",
             "deleteAll": "删除已选",
             "downloadMissingLoras": "下载缺失的 LoRAs",
+            "downloadExamples": "下载示例图片",
             "clear": "清除选择",
             "skipMetadataRefreshCount": "跳过（{count} 个模型）",
             "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
+            "sendToWorkflow": "发送到工作流",
+            "sections": {
+                "workflow": "工作流",
+                "metadata": "元数据",
+                "attributes": "属性",
+                "organize": "整理",
+                "download": "下载"
+            },
             "autoOrganizeProgress": {
                 "initializing": "正在初始化自动整理...",
                 "starting": "正在为 {type} 启动自动整理...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "刷新配方列表",
-                "quick": "同步变更",
-                "quickTooltip": "同步变更 - 快速刷新而不重建缓存",
                 "full": "重建缓存",
                 "fullTooltip": "重建缓存 - 重新扫描所有配方文件"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "已将 {count} 个模型的内容评级设置为 {level}",
             "bulkContentRatingPartial": "已将 {success} 个模型的内容评级设置为 {level}，{failed} 个失败",
             "bulkContentRatingFailed": "未能更新所选模型的内容评级",
+            "bulkFavoriteUpdating": "正在将 {count} 个模型添加到收藏...",
+            "bulkUnfavoriteUpdating": "正在将 {count} 个模型从收藏移除...",
+            "bulkFavoritePartialAdded": "已将 {success} 个模型添加到收藏，{failed} 个失败",
+            "bulkFavoritePartialRemoved": "已将 {success} 个模型从收藏移除，{failed} 个失败",
+            "bulkFavoriteFailed": "更新收藏状态失败",
             "bulkUpdatesChecking": "正在检查所选 {type} 的更新...",
             "bulkUpdatesSuccess": "{count} 个所选 {type} 有可用更新",
             "bulkUpdatesNone": "所选 {type} 未发现更新",

locales/zh-TW.json

@@ -640,8 +640,6 @@
             },
             "refresh": {
                 "title": "重新整理模型列表",
-                "quick": "同步變更",
-                "quickTooltip": "掃描新的或缺少的模型檔案，讓清單保持最新。",
                 "full": "重建快取",
                 "fullTooltip": "從中繼資料檔重新載入所有模型資訊；適用於清單過時或手動編輯後。"
             },
@@ -687,11 +685,23 @@
             "autoOrganize": "自動整理所選模型",
             "skipMetadataRefresh": "跳過所選模型的元數據更新",
             "resumeMetadataRefresh": "恢復所選模型的元數據更新",
+            "setFavorite": "設為收藏",
+            "setFavoriteCount": "設為收藏 ({favorited}/{total})",
+            "unfavorite": "取消收藏",
             "deleteAll": "刪除所選",
             "downloadMissingLoras": "下載缺失的 LoRAs",
+            "downloadExamples": "下載範例圖片",
             "clear": "清除選取",
             "skipMetadataRefreshCount": "跳過（{count} 個模型）",
             "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
+            "sendToWorkflow": "發送到工作流",
+            "sections": {
+                "workflow": "工作流",
+                "metadata": "元數據",
+                "attributes": "屬性",
+                "organize": "整理",
+                "download": "下載"
+            },
             "autoOrganizeProgress": {
                 "initializing": "正在初始化自動整理...",
                 "starting": "正在開始自動整理 {type}...",
@@ -804,8 +814,6 @@
             },
             "refresh": {
                 "title": "重新整理配方列表",
-                "quick": "同步變更",
-                "quickTooltip": "同步變更 - 快速重新整理而不重建快取",
                 "full": "重建快取",
                 "fullTooltip": "重建快取 - 重新掃描所有配方檔案"
             },
@@ -1699,6 +1707,11 @@
             "bulkContentRatingSet": "已將 {count} 個模型的內容分級設定為 {level}",
             "bulkContentRatingPartial": "已將 {success} 個模型的內容分級設定為 {level}，{failed} 個失敗",
             "bulkContentRatingFailed": "無法更新所選模型的內容分級",
+            "bulkFavoriteUpdating": "正在將 {count} 個模型加入收藏...",
+            "bulkUnfavoriteUpdating": "正在將 {count} 個模型從收藏移除...",
+            "bulkFavoritePartialAdded": "已將 {success} 個模型加入收藏，{failed} 個失敗",
+            "bulkFavoritePartialRemoved": "已將 {success} 個模型從收藏移除，{failed} 個失敗",
+            "bulkFavoriteFailed": "更新收藏狀態失敗",
             "bulkUpdatesChecking": "正在檢查所選 {type} 的更新...",
             "bulkUpdatesSuccess": "{count} 個所選 {type} 有可用更新",
             "bulkUpdatesNone": "所選 {type} 未找到更新",

py/config.py

@@ -172,6 +172,12 @@ class Config:
         self.extra_unet_roots: List[str] = []
         self.extra_embeddings_roots: List[str] = []
         self.recipes_path: str = ""
+
+        # Load extra folder paths from active library settings before symlink scan
+        # so both primary and extra paths are discovered in a single pass.
+        if not standalone_mode:
+            self._load_extra_paths_from_settings()
+
         # Scan symbolic links during initialization
         self._initialize_symlink_mappings()
 
@@ -179,6 +185,96 @@ class Config:
             # Save the paths to settings.json when running in ComfyUI mode
             self.save_folder_paths_to_settings()
 
+    def _load_extra_paths_from_settings(self) -> None:
+        """Read extra folder paths from the active library and apply them.
+
+        Called during ``Config.__init__`` before the symlink scan so both primary and
+        extra paths are discovered in a single pass.  Mirrors the extra-path
+        portion of ``_apply_library_paths`` without replacing the primary roots
+        that were already resolved from ComfyUI's ``folder_paths``.
+        """
+        try:
+            from .services.settings_manager import get_settings_manager
+
+            settings_manager = get_settings_manager()
+            library_name = settings_manager.get_active_library_name()
+            libraries = settings_manager.get_libraries()
+
+            if not library_name or library_name not in libraries:
+                return
+
+            library_config = libraries[library_name]
+            if not isinstance(library_config, dict):
+                return
+
+            extra_folder_paths = library_config.get("extra_folder_paths")
+            if not isinstance(extra_folder_paths, dict):
+                return
+
+            extra_lora = extra_folder_paths.get("loras", []) or []
+            extra_checkpoint = extra_folder_paths.get("checkpoints", []) or []
+            extra_unet = extra_folder_paths.get("unet", []) or []
+            extra_embedding = extra_folder_paths.get("embeddings", []) or []
+
+            if not any([extra_lora, extra_checkpoint, extra_unet, extra_embedding]):
+                return
+
+            filtered_extra_lora = self._filter_overlapping_extra_lora_paths(
+                self.loras_roots, extra_lora
+            )
+            self.extra_loras_roots = self._prepare_lora_paths(filtered_extra_lora)
+            (
+                _,
+                self.extra_checkpoints_roots,
+                self.extra_unet_roots,
+            ) = self._prepare_checkpoint_paths(extra_checkpoint, extra_unet)
+            self.extra_embeddings_roots = self._prepare_embedding_paths(
+                extra_embedding
+            )
+
+            recipes_path = library_config.get("recipes_path", "")
+            if isinstance(recipes_path, str) and recipes_path:
+                self.recipes_path = recipes_path
+
+            if self.extra_loras_roots:
+                logger.info(
+                    "Found extra LoRA roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_loras_roots)
+                )
+            if self.extra_checkpoints_roots:
+                logger.info(
+                    "Found extra checkpoint roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_checkpoints_roots)
+                )
+            if self.extra_unet_roots:
+                logger.info(
+                    "Found extra diffusion model roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_unet_roots)
+                )
+            if self.extra_embeddings_roots:
+                logger.info(
+                    "Found extra embedding roots:"
+                    + "\n - "
+                    + "\n - ".join(self.extra_embeddings_roots)
+                )
+
+            logger.info(
+                "Applied library settings for '%s' with extra paths: loras=%s, "
+                "checkpoints=%s, embeddings=%s",
+                library_name,
+                extra_lora,
+                extra_checkpoint,
+                extra_embedding,
+            )
+
+        except Exception as exc:
+            logger.debug(
+                "Could not load extra paths from library settings: %s", exc
+            )
+
     def save_folder_paths_to_settings(self):
         """Persist ComfyUI-derived folder paths to the multi-library settings."""
         try:

py/lora_manager.py

@@ -184,39 +184,6 @@ class LoraManager:
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            # Apply library settings to load extra folder paths before scanning
-            # Only apply if extra paths haven't been loaded yet (preserves test mocks)
-            try:
-                from .services.settings_manager import get_settings_manager
-
-                settings_manager = get_settings_manager()
-                library_name = settings_manager.get_active_library_name()
-                libraries = settings_manager.get_libraries()
-                if library_name and library_name in libraries:
-                    library_config = libraries[library_name]
-                    # Only apply settings if extra paths are not already configured
-                    # This preserves values set by tests via monkeypatch
-                    extra_paths = library_config.get("extra_folder_paths", {})
-                    has_extra_paths = (
-                        config.extra_loras_roots
-                        or config.extra_checkpoints_roots
-                        or config.extra_unet_roots
-                        or config.extra_embeddings_roots
-                    )
-                    if not has_extra_paths and any(extra_paths.values()):
-                        config.apply_library_settings(library_config)
-                        logger.info(
-                            "Applied library settings for '%s' with extra paths: loras=%s, checkpoints=%s, embeddings=%s",
-                            library_name,
-                            extra_paths.get("loras", []),
-                            extra_paths.get("checkpoints", []),
-                            extra_paths.get("embeddings", []),
-                        )
-            except Exception as exc:
-                logger.warning(
-                    "Failed to apply library settings during initialization: %s", exc
-                )
-
             # Initialize CivitaiClient first to ensure it's ready for other services
             await ServiceRegistry.get_civitai_client()
 

py/recipes/enrichment.py

@@ -16,7 +16,9 @@ class RecipeEnricher:
     async def enrich_recipe(
         recipe: Dict[str, Any],
         civitai_client: Any,
-        request_params: Optional[Dict[str, Any]] = None
+        request_params: Optional[Dict[str, Any]] = None,
+        prefetched_civitai_meta_raw: Optional[Dict[str, Any]] = None,
+        prefetched_model_version_id: Optional[int] = None,
     ) -> bool:
         """
         Enrich a recipe dictionary in-place with metadata from Civitai and embedded params.
@@ -25,6 +27,9 @@ class RecipeEnricher:
             recipe: The recipe dictionary to enrich. Must have 'gen_params' initialized.
             civitai_client: Authenticated Civitai client instance.
             request_params: (Optional) Parameters from a user request (e.g. import).
+            prefetched_civitai_meta_raw: (Optional) Pre-fetched raw meta from Civitai
+                get_image_info, avoiding a duplicate API call.
+            prefetched_model_version_id: (Optional) Pre-fetched model version ID.
 
         Returns:
             bool: True if the recipe was modified, False otherwise.
@@ -32,21 +37,27 @@ class RecipeEnricher:
         updated = False
         gen_params = recipe.get("gen_params", {})
 
-        # 1. Fetch Civitai Info if available
+        # 1. Obtain Civitai metadata
         civitai_meta = None
-        model_version_id = None
+        model_version_id = prefetched_model_version_id
 
-        source_url = recipe.get("source_url") or recipe.get("source_path", "")
+        source_path = recipe.get("source_path", "")
 
-        # Check if it's a Civitai image URL
-        image_id = extract_civitai_image_id(str(source_url))
+        if prefetched_civitai_meta_raw is not None:
+            raw_meta = prefetched_civitai_meta_raw
+            if isinstance(raw_meta, dict):
+                if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
+                    civitai_meta = raw_meta["meta"]
+                else:
+                    civitai_meta = raw_meta
+        else:
+            image_id = extract_civitai_image_id(str(source_path))
             if image_id:
                 try:
                     image_info = await civitai_client.get_image_info(
-                    image_id, source_url=str(source_url)
+                        image_id, source_url=str(source_path)
                     )
                     if image_info:
-                    # Handle nested meta often found in Civitai API responses
                         raw_meta = image_info.get("meta")
                         if isinstance(raw_meta, dict):
                             if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
@@ -55,16 +66,15 @@ class RecipeEnricher:
                                 civitai_meta = raw_meta
 
                         model_version_id = image_info.get("modelVersionId")
+                except Exception as e:
+                    logger.warning(f"Failed to fetch Civitai image info: {e}")
 
-                    # If not at top level, check resources in meta
         if not model_version_id and civitai_meta:
             resources = civitai_meta.get("civitaiResources", [])
             for res in resources:
                 if res.get("type") == "checkpoint":
                     model_version_id = res.get("modelVersionId")
                     break
-            except Exception as e:
-                logger.warning(f"Failed to fetch Civitai image info: {e}")
 
         # 2. Merge Parameters
         # Priority: request_params > civitai_meta > embedded (existing gen_params)

py/routes/handlers/misc_handlers.py

@@ -2065,7 +2065,7 @@ class ModelLibraryHandler:
                     file_path=file_path if isinstance(file_path, str) else None,
                 )
             else:
-                await history_service.mark_not_downloaded(model_type, model_version_id)
+                await history_service.mark_as_deleted(model_type, model_version_id)
 
             return web.json_response(
                 {
@@ -2139,8 +2139,19 @@ class ModelLibraryHandler:
                 ]
                 await found_cache.resort()
 
+            scanner_map = {
+                "lora": lora_scanner,
+                "checkpoint": checkpoint_scanner,
+                "embedding": embedding_scanner,
+            }
+            scanner = scanner_map.get(found_type)
+            if scanner:
+                persist = getattr(scanner, "_persist_current_cache", None)
+                if callable(persist):
+                    await persist()
+
             history_service = await self._get_download_history_service()
-            await history_service.mark_not_downloaded(found_type, model_version_id)
+            await history_service.mark_as_deleted(found_type, model_version_id)
 
             return web.json_response(
                 {

py/routes/handlers/recipe_handlers.py

@@ -93,6 +93,8 @@ class RecipeHandlerSet:
             "cancel_batch_import": self.batch_import.cancel_batch_import,
             "start_directory_import": self.batch_import.start_directory_import,
             "browse_directory": self.batch_import.browse_directory,
+            "check_image_exists": self.management.check_image_exists,
+            "import_from_url": self.management.import_from_url,
         }
 
 
@@ -541,7 +543,7 @@ class RecipeQueryHandler:
                     )
                     response_data.append(
                         {
-                            "type": "source_url",
+                            "type": "source_path",
                             "fingerprint": url,
                             "count": len(recipes),
                             "recipes": recipes,
@@ -607,6 +609,7 @@ class RecipeManagementHandler:
         self._downloader_factory = downloader_factory
         self._civitai_client_getter = civitai_client_getter
         self._ws_manager = ws_manager
+        self._import_semaphore = asyncio.Semaphore(2)
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         try:
@@ -767,25 +770,53 @@ class RecipeManagementHandler:
                 sorted(checkpoint_entry.keys()) if isinstance(checkpoint_entry, dict) else [],
             )
 
-            # 2. Initial Metadata Construction
+            # Throttle concurrent imports to avoid starving ComfyUI's event loop
+            async with self._import_semaphore:
+                return await self._do_import_remote_recipe(
+                    image_url=image_url,
+                    name=name,
+                    lora_entries=lora_entries,
+                    checkpoint_entry=checkpoint_entry,
+                    gen_params_request=gen_params_request,
+                    tags=self._parse_tags(params.get("tags")),
+                    base_model=params.get("base_model", "") or "",
+                    source_path=params.get("source_path") or image_url,
+                )
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeDownloadError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error(
+                "Error importing recipe from remote source: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def _do_import_remote_recipe(
+        self,
+        *,
+        image_url: str,
+        name: str,
+        lora_entries: list,
+        checkpoint_entry: dict,
+        gen_params_request: dict,
+        tags: list,
+        base_model: str,
+        source_path: str,
+    ) -> web.Response:
+        recipe_scanner = self._recipe_scanner_getter()
+        if recipe_scanner is None:
+            raise RuntimeError("Recipe scanner unavailable")
+
         metadata: Dict[str, Any] = {
-                "base_model": params.get("base_model", "") or "",
+            "base_model": base_model,
             "loras": lora_entries,
             "gen_params": gen_params_request or {},
-                "source_url": image_url,
+            "source_path": source_path,
         }
 
-            source_path = params.get("source_path")
-            if source_path:
-                metadata["source_path"] = source_path
-
-            # Checkpoint handling
         if checkpoint_entry:
             metadata["checkpoint"] = checkpoint_entry
-                # Ensure checkpoint is also in gen_params for consistency if needed by enricher?
-                # Actually enricher looks at metadata['checkpoint'], so this is fine.
-
-                # Try to resolve base model from checkpoint if not explicitly provided
             if not metadata["base_model"]:
                 base_model_from_metadata = (
                     await self._resolve_base_model_from_checkpoint(checkpoint_entry)
@@ -793,30 +824,17 @@ class RecipeManagementHandler:
                 if base_model_from_metadata:
                     metadata["base_model"] = base_model_from_metadata
 
-            tags = self._parse_tags(params.get("tags"))
-
-            # 3. Download Image
+        # Download image
         (
             image_bytes,
             extension,
-                civitai_meta_from_download,
+            civitai_meta_raw,
+            model_version_id,
         ) = await self._download_remote_media(image_url)
 
-            # 4. Extract Embedded Metadata
-            # Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated
-            # with embedded data if we want it to merge it.
-            # However, logic in Enricher merges: request > civitai > embedded.
-            # So we should gather embedded params and put them into the recipe's gen_params (as initial state)
-            # OR pass them to enricher to handle?
-            # The interface of Enricher.enrich_recipe takes `recipe` (with gen_params) and `request_params`.
-            # So let's extract embedded and put it into recipe['gen_params'] but careful not to overwrite request params.
-            # Actually, `GenParamsMerger` which `Enricher` uses handles 3 layers.
-            # But `Enricher` interface is: recipe['gen_params'] (as embedded) + request_params + civitai (fetched internally).
-            # Wait, `Enricher` fetches Civitai info internally based on URL.
-            # `civitai_meta_from_download` is returned by `_download_remote_media` which might be useful if URL didn't have ID.
-
-            # Let's extract embedded metadata first
+        # Extract embedded EXIF metadata (offloaded to thread pool in this call)
         embedded_gen_params = {}
+        parsed_embedded = None
         try:
             with tempfile.NamedTemporaryFile(
                 suffix=extension, delete=False
@@ -825,7 +843,9 @@ class RecipeManagementHandler:
                 temp_img_path = temp_img.name
 
             try:
-                    raw_embedded = ExifUtils.extract_image_metadata(temp_img_path)
+                raw_embedded = await asyncio.to_thread(
+                    ExifUtils.extract_image_metadata, temp_img_path
+                )
                 if raw_embedded:
                     parser = (
                         self._analysis_service._recipe_parser_factory.create_parser(
@@ -848,27 +868,44 @@ class RecipeManagementHandler:
                 "Failed to extract embedded metadata during import: %s", exc
             )
 
-            # Pre-populate gen_params with embedded data so Enricher treats it as the "base" layer
+        # Fallback: if EXIF extraction yielded nothing, parse Civitai API meta directly
+        # (same approach as analyze_remote_image — downloaded Civitai images often
+        # have no embedded EXIF but the API meta contains resources/hashes)
+        if parsed_embedded is None and civitai_meta_raw:
+            civitai_inner_meta = civitai_meta_raw
+            if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
+                civitai_inner_meta = civitai_meta_raw["meta"]
+            if isinstance(civitai_inner_meta, dict):
+                parser = self._analysis_service._recipe_parser_factory.create_parser(
+                    civitai_inner_meta
+                )
+                if parser:
+                    parsed_embedded = await parser.parse_metadata(
+                        civitai_inner_meta, recipe_scanner=recipe_scanner
+                    )
+                    if parsed_embedded and "gen_params" in parsed_embedded:
+                        embedded_gen_params = parsed_embedded["gen_params"]
+
         if embedded_gen_params:
-                # Merge embedded into existing gen_params (which currently only has request params if any)
-                # But wait, we want request params to override everything.
-                # So we should set recipe['gen_params'] = embedded, and pass request params to enricher.
             metadata["gen_params"] = embedded_gen_params
 
-            # 5. Enrich with unified logic
-            # This will fetch Civitai info (if URL matches) and merge: request > civitai > embedded
+        if parsed_embedded:
+            parsed_loras = parsed_embedded.get("loras")
+            if parsed_loras and not metadata.get("loras"):
+                metadata["loras"] = parsed_loras
+            parsed_model = parsed_embedded.get("model")
+            if parsed_model and not metadata.get("checkpoint"):
+                metadata["checkpoint"] = parsed_model
+
         civitai_client = self._civitai_client_getter()
         await RecipeEnricher.enrich_recipe(
             recipe=metadata,
             civitai_client=civitai_client,
-                request_params=gen_params_request,  # Pass explicit request params here to override
+            request_params=gen_params_request,
+            prefetched_civitai_meta_raw=civitai_meta_raw,
+            prefetched_model_version_id=model_version_id,
         )
 
-            # If we got civitai_meta from download but Enricher didn't fetch it (e.g. not a civitai URL or failed),
-            # we might want to manually merge it?
-            # But usually `import_remote_recipe` is used with Civitai URLs.
-            # For now, relying on Enricher's internal fetch is consistent with repair.
-
         result = await self._persistence_service.save_recipe(
             recipe_scanner=recipe_scanner,
             image_bytes=image_bytes,
@@ -879,15 +916,6 @@ class RecipeManagementHandler:
             extension=extension,
         )
         return web.json_response(result.payload, status=result.status)
-        except RecipeValidationError as exc:
-            return web.json_response({"error": str(exc)}, status=400)
-        except RecipeDownloadError as exc:
-            return web.json_response({"error": str(exc)}, status=400)
-        except Exception as exc:
-            self._logger.error(
-                "Error importing recipe from remote source: %s", exc, exc_info=True
-            )
-            return web.json_response({"error": str(exc)}, status=500)
 
     async def delete_recipe(self, request: web.Request) -> web.Response:
         try:
@@ -1190,7 +1218,7 @@ class RecipeManagementHandler:
             "exclude": False,
         }
 
-    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any]:
+    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any, Any]:
         civitai_client = self._civitai_client_getter()
         downloader = await self._downloader_factory()
         temp_path = None
@@ -1238,10 +1266,18 @@ class RecipeManagementHandler:
                 extension = ".webp"  # Default to webp if unknown
 
             with open(temp_path, "rb") as file_obj:
+                model_ver_id = None
+                if civitai_image_id and image_info:
+                    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]
                 return (
                     file_obj.read(),
                     extension,
                     image_info.get("meta") if civitai_image_id and image_info else None,
+                    model_ver_id,
                 )
         except RecipeDownloadError:
             raise
@@ -1289,6 +1325,205 @@ class RecipeManagementHandler:
 
         return ""
 
+    async def check_image_exists(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            image_ids_raw = request.query.get("image_ids", "")
+            if not image_ids_raw:
+                return web.json_response({"success": True, "results": {}})
+
+            requested_ids = set()
+            for raw in image_ids_raw.split(","):
+                stripped = raw.strip()
+                if stripped and stripped.isdigit():
+                    requested_ids.add(stripped)
+
+            if not requested_ids:
+                return web.json_response({"success": True, "results": {}})
+
+            cache = await recipe_scanner.get_cached_data()
+
+            # Build lookup: image_id -> recipe_id from stored source_path
+            image_to_recipe = {}
+            for recipe in getattr(cache, "raw_data", []):
+                source = recipe.get("source_path")
+                if not source:
+                    continue
+                image_id = extract_civitai_image_id(source)
+                if image_id and image_id not in image_to_recipe:
+                    image_to_recipe[image_id] = recipe.get("id")
+
+            results = {}
+            for img_id in requested_ids:
+                recipe_id = image_to_recipe.get(img_id)
+                results[img_id] = {
+                    "in_library": recipe_id is not None,
+                    "recipe_id": recipe_id,
+                }
+
+            return web.json_response({"success": True, "results": results})
+        except Exception as exc:
+            self._logger.error(
+                "Error checking image existence: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def import_from_url(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            image_url = request.query.get("image_url")
+            if not image_url:
+                raise RecipeValidationError("Missing required field: image_url")
+
+            image_id = extract_civitai_image_id(image_url)
+            if not image_id:
+                raise RecipeValidationError(
+                    "Could not extract Civitai image ID from URL"
+                )
+
+            # Check for duplicate (fast, before acquiring semaphore)
+            cache = await recipe_scanner.get_cached_data()
+            for recipe in getattr(cache, "raw_data", []):
+                source = recipe.get("source_path")
+                if source:
+                    existing_id = extract_civitai_image_id(source)
+                    if existing_id == image_id:
+                        return web.json_response({
+                            "success": True,
+                            "recipe_id": recipe.get("id"),
+                            "name": recipe.get("title", ""),
+                            "already_exists": True,
+                        })
+
+            async with self._import_semaphore:
+                return await self._do_import_from_url(image_url, recipe_scanner)
+        except RecipeValidationError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except RecipeDownloadError as exc:
+            return web.json_response({"error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error(
+                "Error importing recipe from URL: %s", exc, exc_info=True
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
+    async def _do_import_from_url(
+        self,
+        image_url: str,
+        recipe_scanner: Any,
+    ) -> web.Response:
+        image_id = extract_civitai_image_id(image_url)
+        if not image_id:
+            raise RecipeValidationError(
+                "Could not extract Civitai image ID from URL"
+            )
+
+        image_bytes, extension, civitai_meta_raw, model_version_id = (
+            await self._download_remote_media(image_url)
+        )
+
+        # Extract embedded EXIF metadata
+        embedded_gen_params = {}
+        parsed_embedded = None
+        try:
+            with tempfile.NamedTemporaryFile(
+                suffix=extension, delete=False
+            ) as temp_img:
+                temp_img.write(image_bytes)
+                temp_img_path = temp_img.name
+
+            try:
+                raw_embedded = await asyncio.to_thread(
+                    ExifUtils.extract_image_metadata, temp_img_path
+                )
+                if raw_embedded:
+                    parser = (
+                        self._analysis_service._recipe_parser_factory.create_parser(
+                            raw_embedded
+                        )
+                    )
+                    if parser:
+                        parsed_embedded = await parser.parse_metadata(
+                            raw_embedded, recipe_scanner=recipe_scanner
+                        )
+                        if parsed_embedded and "gen_params" in parsed_embedded:
+                            embedded_gen_params = parsed_embedded["gen_params"]
+            finally:
+                if os.path.exists(temp_img_path):
+                    os.unlink(temp_img_path)
+        except Exception as exc:
+            self._logger.warning(
+                "Failed to extract embedded metadata: %s", exc
+            )
+
+        if parsed_embedded is None and civitai_meta_raw:
+            civitai_inner_meta = civitai_meta_raw
+            if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
+                civitai_inner_meta = civitai_meta_raw["meta"]
+            if isinstance(civitai_inner_meta, dict):
+                parser = self._analysis_service._recipe_parser_factory.create_parser(
+                    civitai_inner_meta
+                )
+                if parser:
+                    parsed_embedded = await parser.parse_metadata(
+                        civitai_inner_meta, recipe_scanner=recipe_scanner
+                    )
+                    if parsed_embedded and "gen_params" in parsed_embedded:
+                        embedded_gen_params = parsed_embedded["gen_params"]
+
+        metadata: Dict[str, Any] = {
+            "base_model": "",
+            "loras": [],
+            "gen_params": embedded_gen_params or {},
+            "source_path": image_url,
+        }
+
+        if parsed_embedded:
+            parsed_loras = parsed_embedded.get("loras")
+            if parsed_loras and not metadata.get("loras"):
+                metadata["loras"] = parsed_loras
+            parsed_model = parsed_embedded.get("model")
+            if parsed_model and not metadata.get("checkpoint"):
+                metadata["checkpoint"] = parsed_model
+
+        civitai_client = self._civitai_client_getter()
+        await RecipeEnricher.enrich_recipe(
+            recipe=metadata,
+            civitai_client=civitai_client,
+            request_params={},
+            prefetched_civitai_meta_raw=civitai_meta_raw,
+            prefetched_model_version_id=model_version_id,
+        )
+
+        prompt = (
+            metadata.get("gen_params", {}).get("prompt")
+            or metadata.get("gen_params", {}).get("positivePrompt")
+            or ""
+        )
+        if prompt:
+            name = " ".join(str(prompt).split()[:10])
+        else:
+            name = f"Civitai Image {image_id}"
+
+        result = await self._persistence_service.save_recipe(
+            recipe_scanner=recipe_scanner,
+            image_bytes=image_bytes,
+            image_base64=None,
+            name=name,
+            tags=[],
+            metadata=metadata,
+            extension=extension,
+        )
+        return web.json_response(result.payload, status=result.status)
+
 
 class RecipeAnalysisHandler:
     """Analyze images to extract recipe metadata."""

py/routes/recipe_route_registrar.py

@@ -70,6 +70,10 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "POST", "/api/lm/recipes/batch-import/directory", "start_directory_import"
     ),
     RouteDefinition("POST", "/api/lm/recipes/browse-directory", "browse_directory"),
+    RouteDefinition(
+        "GET", "/api/lm/recipes/check-image-exists", "check_image_exists"
+    ),
+    RouteDefinition("GET", "/api/lm/recipes/import-from-url", "import_from_url"),
 )
 
 

py/services/base_model_service.py

@@ -908,6 +908,17 @@ class BaseModelService(ABC):
         )
         if should_skip or metadata is None:
             return None
+
+        # Prune stale example-image metadata entries whose files no longer
+        # exist on disk (e.g. a user deleted the files manually).
+        from ..utils.example_images_metadata import MetadataUpdater
+
+        was_modified = await MetadataUpdater.prune_stale_example_images(metadata)
+        if was_modified:
+            asyncio.create_task(
+                MetadataManager.save_metadata(file_path, metadata)
+            )
+
         return self.filter_civitai_data(metadata.to_dict().get("civitai", {}))
 
     async def get_model_description(self, file_path: str) -> Optional[str]:

py/services/batch_import_service.py

@@ -224,7 +224,7 @@ class BatchImportService:
                 return False
 
             for recipe in getattr(cache, "raw_data", []):
-                source_path = recipe.get("source_path") or recipe.get("source_url")
+                source_path = recipe.get("source_path")
                 if source_path and source_path == source:
                     return True
             return False

py/services/civitai_base_model_service.py

@@ -193,6 +193,9 @@ class CivitaiBaseModelService:
             "zimageturbo": "ZIT",
             "zimagebase": "ZIB",
             "anima": "ANI",
+            "ernie": "ERNI",
+            "ernie turbo": "ETRB",
+            "nucleus": "NUCL",
             "svd": "SVD",
             "ltxv": "LTXV",
             "ltxv2": "LTV2",
@@ -418,6 +421,9 @@ class CivitaiBaseModelService:
                 "Kolors",
                 "NoobAI",
                 "Anima",
+                "Ernie",
+                "Ernie Turbo",
+                "Nucleus",
             ],
         }
 

py/services/civitai_client.py

@@ -577,6 +577,59 @@ class CivitaiClient:
             logger.error(error_msg)
             return None
 
+    async def get_model_versions_by_hashes(
+        self, hashes: List[str]
+    ) -> Optional[List[Dict]]:
+        """Fetch full version details for up to 100 SHA256 hashes via the batch endpoint.
+
+        Uses POST /api/v1/model-versions/by-hash which returns full version
+        details including ``usageControl`` and ``earlyAccessEndsAt`` that are
+        not available from the model-level API.
+
+        Args:
+            hashes: List of SHA256 hashes (max 100 per batch; auto-split).
+
+        Returns:
+            List of version dicts or None on failure.
+        """
+        if not hashes:
+            return []
+
+        BATCH_SIZE = 100
+        all_versions: List[Dict] = []
+
+        for start in range(0, len(hashes), BATCH_SIZE):
+            batch = hashes[start : start + BATCH_SIZE]
+            try:
+                success, result = await self._make_request(
+                    "POST",
+                    f"{self.base_url}/model-versions/by-hash",
+                    use_auth=True,
+                    json=batch,
+                )
+                if not success:
+                    logger.warning(
+                        "Batch by-hash request failed for %d hashes: %s",
+                        len(batch),
+                        result,
+                    )
+                    continue
+
+                if isinstance(result, list):
+                    all_versions.extend(result)
+                else:
+                    logger.debug(
+                        "Unexpected by-hash response type: %s", type(result)
+                    )
+            except RateLimitError:
+                raise
+            except Exception as exc:  # pragma: no cover - defensive logging
+                logger.error(
+                    "Error fetching model versions by hashes: %s", exc
+                )
+
+        return all_versions if all_versions else None
+
     async def get_user_models(self, username: str) -> Optional[List[Dict]]:
         """Fetch all models for a specific Civitai user."""
         if not username:

py/services/downloaded_version_history_service.py

@@ -206,7 +206,7 @@ class DownloadedVersionHistoryService:
             )
             conn.commit()
 
-    async def mark_not_downloaded(self, model_type: str, version_id: int) -> None:
+    async def mark_as_deleted(self, model_type: str, version_id: int) -> None:
         normalized_type = _normalize_model_type(model_type)
         normalized_version_id = _normalize_int(version_id)
         if normalized_type is None or normalized_version_id is None:

py/services/model_lifecycle_service.py

@@ -111,6 +111,11 @@ class ModelLifecycleService:
             self._scanner._hash_index.remove_by_path(file_path)
 
         await self._sync_update_for_model(model_id)
+
+        persist_current_cache = getattr(self._scanner, "_persist_current_cache", None)
+        if callable(persist_current_cache):
+            await persist_current_cache()
+
         return {"success": True, "deleted_files": deleted_files}
 
     @staticmethod

py/services/model_metadata_provider.py

@@ -109,6 +109,18 @@ class ModelMetadataProvider(ABC):
         """Fetch model versions for multiple model ids when supported."""
         raise NotImplementedError
 
+    async def get_model_versions_by_hashes(
+        self, hashes: List[str]
+    ) -> Optional[List[Dict]]:
+        """Fetch full version details for multiple SHA256 hashes.
+
+        Used specifically to retrieve ``usageControl`` which is only
+        available from the per-version / by-hash API, not from model-level
+        responses.  Providers that cannot resolve hashes should let the
+        default ``NotImplementedError`` propagate.
+        """
+        raise NotImplementedError
+        
     @abstractmethod
     async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
         """Get specific model version with additional metadata"""
@@ -141,6 +153,11 @@ class CivitaiModelMetadataProvider(ModelMetadataProvider):
     ) -> Optional[Dict[int, Dict]]:
         return await self.client.get_model_versions_bulk(model_ids)
 
+    async def get_model_versions_by_hashes(
+        self, hashes: List[str]
+    ) -> Optional[List[Dict]]:
+        return await self.client.get_model_versions_by_hashes(hashes)
+        
     async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
         return await self.client.get_model_version(model_id, version_id)
         
@@ -519,6 +536,32 @@ class FallbackMetadataProvider(ModelMetadataProvider):
                 continue
         return None, "No provider could retrieve the data"
 
+    async def get_model_versions_by_hashes(
+        self, hashes: List[str]
+    ) -> Optional[List[Dict]]:
+        for provider, label in self._iter_providers():
+            try:
+                result = await self._call_with_rate_limit(
+                    label,
+                    provider.get_model_versions_by_hashes,
+                    hashes,
+                )
+                if result is not None:
+                    return result
+            except NotImplementedError:
+                continue
+            except RateLimitError as exc:
+                exc.provider = exc.provider or label
+                raise exc
+            except Exception as e:
+                logger.debug(
+                    "Provider %s failed for get_model_versions_by_hashes: %s",
+                    label,
+                    e,
+                )
+                continue
+        return None
+
     async def get_user_models(self, username: str) -> Optional[List[Dict]]:
         for provider, label in self._iter_providers():
             try:
@@ -593,6 +636,15 @@ class RateLimitRetryingProvider(ModelMetadataProvider):
             model_ids,
         )
 
+    async def get_model_versions_by_hashes(
+        self, hashes: List[str]
+    ) -> Optional[List[Dict]]:
+        return await self._rate_limit_helper.run(
+            self._label,
+            self._provider.get_model_versions_by_hashes,
+            hashes,
+        )
+
     async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
         return await self._rate_limit_helper.run(
             self._label,
@@ -669,6 +721,17 @@ class ModelMetadataProviderManager:
         provider = self._get_provider(provider_name)
         return await provider.get_model_version_info(version_id)
 
+    async def get_model_versions_by_hashes(
+        self,
+        hashes: List[str],
+        provider_name: str = None,
+    ) -> Optional[List[Dict]]:
+        provider = self._get_provider(provider_name)
+        try:
+            return await provider.get_model_versions_by_hashes(hashes)
+        except NotImplementedError:
+            return None
+
     async def get_user_models(self, username: str, provider_name: str = None) -> Optional[List[Dict]]:
         """Fetch models owned by the specified user"""
         provider = self._get_provider(provider_name)

py/services/model_update_service.py

@@ -989,6 +989,11 @@ class ModelUpdateService:
                 fallback_attempted = True
                 try:
                     response = await metadata_provider.get_model_versions(model_id)
+                    if response is not None:
+                        await self._enrich_version_entries(
+                            metadata_provider,
+                            {model_id: response},
+                        )
                 except RateLimitError:
                     raise
                 except ResourceNotFoundError as exc:
@@ -1083,6 +1088,136 @@ class ModelUpdateService:
             self._upsert_record(record)
             return record
 
+    async def _enrich_version_entries(
+        self,
+        metadata_provider,
+        responses_by_model_id: Dict[int, Mapping],
+    ) -> None:
+        """Enrich version entries with ``usageControl`` via batch hash endpoint.
+
+        The model-level API does not include ``usageControl`` on version
+        entries. This method collects SHA256 hashes from every version's
+        primary model file, calls ``POST /api/v1/model-versions/by-hash``
+        (up to 100 hashes per request), and injects ``usageControl`` +
+        ``earlyAccessEndsAt`` into each version entry dict in-place.
+        """
+        if not metadata_provider or not responses_by_model_id:
+            return
+
+        hashes_by_version: Dict[int, str] = {}
+        for response in responses_by_model_id.values():
+            hashes_by_version.update(
+                self._collect_hashes_from_response(response)
+            )
+
+        if not hashes_by_version:
+            return
+
+        version_ids_by_hash: Dict[str, List[int]] = {}
+        for version_id, sha256 in hashes_by_version.items():
+            version_ids_by_hash.setdefault(sha256, []).append(version_id)
+
+        all_hashes = list(version_ids_by_hash.keys())
+        BATCH_SIZE = 100
+
+        enrichment: Dict[int, Dict] = {}
+        try:
+            for start in range(0, len(all_hashes), BATCH_SIZE):
+                batch = all_hashes[start : start + BATCH_SIZE]
+                try:
+                    enriched = await metadata_provider.get_model_versions_by_hashes(
+                        batch
+                    )
+                except NotImplementedError:
+                    return
+                except RateLimitError:
+                    raise
+                except Exception:
+                    continue
+
+                if not enriched:
+                    continue
+
+                for entry in enriched:
+                    if not isinstance(entry, dict):
+                        continue
+                    version_id = entry.get("id")
+                    if version_id is None:
+                        continue
+                    enrichment[version_id] = {
+                        "usageControl": _normalize_string(
+                            entry.get("usageControl")
+                        ),
+                        "earlyAccessEndsAt": _normalize_string(
+                            entry.get("earlyAccessEndsAt")
+                        ),
+                    }
+        except RateLimitError:
+            raise
+
+        if not enrichment:
+            return
+
+        for response in responses_by_model_id.values():
+            versions = response.get("modelVersions")
+            if not isinstance(versions, list):
+                continue
+            for version in versions:
+                if not isinstance(version, dict):
+                    continue
+                version_id = version.get("id")
+                if version_id not in enrichment:
+                    continue
+                extra = enrichment[version_id]
+                if extra.get("usageControl") and not version.get("usageControl"):
+                    version["usageControl"] = extra["usageControl"]
+                if extra.get("earlyAccessEndsAt") and not version.get(
+                    "earlyAccessEndsAt"
+                ):
+                    version["earlyAccessEndsAt"] = extra["earlyAccessEndsAt"]
+
+    @staticmethod
+    def _collect_hashes_from_response(response: Mapping) -> Dict[int, str]:
+        """Extract ``{version_id: sha256}`` from a model-level API response.
+
+        Returns an empty dict if the response structure is unexpected.
+        """
+        result: Dict[int, str] = {}
+        versions = response.get("modelVersions")
+        if not isinstance(versions, list):
+            return result
+        for entry in versions:
+            if not isinstance(entry, dict):
+                continue
+            version_id = _normalize_int(entry.get("id"))
+            if version_id is None:
+                continue
+            sha256 = ModelUpdateService._extract_sha256_from_version_entry(entry)
+            if sha256:
+                result[version_id] = sha256
+        return result
+
+    @staticmethod
+    def _extract_sha256_from_version_entry(entry: Mapping) -> Optional[str]:
+        """Return the SHA256 hash from the primary model file of a version entry."""
+        files = entry.get("files")
+        if not isinstance(files, list):
+            return None
+        for file_info in files:
+            if not isinstance(file_info, dict):
+                continue
+            if file_info.get("type") != "Model":
+                continue
+            primary = file_info.get("primary")
+            if primary is not True and str(primary).strip().lower() != "true":
+                continue
+            hashes = file_info.get("hashes")
+            if isinstance(hashes, dict):
+                sha256 = hashes.get("SHA256")
+                if sha256:
+                    return sha256
+        return None
+
     async def _fetch_model_versions_bulk(
         self,
         metadata_provider,
@@ -1134,6 +1269,7 @@ class ModelUpdateService:
             len(aggregated),
             provider_name,
         )
+        await self._enrich_version_entries(metadata_provider, aggregated)
         return aggregated
 
     async def _collect_local_versions(
@@ -1261,6 +1397,7 @@ class ModelUpdateService:
                     sort_index=sort_map.get(version_id, index),
                     early_access_ends_at=remote_version.early_access_ends_at,
                     is_early_access=remote_version.is_early_access,
+                    usage_control=remote_version.usage_control,
                 )
             )
 

py/services/persistent_recipe_cache.py

@@ -38,6 +38,7 @@ class PersistentRecipeCache:
         "json_path",
         "title",
         "folder",
+        "source_path",
         "base_model",
         "fingerprint",
         "created_date",
@@ -334,6 +335,7 @@ class PersistentRecipeCache:
                             json_path TEXT,
                             title TEXT,
                             folder TEXT,
+                            source_path TEXT,
                             base_model TEXT,
                             fingerprint TEXT,
                             created_date REAL,
@@ -358,6 +360,13 @@ class PersistentRecipeCache:
                         );
                         """
                     )
+                    # Migration: add source_path column to existing databases
+                    try:
+                        conn.execute(
+                            "ALTER TABLE recipes ADD COLUMN source_path TEXT"
+                        )
+                    except Exception:
+                        pass  # column already exists
                     conn.commit()
                 self._schema_initialized = True
             except Exception as exc:
@@ -406,6 +415,7 @@ class PersistentRecipeCache:
             json_path,
             recipe.get("title"),
             recipe.get("folder"),
+            recipe.get("source_path"),
             recipe.get("base_model"),
             recipe.get("fingerprint"),
             float(recipe.get("created_date") or 0.0),
@@ -456,6 +466,7 @@ class PersistentRecipeCache:
             "file_path": row["file_path"] or "",
             "title": row["title"] or "",
             "folder": row["folder"] or "",
+            "source_path": row["source_path"] or "",
             "base_model": row["base_model"] or "",
             "fingerprint": row["fingerprint"] or "",
             "created_date": row["created_date"] or 0.0,

py/services/recipe_scanner.py

@@ -504,6 +504,9 @@ class RecipeScanner:
                     self._cache.raw_data = recipes
                     self._update_folder_metadata(self._cache)
                     self._sort_cache_sync()
+                    # Backfill source_path from JSON files if missing (schema migration)
+                    if self._backfill_source_path_if_needed(recipes, json_paths):
+                        self._persistent_cache.save_cache(recipes, json_paths)
                     return self._cache
                 else:
                     # Partial update: some files changed
@@ -514,6 +517,8 @@ class RecipeScanner:
                     self._cache.raw_data = recipes
                     self._update_folder_metadata(self._cache)
                     self._sort_cache_sync()
+                    # Backfill source_path from JSON files if missing (schema migration)
+                    self._backfill_source_path_if_needed(recipes, json_paths)
                     # Persist updated cache
                     self._persistent_cache.save_cache(recipes, json_paths)
                     return self._cache
@@ -642,6 +647,34 @@ class RecipeScanner:
 
         return recipes, changed, json_paths
 
+    def _backfill_source_path_if_needed(
+        self,
+        recipes: List[Dict],
+        json_paths: Dict[str, str],
+    ) -> bool:
+        """Backfill source_path from recipe JSON files if missing from cache.
+
+        Returns True if any recipes were updated (caller should persist cache).
+        """
+        updated = False
+        for recipe in recipes:
+            if recipe.get("source_path"):
+                continue
+            recipe_id = str(recipe.get("id", ""))
+            json_path = json_paths.get(recipe_id)
+            if not json_path or not os.path.exists(json_path):
+                continue
+            try:
+                with open(json_path, "r", encoding="utf-8") as f:
+                    json_data = json.load(f)
+                file_source_path = json_data.get("source_path")
+                if file_source_path:
+                    recipe["source_path"] = file_source_path
+                    updated = True
+            except Exception:
+                pass
+        return updated
+
     def _full_directory_scan_sync(
         self, recipes_dir: str
     ) -> Tuple[List[Dict], Dict[str, str]]:

py/services/recipes/analysis_service.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import base64
 import io
 import os
@@ -14,6 +15,7 @@ from PIL import Image
 
 from ...utils.utils import calculate_recipe_fingerprint
 from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
+from ...recipes.enrichment import RecipeEnricher
 from .errors import (
     RecipeDownloadError,
     RecipeNotFoundError,
@@ -170,9 +172,11 @@ class RecipeAnalysisService:
                 await self._download_image(url, temp_path)
 
             if metadata is None and not is_video:
-                metadata = self._exif_utils.extract_image_metadata(temp_path)
+                metadata = await asyncio.to_thread(
+                    self._exif_utils.extract_image_metadata, temp_path
+                )
 
-            return await self._parse_metadata(
+            result = await self._parse_metadata(
                 metadata or {},
                 recipe_scanner=recipe_scanner,
                 image_path=temp_path,
@@ -180,6 +184,37 @@ class RecipeAnalysisService:
                 is_video=is_video,
                 extension=extension,
             )
+
+            if civitai_image_id and image_info and not result.payload.get("error"):
+                mvid = image_info.get("modelVersionId")
+                if not mvid:
+                    mvids = image_info.get("modelVersionIds")
+                    if isinstance(mvids, list) and mvids:
+                        mvid = mvids[0]
+
+                recipe_for_enrich = {
+                    "gen_params": result.payload.get("gen_params", {}),
+                    "loras": result.payload.get("loras", []),
+                    "base_model": result.payload.get("base_model", "") or "",
+                    "checkpoint": result.payload.get("checkpoint") or result.payload.get("model"),
+                    "source_path": url,
+                }
+
+                await RecipeEnricher.enrich_recipe(
+                    recipe=recipe_for_enrich,
+                    civitai_client=civitai_client,
+                    request_params=None,
+                    prefetched_civitai_meta_raw=image_info.get("meta"),
+                    prefetched_model_version_id=mvid,
+                )
+
+                result.payload["gen_params"] = recipe_for_enrich["gen_params"]
+                if recipe_for_enrich.get("checkpoint"):
+                    result.payload["checkpoint"] = recipe_for_enrich["checkpoint"]
+                if recipe_for_enrich.get("base_model"):
+                    result.payload["base_model"] = recipe_for_enrich["base_model"]
+
+            return result
         finally:
             if temp_path:
                 self._safe_cleanup(temp_path)
@@ -199,7 +234,9 @@ class RecipeAnalysisService:
         if not os.path.isfile(normalized_path):
             raise RecipeNotFoundError("File not found")
 
-        metadata = self._exif_utils.extract_image_metadata(normalized_path)
+        metadata = await asyncio.to_thread(
+            self._exif_utils.extract_image_metadata, normalized_path
+        )
         if not metadata:
             return self._metadata_not_found_response(normalized_path)
 

py/utils/civitai_utils.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, Iterable, Mapping, Sequence
 from urllib.parse import parse_qs, urlparse, urlunparse
 
 
-_SUPPORTED_CIVITAI_PAGE_HOSTS = frozenset({"civitai.com", "civitai.red"})
+_SUPPORTED_CIVITAI_PAGE_HOSTS = frozenset({"civitai.com", "civitai.red", "civitai.green"})
 DEFAULT_CIVITAI_PAGE_HOST = "civitai.com"
 _DEFAULT_ALLOW_COMMERCIAL_USE: Sequence[str] = ("Sell",)
 _LICENSE_DEFAULTS: Dict[str, Any] = {

py/utils/constants.py

@@ -178,5 +178,8 @@ SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
         "Wan Video 2.5 I2V",
         "Hunyuan Video",
         "Anima",
+        "Ernie",
+        "Ernie Turbo",
+        "Nucleus",
     ]
 )

py/utils/example_images_metadata.py

@@ -452,3 +452,111 @@ class MetadataUpdater:
         except Exception as e:
             logger.error(f"Error parsing image metadata: {e}", exc_info=True)
             return None
+
+    @staticmethod
+    async def prune_stale_example_images(metadata) -> bool:
+        """Remove example-image metadata entries whose files no longer exist on disk.
+
+        Checks ``civitai.customImages`` (by ``id``) and ``civitai.images`` entries
+        that have an empty ``url`` (no remote fallback) against actual files in
+        the model's example-image folder.  Stale entries are removed in-place so
+        the caller can persist the cleaned metadata afterwards.
+
+        Args:
+            metadata: A ``BaseModelMetadata`` instance (modified in place).
+
+        Returns:
+            True if at least one entry was removed.
+        """
+        from ..utils.example_images_paths import get_model_folder
+
+        model_hash = getattr(metadata, "sha256", None)
+        if not model_hash:
+            return False
+
+        model_folder = get_model_folder(model_hash)
+        if not model_folder:
+            return False
+
+        civitai = getattr(metadata, "civitai", None)
+        if not isinstance(civitai, dict):
+            return False
+
+        has_changes = False
+
+        custom_images = civitai.get("customImages")
+        if isinstance(custom_images, list) and custom_images:
+            stale: list[int] = []
+
+            for idx, img in enumerate(custom_images):
+                img_id = img.get("id", "")
+                if not img_id:
+                    continue
+
+                if not os.path.isdir(model_folder):
+                    stale.append(idx)
+                else:
+                    found = False
+                    try:
+                        prefix = f"custom_{img_id}"
+                        for fname in os.listdir(model_folder):
+                            if fname.startswith(prefix) and os.path.isfile(
+                                os.path.join(model_folder, fname)
+                            ):
+                                found = True
+                                break
+                    except OSError:
+                        stale.append(idx)
+                        continue
+
+                    if not found:
+                        stale.append(idx)
+
+            if stale:
+                for idx in reversed(stale):
+                    custom_images.pop(idx)
+                has_changes = True
+                logger.info(
+                    "Pruned %d stale custom image(s) for %s",
+                    len(stale),
+                    getattr(metadata, "model_name", model_hash),
+                )
+
+        images = civitai.get("images")
+        if isinstance(images, list) and images:
+            stale: list[int] = []
+
+            for idx, img in enumerate(images):
+                if img.get("url", ""):
+                    # Has a remote fallback – keep it even if the local copy
+                    # is gone.
+                    continue
+
+                if not os.path.isdir(model_folder):
+                    stale.append(idx)
+                else:
+                    found = False
+                    try:
+                        prefix = f"image_{idx}."
+                        for fname in os.listdir(model_folder):
+                            if fname.startswith(prefix):
+                                found = True
+                                break
+                    except OSError:
+                        stale.append(idx)
+                        continue
+
+                    if not found:
+                        stale.append(idx)
+
+            if stale:
+                for idx in reversed(stale):
+                    images.pop(idx)
+                has_changes = True
+                logger.info(
+                    "Pruned %d stale image entry(ies) for %s",
+                    len(stale),
+                    getattr(metadata, "model_name", model_hash),
+                )
+
+        return has_changes

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.0.5"
+version = "1.0.6"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

static/css/components/batch-import-modal.css

@@ -87,7 +87,7 @@
 
 .checkbox-label input[type="checkbox"]:checked + .checkmark::after {
     content: '\f00c';
-    font-family: 'Font Awesome 6 Free';
+    font-family: 'Font Awesome 6 Free', sans-serif;
     font-weight: 900;
     color: var(--lora-text);
     font-size: 12px;

static/css/components/card.css

@@ -329,7 +329,6 @@
 }
 
 .card-actions i {
-    margin-left: var(--space-1);
     cursor: pointer;
     color: white;
     transition: opacity 0.2s, transform 0.15s ease;

static/css/components/header.css

@@ -141,8 +141,7 @@
 
 .header-search .search-container:focus-within {
   border-color: var(--lora-accent);
-  box-shadow: 0 4px 16px rgba(0, 0, 0, 0.12), 0 0 0 1px var(--lora-accent);
-  transform: translateY(-1px);
+  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08), 0 0 0 1px var(--lora-accent);
 }
 
 .header-search input {

static/css/components/lora-modal/versions.css

@@ -387,6 +387,10 @@
     cursor: not-allowed;
 }
 
+.version-action-disabled-wrapper {
+    display: inline-flex;
+}
+
 .versions-loading-state,
 .versions-empty,
 .versions-error {

static/css/components/media-viewer.css

@@ -0,0 +1,124 @@
+.media-viewer-overlay {
+  position: fixed;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: rgba(0, 0, 0, 0);
+  z-index: 10000;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  transition: background 0.3s ease;
+}
+
+.media-viewer-overlay.active {
+  background: rgba(0, 0, 0, 0.92);
+}
+
+.media-viewer-close {
+  position: fixed;
+  top: 16px;
+  right: 16px;
+  width: 40px;
+  height: 40px;
+  border-radius: 50%;
+  background: rgba(255, 255, 255, 0.1);
+  border: none;
+  color: #fff;
+  font-size: 18px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  z-index: 10001;
+  transition: background 0.2s ease;
+  opacity: 0;
+}
+
+.media-viewer-overlay.active .media-viewer-close {
+  opacity: 1;
+}
+
+.media-viewer-close:hover {
+  background: rgba(255, 255, 255, 0.25);
+}
+
+.media-viewer-content-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  max-width: 90vw;
+  max-height: 95vh;
+  cursor: default;
+}
+
+.media-viewer-media {
+  display: block;
+  max-width: 90vw;
+  max-height: 85vh;
+  object-fit: contain;
+  border-radius: 4px;
+  box-shadow: 0 4px 24px rgba(0, 0, 0, 0.4);
+}
+
+.media-viewer-video {
+  max-height: 80vh;
+}
+
+.media-viewer-counter {
+  margin-top: 8px;
+  color: rgba(255, 255, 255, 0.5);
+  font-size: 0.85em;
+  text-align: center;
+  min-height: 1.2em;
+}
+
+.media-viewer-title {
+  margin-top: 4px;
+  color: rgba(255, 255, 255, 0.7);
+  font-size: 0.9em;
+  text-align: center;
+  max-width: 90vw;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.media-viewer-nav {
+  position: fixed;
+  top: 50%;
+  transform: translateY(-50%);
+  width: 48px;
+  height: 80px;
+  border-radius: 4px;
+  background: rgba(255, 255, 255, 0.06);
+  border: none;
+  color: #fff;
+  font-size: 24px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  z-index: 10001;
+  opacity: 0;
+  transition: opacity 0.2s ease, background 0.2s ease;
+}
+
+.media-viewer-overlay.active .media-viewer-nav {
+  opacity: 1;
+}
+
+.media-viewer-nav:hover {
+  background: rgba(255, 255, 255, 0.18);
+}
+
+.media-viewer-prev {
+  left: 16px;
+}
+
+.media-viewer-next {
+  right: 16px;
+}

static/css/components/menu.css

@@ -41,6 +41,63 @@
     text-align: center;
 }
 
+/* Section Headers */
+.context-menu-section-header {
+    padding: 6px 12px 2px;
+    font-size: 11px;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--text-muted);
+    cursor: default;
+    user-select: none;
+}
+
+/* Submenu */
+.context-menu-item.has-submenu {
+    position: relative;
+    justify-content: space-between;
+}
+
+.submenu-arrow {
+    margin-left: auto;
+    font-size: 10px;
+    width: auto !important;
+}
+
+.context-submenu {
+    position: absolute;
+    left: calc(100% - 4px);
+    top: -1px;
+    display: none;
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: 0;
+    min-width: 200px;
+    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.2);
+    z-index: 1001;
+    backdrop-filter: blur(10px);
+}
+
+.context-submenu .context-menu-item {
+    white-space: nowrap;
+    margin: 0;
+}
+
+.context-submenu .context-menu-item:first-child {
+    padding-top: 9px;
+}
+
+.context-submenu .context-menu-item:last-child {
+    padding-bottom: 9px;
+}
+
+.context-submenu.flip-left {
+    left: auto;
+    right: 100%;
+}
+
 /* NSFW Level Selector */
 .nsfw-level-selector {
     position: fixed;

static/css/components/recipe-modal.css

@@ -396,14 +396,54 @@
     flex-direction: column;
 }
 
-.recipe-gen-params h3 {
-    margin-top: 0;
+.gen-params-header-row {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
     margin-bottom: var(--space-2);
-    font-size: 1.2em;
-    color: var(--text-color);
     padding-bottom: var(--space-1);
     border-bottom: 1px solid var(--border-color);
     flex-shrink: 0;
+    gap: 8px;
+}
+
+.gen-params-header-row h3 {
+    margin: 0;
+    font-size: 1.2em;
+    color: var(--text-color);
+}
+
+/* Inline toggle for lora strip setting */
+.lora-strip-toggle {
+    flex-shrink: 0;
+    gap: 6px;
+}
+
+.lora-strip-toggle .inline-toggle-label {
+    font-size: 0.78em;
+    white-space: nowrap;
+    opacity: 0.7;
+    transition: opacity 0.2s;
+}
+
+.lora-strip-toggle:hover .inline-toggle-label {
+    opacity: 1;
+}
+
+.lora-strip-toggle .toggle-switch {
+    width: 32px;
+    height: 16px;
+}
+
+.lora-strip-toggle .toggle-slider:before {
+    height: 10px;
+    width: 10px;
+    left: 3px;
+    bottom: 3px;
+}
+
+.lora-strip-toggle .toggle-switch input:checked + .toggle-slider:before {
+    transform: translateX(16px);
 }
 
 .gen-params-container {

static/css/components/shared.css

@@ -67,7 +67,6 @@
 
 .early-access-info {
     display: none;
-    position: absolute;
     top: 100%;
     right: 0;
     background: var(--card-bg);
@@ -97,7 +96,6 @@
 
 .local-path {
     display: none;
-    position: absolute;
     top: 100%;
     right: 0;
     background: var(--card-bg);

static/css/style.css

@@ -39,6 +39,7 @@
 @import 'components/keyboard-nav.css'; /* Add keyboard navigation component */
 @import 'components/statistics.css'; /* Add statistics component */
 @import 'components/sidebar.css'; /* Add sidebar component */
+@import 'components/media-viewer.css';
 
 .initialization-notice {
     display: flex;

static/js/components/ContextMenu/BaseContextMenu.js

@@ -3,6 +3,8 @@ export class BaseContextMenu {
         this.menu = document.getElementById(menuId);
         this.cardSelector = cardSelector;
         this.currentCard = null;
+        this.submenuTimeout = null;
+        this.openSubmenu = null;
 
         if (!this.menu) {
             console.error(`Context menu element with ID ${menuId} not found`);
@@ -13,20 +15,99 @@ export class BaseContextMenu {
     }
 
     init() {
-        // Hide menu on regular clicks
-        document.addEventListener('click', () => this.hideMenu());
+        // Hide menu when clicking outside
+        document.addEventListener('click', (e) => {
+            if (!this.menu.contains(e.target)) {
+                this.hideMenu();
+            }
+        });
 
-        // Handle menu item clicks
+        // Handle menu item clicks (including submenu items)
         this.menu.addEventListener('click', (e) => {
             const menuItem = e.target.closest('.context-menu-item');
             if (!menuItem || !this.currentCard) return;
 
+            // Ignore clicks on submenu trigger (has-submenu parent)
+            if (menuItem.classList.contains('has-submenu')) return;
+
             const action = menuItem.dataset.action;
             if (!action) return;
 
             this.handleMenuAction(action, menuItem);
             this.hideMenu();
         });
+
+        // Submenu hover handling
+        // Use mouseover/mouseout (which bubble) with relatedTarget checks
+        // to reliably detect crossing the .has-submenu boundary
+        this.menu.addEventListener('mouseover', (e) => {
+            const trigger = e.target.closest('.has-submenu');
+            if (!trigger) return;
+
+            // Only act when entering from outside this trigger's tree
+            if (e.relatedTarget && trigger.contains(e.relatedTarget)) return;
+
+            this._openSubmenu(trigger);
+        });
+
+        this.menu.addEventListener('mouseout', (e) => {
+            const trigger = e.target.closest('.has-submenu');
+            if (!trigger) return;
+
+            // Only close when leaving the trigger's tree entirely
+            if (e.relatedTarget && trigger.contains(e.relatedTarget)) return;
+
+            this._scheduleSubmenuClose(trigger);
+        });
+    }
+
+    _openSubmenu(trigger) {
+        // Clear any pending close
+        if (this.submenuTimeout) {
+            clearTimeout(this.submenuTimeout);
+            this.submenuTimeout = null;
+        }
+
+        // Hide any previously open submenu
+        if (this.openSubmenu && this.openSubmenu !== trigger) {
+            this._hideSubmenu(this.openSubmenu);
+        }
+
+        const submenu = trigger.querySelector('.context-submenu');
+        if (!submenu) return;
+
+        submenu.style.display = 'block';
+        this.openSubmenu = trigger;
+        this._positionSubmenu(submenu);
+    }
+
+    _scheduleSubmenuClose(trigger) {
+        this.submenuTimeout = setTimeout(() => {
+            this._hideSubmenu(trigger);
+            this.submenuTimeout = null;
+        }, 250);
+    }
+
+    _hideSubmenu(trigger) {
+        const submenu = trigger.querySelector('.context-submenu');
+        if (submenu) {
+            submenu.style.display = 'none';
+            submenu.classList.remove('flip-left');
+        }
+        if (this.openSubmenu === trigger) {
+            this.openSubmenu = null;
+        }
+    }
+
+    _positionSubmenu(submenu) {
+        const submenuRect = submenu.getBoundingClientRect();
+        const viewportWidth = document.documentElement.clientWidth;
+
+        if (submenuRect.right > viewportWidth) {
+            submenu.classList.add('flip-left');
+        } else {
+            submenu.classList.remove('flip-left');
+        }
     }
 
     handleMenuAction(action, menuItem) {
@@ -65,6 +146,13 @@ export class BaseContextMenu {
     }
 
     hideMenu() {
+        if (this.submenuTimeout) {
+            clearTimeout(this.submenuTimeout);
+            this.submenuTimeout = null;
+        }
+        if (this.openSubmenu) {
+            this._hideSubmenu(this.openSubmenu);
+        }
         if (this.menu) {
             this.menu.style.display = 'none';
         }

static/js/components/ContextMenu/BulkContextMenu.js

@@ -4,6 +4,7 @@ import { bulkManager } from '../../managers/BulkManager.js';
 import { updateElementText, translate } from '../../utils/i18nHelpers.js';
 import { bulkMissingLoraDownloadManager } from '../../managers/BulkMissingLoraDownloadManager.js';
 import { showToast } from '../../utils/uiHelpers.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 export class BulkContextMenu extends BaseContextMenu {
     constructor() {
@@ -50,6 +51,14 @@ export class BulkContextMenu extends BaseContextMenu {
         if (copyAllItem) {
             copyAllItem.style.display = config.copyAll ? 'flex' : 'none';
         }
+
+        // Submenu parent visibility
+        const sendToWorkflowSubmenu = this.menu.querySelector('[data-has-submenu="send-to-workflow"]');
+        if (sendToWorkflowSubmenu) {
+            const hasWorkflowActions = config.sendToWorkflow || config.copyAll;
+            sendToWorkflowSubmenu.style.display = hasWorkflowActions ? 'flex' : 'none';
+        }
+
         if (refreshAllItem) {
             refreshAllItem.style.display = config.refreshAll ? 'flex' : 'none';
         }
@@ -74,11 +83,46 @@ export class BulkContextMenu extends BaseContextMenu {
         if (setContentRatingItem) {
             setContentRatingItem.style.display = config.setContentRating ? 'flex' : 'none';
         }
+
+        const setFavoriteItem = this.menu.querySelector('[data-action="set-favorite"]');
+
+        if (setFavoriteItem && config.setFavorite) {
+            setFavoriteItem.style.display = 'flex';
+
+            const total = state.selectedModels.size;
+            const favoritedCount = this.countFavoritedInSelection();
+            const allFavorited = total > 0 && favoritedCount === total;
+
+            const icon = setFavoriteItem.querySelector('i');
+            const label = setFavoriteItem.querySelector('span');
+
+            if (allFavorited) {
+                if (icon) { icon.className = 'far fa-star'; }
+                if (label) { label.textContent = translate('loras.bulkOperations.unfavorite'); }
+            } else {
+                if (icon) { icon.className = 'fas fa-star'; }
+                if (label) {
+                    label.textContent = favoritedCount > 0
+                        ? translate('loras.bulkOperations.setFavoriteCount', { favorited: favoritedCount, total })
+                        : translate('loras.bulkOperations.setFavorite');
+                }
+            }
+        } else if (setFavoriteItem) {
+            setFavoriteItem.style.display = 'none';
+        }
+
         if (downloadMissingLorasItem) {
             // Only show for recipes page
             downloadMissingLorasItem.style.display = currentModelType === 'recipes' ? 'flex' : 'none';
         }
 
+        const downloadExampleImagesItem = this.menu.querySelector('[data-action="download-example-images"]');
+        if (downloadExampleImagesItem) {
+            // Show on model pages (loras, checkpoints, embeddings), hide on recipes
+            const modelPages = ['loras', 'checkpoints', 'embeddings'];
+            downloadExampleImagesItem.style.display = modelPages.includes(currentModelType) ? 'flex' : 'none';
+        }
+
         const skipMetadataRefreshItem = this.menu.querySelector('[data-action="skip-metadata-refresh"]');
         const resumeMetadataRefreshItem = this.menu.querySelector('[data-action="resume-metadata-refresh"]');
 
@@ -112,6 +156,14 @@ export class BulkContextMenu extends BaseContextMenu {
                 );
             }
         }
+
+        // Hide empty sections
+        this.menu.querySelectorAll('.context-menu-section').forEach(section => {
+            const items = Array.from(section.querySelectorAll('.context-menu-item'))
+                .filter(item => !item.closest('.context-submenu'));
+            const allHidden = items.length > 0 && items.every(item => item.style.display === 'none');
+            section.style.display = allHidden ? 'none' : '';
+        });
     }
 
     updateSelectedCountHeader() {
@@ -138,6 +190,20 @@ export class BulkContextMenu extends BaseContextMenu {
         return count;
     }
 
+    countFavoritedInSelection() {
+        let count = 0;
+        for (const filePath of state.selectedModels) {
+            const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+                ? window.CSS.escape(filePath)
+                : filePath.replace(/["\\]/g, '\\$&');
+            const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
+            if (card && card.dataset.favorite === 'true') {
+                count++;
+            }
+        }
+        return count;
+    }
+
     showMenu(x, y, card) {
         this.updateMenuItemsForModelType();
         this.updateSelectedCountHeader();
@@ -185,9 +251,17 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'delete-all':
                 bulkManager.showBulkDeleteModal();
                 break;
+            case 'set-favorite': {
+                const allFavorited = this.countFavoritedInSelection() === state.selectedModels.size;
+                bulkManager.setBulkFavorites(!allFavorited);
+                break;
+            }
             case 'download-missing-loras':
                 this.handleDownloadMissingLoras();
                 break;
+            case 'download-example-images':
+                this.handleDownloadExampleImages();
+                break;
             case 'clear':
                 bulkManager.clearSelection();
                 break;
@@ -230,4 +304,31 @@ export class BulkContextMenu extends BaseContextMenu {
 
         await bulkMissingLoraDownloadManager.downloadMissingLoras(selectedRecipes);
     }
+
+    async handleDownloadExampleImages() {
+        if (state.selectedModels.size === 0) {
+            return;
+        }
+
+        const hashes = new Set();
+        for (const filePath of state.selectedModels) {
+            const escapedPath = CSS.escape(filePath);
+            const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
+            if (card?.dataset?.sha256) {
+                hashes.add(card.dataset.sha256);
+            }
+        }
+
+        if (hashes.size === 0) {
+            showToast('No valid model hashes found in selection', {}, 'warning');
+            return;
+        }
+
+        try {
+            const apiClient = getModelApiClient();
+            await apiClient.downloadExampleImages([...hashes]);
+        } catch (error) {
+            console.error('Bulk download example images failed:', error);
+        }
+    }
 }

static/js/components/RecipeModal.js

@@ -2,10 +2,11 @@
 import { showToast, copyToClipboard, sendLoraToWorkflow, sendModelPathToWorkflow, openCivitaiByMetadata } from '../utils/uiHelpers.js';
 import { translate } from '../utils/i18nHelpers.js';
 import { state } from '../state/index.js';
-import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
+import { setSessionItem, removeSessionItem, getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
 import { fetchRecipeDetails, updateRecipeMetadata } from '../api/recipeApi.js';
 import { downloadManager } from '../managers/DownloadManager.js';
 import { MODEL_TYPES } from '../api/apiConfig.js';
+import { openMediaViewer } from './shared/MediaViewer.js';
 
 const ALLOWED_GEN_PARAM_KEYS = new Set([
     'prompt',
@@ -104,6 +105,7 @@ class RecipeModal {
 
     init() {
         this.setupCopyButtons();
+        this.setupStripLoraToggle();
         this.setupPromptEditors();
         // Set up tooltip positioning handlers after DOM is ready
         document.addEventListener('DOMContentLoaded', () => {
@@ -112,6 +114,23 @@ class RecipeModal {
 
         // Set up document click handler to close edit fields
         document.addEventListener('click', (event) => {
+            const recipeModal = document.getElementById('recipeModal');
+            if (recipeModal && recipeModal.style.display !== 'none') {
+                const mediaEl = event.target.closest('.recipe-preview-media');
+                if (mediaEl && mediaEl.tagName) {
+                    event.stopPropagation();
+                    const isVideo = mediaEl.tagName === 'VIDEO';
+                    const url = mediaEl.src || mediaEl.currentSrc;
+                    if (url) {
+                        openMediaViewer(url, {
+                            type: isVideo ? 'video' : 'image',
+                            title: document.getElementById('recipeModalTitle')?.textContent || ''
+                        });
+                    }
+                    return;
+                }
+            }
+
             // Handle title edit
             const titleEditor = document.getElementById('recipeTitleEditor');
             if (titleEditor && titleEditor.classList.contains('active') &&
@@ -1332,14 +1351,20 @@ class RecipeModal {
 
         if (copyPromptBtn) {
             copyPromptBtn.addEventListener('click', () => {
-                const promptText = this.currentRecipe?.gen_params?.prompt || '';
+                let promptText = this.currentRecipe?.gen_params?.prompt || '';
+                if (this.shouldStripLoraOnCopy()) {
+                    promptText = RecipeModal.stripLoraTags(promptText);
+                }
                 this.copyToClipboard(promptText, 'Prompt copied to clipboard');
             });
         }
 
         if (copyNegativePromptBtn) {
             copyNegativePromptBtn.addEventListener('click', () => {
-                const negativePromptText = this.currentRecipe?.gen_params?.negative_prompt || '';
+                let negativePromptText = this.currentRecipe?.gen_params?.negative_prompt || '';
+                if (this.shouldStripLoraOnCopy()) {
+                    negativePromptText = RecipeModal.stripLoraTags(negativePromptText);
+                }
                 this.copyToClipboard(negativePromptText, 'Negative prompt copied to clipboard');
             });
         }
@@ -1359,6 +1384,43 @@ class RecipeModal {
         }
     }
 
+    /**
+     * Strip <lora:...> tags from prompt text and clean up residual punctuation/whitespace.
+     * Handles both unescaped (<lora:...>) and HTML-escaped (&lt;lora:...&gt;) variants.
+     * Cleans up artifacts like leading ", ", double commas, and extra whitespace.
+     */
+    static stripLoraTags(text) {
+        return text
+            .replace(/<lora:[^>]*>/gi, '')
+            .replace(/&lt;lora:[^&]*&gt;/gi, '')
+            .replace(/,(\s*,)+/g, ',')
+            .replace(/^,\s*/, '')
+            .replace(/,\s*$/, '')
+            .replace(/\s{2,}/g, ' ')
+            .trim();
+    }
+
+    shouldStripLoraOnCopy() {
+        const toggle = document.getElementById('stripLoraOnCopyToggle');
+        return toggle ? toggle.checked : false;
+    }
+
+    setupStripLoraToggle() {
+        const toggle = document.getElementById('stripLoraOnCopyToggle');
+        if (!toggle) return;
+
+        const stored = getStorageItem('strip_lora_on_copy');
+        if (stored !== null) {
+            toggle.checked = stored === true;
+        }
+
+        toggle.addEventListener('change', () => {
+            const checked = toggle.checked;
+            setStorageItem('strip_lora_on_copy', checked);
+            state.global.settings.strip_lora_on_copy = checked;
+        });
+    }
+
     // Fetch recipe syntax from backend and copy to clipboard
     async fetchAndCopyRecipeSyntax() {
         if (!this.recipeId) {

static/js/components/controls/PageControls.js

@@ -166,17 +166,6 @@ export class PageControls {
             });
         });
         
-        // Handle quick refresh option
-        const quickRefreshOption = document.querySelector('[data-action="quick-refresh"]');
-        if (quickRefreshOption) {
-            quickRefreshOption.addEventListener('click', (e) => {
-                e.stopPropagation();
-                this.refreshModels(false);
-                // Close the dropdown
-                document.querySelector('.dropdown-group.active')?.classList.remove('active');
-            });
-        }
-        
         // Handle full rebuild option
         const fullRebuildOption = document.querySelector('[data-action="full-rebuild"]');
         if (fullRebuildOption) {

static/js/components/shared/MediaViewer.js

@@ -0,0 +1,204 @@
+let activeViewer = null;
+
+function createMediaElement(item) {
+    const { url, type = 'image' } = item;
+    if (type === 'video') {
+        const el = document.createElement('video');
+        el.controls = true;
+        el.autoplay = true;
+        el.loop = true;
+        el.muted = true;
+        el.className = 'media-viewer-media media-viewer-video';
+        el.src = url;
+        return el;
+    }
+    const el = document.createElement('img');
+    el.className = 'media-viewer-media media-viewer-image';
+    el.src = url;
+    el.alt = 'Full size preview';
+    el.draggable = false;
+    return el;
+}
+
+function preloadAdjacent(items, index) {
+    [index - 1, index + 1].forEach(i => {
+        if (i >= 0 && i < items.length && items[i].type !== 'video') {
+            const preload = new Image();
+            preload.src = items[i].url;
+        }
+    });
+}
+
+export function openMediaViewer(arg1, arg2, arg3) {
+    closeMediaViewer();
+
+    let items, currentIndex, title = '';
+
+    if (Array.isArray(arg1)) {
+        items = arg1;
+        currentIndex = typeof arg2 === 'number' ? arg2 : 0;
+        title = (arg3 && arg3.title) || '';
+    } else {
+        items = [{ url: arg1, type: (arg2 && arg2.type) || 'image' }];
+        currentIndex = 0;
+        title = (arg2 && arg2.title) || '';
+    }
+
+    if (currentIndex < 0 || currentIndex >= items.length) currentIndex = 0;
+
+    const overlay = document.createElement('div');
+    overlay.className = 'media-viewer-overlay';
+    overlay.setAttribute('role', 'dialog');
+    overlay.setAttribute('aria-label', title || 'Media viewer');
+
+    const closeBtn = document.createElement('button');
+    closeBtn.className = 'media-viewer-close';
+    closeBtn.innerHTML = '<i class="fas fa-times"></i>';
+    closeBtn.title = 'Close (Esc)';
+    closeBtn.addEventListener('click', (e) => {
+        e.stopPropagation();
+        closeMediaViewer();
+    });
+
+    const contentContainer = document.createElement('div');
+    contentContainer.className = 'media-viewer-content-container';
+
+    let mediaElement = createMediaElement(items[currentIndex]);
+    contentContainer.appendChild(mediaElement);
+
+    const hasNavigation = items.length > 1;
+
+    const counter = document.createElement('div');
+    counter.className = 'media-viewer-counter';
+    counter.textContent = hasNavigation ? `${currentIndex + 1} / ${items.length}` : '';
+    contentContainer.appendChild(counter);
+
+    if (title) {
+        const titleBar = document.createElement('div');
+        titleBar.className = 'media-viewer-title';
+        titleBar.textContent = title;
+        contentContainer.appendChild(titleBar);
+    }
+
+    let prevBtn, nextBtn;
+    if (hasNavigation) {
+        prevBtn = document.createElement('button');
+        prevBtn.className = 'media-viewer-nav media-viewer-prev';
+        prevBtn.innerHTML = '<i class="fas fa-chevron-left"></i>';
+        prevBtn.title = 'Previous (←)';
+        nextBtn = document.createElement('button');
+        nextBtn.className = 'media-viewer-nav media-viewer-next';
+        nextBtn.innerHTML = '<i class="fas fa-chevron-right"></i>';
+        nextBtn.title = 'Next (→)';
+
+        const navigate = (delta) => {
+            const newIndex = (currentIndex + delta + items.length) % items.length;
+            currentIndex = newIndex;
+
+            const oldMedia = contentContainer.querySelector('.media-viewer-media');
+            const newMedia = createMediaElement(items[currentIndex]);
+
+            if (oldMedia) {
+                if (oldMedia.tagName === 'VIDEO') {
+                    oldMedia.pause();
+                    oldMedia.src = '';
+                }
+                oldMedia.replaceWith(newMedia);
+            }
+            mediaElement = newMedia;
+
+            counter.textContent = `${currentIndex + 1} / ${items.length}`;
+            preloadAdjacent(items, currentIndex);
+        };
+
+        prevBtn.addEventListener('click', (e) => { e.stopPropagation(); navigate(-1); });
+        nextBtn.addEventListener('click', (e) => { e.stopPropagation(); navigate(1); });
+
+        overlay.appendChild(prevBtn);
+        overlay.appendChild(nextBtn);
+    }
+
+    overlay.appendChild(closeBtn);
+    overlay.appendChild(contentContainer);
+    document.body.appendChild(overlay);
+
+    requestAnimationFrame(() => {
+        overlay.classList.add('active');
+    });
+
+    overlay.addEventListener('click', (e) => {
+        if (e.target === overlay) {
+            closeMediaViewer();
+        }
+    });
+
+    const keyHandler = (e) => {
+        if (e.key === 'Escape') {
+            closeMediaViewer();
+            return;
+        }
+        if (hasNavigation) {
+            if (e.key === 'ArrowLeft') {
+                e.stopPropagation();
+                e.preventDefault();
+                prevBtn.click();
+                return;
+            }
+            if (e.key === 'ArrowRight') {
+                e.stopPropagation();
+                e.preventDefault();
+                nextBtn.click();
+                return;
+            }
+        }
+    };
+    document.addEventListener('keydown', keyHandler, true);
+
+    activeViewer = { overlay, keyHandler };
+    preloadAdjacent(items, currentIndex);
+
+    if (items[currentIndex].type === 'video') {
+        const recipeVideo = document.getElementById('recipeModalVideo');
+        if (recipeVideo && !recipeVideo.paused) {
+            recipeVideo.pause();
+        }
+    }
+}
+
+export function closeMediaViewer() {
+    if (!activeViewer) return;
+
+    const { overlay, keyHandler } = activeViewer;
+
+    const video = overlay.querySelector('video');
+    if (video) {
+        video.pause();
+        video.src = '';
+    }
+
+    const img = overlay.querySelector('img');
+    if (img) {
+        img.src = '';
+    }
+
+    document.removeEventListener('keydown', keyHandler, true);
+
+    overlay.classList.remove('active');
+    overlay.addEventListener('transitionend', () => {
+        if (overlay.parentNode) {
+            overlay.parentNode.removeChild(overlay);
+        }
+    }, { once: true });
+
+    setTimeout(() => {
+        if (overlay.parentNode) {
+            overlay.parentNode.removeChild(overlay);
+        }
+    }, 500);
+
+    activeViewer = null;
+}
+
+export function isMediaViewerOpen() {
+    return activeViewer !== null;
+}

static/js/components/shared/ModelVersionsTab.js

@@ -241,7 +241,7 @@ function buildActionButton(label, variant, action, options = {}) {
     if (action) {
         attributes.push(`data-version-action="${escapeHtml(action)}"`);
     }
-    if (options.title) {
+    if (!options.disabled && options.title) {
         attributes.push(`title="${escapeHtml(options.title)}"`);
         attributes.push(`aria-label="${escapeHtml(options.title)}"`);
     }
@@ -251,7 +251,11 @@ function buildActionButton(label, variant, action, options = {}) {
     if (options.extraAttributes) {
         attributes.push(options.extraAttributes);
     }
-    return `<button ${attributes.join(' ')}>${options.iconMarkup || ''}${escapeHtml(label)}</button>`;
+    const buttonHtml = `<button ${attributes.join(' ')}>${options.iconMarkup || ''}${escapeHtml(label)}</button>`;
+    if (options.disabled && options.title) {
+        return `<span class="version-action-disabled-wrapper" title="${escapeHtml(options.title)}" aria-label="${escapeHtml(options.title)}">${buttonHtml}</span>`;
+    }
+    return buttonHtml;
 }
 
 const DISPLAY_FILTER_MODES = Object.freeze({

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

@@ -17,6 +17,7 @@ import {
 import { generateMetadataPanel } from './MetadataPanel.js';
 import { generateImageWrapper, generateVideoWrapper } from './MediaRenderers.js';
 import { getShowcaseUrl } from '../../../utils/civitaiUtils.js';
+import { openMediaViewer } from '../MediaViewer.js';
 
 export const showcaseListenerMetrics = {
     wheelListeners: 0,
@@ -640,6 +641,27 @@ export function initShowcaseContent(carousel) {
     initMediaControlHandlers(carousel);
     positionAllMediaControls(carousel);
 
+    // Click-to-view: open full-size media viewer when clicking showcase images/videos
+    const viewerElements = carousel.querySelectorAll('.media-wrapper img, .media-wrapper video');
+    const allItems = [];
+    const elementIndexMap = new Map();
+    viewerElements.forEach((el) => {
+        const isVideo = el.tagName === 'VIDEO';
+        const url = el.src || el.dataset.localSrc || el.dataset.remoteSrc;
+        if (url) {
+            elementIndexMap.set(el, allItems.length);
+            allItems.push({ url, type: isVideo ? 'video' : 'image' });
+        }
+    });
+    viewerElements.forEach((mediaEl) => {
+        const idx = elementIndexMap.get(mediaEl);
+        if (idx === undefined) return;
+        mediaEl.addEventListener('click', (e) => {
+            e.stopPropagation();
+            openMediaViewer(allItems, idx);
+        });
+    });
+
     // Bind scroll-indicator click events
     bindScrollIndicatorEvents(carousel);
     

static/js/managers/BulkManager.js

@@ -3,7 +3,7 @@ import { showToast, copyToClipboard, sendLoraToWorkflow, buildLoraSyntax, getNSF
 import { updateCardsForBulkMode } from '../components/shared/ModelCard.js';
 import { modalManager } from './ModalManager.js';
 import { getModelApiClient, resetAndReload } from '../api/modelApiFactory.js';
-import { RecipeSidebarApiClient } from '../api/recipeApi.js';
+import { RecipeSidebarApiClient, updateRecipeMetadata } from '../api/recipeApi.js';
 import { MODEL_TYPES, MODEL_CONFIG } from '../api/apiConfig.js';
 import { BASE_MODEL_CATEGORIES } from '../utils/constants.js';
 import { getPriorityTagSuggestions } from '../utils/priorityTagHelpers.js';
@@ -41,7 +41,9 @@ export class BulkManager {
                 autoOrganize: true,
                 deleteAll: true,
                 setContentRating: true,
-                skipMetadataRefresh: true
+                skipMetadataRefresh: true,
+                setFavorite: true,
+                unfavorite: true
             },
             [MODEL_TYPES.EMBEDDING]: {
                 addTags: true,
@@ -53,7 +55,9 @@ export class BulkManager {
                 autoOrganize: true,
                 deleteAll: true,
                 setContentRating: false,
-                skipMetadataRefresh: true
+                skipMetadataRefresh: true,
+                setFavorite: true,
+                unfavorite: true
             },
             [MODEL_TYPES.CHECKPOINT]: {
                 addTags: true,
@@ -65,7 +69,9 @@ export class BulkManager {
                 autoOrganize: true,
                 deleteAll: true,
                 setContentRating: true,
-                skipMetadataRefresh: true
+                skipMetadataRefresh: true,
+                setFavorite: true,
+                unfavorite: true
             },
             recipes: {
                 addTags: false,
@@ -77,7 +83,9 @@ export class BulkManager {
                 autoOrganize: false,
                 deleteAll: true,
                 setContentRating: false,
-                skipMetadataRefresh: false
+                skipMetadataRefresh: false,
+                setFavorite: true,
+                unfavorite: true
             }
         };
 
@@ -1090,6 +1098,60 @@ export class BulkManager {
         }
     }
 
+    async setBulkFavorites(value) {
+        if (state.selectedModels.size === 0) {
+            showToast('toast.models.noModelsSelected', {}, 'warning');
+            return;
+        }
+
+        const totalCount = state.selectedModels.size;
+        const isRecipesPage = state.currentPageType === 'recipes';
+
+        state.loadingManager.showSimpleLoading(
+            translate(value ? 'toast.models.bulkFavoriteUpdating' : 'toast.models.bulkUnfavoriteUpdating', { count: totalCount })
+        );
+        let cancelled = false;
+        state.loadingManager.showCancelButton(() => {
+            cancelled = true;
+        });
+
+        let successCount = 0;
+        let failureCount = 0;
+
+        try {
+            for (const filePath of state.selectedModels) {
+                if (cancelled) {
+                    showToast('toast.api.operationCancelled', {}, 'info');
+                    break;
+                }
+                try {
+                    if (isRecipesPage) {
+                        await updateRecipeMetadata(filePath, { favorite: value });
+                    } else {
+                        const apiClient = getModelApiClient();
+                        await apiClient.saveModelMetadata(filePath, { favorite: value });
+                    }
+                    successCount++;
+                } catch (error) {
+                    failureCount++;
+                    console.error(`Failed to set favorite=${value} for ${filePath}:`, error);
+                }
+            }
+        } finally {
+            state.loadingManager?.hide?.();
+        }
+
+        if (successCount === totalCount) {
+            const toastKey = value ? 'modelCard.favorites.added' : 'modelCard.favorites.removed';
+            showToast(toastKey, {}, 'success');
+        } else if (successCount > 0) {
+            const toastKey = value ? 'toast.models.bulkFavoritePartialAdded' : 'toast.models.bulkFavoritePartialRemoved';
+            showToast(toastKey, { success: successCount, failed: failureCount }, 'warning');
+        } else {
+            showToast('toast.models.bulkFavoriteFailed', {}, 'error');
+        }
+    }
+
     /**
      * Show bulk base model modal
      */

static/js/recipes.js

@@ -286,16 +286,6 @@ class RecipeManager {
             });
         });
 
-        // Handle quick refresh option (Sync Changes)
-        const quickRefreshOption = document.querySelector('[data-action="quick-refresh"]');
-        if (quickRefreshOption) {
-            quickRefreshOption.addEventListener('click', (e) => {
-                e.stopPropagation();
-                this.pageControls.refreshModels(false);
-                this.closeDropdowns();
-            });
-        }
-
         // Handle full rebuild option (Rebuild Cache)
         const fullRebuildOption = document.querySelector('[data-action="full-rebuild"]');
         if (fullRebuildOption) {

static/js/state/index.js

@@ -50,6 +50,7 @@ const DEFAULT_SETTINGS_BASE = Object.freeze({
     download_skip_base_models: [],
     backup_auto_enabled: true,
     backup_retention_count: 5,
+    strip_lora_on_copy: false,
 });
 
 export function createDefaultSettings() {

static/js/utils/constants.js

@@ -66,6 +66,9 @@ export const BASE_MODELS = {
     HUNYUAN_VIDEO: "Hunyuan Video",
     // Other models
     ANIMA: "Anima",
+    ERNIE: "Ernie",
+    ERNIE_TURBO: "Ernie Turbo",
+    NUCLEUS: "Nucleus",
     PONY_V7: "Pony V7",
     // Default
     UNKNOWN: "Other"
@@ -191,6 +194,9 @@ export const BASE_MODEL_ABBREVIATIONS = {
     [BASE_MODELS.ZIMAGE_TURBO]: 'ZIT',
     [BASE_MODELS.ZIMAGE_BASE]: 'ZIB',
     [BASE_MODELS.ANIMA]: 'ANI',
+    [BASE_MODELS.ERNIE]: 'ERNI',
+    [BASE_MODELS.ERNIE_TURBO]: 'ETRB',
+    [BASE_MODELS.NUCLEUS]: 'NUCL',
 
     // Default
     [BASE_MODELS.UNKNOWN]: 'OTH'
@@ -394,6 +400,7 @@ export const BASE_MODEL_CATEGORIES = {
         BASE_MODELS.QWEN, BASE_MODELS.AURAFLOW, BASE_MODELS.CHROMA, BASE_MODELS.ZIMAGE_TURBO, BASE_MODELS.ZIMAGE_BASE,
         BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
         BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI, BASE_MODELS.ANIMA,
+        BASE_MODELS.ERNIE, BASE_MODELS.ERNIE_TURBO, BASE_MODELS.NUCLEUS,
         BASE_MODELS.UNKNOWN
     ]
 };

templates/components/context_menu.html

@@ -53,32 +53,32 @@
         <span>{{ t('loras.bulkOperations.selected', {'count': 0}) }}</span>
     </div>
     <div class="context-menu-separator"></div>
-    <div class="context-menu-item" data-action="refresh-all">
-        <i class="fas fa-sync-alt"></i> <span>{{ t('loras.bulkOperations.refreshAll') }}</span>
-    </div>
-    <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="copy-all">
-        <i class="fas fa-copy"></i> <span>{{ t('loras.bulkOperations.copyAll') }}</span>
-    </div>
+    <div class="context-menu-section" data-section="workflow">
+        <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.workflow') }}</div>
+        <div class="context-menu-item has-submenu" data-has-submenu="send-to-workflow">
+            <i class="fas fa-paper-plane"></i>
+            <span>{{ t('loras.bulkOperations.sendToWorkflow') }}</span>
+            <i class="fas fa-chevron-right submenu-arrow"></i>
+            <div class="context-submenu">
                 <div class="context-menu-item" data-action="send-to-workflow-append">
                     <i class="fas fa-paper-plane"></i> <span>{{ t('loras.contextMenu.sendToWorkflowAppend') }}</span>
                 </div>
                 <div class="context-menu-item" data-action="send-to-workflow-replace">
                     <i class="fas fa-exchange-alt"></i> <span>{{ t('loras.contextMenu.sendToWorkflowReplace') }}</span>
                 </div>
-    <div class="context-menu-item" data-action="auto-organize">
-        <i class="fas fa-magic"></i> <span>{{ t('loras.bulkOperations.autoOrganize') }}</span>
+                <div class="context-menu-item" data-action="copy-all">
+                    <i class="fas fa-copy"></i> <span>{{ t('loras.bulkOperations.copyAll') }}</span>
                 </div>
-    <div class="context-menu-item" data-action="add-tags">
-        <i class="fas fa-tags"></i> <span>{{ t('loras.bulkOperations.addTags') }}</span>
             </div>
-    <div class="context-menu-item" data-action="set-base-model">
-        <i class="fas fa-layer-group"></i> <span>{{ t('loras.bulkOperations.setBaseModel') }}</span>
         </div>
-    <div class="context-menu-item" data-action="set-content-rating">
-        <i class="fas fa-exclamation-triangle"></i> <span>{{ t('loras.bulkOperations.setContentRating') }}</span>
+    </div>
+    <div class="context-menu-section" data-section="metadata">
+        <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.metadata') }}</div>
+        <div class="context-menu-item" data-action="refresh-all">
+            <i class="fas fa-sync-alt"></i> <span>{{ t('loras.bulkOperations.refreshAll') }}</span>
+        </div>
+        <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="skip-metadata-refresh">
             <i class="fas fa-ban"></i> <span>{{ t('loras.bulkOperations.skipMetadataRefresh') }}</span>
@@ -86,13 +86,41 @@
         <div class="context-menu-item" data-action="resume-metadata-refresh">
             <i class="fas fa-redo"></i> <span>{{ t('loras.bulkOperations.resumeMetadataRefresh') }}</span>
         </div>
-    <div class="context-menu-separator"></div>
-    <div class="context-menu-item" data-action="download-missing-loras">
-        <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadMissingLoras') }}</span>
+    </div>
+    <div class="context-menu-section" data-section="attributes">
+        <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.attributes') }}</div>
+        <div class="context-menu-item" data-action="add-tags">
+            <i class="fas fa-tags"></i> <span>{{ t('loras.bulkOperations.addTags') }}</span>
+        </div>
+        <div class="context-menu-item" data-action="set-base-model">
+            <i class="fas fa-layer-group"></i> <span>{{ t('loras.bulkOperations.setBaseModel') }}</span>
+        </div>
+        <div class="context-menu-item" data-action="set-favorite">
+            <i class="fas fa-star"></i> <span>{{ t('loras.bulkOperations.setFavorite') }}</span>
+        </div>
+        <div class="context-menu-item" data-action="set-content-rating">
+            <i class="fas fa-exclamation-triangle"></i> <span>{{ t('loras.bulkOperations.setContentRating') }}</span>
+        </div>
+    </div>
+    <div class="context-menu-section" data-section="organize">
+        <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.organize') }}</div>
+        <div class="context-menu-item" data-action="auto-organize">
+            <i class="fas fa-magic"></i> <span>{{ t('loras.bulkOperations.autoOrganize') }}</span>
         </div>
         <div class="context-menu-item" data-action="move-all">
             <i class="fas fa-folder-open"></i> <span>{{ t('loras.bulkOperations.moveAll') }}</span>
         </div>
+    </div>
+    <div class="context-menu-section" data-section="download">
+        <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.download') }}</div>
+        <div class="context-menu-item" data-action="download-example-images">
+            <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadExamples') }}</span>
+        </div>
+        <div class="context-menu-item" data-action="download-missing-loras">
+            <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadMissingLoras') }}</span>
+        </div>
+    </div>
+    <div class="context-menu-separator"></div>
     <div class="context-menu-item delete-item" data-action="delete-all">
         <i class="fas fa-trash"></i> <span>{{ t('loras.bulkOperations.deleteAll') }}</span>
     </div>

templates/components/controls.html

@@ -41,9 +41,6 @@
                     <i class="fas fa-caret-down"></i>
                 </button>
                 <div class="dropdown-menu">
-                    <div class="dropdown-item" data-action="quick-refresh" title="{{ t('loras.controls.refresh.quickTooltip') }}">
-                        <i class="fas fa-bolt"></i> <span>{{ t('loras.controls.refresh.quick') }}</span>
-                    </div>
                     <div class="dropdown-item" data-action="full-rebuild" title="{{ t('loras.controls.refresh.fullTooltip') }}">
                         <i class="fas fa-tools"></i> <span>{{ t('loras.controls.refresh.full') }}</span>
                     </div>

templates/components/recipe_modal.html

@@ -22,7 +22,16 @@
                 </div>
                 
                 <div class="info-section recipe-gen-params">
+                    <div class="gen-params-header-row">
                         <h3>Generation Parameters</h3>
+                        <label class="inline-toggle-container lora-strip-toggle" title="When enabled, &lt;lora:...&gt; tags are removed from prompt text when copying">
+                            <span class="inline-toggle-label">Strip &lt;lora:&gt;</span>
+                            <div class="toggle-switch">
+                                <input type="checkbox" id="stripLoraOnCopyToggle">
+                                <span class="toggle-slider"></span>
+                            </div>
+                        </label>
+                    </div>
                     
                     <div class="gen-params-container">
                         <!-- Prompt -->

templates/recipes.html

@@ -75,9 +75,6 @@
                     <i class="fas fa-caret-down"></i>
                 </button>
                 <div class="dropdown-menu">
-                    <div class="dropdown-item" data-action="quick-refresh" title="{{ t('recipes.controls.refresh.quickTooltip', default='Sync changes - quick refresh without rebuilding cache') }}">
-                        <i class="fas fa-bolt"></i> <span>{{ t('loras.controls.refresh.quick', default='Sync Changes') }}</span>
-                    </div>
                     <div class="dropdown-item" data-action="full-rebuild" title="{{ t('recipes.controls.refresh.fullTooltip', default='Rebuild cache - full rescan of all recipe files') }}">
                         <i class="fas fa-tools"></i> <span>{{ t('loras.controls.refresh.full', default='Rebuild Cache') }}</span>
                     </div>

tests/frontend/components/lorasWidgetEvents.interactions.test.js

@@ -114,7 +114,8 @@ describe('LoRA widget drag interactions', () => {
     dragEl.dispatchEvent(new PointerEvent('pointerup', { pointerId: 1 }));
     expect(document.body.classList.contains('lm-lora-strength-dragging')).toBe(false);
     expect(onDragEnd).toHaveBeenCalledTimes(1);
-    expect(renderSpy).toHaveBeenCalledWith(widget.value, widget);
+    // 454210a4 replaced renderFunction() with widget.value setter + widget.callback()
+    expect(widget.callback).toHaveBeenCalledWith(widget.value);
   });
 
   it('deletes the selected LoRA when backspace is pressed outside of strength inputs', async () => {

tests/frontend/components/pageControls.filtering.test.js

@@ -135,7 +135,6 @@ function renderControlsDom(pageKey) {
             <button data-action="refresh" class="dropdown-main"></button>
             <button class="dropdown-toggle"></button>
             <div class="dropdown-menu">
-              <div class="dropdown-item" data-action="quick-refresh"></div>
               <div class="dropdown-item" data-action="full-rebuild"></div>
             </div>
           </div>

tests/routes/test_api_snapshots.py

@@ -79,7 +79,7 @@ class FakeDownloadHistoryService:
     async def mark_downloaded(self, *_args, **_kwargs):
         return None
 
-    async def mark_not_downloaded(self, *_args, **_kwargs):
+    async def mark_as_deleted(self, *_args, **_kwargs):
         return None
 
 

tests/routes/test_misc_routes.py

@@ -903,7 +903,7 @@ class FakeDownloadHistoryService:
             (model_type, version_id, model_id, source, file_path)
         )
 
-    async def mark_not_downloaded(self, model_type, version_id):
+    async def mark_as_deleted(self, model_type, version_id):
         self.marked_not_downloaded.append((model_type, version_id))
 
 

tests/services/test_downloaded_version_history_service.py

@@ -30,7 +30,7 @@ async def test_download_history_roundtrip_and_manual_override(tmp_path: Path) ->
     assert await service.has_been_downloaded("lora", 101) is True
     assert await service.get_downloaded_version_ids("lora", 11) == [101]
 
-    await service.mark_not_downloaded("lora", 101)
+    await service.mark_as_deleted("lora", 101)
     assert await service.has_been_downloaded("lora", 101) is False
     assert await service.get_downloaded_version_ids("lora", 11) == []
 

tests/services/test_recipe_repair.py

@@ -77,7 +77,7 @@ async def test_repair_all_recipes_with_enriched_checkpoint_id(setup_scanner):
     recipe = {
         "id": "r1",
         "title": "Old Recipe",
-        "source_url": "https://civitai.com/images/12345",
+        "source_path": "https://civitai.com/images/12345",
         "checkpoint": None,
         "gen_params": {"prompt": ""}
     }
@@ -127,7 +127,7 @@ async def test_repair_all_recipes_supports_civitai_red_source_url(setup_scanner)
     recipe = {
         "id": "r1",
         "title": "Red Recipe",
-        "source_url": "https://civitai.red/images/12345",
+        "source_path": "https://civitai.red/images/12345",
         "checkpoint": None,
         "gen_params": {"prompt": ""},
     }